anscombes_quartet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anscombe's quartet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!conda install -y numpy pandas matplotlib seaborn statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Anscombe's quartet dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"anscombe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And df is... a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "that we can print, plot, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": {},
   "source": [
    "Print just first dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</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\n",
       "5        I  14.0   9.96\n",
       "6        I   6.0   7.24\n",
       "7        I   4.0   4.26\n",
       "8        I  12.0  10.84\n",
       "9        I   7.0   4.82\n",
       "10       I   5.0   5.68"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.dataset == 'I']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic statistical parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the basic statistical parameters of each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500909\n",
      "std     3.316625   2.031568\n",
      "min     4.000000   4.260000\n",
      "25%     6.500000   6.315000\n",
      "50%     9.000000   7.580000\n",
      "75%    11.500000   8.570000\n",
      "max    14.000000  10.840000\n",
      "\n",
      "II\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500909\n",
      "std     3.316625   2.031657\n",
      "min     4.000000   3.100000\n",
      "25%     6.500000   6.695000\n",
      "50%     9.000000   8.140000\n",
      "75%    11.500000   8.950000\n",
      "max    14.000000   9.260000\n",
      "\n",
      "III\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500000\n",
      "std     3.316625   2.030424\n",
      "min     4.000000   5.390000\n",
      "25%     6.500000   6.250000\n",
      "50%     9.000000   7.110000\n",
      "75%    11.500000   7.980000\n",
      "max    14.000000  12.740000\n",
      "\n",
      "IV\n",
      "               x          y\n",
      "count  11.000000  11.000000\n",
      "mean    9.000000   7.500909\n",
      "std     3.316625   2.030579\n",
      "min     8.000000   5.250000\n",
      "25%     8.000000   6.170000\n",
      "50%     8.000000   7.040000\n",
      "75%     8.000000   8.190000\n",
      "max    19.000000  12.500000\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": {},
   "source": [
    "Let's compare the correlation coefficient for each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fbfa2c5b9b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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0qfLy8uT3+7V8+fLO1I4oYbYSABAO3abNQPPUU09pypQpWrFihRISEoLnMzIydPPNN4fU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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfa2efdc88>"
      ]
     },
     "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": {},
   "source": [
    "## Linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the results of a **linear regression** within each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fbfa021a0b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nMgyDNe9W8NTrJbi9i/7SPz2cObmZJMeGdHk/IuLVWdsRnIji5iIW5T3BQcdB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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfa02e3cf8>"
      ]
     },
     "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": {},
   "source": [
    "It's the same line for all datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot with its 95% confidence interval region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fbf9fc5f438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wr5u377wYwKfulStXYtWqVTloDRWKXK8rE4pqeHu3D2+fFb0GgHK7CZV2BWZF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\nVobi1avhXrYMALv1knn1HhtCZ3svQoMRuMtcqG+I70GUqkzGskMD8V2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2wN/WBnlgAPby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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf9fc5f860>"
      ]
     },
     "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": {},
   "source": [
    "# Key message"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize your data beforehand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nonlinear regression? outliers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can fit a polynomial regression model to explore simple kinds of **nonlinear** trends in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JUUwb29fYgkTE5c4YuP/6179O+7Hbb7+9S4vxJ2+t2utsUDN3+hCCg7RCT8TX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf9fc5ff60>"
      ]
     },
     "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": {},
   "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf9f440978>"
      ]
     },
     "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});"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}