sitc/ml1/2_3_1_Advanced_Visualisation.ipynb

{
 "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, © 2016 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": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 2,
     "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 3,
     "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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f316d1da390>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f316d1da240>"
      ]
     },
     "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4IAAAN9CAYAAADGzcetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTNOjsji5vy5a2IXtuw9
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31664d8940>"
      ]
     },
     "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": {
    "collapsed": false
   },
   "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": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFhCAYAAABOPXDpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsf1vf5flTzf+8bMC/gX
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31669f2080>"
      ]
     },
     "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": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA30AAAN9CAYAAAAnihC5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBn2vW2s3ZK0nIiLEL6F
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f316472ee80>"
      ]
     },
     "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA68AAAN9CAYAAAB8bk/NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPhtzZGWerVjPzP2/ayf
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31647346a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_df, hue=\"species\", size=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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA68AAAN9CAYAAAB8bk/NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1KTW+q1aGSMIwt7CGko
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f315f49c9e8>"
      ]
     },
     "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2758563ef0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAESCAYAAAAbq2nJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIBn6aQPmSpobEddmaL+
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27580d7198>"
      ]
     },
     "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAESCAYAAAD38s6aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRk+1jtaDvx24xlr7x+E
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2758662240>"
      ]
     },
     "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2758c08588>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAESCAYAAAD38s6aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8XGd97/HPrNp3ybIl79vjOI5JQjZnb6gTyEIgJECA\nJNCG4l7SDW5v29tCy9KWFy200Feo0/RSbtkhlwAhCYlDnNVZyWonfuIttiVvkrVYy2i2c+4fM5Jl\nW8vI1pmRjr7v10svzxydOeenKPrNM7/znN8TcF0XERHxn2ChAxAREW8owYuI+JQSvIiITynBi4j4\nlBK8iIhPKcGLiPhU2MuDG2PKgP8GaoAo8EVr7cNenlNERDK8HsF/HNhqrb0CuAn4hsfnExGRLK8T\nfDtQl31cC7R5fD4REckKeH0nqzHmQWApUA1cY6193tMTiogI4PEI3hjzUWC3tXYZ8C7gTi/PJyIi\nR3l6kRW4CHgIwFr7mjGmyRgTsNaO+LEhlUq74XDI45BERHwnMNJGrxP8duAC4F5jzAKgZ7TkDtDZ\n2e9xOCIi/tPQUDHidq8T/F3At40xjwEh4FMen09ERLI8v8g6EW1tPVMnGBGRaaKhoWLEEo3uZBUR\n8SkleBERn1KCFxHxKSV4ERGfUoIXEfEpJXgREZ9SghcR8SkleBERn1KCFxHxKSV4ERGfUoIXEfEp\nJXgR8TXHcejs7Cx0GAWhBC8ivvb973+Hz37202zf/lahQ8k7JXgR8bWNGx8BoKVlb4EjyT8leBGZ\nEaZSa/R8UYIXkRkhHo8XOoS8U4IXkRmhr6+30CHknRK8iPjW8LJMd3d3ASMpDCV4EfGt/v6+ocdd\nXTNvqqSni24bY34PuAVwgQDwTmttpZfnFBEZdPhw+7DHbQWMpDA8TfDW2m8D3wYwxlwK3OTl+URE\nhjt06ODQ4/b2NhzHIRicOYWLfP6knwe+lMfzicgMt3//fgAiBEgmk3R0HC5wRPmVlwRvjDkH2GOt\nPZSP84mIAOzf3wLAkmgUgNbWlkKGk3f5GsHfDnwnT+cSEQFg7949RAIBFkQyCb6lZU+BI8ovT2vw\nw1wO3DHeTjU1pYTDIe+jERHfSyQSHDiwn/pgiPpQJtUdPNhKQ0NFgSPLH88TvDFmDtBjrU2Nt29n\nZ7/X4YjIDPH22ztJp9OkQiE2x2MUBQJs27adtraeQoc26UZ708pHiWYOoNq7iOTVnj27Aeh1HHYm\nE9SFwhw6dJCBgYECR5Y/nid4a+1L1tprvD6PiMhwe/dmEvxg0bcuFMJ13RlVh585E0JFZEYZbA8c\nJAAwVIefSW2D83WRVUQkr1pbW6gMBhnsRlMTyozl9+2bOVMlNYIXEd/p7e2lt7dnKKnD0QR/4MCB\nQoWVd0rwIuI7bW2ZFgWVwaMJPhoIUhIIcuiQEryIyLTV3p5pLDY8wQNUBIN0HD6M4ziFCCvvlOBF\nxHc6OzsAKD+usVh5MEgqnaKnx39z4UeiBC8ivtPZmen9Xnpcgi/LPu/q6sh7TIWgBC8ivjO4uEfZ\ncQl+MOEPvgH4nRK8iPhOd3cXAKWB40bw2eeD3/c7JXgR8Z2OjsOUBIKEAoFjtpcNjeBVohERmXYc\nx6Hj8GEqRli5qSI7q6atbWa0x1KCFxFfOXy4nVQ6RVXoxNbj5cEgQZgxc+GV4EXEVwZ7zdQET0zw\noUCAqmCI1taWGTEXXgleRHxl164dANSHR2611RAOMzAwwIED+/MZVkEowYuIr7z11lYAZodGTvCz\ns4nf2jfzFlOhKMGLiG/09/exfftbzAqFKRrhIivA3HBmfdbNm1/NZ2gFoQQvIr7xyisv4TgOC7OL\nbI+kKhSiJhhi8+bXiMVieYwu/5TgRcQ3nnnmKQCWRIvG3G9JNEoymeSll17IR1gFowQvIr7Q3t7G\nG29spjEUpnqEKZLDmWgxAE88sTEfoRWM5wneGPNRY8wrxpgXjDHv8fp8IjIzbdz4CK7rsrKoeNx9\nK0Mh5oUjbNtm2bPnbe+DKxBPE7wxphb4PHAhcC1wvZfnE5GZKRaL8dhjj1ASCLJ0nPLMoDOKSwB4\n6KEHvAytoLwewf8usMFa22+tPWitXefx+URkBnr88UeJxWKsKiomfFz/mdHMD0eoCYZ47rlNQwuE\n+I3XCX4hUGaM+YUx5nFjzBUen09EZphkMsFDv/4VkUCAM3IozwwKBAKcVVyC4zg8+OCvPIywcEa+\nE2DyBIBa4H3AImAjsGC0nWtqSgmHx744IiIy3K9+9Su6j3RzVlHJqHPfR7MsWsSLAzGeenIjt932\nUerq6jyKsjC8TvAHgU3WWhfYaYzpMcbUW2vbR9q5s7Pf43BExE+SySQ/+clPCQcCrM7W1Efiuu6I\n24PZUfzj/b1873s/4iMfudWrUD3V0FAx4navSzQPA1cYYwLGmDqgbLTkLiIyUZs2PUlnZwcro0Un\nLM8HcDidotdx6HVdftDdyeF06oR9TLSI8mCQxx//DUeOdOcj7LzxNMFba/cB9wDPAvcDd3h5PhGZ\nOTK18/sIEeDMUUbvD/X2MDh273bSPNx74mLboUCAM4tKSCaTPPLIQx5GnH9el2iw1t4N3O31eURk\nZnnlld9y6NBBTosWUTZCa+B+x6HbSR+zrctJ0+84J4z2VxQV8+JAjI0bH+Haa99HNDp6q4PpRHey\nisi0tHHjI8DR+ezHS41Sdx9peyQQ4LSiIvr6enn++WcmL8gCU4IXkWnn8OF23nhjM7PDYepGaQs8\nUSuz7QueeurxSTneVKAELyLTzosvPofrukM9ZSZDZSjEnHCYbdssnZ2dk3bcQlKCF5Fp57XXXgEY\nsy3wyVgcKcJ1Xd/0ileCF5FpJZ1Os337W9QGQyNOjTwVzZEIcHRVqOlOCV5EppUDB/aTTCaZNcqa\nq6eiJhgiHAiwZ8/uST92ISjBi8i00tZ2EGDcnu+DotEoTU1NOU19DAYCVAWDHDp04JRinCqU4EVk\nWjly5AgApYHx01c0GmXdunXcddddrFu3LqckXxoIEo/HiccHTjnWQlOCFxlHR8dhvvSlv2HDhgcL\nHYoAsVimZ1U0h7bA9fX1rF27FoC1a9dSX18/7msGjzswoAQv4nvbt7/Frl07+eEPv1voUITMRVbI\nlFPG097ezoYNGwDYsGED7e3jt8IaPO7geaYzz1sViEx3PT1HCh2CnKREIsH69eu55557aG9vJ5FI\nQHFZocPKGyV4kXEMv+nFcRyCkzw1TyYmmO0744zSiuB4iUSCffv25Xz8wdbCfvg9j5vgjTGrgXeT\nWZ0J4G3g19ba17wLS2TqGD6jorOzg7q68eu44p1QKJN4HY+OP1iYCeU4S2cqGzXBG2PmAP8HmA08\nAmzJfmsB8B1jzH7gdmvtfs+jFCmgvXv3HPNYCb6wQtneM14l+MHjhiapx00hjfUT3AP8rbX2kZG+\naYxZC/wUuNiLwESmgp6eIxw8eHQEv2PHW5x55tkFjEgGSyejrdJ0qgaPO/hJYTob6ye4erTkDmCt\n3QBcM/khiUwdW7a8DkDJ8moIBti8WZXJQhssnXg1gh982/B1Dd5a2w1gjLkF+AxQSWYR7QDgWmsX\nD+4j4lcvvvg8ANF55aS64uze/TZtbYdoaJhV4MhmLq8T/NEa/PQv0eTyFvV54E+BK4G1wO9m/xXx\nte7uLl599SVClVFClRGi88oBePLJxwob2AwXyTYES3tUokm7LqFQyN8j+GHetNb6pwO+SI5+85uH\nSafTlC2qJhAIUNRcRv/rh9m48RGuvvq9FBdPXi9yyV1JSSkACY8SfNx1KSkZeZWo6SaXBH+XMeZh\nMgtnDy1Jbq394ngvNMZcRuZC7GYypZ3XrLV/cpKxiuTNkSNH2PDIrwkWhShaUAFAIBykeHElfVu7\neOSRX3Ptte8rcJQzU3V1NQC9zuQXaVzXpd91aKiumfRjF0IuCf6fyST3ABA5iXM8Zq394Em8TqRg\nfvazHxMfGKD0HXX0v5m
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2758c13ac8>"
      ]
     },
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f275899eb00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAGpCAYAAADbWaxNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XecXGd99v/Pmba9a9WrLemWbMkV9woy2AaDA6abEEgg\nCSGVB55fSEISEpLwMoFQ8iQkhFBDCxiMgzGG4G65N1nllmT1uqvV9tndaef3x8ysVkKrnZ05s2fm\n7PV+xUHanTnz1ay0197lfG/HdV1ERESkdCG/CxAREQkKhaqIiIhHFKoiIiIeUaiKiIh4RKEqIiLi\nEYWqiIiIRyKFPMgYcwdwNRAGPmmt/eGEz+0G9gEZwAVut9YeLkOtIiIiFW3KUDXGXA+cY6290hjT\nDjwH/HDCQ1zgJmvtSHlKFBERqQ6FTP8+CLwl9+s+oN4Y40z4vJP7T0REZFabcqRqrXWB/Cj0fcA9\nuY9N9EVjzArgYWvtn3lco4iISFUoeKOSMeZW4L3A75/yqY8BHwKuA9YbY97kXXkiIiLVwymk968x\n5kbg48CN1tr+MzzuA8Bca+3HJ3tMKpV2I5FwMbWKiEjwVfVyYiEblZqBO4ANpwZq7nPfA15vrU2S\nHa3+95mu19sbL77aMuvsbKK7e9DvMqqS3rvi6H0rnt674lXye9fZ2eR3CSUp5JaatwEdwPdyG5Rc\n4JfAJmvtXcaYnwCPG2PiwHPW2h+Ur1wREZHKVchGpS8BXzrD578AfMHLokRERKqROiqJiIh4RKEq\nIiLiEYWqiIiIRxSqIiIiHlGoioiIeEShKiIi4hGFqoiIiEcUqiIiIh5RqIqIiHhEoSoiIuIRhaqI\niIhHFKqzzM+f2s837rN+lyEiEkgK1VnmZ0/t4/5nD5JIpv0uRUQkcBSqs8hAPMHxgTEA+oYTPlcj\nIhI8CtVZZN+RE4cS9w2O+ViJiEgwKVRnkb1HJ4TqkEJVRMRrCtVZZO+EkWr/kKZ/RUS8plCdRTRS\nFREpL4XqLDE8mqS7b5R57fWAQlVEpBwUqrPEkZ44AOuWtwPQp+lfERHPKVRnifhYCoDWphiNdVGN\nVEVEykChOkuM5EK1NhahtTGmkaqISBkoVGeJfKjW1YRpbaxhZCzFWEJdlUREvKRQnSVGxrIBWheL\n0NpYA0DfsKaARUS8pFCdJUYT+ZFqhNamGKCuSiIiXlOozhLjI9WaCC0NuZGq1lVFRDylUJ0lxjcq\n1YRpaciOVAfiClURES8pVGeJkQnTvzWxMICOfxMR8ZhCdZYYze/+jUWoiWZDdUyhKiLiKYXqLBEf\nSxMJO0QjoROhmsj4XJWISLAoVGeJ0USK2lgEgFg0+2XXSFVExFsK1VliZCxFfU02VPMjVa2pioh4\nS6E6S4wk0tTWZMM0v1FJI1UREW8pVGeBTMZlLJGmLj/9G1GoioiUg0J1FpjYTQkgEnYIOQ6JpDYq\niYh4SaE6C5zoppQdoTqOQ00spJGqiIjHFKqzQL7xQ21upAoQi4YVqiIiHlOozgIjExo/5NUoVEVE\nPKdQnQVOnf6FbKjqlhoREW8pVGeBUzcqQW6kmsjguq5fZYmIBI5CdRaIn3b6N0TGdUmlFaoiIl5R\nqM4Co7np39oJ07+xfFellKaARUS8olCdBfIblepPmf4FGEsoVEVEvKJQnQXGb6mJnXxLDairkoiI\nlxSqs8D4LTWn7P4F1FVJRMRDCtVZYDQ3xTtxpFoT0/FvIiJeU6jOAslUdjSaP0cVJqypKlRFRDyj\nUJ0F8k0e8qfTwIQ1VW1UEhHxjEJ1FkikMtmTaULO+MdqdEuNiIjnFKqzQCKZJjphlAoTp3+1UUlE\nxCsK1VkgkcqctJ4KJ9ZXNf0rIuIdheoskEimiUVO/lKfuKVGoSoi4hWF6iyQTGXGNyblafeviIj3\nFKqzQCKVmXSkqlAVEfGOQjXgMq6bHameslEpFlOoioh4TaEacPnGD9HoZCNV7f4VEfGKQjXgTtf4\nAbLnqU78vIiIlE6hGnCna1GY/b12/4qIeE2hGnBj4yPVk7/UIcchFglpTVVExEMK1YAbH6meMv0L\n2dGq1lRFRLyjUA24xCQblSC7rqqOSiIi3lGoBlx+zbTmNCPVSCRMKq2RqoiIVxSqAXemkWo0HBqf\nHhYRkdIpVANusltqILsjOKmRqoiIZxSqAXdio9LkI1XXdWe6LBGRQFKoBlx+pHra6d9c0KbSClUR\nES9ECnmQMeYO4GogDHzSWvvDCZ+7Afg7IAX81Fr7iXIUKsXJr6mebqNSPlSTqcz4r0VEpHhTfic1\nxlwPnGOtvRK4GfjsKQ/5HPBGsqH7GmPMGq+LlOIVMlLVuqqIiDcKGZ48CLwl9+s+oN4Y4wAYY1YA\nPdbaQ9ZaF7gH2FCWSqUoiTM0f4iG8yNV3asqIuKFKad/c2E5kvvt+4B7ch8DmA90T3h4F3CWpxVK\nSSbr/QsnT/+KiEjpClpTBTDG3Aq8F3jNGR7mlFyReOpMt9REFKoiIp4qdKPSjcBHgRuttYMTPnUI\nWDDh94tyH5tUW1s9kdN8g68UnZ1NfpfgqVDuvZ4/r5nO9vqTPtfSVAtAQ1OtJ3/uoL13M0XvW/H0\n3hVP7115TBmqxphm4A5gg7W2f+LnrLV7jTFNxpilZMP0FuCdZ7peb2+8hHLLq7Ozie7uwakfWEUG\nBkcBGBoYIZQ+ee00mUgB0N09REd9tKTXCeJ7NxP0vhVP713xKvm9q/awL2Sk+jagA/heboOSC/wS\n2GStvQv4APCd3Me/ba3dWa5iZfrG2xServmDdv+KiHiqkI1KXwK+dIbPPwJc6WVR4p3xNdXTblTK\nTg1rTVVExBu64z/gkqkM4ZBDOKTdvyIi5aZQDbhEKnPaUSpMvE9VoSoi4gWFasAlkunT3k4DWlMV\nEfGaQjXgEmfo66vpXxERbylUAy6RTFMTnWKkqjaFIiKeUKgG3JlOoNGaqoiItxSqAea6bnaj0lTT\nv1pTFRHxhEI1wE40059q+lehKiLiBYVqgJ2pm9LEj6cUqiIinlCoBtiJbkqTjFS1pioi4imFaoDl\n10qnvKVGa6oiIp5QqAZYqsDpX41URUS8oVANsPGRalihKiIyExSqAZacYqQa0ZqqiIinFKoBNlWo\nOo5DJBzSmqqIiEcUqgE2HqqTTP9CNnA1UhUR8YZCNcDyYRmZZKQK2VBNKFRFRDyhUA2wqW6pgewo\nNqWG+iIinlCoBpimf0VEZpZCNcCm2qiU/5w2KomIeEOhGmAFh6pGqiIinlCoBliqgDXVWCREKu2S\ncd2ZKktEJLAUqgFWyJpqRCfViIh4RqEaYCd2/57+lBqYcFKN1lVFREqmUA2wQtdUJz5WRESKp1AN\nsPHmD2Fn0scoVEVEvKNQDbDCRqrhkx4rIiLFU6gG2LTWVBWqIiIlU6gGWKrAjkqgjUoiIl5QqAZY\nMtfTVxuVRERmhkI1wJLpbEMHbVQSEZkZCtUAS6YyRCMhHOcMoTq+pqqTakRESqVQDbBkKnPG9VQ4\n0VFJa6oiIqVTqAZYMp0543oqaPeviIiXFKoBlkqliUw5Us1ODafSaqgvIlIqhWqA5ddUzyQaVvMH\nERGvKFQDrKDp3/wpNVpTFREpmUI1wAobqTrjjxURkdIoVAMq47qk0u6Uu3/V+1dExDsK1YBKFdBM\nf+LnNf0rIlI6hWpApdKFhWpE078iIp5RqAZUIce+Tfy8QlVEpHQK1YBKFnBCDTB+H6umf0VESqdQ\nDahkgdO/GqmKiHhHoRpQ+ZCcqqOSzlMVEfGOQjWgCl1Tjaj3r4iIZxSqAVVoqIZDDo6jkaqIiBcU\nqgFV6Jqq4zhEI6Hx+1pFRKR4CtWAKnT3b/4xGqmKiJROoRpQhTZ/gOxB5VpTFREpnUI1oMZ3/xYQ\nqtFwSPepioh4QKEaUIVuVMo/RiNVEZHSKVQD6sSaanjKx2qkKiLiDYVqQBW6+xe0pioi4hWFakCd\nGKk6Uz42O1J1ybhuucs
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f275899e630>"
      ]
     },
     "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\", size=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",
    "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
 ],
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