Demo - BestNormalize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BestNormalize\n",
    "\n",
    "### The goal of this notebook is to help Data Scientists solve a common problem in Machine Learning projects - \"normalizing\" the distribution of numerical variables.\n",
    "\n",
    "Much has been written about the theoretical advantages of dealing with numerical variables that follow a Gaussian distribution. For some algorithms - Linear and Logistic Regression - this is a clear requirement; for many others, it is just helpful. \n",
    "\n",
    "The goal of the BestNormalize class is to reduce the time Data Scientists spend looking for the best way to make a numerical transformation more Gaussian-like by automating this process. In this notebook, \"normalizing\" refers to this process, rather than transforming the variable to a 0-1 scale. Like any other tool, it might not work in 100% of the cases, but for most of them it will provide a fairly reasonable answer.\n",
    "\n",
    "For ease of use, this class follows an API pattern fairly similar to those of scikit-learn - the fit_transform method. When called, this method runs a number of different possible transformations to the numerical variables of the dataset and records the score. Then, it applies the transformation with the best score.\n",
    "\n",
    "#### Disclaimer: This library is similar to the bestNormalize library in R, although it does not support cross-validation of results at this time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's demonstrate a simple use case of this library with the famous housing dataset."
   ]
  },
  {
   "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",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv('datasets/housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains a number of numerical variables. Let's plot the histograms of the numeric variables to check for normality of the distributions. For obvious reasons we will keep latitude and longitude out of this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_num = housing.select_dtypes(include=np.number).drop(['latitude','longitude'], axis=1)\n",
    "housing_num.hist(bins=30, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that none of the numerical variables follow very Gaussian-like distributions. It is feasible that for modelling purposes it would be useful to apply transformations to the distributions. Let's use the **BestNormalize** class for that purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from best_normalize import BestNormalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestNormalize = BestNormalize(allow_quantile=False)\n",
    "housing_num_transformed = bestNormalize.fit_transform(housing_num)"
   ]
  },
  {
   "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>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.953001</td>\n",
       "      <td>-1.158227</td>\n",
       "      <td>-1.572948</td>\n",
       "      <td>-1.619967</td>\n",
       "      <td>-1.521093</td>\n",
       "      <td>1.903289</td>\n",
       "      <td>1.647945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.494219</td>\n",
       "      <td>1.896146</td>\n",
       "      <td>1.436871</td>\n",
       "      <td>1.090355</td>\n",
       "      <td>1.633844</td>\n",
       "      <td>1.897081</td>\n",
       "      <td>1.238401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.599175</td>\n",
       "      <td>-0.534616</td>\n",
       "      <td>-1.130536</td>\n",
       "      <td>-1.139853</td>\n",
       "      <td>-1.136367</td>\n",
       "      <td>1.604361</td>\n",
       "      <td>1.206750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.599175</td>\n",
       "      <td>-0.713802</td>\n",
       "      <td>-0.870963</td>\n",
       "      <td>-1.000270</td>\n",
       "      <td>-0.878626</td>\n",
       "      <td>1.051321</td>\n",
       "      <td>1.152010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.599175</td>\n",
       "      <td>-0.399535</td>\n",
       "      <td>-0.647624</td>\n",
       "      <td>-0.985268</td>\n",
       "      <td>-0.665985</td>\n",
       "      <td>0.205878</td>\n",
       "      <td>1.156638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   housing_median_age  total_rooms  total_bedrooms  population  households  \\\n",
       "0            0.953001    -1.158227       -1.572948   -1.619967   -1.521093   \n",
       "1           -0.494219     1.896146        1.436871    1.090355    1.633844   \n",
       "2            1.599175    -0.534616       -1.130536   -1.139853   -1.136367   \n",
       "3            1.599175    -0.713802       -0.870963   -1.000270   -0.878626   \n",
       "4            1.599175    -0.399535       -0.647624   -0.985268   -0.665985   \n",
       "\n",
       "   median_income  median_house_value  \n",
       "0       1.903289            1.647945  \n",
       "1       1.897081            1.238401  \n",
       "2       1.604361            1.206750  \n",
       "3       1.051321            1.152010  \n",
       "4       0.205878            1.156638  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_transformed.head()"
   ]
  },
  {
   "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>Variable</th>\n",
       "      <th>Transformation</th>\n",
       "      <th>Score</th>\n",
       "      <th>Approx. Normal?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>households</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>949.222942</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>773.754042</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>359.544184</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>median_income</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>8.177807</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>population</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>996.160290</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>959.785760</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>1128.845459</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variable Transformation        Score  Approx. Normal?\n",
       "35          households    yeo-johnson   949.222942            False\n",
       "8   housing_median_age           sqrt   773.754042            False\n",
       "46  median_house_value         arcsin   359.544184            False\n",
       "41       median_income    yeo-johnson     8.177807            False\n",
       "29          population    yeo-johnson   996.160290            False\n",
       "23      total_bedrooms    yeo-johnson   959.785760            False\n",
       "17         total_rooms    yeo-johnson  1128.845459            False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestNormalize.best_scores"
   ]
  },
  {
   "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>Variable</th>\n",
       "      <th>Transformation</th>\n",
       "      <th>Score</th>\n",
       "      <th>Approx. Normal?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>original</td>\n",
       "      <td>1770.127173</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>original</td>\n",
       "      <td>18461.958386</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>original</td>\n",
       "      <td>15680.951552</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>population</td>\n",
       "      <td>original</td>\n",
       "      <td>21882.808473</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>households</td>\n",
       "      <td>original</td>\n",
       "      <td>15709.280182</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>median_income</td>\n",
       "      <td>original</td>\n",
       "      <td>7083.203770</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>original</td>\n",
       "      <td>2430.931051</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>log</td>\n",
       "      <td>3617.654585</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>773.754042</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>exp</td>\n",
       "      <td>14820.756101</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>4377.220855</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>1289.195148</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>1239.902266</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>log</td>\n",
       "      <td>5013.146293</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>6065.577420</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>exp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>5147.629625</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>1128.845459</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>1136.972718</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>log</td>\n",
       "      <td>4486.204394</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5031.116148</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>exp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>4900.969396</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>959.785760</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>987.516960</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>population</td>\n",
       "      <td>log</td>\n",
       "      <td>4683.996745</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>population</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5586.193748</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>population</td>\n",
       "      <td>exp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>population</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>4846.419264</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>population</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>996.160290</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>population</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>1007.094692</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>households</td>\n",
       "      <td>log</td>\n",
       "      <td>4709.123506</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>households</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4729.767522</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>households</td>\n",
       "      <td>exp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>households</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>5117.691472</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>households</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>949.222942</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>households</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>975.366390</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>median_income</td>\n",
       "      <td>log</td>\n",
       "      <td>210.695649</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>median_income</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2653.816639</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>median_income</td>\n",
       "      <td>exp</td>\n",
       "      <td>43444.073726</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>median_income</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>18.068246</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>median_income</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>8.177807</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>median_income</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>74.769622</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>log</td>\n",
       "      <td>359.555288</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>851.848861</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>exp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>arcsin</td>\n",
       "      <td>359.544184</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>yeo-johnson</td>\n",
       "      <td>446.757301</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>median_house_value</td>\n",
       "      <td>boxcox</td>\n",
       "      <td>446.747526</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variable Transformation         Score  Approx. Normal?\n",
       "0   housing_median_age       original   1770.127173            False\n",
       "1          total_rooms       original  18461.958386            False\n",
       "2       total_bedrooms       original  15680.951552            False\n",
       "3           population       original  21882.808473            False\n",
       "4           households       original  15709.280182            False\n",
       "5        median_income       original   7083.203770            False\n",
       "6   median_house_value       original   2430.931051            False\n",
       "7   housing_median_age            log   3617.654585            False\n",
       "8   housing_median_age           sqrt    773.754042            False\n",
       "9   housing_median_age            exp  14820.756101            False\n",
       "10  housing_median_age         arcsin   4377.220855            False\n",
       "11  housing_median_age    yeo-johnson   1289.195148            False\n",
       "12  housing_median_age         boxcox   1239.902266            False\n",
       "13         total_rooms            log   5013.146293            False\n",
       "14         total_rooms           sqrt   6065.577420            False\n",
       "15         total_rooms            exp           NaN            False\n",
       "16         total_rooms         arcsin   5147.629625            False\n",
       "17         total_rooms    yeo-johnson   1128.845459            False\n",
       "18         total_rooms         boxcox   1136.972718            False\n",
       "19      total_bedrooms            log   4486.204394            False\n",
       "20      total_bedrooms           sqrt   5031.116148            False\n",
       "21      total_bedrooms            exp           NaN            False\n",
       "22      total_bedrooms         arcsin   4900.969396            False\n",
       "23      total_bedrooms    yeo-johnson    959.785760            False\n",
       "24      total_bedrooms         boxcox    987.516960            False\n",
       "25          population            log   4683.996745            False\n",
       "26          population           sqrt   5586.193748            False\n",
       "27          population            exp           NaN            False\n",
       "28          population         arcsin   4846.419264            False\n",
       "29          population    yeo-johnson    996.160290            False\n",
       "30          population         boxcox   1007.094692            False\n",
       "31          households            log   4709.123506            False\n",
       "32          households           sqrt   4729.767522            False\n",
       "33          households            exp           NaN            False\n",
       "34          households         arcsin   5117.691472            False\n",
       "35          households    yeo-johnson    949.222942            False\n",
       "36          households         boxcox    975.366390            False\n",
       "37       median_income            log    210.695649            False\n",
       "38       median_income           sqrt   2653.816639            False\n",
       "39       median_income            exp  43444.073726            False\n",
       "40       median_income         arcsin     18.068246            False\n",
       "41       median_income    yeo-johnson      8.177807            False\n",
       "42       median_income         boxcox     74.769622            False\n",
       "43  median_house_value            log    359.555288            False\n",
       "44  median_house_value           sqrt    851.848861            False\n",
       "45  median_house_value            exp           NaN            False\n",
       "46  median_house_value         arcsin    359.544184            False\n",
       "47  median_house_value    yeo-johnson    446.757301            False\n",
       "48  median_house_value         boxcox    446.747526            False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestNormalize.scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_num_transformed.hist(bins=30, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}