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TimestampWhat is your current class standing?Who do you live with?Do you currently live in a house, apartnment, or dorm?How many people live in your household?Do you currently work?How many hours do you work per week on average?Do you work on or off campus?Do you work in a department related to your major?Do you have roommates that are part of your major?
02/9/2024 20:12:14SeniorNeitherHouse6Yes5 - 10Off-campusNoNo
12/9/2024 20:16:34JuniorBothApartment4NoNaNNaNNaNYes
22/9/2024 20:18:55JuniorFriendsHouse4NoNaNNaNNaNNo
32/9/2024 20:24:00SeniorNeitherApartment1NoNaNNaNNoNo
42/9/2024 20:26:16GraduateNeitherApartment1Yes10 - 20Off-campusYesNo
.................................
2552/14/2024 19:46:28JuniorFriendsHouse5Yes10 - 20On-campusNoNo
2562/15/2024 0:28:38NaNFamilyApartmentNorth District 4 bed 2 bathNoNaNNaNNaNNo
2572/15/2024 8:33:45SeniorFamilyHouse9No1 - 5Off-campusNoNo
2582/15/2024 16:10:40SophomoreFamilyApartment4Yes5 - 10On-campusNoNo
2592/15/2024 16:14:11SophomoreFriendsDorm3 (room), 8 (hall), ~70 (building)NoNaNNaNNaNYes
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260 rows × 10 columns

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" ], "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": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "\n", "df = pd.read_csv(\"data.csv\")\n", "df = df.iloc[:, [0, 2, 7, 8, 9, 58, 59, 60, 61, 26]]\n", "df" ] }, { "cell_type": "code", "execution_count": 55, "id": "29889175", "metadata": { "ExecuteTime": { "end_time": "2024-02-23T01:01:41.409516Z", "start_time": "2024-02-23T01:01:41.398267Z" }, "id": "29889175" }, "outputs": [ { "data": { "text/html": [ "
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TimestampWhat is your current class standing?Who do you live with?Do you currently live in a house, apartnment, or dorm?How many people live in your household?Do you currently work?How many hours do you work per week on average?Do you work on or off campus?Do you work in a department related to your major?Do you have roommates that are part of your major?
02/9/2024 20:12:14SeniorNeitherHouse6Yes5 - 10Off-campusNoNo
12/9/2024 20:16:34JuniorBothApartment4No0NaNNaNYes
22/9/2024 20:18:55JuniorFriendsHouse4No0NaNNaNNo
32/9/2024 20:24:00SeniorNeitherApartment1No0NaNNaNNo
42/9/2024 20:26:16GraduateNeitherApartment1Yes10 - 20Off-campusYesNo
.................................
2552/14/2024 19:46:28JuniorFriendsHouse5Yes10 - 20On-campusNoNo
2562/15/2024 0:28:38NaNFamilyApartment4No0NaNNaNNo
2572/15/2024 8:33:45SeniorFamilyHouse9No0Off-campusNaNNo
2582/15/2024 16:10:40SophomoreFamilyApartment4Yes5 - 10On-campusNoNo
2592/15/2024 16:14:11SophomoreFriendsDorm3No0NaNNaNYes
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260 rows × 10 columns

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" ], "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 4 No \n", "257 9 No \n", "258 4 Yes \n", "259 3 No \n", "\n", " How many hours do you work per week on average? \\\n", "0 5 - 10 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 10 - 20 \n", ".. ... \n", "255 10 - 20 \n", "256 0 \n", "257 0 \n", "258 5 - 10 \n", "259 0 \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 NaN \n", "4 Yes \n", ".. ... \n", "255 No \n", "256 NaN \n", "257 NaN \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": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['How many people live in your household?'] = (df['How many people live in your household?']\n", " .fillna(0)\n", " .replace('4 in total', '4')\n", " .replace('4 (Including me)', '4')\n", " .replace('at school 4 including me ', '4')\n", " .replace('3 excluding me', '4')\n", " .replace('5 including me', '5')\n", " .replace('North District 4 bed 2 bath', '4')\n", " .replace('3 (room), 8 (hall), ~70 (building)', '3')\n", " .astype(int))\n", "df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n", "df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace('Family, Friends, Both', 'Both')\n", "df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n", "df" ] }, { "cell_type": "code", "execution_count": 56, "id": "de4448fd64205d85", "metadata": { "ExecuteTime": { "end_time": "2024-02-23T01:01:41.418974Z", "start_time": "2024-02-23T01:01:41.410787Z" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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TimestampWhat is your current class standing?Who do you live with?Do you currently live in a house, apartnment, or dorm?How many people live in your household?Do you currently work?How many hours do you work per week on average?Do you work on or off campus?Do you work in a department related to your major?Do you have roommates that are part of your major?
02/9/2024 20:12:14SeniorNeitherHouse6Yes5 - 10Off-campusNoNo
42/9/2024 20:26:16GraduateNeitherApartment1Yes10 - 20Off-campusYesNo
82/9/2024 22:02:49JuniorFriendsHouse6Yes10 - 20On-campusNoNo
92/9/2024 22:08:43SeniorFamilyHouse5Yes1 - 5On-campusNoNo
132/9/2024 22:15:13JuniorFamilyApartment4Yes10 - 20Off-campusNoNo
.................................
2462/13/2024 19:37:02GraduateFamilyHouse2Yes10 - 20On-campusYesNo
2472/13/2024 21:39:14SeniorFriendsApartment3Yes20 - 40Off-campusNoYes
2522/14/2024 9:48:12JuniorFamilyHouse5Yes20 - 40Off-campusNoNo
2552/14/2024 19:46:28JuniorFriendsHouse5Yes10 - 20On-campusNoNo
2582/15/2024 16:10:40SophomoreFamilyApartment4Yes5 - 10On-campusNoNo
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77 rows × 10 columns

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" ], "text/plain": [ " Timestamp What is your current class standing? \\\n", "0 2/9/2024 20:12:14 Senior \n", "4 2/9/2024 20:26:16 Graduate \n", "8 2/9/2024 22:02:49 Junior \n", "9 2/9/2024 22:08:43 Senior \n", "13 2/9/2024 22:15:13 Junior \n", ".. ... ... \n", "246 2/13/2024 19:37:02 Graduate \n", "247 2/13/2024 21:39:14 Senior \n", "252 2/14/2024 9:48:12 Junior \n", "255 2/14/2024 19:46:28 Junior \n", "258 2/15/2024 16:10:40 Sophomore \n", "\n", " Who do you live with? \\\n", "0 Neither \n", "4 Neither \n", "8 Friends \n", "9 Family \n", "13 Family \n", ".. ... \n", "246 Family \n", "247 Friends \n", "252 Family \n", "255 Friends \n", "258 Family \n", "\n", " Do you currently live in a house, apartnment, or dorm? \\\n", "0 House \n", "4 Apartment \n", "8 House \n", "9 House \n", "13 Apartment \n", ".. ... \n", "246 House \n", "247 Apartment \n", "252 House \n", "255 House \n", "258 Apartment \n", "\n", " How many people live in your household? Do you currently work? \\\n", "0 6 Yes \n", "4 1 Yes \n", "8 6 Yes \n", "9 5 Yes \n", "13 4 Yes \n", ".. ... ... \n", "246 2 Yes \n", "247 3 Yes \n", "252 5 Yes \n", "255 5 Yes \n", "258 4 Yes \n", "\n", " How many hours do you work per week on average? \\\n", "0 5 - 10 \n", "4 10 - 20 \n", "8 10 - 20 \n", "9 1 - 5 \n", "13 10 - 20 \n", ".. ... \n", "246 10 - 20 \n", "247 20 - 40 \n", "252 20 - 40 \n", "255 10 - 20 \n", "258 5 - 10 \n", "\n", " Do you work on or off campus? \\\n", "0 Off-campus \n", "4 Off-campus \n", "8 On-campus \n", "9 On-campus \n", "13 Off-campus \n", ".. ... \n", "246 On-campus \n", "247 Off-campus \n", "252 Off-campus \n", "255 On-campus \n", "258 On-campus \n", "\n", " Do you work in a department related to your major? \\\n", "0 No \n", "4 Yes \n", "8 No \n", "9 No \n", "13 No \n", ".. ... \n", "246 Yes \n", "247 No \n", "252 No \n", "255 No \n", "258 No \n", "\n", " Do you have roommates that are part of your major? \n", "0 No \n", "4 No \n", "8 No \n", "9 No \n", "13 No \n", ".. ... \n", "246 No \n", "247 Yes \n", "252 No \n", "255 No \n", "258 No \n", "\n", "[77 rows x 10 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Working DataFrame\n", "w_df = df[df['Do you currently work?'] == 'Yes']\n", "# Not working DataFrame\n", "nw_df = df[df['Do you currently work?'] == 'No']\n", "w_df" ] }, { "cell_type": "code", "execution_count": 57, "id": "5fe8ec7f22878e60", "metadata": { "ExecuteTime": { "end_time": "2024-02-23T01:01:41.427847Z", "start_time": "2024-02-23T01:01:41.419852Z" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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TimestampWhat is your current class standing?Who do you live with?Do you currently live in a house, apartnment, or dorm?How many people live in your household?Do you currently work?How many hours do you work per week on average?Do you work on or off campus?Do you work in a department related to your major?Do you have roommates that are part of your major?
12/9/2024 20:16:34JuniorBothApartment4No0NaNNaNYes
22/9/2024 20:18:55JuniorFriendsHouse4No0NaNNaNNo
32/9/2024 20:24:00SeniorNeitherApartment1No0NaNNaNNo
52/9/2024 20:45:09JuniorBothApartment4No0NaNNaNNo
62/9/2024 21:55:59SophomoreFriendsApartment4No0NaNNaNNo
.................................
2532/14/2024 13:45:45SeniorFamilyHouse6No0NaNNaNNo
2542/14/2024 16:26:06JuniorFamilyHouse5No0NaNNaNYes
2562/15/2024 0:28:38NaNFamilyApartment4No0NaNNaNNo
2572/15/2024 8:33:45SeniorFamilyHouse9No0Off-campusNaNNo
2592/15/2024 16:14:11SophomoreFriendsDorm3No0NaNNaNYes
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176 rows × 10 columns

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" ], "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": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nw_df" ] }, { "cell_type": "markdown", "id": "899d85626b77db20", "metadata": { "collapsed": false }, "source": [ "
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CS105 Project

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Ali Naqvi, ...

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Topic: Does who a student is living with effect if and how they work jobs?

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\n" ] }, { "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": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Assuming 'df' is your DataFrame\n", "\n", "# Count the number of people who work and don't work\n", "work_counts = df['Do you currently work?'].value_counts()\n", "\n", "# Plotting a pie chart\n", "plt.figure(figsize=(8, 8))\n", "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" ] }, { "cell_type": "code", "execution_count": 65, "id": "9c830283e9b26466", "metadata": { "ExecuteTime": { "end_time": "2024-02-23T01:01:41.532148Z", "start_time": "2024-02-23T01:01:41.528825Z" }, "collapsed": false }, "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": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }