Chapter 3 - Standardization .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.preprocessing as preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.00488661e+03 5.60155714e+04 2.53135326e+04 4.28031496e+00]\n"
     ]
    }
   ],
   "source": [
    "fords=pd.read_csv('fords.csv')\n",
    "fords = fords[['Year','Mileage','Price','Age']]\n",
    "scaler = preprocessing.normalize(fords)\n",
    "print(scaler.scale_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "enc = preprocessing.OrdinalEncoder()\n",
    "X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]\n",
    "enc.fit(X)\n",
    "print(enc.transform([['male', 'from US', 'uses Firefox']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 1. 0. 1.]\n",
      " [0. 1. 1. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "enc = preprocessing.OneHotEncoder()\n",
    "X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]\n",
    "enc.fit(X)\n",
    "\n",
    "print(enc.transform([['female', 'from US', 'uses Safari'],\n",
    "               ['male', 'from Europe', 'uses Safari']]).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>country</th>\n",
       "      <th>browser</th>\n",
       "      <th>numerical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>from US</td>\n",
       "      <td>uses Safari</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>from Europe</td>\n",
       "      <td>Uses Firefox</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex      country       browser  numerical\n",
       "0    male      from US   uses Safari          1\n",
       "1  female  from Europe  Uses Firefox          2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'sex': ['male','female'], 'country': ['from US','from Europe'],\n",
    "                 'browser': ['uses Safari','Uses Firefox'], 'numerical': [1, 2]})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>numerical</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>country_from Europe</th>\n",
       "      <th>country_from US</th>\n",
       "      <th>browser_Uses Firefox</th>\n",
       "      <th>browser_uses Safari</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numerical  sex_female  sex_male  country_from Europe  country_from US  \\\n",
       "0          1           0         1                    0                1   \n",
       "1          2           1         0                    1                0   \n",
       "\n",
       "   browser_Uses Firefox  browser_uses Safari  \n",
       "0                     0                    1  \n",
       "1                     1                    0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [2. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[ -3., 5., 15 ],\n",
    "              [  0., 6., 14 ],\n",
    "              [  6., 3., 11 ]])\n",
    "est = preprocessing.KBinsDiscretizer(n_bins=[3, 2, 2], encode='ordinal').fit(X)\n",
    "print(est.transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "X = [[ 1., -1.,  2.],\n",
    "     [ 2.,  0.,  0.],\n",
    "     [ 0.,  1., -1.]]\n",
    "binarizer = preprocessing.Binarizer()\n",
    "print(binarizer.transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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