PastHires.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Years Experience</th>\n",
       "      <th>Employed?</th>\n",
       "      <th>Previous employers</th>\n",
       "      <th>Level of Education</th>\n",
       "      <th>Top-tier school</th>\n",
       "      <th>Interned</th>\n",
       "      <th>Hired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>BS</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>MS</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>MS</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>N</td>\n",
       "      <td>6</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>Y</td>\n",
       "      <td>5</td>\n",
       "      <td>BS</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Years Experience Employed?  Previous employers Level of Education  \\\n",
       "1                  0         N                   0                 BS   \n",
       "5                  0         N                   0                PhD   \n",
       "9                  0         N                   0                 BS   \n",
       "12                 0         N                   0                PhD   \n",
       "10                 1         N                   1                PhD   \n",
       "3                  2         Y                   1                 MS   \n",
       "7                  3         N                   1                 BS   \n",
       "11                 4         Y                   1                 BS   \n",
       "6                  5         Y                   2                 MS   \n",
       "2                  7         N                   6                 BS   \n",
       "0                 10         Y                   4                 BS   \n",
       "8                 15         Y                   5                 BS   \n",
       "4                 20         N                   2                PhD   \n",
       "\n",
       "   Top-tier school Interned Hired  \n",
       "1                Y        Y     Y  \n",
       "5                Y        Y     Y  \n",
       "9                N        N     N  \n",
       "12               Y        N     Y  \n",
       "10               Y        N     N  \n",
       "3                Y        N     Y  \n",
       "7                N        Y     Y  \n",
       "11               N        Y     Y  \n",
       "6                N        Y     Y  \n",
       "2                N        N     N  \n",
       "0                N        N     Y  \n",
       "8                N        N     Y  \n",
       "4                Y        N     N  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('PastHires.csv')\n",
    "df.head(10)\n",
    "df.sort_values(\"Years Experience\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BS     7\n",
       "PhD    4\n",
       "MS     2\n",
       "Name: Level of Education, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Level of Education\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feb814150d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Level of Education\"].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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