self-methods/pandas-quantile.ipynb Secret

## pandas-quantile.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_boston\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()\n",
    "boston_df = pd.DataFrame(data=boston.data,columns=boston.feature_names)\n",
    "boston_df['target'] = boston.target"
   ]
  },
  {
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  target  \n",
       "0     15.3  396.90   4.98    24.0  \n",
       "1     17.8  396.90   9.14    21.6  \n",
       "2     17.8  392.83   4.03    34.7  \n",
       "3     18.7  394.63   2.94    33.4  \n",
       "4     18.7  396.90   5.33    36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>CRIM</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM      target\n",
       "count  506.000000  506.000000\n",
       "mean     3.613524   22.532806\n",
       "std      8.601545    9.197104\n",
       "min      0.006320    5.000000\n",
       "25%      0.082045   17.025000\n",
       "50%      0.256510   21.200000\n",
       "75%      3.677083   25.000000\n",
       "max     88.976200   50.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df[['CRIM','target']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CRIM', ylabel='Density'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(boston_df.CRIM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=boston_df.CRIM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CRIM', ylabel='target'>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"CRIM\", y=\"target\", data=boston_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25651"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_50 = q = boston_df.CRIM.quantile(0.5)\n",
    "q_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.78915"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = boston_df.CRIM.quantile(0.95)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.092157</td>\n",
       "      <td>23.203542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.648801</td>\n",
       "      <td>8.932629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>6.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.078832</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.222000</td>\n",
       "      <td>21.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.256870</td>\n",
       "      <td>26.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.575700</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM      target\n",
       "count  480.000000  480.000000\n",
       "mean     2.092157   23.203542\n",
       "std      3.648801    8.932629\n",
       "min      0.006320    6.300000\n",
       "25%      0.078832   17.800000\n",
       "50%      0.222000   21.700000\n",
       "75%      2.256870   26.250000\n",
       "max     15.575700   50.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_boston_df = boston_df.query('CRIM < @q')\n",
    "new_boston_df[['CRIM', 'target']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CRIM', ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fAkFERADoet+72AF5vD7+uusYH//tCCerXdgirCTERXFV3wR6X9Yt1OWJSCehQAhzh74+zezX/8qxSifx9kgcPWLx+QzKT9TyZVk1jsRYBvZJoFtyfKhLFZEOToEQxjbtOsabHxTTs3sMM3OvxVnnxmKxAOD1GRw8eootuyv46dK/8aN7h3J1v8TQFiwiHZrOIYSpzbvL+ff3vyCtTwKLZ93CkIFJgTAAiLBauKpvIhPHXEFCXBT/d8Xf+aKkKnQFi0iHp0AIQ/tKT7Jk7S7S+iTw5LShdO8W1exj4+yRPHnvUBw9Yvnlqh2UVlQHsVIR6UwUCGHmVE09v1m9k6TuMTw5LZPoqIhWt4m3RzHr3qFER0Xwyn9u54yzPgiVikhno0AII4Zh8Mb7X+Cs8/DDKRnYYyLbvG3P7jHkT83kVI2LNz/YjWEYJlYqIp2RAiGMfLr9a3YequLeW9PolxzX7u0H9OrOtFvS+Pv+43z0tyMmVCginZkCIUycOOPiDxv3kX55Irde1+eCn+fOkX0ZMrAn73x8gOOnai9hhSLS2ZkaCAUFBYwfP57s7GyWLVt2XvuHH35Ibm4ukyZN4h//8R85deqUmeWEtXc+PoDH6+PhcelYz7maqL0sFgsP56QD8Pv1ezV0JCJtZloglJWVsXjxYpYvX87q1atZsWIF+/fvD7RXV1czf/58lixZwtq1axk0aBCvvfaaWeWEtQNHT7Fp1zGyR11OSg/7RT9fUkIMU8YOZMfBSv67uOwSVCgiXYFpgVBUVERWVhaJiYnY7XZycnJYt25doN3tdjNv3jxSUlIAGDRoEF9//bVZ5YQtwzD4w4f7SOgWxd039L9kz3v7iL4M6NWd//fhPqpr3ZfseUWk8zItEMrLy3E4HIHl5ORkysq++bTao0cP7rzzTgDq6upYsmQJd9xxh1nlhK2/flHGgaOnmXLzQGKjL92N41arhf81Lh1nnYeVH+1vfQMR6fJMm7rC5/M1urPWMIxGy2edOXOGH/7wh6Snp3PPPfe0ax9JSe2/Eiec1Lk8rPqvg6T1TWDyrVdjtTZ/7sBujyY+LqbZNkfP84eaHI54Jt98Je/+eT+5t6aR3r/nJas9XDgcmsPp29QnTVO/tM60QEhNTWXLli2B5YqKCpKTkxs9pry8nEcffZSsrCxmz57d7n1UVlbj83Xck6Z/3FRC5ak68iZcQ2Vl83cYOxzxOJ0uzlTXNdnudLqo8HqbbLt9eG82bv6SX674O88+PLLF0OloHI54KirOhLqMsKI+aZr6pYHVamnxg7RpQ0Zjxoxh06ZNVFVVUVtbS2FhIWPHjg20e71eHnvsMcaNG8ecOXOaPHrozJx1bv701y8ZemWSqZPSxUTZuO+2qzhcdob/2nbUtP2ISMdn2hFCSkoKs2bNYsaMGbjdbqZNm0ZmZiZ5eXnk5+dz7NgxvvjiC7xeL+vXrwdgyJAhLFiwwKySwsq6z7/E6fJwz9iBpu9r9OBkPv6fI7z7yQFGpicTF9v2O6BFpOuwGB34QvWOOmR0uqaeH//bJoamJfFY7pBWH+9wxFNSeoLNzVxCOmpwCt1aOSFdWlHN/N9tZuyw3szIGXRBdYcbDQOcT33SNPVLg5ANGUnz/rjpMPUeL7k3DQjaPvs64rh9RF8++Z8jHD6m/zFE5HwKhCCrOl3Hn/+nlBszetErKbhff5l70wDi7ZEsLdyDr+MeGIqISRQIQbb2sxIMAybdeEXQ922PsXHvrWkcOHqaoh3Hgr5/EQlvCoQgKqty8pftX3PL8D5clhAbkhpuGJLKlX26887H+3HW6Q5mEfmGAiGIVv/lELYICxMu4RQV7WW1WPjenYM443Sz+i+HQlaHiIQfBUKQHD52hv/+oow7RvYjIS46pLX0T43n5uF9+GjrEUrL9ZWbItJAgRAk735ygG4xNsZnXR7qUgCYMnYgsdERLN2gKbJFpIECIQiKS6rYeaiKu2+4ol1fi2mmuNhIpt5yJXu/OknRTp1gFhEFgukMw+A/Pz5Az+7R3D7iwr8JzQxjM3uT1jeB5R/uo+p00/MkiUjXoUAw2ZY9FZQcO8PkmwYSaYsIdTmNWK0WHr17MF6fjzf/tFtDRyJdnALBRG6Pj3c/OUCfy7oxZkhqqMtpUkoPO/fdmsauQ1V8/HdNfifSlSkQTLT+8y8pP1HLfbelhfW007cM78O1A3qy4qN9lJ1whrocEQkRBYJJKk/V8X5RCddd7WDIwCRT92WxWqhxeZr95/G1sr3Fwv8el47NauU3q3fi9jT93Qoi0rmZNv11V/eHj/YBMP32NNP35XJ72ba3otn2UYNTsLUyG2rP7jF8f+I1vPrOdpYW7uV/jUvvct9RIdLV6QjBBLsOVbF1TwV3j7kiZFNUXIhhaZdx9w39+XT712zYUhrqckQkyHSEcInVu70s3bCX5B6x3DU6PG5Ca497xg7k60onKzbu47KEGK672hHqkkQkSHSEcIm99+lByqqcPJQ9iEhbx+teq8VC3sRruKJXd/5tzU52HqoMdUkiEiQd7y9WGNv71UkKP/8qcNVORxUdGcGs7w4ltWc3Xnt3B9sPHA91SSISBAqES6S61s2Sgl1clhjDd2+9MtTlNHIhVyHFxUby1P3D6JVk57V3d/DZjq+DX7iIBJXOIVwCPsPgd38s5nRNPbMfGkFMVHh164VehdTdHsUz91/HL1dt540/FvNVeTXTbrkSW4Q+R4h0Rvo/+xJY/elB/r7/OPfddhVXpHYPdTmXlD3Gxv+5bxi3j+hL4eavePHtLZRWaMpskc5IgXCRPtvxNe8XHWbs0F7cdl14TV53qdgirDx459X88J4Mqk67eP7Nzfxh4z6qa/WNayKdSXiNbXQwW/eU87sPihncvwffyx7U6W/kGjHIwdX9Enj3kwNs2PwVn2w7ys1De/OdzF70ccSFujwRuUgKhAv031+U8e/vf8HA3t15YmpGpx1X9/jA5fYElq0RVu697SpuyuzNus+/ZOPWUgo3f8XA3t0ZPTiFa/r3oLejG9ZOHo4inZECoZ0Mw6Bw81es/Gg/V/VLJH9qRtidRL6UXG4Pm4vLmmwbMqAn0265ku37K/nL9qP8YWPDdB3x9kiu7J1AH0c3el/WjeQesSR0iyKhW1TYTQEuIt/ovH/JTOCsc/P2+j18XlzOiEEO8iZcQ1Rk1/4DF2+PIntUP7JH9aPyVB27vzzBFyUnOFx2hh0HK/H6Gn/HQmy0jXh7JDFRNqIjrcRE2YiJiiA6MoKoSCtRkRFERliJtFmJiLBisUCkzYrNvy7SZiUyworNZuWy0y4Mt8e//pvtbBEWLBbLeUc354qOtNEB7xsUMZUCoQ0Mw+Bve4+zbMMeTte4mXrzQMZn9e/05wzaKykhhhszenFjRi8APF4fJWVn+OvOY9TWe6h1eal1eahzeXB7vdTVe6ir9+L2+Kj3eHH5f3d7fFzMV/VYIBAeBg0nxW0RFiKsDT9tEVYcPWKxR9uItn0TRFGRVqJsEUTZGi9HWC1YrRasFv9PK4HfI85db7FgsTTMHmv1/2xtGWi0nUgoKRBaYBgGOw9V8X5RCftKT9HH0Y0npmYyoFfnurTULLYIK72SutE3ufkTzqOvTcX41lGEYRh4fQb1bh9bdpfh9Rl4vQZen++c3w3Sr0jC7Xbj9vjweHzU+8PkbMA4XR6OHq/B4zXwen14fAYer4+6ei919V48Xh/1bi8utw+Pt5U5woOkUWDQEBQ2m5Uo/1HQ2ZAKHC3526IiG46yeiTEEAFER0UQExnR8POco7CYqIZ10ZERHeq8V0tHe6AjvkvF1EAoKCjgN7/5DR6Ph4cffpgHH3ywUXtxcTFz5syhpqaGkSNH8vzzz2OzhTajDMPgq/Jqth+opGjnMY5VOUmMi2JGziC+M7QXEdbO9647eydzU3wmf6tmSzfNDb3aQWwL03Zfl56Mxdv8dzfUuJo///HtIPL5DNz+gHB7fPgMC866+obffQ0BdDaoDAMsFitujwefYeDz+f8ZDW2GAV7D4PCx0+BfBjBoaO+bHEdkhBXDMPAZDe85w//TZ0C9x8vR4zWBdWdD0OPz4fUaRNqsnKx24fE2LHu8Pjz+n98eomuJLcJyTkjYvvn9W8Fx9vezAdPoqOmco6eIbx1FGQbn9ElDHwV+5+y6xq/f4Gx/+df5H1fn9nKg9FTgyLHxURtcOyCJuJhIbBEWIm1Wf90NQRjOX04Vbkz761tWVsbixYtZtWoVUVFRTJ8+neuvv560tG++H+Dpp5/mxRdfZNiwYcyePZuVK1fywAMPmFXSeTxeHyfOuDh6vIajlTV8WVZNcUkVp50N19df3TeBu28YzPXXpHSoT1Pt1dof5Za0FCZgbqB4vD7qL3Dfrd29PfRqB4eOnmyx/dztrVYLViyN2mOjmj6/1NRR0bfr3rq76SBrat/numl4X+pr3bjcDUNyLnfDUFxdvReX24vT5aXG3x74V//N726Pj5pad8NR1Dlt4W7D5uana4/yn6uKslm/CbooGzHnhF1cbBT26AhioxtC5GyYnF0++7Mz/x0AEwOhqKiIrKwsEhMTAcjJyWHdunU8/vjjABw5coS6ujqGDRsGwJQpU3j11VfbFQgXkvxlJ2r5w8Z9nDjjwlnX+Maq7t2iuGFIL67qm0Ba3wQS46Lb/fxmsUVYscdEtrvtYttb29brMyg+VNVs++ABPU3ct49dF7hvM/ustXYz+8xqsRBhs2K3WbHHnv8YnwE7WpiscOjVjvOS1GcYDcNwbi91bh9fHKpsOPLBwPCBz/9pfkDv7kRZrfho+OQfGP6i4Xef/7VbzhkOO/sYC2BY4EDpKfA//iwLcGW/RA6WnvQv+Y88fASO0K7o3R2b1YrX2zBkeDbg6t1eXPU+6jxejlZU4z57VOXxcdLlweP1Bda1RUTE2aOPhmG6wHkpq4WIiIaLHyKsDeepIiLAZj17cUTD67X6XzeWhv9WZz9DWM/ph3P7B/+6cz5rYIuwMqKVo+fmtPY307RAKC8vx+H45tNlcnIy27dvb7bd4XBQVtb8p6Km9OjRrd11JSXF8UJax5vjv2+vBPr2Smi2fWDfHi1ufzHtZj73xe67X0rL53M66uu62PaWXN67+fdRWwxPT7mo7VuSldH83f6jr+1t2n6lgWnHPz6fr9FVE4ZhNFpurV1ERILLtEBITU2louKbcc6KigqSk5ObbT9+/HijdhERCS7TAmHMmDFs2rSJqqoqamtrKSwsZOzYsYH2Pn36EB0dzdatWwFYs2ZNo3YREQkui2EYpl0HUlBQwG9/+1vcbjfTpk0jLy+PvLw88vPzycjIYPfu3cydO5fq6mquvfZaFi1aRFRUlFnliIhIC0wNBBER6Tg690W1IiLSZgoEEREBFAgiIuKnQBAREUCBEPYKCgoYP3482dnZLFu2LNTlhIWHHnqIu+++m9zcXHJzc9m2bVuoSwqZ6upqJkyYQGlpw1w+RUVFTJw4kezsbBYvXhzi6kLn2/3yz//8z2RnZwfeMxs2bAhxheFJ01+HsbZMENjVGIZBSUkJf/7zn0M+M26obdu2jblz51JSUgJAXV0ds2fP5ve//z29evVi5syZfPLJJ9x8882hLTTIvt0vADt37mTp0qW6+bUVOkIIY+dOEGi32wMTBHZlBw8eBOCRRx5h0qRJLF26NMQVhc7KlSuZN29e4I/c9u3b6d+/P/369cNmszFx4sQu+X75dr/U1tZy9OhRZs+ezcSJE3n11Vfx+cLj+y/CTdf+iBXmWpsgsCs6ffo0N9xwA88++yxut5sZM2YwYMAAbrzxxlCXFnQLFixotNzU+6W9E0Z2Bt/ul+PHj5OVlcW8efOIj49n5syZvPPOO3z3u98NUYXhS0cIYUwTAJ5v+PDhvPzyy8THx9OzZ0+mTZvGJ598EuqywoLeL03r168fv/rVr0hOTiY2NpaHHnpI75lmKBDCWGsTBHZFW7ZsYdOmTYFlwzC6/LmEs/R+adqePXtYv359YFnvmeYpEMJYaxMEdkVnzpzh5ZdfxuVyUV1dzXvvvcedd94Z6rLCwtChQzl06BCHDx/G6/Xy/vvvd/n3CzQEwMKFCzl16hRut5sVK1boPdMMxWQYS0lJYdasWcyYMSMwQWBmZmaoywqpW2+9lW3btjF58mR8Ph8PPPAAw4cPD3VZYSE6OpqXXnqJJ554ApfLxc0338xdd90V6rJCLj09nR/84Afcf//9eDwesrOzmTBhQqjLCkua3E5ERAANGYmIiJ8CQUREAAWCiIj4KRBERARQIIiIiJ8CQaQFXq+XN998kylTppCbm8v48eP52c9+Rn19PatWrWLEiBGBGTQnTZrEbbfdxjPPPIPL5QLgtdde44UXXgj8PmjQIN59991G+3A6nQwfPpyZM2cG/fWJnEv3IYi0YP78+Zw6dYr/+I//ID4+HqfTyVNPPcWcOXO44YYbGDlyJL/97W8Dj3e5XNx///289957TJ8+/bzn6927N2vWrGHq1KmBdYWFhdjt9qC8HpGW6AhBpBmlpaUUFBSwcOFC4uPjAbDb7Tz//PPccccdTW5z8uRJqqurSUhIaLL9O9/5Dvv37+fYsWOBde+99x6TJk269C9ApJ0UCCLN2LVrF2lpacTFxTVa73A4yMnJARrmVsrNzeWuu+4iKyuLJ598kkceeYRx48Y1+Zw2m41x48axdu1aAI4ePUpNTQ1XXXWVuS9GpA0UCCLNsFqtrc6bP3LkSNasWcMHH3zA9773PU6ePNnqdBG5ubkUFBQAsGbNGiZPnnypSha5KAoEkWZkZmZy8OBBqqurG60vKyvjBz/4AXV1dYF1VquVxx9/nD59+vCTn/yk1ef1er0UFxfzwQcfaF4dCRsKBJFmpKSkMHHiRGbPnh0IherqaubPn09iYiIxMTHnbTNv3jw+++wzPvzwwxafOzc3l4ULFzJgwAASExPNKF+k3RQIIi2YN28eaWlpTJ8+ndzcXO69917S0tJ48cUXm3z85ZdfTl5eHosWLQpcetqUSZMmsWXLFu655x6zShdpN812KiIigI4QRETET4EgIiKAAkFERPwUCCIiAigQRETET4EgIiKAAkFERPwUCCIiAsD/B9nzHHCjYlphAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(new_boston_df.CRIM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=new_boston_df.CRIM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CRIM', ylabel='target'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"CRIM\", y=\"target\", data=new_boston_df)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}