lambda_answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *If you are using Google Colab, first run the code cell below. You can run a cell by clicking in the cell and clicking on the arrow that appears on the left side of the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget \"https://raw.githubusercontent.com/aGitHasNoName/lambda/master/plots.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <br>lambda functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br>Common use #1 that you shouldn't do"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naming a lambda function - NO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_two(a, b):\n",
    "    c = a + b\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_two(5, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_two(a, b):\n",
    "    return a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_two(5, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_two = lambda a,b: a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_two(5, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Potato chips'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capitalize_word = lambda word: word.lower().capitalize()\n",
    "capitalize_word(\"POTATO CHIPS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rewrite the lambda function above as a regular function called `capitalize_word2`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def capitalize_word2(word):\n",
    "    return word.lower().capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Potato chips'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capitalize_word2(\"POTATO CHIPS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br>Exercise 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'receiptMarch'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_year(phrase):\n",
    "    return phrase.rstrip(\"2020\")\n",
    "remove_year(\"receiptMarch2020\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rewrite the function above as a lambda function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_year2 = lambda phrase: phrase.rstrip(\"2020\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'receiptMarch'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_year2(\"receiptMarch2020\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br>Common use #2 that you shouldn't do"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br>Using `map()` and lambda to do something to every item in a list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`map()` is a function that will do the same thing to every item in group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lunchSides = [\"POTATO CHIPS\", \"mac 'n cheese\", \"POTATO SALAD\", \"French Bread\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<map object at 0x105624f98>\n"
     ]
    }
   ],
   "source": [
    "cleaned_sides = map(lambda word: word.lower().capitalize(), lunchSides)\n",
    "print(cleaned_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>The `map()` function creates a map object. If we want it to create a useable list, we need to pass all that code to the `list()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Potato chips', \"Mac 'n cheese\", 'Potato salad', 'French bread']\n"
     ]
    }
   ],
   "source": [
    "cleaned_sides = list(map(lambda word: word.lower().capitalize(), lunchSides))\n",
    "print(cleaned_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br><br>Use a list comprehension instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Potato chips', \"Mac 'n cheese\", 'Potato salad', 'French bread']\n"
     ]
    }
   ],
   "source": [
    "cleaned_sides = [word.lower().capitalize() for word in lunchSides]\n",
    "print(cleaned_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Using `filter()` and lambda to filter a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lunchSides = [\"POTATO CHIPS\", \"mac 'n cheese\", \"POTATO SALAD\", \"French Bread\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['POTATO CHIPS', 'POTATO SALAD']\n"
     ]
    }
   ],
   "source": [
    "potato_sides = list(filter(lambda word: \"potato\" in word.lower(), lunchSides))\n",
    "print(potato_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br><br>Use a list comprehension instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['POTATO CHIPS', 'POTATO SALAD']\n"
     ]
    }
   ],
   "source": [
    "potato_sides = [word for word in lunchSides if \"potato\" in word.lower()]\n",
    "print(potato_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br>Plus, with a list comprehension, we can **filter** AND **map** in the same line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Potato chips', 'Potato salad']\n"
     ]
    }
   ],
   "source": [
    "potato_sides = [word.lower().capitalize() for word in lunchSides if \"potato\" in word.lower()]\n",
    "print(potato_sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br>Exercise 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = [7, 3, 28, 47, 2, 49, 29]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49, 9, 784, 2209, 4, 2401, 841]\n"
     ]
    }
   ],
   "source": [
    "squares = list(map(lambda num: num**2, numbers))\n",
    "print(squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rewrite the lambda above as a list comprehension called `squares2`. If you have not yet learned list comprehensions, take a look at the list comprehension/lambda pairs above and try to guess how you might restructure the lambda above as a list comprehension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares2 = [num**2 for num in numbers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49, 9, 784, 2209, 4, 2401, 841]\n"
     ]
    }
   ],
   "source": [
    "print(squares2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br>Using lambdas with pandas Data Frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- pandas Data Frames are Python's version of a data table (like an Excel spreadsheet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It's ok to use lambdas with a pandas Data Frame because there is no \"dataframe comprehension\", no \"row comprehension\", and no \"column comprension\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We're going to use a lamba paired with the `dataframe.apply()` function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>First, let's take a look at a simple pandas Data Frame, which we are going to load as the variable `df`, a commonly used variable name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "             address  length  width\n",
       "0     908 Washington     100    400\n",
       "1         3987 Baker      50    350\n",
       "2        7400 Hadley     150    375\n",
       "3  89304 N. Marcello     200    550\n",
       "4           782 Main      50    200\n",
       "5       7391 Pontiac      60    225\n",
       "6          927 Grand     125    325\n",
       "7       8450 Oakhill     150    400\n",
       "8    400 S. Highland     110    425"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"plots.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One common task is to add a new column that involves data found in other columns. When you want to do this with a simple calculation, you can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"acreage\"] = (df.length * df.width)/43560"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <th>address</th>\n",
       "      <th>length</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>0.918274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>0.401745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>1.291322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>2.525253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>0.229568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>0.309917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>0.932622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>1.377410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>1.073232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width   acreage\n",
       "0     908 Washington     100    400  0.918274\n",
       "1         3987 Baker      50    350  0.401745\n",
       "2        7400 Hadley     150    375  1.291322\n",
       "3  89304 N. Marcello     200    550  2.525253\n",
       "4           782 Main      50    200  0.229568\n",
       "5       7391 Pontiac      60    225  0.309917\n",
       "6          927 Grand     125    325  0.932622\n",
       "7       8450 Oakhill     150    400  1.377410\n",
       "8    400 S. Highland     110    425  1.073232"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>It will be easier to work with less digits, so let's round the acerage number to 3 places after the decimal point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"acreage\"] = round((df.length * df.width)/43560, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>0.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>1.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>2.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>0.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>1.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>1.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width  acreage\n",
       "0     908 Washington     100    400    0.918\n",
       "1         3987 Baker      50    350    0.402\n",
       "2        7400 Hadley     150    375    1.291\n",
       "3  89304 N. Marcello     200    550    2.525\n",
       "4           782 Main      50    200    0.230\n",
       "5       7391 Pontiac      60    225    0.310\n",
       "6          927 Grand     125    325    0.933\n",
       "7       8450 Oakhill     150    400    1.377\n",
       "8    400 S. Highland     110    425    1.073"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>However, when you want to use a more complicated expression, you often get strange results if you try the same method. Here I'm taking the equation we used above and combining it in a string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"acreage\"] = df.address + \" is \" + str(round((df.length * df.width)/43560, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>acreage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>908 Washington is 0    0.918\\n1    0.402\\n2   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>3987 Baker is 0    0.918\\n1    0.402\\n2    1.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>7400 Hadley is 0    0.918\\n1    0.402\\n2    1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>89304 N. Marcello is 0    0.918\\n1    0.402\\n2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>782 Main is 0    0.918\\n1    0.402\\n2    1.291...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>7391 Pontiac is 0    0.918\\n1    0.402\\n2    1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>927 Grand is 0    0.918\\n1    0.402\\n2    1.29...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>8450 Oakhill is 0    0.918\\n1    0.402\\n2    1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>400 S. Highland is 0    0.918\\n1    0.402\\n2  ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width  \\\n",
       "0     908 Washington     100    400   \n",
       "1         3987 Baker      50    350   \n",
       "2        7400 Hadley     150    375   \n",
       "3  89304 N. Marcello     200    550   \n",
       "4           782 Main      50    200   \n",
       "5       7391 Pontiac      60    225   \n",
       "6          927 Grand     125    325   \n",
       "7       8450 Oakhill     150    400   \n",
       "8    400 S. Highland     110    425   \n",
       "\n",
       "                                             acreage  \n",
       "0  908 Washington is 0    0.918\\n1    0.402\\n2   ...  \n",
       "1  3987 Baker is 0    0.918\\n1    0.402\\n2    1.2...  \n",
       "2  7400 Hadley is 0    0.918\\n1    0.402\\n2    1....  \n",
       "3  89304 N. Marcello is 0    0.918\\n1    0.402\\n2...  \n",
       "4  782 Main is 0    0.918\\n1    0.402\\n2    1.291...  \n",
       "5  7391 Pontiac is 0    0.918\\n1    0.402\\n2    1...  \n",
       "6  927 Grand is 0    0.918\\n1    0.402\\n2    1.29...  \n",
       "7  8450 Oakhill is 0    0.918\\n1    0.402\\n2    1...  \n",
       "8  400 S. Highland is 0    0.918\\n1    0.402\\n2  ...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>Don't these results look strange?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br><br>Instead, we can use the function `df.apply()`. We pass it two arguments: 1. a lambda function, and 2. the axis we are looping through - here we are looping through rows, so we pass `axis=1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"acreage\"] = df.apply(lambda row: row.address + \n",
    "                         \" is \" + \n",
    "                         str(round((row.length * row.width)/43560, 3)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>acreage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>908 Washington is 0.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>3987 Baker is 0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>7400 Hadley is 1.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>89304 N. Marcello is 2.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>782 Main is 0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>7391 Pontiac is 0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>927 Grand is 0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>8450 Oakhill is 1.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>400 S. Highland is 1.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width                     acreage\n",
       "0     908 Washington     100    400     908 Washington is 0.918\n",
       "1         3987 Baker      50    350         3987 Baker is 0.402\n",
       "2        7400 Hadley     150    375        7400 Hadley is 1.291\n",
       "3  89304 N. Marcello     200    550  89304 N. Marcello is 2.525\n",
       "4           782 Main      50    200            782 Main is 0.23\n",
       "5       7391 Pontiac      60    225        7391 Pontiac is 0.31\n",
       "6          927 Grand     125    325          927 Grand is 0.933\n",
       "7       8450 Oakhill     150    400       8450 Oakhill is 1.377\n",
       "8    400 S. Highland     110    425    400 S. Highland is 1.073"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br><br>It works, but the lambda function is long and not easy to read. Alternatively, rather than include all the computation in the lambda function, we can define a new function, and then use the lambda to call the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_acreage_statement(row):\n",
    "    acres = round((row.length * row.width)/43560, 3)\n",
    "    return row.address + \" is \" + str(acres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"acreage\"] = df.apply(lambda row: create_acreage_statement(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>acreage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>908 Washington is 0.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>3987 Baker is 0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>7400 Hadley is 1.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>89304 N. Marcello is 2.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>782 Main is 0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>7391 Pontiac is 0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>927 Grand is 0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>8450 Oakhill is 1.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>400 S. Highland is 1.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width                     acreage\n",
       "0     908 Washington     100    400     908 Washington is 0.918\n",
       "1         3987 Baker      50    350         3987 Baker is 0.402\n",
       "2        7400 Hadley     150    375        7400 Hadley is 1.291\n",
       "3  89304 N. Marcello     200    550  89304 N. Marcello is 2.525\n",
       "4           782 Main      50    200            782 Main is 0.23\n",
       "5       7391 Pontiac      60    225        7391 Pontiac is 0.31\n",
       "6          927 Grand     125    325          927 Grand is 0.933\n",
       "7       8450 Oakhill     150    400       8450 Oakhill is 1.377\n",
       "8    400 S. Highland     110    425    400 S. Highland is 1.073"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>As a note, the `row` in our lambda, is just naming a variable. `axis=1` tells the `apply()` function that we are looping through rows. Here's the same code, but using a different variable name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>acreage</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>908 Washington is 0.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>3987 Baker is 0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>7400 Hadley is 1.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>89304 N. Marcello is 2.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>782 Main is 0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>7391 Pontiac is 0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>927 Grand is 0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>8450 Oakhill is 1.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>400 S. Highland is 1.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width                     acreage\n",
       "0     908 Washington     100    400     908 Washington is 0.918\n",
       "1         3987 Baker      50    350         3987 Baker is 0.402\n",
       "2        7400 Hadley     150    375        7400 Hadley is 1.291\n",
       "3  89304 N. Marcello     200    550  89304 N. Marcello is 2.525\n",
       "4           782 Main      50    200            782 Main is 0.23\n",
       "5       7391 Pontiac      60    225        7391 Pontiac is 0.31\n",
       "6          927 Grand     125    325          927 Grand is 0.933\n",
       "7       8450 Oakhill     150    400       8450 Oakhill is 1.377\n",
       "8    400 S. Highland     110    425    400 S. Highland is 1.073"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"acreage\"] = df.apply(lambda x: create_acreage_statement(x), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br>Exercise 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_size_class(row):\n",
    "    acres = (row.length * row.width)/43560\n",
    "    if acres > 2:\n",
    "        size = \"large\"\n",
    "    elif acres > .75:\n",
    "        size = \"medium\"\n",
    "    else:\n",
    "        size = \"small\"\n",
    "    return size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column to the `df` Data Frame called `sizeClass`. Use the `apply` function, a lambda function, and the `calculate_size_class` function above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"sizeClass\"] = df.apply(lambda row: calculate_size_class(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>acreage</th>\n",
       "      <th>sizeClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>908 Washington is 0.918</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>3987 Baker is 0.402</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "      <td>7400 Hadley is 1.291</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "      <td>89304 N. Marcello is 2.525</td>\n",
       "      <td>large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>782 Main is 0.23</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "      <td>7391 Pontiac is 0.31</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "      <td>927 Grand is 0.933</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>8450 Oakhill is 1.377</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "      <td>400 S. Highland is 1.073</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width                     acreage sizeClass\n",
       "0     908 Washington     100    400     908 Washington is 0.918    medium\n",
       "1         3987 Baker      50    350         3987 Baker is 0.402     small\n",
       "2        7400 Hadley     150    375        7400 Hadley is 1.291    medium\n",
       "3  89304 N. Marcello     200    550  89304 N. Marcello is 2.525     large\n",
       "4           782 Main      50    200            782 Main is 0.23     small\n",
       "5       7391 Pontiac      60    225        7391 Pontiac is 0.31     small\n",
       "6          927 Grand     125    325          927 Grand is 0.933    medium\n",
       "7       8450 Oakhill     150    400       8450 Oakhill is 1.377    medium\n",
       "8    400 S. Highland     110    425    400 S. Highland is 1.073    medium"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br>Homework: Filtering a Data Frame on a complex condition:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First I will reload the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"plots.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908 Washington</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3987 Baker</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>782 Main</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7391 Pontiac</td>\n",
       "      <td>60</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width\n",
       "0     908 Washington     100    400\n",
       "1         3987 Baker      50    350\n",
       "2        7400 Hadley     150    375\n",
       "3  89304 N. Marcello     200    550\n",
       "4           782 Main      50    200\n",
       "5       7391 Pontiac      60    225\n",
       "6          927 Grand     125    325\n",
       "7       8450 Oakhill     150    400\n",
       "8    400 S. Highland     110    425"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a new Data Frame that only includes some rows like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df.length > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>927 Grand</td>\n",
       "      <td>125</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width\n",
       "2        7400 Hadley     150    375\n",
       "3  89304 N. Marcello     200    550\n",
       "6          927 Grand     125    325\n",
       "7       8450 Oakhill     150    400\n",
       "8    400 S. Highland     110    425"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br><br> pandas does not work with conditionals for filtering Data Frames in the same way we are used to working with other objects in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of \"and\", \"or\", and \"not\", we use the `&` (and), `|` (or), and `~` (not) operators to combine conditional statements in pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[(df.length > 100) & (df.width > 400)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width\n",
       "3  89304 N. Marcello     200    550\n",
       "8    400 S. Highland     110    425"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>Other conditionals in pandas also don't work in the same way they work in basic Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "False",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: False",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-ccc15894ff88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"N\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddress\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: False"
     ]
    }
   ],
   "source": [
    "new_df = df[\"N\" in df.address]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>There are often specific functions in pandas to deal with conditionals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "             address  length  width\n",
       "3  89304 N. Marcello     200    550"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df.address.str.contains(\"N\")]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>However, these are often difficult to remember."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead, you can always use a lambda. To create a new Data Frame with a conditional, use `df.apply()` with a lambda. Pass a conditional statement to the lambda (`\"N\" in row.address`) and then include the whole thing in `df[]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df.apply(lambda row: \"N\" in row.address, axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "             address  length  width\n",
       "3  89304 N. Marcello     200    550"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df.apply(lambda row: row.width > 400 and row.length > 100, axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width\n",
       "3  89304 N. Marcello     200    550\n",
       "8    400 S. Highland     110    425"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acreage(row):\n",
    "    return (row.length * row.width)/43560"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `df.apply()`, a lambda, and the `acreage` function above. Create a new Data Frame called `large_lots` that includes the rows from `df` that contain lots that are larger than 1 acre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "large_lots = df[df.apply(lambda row: acreage(row) > 1, axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7400 Hadley</td>\n",
       "      <td>150</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89304 N. Marcello</td>\n",
       "      <td>200</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8450 Oakhill</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>400 S. Highland</td>\n",
       "      <td>110</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             address  length  width\n",
       "2        7400 Hadley     150    375\n",
       "3  89304 N. Marcello     200    550\n",
       "7       8450 Oakhill     150    400\n",
       "8    400 S. Highland     110    425"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_lots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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