sitc/ml1/2_3_1_Advanced_Visualisatio...

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, ©  Carlos A. Iglesias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "* [Advanced Visualisation](#Advanced-Visualisation)\n",
    "* [Install seaborn](#Install-seaborn)\n",
    "* [Transform Data into Dataframe](#Transform-Data-into-Dataframe)\n",
    "* [Visualisation with seaborn](#Visualisation-with-seaborn)\n",
    "* [References](#References)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous notebook we developed plots with the [matplotlib](http://matplotlib.org/) plotting library.\n",
    "\n",
    "This notebook introduces another plotting library, [**seaborn**](https://stanford.edu/~mwaskom/software/seaborn/),  which provides advanced facilities for data visualization.\n",
    "\n",
    "*Seaborn* is a library for making attractive and informative statistical graphics in Python. It is built on top of *matplotlib* and tightly integrated with the *PyData* stack, including support for *numpy* and *pandas* data structures and statistical routines from *scipy* and *statsmodels*.\n",
    "\n",
    "*Seaborn* requires its input to be *DataFrames* (a structure created with the library *pandas*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should install the SeaBorn package. Use `conda install seaborn`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform Data into Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Seaborn* requires that data is represented as a *DataFrame* object from the library *pandas*. \n",
    "\n",
    "A *DataFrame* is a 2-dimensional labeled data structure with columns of potentially different types. We will not go into the details of DataFrames in this session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from pandas import DataFrame\n",
    "\n",
    "# iris data set from scikit learn (it is a Bunch object)\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# transform into dataframe\n",
    "iris_df = DataFrame(iris.data)\n",
    "iris_df.columns = iris.feature_names\n",
    "\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   species  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df['species'] = iris.target\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation with seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following examples are taken from [a kaggle tutorial](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook) and [the seaborn tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html).\n",
    "\n",
    "To plot multiple pairwise bivariate distributions in a dataset, you can use the *pairplot()* function and *PairGrid()*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A **scatterplot matrix** (*matriz de diagramas de dispersión*) presents every pairwise relationship between a set of variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fac5787ed68>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac558201d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "# if matplotlib is not set inline, you will not see plots\n",
    "%matplotlib inline \n",
    "\n",
    "sns.pairplot(iris_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PairGrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PairGrid** allows you to quickly draw a grid of small subplots using the same plot type to visualize data in each. In a PairGrid, each row and column is assigned to a different variable, so the resulting plot shows each pairwise relationship in the dataset. This style of plot is sometimes called a “scatterplot matrix”, as this is the most common way to show each relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4YAAAN8CAYAAAAEerRyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXt4HNV9///emd2VtLpZN9uyZFnGl2MLY4MAh4uDGxdIAwmEkgSnQOqkiVv4pkmeOl/69NcSDN980+bCNxCSkjYhmPAkMUkol8QmDVQJNQ6uHcsXsPGxDBKyJVlIsi6WVtJef3/sznpm9sxeZ3dnVp/X8/ix9szMZ87Mvs+ZOXs+5/NxhMNhEARBEARBEARBEHMXqdAVIAiCIAiCIAiCIAoLDQwJgiAIgiAIgiDmODQwJAiCIAiCIAiCmOPQwJAgCIIgCIIgCGKO4yx0BQoBY2wegC8BeIRzPpbP4wt57rl+fKHrboXzmW3T6vZyYdPq9nJlMx/nM8OOlepSrHasVJdCn8/q/QFdszWvudDny6V9O9c91/btUPe5OmM4D8AD0f/zfXwhzz3Xjy903a1wPrNtWt1eLmxa3V6ubObjfGbYsVJditWOlepS6PNZvT+ga7auzUKeL5f27Vz3XNu3fN0tN2PIGPswgIcAOKJFD3HOn9Pt8wCAewH0RYv2cs7/Nn+1JAiCIAiCIAiCKB4sNzAE8GMA13LO32KMXQJgL4DnBPs9xTm/L79VIwiCIAiCIAiCKD6s6EoaxIUp0BoA/Qb7OQzKCYIgCIIgCIIgiDSw4ozhHQBeZIxNAagAcJPBfpsZYzcCGACwnXO+T7RTdCGmxte2srJySVVVFYLBoGxivYm5QzNjTF82ZkIQBtIpYTakVcIOkE4Ju0BaJexAxjp1hMPh3FQpAxhjMoDfALifc76PMXYNgJ8BWM0596r2mw9ghHMeZIxdD+AnAFZxzkcFNrcjshAzRlNTEzo6OnJ4JUSxwhh7FcBGwaYHOefbs7C7HaRTwkRIq4QdIJ0SdoG0StiBbHVqtRnDSwE0KrN/nPM/RGcOVwM4qOzEOX9P9fcrjLHTANYA2COw+QiAHeqCzZs3NwPYMzIyiVAos4FxQ0MlhobO5/1YOr6w9x7ARyGO9pRt2OGc6FSECffAVvZyYdPq9qLYUqtm3Auz7ifZyX1dYFOdqrF6f0B9qmnYXqtqcnSPcm7b7vZzXXdkqVOrDQzPIDL9uZJzfpIxthrAfABvq3dijC3inPdH/74UwBIAXGQwOm1qeq4QYm6SKz2RTgmzIa0SdoB0StgF0iphB7LVk6UGhpzzQcbYPQB+yRgLRos/zTkfY4ztQsTFtBPA1xhj7QBCAGYB3KWeRSQIgiAIgiAIgiBSx1IDQwDgnP8MkXWF+vKbVX9vyWedCIIgCIIgCIIgihkrpqsgCIIgCIIgCIIg8ggNDAmCIAiCIAiCIOY4NDAkCIIgCIIgCIKY49DAkCAIgiAIgiAIYo5DA0OCIAiCIAiCIIg5Dg0MCYIgCIIgCIIg5jg0MCQIgiAIgiAIgpjj0MCQIAiCIAiCIAhijmO5BPeMsQ8DeAiAI1r0EOf8Od0+EoDHAHwQQAjA1znnT+S1ogRBEARBEARBEEWCFWcMfwzgTs75ZQA+BeApwT53AriIc74cwDUAtjPGWvJYR4IgCIIgCIIgiKLBigPDIIB50b9rAPQL9rkDwA8AgHM+DOB5AB/PS+0IgiAIgiAIgiCKDMu5kiIy6HuRMTYFoALATYJ9WgC8q/rcC2CxyBhjbB4uDDQBANu2bWveunWrObUl5hQiPUUZ45yPmWmXdEpkA2mVsAOkU8IukFYJO5CtTh3hcNj8WmUIY0wG8BsA93PO9zHGrgHwMwCrOede1X5HAXyac34w+vl/A2jinH9JYHM7gAfUZU1NTejo6MjdhRCWYXxyFoPnvFhQ60F1RUnW9hhjD0KnpygPcs63Z2F3u94u6bQwmK2ZQkFaJdKlENonnRJ26XNJq0S25EPr2erUajOGlwJo5JzvAwDO+R+iM4erARxU7dcLYImqrAVAj4HNRwDsUBds3ry5GcCekZFJhEKZDYwbGioxNHQ+78fS8akfv+/YWex46QRkyYFgKIwtN63CRzauyOrcEOgpSsa/FhrZNUOnIrK9/3azl45NkWaualuY8zrm4pphU62acS/Mup9zyU6q2je7LrCpTtVYvT8oZJ+aDEV3TqeEQCCUVHf5rp8O22tVTY7uUc5t29V+rrQuICudWm1geAZAM2NsJef8JGNsNYD5AN7W7fcLAJ9jjD0HoB7ArQDeLzIYnTbNttESNmPC68OOl07AFwjFynbsPoHrLs8uRlGu9EQ6LTxGmmlrrUWVx13AmmUGaZVIlUJqn3Q6d1HrTtGelftc0iqRKfnUerZ6slTwGc75IIB7APySMXYIwE8RcRkdY4ztYoy1R3d9GkA3gC4Af0BkevRdoVFiTjIyPgNZcmjKZMmBwXNegyOIuY6RZkbGZwpUI4LID6R9ohCQ7oi5gp20brUZQ3DOf4bIukJ9+c2qv0MA7s1nvQh7UVddiqDO/SIYCmNBrQe+aV+BakVYGSPN1FWXFqhGBJEfSPtEISDdEXMFO2ndUjOGBGEWVR43tty0Cm6nhDK3DLdTwpabVll6YTtRWIw0Y0WXJoIwE9I+UQjUuvOUOkl3RNFiJ61bbsaQIMziqraFaGutxcj4DOqqSy3ZAAlrQZoh5iqkfaIQKLoLOiTI4RDpjiha7KJ1GhgSRcGRriF0dg2jfUU91q1oiJVXedyWbXzFyITXl/DFMpXto72jlu40CcLujE/OontgIq4divrL/uEpdA9MYGljFRbVl8fasLuM2ieRH/TPDb0mCcIudPeN41jvOC5uqda8q1qJrAeGjDEngFsA3AxgHSJJFccAHAHwEoDnOeeBbM9DEEbc/8N96BuOBJXZc3QATfUe/J/PXlXgWs09koW7T3V7HkI5Z3wNBGF39h07ix2/4ZAdSKrxp3/L8bvOvtjnttYanDozHmkfYWDLhxi1DyIrkvX7+j55eXM1jveMxrZvam/CXTeyQlSdINJC/a76ygFY9l01qzWGjLG/AfAOgL9GJKXE/wXwN9H/3wbwOQDvRPcjCNM50jUUa2gKfcNeHOkaKlCN5ibqUMzTviB8gRB27D6BCa8v7e3emUDcditcA0HYnZjG/cGkGu8fntIMCgHgeM/ohfbhD1L7ILIiWb8v6pPVg0IA6OjsQ//wVCGqTxApY6d31WxnDJcDWM85PyvY9hyArzHGGgFsy/I8BBHHhNeHV4/0C7d1dg1bdpq+GEkUirnK4856ez6wQh0IIpeko/HjPeeS2qP2QWSDkR57B8+jvNSFqRl/3HYR3QMT5FJKWJrOrmHDcqu9q2Y1MOScfzmFfQYAJN2PINJBcS8Jh0LC7e0r6vNco7lNslDMqWxXJ9cGAF8glNdQznYKJ00QmZCqxvcdO4ufd3QltUftg8gGkR59gRAe++VROGUJgWAIus1CljZW5aiGBGEO7SvqsefogLDcapiaroIx5mGMrWWMXaP+Z+Y5CGJ8cjbmXuIXjAub6j2W+wWm2EkW7j6lcPhh3RuA/nOOoZD9RLET07hLNtS44r4XEPStba01F9qHS6b2QWSFPoS/yykB4TD8wTCmfUH4g2EgHIZL1Se3tdZobGxqb6LZQsLyLG2qTqu8kJgWlZQx9ikA3wXgAzCt2hQG0JKijSUAno8eAwA1ACo55/W6/R5AJMG9sgBiL+f8bzOvPWEnBs9549xLXBJw8UV12LhuEQ0KC0SycPeJto+Mz8DtkjHtC8bK3C45725qFLKfKHaualuI6y5vAX9nWKhxkXufS3bgrz7chvWrF8QiRLKL6uGbpvWFRHaoQ/j3nR3H48+9GfccuOe2NSgvdVFUUsK2jIzPoMytfccpc+f/HScVzExX8Q0At3POX87UAOf8XQCXKZ8ZY98GIBvs/hTn/L5Mz0XYlwW1njj3E4ckYctNqy3XwOYaydKDGG23khsnpTghip3qihJD9ztRW3Q4HFi1JDJTo7SP6ooSDNHAkDCBKo8bDQ2VkMMh4XOgZUGlpk9eVF9OA0LCVljpHScZZrqS+gD83ixjjDEXgDsB/Mh
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac50a030b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PairGrid\n",
    "g = sns.PairGrid(iris_df)\n",
    "g.map(plt.scatter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very common way to use this plot colors the observations by a separate categorical variable. For example, the iris dataset has four measurements for each of the three different species of iris flowers.\n",
    "\n",
    "We are going to color each class, so that we can easily identify **clustering** and **linear relationships**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f31643557b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA68AAAN9CAYAAAB8bk/NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ne8UZJW1Xqu8ZIcqt9f0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f316455b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(iris_df, hue=\"species\")\n",
    "g.map_diag(plt.hist)\n",
    "g.map_offdiag(plt.scatter)\n",
    "#names = {i: name for i,name in enumerate(iris.target_names)}\n",
    "#g.add_legend(legend_data=names)\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default every numeric column in the dataset is used, but you can focus on particular relationships if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4e2f9908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(iris_df, vars=['sepal length (cm)', 'sepal width (cm)'], hue=\"species\")\n",
    "g.map(plt.scatter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It’s also possible to use a different function in the upper and lower triangles to emphasize different aspects of the relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4e27ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(iris_df)\n",
    "g.map_upper(plt.scatter)\n",
    "g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n",
    "g.map_diag(sns.kdeplot, lw=3, legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pairplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PairGrid is flexible, but to take a quick look at a dataset, it may be easier to use pairplot(). This function uses scatterplots and histograms by default, although a few other kinds will be added (currently, you can also plot regression plots on the off-diagonals and KDEs on the diagonal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cif/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "/home/cif/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4ebd3d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_df, hue=\"species\", height=2.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also control the aesthetics of the plot with keyword arguments, and it returns the PairGrid instance for further tweaking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cif/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "/home/cif/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4c656438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(iris_df, hue=\"species\", palette=\"Set2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin Plots (boxplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**Box plots** or **boxplot** ](https://en.wikipedia.org/wiki/Box_plot) (*diagramas de caja*) are a convenient way of graphically depicting groups of numerical data through their quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fac4d074a20>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4e59df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can look at an individual feature in Seaborn through a boxplot\n",
    "sns.boxplot(x=\"species\", y=\"sepal length (cm)\", data=iris_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4e387128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One way we can extend this plot is adding a layer of individual points on top of\n",
    "# it through Seaborn's striplot\n",
    "# \n",
    "# We'll use jitter=True so that all the points don't fall in single vertical lines\n",
    "# above the species\n",
    "#\n",
    "# Saving the resulting axes as ax each time causes the resulting plot to be shown\n",
    "# on top of the previous axes\n",
    "ax = sns.boxplot(x=\"species\", y=\"petal length (cm)\", data=iris_df)\n",
    "ax = sns.stripplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, jitter=True, edgecolor=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[**Violin plots**](https://en.wikipedia.org/wiki/Violin_plot) (*diagramas de violín*)  are a method of plotting numeric data. A violin plot is a box plot with a rotated kernel density plot on each side.  A violin plot is just a histogram (or more often a smoothed variant like a kernel density) turned on its side and mirrored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fac4e9b6860>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4e23ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A violin plot combines the benefits of the previous two plots and simplifies them\n",
    "# Denser regions of the data are fatter, and sparser thiner in a violin plot\n",
    "sns.violinplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kernel Density Estimation (KDE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful representation is the [Kernel density estimation (KDE)](https://en.wikipedia.org/wiki/Kernel_density_estimation) plot. KDE is a non-parametric way to estimate the probability density function of a random variable. The kdeplot represents the shape of a distribution. Like the histogram, the KDE plots encodes the density of observations on one axis with height along the other axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fac4eb7f7b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac4eb76fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A final seaborn plot useful for looking at univariate relations is the kdeplot,\n",
    "# which creates and visualizes a kernel density estimate of the underlying feature\n",
    "sns.FacetGrid(iris_df, hue=\"species\", height=6) \\\n",
    "   .map(sns.kdeplot, \"petal length (cm)\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choosing the right visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the data, we can choose which visualisation suits better. the following [diagram](http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/) guides this selection.\n",
    "\n",
    "\n",
    "![](files/images/data-chart-type.png \"Graphs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
    "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
    "* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n",
    "* [Matplotlib web page](http://matplotlib.org/index.html)\n",
    "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
    "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",
    "* [Iris dataset visualisation notebook](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook)\n",
    "* [Tutorial plotting with Seaborn](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html)\n",
    "* [Choose the Right Chart Type for your Data](http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licence\n",
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
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