{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Description\" data-toc-modified-id=\"Description-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Description</a></span></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Imports</a></span></li><li><span><a href=\"#Using-plotly-api-in-module-bhishan\" data-toc-modified-id=\"Using-plotly-api-in-module-bhishan-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Using plotly api in module bhishan</a></span></li><li><span><a href=\"#Correlation\" data-toc-modified-id=\"Correlation-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Correlation</a></span></li><li><span><a href=\"#Partial-Correlation\" data-toc-modified-id=\"Partial-Correlation-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Partial Correlation</a></span></li><li><span><a href=\"#Outliers\" data-toc-modified-id=\"Outliers-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Outliers</a></span></li><li><span><a href=\"#Kernel-Density\" data-toc-modified-id=\"Kernel-Density-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Kernel Density</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description\n",
    "This is an example tutorial to use my module bhishan for the plotly\n",
    "extension for pandas.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:08.216693Z",
     "start_time": "2020-09-28T22:49:04.730310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bhishan Poudel 2020-09-28 \n",
      "\n",
      "CPython 3.7.7\n",
      "IPython 7.18.1\n",
      "\n",
      "compiler   : Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 19.6.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 4\n",
      "interpreter: 64bit\n",
      "numpy    1.18.4\n",
      "pandas   1.1.0\n",
      "autopep8 1.5.2\n",
      "seaborn  0.11.0\n",
      "bhishan  0.3.1\n",
      "json     2.0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import bhishan\n",
    "from bhishan import bp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%load_ext autoreload\n",
    "%load_ext watermark\n",
    "\n",
    "%autoreload 2\n",
    "%watermark -a \"Bhishan Poudel\" -d -v -m\n",
    "%watermark -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using plotly api in module bhishan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:08.316782Z",
     "start_time": "2020-09-28T22:49:08.247477Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(sns.get_dataset_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:08.414293Z",
     "start_time": "2020-09-28T22:49:08.319525Z"
    }
   },
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('titanic')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:09.747829Z",
     "start_time": "2020-09-28T22:49:08.417813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.bp.plot_corr(xrot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:11.199539Z",
     "start_time": "2020-09-28T22:49:09.752458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.bp.plot_corr(target='survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:12.539592Z",
     "start_time": "2020-09-28T22:49:11.226233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = 'survived'\n",
    "df_few_cols = df[['age','fare']]\n",
    "df_few_cols.merge(df[target],left_index=True,right_index=True)\\\n",
    "           .bp.plot_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:12.784785Z",
     "start_time": "2020-09-28T22:49:12.567472Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row1_col2{\n",
       "            background-color:  #b72230;\n",
       "            color:  #f1f1f1;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row1_col3{\n",
       "            background-color:  #fdddcb;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row1_col4{\n",
       "            background-color:  #fac8af;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row1_col6,#T_d417b00c_01dc_11eb_bf50_acde48001122row2_col7,#T_d417b00c_01dc_11eb_bf50_acde48001122row5_col2{\n",
       "            background-color:  #fcdfcf;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row1_col7{\n",
       "            background-color:  #fae7dc;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row2_col0{\n",
       "            background-color:  #da6853;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row2_col3{\n",
       "            background-color:  #870a24;\n",
       "            color:  #f1f1f1;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row2_col4{\n",
       "            background-color:  #bd2d35;\n",
       "            color:  #f1f1f1;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row2_col5{\n",
       "            background-color:  #f7b99c;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row2_col6{\n",
       "            background-color:  #f9f0eb;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row3_col0{\n",
       "            background-color:  #f8bda1;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row3_col1,#T_d417b00c_01dc_11eb_bf50_acde48001122row4_col0,#T_d417b00c_01dc_11eb_bf50_acde48001122row6_col1{\n",
       "            background-color:  #fce0d0;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row3_col2{\n",
       "            background-color:  #cc4c44;\n",
       "            color:  #f1f1f1;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row3_col4,#T_d417b00c_01dc_11eb_bf50_acde48001122row4_col3{\n",
       "            background-color:  #bddbea;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row3_col5{\n",
       "            background-color:  #f9efe9;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row3_col6{\n",
       "            background-color:  #d86551;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row4_col1{\n",
       "            background-color:  #fbd0b9;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row4_col2{\n",
       "            background-color:  #e48066;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row4_col5{\n",
       "            background-color:  #f5f6f7;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row4_col6{\n",
       "            background-color:  #c2383a;\n",
       "            color:  #f1f1f1;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row5_col3{\n",
       "            background-color:  #f9ebe3;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row5_col4,#T_d417b00c_01dc_11eb_bf50_acde48001122row7_col2{\n",
       "            background-color:  #f7f5f4;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row5_col6{\n",
       "            background-color:  #e17860;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row5_col7{\n",
       "            background-color:  #ce4f45;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row6_col2{\n",
       "            background-color:  #e9f0f4;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row6_col3{\n",
       "            background-color:  #d35a4a;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row6_col5,#T_d417b00c_01dc_11eb_bf50_acde48001122row7_col0{\n",
       "            background-color:  #e27b62;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row6_col7{\n",
       "            background-color:  #c5dfec;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row7_col1{\n",
       "            background-color:  #faeae1;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row7_col5{\n",
       "            background-color:  #d55d4c;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }#T_d417b00c_01dc_11eb_bf50_acde48001122row7_col6{\n",
       "            background-color:  #c2ddec;\n",
       "            color:  #000000;\n",
       "            max-width:  80px;\n",
       "            font-size:  10pt;\n",
       "        }</style><table id=\"T_d417b00c_01dc_11eb_bf50_acde48001122\" ><caption>Hover to magify</caption><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >survived</th>        <th class=\"col_heading level0 col1\" >pclass</th>        <th class=\"col_heading level0 col2\" >age</th>        <th class=\"col_heading level0 col3\" >sibsp</th>        <th class=\"col_heading level0 col4\" >parch</th>        <th class=\"col_heading level0 col5\" >fare</th>        <th class=\"col_heading level0 col6\" >adult_male</th>        <th class=\"col_heading level0 col7\" >alone</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row0\" class=\"row_heading level0 row0\" >survived</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col1\" class=\"data row0 col1\" >-0.34</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col2\" class=\"data row0 col2\" >-0.08</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col3\" class=\"data row0 col3\" >-0.04</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col4\" class=\"data row0 col4\" >0.08</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col5\" class=\"data row0 col5\" >0.26</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col6\" class=\"data row0 col6\" >-0.56</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row0_col7\" class=\"data row0 col7\" >-0.20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row1\" class=\"row_heading level0 row1\" >pclass</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col0\" class=\"data row1 col0\" >-0.34</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col2\" class=\"data row1 col2\" >-0.37</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col4\" class=\"data row1 col4\" >0.02</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col5\" class=\"data row1 col5\" >-0.55</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col6\" class=\"data row1 col6\" >0.09</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row1_col7\" class=\"data row1 col7\" >0.14</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row2\" class=\"row_heading level0 row2\" >age</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col0\" class=\"data row2 col0\" >-0.08</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col1\" class=\"data row2 col1\" >-0.37</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col3\" class=\"data row2 col3\" >-0.31</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col4\" class=\"data row2 col4\" >-0.19</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col5\" class=\"data row2 col5\" >0.10</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col6\" class=\"data row2 col6\" >0.28</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row2_col7\" class=\"data row2 col7\" >0.20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row3\" class=\"row_heading level0 row3\" >sibsp</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col0\" class=\"data row3 col0\" >-0.04</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col1\" class=\"data row3 col1\" >0.08</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col2\" class=\"data row3 col2\" >-0.31</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col4\" class=\"data row3 col4\" >0.41</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col5\" class=\"data row3 col5\" >0.16</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col6\" class=\"data row3 col6\" >-0.25</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row3_col7\" class=\"data row3 col7\" >-0.58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row4\" class=\"row_heading level0 row4\" >parch</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col1\" class=\"data row4 col1\" >0.02</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col2\" class=\"data row4 col2\" >-0.19</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col3\" class=\"data row4 col3\" >0.41</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col5\" class=\"data row4 col5\" >0.22</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col6\" class=\"data row4 col6\" >-0.35</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row4_col7\" class=\"data row4 col7\" >-0.58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row5\" class=\"row_heading level0 row5\" >fare</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col0\" class=\"data row5 col0\" >0.26</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col1\" class=\"data row5 col1\" >-0.55</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col2\" class=\"data row5 col2\" >0.10</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col3\" class=\"data row5 col3\" >0.16</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col4\" class=\"data row5 col4\" >0.22</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col6\" class=\"data row5 col6\" >-0.18</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row5_col7\" class=\"data row5 col7\" >-0.27</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row6\" class=\"row_heading level0 row6\" >adult_male</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col0\" class=\"data row6 col0\" >-0.56</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col1\" class=\"data row6 col1\" >0.09</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col2\" class=\"data row6 col2\" >0.28</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col3\" class=\"data row6 col3\" >-0.25</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col4\" class=\"data row6 col4\" >-0.35</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col5\" class=\"data row6 col5\" >-0.18</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row6_col7\" class=\"data row6 col7\" >0.40</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d417b00c_01dc_11eb_bf50_acde48001122level0_row7\" class=\"row_heading level0 row7\" >alone</th>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col0\" class=\"data row7 col0\" >-0.20</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col1\" class=\"data row7 col1\" >0.14</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col2\" class=\"data row7 col2\" >0.20</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col3\" class=\"data row7 col3\" >-0.58</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col4\" class=\"data row7 col4\" >-0.58</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col5\" class=\"data row7 col5\" >-0.27</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col6\" class=\"data row7 col6\" >0.40</td>\n",
       "                        <td id=\"T_d417b00c_01dc_11eb_bf50_acde48001122row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdca9e212d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bp.plot_corr_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:12.892783Z",
     "start_time": "2020-09-28T22:49:12.792792Z"
    }
   },
   "outputs": [],
   "source": [
    "# df.bp.plot_corr_sns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:12.998732Z",
     "start_time": "2020-09-28T22:49:12.908848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_d43961ac_01dc_11eb_bf50_acde48001122row0_col6,#T_d43961ac_01dc_11eb_bf50_acde48001122row1_col5,#T_d43961ac_01dc_11eb_bf50_acde48001122row3_col7,#T_d43961ac_01dc_11eb_bf50_acde48001122row4_col7,#T_d43961ac_01dc_11eb_bf50_acde48001122row5_col1,#T_d43961ac_01dc_11eb_bf50_acde48001122row6_col0,#T_d43961ac_01dc_11eb_bf50_acde48001122row7_col3,#T_d43961ac_01dc_11eb_bf50_acde48001122row7_col4{\n",
       "            background:  salmon;\n",
       "        }</style><table id=\"T_d43961ac_01dc_11eb_bf50_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >survived</th>        <th class=\"col_heading level0 col1\" >pclass</th>        <th class=\"col_heading level0 col2\" >age</th>        <th class=\"col_heading level0 col3\" >sibsp</th>        <th class=\"col_heading level0 col4\" >parch</th>        <th class=\"col_heading level0 col5\" >fare</th>        <th class=\"col_heading level0 col6\" >adult_male</th>        <th class=\"col_heading level0 col7\" >alone</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row0\" class=\"row_heading level0 row0\" >survived</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col1\" class=\"data row0 col1\" >-0.338481</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col2\" class=\"data row0 col2\" >-0.077221</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col3\" class=\"data row0 col3\" >-0.035322</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col4\" class=\"data row0 col4\" >0.081629</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col5\" class=\"data row0 col5\" >0.257307</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col6\" class=\"data row0 col6\" >-0.557080</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row0_col7\" class=\"data row0 col7\" >-0.203367</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row1\" class=\"row_heading level0 row1\" >pclass</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col0\" class=\"data row1 col0\" >-0.338481</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col2\" class=\"data row1 col2\" >-0.369226</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col3\" class=\"data row1 col3\" >0.083081</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col4\" class=\"data row1 col4\" >0.018443</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col5\" class=\"data row1 col5\" >-0.549500</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col6\" class=\"data row1 col6\" >0.094035</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row1_col7\" class=\"data row1 col7\" >0.135207</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row2\" class=\"row_heading level0 row2\" >age</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col0\" class=\"data row2 col0\" >-0.077221</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col1\" class=\"data row2 col1\" >-0.369226</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col3\" class=\"data row2 col3\" >-0.308247</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col4\" class=\"data row2 col4\" >-0.189119</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col5\" class=\"data row2 col5\" >0.096067</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col6\" class=\"data row2 col6\" >0.280328</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row2_col7\" class=\"data row2 col7\" >0.198270</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row3\" class=\"row_heading level0 row3\" >sibsp</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col0\" class=\"data row3 col0\" >-0.035322</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col1\" class=\"data row3 col1\" >0.083081</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col2\" class=\"data row3 col2\" >-0.308247</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col6\" class=\"data row3 col6\" >-0.253586</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row3_col7\" class=\"data row3 col7\" >-0.584471</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row4\" class=\"row_heading level0 row4\" >parch</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col0\" class=\"data row4 col0\" >0.081629</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col1\" class=\"data row4 col1\" >0.018443</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col2\" class=\"data row4 col2\" >-0.189119</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col6\" class=\"data row4 col6\" >-0.349943</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row4_col7\" class=\"data row4 col7\" >-0.583398</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row5\" class=\"row_heading level0 row5\" >fare</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col0\" class=\"data row5 col0\" >0.257307</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col1\" class=\"data row5 col1\" >-0.549500</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col2\" class=\"data row5 col2\" >0.096067</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col6\" class=\"data row5 col6\" >-0.182024</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row5_col7\" class=\"data row5 col7\" >-0.271832</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row6\" class=\"row_heading level0 row6\" >adult_male</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col0\" class=\"data row6 col0\" >-0.557080</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col1\" class=\"data row6 col1\" >0.094035</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col2\" class=\"data row6 col2\" >0.280328</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col3\" class=\"data row6 col3\" >-0.253586</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col4\" class=\"data row6 col4\" >-0.349943</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col5\" class=\"data row6 col5\" >-0.182024</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row6_col7\" class=\"data row6 col7\" >0.404744</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d43961ac_01dc_11eb_bf50_acde48001122level0_row7\" class=\"row_heading level0 row7\" >alone</th>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col0\" class=\"data row7 col0\" >-0.203367</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col1\" class=\"data row7 col1\" >0.135207</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col2\" class=\"data row7 col2\" >0.198270</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col3\" class=\"data row7 col3\" >-0.584471</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col4\" class=\"data row7 col4\" >-0.583398</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col5\" class=\"data row7 col5\" >-0.271832</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col6\" class=\"data row7 col6\" >0.404744</td>\n",
       "                        <td id=\"T_d43961ac_01dc_11eb_bf50_acde48001122row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdcafd25f90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().style.apply(lambda x: [\"background: salmon\" if  (abs(v) > 0.5 and v!=1) else \"\" for v in x], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:13.319123Z",
     "start_time": "2020-09-28T22:49:13.006332Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cols_high_corr = ['sibsp', 'pclass', 'adult_male', 'survived', 'fare', 'alone', 'parch']\n",
      "cols_high_corr1 = ['fare', 'survived', 'parch', 'alone']\n",
      "cols_high_corr2 = ['pclass', 'adult_male', 'alone', 'sibsp']\n",
      "cols_high_corr_drop = ['fare', 'parch', 'survived']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_d46a02e4_01dc_11eb_bf50_acde48001122row0_col2{\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_d46a02e4_01dc_11eb_bf50_acde48001122row1_col2{\n",
       "            background-color:  #056aa6;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_d46a02e4_01dc_11eb_bf50_acde48001122row2_col2{\n",
       "            background-color:  #fbf3f9;\n",
       "            color:  #000000;\n",
       "        }#T_d46a02e4_01dc_11eb_bf50_acde48001122row3_col2{\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >feature1</th>        <th class=\"col_heading level0 col1\" >feature2</th>        <th class=\"col_heading level0 col2\" >corr</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row0_col0\" class=\"data row0 col0\" >fare</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row0_col1\" class=\"data row0 col1\" >pclass</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row0_col2\" class=\"data row0 col2\" >-0.549500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row1_col0\" class=\"data row1 col0\" >survived</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row1_col1\" class=\"data row1 col1\" >adult_male</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row1_col2\" class=\"data row1 col2\" >-0.557080</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row2_col0\" class=\"data row2 col0\" >parch</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row2_col1\" class=\"data row2 col1\" >alone</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row2_col2\" class=\"data row2 col2\" >-0.583398</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row3_col0\" class=\"data row3 col0\" >alone</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row3_col1\" class=\"data row3 col1\" >sibsp</td>\n",
       "                        <td id=\"T_d46a02e4_01dc_11eb_bf50_acde48001122row3_col2\" class=\"data row3 col2\" >-0.584471</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdcafc81d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.bp.corr_high(thr=0.5,disp=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:13.462604Z",
     "start_time": "2020-09-28T22:49:13.335583Z"
    }
   },
   "outputs": [],
   "source": [
    "cols_high_corr_drop = ['fare', 'parch', 'survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:13.549766Z",
     "start_time": "2020-09-28T22:49:13.475748Z"
    }
   },
   "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>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.094035</td>\n",
       "      <td>0.135207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>0.280328</td>\n",
       "      <td>0.198270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.253586</td>\n",
       "      <td>-0.584471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adult_male</th>\n",
       "      <td>0.094035</td>\n",
       "      <td>0.280328</td>\n",
       "      <td>-0.253586</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <td>0.135207</td>\n",
       "      <td>0.198270</td>\n",
       "      <td>-0.584471</td>\n",
       "      <td>0.404744</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pclass       age     sibsp  adult_male     alone\n",
       "pclass      1.000000 -0.369226  0.083081    0.094035  0.135207\n",
       "age        -0.369226  1.000000 -0.308247    0.280328  0.198270\n",
       "sibsp       0.083081 -0.308247  1.000000   -0.253586 -0.584471\n",
       "adult_male  0.094035  0.280328 -0.253586    1.000000  0.404744\n",
       "alone       0.135207  0.198270 -0.584471    0.404744  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(cols_high_corr_drop,axis=1)\n",
    "df1.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:13.669935Z",
     "start_time": "2020-09-28T22:49:13.583517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col2,#T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col1{\n",
       "            background:  salmon;\n",
       "        }</style><table id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >survived</th>        <th class=\"col_heading level0 col1\" >pclass</th>        <th class=\"col_heading level0 col2\" >age</th>        <th class=\"col_heading level0 col3\" >sibsp</th>        <th class=\"col_heading level0 col4\" >parch</th>        <th class=\"col_heading level0 col5\" >fare</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122level0_row0\" class=\"row_heading level0 row0\" >survived</th>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row0_col1\" class=\"data row0 col1\" >0.115371</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row0_col2\" class=\"data row0 col2\" >0.111542</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row0_col3\" class=\"data row0 col3\" >-0.028674</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row0_col4\" class=\"data row0 col4\" >0.082447</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row0_col5\" class=\"data row0 col5\" >0.301210</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122level0_row1\" class=\"row_heading level0 row1\" >pclass</th>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col0\" class=\"data row1 col0\" >0.115371</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col2\" class=\"data row1 col2\" >0.571910</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col3\" class=\"data row1 col3\" >0.306844</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col4\" class=\"data row1 col4\" >0.173285</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row1_col5\" class=\"data row1 col5\" >-0.315652</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122level0_row2\" class=\"row_heading level0 row2\" >age</th>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col0\" class=\"data row2 col0\" >0.111542</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col1\" class=\"data row2 col1\" >0.571910</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col3\" class=\"data row2 col3\" >-0.192800</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col4\" class=\"data row2 col4\" >-0.095105</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row2_col5\" class=\"data row2 col5\" >0.392652</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122level0_row3\" class=\"row_heading level0 row3\" >sibsp</th>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row3_col0\" class=\"data row3 col0\" >-0.028674</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row3_col1\" class=\"data row3 col1\" >0.306844</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row3_col2\" class=\"data row3 col2\" >-0.192800</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row3_col4\" class=\"data row3 col4\" >0.324787</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row3_col5\" class=\"data row3 col5\" >0.211403</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122level0_row4\" class=\"row_heading level0 row4\" >parch</th>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row4_col0\" class=\"data row4 col0\" >0.082447</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row4_col1\" class=\"data row4 col1\" >0.173285</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row4_col2\" class=\"data row4 col2\" >-0.095105</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row4_col3\" class=\"data row4 col3\" >0.324787</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row4_col5\" class=\"data row4 col5\" >0.199136</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122level0_row5\" class=\"row_heading level0 row5\" >fare</th>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row5_col0\" class=\"data row5 col0\" >0.301210</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row5_col1\" class=\"data row5 col1\" >-0.315652</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row5_col2\" class=\"data row5 col2\" >0.392652</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row5_col3\" class=\"data row5 col3\" >0.211403</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row5_col4\" class=\"data row5 col4\" >0.199136</td>\n",
       "                        <td id=\"T_d49fcadc_01dc_11eb_bf50_acde48001122row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdcafca99d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bp.partial_corr(thr=0.3,disp=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:13.745759Z",
     "start_time": "2020-09-28T22:49:13.676189Z"
    }
   },
   "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>fare</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.096067</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fare       age\n",
       "fare  1.000000  0.096067\n",
       "age   0.096067  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bp.partial_corr(['fare','age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:14.392042Z",
     "start_time": "2020-09-28T22:49:13.752798Z"
    }
   },
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age\n",
       "33   66.0\n",
       "54   65.0\n",
       "96   71.0\n",
       "116  70.5\n",
       "280  65.0\n",
       "456  65.0\n",
       "493  71.0\n",
       "630  80.0\n",
       "672  70.0\n",
       "745  70.0\n",
       "851  74.0"
      ]
     },
     "execution_count": 15,
     "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": [
    "ser_outliers = df.bp.outliers_tukey('age')\n",
    "ser_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:14.494884Z",
     "start_time": "2020-09-28T22:49:14.396494Z"
    }
   },
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age\n",
       "498  80.0\n",
       "679  74.0\n",
       "74   71.0\n",
       "393  71.0\n",
       "91   70.5\n",
       "531  70.0\n",
       "592  70.0\n",
       "25   66.0\n",
       "366  65.0\n",
       "225  65.0\n",
       "40   65.0\n",
       "351  64.0\n",
       "433  64.0\n",
       "385  63.0\n",
       "221  63.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = 'age'\n",
    "df1 = df.dropna(subset=[col]).reset_index(drop=True)\n",
    "idx_outliers, val_outliers = df1.bp.outliers_kde(col)\n",
    "df1.loc[idx_outliers,[col]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T23:30:23.959237Z",
     "start_time": "2020-06-21T23:30:23.912556Z"
    }
   },
   "source": [
    "# Kernel Density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-28T22:49:15.533264Z",
     "start_time": "2020-09-28T22:49:14.506576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.bp.compare_kde_binn('age','survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (dataSc)",
   "language": "python",
   "name": "datasc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}