anscombes_quartet-in_depth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 1. Anscombe's quartet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this introductory course to data science we will start by introducing the basics of the discipline. In this first part of the course we will explain the first steps towards working with data and the basic theoretical concepts needed to analyse a dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#!conda install -y numpy pandas matplotlib seaborn statsmodels ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We will be using a dataset called the **Anscombe** dataset that can be loaded from the seaborn examples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"anscombe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "And `df` is a pandas dataframe..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "that we can print, plot, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This dataset is comprised of three columns. Two of them, called **'x'** and **'y'**, are filled with *numerical* values while the **'dataset'** column is filled with string values. Each of these values contained in the 'dataset' column will be either **'I'**,**'II'**,**'III'**,**'IV'**. This means that the dataset column is a *categorical* value.\n",
    "\n",
    "We can visualize the first five rows of a DataFrame using the **head()** function. We also have a column comprised of integer values that we will use as an *index*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dataset     x     y\n",
       "0       I  10.0  8.04\n",
       "1       I   8.0  6.95\n",
       "2       I  13.0  7.58\n",
       "3       I   9.0  8.81\n",
       "4       I  11.0  8.33"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 2. Exploring the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When dealing with a new dataset we should start by doing some *data exploration*. This means that we should try to get an intuitive idea of how the data is structured, and how the different columns are related to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2.1 The histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As we don't know anything about the dataset we will start by plotting the numerical values contained in each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The histogram is a plot that is used to display the values that a single column takes. An histogram consists in several vertical bars displayed in parallel, where the *x axis* represents the values that a point in the column can take, and its height is proportional to the number of times that the value appears in the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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LliwBMGZ2sdlsB4xy9+jRA4AdO3bg9/uZOHEizz//PPfee69xIfCkSZNIpVLGZ3Yo1113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\nl5eXU15eTmFhYU7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cR5R4Bw8ezEMPPcSHH37IT3/6U/7xj3/w7W9/m0suuYSzzjrrgGkW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XE/D3s0v7Pt2rA8B6ZjNnNCey4sx0LD1vml26zdDZSNKkpGBRWzirJZhWGb8BHsMCxNjiAqR2CN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\nI0dhYp3BYDAYDAaDwchR/h9W6mDK4QioxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb54dd2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.xkcd():\n",
    "    _=df.plot(kind='hist',title='Histograms of x and y',figsize=(12,5),bins=6,fontsize=20,subplots=True)\n",
    "#plt.xlabel(,fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "A histogram allows us to see **how many items of a column have the same range of values**, but it doesn't allow us to relate the values contained in one column of our dataset to the values contained in a another column for a given row. The histogram doesn't take into account the ordering of the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2.2 The line plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In order to see how the ordering of the data affects its value we can check the following plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KDn932iyy2rb0hPbz05pGo8GWLVsAoE12V+372blzZ5uRQK3m5mbd72HGjBk9akdPaKf7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hPmHsQfJt1LkKvtPU7b6azM1xPrM7ZzpDiNkX7DuT3xBiK97KNq4Zq4OZzencXaM1t4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\nmyIQAgAAAIBN/R9+AxhgWVsgpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb208cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['x','y']].plot( linestyle='--', marker='o',figsize=(15,5))\n",
    "plt.title(\"Anscombe's points\",fontsize=25)\n",
    "plt.xlabel('Index',fontsize=16)\n",
    "_ = plt.ylabel('Value',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is called a *line plot*. In these plots we will represent the values of different columns in the *y axis* relative to its index value, represented in the *x axis*. This kind of plot helps us understand how the index value (and thus the order) affects the structure of the data. \n",
    "\n",
    "This plot also allows us to compare the values of different columns at a given moment (index value)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As we can see, this plot doesn't give us any intuitive idea of how the data is structured. This probably means that the index value does not have a meaning impact on how the data is structured. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2.3 The scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Given that the two columns do not seem to be related to the index value, we will try to figure out if they are related to each other. One graphical way to see how the two series of points are related to each other is by means of a scatter plot.\n",
    "\n",
    "In this plot, instead of plotting the value of a column against an index, we will plot it against the value the other column have at a given index. In this plot the ordering of the data doesn't matter (you cannot see the information contained in the index column in this plot.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZZ5/prbfe0tatW7VmzRrVrVtXvXv31vDhw4tNbnvsscd07bXX6uOPP9by5ctVpUoVxcbG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\n/x+iQWNzQmXMYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb1fd15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='x',y='y',fontsize=14,s=75)\n",
    "plt.title(\"Anscombe's points\",fontsize=20)\n",
    "plt.xlabel('x',fontsize=16)\n",
    "plt.grid()\n",
    "_ = plt.ylabel('y',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is a scatter plot. This speciffic plot answers the following question:\n",
    "\n",
    "When a point of the data set in the column 'x' had a value of x, which was the value y contained in the column 'y'? (or vice versa).\n",
    "\n",
    "In order to answer the question with the plot we only have to substitue the value x for the number we want, and search for that number in the lower *axis* of the plot, labeled *x*. The answer y will be the height of the points that lie in an straight parallel to the grid and starts at x.\n",
    "\n",
    "Now, at least we can infer some structure in our data. In this plot it almost seems as if the points were more or less distributed across an imaginary straight line. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3 Representing categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Since now, we have not paid much attention to the 'dataset' column. This column is comprised of four different values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['I', 'II', 'III', 'IV'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dataset'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It is possible to interpret the values contained in this column as labels attached to each row. This means that a categorical value can be interpreted as a column that allows us to break our dataset in different subsets. Each of the elements of these diferent \"mini datasets\" will have the same value. This is what is known as *grouping a data set by a categorical value\".\n",
    "\n",
    "We can also see that these labels represent numbers, so we will add a column to our database representing the same numeric value in decimal base. This way we can represent the information contained in the labels without getting errors in the pandas plotting interface. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>num_ix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dataset     x     y  num_ix\n",
       "0       I  10.0  8.04       1\n",
       "1       I   8.0  6.95       1\n",
       "2       I  13.0  7.58       1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_mapping = lambda x: x.count('I') if x.count('V')==0 else 4#apply a function value by value to all \n",
    "df['num_ix'] = df['dataset'].map(number_mapping)#the values contained a column\n",
    "df.head(3)#change the number of displayed rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In order to represent the category we will assign a different color to each different value. This means defining a *color map* between the label values and its visual representation. One of the possible ways of doing it is by manually defining a colormap. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "vmax=3\n",
    "cmap = LinearSegmentedColormap.from_list('mycmap', [(0/vmax ,'blue'),\n",
    "                                                    (1/vmax, 'green'),\n",
    "                                                    (2/vmax, 'red'),\n",
    "                                                    (3/vmax, 'yellow')]\n",
    "                                        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now that we are ready to plot,we will see how the different categorical values are distributed across the dataset with the kind of plot we already know: the scatter plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We will make two different scatter plots. First we will plot the value of one numerical-valued column versus the index value, and we will color each point according to its 'dataset' category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7ffbb1fd1668>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/nunTp5cbyH///ffTqVMn3n//fT777DNMJhN9+vRhxowZ9hnOwBbb6969OwsXLmT58uX4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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb2097d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.reset_index().plot(kind='scatter',\n",
    "                      x='index',\n",
    "                      y='x',\n",
    "                      fontsize=14,\n",
    "                      s=150,c='num_ix',\n",
    "                      cmap=cmap,figsize=(15,7),alpha=0.5)\n",
    "plt.title(\"Anscombe's points\",fontsize=20)\n",
    "\n",
    "plt.grid()\n",
    "plt.ylabel('x',fontsize=16)\n",
    "plt.xlabel('index',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We can see in this plot how the index actually represents the different classes ordered. This supports our initial hypothesis that the index is not related to the 'x' and 'y' columns. Now lets take a look at how the\n",
    "coloured scatter plot of x vs y looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DOnWq+phERKQM1+qaIFASJCIiXmCzQY8esGlTxSs8FgsMGuSbuERE5BIHEiDS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb063f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def color_scatter(df):\n",
    "    df.plot(kind='scatter',x='x',y='y',fontsize=14,s=150,c='num_ix',cmap=cmap,figsize=(15,7),alpha=0.5)\n",
    "    plt.title(\"Anscombe's points\",fontsize=20)\n",
    "    plt.xlabel('x',fontsize=16)\n",
    "    plt.grid()\n",
    "    _ = plt.ylabel('y',fontsize=16)\n",
    "color_scatter(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We can clearly see that our Anscombe's dataset actually consists in four different datasets all added up together in the same DataFrame, and each of the category labels are used to separate each different dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ffbb0755438>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YgaZDhw7685//3KaC4R6EGQCAWzQbaLZu3ar8/HwNGDCg3vnMzEzNmjUraoUBkRLOjsYA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb0642518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=None, palette=\"muted\", size=4,\n",
    "           scatter_kws={\"s\": 50, \"alpha\": 1}, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4 Basic statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.1 Subsampling the Anscombe's dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now its time to analise each of the datasets conforming our dataframe independently. And we will start by selecting the data set labeled 'I'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>num_ix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset     x      y  num_ix\n",
       "0        I  10.0   8.04       1\n",
       "1        I   8.0   6.95       1\n",
       "2        I  13.0   7.58       1\n",
       "3        I   9.0   8.81       1\n",
       "4        I  11.0   8.33       1\n",
       "5        I  14.0   9.96       1\n",
       "6        I   6.0   7.24       1\n",
       "7        I   4.0   4.26       1\n",
       "8        I  12.0  10.84       1\n",
       "9        I   7.0   4.82       1\n",
       "10       I   5.0   5.68       1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I = df[df.dataset == 'I'].copy()\n",
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We can see that the new dataset contains 10 points. Now let's plot the \"I\" dataset to see how the points are distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GhYYJgFu4FMer7ofU4cOHKzg4WHv37tXBgwdrvP/w8HD93//9n9566y3t2rVLycnJatq0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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb1fd5d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_scatter(I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NykXXdSO4TKfTnQZCbEKDtdW5bXhue8EGiQy74sfSOdiLtRcsvYCl03k8HmPW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\nQgghhEr+B97ntaYvqlGpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb0581cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    _ =I[['x','y']].plot(kind='hist',title='Histograms of x and y',figsize=(12,5),bins=6,fontsize=20,sharey=True,subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 4.2 Why we need statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It is not easy to infer something meaningfull at first sight from this dataset. We can tell that the points seem to be distributed around an imaginary line, but it is something that is not easy to describe with words.\n",
    "\n",
    "Statistics is a way of describing a dataset, but instead of using plots we are using numbers. That numbers will represent properties of our data set, and they are a way to quantify certain properties of a data set. you can think of it as a language to describe dataset where plots are not available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.3 Density and probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "We will try to define a quantity that tells us **how likely is to find a data point** in any point (x,y) from our scatter plot, and we will call this quantity *density*.\n",
    "\n",
    "This concept is closely related to the histograms, as the higher the frecuency count the higher the *density* will be, as these magnitudes are proportional.\n",
    "\n",
    "That would mean that the density is intuitively the height that an histogram would have at a given point. This makes the density of a 1 dimensional data set really easy to visualize using a histogram, but it is a little more tricky if we try to do this in 2D, as we will se later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>num_ix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.500682</td>\n",
       "      <td>2.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.198837</td>\n",
       "      <td>1.958925</td>\n",
       "      <td>1.13096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.117500</td>\n",
       "      <td>1.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>2.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.747500</td>\n",
       "      <td>3.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.740000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x          y    num_ix\n",
       "count  44.000000  44.000000  44.00000\n",
       "mean    9.000000   7.500682   2.50000\n",
       "std     3.198837   1.958925   1.13096\n",
       "min     4.000000   3.100000   1.00000\n",
       "25%     7.000000   6.117500   1.75000\n",
       "50%     8.000000   7.520000   2.50000\n",
       "75%    11.000000   8.747500   3.25000\n",
       "max    19.000000  12.740000   4.00000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.4 Calculating the probaility density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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iRZUeQgghhBBCCCEqo9xEyMnJiezsv7M+nU5nSGj27t1LWloaL774IsnJyeTl5dG0adMy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\nAAAAeA6NEAAAAADP+Q9ST6GYTwJxrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffbb0622cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "from IPython.core.display import clear_output,display\n",
    "import ipywidgets as widgets\n",
    "def draw_example_dist(n=10000):\n",
    "    a = np.random.normal(loc=-15.,scale=3,size=n)\n",
    "    b = np.random.normal(loc=20,scale=3,size=n)\n",
    "    c = np.random.exponential(size=n,scale=3)\n",
    "    return np.hstack([a,b,c])\n",
    "example = draw_example_dist()\n",
    "def draw_regions(distribution='normal',\n",
    "                 bins=7,\n",
    "                 bw=0.18,\n",
    "                 normed=False,\n",
    "                 mean=False,\n",
    "                 std=False,\n",
    "                 percents=False,\n",
    "                 hist=True):\n",
    "   \n",
    "    x = draw_example_dist() if distribution=='example_2' else np.random.standard_normal(10000)\n",
    "    x = draw_regions_data if distribution=='custom' else x\n",
    "    fig = plt.figure(figsize=(14,8))\n",
    "    #ax = fig.add_subplot(1, 1, 1)\n",
    "    ax = sns.kdeplot(x, cut=0,color='b',shade=True,alpha=0.3,bw=bw)\n",
    "    \n",
    "    d_bins = np.linspace(x.min(),x.max(),num=bins)\n",
    "    if hist:\n",
    "        n, bins, patches = ax.hist(x,bins=d_bins,normed=normed,rwidth=0.8,alpha=0.7)\n",
    "    else:\n",
    "        n=[1]\n",
    "    maxx = 1 if normed else max(n)\n",
    "    if mean:\n",
    "        \n",
    "        ax.axvline(x=x.mean(), ymin=0, ymax = maxx,\n",
    "                    linewidth=6, color='r',label='Mean: {:.3f}'.format(x.mean()),alpha=1.)\n",
    "    if std:\n",
    "        m = x.mean()\n",
    "        \n",
    "        ax.axvline(x=m+x.std(), ymin=0, ymax = maxx, linewidth=5, color='g',label='Std: {:.3f}'.format(x.std()),alpha=0.8)\n",
    "        ax.axvline(x=m-x.std(), ymin=0, ymax = maxx, linewidth=5, color='g',alpha=0.8)\n",
    "    if percents:\n",
    "        d = pd.Series(x).describe()\n",
    "        ax.axvline(x=d.ix['min'],\n",
    "                    ymin=0, ymax = maxx,\n",
    "                    linewidth=5, \n",
    "                    color=(0.19130826141258903, 0.13147472185630074, 0.09409307479747722),\n",
    "                    label='min: {:.2f}'.format(d.ix['min']),alpha=0.8)\n",
    "        ax.axvline(x=d.ix['25%'],\n",
    "                    ymin=0, ymax = maxx,\n",
    "                    linewidth=5, \n",
    "                    color=(0.38717148143023966, 0.26607979423298955, 0.19042646089965626),\n",
    "                    label='25%: {:.2f}'.format(d.ix['25%']),alpha=0.8)\n",
    "        ax.axvline(x=d.ix['50%'],\n",
    "                    ymin=0, ymax = maxx,\n",
    "                    linewidth=5, \n",
    "                    color=(0.5830347014478903, 0.4006848666096784, 0.2867598470018353),\n",
    "                    label='50%: {:.2f}'.format(d.ix['50%']),alpha=0.8)\n",
    "        ax.axvline(x=d.ix['75%'],\n",
    "                    ymin=0, ymax = maxx,\n",
    "                    linewidth=5, \n",
    "                    color=(0.7743429628604792, 0.5321595884659791, 0.3808529217993126),\n",
    "                    label='75%: {:.2f}'.format(d.ix['75%']),alpha=0.8)\n",
    "        ax.axvline(x=d.ix['max'],\n",
    "                    ymin=0, ymax = maxx,\n",
    "                    linewidth=5, \n",
    "                    color=(0.9415558823529412, 0.663581294117647, 0.47400294117647046),\n",
    "                    label='max%: {:.2f}'.format(d.ix['max']),alpha=0.8)\n",
    "        \n",
    "       # ax.plot((m-0.1, m+0.1), (0,max(n)), 'k-')\n",
    "    plt.grid(linewidth=2)\n",
    "    plt.title(\"Basic statistics\",fontsize=20)\n",
    "    plt.legend(loc='upper left',fontsize=18)\n",
    "    plt.show()\n",
    "    \n",
    "    clear_output(True)\n",
    "display(interact(draw_regions,bins=(1,100,2),bw=(0.01,10.,0.025),\n",
    "             normed=False,distribution=['Normal','exmple_2','custom']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 2d density "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.draw_regions>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#from shaolin import KungFu\n",
    "def draw_regions(x_regions,y_regions):\n",
    "    I = df\n",
    "    x,y = I.x,I.y\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.scatter(x,y)\n",
    "    plt.xticks(np.linspace(x.min(),x.max(),num=x_regions))\n",
    "    plt.yticks(np.linspace(y.min(),y.max(),num=y_regions))\n",
    "    plt.grid(linewidth=2)\n",
    "   \n",
    "    plt.show()\n",
    "    \n",
    "    clear_output(True)\n",
    "display(interact(draw_regions,x_regions=(0,30), y_regions=(0,30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def density_plot(linreg_order=1,confidence_intervals=95,bandwidth=1.43,**kwargs):\n",
    "    if False:# 'bandwidth' not in kwargs.keys():\n",
    "        kwargs['bandwith'] = 1\n",
    "    data=jointplot_data\n",
    "    clear_output()\n",
    "    if kwargs['dataset']=='ALL':\n",
    "        subdf = data.copy()\n",
    "    else:\n",
    "        subdf = data[data['dataset']==kwargs['dataset']].copy()\n",
    "    x,y = subdf.x,subdf.y\n",
    "    x_regions = 10\n",
    "    y_regions = 10\n",
    "    x_bins = np.linspace(x.min(),x.max(),num=kwargs['num_xbins'])\n",
    "    y_bins = np.linspace(y.min(),y.max(),num=kwargs['num_ybins'])\n",
    "\n",
    "    g = sns.JointGrid(x=\"x\", y=\"y\", data=subdf)\n",
    "\n",
    "    g.fig.set_figwidth(14)\n",
    "    g.fig.set_figheight(9)\n",
    "    if kwargs['plot_density']:\n",
    "        g = g.plot_joint(sns.kdeplot, shade=True,alpha=0.5,legend=True,\n",
    "                         bw=bandwidth,\n",
    "                         gridsize=int((len(x_bins)+len(y_bins))/2),\n",
    "                         clip=((x.min(),x.max()),(y.min(),y.max())))\n",
    "\n",
    "    if kwargs['scatter']:# and not self.kwargs['regression']:\n",
    "        g = g.plot_joint(plt.scatter)\n",
    "\n",
    "\n",
    "    if kwargs['marginals'] in ['Histogram','Both']:\n",
    "        _ = g.ax_marg_x.hist(x, alpha=.6,\n",
    "                              bins=x_bins,normed=True)\n",
    "        _ = g.ax_marg_y.hist(y, alpha=.6,\n",
    "                               orientation=\"horizontal\",\n",
    "                               bins=y_bins,normed=True)\n",
    "    if kwargs['marginals'] in ['KDE','Both']:\n",
    "        clip = ((x.values.min()-0.1,x.values.max()+0.1),(y.values.min()-0.1,y.values.max()+0.1))\n",
    "        g = g.plot_marginals(sns.kdeplot, **dict(shade=True))#,bw=kwargs['bandwith']))#,\n",
    "                         #gridsize=int((len(x_bins)+len(y_bins))/2)))\n",
    "                         #clip=clip)\n",
    "    if kwargs['regression']:\n",
    "        g = g.plot_joint(sns.regplot,\n",
    "                         truncate=True,\n",
    "                         order=linreg_order,\n",
    "                         ci=confidence_intervals,\n",
    "                         scatter_kws={\"s\": 80}\n",
    "                        )\n",
    "    if kwargs['grid']:\n",
    "        _ = plt.grid(linewidth=2) \n",
    "        _ = plt.xticks(x_bins)\n",
    "        plt.yticks(y_bins)\n",
    "\n",
    "    _ = plt.xlim(x.values.min(),x.values.max())\n",
    "    _ = plt.ylim(y.values.min(),y.values.max())\n",
    "\n",
    "\n",
    "    _ = plt.show()\n",
    "    if kwargs['save']:\n",
    "        _ =plt.savefig(\"data/density_plot_{}.png\".format(0), dpi=100)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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iHMuO2yK/tdXzGuCN8ZkCwIkEQGggewZH4vH9BxauD5fKC9fbNq/Nq6xFm5qp2NofGkjR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\nBEAAAIBE/H8/Ikg2HpADfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7facddef6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs= {'num_xbins':(1,50,2),\n",
    "         'num_ybins':(1,50,2),\n",
    "         'bandwidth':(0.001,10,0.025),\n",
    "         'grid': True,\n",
    "         'plot_density':False,\n",
    "         'scatter':True,\n",
    "         'regression':False,\n",
    "         'linreg_order':(1,5,1),\n",
    "         'confidence_intervals':(1,100,1),\n",
    "         'marginals':['None','Histogram','KDE','Both'],\n",
    "\n",
    "\n",
    "         'dataset':['ALL','I','II','III','IV'],\n",
    "         'save':False,\n",
    "         #'@kernel':['gau','cos','biw','epa','tri','triw']\n",
    "        }\n",
    "jointplot_data = df\n",
    "display(interact(density_plot,**kwargs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Grouping datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>num_ix</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th>I</th>\n",
       "      <th>I</th>\n",
       "      <th>I</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x      y num_ix\n",
       "dataset       I      I      I\n",
       "new_index                    \n",
       "0          10.0   8.04      1\n",
       "1           8.0   6.95      1\n",
       "2          13.0   7.58      1\n",
       "3           9.0   8.81      1\n",
       "4          11.0   8.33      1\n",
       "5          14.0   9.96      1\n",
       "6           6.0   7.24      1\n",
       "7           4.0   4.26      1\n",
       "8          12.0  10.84      1\n",
       "9           7.0   4.82      1\n",
       "10          5.0   5.68      1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['new_index'] = df.groupby('dataset').transform(lambda x:np.arange(len(x)))['x'].values\n",
    "idx = pd.IndexSlice\n",
    "sep = df.pivot(columns='dataset',index='new_index')\n",
    "sep.loc[:,idx[:,'I']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">x</th>\n",
       "      <th colspan=\"4\" halign=\"left\">y</th>\n",
       "      <th colspan=\"4\" halign=\"left\">num_ix</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th>I</th>\n",
       "      <th>II</th>\n",
       "      <th>III</th>\n",
       "      <th>IV</th>\n",
       "      <th>I</th>\n",
       "      <th>II</th>\n",
       "      <th>III</th>\n",
       "      <th>IV</th>\n",
       "      <th>I</th>\n",
       "      <th>II</th>\n",
       "      <th>III</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.58</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.77</td>\n",
       "      <td>5.76</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.74</td>\n",
       "      <td>7.71</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8.84</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8.47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>7.04</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.10</td>\n",
       "      <td>5.39</td>\n",
       "      <td>12.50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "      <td>5.56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>7.91</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.89</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x                        y                     num_ix          \n",
       "dataset       I    II   III    IV      I    II    III     IV      I II III IV\n",
       "new_index                                                                    \n",
       "0          10.0  10.0  10.0   8.0   8.04  9.14   7.46   6.58      1  2   3  4\n",
       "1           8.0   8.0   8.0   8.0   6.95  8.14   6.77   5.76      1  2   3  4\n",
       "2          13.0  13.0  13.0   8.0   7.58  8.74  12.74   7.71      1  2   3  4\n",
       "3           9.0   9.0   9.0   8.0   8.81  8.77   7.11   8.84      1  2   3  4\n",
       "4          11.0  11.0  11.0   8.0   8.33  9.26   7.81   8.47      1  2   3  4\n",
       "5          14.0  14.0  14.0   8.0   9.96  8.10   8.84   7.04      1  2   3  4\n",
       "6           6.0   6.0   6.0   8.0   7.24  6.13   6.08   5.25      1  2   3  4\n",
       "7           4.0   4.0   4.0  19.0   4.26  3.10   5.39  12.50      1  2   3  4\n",
       "8          12.0  12.0  12.0   8.0  10.84  9.13   8.15   5.56      1  2   3  4\n",
       "9           7.0   7.0   7.0   8.0   4.82  7.26   6.42   7.91      1  2   3  4\n",
       "10          5.0   5.0   5.0   8.0   5.68  4.74   5.73   6.89      1  2   3  4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I\n",
      "               x          y  num_ix  new_index\n",
      "count  11.000000  11.000000    11.0  11.000000\n",
      "mean    9.000000   7.500909     1.0   5.000000\n",
      "std     3.316625   2.031568     0.0   3.316625\n",
      "min     4.000000   4.260000     1.0   0.000000\n",
      "25%     6.500000   6.315000     1.0   2.500000\n",
      "50%     9.000000   7.580000     1.0   5.000000\n",
      "75%    11.500000   8.570000     1.0   7.500000\n",
      "max    14.000000  10.840000     1.0  10.000000\n",
      "\n",
      "II\n",
      "               x          y  num_ix  new_index\n",
      "count  11.000000  11.000000    11.0  11.000000\n",
      "mean    9.000000   7.500909     2.0   5.000000\n",
      "std     3.316625   2.031657     0.0   3.316625\n",
      "min     4.000000   3.100000     2.0   0.000000\n",
      "25%     6.500000   6.695000     2.0   2.500000\n",
      "50%     9.000000   8.140000     2.0   5.000000\n",
      "75%    11.500000   8.950000     2.0   7.500000\n",
      "max    14.000000   9.260000     2.0  10.000000\n",
      "\n",
      "III\n",
      "               x          y  num_ix  new_index\n",
      "count  11.000000  11.000000    11.0  11.000000\n",
      "mean    9.000000   7.500000     3.0   5.000000\n",
      "std     3.316625   2.030424     0.0   3.316625\n",
      "min     4.000000   5.390000     3.0   0.000000\n",
      "25%     6.500000   6.250000     3.0   2.500000\n",
      "50%     9.000000   7.110000     3.0   5.000000\n",
      "75%    11.500000   7.980000     3.0   7.500000\n",
      "max    14.000000  12.740000     3.0  10.000000\n",
      "\n",
      "IV\n",
      "               x          y  num_ix  new_index\n",
      "count  11.000000  11.000000    11.0  11.000000\n",
      "mean    9.000000   7.500909     4.0   5.000000\n",
      "std     3.316625   2.030579     0.0   3.316625\n",
      "min     8.000000   5.250000     4.0   0.000000\n",
      "25%     8.000000   6.170000     4.0   2.500000\n",
      "50%     8.000000   7.040000     4.0   5.000000\n",
      "75%     8.000000   8.190000     4.0   7.500000\n",
      "max    19.000000  12.500000     4.0  10.000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "groups = ['I', 'II', 'III', 'IV']\n",
    "for group in groups:\n",
    "    print(group)\n",
    "    print(df[df.dataset == group].describe())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's compare the correlation coefficient for each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.816420516345\n",
      "0.816236506\n",
      "0.81628673949\n",
      "0.816521436889\n"
     ]
    }
   ],
   "source": [
    "for g in groups:\n",
    "    print(df[df.dataset == g]['x'].corr(df[df.dataset == g]['y']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 6 Quick Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plot datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/ori+DNdar8HFyQUhnkGYFTYVgR7+5m6PiIiIqBv/wKEoK6nvf32Ah4TdGI7B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\nYLg00EyEyR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab417af0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cmx\n",
    "import matplotlib.pyplot as plt\n",
    "groups = df.groupby('dataset')\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots()\n",
    "ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "for name, group in groups:\n",
    "    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name)\n",
    "ax.legend(loc=2)\n",
    "plt.title(\"Anscombe's points\",fontsize=20)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kalidus/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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vjXAxN8MD6sksXbqUzZs387Of/czoUkRERMTHKKD6JsO7+E+kpaWFP//5z/zu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\ncgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab41e0ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x=\"x\", y=\"y\",data=df[df['dataset']=='I'], kind=\"kde\", size=7, space=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 6.1 Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lt2bc7HHThwYBx77LER8dEw+pe//GV1kD/72c/GT3/607zX2rlz5/jud78bP/jBD+KMM86ITp06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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab4057c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sep.plot(kind='hist',subplots=True,figsize=(18,10),layout=(3,4),sharex=False,rwidth=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 6.2 Linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Show the results of a **linear regression** within each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fab40434c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j4OfNUxcRkVzU2WvxoXETzlV8K6orocTIDqMwLHwElAqlRN1Rc5PF/yePHz+O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\n5tkNUWEOffeWIZp8Ng/XAUAw2VG7Xa8Uvqx2f7u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AgIAQHgAAAAAEhPAAAAAAICCEBwAA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\nAHxC8QAAAADAJ/8PH3aiXN/zS3wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab402467b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=None, palette=\"muted\", size=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It's the same line for all datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's plot with its 95% confidence interval region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f54e914ff28>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UecW/htBxJHIYnb5tuJA6n3NulnM22ryrcYNzJgP6EiraE3nGjBlob2/HZz7z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\nsa2Sv/I6JAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54e8f150f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=95, palette=\"muted\", size=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Key message"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Visualize your data beforehand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Nonlinear regression? outliers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One can fit a polynomial regression model to explore simple kinds of **nonlinear** trends in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8NISbLutnckUi4i0KXC/796ZDHCuvB+CqS3qp/aJIEFHgelGjzcGba/YBEBVh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54e8ecc390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", data=df[df.dataset == 'II'],\n",
    "           order=2, ci=95, scatter_kws={\"s\": 80});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In the presence of outliers, it can be useful to fit a robust regression, which uses a different loss function to downweight relatively large residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54e9173978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", data=df[df.dataset == 'III'],\n",
    "           robust=True, ci=None, scatter_kws={\"s\": 80});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "widgets": {
   "state": {
    "2c2b2ffcfb8b418da3a118f1ecf3ee88": {
     "views": [
      {
       "cell_index": 60
      }
     ]
    },
    "6226dc75386d4bbc9e451ae9a105d0c9": {
     "views": [
      {
       "cell_index": 58
      }
     ]
    },
    "81dd5cdad1f64d58987481c45cdf987c": {
     "views": [
      {
       "cell_index": 56
      }
     ]
    },
    "d1b053a054b1439b8e458e692ba714c0": {
     "views": [
      {
       "cell_index": 56
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}