17. Engagement Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>user_id</th>\n",
       "      <th>signup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id signup_date\n",
       "0       34  2015-01-01\n",
       "1       59  2015-01-01\n",
       "2      178  2015-01-01\n",
       "3      285  2015-01-01\n",
       "4      383  2015-01-01"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = pd.read_csv('./data/Engagement_Test/user_table.csv', parse_dates=['signup_date'])\n",
    "user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 2 columns):\n",
      "user_id        100000 non-null int64\n",
      "signup_date    100000 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(1)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "user.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>browser</th>\n",
       "      <th>test</th>\n",
       "      <th>pages_visited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600597</td>\n",
       "      <td>2015-08-13</td>\n",
       "      <td>IE</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4410028</td>\n",
       "      <td>2015-08-26</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6004777</td>\n",
       "      <td>2015-08-17</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5990330</td>\n",
       "      <td>2015-08-27</td>\n",
       "      <td>Safari</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3622310</td>\n",
       "      <td>2015-08-07</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id       date  browser  test  pages_visited\n",
       "0   600597 2015-08-13       IE     0              2\n",
       "1  4410028 2015-08-26   Chrome     1              5\n",
       "2  6004777 2015-08-17   Chrome     0              8\n",
       "3  5990330 2015-08-27   Safari     0              8\n",
       "4  3622310 2015-08-07  Firefox     0              1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('./data/Engagement_Test/test_table.csv', parse_dates=['date'])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      "user_id          100000 non-null int64\n",
      "date             100000 non-null datetime64[ns]\n",
      "browser          100000 non-null object\n",
      "test             100000 non-null int64\n",
      "pages_visited    100000 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>user_id</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>date</th>\n",
       "      <th>browser</th>\n",
       "      <th>test</th>\n",
       "      <th>pages_visited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-15</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-12</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-10</td>\n",
       "      <td>Safari</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-03</td>\n",
       "      <td>Opera</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-05</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id signup_date       date  browser  test  pages_visited\n",
       "0       34  2015-01-01 2015-08-15   Chrome     0              6\n",
       "1       59  2015-01-01 2015-08-12   Chrome     1              6\n",
       "2      178  2015-01-01 2015-08-10   Safari     1              3\n",
       "3      285  2015-01-01 2015-08-03    Opera     0              5\n",
       "4      383  2015-01-01 2015-08-05  Firefox     1              9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join user and test table\n",
    "data = pd.merge(left=user, right=test, on='user_id', how='left')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100000 entries, 0 to 99999\n",
      "Data columns (total 6 columns):\n",
      "user_id          100000 non-null int64\n",
      "signup_date      100000 non-null datetime64[ns]\n",
      "date             100000 non-null datetime64[ns]\n",
      "browser          100000 non-null object\n",
      "test             100000 non-null int64\n",
      "pages_visited    100000 non-null int64\n",
      "dtypes: datetime64[ns](2), int64(3), object(1)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Is the test winning? That is, should 100% of the users see the Recommended Friends feature?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test\n",
       "0    4.608394\n",
       "1    4.599693\n",
       "Name: pages_visited, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('test')['pages_visited'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.barplot(x='test', y='pages_visited', data=data, ax=ax)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-test:\t Ttest_indResult(statistic=0.5571118435554797, pvalue=0.5774523171559118)\n"
     ]
    }
   ],
   "source": [
    "# perform student's t-test\n",
    "control_page = data[data['test'] == 0]['pages_visited'].values\n",
    "experiment_page = data[data['test'] == 1]['pages_visited'].values\n",
    "\n",
    "print('T-test:\\t', ttest_ind(a=control_page, b=experiment_page, equal_var=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above analysis, we can find that p-value is $0.577$, which is much larger than $5\\%$, so we cannot reject the null hypothesis that there is no difference between control and experiment group. \n",
    "\n",
    "So, the test doesn't win at tall. This shouldn't expand to all users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "Is the test performing similarly for all user segments or are there differences among different segments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>user_id</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>date</th>\n",
       "      <th>browser</th>\n",
       "      <th>test</th>\n",
       "      <th>pages_visited</th>\n",
       "      <th>interval</th>\n",
       "      <th>is_first</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-15</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-12</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>178</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-10</td>\n",
       "      <td>Safari</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-03</td>\n",
       "      <td>Opera</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-08-05</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id signup_date       date  browser  test  pages_visited  interval  \\\n",
       "0       34  2015-01-01 2015-08-15   Chrome     0              6       226   \n",
       "1       59  2015-01-01 2015-08-12   Chrome     1              6       223   \n",
       "2      178  2015-01-01 2015-08-10   Safari     1              3       221   \n",
       "3      285  2015-01-01 2015-08-03    Opera     0              5       214   \n",
       "4      383  2015-01-01 2015-08-05  Firefox     1              9       216   \n",
       "\n",
       "   is_first  dayofweek  \n",
       "0         0          5  \n",
       "1         0          2  \n",
       "2         0          0  \n",
       "3         0          0  \n",
       "4         0          2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# process the data\n",
    "data['interval'] = (data['date'] - data['signup_date']).apply(lambda x: x.days)\n",
    "data['is_first'] = (data['interval'] == 0).astype(int)\n",
    "data['dayofweek'] = data['date'].apply(lambda x: x.dayofweek)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of browser\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='browser', hue='test', data=data, ax=ax[0])\n",
    "ax[0].set_title('Box Plot of browser', fontsize=16)\n",
    "\n",
    "sns.barplot(x='browser', y='pages_visited', hue='test', data=data, ax=ax[1])\n",
    "ax[1].set_title('Pages Visited vs. browser', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of is_first\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='is_first', hue='test', data=data, ax=ax[0])\n",
    "ax[0].set_title('Box Plot of is_first', fontsize=16)\n",
    "\n",
    "sns.barplot(x='is_first', y='pages_visited', hue='test', data=data, ax=ax[1])\n",
    "ax[1].set_title('Pages Visited vs. is_first', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of dayofweek\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='dayofweek', hue='test', data=data, ax=ax[0])\n",
    "ax[0].set_title('Box Plot of dayofweek', fontsize=16)\n",
    "\n",
    "sns.barplot(x='dayofweek', y='pages_visited', hue='test', data=data, ax=ax[1])\n",
    "ax[1].set_title('Pages Visited vs. dayofweek', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "If you identified segments that responded differently to the test, can you guess the reason? Would this change your point 1 conclusions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visualization in above part, we can see that the number of pages visited is increased for some factors, such as `Chrome`, `Safari`, `Firefox`, and `IE`, but the number of pages visited is always 0 for `Opera`, which shows that there might be some errors or bugs.\n",
    "\n",
    "If user visit the website right after they signup, the number of pages visited actually get decreased when they are exposed to the new feature.\n",
    "\n",
    "Also, there are some small difference for `day of week`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's remove the user who use `Opera`, and conduct t-test again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test\n",
       "0    4.609804\n",
       "1    4.694989\n",
       "Name: pages_visited, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = data[data['browser'] != 'Opera']\n",
    "subset.groupby('test')['pages_visited'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.barplot(x='test', y='pages_visited', data=subset, ax=ax)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-test:\t Ttest_indResult(statistic=-5.474295518566027, pvalue=4.403954129457701e-08)\n"
     ]
    }
   ],
   "source": [
    "# perform student's t-test\n",
    "control_page = subset[subset['test'] == 0]['pages_visited'].values\n",
    "experiment_page = subset[subset['test'] == 1]['pages_visited'].values\n",
    "\n",
    "print('T-test:\\t', ttest_ind(a=control_page, b=experiment_page, equal_var=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it's clear that after removing the records with `Opera` browser, the new feature becomes significantly useful. So, I would suggest expanding this feature to other users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also another solution that have some other good analysis: [Reference Link](https://github.com/stasi009/TakeHomeDataChallenges/blob/master/17.EngagementTest/test_engagement.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of is_first\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='is_first', hue='test', data=subset, ax=ax[0])\n",
    "ax[0].set_title('Box Plot of is_first', fontsize=16)\n",
    "\n",
    "sns.barplot(x='is_first', y='pages_visited', hue='test', data=subset, ax=ax[1])\n",
    "ax[1].set_title('Pages Visited vs. is_first', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}