pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5fCEDCU_qrC0"
   },
   "source": [
    "# Pandas\n",
    "\n",
    "pandas is a software package written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating tabular data and time series. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rMExucF44s_c"
   },
   "source": [
    "## Learning objective\n",
    "\n",
    "Learn how to use pandas to work through the steps social scientists often take to process and analyze raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GJBs_flRovLc"
   },
   "source": [
    "## 1. Import pandas\n",
    "\n",
    "To begin, let's look at a few ways to import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "gJr_9dXGpJ05"
   },
   "outputs": [],
   "source": [
    "# import specific classes and functions: useful if you know you'll only be needing the most common parts of pandas\n",
    "from pandas import DataFrame, read_csv\n",
    "\n",
    "# General syntax to import a package but no functions: \n",
    "#   import (package) as (give the package a nickname/alias)\n",
    "# Most pandas users will import the entire package and give it the short nickname \"pd\" as follows:\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iUTpAqJ6ykG4"
   },
   "source": [
    "## 2. Data example\n",
    "\n",
    "As an example, we'll be working with a dataset containing responses from a survey. The survey specifically asked the following questions:\n",
    "\n",
    "TODO get question text from Sam\n",
    "\n",
    "Now let's take a look at how participants' responses are represented in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9c2d00fn3zDE"
   },
   "source": [
    "## 3. Import csv data\n",
    "\n",
    "As mentioned in the introduction, pandas is designed to work with tabular data. Tabluar data is most commonly provided to researchers in a simple spreadsheet format known as csv, which stands for *comma separated values*. Because of this, pandas has a function to load tabular data from a csv file:  `read_csv`. The most basic way to use the `read_csv` function is to simply give it the path to the csv file you want to load. Here, we will load the example survey data, which is contained in the file \"survey_responses.csv\" in our GitHub repo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "7X7PRKhX4kJV"
   },
   "outputs": [],
   "source": [
    "# The following path is valid for our Binder setup. If you are running this notebook in your local machine instead and have the file downloaded to a different location, you may need to change this path.\n",
    "Location = './data/survey_responses.csv'\n",
    "df = pd.read_csv(Location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oRsvGySgjl6H"
   },
   "source": [
    "`read_csv` loads the data into a structure known as a **DataFrame**, as we can see if we check the type of the variable `df` that we created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "8ZmhPaCsjl6I"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cE0uTGw6jl6J"
   },
   "source": [
    "Think of a **DataFrame** as a table or spreadsheet that you can work with inside your Python code! Like any table, it contains data organized into rows and columns.\n",
    "\n",
    "Now you might be wondering what the data actually looks like. The best way to find out is to use the `display` function, which will show a sample of data from the DataFrame. This is always the first thing you should do after loading a new dataset in pandas!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "NGGMN_Ti28h3"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Time_Stamp</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Response_ID</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parental_Income</th>\n",
       "      <th>Race_Ethnicity</th>\n",
       "      <th>School_Year</th>\n",
       "      <th>Transfer_Student</th>\n",
       "      <th>College</th>\n",
       "      <th>Gender_Identity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Living_Location</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/15/2021 11:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Brooklyn, New York 11225</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/2021 13:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Roslyn Heights, New York 11577</td>\n",
       "      <td>21.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Agriculture and Life Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:31</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>San Antonio, Texas 78218</td>\n",
       "      <td>21.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Hispanic</td>\n",
       "      <td>4th year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>5/19/2022 17:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>895</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Boca Raton, Florida 33496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent),Hispanic,Middle Ea...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>5/19/2022 19:26</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>896</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Harrison, New York, 10528</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>897</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/19/2022 17:22</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>5/19/2022 8:42</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "          Time_Stamp  Year  Month  Response_ID Hunger_Experience Moving_Times  \\\n",
       "0    4/14/2021 19:43  2021  April            1                No            1   \n",
       "1    4/15/2021 11:30  2021  April            2                No            1   \n",
       "2    4/15/2021 13:00  2021  April            3                No            5   \n",
       "3    4/15/2021 14:01  2021  April            4                No            1   \n",
       "4    4/15/2021 14:31  2021  April            5                No            0   \n",
       "..               ...   ...    ...          ...               ...          ...   \n",
       "894  5/19/2022 17:17  2022    May          895                No            2   \n",
       "895  5/19/2022 19:26  2022    May          896               Yes            4   \n",
       "896   5/20/2022 7:13  2022    May          897                No            1   \n",
       "897  5/19/2022 17:22  2022    May          898               NaN          NaN   \n",
       "898   5/19/2022 8:42  2022    May          899               NaN          NaN   \n",
       "\n",
       "    Self_Confidence                            Hometown   Age  \\\n",
       "0    Agree slightly        Coconut Grove, Florida 33133  20.0   \n",
       "1    Agree slightly            Brooklyn, New York 11225  20.0   \n",
       "2    Agree somewhat      Roslyn Heights, New York 11577  21.0   \n",
       "3    Agree slightly           Rye Brook, New York 10573  19.0   \n",
       "4    Agree somewhat            San Antonio, Texas 78218  21.0   \n",
       "..              ...                                 ...   ...   \n",
       "894  Agree somewhat           Boca Raton, Florida 33496  19.0   \n",
       "895  Agree somewhat           Harrison, New York, 10528  20.0   \n",
       "896  Agree slightly  Guangzhou, Guangdong, China 510335  19.0   \n",
       "897             NaN                                 NaN   NaN   \n",
       "898             NaN                                 NaN   NaN   \n",
       "\n",
       "         Parental_Income                                     Race_Ethnicity  \\\n",
       "0     More than $750,000                        White (of European descent)   \n",
       "1          Below $40,000                                              Black   \n",
       "2     More than $750,000                                              Asian   \n",
       "3     More than $750,000                        White (of European descent)   \n",
       "4    $100,000 - $174,999               White (of European descent),Hispanic   \n",
       "..                   ...                                                ...   \n",
       "894  $175,000 - $299,999  White (of European descent),Hispanic,Middle Ea...   \n",
       "895  $175,000 - $299,999                                           Hispanic   \n",
       "896  $300,000 - $499,999                                              Asian   \n",
       "897                  NaN                                                NaN   \n",
       "898                  NaN                                                NaN   \n",
       "\n",
       "                School_Year Transfer_Student  \\\n",
       "0    2nd year undergraduate               No   \n",
       "1    3rd year undergraduate               No   \n",
       "2    3rd year undergraduate               No   \n",
       "3    2nd year undergraduate               No   \n",
       "4    4th year undergraduate               No   \n",
       "..                      ...              ...   \n",
       "894  1st year undergraduate               No   \n",
       "895  2nd year undergraduate              Yes   \n",
       "896  1st year undergraduate               No   \n",
       "897                     NaN              NaN   \n",
       "898                     NaN              NaN   \n",
       "\n",
       "                                      College Gender_Identity     Sex  \\\n",
       "0                    College of Human Ecology           Woman  Female   \n",
       "1                    College of Human Ecology           Woman  Female   \n",
       "2    College of Agriculture and Life Sciences           Woman  Female   \n",
       "3                    College of Human Ecology           Woman  Female   \n",
       "4                College of Arts and Sciences           Woman  Female   \n",
       "..                                        ...             ...     ...   \n",
       "894              College of Arts and Sciences           Woman  Female   \n",
       "895                  College of Human Ecology           Woman  Female   \n",
       "896                  College of Human Ecology           Woman  Female   \n",
       "897                                       NaN             NaN     NaN   \n",
       "898                                       NaN             NaN     NaN   \n",
       "\n",
       "    Living_Location  Unnamed: 17  \n",
       "0        Off campus          NaN  \n",
       "1        Off campus          NaN  \n",
       "2        Off campus          NaN  \n",
       "3         On campus          NaN  \n",
       "4        Off campus          NaN  \n",
       "..              ...          ...  \n",
       "894       On campus          NaN  \n",
       "895       On campus          NaN  \n",
       "896       On campus          NaN  \n",
       "897             NaN          NaN  \n",
       "898             NaN          NaN  \n",
       "\n",
       "[899 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iwv6zshx5ylG"
   },
   "source": [
    "Notice that the top row and the leftmost column are special. The top row is bolded and contains text that looks different from the rest of the table; this is because it is the *header* of the DataFrame. The header does not contain real data; instead, it contains the names of each column. Meanwhile, the leftmost column does not have a name and simply contains consecutive numbers [0,1,2,3,4,...]. This is the *index* of the DataFrame; by default this works likes row numbers in an Excel file, but more generally you can think of the index as containing the name of each row. This index is also similar to the primary key of a sql table with the exception that an index is allowed to have duplicates.\n",
    "\n",
    "You might be wondering where the header and index came from. In pandas, the default behavior is to do the following:\n",
    "- Treat the first row of the CSV file as the header\n",
    "- Create an index from scratch containing consecutive numbers\n",
    "\n",
    "But this behavior is customizable via parameters in `read_csv`, and you may need to customize it depending on what your CSV file looks like. In this case, you might have noticed that the file already contains a column that looks like an index (\"Response_ID\"), which means that pandas' automatically-generated index is redundant. Therefore, we might want to tell pandas to use the \"Response_ID\" column as the index. We can do this using the `index_col` parameter of `read_csv`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(Location, index_col=\"Response_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time_Stamp</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parental_Income</th>\n",
       "      <th>Race_Ethnicity</th>\n",
       "      <th>School_Year</th>\n",
       "      <th>Transfer_Student</th>\n",
       "      <th>College</th>\n",
       "      <th>Gender_Identity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Living_Location</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/2021 11:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Brooklyn, New York 11225</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/2021 13:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Roslyn Heights, New York 11577</td>\n",
       "      <td>21.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Agriculture and Life Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4/15/2021 14:31</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>San Antonio, Texas 78218</td>\n",
       "      <td>21.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Hispanic</td>\n",
       "      <td>4th year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>5/19/2022 17:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Boca Raton, Florida 33496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent),Hispanic,Middle Ea...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>5/19/2022 19:26</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Harrison, New York, 10528</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>5/19/2022 17:22</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>5/19/2022 8:42</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience Moving_Times  \\\n",
       "Response_ID                                                                \n",
       "1            4/14/2021 19:43  2021  April                No            1   \n",
       "2            4/15/2021 11:30  2021  April                No            1   \n",
       "3            4/15/2021 13:00  2021  April                No            5   \n",
       "4            4/15/2021 14:01  2021  April                No            1   \n",
       "5            4/15/2021 14:31  2021  April                No            0   \n",
       "...                      ...   ...    ...               ...          ...   \n",
       "895          5/19/2022 17:17  2022    May                No            2   \n",
       "896          5/19/2022 19:26  2022    May               Yes            4   \n",
       "897           5/20/2022 7:13  2022    May                No            1   \n",
       "898          5/19/2022 17:22  2022    May               NaN          NaN   \n",
       "899           5/19/2022 8:42  2022    May               NaN          NaN   \n",
       "\n",
       "            Self_Confidence                            Hometown   Age  \\\n",
       "Response_ID                                                             \n",
       "1            Agree slightly        Coconut Grove, Florida 33133  20.0   \n",
       "2            Agree slightly            Brooklyn, New York 11225  20.0   \n",
       "3            Agree somewhat      Roslyn Heights, New York 11577  21.0   \n",
       "4            Agree slightly           Rye Brook, New York 10573  19.0   \n",
       "5            Agree somewhat            San Antonio, Texas 78218  21.0   \n",
       "...                     ...                                 ...   ...   \n",
       "895          Agree somewhat           Boca Raton, Florida 33496  19.0   \n",
       "896          Agree somewhat           Harrison, New York, 10528  20.0   \n",
       "897          Agree slightly  Guangzhou, Guangdong, China 510335  19.0   \n",
       "898                     NaN                                 NaN   NaN   \n",
       "899                     NaN                                 NaN   NaN   \n",
       "\n",
       "                 Parental_Income  \\\n",
       "Response_ID                        \n",
       "1             More than $750,000   \n",
       "2                  Below $40,000   \n",
       "3             More than $750,000   \n",
       "4             More than $750,000   \n",
       "5            $100,000 - $174,999   \n",
       "...                          ...   \n",
       "895          $175,000 - $299,999   \n",
       "896          $175,000 - $299,999   \n",
       "897          $300,000 - $499,999   \n",
       "898                          NaN   \n",
       "899                          NaN   \n",
       "\n",
       "                                                Race_Ethnicity  \\\n",
       "Response_ID                                                      \n",
       "1                                  White (of European descent)   \n",
       "2                                                        Black   \n",
       "3                                                        Asian   \n",
       "4                                  White (of European descent)   \n",
       "5                         White (of European descent),Hispanic   \n",
       "...                                                        ...   \n",
       "895          White (of European descent),Hispanic,Middle Ea...   \n",
       "896                                                   Hispanic   \n",
       "897                                                      Asian   \n",
       "898                                                        NaN   \n",
       "899                                                        NaN   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "1            2nd year undergraduate               No   \n",
       "2            3rd year undergraduate               No   \n",
       "3            3rd year undergraduate               No   \n",
       "4            2nd year undergraduate               No   \n",
       "5            4th year undergraduate               No   \n",
       "...                             ...              ...   \n",
       "895          1st year undergraduate               No   \n",
       "896          2nd year undergraduate              Yes   \n",
       "897          1st year undergraduate               No   \n",
       "898                             NaN              NaN   \n",
       "899                             NaN              NaN   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "1                            College of Human Ecology           Woman  Female   \n",
       "2                            College of Human Ecology           Woman  Female   \n",
       "3            College of Agriculture and Life Sciences           Woman  Female   \n",
       "4                            College of Human Ecology           Woman  Female   \n",
       "5                        College of Arts and Sciences           Woman  Female   \n",
       "...                                               ...             ...     ...   \n",
       "895                      College of Arts and Sciences           Woman  Female   \n",
       "896                          College of Human Ecology           Woman  Female   \n",
       "897                          College of Human Ecology           Woman  Female   \n",
       "898                                               NaN             NaN     NaN   \n",
       "899                                               NaN             NaN     NaN   \n",
       "\n",
       "            Living_Location  Unnamed: 17  \n",
       "Response_ID                               \n",
       "1                Off campus          NaN  \n",
       "2                Off campus          NaN  \n",
       "3                Off campus          NaN  \n",
       "4                 On campus          NaN  \n",
       "5                Off campus          NaN  \n",
       "...                     ...          ...  \n",
       "895               On campus          NaN  \n",
       "896               On campus          NaN  \n",
       "897               On campus          NaN  \n",
       "898                     NaN          NaN  \n",
       "899                     NaN          NaN  \n",
       "\n",
       "[899 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that pandas is no longer generating a redundant index column; instead, \"Response_ID\" is being treated as the index, as evidenced by the fact that it has been moved to the left and bolded.\n",
    "\n",
    "This was just one example of how you might need to adjust your usage of `read_csv` depending on what's inside your file. There are other common scenarios, for example some CSV files don't have a header. We won't go into that in detail here, but again, remember you can use the `help` function to see what parameters are available to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0Qfk96MX10T0"
   },
   "outputs": [],
   "source": [
    "help(read_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One last thing we want to point out before we move on: notice that in some cells in the table contain the strange-looking data \"NaN\". NaN is short for \"Not A Number\" and is pandas' way of representing *missing data*. Think of NaNs as equivalent to an empty cell in an Excel file. The meaning of missing data depends on the context of the dataset. In this case, since this data is from a survey and each column represents a survey question, we can infer that in this case missing data means that the survey taker did not give an answer for that question. In research, we often want to do something special to handle missing data; we will return to this later in the workshop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OwuxHmxllTwN"
   },
   "source": [
    "## 4. Checking the structure of the data\n",
    "Usually we first do a couple of sanity checks to make sure the data we imported is in good shape and understand its high level structure. \n",
    "\n",
    "Usually we make sure the data type of each variable is sensible, check the total number of observations, and the number of variables in the data set.\n",
    "\n",
    "Let's take a look at the example data set. First, we'll check data types using the `dtypes` variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "yCC64Uqy6nAY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_Stamp            object\n",
       "Year                   int64\n",
       "Month                 object\n",
       "Hunger_Experience     object\n",
       "Moving_Times          object\n",
       "Self_Confidence       object\n",
       "Hometown              object\n",
       "Age                  float64\n",
       "Parental_Income       object\n",
       "Race_Ethnicity        object\n",
       "School_Year           object\n",
       "Transfer_Student      object\n",
       "College               object\n",
       "Gender_Identity       object\n",
       "Sex                   object\n",
       "Living_Location       object\n",
       "Unnamed: 17          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data type of the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NmbZVWO2BSLU"
   },
   "source": [
    "Next we'll check the number of observations (rows) in the dataset. This can be done by examining the length of the DataFrame's *index*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "QIoJ_maABVOg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "899"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IWQpJOKXBkCm"
   },
   "source": [
    "Similarly, we can check the number of variables (columns) in the dataset by examining the length of the DataFrame's *columns* list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "iwu71C2hBmVG"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g0z8mSwz6ZvH"
   },
   "source": [
    "## 5. Slicing and Indexing\n",
    "Usually, we will not be working with an entire data set all at the same time. Instead, we usually want to pick out specific rows or columns to analyze and work with. In pandas, this can be done using *slicing and indexing*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SwTB_xAcjl6N"
   },
   "source": [
    "The most basic indexing operation is selecting a specific column. This can be done using standard Python indexing syntax (square brackets) and giving it the name of the column you want. For example, the following code will select the \"Age\" column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Qr7i1ojWjl6N"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Response_ID\n",
       "1      20.0\n",
       "2      20.0\n",
       "3      21.0\n",
       "4      19.0\n",
       "5      21.0\n",
       "       ... \n",
       "895    19.0\n",
       "896    20.0\n",
       "897    19.0\n",
       "898     NaN\n",
       "899     NaN\n",
       "Name: Age, Length: 899, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_column = df['Age']\n",
    "display(age_column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bNOAR5qfjl6N"
   },
   "source": [
    "The syntax for selecting a row is similar, except that you need to tell pandas that you are trying to access a row, since indexing defaults to columns. To do this, use the `loc` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "w0IFERlnjl6O"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_Stamp                                4/15/2021 14:31\n",
       "Year                                                 2021\n",
       "Month                                               April\n",
       "Hunger_Experience                                      No\n",
       "Moving_Times                                            0\n",
       "Self_Confidence                            Agree somewhat\n",
       "Hometown                         San Antonio, Texas 78218\n",
       "Age                                                  21.0\n",
       "Parental_Income                       $100,000 - $174,999\n",
       "Race_Ethnicity       White (of European descent),Hispanic\n",
       "School_Year                        4th year undergraduate\n",
       "Transfer_Student                                       No\n",
       "College                      College of Arts and Sciences\n",
       "Gender_Identity                                     Woman\n",
       "Sex                                                Female\n",
       "Living_Location                                Off campus\n",
       "Unnamed: 17                                           NaN\n",
       "Name: 5, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fifth_row = df.loc[5]\n",
    "display(fifth_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hFNKSwHOjl6O"
   },
   "source": [
    "Both row indexing and column indexing allow you to select multiple rows or columns at once. To do this, you can provide a **list** of row or column names, instead of just one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "WvDmj7y6HwJi"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Living_Location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Off campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>Off campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>Off campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>On campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.0</td>\n",
       "      <td>Off campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>19.0</td>\n",
       "      <td>On campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>20.0</td>\n",
       "      <td>On campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>19.0</td>\n",
       "      <td>On campus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Living_Location\n",
       "Response_ID                      \n",
       "1            20.0      Off campus\n",
       "2            20.0      Off campus\n",
       "3            21.0      Off campus\n",
       "4            19.0       On campus\n",
       "5            21.0      Off campus\n",
       "...           ...             ...\n",
       "895          19.0       On campus\n",
       "896          20.0       On campus\n",
       "897          19.0       On campus\n",
       "898           NaN             NaN\n",
       "899           NaN             NaN\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_columns = df[['Age', 'Living_Location']]\n",
    "display(multi_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "m_aQBFI3jl6O"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time_Stamp</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
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       "      <th>Unnamed: 17</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/2021 11:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Brooklyn, New York 11225</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/2021 13:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Roslyn Heights, New York 11577</td>\n",
       "      <td>21.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Agriculture and Life Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience Moving_Times  \\\n",
       "Response_ID                                                                \n",
       "1            4/14/2021 19:43  2021  April                No            1   \n",
       "2            4/15/2021 11:30  2021  April                No            1   \n",
       "3            4/15/2021 13:00  2021  April                No            5   \n",
       "4            4/15/2021 14:01  2021  April                No            1   \n",
       "\n",
       "            Self_Confidence                        Hometown   Age  \\\n",
       "Response_ID                                                         \n",
       "1            Agree slightly    Coconut Grove, Florida 33133  20.0   \n",
       "2            Agree slightly        Brooklyn, New York 11225  20.0   \n",
       "3            Agree somewhat  Roslyn Heights, New York 11577  21.0   \n",
       "4            Agree slightly       Rye Brook, New York 10573  19.0   \n",
       "\n",
       "                Parental_Income               Race_Ethnicity  \\\n",
       "Response_ID                                                    \n",
       "1            More than $750,000  White (of European descent)   \n",
       "2                 Below $40,000                        Black   \n",
       "3            More than $750,000                        Asian   \n",
       "4            More than $750,000  White (of European descent)   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "1            2nd year undergraduate               No   \n",
       "2            3rd year undergraduate               No   \n",
       "3            3rd year undergraduate               No   \n",
       "4            2nd year undergraduate               No   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "1                            College of Human Ecology           Woman  Female   \n",
       "2                            College of Human Ecology           Woman  Female   \n",
       "3            College of Agriculture and Life Sciences           Woman  Female   \n",
       "4                            College of Human Ecology           Woman  Female   \n",
       "\n",
       "            Living_Location  Unnamed: 17  \n",
       "Response_ID                               \n",
       "1                Off campus          NaN  \n",
       "2                Off campus          NaN  \n",
       "3                Off campus          NaN  \n",
       "4                 On campus          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_rows = df.loc[[1,2,3,4]]\n",
    "display(multi_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yAraQcG2jl6O"
   },
   "source": [
    "If a DataFrame's index is numerical (as it is both in our case and in the default settings), Python's **slicing** syntax can also be used to select a range of rows. This can simplify your code a lot because you can specify a range rather than writing out full list of indices! For example, the following code is equivalent to the previous code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "y92xDXcYjl6P"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parental_Income</th>\n",
       "      <th>Race_Ethnicity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/2021 11:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Brooklyn, New York 11225</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/2021 13:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Roslyn Heights, New York 11577</td>\n",
       "      <td>21.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Agriculture and Life Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience Moving_Times  \\\n",
       "Response_ID                                                                \n",
       "1            4/14/2021 19:43  2021  April                No            1   \n",
       "2            4/15/2021 11:30  2021  April                No            1   \n",
       "3            4/15/2021 13:00  2021  April                No            5   \n",
       "4            4/15/2021 14:01  2021  April                No            1   \n",
       "\n",
       "            Self_Confidence                        Hometown   Age  \\\n",
       "Response_ID                                                         \n",
       "1            Agree slightly    Coconut Grove, Florida 33133  20.0   \n",
       "2            Agree slightly        Brooklyn, New York 11225  20.0   \n",
       "3            Agree somewhat  Roslyn Heights, New York 11577  21.0   \n",
       "4            Agree slightly       Rye Brook, New York 10573  19.0   \n",
       "\n",
       "                Parental_Income               Race_Ethnicity  \\\n",
       "Response_ID                                                    \n",
       "1            More than $750,000  White (of European descent)   \n",
       "2                 Below $40,000                        Black   \n",
       "3            More than $750,000                        Asian   \n",
       "4            More than $750,000  White (of European descent)   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "1            2nd year undergraduate               No   \n",
       "2            3rd year undergraduate               No   \n",
       "3            3rd year undergraduate               No   \n",
       "4            2nd year undergraduate               No   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "1                            College of Human Ecology           Woman  Female   \n",
       "2                            College of Human Ecology           Woman  Female   \n",
       "3            College of Agriculture and Life Sciences           Woman  Female   \n",
       "4                            College of Human Ecology           Woman  Female   \n",
       "\n",
       "            Living_Location  Unnamed: 17  \n",
       "Response_ID                               \n",
       "1                Off campus          NaN  \n",
       "2                Off campus          NaN  \n",
       "3                Off campus          NaN  \n",
       "4                 On campus          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_rows = df.loc[1:4] # important difference from regular python: pandas slices include *both* indices!\n",
    "display(multi_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2NYv4_YSKO4f"
   },
   "source": [
    "Additionally, we may sometimes want to pick out a **single** value from the DataFrame. For example, suppose we want to find the Age in the fifth row. We can do this by giving both a row and column to the `loc` indexer. Note that the row and column **must** be given in that order (you cannot give the column first) and should be separated by a comma, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "iSrJcPshKg1N"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.0\n"
     ]
    }
   ],
   "source": [
    "fifth_age = df.loc[5,\"Age\"]\n",
    "print(fifth_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xiAc_0QGjl6P"
   },
   "source": [
    "Finally, the same syntax can be used to select multiple rows **and** columns at the same time; you simply need to provide a list or range of rows, followed by a list of column names, separated by a comma.\n",
    "\n",
    "Let's get the first 4 rows, for columns 'Age' and 'Moving_Times':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "4Y3Ywvp8jl6Q"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Moving_Times</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Moving_Times\n",
       "Response_ID                   \n",
       "1            20.0            1\n",
       "2            20.0            1\n",
       "3            21.0            5\n",
       "4            19.0            1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_row_and_col = df.loc[1:4,[\"Age\", \"Moving_Times\"]]\n",
    "display(multi_row_and_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqhRO0k3BvM2"
   },
   "source": [
    "## Exercise 1\n",
    "\n",
    "Write code to fetch the following data:\n",
    "1. The Hometown in the 10th row\n",
    "2. The Parental_Income and Race_Ethnicity for rows 100 through 110\n",
    "3. The School_Year in rows 8, 42, 47, and 99\n",
    "\n",
    "(Note: each problem should be solved with a *single* indexing operation; for example, you should not need 4 separate indexing operations to answer question 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qAnp6yhjCL68"
   },
   "outputs": [],
   "source": [
    "# Write your answers here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6lBVwk2sjl6Q"
   },
   "source": [
    "## 6. Summarizing column data\n",
    "Previously, we sanity checked our data by checking its structure (data types and number of rows and columns). A second common step in preliminary exploration of the data is to examine individual columns (with the help of indexing, as covered previously). pandas can help with this process by offering several methods to *summarize* the data in a column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MdRkt4Prjl6Q"
   },
   "source": [
    "### Summarizing numerical data\n",
    "Some columns contain numerical data. For example, ages in the \"Age\" column are represented as numbers. For numerical data, pandas offers a number of methods that implement common mathematical summary functions, such as finding the mininum, maximum, and arithmetic mean. We can use these, for instance, to find out who the youngest and oldest participants in the survey were, and what the average age was:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "M__x7ovSjl6R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.0\n",
      "36.0\n",
      "19.84585492227979\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Age\"].min()) # what's the age of the youngest participant?\n",
    "print(df[\"Age\"].max()) # what's the age of the oldest participant?\n",
    "print(df[\"Age\"].mean()) # what's the average age of participants?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_JT9EoIUjl6R"
   },
   "source": [
    "### Summarizing categorical data\n",
    "Mathematical operations like min and max are useful for quickly summarizing numerical data. But not all columns are numerical. For example, the \"College\" column contains text. But notice that the text in the \"College\" column is not just any arbitrary text! It only has specific, fixed values, because in the original survey this was a multiple-choice question. Therefore, \"College\" is what data scientists refer to as a *categorical variable*: one that can only take on values from a fixed, finite set of possibilities. To summarize categorical data, researchers generally want to know what is the set of values the variable can take. In pandas, this can be done using the `unique` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "Nib9a0S_jl6R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['College of Human Ecology' 'College of Agriculture and Life Sciences'\n",
      " 'College of Arts and Sciences' 'College of Engineering'\n",
      " 'School of Industrial and Labor Relations'\n",
      " 'SC Johnson College of Business' nan\n",
      " 'College of Architecture, Art and Planning' 'Weill Cornell Medicine']\n"
     ]
    }
   ],
   "source": [
    "print(df[\"College\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jRw50ceO_b_k"
   },
   "source": [
    "## Exercise 2\n",
    "\n",
    "- What are the possible values for \"Self_Confidence\" and \"Parental_Income\"?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IsL_ZU9BAiSY"
   },
   "outputs": [],
   "source": [
    "print(____________) # fill in your code for Self_Confidence here\n",
    "print(____________) # fill in your code for Parental_Income here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "While the `unique` method is most commonly used to look at categorical text data, it can actually be applied to any data, even numerical data! One possible reason you might want to use the `unique` method on numerical data is if you have integer data representing a count or other finite data (for example, Moving_Times and Age in our data) and want to see what unique values there are. Let's try running `unique` on the Moving_Times column. How many unique values are there? Do you notice anything here that looks like a problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in your code below\n",
    "print(____________)\n",
    "# Looking at the printed output, do you notice any potential problems in the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vIQI7zsSC2FF"
   },
   "source": [
    "## 7. Querying using conditions based on column values\n",
    "\n",
    "Now we can start looking at more advanced data analysis. Previously, we selected subsets of rows by giving the `loc` indexer a list or range of row indices. Most often, however, we don't know ahead of time which rows we want to analyze; instead, we more often want to select rows based on some conditions. For example, if we were interested in studying how undergraduate freshmen specifically answered our survey, we would want to select only the rows where \"School_Year\" is \"1st year undergraduate\". To achieve this, the `loc` indexer can actually be used with conditions, not just with indices! \n",
    "\n",
    "This is best explained through examples. Let's start with the example just discussed, selecting only rows representing undergraduate freshmen. The syntax for the comparison would be `df['School_Year'] == \"1st year undergraduate`, and we can pass this comparison directly to `loc`, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "IHWjCNENBH43"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Time_Stamp</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parental_Income</th>\n",
       "      <th>Race_Ethnicity</th>\n",
       "      <th>School_Year</th>\n",
       "      <th>Transfer_Student</th>\n",
       "      <th>College</th>\n",
       "      <th>Gender_Identity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Living_Location</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4/15/2021 14:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree strongly</td>\n",
       "      <td>Shanghai (China) 200127</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Engineering</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4/16/2021 10:58</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Horseheads, NY 14845</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4/22/2021 14:16</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Horseheads, New York 14845</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4/22/2021 23:45</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree strongly</td>\n",
       "      <td>Sinamangal, Kathmandu</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4/25/2021 13:03</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>St. George, Utah 84790</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>5/7/2022 19:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Bainbridge Island, Washington 98110</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>American Indian or Alaskan Native,Asian,Pacifi...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>School of Industrial and Labor Relations</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>5/9/2022 21:10</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Los Angeles, California 90066</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>White (of European descent),Middle Eastern</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>5/16/2022 22:07</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Albany, New York, 12202</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$60,000 - $99,999</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>5/19/2022 17:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Boca Raton, Florida 33496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent),Hispanic,Middle Ea...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience Moving_Times  \\\n",
       "Response_ID                                                                \n",
       "6            4/15/2021 14:30  2021  April                No            2   \n",
       "14           4/16/2021 10:58  2021  April                No            1   \n",
       "26           4/22/2021 14:16  2021  April                No            1   \n",
       "30           4/22/2021 23:45  2021  April                No            0   \n",
       "42           4/25/2021 13:03  2021  April                No            0   \n",
       "...                      ...   ...    ...               ...          ...   \n",
       "873           5/7/2022 19:17  2022    May                No            1   \n",
       "879           5/9/2022 21:10  2022    May                No            1   \n",
       "890          5/16/2022 22:07  2022    May                No            5   \n",
       "895          5/19/2022 17:17  2022    May                No            2   \n",
       "897           5/20/2022 7:13  2022    May                No            1   \n",
       "\n",
       "                         Self_Confidence                             Hometown  \\\n",
       "Response_ID                                                                     \n",
       "6                         Agree strongly              Shanghai (China) 200127   \n",
       "14                     Disagree slightly                 Horseheads, NY 14845   \n",
       "26           Don't know / not applicable           Horseheads, New York 14845   \n",
       "30                        Agree strongly                Sinamangal, Kathmandu   \n",
       "42           Don't know / not applicable               St. George, Utah 84790   \n",
       "...                                  ...                                  ...   \n",
       "873                       Agree slightly  Bainbridge Island, Washington 98110   \n",
       "879          Don't know / not applicable        Los Angeles, California 90066   \n",
       "890                       Agree somewhat              Albany, New York, 12202   \n",
       "895                       Agree somewhat            Boca Raton, Florida 33496   \n",
       "897                       Agree slightly   Guangzhou, Guangdong, China 510335   \n",
       "\n",
       "              Age      Parental_Income  \\\n",
       "Response_ID                              \n",
       "6            18.0  $175,000 - $299,999   \n",
       "14           19.0  $100,000 - $174,999   \n",
       "26           19.0  $100,000 - $174,999   \n",
       "30           19.0        Below $40,000   \n",
       "42           18.0  $100,000 - $174,999   \n",
       "...           ...                  ...   \n",
       "873          19.0  $300,000 - $499,999   \n",
       "879          18.0  $500,000 - $749,999   \n",
       "890          18.0    $60,000 - $99,999   \n",
       "895          19.0  $175,000 - $299,999   \n",
       "897          19.0  $300,000 - $499,999   \n",
       "\n",
       "                                                Race_Ethnicity  \\\n",
       "Response_ID                                                      \n",
       "6                                                        Asian   \n",
       "14                           White (of European descent),Black   \n",
       "26                           White (of European descent),Black   \n",
       "30                                                       Asian   \n",
       "42                                 White (of European descent)   \n",
       "...                                                        ...   \n",
       "873          American Indian or Alaskan Native,Asian,Pacifi...   \n",
       "879                 White (of European descent),Middle Eastern   \n",
       "890                                                      Black   \n",
       "895          White (of European descent),Hispanic,Middle Ea...   \n",
       "897                                                      Asian   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "6            1st year undergraduate               No   \n",
       "14           1st year undergraduate               No   \n",
       "26           1st year undergraduate               No   \n",
       "30           1st year undergraduate               No   \n",
       "42           1st year undergraduate               No   \n",
       "...                             ...              ...   \n",
       "873          1st year undergraduate               No   \n",
       "879          1st year undergraduate               No   \n",
       "890          1st year undergraduate               No   \n",
       "895          1st year undergraduate               No   \n",
       "897          1st year undergraduate               No   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "6                              College of Engineering           Woman  Female   \n",
       "14                           College of Human Ecology           Woman  Female   \n",
       "26                           College of Human Ecology           Woman  Female   \n",
       "30                       College of Arts and Sciences             Man    Male   \n",
       "42                           College of Human Ecology           Woman  Female   \n",
       "...                                               ...             ...     ...   \n",
       "873          School of Industrial and Labor Relations             Man    Male   \n",
       "879                      College of Arts and Sciences             Man    Male   \n",
       "890                      College of Arts and Sciences             Man    Male   \n",
       "895                      College of Arts and Sciences           Woman  Female   \n",
       "897                          College of Human Ecology           Woman  Female   \n",
       "\n",
       "            Living_Location  Unnamed: 17  \n",
       "Response_ID                               \n",
       "6                Off campus          NaN  \n",
       "14                On campus          NaN  \n",
       "26                On campus          NaN  \n",
       "30               Off campus          NaN  \n",
       "42                On campus          NaN  \n",
       "...                     ...          ...  \n",
       "873               On campus          NaN  \n",
       "879               On campus          NaN  \n",
       "890               On campus          NaN  \n",
       "895               On campus          NaN  \n",
       "897               On campus          NaN  \n",
       "\n",
       "[185 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freshmen = df.loc[df['School_Year'] == \"1st year undergraduate\"]\n",
    "display(freshmen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pW5ot28Fjl6S"
   },
   "source": [
    "Notice that this syntax is similar to the filtering syntax used by numpy (as seen in the previous Python workshop) and by R. Alternatively, if you are familiar with SQL, you can think of the above code as being equivalent to the SQL query `select * from df where School_Year=\"1st year undergraduate\"`.\n",
    "\n",
    "Like in numpy, you can include multiple comparisons in a single query, combining them using operators `&` for \"and\" and `|` for \"or\". So, for example, if we wanted only responses from freshmen *or* sophomores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "B4xk3-AJDAPA"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Time_Stamp</th>\n",
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       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
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       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4/15/2021 14:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree strongly</td>\n",
       "      <td>Shanghai (China) 200127</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Engineering</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4/16/2021 10:58</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Horseheads, NY 14845</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4/16/2021 17:52</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>No</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$40,000 - $59,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>School of Industrial and Labor Relations</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <th>887</th>\n",
       "      <td>5/11/2022 21:20</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>West New York, NJ 07047</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>5/16/2022 22:07</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Albany, New York, 12202</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$60,000 - $99,999</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>5/19/2022 17:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Boca Raton, Florida 33496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent),Hispanic,Middle Ea...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>896</th>\n",
       "      <td>5/19/2022 19:26</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Harrison, New York, 10528</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>Hispanic</td>\n",
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       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>395 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience Moving_Times  \\\n",
       "Response_ID                                                                \n",
       "1            4/14/2021 19:43  2021  April                No            1   \n",
       "4            4/15/2021 14:01  2021  April                No            1   \n",
       "6            4/15/2021 14:30  2021  April                No            2   \n",
       "14           4/16/2021 10:58  2021  April                No            1   \n",
       "15           4/16/2021 17:52  2021  April                No            2   \n",
       "...                      ...   ...    ...               ...          ...   \n",
       "887          5/11/2022 21:20  2022    May                No            3   \n",
       "890          5/16/2022 22:07  2022    May                No            5   \n",
       "895          5/19/2022 17:17  2022    May                No            2   \n",
       "896          5/19/2022 19:26  2022    May               Yes            4   \n",
       "897           5/20/2022 7:13  2022    May                No            1   \n",
       "\n",
       "               Self_Confidence                            Hometown   Age  \\\n",
       "Response_ID                                                                \n",
       "1               Agree slightly        Coconut Grove, Florida 33133  20.0   \n",
       "4               Agree slightly           Rye Brook, New York 10573  19.0   \n",
       "6               Agree strongly             Shanghai (China) 200127  18.0   \n",
       "14           Disagree slightly                Horseheads, NY 14845  19.0   \n",
       "15              Agree slightly                                  No  19.0   \n",
       "...                        ...                                 ...   ...   \n",
       "887             Agree slightly             West New York, NJ 07047  19.0   \n",
       "890             Agree somewhat             Albany, New York, 12202  18.0   \n",
       "895             Agree somewhat           Boca Raton, Florida 33496  19.0   \n",
       "896             Agree somewhat           Harrison, New York, 10528  20.0   \n",
       "897             Agree slightly  Guangzhou, Guangdong, China 510335  19.0   \n",
       "\n",
       "                 Parental_Income  \\\n",
       "Response_ID                        \n",
       "1             More than $750,000   \n",
       "4             More than $750,000   \n",
       "6            $175,000 - $299,999   \n",
       "14           $100,000 - $174,999   \n",
       "15             $40,000 - $59,999   \n",
       "...                          ...   \n",
       "887                Below $40,000   \n",
       "890            $60,000 - $99,999   \n",
       "895          $175,000 - $299,999   \n",
       "896          $175,000 - $299,999   \n",
       "897          $300,000 - $499,999   \n",
       "\n",
       "                                                Race_Ethnicity  \\\n",
       "Response_ID                                                      \n",
       "1                                  White (of European descent)   \n",
       "4                                  White (of European descent)   \n",
       "6                                                        Asian   \n",
       "14                           White (of European descent),Black   \n",
       "15                                 White (of European descent)   \n",
       "...                                                        ...   \n",
       "887                                                   Hispanic   \n",
       "890                                                      Black   \n",
       "895          White (of European descent),Hispanic,Middle Ea...   \n",
       "896                                                   Hispanic   \n",
       "897                                                      Asian   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "1            2nd year undergraduate               No   \n",
       "4            2nd year undergraduate               No   \n",
       "6            1st year undergraduate               No   \n",
       "14           1st year undergraduate               No   \n",
       "15           2nd year undergraduate               No   \n",
       "...                             ...              ...   \n",
       "887          2nd year undergraduate               No   \n",
       "890          1st year undergraduate               No   \n",
       "895          1st year undergraduate               No   \n",
       "896          2nd year undergraduate              Yes   \n",
       "897          1st year undergraduate               No   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "1                            College of Human Ecology           Woman  Female   \n",
       "4                            College of Human Ecology           Woman  Female   \n",
       "6                              College of Engineering           Woman  Female   \n",
       "14                           College of Human Ecology           Woman  Female   \n",
       "15           School of Industrial and Labor Relations             Man    Male   \n",
       "...                                               ...             ...     ...   \n",
       "887                          College of Human Ecology           Woman  Female   \n",
       "890                      College of Arts and Sciences             Man    Male   \n",
       "895                      College of Arts and Sciences           Woman  Female   \n",
       "896                          College of Human Ecology           Woman  Female   \n",
       "897                          College of Human Ecology           Woman  Female   \n",
       "\n",
       "            Living_Location  Unnamed: 17  \n",
       "Response_ID                               \n",
       "1                Off campus          NaN  \n",
       "4                 On campus          NaN  \n",
       "6                Off campus          NaN  \n",
       "14                On campus          NaN  \n",
       "15                On campus          NaN  \n",
       "...                     ...          ...  \n",
       "887               On campus          NaN  \n",
       "890               On campus          NaN  \n",
       "895               On campus          NaN  \n",
       "896               On campus          NaN  \n",
       "897               On campus          NaN  \n",
       "\n",
       "[395 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fresh_soph = df.loc[(df['School_Year']==\"1st year undergraduate\")|(df['School_Year']==\"2nd year undergraduate\")]\n",
    "display(fresh_soph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we wanted responses only from freshmen in the College of Human Ecology:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4/22/2021 14:16</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Horseheads, New York 14845</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4/25/2021 13:03</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>St. George, Utah 84790</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4/26/2021 14:05</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>San Diego, CA 92129</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>4/29/2021 8:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Scarsdale, NY 10583</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent),Unknown</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5/3/2021 11:37</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>New Rochelle, NY 10804</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5/3/2021 21:05</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Secaucus, New Jersey 07094</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5/3/2021 22:17</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Port Washington, New York 11050</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>5/6/2021 14:46</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Orleans, Massachusetts 02653</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5/7/2021 7:41</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Upper East Side, Manhattan, New York City, 10075</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>5/7/2021 21:28</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Durham, North Carolina  27713</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>5/10/2021 15:08</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Bloomfield Hills, Michigan 48302</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>5/11/2021 17:17</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Tegucigalpa, Honduras 11101</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$40,000 - $59,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>5/13/2021 22:16</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Ithaca, New York, 14853</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>5/14/2021 2:37</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Dogokdong, Seoul, 06294</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>5/13/2021 19:15</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree somewhat</td>\n",
       "      <td>Roslyn, New York 11576</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>5/14/2021 10:42</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Modesto, California 95356</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>5/20/2021 11:58</td>\n",
       "      <td>2021</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Wilmette, Illinois, 60091</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>9/23/2021 16:10</td>\n",
       "      <td>2021</td>\n",
       "      <td>September</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>New Rochelle New York 10804</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>9/28/2021 17:40</td>\n",
       "      <td>2021</td>\n",
       "      <td>September</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Changchun, Jilin Province, China</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$60,000 - $99,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>9/30/2021 10:04</td>\n",
       "      <td>2021</td>\n",
       "      <td>September</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Hong Kong, China</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>10/4/2021 12:51</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Bellerose Village, NY 11001</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>White (of European descent),Asian,Mixed Race</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>10/12/2021 13:06</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>New York, New York 10038</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>10/20/2021 8:22</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Shanghai, China, 200060</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>10/20/2021 8:16</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree strongly</td>\n",
       "      <td>Newnan, Georgia 30263</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>10/24/2021 12:54</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Songdo, Incheon, 22003</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>10/25/2021 13:04</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>San Diego, California 92173</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>10/26/2021 8:33</td>\n",
       "      <td>2021</td>\n",
       "      <td>October</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>New Brunswick, NJ 08901</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>11/1/2021 11:29</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Atlanta, Georgia 30327</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>11/2/2021 14:09</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Essex, Vermont 05452</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>11/5/2021 20:20</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Shanghai, China; Princeton, NJ 08540</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>11/8/2021 12:33</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Shenzhen Guangdong China</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Gender non-conforming</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>11/10/2021 6:35</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>New York City, New York 10128</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>11/12/2021 13:03</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Harrison, New York 10528</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Asian,Pacific Isla...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>11/14/2021 15:40</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree strongly</td>\n",
       "      <td>Boston, MA 02119</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>11/15/2021 19:09</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Tenafly, NJ, 07670</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>11/15/2021 21:41</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>11/16/2021 21:20</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>new hyde park, new york,11040</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>11/18/2021 21:12</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Snellville,Georgia,30039</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>11/20/2021 16:09</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>New York, New York 10028</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent),Hispanic</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>11/23/2021 22:51</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>New york ny 10028</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>11/29/2021 20:02</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>La Canada, CA 91011</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>11/30/2021 15:32</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Williston Park, New York 11596</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>11/30/2021 20:16</td>\n",
       "      <td>2021</td>\n",
       "      <td>November</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$500,000 - $749,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>12/1/2021 19:02</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coral Springs, Florida, 33076</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>12/4/2021 11:17</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Southampton, NY, 11968</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>12/4/2021 17:06</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>silver spring, md , 20904</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$60,000 - $99,999</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>12/7/2021 21:51</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>Agree strongly</td>\n",
       "      <td>Boynton Beach, FL</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$60,000 - $99,999</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>12/9/2021 13:15</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Austin Texas 78746</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>12/9/2021 18:14</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Ithaca, New York `4850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Hispanic,Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Gender non-conforming</td>\n",
       "      <td>Male</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>12/10/2021 17:12</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Don't know / not applicable</td>\n",
       "      <td>Carrollton, TX 75007</td>\n",
       "      <td>18.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>Black</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>12/12/2021 10:53</td>\n",
       "      <td>2021</td>\n",
       "      <td>December</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Wellesley, Massachusetts 02481</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>4/18/2022 7:46</td>\n",
       "      <td>2022</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Disagree slightly</td>\n",
       "      <td>Bellevue, Washington 98005</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Time_Stamp  Year      Month Hunger_Experience Moving_Times  \\\n",
       "Response_ID                                                                     \n",
       "14            4/16/2021 10:58  2021      April                No            1   \n",
       "26            4/22/2021 14:16  2021      April                No            1   \n",
       "42            4/25/2021 13:03  2021      April                No            0   \n",
       "49            4/26/2021 14:05  2021      April                No            2   \n",
       "62             4/29/2021 8:01  2021      April               Yes            1   \n",
       "87             5/3/2021 11:37  2021        May                No            1   \n",
       "95             5/3/2021 21:05  2021        May                No            0   \n",
       "96             5/3/2021 22:17  2021        May                No            1   \n",
       "116            5/6/2021 14:46  2021        May                No            1   \n",
       "120             5/7/2021 7:41  2021        May                No            3   \n",
       "130            5/7/2021 21:28  2021        May                No            1   \n",
       "159           5/10/2021 15:08  2021        May                No            1   \n",
       "186           5/11/2021 17:17  2021        May                No            0   \n",
       "229           5/13/2021 22:16  2021        May                No            1   \n",
       "243            5/14/2021 2:37  2021        May                No            7   \n",
       "247           5/13/2021 19:15  2021        May                No            1   \n",
       "269           5/14/2021 10:42  2021        May                No            2   \n",
       "324           5/20/2021 11:58  2021        May                No            1   \n",
       "454           9/23/2021 16:10  2021  September                No            1   \n",
       "471           9/28/2021 17:40  2021  September               Yes            3   \n",
       "477           9/30/2021 10:04  2021  September                No            5   \n",
       "492           10/4/2021 12:51  2021    October                No            1   \n",
       "510          10/12/2021 13:06  2021    October                No            3   \n",
       "535           10/20/2021 8:22  2021    October                No            3   \n",
       "538           10/20/2021 8:16  2021    October                No            3   \n",
       "543          10/24/2021 12:54  2021    October                No            1   \n",
       "549          10/25/2021 13:04  2021    October                No            1   \n",
       "553           10/26/2021 8:33  2021    October                No            7   \n",
       "564           11/1/2021 11:29  2021   November                No            0   \n",
       "569           11/2/2021 14:09  2021   November                No            0   \n",
       "574           11/5/2021 20:20  2021   November                No            2   \n",
       "580           11/8/2021 12:33  2021   November                No            3   \n",
       "587           11/10/2021 6:35  2021   November                No            1   \n",
       "595          11/12/2021 13:03  2021   November                No            1   \n",
       "603          11/14/2021 15:40  2021   November        Don't know            1   \n",
       "606          11/15/2021 19:09  2021   November                No            3   \n",
       "607          11/15/2021 21:41  2021   November                No            3   \n",
       "615          11/16/2021 21:20  2021   November                No            0   \n",
       "625          11/18/2021 21:12  2021   November                No            2   \n",
       "629          11/20/2021 16:09  2021   November                No            2   \n",
       "639          11/23/2021 22:51  2021   November                No            0   \n",
       "672          11/29/2021 20:02  2021   November                No            2   \n",
       "677          11/30/2021 15:32  2021   November                No            4   \n",
       "680          11/30/2021 20:16  2021   November                No            4   \n",
       "693           12/1/2021 19:02  2021   December                No            0   \n",
       "719           12/4/2021 11:17  2021   December                No            0   \n",
       "723           12/4/2021 17:06  2021   December                No            1   \n",
       "790           12/7/2021 21:51  2021   December               Yes            5   \n",
       "800           12/9/2021 13:15  2021   December                No            7   \n",
       "802           12/9/2021 18:14  2021   December                No            0   \n",
       "811          12/10/2021 17:12  2021   December                No            3   \n",
       "828          12/12/2021 10:53  2021   December                No            2   \n",
       "852            4/18/2022 7:46  2022      April                No            1   \n",
       "897            5/20/2022 7:13  2022        May                No            1   \n",
       "\n",
       "                         Self_Confidence  \\\n",
       "Response_ID                                \n",
       "14                     Disagree slightly   \n",
       "26           Don't know / not applicable   \n",
       "42           Don't know / not applicable   \n",
       "49                        Agree somewhat   \n",
       "62                        Agree slightly   \n",
       "87                        Agree slightly   \n",
       "95                        Agree slightly   \n",
       "96                     Disagree slightly   \n",
       "116                    Disagree slightly   \n",
       "120                       Agree slightly   \n",
       "130          Don't know / not applicable   \n",
       "159                       Agree slightly   \n",
       "186                       Agree somewhat   \n",
       "229                    Disagree slightly   \n",
       "243                       Agree slightly   \n",
       "247                    Disagree somewhat   \n",
       "269          Don't know / not applicable   \n",
       "324                    Disagree slightly   \n",
       "454                       Agree slightly   \n",
       "471          Don't know / not applicable   \n",
       "477                       Agree slightly   \n",
       "492                       Agree slightly   \n",
       "510                       Agree slightly   \n",
       "535                       Agree slightly   \n",
       "538                       Agree strongly   \n",
       "543                       Agree slightly   \n",
       "549                       Agree somewhat   \n",
       "553                    Disagree slightly   \n",
       "564          Don't know / not applicable   \n",
       "569                    Disagree slightly   \n",
       "574          Don't know / not applicable   \n",
       "580                       Agree slightly   \n",
       "587          Don't know / not applicable   \n",
       "595                       Agree somewhat   \n",
       "603                       Agree strongly   \n",
       "606          Don't know / not applicable   \n",
       "607                    Disagree slightly   \n",
       "615                       Agree slightly   \n",
       "625                    Disagree slightly   \n",
       "629                       Agree somewhat   \n",
       "639                    Disagree slightly   \n",
       "672                       Agree somewhat   \n",
       "677                       Agree slightly   \n",
       "680                       Agree slightly   \n",
       "693                       Agree slightly   \n",
       "719                       Agree slightly   \n",
       "723                    Disagree slightly   \n",
       "790                       Agree strongly   \n",
       "800          Don't know / not applicable   \n",
       "802          Don't know / not applicable   \n",
       "811          Don't know / not applicable   \n",
       "828                       Agree somewhat   \n",
       "852                    Disagree slightly   \n",
       "897                       Agree slightly   \n",
       "\n",
       "                                                     Hometown   Age  \\\n",
       "Response_ID                                                           \n",
       "14                                       Horseheads, NY 14845  19.0   \n",
       "26                                 Horseheads, New York 14845  19.0   \n",
       "42                                     St. George, Utah 84790  18.0   \n",
       "49                                        San Diego, CA 92129  18.0   \n",
       "62                                        Scarsdale, NY 10583  18.0   \n",
       "87                                     New Rochelle, NY 10804  20.0   \n",
       "95                                 Secaucus, New Jersey 07094  18.0   \n",
       "96                            Port Washington, New York 11050  19.0   \n",
       "116                              Orleans, Massachusetts 02653  19.0   \n",
       "120          Upper East Side, Manhattan, New York City, 10075  18.0   \n",
       "130                             Durham, North Carolina  27713  19.0   \n",
       "159                          Bloomfield Hills, Michigan 48302  18.0   \n",
       "186                               Tegucigalpa, Honduras 11101  19.0   \n",
       "229                                   Ithaca, New York, 14853  19.0   \n",
       "243                                   Dogokdong, Seoul, 06294  19.0   \n",
       "247                                    Roslyn, New York 11576  18.0   \n",
       "269                                 Modesto, California 95356  19.0   \n",
       "324                                 Wilmette, Illinois, 60091  19.0   \n",
       "454                               New Rochelle New York 10804  18.0   \n",
       "471                          Changchun, Jilin Province, China  19.0   \n",
       "477                                          Hong Kong, China  18.0   \n",
       "492                               Bellerose Village, NY 11001  18.0   \n",
       "510                                  New York, New York 10038  18.0   \n",
       "535                                   Shanghai, China, 200060  18.0   \n",
       "538                                     Newnan, Georgia 30263  18.0   \n",
       "543                                    Songdo, Incheon, 22003  18.0   \n",
       "549                               San Diego, California 92173  18.0   \n",
       "553                                   New Brunswick, NJ 08901  18.0   \n",
       "564                                    Atlanta, Georgia 30327  18.0   \n",
       "569                                      Essex, Vermont 05452  18.0   \n",
       "574                      Shanghai, China; Princeton, NJ 08540  19.0   \n",
       "580                                  Shenzhen Guangdong China  19.0   \n",
       "587                             New York City, New York 10128  18.0   \n",
       "595                                  Harrison, New York 10528  18.0   \n",
       "603                                          Boston, MA 02119  18.0   \n",
       "606                                        Tenafly, NJ, 07670  18.0   \n",
       "607                                                  Shanghai  18.0   \n",
       "615                             new hyde park, new york,11040  18.0   \n",
       "625                                  Snellville,Georgia,30039  18.0   \n",
       "629                                  New York, New York 10028  19.0   \n",
       "639                                         New york ny 10028  19.0   \n",
       "672                                       La Canada, CA 91011  18.0   \n",
       "677                            Williston Park, New York 11596  18.0   \n",
       "680                                                 Hong Kong  19.0   \n",
       "693                             Coral Springs, Florida, 33076  18.0   \n",
       "719                                    Southampton, NY, 11968  18.0   \n",
       "723                                 silver spring, md , 20904  19.0   \n",
       "790                                         Boynton Beach, FL  19.0   \n",
       "800                                        Austin Texas 78746  19.0   \n",
       "802                                    Ithaca, New York `4850  18.0   \n",
       "811                                      Carrollton, TX 75007  18.0   \n",
       "828                            Wellesley, Massachusetts 02481  20.0   \n",
       "852                                Bellevue, Washington 98005  19.0   \n",
       "897                        Guangzhou, Guangdong, China 510335  19.0   \n",
       "\n",
       "                 Parental_Income  \\\n",
       "Response_ID                        \n",
       "14           $100,000 - $174,999   \n",
       "26           $100,000 - $174,999   \n",
       "42           $100,000 - $174,999   \n",
       "49           $300,000 - $499,999   \n",
       "62            More than $750,000   \n",
       "87           $175,000 - $299,999   \n",
       "95           $100,000 - $174,999   \n",
       "96            More than $750,000   \n",
       "116          $175,000 - $299,999   \n",
       "120          $500,000 - $749,999   \n",
       "130                Below $40,000   \n",
       "159          $500,000 - $749,999   \n",
       "186            $40,000 - $59,999   \n",
       "229                Below $40,000   \n",
       "243          $175,000 - $299,999   \n",
       "247          $500,000 - $749,999   \n",
       "269          $500,000 - $749,999   \n",
       "324           More than $750,000   \n",
       "454          $300,000 - $499,999   \n",
       "471            $60,000 - $99,999   \n",
       "477          $300,000 - $499,999   \n",
       "492          $500,000 - $749,999   \n",
       "510          $500,000 - $749,999   \n",
       "535          $500,000 - $749,999   \n",
       "538          $100,000 - $174,999   \n",
       "543           More than $750,000   \n",
       "549                Below $40,000   \n",
       "553                Below $40,000   \n",
       "564          $175,000 - $299,999   \n",
       "569          $175,000 - $299,999   \n",
       "574           More than $750,000   \n",
       "580          $100,000 - $174,999   \n",
       "587           More than $750,000   \n",
       "595          $100,000 - $174,999   \n",
       "603                Below $40,000   \n",
       "606           More than $750,000   \n",
       "607           More than $750,000   \n",
       "615          $100,000 - $174,999   \n",
       "625                Below $40,000   \n",
       "629           More than $750,000   \n",
       "639          $175,000 - $299,999   \n",
       "672                Below $40,000   \n",
       "677          $175,000 - $299,999   \n",
       "680          $500,000 - $749,999   \n",
       "693          $100,000 - $174,999   \n",
       "719           More than $750,000   \n",
       "723            $60,000 - $99,999   \n",
       "790            $60,000 - $99,999   \n",
       "800          $100,000 - $174,999   \n",
       "802           More than $750,000   \n",
       "811          $100,000 - $174,999   \n",
       "828          $175,000 - $299,999   \n",
       "852          $175,000 - $299,999   \n",
       "897          $300,000 - $499,999   \n",
       "\n",
       "                                                Race_Ethnicity  \\\n",
       "Response_ID                                                      \n",
       "14                           White (of European descent),Black   \n",
       "26                           White (of European descent),Black   \n",
       "42                                 White (of European descent)   \n",
       "49                                                       Asian   \n",
       "62                         White (of European descent),Unknown   \n",
       "87                                 White (of European descent)   \n",
       "95                                 White (of European descent)   \n",
       "96                                 White (of European descent)   \n",
       "116                                White (of European descent)   \n",
       "120                                                   Hispanic   \n",
       "130                                                      Asian   \n",
       "159                                White (of European descent)   \n",
       "186                                                   Hispanic   \n",
       "229                                                      Asian   \n",
       "243                                                      Asian   \n",
       "247                                White (of European descent)   \n",
       "269                                                      Asian   \n",
       "324                                White (of European descent)   \n",
       "454                                White (of European descent)   \n",
       "471                                                      Asian   \n",
       "477                                                      Asian   \n",
       "492               White (of European descent),Asian,Mixed Race   \n",
       "510                                                      Asian   \n",
       "535                                                      Asian   \n",
       "538                                White (of European descent)   \n",
       "543                                                      Asian   \n",
       "549                                                   Hispanic   \n",
       "553                                                      Asian   \n",
       "564                                White (of European descent)   \n",
       "569                                White (of European descent)   \n",
       "574                                                      Asian   \n",
       "580                                                      Asian   \n",
       "587                                White (of European descent)   \n",
       "595          White (of European descent),Asian,Pacific Isla...   \n",
       "603                                                      Black   \n",
       "606                                White (of European descent)   \n",
       "607                                                      Asian   \n",
       "615                                                      Asian   \n",
       "625                                                      Black   \n",
       "629                       White (of European descent),Hispanic   \n",
       "639                                White (of European descent)   \n",
       "672                                                      Asian   \n",
       "677                                                      Asian   \n",
       "680                                                      Asian   \n",
       "693                                                   Hispanic   \n",
       "719                                White (of European descent)   \n",
       "723                                                      Black   \n",
       "790                                                      Black   \n",
       "800                                White (of European descent)   \n",
       "802                                             Hispanic,Asian   \n",
       "811                                                      Black   \n",
       "828                                White (of European descent)   \n",
       "852                                             Middle Eastern   \n",
       "897                                                      Asian   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "14           1st year undergraduate               No   \n",
       "26           1st year undergraduate               No   \n",
       "42           1st year undergraduate               No   \n",
       "49           1st year undergraduate               No   \n",
       "62           1st year undergraduate               No   \n",
       "87           1st year undergraduate               No   \n",
       "95           1st year undergraduate               No   \n",
       "96           1st year undergraduate               No   \n",
       "116          1st year undergraduate               No   \n",
       "120          1st year undergraduate               No   \n",
       "130          1st year undergraduate               No   \n",
       "159          1st year undergraduate               No   \n",
       "186          1st year undergraduate               No   \n",
       "229          1st year undergraduate               No   \n",
       "243          1st year undergraduate               No   \n",
       "247          1st year undergraduate               No   \n",
       "269          1st year undergraduate               No   \n",
       "324          1st year undergraduate               No   \n",
       "454          1st year undergraduate               No   \n",
       "471          1st year undergraduate               No   \n",
       "477          1st year undergraduate               No   \n",
       "492          1st year undergraduate               No   \n",
       "510          1st year undergraduate               No   \n",
       "535          1st year undergraduate               No   \n",
       "538          1st year undergraduate               No   \n",
       "543          1st year undergraduate               No   \n",
       "549          1st year undergraduate               No   \n",
       "553          1st year undergraduate               No   \n",
       "564          1st year undergraduate               No   \n",
       "569          1st year undergraduate               No   \n",
       "574          1st year undergraduate               No   \n",
       "580          1st year undergraduate               No   \n",
       "587          1st year undergraduate               No   \n",
       "595          1st year undergraduate               No   \n",
       "603          1st year undergraduate               No   \n",
       "606          1st year undergraduate               No   \n",
       "607          1st year undergraduate               No   \n",
       "615          1st year undergraduate               No   \n",
       "625          1st year undergraduate               No   \n",
       "629          1st year undergraduate               No   \n",
       "639          1st year undergraduate               No   \n",
       "672          1st year undergraduate               No   \n",
       "677          1st year undergraduate               No   \n",
       "680          1st year undergraduate               No   \n",
       "693          1st year undergraduate               No   \n",
       "719          1st year undergraduate               No   \n",
       "723          1st year undergraduate               No   \n",
       "790          1st year undergraduate               No   \n",
       "800          1st year undergraduate               No   \n",
       "802          1st year undergraduate               No   \n",
       "811          1st year undergraduate               No   \n",
       "828          1st year undergraduate               No   \n",
       "852          1st year undergraduate               No   \n",
       "897          1st year undergraduate               No   \n",
       "\n",
       "                              College        Gender_Identity     Sex  \\\n",
       "Response_ID                                                            \n",
       "14           College of Human Ecology                  Woman  Female   \n",
       "26           College of Human Ecology                  Woman  Female   \n",
       "42           College of Human Ecology                  Woman  Female   \n",
       "49           College of Human Ecology                  Woman  Female   \n",
       "62           College of Human Ecology                    Man    Male   \n",
       "87           College of Human Ecology                  Woman  Female   \n",
       "95           College of Human Ecology                  Woman  Female   \n",
       "96           College of Human Ecology                  Woman  Female   \n",
       "116          College of Human Ecology                  Woman  Female   \n",
       "120          College of Human Ecology                  Woman  Female   \n",
       "130          College of Human Ecology                  Woman  Female   \n",
       "159          College of Human Ecology                  Woman  Female   \n",
       "186          College of Human Ecology                  Woman  Female   \n",
       "229          College of Human Ecology                    Man    Male   \n",
       "243          College of Human Ecology                    Man    Male   \n",
       "247          College of Human Ecology                  Woman  Female   \n",
       "269          College of Human Ecology                  Woman  Female   \n",
       "324          College of Human Ecology                  Woman  Female   \n",
       "454          College of Human Ecology                  Woman  Female   \n",
       "471          College of Human Ecology                  Woman  Female   \n",
       "477          College of Human Ecology                  Woman  Female   \n",
       "492          College of Human Ecology                  Woman  Female   \n",
       "510          College of Human Ecology                  Woman  Female   \n",
       "535          College of Human Ecology                  Woman  Female   \n",
       "538          College of Human Ecology                  Woman  Female   \n",
       "543          College of Human Ecology                  Woman  Female   \n",
       "549          College of Human Ecology                  Woman  Female   \n",
       "553          College of Human Ecology                  Woman  Female   \n",
       "564          College of Human Ecology                  Woman  Female   \n",
       "569          College of Human Ecology                    Man    Male   \n",
       "574          College of Human Ecology                  Woman  Female   \n",
       "580          College of Human Ecology  Gender non-conforming  Female   \n",
       "587          College of Human Ecology                    Man    Male   \n",
       "595          College of Human Ecology                  Woman  Female   \n",
       "603          College of Human Ecology                  Woman  Female   \n",
       "606          College of Human Ecology                  Woman  Female   \n",
       "607          College of Human Ecology                  Woman  Female   \n",
       "615          College of Human Ecology                    Man    Male   \n",
       "625          College of Human Ecology                  Woman  Female   \n",
       "629          College of Human Ecology                  Woman  Female   \n",
       "639          College of Human Ecology                  Woman  Female   \n",
       "672          College of Human Ecology                  Woman  Female   \n",
       "677          College of Human Ecology                    Man    Male   \n",
       "680          College of Human Ecology                    Man    Male   \n",
       "693          College of Human Ecology                  Woman  Female   \n",
       "719          College of Human Ecology                  Woman  Female   \n",
       "723          College of Human Ecology                  Woman  Female   \n",
       "790          College of Human Ecology                  Woman  Female   \n",
       "800          College of Human Ecology                  Woman  Female   \n",
       "802          College of Human Ecology  Gender non-conforming    Male   \n",
       "811          College of Human Ecology                  Woman  Female   \n",
       "828          College of Human Ecology                  Woman  Female   \n",
       "852          College of Human Ecology                    Man    Male   \n",
       "897          College of Human Ecology                  Woman  Female   \n",
       "\n",
       "            Living_Location  Unnamed: 17  \n",
       "Response_ID                               \n",
       "14                On campus          NaN  \n",
       "26                On campus          NaN  \n",
       "42                On campus          NaN  \n",
       "49                On campus          NaN  \n",
       "62                On campus          NaN  \n",
       "87                On campus          NaN  \n",
       "95                On campus          NaN  \n",
       "96                On campus          NaN  \n",
       "116               On campus          NaN  \n",
       "120              Off campus          NaN  \n",
       "130              Off campus          NaN  \n",
       "159               On campus          NaN  \n",
       "186               On campus          NaN  \n",
       "229               On campus          NaN  \n",
       "243              Off campus          NaN  \n",
       "247               On campus          NaN  \n",
       "269               On campus          NaN  \n",
       "324               On campus          NaN  \n",
       "454               On campus          NaN  \n",
       "471               On campus          NaN  \n",
       "477               On campus          NaN  \n",
       "492               On campus          NaN  \n",
       "510               On campus          NaN  \n",
       "535               On campus          NaN  \n",
       "538               On campus          NaN  \n",
       "543               On campus          NaN  \n",
       "549               On campus          NaN  \n",
       "553               On campus          NaN  \n",
       "564               On campus          NaN  \n",
       "569               On campus          NaN  \n",
       "574               On campus          NaN  \n",
       "580               On campus          NaN  \n",
       "587               On campus          NaN  \n",
       "595               On campus          NaN  \n",
       "603               On campus          NaN  \n",
       "606               On campus          NaN  \n",
       "607               On campus          NaN  \n",
       "615               On campus          NaN  \n",
       "625               On campus          NaN  \n",
       "629               On campus          NaN  \n",
       "639               On campus          NaN  \n",
       "672               On campus          NaN  \n",
       "677               On campus          NaN  \n",
       "680               On campus          NaN  \n",
       "693               On campus          NaN  \n",
       "719               On campus          NaN  \n",
       "723               On campus          NaN  \n",
       "790               On campus          NaN  \n",
       "800               On campus          NaN  \n",
       "802              Off campus          NaN  \n",
       "811               On campus          NaN  \n",
       "828              Off campus          NaN  \n",
       "852               On campus          NaN  \n",
       "897               On campus          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eco_fresh = df.loc[(df['School_Year']==\"1st year undergraduate\")&(df['College']==\"College of Human Ecology\")]\n",
    "display(eco_fresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6QlU0jFUjl6S"
   },
   "source": [
    "Notice that when we run a query, the matching rows get returned in a new DataFrame, which is a subset of the original, containing only rows that matched the query. We can confirm that the result is still a DataFrame by checking the type of the variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "hG979xWhjl6S"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(eco_fresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OppCv7_Ojl6T"
   },
   "source": [
    "This means that we can run all of the previously described DataFrame methods on the query results! For example, this can be useful for finding out how many rows matched the query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "RuZIkruEjl6T"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54\n"
     ]
    }
   ],
   "source": [
    "print(len(eco_fresh.index)) # we can use the same code we saw earlier for counting rows, because eco_fresh is still a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "90HyVcgYjl6T"
   },
   "source": [
    "## Exercise 4\n",
    "\n",
    "How many freshmen in the data have experienced hunger (i.e., answered \"Yes\" for Hunger_Experience)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X-DlbQVWjl6T"
   },
   "outputs": [],
   "source": [
    "fresh_hunger = df.loc[______________] # first, fill in your query here\n",
    "print(_____________) # then, fill in the code to count the number of rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 5\n",
    "Do more seniors live on-campus or off-campus?\n",
    "\n",
    "(Hint: you will need to run two queries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your solution below\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yZ-cXW6P6Opk"
   },
   "source": [
    "## 8. Modifying and adding rows\n",
    "Queries aren't just useful for accessing data; we may sometimes also want to *modify* data that matches a query! One common reason to do this is for data preprocessing. Let's consider an example that's quite common in survey work. The \"Self_Confidence\" column contains answers to a multiple-choice question meant to represent a scale of *intensity*, with seven options representing increasing levels of confidence: \"Disagree strongly\", \"Disagree somewhat\", \"Disagree slightly\", \"Don't know / not applicable\", \"Agree slightly\", \"Agree somewhat\", and \"Agree strongly\". We might wish to turn this into a numerical Likert Scale, with values from 0 to 6 where 0 is \"Disagree strongly\" and 6 is \"Agree strongly\". This is commonly done in social science research to enable more quantitative comparisons of responses on scale-based questions like this one.\n",
    "\n",
    "Let's consider one step in this Likert scale conversion, changing \"Disagree strongly\" to 0. We can achieve this with the following steps:\n",
    "\n",
    "1. Write a query to find all instances of the thing you want to preprocess (in this case, rows where the Self_Confidence is \"Disagree strongly\") (See Section 7)\n",
    "2. Use row-and-column indexing to combine this query (which selects certain rows) with column selection (See Section 5)\n",
    "3. Use Python assigment syntax (the \"=\" operator) to set the new value (in this case, 0)\n",
    "\n",
    "Now let's look at the code for these steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "EVP5mQ1R6Tl6"
   },
   "outputs": [],
   "source": [
    "# Step 1: the query syntax for selecting rows where Self_Confidence is \"Disagree strongly\"\n",
    "query = (df[\"Self_Confidence\"] == \"Disagree strongly\")\n",
    "# Step 2 and 3: row-and-column indexing followed by new value assignment\n",
    "df.loc[query,\"Self_Confidence\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# To confirm that it worked, show that there now exist some rows where \"Self_Confidence\" is 0\n",
    "print(len(df.loc[df[\"Self_Confidence\"] == 0].index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that although for clearer illustration we split the code into multiple lines to correspond to the multiple steps, in practice most pandas users will do the entire operation in a single line of code (this concision is part of what makes pandas so powerful!). Thus, the following single line of code would have been equivalent to what we did above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df[\"Self_Confidence\"] == \"Disagree strongly\"),\"Self_Confidence\"] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 6\n",
    "Repeat this process to convert the remaining 6 response options. The conversions should be as follows:\n",
    "- Disagree somewhat --> 1\n",
    "- Disagree slightly --> 2\n",
    "- Don't know / not applicable --> 3\n",
    "- Agree slightly --> 4\n",
    "- Agree somewhat --> 5\n",
    "- Agree strongly --> 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finish the exercise by filling in the blanks below:\n",
    "df.loc[___________________,\"Self_Confidence\"] = 1\n",
    "df.loc[___________________,\"Self_Confidence\"] = 2\n",
    "df.loc[___________________,\"Self_Confidence\"] = 3\n",
    "df.loc[___________________,\"Self_Confidence\"] = 4\n",
    "df.loc[___________________,\"Self_Confidence\"] = 5\n",
    "df.loc[___________________,\"Self_Confidence\"] = 6\n",
    "# Once you've finished your solution, the following code should be \n",
    "# able to run successfully now that Self_Confidence contains numerical data\n",
    "print(df[\"Self_Confidence\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 7\n",
    "Another common reason for modifying rows is data cleaning. Let's work through an example using the Moving_Times column. Earlier in Exercise 3, we found that while most participants gave a numerical answer for the moving times question (which is what we wanted), some participants instead filled in free text. This prevents us from running numerical summarization methods like min, max, and mean. We can fix this by modifying the Moving_Times data, similar to what we did previously for Self_Confidence. For each non-numerical answer, we will replace it with the best possible numerical interpretation we can come up with; for example, we can replace \"twice\" with 2.\n",
    "\n",
    "As a reminder, in Exercise 3 you should have discovered that the following non-numerical answers exist in the data:\n",
    "- 'twice' \n",
    "- 'None' \n",
    "- 'III'\n",
    "- '1 (but when my family was renovating my house we briefly moved into another house in the same town then moved back, so if that counts as moving then 3 times)'\n",
    "- '2-3 times'\n",
    "- '2 times'\n",
    "\n",
    "We will allow you some freedom to make your own judgments in cases that are ambigious - for example, it is up to you how you want to handle the answer \"2-3 times\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8753180661577609\n"
     ]
    }
   ],
   "source": [
    "# Fill in your answer below\n",
    "\n",
    "\n",
    "# Once you are done, the following lines of code should be able to run successfuly,\n",
    "# printing out the average number of moving times in the data\n",
    "df[\"Moving_Times\"] = pd.to_numeric(df[\"Moving_Times\"])\n",
    "print(df[\"Moving_Times\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G5lS8Sf8jl6U"
   },
   "source": [
    "In the previous examples, we *replaced* values in an existing column. But there are other cases where instead, you might want to preserve the original data, and instead put the cleaned data in a new column. pandas lets us do this too! As an example, let's consider the \"Moving_Times\" column. The data in this column counts how many times the survey participant has moved prior to taking the survey. You may imagine that for some research questions we don't necessarily care about the exact number of moving times, but instead only need to know whether the survey participant has moved at all (a boolean value). But this does not mean we want to completely replace the \"Moving_Times\" column, because for *other* research questions we might actually care about the exact number of moving times! So instead, we might want to create a *new* column that contains the simplified information of whether or not a survey participant has moved.\n",
    "\n",
    "We will start by creating a new column, let's call it \"Has_Moved\". Initially, we'll create the column as a copy of \"Moving_Times\"; we can then modify it (using code similar to what we did previously for \"Self_Confidence\") without affecting the original column. To create a new column, we can simply use Python assigment syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "hNWb6wQAjl6U"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Time_Stamp</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parental_Income</th>\n",
       "      <th>Race_Ethnicity</th>\n",
       "      <th>School_Year</th>\n",
       "      <th>Transfer_Student</th>\n",
       "      <th>College</th>\n",
       "      <th>Gender_Identity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Living_Location</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Has_Moved</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/2021 11:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Brooklyn, New York 11225</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/2021 13:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Roslyn Heights, New York 11577</td>\n",
       "      <td>21.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Agriculture and Life Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4/15/2021 14:31</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>San Antonio, Texas 78218</td>\n",
       "      <td>21.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Hispanic</td>\n",
       "      <td>4th year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>5/19/2022 17:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Boca Raton, Florida 33496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent),Hispanic,Middle Ea...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>5/19/2022 19:26</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Harrison, New York, 10528</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>5/19/2022 17:22</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>5/19/2022 8:42</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience  Moving_Times  \\\n",
       "Response_ID                                                                 \n",
       "1            4/14/2021 19:43  2021  April                No           1.0   \n",
       "2            4/15/2021 11:30  2021  April                No           1.0   \n",
       "3            4/15/2021 13:00  2021  April                No           5.0   \n",
       "4            4/15/2021 14:01  2021  April                No           1.0   \n",
       "5            4/15/2021 14:31  2021  April                No           0.0   \n",
       "...                      ...   ...    ...               ...           ...   \n",
       "895          5/19/2022 17:17  2022    May                No           2.0   \n",
       "896          5/19/2022 19:26  2022    May               Yes           4.0   \n",
       "897           5/20/2022 7:13  2022    May                No           1.0   \n",
       "898          5/19/2022 17:22  2022    May               NaN           NaN   \n",
       "899           5/19/2022 8:42  2022    May               NaN           NaN   \n",
       "\n",
       "            Self_Confidence                            Hometown   Age  \\\n",
       "Response_ID                                                             \n",
       "1            Agree slightly        Coconut Grove, Florida 33133  20.0   \n",
       "2            Agree slightly            Brooklyn, New York 11225  20.0   \n",
       "3            Agree somewhat      Roslyn Heights, New York 11577  21.0   \n",
       "4            Agree slightly           Rye Brook, New York 10573  19.0   \n",
       "5            Agree somewhat            San Antonio, Texas 78218  21.0   \n",
       "...                     ...                                 ...   ...   \n",
       "895          Agree somewhat           Boca Raton, Florida 33496  19.0   \n",
       "896          Agree somewhat           Harrison, New York, 10528  20.0   \n",
       "897          Agree slightly  Guangzhou, Guangdong, China 510335  19.0   \n",
       "898                     NaN                                 NaN   NaN   \n",
       "899                     NaN                                 NaN   NaN   \n",
       "\n",
       "                 Parental_Income  \\\n",
       "Response_ID                        \n",
       "1             More than $750,000   \n",
       "2                  Below $40,000   \n",
       "3             More than $750,000   \n",
       "4             More than $750,000   \n",
       "5            $100,000 - $174,999   \n",
       "...                          ...   \n",
       "895          $175,000 - $299,999   \n",
       "896          $175,000 - $299,999   \n",
       "897          $300,000 - $499,999   \n",
       "898                          NaN   \n",
       "899                          NaN   \n",
       "\n",
       "                                                Race_Ethnicity  \\\n",
       "Response_ID                                                      \n",
       "1                                  White (of European descent)   \n",
       "2                                                        Black   \n",
       "3                                                        Asian   \n",
       "4                                  White (of European descent)   \n",
       "5                         White (of European descent),Hispanic   \n",
       "...                                                        ...   \n",
       "895          White (of European descent),Hispanic,Middle Ea...   \n",
       "896                                                   Hispanic   \n",
       "897                                                      Asian   \n",
       "898                                                        NaN   \n",
       "899                                                        NaN   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "1            2nd year undergraduate               No   \n",
       "2            3rd year undergraduate               No   \n",
       "3            3rd year undergraduate               No   \n",
       "4            2nd year undergraduate               No   \n",
       "5            4th year undergraduate               No   \n",
       "...                             ...              ...   \n",
       "895          1st year undergraduate               No   \n",
       "896          2nd year undergraduate              Yes   \n",
       "897          1st year undergraduate               No   \n",
       "898                             NaN              NaN   \n",
       "899                             NaN              NaN   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "1                            College of Human Ecology           Woman  Female   \n",
       "2                            College of Human Ecology           Woman  Female   \n",
       "3            College of Agriculture and Life Sciences           Woman  Female   \n",
       "4                            College of Human Ecology           Woman  Female   \n",
       "5                        College of Arts and Sciences           Woman  Female   \n",
       "...                                               ...             ...     ...   \n",
       "895                      College of Arts and Sciences           Woman  Female   \n",
       "896                          College of Human Ecology           Woman  Female   \n",
       "897                          College of Human Ecology           Woman  Female   \n",
       "898                                               NaN             NaN     NaN   \n",
       "899                                               NaN             NaN     NaN   \n",
       "\n",
       "            Living_Location  Unnamed: 17  Has_Moved  \n",
       "Response_ID                                          \n",
       "1                Off campus          NaN        1.0  \n",
       "2                Off campus          NaN        1.0  \n",
       "3                Off campus          NaN        5.0  \n",
       "4                 On campus          NaN        1.0  \n",
       "5                Off campus          NaN        0.0  \n",
       "...                     ...          ...        ...  \n",
       "895               On campus          NaN        2.0  \n",
       "896               On campus          NaN        4.0  \n",
       "897               On campus          NaN        1.0  \n",
       "898                     NaN          NaN        NaN  \n",
       "899                     NaN          NaN        NaN  \n",
       "\n",
       "[899 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Has_Moved\"] = df[\"Moving_Times\"]\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oIMcZ7Cbjl6U"
   },
   "source": [
    "We see that there is now a new column \"Has_Moved\" that is an exact copy of \"Moving_Times\". Next we will modify it as follows: rows containing nonzero values will be set to 1, representing that the participant has moved (rows containing zero will not need to be changed, as they will remain as 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "oj2Fb7hhjl6V"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time_Stamp</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hunger_Experience</th>\n",
       "      <th>Moving_Times</th>\n",
       "      <th>Self_Confidence</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parental_Income</th>\n",
       "      <th>Race_Ethnicity</th>\n",
       "      <th>School_Year</th>\n",
       "      <th>Transfer_Student</th>\n",
       "      <th>College</th>\n",
       "      <th>Gender_Identity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Living_Location</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Has_Moved</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/14/2021 19:43</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Coconut Grove, Florida 33133</td>\n",
       "      <td>20.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/15/2021 11:30</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Brooklyn, New York 11225</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Below $40,000</td>\n",
       "      <td>Black</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/15/2021 13:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Roslyn Heights, New York 11577</td>\n",
       "      <td>21.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>Asian</td>\n",
       "      <td>3rd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Agriculture and Life Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/15/2021 14:01</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Rye Brook, New York 10573</td>\n",
       "      <td>19.0</td>\n",
       "      <td>More than $750,000</td>\n",
       "      <td>White (of European descent)</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4/15/2021 14:31</td>\n",
       "      <td>2021</td>\n",
       "      <td>April</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>San Antonio, Texas 78218</td>\n",
       "      <td>21.0</td>\n",
       "      <td>$100,000 - $174,999</td>\n",
       "      <td>White (of European descent),Hispanic</td>\n",
       "      <td>4th year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>Off campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>5/19/2022 17:17</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Boca Raton, Florida 33496</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>White (of European descent),Hispanic,Middle Ea...</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Arts and Sciences</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>5/19/2022 19:26</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Agree somewhat</td>\n",
       "      <td>Harrison, New York, 10528</td>\n",
       "      <td>20.0</td>\n",
       "      <td>$175,000 - $299,999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2nd year undergraduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>5/20/2022 7:13</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree slightly</td>\n",
       "      <td>Guangzhou, Guangdong, China 510335</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$300,000 - $499,999</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1st year undergraduate</td>\n",
       "      <td>No</td>\n",
       "      <td>College of Human Ecology</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Female</td>\n",
       "      <td>On campus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>5/19/2022 17:22</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>5/19/2022 8:42</td>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time_Stamp  Year  Month Hunger_Experience  Moving_Times  \\\n",
       "Response_ID                                                                 \n",
       "1            4/14/2021 19:43  2021  April                No           1.0   \n",
       "2            4/15/2021 11:30  2021  April                No           1.0   \n",
       "3            4/15/2021 13:00  2021  April                No           5.0   \n",
       "4            4/15/2021 14:01  2021  April                No           1.0   \n",
       "5            4/15/2021 14:31  2021  April                No           0.0   \n",
       "...                      ...   ...    ...               ...           ...   \n",
       "895          5/19/2022 17:17  2022    May                No           2.0   \n",
       "896          5/19/2022 19:26  2022    May               Yes           4.0   \n",
       "897           5/20/2022 7:13  2022    May                No           1.0   \n",
       "898          5/19/2022 17:22  2022    May               NaN           NaN   \n",
       "899           5/19/2022 8:42  2022    May               NaN           NaN   \n",
       "\n",
       "            Self_Confidence                            Hometown   Age  \\\n",
       "Response_ID                                                             \n",
       "1            Agree slightly        Coconut Grove, Florida 33133  20.0   \n",
       "2            Agree slightly            Brooklyn, New York 11225  20.0   \n",
       "3            Agree somewhat      Roslyn Heights, New York 11577  21.0   \n",
       "4            Agree slightly           Rye Brook, New York 10573  19.0   \n",
       "5            Agree somewhat            San Antonio, Texas 78218  21.0   \n",
       "...                     ...                                 ...   ...   \n",
       "895          Agree somewhat           Boca Raton, Florida 33496  19.0   \n",
       "896          Agree somewhat           Harrison, New York, 10528  20.0   \n",
       "897          Agree slightly  Guangzhou, Guangdong, China 510335  19.0   \n",
       "898                     NaN                                 NaN   NaN   \n",
       "899                     NaN                                 NaN   NaN   \n",
       "\n",
       "                 Parental_Income  \\\n",
       "Response_ID                        \n",
       "1             More than $750,000   \n",
       "2                  Below $40,000   \n",
       "3             More than $750,000   \n",
       "4             More than $750,000   \n",
       "5            $100,000 - $174,999   \n",
       "...                          ...   \n",
       "895          $175,000 - $299,999   \n",
       "896          $175,000 - $299,999   \n",
       "897          $300,000 - $499,999   \n",
       "898                          NaN   \n",
       "899                          NaN   \n",
       "\n",
       "                                                Race_Ethnicity  \\\n",
       "Response_ID                                                      \n",
       "1                                  White (of European descent)   \n",
       "2                                                        Black   \n",
       "3                                                        Asian   \n",
       "4                                  White (of European descent)   \n",
       "5                         White (of European descent),Hispanic   \n",
       "...                                                        ...   \n",
       "895          White (of European descent),Hispanic,Middle Ea...   \n",
       "896                                                   Hispanic   \n",
       "897                                                      Asian   \n",
       "898                                                        NaN   \n",
       "899                                                        NaN   \n",
       "\n",
       "                        School_Year Transfer_Student  \\\n",
       "Response_ID                                            \n",
       "1            2nd year undergraduate               No   \n",
       "2            3rd year undergraduate               No   \n",
       "3            3rd year undergraduate               No   \n",
       "4            2nd year undergraduate               No   \n",
       "5            4th year undergraduate               No   \n",
       "...                             ...              ...   \n",
       "895          1st year undergraduate               No   \n",
       "896          2nd year undergraduate              Yes   \n",
       "897          1st year undergraduate               No   \n",
       "898                             NaN              NaN   \n",
       "899                             NaN              NaN   \n",
       "\n",
       "                                              College Gender_Identity     Sex  \\\n",
       "Response_ID                                                                     \n",
       "1                            College of Human Ecology           Woman  Female   \n",
       "2                            College of Human Ecology           Woman  Female   \n",
       "3            College of Agriculture and Life Sciences           Woman  Female   \n",
       "4                            College of Human Ecology           Woman  Female   \n",
       "5                        College of Arts and Sciences           Woman  Female   \n",
       "...                                               ...             ...     ...   \n",
       "895                      College of Arts and Sciences           Woman  Female   \n",
       "896                          College of Human Ecology           Woman  Female   \n",
       "897                          College of Human Ecology           Woman  Female   \n",
       "898                                               NaN             NaN     NaN   \n",
       "899                                               NaN             NaN     NaN   \n",
       "\n",
       "            Living_Location  Unnamed: 17  Has_Moved  \n",
       "Response_ID                                          \n",
       "1                Off campus          NaN        1.0  \n",
       "2                Off campus          NaN        1.0  \n",
       "3                Off campus          NaN        1.0  \n",
       "4                 On campus          NaN        1.0  \n",
       "5                Off campus          NaN        0.0  \n",
       "...                     ...          ...        ...  \n",
       "895               On campus          NaN        1.0  \n",
       "896               On campus          NaN        1.0  \n",
       "897               On campus          NaN        1.0  \n",
       "898                     NaN          NaN        NaN  \n",
       "899                     NaN          NaN        NaN  \n",
       "\n",
       "[899 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['Has_Moved'] > 0), 'Has_Moved'] = 1\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TwLU5fCWjl6V"
   },
   "source": [
    "## 9. Fine-grained analysis using groupby\n",
    "In section 6, we showed how to summarize data from an *entire* column, such as taking the min and max of numerical data. This is useful for initial exploration, but when tackling actual research questions, we often want to do more complicated operations involving interactions between multiple variables. For example, instead of just finding average moving times in the data, we might be interested in finding the average moving times among students in each college. Using what we have learned so far, one way to do this would be to use queries: for each college, you could write a query to select only the rows matching that college, and call the `mean` method each time. But this is extremely tedious, given that there are so many colleges in the data! Thankfully, pandas offers a faster alternative: `groupby`.\n",
    "\n",
    "The `groupby` method can be thought of a splitting a DataFrame based on some categorical variable. For example, if we use `groupby` on the \"College\" column, we'll have one group containing rows where the College is \"College of Human Ecology\", another containing rows where the College is \"College of Arts and Sciences\", and so on, for every unique college. Now, if we just run `groupby` on its own, we won't immediately see anything useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "D0LNym1Pjl6V"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f7d98322f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_grouped = df.groupby(\"College\")\n",
    "display(df_grouped) # prints out some strange code that isn't super useful..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BG_OSJQ3jl6V"
   },
   "source": [
    "But the key difference happens when we call summarization methods on the grouped variable. Instead of running the summarization on the entire data, as happens normally, the summarization will be run separately on each group! Let's try using the `min` summarization method on \"year\" in the grouped data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "4qk2lYsKjl6V"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College\n",
       "College of Agriculture and Life Sciences     1.918033\n",
       "College of Architecture, Art and Planning    2.000000\n",
       "College of Arts and Sciences                 1.973282\n",
       "College of Engineering                       1.637255\n",
       "College of Human Ecology                     1.810680\n",
       "SC Johnson College of Business               1.833333\n",
       "School of Industrial and Labor Relations     1.920000\n",
       "Weill Cornell Medicine                       1.000000\n",
       "Name: Moving_Times, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_grouped['Moving_Times'].mean()) # we can use the same indexing and summarization syntax as we did for regular DataFrames, but the operation now happens separately for each group!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6zx0vHUmjl6W"
   },
   "source": [
    "As we can see, instead of a single mean, we get multiple: one for each group! Specifically, we are seeing the mean moving times for participants in each college. We can immediately see how this might be useful for running comparative analyses; for example, it appears that on average students in the College of Engineering have moved fewer times than students in the College of Agriculture and Life Sciences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-QL_oq4Cjl6W"
   },
   "source": [
    "There are other kinds of summarization methods that can be useful when combined with `groupby`. Here are just a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "D6A5wTMQjl6W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College\n",
       "College of Agriculture and Life Sciences     123\n",
       "College of Architecture, Art and Planning      5\n",
       "College of Arts and Sciences                 264\n",
       "College of Engineering                       103\n",
       "College of Human Ecology                     208\n",
       "SC Johnson College of Business                36\n",
       "School of Industrial and Labor Relations      25\n",
       "Weill Cornell Medicine                         8\n",
       "Name: College, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The count() method counts the total number of items in a column.\n",
    "# When combined with groupby, the counting will happen separately per group.\n",
    "# Let's try using it to find out how many participants we got from each college\n",
    "df_grouped = df.groupby(\"College\")\n",
    "display(df_grouped[\"College\"].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "omhvRRokjl6W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College\n",
       "College of Agriculture and Life Sciences      89.0\n",
       "College of Architecture, Art and Planning      4.0\n",
       "College of Arts and Sciences                 184.0\n",
       "College of Engineering                        71.0\n",
       "College of Human Ecology                     144.0\n",
       "SC Johnson College of Business                25.0\n",
       "School of Industrial and Labor Relations      18.0\n",
       "Weill Cornell Medicine                         4.0\n",
       "Name: Has_Moved, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The sum() method computes the sum of all items in a column\n",
    "# When combined with groupby, sums are computed separately per group\n",
    "# Let's try using it to find out how many participants in each college have ever moved.\n",
    "df_grouped = df.groupby(\"College\")\n",
    "display(df_grouped[\"Has_Moved\"].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lKEE8WO0jl6W"
   },
   "source": [
    "Finally, note that it is possible to group on multiple columns at once! This will create a group for each unique pair of possible values in the two columns. For example, we might be interested in splitting our analysis not just by college, but by different school years in each college:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "gjbzKU5Ejl6W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College                                    School_Year           \n",
       "College of Agriculture and Life Sciences   1st year undergraduate    1.636364\n",
       "                                           2nd year undergraduate    1.555556\n",
       "                                           3rd year undergraduate    2.025000\n",
       "                                           4th year undergraduate    2.250000\n",
       "                                           Graduate student          3.000000\n",
       "College of Architecture, Art and Planning  1st year undergraduate    3.000000\n",
       "                                           2nd year undergraduate    1.000000\n",
       "                                           4th year undergraduate    2.000000\n",
       "College of Arts and Sciences               1st year undergraduate    1.931818\n",
       "                                           2nd year undergraduate    2.118644\n",
       "                                           3rd year undergraduate    1.851852\n",
       "                                           4th year undergraduate    2.000000\n",
       "                                           Graduate student               NaN\n",
       "College of Engineering                     1st year undergraduate    2.222222\n",
       "                                           2nd year undergraduate    1.512821\n",
       "                                           3rd year undergraduate    1.218750\n",
       "                                           4th year undergraduate    2.227273\n",
       "College of Human Ecology                   1st year undergraduate    1.870370\n",
       "                                           2nd year undergraduate    1.571429\n",
       "                                           3rd year undergraduate    2.222222\n",
       "                                           4th year undergraduate    1.514286\n",
       "SC Johnson College of Business             1st year undergraduate    3.333333\n",
       "                                           2nd year undergraduate    1.444444\n",
       "                                           3rd year undergraduate    2.200000\n",
       "                                           4th year undergraduate    1.500000\n",
       "School of Industrial and Labor Relations   1st year undergraduate    0.800000\n",
       "                                           2nd year undergraduate    1.400000\n",
       "                                           3rd year undergraduate    3.250000\n",
       "                                           4th year undergraduate    2.181818\n",
       "Weill Cornell Medicine                     2nd year undergraduate    0.800000\n",
       "                                           3rd year undergraduate    1.500000\n",
       "                                           Graduate student          1.000000\n",
       "Name: Moving_Times, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_grouped = df.groupby([\"College\", \"School_Year\"])\n",
    "display(df_grouped[\"Moving_Times\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Exercise\n",
    "In this wrap-up, you will combine various pandas skills that we have learned in order to explore an example research question on this data.\n",
    "\n",
    "Here is the research question we will be exploring: Is there a relationship between parental income and hunger experience? More concretely, are participants who come from a lower parental income more likely to report having experienced hunger?\n",
    "\n",
    "In order to answer this question, you will need to take the following steps. Each step can be achieved using one of the techniques we have discussed today.\n",
    "1. The Hunger_Experience column is currently coded as a string, with possible answers \"Yes\" and \"No\". In order to perform numeric analyses on it, you will need to convert it to numerical data, like we discussed in Section 8.\n",
    "2. You will then need to use a groupby operation to split the data into the groups we are interested in comparing\n",
    "3. Finally, you will need to pick the right summarization method to use on the grouped DataFrame.\n",
    "\n",
    "HINT: for binary-coded data (that is, data where 1 means yes and 0 means no), the percentage of answers that are \"yes\" can be computed using the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your answer below\n"
   ]
  }
 ],
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