DataPreprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing Part I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model_year origin                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('mpg')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>38.491160</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
       "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
       "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
       "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model_year  \n",
       "count    398.000000  398.000000  \n",
       "mean      15.568090   76.010050  \n",
       "std        2.757689    3.697627  \n",
       "min        8.000000   70.000000  \n",
       "25%       13.825000   73.000000  \n",
       "50%       15.500000   76.000000  \n",
       "75%       17.175000   79.000000  \n",
       "max       24.800000   82.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note that all the features are of different scale, weight is in thousands and horsepower is around 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some model may not be smart enough and mistakenly assume weight is a more important factor simply because they are huge number and therefore a small change in weight may give arise to a bigger impact in the target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example of such a model would be in K-Means clustering where we calculate a Euclidean distance between two data point to capture how similar the two data points are. In the above example, the similarity meansure will be dominate by the weights, other attributes such as the number of cylinders will have almost no impact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One may want to \"standardize\" all features to be around the same magnitude. To do that we use the StandardScalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower  weight  acceleration\n",
       "0          8         307.0       130.0    3504          12.0\n",
       "1          8         350.0       165.0    3693          11.5\n",
       "2          8         318.0       150.0    3436          11.0\n",
       "3          8         304.0       150.0    3433          12.0\n",
       "4          8         302.0       140.0    3449          10.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = df[[\"cylinders\",\"displacement\",\"horsepower\",\"weight\", \"acceleration\"]]\n",
    "numerical_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.090604</td>\n",
       "      <td>0.664133</td>\n",
       "      <td>0.630870</td>\n",
       "      <td>-1.295498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.503514</td>\n",
       "      <td>1.574594</td>\n",
       "      <td>0.854333</td>\n",
       "      <td>-1.477038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.196232</td>\n",
       "      <td>1.184397</td>\n",
       "      <td>0.550470</td>\n",
       "      <td>-1.658577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.061796</td>\n",
       "      <td>1.184397</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>-1.295498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.042591</td>\n",
       "      <td>0.924265</td>\n",
       "      <td>0.565841</td>\n",
       "      <td>-1.840117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower    weight  acceleration\n",
       "0   1.498191      1.090604    0.664133  0.630870     -1.295498\n",
       "1   1.498191      1.503514    1.574594  0.854333     -1.477038\n",
       "2   1.498191      1.196232    1.184397  0.550470     -1.658577\n",
       "3   1.498191      1.061796    1.184397  0.546923     -1.295498\n",
       "4   1.498191      1.042591    0.924265  0.565841     -1.840117"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "normalized_df = scaler.fit_transform(numerical_features)\n",
    "normalized_df = pd.DataFrame(normalized_df, index = df.index, columns = [\"cylinders\",\"displacement\",\"horsepower\",\"weight\", \"acceleration\"] )\n",
    "normalized_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.980000e+02</td>\n",
       "      <td>3.980000e+02</td>\n",
       "      <td>3.920000e+02</td>\n",
       "      <td>3.980000e+02</td>\n",
       "      <td>3.980000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.570567e-17</td>\n",
       "      <td>-1.785283e-17</td>\n",
       "      <td>-1.812609e-16</td>\n",
       "      <td>-1.606755e-16</td>\n",
       "      <td>-2.677925e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.001259e+00</td>\n",
       "      <td>1.001259e+00</td>\n",
       "      <td>1.001278e+00</td>\n",
       "      <td>1.001259e+00</td>\n",
       "      <td>1.001259e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.444949e+00</td>\n",
       "      <td>-1.204411e+00</td>\n",
       "      <td>-1.520975e+00</td>\n",
       "      <td>-1.604943e+00</td>\n",
       "      <td>-2.747814e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.563206e-01</td>\n",
       "      <td>-8.563178e-01</td>\n",
       "      <td>-7.665929e-01</td>\n",
       "      <td>-8.828266e-01</td>\n",
       "      <td>-6.328794e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-8.563206e-01</td>\n",
       "      <td>-4.314040e-01</td>\n",
       "      <td>-2.853488e-01</td>\n",
       "      <td>-1.973624e-01</td>\n",
       "      <td>-2.472221e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.498191e+00</td>\n",
       "      <td>6.584879e-01</td>\n",
       "      <td>5.600800e-01</td>\n",
       "      <td>7.538337e-01</td>\n",
       "      <td>5.834349e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.498191e+00</td>\n",
       "      <td>2.511784e+00</td>\n",
       "      <td>3.265452e+00</td>\n",
       "      <td>2.565185e+00</td>\n",
       "      <td>3.351912e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cylinders  displacement    horsepower        weight  acceleration\n",
       "count  3.980000e+02  3.980000e+02  3.920000e+02  3.980000e+02  3.980000e+02\n",
       "mean  -3.570567e-17 -1.785283e-17 -1.812609e-16 -1.606755e-16 -2.677925e-16\n",
       "std    1.001259e+00  1.001259e+00  1.001278e+00  1.001259e+00  1.001259e+00\n",
       "min   -1.444949e+00 -1.204411e+00 -1.520975e+00 -1.604943e+00 -2.747814e+00\n",
       "25%   -8.563206e-01 -8.563178e-01 -7.665929e-01 -8.828266e-01 -6.328794e-01\n",
       "50%   -8.563206e-01 -4.314040e-01 -2.853488e-01 -1.973624e-01 -2.472221e-02\n",
       "75%    1.498191e+00  6.584879e-01  5.600800e-01  7.538337e-01  5.834349e-01\n",
       "max    1.498191e+00  2.511784e+00  3.265452e+00  2.565185e+00  3.351912e+00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now all features has mean of 0 and standard deviation of 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.617571</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.536150</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.728682</td>\n",
       "      <td>0.646739</td>\n",
       "      <td>0.589736</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.645995</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.516870</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.609819</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.516019</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.510870</td>\n",
       "      <td>0.520556</td>\n",
       "      <td>0.148810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower    weight  acceleration\n",
       "0        1.0      0.617571    0.456522  0.536150      0.238095\n",
       "1        1.0      0.728682    0.646739  0.589736      0.208333\n",
       "2        1.0      0.645995    0.565217  0.516870      0.178571\n",
       "3        1.0      0.609819    0.565217  0.516019      0.238095\n",
       "4        1.0      0.604651    0.510870  0.520556      0.148810"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minMaxScaler = MinMaxScaler()\n",
    "minMax_df = minMaxScaler.fit_transform(numerical_features)\n",
    "minMax_df = pd.DataFrame(minMax_df, columns = [\"cylinders\",\"displacement\",\"horsepower\",\"weight\", \"acceleration\"] )\n",
    "minMax_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.490955</td>\n",
       "      <td>0.324098</td>\n",
       "      <td>0.317768</td>\n",
       "      <td>0.384867</td>\n",
       "      <td>0.450482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.340201</td>\n",
       "      <td>0.269431</td>\n",
       "      <td>0.209191</td>\n",
       "      <td>0.240103</td>\n",
       "      <td>0.164148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.093669</td>\n",
       "      <td>0.157609</td>\n",
       "      <td>0.173164</td>\n",
       "      <td>0.346726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.208010</td>\n",
       "      <td>0.258152</td>\n",
       "      <td>0.337539</td>\n",
       "      <td>0.446429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.501292</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.565637</td>\n",
       "      <td>0.546131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cylinders  displacement  horsepower      weight  acceleration\n",
       "count  398.000000    398.000000  392.000000  398.000000    398.000000\n",
       "mean     0.490955      0.324098    0.317768    0.384867      0.450482\n",
       "std      0.340201      0.269431    0.209191    0.240103      0.164148\n",
       "min      0.000000      0.000000    0.000000    0.000000      0.000000\n",
       "25%      0.200000      0.093669    0.157609    0.173164      0.346726\n",
       "50%      0.200000      0.208010    0.258152    0.337539      0.446429\n",
       "75%      1.000000      0.501292    0.434783    0.565637      0.546131\n",
       "max      1.000000      1.000000    1.000000    1.000000      1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minMax_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/Salaries2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(403, 7)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(8)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Prof', 'AsstProf', 'AssocProf'], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Rank\"].unique()\n",
    "df.isnull().any() # Before Removing Rows that are null or NA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank              False\n",
       "Discipline        False\n",
       "YrsSincePhd       False\n",
       "YrsOfService      False\n",
       "Sex               False\n",
       "Salary            False\n",
       "numerical_rank    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[ ~np.isnan(df.YrsSincePhd)] # Remove YrsSincePhd NA\n",
    "df2 = df2[ ~np.isnan(df2.YrsOfService)] # Remove YrsOfService NA\n",
    "df2 = df2[ ~pd.isnull(df2.Sex)] # Remove Sex Null\n",
    "df2.isnull().any() # After Removing Rows that are null or NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 7)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[ df2['YrsOfService'] <= 80] # Remove Past 80 Years "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 7)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female', nan], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "encoder = OrdinalEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [2.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = encoder.fit_transform(df[['Rank']])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['AssocProf', 'AsstProf', 'Prof'], dtype=object)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Rank</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>YrsSincePhd</th>\n",
       "      <th>YrsOfService</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Salary</th>\n",
       "      <th>numerical_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>139750</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>173200</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>79750</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>115000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>141500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>97000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>175000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>147765</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rank Discipline  YrsSincePhd  YrsOfService   Sex  Salary  \\\n",
       "0       Prof          B         19.0          18.0  Male  139750   \n",
       "1       Prof          B         20.0          16.0  Male  173200   \n",
       "2   AsstProf          B          4.0           3.0  Male   79750   \n",
       "3       Prof          B         45.0          39.0  Male  115000   \n",
       "4       Prof          B         40.0          41.0  Male  141500   \n",
       "5  AssocProf          B          6.0           6.0  Male   97000   \n",
       "6       Prof          B         30.0          23.0  Male  175000   \n",
       "7       Prof          B         45.0          45.0  Male  147765   \n",
       "\n",
       "   numerical_rank  \n",
       "0             2.0  \n",
       "1             2.0  \n",
       "2             1.0  \n",
       "3             2.0  \n",
       "4             2.0  \n",
       "5             0.0  \n",
       "6             2.0  \n",
       "7             2.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['numerical_rank'] = result\n",
    "df.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_encoder = OneHotEncoder()\n",
    "cat_1hot = cat_encoder.fit_transform(df2[['Rank']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['AssocProf', 'AsstProf', 'Prof'], dtype=object)]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 3)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_1hot_df = pd.DataFrame(cat_1hot.toarray(), columns = cat_encoder.categories_)\n",
    "cat_1hot_df.head()\n",
    "cat_1hot_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AssocProf</th>\n",
       "      <th>AsstProf</th>\n",
       "      <th>Prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AssocProf AsstProf Prof\n",
       "393       0.0      0.0  1.0\n",
       "394       0.0      0.0  1.0\n",
       "395       0.0      0.0  1.0\n",
       "396       0.0      0.0  1.0\n",
       "397       0.0      1.0  0.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_1hot_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Rank</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>YrsSincePhd</th>\n",
       "      <th>YrsOfService</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Salary</th>\n",
       "      <th>numerical_rank</th>\n",
       "      <th>(AssocProf,)</th>\n",
       "      <th>(AsstProf,)</th>\n",
       "      <th>(Prof,)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>139750</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>173200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>79750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>115000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>141500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>97000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>175000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>147765</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rank Discipline  YrsSincePhd  YrsOfService   Sex  Salary  \\\n",
       "0       Prof          B         19.0          18.0  Male  139750   \n",
       "1       Prof          B         20.0          16.0  Male  173200   \n",
       "2   AsstProf          B          4.0           3.0  Male   79750   \n",
       "3       Prof          B         45.0          39.0  Male  115000   \n",
       "4       Prof          B         40.0          41.0  Male  141500   \n",
       "5  AssocProf          B          6.0           6.0  Male   97000   \n",
       "6       Prof          B         30.0          23.0  Male  175000   \n",
       "7       Prof          B         45.0          45.0  Male  147765   \n",
       "\n",
       "   numerical_rank  (AssocProf,)  (AsstProf,)  (Prof,)  \n",
       "0             2.0           0.0          0.0      1.0  \n",
       "1             2.0           0.0          0.0      1.0  \n",
       "2             1.0           0.0          1.0      0.0  \n",
       "3             2.0           0.0          0.0      1.0  \n",
       "4             2.0           0.0          0.0      1.0  \n",
       "5             0.0           1.0          0.0      0.0  \n",
       "6             2.0           0.0          0.0      1.0  \n",
       "7             2.0           0.0          0.0      1.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df = pd.concat([df, cat_1hot_df], axis=1)\n",
    "combined_df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Rank</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>YrsSincePhd</th>\n",
       "      <th>YrsOfService</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Salary</th>\n",
       "      <th>numerical_rank</th>\n",
       "      <th>(AssocProf,)</th>\n",
       "      <th>(AsstProf,)</th>\n",
       "      <th>(Prof,)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>103106</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>150564</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>42.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>101738</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>95329</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>81035</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Rank Discipline  YrsSincePhd  YrsOfService   Sex  Salary  \\\n",
       "398      Prof          A         33.0          30.0  Male  103106   \n",
       "399      Prof          A         31.0          19.0  Male  150564   \n",
       "400      Prof          A         42.0          25.0  Male  101738   \n",
       "401      Prof          A         25.0          15.0  Male   95329   \n",
       "402  AsstProf          A          8.0           4.0  Male   81035   \n",
       "\n",
       "     numerical_rank  (AssocProf,)  (AsstProf,)  (Prof,)  \n",
       "398             2.0           0.0          0.0      1.0  \n",
       "399             2.0           0.0          0.0      1.0  \n",
       "400             2.0           0.0          0.0      1.0  \n",
       "401             2.0           0.0          0.0      1.0  \n",
       "402             1.0           0.0          1.0      0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Rank</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>YrsSincePhd</th>\n",
       "      <th>YrsOfService</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Salary</th>\n",
       "      <th>numerical_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>103106</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>150564</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>42.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>101738</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>95329</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>81035</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Rank Discipline  YrsSincePhd  YrsOfService   Sex  Salary  \\\n",
       "398      Prof          A         33.0          30.0  Male  103106   \n",
       "399      Prof          A         31.0          19.0  Male  150564   \n",
       "400      Prof          A         42.0          25.0  Male  101738   \n",
       "401      Prof          A         25.0          15.0  Male   95329   \n",
       "402  AsstProf          A          8.0           4.0  Male   81035   \n",
       "\n",
       "     numerical_rank  \n",
       "398             2.0  \n",
       "399             2.0  \n",
       "400             2.0  \n",
       "401             2.0  \n",
       "402             1.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>AssocProf</th>\n",
       "      <th>AsstProf</th>\n",
       "      <th>Prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AssocProf  AsstProf  Prof\n",
       "0          0         0     1\n",
       "1          0         0     1\n",
       "2          0         1     0\n",
       "3          0         0     1\n",
       "4          0         0     1\n",
       "5          1         0     0\n",
       "6          0         0     1\n",
       "7          0         0     1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(df['rank'])\n",
    "dummy.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>rank_AssocProf</th>\n",
       "      <th>rank_AsstProf</th>\n",
       "      <th>rank_Prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex_Female  sex_Male  rank_AssocProf  rank_AsstProf  rank_Prof\n",
       "0           0         1               0              0          1\n",
       "1           0         1               0              0          1\n",
       "2           0         1               0              1          0\n",
       "3           0         1               0              0          1\n",
       "4           0         1               0              0          1\n",
       "5           0         1               1              0          0\n",
       "6           0         1               0              0          1\n",
       "7           0         1               0              0          1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(df[['sex','rank']])\n",
    "dummy.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}