pandas-Answers.ipynb

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   "source": [
    "# pandas\n",
    "<br><br>\n",
    "**PLEASE NOTE THAT THIS NOTEBOOK IS NOT EXACTLY THE SAME AS pandas.ipynb, BUT ALL THE EXERCISES AND ANSWERS ARE THE SAME.**\n",
    "<br><br><br><br>"
   ]
  },
  {
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   "id": "e7f4b330-9e0a-4ad5-98b4-e54bebd3a3d7",
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   "source": [
    "- Pandas is one of the most commonly used Python packages/libraries/modules for data science.<br><br>\n",
    "- Pandas is Python's answer for making two dimensional tables (ala Excel and SQL).<br><br>\n",
    "- Pandas calls a table a \"DataFrame\".<br><br>\n",
    "- Pandas DataFrames are used by Python's other packages for statistical analysis, data manipulation, and data visualization.<br><br>\n",
    "- Pandas DataFrames can be exported as .csv and other files.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d00bfc0-4a81-47d9-ab3a-a25a1fe60aaa",
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   "source": [
    "The pandas syntax isn't very instinctual. Some of the syntax will differ from basic Python. I still have to look a lot of things up in pandas, if it's something I don't do very often. However, it is the tool for working with spreadsheets in Python, so you'll need to learn it at some point.<br><br>\n",
    "Pandas is written as more of a *functional* language than basic Python. This means that instead of manipulating objects, we'll be applying more functions across our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2d4bf7-cc0c-401e-8d7f-0991ff5e7a85",
   "metadata": {},
   "source": [
    "#### <br>Why do we work with Jupyter Notebooks for data science?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033b772e-5946-432b-aa51-9d501b3e7758",
   "metadata": {},
   "source": [
    "Jupyter Notebooks allow us to view nicely formatted output (such as pandas DataFrames and data visualizations) directly below the code used to create the object. They also allow you to scroll through large DataFrames or images."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### <br>NumPy arrays\n",
    "This week is going to focus on the Python package Pandas. However, Pandas (and many other Python packages) are built on NumPy arrays. NumPy is another Python module, and NumPy arrays are multi-dimensional datasets made up entirely of numerical data. They allow for much faster calculations than other basic Python objects. If you work with large numerical datasets, you will also want to look into the NumPy package. NumPy arrays do not have the features that many of us want to work with, such as column headers and the ability to work with non-numerical data; that's why pandas is so popular."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf83b2a9-28b9-44f0-9ae1-1dd9367d7162",
   "metadata": {},
   "source": [
    "## <br><br><br>Importing pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b493b70-6ed7-4af4-bea4-1effec56e603",
   "metadata": {},
   "source": [
    "Because pandas is one of the most commonly used Python packages, it often gets imported as a shortened version of it's actual name. This makes it quicker to type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b96aee5-766e-4ace-b7c1-21ba17809d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caeb892-3b60-42d3-a172-832db988e604",
   "metadata": {},
   "source": [
    "Pandas comes with the Anaconda distribution of Python and is available on Google Colab."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b726cf1-7ef4-4530-ad63-374715e48b0c",
   "metadata": {},
   "source": [
    "## <br><br><br>PART ONE: LOADING AND VIEWING DATAFRAMES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17684dc2-0354-416a-b777-bf3430e27567",
   "metadata": {},
   "source": [
    "### <br>Opening files from your computer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79718441-3bcb-472c-a4c7-036ddfde025d",
   "metadata": {},
   "source": [
    "#### If you are using Google Colab, you must run the next line of code. *If you are NOT using Google Colab, do NOT run the next line.*\n",
    "Google Colab requires you to load data files into your workspace by hand (or by using this trick to pull them in from github)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a46bce5-8d87-4b71-bec6-10c3a1f4e2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/aGitHasNoName/pandasBasics/main/wnba-team-elo-ratings.csv\n",
    "!wget https://raw.githubusercontent.com/aGitHasNoName/pandasBasics/main/pigeonRacing.txt\n",
    "!wget https://raw.githubusercontent.com/aGitHasNoName/pandasBasics/main/zoo.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bee105d-c718-4ee8-95a8-77d8b98eb19e",
   "metadata": {},
   "source": [
    "### <br><br><br>Loading a csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f470eb-6b9d-4007-a2fa-7cb028d63c7d",
   "metadata": {},
   "source": [
    "We will use the function `pd.read_csv()`. As a reminder, when we use a function from an imported module, we first give the module's name, followed by a dot, followed by the function name.\n",
    "<br><br>This will automatically create a **DataFrame** object, which we are saving as `df`. `df` is a common variable name for a DataFrame. You can open the file, define it as a Pandas DataFrame, assign it to a variable, and close the file in one line. (Already we're seeing the differences from basic Python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ed11452-979d-4d67-853e-e1cacd5baf89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"wnba-team-elo-ratings.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b8b8a0-e1f6-4a2e-9a21-ff52206ff561",
   "metadata": {},
   "source": [
    "<br><br>Our sample dataset was taken from FiveThirtyEight. It contains game data for WNBA games since 1997."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abad5eb-2de4-4ed2-8285-6d0d62ec704b",
   "metadata": {},
   "source": [
    "### <br><br><br>Viewing the DataFrame"
   ]
  },
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   "execution_count": 3,
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       "       season        date team1 team2                name1  \\\n",
       "0        2019  10/10/2019   WAS   CON   Washington Mystics   \n",
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       "4        2019   10/6/2019   WAS   CON   Washington Mystics   \n",
       "...       ...         ...   ...   ...                  ...   \n",
       "10483    1997   6/21/1997   SAC   LVA  Sacramento Monarchs   \n",
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       "10485    1997   6/21/1997   LAS   NYL   Los Angeles Sparks   \n",
       "10486    1997   6/21/1997   LVA   SAC          Utah Starzz   \n",
       "10487    1997   6/21/1997   CLE   HOU    Cleveland Rockers   \n",
       "\n",
       "                     name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "0          Connecticut Sun        0        1      89      78      1684   \n",
       "1       Washington Mystics        0        1      78      89      1634   \n",
       "2          Connecticut Sun        0        1      86      90      1693   \n",
       "3       Washington Mystics        0        1      90      86      1626   \n",
       "4          Connecticut Sun        0        1      94      81      1671   \n",
       "...                    ...      ...      ...     ...     ...       ...   \n",
       "10483          Utah Starzz        0        0      73      61      1500   \n",
       "10484   Los Angeles Sparks        0        0      67      57      1500   \n",
       "10485     New York Liberty        0        0      57      67      1500   \n",
       "10486  Sacramento Monarchs        0        0      61      73      1500   \n",
       "10487       Houston Comets        0        0      56      76      1500   \n",
       "\n",
       "       elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "0          1634       1692       1627  0.718         1  \n",
       "1          1684       1627       1692  0.282         0  \n",
       "2          1626       1684       1634  0.476         0  \n",
       "3          1693       1634       1684  0.524         1  \n",
       "4          1648       1693       1626  0.399         0  \n",
       "...         ...        ...        ...    ...       ...  \n",
       "10483      1500       1521       1479  0.387         0  \n",
       "10484      1500       1519       1481  0.387         0  \n",
       "10485      1500       1481       1519  0.613         1  \n",
       "10486      1500       1479       1521  0.613         1  \n",
       "10487      1500       1470       1530  0.613         1  \n",
       "\n",
       "[10488 rows x 16 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada87b22-8011-4e98-b349-74d7892cd475",
   "metadata": {},
   "source": [
    "<br>Take a minute to look at the data. The DataFrame will have a slightly different look on Colab and Jupyter, and on different versions of Jupyter.\n",
    "<br><br>The number at the beginning of each row is called an **index**. The index was automatically assigned by pandas when the dataset was loaded. It was not in the original csv file. It is merely a series of consecutive numbers going down the rows. The rows were loaded in whatever order they were in the csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139e5c60-4bd8-4a6a-a708-c1a2d22ad0e3",
   "metadata": {},
   "source": [
    "If you are working in Google Colab, there is a new feature that lets you magically convert your DataFrame into an interactive table. We're NOT going to use that feature, though you can feel free to explore it on your own time. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ffe23c-6090-4253-8136-db80aabfc9da",
   "metadata": {},
   "source": [
    "<br><br>There are ways to view pieces of the DataFrame. Try these to see what they do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21b419ef-4fd1-42b2-8033-e0609b240f78",
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   "outputs": [
    {
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       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d06549f4-9340-4f19-bc15-bdf5eaa9da8b",
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   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
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       "      <td>Washington Mystics</td>\n",
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       "      <td>1671</td>\n",
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       "      <td>0.763</td>\n",
       "      <td>1</td>\n",
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       "      <td>1648</td>\n",
       "      <td>1671</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
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       "      <td>1618</td>\n",
       "      <td>1700</td>\n",
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      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "5    2019   10/6/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "6    2019   10/1/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "7    2019   10/1/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "8    2019   9/29/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "9    2019   9/29/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "5        0        1      81      94      1648      1671       1626       1693   \n",
       "6        0        1      87      99      1700      1618       1671       1648   \n",
       "7        0        1      99      87      1618      1700       1648       1671   \n",
       "8        0        1      95      86      1694      1624       1700       1618   \n",
       "9        0        1      86      95      1624      1694       1618       1700   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  \n",
       "5  0.601         1  \n",
       "6  0.763         1  \n",
       "7  0.237         0  \n",
       "8  0.747         1  \n",
       "9  0.253         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "848ea7e2-dbc2-43e3-a879-1864bc5bce05",
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   "outputs": [
    {
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       "      <th>10484</th>\n",
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       "      <th>10485</th>\n",
       "      <td>1997</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>10486</th>\n",
       "      <td>1997</td>\n",
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       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>1500</td>\n",
       "      <td>1500</td>\n",
       "      <td>1479</td>\n",
       "      <td>1521</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/21/1997</td>\n",
       "      <td>CLE</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
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       "      <td>1470</td>\n",
       "      <td>1530</td>\n",
       "      <td>0.613</td>\n",
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      "text/plain": [
       "       season       date team1 team2                name1  \\\n",
       "10483    1997  6/21/1997   SAC   LVA  Sacramento Monarchs   \n",
       "10484    1997  6/21/1997   NYL   LAS     New York Liberty   \n",
       "10485    1997  6/21/1997   LAS   NYL   Los Angeles Sparks   \n",
       "10486    1997  6/21/1997   LVA   SAC          Utah Starzz   \n",
       "10487    1997  6/21/1997   CLE   HOU    Cleveland Rockers   \n",
       "\n",
       "                     name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "10483          Utah Starzz        0        0      73      61      1500   \n",
       "10484   Los Angeles Sparks        0        0      67      57      1500   \n",
       "10485     New York Liberty        0        0      57      67      1500   \n",
       "10486  Sacramento Monarchs        0        0      61      73      1500   \n",
       "10487       Houston Comets        0        0      56      76      1500   \n",
       "\n",
       "       elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "10483      1500       1521       1479  0.387         0  \n",
       "10484      1500       1519       1481  0.387         0  \n",
       "10485      1500       1481       1519  0.613         1  \n",
       "10486      1500       1479       1521  0.613         1  \n",
       "10487      1500       1470       1530  0.613         1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f99ffae-91cb-47dc-8fb1-e0d01c026c7a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>10486</th>\n",
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       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/21/1997</td>\n",
       "      <td>CLE</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Cleveland Rockers</td>\n",
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       "      <td>1500</td>\n",
       "      <td>1470</td>\n",
       "      <td>1530</td>\n",
       "      <td>0.613</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       season       date team1 team2              name1                name2  \\\n",
       "10486    1997  6/21/1997   LVA   SAC        Utah Starzz  Sacramento Monarchs   \n",
       "10487    1997  6/21/1997   CLE   HOU  Cleveland Rockers       Houston Comets   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "10486        0        0      61      73      1500      1500       1479   \n",
       "10487        0        0      56      76      1500      1500       1470   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "10486       1521  0.613         1  \n",
       "10487       1530  0.613         1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3c93e9c-262c-44b1-9666-96ec5c4407e5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>10375</th>\n",
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       "       season       date team1 team2                name1           name2  \\\n",
       "10375    1997  7/25/1997   SAC   HOU  Sacramento Monarchs  Houston Comets   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "10375        0        0      76      86      1427      1585       1417   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "10375       1595   0.39         1  "
      ]
     },
     "execution_count": 8,
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   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5959de80-7806-4c3c-b974-1941c4c875d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>8451</th>\n",
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       "    <tr>\n",
       "      <th>10470</th>\n",
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       "      <td>LVA</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>74</td>\n",
       "      <td>1491</td>\n",
       "      <td>1470</td>\n",
       "      <td>1479</td>\n",
       "      <td>1482</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8025</th>\n",
       "      <td>2002</td>\n",
       "      <td>8/1/2002</td>\n",
       "      <td>LVA</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "      <td>1571</td>\n",
       "      <td>1458</td>\n",
       "      <td>1556</td>\n",
       "      <td>1473</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7787</th>\n",
       "      <td>2003</td>\n",
       "      <td>6/17/2003</td>\n",
       "      <td>IND</td>\n",
       "      <td>CHA</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>60</td>\n",
       "      <td>1502</td>\n",
       "      <td>1511</td>\n",
       "      <td>1514</td>\n",
       "      <td>1499</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8990</th>\n",
       "      <td>2001</td>\n",
       "      <td>5/28/2001</td>\n",
       "      <td>HOU</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>1621</td>\n",
       "      <td>1581</td>\n",
       "      <td>1610</td>\n",
       "      <td>1592</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>2014</td>\n",
       "      <td>8/5/2014</td>\n",
       "      <td>PHO</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>67</td>\n",
       "      <td>1698</td>\n",
       "      <td>1543</td>\n",
       "      <td>1703</td>\n",
       "      <td>1538</td>\n",
       "      <td>0.795</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "       season       date team1 team2             name1                name2  \\\n",
       "8451     2001  8/27/2001   NYL   CHA  New York Liberty      Charlotte Sting   \n",
       "10470    1997  6/26/1997   LVA   CLE       Utah Starzz    Cleveland Rockers   \n",
       "8025     2002   8/1/2002   LVA   SAC       Utah Starzz  Sacramento Monarchs   \n",
       "7787     2003  6/17/2003   IND   CHA     Indiana Fever      Charlotte Sting   \n",
       "8990     2001  5/28/2001   HOU   LAS    Houston Comets   Los Angeles Sparks   \n",
       "2306     2014   8/5/2014   PHO   ATL   Phoenix Mercury        Atlanta Dream   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "8451         0        1      44      48      1534      1549       1523   \n",
       "10470        0        0      63      74      1491      1470       1479   \n",
       "8025         0        0      71      80      1571      1458       1556   \n",
       "7787         0        0      71      60      1502      1511       1514   \n",
       "8990         0        0      63      66      1621      1581       1610   \n",
       "2306         0        0      75      67      1698      1543       1703   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "8451        1561  0.615         1  \n",
       "10470       1482  0.416         0  \n",
       "8025        1473  0.548         0  \n",
       "7787        1499  0.601         1  \n",
       "8990        1592  0.667         1  \n",
       "2306        1538  0.795         1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7804e54b-201f-47e4-9207-053aee88be97",
   "metadata": {},
   "source": [
    "### <br><br><br>Loading other types of files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0559c0d4-8019-441a-985b-2c00dc293254",
   "metadata": {},
   "source": [
    "We can open a tab-separated file using the same function we used to open a csv. We just have to pass a second argument, a **keyword argument**, to tell it that the delimiter is a tab instead of the default (comma). This dataset contains rankings of profressional racing pigeons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "702d1bf6-ab06-4840-b454-109c84348cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "pigeon_df = pd.read_csv(\"pigeonRacing.txt\", delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7c0f42d-0e34-40e5-8338-b326095ee00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26%</td>\n",
       "      <td>Dean Schultz</td>\n",
       "      <td>751 AU 18 PURP</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>469, 469</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>Dick Fassio</td>\n",
       "      <td>9027 AU 19 SLI</td>\n",
       "      <td>BBAR</td>\n",
       "      <td>F</td>\n",
       "      <td>579, 500</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.42%</td>\n",
       "      <td>Gary Mosher</td>\n",
       "      <td>32826 AU 17 AA</td>\n",
       "      <td>BKC</td>\n",
       "      <td>F</td>\n",
       "      <td>494, 539</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.21%</td>\n",
       "      <td>Todd Bartholomew</td>\n",
       "      <td>35624 AU 17 JEDD</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>547, 468</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>Dustin Maxfield</td>\n",
       "      <td>3322 AU 17 OGN</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>462, 462</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Position Avg Unirate              Name     Racing Pigeon Color Sex  \\\n",
       "0         1       0.26%      Dean Schultz    751 AU 18 PURP    BB   F   \n",
       "1         2       1.08%       Dick Fassio    9027 AU 19 SLI  BBAR   F   \n",
       "2         3       1.42%       Gary Mosher    32826 AU 17 AA   BKC   F   \n",
       "3         4       2.21%  Todd Bartholomew  35624 AU 17 JEDD    BC   F   \n",
       "4         5       2.61%   Dustin Maxfield    3322 AU 17 OGN    BB   M   \n",
       "\n",
       "  Qualifying Race Miles  Average Birdage  \n",
       "0              469, 469            612.0  \n",
       "1              579, 500            139.0  \n",
       "2              494, 539            103.0  \n",
       "3              547, 468            226.0  \n",
       "4              462, 462            171.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pigeon_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8837778b-8e96-42ab-9279-5e75bceb199c",
   "metadata": {},
   "source": [
    "<br><br>We will use a different function to open an Excel file. This file has information about animals and has two sheets within the excel file. We will first load sheet 1 and then sheet 2. We have to pass the `read_excel()` function one extra argument to specify the sheet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c158d46-2f49-483d-8a62-e14f20b0a6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "zoo_df = pd.read_excel(\"zoo.xlsx\", sheet_name=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9cdf71f-c051-42db-8d35-b301ebdaf1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airbourne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     animal  hair  feathers  eggs  milk  airbourne  aquatic  predator  \\\n",
       "0  aardvark     1         0     0     1          0        0         1   \n",
       "1  antelope     1         0     0     1          0        0         0   \n",
       "2      bass     0         0     1     0          0        1         1   \n",
       "3      bear     1         0     0     1          0        0         1   \n",
       "4      boar     1         0     0     1          0        0         1   \n",
       "\n",
       "   toothed  backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \\\n",
       "0        1         1         1         0     0     4     0         0        1   \n",
       "1        1         1         1         0     0     4     1         0        1   \n",
       "2        1         1         0         0     1     0     1         0        0   \n",
       "3        1         1         1         0     0     4     0         0        1   \n",
       "4        1         1         1         0     0     4     1         0        1   \n",
       "\n",
       "   type  \n",
       "0     1  \n",
       "1     1  \n",
       "2     4  \n",
       "3     1  \n",
       "4     1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf493098-de3d-48e9-b9f8-5d2c5fc99a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "zoo_class_df = pd.read_excel(\"zoo.xlsx\", sheet_name=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a614aa41-3e12-457a-9f65-584a3b98b0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>mammal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>bird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>reptile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>fish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>amphibian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      class\n",
       "0           1     mammal\n",
       "1           2       bird\n",
       "2           3    reptile\n",
       "3           4       fish\n",
       "4           5  amphibian"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_class_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41047684-b6e4-4983-ab11-169782c9b029",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ba836d-4925-4026-a4ff-8e0af898f2c7",
   "metadata": {},
   "source": [
    "Try to load two or three files from your own computer into pandas. Try with at least two different file types (csv, tab-delimited, excel). You can copy and paste the files into today's workshop folder, or you can open them by using the full path to the file (for example, \"\\~/Documents/myfolder/myfile.xlsx\" or \"\\~\\Documents\\myfolder\\myfile.csv\")."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12decee2-1668-4ae5-b58a-ed1a2038c404",
   "metadata": {},
   "source": [
    "<br>**If you are using Google Colab**, you will need to upload the files to Colab yourself. You can do this by clicking on the folder on the left menu. You should see a file tree come up that includes sample_data. Right click anywhere in this space and choose upload to upload your own files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be593ebd-4ae2-4d4f-bdb3-ff743c9498ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e537b55-d918-4df4-b43b-ee18dbdbad2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bfe1681-6b7b-44e2-8e0c-65751464d927",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f5f6c568-a611-4bb1-b36c-11244906b5ca",
   "metadata": {},
   "source": [
    "### <br><br><br>Getting basic info about the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca39c8b-4b2f-4baf-90d6-b45b5c6bd4c0",
   "metadata": {},
   "source": [
    "You can use the `len()` function to find out how many rows are in a DataFrame object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47e27d66-48b1-4e0c-99f7-69d1e9755aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10488"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b5146c-77eb-4923-84c3-0d771fddbbdd",
   "metadata": {},
   "source": [
    "<br>The `describe()` method will give you some very basic stats about each column in your DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ee1b17d4-81d4-42f1-b584-18e49361ce8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.0</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "      <td>10488.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2008.094775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.073227</td>\n",
       "      <td>74.171815</td>\n",
       "      <td>74.171815</td>\n",
       "      <td>1493.718440</td>\n",
       "      <td>1493.718440</td>\n",
       "      <td>1493.717868</td>\n",
       "      <td>1493.717868</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.361955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260520</td>\n",
       "      <td>12.409629</td>\n",
       "      <td>12.409629</td>\n",
       "      <td>87.037547</td>\n",
       "      <td>87.037547</td>\n",
       "      <td>88.109043</td>\n",
       "      <td>88.109043</td>\n",
       "      <td>0.184767</td>\n",
       "      <td>0.500024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1183.000000</td>\n",
       "      <td>1183.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>0.049000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>1441.000000</td>\n",
       "      <td>1441.000000</td>\n",
       "      <td>0.358000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1498.000000</td>\n",
       "      <td>1498.000000</td>\n",
       "      <td>1497.000000</td>\n",
       "      <td>1497.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1549.000000</td>\n",
       "      <td>1549.000000</td>\n",
       "      <td>1549.000000</td>\n",
       "      <td>1549.000000</td>\n",
       "      <td>0.642000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>1741.000000</td>\n",
       "      <td>1741.000000</td>\n",
       "      <td>1743.000000</td>\n",
       "      <td>1743.000000</td>\n",
       "      <td>0.951000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             season  neutral       playoff        score1        score2  \\\n",
       "count  10488.000000  10488.0  10488.000000  10488.000000  10488.000000   \n",
       "mean    2008.094775      0.0      0.073227     74.171815     74.171815   \n",
       "std        6.361955      0.0      0.260520     12.409629     12.409629   \n",
       "min     1997.000000      0.0      0.000000      0.000000      0.000000   \n",
       "25%     2003.000000      0.0      0.000000     66.000000     66.000000   \n",
       "50%     2008.000000      0.0      0.000000     74.000000     74.000000   \n",
       "75%     2014.000000      0.0      0.000000     82.000000     82.000000   \n",
       "max     2019.000000      0.0      1.000000    127.000000    127.000000   \n",
       "\n",
       "           elo1_pre      elo2_pre     elo1_post     elo2_post         prob1  \\\n",
       "count  10488.000000  10488.000000  10488.000000  10488.000000  10488.000000   \n",
       "mean    1493.718440   1493.718440   1493.717868   1493.717868      0.500000   \n",
       "std       87.037547     87.037547     88.109043     88.109043      0.184767   \n",
       "min     1183.000000   1183.000000   1168.000000   1168.000000      0.049000   \n",
       "25%     1442.000000   1442.000000   1441.000000   1441.000000      0.358000   \n",
       "50%     1498.000000   1498.000000   1497.000000   1497.000000      0.500000   \n",
       "75%     1549.000000   1549.000000   1549.000000   1549.000000      0.642000   \n",
       "max     1741.000000   1741.000000   1743.000000   1743.000000      0.951000   \n",
       "\n",
       "           is_home1  \n",
       "count  10488.000000  \n",
       "mean       0.500000  \n",
       "std        0.500024  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.500000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0772ca9-9784-4ea2-8b79-33bfc23efe1c",
   "metadata": {},
   "source": [
    "<br>The `shape` attribute will return the number of rows and columns as a tuple. An attribute gives us some stored data about an object - it is not a method function, so it does not get parentheses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c198928-2fd2-41ab-9f50-01bd57347c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10488, 16)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df6da40-ca29-437b-8d15-4a91e95e92b6",
   "metadata": {},
   "source": [
    "You can even save the shape tuple as an object, in case you need to include it in any code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ef080b39-916d-49ab-8e4e-c8f46c9504bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shape = df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "24546350-5e1f-4b44-a7b3-c86b8a99244b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our DataFrame has 10488 rows and 16 columns.\n"
     ]
    }
   ],
   "source": [
    "print(\"Our DataFrame has \" + str(df_shape[0]) + \" rows and \" + str(df_shape[1]) + \" columns.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1678f7ec-7f18-42ce-8fd6-033afd48b6c5",
   "metadata": {},
   "source": [
    "<br>The `size` attribute will tell you the total number of elements in the DataFrame (size = rows x columns):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "42785a65-750e-41ad-b7ec-316a08d92f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167808"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d18135-10e0-4b80-b033-8bcb7893492c",
   "metadata": {},
   "source": [
    "<br>To return a list of the column names, you can start with the `columns` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f76d1e85-ec06-4a20-a880-e7c7ef6ec31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'date', 'team1', 'team2', 'name1', 'name2', 'neutral',\n",
       "       'playoff', 'score1', 'score2', 'elo1_pre', 'elo2_pre', 'elo1_post',\n",
       "       'elo2_post', 'prob1', 'is_home1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f1c8cb-d4f3-497b-b2f7-0322ef672caf",
   "metadata": {},
   "source": [
    "Hmm. That looks strange because it is a pandas object. You can make it into a list so that it is easier to work with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "816c7e65-6a5b-4a1a-9abb-0b9074712016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['season', 'date', 'team1', 'team2', 'name1', 'name2', 'neutral', 'playoff', 'score1', 'score2', 'elo1_pre', 'elo2_pre', 'elo1_post', 'elo2_post', 'prob1', 'is_home1']\n"
     ]
    }
   ],
   "source": [
    "column_names = list(df.columns)\n",
    "print(column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76131bc7-fd7a-4ca1-ad54-2cfd48a801d9",
   "metadata": {},
   "source": [
    "<br>To find out the data types of the data found in each column, use the `dtypes` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "29e11ace-5786-4240-ac4c-695c8cdd0aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season         int64\n",
       "date          object\n",
       "team1         object\n",
       "team2         object\n",
       "name1         object\n",
       "name2         object\n",
       "neutral        int64\n",
       "playoff        int64\n",
       "score1         int64\n",
       "score2         int64\n",
       "elo1_pre       int64\n",
       "elo2_pre       int64\n",
       "elo1_post      int64\n",
       "elo2_post      int64\n",
       "prob1        float64\n",
       "is_home1       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a727130-0d81-4959-b88d-a2b0509252d7",
   "metadata": {},
   "source": [
    "<br>To **transpose** a DataFrame (swap the rows and columns), you also use an attribute. Let's transpose `zoo_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4785770-ec65-4901-b948-a17b4291d727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>animal</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>antelope</td>\n",
       "      <td>bass</td>\n",
       "      <td>bear</td>\n",
       "      <td>boar</td>\n",
       "      <td>buffalo</td>\n",
       "      <td>calf</td>\n",
       "      <td>carp</td>\n",
       "      <td>catfish</td>\n",
       "      <td>cavy</td>\n",
       "      <td>...</td>\n",
       "      <td>tuatara</td>\n",
       "      <td>tuna</td>\n",
       "      <td>vampire</td>\n",
       "      <td>vole</td>\n",
       "      <td>vulture</td>\n",
       "      <td>wallaby</td>\n",
       "      <td>wasp</td>\n",
       "      <td>wolf</td>\n",
       "      <td>worm</td>\n",
       "      <td>wren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hair</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feathers</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggs</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milk</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airbourne</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aquatic</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predator</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toothed</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>backbone</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breathes</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>venomous</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fins</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>legs</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tail</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>domestic</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catsize</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0         1     2     3     4        5     6     7        8    \\\n",
       "animal     aardvark  antelope  bass  bear  boar  buffalo  calf  carp  catfish   \n",
       "hair              1         1     0     1     1        1     1     0        0   \n",
       "feathers          0         0     0     0     0        0     0     0        0   \n",
       "eggs              0         0     1     0     0        0     0     1        1   \n",
       "milk              1         1     0     1     1        1     1     0        0   \n",
       "airbourne         0         0     0     0     0        0     0     0        0   \n",
       "aquatic           0         0     1     0     0        0     0     1        1   \n",
       "predator          1         0     1     1     1        0     0     0        1   \n",
       "toothed           1         1     1     1     1        1     1     1        1   \n",
       "backbone          1         1     1     1     1        1     1     1        1   \n",
       "breathes          1         1     0     1     1        1     1     0        0   \n",
       "venomous          0         0     0     0     0        0     0     0        0   \n",
       "fins              0         0     1     0     0        0     0     1        1   \n",
       "legs              4         4     0     4     4        4     4     0        0   \n",
       "tail              0         1     1     0     1        1     1     1        1   \n",
       "domestic          0         0     0     0     0        0     1     1        0   \n",
       "catsize           1         1     0     1     1        1     1     0        0   \n",
       "type              1         1     4     1     1        1     1     4        4   \n",
       "\n",
       "            9    ...      91    92       93    94       95       96    97   \\\n",
       "animal     cavy  ...  tuatara  tuna  vampire  vole  vulture  wallaby  wasp   \n",
       "hair          1  ...        0     0        1     1        0        1     1   \n",
       "feathers      0  ...        0     0        0     0        1        0     0   \n",
       "eggs          0  ...        1     1        0     0        1        0     1   \n",
       "milk          1  ...        0     0        1     1        0        1     0   \n",
       "airbourne     0  ...        0     0        1     0        1        0     1   \n",
       "aquatic       0  ...        0     1        0     0        0        0     0   \n",
       "predator      0  ...        1     1        0     0        1        0     0   \n",
       "toothed       1  ...        1     1        1     1        0        1     0   \n",
       "backbone      1  ...        1     1        1     1        1        1     0   \n",
       "breathes      1  ...        1     0        1     1        1        1     1   \n",
       "venomous      0  ...        0     0        0     0        0        0     1   \n",
       "fins          0  ...        0     1        0     0        0        0     0   \n",
       "legs          4  ...        4     0        2     4        2        2     6   \n",
       "tail          0  ...        1     1        1     1        1        1     0   \n",
       "domestic      1  ...        0     0        0     0        0        0     0   \n",
       "catsize       0  ...        0     1        0     0        1        1     0   \n",
       "type          1  ...        3     4        1     1        2        1     6   \n",
       "\n",
       "            98    99    100  \n",
       "animal     wolf  worm  wren  \n",
       "hair          1     0     0  \n",
       "feathers      0     0     1  \n",
       "eggs          0     1     1  \n",
       "milk          1     0     0  \n",
       "airbourne     0     0     1  \n",
       "aquatic       0     0     0  \n",
       "predator      1     0     0  \n",
       "toothed       1     0     0  \n",
       "backbone      1     0     1  \n",
       "breathes      1     1     1  \n",
       "venomous      0     0     0  \n",
       "fins          0     0     0  \n",
       "legs          4     0     2  \n",
       "tail          1     0     1  \n",
       "domestic      0     0     0  \n",
       "catsize       1     0     0  \n",
       "type          1     7     2  \n",
       "\n",
       "[18 rows x 101 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ed919c-2376-461d-bd6d-2a53b1f80c55",
   "metadata": {},
   "source": [
    "<br>Let's see if that changed our DataFrame object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e21da875-ab56-4734-9d50-7d5f497f0f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
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       "      <td>bass</td>\n",
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       "    <tr>\n",
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       "      <td>wasp</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>worm</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>wren</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       animal  hair  feathers  eggs  milk  airbourne  aquatic  predator  \\\n",
       "0    aardvark     1         0     0     1          0        0         1   \n",
       "1    antelope     1         0     0     1          0        0         0   \n",
       "2        bass     0         0     1     0          0        1         1   \n",
       "3        bear     1         0     0     1          0        0         1   \n",
       "4        boar     1         0     0     1          0        0         1   \n",
       "..        ...   ...       ...   ...   ...        ...      ...       ...   \n",
       "96    wallaby     1         0     0     1          0        0         0   \n",
       "97       wasp     1         0     1     0          1        0         0   \n",
       "98       wolf     1         0     0     1          0        0         1   \n",
       "99       worm     0         0     1     0          0        0         0   \n",
       "100      wren     0         1     1     0          1        0         0   \n",
       "\n",
       "     toothed  backbone  breathes  venomous  fins  legs  tail  domestic  \\\n",
       "0          1         1         1         0     0     4     0         0   \n",
       "1          1         1         1         0     0     4     1         0   \n",
       "2          1         1         0         0     1     0     1         0   \n",
       "3          1         1         1         0     0     4     0         0   \n",
       "4          1         1         1         0     0     4     1         0   \n",
       "..       ...       ...       ...       ...   ...   ...   ...       ...   \n",
       "96         1         1         1         0     0     2     1         0   \n",
       "97         0         0         1         1     0     6     0         0   \n",
       "98         1         1         1         0     0     4     1         0   \n",
       "99         0         0         1         0     0     0     0         0   \n",
       "100        0         1         1         0     0     2     1         0   \n",
       "\n",
       "     catsize  type  \n",
       "0          1     1  \n",
       "1          1     1  \n",
       "2          0     4  \n",
       "3          1     1  \n",
       "4          1     1  \n",
       "..       ...   ...  \n",
       "96         1     1  \n",
       "97         0     6  \n",
       "98         1     1  \n",
       "99         0     7  \n",
       "100        0     2  \n",
       "\n",
       "[101 rows x 18 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d612c4f-45d9-446f-9522-eb3ace390356",
   "metadata": {},
   "source": [
    "<br><br>It didn't change! DataFrames are **immutable objects** like strings and numpy arrays. To save the transposed DataFrame, we would have to reassign it to a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b63fdd7d-62a9-4a3f-9d36-1c74911ccc0c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggs</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milk</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airbourne</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aquatic</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predator</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toothed</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>backbone</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breathes</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>venomous</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fins</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>legs</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tail</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>domestic</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catsize</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0         1     2     3     4        5     6     7        8    \\\n",
       "animal     aardvark  antelope  bass  bear  boar  buffalo  calf  carp  catfish   \n",
       "hair              1         1     0     1     1        1     1     0        0   \n",
       "feathers          0         0     0     0     0        0     0     0        0   \n",
       "eggs              0         0     1     0     0        0     0     1        1   \n",
       "milk              1         1     0     1     1        1     1     0        0   \n",
       "airbourne         0         0     0     0     0        0     0     0        0   \n",
       "aquatic           0         0     1     0     0        0     0     1        1   \n",
       "predator          1         0     1     1     1        0     0     0        1   \n",
       "toothed           1         1     1     1     1        1     1     1        1   \n",
       "backbone          1         1     1     1     1        1     1     1        1   \n",
       "breathes          1         1     0     1     1        1     1     0        0   \n",
       "venomous          0         0     0     0     0        0     0     0        0   \n",
       "fins              0         0     1     0     0        0     0     1        1   \n",
       "legs              4         4     0     4     4        4     4     0        0   \n",
       "tail              0         1     1     0     1        1     1     1        1   \n",
       "domestic          0         0     0     0     0        0     1     1        0   \n",
       "catsize           1         1     0     1     1        1     1     0        0   \n",
       "type              1         1     4     1     1        1     1     4        4   \n",
       "\n",
       "            9    ...      91    92       93    94       95       96    97   \\\n",
       "animal     cavy  ...  tuatara  tuna  vampire  vole  vulture  wallaby  wasp   \n",
       "hair          1  ...        0     0        1     1        0        1     1   \n",
       "feathers      0  ...        0     0        0     0        1        0     0   \n",
       "eggs          0  ...        1     1        0     0        1        0     1   \n",
       "milk          1  ...        0     0        1     1        0        1     0   \n",
       "airbourne     0  ...        0     0        1     0        1        0     1   \n",
       "aquatic       0  ...        0     1        0     0        0        0     0   \n",
       "predator      0  ...        1     1        0     0        1        0     0   \n",
       "toothed       1  ...        1     1        1     1        0        1     0   \n",
       "backbone      1  ...        1     1        1     1        1        1     0   \n",
       "breathes      1  ...        1     0        1     1        1        1     1   \n",
       "venomous      0  ...        0     0        0     0        0        0     1   \n",
       "fins          0  ...        0     1        0     0        0        0     0   \n",
       "legs          4  ...        4     0        2     4        2        2     6   \n",
       "tail          0  ...        1     1        1     1        1        1     0   \n",
       "domestic      1  ...        0     0        0     0        0        0     0   \n",
       "catsize       0  ...        0     1        0     0        1        1     0   \n",
       "type          1  ...        3     4        1     1        2        1     6   \n",
       "\n",
       "            98    99    100  \n",
       "animal     wolf  worm  wren  \n",
       "hair          1     0     0  \n",
       "feathers      0     0     1  \n",
       "eggs          0     1     1  \n",
       "milk          1     0     0  \n",
       "airbourne     0     0     1  \n",
       "aquatic       0     0     0  \n",
       "predator      1     0     0  \n",
       "toothed       1     0     0  \n",
       "backbone      1     0     1  \n",
       "breathes      1     1     1  \n",
       "venomous      0     0     0  \n",
       "fins          0     0     0  \n",
       "legs          4     0     2  \n",
       "tail          1     0     1  \n",
       "domestic      0     0     0  \n",
       "catsize       1     0     0  \n",
       "type          1     7     2  \n",
       "\n",
       "[18 rows x 101 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_df_t = zoo_df.T\n",
    "zoo_df_t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0937b97b-0076-485c-acca-8e05fc541051",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e36cb80-e4a1-4b1c-91f2-da0785deccbb",
   "metadata": {},
   "source": [
    "Write code to create a list of column names from `zoo_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21c02665-e5d1-4b40-8766-67f6685e8302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['animal', 'hair', 'feathers', 'eggs', 'milk', 'airbourne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'type']\n"
     ]
    }
   ],
   "source": [
    "zoo_list = list(zoo_df.columns)\n",
    "print(zoo_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986a65d3-7135-443c-a3ae-de17cbdb700a",
   "metadata": {},
   "source": [
    "Write code to return the data type for each column in `zoo_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b051b1c-d698-4722-bc96-19d12cf1d2aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal       object\n",
       "hair          int64\n",
       "feathers      int64\n",
       "eggs          int64\n",
       "milk          int64\n",
       "airbourne     int64\n",
       "aquatic       int64\n",
       "predator      int64\n",
       "toothed       int64\n",
       "backbone      int64\n",
       "breathes      int64\n",
       "venomous      int64\n",
       "fins          int64\n",
       "legs          int64\n",
       "tail          int64\n",
       "domestic      int64\n",
       "catsize       int64\n",
       "type          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536f593c-9941-45cf-9f44-a737c33f9560",
   "metadata": {},
   "source": [
    "## <br><br><br>PART TWO: SELECTING DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084353cd-6545-4c22-9656-f9d2f8f7189a",
   "metadata": {},
   "source": [
    "There are multiple ways to select data in pandas. You will need to learn all of the ways because you will see these techniques being used in other people's code and in answers to your pandas questions when you search online.\n",
    "<br><br>We will cover:\n",
    "- Selecting columns using DataFrame indexing\n",
    "- Selecting rows based on a boolean condition using DataFrame indexing\n",
    "- Selecting columns, rows, and individual data points with `loc` and `iloc`\n",
    "- Selecting individual data points with `at` and `iat`\n",
    "*You cannot select individual data points using indexing.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9895468a-410e-4174-b676-b5503ec04b17",
   "metadata": {},
   "source": [
    "We will work with the WNBA game data. Let's look at it again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e42066c8-60b0-449a-a3d3-846aa7b1a81e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324f3565-5dc2-4482-b9d3-7fd03b8aaf1c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### <br><br>Selecting columns or rows using indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f3e993-04b8-43e2-a608-cc2a81356029",
   "metadata": {},
   "source": [
    "To create a DataFrame with only some columns, you use indexing, and you pass it a list of the columns that you want to include:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "26f7e111-8e1b-4abb-abe5-8932d4e6e6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10483</th>\n",
       "      <td>1997</td>\n",
       "      <td>SAC</td>\n",
       "      <td>LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>1997</td>\n",
       "      <td>NYL</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>1997</td>\n",
       "      <td>LAS</td>\n",
       "      <td>NYL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>SAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>1997</td>\n",
       "      <td>CLE</td>\n",
       "      <td>HOU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10488 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season team1 team2\n",
       "0        2019   WAS   CON\n",
       "1        2019   CON   WAS\n",
       "2        2019   WAS   CON\n",
       "3        2019   CON   WAS\n",
       "4        2019   WAS   CON\n",
       "...       ...   ...   ...\n",
       "10483    1997   SAC   LVA\n",
       "10484    1997   NYL   LAS\n",
       "10485    1997   LAS   NYL\n",
       "10486    1997   LVA   SAC\n",
       "10487    1997   CLE   HOU\n",
       "\n",
       "[10488 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_columns = [\"season\", \"team1\", \"team2\"]\n",
    "df[my_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758552dc-d1aa-4438-9c7f-059c0dbc1757",
   "metadata": {},
   "source": [
    "<br>OR you could just include the list inside the indexing. This creates two sets of square brackets, which looks a little silly, but it works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c605f4b-db6a-4430-9c7c-b8e78e01bd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10483</th>\n",
       "      <td>1997</td>\n",
       "      <td>SAC</td>\n",
       "      <td>LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>1997</td>\n",
       "      <td>NYL</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>1997</td>\n",
       "      <td>LAS</td>\n",
       "      <td>NYL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>SAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>1997</td>\n",
       "      <td>CLE</td>\n",
       "      <td>HOU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10488 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season team1 team2\n",
       "0        2019   WAS   CON\n",
       "1        2019   CON   WAS\n",
       "2        2019   WAS   CON\n",
       "3        2019   CON   WAS\n",
       "4        2019   WAS   CON\n",
       "...       ...   ...   ...\n",
       "10483    1997   SAC   LVA\n",
       "10484    1997   NYL   LAS\n",
       "10485    1997   LAS   NYL\n",
       "10486    1997   LVA   SAC\n",
       "10487    1997   CLE   HOU\n",
       "\n",
       "[10488 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"season\", \"team1\", \"team2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ee3f9b-7ca4-41d5-9344-3678085c1441",
   "metadata": {},
   "source": [
    "<br>If you want to return just one column as a DataFrame, you still use the list inside the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9c8c54eb-5adb-4e90-97ca-1fc0f46b70d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/10/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/10/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/8/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/8/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/6/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10483</th>\n",
       "      <td>6/21/1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>6/21/1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>6/21/1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>6/21/1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>6/21/1997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10488 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date\n",
       "0      10/10/2019\n",
       "1      10/10/2019\n",
       "2       10/8/2019\n",
       "3       10/8/2019\n",
       "4       10/6/2019\n",
       "...           ...\n",
       "10483   6/21/1997\n",
       "10484   6/21/1997\n",
       "10485   6/21/1997\n",
       "10486   6/21/1997\n",
       "10487   6/21/1997\n",
       "\n",
       "[10488 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"date\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da91f6c3-8548-402d-8283-fbfa71764cf4",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ab06d0-1613-440d-a350-45008be17d4a",
   "metadata": {},
   "source": [
    "Here's a reminder of what the DataFrame looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "089c2dd0-02e9-4d21-b543-b57a8804e4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce13baab-eb29-4c8b-84d3-72a2533f9bd6",
   "metadata": {},
   "source": [
    "Write code to return the name1, name2, and is_home1 columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d689a06-5424-4d8a-875b-eec479243a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington Mystics</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10483</th>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10488 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name1                name2  is_home1\n",
       "0       Washington Mystics      Connecticut Sun         1\n",
       "1          Connecticut Sun   Washington Mystics         0\n",
       "2       Washington Mystics      Connecticut Sun         0\n",
       "3          Connecticut Sun   Washington Mystics         1\n",
       "4       Washington Mystics      Connecticut Sun         0\n",
       "...                    ...                  ...       ...\n",
       "10483  Sacramento Monarchs          Utah Starzz         0\n",
       "10484     New York Liberty   Los Angeles Sparks         0\n",
       "10485   Los Angeles Sparks     New York Liberty         1\n",
       "10486          Utah Starzz  Sacramento Monarchs         1\n",
       "10487    Cleveland Rockers       Houston Comets         1\n",
       "\n",
       "[10488 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"name1\", \"name2\", \"is_home1\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50dd3462-a9c5-4232-bd42-3193aa424715",
   "metadata": {},
   "source": [
    "Write code to return the playoff column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "77d5766f-95cb-4237-927e-5f797d74978d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10483</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10488 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playoff\n",
       "0            1\n",
       "1            1\n",
       "2            1\n",
       "3            1\n",
       "4            1\n",
       "...        ...\n",
       "10483        0\n",
       "10484        0\n",
       "10485        0\n",
       "10486        0\n",
       "10487        0\n",
       "\n",
       "[10488 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"playoff\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d26f14-13b5-4b16-b940-f4764d22fb2d",
   "metadata": {},
   "source": [
    "<br><br><br>If you only index the column name, without putting it in a list, you get a different type of pandas object - the **Series** object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99c46638-438f-486b-a12d-6e19206453d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        10/10/2019\n",
       "1        10/10/2019\n",
       "2         10/8/2019\n",
       "3         10/8/2019\n",
       "4         10/6/2019\n",
       "            ...    \n",
       "10483     6/21/1997\n",
       "10484     6/21/1997\n",
       "10485     6/21/1997\n",
       "10486     6/21/1997\n",
       "10487     6/21/1997\n",
       "Name: date, Length: 10488, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"date\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a56797-3221-454f-b483-8eca893cffdf",
   "metadata": {},
   "source": [
    "<br>A Series object only returns the values from one column. It can be turned into a list, which is very convenient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "44e0725d-677d-4255-9010-a5ac78ccddce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10488\n",
      "<class 'list'>\n",
      "['10/10/2019', '10/10/2019', '10/8/2019', '10/8/2019', '10/6/2019']\n"
     ]
    }
   ],
   "source": [
    "date_list = list(df[\"date\"])\n",
    "print(len(date_list))\n",
    "print(type(date_list))\n",
    "print(date_list[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783476a5-2155-4ab8-8dab-64779bb726d8",
   "metadata": {},
   "source": [
    "<br>**A Series object is a one-dimensional object, while a DataFrame is a two-dimensional object. A Series can be turned into a list, while a DataFrame can be indexed based on row number, so they both have their uses.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee3cb99-6f57-401e-a16c-a06cfdf8fd0c",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20546404-a317-4826-8580-de1f26b90cb2",
   "metadata": {},
   "source": [
    "Write code to return a list of data in the prob1 column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "981427e2-0002-4bda-beb8-059cf083ee70",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_data = list(df[\"prob1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4352ed8f-3522-426f-8a6e-9cf3a5cbabeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10488\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(len(prob_data))\n",
    "print(type(prob_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990a1af9-622c-4d0a-82b3-ad64c6623698",
   "metadata": {},
   "source": [
    "### <br><br><br>Selecting rows using indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11891ffa-6371-4753-b624-0c3bc74a85ab",
   "metadata": {},
   "source": [
    "If we want to return a DataFrame with only some **rows**, we can index a range. DataFrame indexing uses regular Python indexing, so we ask for the first item we want, and then a colon, and then we go one position past the last item we want. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0701704b-1fd2-4c37-8043-76d8af913728",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
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       "      <td>WAS</td>\n",
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       "      <td>1684</td>\n",
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       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
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       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>1648</td>\n",
       "      <td>1671</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>1700</td>\n",
       "      <td>1618</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>0.763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>87</td>\n",
       "      <td>1618</td>\n",
       "      <td>1700</td>\n",
       "      <td>1648</td>\n",
       "      <td>1671</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>86</td>\n",
       "      <td>1694</td>\n",
       "      <td>1624</td>\n",
       "      <td>1700</td>\n",
       "      <td>1618</td>\n",
       "      <td>0.747</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>95</td>\n",
       "      <td>1624</td>\n",
       "      <td>1694</td>\n",
       "      <td>1618</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "5    2019   10/6/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "6    2019   10/1/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "7    2019   10/1/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "8    2019   9/29/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "9    2019   9/29/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "5        0        1      81      94      1648      1671       1626       1693   \n",
       "6        0        1      87      99      1700      1618       1671       1648   \n",
       "7        0        1      99      87      1618      1700       1648       1671   \n",
       "8        0        1      95      86      1694      1624       1700       1618   \n",
       "9        0        1      86      95      1624      1694       1618       1700   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  \n",
       "5  0.601         1  \n",
       "6  0.763         1  \n",
       "7  0.237         0  \n",
       "8  0.747         1  \n",
       "9  0.253         0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fa19de-1035-4dbd-8ff7-4ae98397255b",
   "metadata": {},
   "source": [
    "<br>Because this indexing is referencing the position of the row in the DataFrame, not the index number, we an use negative indexing in either spot to count from the bottom of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cef717f4-5ddc-438b-811b-058b2ba89f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
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       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>2018</td>\n",
       "      <td>8/17/2018</td>\n",
       "      <td>DAL</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>102</td>\n",
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       "      <td>1451</td>\n",
       "      <td>0.618</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2018</td>\n",
       "      <td>8/17/2018</td>\n",
       "      <td>LVA</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>107</td>\n",
       "      <td>1458</td>\n",
       "      <td>1461</td>\n",
       "      <td>1451</td>\n",
       "      <td>1468</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2018</td>\n",
       "      <td>8/17/2018</td>\n",
       "      <td>NYL</td>\n",
       "      <td>SEA</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "      <td>1328</td>\n",
       "      <td>1653</td>\n",
       "      <td>1326</td>\n",
       "      <td>1655</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>2018</td>\n",
       "      <td>8/15/2018</td>\n",
       "      <td>WAS</td>\n",
       "      <td>IND</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>62</td>\n",
       "      <td>1591</td>\n",
       "      <td>1330</td>\n",
       "      <td>1599</td>\n",
       "      <td>1322</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2018</td>\n",
       "      <td>8/15/2018</td>\n",
       "      <td>LVA</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>72</td>\n",
       "      <td>1451</td>\n",
       "      <td>1335</td>\n",
       "      <td>1458</td>\n",
       "      <td>1328</td>\n",
       "      <td>0.755</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10471</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/26/1997</td>\n",
       "      <td>CHA</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>1467</td>\n",
       "      <td>1501</td>\n",
       "      <td>1459</td>\n",
       "      <td>1509</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10472</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/25/1997</td>\n",
       "      <td>LAS</td>\n",
       "      <td>CHA</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>54</td>\n",
       "      <td>1468</td>\n",
       "      <td>1485</td>\n",
       "      <td>1486</td>\n",
       "      <td>1467</td>\n",
       "      <td>0.590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10473</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/25/1997</td>\n",
       "      <td>CHA</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>74</td>\n",
       "      <td>1485</td>\n",
       "      <td>1468</td>\n",
       "      <td>1467</td>\n",
       "      <td>1486</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10474</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/24/1997</td>\n",
       "      <td>HOU</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>55</td>\n",
       "      <td>1530</td>\n",
       "      <td>1515</td>\n",
       "      <td>1544</td>\n",
       "      <td>1501</td>\n",
       "      <td>0.634</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10475</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/24/1997</td>\n",
       "      <td>PHO</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>72</td>\n",
       "      <td>1515</td>\n",
       "      <td>1530</td>\n",
       "      <td>1501</td>\n",
       "      <td>1544</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9981 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season       date team1 team2               name1                name2  \\\n",
       "495      2018  8/17/2018   DAL   LVA        Dallas Wings       Las Vegas Aces   \n",
       "496      2018  8/17/2018   LVA   DAL      Las Vegas Aces         Dallas Wings   \n",
       "497      2018  8/17/2018   NYL   SEA    New York Liberty        Seattle Storm   \n",
       "498      2018  8/15/2018   WAS   IND  Washington Mystics        Indiana Fever   \n",
       "499      2018  8/15/2018   LVA   NYL      Las Vegas Aces     New York Liberty   \n",
       "...       ...        ...   ...   ...                 ...                  ...   \n",
       "10471    1997  6/26/1997   CHA   SAC     Charlotte Sting  Sacramento Monarchs   \n",
       "10472    1997  6/25/1997   LAS   CHA  Los Angeles Sparks      Charlotte Sting   \n",
       "10473    1997  6/25/1997   CHA   LAS     Charlotte Sting   Los Angeles Sparks   \n",
       "10474    1997  6/24/1997   HOU   PHO      Houston Comets      Phoenix Mercury   \n",
       "10475    1997  6/24/1997   PHO   HOU     Phoenix Mercury       Houston Comets   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "495          0        0     107     102      1461      1458       1468   \n",
       "496          0        0     102     107      1458      1461       1451   \n",
       "497          0        0      77      85      1328      1653       1326   \n",
       "498          0        0      76      62      1591      1330       1599   \n",
       "499          0        0      85      72      1451      1335       1458   \n",
       "...        ...      ...     ...     ...       ...       ...        ...   \n",
       "10471        0        0      70      78      1467      1501       1459   \n",
       "10472        0        0      74      54      1468      1485       1486   \n",
       "10473        0        0      54      74      1485      1468       1467   \n",
       "10474        0        0      72      55      1530      1515       1544   \n",
       "10475        0        0      55      72      1515      1530       1501   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "495         1451  0.618         1  \n",
       "496         1468  0.382         0  \n",
       "497         1655  0.088         0  \n",
       "498         1322  0.740         0  \n",
       "499         1328  0.755         1  \n",
       "...          ...    ...       ...  \n",
       "10471       1509  0.342         0  \n",
       "10472       1467  0.590         1  \n",
       "10473       1486  0.410         0  \n",
       "10474       1501  0.634         1  \n",
       "10475       1544  0.366         0  \n",
       "\n",
       "[9981 rows x 16 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[495:-12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7923721a-9f65-4379-9d63-d9e8f72cb83f",
   "metadata": {},
   "source": [
    "<br>If you only want a single row, you still need to use indexing with a `:`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ad47fd6f-2dea-4762-b0ff-fffd94d64836",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   season       date team1 team2               name1            name2  \\\n",
       "4    2019  10/6/2019   WAS   CON  Washington Mystics  Connecticut Sun   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "\n",
       "   prob1  is_home1  \n",
       "4  0.399         0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[4:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97f64b6-db27-4a66-9226-cd465e0e51fa",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7496515a-3b53-421e-8d0b-8c75b7fe5922",
   "metadata": {},
   "source": [
    "Write code to return row number 9,999."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c63c905b-4bc9-4b36-9cbb-3c3e6625b38a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1998</td>\n",
       "      <td>8/11/1998</td>\n",
       "      <td>SAC</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>Detroit Shock</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>1369</td>\n",
       "      <td>1424</td>\n",
       "      <td>1354</td>\n",
       "      <td>1439</td>\n",
       "      <td>0.537</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      season       date team1 team2                name1          name2  \\\n",
       "9999    1998  8/11/1998   SAC   DAL  Sacramento Monarchs  Detroit Shock   \n",
       "\n",
       "      neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "9999        0        0      41      50      1369      1424       1354   \n",
       "\n",
       "      elo2_post  prob1  is_home1  \n",
       "9999       1439  0.537         1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[9999:10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0158700-008b-4780-950a-519fe8f9f02b",
   "metadata": {},
   "source": [
    "Write code to return the second row in our DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1b67e7fc-578c-4cc3-8ac5-2e11d9c36371",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2            name1               name2  \\\n",
       "1    2019  10/10/2019   CON   WAS  Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "\n",
       "   prob1  is_home1  \n",
       "1  0.282         0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55baa66e-10dd-4eae-9ba1-3155b117a567",
   "metadata": {},
   "source": [
    "### <br><br><br>Selecting data with a boolean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e2ea5e-5e03-4272-b2df-e115e8a5543c",
   "metadata": {},
   "source": [
    "To return a DataFrame that only has rows that meet a certain condition, we use this syntax. The outer `df[]` lets Python know that you want the answer to be returned as a DataFrame, meaning you can return all the columns included in the output. Inside the indexing, we include our boolean statement, which usually means we need to index a particular column in the dataset to filter the data on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aa426ded-096c-4d2f-9394-f45c8db13862",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>season</th>\n",
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       "      <td>LVA</td>\n",
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       "      <th>14</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/22/2019</td>\n",
       "      <td>LVA</td>\n",
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       "      <td>75</td>\n",
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       "      <td>1717</td>\n",
       "      <td>1570</td>\n",
       "      <td>1687</td>\n",
       "      <td>0.331</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/19/2019</td>\n",
       "      <td>LVA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>Washington Mystics</td>\n",
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       "      <td>103</td>\n",
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       "      <td>1714</td>\n",
       "      <td>1540</td>\n",
       "      <td>1717</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/17/2019</td>\n",
       "      <td>LVA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>97</td>\n",
       "      <td>1545</td>\n",
       "      <td>1712</td>\n",
       "      <td>1543</td>\n",
       "      <td>1714</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/15/2019</td>\n",
       "      <td>LVA</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>92</td>\n",
       "      <td>1541</td>\n",
       "      <td>1564</td>\n",
       "      <td>1545</td>\n",
       "      <td>1559</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10453</th>\n",
       "      <td>1997</td>\n",
       "      <td>7/2/1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>68</td>\n",
       "      <td>1456</td>\n",
       "      <td>1497</td>\n",
       "      <td>1470</td>\n",
       "      <td>1483</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10463</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/28/1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>76</td>\n",
       "      <td>1479</td>\n",
       "      <td>1535</td>\n",
       "      <td>1456</td>\n",
       "      <td>1558</td>\n",
       "      <td>0.534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10470</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/26/1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>74</td>\n",
       "      <td>1491</td>\n",
       "      <td>1470</td>\n",
       "      <td>1479</td>\n",
       "      <td>1482</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10478</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/23/1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>89</td>\n",
       "      <td>1479</td>\n",
       "      <td>1481</td>\n",
       "      <td>1491</td>\n",
       "      <td>1468</td>\n",
       "      <td>0.610</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/21/1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>1500</td>\n",
       "      <td>1500</td>\n",
       "      <td>1479</td>\n",
       "      <td>1521</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season       date team1 team2           name1                name2  \\\n",
       "11       2019  9/24/2019   LVA   WAS  Las Vegas Aces   Washington Mystics   \n",
       "14       2019  9/22/2019   LVA   WAS  Las Vegas Aces   Washington Mystics   \n",
       "19       2019  9/19/2019   LVA   WAS  Las Vegas Aces   Washington Mystics   \n",
       "23       2019  9/17/2019   LVA   WAS  Las Vegas Aces   Washington Mystics   \n",
       "26       2019  9/15/2019   LVA   CHI  Las Vegas Aces          Chicago Sky   \n",
       "...       ...        ...   ...   ...             ...                  ...   \n",
       "10453    1997   7/2/1997   LVA   SAC     Utah Starzz  Sacramento Monarchs   \n",
       "10463    1997  6/28/1997   LVA   HOU     Utah Starzz       Houston Comets   \n",
       "10470    1997  6/26/1997   LVA   CLE     Utah Starzz    Cleveland Rockers   \n",
       "10478    1997  6/23/1997   LVA   LAS     Utah Starzz   Los Angeles Sparks   \n",
       "10486    1997  6/21/1997   LVA   SAC     Utah Starzz  Sacramento Monarchs   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "11           0        1      90      94      1570      1687       1563   \n",
       "14           0        1      92      75      1540      1717       1570   \n",
       "19           0        1      91     103      1543      1714       1540   \n",
       "23           0        1      95      97      1545      1712       1543   \n",
       "26           0        1      93      92      1541      1564       1545   \n",
       "...        ...      ...     ...     ...       ...       ...        ...   \n",
       "10453        0        0      73      68      1456      1497       1470   \n",
       "10463        0        0      58      76      1479      1535       1456   \n",
       "10470        0        0      63      74      1491      1470       1479   \n",
       "10478        0        0     102      89      1479      1481       1491   \n",
       "10486        0        0      61      73      1500      1500       1479   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "11          1694  0.434         1  \n",
       "14          1687  0.331         1  \n",
       "19          1717  0.142         0  \n",
       "23          1714  0.144         0  \n",
       "26          1559  0.601         1  \n",
       "...          ...    ...       ...  \n",
       "10453       1483  0.332         0  \n",
       "10463       1558  0.534         1  \n",
       "10470       1482  0.416         0  \n",
       "10478       1468  0.610         1  \n",
       "10486       1521  0.613         1  \n",
       "\n",
       "[802 rows x 16 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"team1\"] == \"LVA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "68c02143-ef3b-4647-bc58-2fb9c45e56e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/19/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>1714</td>\n",
       "      <td>1543</td>\n",
       "      <td>1717</td>\n",
       "      <td>1540</td>\n",
       "      <td>0.858</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/11/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "      <td>1553</td>\n",
       "      <td>1450</td>\n",
       "      <td>1564</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/8/2019</td>\n",
       "      <td>IND</td>\n",
       "      <td>CON</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>76</td>\n",
       "      <td>1424</td>\n",
       "      <td>1599</td>\n",
       "      <td>1464</td>\n",
       "      <td>1559</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/6/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>109</td>\n",
       "      <td>1615</td>\n",
       "      <td>1543</td>\n",
       "      <td>1599</td>\n",
       "      <td>1559</td>\n",
       "      <td>0.705</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/6/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "      <td>1543</td>\n",
       "      <td>1615</td>\n",
       "      <td>1559</td>\n",
       "      <td>1599</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9872</th>\n",
       "      <td>1999</td>\n",
       "      <td>6/24/1999</td>\n",
       "      <td>SAC</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>69</td>\n",
       "      <td>1465</td>\n",
       "      <td>1396</td>\n",
       "      <td>1484</td>\n",
       "      <td>1376</td>\n",
       "      <td>0.702</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9961</th>\n",
       "      <td>1998</td>\n",
       "      <td>8/19/1998</td>\n",
       "      <td>CHA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>69</td>\n",
       "      <td>1460</td>\n",
       "      <td>1183</td>\n",
       "      <td>1475</td>\n",
       "      <td>1168</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9966</th>\n",
       "      <td>1998</td>\n",
       "      <td>8/17/1998</td>\n",
       "      <td>HOU</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>65</td>\n",
       "      <td>1695</td>\n",
       "      <td>1188</td>\n",
       "      <td>1700</td>\n",
       "      <td>1183</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10464</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/27/1997</td>\n",
       "      <td>LAS</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>92</td>\n",
       "      <td>1486</td>\n",
       "      <td>1509</td>\n",
       "      <td>1498</td>\n",
       "      <td>1497</td>\n",
       "      <td>0.582</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10478</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/23/1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>89</td>\n",
       "      <td>1479</td>\n",
       "      <td>1481</td>\n",
       "      <td>1491</td>\n",
       "      <td>1468</td>\n",
       "      <td>0.610</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season       date team1 team2                name1  \\\n",
       "16       2019  9/19/2019   WAS   LVA   Washington Mystics   \n",
       "28       2019  9/11/2019   CHI   PHO          Chicago Sky   \n",
       "39       2019   9/8/2019   IND   CON        Indiana Fever   \n",
       "45       2019   9/6/2019   CON   CHI      Connecticut Sun   \n",
       "47       2019   9/6/2019   CHI   CON          Chicago Sky   \n",
       "...       ...        ...   ...   ...                  ...   \n",
       "9872     1999  6/24/1999   SAC   LVA  Sacramento Monarchs   \n",
       "9961     1998  8/19/1998   CHA   WAS      Charlotte Sting   \n",
       "9966     1998  8/17/1998   HOU   WAS       Houston Comets   \n",
       "10464    1997  6/27/1997   LAS   SAC   Los Angeles Sparks   \n",
       "10478    1997  6/23/1997   LVA   LAS          Utah Starzz   \n",
       "\n",
       "                     name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "16          Las Vegas Aces        0        1     103      91      1714   \n",
       "28         Phoenix Mercury        0        1     105      76      1553   \n",
       "39         Connecticut Sun        0        0     104      76      1424   \n",
       "45             Chicago Sky        0        0     104     109      1615   \n",
       "47         Connecticut Sun        0        0     109     104      1543   \n",
       "...                    ...      ...      ...     ...     ...       ...   \n",
       "9872           Utah Starzz        0        0     107      69      1465   \n",
       "9961    Washington Mystics        0        0     105      69      1460   \n",
       "9966    Washington Mystics        0        0     110      65      1695   \n",
       "10464  Sacramento Monarchs        0        0     102      92      1486   \n",
       "10478   Los Angeles Sparks        0        0     102      89      1479   \n",
       "\n",
       "       elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "16         1543       1717       1540  0.858         1  \n",
       "28         1450       1564       1440  0.788         1  \n",
       "39         1599       1464       1559  0.367         1  \n",
       "45         1543       1599       1559  0.705         1  \n",
       "47         1615       1559       1599  0.295         0  \n",
       "...         ...        ...        ...    ...       ...  \n",
       "9872       1396       1484       1376  0.702         1  \n",
       "9961       1183       1475       1168  0.757         0  \n",
       "9966       1188       1700       1183  0.921         0  \n",
       "10464      1509       1498       1497  0.582         1  \n",
       "10478      1481       1491       1468  0.610         1  \n",
       "\n",
       "[225 rows x 16 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"score1\"] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f6ef09-d54f-45e7-b22b-cb2a996939ae",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21a3f93-6549-440c-8edc-ce6ec8b38613",
   "metadata": {},
   "source": [
    "Write code to return a DataFrame that only includes games that were playoff games (playoff games are coded as 1 in that column):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "192e4345-04e9-4fc2-a584-f00ce6d8fc9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10259</th>\n",
       "      <td>1997</td>\n",
       "      <td>8/30/1997</td>\n",
       "      <td>NYL</td>\n",
       "      <td>HOU</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>65</td>\n",
       "      <td>1532</td>\n",
       "      <td>1597</td>\n",
       "      <td>1524</td>\n",
       "      <td>1605</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10260</th>\n",
       "      <td>1997</td>\n",
       "      <td>8/28/1997</td>\n",
       "      <td>HOU</td>\n",
       "      <td>CHA</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>54</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>1597</td>\n",
       "      <td>1503</td>\n",
       "      <td>0.755</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10261</th>\n",
       "      <td>1997</td>\n",
       "      <td>8/28/1997</td>\n",
       "      <td>NYL</td>\n",
       "      <td>PHO</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>41</td>\n",
       "      <td>1496</td>\n",
       "      <td>1556</td>\n",
       "      <td>1532</td>\n",
       "      <td>1520</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10262</th>\n",
       "      <td>1997</td>\n",
       "      <td>8/28/1997</td>\n",
       "      <td>PHO</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>59</td>\n",
       "      <td>1556</td>\n",
       "      <td>1496</td>\n",
       "      <td>1520</td>\n",
       "      <td>1532</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10263</th>\n",
       "      <td>1997</td>\n",
       "      <td>8/28/1997</td>\n",
       "      <td>CHA</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>70</td>\n",
       "      <td>1512</td>\n",
       "      <td>1588</td>\n",
       "      <td>1503</td>\n",
       "      <td>1597</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season        date team1 team2               name1               name2  \\\n",
       "0        2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1        2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2        2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3        2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4        2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "...       ...         ...   ...   ...                 ...                 ...   \n",
       "10259    1997   8/30/1997   NYL   HOU    New York Liberty      Houston Comets   \n",
       "10260    1997   8/28/1997   HOU   CHA      Houston Comets     Charlotte Sting   \n",
       "10261    1997   8/28/1997   NYL   PHO    New York Liberty     Phoenix Mercury   \n",
       "10262    1997   8/28/1997   PHO   NYL     Phoenix Mercury    New York Liberty   \n",
       "10263    1997   8/28/1997   CHA   HOU     Charlotte Sting      Houston Comets   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "0            0        1      89      78      1684      1634       1692   \n",
       "1            0        1      78      89      1634      1684       1627   \n",
       "2            0        1      86      90      1693      1626       1684   \n",
       "3            0        1      90      86      1626      1693       1634   \n",
       "4            0        1      94      81      1671      1648       1693   \n",
       "...        ...      ...     ...     ...       ...       ...        ...   \n",
       "10259        0        1      51      65      1532      1597       1524   \n",
       "10260        0        1      70      54      1588      1512       1597   \n",
       "10261        0        1      59      41      1496      1556       1532   \n",
       "10262        0        1      41      59      1556      1496       1520   \n",
       "10263        0        1      54      70      1512      1588       1503   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "0           1627  0.718         1  \n",
       "1           1692  0.282         0  \n",
       "2           1634  0.476         0  \n",
       "3           1684  0.524         1  \n",
       "4           1626  0.399         0  \n",
       "...          ...    ...       ...  \n",
       "10259       1605  0.261         0  \n",
       "10260       1503  0.755         1  \n",
       "10261       1520  0.267         0  \n",
       "10262       1532  0.733         1  \n",
       "10263       1597  0.245         0  \n",
       "\n",
       "[768 rows x 16 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"playoff\"] == 1]"
   ]
  },
  {
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   "execution_count": 50,
   "id": "9c19ffc6-41aa-4e5e-9274-22f8bfa6bc69",
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      ],
      "text/plain": [
       "       season date team1 team2 name1 name2  neutral  playoff  score1  score2  \\\n",
       "0         NaN  NaN   NaN   NaN   NaN   NaN      NaN      1.0     NaN     NaN   \n",
       "1         NaN  NaN   NaN   NaN   NaN   NaN      NaN      1.0     NaN     NaN   \n",
       "2         NaN  NaN   NaN   NaN   NaN   NaN      NaN      1.0     NaN     NaN   \n",
       "3         NaN  NaN   NaN   NaN   NaN   NaN      NaN      1.0     NaN     NaN   \n",
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       "10487     NaN  NaN   NaN   NaN   NaN   NaN      NaN      NaN     NaN     NaN   \n",
       "\n",
       "       elo1_pre  elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "0           NaN       NaN        NaN        NaN    NaN       NaN  \n",
       "1           NaN       NaN        NaN        NaN    NaN       NaN  \n",
       "2           NaN       NaN        NaN        NaN    NaN       NaN  \n",
       "3           NaN       NaN        NaN        NaN    NaN       NaN  \n",
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       "...         ...       ...        ...        ...    ...       ...  \n",
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       "10487       NaN       NaN        NaN        NaN    NaN       NaN  \n",
       "\n",
       "[10488 rows x 16 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[[\"playoff\"]] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe21cd6-89e5-463b-a880-29043b02c3ae",
   "metadata": {},
   "source": [
    "Write code to return a DataFrame that only includes games where score2 was greater than 100:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "72532c89-5377-4ee0-8e77-c6b11d88be24",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1168</td>\n",
       "      <td>1475</td>\n",
       "      <td>0.243</td>\n",
       "      <td>1</td>\n",
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       "      <td>Houston Comets</td>\n",
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       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>110</td>\n",
       "      <td>1188</td>\n",
       "      <td>1695</td>\n",
       "      <td>1183</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.079</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10465</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/27/1997</td>\n",
       "      <td>SAC</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>102</td>\n",
       "      <td>1509</td>\n",
       "      <td>1486</td>\n",
       "      <td>1497</td>\n",
       "      <td>1498</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10479</th>\n",
       "      <td>1997</td>\n",
       "      <td>6/23/1997</td>\n",
       "      <td>LAS</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>1481</td>\n",
       "      <td>1479</td>\n",
       "      <td>1468</td>\n",
       "      <td>1491</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season       date team1 team2                name1  \\\n",
       "19       2019  9/19/2019   LVA   WAS       Las Vegas Aces   \n",
       "31       2019  9/11/2019   PHO   CHI      Phoenix Mercury   \n",
       "35       2019   9/8/2019   CON   IND      Connecticut Sun   \n",
       "45       2019   9/6/2019   CON   CHI      Connecticut Sun   \n",
       "47       2019   9/6/2019   CHI   CON          Chicago Sky   \n",
       "...       ...        ...   ...   ...                  ...   \n",
       "9876     1999  6/24/1999   LVA   SAC          Utah Starzz   \n",
       "9965     1998  8/19/1998   WAS   CHA   Washington Mystics   \n",
       "9971     1998  8/17/1998   WAS   HOU   Washington Mystics   \n",
       "10465    1997  6/27/1997   SAC   LAS  Sacramento Monarchs   \n",
       "10479    1997  6/23/1997   LAS   LVA   Los Angeles Sparks   \n",
       "\n",
       "                     name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "19      Washington Mystics        0        1      91     103      1543   \n",
       "31             Chicago Sky        0        1      76     105      1450   \n",
       "35           Indiana Fever        0        0      76     104      1599   \n",
       "45             Chicago Sky        0        0     104     109      1615   \n",
       "47         Connecticut Sun        0        0     109     104      1543   \n",
       "...                    ...      ...      ...     ...     ...       ...   \n",
       "9876   Sacramento Monarchs        0        0      69     107      1396   \n",
       "9965       Charlotte Sting        0        0      69     105      1183   \n",
       "9971        Houston Comets        0        0      65     110      1188   \n",
       "10465   Los Angeles Sparks        0        0      92     102      1509   \n",
       "10479          Utah Starzz        0        0      89     102      1481   \n",
       "\n",
       "       elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "19         1714       1540       1717  0.142         0  \n",
       "31         1553       1440       1564  0.212         0  \n",
       "35         1424       1559       1464  0.633         0  \n",
       "45         1543       1599       1559  0.705         1  \n",
       "47         1615       1559       1599  0.295         0  \n",
       "...         ...        ...        ...    ...       ...  \n",
       "9876       1465       1376       1484  0.298         0  \n",
       "9965       1460       1168       1475  0.243         1  \n",
       "9971       1695       1183       1700  0.079         1  \n",
       "10465      1486       1497       1498  0.418         0  \n",
       "10479      1479       1468       1491  0.390         0  \n",
       "\n",
       "[225 rows x 16 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"score2\"] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbdb9a46-a1e2-485b-a702-bec72f91ccce",
   "metadata": {},
   "source": [
    "<br><br><br>If you don't use the outer `df[]` the return is a Series object that returns the boolean value for each row based on the condition you set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6e7a6d14-4419-4a0c-840e-3cc3c0d1db23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "10483    False\n",
       "10484    False\n",
       "10485    False\n",
       "10486     True\n",
       "10487    False\n",
       "Name: team1, Length: 10488, dtype: bool"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"team1\"] == \"LVA\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a315316-6097-49eb-baa3-a15498a86024",
   "metadata": {},
   "source": [
    "### <br><br><br>Combining boolean indexing with column indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72bd8f65-00d3-419c-ac2b-d95050289afe",
   "metadata": {},
   "source": [
    "You can also combine a boolean with column indexing to return only some columns for your filtered data. Here I am returning only the team2, score1, and score2 columns for any rows with \"CHI\" in the team1 column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b7a0d120-2ca5-49ce-9b52-c97964f82f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team2</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LVA</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PHO</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>WAS</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CON</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PHO</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6371</th>\n",
       "      <td>HOU</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>LAS</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6393</th>\n",
       "      <td>IND</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>SAC</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>CHA</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>497 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team2  score1  score2\n",
       "25     LVA      92      93\n",
       "28     PHO     105      76\n",
       "36     WAS      86     100\n",
       "47     CON     109     104\n",
       "65     PHO     105      78\n",
       "...    ...     ...     ...\n",
       "6371   HOU      60      71\n",
       "6387   LAS      55      64\n",
       "6393   IND      60      75\n",
       "6415   SAC      63      76\n",
       "6427   CHA      83      82\n",
       "\n",
       "[497 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"team1\"] == \"CHI\"][[\"team2\", \"score1\", \"score2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aafd74e-0f99-469d-8979-60a425834444",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5e5488-1ef6-475d-859a-9f202ea851eb",
   "metadata": {},
   "source": [
    "Write code to return all the games that were played in the 2017 season. Return only columns for date, team1, and team2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6535f219-0f60-4ed5-8f47-13bdbf76f026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>10/4/2017</td>\n",
       "      <td>MIN</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>10/4/2017</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>10/1/2017</td>\n",
       "      <td>MIN</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>10/1/2017</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>9/29/2017</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>5/13/2017</td>\n",
       "      <td>SEA</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>5/13/2017</td>\n",
       "      <td>ATL</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>5/13/2017</td>\n",
       "      <td>NYL</td>\n",
       "      <td>LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>5/13/2017</td>\n",
       "      <td>CON</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>5/13/2017</td>\n",
       "      <td>LVA</td>\n",
       "      <td>NYL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>438 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date team1 team2\n",
       "882   10/4/2017   MIN   LAS\n",
       "883   10/4/2017   LAS   MIN\n",
       "884   10/1/2017   MIN   LAS\n",
       "885   10/1/2017   LAS   MIN\n",
       "886   9/29/2017   LAS   MIN\n",
       "...         ...   ...   ...\n",
       "1315  5/13/2017   SEA   LAS\n",
       "1316  5/13/2017   ATL   CON\n",
       "1317  5/13/2017   NYL   LVA\n",
       "1318  5/13/2017   CON   ATL\n",
       "1319  5/13/2017   LVA   NYL\n",
       "\n",
       "[438 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"season\"] == 2017][[\"date\", \"team1\", \"team2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0f0b66-2e73-4b3a-8e1e-d79d32da2903",
   "metadata": {},
   "source": [
    "<br><br><br>**Using the indexing method, we cannot refer to individual rows by name or pull up individual cells in our DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e5982808-88ee-4c37-b568-1d2c2a8a510e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "25",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 25",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [57]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3504\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3507\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3623\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3625\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3626\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3627\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3628\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 25"
     ]
    }
   ],
   "source": [
    "df[25]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6240b7a2-3559-4539-8773-1abab458b598",
   "metadata": {
    "tags": []
   },
   "source": [
    "### <br><br><br>pandas loc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ace5f2-9ed6-4eb1-b0cc-5275a41fff70",
   "metadata": {},
   "source": [
    "The `loc` **attribute** allows us to call up certain rows and columns. The syntax is:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6c9669-b721-4fe3-8009-f976cb1ee68a",
   "metadata": {},
   "source": [
    "#### `df.loc[row, column]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6caa83-81e7-4567-ae37-1222893647c3",
   "metadata": {},
   "source": [
    "#### `df.loc[list of rows, list of columns]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5600beb6-bc6c-4c01-85c7-12879ba4c1c4",
   "metadata": {},
   "source": [
    "#### `df.loc[range of rows, range of columns]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa9e930-d69c-484a-96c7-de09e81a0c36",
   "metadata": {},
   "source": [
    "`loc` can take a row, a list of rows, or a range of rows, followed by a comma, and then a column, list of columns, or range of columns. <br><br>If you want all the rows or all the columns, you can use a `:`. <br><br>**The rows that we refer to here are the row names (index names) that are found in bold on the far left of our DataFrame.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decd9344-c813-4398-9384-b85fab700fdc",
   "metadata": {},
   "source": [
    "<br>To reference one cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "772ca4d2-c62c-443d-b68c-8c25e48571f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9/15/2019'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[25, \"date\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9980ff86-3d83-47c3-8b6c-090c8c66f271",
   "metadata": {},
   "source": [
    "<br>All rows for one column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1acaa43d-6671-45c5-8c14-f8beae14884e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        WAS\n",
       "1        CON\n",
       "2        WAS\n",
       "3        CON\n",
       "4        WAS\n",
       "        ... \n",
       "10483    SAC\n",
       "10484    NYL\n",
       "10485    LAS\n",
       "10486    LVA\n",
       "10487    CLE\n",
       "Name: team1, Length: 10488, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, \"team1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36aa9c5d-1f1a-461c-9f5d-7092c2254d69",
   "metadata": {},
   "source": [
    "<br>All columns for one row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5d3313b7-834c-4bff-84fb-6624219bd2dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season                     2019\n",
       "date                  9/22/2019\n",
       "team1                       WAS\n",
       "team2                       LVA\n",
       "name1        Washington Mystics\n",
       "name2            Las Vegas Aces\n",
       "neutral                       0\n",
       "playoff                       1\n",
       "score1                       75\n",
       "score2                       92\n",
       "elo1_pre                   1717\n",
       "elo2_pre                   1540\n",
       "elo1_post                  1687\n",
       "elo2_post                  1570\n",
       "prob1                     0.669\n",
       "is_home1                      0\n",
       "Name: 12, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[12, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cbeb77-3706-4e0d-b7ee-253c2698c71c",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b80fb3-dda5-4284-bf4b-218c25c4d231",
   "metadata": {},
   "source": [
    "The very first game played by the Chicago Sky is in the row with index 6427."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df46706-14cf-4ef9-b306-e6b291bcef36",
   "metadata": {},
   "source": [
    "Write code to return all columns in that row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bd21eb3f-7d4c-49e7-90bf-2c26146f6772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season                  2006\n",
       "date               5/20/2006\n",
       "team1                    CHI\n",
       "team2                    CHA\n",
       "name1            Chicago Sky\n",
       "name2        Charlotte Sting\n",
       "neutral                    0\n",
       "playoff                    0\n",
       "score1                    83\n",
       "score2                    82\n",
       "elo1_pre                1300\n",
       "elo2_pre                1428\n",
       "elo1_post               1310\n",
       "elo2_post               1418\n",
       "prob1                  0.232\n",
       "is_home1                   0\n",
       "Name: 6427, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[6427, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9e604f-8009-46f2-99ae-a41ed0a28233",
   "metadata": {},
   "source": [
    "Did the Chicago Sky play their very first game at home or away? Write code to return the data in the column \"is_home1\" for that row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "98f0ca53-9ada-4c8e-9a4e-1e3ed7d89567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[6427, \"is_home1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af06382-19bd-4b26-aa4e-cf50c5b03343",
   "metadata": {},
   "source": [
    "*(1 is True and 0 is False)* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c8515d-ce89-4d59-ab12-8b9e9d8357fd",
   "metadata": {},
   "source": [
    "### <br><br>`loc` with a range and a list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9124c5b-b601-43ad-ae57-147da56cfb80",
   "metadata": {},
   "source": [
    "This code will return all columns for the rows 0 through 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c18caf4f-ea30-4c96-925d-8948587a0d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>1648</td>\n",
       "      <td>1671</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>1700</td>\n",
       "      <td>1618</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>0.763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>87</td>\n",
       "      <td>1618</td>\n",
       "      <td>1700</td>\n",
       "      <td>1648</td>\n",
       "      <td>1671</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>86</td>\n",
       "      <td>1694</td>\n",
       "      <td>1624</td>\n",
       "      <td>1700</td>\n",
       "      <td>1618</td>\n",
       "      <td>0.747</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>95</td>\n",
       "      <td>1624</td>\n",
       "      <td>1694</td>\n",
       "      <td>1618</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/24/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>90</td>\n",
       "      <td>1687</td>\n",
       "      <td>1570</td>\n",
       "      <td>1694</td>\n",
       "      <td>1563</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season        date team1 team2               name1               name2  \\\n",
       "0     2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1     2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2     2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3     2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4     2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "5     2019   10/6/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "6     2019   10/1/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "7     2019   10/1/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "8     2019   9/29/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "9     2019   9/29/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "10    2019   9/24/2019   WAS   LVA  Washington Mystics      Las Vegas Aces   \n",
       "\n",
       "    neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "0         0        1      89      78      1684      1634       1692   \n",
       "1         0        1      78      89      1634      1684       1627   \n",
       "2         0        1      86      90      1693      1626       1684   \n",
       "3         0        1      90      86      1626      1693       1634   \n",
       "4         0        1      94      81      1671      1648       1693   \n",
       "5         0        1      81      94      1648      1671       1626   \n",
       "6         0        1      87      99      1700      1618       1671   \n",
       "7         0        1      99      87      1618      1700       1648   \n",
       "8         0        1      95      86      1694      1624       1700   \n",
       "9         0        1      86      95      1624      1694       1618   \n",
       "10        0        1      94      90      1687      1570       1694   \n",
       "\n",
       "    elo2_post  prob1  is_home1  \n",
       "0        1627  0.718         1  \n",
       "1        1692  0.282         0  \n",
       "2        1634  0.476         0  \n",
       "3        1684  0.524         1  \n",
       "4        1626  0.399         0  \n",
       "5        1693  0.601         1  \n",
       "6        1648  0.763         1  \n",
       "7        1671  0.237         0  \n",
       "8        1618  0.747         1  \n",
       "9        1700  0.253         0  \n",
       "10       1563  0.566         0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:10, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64822241-5c4e-465e-8abb-9db40cf8009e",
   "metadata": {},
   "source": [
    "<br>**Unlike Python indexing, `loc` is referencing the rows by their index names, so row 10 is included.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0271056-16c5-4c05-be25-ae6897d29d25",
   "metadata": {},
   "source": [
    "<br><br>We can also ask for a range of columns, from left to right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b11ba013-fddd-4499-ad55-15eceee4ef86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/24/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season        date team1 team2               name1               name2\n",
       "0     2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun\n",
       "1     2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics\n",
       "2     2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun\n",
       "3     2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics\n",
       "4     2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun\n",
       "5     2019   10/6/2019   CON   WAS     Connecticut Sun  Washington Mystics\n",
       "6     2019   10/1/2019   WAS   CON  Washington Mystics     Connecticut Sun\n",
       "7     2019   10/1/2019   CON   WAS     Connecticut Sun  Washington Mystics\n",
       "8     2019   9/29/2019   WAS   CON  Washington Mystics     Connecticut Sun\n",
       "9     2019   9/29/2019   CON   WAS     Connecticut Sun  Washington Mystics\n",
       "10    2019   9/24/2019   WAS   LVA  Washington Mystics      Las Vegas Aces"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:10, \"season\":\"name2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468126ba-ef8f-4804-a5ca-f5a68bb4ddba",
   "metadata": {},
   "source": [
    "<br><br>Again, `loc` uses the column and row names, not their positions, so this will not work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a37f9b9c-acc0-4ce7-b4d6-12a20ed6828f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do slice indexing on Index with these indexers [0] of type int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [65]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:961\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    959\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m    960\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m--> 961\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    963\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m    964\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1149\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take_opportunity(tup):\n\u001b[1;32m   1147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take(tup)\n\u001b[0;32m-> 1149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple_same_dim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:827\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_tuple_same_dim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(key):\n\u001b[1;32m    825\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 827\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mretval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    828\u001b[0m \u001b[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001b[39;00m\n\u001b[1;32m    829\u001b[0m \u001b[38;5;66;03m#  be handled by the _getitem_lowerdim call above.\u001b[39;00m\n\u001b[1;32m    830\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m retval\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1180\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m   1179\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1180\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_slice_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1181\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[1;32m   1182\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1214\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m   1211\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   1213\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[0;32m-> 1214\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43mlabels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m   1217\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6274\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   6231\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   6232\u001b[0m \u001b[38;5;124;03mCompute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[1;32m   6233\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   6270\u001b[0m \u001b[38;5;124;03mslice(1, 3, None)\u001b[39;00m\n\u001b[1;32m   6271\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   6272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecated_arg(kind, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_indexer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 6274\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_locs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6276\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[1;32m   6277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6484\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   6482\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   6483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 6484\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mleft\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   6486\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6393\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   6389\u001b[0m original_label \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m   6391\u001b[0m \u001b[38;5;66;03m# For datetime indices label may be a string that has to be converted\u001b[39;00m\n\u001b[1;32m   6392\u001b[0m \u001b[38;5;66;03m# to datetime boundary according to its resolution.\u001b[39;00m\n\u001b[0;32m-> 6393\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_cast_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mside\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6395\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[1;32m   6396\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6340\u001b[0m, in \u001b[0;36mIndex._maybe_cast_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   6335\u001b[0m \u001b[38;5;66;03m# We are a plain index here (sub-class override this method if they\u001b[39;00m\n\u001b[1;32m   6336\u001b[0m \u001b[38;5;66;03m# wish to have special treatment for floats/ints, e.g. Float64Index and\u001b[39;00m\n\u001b[1;32m   6337\u001b[0m \u001b[38;5;66;03m# datetimelike Indexes\u001b[39;00m\n\u001b[1;32m   6338\u001b[0m \u001b[38;5;66;03m# reject them, if index does not contain label\u001b[39;00m\n\u001b[1;32m   6339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (is_float(label) \u001b[38;5;129;01mor\u001b[39;00m is_integer(label)) \u001b[38;5;129;01mand\u001b[39;00m label \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m-> 6340\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalid_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, label)\n\u001b[1;32m   6342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m label\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot do slice indexing on Index with these indexers [0] of type int"
     ]
    }
   ],
   "source": [
    "df.loc[0:10, 0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038b4aa4-9a43-4d57-8cc0-db2bec1284c5",
   "metadata": {},
   "source": [
    "<br><br>We can also pass a list of rows or columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "225ae36d-17bf-4cd3-b90e-40fca646376d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team1</th>\n",
       "      <th>score1</th>\n",
       "      <th>team2</th>\n",
       "      <th>score2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WAS</td>\n",
       "      <td>89</td>\n",
       "      <td>CON</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WAS</td>\n",
       "      <td>94</td>\n",
       "      <td>LVA</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WAS</td>\n",
       "      <td>95</td>\n",
       "      <td>CON</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team1  score1 team2  score2\n",
       "0    WAS      89   CON      78\n",
       "10   WAS      94   LVA      90\n",
       "8    WAS      95   CON      86"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[0, 10, 8], [\"team1\", \"score1\", \"team2\", \"score2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04362fa-f2a7-4d33-ac6c-969c71f53e4b",
   "metadata": {},
   "source": [
    "*Notice how the returned DataFrame used the same order given in the lists.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9eeeac8-9cd8-4862-baa7-f92a27eb5aea",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d74301-06a2-4e67-aaaf-b094d4503707",
   "metadata": {},
   "source": [
    "Run the following cell to store the list of row indexes for the first 5 games played by the Chicago Sky:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f786bfc7-0817-4ef8-ba70-f32252c4fc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "first5 = [6427, 6415, 6393, 6387, 6371]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1ea4db-0a47-4774-9818-f62603645904",
   "metadata": {},
   "source": [
    "Write code to return the rows for the first 5 Chicago Sky games, and return only the columns \"team2\", \"score1\", \"score2\", and \"is_home1\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "66c4ed80-75b9-4180-bbb9-0b7cf8a18828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team2</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>CHA</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>SAC</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6393</th>\n",
       "      <td>IND</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>LAS</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6371</th>\n",
       "      <td>HOU</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     team2  score1  score2  is_home1\n",
       "6427   CHA      83      82         0\n",
       "6415   SAC      63      76         1\n",
       "6393   IND      60      75         1\n",
       "6387   LAS      55      64         1\n",
       "6371   HOU      60      71         0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[first5, [\"team2\", \"score1\", \"score2\", \"is_home1\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd3da9a-e5b4-4638-84cb-5b6e50449d29",
   "metadata": {},
   "source": [
    "### <br><br>`loc` with a conditional"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63fd90a8-01e6-42a4-a99c-0f5d346b4a13",
   "metadata": {},
   "source": [
    "You can use a conditional to filter rows. The conditional is written the same way as we would write it without using loc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7c0ddafb-d51e-4401-a9e1-70bf2b4b3e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/15/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>1564</td>\n",
       "      <td>1541</td>\n",
       "      <td>1559</td>\n",
       "      <td>1545</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/11/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "      <td>1553</td>\n",
       "      <td>1450</td>\n",
       "      <td>1564</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/8/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>1559</td>\n",
       "      <td>1706</td>\n",
       "      <td>1553</td>\n",
       "      <td>1712</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/6/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "      <td>1543</td>\n",
       "      <td>1615</td>\n",
       "      <td>1559</td>\n",
       "      <td>1599</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/1/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1522</td>\n",
       "      <td>1527</td>\n",
       "      <td>1543</td>\n",
       "      <td>1506</td>\n",
       "      <td>0.607</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6371</th>\n",
       "      <td>2006</td>\n",
       "      <td>6/2/2006</td>\n",
       "      <td>CHI</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>1286</td>\n",
       "      <td>1575</td>\n",
       "      <td>1284</td>\n",
       "      <td>1577</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>2006</td>\n",
       "      <td>5/30/2006</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>1294</td>\n",
       "      <td>1500</td>\n",
       "      <td>1286</td>\n",
       "      <td>1508</td>\n",
       "      <td>0.326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6393</th>\n",
       "      <td>2006</td>\n",
       "      <td>5/26/2006</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>1304</td>\n",
       "      <td>1548</td>\n",
       "      <td>1294</td>\n",
       "      <td>1557</td>\n",
       "      <td>0.280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>2006</td>\n",
       "      <td>5/23/2006</td>\n",
       "      <td>CHI</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "      <td>1310</td>\n",
       "      <td>1603</td>\n",
       "      <td>1304</td>\n",
       "      <td>1610</td>\n",
       "      <td>0.227</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>2006</td>\n",
       "      <td>5/20/2006</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CHA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>1300</td>\n",
       "      <td>1428</td>\n",
       "      <td>1310</td>\n",
       "      <td>1418</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>497 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season       date team1 team2        name1                name2  \\\n",
       "25      2019  9/15/2019   CHI   LVA  Chicago Sky       Las Vegas Aces   \n",
       "28      2019  9/11/2019   CHI   PHO  Chicago Sky      Phoenix Mercury   \n",
       "36      2019   9/8/2019   CHI   WAS  Chicago Sky   Washington Mystics   \n",
       "47      2019   9/6/2019   CHI   CON  Chicago Sky      Connecticut Sun   \n",
       "65      2019   9/1/2019   CHI   PHO  Chicago Sky      Phoenix Mercury   \n",
       "...      ...        ...   ...   ...          ...                  ...   \n",
       "6371    2006   6/2/2006   CHI   HOU  Chicago Sky       Houston Comets   \n",
       "6387    2006  5/30/2006   CHI   LAS  Chicago Sky   Los Angeles Sparks   \n",
       "6393    2006  5/26/2006   CHI   IND  Chicago Sky        Indiana Fever   \n",
       "6415    2006  5/23/2006   CHI   SAC  Chicago Sky  Sacramento Monarchs   \n",
       "6427    2006  5/20/2006   CHI   CHA  Chicago Sky      Charlotte Sting   \n",
       "\n",
       "      neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "25          0        1      92      93      1564      1541       1559   \n",
       "28          0        1     105      76      1553      1450       1564   \n",
       "36          0        0      86     100      1559      1706       1553   \n",
       "47          0        0     109     104      1543      1615       1559   \n",
       "65          0        0     105      78      1522      1527       1543   \n",
       "...       ...      ...     ...     ...       ...       ...        ...   \n",
       "6371        0        0      60      71      1286      1575       1284   \n",
       "6387        0        0      55      64      1294      1500       1286   \n",
       "6393        0        0      60      75      1304      1548       1294   \n",
       "6415        0        0      63      76      1310      1603       1304   \n",
       "6427        0        0      83      82      1300      1428       1310   \n",
       "\n",
       "      elo2_post  prob1  is_home1  \n",
       "25         1545  0.399         0  \n",
       "28         1440  0.788         1  \n",
       "36         1712  0.214         0  \n",
       "47         1599  0.295         0  \n",
       "65         1506  0.607         1  \n",
       "...         ...    ...       ...  \n",
       "6371       1577  0.107         0  \n",
       "6387       1508  0.326         1  \n",
       "6393       1557  0.280         1  \n",
       "6415       1610  0.227         1  \n",
       "6427       1418  0.232         0  \n",
       "\n",
       "[497 rows x 16 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"team1\"] == \"CHI\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87fed3ca-c071-4b73-9f1e-6928c2b1ab7c",
   "metadata": {},
   "source": [
    "<br><br>Here I use the same filter for the rows, but I only ask for three columns to be returned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2ed22201-4e97-4a76-804d-66fb00c07163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team2</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LVA</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PHO</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>WAS</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CON</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PHO</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6371</th>\n",
       "      <td>HOU</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6387</th>\n",
       "      <td>LAS</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6393</th>\n",
       "      <td>IND</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>SAC</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>CHA</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>497 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team2  score1  score2\n",
       "25     LVA      92      93\n",
       "28     PHO     105      76\n",
       "36     WAS      86     100\n",
       "47     CON     109     104\n",
       "65     PHO     105      78\n",
       "...    ...     ...     ...\n",
       "6371   HOU      60      71\n",
       "6387   LAS      55      64\n",
       "6393   IND      60      75\n",
       "6415   SAC      63      76\n",
       "6427   CHA      83      82\n",
       "\n",
       "[497 rows x 3 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"team1\"] == \"CHI\", [\"team2\", \"score1\", \"score2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cfdb905-4287-45f2-8072-f7f1c103cf19",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3de793b-0f6e-46b2-b454-a20298460686",
   "metadata": {},
   "source": [
    "Write code to return all games played in the 2012 season. Only return the columns \"date\", \"name1\", and \"name2\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ad94e9b5-6d92-4f47-86bb-5ece18aae7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3096</th>\n",
       "      <td>10/21/2012</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>10/21/2012</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>Indiana Fever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3098</th>\n",
       "      <td>10/19/2012</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3099</th>\n",
       "      <td>10/19/2012</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>Indiana Fever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3100</th>\n",
       "      <td>10/17/2012</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>Indiana Fever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3537</th>\n",
       "      <td>5/19/2012</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3538</th>\n",
       "      <td>5/19/2012</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Chicago Sky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3539</th>\n",
       "      <td>5/19/2012</td>\n",
       "      <td>Tulsa Shock</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>5/18/2012</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3541</th>\n",
       "      <td>5/18/2012</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Seattle Storm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>446 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date               name1                     name2\n",
       "3096  10/21/2012       Indiana Fever            Minnesota Lynx\n",
       "3097  10/21/2012      Minnesota Lynx             Indiana Fever\n",
       "3098  10/19/2012       Indiana Fever            Minnesota Lynx\n",
       "3099  10/19/2012      Minnesota Lynx             Indiana Fever\n",
       "3100  10/17/2012      Minnesota Lynx             Indiana Fever\n",
       "...          ...                 ...                       ...\n",
       "3537   5/19/2012         Chicago Sky        Washington Mystics\n",
       "3538   5/19/2012  Washington Mystics               Chicago Sky\n",
       "3539   5/19/2012         Tulsa Shock  San Antonio Silver Stars\n",
       "3540   5/18/2012       Seattle Storm        Los Angeles Sparks\n",
       "3541   5/18/2012  Los Angeles Sparks             Seattle Storm\n",
       "\n",
       "[446 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"season\"] == 2012, [\"date\", \"name1\", \"name2\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cf2e2b8-8e41-499b-a4e9-39311043513c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0f45a137-6703-4ff1-9258-7a35bdff1967",
   "metadata": {},
   "source": [
    "### <br><br><br>pandas `iloc`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4703e090-0552-4372-ab48-75ca19150e7e",
   "metadata": {},
   "source": [
    "**While `loc` searches by row and column names, `iloc` searches only by the indexed positions in the DataFrame.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313ab21d-5b95-405d-94c5-f3967316b390",
   "metadata": {},
   "source": [
    "Here, I'm asking for the top 10 rows and the first four columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ada588f8-1cf2-40c6-9056-220ec1af6747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/1/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/29/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2\n",
       "0    2019  10/10/2019   WAS   CON\n",
       "1    2019  10/10/2019   CON   WAS\n",
       "2    2019   10/8/2019   WAS   CON\n",
       "3    2019   10/8/2019   CON   WAS\n",
       "4    2019   10/6/2019   WAS   CON\n",
       "5    2019   10/6/2019   CON   WAS\n",
       "6    2019   10/1/2019   WAS   CON\n",
       "7    2019   10/1/2019   CON   WAS\n",
       "8    2019   9/29/2019   WAS   CON\n",
       "9    2019   9/29/2019   CON   WAS"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:10, 0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7129da68-671e-4eed-9895-e1b2b37cc566",
   "metadata": {},
   "source": [
    "<br>**Notice that `iloc` uses Python indexing!** When we ask for rows 0:10, it returns rows 0 to 9. Also notice that the index (the bold number on the left side of each row) does not count as a true column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4932b14-ae20-4e38-bb3c-ee7bb83d9993",
   "metadata": {},
   "source": [
    "<br>Because `iloc` uses Python indexing, we can use negative numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "47c808dc-2c7a-4da5-880e-b77689ad8233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5483</th>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Houston Comets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5484</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5485</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>New York Liberty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5486</th>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5487</th>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>Indiana Fever</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5488 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name1                     name2\n",
       "0           Washington Mystics           Connecticut Sun\n",
       "1              Connecticut Sun        Washington Mystics\n",
       "2           Washington Mystics           Connecticut Sun\n",
       "3              Connecticut Sun        Washington Mystics\n",
       "4           Washington Mystics           Connecticut Sun\n",
       "...                        ...                       ...\n",
       "5483        Los Angeles Sparks            Houston Comets\n",
       "5484           Phoenix Mercury       Sacramento Monarchs\n",
       "5485           Connecticut Sun          New York Liberty\n",
       "5486             Indiana Fever  San Antonio Silver Stars\n",
       "5487  San Antonio Silver Stars             Indiana Fever\n",
       "\n",
       "[5488 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:-5000, 4:-10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475d891a-5526-446c-8c4a-c9c09071c099",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1aafd4-3c9a-4ad6-b469-d004221b81f4",
   "metadata": {},
   "source": [
    "The games are included in reverse chronological order, so the last row in the table is the very first game ever played."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3c7ded-25c7-4ce5-a0ea-1e9abed00744",
   "metadata": {},
   "source": [
    "Was the very first game played at home or away for team1? Use iloc to return the column \"is_home1\" for the very last row in the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b3fab7c2-c4a1-4b8d-be0c-dee6af82975b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfbf7fa9-acaa-4820-867c-a36de1eecb95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3b46ae29-2a4e-4005-8db4-b065945b7147",
   "metadata": {},
   "source": [
    "Use iloc to write code to return the columns \"team1\" and \"team2\" for the most recent 20 games:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b47bdd95-8860-4308-b48d-4b86c5cb5e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WAS</td>\n",
       "      <td>LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LVA</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WAS</td>\n",
       "      <td>LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CON</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LVA</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>WAS</td>\n",
       "      <td>LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CON</td>\n",
       "      <td>LAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LAS</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LVA</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team1 team2\n",
       "0    WAS   CON\n",
       "1    CON   WAS\n",
       "2    WAS   CON\n",
       "3    CON   WAS\n",
       "4    WAS   CON\n",
       "5    CON   WAS\n",
       "6    WAS   CON\n",
       "7    CON   WAS\n",
       "8    WAS   CON\n",
       "9    CON   WAS\n",
       "10   WAS   LVA\n",
       "11   LVA   WAS\n",
       "12   WAS   LVA\n",
       "13   CON   LAS\n",
       "14   LVA   WAS\n",
       "15   LAS   CON\n",
       "16   WAS   LVA\n",
       "17   CON   LAS\n",
       "18   LAS   CON\n",
       "19   LVA   WAS"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:20, 2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af803bad-537f-4d2e-9d78-6aaa6de29a3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "89c8f396-cae6-49ec-888e-3ef23dcad856",
   "metadata": {},
   "source": [
    "### <br><br><br>pandas `at` and `iat`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcee7b7c-8076-4ab9-b620-03badbe13a49",
   "metadata": {},
   "source": [
    "If you are looking for the contents of only a single cell (called a **scalar**) in the DataFrame, you can use `loc` or `iloc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "76578098-c5d6-4203-af99-1afa690a3a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0, \"season\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d3b8de50-8dd8-4e67-a5e1-7840b542cf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7152378e-8df4-49cf-a48f-97c2e6211693",
   "metadata": {},
   "source": [
    "<br>However, there is another set of pandas functions designed to look up only a single cell. `at` will look up a single cell by row name and column name (like `loc`), and `iat` will look up a single cell by index position (like `iloc`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae554fa-566f-40d1-9177-fffda024f636",
   "metadata": {},
   "source": [
    "Why does pandas have a separate way to look up a single cell? Because `at` and `iat` are very fast. If you write code to look up 10,000 single points in a DataFrame, it would be much faster to use `at` or `iat` than `loc` or `iloc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "aa20d245-c1e0-4f61-b8ab-b8c74b438123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[0, \"season\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a924d777-0551-4918-be99-257d87a7ef1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2167852c-1370-4350-923e-6078cf1ae455",
   "metadata": {},
   "source": [
    "<br><br>Just to reiterate, `at` and `iat` cannot be used with multiple rows or columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1d74620a-5e25-4d1e-b2bc-9b2cf525207b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [80]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mat\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseason\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:2270\u001b[0m, in \u001b[0;36m_AtIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2267\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid call for scalar access (getting)!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2268\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mloc[key]\n\u001b[0;32m-> 2270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:2221\u001b[0m, in \u001b[0;36m_ScalarAccessIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2218\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid call for scalar access (getting)!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2220\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_key(key)\n\u001b[0;32m-> 2221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtakeable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_takeable\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:3615\u001b[0m, in \u001b[0;36mDataFrame._get_value\u001b[0;34m(self, index, col, takeable)\u001b[0m\n\u001b[1;32m   3612\u001b[0m     series \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ixs(col, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m   3613\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m series\u001b[38;5;241m.\u001b[39m_values[index]\n\u001b[0;32m-> 3615\u001b[0m series \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_item_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3616\u001b[0m engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_engine\n\u001b[1;32m   3618\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n\u001b[1;32m   3619\u001b[0m     \u001b[38;5;66;03m# CategoricalIndex: Trying to use the engine fastpath may give incorrect\u001b[39;00m\n\u001b[1;32m   3620\u001b[0m     \u001b[38;5;66;03m#  results if our categories are integers that dont match our codes\u001b[39;00m\n\u001b[1;32m   3621\u001b[0m     \u001b[38;5;66;03m# IntervalIndex: IntervalTree has no get_loc\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:3926\u001b[0m, in \u001b[0;36mDataFrame._get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   3924\u001b[0m \u001b[38;5;124;03m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[39;00m\n\u001b[1;32m   3925\u001b[0m cache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_item_cache\n\u001b[0;32m-> 3926\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mcache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3927\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3928\u001b[0m     \u001b[38;5;66;03m# All places that call _get_item_cache have unique columns,\u001b[39;00m\n\u001b[1;32m   3929\u001b[0m     \u001b[38;5;66;03m#  pending resolution of GH#33047\u001b[39;00m\n\u001b[1;32m   3931\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(item)\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "df.at[0, [\"season\", \"date\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ad6dfd-7069-4dc8-bf61-b328180f466d",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c283a4-934e-4811-811a-e88daa18d177",
   "metadata": {},
   "source": [
    "Use `at` to write code to find out if the game in row 5485 was played at home or away:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "721e26eb-cc6c-4d04-8ab4-bf46829ad55b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[5485, \"is_home1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789d0876-8331-46b5-9908-c15a5a85830a",
   "metadata": {},
   "source": [
    "Now use `iat` to find the same answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fdea5139-9c30-4833-b71e-b7b8f6698e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[5485, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b112b76-3066-4764-a492-313bc525fa84",
   "metadata": {},
   "source": [
    "### <br><br>A note about index labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57fe6d0c-38b2-4a09-a241-76247bbb7001",
   "metadata": {},
   "source": [
    "The bold numbers on the far left of each column were assigned to each row when the csv file was originally loaded into pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fd71c70c-ed04-4d7b-8745-6877a8257bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d55d3e-ae1d-4d35-a248-5906775dadf0",
   "metadata": {},
   "source": [
    "<br>If we make a new DataFrame out of only some rows, the index labels will stay the same, leaving gaps. Let's make a new DataFrame that only includes games played by the Chicago Sky:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "aaf0b714-15ca-4f25-bbf8-9fcb599e6d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/15/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>1564</td>\n",
       "      <td>1541</td>\n",
       "      <td>1559</td>\n",
       "      <td>1545</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/11/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "      <td>1553</td>\n",
       "      <td>1450</td>\n",
       "      <td>1564</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/8/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>1559</td>\n",
       "      <td>1706</td>\n",
       "      <td>1553</td>\n",
       "      <td>1712</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/6/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "      <td>1543</td>\n",
       "      <td>1615</td>\n",
       "      <td>1559</td>\n",
       "      <td>1599</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/1/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1522</td>\n",
       "      <td>1527</td>\n",
       "      <td>1543</td>\n",
       "      <td>1506</td>\n",
       "      <td>0.607</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season       date team1 team2        name1               name2  neutral  \\\n",
       "25    2019  9/15/2019   CHI   LVA  Chicago Sky      Las Vegas Aces        0   \n",
       "28    2019  9/11/2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "36    2019   9/8/2019   CHI   WAS  Chicago Sky  Washington Mystics        0   \n",
       "47    2019   9/6/2019   CHI   CON  Chicago Sky     Connecticut Sun        0   \n",
       "65    2019   9/1/2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "\n",
       "    playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  prob1  \\\n",
       "25        1      92      93      1564      1541       1559       1545  0.399   \n",
       "28        1     105      76      1553      1450       1564       1440  0.788   \n",
       "36        0      86     100      1559      1706       1553       1712  0.214   \n",
       "47        0     109     104      1543      1615       1559       1599  0.295   \n",
       "65        0     105      78      1522      1527       1543       1506  0.607   \n",
       "\n",
       "    is_home1  \n",
       "25         0  \n",
       "28         1  \n",
       "36         0  \n",
       "47         0  \n",
       "65         1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf = df.loc[df[\"team1\"] == \"CHI\", :]\n",
    "CHIdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d27d467-590f-403e-bc2c-2a379c1e08e6",
   "metadata": {},
   "source": [
    "<br>I can now use `iloc` to get Chicago's most recent 30 games:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8962c887-bd77-4cb1-b450-06a98eab0099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/15/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>1564</td>\n",
       "      <td>1541</td>\n",
       "      <td>1559</td>\n",
       "      <td>1545</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/11/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "      <td>1553</td>\n",
       "      <td>1450</td>\n",
       "      <td>1564</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/8/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>1559</td>\n",
       "      <td>1706</td>\n",
       "      <td>1553</td>\n",
       "      <td>1712</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/6/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "      <td>1543</td>\n",
       "      <td>1615</td>\n",
       "      <td>1559</td>\n",
       "      <td>1599</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/1/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1522</td>\n",
       "      <td>1527</td>\n",
       "      <td>1543</td>\n",
       "      <td>1506</td>\n",
       "      <td>0.607</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/29/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>88</td>\n",
       "      <td>1541</td>\n",
       "      <td>1395</td>\n",
       "      <td>1522</td>\n",
       "      <td>1413</td>\n",
       "      <td>0.787</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/27/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>MIN</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>93</td>\n",
       "      <td>1551</td>\n",
       "      <td>1539</td>\n",
       "      <td>1541</td>\n",
       "      <td>1548</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/25/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>86</td>\n",
       "      <td>1535</td>\n",
       "      <td>1522</td>\n",
       "      <td>1551</td>\n",
       "      <td>1506</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/23/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>78</td>\n",
       "      <td>1519</td>\n",
       "      <td>1692</td>\n",
       "      <td>1535</td>\n",
       "      <td>1676</td>\n",
       "      <td>0.369</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/20/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>83</td>\n",
       "      <td>1513</td>\n",
       "      <td>1347</td>\n",
       "      <td>1519</td>\n",
       "      <td>1341</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/18/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1536</td>\n",
       "      <td>1552</td>\n",
       "      <td>1513</td>\n",
       "      <td>1576</td>\n",
       "      <td>0.591</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/16/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>81</td>\n",
       "      <td>1524</td>\n",
       "      <td>1560</td>\n",
       "      <td>1536</td>\n",
       "      <td>1548</td>\n",
       "      <td>0.563</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/11/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>84</td>\n",
       "      <td>1529</td>\n",
       "      <td>1570</td>\n",
       "      <td>1524</td>\n",
       "      <td>1575</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/9/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>84</td>\n",
       "      <td>1518</td>\n",
       "      <td>1551</td>\n",
       "      <td>1529</td>\n",
       "      <td>1539</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/7/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>92</td>\n",
       "      <td>1512</td>\n",
       "      <td>1375</td>\n",
       "      <td>1518</td>\n",
       "      <td>1370</td>\n",
       "      <td>0.777</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2019</td>\n",
       "      <td>8/3/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "      <td>1498</td>\n",
       "      <td>1393</td>\n",
       "      <td>1512</td>\n",
       "      <td>1378</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/30/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>1503</td>\n",
       "      <td>1585</td>\n",
       "      <td>1498</td>\n",
       "      <td>1590</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/21/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>1497</td>\n",
       "      <td>1400</td>\n",
       "      <td>1503</td>\n",
       "      <td>1394</td>\n",
       "      <td>0.734</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/17/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>1494</td>\n",
       "      <td>1436</td>\n",
       "      <td>1497</td>\n",
       "      <td>1433</td>\n",
       "      <td>0.688</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/14/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>79</td>\n",
       "      <td>1478</td>\n",
       "      <td>1418</td>\n",
       "      <td>1494</td>\n",
       "      <td>1403</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/12/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>83</td>\n",
       "      <td>1467</td>\n",
       "      <td>1415</td>\n",
       "      <td>1478</td>\n",
       "      <td>1404</td>\n",
       "      <td>0.681</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/10/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>MIN</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>1473</td>\n",
       "      <td>1522</td>\n",
       "      <td>1467</td>\n",
       "      <td>1528</td>\n",
       "      <td>0.545</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/7/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>66</td>\n",
       "      <td>1464</td>\n",
       "      <td>1417</td>\n",
       "      <td>1473</td>\n",
       "      <td>1407</td>\n",
       "      <td>0.675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>2019</td>\n",
       "      <td>7/2/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>90</td>\n",
       "      <td>1472</td>\n",
       "      <td>1505</td>\n",
       "      <td>1464</td>\n",
       "      <td>1512</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2019</td>\n",
       "      <td>6/30/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>94</td>\n",
       "      <td>1492</td>\n",
       "      <td>1484</td>\n",
       "      <td>1472</td>\n",
       "      <td>1504</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>2019</td>\n",
       "      <td>6/28/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>79</td>\n",
       "      <td>1495</td>\n",
       "      <td>1592</td>\n",
       "      <td>1492</td>\n",
       "      <td>1595</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2019</td>\n",
       "      <td>6/26/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>1505</td>\n",
       "      <td>1633</td>\n",
       "      <td>1495</td>\n",
       "      <td>1643</td>\n",
       "      <td>0.431</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2019</td>\n",
       "      <td>6/23/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>75</td>\n",
       "      <td>1479</td>\n",
       "      <td>1618</td>\n",
       "      <td>1505</td>\n",
       "      <td>1592</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2019</td>\n",
       "      <td>6/21/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>76</td>\n",
       "      <td>1498</td>\n",
       "      <td>1417</td>\n",
       "      <td>1479</td>\n",
       "      <td>1436</td>\n",
       "      <td>0.717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2019</td>\n",
       "      <td>6/19/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>83</td>\n",
       "      <td>1486</td>\n",
       "      <td>1408</td>\n",
       "      <td>1498</td>\n",
       "      <td>1395</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season       date team1 team2        name1               name2  neutral  \\\n",
       "25     2019  9/15/2019   CHI   LVA  Chicago Sky      Las Vegas Aces        0   \n",
       "28     2019  9/11/2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "36     2019   9/8/2019   CHI   WAS  Chicago Sky  Washington Mystics        0   \n",
       "47     2019   9/6/2019   CHI   CON  Chicago Sky     Connecticut Sun        0   \n",
       "65     2019   9/1/2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "78     2019  8/29/2019   CHI   DAL  Chicago Sky        Dallas Wings        0   \n",
       "86     2019  8/27/2019   CHI   MIN  Chicago Sky      Minnesota Lynx        0   \n",
       "96     2019  8/25/2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "107    2019  8/23/2019   CHI   WAS  Chicago Sky  Washington Mystics        0   \n",
       "117    2019  8/20/2019   CHI   ATL  Chicago Sky       Atlanta Dream        0   \n",
       "128    2019  8/18/2019   CHI   LVA  Chicago Sky      Las Vegas Aces        0   \n",
       "135    2019  8/16/2019   CHI   LAS  Chicago Sky  Los Angeles Sparks        0   \n",
       "157    2019  8/11/2019   CHI   LAS  Chicago Sky  Los Angeles Sparks        0   \n",
       "167    2019   8/9/2019   CHI   LVA  Chicago Sky      Las Vegas Aces        0   \n",
       "174    2019   8/7/2019   CHI   NYL  Chicago Sky    New York Liberty        0   \n",
       "187    2019   8/3/2019   CHI   ATL  Chicago Sky       Atlanta Dream        0   \n",
       "206    2019  7/30/2019   CHI   CON  Chicago Sky     Connecticut Sun        0   \n",
       "221    2019  7/21/2019   CHI   IND  Chicago Sky       Indiana Fever        0   \n",
       "239    2019  7/17/2019   CHI   ATL  Chicago Sky       Atlanta Dream        0   \n",
       "247    2019  7/14/2019   CHI   DAL  Chicago Sky        Dallas Wings        0   \n",
       "259    2019  7/12/2019   CHI   NYL  Chicago Sky    New York Liberty        0   \n",
       "269    2019  7/10/2019   CHI   MIN  Chicago Sky      Minnesota Lynx        0   \n",
       "278    2019   7/7/2019   CHI   DAL  Chicago Sky        Dallas Wings        0   \n",
       "294    2019   7/2/2019   CHI   LVA  Chicago Sky      Las Vegas Aces        0   \n",
       "300    2019  6/30/2019   CHI   LAS  Chicago Sky  Los Angeles Sparks        0   \n",
       "310    2019  6/28/2019   CHI   SEA  Chicago Sky       Seattle Storm        0   \n",
       "318    2019  6/26/2019   CHI   WAS  Chicago Sky  Washington Mystics        0   \n",
       "327    2019  6/23/2019   CHI   CON  Chicago Sky     Connecticut Sun        0   \n",
       "339    2019  6/21/2019   CHI   IND  Chicago Sky       Indiana Fever        0   \n",
       "346    2019  6/19/2019   CHI   NYL  Chicago Sky    New York Liberty        0   \n",
       "\n",
       "     playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  prob1  \\\n",
       "25         1      92      93      1564      1541       1559       1545  0.399   \n",
       "28         1     105      76      1553      1450       1564       1440  0.788   \n",
       "36         0      86     100      1559      1706       1553       1712  0.214   \n",
       "47         0     109     104      1543      1615       1559       1599  0.295   \n",
       "65         0     105      78      1522      1527       1543       1506  0.607   \n",
       "78         0      83      88      1541      1395       1522       1413  0.787   \n",
       "86         0      85      93      1551      1539       1541       1548  0.404   \n",
       "96         0      94      86      1535      1522       1551       1506  0.405   \n",
       "107        0      85      78      1519      1692       1535       1676  0.369   \n",
       "117        0      87      83      1513      1347       1519       1341  0.621   \n",
       "128        0      85     100      1536      1552       1513       1576  0.591   \n",
       "135        0      91      81      1524      1560       1536       1548  0.563   \n",
       "157        0      81      84      1529      1570       1524       1575  0.332   \n",
       "167        0      87      84      1518      1551       1529       1539  0.343   \n",
       "174        0     101      92      1512      1375       1518       1370  0.777   \n",
       "187        0      87      75      1498      1393       1512       1378  0.536   \n",
       "206        0      94     100      1503      1585       1498       1590  0.282   \n",
       "221        0      78      70      1497      1400       1503       1394  0.734   \n",
       "239        0      77      76      1494      1436       1497       1433  0.688   \n",
       "247        0      89      79      1478      1418       1494       1403  0.471   \n",
       "259        0      99      83      1467      1415       1478       1404  0.681   \n",
       "269        0      72      73      1473      1522       1467       1528  0.545   \n",
       "278        0      78      66      1464      1417       1473       1407  0.675   \n",
       "294        0      82      90      1472      1505       1464       1512  0.343   \n",
       "300        0      69      94      1492      1484       1472       1504  0.397   \n",
       "310        0      76      79      1495      1592       1492       1595  0.266   \n",
       "318        0      74      81      1505      1633       1495       1643  0.431   \n",
       "327        0      93      75      1479      1618       1505       1592  0.416   \n",
       "339        0      69      76      1498      1417       1479       1436  0.717   \n",
       "346        0      91      83      1486      1408       1498       1395  0.497   \n",
       "\n",
       "     is_home1  \n",
       "25          0  \n",
       "28          1  \n",
       "36          0  \n",
       "47          0  \n",
       "65          1  \n",
       "78          1  \n",
       "86          0  \n",
       "96          0  \n",
       "107         1  \n",
       "117         0  \n",
       "128         1  \n",
       "135         1  \n",
       "157         0  \n",
       "167         0  \n",
       "174         1  \n",
       "187         0  \n",
       "206         0  \n",
       "221         1  \n",
       "239         1  \n",
       "247         0  \n",
       "259         1  \n",
       "269         1  \n",
       "278         1  \n",
       "294         0  \n",
       "300         0  \n",
       "310         0  \n",
       "318         1  \n",
       "327         1  \n",
       "339         1  \n",
       "346         0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.iloc[0:30, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0937d5ea-01f1-4592-801b-369200d19166",
   "metadata": {},
   "source": [
    "<br>But I could not use `loc` to get the same thing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ed49cc57-7508-4d65-ad9d-33e39acc1aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/15/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>1564</td>\n",
       "      <td>1541</td>\n",
       "      <td>1559</td>\n",
       "      <td>1545</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>9/11/2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "      <td>1553</td>\n",
       "      <td>1450</td>\n",
       "      <td>1564</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season       date team1 team2        name1            name2  neutral  \\\n",
       "25    2019  9/15/2019   CHI   LVA  Chicago Sky   Las Vegas Aces        0   \n",
       "28    2019  9/11/2019   CHI   PHO  Chicago Sky  Phoenix Mercury        0   \n",
       "\n",
       "    playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  prob1  \\\n",
       "25        1      92      93      1564      1541       1559       1545  0.399   \n",
       "28        1     105      76      1553      1450       1564       1440  0.788   \n",
       "\n",
       "    is_home1  \n",
       "25         0  \n",
       "28         1  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.loc[0:30, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed6ce50-ad66-4a22-a0e0-e1d073b6bae9",
   "metadata": {},
   "source": [
    "<br>You can, however, set one of your columns as the index labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5b52d864-3cb8-420b-97a9-6083f121d476",
   "metadata": {},
   "outputs": [],
   "source": [
    "CHIdf = CHIdf.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1b7c5d09-b5de-451c-a964-204644c08d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
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       "      <th>score1</th>\n",
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       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9/15/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>1564</td>\n",
       "      <td>1541</td>\n",
       "      <td>1559</td>\n",
       "      <td>1545</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/11/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>76</td>\n",
       "      <td>1553</td>\n",
       "      <td>1450</td>\n",
       "      <td>1564</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/8/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>1559</td>\n",
       "      <td>1706</td>\n",
       "      <td>1553</td>\n",
       "      <td>1712</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/6/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>104</td>\n",
       "      <td>1543</td>\n",
       "      <td>1615</td>\n",
       "      <td>1559</td>\n",
       "      <td>1599</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/1/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>1522</td>\n",
       "      <td>1527</td>\n",
       "      <td>1543</td>\n",
       "      <td>1506</td>\n",
       "      <td>0.607</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season team1 team2        name1               name2  neutral  \\\n",
       "date                                                                      \n",
       "9/15/2019    2019   CHI   LVA  Chicago Sky      Las Vegas Aces        0   \n",
       "9/11/2019    2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "9/8/2019     2019   CHI   WAS  Chicago Sky  Washington Mystics        0   \n",
       "9/6/2019     2019   CHI   CON  Chicago Sky     Connecticut Sun        0   \n",
       "9/1/2019     2019   CHI   PHO  Chicago Sky     Phoenix Mercury        0   \n",
       "\n",
       "           playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "date                                                                           \n",
       "9/15/2019        1      92      93      1564      1541       1559       1545   \n",
       "9/11/2019        1     105      76      1553      1450       1564       1440   \n",
       "9/8/2019         0      86     100      1559      1706       1553       1712   \n",
       "9/6/2019         0     109     104      1543      1615       1559       1599   \n",
       "9/1/2019         0     105      78      1522      1527       1543       1506   \n",
       "\n",
       "           prob1  is_home1  \n",
       "date                        \n",
       "9/15/2019  0.399         0  \n",
       "9/11/2019  0.788         1  \n",
       "9/8/2019   0.214         0  \n",
       "9/6/2019   0.295         0  \n",
       "9/1/2019   0.607         1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0f598e-1b04-4661-8512-b64571164384",
   "metadata": {},
   "source": [
    "<br>Now I can use `loc` to reference the games by date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cb15da68-db7d-4287-a605-2c7b834029ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season                     2012\n",
       "team1                       CHI\n",
       "team2                       LAS\n",
       "name1               Chicago Sky\n",
       "name2        Los Angeles Sparks\n",
       "neutral                       0\n",
       "playoff                       0\n",
       "score1                       77\n",
       "score2                       86\n",
       "elo1_pre                   1490\n",
       "elo2_pre                   1566\n",
       "elo1_post                  1483\n",
       "elo2_post                  1573\n",
       "prob1                      0.29\n",
       "is_home1                      0\n",
       "Name: 9/13/2012, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.loc[\"9/13/2012\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df475d5-3c8c-47a0-bd3d-2dea8e4040f9",
   "metadata": {},
   "source": [
    "<br><br>I can still use a range of row labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9f03590a-5236-4f84-834c-6098680948f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9/13/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>1490</td>\n",
       "      <td>1566</td>\n",
       "      <td>1483</td>\n",
       "      <td>1573</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/11/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>MIN</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>70</td>\n",
       "      <td>1460</td>\n",
       "      <td>1727</td>\n",
       "      <td>1490</td>\n",
       "      <td>1697</td>\n",
       "      <td>0.254</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/9/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>82</td>\n",
       "      <td>1465</td>\n",
       "      <td>1552</td>\n",
       "      <td>1460</td>\n",
       "      <td>1556</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/7/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>83</td>\n",
       "      <td>1446</td>\n",
       "      <td>1460</td>\n",
       "      <td>1465</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/2/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>74</td>\n",
       "      <td>1426</td>\n",
       "      <td>1585</td>\n",
       "      <td>1446</td>\n",
       "      <td>1565</td>\n",
       "      <td>0.389</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9/1/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>1433</td>\n",
       "      <td>1592</td>\n",
       "      <td>1426</td>\n",
       "      <td>1599</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/28/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>1447</td>\n",
       "      <td>1550</td>\n",
       "      <td>1433</td>\n",
       "      <td>1564</td>\n",
       "      <td>0.467</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/26/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>70</td>\n",
       "      <td>1415</td>\n",
       "      <td>1583</td>\n",
       "      <td>1447</td>\n",
       "      <td>1550</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/24/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Tulsa Shock</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>1423</td>\n",
       "      <td>1325</td>\n",
       "      <td>1415</td>\n",
       "      <td>1333</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/22/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>82</td>\n",
       "      <td>1430</td>\n",
       "      <td>1539</td>\n",
       "      <td>1423</td>\n",
       "      <td>1545</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/21/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>1449</td>\n",
       "      <td>1452</td>\n",
       "      <td>1430</td>\n",
       "      <td>1471</td>\n",
       "      <td>0.609</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/19/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>75</td>\n",
       "      <td>1458</td>\n",
       "      <td>1358</td>\n",
       "      <td>1449</td>\n",
       "      <td>1368</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8/17/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>82</td>\n",
       "      <td>1469</td>\n",
       "      <td>1540</td>\n",
       "      <td>1458</td>\n",
       "      <td>1552</td>\n",
       "      <td>0.513</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7/13/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>CON</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>1475</td>\n",
       "      <td>1592</td>\n",
       "      <td>1469</td>\n",
       "      <td>1598</td>\n",
       "      <td>0.447</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7/11/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>77</td>\n",
       "      <td>1486</td>\n",
       "      <td>1620</td>\n",
       "      <td>1475</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.422</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7/7/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>88</td>\n",
       "      <td>1490</td>\n",
       "      <td>1553</td>\n",
       "      <td>1486</td>\n",
       "      <td>1557</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7/6/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>64</td>\n",
       "      <td>1504</td>\n",
       "      <td>1480</td>\n",
       "      <td>1490</td>\n",
       "      <td>1494</td>\n",
       "      <td>0.645</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7/1/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>1498</td>\n",
       "      <td>1549</td>\n",
       "      <td>1504</td>\n",
       "      <td>1542</td>\n",
       "      <td>0.542</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/29/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>84</td>\n",
       "      <td>1511</td>\n",
       "      <td>1406</td>\n",
       "      <td>1498</td>\n",
       "      <td>1420</td>\n",
       "      <td>0.744</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/27/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>81</td>\n",
       "      <td>1527</td>\n",
       "      <td>1563</td>\n",
       "      <td>1511</td>\n",
       "      <td>1579</td>\n",
       "      <td>0.563</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/23/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>MIN</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>79</td>\n",
       "      <td>1533</td>\n",
       "      <td>1688</td>\n",
       "      <td>1527</td>\n",
       "      <td>1693</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/16/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>84</td>\n",
       "      <td>1547</td>\n",
       "      <td>1535</td>\n",
       "      <td>1533</td>\n",
       "      <td>1549</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/13/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>58</td>\n",
       "      <td>1536</td>\n",
       "      <td>1471</td>\n",
       "      <td>1547</td>\n",
       "      <td>1460</td>\n",
       "      <td>0.698</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/10/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>64</td>\n",
       "      <td>1519</td>\n",
       "      <td>1495</td>\n",
       "      <td>1536</td>\n",
       "      <td>1479</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/8/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Tulsa Shock</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>91</td>\n",
       "      <td>1516</td>\n",
       "      <td>1335</td>\n",
       "      <td>1519</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/2/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>92</td>\n",
       "      <td>1506</td>\n",
       "      <td>1544</td>\n",
       "      <td>1516</td>\n",
       "      <td>1534</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/1/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>1503</td>\n",
       "      <td>1400</td>\n",
       "      <td>1506</td>\n",
       "      <td>1397</td>\n",
       "      <td>0.741</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5/30/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>63</td>\n",
       "      <td>1476</td>\n",
       "      <td>1519</td>\n",
       "      <td>1503</td>\n",
       "      <td>1492</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5/25/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>1494</td>\n",
       "      <td>1526</td>\n",
       "      <td>1476</td>\n",
       "      <td>1544</td>\n",
       "      <td>0.569</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5/19/2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>CHI</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>57</td>\n",
       "      <td>1477</td>\n",
       "      <td>1416</td>\n",
       "      <td>1494</td>\n",
       "      <td>1399</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season team1 team2        name1                     name2  neutral  \\\n",
       "date                                                                            \n",
       "9/13/2012    2012   CHI   LAS  Chicago Sky        Los Angeles Sparks        0   \n",
       "9/11/2012    2012   CHI   MIN  Chicago Sky            Minnesota Lynx        0   \n",
       "9/9/2012     2012   CHI   CON  Chicago Sky           Connecticut Sun        0   \n",
       "9/7/2012     2012   CHI   NYL  Chicago Sky          New York Liberty        0   \n",
       "9/2/2012     2012   CHI   LAS  Chicago Sky        Los Angeles Sparks        0   \n",
       "9/1/2012     2012   CHI   IND  Chicago Sky             Indiana Fever        0   \n",
       "8/28/2012    2012   CHI   CON  Chicago Sky           Connecticut Sun        0   \n",
       "8/26/2012    2012   CHI   CON  Chicago Sky           Connecticut Sun        0   \n",
       "8/24/2012    2012   CHI   DAL  Chicago Sky               Tulsa Shock        0   \n",
       "8/22/2012    2012   CHI   ATL  Chicago Sky             Atlanta Dream        0   \n",
       "8/21/2012    2012   CHI   NYL  Chicago Sky          New York Liberty        0   \n",
       "8/19/2012    2012   CHI   WAS  Chicago Sky        Washington Mystics        0   \n",
       "8/17/2012    2012   CHI   ATL  Chicago Sky             Atlanta Dream        0   \n",
       "7/13/2012    2012   CHI   CON  Chicago Sky           Connecticut Sun        0   \n",
       "7/11/2012    2012   CHI   LVA  Chicago Sky  San Antonio Silver Stars        0   \n",
       "7/7/2012     2012   CHI   IND  Chicago Sky             Indiana Fever        0   \n",
       "7/6/2012     2012   CHI   NYL  Chicago Sky          New York Liberty        0   \n",
       "7/1/2012     2012   CHI   ATL  Chicago Sky             Atlanta Dream        0   \n",
       "6/29/2012    2012   CHI   PHO  Chicago Sky           Phoenix Mercury        0   \n",
       "6/27/2012    2012   CHI   IND  Chicago Sky             Indiana Fever        0   \n",
       "6/23/2012    2012   CHI   MIN  Chicago Sky            Minnesota Lynx        0   \n",
       "6/16/2012    2012   CHI   IND  Chicago Sky             Indiana Fever        0   \n",
       "6/13/2012    2012   CHI   SEA  Chicago Sky             Seattle Storm        0   \n",
       "6/10/2012    2012   CHI   NYL  Chicago Sky          New York Liberty        0   \n",
       "6/8/2012     2012   CHI   DAL  Chicago Sky               Tulsa Shock        0   \n",
       "6/2/2012     2012   CHI   ATL  Chicago Sky             Atlanta Dream        0   \n",
       "6/1/2012     2012   CHI   WAS  Chicago Sky        Washington Mystics        0   \n",
       "5/30/2012    2012   CHI   LVA  Chicago Sky  San Antonio Silver Stars        0   \n",
       "5/25/2012    2012   CHI   IND  Chicago Sky             Indiana Fever        0   \n",
       "5/19/2012    2012   CHI   WAS  Chicago Sky        Washington Mystics        0   \n",
       "\n",
       "           playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "date                                                                           \n",
       "9/13/2012        0      77      86      1490      1566       1483       1573   \n",
       "9/11/2012        0      83      70      1460      1727       1490       1697   \n",
       "9/9/2012         0      77      82      1465      1552       1460       1556   \n",
       "9/7/2012         0      92      83      1446      1460       1465       1442   \n",
       "9/2/2012         0      85      74      1426      1585       1446       1565   \n",
       "9/1/2012         0      64      81      1433      1592       1426       1599   \n",
       "8/28/2012        0      72      83      1447      1550       1433       1564   \n",
       "8/26/2012        0      82      70      1415      1583       1447       1550   \n",
       "8/24/2012        0      78      81      1423      1325       1415       1333   \n",
       "8/22/2012        0      71      82      1430      1539       1423       1545   \n",
       "8/21/2012        0      67      77      1449      1452       1430       1471   \n",
       "8/19/2012        0      71      75      1458      1358       1449       1368   \n",
       "8/17/2012        0      76      82      1469      1540       1458       1552   \n",
       "7/13/2012        0      78      80      1475      1592       1469       1598   \n",
       "7/11/2012        0      68      77      1486      1620       1475       1631   \n",
       "7/7/2012         0      86      88      1490      1553       1486       1557   \n",
       "7/6/2012         0      59      64      1504      1480       1490       1494   \n",
       "7/1/2012         0      71      69      1498      1549       1504       1542   \n",
       "6/29/2012        0      81      84      1511      1406       1498       1420   \n",
       "6/27/2012        0      72      81      1527      1563       1511       1579   \n",
       "6/23/2012        0      67      79      1533      1688       1527       1693   \n",
       "6/16/2012        0      70      84      1547      1535       1533       1549   \n",
       "6/13/2012        0      74      58      1536      1471       1547       1460   \n",
       "6/10/2012        0      73      64      1519      1495       1536       1479   \n",
       "6/8/2012         0      98      91      1516      1335       1519       1331   \n",
       "6/2/2012         0      94      92      1506      1544       1516       1534   \n",
       "6/1/2012         0      65      63      1503      1400       1506       1397   \n",
       "5/30/2012        0      77      63      1476      1519       1503       1492   \n",
       "5/25/2012        0      72      83      1494      1526       1476       1544   \n",
       "5/19/2012        0      69      57      1477      1416       1494       1399   \n",
       "\n",
       "           prob1  is_home1  \n",
       "date                        \n",
       "9/13/2012  0.290         0  \n",
       "9/11/2012  0.254         1  \n",
       "9/9/2012   0.277         0  \n",
       "9/7/2012   0.368         0  \n",
       "9/2/2012   0.389         1  \n",
       "9/1/2012   0.202         0  \n",
       "8/28/2012  0.467         1  \n",
       "8/26/2012  0.193         0  \n",
       "8/24/2012  0.526         0  \n",
       "8/22/2012  0.252         0  \n",
       "8/21/2012  0.609         1  \n",
       "8/19/2012  0.529         0  \n",
       "8/17/2012  0.513         1  \n",
       "7/13/2012  0.447         1  \n",
       "7/11/2012  0.422         1  \n",
       "7/7/2012   0.305         0  \n",
       "7/6/2012   0.645         1  \n",
       "7/1/2012   0.542         1  \n",
       "6/29/2012  0.744         1  \n",
       "6/27/2012  0.563         1  \n",
       "6/23/2012  0.206         0  \n",
       "6/16/2012  0.402         0  \n",
       "6/13/2012  0.698         1  \n",
       "6/10/2012  0.420         0  \n",
       "6/8/2012   0.818         1  \n",
       "6/2/2012   0.337         0  \n",
       "6/1/2012   0.741         1  \n",
       "5/30/2012  0.330         0  \n",
       "5/25/2012  0.569         1  \n",
       "5/19/2012  0.472         0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.loc[\"9/13/2012\":\"5/19/2012\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e90308cb-3a08-4e91-9dc9-f47a556442ee",
   "metadata": {},
   "source": [
    "<br>If you've been wondering why referencing rows by index numbers would ever be useful, now you can set your index names to any unique value that would be useful to you - a sample ID, a name, a date, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e252a57-5b3d-480c-a1a6-9d8c84f9bd0e",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96efaeba-d1a2-4443-a8c3-8d3d805721c6",
   "metadata": {},
   "source": [
    "Using the CHIdf, write code (use either `loc` or `at`) to find out which team Chicago played against on 6/16/2017:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4e301a87-5375-4fb2-81b4-b49ef62c6731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PHO'"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.loc[\"6/16/2017\", \"team2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4a3aa4e3-8ad2-49f1-a079-146675f7a8d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PHO'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHIdf.at[\"6/16/2017\", \"team2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d3af5ba-a5d2-414f-a7c5-24d315accdaf",
   "metadata": {},
   "source": [
    "### <br><br><br>Searching for multiple conditionals in pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ca5570-8514-44b3-b54f-f2437c1d41b2",
   "metadata": {},
   "source": [
    "Let's say we want to search through the original DataFrame, `df`, for all games played by the Chicago Sky where the Chicago Sky won. For each of these games, we want to return only the columns for season and the name of the opposing team."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66878e7-22b0-400c-bae9-04ee8033cf31",
   "metadata": {},
   "source": [
    "The conditional for only Chicago Sky games is:\n",
    "<br>`df[\"team1\"] == \"CHI\"`\n",
    "<br><br>The conditional for games that Chicago won is:\n",
    "<br>`df[\"score1\"] > df[\"score2\"]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc02ea7-4c8e-4ca9-be9c-4c2c8f077909",
   "metadata": {},
   "source": [
    "We might try to use Python operators (`and`, `or`, `not`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cd31f7fd-959e-45c5-9312-aa8bccea7295",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [95]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mloc[df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mteam1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCHI\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore2\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseason\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname2\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:1527\u001b[0m, in \u001b[0;36mNDFrame.__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1525\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   1526\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__nonzero__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1528\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe truth value of a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is ambiguous. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1529\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse a.empty, a.bool(), a.item(), a.any() or a.all().\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1530\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "df.loc[df[\"team1\"] == \"CHI\" and df[\"score1\"] > df[\"score2\"], [\"season\", \"name2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce07b50-7657-494d-8c8c-49340ca0ea34",
   "metadata": {},
   "source": [
    "<br><br>**However, pandas uses the operators `&`, `|`, `!` for and, or, and not. Pandas also requires you to include each conditional inside parentheses.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "db9e6f19-9760-4106-b878-130f9cb3cd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>PHO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2019</td>\n",
       "      <td>PHO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2019</td>\n",
       "      <td>PHO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5961</th>\n",
       "      <td>2006</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6069</th>\n",
       "      <td>2006</td>\n",
       "      <td>MIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>2006</td>\n",
       "      <td>NYL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>2006</td>\n",
       "      <td>CHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>2006</td>\n",
       "      <td>CHA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season team2\n",
       "28      2019   PHO\n",
       "47      2019   CON\n",
       "65      2019   PHO\n",
       "96      2019   PHO\n",
       "107     2019   WAS\n",
       "...      ...   ...\n",
       "5961    2006   IND\n",
       "6069    2006   MIN\n",
       "6183    2006   NYL\n",
       "6223    2006   CHA\n",
       "6427    2006   CHA\n",
       "\n",
       "[220 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df[\"team1\"] == \"CHI\") & (df[\"score1\"] > df[\"score2\"]), [\"season\", \"team2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6172a13-a154-4b4f-9b81-8aa88f1805b1",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1702a7-2220-457d-9826-967ffdaed76e",
   "metadata": {},
   "source": [
    "Use `loc` to return rows in the DataFrame that were played in either the 1999 or 2000 seasons. For each row, return all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "236a8c1e-c5ad-4241-b9b2-b050f850c2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8992</th>\n",
       "      <td>2000</td>\n",
       "      <td>8/26/2000</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>73</td>\n",
       "      <td>1741</td>\n",
       "      <td>1523</td>\n",
       "      <td>1743</td>\n",
       "      <td>1522</td>\n",
       "      <td>0.895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993</th>\n",
       "      <td>2000</td>\n",
       "      <td>8/26/2000</td>\n",
       "      <td>NYL</td>\n",
       "      <td>HOU</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>79</td>\n",
       "      <td>1523</td>\n",
       "      <td>1741</td>\n",
       "      <td>1522</td>\n",
       "      <td>1743</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8994</th>\n",
       "      <td>2000</td>\n",
       "      <td>8/24/2000</td>\n",
       "      <td>HOU</td>\n",
       "      <td>NYL</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>52</td>\n",
       "      <td>1735</td>\n",
       "      <td>1529</td>\n",
       "      <td>1741</td>\n",
       "      <td>1523</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8995</th>\n",
       "      <td>2000</td>\n",
       "      <td>8/24/2000</td>\n",
       "      <td>NYL</td>\n",
       "      <td>HOU</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>59</td>\n",
       "      <td>1529</td>\n",
       "      <td>1735</td>\n",
       "      <td>1523</td>\n",
       "      <td>1741</td>\n",
       "      <td>0.288</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8996</th>\n",
       "      <td>2000</td>\n",
       "      <td>8/21/2000</td>\n",
       "      <td>NYL</td>\n",
       "      <td>CLE</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>1521</td>\n",
       "      <td>1460</td>\n",
       "      <td>1529</td>\n",
       "      <td>1451</td>\n",
       "      <td>0.734</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9937</th>\n",
       "      <td>1999</td>\n",
       "      <td>6/10/1999</td>\n",
       "      <td>LAS</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>78</td>\n",
       "      <td>1482</td>\n",
       "      <td>1423</td>\n",
       "      <td>1496</td>\n",
       "      <td>1410</td>\n",
       "      <td>0.689</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9938</th>\n",
       "      <td>1999</td>\n",
       "      <td>6/10/1999</td>\n",
       "      <td>CHA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>1482</td>\n",
       "      <td>1334</td>\n",
       "      <td>1493</td>\n",
       "      <td>1323</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9939</th>\n",
       "      <td>1999</td>\n",
       "      <td>6/10/1999</td>\n",
       "      <td>SAC</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>100</td>\n",
       "      <td>1423</td>\n",
       "      <td>1482</td>\n",
       "      <td>1410</td>\n",
       "      <td>1496</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9940</th>\n",
       "      <td>1999</td>\n",
       "      <td>6/10/1999</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CHA</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Charlotte Sting</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>83</td>\n",
       "      <td>1334</td>\n",
       "      <td>1482</td>\n",
       "      <td>1323</td>\n",
       "      <td>1493</td>\n",
       "      <td>0.403</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9941</th>\n",
       "      <td>1999</td>\n",
       "      <td>6/10/1999</td>\n",
       "      <td>CON</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Orlando Miracle</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>77</td>\n",
       "      <td>1300</td>\n",
       "      <td>1606</td>\n",
       "      <td>1293</td>\n",
       "      <td>1613</td>\n",
       "      <td>0.214</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>950 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season       date team1 team2                name1                name2  \\\n",
       "8992    2000  8/26/2000   HOU   NYL       Houston Comets     New York Liberty   \n",
       "8993    2000  8/26/2000   NYL   HOU     New York Liberty       Houston Comets   \n",
       "8994    2000  8/24/2000   HOU   NYL       Houston Comets     New York Liberty   \n",
       "8995    2000  8/24/2000   NYL   HOU     New York Liberty       Houston Comets   \n",
       "8996    2000  8/21/2000   NYL   CLE     New York Liberty    Cleveland Rockers   \n",
       "...      ...        ...   ...   ...                  ...                  ...   \n",
       "9937    1999  6/10/1999   LAS   SAC   Los Angeles Sparks  Sacramento Monarchs   \n",
       "9938    1999  6/10/1999   CHA   WAS      Charlotte Sting   Washington Mystics   \n",
       "9939    1999  6/10/1999   SAC   LAS  Sacramento Monarchs   Los Angeles Sparks   \n",
       "9940    1999  6/10/1999   WAS   CHA   Washington Mystics      Charlotte Sting   \n",
       "9941    1999  6/10/1999   CON   HOU      Orlando Miracle       Houston Comets   \n",
       "\n",
       "      neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "8992        0        1      79      73      1741      1523       1743   \n",
       "8993        0        1      73      79      1523      1741       1522   \n",
       "8994        0        1      59      52      1735      1529       1741   \n",
       "8995        0        1      52      59      1529      1735       1523   \n",
       "8996        0        1      81      67      1521      1460       1529   \n",
       "...       ...      ...     ...     ...       ...       ...        ...   \n",
       "9937        0        0     100      78      1482      1423       1496   \n",
       "9938        0        0      83      73      1482      1334       1493   \n",
       "9939        0        0      78     100      1423      1482       1410   \n",
       "9940        0        0      73      83      1334      1482       1323   \n",
       "9941        0        0      63      77      1300      1606       1293   \n",
       "\n",
       "      elo2_post  prob1  is_home1  \n",
       "8992       1522  0.895         1  \n",
       "8993       1743  0.105         0  \n",
       "8994       1523  0.712         0  \n",
       "8995       1741  0.288         1  \n",
       "8996       1451  0.734         1  \n",
       "...         ...    ...       ...  \n",
       "9937       1410  0.689         1  \n",
       "9938       1323  0.597         0  \n",
       "9939       1496  0.311         0  \n",
       "9940       1493  0.403         1  \n",
       "9941       1613  0.214         1  \n",
       "\n",
       "[950 rows x 16 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df[\"season\"] == 1999) | (df[\"season\"] == 2000), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a308f1ea-a742-4784-8d60-6cc7a9a0a065",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "db643dbe-e30e-4220-8524-8039a4bebedc",
   "metadata": {},
   "source": [
    "Has Chicago ever played in any playoff games? Return rows that have CHI in the \"team1\" column and 1 in the \"playoff\" column. Only return the columns \"season\", \"team2\", and \"date\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a32e2a01-6424-4f7b-9ab5-cecaa74c1693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team2</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>LVA</td>\n",
       "      <td>9/15/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>PHO</td>\n",
       "      <td>9/11/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>2016</td>\n",
       "      <td>LAS</td>\n",
       "      <td>10/4/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>2016</td>\n",
       "      <td>LAS</td>\n",
       "      <td>10/2/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>2016</td>\n",
       "      <td>LAS</td>\n",
       "      <td>9/30/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>2016</td>\n",
       "      <td>LAS</td>\n",
       "      <td>9/28/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>2016</td>\n",
       "      <td>ATL</td>\n",
       "      <td>9/25/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>2015</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/21/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>2015</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/19/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>2015</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/17/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>2014</td>\n",
       "      <td>PHO</td>\n",
       "      <td>9/12/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>2014</td>\n",
       "      <td>PHO</td>\n",
       "      <td>9/9/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>2014</td>\n",
       "      <td>PHO</td>\n",
       "      <td>9/7/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>2014</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/3/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>2014</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/1/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>2014</td>\n",
       "      <td>IND</td>\n",
       "      <td>8/30/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>2014</td>\n",
       "      <td>ATL</td>\n",
       "      <td>8/26/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>2014</td>\n",
       "      <td>ATL</td>\n",
       "      <td>8/24/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>2014</td>\n",
       "      <td>ATL</td>\n",
       "      <td>8/22/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>2013</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/22/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>2013</td>\n",
       "      <td>IND</td>\n",
       "      <td>9/20/2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      season team2       date\n",
       "25      2019   LVA  9/15/2019\n",
       "28      2019   PHO  9/11/2019\n",
       "1331    2016   LAS  10/4/2016\n",
       "1335    2016   LAS  10/2/2016\n",
       "1339    2016   LAS  9/30/2016\n",
       "1343    2016   LAS  9/28/2016\n",
       "1344    2016   ATL  9/25/2016\n",
       "1785    2015   IND  9/21/2015\n",
       "1791    2015   IND  9/19/2015\n",
       "1799    2015   IND  9/17/2015\n",
       "2211    2014   PHO  9/12/2014\n",
       "2213    2014   PHO   9/9/2014\n",
       "2215    2014   PHO   9/7/2014\n",
       "2216    2014   IND   9/3/2014\n",
       "2221    2014   IND   9/1/2014\n",
       "2225    2014   IND  8/30/2014\n",
       "2229    2014   ATL  8/26/2014\n",
       "2233    2014   ATL  8/24/2014\n",
       "2241    2014   ATL  8/22/2014\n",
       "2674    2013   IND  9/22/2013\n",
       "2681    2013   IND  9/20/2013"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df[\"team1\"] == \"CHI\") & (df[\"playoff\"] == 1), [\"season\", \"team2\", \"date\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25340a60-bc81-42ae-b11e-520266bb6ab1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2f230881-4e23-4643-ac45-5f402dddd9cd",
   "metadata": {},
   "source": [
    "## <br><br><br>PART THREE: *OPEN UP THE NOTEBOOK PANDAS2.IPYNB*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afabe6ab-58ba-47f7-9c07-8974288ec34c",
   "metadata": {},
   "source": [
    "We don't want our notebook to get too slow as we continue to work with additional dataframes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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