{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anscombe's quartet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/carData/Quartet.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
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       "      <td>6.58</td>\n",
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       "      <td>6.77</td>\n",
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       "      <td>5.76</td>\n",
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       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
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       "      <td>8</td>\n",
       "      <td>7.71</td>\n",
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       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
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       "      <td>8.84</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8</td>\n",
       "      <td>7.04</td>\n",
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       "      <th>6</th>\n",
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       "      <td>19</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>8</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>8</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Unnamed: 0   x     y1    y2     y3  x4     y4\n",
       "0            1  10   8.04  9.14   7.46   8   6.58\n",
       "1            2   8   6.95  8.14   6.77   8   5.76\n",
       "2            3  13   7.58  8.74  12.74   8   7.71\n",
       "3            4   9   8.81  8.77   7.11   8   8.84\n",
       "4            5  11   8.33  9.26   7.81   8   8.47\n",
       "5            6  14   9.96  8.10   8.84   8   7.04\n",
       "6            7   6   7.24  6.13   6.08   8   5.25\n",
       "7            8   4   4.26  3.10   5.39  19  12.50\n",
       "8            9  12  10.84  9.13   8.15   8   5.56\n",
       "9           10   7   4.82  7.26   6.42   8   7.91\n",
       "10          11   5   5.68  4.74   5.73   8   6.89"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.84</td>\n",
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       "      <th>4</th>\n",
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       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8</td>\n",
       "      <td>8.47</td>\n",
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       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8</td>\n",
       "      <td>7.04</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
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       "      <th>8</th>\n",
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       "      <td>5.56</td>\n",
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       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>8</td>\n",
       "      <td>7.91</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
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       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
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      ],
      "text/plain": [
       "     x     y1    y2     y3  x4     y4\n",
       "0   10   8.04  9.14   7.46   8   6.58\n",
       "1    8   6.95  8.14   6.77   8   5.76\n",
       "2   13   7.58  8.74  12.74   8   7.71\n",
       "3    9   8.81  8.77   7.11   8   8.84\n",
       "4   11   8.33  9.26   7.81   8   8.47\n",
       "5   14   9.96  8.10   8.84   8   7.04\n",
       "6    6   7.24  6.13   6.08   8   5.25\n",
       "7    4   4.26  3.10   5.39  19  12.50\n",
       "8   12  10.84  9.13   8.15   8   5.56\n",
       "9    7   4.82  7.26   6.42   8   7.91\n",
       "10   5   5.68  4.74   5.73   8   6.89"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a50e6edda0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l5g+BH0TEle2HbgC+28ORuu15oBUReyMiOLO/xb4JfB7fAO5of38H8PUeztJ1EXET8KfALZn5Wq/n6bbMfCozL83M/e2WnQSuaf9/XqsdHfQ+93FgOiK+A7wb+PMez9M17b+JHAUeB57izH+XRV5JGBFHgH8DroyIkxHxMeAe4MaIeJYzZ0Hc08sZ67TO/v4t8BbgWEQ8ERF/19Mha7bOPm/Ptsv/244k9QeP0CWpEAZdkgph0CWpEAZdkgph0CWpEAZdkgph0CWpEAZdkgrx/1z1Pcjh+WE7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a50e663358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(df['x'], df['y1'], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a50e7592b0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a50e6dab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(df['x'], df['y2'], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a50e7b9278>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a50e7369e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(df['x'], df['y3'], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a50e819898>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a50e701fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(df['x'], df['y4'], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a50f7e22b0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a50f7e2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "# Load the example dataset for Anscombe's quartet\n",
    "df = sns.load_dataset(\"anscombe\")\n",
    "\n",
    "# Show the results of a linear regression within each dataset\n",
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=None, palette=\"muted\",\n",
    "           scatter_kws={\"s\": 50, \"alpha\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}