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/),  that 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 that the data is provides as *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": 19,
   "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": 19,
     "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()*.\n",
    "\n",
    "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,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb8581e7198>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8581e70f0>"
      ]
     },
     "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** 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": "iVBORw0KGgoAAAANSUhEUgAAA4MAAAN9CAYAAAApD6yTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRSnk+ppIJHKWy92uNF3
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f76a815f8>"
      ]
     },
     "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 three different species of iris flowers so you can see how they differ.\n",
    "\n",
    "We are going to color each class, so that we can identify easily **clustering** and **linear relationships**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f4f749f4320>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7AAAAN9CAYAAACqy5WoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5YiZzNzfJ8d8KfuEhwd
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f749f4dd8>"
      ]
     },
     "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": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFhCAYAAACh/xvXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5cpKaeHMeQgBu914axC
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f749f0d30>"
      ]
     },
     "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": [
      "/home/cif/anaconda3/lib/python3.5/site-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": "iVBORw0KGgoAAAANSUhEUgAAA4MAAAN9CAYAAAApD6yTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ6QUxGL3ja0H6OqNj8T
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f748b53c8>"
      ]
     },
     "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": [
    "PairGrid is flexible, but to take a quick look at a dataset, it can 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": "iVBORw0KGgoAAAANSUhEUgAAA7AAAAN9CAYAAACqy5WoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUGt9V86jEkKsCOY5sfg
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f749a94e0>"
      ]
     },
     "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": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7AAAAN9CAYAAACqy5WoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmUI9d92PtvYUejge7GNr3N0jPk3OEyJjkcUhqSIrWZ\nlOWxjuNVduI4cWzKeXbyHDs5sZLn5Dg5yfE7TuzYL07yEiqKXqLIsS2bckRFUkRLpChKpEaUaC4z\nl+RwODM909P7gu7Gjnp/YGmgGg2gATSWnt/nHB5OoVBVF9W3Luqifvf+DNM0EUIIIYQQQgghep2t\n2wUQQgghhBBCCCEaIR1YIYQQQgghhBB9QTqwQgghhBBCCCH6gnRghRBCCCGEEEL0BenACiGEEEII\nIYToC9KBFUIIIYQQQgjRFxydPqBSygF8CjgCZIBf0Fq/Ubb+V4CfB+YKL31Ma/1mp8sphBBCCCGE\nEKK3dLwDC3wYsGutH1RKfRD4l8CPla2/F/gZrfV3u1A2IYQQQgghhBA9qhshxG8ADqWUAQwBKcv6\ne4GPK6W+rpT69Y6XTgghhBBCCCFET+pGB3YdmAIuAP8v8PuW9Z8BfhF4H/CQUurDnS2eEEIIIYQQ\nQohe1I0Q4r8HfFFr/Y+VUhPAV5VSd2qti09if09rvQaglHoKuAf4Qq0dmqZpGoaxp4UWN509r1BS\nb0WbSZ0V/Ujqreg3UmdFP9pXFaobHdglIF3490qhDHYApVQAeFUpdQKIA+8HPlFvh4ZhMD8f25vS\nFkQi/r4/xn74DJ08xl5rtd624zy0ug8pQ2+VYa+1o63dL+daytC+Muy1dt0jtPO7p137kjJ1fl/9\nVGdr2Q/3a/vhM3TyGPtJN0KI/w1wr1LqWeArwD8Cflgp9fOFJ68fB74GPAO8qrX+YhfKKIQQQggh\nhBCix3T8CazWegP4yRrrPw18unMl6g2vx2awZWZR9igSNiKEEEIIIYQQ23UjhFhYXN5c4r9fOwfA\nX524j9v8o10ukRBCCCGEEEL0nm6EEAuL76xeKf375bXpLpZECCGEEEIIIXqXPIHtAZc2F/HanHid\nTi5uLGCapoQRCyGEEEIIIYSFPIHtslgmwXJ6k0PeIEf9YeK5NCuZeLeLJYQQQgghhBA9RzqwXTaf\nXAdg1ONnwjcCwGxirZtFEkIIIYQQQoieJB3YLptP5TuwYdcg4wOBiteEEEIIIYQQQmyRDmyXLRQ6\nqxGXn5BnEICVtIQQCyGEEEIIIYSVdGC7bKH0BNZHyOMDYCW92c0iCSGEEEIIIURPkg5sl80n1/E7\n3HjsTgYcLtw2h0ziJIQQQgghhBBVdDyNjlLKAXwKOAJkgF/QWr9Rtv6HgN8A0sAntdZPdLqMnZLJ\nZVnNxDnsDZVeG3Z6WUnHJZWOEEIIIYQQQlh04wnshwG71vpB4J8D/7K4otC5/R3gg8B7gceVUpEu\nlLEj1jJJTPKd1qJhxwDJXIZ4Lt29ggkhhBBCCCFED+pGB/YNwKGUMoAhIFW27jbgTa31mtY6DTwH\nPNyFMnbEWiFUOODwlF4rdmZlIichhBBCCCGEqNTxEGJgHZgCLgAh4GzZugCwWrYcI9/J3ZfWMgkA\nAs5qHdhNxj379qMLIYQQQgghxK4Zpml29IBKqX8NJLTW/1gpNQF8FbhTa51SSp0Efktr/YOF9/4O\n8JzW+k/r7LazH6JN/vf0ef7k0nf527e9h7vDBwF4ce4dPqGf56eP3ccj47d2uYQ3tU4MQO7Leit6\nltRZ0Y+k3op+I3VW9KN9NbFON57ALpGfoAlgpVAGe2H5PHCLUmoY2CQfPvzbjex0fj7W5mJWikT8\nbT/G9ZX8w2ZzM1/+SMRPbjPfZs2srDLvbO/x9uIz7OdjdEIrn6Md56HVffRDGUxM5peTLMUSBAMe\nosNurO34fjkPndALn1PKUH37Rur6XpehmX10Qju+M9r53dOuffVSmYr1LxZP4x9w1q1/nSjTXuyr\nn+psLfvhfq3fP8NeXDM76VS97ZRudGD/DfCflVLPAk7gHwE/rJTyaa2fUEr9KvBl8n/BJ7TWM10o\nY0eUQojLxsAOOtwArGeTXSmTEPvN/HKSL33zcmn5sTOHiY54amwhRH+Sui66SeqfELsj10zzOt6B\n1VpvAD9ZY/1TwFOdK1H3rGXi2DBKnVYAf7EDW+jcCiFasxRLbFuWLwixH0ldF90k9U+I3ZFrpnnd\nmIVYFKxlEgw63NjK8r16bE5sGKxnUjW2FEI0Khio/DII+uXLQexPUtdFN0n9E2J35JppXjdCiAVg\nmiaxTJIxd6DidZuRfyIby8oTWCHaITrs5rEzh/PjAv0eoiPu+hsJ0YekrotuKta/WDyN3+uU+idE\nHXLNNE86sF2ymU2RNXMV41+LBh1u5pIxTNPEMPbVpGFCdIFBdMQjYTniJiB1XXRTvv7dcTyy5xPr\nCLE/yDXTLAkh7pJqEzgVDdrdZMwcyVym08USQgghhBBCiJ4lT2C7pNiB9TurP4GF/EzEHruzo+US\nYrdqpe5oZN3F67E9nT5+t6lFhOgHuZzJ3HKibr3OkWN6Ls7iapzwkBePy8biWpJgwEM4PNj5got9\nq16bbm2Lw8Ouirp5MOpFnquIm0mGHFdubPIdvUBwyMPU6AByDTRGOrBdUvMJbKEDG8skCbvkBkP0\ntlrTwDe7rlPlE6JfvT290lC9np6L88y56dKymgqiLy0B4HY7GPLKbYBoj3ptrXX9Q6cmeO6la6Xl\nR05Pcijq60xhhegBV25s8o2ya8A8NcHRUbnvb4R087tkNR0HYMjh3bZu0J7vwG5kJBes6H3VpoFv\ndV07deo4QnTSwnK8Ynmner24Wvm+dDq74z6EaEW9tta6vLxWuWytq0Lsd8uriZrLYmfy02uXlJ7A\nVgkh9hWewG5kJZWO6H21poGvvc5tWbc3s+/JNPViPwqPVP74aa3XxXDNwGDldeV02nfchxCt2N7W\nuivC3Le1+Zb3h4akPoqbS3Co8hoYGZL7k0ZJB7ZLYjUncXIBsC5PYEUfqJW6o9Y6AwM1FSSdzuJ0\n2vdsxm1JLSL2o2MHh2vW62K45qDXyek7D5BIZAgNe/G6bAR8ToJ+D8cODrOwsN6lTyD2G2tKEMOA\nLz6/FTL8oQcOW+qsC9vpSRZX44SGvByKSgdW3FyGfA4euHuclViSYb+bYZ90yxrVlTOllPpZ4G8A\nJuAF7gJGtdZrhfW/Avw8MFfY5GNa6ze7UNQ9s5ZJ4LE5cNm2/wl8xRDirHRgRT+olbpj53WLa4nS\nWDyAgM9JZHgvfn2U1CJi/zGM2vW6GK65Hk9z7tVZ7rvzQGl8YWTYW9qHEO1TmRLkwpWVirWLawlO\nHBquqLOHoj4Z9ypuWnPLCb796mxp+b47D0iUWIO60oHVWn8K+BSAUurfAk8UO68F9wI/o7X+bjfK\n1wlr6UTVp6+wNYmThBCL/UxCe4XYO3J9iW6TOihEbXKNNK+rz6qVUqeB27XWv2xZdS/wcaXUGPCU\n1vq3Ol+6vZPOZYnn0ow7hqqu99qdGEgIsdjfrOFmEtorRPtI6LzoNqmDQtQm90HN63aw9ceB36zy\n+meAPwDWgCeVUh/WWn+hoyXbQ1sTOFUf72E3bAzYXRJCLPa5ynAzIUQ7Sei86Dapg0LUJvdBzTJM\n0+zKgZVSQ8BzWuuTVdYFysbD/m0gqLX+FzV2150P0aQ3Vmb51688zQ8cvIMfPnJX1ff85neeYiUV\n53fP/FiHSycKOjE4rK/qreh5UmdFP5J6K/qN1FnRj/bVpAfdfAL7MPC09UWlVAB4VSl1AogD7wc+\nUW9ne/3LRSTib9sxrqzmJ65xpGwV+yw/hhsHm5kUM3OrOIz2pOtt52e4GY7RCa18jnach1b3UW/7\nYiqPYhqF6LAbaxu612XoxD56pQyd0AufU8oAodAg599aqHlt7XUZ2nUeOqEd3xnt/O5p1766XaZq\nbXwkEui589TOffVTna1
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6e91ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(iris_df, hue=\"species\", palette=\"Set2\", diag_kind=\"kde\", size=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to show other representions.\n",
    "\n",
    "[**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 0x7f4f6df755f8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAERCAYAAACdPxtnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGV1JREFUeJzt3X+UXGWd5/F3upuwSZtAZ2hHGMVfrF9gjwcwZBhZNw6M\nriNjdFzXoAP+hBV2UWRwMxJ2UHB3OWpcWVh/jQZZXCNrZlhByQwioPJDZaKDjgh8CSOjs8qahrQk\ndBLyo3v/uNWkaDuVm1C3KtX3/TqnT/reW/XUtzt96lPPfe59nlkTExNIkuqpr9sFSJK6xxCQpBoz\nBCSpxgwBSaoxQ0CSaswQkKQaG6iy8YgYAK4GngfsAP5dZj7QdHwJcBGwHbgqM1dWWY8k6amq7gmc\nAvRn5r8E/jNw6eSBRkB8HHgF8PvAuyJiuOJ6JElNqg6BB4CBiJgFHARsazp2FLAuMzdm5nbgDmBx\nxfVIkppUejoIeBx4PnA/8FvAa5qOzQcea9reRBEUkqQOqbon8KfAjZkZwDHAFyJiduPYRoogmDQP\n+HXF9UiSmlTdE9hAMegLxRv8ANDf2L4POCIiDgY2U5wKWtGqsR07dk4MDPS3eogk6TfN2u2BKieQ\ni4hB4PPAocABwOWNYgYzc2VE/BHwwca+KzPzM63aGxnZ5Gx3krSXhofndScE2s0QkKS91yoEvFlM\nkmqs6jEBSWqL1atXsXbtXW1vd2xsDIDBwcG2tw2waNEJLF16WiVtt4M9AUm1tm3bE2zb9kS3y+ga\nxwQk1dqyZecCsGLFFV2upDqOCUiSpmUISFKNGQKSVGOGgCTVmCEgSTVmCEhSjRkCklRjhoAk1Zgh\nIEk1ZghIUo0ZApJUY4aAJNWYISBJNWYISFKNGQKSVGOGgCTVWKXLS0bE24C3AxPAHOAY4FmZubFx\n/DzgTGB94ylnZea6KmuSJO1SaQhk5tXA1QAR8Qlg5WQANCwE3pKZd1dZhyRpeh05HRQRxwNHZ+aV\nUw4tBJZHxO0RcUEnapEk7dKpMYHlwCXT7L8GOBs4CXhZRJzSoXokSXQgBCLiIOBFmfntaQ5fnpkb\nMnMHsAY4rup6JEm7VDom0LAYuGXqzoiYD9wTEUcCW4CTgamni55iaGguAwP9lRQpqZ76+4vPwsPD\n87pcSXd0IgQC+OmTGxFvBgYzc2VELAe+BWwFbsnMG1s1NDq6uco6JdXQzp3jAIyMbOpyJdVpFXCV\nh0BmfmzK9jVN368CVlVdgyRpet4sJkk1ZghIUo0ZApJUY4aAJNWYISBJNWYISFKNGQKSVGOGgCTV\nmCEgSTVmCEhSjRkCklRjhoAk1ZghIEk1ZghIUo0ZApJUY4aAJNWYISBJNWYISFKNGQKSVGOGgCTV\nWKULzUfE24C3AxPAHOAY4FmZubFxfAlwEbAduCozV1ZZjyTpqSoNgcy8GrgaICI+AaxsCoAB4OPA\nQmALcGdEXJ+ZI1XWJEnapSOngyLieODozLyyafdRwLrM3JiZ24E7gMWdqEeSVKi0J9BkOXDJlH3z\ngceatjcBB3WoHtXQ6tWrWLv2rkraHhsbA2BwcLDtbS9adAJLl57W9nYl6EAIRMRBwIsy89tTDm2k\nCIJJ84Bft2praGguAwP9ba5QdTFnzmz6+6vp/G7b9gQA8+fPa3vbc+bMZni4/e2qMPk3UdffcSd6\nAouBW6bZfx9wREQcDGxuPG5Fq4ZGRze3vzrVxpIlb2TJkjdW0vayZecC8OEP//dK2h8Z2VRJu4Kd\nO8eBmf07bhVwnQiBAH765EbEm4HBzFwZEecDNwGzKAaNH+5APZKkhspDIDM/NmX7mqbv1wBrqq5B\nkjQ9bxaTpBrr1NVBkmri0ksvZnR0Q7fLKG2y1slxnV4xNLSACy+8+Gm3YwhIaqvR0Q1sePQRntHX\nGyca+seLgeFtPRRcjzdqbgdDQFLbPaOvj9MPWtDtMmasLz7WvsDqjaiWJFXCEJCkGjMEJKnGDAFJ\nqjFDQJJqzBCQpBozBCSpxlreJxARhwDvBl4LHAGMAw8C1wOfzsxHKq9QklSZ3fYEIuIc4MvACPA2\n4NnAocBbgQ3AVyKit+6zliQ9RauewC8y8w+m2X9v4+uTEfGGasqSJHXCbnsCmXndnp6cmde2txxJ\nUiftce6giDgP+AC71v+dBUxkpus8SlKPKzOB3HnAsZn586qLkSR1VplLRO8FflV1IZKkzivTE7gC\n+HFEfA/YMbkzM99ZWVWSpI4oGwJfBH5WcS2SpA4rEwJbM/ND+/oCEXEBxc1mBwCfysyrmo6dB5wJ\nrG/sOisz1+3ra0mS9k6ZELg5Iv4b8DfAtsmdmXnbnp4YES8HXpqZJ0bEIPC+KQ9ZCLwlM+/ei5ol\nSW1SJgSOa/z7kqZ9E8DJJZ77KuCeiLgOmAcsm3J8IbA8Ig4F1mTmh0u0KUlqkz2GQGaeFBHPzMz1\nETEXOCwzHyzZ/iHA4cBrgBcAXwWObDp+DfBJYCNwXUSckpl/vVc/gaT9ytjYGE+Mj7d1HVw91ePj\n4xw4NtaWtsrcLPYe4B0UPYFh4GsRcVlmfrZE+48C92XmDuCBiNgaEYc0TTx3eWZubLzOGopex25D\nYGhoLgMD3qOm/U9/f3G19fDwvC5X0n19fbO6XUIt9PXNasvfW5nTQWcBJwBk5s8iYiFwF1AmBO4A\nzgUui4jDgLkUwUBEzKc4VXQksIXi9NKVrRobHd1c4iWlztu5cxyAkZFNXa6k++bMmUv/1q2cftCC\nbpcyY33xsQ3MnjO39N9bq7Aoc7PYAcATTdvbKMYE9igz1wB3R8TfUkw/fQ7wpog4s9EDWA58C/g2\ncE9m3limXUlSe5TpCVwH3BoRqxvb/4biDb2UzLygxbFVwKqybUmS2muPPYHMfD/FDWNBMbh7RWZe\nVHVhkqTq7bYnEBGvycwbADLzr4C/muYxr83Mr1ZYnySpQq1OBz0/Im6iePO/Dfi/FHMHPZdiEPdU\n4CuVVyhJqkyrRWX+B3A68DsU1/P/P4rZRK8BngWcmpmXd6JISVI1Wg4MZ+Z64IONL0nSDFPmElFJ\n0gxV5hJRqaMuvfRiRkd7a8qByXqXLTu3y5WUNzS0gAsvvLjbZajLDAHtd0ZHN/Dohkfom9M7f57j\nfcX9k6Nbft3lSsoZ37Jjzw9SLZSZO+gFFFNHHEKxyDzgymKqVt+cAYb+8PBulzFjjd7okuEqlPmo\ndS1wM3A7JaeLkCT1hjIhMCszp64DIEmaAcqEwHci4vXA9Zk5XnVBknrf4z20nsDW8eJt7Z/19c7F\nko+Pj9OuOVpbTRsxTnH6ZxZwNjARETS2JzLTif0l/Yahod6aQnqscWXX7B6qewHt+z3vNgQyc7ex\nGBEHtuXVJc04vXbZ6eRlvStWXNHlSrpjj/2fiPjulO0+4PuVVSRJ6phWp4NuBX6/8X3zWMAOirWC\nJUk9rtXpoJMBIuLyzHxv50qSJHVKmauD/i4i3tq0PUGxJvD9mXlPNWVJkjqhTAi8FjiOYplJgNcA\nvwCeERFfyszLqipOklStMhfGPgt4SWaen5nnA8c3nvdS4O0V1iZJqliZnsAwsKlpewuwIDN3RMQe\np5GIiAsoehMHAJ/KzKuaji0BLgK2A1dl5sq9KV6S9PSU6QlcC9waEedExHuAbwDXNcYJHm71xIh4\nOfDSzDyR4kqj5zQdGwA+DryicexdETG8Lz+EJGnf7DEEMnM5sAJ4EfB84COZeRHwAPAne3j6q4B7\nIuI6istKb2g6dhSwLjM3ZuZ24A5g8d7/CJKkfVV2wvaHKBacnwUQEYsz87YSzzsEOJxiMPkFFEFw\nZOPYfOCxpsduAg4qWY8kqQ3KrCfwSWAJ8A9NuyeAk0u0/yhwX2buAB6IiK0RcUhmPgJspAiCSfOA\nlityDA3NZWDAKYtmuv7+3pnIq5f19/cxPDyv22V03eTfW11/F2V6Av8aiMzcsg/t3wGcC1wWEYcB\ncymCAeA+4IiIOBjYTHE
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6e645908>"
      ]
     },
     "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": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAERCAYAAABxZrw0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsL9lUryh0mHJPhzXJZF
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6f19cf60>"
      ]
     },
     "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 0x7f4f6ed5ee10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6fee19b0>"
      ]
     },
     "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": [
    "Another useful representation is the kde (kernel density estimate) plot.  [Kernel density estimation (KDE)](https://en.wikipedia.org/wiki/Kernel_density_estimation) 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 0x7f4f6f1c2400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAGpCAYAAADbWaxNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n83rwegzN/hURKRAKVRd
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6f1c2240>"
      ]
     },
     "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": [
    "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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