statistical_significance_of_information_value.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Significance of Information Value\n",
    "\n",
    "While Information Value and Weight of Evidence may be a totally new concepts for many people (especially collegues from other departments, not statisticians), statistical significance is much more familiar term. During presentation of analysis you might be asked if the obtained results are statistically significant. IV & WOE technique doesn't give explicit answer to this question (although it gives some intuition which results are relevant).\n",
    "\n",
    "This analysis shows how differences in distribution of 'goods' and 'bads' are measured by two methods: IV & WOE and p-value & effect size.\n",
    "\n",
    "- In order to see how it can be used in practice, check [**Telco Customer Churn Analysis**](https://github.com/klaudia-nazarko/iv-and-woe-python/blob/master/telco_customer_churn_analysis.ipynb).\n",
    "- Get to know how to use Information Value module, check [**IV and WOE in Python**](https://github.com/klaudia-nazarko/iv-and-woe-python/blob/master/iv_and_woe_in_python.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P-value and effect size\n",
    "\n",
    "In order to measure p-value and effect size in this analysis it was used:\n",
    "* chi-square test of independence of variables\n",
    "* cramers' v effect size\n",
    "\n",
    ">Pearson's chi-squared test (χ2) is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. A test of independence assesses whether observations consisting of measures on two variables, expressed in a contingency table, are independent of each other (e.g. polling responses from people of different nationalities to see if one's nationality is related to the response).\n",
    "\n",
    "Chi-square says that there is a significant relationship between variables, but it does not say just how strong and important this is.\n",
    "\n",
    ">Cramér's V is a measure of association between two nominal variables. Cramer's V is a post-test to give this additional information. It varies between 0 and 1: close to 0 it shows little association between variables; close to 1, it indicates a strong association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "\n",
    "from Features import *\n",
    "from AttributeRelevance import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "pd.set_option('display.max_columns', None)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/telco_churn.csv', na_values=[' '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [c[0].lower() + c[1:] for c in df.columns]\n",
    "df['label'] = df['churn'].map({'Yes': 0, 'No': 1})\n",
    "df['seniorCitizen'] = df['seniorCitizen'].map({1: 'Yes', 0: 'No'})\n",
    "df.drop(['customerID', 'churn'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>seniorCitizen</th>\n",
       "      <th>partner</th>\n",
       "      <th>dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>phoneService</th>\n",
       "      <th>multipleLines</th>\n",
       "      <th>internetService</th>\n",
       "      <th>onlineSecurity</th>\n",
       "      <th>onlineBackup</th>\n",
       "      <th>deviceProtection</th>\n",
       "      <th>techSupport</th>\n",
       "      <th>streamingTV</th>\n",
       "      <th>streamingMovies</th>\n",
       "      <th>contract</th>\n",
       "      <th>paperlessBilling</th>\n",
       "      <th>paymentMethod</th>\n",
       "      <th>monthlyCharges</th>\n",
       "      <th>totalCharges</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4831</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>18</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>40.20</td>\n",
       "      <td>711.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>24.60</td>\n",
       "      <td>592.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4590</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>57</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>101.30</td>\n",
       "      <td>5,779.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>61</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>20.55</td>\n",
       "      <td>1,252.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>83.90</td>\n",
       "      <td>497.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender seniorCitizen partner dependents  tenure phoneService  \\\n",
       "4831  Female            No      No         No      18           No   \n",
       "3739    Male            No      No         No      24          Yes   \n",
       "4590  Female           Yes      No         No      57          Yes   \n",
       "179     Male            No     Yes         No      61          Yes   \n",
       "882   Female            No      No         No       6          Yes   \n",
       "\n",
       "         multipleLines internetService       onlineSecurity  \\\n",
       "4831  No phone service             DSL                  Yes   \n",
       "3739               Yes              No  No internet service   \n",
       "4590               Yes     Fiber optic                  Yes   \n",
       "179                 No              No  No internet service   \n",
       "882                 No     Fiber optic                   No   \n",
       "\n",
       "             onlineBackup     deviceProtection          techSupport  \\\n",
       "4831                  Yes                   No                  Yes   \n",
       "3739  No internet service  No internet service  No internet service   \n",
       "4590                   No                  Yes                  Yes   \n",
       "179   No internet service  No internet service  No internet service   \n",
       "882                    No                  Yes                   No   \n",
       "\n",
       "              streamingTV      streamingMovies        contract  \\\n",
       "4831                   No                   No        One year   \n",
       "3739  No internet service  No internet service        One year   \n",
       "4590                   No                  Yes        Two year   \n",
       "179   No internet service  No internet service        One year   \n",
       "882                    No                  Yes  Month-to-month   \n",
       "\n",
       "     paperlessBilling            paymentMethod  monthlyCharges  totalCharges  \\\n",
       "4831              Yes             Mailed check           40.20        711.95   \n",
       "3739               No         Electronic check           24.60        592.65   \n",
       "4590               No  Credit card (automatic)          101.30      5,779.60   \n",
       "179               Yes  Credit card (automatic)           20.55      1,252.00   \n",
       "882                No         Electronic check           83.90        497.55   \n",
       "\n",
       "      label  \n",
       "4831      1  \n",
       "3739      1  \n",
       "4590      1  \n",
       "179       1  \n",
       "882       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats_dict = {}\n",
    "\n",
    "for col in [c for c in df.columns if c != 'label']:\n",
    "    if is_numeric_dtype(df[col]):\n",
    "        feats_dict[col] = ContinuousFeature(df, col)\n",
    "    else:\n",
    "        feats_dict[col] = CategoricalFeature(df, col)\n",
    "\n",
    "feats = list(feats_dict.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv = IV()\n",
    "s = StatsSignificance()\n",
    "\n",
    "ar = AttributeRelevance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Information value vs statistical significance\n",
    "\n",
    "Analysis of features with both IV & WOE and Chi-square test & Cramers'V shows some interesting relations between results of those two methods.\n",
    "\n",
    "**P-value vs information value**\n",
    "\n",
    "P-value for almost all featues is very, very small (less than 0.01 which gives us 99% confidence level). Only the differences of distribution for two features (with IV = 0) aren't statistically significant. It leads to the conclusion that for features that were recognized as at least medium predictor (the most interesting from analysis perspective) the differences in distribution of 'goods' and 'bads' is statistically significant. However, it's good to note that low p-value doesn't give information about the strength of relationship.\n",
    "\n",
    "**Information value vs Cramers' V**\n",
    "\n",
    "There is strong, almost linear, relationship between information value and effect size. Features with high information value have high effect size as well. Correlation coefficient for these values is: 0.94 (Pearson) and 0.98 (Spearman).\n",
    "\n",
    "**Interpretation of information value and effect size**\n",
    "\n",
    "Although the interpretation of information value and effect size differs a bit, the rules are very similar: values closer to 0 imply very weak (or lack of) relationship, while higher values suggest stronger relation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>iv</th>\n",
       "      <th>p-value</th>\n",
       "      <th>effect_size</th>\n",
       "      <th>iv_interpretation</th>\n",
       "      <th>es_interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>contract</th>\n",
       "      <td>1.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlineSecurity</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>techSupport</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internetService</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlineBackup</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deviceProtection</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>strong</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paymentMethod</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>strong</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingMovies</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>strong</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingTV</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>strong</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalCharges</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>strong</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paperlessBilling</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>medium</td>\n",
       "      <td>weak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthlyCharges</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>medium</td>\n",
       "      <td>weak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dependents</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>medium</td>\n",
       "      <td>weak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partner</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>medium</td>\n",
       "      <td>weak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seniorCitizen</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>medium</td>\n",
       "      <td>weak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multipleLines</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>useless</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phoneService</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>useless</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.01</td>\n",
       "      <td>useless</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   iv  p-value  effect_size iv_interpretation  \\\n",
       "contract         1.24     0.00         0.41        suspicious   \n",
       "tenure           0.78     0.00         0.37        suspicious   \n",
       "onlineSecurity   0.72     0.00         0.35        suspicious   \n",
       "techSupport      0.70     0.00         0.34        suspicious   \n",
       "internetService  0.62     0.00         0.32        suspicious   \n",
       "onlineBackup     0.53     0.00         0.29        suspicious   \n",
       "deviceProtection 0.50     0.00         0.28            strong   \n",
       "paymentMethod    0.46     0.00         0.30            strong   \n",
       "streamingMovies  0.38     0.00         0.23            strong   \n",
       "streamingTV      0.38     0.00         0.23            strong   \n",
       "totalCharges     0.32     0.00         0.25            strong   \n",
       "paperlessBilling 0.20     0.00         0.19            medium   \n",
       "monthlyCharges   0.17     0.00         0.18            medium   \n",
       "dependents       0.16     0.00         0.16            medium   \n",
       "partner          0.12     0.00         0.15            medium   \n",
       "seniorCitizen    0.11     0.00         0.15            medium   \n",
       "multipleLines    0.01     0.00         0.04           useless   \n",
       "phoneService     0.00     0.34         0.01           useless   \n",
       "gender           0.00     0.49         0.01           useless   \n",
       "\n",
       "                 es_interpretation  \n",
       "contract                    strong  \n",
       "tenure                      medium  \n",
       "onlineSecurity              medium  \n",
       "techSupport                 medium  \n",
       "internetService             medium  \n",
       "onlineBackup                medium  \n",
       "deviceProtection            medium  \n",
       "paymentMethod               medium  \n",
       "streamingMovies             medium  \n",
       "streamingTV                 medium  \n",
       "totalCharges                medium  \n",
       "paperlessBilling              weak  \n",
       "monthlyCharges                weak  \n",
       "dependents                    weak  \n",
       "partner                       weak  \n",
       "seniorCitizen                 weak  \n",
       "multipleLines              useless  \n",
       "phoneService               useless  \n",
       "gender                     useless  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_analysis = ar.analyze(feats, iv, s, interpretation=True)\n",
    "display(df_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation: 0.94\n",
      "Spearman correlation: 0.98\n"
     ]
    }
   ],
   "source": [
    "df_analysis_sign = df_analysis[df_analysis['p-value']<0.05]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "sns.regplot(x='iv', y='effect_size', data=df_analysis_sign, color='#455872')\n",
    "ax.set_title('Information value vs effect size')\n",
    "plt.show()\n",
    "\n",
    "print('Pearson correlation: %0.2f' % df_analysis_sign['iv'].corr(df_analysis_sign['effect_size']))\n",
    "print('Spearman correlation: %0.2f' % df_analysis_sign['iv'].corr(df_analysis_sign['effect_size'], method='spearman'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight of Evidence vs share of 'goods' in total\n",
    "\n",
    "WOE is one of ways to show the proportion of 'good' observations in a group (bin). Another way of doing it is to calculate a share of 'goods' in total. It's not a surprise that results obtained in two ways are very similar. The relation remains the same, it's just presented on a different scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iv.draw_woe(feats_dict['tenure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tenure = iv.group_by_feature(feats_dict['tenure'])\n",
    "df_tenure['total'] = 1\n",
    "df_tenure['good_share'] = df_tenure['good'] / df_tenure['count']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "sns.barplot(x='tenure', y='total', data=df_tenure, color='#a9c6a3')\n",
    "sns.barplot(x='tenure', y='good_share', data=df_tenure, color='#4a6a7c')\n",
    "ax.set_title(\"Share of 'goods' in total\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference\n",
    "\n",
    "1. https://en.wikipedia.org/wiki/Chi-squared_test\n",
    "2. https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V"
   ]
  }
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