Day08_13Mar2021_Data_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://apssdc.in\"><img src=\"https://camo.githubusercontent.com/e7501c5948d48f88dad8ab2ab6bd448e1cfd6c79/68747470733a2f2f64726976652e676f6f676c652e636f6d2f75633f6578706f72743d646f776e6c6f61642669643d3135414b51365f2d42697857344b366d4c36525070684635454b58715946327a6a\" width=\"900\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Data Preprocessing using Scikit Learn\n",
    "</center></h1>\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/Scikit_learn_logo_small.svg/1200px-Scikit_learn_logo_small.svg.png\" width=\"150\" align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Datasets/master/Data%20Analysis/Advertising.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "1  230.1   37.8       69.2   22.1\n",
       "2   44.5   39.3       45.1   10.4\n",
       "3   17.2   45.9       69.3    9.3\n",
       "4  151.5   41.3       58.5   18.5\n",
       "5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(url, index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution befor Standardization of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ddcc534ee0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['TV'])\n",
    "sns.kdeplot(df['radio'])\n",
    "sns.kdeplot(df['newspaper'])\n",
    "sns.kdeplot(df['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "std = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stdScale = std.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.69852266e-01,  9.81522472e-01,  1.77894547e+00,\n",
       "         1.55205313e+00],\n",
       "       [-1.19737623e+00,  1.08280781e+00,  6.69578760e-01,\n",
       "        -6.96046111e-01],\n",
       "       [-1.51615499e+00,  1.52846331e+00,  1.78354865e+00,\n",
       "        -9.07405869e-01],\n",
       "       [ 5.20496822e-02,  1.21785493e+00,  1.28640506e+00,\n",
       "         8.60330287e-01],\n",
       "       [ 3.94182198e-01, -8.41613655e-01,  1.28180188e+00,\n",
       "        -2.15683025e-01],\n",
       "       [-1.61540845e+00,  1.73103399e+00,  2.04592999e+00,\n",
       "        -1.31091086e+00],\n",
       "       [-1.04557682e+00,  6.43904671e-01, -3.24708413e-01,\n",
       "        -4.27042783e-01],\n",
       "       [-3.13436589e-01, -2.47406325e-01, -8.72486994e-01,\n",
       "        -1.58039455e-01],\n",
       "       [-1.61657614e+00, -1.42906863e+00, -1.36042422e+00,\n",
       "        -1.77205942e+00],\n",
       "       [ 6.16042873e-01, -1.39530685e+00, -4.30581584e-01,\n",
       "        -6.57617064e-01],\n",
       "       [-9.45155670e-01, -1.17923146e+00, -2.92486143e-01,\n",
       "        -1.04190753e+00],\n",
       "       [ 7.90028350e-01,  4.96973404e-02, -1.22232878e+00,\n",
       "         6.48970529e-01],\n",
       "       [-1.43908760e+00,  7.99208859e-01,  1.62704048e+00,\n",
       "        -9.26620392e-01],\n",
       "       [-5.78501712e-01, -1.05768905e+00, -1.07502697e+00,\n",
       "        -8.30547775e-01],\n",
       "       [ 6.66253447e-01,  6.50657027e-01,  7.11007392e-01,\n",
       "         9.56402904e-01],\n",
       "       [ 5.64664612e-01,  1.65000572e+00,  1.02862691e+00,\n",
       "         1.60969670e+00],\n",
       "       [-9.25304978e-01,  9.00494200e-01,  3.84117072e+00,\n",
       "        -2.92541119e-01],\n",
       "       [ 1.56887609e+00,  1.10306488e+00,  1.16211917e+00,\n",
       "         1.99398717e+00],\n",
       "       [-9.08957349e-01, -1.86635121e-01, -5.64073843e-01,\n",
       "        -5.23115400e-01],\n",
       "       [ 3.00679600e-03,  4.29449843e-02, -5.27248393e-01,\n",
       "         1.10963873e-01],\n",
       "       [ 8.33232798e-01,  2.99534513e-01,  1.05164281e+00,\n",
       "         7.64257669e-01],\n",
       "       [ 1.05509347e+00, -1.22649795e+00, -3.24708413e-01,\n",
       "        -2.92541119e-01],\n",
       "       [-1.56286250e+00, -4.97243498e-01,  8.76721921e-01,\n",
       "        -1.61834324e+00],\n",
       "       [ 9.48833887e-01, -4.29719938e-01, -2.00422516e-01,\n",
       "         2.83894584e-01],\n",
       "       [-9.89527805e-01, -7.20071247e-01, -5.64073843e-01,\n",
       "        -8.30547775e-01],\n",
       "       [ 1.35285385e+00, -1.33453565e+00, -5.08835667e-01,\n",
       "        -3.88613736e-01],\n",
       "       [-4.83714657e-02,  4.07572210e-01, -8.26455181e-01,\n",
       "         1.87821967e-01],\n",
       "       [ 1.08662104e+00, -4.43224650e-01, -3.52327501e-01,\n",
       "         3.60752677e-01],\n",
       "       [ 1.18820988e+00,  2.59020377e-01, -3.52327501e-01,\n",
       "         9.37188380e-01],\n",
       "       [-8.92609721e-01, -4.90491142e-01,  4.71641962e-01,\n",
       "        -6.76831588e-01],\n",
       "       [ 1.70316018e+00,  3.40048650e-01,  5.82118314e-01,\n",
       "         1.41755147e+00],\n",
       "       [-3.98677796e-01, -3.95958157e-01,  3.70371972e-01,\n",
       "        -4.07828260e-01],\n",
       "       [-5.82004775e-01, -1.46958277e+00, -2.55016247e-02,\n",
       "        -8.49762299e-01],\n",
       "       [ 1.38438142e+00, -2.20396901e-01, -1.39264649e+00,\n",
       "         6.48970529e-01],\n",
       "       [-5.99520091e-01, -1.47633512e+00, -1.06582061e+00,\n",
       "        -8.68976822e-01],\n",
       "       [ 1.67747105e+00, -1.29402151e+00, -1.01518562e+00,\n",
       "        -2.34897549e-01],\n",
       "       [ 1.39956136e+00,  1.38666383e+00, -1.17629696e+00,\n",
       "         2.18613240e+00],\n",
       "       [-8.44734522e-01,  1.76479577e+00,  6.97197848e-01,\n",
       "         1.30178396e-01],\n",
       "       [-1.21372386e+00,  2.32010953e-01,  2.09260624e-01,\n",
       "        -7.53689682e-01],\n",
       "       [ 9.45330823e-01,  9.74770116e-01,  6.65620024e-02,\n",
       "         1.43676599e+00],\n",
       "       [ 6.47570443e-01, -6.50927121e-02,  4.81492770e-02,\n",
       "         4.95254341e-01],\n",
       "       [ 3.49810063e-01,  6.84418807e-01,  3.74975153e-01,\n",
       "         5.91326959e-01],\n",
       "       [ 1.71133400e+00,  2.99534513e-01, -1.32359877e+00,\n",
       "         1.28304980e+00],\n",
       "       [ 6.98948705e-01, -1.00367020e+00, -1.91216154e-01,\n",
       "        -2.15683025e-01],\n",
       "       [-1.42390765e+00,  1.64487393e-01,  5.86721496e-01,\n",
       "        -1.06112206e+00],\n",
       "       [ 3.27623995e-01, -5.15880000e-02,  4.35460956e-02,\n",
       "         1.68607443e-01],\n",
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       "       [-8.35393020e-01, -8.41613655e-01, -1.13026515e+00,\n",
       "        -7.92118728e-01],\n",
       "       [-1.51615499e+00, -1.29402151e+00,  4.81492770e-02,\n",
       "        -1.56069967e+00],\n",
       "       [ 2.30705910e-01,  1.26512143e+00, -1.24074150e+00,\n",
       "         1.07169004e+00],\n",
       "       [ 3.10313024e-02,  8.32970639e-01, -1.13026515e+00,\n",
       "         6.29756005e-01],\n",
       "       [-1.27094056e+00, -1.32103093e+00, -7.71217005e-01,\n",
       "        -1.23405277e+00],\n",
       "       [-6.17035408e-01, -1.24000266e+00, -1.03359834e+00,\n",
       "        -8.30547775e-01],\n",
       "       [ 3.49810063e-01, -9.42898996e-01, -1.11185242e+00,\n",
       "        -2.34897549e-01],\n",
       "       [ 1.59456522e+00,  1.26512143e+00,  1.64085003e+00,\n",
       "         2.20534693e+00],\n",
       "       [ 9.93206022e-01, -9.90165488e-01, -1.00597925e+00,\n",
       "        -1.19610408e-01]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stdScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.969852</td>\n",
       "      <td>0.981522</td>\n",
       "      <td>1.778945</td>\n",
       "      <td>1.552053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.197376</td>\n",
       "      <td>1.082808</td>\n",
       "      <td>0.669579</td>\n",
       "      <td>-0.696046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.516155</td>\n",
       "      <td>1.528463</td>\n",
       "      <td>1.783549</td>\n",
       "      <td>-0.907406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.052050</td>\n",
       "      <td>1.217855</td>\n",
       "      <td>1.286405</td>\n",
       "      <td>0.860330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.394182</td>\n",
       "      <td>-0.841614</td>\n",
       "      <td>1.281802</td>\n",
       "      <td>-0.215683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TV     radio  newspaper     sales\n",
       "0  0.969852  0.981522   1.778945  1.552053\n",
       "1 -1.197376  1.082808   0.669579 -0.696046\n",
       "2 -1.516155  1.528463   1.783549 -0.907406\n",
       "3  0.052050  1.217855   1.286405  0.860330\n",
       "4  0.394182 -0.841614   1.281802 -0.215683"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scale = pd.DataFrame(stdScale, columns=df.columns)\n",
    "\n",
    "df_scale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0.0\n",
       "radio       -0.0\n",
       "newspaper    0.0\n",
       "sales       -0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scale.mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           1.0\n",
       "radio        1.0\n",
       "newspaper    1.0\n",
       "sales        1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scale.std().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ddcf8cf2b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_scale['TV'])\n",
    "sns.kdeplot(df_scale['radio'])\n",
    "sns.kdeplot(df_scale['newspaper'])\n",
    "sns.kdeplot(df_scale['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ddcb4e88e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import robust_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsc = robust_scale(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scale = pd.DataFrame(rsc, columns = df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ddd189e3a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_scale['TV'])\n",
    "sns.kdeplot(df_scale['radio'])\n",
    "sns.kdeplot(df_scale['newspaper'])\n",
    "sns.kdeplot(df_scale['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmaxData = minmax.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.77578627, 0.76209677, 0.60598065, 0.80708661],\n",
       "       [0.1481231 , 0.79233871, 0.39401935, 0.34645669],\n",
       "       [0.0557998 , 0.92540323, 0.60686016, 0.30314961],\n",
       "       [0.50997633, 0.83266129, 0.51187335, 0.66535433],\n",
       "       [0.60906324, 0.21774194, 0.51099384, 0.44488189],\n",
       "       [0.02705445, 0.9858871 , 0.65699208, 0.22047244],\n",
       "       [0.19208657, 0.66129032, 0.20404573, 0.4015748 ],\n",
       "       [0.4041258 , 0.39516129, 0.09938434, 0.45669291],\n",
       "       [0.02671627, 0.04233871, 0.00615655, 0.12598425],\n",
       "       [0.67331755, 0.05241935, 0.18381706, 0.35433071],\n",
       "       [0.2211701 , 0.11693548, 0.21020229, 0.27559055],\n",
       "       [0.72370646, 0.48387097, 0.03254178, 0.62204724],\n",
       "       [0.07811972, 0.70766129, 0.5769569 , 0.2992126 ],\n",
       "       [0.32735881, 0.15322581, 0.06068602, 0.31889764],\n",
       "       [0.68785932, 0.66330645, 0.40193492, 0.68503937],\n",
       "       [0.65843761, 0.96169355, 0.46262093, 0.81889764],\n",
       "       [0.22691917, 0.73790323, 1.        , 0.42913386],\n",
       "       [0.94927291, 0.7983871 , 0.48812665, 0.8976378 ],\n",
       "       [0.2316537 , 0.41330645, 0.15831135, 0.38188976],\n",
       "       [0.49577274, 0.48185484, 0.16534741, 0.51181102],\n",
       "       [0.73621914, 0.55846774, 0.46701847, 0.64566929],\n",
       "       [0.80047345, 0.10282258, 0.20404573, 0.42913386],\n",
       "       [0.04227257, 0.32056452, 0.43359719, 0.15748031],\n",
       "       [0.76969902, 0.34072581, 0.22779244, 0.54724409],\n",
       "       [0.20831924, 0.25403226, 0.15831135, 0.31889764],\n",
       "       [0.8867095 , 0.07056452, 0.16886544, 0.40944882],\n",
       "       [0.4808928 , 0.59072581, 0.10817942, 0.52755906],\n",
       "       [0.80960433, 0.33669355, 0.19876869, 0.56299213],\n",
       "       [0.83902604, 0.54637097, 0.19876869, 0.68110236],\n",
       "       [0.23638823, 0.32258065, 0.35620053, 0.3503937 ],\n",
       "       [0.98816368, 0.57056452, 0.37730871, 0.77952756],\n",
       "       [0.37943862, 0.35080645, 0.33685136, 0.40551181],\n",
       "       [0.32634427, 0.03024194, 0.26121372, 0.31496063],\n",
       "       [0.89584038, 0.40322581, 0.        , 0.62204724],\n",
       "       [0.32127156, 0.02822581, 0.06244503, 0.31102362],\n",
       "       [0.98072371, 0.08266129, 0.07211961, 0.44094488],\n",
       "       [0.90023673, 0.88306452, 0.04133685, 0.93700787],\n",
       "       [0.25025364, 0.99596774, 0.39929639, 0.51574803],\n",
       "       [0.14338857, 0.53830645, 0.3060686 , 0.33464567],\n",
       "       [0.76868448, 0.76008065, 0.27880387, 0.78346457],\n",
       "       [0.68244843, 0.44959677, 0.27528584, 0.59055118],\n",
       "       [0.59621238, 0.6733871 , 0.33773087, 0.61023622],\n",
       "       [0.99053094, 0.55846774, 0.01319261, 0.7519685 ],\n",
       "       [0.69732837, 0.16935484, 0.22955145, 0.44488189],\n",
       "       [0.08251606, 0.51814516, 0.37818821, 0.27165354],\n",
       "       [0.58978695, 0.45362903, 0.27440633, 0.52362205],\n",
       "       [0.30098072, 0.19959677, 0.31134565, 0.35433071],\n",
       "       [0.80892797, 0.83669355, 0.16007036, 0.8503937 ],\n",
       "       [0.76597903, 0.31854839, 0.43623571, 0.51968504],\n",
       "       [0.22387555, 0.2358871 , 0.32102023, 0.31889764],\n",
       "       [0.67331755, 0.0625    , 0.30167106, 0.38582677],\n",
       "       [0.33716605, 0.19354839, 0.02902375, 0.35826772],\n",
       "       [0.72945553, 0.84072581, 0.34564644, 0.82677165],\n",
       "       [0.61515049, 0.93145161, 0.51363237, 0.77165354],\n",
       "       [0.88603314, 0.58064516, 0.13720317, 0.73228346],\n",
       "       [0.67027393, 0.99596774, 0.52506596, 0.87007874],\n",
       "       [0.02231992, 0.56653226, 0.36147757, 0.15354331],\n",
       "       [0.4582347 , 0.38709677, 0.14335972, 0.45669291],\n",
       "       [0.71051742, 1.        , 0.3289358 , 0.87401575],\n",
       "       [0.71017924, 0.59475806, 0.07915567, 0.66141732],\n",
       "       [0.17855935, 0.04032258, 0.18557608, 0.25590551],\n",
       "       [0.88129861, 0.8608871 , 0.47845207, 0.88976378],\n",
       "       [0.80689888, 0.3125    , 0.23746702, 0.55511811],\n",
       "       [0.3449442 , 0.59677419, 0.07124011, 0.48818898],\n",
       "       [0.44098749, 0.86290323, 0.25153914, 0.64566929],\n",
       "       [0.23097734, 0.1875    , 0.00527704, 0.30314961],\n",
       "       [0.10415962, 0.49596774, 0.01671064, 0.31102362],\n",
       "       [0.4687183 , 0.29233871, 0.08707124, 0.46456693],\n",
       "       [0.80047345, 0.55443548, 0.0941073 , 0.68110236],\n",
       "       [0.73080825, 0.88508065, 0.23658751, 0.81496063],\n",
       "       [0.67095029, 0.61693548, 0.33773087, 0.65748031],\n",
       "       [0.36895502, 0.28830645, 0.27616535, 0.42519685],\n",
       "       [0.08826513, 0.66532258, 0.16710642, 0.28346457],\n",
       "       [0.43523842, 0.11491935, 0.27264732, 0.37007874],\n",
       "       [0.71931011, 0.49596774, 0.11257696, 0.60629921],\n",
       "       [0.05478526, 0.88104839, 0.78364116, 0.27952756],\n",
       "       [0.0906324 , 0.03225806, 0.17941953, 0.20866142],\n",
       "       [0.40514034, 0.57459677, 0.12225154, 0.49606299],\n",
       "       [0.01589449, 0.60282258, 0.08003518, 0.14566929],\n",
       "       [0.38992222, 0.15524194, 0.2005277 , 0.37007874],\n",
       "       [0.25600271, 0.53830645, 0.19349164, 0.4015748 ],\n",
       "       [0.80858979, 0.08266129, 0.32189974, 0.42125984],\n",
       "       [0.25228272, 0.40927419, 0.28320141, 0.38188976],\n",
       "       [0.22894826, 0.89717742, 0.31046614, 0.47244094],\n",
       "       [0.71964829, 0.86693548, 0.294635  , 0.79133858],\n",
       "       [0.65099763, 0.37096774, 0.57519789, 0.53543307],\n",
       "       [0.25566452, 0.55443548, 0.13808267, 0.40944882],\n",
       "       [0.37199865, 0.81854839, 0.5532102 , 0.56692913],\n",
       "       [0.2962462 , 0.5141129 , 0.64291996, 0.44488189],\n",
       "       [0.36895502, 0.96370968, 0.44942832, 0.59448819],\n",
       "       [0.45180927, 0.09879032, 0.07915567, 0.37795276],\n",
       "       [0.09435238, 0.03024194, 0.28759894, 0.22440945],\n",
       "       [0.73385188, 0.67540323, 0.51627089, 0.7007874 ],\n",
       "       [0.84612783, 0.7358871 , 0.63324538, 0.81102362],\n",
       "       [0.36083869, 0.28225806, 0.09322779, 0.38976378],\n",
       "       [0.54988164, 0.63709677, 0.46262093, 0.6023622 ],\n",
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       "       [0.62292864, 0.4233871 , 0.19085312, 0.54724409],\n",
       "       [0.9773419 , 0.85282258, 0.44766931, 0.93700787],\n",
       "       [0.45485289, 0.84072581, 0.40105541, 0.61417323],\n",
       "       [0.74974636, 0.08669355, 0.4353562 , 0.3976378 ],\n",
       "       [1.        , 0.73185484, 0.88478452, 0.87401575],\n",
       "       [0.94521474, 0.20362903, 0.18557608, 0.51968504],\n",
       "       [0.63307406, 0.34677419, 0.15479332, 0.51574803],\n",
       "       [0.8031789 , 0.69153226, 0.04397537, 0.7519685 ],\n",
       "       [0.46398377, 0.93548387, 0.51627089, 0.69291339],\n",
       "       [0.08217788, 0.22177419, 0.2585752 , 0.22047244],\n",
       "       [0.30334799, 0.00604839, 0.20140721, 0.27952756],\n",
       "       [0.04193439, 0.00806452, 0.22251539, 0.14566929],\n",
       "       [0.86134596, 0.54233871, 0.04573439, 0.71653543],\n",
       "       [0.7612445 , 0.16532258, 0.4942832 , 0.46456693],\n",
       "       [0.81501522, 0.76612903, 0.20140721, 0.79527559],\n",
       "       [0.59181603, 0.31048387, 0.01846966, 0.49212598],\n",
       "       [0.70645925, 0.41532258, 0.09146878, 0.56299213],\n",
       "       [0.26208996, 0.94354839, 0.30079156, 0.51181102],\n",
       "       [0.25160636, 0.70564516, 0.46086192, 0.43307087],\n",
       "       [0.46838011, 0.28830645, 0.22251539, 0.41732283],\n",
       "       [0.25600271, 0.01612903, 0.12752858, 0.30708661],\n",
       "       [0.42272574, 0.74395161, 0.6939314 , 0.56299213],\n",
       "       [0.06323977, 0.32258065, 0.19349164, 0.19685039],\n",
       "       [0.47548191, 0.54032258, 0.40369393, 0.54724409],\n",
       "       [0.06121069, 0.4375    , 0.44063325, 0.21259843],\n",
       "       [0.75515725, 0.0483871 , 0.13456464, 0.39370079],\n",
       "       [0.41393304, 0.69758065, 0.1064204 , 0.53543307],\n",
       "       [0.77375719, 0.65120968, 0.64995602, 0.71259843],\n",
       "       [0.29252621, 0.23790323, 0.22515391, 0.35433071],\n",
       "       [0.02401082, 0.78427419, 0.44239226, 0.19685039],\n",
       "       [0.26885357, 0.        , 0.07827617, 0.28346457],\n",
       "       [0.74264457, 0.98790323, 0.02550572, 0.90944882],\n",
       "       [0.19918837, 0.24193548, 0.3764292 , 0.31889764],\n",
       "       [0.        , 0.7983871 , 0.07387863, 0.        ],\n",
       "       [0.89448766, 0.05846774, 0.37554969, 0.43700787],\n",
       "       [0.02603991, 0.5483871 , 0.01583113, 0.16141732],\n",
       "       [0.74095367, 0.67540323, 0.39401935, 0.70866142],\n",
       "       [0.12242137, 0.77822581, 0.57431838, 0.36220472],\n",
       "       [0.16097396, 0.94758065, 0.07211961, 0.39370079],\n",
       "       [0.08420697, 0.78629032, 0.07915567, 0.31102362],\n",
       "       [0.92323301, 0.58266129, 0.52242744, 0.75590551],\n",
       "       [0.14305039, 0.52217742, 0.17766051, 0.31496063],\n",
       "       [0.62292864, 0.88508065, 0.0123131 , 0.7519685 ],\n",
       "       [0.24585729, 0.34274194, 0.11081794, 0.36614173],\n",
       "       [0.65268854, 0.71370968, 0.66226913, 0.69291339],\n",
       "       [0.74332093, 0.66935484, 0.33069481, 0.72834646],\n",
       "       [0.35136963, 0.11491935, 0.29991205, 0.34645669],\n",
       "       [0.32296246, 0.2983871 , 0.33948989, 0.38582677],\n",
       "       [0.4721001 , 0.03830645, 0.07651715, 0.34251969],\n",
       "       [0.80960433, 0.14717742, 0.07387863, 0.45669291],\n",
       "       [0.82008793, 0.98790323, 0.38698329, 0.93700787],\n",
       "       [0.12614136, 0.8125    , 0.10202287, 0.36614173],\n",
       "       [0.14879946, 0.52016129, 0.17854002, 0.33464567],\n",
       "       [0.94690565, 0.28024194, 0.32277924, 0.57086614],\n",
       "       [0.40683125, 0.16935484, 0.42568162, 0.39370079],\n",
       "       [0.66587758, 0.46975806, 0.12225154, 0.59055118],\n",
       "       [0.57693608, 0.80040323, 0.3289358 , 0.68503937],\n",
       "       [0.63273588, 0.42540323, 0.08091469, 0.5511811 ],\n",
       "       [0.01149814, 0.23387097, 0.0474934 , 0.06299213],\n",
       "       [0.31518431, 0.87701613, 0.44151275, 0.53937008],\n",
       "       [0.50422726, 0.02620968, 0.21108179, 0.33464567],\n",
       "       [0.03719986, 0.74395161, 0.39489886, 0.22440945],\n",
       "       [0.44301657, 0.37096774, 0.30167106, 0.44488189],\n",
       "       [0.58099425, 0.36491935, 0.26737027, 0.50393701],\n",
       "       [0.2874535 , 0.72177419, 0.43095866, 0.46062992],\n",
       "       [0.63476496, 0.36491935, 0.22251539, 0.52362205],\n",
       "       [0.550558  , 0.74193548, 0.06244503, 0.64566929],\n",
       "       [0.39398039, 0.29637097, 0.04485488, 0.40551181],\n",
       "       [0.79066622, 0.06854839, 0.74318382, 0.40551181],\n",
       "       [0.05816706, 0.75806452, 0.18733509, 0.2519685 ],\n",
       "       [0.69699019, 0.10483871, 0.16798593, 0.41732283],\n",
       "       [0.72607372, 0.47580645, 0.50395778, 0.61023622],\n",
       "       [0.95908015, 0.21370968, 0.05364996, 0.52755906],\n",
       "       [0.16672303, 0.23387097, 0.15919085, 0.26771654],\n",
       "       [0.5539398 , 0.42137097, 0.41424802, 0.50787402],\n",
       "       [0.06391613, 0.40524194, 0.14687775, 0.23622047],\n",
       "       [0.56712885, 0.14314516, 0.10993843, 0.3976378 ],\n",
       "       [0.74974636, 0.06854839, 0.11257696, 0.38976378],\n",
       "       [0.93405479, 0.9858871 , 0.3649956 , 1.        ],\n",
       "       [0.83767332, 0.60887097, 0.1759015 , 0.73228346],\n",
       "       [0.5732161 , 0.15725806, 0.30694811, 0.3976378 ],\n",
       "       [0.93337842, 0.04637097, 0.20580475, 0.4015748 ],\n",
       "       [0.55765979, 0.2016129 , 0.15215479, 0.43307087],\n",
       "       [0.52722354, 0.05241935, 0.0703606 , 0.3503937 ],\n",
       "       [0.73655732, 0.10887097, 0.23834653, 0.41732283],\n",
       "       [0.18769023, 0.11491935, 0.2585752 , 0.27952756],\n",
       "       [0.97024011, 0.86693548, 0.62884785, 0.96850394],\n",
       "       [0.85593507, 0.42943548, 0.26121372, 0.62992126],\n",
       "       [0.69090294, 0.90927419, 0.16974494, 0.82677165],\n",
       "       [0.46939466, 0.04233871, 0.23131047, 0.34251969],\n",
       "       [0.64389584, 0.57862903, 0.15743184, 0.61811024],\n",
       "       [0.96482922, 0.28024194, 0.02990325, 0.56299213],\n",
       "       [0.06087251, 0.24395161, 0.20316623, 0.2007874 ],\n",
       "       [0.13121407, 0.82862903, 0.04837291, 0.36220472],\n",
       "       [0.25295908, 0.21774194, 0.05013193, 0.32677165],\n",
       "       [0.0557998 , 0.08266129, 0.27528584, 0.16929134],\n",
       "       [0.56171796, 0.84677419, 0.02902375, 0.70866142],\n",
       "       [0.50388908, 0.71774194, 0.05013193, 0.61811024],\n",
       "       [0.12681772, 0.07459677, 0.11873351, 0.23622047],\n",
       "       [0.31619885, 0.09879032, 0.06860158, 0.31889764],\n",
       "       [0.59621238, 0.1875    , 0.05364996, 0.44094488],\n",
       "       [0.95671288, 0.84677419, 0.57959543, 0.94094488],\n",
       "       [0.78254988, 0.1733871 , 0.07387863, 0.46456693]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmaxData"
   ]
  }
 ],
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