two_sample_t-test_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the notebook containing all the code for the post about [carrying out two-sample t-test with Python](https://www.marsja.se/how-to-perform-a-two-sample-t-test-with-python-3-different-methods/). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data\n",
    "Here's the code to import the example data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grouping</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>men</td>\n",
       "      <td>181.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>men</td>\n",
       "      <td>187.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>men</td>\n",
       "      <td>175.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>men</td>\n",
       "      <td>178.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>men</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  grouping  height\n",
       "0      men   181.5\n",
       "1      men   187.3\n",
       "2      men   175.3\n",
       "3      men   178.3\n",
       "4      men   169.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = 'https://gist.githubusercontent.com/baskaufs/1a7a995c1b25d6e88b45/raw/4bb17ccc5c1e62c27627833a4f25380f27d30b35/t-test.csv'\n",
    "df = pd.read_csv(data)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Subset Data\n",
    "Here's how to create two variables by subsetting them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset data\n",
    "male = df.query('grouping == \"men\"')['height']\n",
    "female = df.query('grouping == \"women\"')['height']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Statistics\n",
    "Here's how to quickly do summary statistics of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grouping</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>men</th>\n",
       "      <td>7.0</td>\n",
       "      <td>179.871429</td>\n",
       "      <td>6.216836</td>\n",
       "      <td>169.0</td>\n",
       "      <td>176.80</td>\n",
       "      <td>181.5</td>\n",
       "      <td>183.85</td>\n",
       "      <td>187.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>women</th>\n",
       "      <td>7.0</td>\n",
       "      <td>171.057143</td>\n",
       "      <td>5.697619</td>\n",
       "      <td>165.2</td>\n",
       "      <td>166.65</td>\n",
       "      <td>170.3</td>\n",
       "      <td>173.75</td>\n",
       "      <td>181.1</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         height                                                           \n",
       "          count        mean       std    min     25%    50%     75%    max\n",
       "grouping                                                                  \n",
       "men         7.0  179.871429  6.216836  169.0  176.80  181.5  183.85  187.3\n",
       "women       7.0  171.057143  5.697619  165.2  166.65  170.3  173.75  181.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('grouping').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normality Tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "stats.shapiro(male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.shapiro(female)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Levene's test of equal variances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.levene(male, female)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T-test using Scipy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = stats.ttest_ind(male, female, \n",
    "                      equal_var=True)\n",
    "\n",
    "display(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T-test with Pingouin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pingouin as pg\n",
    "\n",
    "res = pg.ttest(male, female, correction=False)\n",
    "\n",
    "\n",
    "display(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T-test using Statsmodels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.weightstats import ttest_ind\n",
    "\n",
    "\n",
    "ttest_ind(male, female)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "Here's how to do a boxplot and a violin plot:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot in Python:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.boxplot(x='grouping', y='height', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin Plot with Python:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.violinplot(x='grouping', y='height', data=df)"
   ]
  }
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