Adds some analysis for visualizations

author: Anshul Gupta <ansg191@anshulg.com> 2024-02-23 20:34:42 -0800
committer: Anshul Gupta <ansg191@anshulg.com> 2024-02-23 21:05:40 -0800
commit: 813b153ef6b0669c1561fd2b49a6c95d40943a61 (patch)
tree: 9d12c116a3ae5d40c0a244eceaa2ffe56b6771b4 /CS105MiniProject.ipynb
parent: 7199637c662d5f10bf41ce3ded5480f2501a6a45 (diff)
download: CS105MiniProject-813b153ef6b0669c1561fd2b49a6c95d40943a61.tar.gz
CS105MiniProject-813b153ef6b0669c1561fd2b49a6c95d40943a61.tar.zst
CS105MiniProject-813b153ef6b0669c1561fd2b49a6c95d40943a61.zip
1 files changed, 140 insertions, 80 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index 35b9887..39cffcd 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -26,6 +26,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
+   "id": "b68b27041fdab1a5",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-24T05:05:30.261607Z",
+     "start_time": "2024-02-24T05:05:30.239183Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -51,29 +60,29 @@
     "# 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": "2024-02-24T04:30:18.761606Z",
-     "start_time": "2024-02-24T04:30:18.740080Z"
-    }
-   },
-   "id": "b68b27041fdab1a5",
-   "execution_count": 1
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Preprocessing"
-   ],
+   "id": "f7ee1fc9a8abba2b",
    "metadata": {
     "collapsed": false
    },
-   "id": "f7ee1fc9a8abba2b"
+   "source": [
+    "## Preprocessing"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f72adcb3bc0285e",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-24T05:05:30.273335Z",
+     "start_time": "2024-02-24T05:05:30.262297Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -122,19 +131,19 @@
     "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": "285236650ff590d8",
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:18.773188Z",
-     "start_time": "2024-02-24T04:30:18.762221Z"
+     "end_time": "2024-02-24T05:05:30.281906Z",
+     "start_time": "2024-02-24T05:05:30.274748Z"
     }
    },
-   "id": "3f72adcb3bc0285e",
-   "execution_count": 2
-  },
-  {
-   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -152,19 +161,19 @@
     "# Not working DataFrame\n",
     "nw_df = df[df['Do you currently work?'] == 'No']\n",
     "w_df"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6516c926e6efd1c3",
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:18.781369Z",
-     "start_time": "2024-02-24T04:30:18.774710Z"
+     "end_time": "2024-02-24T05:05:30.288976Z",
+     "start_time": "2024-02-24T05:05:30.282571Z"
     }
    },
-   "id": "285236650ff590d8",
-   "execution_count": 3
-  },
-  {
-   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -178,29 +187,29 @@
    ],
    "source": [
     "nw_df"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:18.787562Z",
-     "start_time": "2024-02-24T04:30:18.782006Z"
-    }
-   },
-   "id": "6516c926e6efd1c3",
-   "execution_count": 4
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "# Analysis"
-   ],
+   "id": "7efd20d58edbb05d",
    "metadata": {
     "collapsed": false
    },
-   "id": "7efd20d58edbb05d"
+   "source": [
+    "# Analysis"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
+   "id": "6deea60d8966fa15",
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-24T05:05:30.394575Z",
+     "start_time": "2024-02-24T05:05:30.289517Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -220,58 +229,75 @@
     "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()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The majority of student respondents (70.4%) do **not** work while attending school."
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:19.032770Z",
-     "start_time": "2024-02-24T04:30:18.788319Z"
-    }
+    "collapsed": false
    },
-   "id": "6deea60d8966fa15",
-   "execution_count": 5
+   "id": "5dbc734405a3858d"
   },
   {
    "cell_type": "code",
    "outputs": [
     {
      "data": {
+      "text/plain": "Do you currently live in a house, apartment, or dorm?  Apartment      Dorm  \\\nDo you currently work?                                                       \nNo                                                      0.500000  0.131868   \nYes                                                     0.493506  0.064935   \n\nDo you currently live in a house, apartment, or dorm?     House      Room  \nDo you currently work?                                                     \nNo                                                     0.362637  0.005495  \nYes                                                    0.441558  0.000000  ",
+      "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 currently live in a house, apartment, or dorm?</th>\n      <th>Apartment</th>\n      <th>Dorm</th>\n      <th>House</th>\n      <th>Room</th>\n    </tr>\n    <tr>\n      <th>Do you currently work?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.500000</td>\n      <td>0.131868</td>\n      <td>0.362637</td>\n      <td>0.005495</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.493506</td>\n      <td>0.064935</td>\n      <td>0.441558</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
       "text/plain": "<Figure size 800x800 with 2 Axes>",
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "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, apartment, or dorm?')\n",
-    "})\n",
+    "df_2dhist = pd.crosstab(df.loc[:, 'Do you currently work?'],\n",
+    "                        df.loc[:, 'Do you currently live in a house, apartment, or dorm?'],\n",
+    "                        normalize='index')\n",
     "\n",
     "# Plot heatmap\n",
     "plt.subplots(figsize=(8, 8))\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?')"
+    "_ = plt.ylabel('Do you currently work?')\n",
+    "df_2dhist"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:19.179017Z",
-     "start_time": "2024-02-24T04:30:19.034991Z"
+     "end_time": "2024-02-24T05:05:30.657400Z",
+     "start_time": "2024-02-24T05:05:30.402484Z"
     }
    },
-   "id": "c6372820e5ee501f",
+   "id": "c533e52f7d64a4df",
    "execution_count": 6
   },
   {
    "cell_type": "markdown",
-   "source": [],
+   "source": [
+    "For both working & non-working participants, the proportion who live in an apartment are equivalent (50%).\n",
+    "\n",
+    "However, 13% of non-working participants live in a dorm while only 6% of working participants live in a dorm.\n",
+    "This 7% drop is matched in participants who live a house, with 44% of working participants living in a house compared to 36% of non-working participants.\n",
+    "\n",
+    "This indicates that working participants tend to live off-campus and in living situations that have a higher cost of living."
+   ],
    "metadata": {
     "collapsed": false
    },
-   "id": "3ef5084b2abd603e"
+   "id": "6294dcc03fa9f516"
   },
   {
    "cell_type": "code",
@@ -287,24 +313,41 @@
    ],
    "source": [
     "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",
+    "                                                                                color=sns.palettes.mpl_palette(\n",
+    "                                                                                    'Dark2'))\n",
     "plt.gca().spines[['top', 'right', ]].set_visible(False)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:19.283024Z",
-     "start_time": "2024-02-24T04:30:19.181016Z"
+     "end_time": "2024-02-24T05:05:30.780026Z",
+     "start_time": "2024-02-24T05:05:30.659455Z"
     }
    },
-   "id": "67df9b48e43a5307",
+   "id": "450665f2272bb3a2",
    "execution_count": 7
   },
   {
+   "cell_type": "markdown",
+   "source": [
+    "Most participants live in either an Apartment or a House. This would indicate that most students either live off-campus or on-campus apartments."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "4d64d891924f201e"
+  },
+  {
    "cell_type": "code",
    "outputs": [
     {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Average GPA:  3.6520247933884296\n"
+     ]
+    },
+    {
      "data": {
       "text/plain": "<Figure size 640x480 with 1 Axes>",
       "image/png": 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"
@@ -316,27 +359,44 @@
    "source": [
     "dataTable1 = pd.pivot_table(data=df, values='What was your GPA your very first quarter at UCR?',\n",
     "                            index='How many hours do you work per week on average?', aggfunc='mean')\n",
-    "_ = dataTable1.plot(kind='bar')"
+    "_ = dataTable1.plot(kind='bar')\n",
+    "print(\"Total Average GPA: \", df['What was your GPA your very first quarter at UCR?'].mean())"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:19.368513Z",
-     "start_time": "2024-02-24T04:30:19.284400Z"
+     "end_time": "2024-02-24T05:05:30.918126Z",
+     "start_time": "2024-02-24T05:05:30.781852Z"
     }
    },
-   "id": "1163d27db8106025",
+   "id": "1a704a4702ea3f9c",
    "execution_count": 8
   },
   {
    "cell_type": "markdown",
    "source": [
+    "The average GPA seems to be independent in respect to working hours per week.\n",
+    "Most students who work less than 20 hours have an equivalent average GPA to the total average GPA of all participants (3.65).\n",
+    "\n",
+    "There is a small drop in GPA associated with students who work more than 20 hours (3.5 GPA), which may mean some of those students may struggle maintaining balance between work and school.  \n",
+    "\n",
+    "This would indicate that most students seem to be able to balance work with school. However, it would also indicate that\n",
+    "students who work full-time jobs may struggle slightly in school."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "cb1fd8e56d403466"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
     "## Hypotheses"
    ],
    "metadata": {
     "collapsed": false
    },
-   "id": "8f2599d399d14333"
+   "id": "f573ee142b931496"
   },
   {
    "cell_type": "markdown",
@@ -346,7 +406,7 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "85d89eaa6a2c8057"
+   "id": "dcc6d91b3e660c2e"
   },
   {
    "cell_type": "code",
@@ -368,11 +428,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:19.387872Z",
-     "start_time": "2024-02-24T04:30:19.371005Z"
+     "end_time": "2024-02-24T05:05:30.953549Z",
+     "start_time": "2024-02-24T05:05:30.925037Z"
     }
    },
-   "id": "5cbb7ab4d38de9ef",
+   "id": "24d1f01fdd4ca1d6",
    "execution_count": 9
   },
   {
@@ -397,18 +457,18 @@
     "        col_totals = roommates_major_table.iloc[-1, j]\n",
     "        expected_frequency = (row_totals * col_totals) / roommates_major_table.iloc[-1, -1]\n",
     "        expected_frequencies.append(expected_frequency)\n",
-    "        chi_squared += ((roommates_major_table.iloc[i, j] - expected_frequency)**2) / expected_frequency\n",
+    "        chi_squared += ((roommates_major_table.iloc[i, j] - expected_frequency) ** 2) / expected_frequency\n",
     "\n",
     "print(\"Chi-squared value:\", chi_squared)"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-24T04:30:19.394678Z",
-     "start_time": "2024-02-24T04:30:19.389670Z"
+     "end_time": "2024-02-24T05:05:30.962589Z",
+     "start_time": "2024-02-24T05:05:30.955712Z"
     }
    },
-   "id": "2fbaac2d0722a7e3",
+   "id": "fd3e73d9f461afd1",
    "execution_count": 10
   }
  ],
author	Anshul Gupta <ansg191@anshulg.com>	2024-02-23 20:34:42 -0800
committer	Anshul Gupta <ansg191@anshulg.com>	2024-02-23 21:05:40 -0800
commit	813b153ef6b0669c1561fd2b49a6c95d40943a61 (patch)
tree	9d12c116a3ae5d40c0a244eceaa2ffe56b6771b4 /CS105MiniProject.ipynb
parent	7199637c662d5f10bf41ce3ded5480f2501a6a45 (diff)
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