{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter1 - Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Initial Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in the Pima Indians data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnant</th>\n",
       "      <th>glucose</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>age</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnant  glucose  diastolic  triceps  insulin   bmi  diabetes  age  test\n",
       "0         6      148         72       35        0  33.6     0.627   50     1\n",
       "1         1       85         66       29        0  26.6     0.351   31     0\n",
       "2         8      183         64        0        0  23.3     0.672   32     1\n",
       "3         1       89         66       23       94  28.1     0.167   21     0\n",
       "4         0      137         40       35      168  43.1     2.288   33     1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima = pd.read_csv(\"data/pima.csv\")\n",
    "pima.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct numerical summary. In contrast to R, only numerical variables are summarized (but that's all of them here). Round improves the presentation but is not well controlled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnant</th>\n",
       "      <th>glucose</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>age</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.8</td>\n",
       "      <td>120.9</td>\n",
       "      <td>69.1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>79.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>115.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>140.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>127.2</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>846.0</td>\n",
       "      <td>67.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pregnant  glucose  diastolic  triceps  insulin    bmi  diabetes    age  \\\n",
       "count     768.0    768.0      768.0    768.0    768.0  768.0     768.0  768.0   \n",
       "mean        3.8    120.9       69.1     20.5     79.8   32.0       0.5   33.2   \n",
       "std         3.4     32.0       19.4     16.0    115.2    7.9       0.3   11.8   \n",
       "min         0.0      0.0        0.0      0.0      0.0    0.0       0.1   21.0   \n",
       "25%         1.0     99.0       62.0      0.0      0.0   27.3       0.2   24.0   \n",
       "50%         3.0    117.0       72.0     23.0     30.5   32.0       0.4   29.0   \n",
       "75%         6.0    140.2       80.0     32.0    127.2   36.6       0.6   41.0   \n",
       "max        17.0    199.0      122.0     99.0    846.0   67.1       2.4   81.0   \n",
       "\n",
       "        test  \n",
       "count  768.0  \n",
       "mean     0.3  \n",
       "std      0.5  \n",
       "min      0.0  \n",
       "25%      0.0  \n",
       "50%      0.0  \n",
       "75%      1.0  \n",
       "max      1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.describe().round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort diastolic and show the first few values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "347    0\n",
       "494    0\n",
       "222    0\n",
       "81     0\n",
       "78     0\n",
       "Name: diastolic, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima['diastolic'].sort_values().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace all the instances of zero in five variables with the missing value code. Python code neater than R here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnant</th>\n",
       "      <th>glucose</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>age</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnant  glucose  diastolic  triceps  insulin   bmi  diabetes  age  test\n",
       "0         6    148.0       72.0     35.0      NaN  33.6     0.627   50     1\n",
       "1         1     85.0       66.0     29.0      NaN  26.6     0.351   31     0\n",
       "2         8    183.0       64.0      NaN      NaN  23.3     0.672   32     1\n",
       "3         1     89.0       66.0     23.0     94.0  28.1     0.167   21     0\n",
       "4         0    137.0       40.0     35.0    168.0  43.1     2.288   33     1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.replace({'diastolic' : 0, 'triceps' : 0, 'insulin' : 0, 'glucose' : 0, 'bmi' : 0}, np.nan, inplace=True)\n",
    "pima.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn test into a categorical variable. Describe is not like R table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          768\n",
       "unique           2\n",
       "top       Negative\n",
       "freq           500\n",
       "Name: test, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima['test'] = pima['test'].astype('category')\n",
    "pima['test'] = pima['test'].cat.rename_categories(['Negative','Positive'])\n",
    "pima['test'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This produces output like R table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Negative    500\n",
       "Positive    268\n",
       "Name: test, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima['test'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce histogram using `seaborn`. Necessary to drop the missing values. The kernel density estimate is also plotted. (Warning message can be ignored)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20aee160>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pima.diastolic.dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort data, remove missing values and make a line plot using an index created over the range of the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20ecf4e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pimad = pima.diastolic.dropna().sort_values()\n",
    "sns.lineplot(range(0, len(pimad)), pimad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot. Need to use smaller plotting character size with s=20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20f2a6d8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='diastolic',y='diabetes',data=pima, s=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produces the boxplot using the `seaborn` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20f588d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"test\", y=\"diabetes\", data=pima)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use some alpha transparency in the following plot to distinguish the different levels of test on the scatter plot of the two quantitative variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a211747f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"diastolic\", y=\"diabetes\", data=pima, hue=\"test\", alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can do faceting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a21200d30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"diastolic\", y=\"diabetes\", data=pima, col=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 History"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in manilius data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arc</th>\n",
       "      <th>sinang</th>\n",
       "      <th>cosang</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.166667</td>\n",
       "      <td>0.8836</td>\n",
       "      <td>-0.4682</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.133333</td>\n",
       "      <td>0.9996</td>\n",
       "      <td>-0.0282</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.200000</td>\n",
       "      <td>0.9899</td>\n",
       "      <td>0.1421</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.250000</td>\n",
       "      <td>0.2221</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.700000</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         arc  sinang  cosang  group\n",
       "0  13.166667  0.8836 -0.4682      1\n",
       "1  13.133333  0.9996 -0.0282      1\n",
       "2  13.200000  0.9899  0.1421      1\n",
       "3  14.250000  0.2221  0.9750      3\n",
       "4  14.700000  0.0006  1.0000      3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manilius = pd.read_csv(\"data/manilius.csv\")\n",
    "manilius.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do ancient calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arc</th>\n",
       "      <th>sinang</th>\n",
       "      <th>cosang</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118.133333</td>\n",
       "      <td>8.4987</td>\n",
       "      <td>-0.7932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140.283333</td>\n",
       "      <td>-6.1404</td>\n",
       "      <td>1.7443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>127.533333</td>\n",
       "      <td>2.9777</td>\n",
       "      <td>7.9649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              arc  sinang  cosang\n",
       "group                            \n",
       "1      118.133333  8.4987 -0.7932\n",
       "2      140.283333 -6.1404  1.7443\n",
       "3      127.533333  2.9777  7.9649"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moon3 = manilius.groupby('group')[['arc','sinang', 'cosang']].sum()\n",
    "moon3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add an intercept column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arc</th>\n",
       "      <th>sinang</th>\n",
       "      <th>cosang</th>\n",
       "      <th>intercept</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118.133333</td>\n",
       "      <td>8.4987</td>\n",
       "      <td>-0.7932</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140.283333</td>\n",
       "      <td>-6.1404</td>\n",
       "      <td>1.7443</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>127.533333</td>\n",
       "      <td>2.9777</td>\n",
       "      <td>7.9649</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              arc  sinang  cosang  intercept\n",
       "group                                       \n",
       "1      118.133333  8.4987 -0.7932          9\n",
       "2      140.283333 -6.1404  1.7443          9\n",
       "3      127.533333  2.9777  7.9649          9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moon3['intercept'] = [9]*3\n",
    "moon3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solve the linear equation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.54458591, -1.48982207,  0.13412639])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.solve(moon3[['intercept','sinang','cosang']],moon3['arc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can do linear models with R style formulae."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    14.561624\n",
       "sinang       -1.504581\n",
       "cosang        0.091365\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula='arc ~ sinang + cosang', data=manilius)\n",
    "res = mod.fit()\n",
    "res.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in family height data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family</th>\n",
       "      <th>father</th>\n",
       "      <th>mother</th>\n",
       "      <th>midparentHeight</th>\n",
       "      <th>children</th>\n",
       "      <th>childNum</th>\n",
       "      <th>gender</th>\n",
       "      <th>childHeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>001</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>001</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002</td>\n",
       "      <td>75.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>73.66</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  family  father  mother  midparentHeight  children  childNum  gender  \\\n",
       "0    001    78.5    67.0            75.43         4         1    male   \n",
       "1    001    78.5    67.0            75.43         4         2  female   \n",
       "2    001    78.5    67.0            75.43         4         3  female   \n",
       "3    001    78.5    67.0            75.43         4         4  female   \n",
       "4    002    75.5    66.5            73.66         4         1    male   \n",
       "\n",
       "   childHeight  \n",
       "0         73.2  \n",
       "1         69.2  \n",
       "2         69.0  \n",
       "3         69.0  \n",
       "4         73.5  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "families = pd.read_csv(\"data/families.csv\")\n",
    "families.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot - need to reduce point size with s=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2169c240>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='midparentHeight', y='childHeight',data=families,s=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate lm fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          22.636241\n",
       "midparentHeight     0.637361\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(formula='childHeight ~ midparentHeight', data=families)\n",
    "res = mod.fit()\n",
    "res.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the regression line to the plot. Seaborn's lmplot function does this automatically. We don't want the confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2164b320>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('midparentHeight', 'childHeight', families, ci=None, scatter_kws={'s':2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate using the direct formulae."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.64,  0.64])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = sp.stats.pearsonr(families['childHeight'],families['midparentHeight'])[0]\n",
    "sdy = np.std(families['childHeight'])\n",
    "sdx = np.std(families['midparentHeight'])\n",
    "beta = cor*sdy/sdx\n",
    "alpha = np.mean(families['childHeight']) - beta*np.mean(families['midparentHeight'])\n",
    "np.round([alpha,beta],2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the appropriate y=x line to show the regression effect. Python appears to lack a convenient abline() function as in R so we have to manually calculate the endpoints of the added line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a21346cf8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "beta1 = sdy/sdx\n",
    "alpha1 = np.mean(families['childHeight']) - beta1*np.mean(families['midparentHeight'])\n",
    "sns.lmplot('midparentHeight', 'childHeight', families, ci=None, scatter_kws={'s':2})\n",
    "xl,xu = [64, 76]\n",
    "plt.plot([xl,xu], [alpha1+xl*beta1,alpha1+xu*beta1],c='Red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]"
        },
        {
         "module": "IPython",
         "version": "6.5.0"
        },
        {
         "module": "OS",
         "version": "Darwin 17.7.0 x86_64 i386 64bit"
        },
        {
         "module": "pandas",
         "version": "0.23.4"
        },
        {
         "module": "numpy",
         "version": "1.15.1"
        },
        {
         "module": "matplotlib",
         "version": "2.2.3"
        },
        {
         "module": "seaborn",
         "version": "0.9.0"
        },
        {
         "module": "scipy",
         "version": "1.1.0"
        },
        {
         "module": "patsy",
         "version": "0.5.0"
        },
        {
         "module": "statsmodels",
         "version": "0.9.0"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>IPython</td><td>6.5.0</td></tr><tr><td>OS</td><td>Darwin 17.7.0 x86_64 i386 64bit</td></tr><tr><td>pandas</td><td>0.23.4</td></tr><tr><td>numpy</td><td>1.15.1</td></tr><tr><td>matplotlib</td><td>2.2.3</td></tr><tr><td>seaborn</td><td>0.9.0</td></tr><tr><td>scipy</td><td>1.1.0</td></tr><tr><td>patsy</td><td>0.5.0</td></tr><tr><td>statsmodels</td><td>0.9.0</td></tr><tr><td colspan='2'>Fri Sep 07 15:10:46 2018 BST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE\\_401/final)] \\\\ \\hline\n",
       "IPython & 6.5.0 \\\\ \\hline\n",
       "OS & Darwin 17.7.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "pandas & 0.23.4 \\\\ \\hline\n",
       "numpy & 1.15.1 \\\\ \\hline\n",
       "matplotlib & 2.2.3 \\\\ \\hline\n",
       "seaborn & 0.9.0 \\\\ \\hline\n",
       "scipy & 1.1.0 \\\\ \\hline\n",
       "patsy & 0.5.0 \\\\ \\hline\n",
       "statsmodels & 0.9.0 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Fri Sep 07 15:10:46 2018 BST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]\n",
       "IPython 6.5.0\n",
       "OS Darwin 17.7.0 x86_64 i386 64bit\n",
       "pandas 0.23.4\n",
       "numpy 1.15.1\n",
       "matplotlib 2.2.3\n",
       "seaborn 0.9.0\n",
       "scipy 1.1.0\n",
       "patsy 0.5.0\n",
       "statsmodels 0.9.0\n",
       "Fri Sep 07 15:10:46 2018 BST"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext version_information\n",
    "%version_information pandas, numpy, matplotlib, seaborn, scipy, patsy, statsmodels"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}