Box_whisker.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Gender</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Grade\n",
       "0  Female     28\n",
       "1  Female     33\n",
       "2    Male     34\n",
       "3    Male     34\n",
       "4    Male     34"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"data/course_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "xnames = ['Male','Female']\n",
    "male_df = df[[\"Grade\"]][df[\"Gender\"]==\"Male\"]\n",
    "female_df =df[[\"Grade\"]][df[\"Gender\"]==\"Female\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_list=male_df[\"Grade\"].values\n",
    "female_list=female_df[\"Grade\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "male=np.array(male_list)\n",
    "female=np.array(female_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jitered x-ccords for data point overlay\n",
    "eps = 0.05 #controls amount of jitter\n",
    "xm = [random.uniform(1-eps,1+eps) for i in range(0,60)]\n",
    "xf = [random.uniform(2-eps,2+eps) for i in range(0,40)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot data is a list of lists\n",
    "box_data = [male,female]\n",
    "\n",
    "\n",
    "# Box-and-Whisker Pipeline\n",
    "plt.plot(xm,male,'ro')\n",
    "plt.plot(xf,female,'bo')\n",
    "plt.boxplot(box_data,labels=xnames,sym=\"\") #dont show outliers\n",
    "plt.title(\"Grade Differentials for Gender\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Grade\")\n",
    "plt.savefig('Box_whisker.png',dpi =100)\n",
    "plt.show() # render pipeline\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>71.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.611895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>72.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Grade\n",
       "count   40.000000\n",
       "mean    71.775000\n",
       "std     17.611895\n",
       "min     28.000000\n",
       "25%     60.750000\n",
       "50%     72.500000\n",
       "75%     85.000000\n",
       "max    100.000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>71.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.611895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>72.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Grade\n",
       "count   40.000000\n",
       "mean    71.775000\n",
       "std     17.611895\n",
       "min     28.000000\n",
       "25%     60.750000\n",
       "50%     72.500000\n",
       "75%     85.000000\n",
       "max    100.000000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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