lecture-1B.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Week 1B: Exploratory Data Science in Python\n",
    "\n",
    "9/7/2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Today\n",
    "\n",
    "- Introduction to Pandas\n",
    "- Key data analysis concepts\n",
    "- Example: Zillow + Census housing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Reminder: The weekly workflow\n",
    "\n",
    "- You'll set up your local Python environment as part of first homework assignment (posted today!)\n",
    "- Each week, you will have two options to follow along with lectures:\n",
    "    1. Using Binder in the cloud, launching via the button on the week's repository\n",
    "    1. Download the week's repository to your laptop and launch the notebook locally\n",
    "- Work on homeworks locally on your laptop — Binder is only a *temporary* environment (no save features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To follow along today, go to https://github.com/MUSA-550-Fall-2022/week-1"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![Screen%20Shot%202020-09-02%20at%2010.31.40%20PM.png](attachment:Screen%20Shot%202020-09-02%20at%2010.31.40%20PM.png)"
   ]
  },
  {
   "attachments": {
    "download.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Python Data Analysis: the *pandas* package\n",
    "\n",
    "Documentation is available at https://pandas.pydata.org\n",
    "\n",
    "<center>\n",
    "<img src=attachment:download.png width=400></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The new, less fun logo\n",
    "\n",
    "<center>\n",
    "    <img width=500 src=\"https://raw.githubusercontent.com/pandas-dev/pandas/master/web/pandas/static/img/pandas.svg\"></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The following line imports the *pandas* package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Basic *pandas* concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The primary *objects* in *pandas* are the:\n",
    "\n",
    "  * **`DataFrame`**, which is very similar to an Excel spreadsheet, and has rows and named columns\n",
    "  * **`Series`**, which represents a single column of data. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
    "\n",
    "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [R](https://www.r-project.org/about.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can think `Series` objects as fancier versions of Python's built-in list data type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To create a `Series` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# city names and population\n",
    "city_names = pd.Series([\"San Francisco\", \"San Jose\", \"Sacramento\"])\n",
    "population = pd.Series([852469, 1015785, 485199])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    San Francisco\n",
       "1         San Jose\n",
       "2       Sacramento\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population\n",
       "0  San Francisco      852469\n",
       "1       San Jose     1015785\n",
       "2     Sacramento      485199"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df = pd.DataFrame({\"City Name\": city_names, \"Population\": population})\n",
    "cities_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Note:** always try to give your variables *meaningful names*. It will help immensely when you are trying to debug problems or when you're trying to understand the code you've written months later. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Accessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     852469\n",
       "1    1015785\n",
       "2     485199\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access columns with a dict-like syntax\n",
    "cities_df[\"Population\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rows can be accessed using Python's syntax for slicing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['San Francisco', 'San Jose']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list slicing: get the elements with indices 1 and 2 (but NOT 3)\n",
    "cities_list = [\"San Francisco\", \"San Jose\", \"Sacramento\"]\n",
    "cities_list[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Unfortunately the functionality for slicing lists is not that powerful...but pandas will have many more features!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population\n",
       "0  San Francisco      852469\n",
       "1       San Jose     1015785"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing data frame rows is very similar!\n",
    "cities_df[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "*pandas* includes functionality for many different ways of selecting data. See [the documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html) for many more examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Manipulating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "852469.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df[\"Population\"].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. \n",
    "\n",
    "*pandas* `Series` can be used as arguments to most NumPy functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "852469.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# calculate the median population value\n",
    "np.median(cities_df[\"Population\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For more complex single-column transformations, you can use `Series.apply`. It accepts a function that is applied to each value in the `Series`.\n",
    "\n",
    "For example, we can find which cities have a population greater than a million:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# define our function\n",
    "def get_large_cities(population):\n",
    "    return population > 1e6\n",
    "\n",
    "\n",
    "large_cities_sel = cities_df[\"Population\"].apply(get_large_cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "Name: Population, dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_cities_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Large Cities</th>\n",
       "      <th>Small Cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population  Large Cities  Small Cities\n",
       "0  San Francisco      852469         False          True\n",
       "1       San Jose     1015785          True         False\n",
       "2     Sacramento      485199         False          True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the new computed column to our original data frame\n",
    "cities_df[\"Large Cities\"] = large_cities_sel\n",
    "\n",
    "small_cities_func = lambda population: population < 1e6\n",
    "\n",
    "# we can also use lambda (unnamed, inline) functions\n",
    "cities_df[\"Small Cities\"] = cities_df[\"Population\"].apply(small_cities_func)\n",
    "\n",
    "# print out\n",
    "cities_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data selection\n",
    "\n",
    "We can select the \"large\" cities by passing the boolean values to the `.loc()` function of a `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "Name: Large Cities, dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df[\"Large Cities\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Large Cities</th>\n",
       "      <th>Small Cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  City Name  Population  Large Cities  Small Cities\n",
       "1  San Jose     1015785          True         False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df.loc[cities_df[\"Large Cities\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### There is a faster, more concise, to achieve this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "Name: Population, dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df[\"Population\"] > 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Large Cities</th>\n",
       "      <th>Small Cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population  Large Cities  Small Cities\n",
       "0  San Francisco      852469         False          True\n",
       "2     Sacramento      485199         False          True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is equivalent to doing\n",
    "cities_df.loc[cities_df[\"Population\"] < 1e6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What about getting rows where \"Large Cities\" is False?\n",
    "\n",
    "Use the Python tilde operator to do a logicial *not* operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Large Cities</th>\n",
       "      <th>Small Cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population  Large Cities  Small Cities\n",
       "0  San Francisco      852469         False          True\n",
       "2     Sacramento      485199         False          True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reverse the large cities boolean selection\n",
    "cities_df.loc[~large_cities_sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Large Cities</th>\n",
       "      <th>Small Cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population  Large Cities  Small Cities\n",
       "0  San Francisco      852469         False          True\n",
       "2     Sacramento      485199         False          True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or equivalently:\n",
    "# NOTE: you need to put the whole expression in () and then apply the tilde!\n",
    "cities_df.loc[~(cities_df[\"Population\"] > 1e6)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### An even faster way!\n",
    "\n",
    "We can use the [pandas query function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html). \n",
    "\n",
    "The query() function will return a subset of your dataframe based on a string version of the boolean expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City Name</th>\n",
       "      <th>Population</th>\n",
       "      <th>Large Cities</th>\n",
       "      <th>Small Cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City Name  Population  Large Cities  Small Cities\n",
       "0  San Francisco      852469         False          True\n",
       "2     Sacramento      485199         False          True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df.query(\"Population < 1e6\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## How to remember the specifics of all of these functions?\n",
    "\n",
    "The documentation is your best friend! Use the question mark operator!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# use the question mark\n",
    "pd.DataFrame.loc?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Now onto a more interesting example..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Analyzing trends in rentals rates over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's load census data on rentals rates from the 2019 Amercian Community Survey. \n",
    "\n",
    "We've stored the data locally in the comma-separated value (CSV) format. Later in the course, you'll learn how to download them directly using Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Use the `read_csv()` function from *pandas*. The first argument to `read_csv()` is the file path to load."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "census_df = pd.read_csv(\"./data/census/ACS_19_1YR_B25003.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>GEO.id</th>\n",
       "      <th>GEO.display-label</th>\n",
       "      <th>HD01_VD01</th>\n",
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       "      <th>0</th>\n",
       "      <td>1600000US0103076</td>\n",
       "      <td>Auburn city, Alabama</td>\n",
       "      <td>26156</td>\n",
       "      <td>2687</td>\n",
       "      <td>13095</td>\n",
       "      <td>1829</td>\n",
       "      <td>13061</td>\n",
       "      <td>2108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1600000US0107000</td>\n",
       "      <td>Birmingham city, Alabama</td>\n",
       "      <td>93300</td>\n",
       "      <td>3618</td>\n",
       "      <td>37788</td>\n",
       "      <td>2635</td>\n",
       "      <td>55512</td>\n",
       "      <td>2788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1600000US0121184</td>\n",
       "      <td>Dothan city, Alabama</td>\n",
       "      <td>27103</td>\n",
       "      <td>1044</td>\n",
       "      <td>15891</td>\n",
       "      <td>824</td>\n",
       "      <td>11212</td>\n",
       "      <td>817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1600000US0135896</td>\n",
       "      <td>Hoover city, Alabama</td>\n",
       "      <td>32461</td>\n",
       "      <td>1606</td>\n",
       "      <td>22391</td>\n",
       "      <td>1515</td>\n",
       "      <td>10070</td>\n",
       "      <td>1496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600000US0137000</td>\n",
       "      <td>Huntsville city, Alabama</td>\n",
       "      <td>88930</td>\n",
       "      <td>2823</td>\n",
       "      <td>48728</td>\n",
       "      <td>2758</td>\n",
       "      <td>40202</td>\n",
       "      <td>2550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             GEO.id         GEO.display-label  HD01_VD01  HD02_VD01  \\\n",
       "0  1600000US0103076      Auburn city, Alabama      26156       2687   \n",
       "1  1600000US0107000  Birmingham city, Alabama      93300       3618   \n",
       "2  1600000US0121184      Dothan city, Alabama      27103       1044   \n",
       "3  1600000US0135896      Hoover city, Alabama      32461       1606   \n",
       "4  1600000US0137000  Huntsville city, Alabama      88930       2823   \n",
       "\n",
       "   HD01_VD02  HD02_VD02  HD01_VD03  HD02_VD03  \n",
       "0      13095       1829      13061       2108  \n",
       "1      37788       2635      55512       2788  \n",
       "2      15891        824      11212        817  \n",
       "3      22391       1515      10070       1496  \n",
       "4      48728       2758      40202       2550  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_df.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Select certain columns and rename them.\n",
    "\n",
    "- Total: Total occupied housing units\n",
    "- Rentals: Renter-occupied housing units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "## FIRST STEP: let's trim to the columns we want\n",
    "\n",
    "# columns we want\n",
    "columns = [\"GEO.display-label\", \"HD01_VD01\", \"HD01_VD03\"]\n",
    "census_df = census_df[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>GEO.display-label</th>\n",
       "      <th>HD01_VD01</th>\n",
       "      <th>HD01_VD03</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Auburn city, Alabama</td>\n",
       "      <td>26156</td>\n",
       "      <td>13061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birmingham city, Alabama</td>\n",
       "      <td>93300</td>\n",
       "      <td>55512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dothan city, Alabama</td>\n",
       "      <td>27103</td>\n",
       "      <td>11212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hoover city, Alabama</td>\n",
       "      <td>32461</td>\n",
       "      <td>10070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Huntsville city, Alabama</td>\n",
       "      <td>88930</td>\n",
       "      <td>40202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GEO.display-label  HD01_VD01  HD01_VD03\n",
       "0      Auburn city, Alabama      26156      13061\n",
       "1  Birmingham city, Alabama      93300      55512\n",
       "2      Dothan city, Alabama      27103      11212\n",
       "3      Hoover city, Alabama      32461      10070\n",
       "4  Huntsville city, Alabama      88930      40202"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peak at the dataframe with trimmed columns\n",
    "census_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Auburn city, Alabama</td>\n",
       "      <td>26156</td>\n",
       "      <td>13061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birmingham city, Alabama</td>\n",
       "      <td>93300</td>\n",
       "      <td>55512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dothan city, Alabama</td>\n",
       "      <td>27103</td>\n",
       "      <td>11212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hoover city, Alabama</td>\n",
       "      <td>32461</td>\n",
       "      <td>10070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Huntsville city, Alabama</td>\n",
       "      <td>88930</td>\n",
       "      <td>40202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City  Total  Rentals\n",
       "0      Auburn city, Alabama  26156    13061\n",
       "1  Birmingham city, Alabama  93300    55512\n",
       "2      Dothan city, Alabama  27103    11212\n",
       "3      Hoover city, Alabama  32461    10070\n",
       "4  Huntsville city, Alabama  88930    40202"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## STEP 2: Let's rename the columns!\n",
    "\n",
    "# rename columns\n",
    "census_df.columns = [\"City\", \"Total\", \"Rentals\"]\n",
    "\n",
    "# Return the head (first 5 rows) from the cell\n",
    "census_df.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Auburn city, Alabama</td>\n",
       "      <td>26156</td>\n",
       "      <td>13061</td>\n",
       "      <td>49.935005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birmingham city, Alabama</td>\n",
       "      <td>93300</td>\n",
       "      <td>55512</td>\n",
       "      <td>59.498392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dothan city, Alabama</td>\n",
       "      <td>27103</td>\n",
       "      <td>11212</td>\n",
       "      <td>41.368114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hoover city, Alabama</td>\n",
       "      <td>32461</td>\n",
       "      <td>10070</td>\n",
       "      <td>31.021842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Huntsville city, Alabama</td>\n",
       "      <td>88930</td>\n",
       "      <td>40202</td>\n",
       "      <td>45.206342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City  Total  Rentals  Rental Rate\n",
       "0      Auburn city, Alabama  26156    13061    49.935005\n",
       "1  Birmingham city, Alabama  93300    55512    59.498392\n",
       "2      Dothan city, Alabama  27103    11212    41.368114\n",
       "3      Hoover city, Alabama  32461    10070    31.021842\n",
       "4  Huntsville city, Alabama  88930    40202    45.206342"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 3: Add the rental rate as a new column\n",
    "census_df['Rental Rate'] = census_df['Rentals'] / census_df['Total'] * 100\n",
    "\n",
    "census_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "634"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many rows are in the dataframe? --> use the len() operator\n",
    "\n",
    "len(census_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can select the largest cities by population using the `Series.isin()` function, which checks if each value in the `Series` is in the specified list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Define the list of city names we want\n",
    "top_cities_list = [\n",
    "    \"Philadelphia city, Pennsylvania\",\n",
    "    \"New York city, New York\",\n",
    "    \"Los Angeles city, California\",\n",
    "    \"Houston city, Texas\",\n",
    "    \"Chicago city, Illinois\",\n",
    "    \"Phoenix city, Arizona\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "629    False\n",
       "630    False\n",
       "631    False\n",
       "632    False\n",
       "633    False\n",
       "Name: City, Length: 634, dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the isin() to test whether a row value is in a city\n",
    "census_df[\"City\"].isin(top_cities_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Phoenix city, Arizona</td>\n",
       "      <td>586878</td>\n",
       "      <td>267225</td>\n",
       "      <td>45.533314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Los Angeles city, California</td>\n",
       "      <td>1398900</td>\n",
       "      <td>887785</td>\n",
       "      <td>63.463078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Chicago city, Illinois</td>\n",
       "      <td>1080345</td>\n",
       "      <td>603352</td>\n",
       "      <td>55.848086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>New York city, New York</td>\n",
       "      <td>3211033</td>\n",
       "      <td>2188214</td>\n",
       "      <td>68.146730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>619505</td>\n",
       "      <td>295783</td>\n",
       "      <td>47.745055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Houston city, Texas</td>\n",
       "      <td>876504</td>\n",
       "      <td>522942</td>\n",
       "      <td>59.662249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                City    Total  Rentals  Rental Rate\n",
       "19             Phoenix city, Arizona   586878   267225    45.533314\n",
       "94      Los Angeles city, California  1398900   887785    63.463078\n",
       "284           Chicago city, Illinois  1080345   603352    55.848086\n",
       "439          New York city, New York  3211033  2188214    68.146730\n",
       "487  Philadelphia city, Pennsylvania   619505   295783    47.745055\n",
       "538              Houston city, Texas   876504   522942    59.662249"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now do the selection!\n",
    "census_df.loc[census_df[\"City\"].isin(top_cities_list)]  # select the valid rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Only 6 rows as expected!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Alternatively, we could use the query() function. The syntax is a bit different (and I always forget and have to look at the documentation).\n",
    "\n",
    "In the query() string, you can reference existing variables by prefacing the variable name with an \"@\" symbol. \n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
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       "      <th>19</th>\n",
       "      <td>Phoenix city, Arizona</td>\n",
       "      <td>586878</td>\n",
       "      <td>267225</td>\n",
       "      <td>45.533314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Los Angeles city, California</td>\n",
       "      <td>1398900</td>\n",
       "      <td>887785</td>\n",
       "      <td>63.463078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Chicago city, Illinois</td>\n",
       "      <td>1080345</td>\n",
       "      <td>603352</td>\n",
       "      <td>55.848086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>New York city, New York</td>\n",
       "      <td>3211033</td>\n",
       "      <td>2188214</td>\n",
       "      <td>68.146730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>619505</td>\n",
       "      <td>295783</td>\n",
       "      <td>47.745055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Houston city, Texas</td>\n",
       "      <td>876504</td>\n",
       "      <td>522942</td>\n",
       "      <td>59.662249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                City    Total  Rentals  Rental Rate\n",
       "19             Phoenix city, Arizona   586878   267225    45.533314\n",
       "94      Los Angeles city, California  1398900   887785    63.463078\n",
       "284           Chicago city, Illinois  1080345   603352    55.848086\n",
       "439          New York city, New York  3211033  2188214    68.146730\n",
       "487  Philadelphia city, Pennsylvania   619505   295783    47.745055\n",
       "538              Houston city, Texas   876504   522942    59.662249"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the rows where the City column is in \"top_cities_list\"\n",
    "census_df.query(\"City in @top_cities_list\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Python f-strings\n",
    "\n",
    "Python version 3.6 (we are using python 3.9) introduced a new and improved string formatting syntax, where the variable that is being inserted can be referenced directly using the variable name.\n",
    "\n",
    "The variable name should be enclosed in curly braces inside the string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/census/ACS_19_1YR_B25003.csv\n"
     ]
    }
   ],
   "source": [
    "year_str = \"19\"\n",
    "path = f\"./data/census/ACS_{year_str}_1YR_B25003.csv\"\n",
    "print(path) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For more info: see [this guide](https://realpython.com/python-f-strings/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Now let's load data from 2005 to 2019\n",
    "\n",
    "Let's use a for loop to loop over each year value from 2005 to 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for year = 2005...\n",
      "Loading data for year = 2006...\n",
      "Loading data for year = 2007...\n",
      "Loading data for year = 2008...\n",
      "Loading data for year = 2009...\n",
      "Loading data for year = 2010...\n",
      "Loading data for year = 2011...\n",
      "Loading data for year = 2012...\n",
      "Loading data for year = 2013...\n",
      "Loading data for year = 2014...\n",
      "Loading data for year = 2015...\n",
      "Loading data for year = 2016...\n",
      "Loading data for year = 2017...\n",
      "Loading data for year = 2018...\n",
      "Loading data for year = 2019...\n"
     ]
    }
   ],
   "source": [
    "all_rental_data = []\n",
    "start_year = 2005\n",
    "stop_year = 2019\n",
    "for year in range(start_year, stop_year + 1):\n",
    "    \n",
    "    #---------------------------------------------\n",
    "    # Step 0: Print out year \n",
    "    # The variable year changes for each iteration of the loop\n",
    "    #----------------------------------------------\n",
    "    print(f\"Loading data for year = {year}...\")\n",
    "    \n",
    "    #---------------------------------\n",
    "    # Step 1: Read data for this year\n",
    "    #---------------------------------\n",
    "    year_str = str(year) # convert integer value of \"year\" to a string\n",
    "    year_str = year_str[2:] # extract the last two digits of the year string\n",
    "    rental_df = pd.read_csv(f\"./data/census/ACS_{year_str}_1YR_B25003.csv\")\n",
    "     \n",
    "    #---------------------------------\n",
    "    # Step 2: Columns we want\n",
    "    #---------------------------------\n",
    "    columns = ['GEO.display-label', 'HD01_VD01', 'HD01_VD03']\n",
    "    rental_df = rental_df[columns]\n",
    "\n",
    "    #---------------------------------\n",
    "    # Step 3: Rename columns\n",
    "    #---------------------------------\n",
    "    rental_df.columns = ['City', 'Total', 'Rentals']\n",
    "    \n",
    "    #----------------------------------\n",
    "    # Step 4: Calculate the rental rate\n",
    "    #----------------------------------\n",
    "    rental_df['Rental Rate'] = rental_df['Rentals'] / rental_df['Total'] * 100\n",
    "    \n",
    "    #----------------------------------\n",
    "    # Step 5: Select the cities we want\n",
    "    #----------------------------------\n",
    "    selection = rental_df['City'].isin(top_cities_list)\n",
    "    top_cities_df = rental_df.loc[selection].copy()\n",
    "    \n",
    "    #----------------------------------\n",
    "    # Step 6: Add a column for the year\n",
    "    #----------------------------------\n",
    "    top_cities_df['Year'] = year\n",
    "    \n",
    "    #-----------------------------------\n",
    "    # Step 7: Save this dataframe in the list\n",
    "    #-----------------------------------\n",
    "    all_rental_data.append(top_cities_df)\n",
    "\n",
    "#---------------------------------------------------\n",
    "# Step 8: After the for loop, combine all of the \n",
    "# data frames into one along the row axis\n",
    "#---------------------------------------------------\n",
    "all_rental_data = pd.concat(all_rental_data, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: the pd.concat() took a list of dataframes and converted them \n",
    "# into a single dataframe!\n",
    "type(all_rental_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Phoenix city, Arizona</td>\n",
       "      <td>503753</td>\n",
       "      <td>204914</td>\n",
       "      <td>40.677475</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Los Angeles city, California</td>\n",
       "      <td>1284124</td>\n",
       "      <td>771325</td>\n",
       "      <td>60.066240</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Chicago city, Illinois</td>\n",
       "      <td>1020605</td>\n",
       "      <td>525620</td>\n",
       "      <td>51.500825</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>New York city, New York</td>\n",
       "      <td>3026196</td>\n",
       "      <td>2023924</td>\n",
       "      <td>66.880136</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>565433</td>\n",
       "      <td>244792</td>\n",
       "      <td>43.292839</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                City    Total  Rentals  Rental Rate  Year\n",
       "13             Phoenix city, Arizona   503753   204914    40.677475  2005\n",
       "75      Los Angeles city, California  1284124   771325    60.066240  2005\n",
       "218           Chicago city, Illinois  1020605   525620    51.500825  2005\n",
       "349          New York city, New York  3026196  2023924    66.880136  2005\n",
       "393  Philadelphia city, Pennsylvania   565433   244792    43.292839  2005"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rental_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Los Angeles city, California</td>\n",
       "      <td>1398900</td>\n",
       "      <td>887785</td>\n",
       "      <td>63.463078</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Chicago city, Illinois</td>\n",
       "      <td>1080345</td>\n",
       "      <td>603352</td>\n",
       "      <td>55.848086</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>New York city, New York</td>\n",
       "      <td>3211033</td>\n",
       "      <td>2188214</td>\n",
       "      <td>68.146730</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>619505</td>\n",
       "      <td>295783</td>\n",
       "      <td>47.745055</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Houston city, Texas</td>\n",
       "      <td>876504</td>\n",
       "      <td>522942</td>\n",
       "      <td>59.662249</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                City    Total  Rentals  Rental Rate  Year\n",
       "94      Los Angeles city, California  1398900   887785    63.463078  2019\n",
       "284           Chicago city, Illinois  1080345   603352    55.848086  2019\n",
       "439          New York city, New York  3211033  2188214    68.146730  2019\n",
       "487  Philadelphia city, Pennsylvania   619505   295783    47.745055  2019\n",
       "538              Houston city, Texas   876504   522942    59.662249  2019"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What about the tail?\n",
    "all_rental_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many rows? Hopefully (6 cities x 15 years = 90!)\n",
    "len(all_rental_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Basic plotting: `matplotlib`\n",
    "\n",
    "Much more to come next week. For now, we'll use some very simple plotting commands with Python's main plotting libary `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Import the library we'll use from `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We'll use the `plot()` function which plots a simple `x` vs `y` line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The strategy\n",
    "\n",
    "1. Create a figure axes to plot to\n",
    "1. Loop over each unique city\n",
    "1. Select the subset of the data for each city\n",
    "1. Plot the 'Year' vs. 'Rental Rate' columns using the `plot()` function for the sub-selection data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Note\n",
    "\n",
    "To find the unique city names in the 'City' column, we can use the `unique()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Phoenix city, Arizona', 'Los Angeles city, California',\n",
       "       'Chicago city, Illinois', 'New York city, New York',\n",
       "       'Philadelphia city, Pennsylvania', 'Houston city, Texas'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rental_data['City'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phoenix city, Arizona\n",
      "Los Angeles city, California\n",
      "Chicago city, Illinois\n",
      "New York city, New York\n",
      "Philadelphia city, Pennsylvania\n",
      "Houston city, Texas\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    \n",
    "    # Step 1: Create a figure and axes to plot on\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "    \n",
    "    # Step 2: Loop over each city and plot\n",
    "    for city in all_rental_data['City'].unique():\n",
    "        \n",
    "        # Print out the city in each loop iteration\n",
    "        print(city)\n",
    "        \n",
    "        # Select data for this city\n",
    "        this_city = all_rental_data['City'] == city\n",
    "        city_df = all_rental_data.loc[this_city]\n",
    "        \n",
    "        # Plot Year vs Rental Rate\n",
    "        ax.plot(city_df['Year'], city_df['Rental Rate'], label=city, linewidth=4)\n",
    "\n",
    "    # Add a legend\n",
    "    ax.legend(loc=0, ncol=3, fontsize=12)\n",
    "\n",
    "    # Set the y limit\n",
    "    ax.set_ylim(30, 72)\n",
    "\n",
    "    # Add a y label\n",
    "    ax.set_ylabel(\"Rental Rate in Percent\")\n",
    "    \n",
    "    # Show\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Note:** we used the \"ggplot\" theme to style our plots. Matplotlib comes with a number of built in styles: see all of the examples [here](https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## From 2005 to 2019, Philadelphia had the third largest percent increase\n",
    "\n",
    "### Exercise: Can you calculate what the increase was for Philadelphia? \n",
    "\n",
    "You will need to:\n",
    "1. Trim the full data set to just Philadelphia \n",
    "1. Select the rental rate for 2005\n",
    "1. Select the rental rate for 2019\n",
    "1. Calculate the percent change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. Trim the full data set to just Philadelphia "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13     False\n",
       "75     False\n",
       "218    False\n",
       "349    False\n",
       "393     True\n",
       "       ...  \n",
       "94     False\n",
       "284    False\n",
       "439    False\n",
       "487     True\n",
       "538    False\n",
       "Name: City, Length: 90, dtype: bool"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trim to just Philadelphia first, using this boolean selection index\n",
    "all_rental_data[\"City\"] == \"Philadelphia city, Pennsylvania\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>565433</td>\n",
       "      <td>244792</td>\n",
       "      <td>43.292839</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>554048</td>\n",
       "      <td>231323</td>\n",
       "      <td>41.751437</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>562384</td>\n",
       "      <td>239363</td>\n",
       "      <td>42.562199</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>578263</td>\n",
       "      <td>253084</td>\n",
       "      <td>43.766245</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>569835</td>\n",
       "      <td>258459</td>\n",
       "      <td>45.356814</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>575413</td>\n",
       "      <td>264129</td>\n",
       "      <td>45.902508</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>576429</td>\n",
       "      <td>265126</td>\n",
       "      <td>45.994563</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>579874</td>\n",
       "      <td>277323</td>\n",
       "      <td>47.824700</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>582528</td>\n",
       "      <td>285430</td>\n",
       "      <td>48.998503</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>577862</td>\n",
       "      <td>277301</td>\n",
       "      <td>47.987409</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>581604</td>\n",
       "      <td>275397</td>\n",
       "      <td>47.351291</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>580205</td>\n",
       "      <td>277707</td>\n",
       "      <td>47.863600</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>606142</td>\n",
       "      <td>309279</td>\n",
       "      <td>51.024182</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>608233</td>\n",
       "      <td>287543</td>\n",
       "      <td>47.275140</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>619505</td>\n",
       "      <td>295783</td>\n",
       "      <td>47.745055</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                City   Total  Rentals  Rental Rate  Year\n",
       "393  Philadelphia city, Pennsylvania  565433   244792    43.292839  2005\n",
       "400  Philadelphia city, Pennsylvania  554048   231323    41.751437  2006\n",
       "408  Philadelphia city, Pennsylvania  562384   239363    42.562199  2007\n",
       "415  Philadelphia city, Pennsylvania  578263   253084    43.766245  2008\n",
       "419  Philadelphia city, Pennsylvania  569835   258459    45.356814  2009\n",
       "427  Philadelphia city, Pennsylvania  575413   264129    45.902508  2010\n",
       "434  Philadelphia city, Pennsylvania  576429   265126    45.994563  2011\n",
       "437  Philadelphia city, Pennsylvania  579874   277323    47.824700  2012\n",
       "449  Philadelphia city, Pennsylvania  582528   285430    48.998503  2013\n",
       "454  Philadelphia city, Pennsylvania  577862   277301    47.987409  2014\n",
       "458  Philadelphia city, Pennsylvania  581604   275397    47.351291  2015\n",
       "465  Philadelphia city, Pennsylvania  580205   277707    47.863600  2016\n",
       "474  Philadelphia city, Pennsylvania  606142   309279    51.024182  2017\n",
       "486  Philadelphia city, Pennsylvania  608233   287543    47.275140  2018\n",
       "487  Philadelphia city, Pennsylvania  619505   295783    47.745055  2019"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select Philadelphia only\n",
    "philly = all_rental_data.loc[\n",
    "    all_rental_data[\"City\"] == \"Philadelphia city, Pennsylvania\"\n",
    "]\n",
    "\n",
    "philly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### An aside about labels and the index in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Note how the first row of the above dataframe has an index label of 393. This means we can access it using the `.loc[]` function like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City           Philadelphia city, Pennsylvania\n",
       "Total                                   565433\n",
       "Rentals                                 244792\n",
       "Rental Rate                          43.292839\n",
       "Year                                      2005\n",
       "Name: 393, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "philly.loc[393]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This is different than the `.iloc[]` function, which also indexes based on the integer value of the row, regardless of what the labels in the index are. For example, the first row of a dataframe can always be accessed using:\n",
    "\n",
    "\n",
    "For more details on the `iloc()` function, see the documentation on [indexing by position](https://pandas.pydata.org/pandas-docs/version/0.17.0/indexing.html#indexing-integer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hide_input": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City           Philadelphia city, Pennsylvania\n",
       "Total                                   565433\n",
       "Rentals                                 244792\n",
       "Rental Rate                          43.292839\n",
       "Year                                      2005\n",
       "Name: 393, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "philly.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City           Philadelphia city, Pennsylvania\n",
       "Total                                   565433\n",
       "Rentals                                 244792\n",
       "Rental Rate                          43.292839\n",
       "Year                                      2005\n",
       "Name: 393, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rental_data.loc[393]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can reset the index labels so they range from 0 to the length of the dataframe, using the `reset_index()` function. For example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>565433</td>\n",
       "      <td>244792</td>\n",
       "      <td>43.292839</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>554048</td>\n",
       "      <td>231323</td>\n",
       "      <td>41.751437</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>562384</td>\n",
       "      <td>239363</td>\n",
       "      <td>42.562199</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>578263</td>\n",
       "      <td>253084</td>\n",
       "      <td>43.766245</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>569835</td>\n",
       "      <td>258459</td>\n",
       "      <td>45.356814</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>575413</td>\n",
       "      <td>264129</td>\n",
       "      <td>45.902508</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>576429</td>\n",
       "      <td>265126</td>\n",
       "      <td>45.994563</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>579874</td>\n",
       "      <td>277323</td>\n",
       "      <td>47.824700</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>582528</td>\n",
       "      <td>285430</td>\n",
       "      <td>48.998503</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>577862</td>\n",
       "      <td>277301</td>\n",
       "      <td>47.987409</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>581604</td>\n",
       "      <td>275397</td>\n",
       "      <td>47.351291</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>580205</td>\n",
       "      <td>277707</td>\n",
       "      <td>47.863600</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>606142</td>\n",
       "      <td>309279</td>\n",
       "      <td>51.024182</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>608233</td>\n",
       "      <td>287543</td>\n",
       "      <td>47.275140</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>619505</td>\n",
       "      <td>295783</td>\n",
       "      <td>47.745055</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               City   Total  Rentals  Rental Rate  Year\n",
       "0   Philadelphia city, Pennsylvania  565433   244792    43.292839  2005\n",
       "1   Philadelphia city, Pennsylvania  554048   231323    41.751437  2006\n",
       "2   Philadelphia city, Pennsylvania  562384   239363    42.562199  2007\n",
       "3   Philadelphia city, Pennsylvania  578263   253084    43.766245  2008\n",
       "4   Philadelphia city, Pennsylvania  569835   258459    45.356814  2009\n",
       "5   Philadelphia city, Pennsylvania  575413   264129    45.902508  2010\n",
       "6   Philadelphia city, Pennsylvania  576429   265126    45.994563  2011\n",
       "7   Philadelphia city, Pennsylvania  579874   277323    47.824700  2012\n",
       "8   Philadelphia city, Pennsylvania  582528   285430    48.998503  2013\n",
       "9   Philadelphia city, Pennsylvania  577862   277301    47.987409  2014\n",
       "10  Philadelphia city, Pennsylvania  581604   275397    47.351291  2015\n",
       "11  Philadelphia city, Pennsylvania  580205   277707    47.863600  2016\n",
       "12  Philadelphia city, Pennsylvania  606142   309279    51.024182  2017\n",
       "13  Philadelphia city, Pennsylvania  608233   287543    47.275140  2018\n",
       "14  Philadelphia city, Pennsylvania  619505   295783    47.745055  2019"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "philly.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2. Select the rental rate for 2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>City</th>\n",
       "      <th>Total</th>\n",
       "      <th>Rentals</th>\n",
       "      <th>Rental Rate</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Philadelphia city, Pennsylvania</td>\n",
       "      <td>565433</td>\n",
       "      <td>244792</td>\n",
       "      <td>43.292839</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                City   Total  Rentals  Rental Rate  Year\n",
       "393  Philadelphia city, Pennsylvania  565433   244792    43.292839  2005"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select 20\n",
    "philly_2005 = philly.loc[philly['Year']==2005]\n",
    "\n",
    "\n",
    "philly_2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "393    43.292839\n",
       "Name: Rental Rate, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice b/c we have a dataframe with length of 1, we don't get back just a number\n",
    "philly_2005[\"Rental Rate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.292839293072745"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "philly_2005['Rental Rate'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(philly_2005[\"Rental Rate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## An aside: the squeeze() function\n",
    "\n",
    "Notice that `philly_2005` has only a single row. It's more useful if we had this a scalar (just a single number)\n",
    "\n",
    "We can use the `.squeeze()` function to do this. It does just one it sounds like: if you have a DataFrame with only one row, it will \"squeeze\" the row dimension by removing it, returning just a Series object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City           Philadelphia city, Pennsylvania\n",
       "Total                                   565433\n",
       "Rentals                                 244792\n",
       "Rental Rate                          43.292839\n",
       "Year                                      2005\n",
       "Name: 393, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "philly_2005 = philly.loc[philly['Year']==2005].squeeze()\n",
    "\n",
    "philly_2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.292839293072745"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_2005 = philly_2005[\"Rental Rate\"]\n",
    "\n",
    "value_2005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 3. Select the rental rate for 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.74505451933398"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do the same thing for 2019 — make sure you squeeze!\n",
    "philly_2019 = philly.loc[philly['Year']==2019].squeeze()\n",
    "\n",
    "# Get the value\n",
    "value_2019 = philly_2019['Rental Rate']\n",
    "\n",
    "value_2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "###  4. Calculate the percent change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [],
   "source": [
    "# calculate percent change\n",
    "percent_change = (value_2019 - value_2005)/ value_2005 * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hide_input": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "change in rental rate in Philadelphia from 2005 to 2019 = 10.283952956103837\n"
     ]
    }
   ],
   "source": [
    "print(f\"change in rental rate in Philadelphia from 2005 to 2019 = {percent_change}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### To be continued next time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## First Homework Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Available on GitHub: \n",
    "\n",
    "https://github.com/MUSA-550-Fall-2022/assignment-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Two parts: \n",
    "\n",
    "1. Download and install Python locally on your computer. Instructions in the assignment README!\n",
    "1. Analyze Zillow ZHVI data by ZIP code in Philadelphia, submitting your Jupyter notebook.\n",
    "\n",
    "**Due date: Monday 9/19 by the start of class (7pm)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# That's it!\n",
    "\n",
    "- Next week: Data Visualization Fundamentals\n",
    "- Office hours:\n",
    "    - Nick: Saturdays, 10AM-12PM\n",
    "    - Kristin: Tuesday/Thursday 11AM-12PM, remote via Zoom\n",
    "    - Sign up for 15-minute time slots on Canvas (Zoom info in calendar invite)\n",
    "- Post questions on Piazza!\n",
    "- Email questions/concerns to nhand@design.upenn.edu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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