diff options
| author |  ENathanLe <Nath.wessix@gmail.com>
| 2024-02-22 23:46:11 -0800 |
|---|---|---|
| committer |  Anshul Gupta <ansg191@anshulg.com>
| 2024-02-23 18:31:47 -0800 |
| commit | 2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b (patch) | |
| tree | 9a448b765f742b1147d5c7cf70ef1032c74c8036 /CS105MiniProject.ipynb | |
| parent | d2854b75fdd15f1313ca3a5cd1c4929ce868fb2f (diff) | |
| download | CS105MiniProject-2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b.tar.gz CS105MiniProject-2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b.tar.zst CS105MiniProject-2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b.zip | |
Minor fixes to remove trailing and starting spaces from index names
Diffstat (limited to 'CS105MiniProject.ipynb')
| -rw-r--r-- | CS105MiniProject.ipynb | 186 |
1 files changed, 90 insertions, 96 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index b8d92b8..3e15525 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -2,49 +2,35 @@ "cells": [ { "cell_type": "markdown", + "id": "21abd26c73fd0070", + "metadata": { + "collapsed": false + }, "source": [ "<div>\n", " <h1><center>CS105 Mini-Project</center></h1>\n", " <h2><center>Does who a student is living with effect if and how they work jobs?</center></h2>\n", " <p>By: <b>NAMES HERE</b></p>\n", "</div>" - ], - "metadata": { - "collapsed": false - }, - "id": "21abd26c73fd0070" + ] }, { "cell_type": "markdown", - "source": [ - "# Data Loading & Preprocessing" - ], + "id": "69d8e8ad7c61ba61", "metadata": { "collapsed": false }, - "id": "69d8e8ad7c61ba61" + "source": [ + "# Data Loading & Preprocessing" + ] }, { "cell_type": "code", - "execution_count": 1, - "id": "daa13044", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 614 - }, - "id": "daa13044", - "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a", - "ExecuteTime": { - "end_time": "2024-02-23T06:53:02.933496Z", - "start_time": "2024-02-23T06:53:02.907444Z" - } - }, "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 North District 4 bed 2 bath \n257 9 \n258 4 \n259 3 (room), 8 (hall), ~70 (building) \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.7 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4 Yes \n256 3.5 No \n257 3.7 No \n258 3 Yes \n259 4 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 NaN \n2 NaN \n3 NaN \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 NaN \n257 1 - 5 \n258 5 - 10 \n259 NaN \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 No \n4 Yes \n.. ... \n255 No \n256 NaN \n257 No \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 11 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.7</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>3.5</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.7</td>\n <td>No</td>\n <td>1 - 5</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 North District 4 bed 2 bath \n257 9 \n258 4 \n259 3 (room), 8 (hall), ~70 (building) \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.7 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4 Yes \n256 3.5 No \n257 3.7 No \n258 3 Yes \n259 4 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 NaN \n2 NaN \n3 NaN \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 NaN \n257 1 - 5 \n258 5 - 10 \n259 NaN \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 No \n4 Yes \n.. ... \n255 No \n256 NaN \n257 No \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 11 columns]", + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.7</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>3.5</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.7</td>\n <td>No</td>\n <td>1 - 5</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>" }, "execution_count": 1, "metadata": {}, @@ -65,7 +51,16 @@ "# Select relevant columns\n", "df = df.iloc[:, [2, 3, 7, 8, 9, 34, 58, 59, 60, 61, 26]]\n", "df" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:50.385493Z", + "start_time": "2024-02-24T02:30:50.364241Z" + } + }, + "id": "b68b27041fdab1a5", + "execution_count": 1 }, { "cell_type": "markdown", @@ -75,24 +70,15 @@ "metadata": { "collapsed": false }, - "id": "3f7614a5665d55b6" + "id": "f7ee1fc9a8abba2b" }, { "cell_type": "code", - "execution_count": 2, - "id": "29889175", - "metadata": { - "id": "29889175", - "ExecuteTime": { - "end_time": "2024-02-23T06:53:02.952629Z", - "start_time": "2024-02-23T06:53:02.936631Z" - } - }, "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 4 \n257 9 \n258 4 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.70 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4.00 Yes \n256 3.50 No \n257 3.70 No \n258 3.00 Yes \n259 4.00 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 0 \n2 0 \n3 0 \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 0 \n257 0 \n258 5 - 10 \n259 0 \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 NaN \n4 Yes \n.. ... \n255 No \n256 NaN \n257 NaN \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 11 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 4 \n257 9 \n258 4 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.70 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4.00 Yes \n256 3.50 No \n257 3.70 No \n258 3.00 Yes \n259 4.00 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 0 \n2 0 \n3 0 \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 0 \n257 0 \n258 5 - 10 \n259 0 \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 NaN \n4 Yes \n.. ... \n255 No \n256 NaN \n257 NaN \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 11 columns]", + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>" }, "execution_count": 2, "metadata": {}, @@ -114,10 +100,10 @@ " .replace('North District 4 bed 2 bath', '4')\n", " .replace('3 (room), 8 (hall), ~70 (building)', '3')\n", " .astype(int))\n", - "df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace(\n", + "df['Who do you live with?'] = df['Who do you live with?'].replace('Family, Friends', 'Both').replace(\n", " 'Family, Friends, Both', 'Both')\n", - "df['Do you currently live in a house, apartnment, or dorm? '] = (\n", - " df['Do you currently live in a house, apartnment, or dorm? ']\n", + "df['Do you currently live in a house, apartment, or dorm?'] = (\n", + " df['Do you currently live in a house, apartment, or dorm?']\n", " .replace('house (renting)', 'House'))\n", "\n", "df.loc[df['What was your GPA your very first quarter at UCR?'].str.contains(\n", @@ -131,30 +117,29 @@ " .replace('3.0?', '3.0')\n", " .replace('about 3.0', '3.0')\n", " .astype(np.float64))\n", - "\n", "# Normalizes non-applicable answers\n", "df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n", "df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n", "\n", "df" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "de4448fd64205d85", + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:02.965372Z", - "start_time": "2024-02-23T06:53:02.954441Z" + "end_time": "2024-02-24T02:30:50.398700Z", + "start_time": "2024-02-24T02:30:50.386214Z" } }, + "id": "3f72adcb3bc0285e", + "execution_count": 2 + }, + { + "cell_type": "code", "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? \\\n0 6 \n4 1 \n8 6 \n9 5 \n13 4 \n.. ... \n246 2 \n247 3 \n252 5 \n255 5 \n258 4 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n4 3.23 Yes \n8 3.40 Yes \n9 NaN Yes \n13 3.50 Yes \n.. ... ... \n246 4.00 Yes \n247 3.60 Yes \n252 3.50 Yes \n255 4.00 Yes \n258 3.00 Yes \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n4 10 - 20 \n8 10 - 20 \n9 1 - 5 \n13 10 - 20 \n.. ... \n246 10 - 20 \n247 20 - 40 \n252 20 - 40 \n255 10 - 20 \n258 5 - 10 \n\n Do you work on or off campus? \\\n0 Off-campus \n4 Off-campus \n8 On-campus \n9 On-campus \n13 Off-campus \n.. ... \n246 On-campus \n247 Off-campus \n252 Off-campus \n255 On-campus \n258 On-campus \n\n Do you work in a department related to your major? \\\n0 No \n4 Yes \n8 No \n9 No \n13 No \n.. ... \n246 Yes \n247 No \n252 No \n255 No \n258 No \n\n Do you have roommates that are part of your major? \n0 No \n4 No \n8 No \n9 No \n13 No \n.. ... \n246 No \n247 Yes \n252 No \n255 No \n258 No \n\n[77 rows x 11 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Junior</td>\n <td>20</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>3.40</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Senior</td>\n <td>22</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>NaN</td>\n <td>Yes</td>\n <td>1 - 5</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Junior</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>Graduate</td>\n <td>23+</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>247</th>\n <td>Senior</td>\n <td>21</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>3.60</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>252</th>\n <td>Junior</td>\n <td>20</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.50</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n </tbody>\n</table>\n<p>77 rows × 11 columns</p>\n</div>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? \\\n0 6 \n4 1 \n8 6 \n9 5 \n13 4 \n.. ... \n246 2 \n247 3 \n252 5 \n255 5 \n258 4 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n4 3.23 Yes \n8 3.40 Yes \n9 NaN Yes \n13 3.50 Yes \n.. ... ... \n246 4.00 Yes \n247 3.60 Yes \n252 3.50 Yes \n255 4.00 Yes \n258 3.00 Yes \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n4 10 - 20 \n8 10 - 20 \n9 1 - 5 \n13 10 - 20 \n.. ... \n246 10 - 20 \n247 20 - 40 \n252 20 - 40 \n255 10 - 20 \n258 5 - 10 \n\n Do you work on or off campus? \\\n0 Off-campus \n4 Off-campus \n8 On-campus \n9 On-campus \n13 Off-campus \n.. ... \n246 On-campus \n247 Off-campus \n252 Off-campus \n255 On-campus \n258 On-campus \n\n Do you work in a department related to your major? \\\n0 No \n4 Yes \n8 No \n9 No \n13 No \n.. ... \n246 Yes \n247 No \n252 No \n255 No \n258 No \n\n Do you have roommates that are part of your major? \n0 No \n4 No \n8 No \n9 No \n13 No \n.. ... \n246 No \n247 Yes \n252 No \n255 No \n258 No \n\n[77 rows x 11 columns]", + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Junior</td>\n <td>20</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>3.40</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Senior</td>\n <td>22</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>NaN</td>\n <td>Yes</td>\n <td>1 - 5</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Junior</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>Graduate</td>\n <td>23+</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>247</th>\n <td>Senior</td>\n <td>21</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>3.60</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>252</th>\n <td>Junior</td>\n <td>20</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.50</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n </tbody>\n</table>\n<p>77 rows × 11 columns</p>\n</div>" }, "execution_count": 3, "metadata": {}, @@ -167,24 +152,24 @@ "# Not working DataFrame\n", "nw_df = df[df['Do you currently work?'] == 'No']\n", "w_df" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5fe8ec7f22878e60", + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:02.975332Z", - "start_time": "2024-02-23T06:53:02.968284Z" + "end_time": "2024-02-24T02:30:50.408153Z", + "start_time": "2024-02-24T02:30:50.400240Z" } }, + "id": "285236650ff590d8", + "execution_count": 3 + }, + { + "cell_type": "code", "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? \\\n1 4 \n2 4 \n3 1 \n5 4 \n6 4 \n.. ... \n253 6 \n254 5 \n256 4 \n257 9 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n1 3.70 No \n2 3.75 No \n3 3.81 No \n5 4.00 No \n6 4.00 No \n.. ... ... \n253 4.00 No \n254 3.80 No \n256 3.50 No \n257 3.70 No \n259 4.00 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[183 rows x 11 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Junior</td>\n <td>21</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Sophomore</td>\n <td>19</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>254</th>\n <td>Junior</td>\n <td>19</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.80</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>183 rows × 11 columns</p>\n</div>" + "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? \\\n1 4 \n2 4 \n3 1 \n5 4 \n6 4 \n.. ... \n253 6 \n254 5 \n256 4 \n257 9 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n1 3.70 No \n2 3.75 No \n3 3.81 No \n5 4.00 No \n6 4.00 No \n.. ... ... \n253 4.00 No \n254 3.80 No \n256 3.50 No \n257 3.70 No \n259 4.00 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[183 rows x 11 columns]", + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Junior</td>\n <td>21</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Sophomore</td>\n <td>19</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>254</th>\n <td>Junior</td>\n <td>19</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.80</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>183 rows × 11 columns</p>\n</div>" }, "execution_count": 4, "metadata": {}, @@ -193,17 +178,26 @@ ], "source": [ "nw_df" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:50.417032Z", + "start_time": "2024-02-24T02:30:50.408722Z" + } + }, + "id": "6516c926e6efd1c3", + "execution_count": 4 }, { "cell_type": "markdown", - "id": "899d85626b77db20", + "source": [ + "# Analysis" + ], "metadata": { "collapsed": false }, - "source": [ - "# Analysis" - ] + "id": "7efd20d58edbb05d" }, { "cell_type": "code", @@ -230,11 +224,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.191673Z", - "start_time": "2024-02-23T06:53:02.976617Z" + "end_time": "2024-02-24T02:30:50.608877Z", + "start_time": "2024-02-24T02:30:50.418071Z" } }, - "id": "6bc50ddc195d88a", + "id": "6deea60d8966fa15", "execution_count": 5 }, { @@ -243,7 +237,7 @@ { "data": { "text/plain": "<Figure size 800x800 with 2 Axes>", - "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -252,23 +246,23 @@ "source": [ "df_2dhist = pd.DataFrame({\n", " x_label: grp['Do you currently work?'].value_counts()\n", - " for x_label, grp in df.groupby('Do you currently live in a house, apartnment, or dorm? ')\n", + " for x_label, grp in df.groupby('Do you currently live in a house, apartment, or dorm?')\n", "})\n", "\n", "# Plot heatmap\n", "plt.subplots(figsize=(8, 8))\n", - "sns.heatmap(df_2dhist, cmap='viridis')\n", - "plt.xlabel('Do you currently live in a house, apartnment, or dorm? ')\n", + "sns.heatmap(df_2dhist, cmap=\"Blues\")\n", + "plt.xlabel('Do you currently live in a house, apartment, or dorm?')\n", "_ = plt.ylabel('Do you currently work?')" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.404019Z", - "start_time": "2024-02-23T06:53:03.194598Z" + "end_time": "2024-02-24T02:30:50.805764Z", + "start_time": "2024-02-24T02:30:50.611563Z" } }, - "id": "15f1e14311b1b17f", + "id": "c6372820e5ee501f", "execution_count": 6 }, { @@ -277,7 +271,7 @@ "metadata": { "collapsed": false }, - "id": "2b499b750ea3aec9" + "id": "3ef5084b2abd603e" }, { "cell_type": "code", @@ -285,14 +279,14 @@ { "data": { "text/plain": "<Figure size 640x480 with 1 Axes>", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "df.groupby('Do you currently live in a house, apartnment, or dorm? ').size().plot(kind='barh',\n", + "df.groupby('Do you currently live in a house, apartment, or dorm?').size().plot(kind='barh',\n", " color=sns.palettes.mpl_palette(\n", " 'Dark2'))\n", "plt.gca().spines[['top', 'right', ]].set_visible(False)" @@ -300,11 +294,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.516283Z", - "start_time": "2024-02-23T06:53:03.408298Z" + "end_time": "2024-02-24T02:30:50.904997Z", + "start_time": "2024-02-24T02:30:50.807674Z" } }, - "id": "a3d9a4a3b5eba149", + "id": "67df9b48e43a5307", "execution_count": 7 }, { @@ -327,11 +321,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.640403Z", - "start_time": "2024-02-23T06:53:03.518082Z" + "end_time": "2024-02-24T02:30:51.009476Z", + "start_time": "2024-02-24T02:30:50.906524Z" } }, - "id": "36727f07413da341", + "id": "1163d27db8106025", "execution_count": 8 }, { @@ -342,7 +336,7 @@ "metadata": { "collapsed": false }, - "id": "4df3824f641fb18b" + "id": "8f2599d399d14333" }, { "cell_type": "markdown", @@ -352,15 +346,15 @@ "metadata": { "collapsed": false }, - "id": "796d474b4650e712" + "id": "85d89eaa6a2c8057" }, { "cell_type": "code", "outputs": [ { "data": { - "text/plain": "Do you work in a department related to your major? No Yes Total\nDo you currently live in a house, apartnment, o... \nApartment 22 16 38\nDorm 4 1 5\nHouse 27 7 34\nTotal 53 24 77", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>Do you work in a department related to your major?</th>\n <th>No</th>\n <th>Yes</th>\n <th>Total</th>\n </tr>\n <tr>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Apartment</th>\n <td>22</td>\n <td>16</td>\n <td>38</td>\n </tr>\n <tr>\n <th>Dorm</th>\n <td>4</td>\n <td>1</td>\n <td>5</td>\n </tr>\n <tr>\n <th>House</th>\n <td>27</td>\n <td>7</td>\n <td>34</td>\n </tr>\n <tr>\n <th>Total</th>\n <td>53</td>\n <td>24</td>\n <td>77</td>\n </tr>\n </tbody>\n</table>\n</div>" + "text/plain": "Do you work in a department related to your major? No Yes Total\nDo you currently live in a house, apartment, or... \nApartment 22 16 38\nDorm 4 1 5\nHouse 27 7 34\nTotal 53 24 77", + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>Do you work in a department related to your major?</th>\n <th>No</th>\n <th>Yes</th>\n <th>Total</th>\n </tr>\n <tr>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Apartment</th>\n <td>22</td>\n <td>16</td>\n <td>38</td>\n </tr>\n <tr>\n <th>Dorm</th>\n <td>4</td>\n <td>1</td>\n <td>5</td>\n </tr>\n <tr>\n <th>House</th>\n <td>27</td>\n <td>7</td>\n <td>34</td>\n </tr>\n <tr>\n <th>Total</th>\n <td>53</td>\n <td>24</td>\n <td>77</td>\n </tr>\n </tbody>\n</table>\n</div>" }, "execution_count": 9, "metadata": {}, @@ -374,11 +368,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.657146Z", - "start_time": "2024-02-23T06:53:03.642872Z" + "end_time": "2024-02-24T02:30:51.029851Z", + "start_time": "2024-02-24T02:30:51.010794Z" } }, - "id": "2ee7f39b5d8df8de", + "id": "5cbb7ab4d38de9ef", "execution_count": 9 }, { @@ -416,11 +410,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.664072Z", - "start_time": "2024-02-23T06:53:03.659800Z" + "end_time": "2024-02-24T02:30:51.035050Z", + "start_time": "2024-02-24T02:30:51.030607Z" } }, - "id": "957406c164cf2ef1", + "id": "2fbaac2d0722a7e3", "execution_count": 10 } ], |