{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/src/iris.csv', index_col=0)\n",
    "# df = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal_length  sepal_width  petal_length  petal_width species\n",
      "0           5.1          3.5           1.4          0.2  setosa\n",
      "1           4.9          3.0           1.4          0.2  setosa\n",
      "2           4.7          3.2           1.3          0.2  setosa\n",
      "3           4.6          3.1           1.5          0.2  setosa\n",
      "4           5.0          3.6           1.4          0.2  setosa\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal_length    float64\n",
      "sepal_width     float64\n",
      "petal_length    float64\n",
      "petal_width     float64\n",
      "species          object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "versicolor    50\n",
      "setosa        50\n",
      "virginica     50\n",
      "Name: species, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['species'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'seaborn.axisgrid.PairGrid'>\n"
     ]
    }
   ],
   "source": [
    "pg = sns.pairplot(df)\n",
    "print(type(pg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pg.savefig('data/dst/seaborn_pairplot_default.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df).savefig('data/dst/seaborn_pairplot_default.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species').savefig('data/dst/seaborn_pairplot_hue.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species',\n",
    "             hue_order=['virginica', 'versicolor', 'setosa']).savefig('data/dst/seaborn_pairplot_hue_order.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', palette='Blues').savefig('data/dst/seaborn_pairplot_palette.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species',\n",
    "             palette={'setosa': 'red',\n",
    "                      'versicolor': '#00ff00',\n",
    "                      'virginica': 'blue'}).savefig('data/dst/seaborn_pairplot_palette_dict.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species',\n",
    "             vars=['sepal_length', 'sepal_width']).savefig('data/dst/seaborn_pairplot_vars.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species',\n",
    "             x_vars=['sepal_length', 'sepal_width'],\n",
    "             y_vars=['petal_length', 'petal_width']).savefig('data/dst/seaborn_pairplot_xy_vars.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', markers='+').savefig('data/dst/seaborn_pairplot_markers.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', markers=['+', 's', 'd']).savefig('data/dst/seaborn_pairplot_markers_multi.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', kind='reg').savefig('data/dst/seaborn_pairplot_kind_reg.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', diag_kind='kde').savefig('data/dst/seaborn_pairplot_diag_kind_kde.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', size=2).savefig('data/dst/seaborn_pairplot_size.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species',\n",
    "             plot_kws={'alpha': 0.2},\n",
    "             diag_kws={'bins': 20, 'histtype': 'step'}).savefig('data/dst/seaborn_pairplot_kws.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(df, hue='species', kind='reg',\n",
    "             plot_kws={'ci': None,\n",
    "                       'marker': '+',\n",
    "                       'scatter_kws': {'alpha': 0.4},\n",
    "                       'line_kws': {'linestyle': '--'}}).savefig('data/dst/seaborn_pairplot_kind_reg_kws.png')"
   ]
  }
 ],
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