Exercise_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:98% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML, Markdown\n",
    "display(HTML(\"<style>.container { width:98% !important; }</style>\"))\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_columns\",999)\n",
    "pd.options.display.max_rows = 9999\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>LogId</th>\n",
       "      <th>EmailSentDateTimeUtc</th>\n",
       "      <th>EmailCampaignId</th>\n",
       "      <th>EmailOpenedDateTimeUtc</th>\n",
       "      <th>EmailClickedDateTimeUtc</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>AddressData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208728980120</td>\n",
       "      <td>2019-07-31 15:13:25.817</td>\n",
       "      <td>792229022220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>851332105693</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>292563123584</td>\n",
       "      <td>2019-08-02 02:15:52.447</td>\n",
       "      <td>866429603262</td>\n",
       "      <td>2019-08-02 15:16:01.7533333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369293054977</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72552047640</td>\n",
       "      <td>2019-08-07 16:21:37.293</td>\n",
       "      <td>383130475294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>938684493058</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1089121462891</td>\n",
       "      <td>2019-08-07 16:38:03.323</td>\n",
       "      <td>792229022220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50233193142</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>828770719378</td>\n",
       "      <td>2019-08-12 11:45:43.293</td>\n",
       "      <td>479810600058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369293054977</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LogId     EmailSentDateTimeUtc  EmailCampaignId  \\\n",
       "0   208728980120  2019-07-31 15:13:25.817     792229022220   \n",
       "1   292563123584  2019-08-02 02:15:52.447     866429603262   \n",
       "2    72552047640  2019-08-07 16:21:37.293     383130475294   \n",
       "3  1089121462891  2019-08-07 16:38:03.323     792229022220   \n",
       "4   828770719378  2019-08-12 11:45:43.293     479810600058   \n",
       "\n",
       "        EmailOpenedDateTimeUtc EmailClickedDateTimeUtc        UserId  ZipCode  \\\n",
       "0                          NaN                     NaN  851332105693      nan   \n",
       "1  2019-08-02 15:16:01.7533333                     NaN  369293054977      nan   \n",
       "2                          NaN                     NaN  938684493058      nan   \n",
       "3                          NaN                     NaN   50233193142      nan   \n",
       "4                          NaN                     NaN  369293054977      nan   \n",
       "\n",
       "  AddressData  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>UserId</th>\n",
       "      <th>DateOfActivityPST</th>\n",
       "      <th>ActivityCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241710509395</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241710509395</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>435228414681</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>435228414681</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>435228414681</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         UserId DateOfActivityPST  ActivityCount\n",
       "0  241710509395        2019-09-24             29\n",
       "1  241710509395        2020-09-11             60\n",
       "2  435228414681        2018-01-06             12\n",
       "3  435228414681        2018-04-20             20\n",
       "4  435228414681        2018-05-10            136"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_emails = pd.read_csv('/Users/jinyaotian/Desktop/Data-Science-Take-Home-Project-main/data/email_data.csv',delimiter=',')\n",
    "display(df_emails.head())\n",
    "\n",
    "df_activity = pd.read_csv('/Users/jinyaotian/Desktop/Data-Science-Take-Home-Project-main/data/activity_data.csv',delimiter=',')\n",
    "display(df_activity.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1. How many rows are in the two datasets? If the log of emails represents 3% of the emails we sent over this period, how many emails did we send? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer: there are 474163 rows in df_emails dataset and 4559973 rows in df_activity dataset.\n"
     ]
    }
   ],
   "source": [
    "print(\"Answer: there are %d rows in df_emails dataset and %d rows in df_activity dataset.\"%(df_emails.shape[0],df_activity.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of emails have been sent over this period is 15805433.\n"
     ]
    }
   ],
   "source": [
    "print('Total number of emails have been sent over this period is %d.'%(df_emails.shape[0]/0.03))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: What are the earliest and most recent dates of emails sent in this dataset in California time (Pacific Standard Time Zone)? (use python to print the date in format mm/dd/yyy hour:minute in 24 hour time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LogId', 'EmailSentDateTimeUtc', 'EmailCampaignId',\n",
       "       'EmailOpenedDateTimeUtc', 'EmailClickedDateTimeUtc', 'UserId',\n",
       "       'ZipCode', 'AddressData'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emails.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogId                        int64\n",
       "EmailSentDateTimeUtc        object\n",
       "EmailCampaignId              int64\n",
       "EmailOpenedDateTimeUtc      object\n",
       "EmailClickedDateTimeUtc     object\n",
       "UserId                       int64\n",
       "ZipCode                    float64\n",
       "AddressData                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emails.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert time zone from Universal time into US Pacific time\n",
    "df_emails['EmailSentDateTimeUtc'] = pd.DatetimeIndex(df_emails['EmailSentDateTimeUtc'],tz = 'utc').tz_convert('US/Pacific')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The earliest date of emails sent in this dataset in California time (Pacific Standard Time Zone) is 1/3/2018 20:29\n"
     ]
    }
   ],
   "source": [
    "# Note that the date and time are in coordinated universal time. \n",
    "# Coordinated Universal Time is 7 hours ahead of Pacific Standard Time\n",
    "t_1 = df_emails['EmailSentDateTimeUtc'].min()\n",
    "print('The earliest date of emails sent in this dataset in California time (Pacific Standard Time Zone) is %d/%d/%d %d:%d' %(t_1.month,t_1.day,t_1.year,t_1.hour,t_1.minute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most recent date of emails sent in this dataset in California time (Pacific Standard Time Zone) is 4/19/2021 6:45\n"
     ]
    }
   ],
   "source": [
    "t_2 = df_emails['EmailSentDateTimeUtc'].max()\n",
    "print('The most recent date of emails sent in this dataset in California time (Pacific Standard Time Zone) is %d/%d/%d %d:%d' %(t_2.month,t_2.day,t_2.year,t_2.hour,t_2.minute))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: Which 10 zip codes receive the most emails and how many emails do they receive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group1 = df_emails.groupby(['ZipCode']).count()['UserId'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = df_group1.index[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ZipCode\n",
       "63301.000    312\n",
       "33801.000    310\n",
       "34210.000    299\n",
       "38301.000    295\n",
       "67460.000    285\n",
       "95971.000    277\n",
       "42301.000    266\n",
       "36104.000    254\n",
       "40769.000    234\n",
       "85225.000    229\n",
       "Name: UserId, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 zip codes for the most emails received\n",
    "df_group1.loc[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4: Plot two lines on a single graph. \n",
    "#### 1) total distinct users who were sent at least 1 email by month and year.\n",
    "#### 2) the monthly percentage of users who were sent an email and were active the same day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column with month of date field 'EmailSentDateTimeUtc'\n",
    "df_emails['month'] = pd.DatetimeIndex(df_emails['EmailSentDateTimeUtc']).month\n",
    "\n",
    "# create a new column with Year of date field 'EmailSentDateTimeUtc'\n",
    "df_emails['year'] = pd.DatetimeIndex(df_emails['EmailSentDateTimeUtc']).year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count the total distinct users who were sent at least 1 email by month and year.\n",
    "grouped = df_emails.groupby(['year','month']).agg({'UserId':['nunique']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) \n",
    "# list_1 to store total distint users who were sent at least 1 by month and by year\n",
    "list_1 = []\n",
    "year = [2018,2019,2020,2021]\n",
    "for y in year:\n",
    "    if y != 2021: \n",
    "        for i in range(1,13):\n",
    "            list_1.append(grouped.loc[(y,i)][0])\n",
    "    else: \n",
    "        for i in range(1,5):\n",
    "            list_1.append(grouped.loc[(y,i)][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2)\n",
    "#Convert timestamp back to string, in the form 'xxxx/xx/xx'.\n",
    "df_emails['EmailSentDateTimeUtc'] = df_emails['EmailSentDateTimeUtc'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# left join two data frame\n",
    "df_merge = df_emails.merge(df_activity[['UserId','DateOfActivityPST']],on = 'UserId', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge['Same day'] = np.zeros(len(df_merge))\n",
    "for i in range(len(df_merge)):\n",
    "    if df_merge.loc[i,'EmailSentDateTimeUtc'] == df_merge.loc[i,'DateOfActivityPST']:\n",
    "        df_merge.loc[i,'Same day'] = 1\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract those rows where users who were sent an email and were active the same day\n",
    "df_sameday = df_merge.loc[df_merge['Same day'] == 1]\n",
    "\n",
    "grouped_2 = df_sameday.groupby(['year','month']).agg({'UserId':['nunique']})\n",
    "list_2 = []\n",
    "year = [2018,2019,2020,2021]\n",
    "for y in year:\n",
    "    if y != 2021: \n",
    "        for i in range(1,13):\n",
    "            list_2.append(grouped_2.loc[(y,i)][0])\n",
    "    else: \n",
    "        for i in range(1,5):\n",
    "            list_2.append(grouped_2.loc[(y,i)][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list_perc : the monthly percentage of users who were sent an email and were active the same day \n",
    "list_perc = []\n",
    "for i in range(len(list_1)):\n",
    "    list_perc.append(100*list_2[i]/list_1[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot two lines on a single graph\n",
    "plt.plot(list_1,'r-')\n",
    "plt.plot(list_perc,'b--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that it is meaningless to draw the two lines on a single praph, because their values differ too large."
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}