Code Samples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats\n",
    "from sklearn import linear_model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 Types of Data in Statistics\n",
    "\n",
    "## 3.2.1 Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>temp</th>\n",
       "      <th>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>75</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp weather\n",
       "0    55   windy\n",
       "1    34  cloudy\n",
       "2    80   sunny\n",
       "3    75    rain\n",
       "4    53   sunny"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df = pd.DataFrame({\n",
    "    'temp': [55, 34, 80, 75, 53],\n",
    "    'weather': ['windy', 'cloudy', 'sunny', 'rain', 'sunny']\n",
    "})\n",
    "\n",
    "weather_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>temp</th>\n",
       "      <th>weather</th>\n",
       "      <th>weather_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>windy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>sunny</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>75</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>sunny</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp weather  weather_encoded\n",
       "0    55   windy                0\n",
       "1    34  cloudy                1\n",
       "2    80   sunny                2\n",
       "3    75    rain                3\n",
       "4    53   sunny                2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df['weather_encoded'] = weather_df['weather'].map({'windy': 0, 'cloudy': 1, 'sunny': 2, 'rain': 3})\n",
    "weather_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>cloudy</th>\n",
       "      <th>rain</th>\n",
       "      <th>sunny</th>\n",
       "      <th>windy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cloudy  rain  sunny  windy\n",
       "0       0     0      0      1\n",
       "1       1     0      0      0\n",
       "2       0     0      1      0\n",
       "3       0     1      0      0\n",
       "4       0     0      1      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(weather_df['weather'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_df['weather'].value_counts().plot.pie(autopct='%1.1f%%')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>class</th>\n",
       "      <th>gpa</th>\n",
       "      <th>num_classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Alice</td>\n",
       "      <td>female</td>\n",
       "      <td>FY</td>\n",
       "      <td>90</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Bob</td>\n",
       "      <td>male</td>\n",
       "      <td>SO</td>\n",
       "      <td>93</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Carol</td>\n",
       "      <td>female</td>\n",
       "      <td>SR</td>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dan</td>\n",
       "      <td>male</td>\n",
       "      <td>SO</td>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Eli</td>\n",
       "      <td>male</td>\n",
       "      <td>JR</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Fran</td>\n",
       "      <td>female</td>\n",
       "      <td>SR</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name     sex class  gpa  num_classes\n",
       "0  Alice  female    FY   90            4\n",
       "1    Bob    male    SO   93            3\n",
       "2  Carol  female    SR   97            4\n",
       "3    Dan    male    SO   89            4\n",
       "4    Eli    male    JR   95            3\n",
       "5   Fran  female    SR   92            2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_df = pd.DataFrame({\n",
    "    'name': ['Alice', 'Bob', 'Carol', 'Dan', 'Eli', 'Fran'],\n",
    "    'sex': ['female', 'male', 'female', 'male', 'male', 'female'],\n",
    "    'class': ['FY', 'SO', 'SR', 'SO', 'JR', 'SR'],\n",
    "    'gpa': [90, 93, 97, 89, 95, 92],\n",
    "    'num_classes': [4, 3, 4, 4, 3, 2]\n",
    "})\n",
    "student_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>name</th>\n",
       "      <th>class</th>\n",
       "      <th>gpa</th>\n",
       "      <th>num_classes</th>\n",
       "      <th>female_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Alice</td>\n",
       "      <td>FY</td>\n",
       "      <td>90</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Bob</td>\n",
       "      <td>SO</td>\n",
       "      <td>93</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Carol</td>\n",
       "      <td>SR</td>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Dan</td>\n",
       "      <td>SO</td>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Eli</td>\n",
       "      <td>JR</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Fran</td>\n",
       "      <td>SR</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name class  gpa  num_classes  female_flag\n",
       "0  Alice    FY   90            4         True\n",
       "1    Bob    SO   93            3        False\n",
       "2  Carol    SR   97            4         True\n",
       "3    Dan    SO   89            4        False\n",
       "4    Eli    JR   95            3        False\n",
       "5   Fran    SR   92            2         True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_df['female_flag'] = student_df['sex'] == 'female'\n",
    "student_df = student_df.drop('sex', axis=1)\n",
    "student_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2.2 Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = np.random.normal(4, 10, size=1000)\n",
    "\n",
    "plt.hist(samples, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalized_samples = (samples - 4) / 10\n",
    "\n",
    "plt.hist(normalized_samples, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.775724553212789, 9.87450069513982)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_mean = np.mean(samples)\n",
    "sample_sd = np.std(samples)\n",
    "\n",
    "sample_mean, sample_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalized_samples = (samples - sample_mean) / sample_sd\n",
    "\n",
    "plt.hist(normalized_samples, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3 Descriptive Statistics\n",
    "\n",
    "## Python-Related Descriptive Statistics"
   ]
  },
  {
   "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",
       "    }\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>numerical</th>\n",
       "      <th>categorical</th>\n",
       "      <th>ordinal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.111691</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.209548</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.019181</td>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.125899</td>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.562629</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numerical categorical  ordinal\n",
       "0  -0.111691           a        1\n",
       "1   1.209548           b        2\n",
       "2   0.019181           a        3\n",
       "3  -0.125899           c        5\n",
       "4  -1.562629           b        4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'numerical': np.random.normal(size=5),\n",
    "    'categorical': ['a', 'b', 'a', 'c', 'b'],\n",
    "    'ordinal': [1, 2, 3, 5, 4]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "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",
       "    .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>numerical</th>\n",
       "      <th>ordinal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-0.114298</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.983385</td>\n",
       "      <td>1.581139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.562629</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.125899</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-0.111691</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.019181</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.209548</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       numerical   ordinal\n",
       "count   5.000000  5.000000\n",
       "mean   -0.114298  3.000000\n",
       "std     0.983385  1.581139\n",
       "min    -1.562629  1.000000\n",
       "25%    -0.125899  2.000000\n",
       "50%    -0.111691  3.000000\n",
       "75%     0.019181  4.000000\n",
       "max     1.209548  5.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>numerical</th>\n",
       "      <th>categorical</th>\n",
       "      <th>ordinal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-0.114298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.983385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.581139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.562629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.125899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-0.111691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.019181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.209548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        numerical categorical   ordinal\n",
       "count    5.000000           5  5.000000\n",
       "unique        NaN           3       NaN\n",
       "top           NaN           a       NaN\n",
       "freq          NaN           2       NaN\n",
       "mean    -0.114298         NaN  3.000000\n",
       "std      0.983385         NaN  1.581139\n",
       "min     -1.562629         NaN  1.000000\n",
       "25%     -0.125899         NaN  2.000000\n",
       "50%     -0.111691         NaN  3.000000\n",
       "75%      0.019181         NaN  4.000000\n",
       "max      1.209548         NaN  5.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(np.random.normal(2, 5, size=1000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        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>numerical</th>\n",
       "      <th>categorical</th>\n",
       "      <th>ordinal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.030559</td>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.036235</td>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.453510</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-2.169020</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.384212</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numerical categorical  ordinal\n",
       "0   2.030559           c        5\n",
       "1   0.036235           c        5\n",
       "2  -0.453510           b        1\n",
       "3  -2.169020           c        2\n",
       "4   1.384212           c        2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'numerical': np.random.normal(size=1000),\n",
    "    'categorical': np.random.choice(['a', 'b', 'c'], size=1000),\n",
    "    'ordinal': np.random.choice([1, 2, 3, 4, 5], size=1000)\n",
    "})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='numerical', x='categorical', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='numerical', x='ordinal', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.4 Inferential Statistics\n",
    "\n",
    "## 3.4.1 T-Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_a = np.random.normal(size=20)\n",
    "samples_b = np.random.normal(0.2, 1, size=20)\n",
    "\n",
    "plt.hist(samples_a, alpha=0.2)\n",
    "plt.hist(samples_b, alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8616483548091348"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(samples_a, samples_b).pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_a = np.random.normal(size=1000)\n",
    "samples_b = np.random.normal(0.2, 1, size=1000)\n",
    "\n",
    "plt.hist(samples_a, alpha=0.2)\n",
    "plt.hist(samples_b, alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1445050317071093e-06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(samples_a, samples_b).pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.2 Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.117481</td>\n",
       "      <td>0.325332</td>\n",
       "      <td>0.550776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.760875</td>\n",
       "      <td>1.285527</td>\n",
       "      <td>0.323788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.919157</td>\n",
       "      <td>1.937977</td>\n",
       "      <td>0.597903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.399931</td>\n",
       "      <td>0.399549</td>\n",
       "      <td>0.504054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.371473</td>\n",
       "      <td>0.618477</td>\n",
       "      <td>0.819181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y         z\n",
       "0  0.117481  0.325332  0.550776\n",
       "1  0.760875  1.285527  0.323788\n",
       "2  0.919157  1.937977  0.597903\n",
       "3  0.399931  0.399549  0.504054\n",
       "4  0.371473  0.618477  0.819181"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.rand(500,)\n",
    "y = x * 2 + np.random.normal(0, 0.3, 500)\n",
    "z = np.random.rand(500,)\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'x': x,\n",
    "    'y': y,\n",
    "    'z': z\n",
    "})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>x</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897084</td>\n",
       "      <td>0.007261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y</td>\n",
       "      <td>0.897084</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>z</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>-0.009610</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y         z\n",
       "x  1.000000  0.897084  0.007261\n",
       "y  0.897084  1.000000 -0.009610\n",
       "z  0.007261 -0.009610  1.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), center=0, annot=True)\n",
    "bottom, top = plt.ylim()\n",
    "plt.ylim(bottom + 0.5, top - 0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.3 Statistical and Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.98861194, 0.05436268])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(df[['x', 'z']], df['y'])\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.10790143, 4.15087605])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[1, 2], [2, 3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}