diff options
| author |  Anaqvi2020 <anaqvi2020@gmail.com>
| 2024-02-22 22:09:34 -0800 |
|---|---|---|
| committer | Anaqvi2020 <anaqvi2020@gmail.com>
| 2024-02-22 22:09:34 -0800 |
| commit | 431bff4fe84778d8bfa70ee7f712ec892883b969 (patch) | |
| tree | 516662d42965267a3a1dd0bc8ca08288f3ae1ee5 /CS105MiniProject.ipynb | |
| parent | d81c2b6555baa01aafdb5b68b38fa769503151d2 (diff) | |
| download | CS105MiniProject-431bff4fe84778d8bfa70ee7f712ec892883b969.tar.gz CS105MiniProject-431bff4fe84778d8bfa70ee7f712ec892883b969.tar.zst CS105MiniProject-431bff4fe84778d8bfa70ee7f712ec892883b969.zip | |
Chi-square equation
Diffstat (limited to 'CS105MiniProject.ipynb')
| -rw-r--r-- | CS105MiniProject.ipynb | 1312 |
1 files changed, 1255 insertions, 57 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index ffb4fa4..dd77c8f 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -2,27 +2,312 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 54, "id": "daa13044", "metadata": { + "ExecuteTime": { + "end_time": "2024-02-23T01:01:41.396867Z", + "start_time": "2024-02-23T01:01:40.758392Z" + }, "colab": { "base_uri": "https://localhost:8080/", "height": 614 }, "id": "daa13044", - "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a", - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.396867Z", - "start_time": "2024-02-23T01:01:40.758392Z" - } + "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a" }, "outputs": [ { "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n4 2/9/2024 20:26:16 Graduate \n.. ... ... \n255 2/14/2024 19:46:28 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n258 2/15/2024 16:10:40 Sophomore \n259 2/15/2024 16:14:11 Sophomore \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? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 North District 4 bed 2 bath No \n257 9 No \n258 4 Yes \n259 3 (room), 8 (hall), ~70 (building) 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 10 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>Timestamp</th>\n <th>What is your current class standing?</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>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>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</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>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</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 <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</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>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</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>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</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 </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</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>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</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>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</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>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</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>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</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 × 10 columns</p>\n</div>" + "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>Timestamp</th>\n", + " <th>What is your current class standing?</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>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>2/9/2024 20:12:14</td>\n", + " <td>Senior</td>\n", + " <td>Neither</td>\n", + " <td>House</td>\n", + " <td>6</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>2/9/2024 20:16:34</td>\n", + " <td>Junior</td>\n", + " <td>Both</td>\n", + " <td>Apartment</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", + " <tr>\n", + " <th>2</th>\n", + " <td>2/9/2024 20:18:55</td>\n", + " <td>Junior</td>\n", + " <td>Friends</td>\n", + " <td>House</td>\n", + " <td>4</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>2/9/2024 20:24:00</td>\n", + " <td>Senior</td>\n", + " <td>Neither</td>\n", + " <td>Apartment</td>\n", + " <td>1</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>2/9/2024 20:26:16</td>\n", + " <td>Graduate</td>\n", + " <td>Neither</td>\n", + " <td>Apartment</td>\n", + " <td>1</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", + " </tr>\n", + " <tr>\n", + " <th>255</th>\n", + " <td>2/14/2024 19:46:28</td>\n", + " <td>Junior</td>\n", + " <td>Friends</td>\n", + " <td>House</td>\n", + " <td>5</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>2/15/2024 0:28:38</td>\n", + " <td>NaN</td>\n", + " <td>Family</td>\n", + " <td>Apartment</td>\n", + " <td>North District 4 bed 2 bath</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>2/15/2024 8:33:45</td>\n", + " <td>Senior</td>\n", + " <td>Family</td>\n", + " <td>House</td>\n", + " <td>9</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>2/15/2024 16:10:40</td>\n", + " <td>Sophomore</td>\n", + " <td>Family</td>\n", + " <td>Apartment</td>\n", + " <td>4</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>2/15/2024 16:14:11</td>\n", + " <td>Sophomore</td>\n", + " <td>Friends</td>\n", + " <td>Dorm</td>\n", + " <td>3 (room), 8 (hall), ~70 (building)</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 × 10 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Timestamp What is your current class standing? \\\n", + "0 2/9/2024 20:12:14 Senior \n", + "1 2/9/2024 20:16:34 Junior \n", + "2 2/9/2024 20:18:55 Junior \n", + "3 2/9/2024 20:24:00 Senior \n", + "4 2/9/2024 20:26:16 Graduate \n", + ".. ... ... \n", + "255 2/14/2024 19:46:28 Junior \n", + "256 2/15/2024 0:28:38 NaN \n", + "257 2/15/2024 8:33:45 Senior \n", + "258 2/15/2024 16:10:40 Sophomore \n", + "259 2/15/2024 16:14:11 Sophomore \n", + "\n", + " Who do you live with? \\\n", + "0 Neither \n", + "1 Both \n", + "2 Friends \n", + "3 Neither \n", + "4 Neither \n", + ".. ... \n", + "255 Friends \n", + "256 Family \n", + "257 Family \n", + "258 Family \n", + "259 Friends \n", + "\n", + " Do you currently live in a house, apartnment, or dorm? \\\n", + "0 House \n", + "1 Apartment \n", + "2 House \n", + "3 Apartment \n", + "4 Apartment \n", + ".. ... \n", + "255 House \n", + "256 Apartment \n", + "257 House \n", + "258 Apartment \n", + "259 Dorm \n", + "\n", + " How many people live in your household? Do you currently work? \\\n", + "0 6 Yes \n", + "1 4 No \n", + "2 4 No \n", + "3 1 No \n", + "4 1 Yes \n", + ".. ... ... \n", + "255 5 Yes \n", + "256 North District 4 bed 2 bath No \n", + "257 9 No \n", + "258 4 Yes \n", + "259 3 (room), 8 (hall), ~70 (building) No \n", + "\n", + " How many hours do you work per week on average? \\\n", + "0 5 - 10 \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 10 - 20 \n", + ".. ... \n", + "255 10 - 20 \n", + "256 NaN \n", + "257 1 - 5 \n", + "258 5 - 10 \n", + "259 NaN \n", + "\n", + " Do you work on or off campus? \\\n", + "0 Off-campus \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 Off-campus \n", + ".. ... \n", + "255 On-campus \n", + "256 NaN \n", + "257 Off-campus \n", + "258 On-campus \n", + "259 NaN \n", + "\n", + " Do you work in a department related to your major? \\\n", + "0 No \n", + "1 NaN \n", + "2 NaN \n", + "3 No \n", + "4 Yes \n", + ".. ... \n", + "255 No \n", + "256 NaN \n", + "257 No \n", + "258 No \n", + "259 NaN \n", + "\n", + " Do you have roommates that are part of your major? \n", + "0 No \n", + "1 Yes \n", + "2 No \n", + "3 No \n", + "4 No \n", + ".. ... \n", + "255 No \n", + "256 No \n", + "257 No \n", + "258 No \n", + "259 Yes \n", + "\n", + "[260 rows x 10 columns]" + ] }, - "execution_count": 1, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -39,22 +324,307 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 55, "id": "29889175", "metadata": { - "id": "29889175", "ExecuteTime": { "end_time": "2024-02-23T01:01:41.409516Z", "start_time": "2024-02-23T01:01:41.398267Z" - } + }, + "id": "29889175" }, "outputs": [ { "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n4 2/9/2024 20:26:16 Graduate \n.. ... ... \n255 2/14/2024 19:46:28 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n258 2/15/2024 16:10:40 Sophomore \n259 2/15/2024 16:14:11 Sophomore \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? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 4 No \n257 9 No \n258 4 Yes \n259 3 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 10 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>Timestamp</th>\n <th>What is your current class standing?</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>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>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</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>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</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>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</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>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</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>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</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 </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 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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>2/13/2024 21:39:14</td>\n <td>Senior</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</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>2/14/2024 9:48:12</td>\n <td>Junior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</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>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</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>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</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 × 10 columns</p>\n</div>" + "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>Timestamp</th>\n", + " <th>What is your current class standing?</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>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>2/9/2024 20:12:14</td>\n", + " <td>Senior</td>\n", + " <td>Neither</td>\n", + " <td>House</td>\n", + " <td>6</td>\n", + " <td>Yes</td>\n", + " <td>5 - 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<td>4</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>2/9/2024 20:18:55</td>\n", + " <td>Junior</td>\n", + " <td>Friends</td>\n", + " <td>House</td>\n", + " <td>4</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>2/9/2024 20:24:00</td>\n", + " <td>Senior</td>\n", + " <td>Neither</td>\n", + " <td>Apartment</td>\n", + " <td>1</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>2/9/2024 20:45:09</td>\n", + " <td>Junior</td>\n", + " <td>Both</td>\n", + " <td>Apartment</td>\n", + " <td>4</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>2/9/2024 21:55:59</td>\n", + " <td>Sophomore</td>\n", + " <td>Friends</td>\n", + " <td>Apartment</td>\n", + " <td>4</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", + " </tr>\n", + " <tr>\n", + " <th>253</th>\n", + " <td>2/14/2024 13:45:45</td>\n", + " <td>Senior</td>\n", + " <td>Family</td>\n", + " <td>House</td>\n", + " <td>6</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>2/14/2024 16:26:06</td>\n", + " <td>Junior</td>\n", + " <td>Family</td>\n", + " <td>House</td>\n", + " <td>5</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>2/15/2024 0:28:38</td>\n", + " <td>NaN</td>\n", + " <td>Family</td>\n", + " <td>Apartment</td>\n", + " <td>4</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>2/15/2024 8:33:45</td>\n", + " <td>Senior</td>\n", + " <td>Family</td>\n", + " <td>House</td>\n", + " <td>9</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>2/15/2024 16:14:11</td>\n", + " <td>Sophomore</td>\n", + " <td>Friends</td>\n", + " <td>Dorm</td>\n", + " <td>3</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>176 rows × 10 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Timestamp What is your current class standing? \\\n", + "1 2/9/2024 20:16:34 Junior \n", + "2 2/9/2024 20:18:55 Junior \n", + "3 2/9/2024 20:24:00 Senior \n", + "5 2/9/2024 20:45:09 Junior \n", + "6 2/9/2024 21:55:59 Sophomore \n", + ".. ... ... \n", + "253 2/14/2024 13:45:45 Senior \n", + "254 2/14/2024 16:26:06 Junior \n", + "256 2/15/2024 0:28:38 NaN \n", + "257 2/15/2024 8:33:45 Senior \n", + "259 2/15/2024 16:14:11 Sophomore \n", + "\n", + " Who do you live with? \\\n", + "1 Both \n", + "2 Friends \n", + "3 Neither \n", + "5 Both \n", + "6 Friends \n", + ".. ... \n", + "253 Family \n", + "254 Family \n", + "256 Family \n", + "257 Family \n", + "259 Friends \n", + "\n", + " Do you currently live in a house, apartnment, or dorm? \\\n", + "1 Apartment \n", + "2 House \n", + "3 Apartment \n", + "5 Apartment \n", + "6 Apartment \n", + ".. ... \n", + "253 House \n", + "254 House \n", + "256 Apartment \n", + "257 House \n", + "259 Dorm \n", + "\n", + " How many people live in your household? Do you currently work? \\\n", + "1 4 No \n", + "2 4 No \n", + "3 1 No \n", + "5 4 No \n", + "6 4 No \n", + ".. ... ... \n", + "253 6 No \n", + "254 5 No \n", + "256 4 No \n", + "257 9 No \n", + "259 3 No \n", + "\n", + " How many hours do you work per week on average? \\\n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "5 0 \n", + "6 0 \n", + ".. ... \n", + "253 0 \n", + "254 0 \n", + "256 0 \n", + "257 0 \n", + "259 0 \n", + "\n", + " Do you work on or off campus? \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "5 NaN \n", + "6 NaN \n", + ".. ... \n", + "253 NaN \n", + "254 NaN \n", + "256 NaN \n", + "257 Off-campus \n", + "259 NaN \n", + "\n", + " Do you work in a department related to your major? \\\n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "5 NaN \n", + "6 NaN \n", + ".. ... \n", + "253 NaN \n", + "254 NaN \n", + "256 NaN \n", + "257 NaN \n", + "259 NaN \n", + "\n", + " Do you have roommates that are part of your major? \n", + "1 Yes \n", + "2 No \n", + "3 No \n", + "5 No \n", + "6 No \n", + ".. ... \n", + "253 No \n", + "254 Yes \n", + "256 No \n", + "257 No \n", + "259 Yes \n", + "\n", + "[176 rows x 10 columns]" + ] }, - "execution_count": 4, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw_df" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.427847Z", - "start_time": "2024-02-23T01:01:41.419852Z" - } - }, - "id": "5fe8ec7f22878e60", - "execution_count": 4 + ] }, { "cell_type": "markdown", + "id": "899d85626b77db20", + "metadata": { + "collapsed": false + }, "source": [ "<div>\n", " <h1>CS105 Project</h2>\n", " <p>Ali Naqvi, ...</p>\n", " <p>Topic: Does who a student is living with effect if and how they work jobs?</p>\n", "</div>\n" - ], - "metadata": { - "collapsed": false - }, - "id": "899d85626b77db20" + ] }, { "cell_type": "code", + "execution_count": 58, + "id": "bfa40c9e9693481d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-23T01:01:41.526696Z", + "start_time": "2024-02-23T01:01:41.430135Z" + }, + "collapsed": false + }, "outputs": [ { "data": { - "text/plain": "<Figure size 800x800 with 1 Axes>", - "image/png": 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" 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", + "text/plain": [ + "<Figure size 800x800 with 1 Axes>" + ] }, "metadata": {}, "output_type": "display_data" @@ -171,30 +1322,77 @@ "plt.pie(work_counts, labels=work_counts.index, autopct='%1.1f%%', startangle=90, colors=['lightblue', 'lightcoral'])\n", "plt.title('Distribution of People Who Work and Don\\'t Work')\n", "plt.show()\n" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.526696Z", - "start_time": "2024-02-23T01:01:41.430135Z" - } - }, - "id": "bfa40c9e9693481d", - "execution_count": 5 + ] }, { "cell_type": "code", - "outputs": [], - "source": [], + "execution_count": 65, + "id": "9c830283e9b26466", "metadata": { - "collapsed": false, "ExecuteTime": { "end_time": "2024-02-23T01:01:41.532148Z", "start_time": "2024-02-23T01:01:41.528825Z" - } + }, + "collapsed": false }, - "id": "9c830283e9b26466", - "execution_count": 5 + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Do you have roommates that are part of your major? No Yes Total\n", + "Do you currently live in a house, apartnment, o... \n", + "Apartment 83 44 127\n", + "Dorm 17 11 28\n", + "House 77 21 98\n", + "Room 1 0 1\n", + "house (renting) 1 0 1\n", + "Total 179 76 255\n" + ] + } + ], + "source": [ + "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n", + "\n", + "# Print the table\n", + "print(roommates_major_table)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "aef1e802", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chi-squared Value: 6.761293513057266\n", + "Degrees of Freedom: 10\n" + ] + } + ], + "source": [ + "# Extract the observed values from the contingency table\n", + "observed_values = roommates_major_table.iloc[:-1, :-1].values\n", + "\n", + "# Calculate expected values\n", + "row_totals = roommates_major_table.iloc[:-1, -1].values\n", + "col_totals = roommates_major_table.iloc[-1, :-1].values\n", + "total = np.sum(row_totals)\n", + "\n", + "expected_values = np.outer(row_totals, col_totals) / total\n", + "\n", + "# Calculate chi-squared statistic\n", + "chi2_statistic = np.sum((observed_values - expected_values)**2 / expected_values)\n", + "\n", + "# Degrees of freedom\n", + "degrees_of_freedom = (roommates_major_table.shape[0] - 1) * (roommates_major_table.shape[1] - 1)\n", + "\n", + "# Print results\n", + "print(f\"Chi-squared Value: {chi2_statistic}\\nDegrees of Freedom: {degrees_of_freedom}\")\n" + ] } ], "metadata": { |