Capstone part 1 quiz answers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_data(data_dir):\n",
    "    \"\"\"\n",
    "    laod all json formatted files into a dataframe\n",
    "    \"\"\"\n",
    "\n",
    "    ## input testing\n",
    "    if not os.path.isdir(data_dir):\n",
    "        raise Exception(\"specified data dir does not exist\")\n",
    "    if not len(os.listdir(data_dir)) > 0:\n",
    "        raise Exception(\"specified data dir does not contain any files\")\n",
    "\n",
    "    file_list = [os.path.join(data_dir,f) for f in os.listdir(data_dir) if re.search(\"\\.json\",f)]\n",
    "    correct_columns = ['country', 'customer_id', 'day', 'invoice', 'month',\n",
    "                       'price', 'stream_id', 'times_viewed', 'year']\n",
    "\n",
    "    ## read data into a temp structure\n",
    "    all_months = {}\n",
    "    for file_name in file_list:\n",
    "        df = pd.read_json(file_name)\n",
    "        all_months[os.path.split(file_name)[-1]] = df\n",
    "\n",
    "    ## ensure the data are formatted with correct columns\n",
    "    for f,df in all_months.items():\n",
    "        cols = set(df.columns.tolist())\n",
    "        if 'StreamID' in cols:\n",
    "             df.rename(columns={'StreamID':'stream_id'},inplace=True)\n",
    "        if 'TimesViewed' in cols:\n",
    "            df.rename(columns={'TimesViewed':'times_viewed'},inplace=True)\n",
    "        if 'total_price' in cols:\n",
    "            df.rename(columns={'total_price':'price'},inplace=True)\n",
    "\n",
    "        cols = df.columns.tolist()\n",
    "        if sorted(cols) != correct_columns:\n",
    "            raise Exception(\"columns name could not be matched to correct cols\")\n",
    "\n",
    "    ## concat all of the data\n",
    "    df = pd.concat(list(all_months.values()),sort=True)\n",
    "    years,months,days = df['year'].values,df['month'].values,df['day'].values \n",
    "    dates = [\"{}-{}-{}\".format(years[i],str(months[i]).zfill(2),str(days[i]).zfill(2)) for i in range(df.shape[0])]\n",
    "    df['invoice_date'] = np.array(dates,dtype='datetime64[D]')\n",
    "    df['invoice'] = [re.sub(\"\\D+\",\"\",i) for i in df['invoice'].values]\n",
    "    \n",
    "    ## sort by date and reset the index\n",
    "    df.sort_values(by='invoice_date',inplace=True)\n",
    "    df.reset_index(drop=True,inplace=True)\n",
    "    \n",
    "    return(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import re\n",
    "import shutil\n",
    "import time\n",
    "import pickle\n",
    "from collections import defaultdict\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "COLORS = [\"darkorange\",\"royalblue\",\"slategrey\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "\n",
    "SMALL_SIZE = 12\n",
    "MEDIUM_SIZE = 14\n",
    "LARGE_SIZE = 16\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=LARGE_SIZE)   # fontsize of the figure title\n",
    "\n",
    "## specify the directory you saved the data and images in\n",
    "DATA_DIR = os.path.join(\"..\",\"data\")\n",
    "IMAGE_DIR = os.path.join(\"..\",\"images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_ts(df_orig, country=None):\n",
    "    \"\"\"\n",
    "    given the original DataFrame (fetch_data())\n",
    "    return a numerically indexed time-series DataFrame \n",
    "    by aggregating over each day\n",
    "    \"\"\"\n",
    "\n",
    "    if country:\n",
    "        if country not in np.unique(df_orig['country'].values):\n",
    "            raise Excpetion(\"country not found\")\n",
    "    \n",
    "        mask = df_orig['country'] == country\n",
    "        df = df_orig[mask]\n",
    "    else:\n",
    "        df = df_orig\n",
    "        \n",
    "    ## use a date range to ensure all days are accounted for in the data\n",
    "    invoice_dates = df['invoice_date'].values\n",
    "    start_month = '{}-{}'.format(df['year'].values[0],str(df['month'].values[0]).zfill(2))\n",
    "    stop_month = '{}-{}'.format(df['year'].values[-1],str(df['month'].values[-1]).zfill(2))\n",
    "    df_dates = df['invoice_date'].values.astype('datetime64[D]')\n",
    "    days = np.arange(start_month,stop_month,dtype='datetime64[D]')\n",
    "    \n",
    "    purchases = np.array([np.where(df_dates==day)[0].size for day in days])\n",
    "    invoices = [np.unique(df[df_dates==day]['invoice'].values).size for day in days]\n",
    "    streams = [np.unique(df[df_dates==day]['stream_id'].values).size for day in days]\n",
    "    views =  [df[df_dates==day]['times_viewed'].values.sum() for day in days]\n",
    "    revenue = [df[df_dates==day]['price'].values.sum() for day in days]\n",
    "    year_month = [\"-\".join(re.split(\"-\",str(day))[:2]) for day in days]\n",
    "\n",
    "    df_time = pd.DataFrame({'date':days,\n",
    "                            'purchases':purchases,\n",
    "                            'unique_invoices':invoices,\n",
    "                            'unique_streams':streams,\n",
    "                            'total_views':views,\n",
    "                            'year_month':year_month,\n",
    "                            'revenue':revenue})\n",
    "    return(df_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fetch_data(\"cs-train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "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>country</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>day</th>\n",
       "      <th>invoice</th>\n",
       "      <th>month</th>\n",
       "      <th>price</th>\n",
       "      <th>stream_id</th>\n",
       "      <th>times_viewed</th>\n",
       "      <th>year</th>\n",
       "      <th>invoice_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17998.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489562</td>\n",
       "      <td>11</td>\n",
       "      <td>5.95</td>\n",
       "      <td>84656</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>6.75</td>\n",
       "      <td>79323W</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>2.10</td>\n",
       "      <td>22041</td>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>1.25</td>\n",
       "      <td>21232</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>1.65</td>\n",
       "      <td>22064</td>\n",
       "      <td>17</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  customer_id  day invoice  month  price stream_id  \\\n",
       "0  United Kingdom      17998.0   28  489562     11   5.95     84656   \n",
       "1  United Kingdom      13085.0   28  489434     11   6.75    79323W   \n",
       "2  United Kingdom      13085.0   28  489434     11   2.10     22041   \n",
       "3  United Kingdom      13085.0   28  489434     11   1.25     21232   \n",
       "4  United Kingdom      13085.0   28  489434     11   1.65     22064   \n",
       "\n",
       "   times_viewed  year invoice_date  \n",
       "0             1  2017   2017-11-28  \n",
       "1            12  2017   2017-11-28  \n",
       "2            21  2017   2017-11-28  \n",
       "3             5  2017   2017-11-28  \n",
       "4            17  2017   2017-11-28  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index = convert_to_ts(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>date</th>\n",
       "      <th>purchases</th>\n",
       "      <th>unique_invoices</th>\n",
       "      <th>unique_streams</th>\n",
       "      <th>total_views</th>\n",
       "      <th>year_month</th>\n",
       "      <th>revenue</th>\n",
       "      <th>diff_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1358</td>\n",
       "      <td>67</td>\n",
       "      <td>999</td>\n",
       "      <td>6420</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>4903.17</td>\n",
       "      <td>606 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1620</td>\n",
       "      <td>80</td>\n",
       "      <td>944</td>\n",
       "      <td>9435</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>5499.38</td>\n",
       "      <td>606 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1027</td>\n",
       "      <td>70</td>\n",
       "      <td>607</td>\n",
       "      <td>5539</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>3570.60</td>\n",
       "      <td>606 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>606 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>602</td>\n",
       "      <td>27</td>\n",
       "      <td>423</td>\n",
       "      <td>2534</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>1793.98</td>\n",
       "      <td>606 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  purchases  unique_invoices  unique_streams  total_views  \\\n",
       "602 2019-06-26       1358               67             999         6420   \n",
       "603 2019-06-27       1620               80             944         9435   \n",
       "604 2019-06-28       1027               70             607         5539   \n",
       "605 2019-06-29          0                0               0            0   \n",
       "606 2019-06-30        602               27             423         2534   \n",
       "\n",
       "    year_month  revenue diff_days  \n",
       "602    2019-06  4903.17  606 days  \n",
       "603    2019-06  5499.38  606 days  \n",
       "604    2019-06  3570.60  606 days  \n",
       "605    2019-06     0.00  606 days  \n",
       "606    2019-06  1793.98  606 days  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>3521514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <td>107069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>49272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>40565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>38495.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    price\n",
       "country                  \n",
       "United Kingdom  3521514.0\n",
       "EIRE             107069.0\n",
       "Germany           49272.0\n",
       "France            40565.0\n",
       "Norway            38495.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate most revenue, answer is UK.\n",
    "columns_to_show = [\"price\"]\n",
    "pd.pivot_table(df, index= 'country', values=columns_to_show,aggfunc='sum').round(0)\\\n",
    "        .sort_values(by='price', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "606 days 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# The number of days between first and last invoice. = 606, so answer is 600\n",
    "print (df_index['date'][606] - df_index['date'][0])\n",
    "\n",
    "#datemin = np.datetime64(df_index['date'][0])\n",
    "#datemax = np.datetime64(df_index['date'][606])\n",
    "#print (datemax)\n",
    "#datemax = np.datetime64(data['date'][-1], 'Y') + np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count unique days, answer to question 2: \n",
    "df['invoice_date'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer part 2: in Nov-Dec meest views, purchases en unique_streams\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.cbook as cbook\n",
    "\n",
    "years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "years_fmt = mdates.DateFormatter('%Y')\n",
    "months_fmt = mdates.DateFormatter('%m')\n",
    "\n",
    "# Load a numpy structured array from yahoo csv data with fields date, open,\n",
    "# close, volume, adj_close from the mpl-data/example directory.  This array\n",
    "# stores the date as an np.datetime64 with a day unit ('D') in the 'date'\n",
    "# column.\n",
    "#with cbook.get_sample_data('goog.npz') as datafile:\n",
    "#    data = np.load(datafile)['price_data']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,3))\n",
    "ax.plot('date', 'unique_streams', data=df_index)\n",
    "\n",
    "# format the ticks\n",
    "ax.xaxis.set_major_locator(months)\n",
    "ax.xaxis.set_major_formatter(months_fmt)\n",
    "ax.xaxis.set_minor_locator(years)\n",
    "\n",
    "# round to nearest years.\n",
    "datemin = np.datetime64(df_index['date'][0])\n",
    "datemax = np.datetime64(df_index['date'][606])\n",
    "#datemin = np.datetime64(data['date'][0], 'Y')\n",
    "#datemax = np.datetime64(data['date'][-1], 'Y') + np.timedelta64(1, 'Y')\n",
    "ax.set_xlim(datemin, datemax)\n",
    "\n",
    "# format the coords message box\n",
    "ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "#ax.format_xdata = mdates.DateFormatter('%m')\n",
    "ax.format_ydata = lambda x: '$%1.2f' % x  # format the price.\n",
    "ax.grid(True)\n",
    "\n",
    "# rotates and right aligns the x labels, and moves the bottom of the\n",
    "# axes up to make room for them\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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