chi2_test_census_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if we want to determine the statistical significance of two independent categorical groups of data?\n",
    "\n",
    "This is where the Chi-squared test for independence is useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be looking at data from the census in 1994.\n",
    "Specifically, we are interested in the relationship between 'sex' and 'hours-per-week' worked. \n",
    "\n",
    "\n",
    "For the sake of this example, we'll convert the numerical column 'hours-per-week' into a categorical column using pandas. Then we'll assign 'sex' and 'hours_per_week_categories' to a new dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwg</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   fnlwg   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "cols = ['age', 'workclass', 'fnlwg', 'education', 'education-num', \n",
    "        'marital-status','occupation','relationship', 'race','sex',\n",
    "        'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\n",
    "data = pd.read_csv('census.csv', names=cols)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>hours_per_week_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       sex hours_per_week_categories\n",
       "0     Male                     40-49\n",
       "1     Male                     10-19\n",
       "2     Male                     40-49\n",
       "3     Male                     40-49\n",
       "4   Female                     40-49"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a column for work hour categories.\n",
    "def process_hours(df):\n",
    "    cut_points = [0,9,19,29,39,49,1000]\n",
    "    label_names = [\"0-9\",\"10-19\",\"20-29\",\"30-39\",\"40-49\",\"50+\"]\n",
    "    df[\"hours_per_week_categories\"] = pd.cut(df[\"hours-per-week\"],\n",
    "                                             cut_points, labels=label_names)\n",
    "    return df\n",
    "\n",
    "data = process_hours(data)\n",
    "workhour_by_sex = data[['sex', 'hours_per_week_categories']]\n",
    "workhour_by_sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>hours_per_week_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       sex hours_per_week_categories\n",
       "0     Male                     40-49\n",
       "1     Male                     10-19\n",
       "2     Male                     40-49\n",
       "3     Male                     40-49\n",
       "4   Female                     40-49"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column for work hour categories.\n",
    "def process_hours(df):\n",
    "    cut_points = [0,9,19,29,39,49,1000]\n",
    "    label_names = [\"0-9\",\"10-19\",\"20-29\",\"30-39\",\"40-49\",\"50+\"]\n",
    "    df[\"hours_per_week_categories\"] = pd.cut(df[\"hours-per-week\"],\n",
    "                                             cut_points, labels=label_names)\n",
    "    return df\n",
    "\n",
    "data = process_hours(data)\n",
    "workhour_by_sex = data[['sex', 'hours_per_week_categories']]\n",
    "workhour_by_sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40-49    18336\n",
       "50+       6462\n",
       "30-39     3667\n",
       "20-29     2392\n",
       "10-19     1246\n",
       "0-9        458\n",
       "Name: hours_per_week_categories, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workhour_by_sex['hours_per_week_categories'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For relationship between 'sex' and 'hours_per_week_categories', we need Chi-squared test.\n",
    "\n",
    "H0:There is NO statistically significant relationship between sex and the # of hours per week worked\n",
    "\n",
    "Ha:There is statistically significant relationship between sex and the # of hours per week worked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constructing the Contingency Table\n",
    "Format the data into a frequency count table. This is called a Contingency Table.\n",
    "\n",
    "We can accomplish this by using the pd.crosstab() function in pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "contingency_table = pd.crosstab(\n",
    "    workhour_by_sex['sex'],\n",
    "    workhour_by_sex['hours_per_week_categories'],\n",
    "    margins = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>hours_per_week_categories</th>\n",
       "      <th>0-9</th>\n",
       "      <th>10-19</th>\n",
       "      <th>20-29</th>\n",
       "      <th>30-39</th>\n",
       "      <th>40-49</th>\n",
       "      <th>50+</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</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>Female</th>\n",
       "      <td>235</td>\n",
       "      <td>671</td>\n",
       "      <td>1287</td>\n",
       "      <td>1914</td>\n",
       "      <td>5636</td>\n",
       "      <td>1028</td>\n",
       "      <td>10771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>223</td>\n",
       "      <td>575</td>\n",
       "      <td>1105</td>\n",
       "      <td>1753</td>\n",
       "      <td>12700</td>\n",
       "      <td>5434</td>\n",
       "      <td>21790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>6462</td>\n",
       "      <td>1246</td>\n",
       "      <td>18336</td>\n",
       "      <td>3667</td>\n",
       "      <td>458</td>\n",
       "      <td>2392</td>\n",
       "      <td>32561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "hours_per_week_categories   0-9  10-19  20-29  30-39  40-49   50+    All\n",
       "sex                                                                     \n",
       " Female                     235    671   1287   1914   5636  1028  10771\n",
       " Male                       223    575   1105   1753  12700  5434  21790\n",
       "All                        6462   1246  18336   3667    458  2392  32561"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Contingency Table with a Stacked Bar Chart\n",
    "\n",
    "#Assigns the frequency values\n",
    "malecount = contingency_table.iloc[0][0:6].values\n",
    "femalecount = contingency_table.iloc[1][0:6].values\n",
    "\n",
    "#Plots the bar chart\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "sns.set(font_scale=1.8)\n",
    "categories = [\"0-9\",\"10-19\",\"20-29\",\"30-39\",\"40-49\",\"50+\"]\n",
    "p1 = plt.bar(categories, malecount, 0.55, color='#d62728')\n",
    "p2 = plt.bar(categories, femalecount, 0.55, bottom=malecount)\n",
    "plt.legend((p2[0], p1[0]), ('Male', 'Female'))\n",
    "plt.xlabel('Hours per Week Worked')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  235,   671,  1287,  1914,  5636,  1028],\n",
       "       [  223,   575,  1105,  1753, 12700,  5434]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_obs = np.array([contingency_table.iloc[0][0:6].values,\n",
    "                  contingency_table.iloc[1][0:6].values])\n",
    "f_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "chi2_stat, pvalue, dof = stats.chi2_contingency(f_obs)[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi Squared Statistic: 2287.190943926107\n",
      "p value: 0.0\n",
      "Degrees of Freedom: 5\n"
     ]
    }
   ],
   "source": [
    "print(\"Chi Squared Statistic: {}\".format(chi2_stat))\n",
    "print(\"p value: {}\".format(pvalue))\n",
    "print(\"Degrees of Freedom: {}\".format(dof))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to look into the Chi-squared distribution table from below\n",
    "http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n",
    "\n",
    "The cut off for a p-value of 0.05 with degree of freedom of 5 was 11.070. \n",
    "\n",
    "Our X2 statistic was so large that the p-value is approximately zero. \n",
    "So we have evidence against the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusions:\n",
    "With a p-value < 0.05 , we can reject the null hypothesis. \n",
    "There is definitely some sort of relationship between 'sex' and the 'hours-per-week' column. \n",
    "We don't know what this relationship is, but we do know that these two variables are not independent of each other."
   ]
  },
  {
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
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}