pandas2-Answers.ipynb

{
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   "cell_type": "markdown",
   "id": "a44c3788-1b87-4f0b-aec7-c6d6f849fa26",
   "metadata": {},
   "source": [
    "# pandas2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d160743-5bc8-4085-a377-0dae8ab60314",
   "metadata": {},
   "source": [
    "Let's import pandas!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb4373f9-19c3-4ffa-a7a0-f8a923cafc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e0843c-5b80-482c-b767-c207073f7028",
   "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.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4efd26fc-e177-44da-9047-ea995943d06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/aGitHasNoName/pandasBasics/main/forestfires.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3537f5-ad45-4ec3-a78d-1f087ad2344a",
   "metadata": {},
   "source": [
    "<br><br>We're going to work with a new dataframe. This is a dataset from forest fires in NE Portugal. I have included the dataset as a csv file in today's materials, but the data is available publicly at this site: https://archive.ics.uci.edu/ml/datasets/Forest+Fires"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e8a3d8-b6a6-433b-960c-e74335832a85",
   "metadata": {},
   "source": [
    "<br><br>Everyone can now run the next line of code to create a DataFrame from our csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2011bd39-be5f-4ac7-beab-6f137951e79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"forestfires.csv\")"
   ]
  },
  {
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   "execution_count": 3,
   "id": "1e81d576-c2aa-4b7c-9e2d-9f37389bc904",
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   "outputs": [
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       "      <td>89.3</td>\n",
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       "      <td>102.2</td>\n",
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       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
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      "text/plain": [
       "   X  Y month  day  fuel_code  moisture_code  drought_code  \\\n",
       "0  7  5   mar  fri       86.2           26.2          94.3   \n",
       "1  7  4   oct  tue       90.6           35.4         669.1   \n",
       "2  7  4   oct  sat       90.6           43.7         686.9   \n",
       "3  8  6   mar  fri       91.7           33.3          77.5   \n",
       "4  8  6   mar  sun       89.3           51.3         102.2   \n",
       "\n",
       "   initial_spread_code  temp  humidity  wind  rain  area_burned  \n",
       "0                  5.1   8.2        51   6.7   0.0          0.0  \n",
       "1                  6.7  18.0        33   0.9   0.0          0.0  \n",
       "2                  6.7  14.6        33   1.3   0.0          0.0  \n",
       "3                  9.0   8.3        97   4.0   0.2          0.0  \n",
       "4                  9.6  11.4        99   1.8   0.0          0.0  "
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   "source": [
    "df.head()"
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   "cell_type": "markdown",
   "id": "a485479b-3eb4-4e2e-86bb-d2cdddb24b6b",
   "metadata": {},
   "source": [
    "Take a look at the new dataframe to get to know our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27abf411-c252-4122-8018-11a09bef469f",
   "metadata": {},
   "source": [
    "How many rows and columns are in our data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b52eabc4-1876-4af6-a80c-dbba6e7785e2",
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       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
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       "      <td>21.9</td>\n",
       "      <td>71</td>\n",
       "      <td>5.8</td>\n",
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       "      <td>54.29</td>\n",
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       "      <td>7</td>\n",
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       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
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       "      <td>94.4</td>\n",
       "      <td>146.0</td>\n",
       "      <td>614.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>42</td>\n",
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       "      <td>79.5</td>\n",
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       "      <td>106.7</td>\n",
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       "<p>517 rows × 13 columns</p>\n",
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      "text/plain": [
       "     X  Y month  day  fuel_code  moisture_code  drought_code  \\\n",
       "0    7  5   mar  fri       86.2           26.2          94.3   \n",
       "1    7  4   oct  tue       90.6           35.4         669.1   \n",
       "2    7  4   oct  sat       90.6           43.7         686.9   \n",
       "3    8  6   mar  fri       91.7           33.3          77.5   \n",
       "4    8  6   mar  sun       89.3           51.3         102.2   \n",
       "..  .. ..   ...  ...        ...            ...           ...   \n",
       "512  4  3   aug  sun       81.6           56.7         665.6   \n",
       "513  2  4   aug  sun       81.6           56.7         665.6   \n",
       "514  7  4   aug  sun       81.6           56.7         665.6   \n",
       "515  1  4   aug  sat       94.4          146.0         614.7   \n",
       "516  6  3   nov  tue       79.5            3.0         106.7   \n",
       "\n",
       "     initial_spread_code  temp  humidity  wind  rain  area_burned  \n",
       "0                    5.1   8.2        51   6.7   0.0         0.00  \n",
       "1                    6.7  18.0        33   0.9   0.0         0.00  \n",
       "2                    6.7  14.6        33   1.3   0.0         0.00  \n",
       "3                    9.0   8.3        97   4.0   0.2         0.00  \n",
       "4                    9.6  11.4        99   1.8   0.0         0.00  \n",
       "..                   ...   ...       ...   ...   ...          ...  \n",
       "512                  1.9  27.8        32   2.7   0.0         6.44  \n",
       "513                  1.9  21.9        71   5.8   0.0        54.29  \n",
       "514                  1.9  21.2        70   6.7   0.0        11.16  \n",
       "515                 11.3  25.6        42   4.0   0.0         0.00  \n",
       "516                  1.1  11.8        31   4.5   0.0         0.00  \n",
       "\n",
       "[517 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "832e90a7-2c31-4dc7-a8ff-c6af21eda7d8",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(517, 13)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
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  {
   "cell_type": "markdown",
   "id": "8a26a640-5f0e-4786-b8f1-0159b644ab81",
   "metadata": {},
   "source": [
    "## <br><br><br>PART THREE: RENAMING COLUMNS, DROPPING ROWS AND COLUMNS, SORTING DATA, AND SAVING DATAFRAMES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9455b792-35b5-44d9-9439-b80b394dc8fc",
   "metadata": {},
   "source": [
    "### <br>Renaming columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4c34b4-4a3b-423e-878a-1ebc91e55c7e",
   "metadata": {},
   "source": [
    "Here's what our column names look like in the forest fire dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8107a48d-23ad-4469-96c8-c9b1a0e1b1ac",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  fuel_code  moisture_code  drought_code  \\\n",
       "0  7  5   mar  fri       86.2           26.2          94.3   \n",
       "1  7  4   oct  tue       90.6           35.4         669.1   \n",
       "2  7  4   oct  sat       90.6           43.7         686.9   \n",
       "3  8  6   mar  fri       91.7           33.3          77.5   \n",
       "4  8  6   mar  sun       89.3           51.3         102.2   \n",
       "\n",
       "   initial_spread_code  temp  humidity  wind  rain  area_burned  \n",
       "0                  5.1   8.2        51   6.7   0.0          0.0  \n",
       "1                  6.7  18.0        33   0.9   0.0          0.0  \n",
       "2                  6.7  14.6        33   1.3   0.0          0.0  \n",
       "3                  9.0   8.3        97   4.0   0.2          0.0  \n",
       "4                  9.6  11.4        99   1.8   0.0          0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57d3dc4-8241-4be9-9023-3d62076a656d",
   "metadata": {},
   "source": [
    "Four of the columns end in \"\\_code\". Let's remove that part from the column names. We can use the `rename()` method. We need to pass the function a **dictionary** of the old name to be replaced as the key and the new name as the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe94b5a2-4e3f-452a-b978-64a625395c68",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought_code</th>\n",
       "      <th>initial_spread_code</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area_burned</th>\n",
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       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
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       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
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       "      <td>5.1</td>\n",
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       "      <td>7</td>\n",
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       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
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       "      <td>0.00</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>27.8</td>\n",
       "      <td>32</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.44</td>\n",
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       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>71</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.29</td>\n",
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       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>94.4</td>\n",
       "      <td>146.0</td>\n",
       "      <td>614.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>516</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>nov</td>\n",
       "      <td>tue</td>\n",
       "      <td>79.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>106.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>31</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>517 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     X  Y month  day  fuel  moisture  drought_code  initial_spread_code  temp  \\\n",
       "0    7  5   mar  fri  86.2      26.2          94.3                  5.1   8.2   \n",
       "1    7  4   oct  tue  90.6      35.4         669.1                  6.7  18.0   \n",
       "2    7  4   oct  sat  90.6      43.7         686.9                  6.7  14.6   \n",
       "3    8  6   mar  fri  91.7      33.3          77.5                  9.0   8.3   \n",
       "4    8  6   mar  sun  89.3      51.3         102.2                  9.6  11.4   \n",
       "..  .. ..   ...  ...   ...       ...           ...                  ...   ...   \n",
       "512  4  3   aug  sun  81.6      56.7         665.6                  1.9  27.8   \n",
       "513  2  4   aug  sun  81.6      56.7         665.6                  1.9  21.9   \n",
       "514  7  4   aug  sun  81.6      56.7         665.6                  1.9  21.2   \n",
       "515  1  4   aug  sat  94.4     146.0         614.7                 11.3  25.6   \n",
       "516  6  3   nov  tue  79.5       3.0         106.7                  1.1  11.8   \n",
       "\n",
       "     humidity  wind  rain  area_burned  \n",
       "0          51   6.7   0.0         0.00  \n",
       "1          33   0.9   0.0         0.00  \n",
       "2          33   1.3   0.0         0.00  \n",
       "3          97   4.0   0.2         0.00  \n",
       "4          99   1.8   0.0         0.00  \n",
       "..        ...   ...   ...          ...  \n",
       "512        32   2.7   0.0         6.44  \n",
       "513        71   5.8   0.0        54.29  \n",
       "514        70   6.7   0.0        11.16  \n",
       "515        42   4.0   0.0         0.00  \n",
       "516        31   4.5   0.0         0.00  \n",
       "\n",
       "[517 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {\"moisture_code\": \"moisture\", \n",
    "                     \"fuel_code\": \"fuel\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ecd9b2a-6136-4466-babb-73312f3ef0af",
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel_code</th>\n",
       "      <th>moisture_code</th>\n",
       "      <th>drought_code</th>\n",
       "      <th>initial_spread_code</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area_burned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  fuel_code  moisture_code  drought_code  \\\n",
       "0  7  5   mar  fri       86.2           26.2          94.3   \n",
       "1  7  4   oct  tue       90.6           35.4         669.1   \n",
       "2  7  4   oct  sat       90.6           43.7         686.9   \n",
       "3  8  6   mar  fri       91.7           33.3          77.5   \n",
       "4  8  6   mar  sun       89.3           51.3         102.2   \n",
       "\n",
       "   initial_spread_code  temp  humidity  wind  rain  area_burned  \n",
       "0                  5.1   8.2        51   6.7   0.0          0.0  \n",
       "1                  6.7  18.0        33   0.9   0.0          0.0  \n",
       "2                  6.7  14.6        33   1.3   0.0          0.0  \n",
       "3                  9.0   8.3        97   4.0   0.2          0.0  \n",
       "4                  9.6  11.4        99   1.8   0.0          0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3edc670-98af-4547-9e32-88b0fe7bf667",
   "metadata": {},
   "source": [
    "Uh-oh, the change didn't stick. We've encountered this before with strings, so we know the answer - reassign it to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62cd4555-5fc7-4f62-9fb6-61c36295db20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns = {\"moisture_code\": \"moisture\", \n",
    "                          \"fuel_code\": \"fuel\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bda3138b-584f-4006-9bce-42399e1f4751",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought_code</th>\n",
       "      <th>initial_spread_code</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
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       "      <th>area_burned</th>\n",
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       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  fuel  moisture  drought_code  initial_spread_code  temp  \\\n",
       "0  7  5   mar  fri  86.2      26.2          94.3                  5.1   8.2   \n",
       "1  7  4   oct  tue  90.6      35.4         669.1                  6.7  18.0   \n",
       "2  7  4   oct  sat  90.6      43.7         686.9                  6.7  14.6   \n",
       "3  8  6   mar  fri  91.7      33.3          77.5                  9.0   8.3   \n",
       "4  8  6   mar  sun  89.3      51.3         102.2                  9.6  11.4   \n",
       "\n",
       "   humidity  wind  rain  area_burned  \n",
       "0        51   6.7   0.0          0.0  \n",
       "1        33   0.9   0.0          0.0  \n",
       "2        33   1.3   0.0          0.0  \n",
       "3        97   4.0   0.2          0.0  \n",
       "4        99   1.8   0.0          0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16eb5d84-6020-4266-b4f7-b2fbe1aa17b8",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63ea6f1-93b4-4184-8279-651adf26c0c5",
   "metadata": {},
   "source": [
    "Write code to remove \"\\_code\" from the ends of the drought and initial_spread column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "526c4262-0488-4527-bf01-d5ca75393406",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"drought_code\": \"drought\", \n",
    "                        \"initial_spread_code\": \"initial_spread\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c11bb1e-9367-4568-afbc-80bf32196639",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   X  Y month  day  fuel  moisture  drought  initial_spread  temp  humidity  \\\n",
       "0  7  5   mar  fri  86.2      26.2     94.3             5.1   8.2        51   \n",
       "1  7  4   oct  tue  90.6      35.4    669.1             6.7  18.0        33   \n",
       "2  7  4   oct  sat  90.6      43.7    686.9             6.7  14.6        33   \n",
       "3  8  6   mar  fri  91.7      33.3     77.5             9.0   8.3        97   \n",
       "4  8  6   mar  sun  89.3      51.3    102.2             9.6  11.4        99   \n",
       "\n",
       "   wind  rain  area_burned  \n",
       "0   6.7   0.0          0.0  \n",
       "1   0.9   0.0          0.0  \n",
       "2   1.3   0.0          0.0  \n",
       "3   4.0   0.2          0.0  \n",
       "4   1.8   0.0          0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22b88d0-fa5f-4fab-9c77-1e82a788c0fe",
   "metadata": {},
   "source": [
    "### <br><br><br>Dropping rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454c2b6b-5bdc-4c3c-9169-d66b8a146475",
   "metadata": {},
   "source": [
    "Let's drop a single row from the DataFrame. How about row 2? You still have to assign `df` to a variable to make the change permanent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b61d15dc-a17c-4770-b8ae-be2aca14ac3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88178e00-0160-484d-b413-7bb6e276a059",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   X  Y month  day  fuel  moisture  drought  initial_spread  temp  humidity  \\\n",
       "0  7  5   mar  fri  86.2      26.2     94.3             5.1   8.2        51   \n",
       "1  7  4   oct  tue  90.6      35.4    669.1             6.7  18.0        33   \n",
       "3  8  6   mar  fri  91.7      33.3     77.5             9.0   8.3        97   \n",
       "4  8  6   mar  sun  89.3      51.3    102.2             9.6  11.4        99   \n",
       "5  8  6   aug  sun  92.3      85.3    488.0            14.7  22.2        29   \n",
       "\n",
       "   wind  rain  area_burned  \n",
       "0   6.7   0.0          0.0  \n",
       "1   0.9   0.0          0.0  \n",
       "3   4.0   0.2          0.0  \n",
       "4   1.8   0.0          0.0  \n",
       "5   5.4   0.0          0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1c25e4-af49-41b5-87e8-1c93a7151fc1",
   "metadata": {},
   "source": [
    "<br>The index numbers did not reset when we dropped a row. 2 is missing!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fc3b6d-08e4-4844-b69f-ccf60f10c010",
   "metadata": {},
   "source": [
    "We can reset the index and pretend like 2 was never there. The `reset_index()` function takes one keyword argument. If we don't pass the argument, `drop=True`, an extra column will get added to our DataFrame containing the old index numbers. Let's first reset the index without passing the argument, but we won't save that DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5cec15ce-7f86-457c-9b20-8693c2d257d9",
   "metadata": {},
   "outputs": [
    {
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       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>92.3</td>\n",
       "      <td>85.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>27.8</td>\n",
       "      <td>32</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>513</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>71</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>514</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>94.4</td>\n",
       "      <td>146.0</td>\n",
       "      <td>614.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>516</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>nov</td>\n",
       "      <td>tue</td>\n",
       "      <td>79.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>106.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>31</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  X  Y month  day  fuel  moisture  drought  initial_spread  temp  \\\n",
       "0        0  7  5   mar  fri  86.2      26.2     94.3             5.1   8.2   \n",
       "1        1  7  4   oct  tue  90.6      35.4    669.1             6.7  18.0   \n",
       "2        3  8  6   mar  fri  91.7      33.3     77.5             9.0   8.3   \n",
       "3        4  8  6   mar  sun  89.3      51.3    102.2             9.6  11.4   \n",
       "4        5  8  6   aug  sun  92.3      85.3    488.0            14.7  22.2   \n",
       "..     ... .. ..   ...  ...   ...       ...      ...             ...   ...   \n",
       "511    512  4  3   aug  sun  81.6      56.7    665.6             1.9  27.8   \n",
       "512    513  2  4   aug  sun  81.6      56.7    665.6             1.9  21.9   \n",
       "513    514  7  4   aug  sun  81.6      56.7    665.6             1.9  21.2   \n",
       "514    515  1  4   aug  sat  94.4     146.0    614.7            11.3  25.6   \n",
       "515    516  6  3   nov  tue  79.5       3.0    106.7             1.1  11.8   \n",
       "\n",
       "     humidity  wind  rain  area_burned  \n",
       "0          51   6.7   0.0         0.00  \n",
       "1          33   0.9   0.0         0.00  \n",
       "2          97   4.0   0.2         0.00  \n",
       "3          99   1.8   0.0         0.00  \n",
       "4          29   5.4   0.0         0.00  \n",
       "..        ...   ...   ...          ...  \n",
       "511        32   2.7   0.0         6.44  \n",
       "512        71   5.8   0.0        54.29  \n",
       "513        70   6.7   0.0        11.16  \n",
       "514        42   4.0   0.0         0.00  \n",
       "515        31   4.5   0.0         0.00  \n",
       "\n",
       "[516 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c851da94-8756-404d-815f-9b25c3b901de",
   "metadata": {},
   "source": [
    "You can see that new column `index` contains the original index positions. Now let's save a new version of our DataFrame, with the indexes reset, but without that new column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "64eed170-ea90-40e5-9017-c32c73b077ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "706ca53c-f75c-4fb2-af94-a074992b2efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
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       "      <td>tue</td>\n",
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       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
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       "      <td>mar</td>\n",
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       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
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       "      <td>11.4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
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       "      <td>sun</td>\n",
       "      <td>92.3</td>\n",
       "      <td>85.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  fuel  moisture  drought  initial_spread  temp  humidity  \\\n",
       "0  7  5   mar  fri  86.2      26.2     94.3             5.1   8.2        51   \n",
       "1  7  4   oct  tue  90.6      35.4    669.1             6.7  18.0        33   \n",
       "2  8  6   mar  fri  91.7      33.3     77.5             9.0   8.3        97   \n",
       "3  8  6   mar  sun  89.3      51.3    102.2             9.6  11.4        99   \n",
       "4  8  6   aug  sun  92.3      85.3    488.0            14.7  22.2        29   \n",
       "\n",
       "   wind  rain  area_burned  \n",
       "0   6.7   0.0          0.0  \n",
       "1   0.9   0.0          0.0  \n",
       "2   4.0   0.2          0.0  \n",
       "3   1.8   0.0          0.0  \n",
       "4   5.4   0.0          0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00a34e80-97bd-4e47-808b-7ee2d7d38a5c",
   "metadata": {},
   "source": [
    "<br><br><br>The `drop()` function defaults to dropping rows. If we want to drop a column, we need to add one more argument. `axis=1` is used in pandas to refer to columns as opposed to rows (`axis=0`). The `axis` argument is used elsewhere in pandas, too. Let's drop the \"X\" column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2f9fa114-21b7-4dce-9d86-41067dcf0944",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"X\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f140b38-5a8f-4ee4-88ea-3c5e77f14300",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
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       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>77.5</td>\n",
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       "      <td>97</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
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       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Y month  day  fuel  moisture  drought  initial_spread  temp  humidity  \\\n",
       "0  5   mar  fri  86.2      26.2     94.3             5.1   8.2        51   \n",
       "1  4   oct  tue  90.6      35.4    669.1             6.7  18.0        33   \n",
       "2  6   mar  fri  91.7      33.3     77.5             9.0   8.3        97   \n",
       "3  6   mar  sun  89.3      51.3    102.2             9.6  11.4        99   \n",
       "4  6   aug  sun  92.3      85.3    488.0            14.7  22.2        29   \n",
       "\n",
       "   wind  rain  area_burned  \n",
       "0   6.7   0.0          0.0  \n",
       "1   0.9   0.0          0.0  \n",
       "2   4.0   0.2          0.0  \n",
       "3   1.8   0.0          0.0  \n",
       "4   5.4   0.0          0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4047294-1b5a-450c-b3b4-7d765031a2c8",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b181b50b-a1d8-44af-b60b-c2ee7e6a3ddd",
   "metadata": {},
   "source": [
    "Write code to view the last 5 rows of the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3f84ca95-0d09-458c-bc5b-2ce088116907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area_burned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>3</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>27.8</td>\n",
       "      <td>32</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>71</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>94.4</td>\n",
       "      <td>146.0</td>\n",
       "      <td>614.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>3</td>\n",
       "      <td>nov</td>\n",
       "      <td>tue</td>\n",
       "      <td>79.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>106.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>31</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Y month  day  fuel  moisture  drought  initial_spread  temp  humidity  \\\n",
       "511  3   aug  sun  81.6      56.7    665.6             1.9  27.8        32   \n",
       "512  4   aug  sun  81.6      56.7    665.6             1.9  21.9        71   \n",
       "513  4   aug  sun  81.6      56.7    665.6             1.9  21.2        70   \n",
       "514  4   aug  sat  94.4     146.0    614.7            11.3  25.6        42   \n",
       "515  3   nov  tue  79.5       3.0    106.7             1.1  11.8        31   \n",
       "\n",
       "     wind  rain  area_burned  \n",
       "511   2.7   0.0         6.44  \n",
       "512   5.8   0.0        54.29  \n",
       "513   6.7   0.0        11.16  \n",
       "514   4.0   0.0         0.00  \n",
       "515   4.5   0.0         0.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f073f98-0ac3-4806-a563-fe9fd5b78073",
   "metadata": {},
   "source": [
    "Now write code to drop the very last row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ecdb32f7-21ac-4114-8c38-f97d4a98c6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(515)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "70255ad3-3185-470a-8602-4391f83fff5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
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       "      <th>moisture</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>510</th>\n",
       "      <td>6</td>\n",
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       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.29</td>\n",
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       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.16</td>\n",
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       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>4</td>\n",
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       "      <td>42</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Y month  day  fuel  moisture  drought  initial_spread  temp  humidity  \\\n",
       "510  6   aug  sun  81.6      56.7    665.6             1.9  27.8        35   \n",
       "511  3   aug  sun  81.6      56.7    665.6             1.9  27.8        32   \n",
       "512  4   aug  sun  81.6      56.7    665.6             1.9  21.9        71   \n",
       "513  4   aug  sun  81.6      56.7    665.6             1.9  21.2        70   \n",
       "514  4   aug  sat  94.4     146.0    614.7            11.3  25.6        42   \n",
       "\n",
       "     wind  rain  area_burned  \n",
       "510   2.7   0.0         0.00  \n",
       "511   2.7   0.0         6.44  \n",
       "512   5.8   0.0        54.29  \n",
       "513   6.7   0.0        11.16  \n",
       "514   4.0   0.0         0.00  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbc6186-e4b4-4658-83f7-e40aca964839",
   "metadata": {},
   "source": [
    "Write code to remove the \"Y\" column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5feb4c22-9dc6-4077-9aa6-773e62f88299",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(\"Y\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f77b9457-a4d6-4aa2-b12f-6cf94277595b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>mar</td>\n",
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       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
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       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oct</td>\n",
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       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
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       "      <td>77.5</td>\n",
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       "      <td>97</td>\n",
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       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>aug</td>\n",
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      ],
      "text/plain": [
       "  month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "0   mar  fri  86.2      26.2     94.3             5.1   8.2        51   6.7   \n",
       "1   oct  tue  90.6      35.4    669.1             6.7  18.0        33   0.9   \n",
       "2   mar  fri  91.7      33.3     77.5             9.0   8.3        97   4.0   \n",
       "3   mar  sun  89.3      51.3    102.2             9.6  11.4        99   1.8   \n",
       "4   aug  sun  92.3      85.3    488.0            14.7  22.2        29   5.4   \n",
       "\n",
       "   rain  area_burned  \n",
       "0   0.0          0.0  \n",
       "1   0.0          0.0  \n",
       "2   0.2          0.0  \n",
       "3   0.0          0.0  \n",
       "4   0.0          0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b8f215-7b5d-4cf3-a791-15645c00d26a",
   "metadata": {},
   "source": [
    "### <br><br><br>Sorting a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0536e871-d413-4da5-a224-539a41349a51",
   "metadata": {},
   "source": [
    "There are two functions for sorting your DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa9d7b7-dcd2-472a-86a1-824022861707",
   "metadata": {},
   "source": [
    "If you want to sort by the index numbers, or if you want to sort by the column names (alphabetically), you use `sort_index`. It can take two arguments: the axis to sort by (row or column) and the order (ascending or not):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e11db2-60c9-4933-8eba-271972c77d87",
   "metadata": {},
   "source": [
    "The default arguments are to sort by row index with 0 at the top, which is how we've already been viewing the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "762345f6-8b7a-43ec-b0b7-dc443a2b516b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.00</td>\n",
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       "      <th>3</th>\n",
       "      <td>mar</td>\n",
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       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
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       "      <th>4</th>\n",
       "      <td>aug</td>\n",
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       "      <td>488.0</td>\n",
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       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>27.8</td>\n",
       "      <td>35</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>27.8</td>\n",
       "      <td>32</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>71</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>81.6</td>\n",
       "      <td>56.7</td>\n",
       "      <td>665.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>94.4</td>\n",
       "      <td>146.0</td>\n",
       "      <td>614.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "0     mar  fri  86.2      26.2     94.3             5.1   8.2        51   6.7   \n",
       "1     oct  tue  90.6      35.4    669.1             6.7  18.0        33   0.9   \n",
       "2     mar  fri  91.7      33.3     77.5             9.0   8.3        97   4.0   \n",
       "3     mar  sun  89.3      51.3    102.2             9.6  11.4        99   1.8   \n",
       "4     aug  sun  92.3      85.3    488.0            14.7  22.2        29   5.4   \n",
       "..    ...  ...   ...       ...      ...             ...   ...       ...   ...   \n",
       "510   aug  sun  81.6      56.7    665.6             1.9  27.8        35   2.7   \n",
       "511   aug  sun  81.6      56.7    665.6             1.9  27.8        32   2.7   \n",
       "512   aug  sun  81.6      56.7    665.6             1.9  21.9        71   5.8   \n",
       "513   aug  sun  81.6      56.7    665.6             1.9  21.2        70   6.7   \n",
       "514   aug  sat  94.4     146.0    614.7            11.3  25.6        42   4.0   \n",
       "\n",
       "     rain  area_burned  \n",
       "0     0.0         0.00  \n",
       "1     0.0         0.00  \n",
       "2     0.2         0.00  \n",
       "3     0.0         0.00  \n",
       "4     0.0         0.00  \n",
       "..    ...          ...  \n",
       "510   0.0         0.00  \n",
       "511   0.0         6.44  \n",
       "512   0.0        54.29  \n",
       "513   0.0        11.16  \n",
       "514   0.0         0.00  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e310e557-efc5-4caf-bd8c-0ad0579d3b3a",
   "metadata": {},
   "source": [
    "Let's try more arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f061cf48-7edf-4c2e-b7be-51614091b78c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>month</th>\n",
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       "      <th>4</th>\n",
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       "      <td>85.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.00</td>\n",
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       "      <th>1</th>\n",
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       "      <td>tue</td>\n",
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       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
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      ],
      "text/plain": [
       "    month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "514   aug  sat  94.4     146.0    614.7            11.3  25.6        42   4.0   \n",
       "513   aug  sun  81.6      56.7    665.6             1.9  21.2        70   6.7   \n",
       "512   aug  sun  81.6      56.7    665.6             1.9  21.9        71   5.8   \n",
       "511   aug  sun  81.6      56.7    665.6             1.9  27.8        32   2.7   \n",
       "510   aug  sun  81.6      56.7    665.6             1.9  27.8        35   2.7   \n",
       "..    ...  ...   ...       ...      ...             ...   ...       ...   ...   \n",
       "4     aug  sun  92.3      85.3    488.0            14.7  22.2        29   5.4   \n",
       "3     mar  sun  89.3      51.3    102.2             9.6  11.4        99   1.8   \n",
       "2     mar  fri  91.7      33.3     77.5             9.0   8.3        97   4.0   \n",
       "1     oct  tue  90.6      35.4    669.1             6.7  18.0        33   0.9   \n",
       "0     mar  fri  86.2      26.2     94.3             5.1   8.2        51   6.7   \n",
       "\n",
       "     rain  area_burned  \n",
       "514   0.0         0.00  \n",
       "513   0.0        11.16  \n",
       "512   0.0        54.29  \n",
       "511   0.0         6.44  \n",
       "510   0.0         0.00  \n",
       "..    ...          ...  \n",
       "4     0.0         0.00  \n",
       "3     0.0         0.00  \n",
       "2     0.2         0.00  \n",
       "1     0.0         0.00  \n",
       "0     0.0         0.00  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "52bafeba-a837-4a0e-b287-448ca4422622",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>56.7</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>6.7</td>\n",
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       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>0.00</td>\n",
       "      <td>sat</td>\n",
       "      <td>614.7</td>\n",
       "      <td>94.4</td>\n",
       "      <td>42</td>\n",
       "      <td>11.3</td>\n",
       "      <td>146.0</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 11 columns</p>\n",
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      "text/plain": [
       "     area_burned  day  drought  fuel  humidity  initial_spread  moisture  \\\n",
       "0           0.00  fri     94.3  86.2        51             5.1      26.2   \n",
       "1           0.00  tue    669.1  90.6        33             6.7      35.4   \n",
       "2           0.00  fri     77.5  91.7        97             9.0      33.3   \n",
       "3           0.00  sun    102.2  89.3        99             9.6      51.3   \n",
       "4           0.00  sun    488.0  92.3        29            14.7      85.3   \n",
       "..           ...  ...      ...   ...       ...             ...       ...   \n",
       "510         0.00  sun    665.6  81.6        35             1.9      56.7   \n",
       "511         6.44  sun    665.6  81.6        32             1.9      56.7   \n",
       "512        54.29  sun    665.6  81.6        71             1.9      56.7   \n",
       "513        11.16  sun    665.6  81.6        70             1.9      56.7   \n",
       "514         0.00  sat    614.7  94.4        42            11.3     146.0   \n",
       "\n",
       "    month  rain  temp  wind  \n",
       "0     mar   0.0   8.2   6.7  \n",
       "1     oct   0.0  18.0   0.9  \n",
       "2     mar   0.2   8.3   4.0  \n",
       "3     mar   0.0  11.4   1.8  \n",
       "4     aug   0.0  22.2   5.4  \n",
       "..    ...   ...   ...   ...  \n",
       "510   aug   0.0  27.8   2.7  \n",
       "511   aug   0.0  27.8   2.7  \n",
       "512   aug   0.0  21.9   5.8  \n",
       "513   aug   0.0  21.2   6.7  \n",
       "514   aug   0.0  25.6   4.0  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4bac656-ba2e-4258-9337-efb7e32d500b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     wind  temp  rain month  moisture  initial_spread  humidity  fuel  \\\n",
       "0     6.7   8.2   0.0   mar      26.2             5.1        51  86.2   \n",
       "1     0.9  18.0   0.0   oct      35.4             6.7        33  90.6   \n",
       "2     4.0   8.3   0.2   mar      33.3             9.0        97  91.7   \n",
       "3     1.8  11.4   0.0   mar      51.3             9.6        99  89.3   \n",
       "4     5.4  22.2   0.0   aug      85.3            14.7        29  92.3   \n",
       "..    ...   ...   ...   ...       ...             ...       ...   ...   \n",
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       "511   2.7  27.8   0.0   aug      56.7             1.9        32  81.6   \n",
       "512   5.8  21.9   0.0   aug      56.7             1.9        71  81.6   \n",
       "513   6.7  21.2   0.0   aug      56.7             1.9        70  81.6   \n",
       "514   4.0  25.6   0.0   aug     146.0            11.3        42  94.4   \n",
       "\n",
       "     drought  day  area_burned  \n",
       "0       94.3  fri         0.00  \n",
       "1      669.1  tue         0.00  \n",
       "2       77.5  fri         0.00  \n",
       "3      102.2  sun         0.00  \n",
       "4      488.0  sun         0.00  \n",
       "..       ...  ...          ...  \n",
       "510    665.6  sun         0.00  \n",
       "511    665.6  sun         6.44  \n",
       "512    665.6  sun        54.29  \n",
       "513    665.6  sun        11.16  \n",
       "514    614.7  sat         0.00  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b294a125-663b-4af9-b195-ce5d7e301884",
   "metadata": {},
   "source": [
    "<br><br><br>The second sort function, `sort_values()`, will sort the frame by the data in a column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "858a2967-f453-4b58-9f1b-4b704adceb0e",
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   "outputs": [
    {
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       "      <td>103.9</td>\n",
       "      <td>431.6</td>\n",
       "      <td>6.4</td>\n",
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       "      <td>57</td>\n",
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       "      <td>222.4</td>\n",
       "      <td>698.6</td>\n",
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       "      <td>27</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "    month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "0     mar  fri  86.2      26.2     94.3             5.1   8.2        51   6.7   \n",
       "298   jun  sat  53.4      71.0    233.8             0.4  10.6        90   2.7   \n",
       "299   jun  mon  90.4      93.3    298.1             7.5  20.7        25   4.9   \n",
       "301   jun  fri  91.1      94.1    232.1             7.1  19.2        38   4.5   \n",
       "302   jun  fri  91.1      94.1    232.1             7.1  19.2        38   4.5   \n",
       "..    ...  ...   ...       ...      ...             ...   ...       ...   ...   \n",
       "235   sep  sat  92.5     121.1    674.4             8.6  18.2        46   1.8   \n",
       "236   sep  tue  91.0     129.5    692.6             7.0  18.8        40   2.2   \n",
       "478   jul  mon  89.2     103.9    431.6             6.4  22.6        57   4.9   \n",
       "414   aug  thu  94.8     222.4    698.6            13.9  27.5        27   4.9   \n",
       "237   sep  sat  92.5     121.1    674.4             8.6  25.1        27   4.0   \n",
       "\n",
       "     rain  area_burned  \n",
       "0     0.0         0.00  \n",
       "298   0.0         0.00  \n",
       "299   0.0         0.00  \n",
       "301   0.0         0.00  \n",
       "302   0.0         0.00  \n",
       "..    ...          ...  \n",
       "235   0.0       200.94  \n",
       "236   0.0       212.88  \n",
       "478   0.0       278.53  \n",
       "414   0.0       746.28  \n",
       "237   0.0      1090.84  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"area_burned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2a0a7d97-c192-4194-8991-dea8617cfd80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>111.2</td>\n",
       "      <td>654.1</td>\n",
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       "      <td>45</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.63</td>\n",
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       "      <td>95.2</td>\n",
       "      <td>217.7</td>\n",
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       "    month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "0     mar  fri  86.2      26.2     94.3             5.1   8.2        51   6.7   \n",
       "352   sep  fri  92.1      99.0    745.3             9.6  19.8        47   2.7   \n",
       "104   mar  fri  85.9      19.5     57.3             2.8  12.7        52   6.3   \n",
       "354   sep  fri  92.1      99.0    745.3             9.6  20.8        35   4.9   \n",
       "355   sep  fri  92.1      99.0    745.3             9.6  20.8        35   4.9   \n",
       "..    ...  ...   ...       ...      ...             ...   ...       ...   ...   \n",
       "43    sep  wed  90.1      82.9    735.7             6.2  12.9        74   4.9   \n",
       "44    sep  wed  94.3      85.1    692.3            15.9  25.9        24   4.0   \n",
       "156   aug  wed  92.1     111.2    654.1             9.6  18.4        45   3.6   \n",
       "449   aug  wed  95.2     217.7    690.0            18.0  23.4        49   5.4   \n",
       "239   apr  wed  88.0      17.2     43.5             3.8  15.2        51   2.7   \n",
       "\n",
       "     rain  area_burned  \n",
       "0     0.0         0.00  \n",
       "352   0.0         1.72  \n",
       "104   0.0         0.00  \n",
       "354   0.0        13.06  \n",
       "355   0.0         1.26  \n",
       "..    ...          ...  \n",
       "43    0.0         0.00  \n",
       "44    0.0         0.00  \n",
       "156   0.0         1.63  \n",
       "449   0.0         6.43  \n",
       "239   0.0         0.00  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e419243-6667-4862-9d57-c955c146d2d2",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80170267-0083-4131-9686-c2ee39f331dd",
   "metadata": {},
   "source": [
    "Write code to sort the DataFrame by the rain column, with the largest values at the top:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ad1e5b7f-d696-43d7-be4c-fd9c5eab41f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>97.8</td>\n",
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       "      <td>26</td>\n",
       "      <td>9.4</td>\n",
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       "      <td>33</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.51</td>\n",
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       "      <td>aug</td>\n",
       "      <td>wed</td>\n",
       "      <td>92.1</td>\n",
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       "      <td>16.6</td>\n",
       "      <td>47</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.29</td>\n",
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       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>70</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>aug</td>\n",
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       "      <td>614.7</td>\n",
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       "      <td>42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "<p>515 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "    month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "498   aug  tue  96.1     181.1    671.2            14.3  27.3        63   4.9   \n",
       "508   aug  fri  91.0     166.9    752.6             7.1  21.1        71   7.6   \n",
       "242   aug  sun  91.8     175.1    700.7            13.8  21.9        73   7.6   \n",
       "499   aug  tue  96.1     181.1    671.2            14.3  21.6        65   4.9   \n",
       "500   aug  tue  96.1     181.1    671.2            14.3  21.6        65   4.9   \n",
       "..    ...  ...   ...       ...      ...             ...   ...       ...   ...   \n",
       "167   mar  fri  91.2      48.3     97.8            12.5  14.6        26   9.4   \n",
       "166   aug  wed  96.0     127.1    570.5            16.5  23.4        33   4.5   \n",
       "165   aug  wed  92.1     111.2    654.1             9.6  16.6        47   0.9   \n",
       "164   mar  thu  84.9      18.2     55.0             3.0   5.3        70   4.5   \n",
       "514   aug  sat  94.4     146.0    614.7            11.3  25.6        42   4.0   \n",
       "\n",
       "     rain  area_burned  \n",
       "498   6.4        10.82  \n",
       "508   1.4         2.17  \n",
       "242   1.0         0.00  \n",
       "499   0.8         0.00  \n",
       "500   0.8         0.00  \n",
       "..    ...          ...  \n",
       "167   0.0         2.53  \n",
       "166   0.0         2.51  \n",
       "165   0.0         2.29  \n",
       "164   0.0         2.14  \n",
       "514   0.0         0.00  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"rain\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd065b40-884c-4b0b-b0a6-d2ccad291235",
   "metadata": {},
   "source": [
    "<br><br><br>You can also sort on multiple values by passing the `sort_values` function a list of column names instead of a single name. If we want to first sort by day, then by area burned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "50d6dddf-9c37-4511-acfd-a06705b9dff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>470</th>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>89.6</td>\n",
       "      <td>25.4</td>\n",
       "      <td>73.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>jul</td>\n",
       "      <td>fri</td>\n",
       "      <td>88.3</td>\n",
       "      <td>150.3</td>\n",
       "      <td>309.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>79</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>sep</td>\n",
       "      <td>fri</td>\n",
       "      <td>93.3</td>\n",
       "      <td>141.2</td>\n",
       "      <td>713.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>49</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>sep</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.1</td>\n",
       "      <td>91.3</td>\n",
       "      <td>738.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>20.7</td>\n",
       "      <td>46</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>aug</td>\n",
       "      <td>wed</td>\n",
       "      <td>95.2</td>\n",
       "      <td>217.7</td>\n",
       "      <td>690.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>19</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>mar</td>\n",
       "      <td>wed</td>\n",
       "      <td>93.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>28.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>89.7</td>\n",
       "      <td>284.9</td>\n",
       "      <td>844.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>77</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>aug</td>\n",
       "      <td>wed</td>\n",
       "      <td>95.1</td>\n",
       "      <td>141.3</td>\n",
       "      <td>605.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>20.6</td>\n",
       "      <td>58</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>aug</td>\n",
       "      <td>wed</td>\n",
       "      <td>95.1</td>\n",
       "      <td>141.3</td>\n",
       "      <td>605.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "492   aug  fri  95.9     158.0    633.6            11.3  27.5        29   4.5   \n",
       "470   may  fri  89.6      25.4     73.7             5.7  18.0        40   4.0   \n",
       "222   jul  fri  88.3     150.3    309.9             6.8  13.4        79   3.6   \n",
       "220   sep  fri  93.3     141.2    713.9            13.9  18.6        49   3.6   \n",
       "390   sep  fri  91.1      91.3    738.1             7.2  20.7        46   2.7   \n",
       "..    ...  ...   ...       ...      ...             ...   ...       ...   ...   \n",
       "420   aug  wed  95.2     217.7    690.0            18.0  30.8        19   4.5   \n",
       "446   mar  wed  93.4      17.3     28.3             9.9   8.9        35   8.0   \n",
       "447   sep  wed  89.7     284.9    844.0            10.1  10.5        77   4.0   \n",
       "488   aug  wed  95.1     141.3    605.8            17.7  20.6        58   1.3   \n",
       "489   aug  wed  95.1     141.3    605.8            17.7  28.7        33   4.0   \n",
       "\n",
       "     rain  area_burned  \n",
       "492   0.0        43.32  \n",
       "470   0.0        38.48  \n",
       "222   0.0        37.02  \n",
       "220   0.0        35.88  \n",
       "390   0.0        30.18  \n",
       "..    ...          ...  \n",
       "420   0.0         0.00  \n",
       "446   0.0         0.00  \n",
       "447   0.0         0.00  \n",
       "488   0.0         0.00  \n",
       "489   0.0         0.00  \n",
       "\n",
       "[515 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"day\", \"area_burned\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17be944d-c7d8-459c-8b1b-3b3d6db0e70f",
   "metadata": {},
   "source": [
    "### <br><br><br>Saving your changed DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09651f16-096c-42e8-837a-63d72d7a5cab",
   "metadata": {},
   "source": [
    "We've made a lot of changes to the forest fire dataset. Let's save it as a new csv file. First, we can decide what we're going to call the new file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "501ecd8d-802a-46bc-a101-62e60cd16dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_filename = \"fire_changed.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be6ed49-9fea-4118-887a-aae610e4f070",
   "metadata": {},
   "source": [
    "Next, we can use the `to_csv()` method function to save the new file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a0c28fbc-2ace-407d-84e4-5918092ba5db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(new_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1795b3cb-5646-45b6-8324-3265dc261feb",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23287bf0-8bb4-4769-a6e0-7e670b5c604e",
   "metadata": {},
   "source": [
    "Remember what you learned from Part Two: Selecting Data. Create a new dataframe called df_rain that contains only the columns day, month, and rain: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dde0cf34-7429-49d0-ae39-e74dc7cd0c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rain = df[[\"day\", \"month\", \"rain\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a54eff20-b23d-40b5-a088-e07c8a8d0e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rain = df.loc[:, [\"day\", \"month\", \"rain\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c03a55d7-3677-4c1a-a60e-6c768d735267",
   "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>day</th>\n",
       "      <th>month</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fri</td>\n",
       "      <td>mar</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tue</td>\n",
       "      <td>oct</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fri</td>\n",
       "      <td>mar</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sun</td>\n",
       "      <td>mar</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sun</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>sun</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>sun</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>sun</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>sun</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>sat</td>\n",
       "      <td>aug</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     day month  rain\n",
       "0    fri   mar   0.0\n",
       "1    tue   oct   0.0\n",
       "2    fri   mar   0.2\n",
       "3    sun   mar   0.0\n",
       "4    sun   aug   0.0\n",
       "..   ...   ...   ...\n",
       "510  sun   aug   0.0\n",
       "511  sun   aug   0.0\n",
       "512  sun   aug   0.0\n",
       "513  sun   aug   0.0\n",
       "514  sat   aug   0.0\n",
       "\n",
       "[515 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514a2113-ff40-41bb-bdf7-640161d0acd8",
   "metadata": {},
   "source": [
    "Run the next line of code to save a new filename for the rain dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "57e91f70-f3f9-4500-8570-5fb90285de3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_filename = \"forestFiresRain.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4388e937-5c75-4a85-afea-09e1ea0942c4",
   "metadata": {},
   "source": [
    "Write code to save the df_rain dataframe as a csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2a0a9270-43f0-4a5f-85ca-c9af9711131c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rain.to_csv(rain_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f81cb4-10a3-486d-91cc-13ead798cb6a",
   "metadata": {},
   "source": [
    "## <br><br><br>PART FOUR: DATA AGGREGATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354d7ba4-b6d9-4946-863f-4c6c0e00975b",
   "metadata": {},
   "source": [
    "Data aggregation means taking many data points and reducing them to one number, whether it's a count, sum, mean, or other single statistic. Here are some DataFrame method functions:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c9cea4-b108-426a-b45c-4e34aaaf9161",
   "metadata": {},
   "source": [
    "- [`.count()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.count.html)\n",
    "- [`.sum()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html)\n",
    "- [`.mean()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mean.html)\n",
    "- [`.median()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.median.html)\n",
    "- [`.min()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.min.html)\n",
    "- [`.max()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.max.html)\n",
    "- [`.unique()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.unique.html)\n",
    "- [`.nunique()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.nunique.html)\n",
    "- [`.std()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html)   #Standard error\n",
    "- [`.var()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.var.html)   #Variance\n",
    "- And more! https://pandas.pydata.org/docs/reference/series.html#computations-descriptive-stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7512f421-b3ec-4907-91fd-133db3843f05",
   "metadata": {},
   "source": [
    "If you use a method function on the entire dataset, it will try its best to execute the method for all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "506bf8c0-aa7d-46a4-b8ac-805e5abcac51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month             515\n",
       "day               515\n",
       "fuel              515\n",
       "moisture          515\n",
       "drought           515\n",
       "initial_spread    515\n",
       "temp              515\n",
       "humidity          515\n",
       "wind              515\n",
       "rain              515\n",
       "area_burned       515\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1aff8bdf-0fce-4814-a4f3-c8b78ad3ec79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month              apr\n",
       "day                fri\n",
       "fuel              18.7\n",
       "moisture           1.1\n",
       "drought            7.9\n",
       "initial_spread     0.0\n",
       "temp               2.2\n",
       "humidity            15\n",
       "wind               0.4\n",
       "rain               0.0\n",
       "area_burned        0.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "06e8f8f8-abd9-44b2-b266-54dd50bf02c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month             maroctmarmaraugaugaugsepsepsepsepaugsepsepsepm...\n",
       "day               frituefrisunsunmonmontuesatsatsatfrimonwedfris...\n",
       "fuel                                                        46693.2\n",
       "moisture                                                    57274.3\n",
       "drought                                                    282491.4\n",
       "initial_spread                                               4656.4\n",
       "temp                                                         9739.3\n",
       "humidity                                                      22833\n",
       "wind                                                         2071.3\n",
       "rain                                                           11.2\n",
       "area_burned                                                 6642.05\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f182ddbf-8165-4534-9bdb-05c5e221ce42",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'unique'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [47]\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[43munique\u001b[49m()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m   5573\u001b[0m ):\n\u001b[1;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'unique'"
     ]
    }
   ],
   "source": [
    "df.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd03b7de-3a91-4be9-8095-32fb2ebfc3da",
   "metadata": {},
   "source": [
    "<br>Not all functions will work on the entire DataFrame. Most of the time you are interested in only a subset of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "77cd48fc-6e9e-4c7a-9abd-09cbbc8a570b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fri', 'tue', 'sun', 'mon', 'sat', 'wed', 'thu'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f275f4e6-83c9-48e0-afd0-8b18afbdbc29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fri', 'tue', 'sun', 'mon', 'sat', 'wed', 'thu']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df[\"day\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cf96f512-021d-4bea-af0f-b7c021208ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"month\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a825e3ff-8ba2-4b0b-9cb1-a0037a1d5d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.714036341656936"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"temp\"].var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb489dd-5be6-4a1f-b9d0-04ee303a1dfa",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c53f90-e776-47b0-8746-9759c737109d",
   "metadata": {},
   "source": [
    "Write code to find the mean humidity of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b7fc17e6-a05d-44a6-acb4-3f37886c2907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.335922330097084"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"humidity\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6a45ac-3652-432d-aa9c-58e6aaeb5260",
   "metadata": {},
   "source": [
    "Write code to find the coldest temperature in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2cbf6213-e015-4ae3-8a2d-a50a4b9500d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"temp\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dae13127-8e18-441a-8555-d2de21942526",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "094e8a28-eca5-4636-b4cf-d55fa47189c0",
   "metadata": {},
   "source": [
    "## <br><br>groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac008ba8-c273-4186-b98b-0535c3a60c2c",
   "metadata": {},
   "source": [
    "Often, you will want to calculate the statistics for a particular subgroup of a data column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90224728-5435-41ed-ac5a-a25789d9b977",
   "metadata": {},
   "source": [
    "For example, let's say we want to ask if more fires happen on certain days of the week. This code will tell you the count for every column in the DataFrame except the column that you are using to group your data (i.e. \"day\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "60656711-6831-46ca-8d23-01eef5f89d1f",
   "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>month</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fri</th>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
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       "      <td>85</td>\n",
       "      <td>85</td>\n",
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       "      <th>mon</th>\n",
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       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sat</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thu</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tue</th>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wed</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "day                                                                         \n",
       "fri     85    85        85       85              85    85        85    85   \n",
       "mon     74    74        74       74              74    74        74    74   \n",
       "sat     83    83        83       83              83    83        83    83   \n",
       "sun     95    95        95       95              95    95        95    95   \n",
       "thu     61    61        61       61              61    61        61    61   \n",
       "tue     63    63        63       63              63    63        63    63   \n",
       "wed     54    54        54       54              54    54        54    54   \n",
       "\n",
       "     rain  area_burned  \n",
       "day                     \n",
       "fri    85           85  \n",
       "mon    74           74  \n",
       "sat    83           83  \n",
       "sun    95           95  \n",
       "thu    61           61  \n",
       "tue    63           63  \n",
       "wed    54           54  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba2699ef-74bb-4884-a7d3-8a0a2e480679",
   "metadata": {},
   "source": [
    "<br>We can make this easier to read by sorting the rows from lowest count to highest count. We can string multiple method functions onto our code, as long as they go in the correct logical order. We will pick any column to sort on, since they are all the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1c0198a1-bb90-4478-886d-3ee199ab4bfd",
   "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>month</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
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       "      <th>area_burned</th>\n",
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       "      <th>day</th>\n",
       "      <th></th>\n",
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       "      <th>wed</th>\n",
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       "      <th>thu</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
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       "      <th>tue</th>\n",
       "      <td>63</td>\n",
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       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fri</th>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "day                                                                         \n",
       "wed     54    54        54       54              54    54        54    54   \n",
       "thu     61    61        61       61              61    61        61    61   \n",
       "tue     63    63        63       63              63    63        63    63   \n",
       "mon     74    74        74       74              74    74        74    74   \n",
       "sat     83    83        83       83              83    83        83    83   \n",
       "fri     85    85        85       85              85    85        85    85   \n",
       "sun     95    95        95       95              95    95        95    95   \n",
       "\n",
       "     rain  area_burned  \n",
       "day                     \n",
       "wed    54           54  \n",
       "thu    61           61  \n",
       "tue    63           63  \n",
       "mon    74           74  \n",
       "sat    83           83  \n",
       "fri    85           85  \n",
       "sun    95           95  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day\").count().sort_values(\"rain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705beddf-c156-4f7d-a977-91b4d01f5b1d",
   "metadata": {},
   "source": [
    "<br>It looks like weekends have more fires than weekdays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e598c64a-fe48-4d1e-be96-9019ea4f5daa",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c332211-9a7c-49fe-9141-40215da1233d",
   "metadata": {},
   "source": [
    "Write code to find the mean for all the columns when grouped by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "56f32a1e-5e73-4a5d-83e3-04e039640e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area_burned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>apr</th>\n",
       "      <td>85.788889</td>\n",
       "      <td>15.911111</td>\n",
       "      <td>48.555556</td>\n",
       "      <td>5.377778</td>\n",
       "      <td>12.044444</td>\n",
       "      <td>46.888889</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.891111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug</th>\n",
       "      <td>92.336957</td>\n",
       "      <td>153.732609</td>\n",
       "      <td>641.077717</td>\n",
       "      <td>11.072283</td>\n",
       "      <td>21.631522</td>\n",
       "      <td>45.489130</td>\n",
       "      <td>4.086413</td>\n",
       "      <td>0.058696</td>\n",
       "      <td>12.489076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>84.966667</td>\n",
       "      <td>26.122222</td>\n",
       "      <td>351.244444</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>4.522222</td>\n",
       "      <td>38.444444</td>\n",
       "      <td>7.644444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>82.905000</td>\n",
       "      <td>9.475000</td>\n",
       "      <td>54.670000</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>9.635000</td>\n",
       "      <td>55.700000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>50.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>90.350000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul</th>\n",
       "      <td>91.328125</td>\n",
       "      <td>110.387500</td>\n",
       "      <td>450.603125</td>\n",
       "      <td>9.393750</td>\n",
       "      <td>22.109375</td>\n",
       "      <td>45.125000</td>\n",
       "      <td>3.734375</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>14.369687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun</th>\n",
       "      <td>89.429412</td>\n",
       "      <td>93.382353</td>\n",
       "      <td>297.705882</td>\n",
       "      <td>11.776471</td>\n",
       "      <td>20.494118</td>\n",
       "      <td>45.117647</td>\n",
       "      <td>4.135294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.841176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>89.444444</td>\n",
       "      <td>34.542593</td>\n",
       "      <td>75.942593</td>\n",
       "      <td>7.107407</td>\n",
       "      <td>13.083333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.968519</td>\n",
       "      <td>0.003704</td>\n",
       "      <td>4.356667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>may</th>\n",
       "      <td>87.350000</td>\n",
       "      <td>26.700000</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>14.650000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>4.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>90.442857</td>\n",
       "      <td>41.257143</td>\n",
       "      <td>681.300000</td>\n",
       "      <td>7.178571</td>\n",
       "      <td>17.271429</td>\n",
       "      <td>37.785714</td>\n",
       "      <td>3.614286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.112143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>91.243023</td>\n",
       "      <td>120.922674</td>\n",
       "      <td>734.615698</td>\n",
       "      <td>8.577326</td>\n",
       "      <td>19.612209</td>\n",
       "      <td>42.843023</td>\n",
       "      <td>3.557558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.942616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fuel    moisture     drought  initial_spread       temp  \\\n",
       "month                                                                 \n",
       "apr    85.788889   15.911111   48.555556        5.377778  12.044444   \n",
       "aug    92.336957  153.732609  641.077717       11.072283  21.631522   \n",
       "dec    84.966667   26.122222  351.244444        3.466667   4.522222   \n",
       "feb    82.905000    9.475000   54.670000        3.350000   9.635000   \n",
       "jan    50.400000    2.400000   90.350000        1.450000   5.250000   \n",
       "jul    91.328125  110.387500  450.603125        9.393750  22.109375   \n",
       "jun    89.429412   93.382353  297.705882       11.776471  20.494118   \n",
       "mar    89.444444   34.542593   75.942593        7.107407  13.083333   \n",
       "may    87.350000   26.700000   93.750000        4.600000  14.650000   \n",
       "oct    90.442857   41.257143  681.300000        7.178571  17.271429   \n",
       "sep    91.243023  120.922674  734.615698        8.577326  19.612209   \n",
       "\n",
       "        humidity      wind      rain  area_burned  \n",
       "month                                              \n",
       "apr    46.888889  4.666667  0.000000     8.891111  \n",
       "aug    45.489130  4.086413  0.058696    12.489076  \n",
       "dec    38.444444  7.644444  0.000000    13.330000  \n",
       "feb    55.700000  3.755000  0.000000     6.275000  \n",
       "jan    89.000000  2.000000  0.000000     0.000000  \n",
       "jul    45.125000  3.734375  0.006250    14.369687  \n",
       "jun    45.117647  4.135294  0.000000     5.841176  \n",
       "mar    40.000000  4.968519  0.003704     4.356667  \n",
       "may    67.000000  4.450000  0.000000    19.240000  \n",
       "oct    37.785714  3.614286  0.000000     7.112143  \n",
       "sep    42.843023  3.557558  0.000000    17.942616  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"month\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19995e59-4205-44c1-84f9-7f8f6b41cc1c",
   "metadata": {},
   "source": [
    "<br><br><br><br>If you only want to see an aggregate total for one column in the DataFrame, you can add on the indexing technique we learned yesterday. With this code I will ask, What is the mean area burned on each day of the week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "34fb38d1-e20c-4c3b-8cf2-879978c31eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_burned</th>\n",
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       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fri</th>\n",
       "      <td>5.261647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mon</th>\n",
       "      <td>9.547703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sat</th>\n",
       "      <td>25.841687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun</th>\n",
       "      <td>10.104526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thu</th>\n",
       "      <td>16.345902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tue</th>\n",
       "      <td>12.822063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wed</th>\n",
       "      <td>10.714815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area_burned\n",
       "day             \n",
       "fri     5.261647\n",
       "mon     9.547703\n",
       "sat    25.841687\n",
       "sun    10.104526\n",
       "thu    16.345902\n",
       "tue    12.822063\n",
       "wed    10.714815"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day\")[[\"area_burned\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e4cb01-37c8-4df7-92e6-a20f333a4e32",
   "metadata": {},
   "source": [
    "<br>Again, we can also sort the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8704bd13-65db-4738-bd92-99a3308ac002",
   "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>area_burned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>fri</th>\n",
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       "      <th>wed</th>\n",
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       "      <th>tue</th>\n",
       "      <td>12.822063</td>\n",
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       "      <th>thu</th>\n",
       "      <td>16.345902</td>\n",
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       "      <th>sat</th>\n",
       "      <td>25.841687</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area_burned\n",
       "day             \n",
       "fri     5.261647\n",
       "mon     9.547703\n",
       "sun    10.104526\n",
       "wed    10.714815\n",
       "tue    12.822063\n",
       "thu    16.345902\n",
       "sat    25.841687"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day\")[[\"area_burned\"]].mean().sort_values(\"area_burned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6d8bd1-82b5-4b45-818f-059121c6ef30",
   "metadata": {},
   "source": [
    "<br>So Saturday fires are the most destructive fires."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e788cde-12fa-42ff-ac69-e33ecf0f9005",
   "metadata": {},
   "source": [
    "<br>We can also add some other functions to our code, like round:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b8949930-f8a2-4d6d-b3e4-c39a27520602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_burned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fri</th>\n",
       "      <td>5.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mon</th>\n",
       "      <td>9.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun</th>\n",
       "      <td>10.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wed</th>\n",
       "      <td>10.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tue</th>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thu</th>\n",
       "      <td>16.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sat</th>\n",
       "      <td>25.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area_burned\n",
       "day             \n",
       "fri         5.26\n",
       "mon         9.55\n",
       "sun        10.10\n",
       "wed        10.71\n",
       "tue        12.82\n",
       "thu        16.35\n",
       "sat        25.84"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day\")[[\"area_burned\"]].mean().round(2).sort_values(\"area_burned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d7dd88-ca88-4792-b56e-3101ca17b6ca",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ffe388d-9ea7-4fb1-b164-a3c7cf6e68b1",
   "metadata": {},
   "source": [
    "Write code to count how many fires happened in each month (as a bonus, sort the results):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0498befa-df51-4384-b12f-89ba6678443a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
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       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>apr</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug</th>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "month                                                                       \n",
       "jan      2     2         2        2               2     2         2     2   \n",
       "may      2     2         2        2               2     2         2     2   \n",
       "apr      9     9         9        9               9     9         9     9   \n",
       "dec      9     9         9        9               9     9         9     9   \n",
       "oct     14    14        14       14              14    14        14    14   \n",
       "jun     17    17        17       17              17    17        17    17   \n",
       "feb     20    20        20       20              20    20        20    20   \n",
       "jul     32    32        32       32              32    32        32    32   \n",
       "mar     54    54        54       54              54    54        54    54   \n",
       "sep    172   172       172      172             172   172       172   172   \n",
       "aug    184   184       184      184             184   184       184   184   \n",
       "\n",
       "       rain  area_burned  \n",
       "month                     \n",
       "jan       2            2  \n",
       "may       2            2  \n",
       "apr       9            9  \n",
       "dec       9            9  \n",
       "oct      14           14  \n",
       "jun      17           17  \n",
       "feb      20           20  \n",
       "jul      32           32  \n",
       "mar      54           54  \n",
       "sep     172          172  \n",
       "aug     184          184  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"month\").count().sort_values(\"rain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c3d928-c9eb-4f89-b242-1184f31be343",
   "metadata": {},
   "source": [
    "Write code to see the mean area burned for fires in each month (as a bonus, round and sort the results):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "735a6427-3a2b-46fb-814a-e25eb0ae6b58",
   "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>area_burned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun</th>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>6.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apr</th>\n",
       "      <td>8.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug</th>\n",
       "      <td>12.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul</th>\n",
       "      <td>14.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>17.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>may</th>\n",
       "      <td>19.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       area_burned\n",
       "month             \n",
       "jan           0.00\n",
       "mar           4.36\n",
       "jun           5.84\n",
       "feb           6.28\n",
       "oct           7.11\n",
       "apr           8.89\n",
       "aug          12.49\n",
       "dec          13.33\n",
       "jul          14.37\n",
       "sep          17.94\n",
       "may          19.24"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"month\")[[\"area_burned\"]].mean().round(2).sort_values(\"area_burned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f634f961-e132-4969-b56d-9a7ea18cd45e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "648e16e3-1d26-4984-a724-db61f3783a94",
   "metadata": {},
   "source": [
    "## <br><br><br>PART FIVE: BASIC PLOTTING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea11ba1-3722-46f4-9a25-7f9b220c5742",
   "metadata": {},
   "source": [
    "Pandas allows you to make some basic plots without loading other packages. Plotting in pandas is good for exploring your data, and we'll focus on that today.\n",
    "<br><br>Pandas is not good for making good-looking, high-quality data visualizations. The Python library for that is called matplotlib. We will not be covering how to make data visualizations for publication, since this is not a matplotlib workshop.\n",
    "<br><br>Pandas' plotting capabilities are actually built on matplotlib, but in a much simpler format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4324f8-3119-441b-b98c-1be6e18d23a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "### <br><br>Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a4fc7c-688f-455d-a258-b31dd597e811",
   "metadata": {},
   "source": [
    "One of the most common data exploration tasks you might do is to check the distributions of the columns in your dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded9bb2d-8a27-4a36-b3a1-e0649f2e2651",
   "metadata": {},
   "source": [
    "We will use the `hist()` method function on the `temp` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "555464fd-90e2-47a8-b3a3-9fd65debcfa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"temp\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a818f4a9-fe92-46fb-bf02-b1890e0b9e3e",
   "metadata": {},
   "source": [
    "<br><br>Like I said, it's not pretty, but it tells us the story of our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc92213-b34c-4695-88cb-cf62f6d1f634",
   "metadata": {},
   "source": [
    "By default, `hist()` will divide the data into 10 bins. We can change that by passing a keyword argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "56c99958-2254-43d6-9761-f34936b69c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"temp\"].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e767d2bf-7a9c-4732-a039-a9ccead148ee",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd89dcae-ce08-457e-bbf6-1b9b302a6601",
   "metadata": {},
   "source": [
    "Make a histogram of the `humidity` column. Specify that you want the data grouped into 15 bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "64c7c319-1706-49b5-b7df-15bee4f874c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jwBitS8aeAu4F/pFF+hERfw78Ca3+fSIz/2lZj7taA1ySVrtVu4QiSaudAS5JRRngklSUAS5JRRngklSUAS5JRRngklTU/wOikbytsq+OjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"humidity\"].hist(bins=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340c8215-eace-46df-a971-4f21fe709b2f",
   "metadata": {},
   "source": [
    "<br><br><br>Another way to create a histogram in pandas is to do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "00aa5519-8eb5-45bd-ac38-2a391caacc53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'temp'}>]], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"temp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa15b86f-ee3b-49ff-aabd-8c0d79451116",
   "metadata": {},
   "source": [
    "<br>Both ways are doing the same thing. In one, we're making a histogram on a sample of our dataframe. In the second, we're calling `hist()` on the entire dataframe and then specifying the column with an argument."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb80c77a-0faa-437b-80db-eb026175048c",
   "metadata": {},
   "source": [
    "<br>If we don't specify a column, we will get histograms of all columns with numerical data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7a95ab51-51c5-42a4-b69b-e53c3153d51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'fuel'}>,\n",
       "        <AxesSubplot:title={'center':'moisture'}>,\n",
       "        <AxesSubplot:title={'center':'drought'}>],\n",
       "       [<AxesSubplot:title={'center':'initial_spread'}>,\n",
       "        <AxesSubplot:title={'center':'temp'}>,\n",
       "        <AxesSubplot:title={'center':'humidity'}>],\n",
       "       [<AxesSubplot:title={'center':'wind'}>,\n",
       "        <AxesSubplot:title={'center':'rain'}>,\n",
       "        <AxesSubplot:title={'center':'area_burned'}>]], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a4FP8WXEGM7sT+A/gOjMbYmZ7lziOgB8BOwEfIHRQnVGnLOcB55nZVsBuwLSi7QcBo4DPApOKfJHjgOvjeUwFLgf6gd2BfeM+X6smq4J/9VfAlcC2wC+Bf6jzPLoaMzsOeAH4nJkNIdT3dOBMQp39K3CDpO0Tu40HjiOEJ+4GPAhcGvPPA04vKubvgTHAhwi6/ee0zieHfAk4FBgJfBCYUMd+3yeEVr5N0OFjcf164Jwy+51O0PluwCFAyX6/MnbmFmCkpA8ksh5LuD/bTica/AfM7HYze5dQiaWMeVXMbKGZzTCzt83sZYLyvyFpjqTZwDAASdtKmiFpQfzfJnGYd4DdJfWY2Soze6iomB+Y2Wozm0MwFkcntj1oZr8yszXAVoQvGr8d8y8HfkowOuVk/WQ8zgHAxsC5ZvaOmV1P6HA7UtJySU8WCqx0LpImS1ooab6kQwZSp7Ui6dBYzkJJk9IsqwzHArfH62iNmc0AZgKHJ/JcambPmtkbwB3As2Z2T3S9/ZLwUEZSIZJlN+C3BMN/LnBcFuq6HaSg3/82syVm9hpwK7BPjfvdZGazzOwvwE3AX8zsimg7rgP2ja31OYTG1AVxv/HAYOBh4BLg4sS5TSY0Ai4tpTszezse+9iYfy+gF7itvlNOh040+H9KLL8FbCap7r4ISTtIuja6T94EriJ0Th1kZvsAS2PWScC9ZjYKuDeuFzgB2AN4Or7mH1lUTPLDlecJF1WpbbsSjPbS6GJYAfwc2KGCrD1x352Al8wsGSP9PPAEoVWUpOS5KHwyPx7YK+5zoaI7qdkoG5/s7wp8sVDXsb4/RnzIR5Yllv9cYn1IXC70vRxGePieBKwB/pY213U7SEm/xff8kHIZi6hVhwcRQk5PiusjgL6E7v4O1tPdS8Bk4EJKf69wOfBPkkR4S5wWHwRtpxMNfq1s8JFIET+KeT4YXTLletnHERRI/P/C2gLMFpjZ0QTDfDZwvaTBiX2Tccy7sH4cc1K+FwmvnD1mtnX8bZWIBigla+FCWwoMjxdXsqwXgNdqPJdxwLXxDeI5YCHh0/o0WPvJvpn9leCeG5dSWUmK6/vKRF1vbWaDBzI4mJkVGgY7m9lKgrvnA4R+lHbXdTtolX5XA1sUViTt2MRjryG8JUPQ3Sfi8jjC+UB4CC1k/UYcAPFN/6/Ax4F/IiPuHOhug78M6K3QebYlsApYIWk48N2YfnfsdCk8/YcWbur4v0PhAJKOlbR9dMusiMnvJsr4N0lbxNe6rxJe9TYgHvdu4CeStpL0HoWO2oLbppysEPyS/cDJkjaSdBTlDUi5cyn1Gf3wMsdolFaWlWQZ8L64fBXwOUmHSBoUOwHHShrRwPG/K+mDBD/+YYAyUNftoFXn9ziwl6R9JG1G/f1vBYxw7w1jnUtvDfD16IYbxLq36VLntobSduYKQv9iv5k9QEboZoP/y/j/qqTHSmz/AaGD7Q1CB96NwHIz+xDhht0KGF2ljEOBuZJWETpwx0d/YYHfEFoB9wL/ZWZ3VzjWV4BNCINTvU7oVCq4GErJCkBsRR1F6Mh6HfhycnuN1PQZfZNoZVlJfgR8P7pvvkxorZ0KvEy4ib9LY/fDHYRW4VYEP/Nfy+Rr1/m3ipacn5k9A/wQuAdYAAzUqB4Y7/llwDhJnwD+QnCLPkd4GBR0Wercfh//i+3MlQS3XmZa9wCYmf9K/Agthn8F5gPDYtowYH4N+/YSLvKN2nwOvcCTifWS50LwR05O5LsL+GhKMn0UuCuxvl7ZnfiLur4f+E6W6rpNddGx+q12z9ejO2BzYCUwqt3nlfx1cwu/LiQNlrRlYZkQFvkkIcyqEJZ1PHBzeyRsCuXO5RZgvKRNJY0khJI+kpIMmfhkv1kk+k6eNbNkmF8W6roddIx+B3DP16O7bwCPmtmCtOQfEO1+4mTlR/DvPh5/c4HTYvo9BB/5mvi/Kv5OrXCsXtrcwgeuIXTovkPwNZ4AbEdwLy2I/9sm8p8GPEto3RyWsmyHA8/E8k5rt+4bPJePRV0/DcyOv8OzUtdtqpOO0G+Fe74h3QGLCC6hfdt9jsU/RQEdx3GcLsddOo7jODkhtcHT6qGnp8d6e3vXS1u9ejWDBw8uvUObyKJMMDC5Zs2a9YqZbV89Z3MopeM0aKWOsl5Wt+p4IGT13q2X4vOoW8ft9imZGfvtt58Vc999922Q1m6yKJPZwOQCZlqbdZwGrdRR1svqVh0PhKzeu/VSfB716thdOo7jODkhEy6dgdA7aXrF7YumHNEiSZxup9y1NnF0PxMmTfdrLUWq3efg93o9eAvfcRwnJ7jBdxzHyQlu8B3HcXKCG3zHcZyc4AbfcRwnJ7jBdxzHyQlu8B3HcXKCG3zHcZyc4AbfQdIlkpZLejKRtq2kGZIWxP9tEtsmS1ooab6kQ9ojteM49eIG3wG4jDBdY5JJwL1mNoowJvgkAEl7Eia12Cvuc6GkQa0T1RkI/lB3oIOHVnCah5ndL6m3KHkcMDYuXw70AafE9GvN7G3gOUkLCZOmP9gSYZ2BchlhUu0rEmmFh/oUSZPi+ilFD/WdgHsk7WFm77ZY5prwYVZqxw2+U46hZrYUwMyWStohpg8HHkrkWxzTNkDSicCJAEOHDqWvry89aSOrVq1qejkTR/eXTB+6edh2/tTqs16OHv7ehmRo9Lz8oe6AG3ynflQireS0aWZ2EXARwJgxY2zs2LEpihXo6+uj2eVMqDB42k/m1HYLLTpmbEMypHFedMBDvdzDth76+vpSaQi0g0bPww2+U45lkoZFQzAMWB7TFwM7J/KNAJa0XDonTTLzUC/3sK2HRceMTeuB2XIaPQ83+E45bgGOB6bE/5sT6VdLOofg3x0FPNIWCZ1GaftDvZbhj53m4QbfQdI1BF9uj6TFwOkEQz9N0gnAC8AXAcxsrqRpwFNAP3BSVjvzaiHnBscf6jmjqsGXtDOhZ39HYA1wkZmdJ2lb4DqgF1gEfMnMXo/7TAZOAN4FTjazu1KR3mkKZnZ0mU2fLpP/LOCs9CRymk2eH+rOOmpp4fcDE83sMUlbArMkzQAm0AUhXY6TB/yh7kANH16Z2VIzeywurwTmEXrsxxFCuYj/X4jLa0O6zOw5oBDS5TiO47SRunz4MY53X+BhGgzpqhbOVS38qFq4VhohWFkN7cqqXI7jZIuaDb6kIcANwLfN7E2pVORWyFoibYOQrmrhXNXCj6qFazUa91yKrIZ2ZVUux3GyRU1j6UjamGDsp5rZjTF5WQzlwuO0Hcdxsk9Vg6/QlP8FMM/MzklsKoR0wYYhXeMlbSppJB7S5TiOkwlqcekcCBwHzJE0O6adiod0OY7jdBRVDb6ZPUBpvzx4SJfjOE7H4OPhO47j5AQ3+I7jODnBDb7jOE5O8MHTHMfpanonTWfi6P6K3+7kZVYsb+E7juPkBG/hOxWRtAhYSRj5tN/MxlQaKdVxnOziBt+phYPM7JXEesnJr9sjWmeQ5Ym2/aGeH9yl4wyEciOlOp3LQWa2j5mNieuFh/oo4N647nQ4bvCdahhwt6RZcYRTKBopFdih7N5Op+IP9S7EXTpONQ40syVx+OsZkp6udcdqQ2CnQb1DRVcbZrsSQzdvbP8k1WROeQjswkPdgJ/HkWzLDX++Ho3quFn1V41quuqU4cUbvQ7c4DsVMbMl8X+5pJsIk9mUm/y6eN+KQ2CnQb1DRVcbZrsSE0f385M5zbmFqg3nnfIQ2AN+qDeq40bqvx6q6SqN4dTToNHrwA2+UxZJg4H3mNnKuPxZ4IeUn/w6c+R8kvKaaOSh7nQW7sN3KjEUeEDS44Qhrqeb2Z0EQ3+wpAXAwXHd6UAkDY5zVZN4qD9J+eHPnQ7GW/hOWczsj8DeJdJfpcxIqU7HMRS4Kc5gtxFwtZndKelRSgx/3q1kOWy2mXStwa/lVb5blOg4A8Uf6vnCXTqO4zg5IbMt/DkvvdGyHnzHcZw8kFmD7zjVKOW2qzYqouMMhG5xEbtLx3EcJye4wXccx8kJ7tJxnAxQzWUwcXQ/Y1sjitPFeAvfcRwnJ3gL33GcVMjbsBad8PGWG3ynLXRL1IPjdBLu0nEcx8kJ3sJ3nA6hE1wGTrbJtcH3Gyjb5M0H7Dhp4y4dx3GcnJBaC1/SocB5wCDgYjPzMdO7CNdv9+M6bi5Z8CikYvAlDQIuIEyOsRh4VNItZvZUGuU5rcX12/24jltPLS7Myw4d3FAZabl09gcWmtkfzeyvwLXAuJTKclqP67f7cR13IWm5dIYDLybWFwMfSWZIznYPrJI0v+gYPcArKclXEzp7g6S2y1SGgci1awPlVdUv1KTjpnNyC3WUtbJKXK9dqeOB0EpdpclBZ29wHnXpOC2DrxJptt5KYrb7kgeQZprZmGYL1ghZlAnaIldV/UJ1HadBK+uiW8sqFFkiLRM6HghZvXfrpdHzSMulsxjYObE+AliSUllO63H9dj+u4y4kLYP/KDBK0khJmwDjgVtSKqulSFol6X0D3LdP0teaLVMb6Fr9lkLSMZLubrccLSZXOs4LqRh8M+sHvgncBcwDppnZ3DoPk8XXxIvMbEic+DlLtLSumqTftGh6XZjZVDP7bCvKqoDruDHW1l+WG16SzpB0VYUsDV0HMtvALeekhKQ+4Cozu7jdsjjrkLRRNHBODsjyfSjpDGB3Mzs2jeP7l7YRSV+VdGtifaGkaYn1FyXtI8kk7R7TLpN0gaTpklZKeljSbol9Dpb0tKQ3JP2M0h1hThuQtEjSKZKeAFZL+r6kZ6Men5L094m8EyQ9kFg3SV+XtEDS6/EacN02GUmZH/qlE2RM4gZ/Hb8BPi7pPZKGARsDBwJEn/0Q4IkS+x0N/ADYBlgInBX36QFuAL5PCAl7tnA8JzMcDRwBbA3MBz4OvJegz6vidVCOI4EPA3sDXwIOSVXSLkLSpFIP1/hg/Z2kn0p6DThD0qaS/kvSC5KWSfofSZvH/NtIuk3Sy/HBe5ukETWKsZukR2Jj7GZJ28ZjjpW0uEjeRZI+E5fPkHS9pKskvQlMiC6i/xtlXynp7nj/F/Y/QNLvJa2Q9LiksYltIyX9Ju43g2Ar0sPMMvUDDiXcfAuBSS0u+0XgQ4QOqqnAmwRD/RIwJ+Yx4E/AbOA14M7E/ocDT8flrwAPJbaJEPnwtQZlXATMieXPjGnbAjOABfF/m3brsQW6ugRYDjyZSKu5HmI9/nOF7bOBcXH5O8AKgi97brwGPpYob2Uss+F6BzYDHgEej2X9oNt0DHwR2InQ4PwysBoYBkwA+oH/QwgZ3xw4l9BZvC2wJXAr8KN4nO2AfwC2iNt+Cfwqod9y98lbwF+AvwMGExpmV8U8Y4HFJa6Vz8TlM4B3gC9E+TcH+gh2Yo/E+pSYfzjwKsE2vIfw5fKrwPZx+4PAOcCmwHRgDbCilmsamEywk/OBQ2qp+0y18LXuc+7DgD2BoyXt2UIRfkNQ+CeA3wM3AT8G7gN2TMhyqZntQ7gQZyb2f4vwJgDhgl774YoFDSU/ZGmEg8xsH1sXjzsJuNfMRgH3xvVu5zJC4yBJvfWwVh+SviJpdmyFrQD+lnWtrXeB58zsA8ABMW1IoTyCwXixhvJq4W3gU2a2N7APcKikAwZwbpnFzH5pZkvMbI2ZXUcwZvvHzUvM7HwLfSp/Af4X8C9m9pqZrQT+g9Agw8xeNbMbzOytuO0s4JOJokreJ4QH6kOEB/pq4N+AL0X7UwsPmtmvovx/jmmXmtkzcX0aQXcAxwK3m9ntMf8Mgs04XNIuhLfEfzOzt4GzCZ3kSUrqPdqi8cBehPvgwlrkz5TBp/2fcxcM/seBmwmK+yTBFfMU4WldK0tJxDFHH+/O5bM3xDjg8rh8OaH10dWY2f2EN6wk9dZDePWSdgX+HyEqZTsz2xp4knV9Lm8QWqFEwwIwtKi8J2ooryoWWBVXN44/o4t0XOXhmmwUbU9ovc9K5L0zpiNpC0k/l/R8dK/cD2xdwfAl6/Au1tXh84R6rtWdUqrh9qfEcrLhtyvwxYL88Rw+Rnij2Ql4PT50Ctf0cxVkTup9HHCtmb1tZs8RWvr7U4WsGfxSn3PXY2Qb5TfAQcDmZrYY+C3h6dkDjAQejvmOi519BxJewUsxHdhL0lGxY+dkYMcmyGjA3ZJmKXzWDjDUzJYCxP8dmlBOJzLQehhMqNeXIXTgE4zQBkjqjYuPJ8sDVtVRXkUkDZI0m+CymmFmD9MlOq7h4ZoMG3wF+DOwl5ltHX/vNbOCMZ0IvB/4iJltRXgzJx6r4n1C6Ksp1OEuBDfNK4QH+xYJeQcRHzAJ6gltfBG4MiH/1mY22MLIo0uBbSQlR0TbqWj/cnofkK3MmsGv6XPutDCzZwg37m/j+psE/90a4NtxHeBThFe2t4DPlDnWKwRf5RSCz24U8LsmiHmgmX2I4PY6SdInqu3gVMbCCJA/IfhTlwGjKaErSUMI7hsI10la8rwbXYYjgP0llXz4dCg1P1zNbA3h4fBTSTvE/MMlFTrItyQ8EFbETtfTE7tXu0+OBd4jaQvgh8D1ZvYu8AywmaQjJG1MCLrYtIHzvQr4nKRD4oN8s9gxPMLMnie4d34gaRNJHwM+XeNxB2Qrs2bw2/45t5kNM7OvAkSFv0zoOLsxblf01a0hvFZtlNi3z8xGJNbvNLM9Yqvkm2b2SWsw9tfMlsT/5YQ+hv2BZYWIkvi/vJEyOpia68HMes3snsT6aWa2rZn1mNl3kroys8sIb343AFPjNbCwUJ6ZTSD0PTW13s1sBaED8NB6zi3L1PpwTXAKwV3xUHTb3ENo1UPo0N2c0DJ/iODuKZRT8T4BbgQ2IbhiNiO8gWNmbwD/G7iYEKyxmmCXBnq+LxLsxKkEW/Ii8F3W2d5/IgxK9xrhgXVj0SHK6X1gttIy0Gtf+BGM5x8J7pNNCK/Ne7VJFgFXAOcWpQ9LLP8LwY/WKpkGA1smln9PMAb/SYxoInTq/LjdumxRffSyfpROKvVQ4VpoenkE98HWcXlzwtvmkXnV8QDrsGPvk1qvaUJn7eOEt4+R0W4Oqnr8dp9giRM+nPBa9SxwWhvl+BjhFekJQmjX7CjblYRwrycIUTrDWijT+6KSCyF7p8X07Qg9+Avi/7bt1mML6uIagg/0HUJr54S06qHCtdD08oAPAn+IZT0J/HteddxAHXbkfVLvNQ2cFu3kfOCwWsrwoRUcx+k6JJXrYznMzH7bUmEyhBt8x3GcnJCJcSB6enps++23Z/DgxuZrbBerV6/uONlnzZr1ipkVh5ulRk9Pj/X29q5dz0KdZUGGNOVwHZenW2SrW8ft9luZGfvtt5/dd9991ql0ouzEz81b9dtvv/3WKz8LdZYFGczSk8N1XJ5uka1eHWctLNNxHMdJiUy4dDqZ3knTmTi6nwmTppfcvmjKES2WqDOY89IbZeusgNddZ+M6zh7ewnccx8kJbvAdx3Fyght8x3GcnFDV4Eu6RNJySU8m0raVNENhircZkrZJbJusMD3g/MQgR47jOE6bqaWFfxk1TjQx0EH5HcdxnPSpavCtvokmBjQov+M4jpM+Aw3LXG9Q/sJY1YQB+B9K5Cs7KH+clOBEgKFDh7Jq1Sr6+voGKE77mDi6n6Gbh/9SdOI5OY7TnTQ7Dr/mQfnN7CLgIoAxY8bYkCFDGDt2bNME6a0S/wvNiQGeEOPwfzKndFUuOmZsw2U4juM0g4FG6TR3UH7HcVInzrj0B0m3xXUPvsgZA23h3wIcT5i+73jChN+F9KslnUOYm3EUYYb4TFLtLSBvXwHGDvaZwEtmdmScNu46wqQMi4AvmdnrMe9kwnjd7wInm9ldbRHaqYdvAfOAreJ6IfhiiqRJcf2UouCLnYB7JO1hYQpAp4OpavAlXQOMBXokLSZMwzUFmCbpBOAFwtytmNlcSdOAp4B+4KROvkhqcQt1GW4QuhRJI4AjgLOA78TkcYR7G0LwRR9hSsG1wRfAc5IKwRcPtlBkJwWqGnwzO7rMppKT7ZrZWYSLyukg3CB0PecC3yNM/F2g6cEXySCFSsEMBdoV1JDlIJE0ZfPB05wC59Jkg5B1Y5CVmz5tOSQdCSw3s1mSxtayS4m0moIvkoEX50+9uWwwQ4F2BTX09fU1NUikmaQpmxt8JzWDkHVjkJWbvgVyHAh8XtLhwGbAVpKuIgZfxIe5B1/kAB9Lx4F1BmERcC3wqaRBAI/G6mTMbLKZjTCzXkLfy6/N7FjWBV/AhsEX4yVtKmkkGQ++cGrHDb7jBiG/TAEOlrQAODiuY2ZzgULwxZ10ePCFsw536TiVyEU0Vp4wsz5C5ztm9ioefJEr3OA76+EGwXG6F3fpOI7j5AQ3+I7jODnBDb7jOE5OcIPvOI6TE9zgO47j5AQ3+I7jODnBDb7jOE5OcIPvOI6TE9zgO47j5AQ3+I7jODmhoaEV4uiKKwnT3PWb2ZhK0+I5juM47aMZLfyDzGwfMxsT1wvT4o0C7o3rjuM4TptJw6UzjjAdHvH/CymU4TiO49RJo6NlGnC3JAN+Hmc4Kjct3noUT3/X7Gneqk2f10wqTdeXhSn0qiFpZ+AKYEdgDXCRmZ1XyT0naTJwAsGdd7KZ3dUG0Z0acR070LjBP9DMlkSjPkPS07XuWDz93ZAhQ5o6zduESdObdqxqTBzdX3a6vnbN2Vkn/cBEM3tM0pbALEkzgAkE99wUSZMI7rlTJO1JmChlL2An4B5Je/iY+JnGdew05tIxsyXxfzlwE7A/5afFczKKmS01s8fi8kpgHmFS8nLuuXHAtWb2tpk9Bywk6N7JKK5jBxpo4UsaDLzHzFbG5c8CP2TdtHhTWH9avKbS28IWfJ6Q1AvsCzxMeffccOChxG6LY1rxsdZz2yXdW5XcYAXSdoc1243YKXLkScflyIruS5GmbI24dIYCN0kqHOdqM7tT0qOUmBbPyT6ShgA3AN82szejbktmLZFmGyQUue2SLrvzp95c1g1WIG13WF9fX1PdiJ0gR950XI6s6L4Uaco2YINvZn8E9i6RXnZaPCe7SNqYYAimmtmNMXmZpGGx5Zd0zy0Gdk7sPgJY0jppnYHgOnb8S1sHhWbeL4B5ZnZOYlPBPQfru+duAcZL2lTSSGAU8Eir5HXqx3XsgE9i7gQOBI4D5kiaHdNOJfTDbOCeM7O5kqYBTxGiP07y6I3M4zp23OA7YGYPUNpnC2Xcc2Z2FnBWakI5TcV17IC7dBzHcXKDG3zHcZyc4AbfcRwnJ7jBdxzHyQneaZsBqn01vGjKES2SxHGcbsZb+I7jODnBDb7jOE5OcJdOyvggb47jZAVv4TuO4+QEN/iO4zg5IbMuHXeF1IdH+jiOUw1v4TuO4+QEN/iO4zg5wQ2+4zhOTkjNhy/pUOA8YBBwsZlNSausbieL/Rmu3+7Hddx9pNLClzQIuAA4DNgTOFrSnmmU5bQe12/34zruTtJq4e8PLIzz3iLpWmAcYfYcp/NpiX498qituI67kLQM/nDgxcT6YuAjyQySTgROjKurDjrooFeBV1KSJ1VOhh4yLrvO3iBp1wYOV1W/sKGOJc1PbG64zkqcU71kRW9pyeE6Lk9WdF+KemSrS8dpGfxSU6nZeitmFwEXrd1BmmlmY1KSJ1U6WfYBUlW/sKGO1ztABuosCzJkSY4iukLH5cirbGlF6SwGdk6sjwCWpFSW03pcv92P67gLScvgPwqMkjRS0ibAeOCWlMpyWo/rt/txHXchqbh0zKxf0jeBuwghXZeY2dwqu5V8LewQOln2uhmgfovJQp1lQQbIjhxr6SIdlyOXsslsA7ec4ziO04X4l7aO4zg5wQ2+4zhOTmi7wZd0qKT5khZKmtRueepB0iJJcyTNljSz3fJkkWr6VeC/4/YnJH2oyeXvLOk+SfMkzZX0rRJ5xkp6I+pxtqR/b6YMsYyK10ra9dBq2n1fl6pvSdtKmiFpQfzfJpF/cpR1vqRDmizLJZKWS3oykVa3LJL2i+e0MF4rpUJnK2NmbfsROoOeBd4HbAI8DuzZTpnqlH8R0NNuObL6q0W/wOHAHYS47wOAh5sswzDgQ3F5S+CZEjKMBW5r57WSdj1kTe8tkGGD+gZ+DEyKy5OAs+PynlHGTYGRUfZBTZTlE8CHgCcbkQV4BPhovEbuAA6rV5Z2t/DXfr5tZn8FCp9vO91BLfodB1xhgYeArSUNa5YAZrbUzB6LyyuBeYSvSLNGqvXQYrJ6X48DLo/LlwNfSKRfa2Zvm9lzwELCOTQFM7sfeK0RWeK1sJWZPWjB+l+R2Kdm2m3wS32+ncWbsRwG3C1pVvzE3FmfWvTbsmtAUi+wL/Bwic0flfS4pDsk7ZVC8dWulU6/F5Jk4VxK1fdQM1sKoSEA7BDT2yFvvbIMj8sNydjuKQ5r+nw7wxxoZksk7QDMkPR0fJo7gVr025JrQNIQ4Abg22b2ZtHmx4BdzWyVpMOBXwGjmixCtWul0++FJFk4lw3qu0LeLMhboJwsTZGx3S38jv5828yWxP/lwE008TWwS6hFv6lfA5I2Jhj7qWZ2Y/F2M3vTzFbF5duBjSX1NFOGGq6Vjr4Ximj7uZSp72UFN1n8Xx6zt0PeemVZHJcbkrHdBr9jP9+WNFjSloVl4LPAk5X3yh216PcW4CsxSuUA4I3Cq24ziJEMvwDmmdk5ZfLsWIh4kLQ/4b54tYky1HKtpFoPLaat93WF+r4FOD5mOx64OS7fAoyXtKmkkYS3u0dSFrMuWeK1sFLSAfFa/Upin5ppq0vHmvP5drsYCtwU7cRGwNVmdmd7RcoW5fQr6etx+/8AtxMiVBYCbwFfbbIYBwLHAXMkzY5ppwK7JGT4R+AbkvqBPwPjY8dYsyh5rbS4HlpGBu7rcvX9KDBN0gnAC8AXo7xzJU0jjPXfD5xkZu82SxhJ1xAiwXokLQZOB6YMQJZvAJcBmxOidO6oW5bmXteO4zhOVmm3S8dxHMdpEW7wHcdxcoIbfMdxnJzgBt9xHCcnuMF3HMfJCW7wHcdxcoIbfMdxnJzw/wFyIZOKcPUSOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ff2372-aeee-4e76-84fa-a8a74557ca0a",
   "metadata": {},
   "source": [
    "<br>We can also ask for a list of columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "becdce2d-6f12-4ea8-b683-ca3e47c75176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'temp'}>,\n",
       "        <AxesSubplot:title={'center':'humidity'}>]], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=[\"temp\", \"humidity\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125a5822-a1d7-4b79-a853-1cf178102b3f",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6ca92c-54b6-45fb-8bf6-a1b544af93eb",
   "metadata": {},
   "source": [
    "In one line of code, create histograms for the `moisture` and `drought` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "965646e5-c458-4232-b221-a20199f84417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'moisture'}>,\n",
       "        <AxesSubplot:title={'center':'drought'}>]], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=[\"moisture\", \"drought\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0c59fd-1852-45c1-b470-8e8c98d6d168",
   "metadata": {},
   "source": [
    "**<br><br><br>What other changes can we make to our histogram?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85dc43f-5eed-4402-9c71-d58508a64516",
   "metadata": {},
   "source": [
    "Let's look at the documentation. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f86bd1a-e985-4d42-a6ca-fae4bbad16f4",
   "metadata": {},
   "source": [
    "<br>We can get rid of the grid lines!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e9b4df72-1afb-477d-995e-41e79d3be624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'temp'}>]], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"temp\", grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3212ca45-18b4-4653-b8c4-cecc3cff721c",
   "metadata": {},
   "source": [
    "<br>We can also change the figure size. The `figsize` keyword argument takes a list containing two numbers: width in inches and height in inches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ccda794-1719-44d5-b428-a4389cbcc829",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90f4910-934d-48ba-962e-3be34421b37e",
   "metadata": {},
   "source": [
    "Remember this plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "990ff98f-48e2-4c60-b83e-821157b8ce8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'fuel'}>,\n",
       "        <AxesSubplot:title={'center':'moisture'}>,\n",
       "        <AxesSubplot:title={'center':'drought'}>],\n",
       "       [<AxesSubplot:title={'center':'initial_spread'}>,\n",
       "        <AxesSubplot:title={'center':'temp'}>,\n",
       "        <AxesSubplot:title={'center':'humidity'}>],\n",
       "       [<AxesSubplot:title={'center':'wind'}>,\n",
       "        <AxesSubplot:title={'center':'rain'}>,\n",
       "        <AxesSubplot:title={'center':'area_burned'}>]], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c88d495-d653-40f1-833a-5c6ffed608bc",
   "metadata": {},
   "source": [
    "<br>Change the figure size in the next line of code until you can see the plots better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0d3d456c-2cfa-4672-acf2-f22ff122af6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'fuel'}>,\n",
       "        <AxesSubplot:title={'center':'moisture'}>,\n",
       "        <AxesSubplot:title={'center':'drought'}>],\n",
       "       [<AxesSubplot:title={'center':'initial_spread'}>,\n",
       "        <AxesSubplot:title={'center':'temp'}>,\n",
       "        <AxesSubplot:title={'center':'humidity'}>],\n",
       "       [<AxesSubplot:title={'center':'wind'}>,\n",
       "        <AxesSubplot:title={'center':'rain'}>,\n",
       "        <AxesSubplot:title={'center':'area_burned'}>]], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J50i6v6H8hEHHGlNTEnVMdRuBjbavL/OXUCXuLZJmAZTHrQOKL4ac7btsH2b7MOD/AX4MfLE8/aHR52x/aWBBxpSWRB1Tmu3vA/dJekEpOga4A1gBLCpli4DLBxBeTD/HAHfb/t6gA4nhkcuzYhj8CXChpKcB3wV+n2ondLmk04B7gdcPML6YPhYCFzXMv1XSm4BVwDuaXSIo6XTgdICDDjqoL0HG1JIj6pjybK8u55l/1fbJth+y/YDtY2zPL48PDjrOGG5lR/E1wBdK0ceA5wKHAZuBDzZbL/0kYjzjJmpJcyV9XdJaSWsknVnK95W0UtK68rhPwzpnSVov6S5Jx/XyA0RE1MSrgZtsbwGwvcX2E7afBD4OHDHQ6GLKaueIejtVk82LgCOBMyQdQosBJcpzC4FDgeOBj0qa0YvgIyJq5BQamr1HOzMWrwVu73tEMRTGTdS2N9u+qUw/SjXq02xaDyixALjY9uO27wHWkz3JiBhikp4BHAtc1lD8t5Juk3Qr8HLgTwcSXEx5HXUmkzQPOBy4njEDSkgaHVBiNvCthtU2lrKxr5UOFBExFGz/GHjWmLI3DiicGDJtdyaTtCdwKfB224/sbNEmZd6hIB0oIiIixtXWEbWkXamS9IW2R5t2tkiaVY6mGweU2AjMbVh9DrCpWwFHREwnnd7BLXdlGz7t9PoW1Q0P1to+r+GpVgNKrAAWStpN0sHAfOCG7oUcERExfbRzRH0U8EbgNkmrS9m7gSU0GVDC9hpJy6lGh9oOnGH7iW4HHjHV9fJe1xExPMZN1La/SfPzzlANl9dsnXOBcycRV0RERJCRySIiImotiToiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImqso5tyRNRRuY3qKuB+2ydJ2hf4PDAP2AC8wfZDg4uw3joZeCXDU0b0X46oYxicSXX71VFN75UeETEVJVHHlCZpDnAi8ImG4lb3So+ImHKSqGOq+zDwTuDJhrKn3Csd2L/JekB1X3RJqySt2rZtW08DjeElaYOk2yStlrSqlO0raaWkdeVxn0HHGVNTEnVMWZJOArbavnGir5H7okcXvdz2YbZHynxOwURXJFHHVHYU8BpJG4CLgVdI+izlXukAY+6VHtFPOQUTXZFEHVOW7bNsz7E9D1gIfM32qbS+V3pErxj4iqQbJZ1eyto6BZPTLzGeXJ4Vw6jpvdIjeugo25sk7Q+slHRnuyvaXgosBRgZGXGvAoypK4k6hoLta4BryvQDtLhXekQv2N5UHrdK+iJwBOUUjO3NOQUTk5Gm74iISZC0h6Rnjk4DrwJuJ6dgoktyRB0RMTkHAF+UBNU29XO2r5L0bXIKJrogiToiYhJsfxd4SZPynIKJrkjTd0RERI0lUUdERNRYEnVERESN5Rx1RERMGZ3clnVYJFFHRER0qJ/3cR+36VvSJyVtlXR7Q1nLu8JIOkvSekl3STpuUtFFRERMc+2co74AOH5MWdO7wkg6hGrM5UPLOh+VNKNr0UZEREwz4yZq29cCD44pbnVXmAXAxbYft30PsJ5qKL2IiIiYgIn2+m51V5jZwH0Ny20sZTvIHWMiIiLG1+3Ls9SkrOndYGwvtT1ie2TmzJldDiMiImI4TLTXd6u7wmwE5jYsNwfYNJkAI6I++tnTNSIqEz2ibnVXmBXAQkm7SToYmA/cMLkQIyIipq9xj6glXQQcDewnaSNwNrCEJneFsb1G0nLgDmA7cIbtJ3oUe0TUWI6+I7pj3ERt+5QWTzW9K4ztc4FzJxNURLskzQU+DfwK8CSw1PZHJO0LfB6YB2wA3mD7oUHFGRExURnrO6a67cA7bL8IOBI4o1zP3/Ra/4hukzRX0tclrZW0RtKZpfwcSfdLWl3+Thh0rDE1ZQjRmNLK5YGjlwo+Kmkt1SWBC6hO2UB1rf81wLsGEGIMv9GdxZskPRO4UdLK8tyHbH9ggLHFEEiijqEhaR5wOHA9Y671l7R/i3VOB04HOOigg/oUaQyTnewsRnRFEnUMBUl7ApcCb7f9iNTskv4d2V4KLAUYGRlpes1/RLvG7CweBbxV0puAVVRH3Tv0kxjGncVO73CVzoQ7l3PUMeVJ2pUqSV9o+7JSvKVc48+Ya/0jemLsziLwMeC5wGFUR9wfbLZeBn+K8eSIOqb0/V1VHTqfD6y1fV7DU6PX+i/hqdf6R3Rds51F21sanv84cMWAwospLok6prqjgDcCt0laXcreTYtr/SO6rdXO4ujojWX2tcDtzdaPqX2w0A9J1DGl2f4mzceYhxbX+sfUV7PBVFrtLJ4i6TCq+x1sAP6w14HEcEqijoiYhJ3sLH6p37FA7XZiogvSmSwiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGch11RAxcRqaKaC1H1BERETWWI+qIiBhXWj0GJ0fUERERNZZEHRERUWNp+o6ImKbSnD015Ig6IiKixpKoIyIiaqxniVrS8ZLukrRe0uJevU9EK6mDMWipg9ENPUnUkmYA/wS8GjgEOEXSIb14r4hmUgdj0FIHo1t61ZnsCGC97e8CSLoYWADc0aP3izHSSSR1MAYudTC6oleJejZwX8P8RuDXGxeQdDpwepl9TNJdE3yv/YAfTHDdXqhbPDDAmPT+psUv6MNbj1sHoa16WMffc1RdY6tVXGPqYGNsz+7xW3ejDtbhu0wMk4yhxXYQ2qyDvUrUalLmp8zYS4Glk34jaZXtkcm+TrfULR6oX0ySVvXjbZqUeYeCceph3b67RnWNra5xQd9jm3QdrMN3mRgGH0OvOpNtBOY2zM8BNvXovSKaSR2MQUsdjK7oVaL+NjBf0sGSngYsBFb06L0imkkdjEFLHYyu6EnTt+3tkt4K/BswA/ik7TW9eC+60HzeZXWLB+oXU8/j6WIdrNt316iusdU1LuhjbF2qg3X4LhNDZWAxyN7hlElERETUREYmi4iIqLEk6oiIiBqbMola0lxJX5e0VtIaSWeW8nMk3S9pdfk7oc9xbZB0W3nvVaVsX0krJa0rj/v0KZYXNHwPqyU9Iunt/f6OJH1S0lZJtzeUtfxOJJ1Vhli8S9JxvYytXYMe+rHO3+FO/hcHGp+k3SXdIOmWEtd76xDXRPWrDk5k29qL763TbWm3Y5jI9rNv9cf2lPgDZgEvLdPPBL5DNSzfOcCfDzCuDcB+Y8r+FlhcphcD7x9AXDOA71NdUN/X7wh4GfBS4PbxvpPyG94C7AYcDNwNzBhwXZtR4ngO8LQS3yF9jqG23+FO/hcHGh/Vdct7luldgeuBIwcdV93rYKfb1l59b51sS3v927Wz/exn/ZkyR9S2N9u+qUw/CqylGvmnjhYAy8r0MuDkAcRwDHC37e/1+41tXws8OKa41XeyALjY9uO27wHWUw29OEg/H/rR9k+B0aEf+6bO3+FO/hcHGp8rj5XZXcufBx3XBPWtDk5g29rP721Qv10728++fQ9TJlE3kjQPOJxqjxngrZJuLc2FfWlmbmDgK5JuVDUUIMABtjdD9U8A7N/nmKC6ZvOihvlBfkfQ+jtpNszioHfA6hgT1PA7HPO/OPD4JM2QtBrYCqy0XYu4JmAgsbW5be1VbJ1sS3v9/bSz/ezbbzTlErWkPYFLgbfbfgT4GPBc4DBgM/DBPod0lO2XUt0h5wxJL+vz++9A1eAKrwG+UIoG/R3tTFvDLPZZHWPamYHE2+R/seWiTcp6Ep/tJ2wfRjUK2BGSXlyHuCag77F1sG3tVWydbEt79v10sP3s2280pRK1pF2pKtKFti8DsL2l/HM+CXycPjdd2d5UHrcCXyzvv0XSrBLzLKq9+356NXCT7S0ltoF+R0Wr76SOwyzWMSao0XfY7H+xTvHZfhi4Bji+TnF1oK+xdbht7UlsHW5Le/n9tLv97NtvNGUStSQB5wNrbZ/XUD6rYbHXArePXbeHMe0h6Zmj08CryvuvABaVxRYBl/crpuIUGpptBvkdNWj1nawAFkraTdLBwHzghgHE16iuQz/W4jts9b846PgkzZS0d5l+OvBK4M5BxzVBfauDE9i2dv17m8C2tJe/Xbvbz/7Vn170UOvFH/BbVM0KtwKry98JwGeA20r5CmBWH2N6DlWvv1uANcB7SvmzgKuBdeVx3z7G9AzgAeCXG8r6+h1RVfLNwM+o9jpP29l3AryHqsfkXcCrB13XSkwnUPV+vXv0d+3z+9f2O9zJ/+JA4wN+Fbi5xHU78FelvBbfW13r4ES2rd3+3iayLe3Fb9fp9rNf9SdDiEZERNTYlGn6joiImI6SqCMiImosiToiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaGPlFLWiPp6MkuJ+nLkha1en7MshskvbLdGOtE0gWS3jfoOCJi8Pq1LdvZ9lfS0ZI2trPssNpl0AH0mu1DO11O0jnA82yf2vD8q7sfXUR3SNoA/IHtrw46lohOtbudHrtss231MBr6I+phJ2nod7YiIqazoU/Uo003ks6RtFzSpyU9WppPRposdzzwbuB3JT0m6Zby/DWS/qBMP1fS1yQ9IOkHki6UtHeHcR0haZWkRyRtkXReKZ8nyZJOl7RJ0mZJ72hY7xxJl0j6rKRHgDdL+mVJ55dl75f0Pkkz2olV0uGSbirfyeeB3Sf6XcdgSPoMcBDwr6XOvlPSkZL+r6SHJd3S2FRY6vL7yvOPSfpXSc8qdeMRSd+WNK9heUt6m6Tvljr0d5KGftsRP3eYpFsl/VDS5yXtLunNkr7ZuFCpJ88r0xdI+mg5ZfiYpP+Q9CuSPizpIUl3Sjq8Yd2fN7FLenpZ/yFJdwC/NuZ9Wm6rJb1e0o1jln+HpH/pzVfTH9Ptn+01wMXA3sAK4B/HLmD7KuBvgM/b3tP2S5q8joD/DRwIvAiYC5zTYSwfAT5iey/gucDyMc+/HJgPvApYPOY80QLgkvI5LgSWAduB5wGHl3X+YLxYJT0N+BfgM8C+wBeA/9bh54gBs/1G4F7gd2zvSVUnrgTeR/W7/jlwqaSZDastBN4IzKaqf9cBnyrLrwXOHvM2rwVGgJdS1b+39OrzRO28ATgeOBj4VeDNHaz3l8B+wONUdeymMn8JcF6L9c6mqpPPBY4DmvYNarGtXgEcLOlFDYueSrWNm7KmW6L+pu0v2X6C6odrloTHZXu97ZW2H7e9jarC/XaHL/Mz4HmS9rP9mO1vjXn+vbZ/ZPs2qg3oKQ3PXWf7X2w/CewFvBp4e1l+K/Ahqg3xeLEeCewKfNj2z2xfAny7w88R9XMq8KVS15+0vRJYBZzQsMynbN9t+4fAl4G7bX/V9naqHbbDx7zm+20/aPte4MM8tT7GcPt725tsPwj8K3BYm+t90faNtv8T+CLwn7Y/Xba/n2fHOjbqDcC5pb7dB/x9u4Hafry89qkAkg4F5gFXtPsadTTdEvX3G6Z/DOw+kXO8kvaXdHFpZn4E+CzVXmInTgOeD9xZmhpPGvP8fQ3T36M6Im723LOpku3m0sz5MPB/gP3biPVA4H7bHvNeMbU9G3j9aH0odeK3gFkNy2xpmP5Jk/k9x7zmzupjDLex282xdaOVTuvYqAPZsb51YhnwPySJqtVoeUngU9Z0S9Tt8jjP/++yzK+WputTqZqY238De53tU6gS6vuBSyTt0bDI3Ibpg4BNLeK7j6pZaT/be5e/vRp6Ru4s1s3A7FKhG98rpp6xdeIzDfVhb9t72F4yidffWX2M6edHwDNGZyT9ShdfezM71rdWdthWl9bJnwL/FfgfTPFmb0iibmULMG8nHWaeCTwGPCxpNvAXnb6BpFMlzSzN1w+X4icaFvmfkp5Rmm5+n6o5Zwe2NwNfAT4oaS9Jv1Q6kI02b+8s1uuozm2/TdIukl4HHNHpZ4la2AI8p0x/FvgdScdJmlE6/xwtac4kXv8vJO0jaS5wJi3qY0wbtwCHSjpM0u503kdnZ5YDZ5X6Ngf4k50s22pb/WmqPkjbbX9zx9WmliTq5r5QHh+QdFOT599L1anmh1Sddi6bwHscD6yR9BhVx7KF5VzOqG8A64GrgQ/Y/spOXutNwNOAO4CHqDpqjDZztozV9k+B11F1DnkI+N0JfpYYvP8N/GVp5v5dqg5f7wa2UR1h/wWT+3+/HLgRWE1Vj86fxGvFFGf7O8BfA18F1gHdTIbvpWruvofqIGRnR8StttWfAV48zrpThp56ejIGrVwWcw+wa+nYEzFQkgzMt71+0LFEtEPS04GtwEttrxt0PJOVI+qIiBg2fwx8exiSNEyDIUQHSdKXqTo0jPU3tv+m3/FERAw7VcPpCjh5sJF0T5q+IyIiaixN3xERETWWRB0REVFjtThHvd9++3nevHmDDiP65MYbb/yB7ZnjL9lfqYfTSx3rYerg9NJuHaxFop43bx6rVq0adBjRJ5JqOUxp6uH0Usd6mDo4vbRbB9P0HRERUWNJ1BERETWWRB0REVFjSdQRERE1lkQdERFRY7Xo9d0N8xZf2dHyG5ac2KNIIqaWTv538n8zXPLbTw05oo6IiKixcRN1uen8DZJukbRG0ntL+b6SVkpaVx73aVjnLEnrJd0l6bhefoCIiIhh1s4R9ePAK2y/BDgMOF7SkcBi4Grb84GryzySDgEWAocCxwMflTSjB7FHREQMvXETtSuPldldy5+BBcCyUr6MX9xSbAFwse3Hbd8DrAeO6GbQERER00Vb56glzZC0GtgKrLR9PXCA7c0A5XH/svhs4L6G1TeWsrGvebqkVZJWbdu2bRIfIaYDSZ+UtFXS7Q1lfyfpTkm3SvqipL0bnsvpl4gYCm0lattP2D4MmAMcIenFO1lczV6iyWsutT1ie2TmzFqNix/1dAHVqZRGK4EX2/5V4DvAWZDTLxExXDrq9W37YeAaqo3fFkmzAMrj1rLYRmBuw2pzgE2TDTSmN9vXAg+OKfuK7e1l9ltUdQ1y+iV6IK06MSjt9PqeOVr5JD0deCVwJ7ACWFQWWwRcXqZXAAsl7SbpYGA+cEOX444Y6y3Al8t0W6dfIKdgoiMXkFadGIB2jqhnAV+XdCvwbapz1FcAS4BjJa0Dji3z2F4DLAfuAK4CzrD9RC+CjwCQ9B5gO3DhaFGTxXY4/QI5BRPtS6tODMq4I5PZvhU4vEn5A8AxLdY5Fzh30tFFjEPSIuAk4Bjbo8k4p19iEN4CfL5Mz6ZK3KN22qoDnA5w0EEH9TK+mKIyMllMWZKOB94FvMb2jxueyumX6Ku06kQvDc1Y3zHcJF0EHA3sJ2kjcDbV+cDdgJWSAL5l+49sr5E0evplOzn9Ej2UVp3otSTqmBJsn9Kk+PydLJ/TL9FzDa06v92kVedzks4DDiStOjEJSdQREW1Iq04MShJ1REQb0qoTg5LOZBERETWWRB0REVFjSdQRERE1lkQdERFRY0nUERERNZZEHRERUWNJ1BERETWWRB0REVFjSdQRERE1Nm6iljRX0tclrZW0RtKZpfwcSfdLWl3+TmhY5yxJ6yXdJem4Xn6AiIiIYdbOEKLbgXfYvknSM4EbJa0sz33I9gcaF5Z0CLAQOJRqMPqvSnp+xrmNiIjo3LhH1LY3276pTD8KrKXFDdCLBcDFth+3fQ+wHjiiG8HG9CXpk5K2Srq9oWxfSSslrSuP+zQ8l1adiBgKHd2UQ9I84HDgeuAo4K2S3gSsojrqfogqiX+rYbWNNEnskk4HTgc46KCDJhJ7TC8XAP8IfLqhbDFwte0lkhaX+XelVSei++YtvrLtZTcsObGHkUw/bXcmk7QncCnwdtuPAB8DngscBmwGPji6aJPVvUOBvdT2iO2RmTNndhp3TDO2rwUeHFO8AFhWppcBJzeUp1UnuiqtOjEobSVqSbtSJekLbV8GYHuL7SdsPwl8nF9sCDcCcxtWnwNs6l7IET93gO3NUJ2iAfYv5bOB+xqWa9qqA1XLjqRVklZt27atp8HGlHcBcPyYstFWnfnA1WV+bF+d44GPSprRv1BjmIzb9K3qbujnA2ttn9dQPmt0Iwm8Fhjdy1wBfE7SeVTNjvOBG7oadcTOtdWqA1XLDrAUYGRkpOky8QvTufnT9rXl9F+jBcDRZXoZcA3wLhpadYB7JI226lzXl2BjqLRzjvoo4I3AbZJWl7J3A6dIOoxqA7gB+EMA22skLQfuoOoxfkbODUaPbBndYZQ0C9haytOqE/3ylFYdSY2tOuP21YH014nxjZuobX+T5kcoX9rJOucC504iroh2rAAWAUvK4+UN5WnViUFKq050TUe9viMGRdJFVE2M+0naCJxNlaCXSzoNuBd4PaRVJ/oqrTrRc0nUMSXYPqXFU8e0WD6tOtEPadWJnkuijohoQ1p1YlCSqCMi2pBWnRiUJOqIiCHSySV0MTUkUUcMmWyoI4ZL7kcdERFRY0nUERERNZZEHRERUWNJ1BERETWWRB0REVFjSdQRERE1lkQdERFRY0nUERERNTZuopY0V9LXJa2VtEbSmaV8X0krJa0rj/s0rHOWpPWS7pJ0XC8/QERExDBr54h6O/AO2y8CjgTOkHQIsBi42vZ84OoyT3luIXAocDzwUUkzehF8RETEsBs3UdvebPumMv0osBaYDSwAlpXFlgEnl+kFwMW2H7d9D7AeOKLLcUcAIOlPS0vP7ZIukrT7zlp7IiKmmo7OUUuaBxwOXA8cYHszVMkc2L8sNhu4r2G1jaVs7GudLmmVpFXbtm2bQOgx3UmaDbwNGLH9YmAGVWtO09aeiF7JDmP0UtuJWtKewKXA220/srNFm5R5hwJ7qe0R2yMzZ85sN4yIsXYBni5pF+AZwCZat/ZEdF12GKPX2krUknalStIX2r6sFG+RNKs8PwvYWso3AnMbVp9DtfGM6Crb9wMfAO4FNgM/tP0VWrf27CAtO9El2WGMnmmn17eA84G1ts9reGoFsKhMLwIubyhfKGk3SQcD84EbuhdyRKU0JS4ADgYOBPaQdGonr5GWnZisye4wZmcxxtPOEfVRwBuBV0haXf5OAJYAx0paBxxb5rG9BlgO3AFcBZxh+4meRB/T3SuBe2xvs/0z4DLgN2nd2hPRdZPdYczOYoxnl/EWsP1Nmp93BjimxTrnAudOIq6IdtwLHCnpGcBPqOrjKuBHVK08S3hqa09EL/x8hxFA0lN2GG1vzg5jTMa4iTqirmxfL+kS4Caq6/1vBpYCewLLJZ1GlcxfP7goYxrIDmP0VBJ1TGm2zwbOHlP8OC1aeyK6LTuM0WtJ1BERk5Qdxuil3JQjIiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGxk3Ukj4paauk2xvKzpF0/5j7U48+d5ak9ZLuknRcrwKPiIiYDto5or4AOL5J+YdsH1b+vgQg6RBgIXBoWeejkmZ0K9iIiIjpZty7Z9m+VtK8Nl9vAXCx7ceBeyStB44Arpt4iBE7J2lv4BPAiwEDbwHuAj4PzAM2AG+w/dBgIoyYXuYtvrKj5TcsObFHkQyHyZyjfqukW0vT+D6lbDZwX8MyG0vZDiSdLmmVpFXbtm2bRBgRfAS4yvYLgZcAa4HFwNW25wNXl/mInpC0t6RLJN0paa2k35C0r6SVktaVx33Gf6WIHU00UX8MeC5wGLAZ+GApV5Nl3ewFbC+1PWJ7ZObMmRMMI6Y7SXsBLwPOB7D9U9sPU7XuLCuLLQNOHkR8MW1kZzF6ZkKJ2vYW20/YfhL4OFXzNlRH0HMbFp0DbJpciBE79RxgG/ApSTdL+oSkPYADbG8GKI/7N1s5LTsxWdlZjF6bUKKWNKth9rXAaI/wFcBCSbtJOhiYD9wwuRAjdmoX4KXAx2wfDvyIDo5c0rITXZCdxeipdi7PuoiqM9gLJG2UdBrwt5Juk3Qr8HLgTwFsrwGWA3cAVwFn2H6iZ9FHVK04G21fX+YvoUrcW0Z3KMvj1gHFF8MvO4vRU+30+j6lSfH5O1n+XODcyQQV0S7b35d0n6QX2L4LOIZqR/EOYBGwpDxePsAwY7g121lcTNlZtL05O4sxGeMm6ogp4E+ACyU9Dfgu8PtUrUXLSwvQvcDrBxhfDLHsLEavJVHHlGd7NTDS5Klj+hxKNOjkWtohuI42O4vRM0nUERGTlJ3F6KXclCMiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGkqgjIiJqLIk6IiKixpKoIyIiaixDiEZMAZ2Mmx0Rw6Wd+1F/UtJWSbc3lO0raaWkdeVxn4bnzpK0XtJdko7rVeARERHTQTtN3xcAx48pWwxcbXs+cHWZR9IhwELg0LLORyXN6Fq0EU1ImiHpZklXlPmWO5IREVPNuIna9rXAg2OKFwDLyvQy4OSG8ottP277HmA9cER3Qo1o6UxgbcN80x3JiF7JzmL00kQ7kx1gezNAedy/lM8G7mtYbmMp24Gk0yWtkrRq27ZtEwwjpjtJc4ATgU80FLfakYzolewsRs90u9e3mpS52YK2l9oesT0yc+bMLocR08iHgXcCTzaUtdqR3EF2GGOysrMYvTbRXt9bJM2yvVnSLGBrKd8IzG1Ybg6waTIBRrQi6SRgq+0bJR09kdewvRRYCjAyMtJ0pzJiHB+m2ll8ZkPZU3YWJe10ZxE4HeCggw7qYZj11clVDRuWnNjDSOppool6BbAIWFIeL28o/5yk84ADgfnADZMNshdSMYbCUcBrJJ0A7A7sJemztN6RjOiq7CxGP7RzedZFwHXACyRtlHQaVYI+VtI64Ngyj+01wHLgDuAq4AzbT/Qq+JjebJ9le47teVRXG3zN9qn8YkcSnrojGdFtozuLG4CLgVc07iwCZGcxJmvcI2rbp7R46pgWy58LnDuZoCImaQmwvOxU3gu8fsDxxJCyfRZwFkA5ov5z26dK+juatzpGdCwjk8VQsH0NcE2ZfoAWO5IRfZKdxeiaJOqIiC7IzmL0Sm7KERERUWO1PqLOjQgiImK6yxF1REREjdX6iDpimKXFKCLakUQdERFTxnQcrCpN3xERETWWRB0REVFjSdQRERE1lkQdERFRY0nUERERNZZEHRERUWO5PCsiBq7Ta8qH5bKbiHZMKlGXe7A+CjwBbLc9Imlf4PPAPGAD8AbbD00uzIiIiOmpG03fL7d9mO2RMr8YuNr2fODqMh/RE5LmSvq6pLWS1kg6s5TvK2mlpHXlcZ9BxxrDKXUweq0X56gXAMvK9DLg5B68R8So7cA7bL8IOBI4Q9IhZIcx+id1MHpqsueoDXxFkoH/Y3spcIDtzQC2N0vav9mKkk4HTgc46KCDJhlGTFelro3Wt0clrQVmU+0wHl0WW0Z1n+B3DSDEGHK9roMZEz4mm6iPsr2pJOOVku5sd8WS1JcCjIyMeJJxRCBpHnA4cD3ZYYwBSB2sl2HppDippm/bm8rjVuCLwBHAFkmzAMrj1skGGTEeSXsClwJvt/1Iu+vZXmp7xPbIzJkzexdgDL3UweiVCR9RS9oD+KXS1LMH8Crgr4EVwCJgSXm8vBuBRrQiaVeqDeSFti8rxVskzSpHMhPeYZyOd+qJzvWyDkZM5oj6AOCbkm4BbgCutH0VVYI+VtI64NgyH9ETkgScD6y1fV7DU6M7jJAdxuih1MHotQkfUdv+LvCSJuUPAMdMJqiIDhwFvBG4TdLqUvZuqh3E5ZJOA+4FXj+Y8GIaSB2MnsrIZDGl2f4moBZPZ4cxei51MHotY31HRETUWBJ1REREjaXpOyKmnPTGj+kkR9QRERE1liPqNmTvPdqRoR4johdyRB0REVFjSdQRERE1lkQdERFRY0nUERERNZbOZBEREdS343COqCMiImosR9QREREd6ufRd46oIyIiaqxniVrS8ZLukrRe0uJevU9EK6mDMWipg9ENPUnUkmYA/wS8GjgEOEXSIb14r4hmUgdj0FIHo1t6dUR9BLDe9ndt/xS4GFjQo/eKaCZ1MAYtdTC6oledyWYD9zXMbwR+vUfvVSt17d7fLZ2OZz3Azzht62DURupgdEWvErWalPkpC0inA6eX2cck3dVknf2AH3Q5tm6aVHx6fxcjaW7g31+Lz/jsfrx1kzLvsFB79bCfBv6bNVG3mDqKZyf/Z72uh4Oug3X43RIDoPe3jKGtOtirRL0RmNswPwfY1LiA7aXA0p29iKRVtke6H153JL5aG7cOQnv1sJ/q+JvVLaa6xbMTA62DdfieEkN3YujVOepvA/MlHSzpacBCYEWP3iuimdTBGLTUweiKnhxR294u6a3AvwEzgE/aXtOL94poJnUwBi11MLqlZyOT2f4S8KVJvkxtmiRbSHw11qU62G91/M3qFlPd4mlpwHWwDt9TYqhMKgbZO/RtiIiIiJrIEKIRERE1VstEXcdh9yTNlfR1SWslrZF0ZinfV9JKSevK4z4DjHGGpJslXVG32GJHkj4paauk2xvKBvab1a2OS9pd0g2SbinxvHeQ8dTVTn63cyTdL2l1+Tuhx3FskHRbea9Vpaxvv5WkFzR81tWSHpH09n58D53+L0s6q+S3uyQdN+7r163puwy79x3gWKrLG74NnGL7jgHHNQuYZfsmSc8EbgROBt4MPGh7Sdmp2Mf2uwYU458BI8Betk+S9Ld1iS12JOllwGPAp22/uJQN7DerWx2XJGAP249J2hX4JnAm8LpBxFNXO/nd3gA8ZvsDfYpjAzBi+wcNZQOpzyWP3E81wMzv0+PvoZP/ZVXDyF5ENXLdgcBXgefbfqLV69fxiLqWw+7Z3mz7pjL9KLCWauShBcCystgyqn+QvpM0BzgR+ERDcS1ii+ZsXws8OKZ4YL9Z3eq4K4+V2V3LnwcVT13t5Herg0H9VscAd9v+Xj/erMP/5QXAxbYft30PsJ4q77VUx0TdbNi9ulQ6ACTNAw4HrgcOsL0Zqn8YYP8BhfVh4J3Akw1ldYkt2leL36wudbyczlkNbAVW2q7T/1ztjPndAN4q6dbSNNvrUwQGviLpRlWjrcHgfquFVEeto/r5PYxq9dk7znF1TNRtDbs3KJL2BC4F3m77kUHHAyDpJGCr7RsHHUtMfXWq47afsH0Y1aheR0h68SDjqbMmv9vHgOcChwGbgQ/2OISjbL+U6m5hZ5Tm4L5TNbjMa4AvlKJ+fw/j6TjH1TFRtzXs3iCU82SXAhfavqwUbynniEbPFW0dQGhHAa8p54guBl4h6bM1iS06M9DfrK513PbDwDXA8XWIp26a/W62t5QdnSeBjzNO8+pk2d5UHrcCXyzvN4jf6tXATba3lHj6+j00aPXZO85xdUzUtRx2r3RsOR9Ya/u8hqdWAIvK9CLg8n7HZvss23Nsz6P6vr5m+9Q6xBYdG9hvVrc6LmmmpL3L9NOBVwJ3Diqeumr1u40mieK1wO1j1+1iDHuUjmxI2gN4VXm/QfxWp9DQ7N3P72GMVp99BbBQ0m6SDgbmAzfs9JVs1+4POIGq5/fdwHsGHU+J6beomiduBVaXvxOAZwFXA+vK474DjvNo4IoyXavY8rfDb3URVVPcz6j2sk8b5G9WtzoO/Cpwc4nnduCvSnnqdXu/22eA20r5Cqqe4b2K4TnALeVvzeh2u9+/FfAM4AHglxvKev49dPq/DLyn5Le7gFeP9/q1uzwrIiIifqGOTd8RERFRJFFHRETUWBJ1REREjSVRR0RE1FgSdURERI0lUUdERNRYEnVERESNJVFHRETUWBJ1REREjSVRR0RE1FgSdURERI0lUUdERNRYEnVERESNJVFHRETUWBJ1n0l6TNJzJrjuNZL+oNsxxfQm6fckfWXQcUREc0nUfWZ7T9vfHXQcEaNsX2j7VYOOI6aPOh90SDpH0mcHHUejJOqIISdpl0HHEBETl0TdJZJ+X9K/Nsyvl7S8Yf4+SYdJsqTnlbILJP2TpCslPSrpeknPbVjnWEl3SvqhpH8E1NcPFVOWpA2S3iXpVuBHkv5S0t2lnt0h6bUNy75Z0jcb5i3pjyStk/RQqaOpe9PQVNjJmwoxTlYSdfd8A/ivkn5J0ixgV+AogHJOek/g1ibrnQK8F9gHWA+cW9bZD7gU+EtgP+Du0deLaNMpwInA3sBdwH8Ffpmqvn221NNWTgJ+DXgJ8AbguJ5GGn0laXGzHbey0/Yfkj4k6UHgHEm7SfqApHslbZH0z5KeXpbfR9IVkraVnborJM1pM4znSrqhHIhcLmnf8ppHS9o4Jt4Nkl5Zps+RdImkz0p6BHhzaUr/XyX2RyV9pWxDR9c/UtL/lfSwpFskHd3w3MGSvlHWW0m1va2VJOouKeedHwUOA34b+DfgfkkvLPP/bvvJJqteZvsG29uBC8v6ACcAd9i+xPbPgA8D3+/ph4hh8/e277P9E9tfsL3J9pO2Pw+sA47YybpLbD9s+17g6/yiXsZwuJvWO26/DnwX2J/qwOH9wPOp6sDzgNnAX5Vlfwn4FPBs4CDgJ8A/thnDm4C3AAcC24G/7yD+BcAlVDuhF5ay/wH8fon7acCfA0iaDVwJvA/Yt5RfKmlmWe9zwI1UCfp/AYs6iKMvkqi76xvA0cDLyvQ1VEn6t8t8M43J98dUR95QVd77Rp+w7cb5iDb8vL5IepOk1eWI4mHgxez8yKFVvYwhMM6O2ybb/1AOHv4T+H+BP7X9oO1Hgb8BFpbXecD2pbZ/XJ47l2p7147P2L7d9o+A/wm8QdKMNte9zva/lPh/Uso+Zfs7ZX45v9i5PBX4ku0vleVXAquAEyQdRNVy9D9tP277WuBfqZmhb9vvs28AvwMcTFWZHwZ+D/gN2t/LHLUZmDs6U84Rzm29eMQODCDp2cDHgWOoNnBPSFpN+jxMW5LeBPwZMK8U7Um14/YETz0gmAk8A7ixoZuCgBnldZ4BfAg4nur0HcAzJc2w/cQ4YTS+z/eoThe22+zc7KCl1c7ls4HXS/qdhud3pWopOhB4qOwsNMZSq21tjqi76xvAy4Gn294I/DtVBX4WcHOHr3UlcKik15XOEm8DfqWbwca0sQdV0t4GVcdHqiPqmIYadtzeCjzL9t7A7fxix80Ni/+Aqjn7UNt7l79ftj2aBN8BvAD4ddt7UbUmQns7gY3J8CDgZ+X9fkS1czAa7wyqHYZGpn33UR29793wt4ftJVQHRPtI2mNMLLWSRN1Ftr8DPEaVoLH9CNW5nv9oY+9y7Gv9AHg9sAR4AJgP/EdXA45pwfYdwAeB64AtwH8hdWk6a3vHrfSr+TjwIUn7l+VnSxrtXPhMqkT+cOkMdnYHcZwq6ZByVP7XwCVlO/kdYHdJJ0ralapD7W6dfsgGnwV+R9JxkmZI2r10WJtj+3tUzeDvlfQ0Sb9F1SpaK2n67jLbs8bMj4yZV8P0m8c8dw0wp2H+KqpOHBEdsT1vzPx7gPe0WPYC4IKGeY15/s3dji8Gx/YdkkZ33J4EPs3Od9zeRdV57FulJ/X9wMeoOsx+mKoz1g+ATVQ7hCe3GcpnqOrdC6laI/+4xPdDSf8f8AmqJva/BTa2eI1x2b5P0oLyOhdRNe/fMPp+VJ3QlgEPUn0nn6bqpFYbqvooRURERB2l6TsiIqLG0vQdERFdJ+mxFk+92va/9zWYKS5N3xERETVWiyPq/fbbz/PmzRt0GNEnN9544w9sj73cYuBSD6eXOtbD1MHppd06WItEPW/ePFatWjXoMKJPJH1v0DE0k3o4vdSxHqYOTi/t1sF0JouIiKixJOqIiIgaS6KOiIiosSTqiIiIGkuijohog6QNkm4rtwtdVcr2lbRS0rryuE/D8mdJWi/proaxsSM6Vote39PVvMVXdrT8hiUn9iiS+pO0AXiUapze7bZHyk0APk91q74NwBtsP1SWPws4rSz/Ntv/NpH37eQ3ms6/zzTy8nLDnFGLgattL5G0uMy/S9IhVPdsPpTqVopflfT8Tm/OA9lORI6oY2p5ue3DGm50MrqRnA9cXeYZs5E8HvhoBzekj+jEAqobOlAeT24ov9j247bvAdYDR/Q/vBgGSdQxlWUjGf1k4CuSbpR0eik7wPZmgPK4fymfTXUf5FEbS9kOJJ0uaZWkVdu2betR6DGVJVHHVJGNZAzaUbZfCrwaOEPSy3ayrJqUNR2v2fZS2yO2R2bOrNVAaVETOUcdU8VRtjeVm9evlHTnTpbtaCMJLAUYGRnJwPfRku1N5XGrpC9StdJskTTL9mZJs4CtZfGNwNyG1edQ3a85omM5oo4poXEjCTxlIwmQjWT0kqQ9JD1zdBp4FXA7sAJYVBZbBFxeplcACyXtJulgYD5wQ3+jjmGRRB21l41k1MABwDcl3UJVl660fRWwBDhW0jrg2DKP7TXAcuAO4CrgjIn0+I6ANH3H1HAA8EVJUNXZz9m+StK3geWSTgPuBV4P1UZS0uhGcjvZSMYk2f4u8JIm5Q8Ax7RY51zg3B6HFtNAEnXUXjaSETGdpek7IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGxk3Ukj4paauk2xvKcmu3iIiIPmjniPoCqjsQNcpdiyIiIvpg3ERt+1rgwTHFuWtRREREH0z0HHXuWhQREdEH3e5Mllu7RUREdNFEE3XuWhQREdEHEx3re/SuRUvY8a5Fn5N0HnAgNb5r0bzFV7a97IYlJ/YwkoiIiNbGTdSSLgKOBvaTtBE4mypB565FERERPTZuorZ9SouncteiiIiIHsvIZBERETWWRB0REVFjE+1MNq2k41lERAxKjqgjIiJqLIk6IqJNkmZIulnSFWU+NyiKnkvTd5d10kwenSk3eFkF3G/7JEn7Ap8H5gEbgDfYfqgsexZwGvAE8Dbb/zaQoGPYnAmsBfYq86M3KFoiaXGZf9eYGxQdCHxV0vNzuWpMRI6oYyoZ3UiOyl3com8kzQFOBD7RUJwbFEXPJVHHlJCNZNTAh4F3Ak82lOUGRdFzSdQxVXyYbCRjQCSdBGy1fWO7qzQpyw2KYkKSqKP2spGMGjgKeI2kDcDFwCskfZbcoCj6IIk6poJsJGOgbJ9le47teVT9H75m+1R+cYMi2PEGRQsl7SbpYGp8g6KovyTqqL1sJKPGlgDHSloHHFvmsb0GGL1B0VXkBkUxCbk8K6ay3MUt+s72NcA1ZfoBcoOi6LEk6phSspGMiOkmTd8RERE1lkQdERFRY5NK1JL+VNIaSbdLukjS7jsb+zYiIiI6M+FELWk28DZgxPaLgRlUPXKbDusYERERnZts0/cuwNMl7QI8g+pa1VbDOkZERESHJpyobd8PfIDqspjNwA9tf4XWwzo+RYZujIiIGN9kmr73oTp6PpjqNm57SDq13fUzdGNERMT4JtP0/UrgHtvbbP8MuAz4TVoP6xgREREdmkyivhc4UtIzJIlq4Im1tB7WMSIiIjo04ZHJbF8v6RLgJqphGm8GlgJ70mRYx4iIiOjcpIYQtX02cPaY4sdpMaxjREREdCYjk0VERNRYEnVERESNJVFHRETUWBJ1REREjSVRR0RE1FgSdURERI0lUUdERNRYEnVERESNJVFH7UnaXdINkm6RtEbSe0v5vpJWSlpXHvdpWOcsSesl3SXpuMFFH8MgdTAGKYk6poLHgVfYfglwGHC8pCOBxcDVtucDV5d5JB0CLAQOBY4HPippxiACj6GROhgDk0QdtefKY2V21/JnqtusLivly4CTy/QC4GLbj9u+B1gPHNG/iGPYpA7GICVRx5QgaYak1VS3TV1p+3rgANubAcrj/mXx2cB9DatvLGXNXvd0Saskrdq2bVvP4o+pL3UwBiWJOqYE20/YPgyYAxwh6cU7WVzNXqLF6y61PWJ7ZObMmV2INIZV6mAMShJ1TCm2HwauoTrvt0XSLIDyuLUsthGY27DaHGBT/6KMYZY6GP2WRB21J2mmpL3L9NOBVwJ3AiuARWWxRcDlZXoFsFDSbpIOBuYDN/Q16BgqqYMxSJO6H3VEn8wClpVes78ELLd9haTrgOWSTgPuBV4PYHuNpOXAHcB24AzbTwwo9hgOqYMxMJNK1GUP8xPAi6nOv7wFuAv4PDAP2AC8wfZDk3mfmN5s3woc3qT8AeCYFuucC5zb49BimkgdjEGabNP3R4CrbL8QeAmwlhbXFUZERETnJpyoJe0FvAw4H8D2T0sni1bXFUZERESHJnNE/RxgG/ApSTdL+oSkPWh9XeFT5NrBiIiI8U0mUe8CvBT4mO3DgR/RQTN3rh2MiIgY32QS9UZgYxmdB+ASqsTd6rrCiIiI6NCEE7Xt7wP3SXpBKTqG6lKEVtcVRkRERIcmex31nwAXSnoa8F3g9ynXGI69rjAiIiI6N6lEbXs1MNLkqabXFUZERERnMoRoREREjSVRR0RE1FgSdURERI0lUUdERNRYEnVERESNJVFHRETUWBJ1REREjSVRR0RE1FgSdURERI0lUUdERNRYEnVERESNJVFH7UmaK+nrktZKWiPpzFK+r6SVktaVx30a1jlL0npJd0k6bnDRxzBIHYxBSqKOqWA78A7bLwKOBM6QdAiwGLja9nzg6jJPeW4hcChwPPBRSTMGEnkMi9TBGJgk6qg925tt31SmHwXWArOBBcCystgy4OQyvQC42Pbjtu8B1gNH9DXoGCqpgzFISdQxpUiaBxwOXA8cYHszVBtSYP+y2GzgvobVNpayZq93uqRVklZt27atZ3HH8EgdjH6b1P2o62Te4isHHUL0mKQ9gUuBt9t+RFLLRZuUudmCtpcCSwFGRkaaLhMxKnUwBiFH1DElSNqVagN5oe3LSvEWSbPK87OAraV8IzC3YfU5wKZ+xRrDKXUwBmXSiVrSDEk3S7qizLfsBRkxEaoOW84H1to+r+GpFcCiMr0IuLyhfKGk3SQdDMwHbuhXvDF8UgdjkLpxRH0mVceKUU17QUZMwlHAG4FXSFpd/k4AlgDHSloHHFvmsb0GWA7cAVwFnGH7icGEHkMidTAGZlLnqCXNAU4EzgX+rBQvAI4u08uAa4B3TeZ9Ynqz/U2an/MDOKbFOudS1cuISUsdjEGa7BH1h4F3Ak82lLXqBfkU6ekYERExvgknakknAVtt3ziR9W0vtT1ie2TmzJkTDSMiImKoTabp+yjgNeU8ze7AXpI+S+kFaXvzmF6QERER0aEJH1HbPsv2HNvzqIbK+5rtU2ndCzIiIiI61IvrqJv2goyIiIjOdWVkMtvXUPXuxvYDtOgFGREREZ3JyGQRERE1lkQdERFRY0nUERERNZZEHRERUWNJ1BERETWWRB0REVFjSdQRERE1lkQdERFRY0nUERERNZZEHRERUWNJ1BERETWWRB0REVFjXbkpR9TPvMVXtr3shiUn9jCSiIiYjBxRR0S0QdInJW2VdHtD2b6SVkpaVx73aXjuLEnrJd0l6bjBRB3DIIk6poRsJKMGLgCOH1O2GLja9nzg6jKPpEOAhcChZZ2PSprRv1BjmCRRx1RxAdlIxgDZvhZ4cEzxAmBZmV4GnNxQfrHtx23fA6wHjuhHnDF8JpyoJc2V9HVJayWtkXRmKW95lBMxUdlIRk0dYHszQHncv5TPBu5rWG5jKduBpNMlrZK0atu2bT0NNqamyRxRbwfeYftFwJHAGeVIpulRTkQPZCMZdaUmZW62oO2ltkdsj8ycObPHYcVUNOFEbXuz7ZvK9KPAWqqNYaujnIh+yUYy+mWLpFkA5XFrKd8IzG1Ybg6wqc+xxZDoyjlqSfOAw4HraX2UM3adHMnEZGUjGYO2AlhUphcBlzeUL5S0m6SDgfnADQOIL4bApBO1pD2BS4G3236k3fVyJBNdkI1k9I2ki4DrgBdI2ijpNGAJcKykdcCxZR7ba4DlwB3AVcAZtp8YTOQx1U1qwBNJu1Il6QttX1aKt0iaZXvzmKOcmKROBjEZNmUjeTSwn6SNwNlUG8XlZYN5L/B6qDaSkkY3ktvJRjK6wPYpLZ46psXy5wLn9i6imC4mnKglCTgfWGv7vIanRo9ylvDUo5yICctGMiKmq8kcUR8FvBG4TdLqUvZuWhzlREREROcmnKhtf5PmvWuhxVFOREREdCYjk0VERNRYEnVERESN1fo2l9O5l3NERATUPFFH/XS685R7XUdETE6aviMiImosiToiIqLGkqgjIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGkqgjIiJqLCOTRYZqjYiosRxRR0RE1FgSdURERI0lUUdERNRYzxK1pOMl3SVpvaTFvXqfiFZSB2PQUgejG3rSmUzSDOCfgGOBjcC3Ja2wfUcv3i9irNTBGLRB1cFOOofmNrRTQ696fR8BrLf9XQBJFwMLgGwko1/6Xgdzr+4YI9vB6IpeJerZwH0N8xuBX29cQNLpwOll9jFJDwA/6FE849lvgO896Pfv6Xvr/U2Ln92r92swbh2EpvXwrjGL9Oz7afHd7Myg6+lYdYpnIrH0uh4OYx3shjrVm26YzOdpqw72KlGrSZmfMmMvBZb+fAVple2RHsWzU4N870G//6A/ew+NWwdhx3q4w4vU6PupUyxQr3jqFEuDoauD3ZDP07ledSbbCMxtmJ8DbOrRe0U0kzoYg5Y6GF3Rq0T9bWC+pIMlPQ1YCKzo0XtFNJM6GIOWOhhd0ZOmb9vbJb0V+DdgBvBJ22vGWa1l008fDPK9B/3+g/7sPTHBOthMnb6fOsUC9YqnTrEAQ1sHuyGfp0OydzhlEhERETWRkckiIiJqLIk6IiKixvqeqMcbUk+Vvy/P3yrppV1637mSvi5praQ1ks5ssszRkn4oaXX5+6tuvHd57Q2Sbiuvu6rJ8z353OW1X9DwmVZLekTS28cs07PPPlXVZfjHduruAGKaIelmSVcMOI69JV0i6c7y/fzGIOPptrrUwU4029ZJ2lfSSknryuM+DcufVT7fXZKOG1zkP4/nk5K2Srq9oazj+CX9P+V7WF+27c0u12uP7b79UXWouBt4DvA04BbgkDHLnAB8meoaxCOB67v03rOAl5bpZwLfafLeRwNX9OizbwD228nzPfncLX6D7wPP7tdnn4p/7dTVPsYybt0dQEx/Bnxu0HUGWAb8QZl+GrD3oOtOFz9bbepgh3HvsK0D/hZYXKYXA+8v04eUz7UbcHD5vDMGHP/LgJcCt08mfuAG4DfKNv3LwKsnGlO/j6h/PqSe7Z8Co0PqNVoAfNqVbwF7S5o12Te2vdn2TWX6UWAt1chBddGTz93EMcDdtr/Xg9ceJu3U1b6oW92VNAc4EfjEoGIocexFtVE9H8D2T20/PMiYuqw2dbALFlDtVFEeT24ov9j247bvAdZTfe6BsX0t8OCY4o7iL9vuvWxf5yprf7phnY71O1E3G1Jv7AannWUmRdI84HDg+iZP/4akWyR9WdKhXXxbA1+RdKOqIQPH6vnnLhYCF7V4rleffSrq1+/RkXHqbr98GHgn8OQAY4DqSHMb8KnSDP8JSXsMOKZuqmUdbEOzbd0BtjdDteMJ7F/Kp8pn7DT+2WV6bPmE9DtRtzOkXlvD7k04AGlP4FLg7bYfGfP0TVRNwi8B/gH4l269L3CU7ZcCrwbOkPSysaE1Waer186VQRdeA3yhydO9/OxTUc9/j06NU3f7FcNJwFbbNw7i/cfYhaqJ8mO2Dwd+RNUsOSxqVwfbNN62rtFU/YyjWsXf1c/V70TdzpB6PRt2T9KuVBu6C21fNvZ524/YfqxMfwnYVdJ+3Xhv25vK41bgi+zYvNOP4QZfDdxke0uT+Hr22aeoWg3/OF7d7aOjgNdI2kDVFPsKSZ8dUCwbgY22R1sXLqFK3MOiVnWwXS22dVtGT+WVx61l8anyGTuNf2OZHls+If1O1O0MqbcCeFPpBX0k8MPRJofJKD3uzgfW2j6vxTK/MtozT9IRVN/PA1147z0kPXN0GngVcPuYxXryucc4hRbN3r367FNYbYZ/bKfu9ovts2zPsT2P6jv5mu1TBxTL94H7JL2gFB3DcN1CsjZ1sF072datABaVxRYBl5fpFcBCSbtJOhiYT9UJq246ir9sux+VdGT5/31Twzod69Xds5pyiyH1JP1Ref6fgS9R9YBeD/wY+P0uvf1RwBuB2yStLmXvBg5qeO//DvyxpO3AT4CFpSPAZB0AfLHkwV2Az9m+qk+fGwBJz6C6gf0fNpQ1vn+vPvuU1KquDiicpnW3tHxMd38CXFgS2Xfp8v/NINWsDrar1bbu28BySacB9wKvByjb/+VUO1jbgTNsPzGY0CuSLqK6CmY/SRuBs4EldB7/HwMXAE+n6vX95QnHNI23xREREbWXkckiIiJqLIk6IiKixpKoIyIiaiyJOiIiosaSqCMiImosiToiIqLGkqgjIiJq7P8HoJr4GZusUmsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(grid=False, figsize=[8,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a468baab-4db5-4059-9ff2-dd9be4355c05",
   "metadata": {},
   "source": [
    "## <br><br><br>Scatter plots to check for correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5291673f-b5cb-4dbf-991e-cb46346f7695",
   "metadata": {},
   "source": [
    "We can make a quick scatter plot to check for correlation between 2 columns. We have to use a slightly different format for our scatter plot function. We are going to do `df.plot.scatter()`. This function requires two arguments, the columns for your x and y axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e156aa94-2419-44c8-a12f-efc70c5d6743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='temp', ylabel='humidity'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"temp\", y=\"humidity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c800bf20-aa96-4993-a010-5da0e591f5b7",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bccf2886-c32d-4cff-bd3e-be52f279245b",
   "metadata": {},
   "source": [
    "Write code to create a scatter plot with the moisture column on the x-axis and the drought column on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8c91c4a1-068b-48ba-9e30-6c813383ad61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='moisture', ylabel='drought'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"moisture\", y=\"drought\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01406ba-81f4-448f-a713-0f24821809fa",
   "metadata": {},
   "source": [
    "Based on what we learned with `hist()`, can you add a grid to the scatter plot you just made and change the size so that it is a perfect square?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a79e81cc-f203-4ee2-ae7f-d815adff0851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='moisture', ylabel='drought'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"moisture\", y=\"drought\", grid=True, figsize=[6,6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d257c1-0fba-4edf-a5ca-f002cc1a01b8",
   "metadata": {},
   "source": [
    "## <br><br><br>Slightly more complicated examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da86ac36-ba9c-45c4-9b9d-12d0c603e7d7",
   "metadata": {},
   "source": [
    "### <br>Removing Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4b1213-177e-4100-9840-c505088351f5",
   "metadata": {},
   "source": [
    "Let's look at the correlation between humidity and area_burned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "19b001bd-2d81-4156-9ff3-3645b596cda0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='humidity', ylabel='area_burned'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"humidity\", y=\"area_burned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e012b16-3d1d-4a67-8a01-4e423fa3fd4e",
   "metadata": {},
   "source": [
    "<br>We can see that a few outliers are clouding any relationship. We can remove them with a boolean, but we need to look at the plot above and decide where to make the cutoff. Let's try getting rid of only points above 400."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "42ea18a5-4ba0-44b0-bfce-00044382322a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_outliers = df[df[\"area_burned\"] < 400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a28e8e27-4b86-498e-bf15-02b8d1008dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='humidity', ylabel='area_burned'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_no_outliers.plot.scatter(x=\"humidity\", y=\"area_burned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c44508-92b1-4c6c-b6b5-9fe0a154a0cd",
   "metadata": {},
   "source": [
    "<br><br>There are a large number of points with 0 for area_burned. Let's remove those, and only look at days where the fires spread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "edf78f87-5f15-4ccc-a395-ca16c18880dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_outliers = df[(df[\"area_burned\"] > 0) & (df[\"area_burned\"] < 400)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8918fc00-3fba-4e28-9598-865a485a286c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='humidity', ylabel='area_burned'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_no_outliers.plot.scatter(x=\"humidity\", y=\"area_burned\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ce08e4-c584-4c5e-acbe-1b11fd8f67e7",
   "metadata": {},
   "source": [
    "### <br><br><br>Plotting with categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cdcc66-3f16-4d9f-a261-9eb3f8cfd867",
   "metadata": {},
   "source": [
    "Let's say we want to see the relationship between month and humidity. We can try a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a556cfcd-6347-4cc2-a489-82d73de36abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month', ylabel='humidity'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"month\", y=\"humidity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a2c3c5-fd85-473d-82dd-c5d8f3306605",
   "metadata": {},
   "source": [
    "<br>That doesn't work! Let's try another type of plot - a bar plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ae535333-18b0-4c83-875e-2f9e31407eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x=\"month\", y=\"humidity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bd9942-37c8-4177-9c0b-f156a8b17518",
   "metadata": {},
   "source": [
    "<br>Also not what we're looking for. It's plotting each data point individually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2395c937-850f-4016-b867-989418e1cb46",
   "metadata": {},
   "source": [
    "What we really want to see is how the mean humidity changes each month. We can create a new DataFrame that only includes the data we need. We will group by month, select only the humidity column, and then find the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1dfbe18e-1219-4eed-8f1e-9fac72bbb5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "apr    46.888889\n",
       "aug    45.489130\n",
       "dec    38.444444\n",
       "feb    55.700000\n",
       "jan    89.000000\n",
       "jul    45.125000\n",
       "jun    45.117647\n",
       "mar    40.000000\n",
       "may    67.000000\n",
       "oct    37.785714\n",
       "sep    42.843023\n",
       "Name: humidity, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hum_mean = df.groupby(\"month\")[\"humidity\"].mean()\n",
    "hum_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4c2300-d705-45e0-9e5b-e1208a27e3bc",
   "metadata": {},
   "source": [
    "<br>Now we have a nice series object that we can plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e6c12c40-4f3c-4bc2-9a0c-08822680d957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hum_mean.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf1c808-0b6d-4fd8-a2da-ea6f1b71dee5",
   "metadata": {},
   "source": [
    "<br>This is looking better, but we need to sort them by month, not alphabetically. We're going to worry about that in just a minute."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d907f2-0f28-44a3-80d1-198e31df57cd",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57738428-6871-4532-81c5-d81655baaa55",
   "metadata": {},
   "source": [
    "Create a bar graph that shows mean temperature grouped by month. First you'll need to create a new series object with the means. Refer back to the humidity exercise directly above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "06cd8ec2-0532-442b-bd9e-a844543a2d14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tem_mean = df.groupby(\"month\")[\"temp\"].mean()\n",
    "tem_mean.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eea6f57-4ee7-4816-9fff-d3ceb8e29290",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "79d992a1-9827-4ee5-94f1-9f68f7acb7b3",
   "metadata": {},
   "source": [
    "<br><br><br>Let's deal with the sorting issue! We can create a new column in our DataFrame that contains a numerical value for the month. We will use a handy pandas function called `replace()`. It takes two arguments: a list of items to replace, and a list of the replacement values in the same order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "990ddb12-2223-4c8b-bd69-3314b7ad892b",
   "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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fuel</th>\n",
       "      <th>moisture</th>\n",
       "      <th>drought</th>\n",
       "      <th>initial_spread</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area_burned</th>\n",
       "      <th>month_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>92.3</td>\n",
       "      <td>85.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  month  day  fuel  moisture  drought  initial_spread  temp  humidity  wind  \\\n",
       "0   mar  fri  86.2      26.2     94.3             5.1   8.2        51   6.7   \n",
       "1   oct  tue  90.6      35.4    669.1             6.7  18.0        33   0.9   \n",
       "2   mar  fri  91.7      33.3     77.5             9.0   8.3        97   4.0   \n",
       "3   mar  sun  89.3      51.3    102.2             9.6  11.4        99   1.8   \n",
       "4   aug  sun  92.3      85.3    488.0            14.7  22.2        29   5.4   \n",
       "\n",
       "   rain  area_burned  month_num  \n",
       "0   0.0          0.0          3  \n",
       "1   0.0          0.0         10  \n",
       "2   0.2          0.0          3  \n",
       "3   0.0          0.0          3  \n",
       "4   0.0          0.0          8  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"month_num\"] = df[\"month\"].replace([\"jan\", \"feb\", \"mar\", \"apr\", \n",
    "                          \"may\", \"jun\", \"jul\", \"aug\", \n",
    "                          \"sep\", \"oct\", \"nov\", \"dec\"], \n",
    "                         [1, 2, 3, 4, 5, 6, 7, 8, 9, \n",
    "                          10, 11, 12])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b48313-aec3-485c-9d20-a9733f7699a5",
   "metadata": {},
   "source": [
    "<br>Now we will repeat the humidity plot we just did, but we will group by the new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "98b18090-2fb9-42ae-9573-8606fa837e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month_num'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hum_mean = df.groupby(\"month_num\")[\"humidity\"].mean()\n",
    "hum_mean.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7f80e1-e51f-45b6-837f-64584f1cfd87",
   "metadata": {},
   "source": [
    "## <br><br><br>Other packages for exploring data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619f5d0e-2e0c-4356-9a66-4e22140255ff",
   "metadata": {},
   "source": [
    "There are a few packages that have been created to help you visualize your data during the data exploration step without writing the code for yourself. They also have their limitations, however."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6eee4b-9450-4b73-97a4-e0154654d31a",
   "metadata": {},
   "source": [
    "- pandas-profiling (gives in-depth summaries about your dataset) (I've been having trouble with the installation/dependencies lately)\n",
    "- sweetviz (designed as exploration prior to machine learning, so it requires you to split your data into train and test and select a target variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bcc12c-1f6a-422a-9f0e-0e80a640e2c8",
   "metadata": {},
   "source": [
    "Both packages cover:\n",
    "- data type, unique values, missing values\n",
    "- Quantile statistics like minimum value, Q1, median, Q3, maximum, range, interquartile range\n",
    "- Descriptive statistics like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness\n",
    "- Histograms\n",
    "- Correlations of variables\n",
    "- Missing values matrix, count, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cab6242-4484-479a-afbc-7cb466b8592a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}