ADM-HW-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW2 - eCommerce analytics\n",
    "#### *Elisa Valeriani & Iyuele A. Korsaye* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries\n",
    "First of all, we need to import the libraries we'll need to solve our exercises, like pandas to work with dataframes and matplotlib to plot graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import dask\n",
    "import dask.dataframe as dd\n",
    "import dask.array as da\n",
    "from dask.distributed import Client, progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start Dask Client for Dashboard\n",
    "- The Dask Client will provide a dashboard which is useful to gain insight on the computation going on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:57331</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>8.46 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:57331' processes=4 threads=8, memory=8.46 GB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client()  \n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Dask DataFrame        \n",
    "- Read all the csv dataframes as one logical dataframe using the dd.read_csv function     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dd.read_csv('2019-*.csv', parse_dates=['event_time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write to Parquet\n",
    "- a format that is more efficient for computers to read and write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_parquet('2019.parquet', engine='pyarrow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read from Parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dd.read_parquet('2019.parquet', engine='pyarrow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ1.1*\n",
    "#### *What's the operation users repeat more on average within a session? Produce a plot that shows the average number of times users perform each operation (view/removefromchart etc etc).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ex 1.1\n",
    "\n",
    "#initialize 3 empty dataframes\n",
    "v_view = pd.DataFrame()\n",
    "v_cart = pd.DataFrame()\n",
    "v_purchase = pd.DataFrame()\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['user_session', 'event_type'], nrows =100000, chunksize=1000, iterator = True):\n",
    "\n",
    "#...count the number of times per session a product was seen...\n",
    "        views = pd.DataFrame(chunck[chunck.event_type == 'view'].groupby(['user_session']).event_type.count())\n",
    "        v_view = v_view.append(views)\n",
    "        \n",
    "#...put in the cart...\n",
    "        carts = pd.DataFrame(chunck[chunck.event_type == 'cart'].groupby(['user_session']).event_type.count())\n",
    "        v_cart = v_cart.append(carts)\n",
    "        \n",
    "#...bought...\n",
    "        purchases = pd.DataFrame(chunck[chunck.event_type == 'purchase'].groupby(['user_session']).event_type.count())\n",
    "        v_purchase = v_purchase.append(purchases)\n",
    "        \n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['user_session', 'event_type'], nrows =100000, chunksize=1000, iterator = True):\n",
    "    \n",
    "        views = pd.DataFrame(chunck[chunck.event_type == 'view'].groupby(['user_session']).event_type.count())\n",
    "        v_view = v_view.append(views)\n",
    "        carts = pd.DataFrame(chunck[chunck.event_type == 'cart'].groupby(['user_session']).event_type.count())\n",
    "        v_cart = v_cart.append(carts)\n",
    "        purchases = pd.DataFrame(chunck[chunck.event_type == 'purchase'].groupby(['user_session']).event_type.count())\n",
    "        v_purchase = v_purchase.append(purchases)\n",
    "        \n",
    "#repeat the same process for the dataframes filled with information\n",
    "v_view = v_view.groupby('user_session').event_type.count().mean()\n",
    "v_cart = v_cart.groupby('user_session').event_type.count().mean()\n",
    "v_purchase = v_purchase.groupby('user_session').event_type.count().mean()\n",
    "\n",
    "#plot a pie plot (suitable because we have just a few numeric quantities to show)\n",
    "my_labels = (\"view\", \"cart\", \"purchase\")\n",
    "my_colors = (\"pink\", \"lightgreen\", \"lightblue\")\n",
    "plt.pie([v_view, v_cart, v_purchase], labels = my_labels, autopct = \"%.2f%%\", colors= my_colors, shadow = True)\n",
    "plt.title(\"Percentages of the average number of times users perform each operation\", fontsize = 20)\n",
    "plt.legend(bbox_to_anchor=(0,.7))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "From this pie plot we can see that the most repeated operation within a session is 'view' (almost half of the times), while cart and purchase follow (togheter these operations are repeated more than half of the times) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ1.2*\n",
    "#### *How many times, on average, a user views a product before adding it to the cart?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, a user views a product before adding it to the cart 0 times\n"
     ]
    }
   ],
   "source": [
    "#Ex 1.2\n",
    "\n",
    "#initialize 2 empty numeric variables\n",
    "my_sum = 0\n",
    "i = 0\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['user_id', 'product_id', 'event_type'], nrows =100000, chunksize=1000, iterator = True):\n",
    "\n",
    "#...for each couple (user, product) where the user put the product in the cart...\n",
    "    for e, frame in chunck[chunck.event_type == 'cart'].groupby(['user_id', 'product_id']):\n",
    "\n",
    "#...increase my_sum of one and i of the number of times THE user has seen THE product\n",
    "        my_sum += 1\n",
    "        i += chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'views')].shape[0]\n",
    "\n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['user_id', 'product_id', 'event_type'], nrows =100000, chunksize=1000, iterator = True):\n",
    "    \n",
    "    for e, frame in chunck[chunck.event_type == 'cart'].groupby(['user_id', 'product_id']):\n",
    "        \n",
    "        my_sum += 1\n",
    "        i += chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'views')].shape[0]\n",
    "\n",
    "#if at least a user put a product in the cart...\n",
    "if my_sum != 0:\n",
    "    \n",
    "    res = int(i/my_sum) \n",
    "    print(\"On average, a user views a product before adding it to the cart \"+ str(res) +\" times\")\n",
    "    \n",
    "#...else...\n",
    "else:\n",
    "    print(\"Not even one user has added a product to the cart\")\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "This result shows that, on average, the user never looks at the product before putting it in the cart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ1.3*\n",
    "#### *What's the probability that products added once to the cart are effectively bought?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that a user boughts a product after adding it to the cart is 18.0%\n"
     ]
    }
   ],
   "source": [
    "#Ex 1.3\n",
    "\n",
    "#initialize 2 empty numeric variables\n",
    "my_sum = 0\n",
    "i = 0\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['user_id', 'product_id', 'event_type'], nrows =100000, chunksize=1000, iterator = True):\n",
    "    \n",
    "#...for each couple (user, product) where the user put the product in the cart...\n",
    "    for e, frame in chunck[chunck.event_type == 'cart'].groupby(['user_id', 'product_id']):\n",
    "        \n",
    "#...if THE user put THE product in the cart just once...\n",
    "        if len(chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'cart')]) == 1: \n",
    "        \n",
    "#...increase my_sum of one and i of the number of times the user bought the product\n",
    "                my_sum += 1\n",
    "                i += chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'purchase')].shape[0]\n",
    "                \n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['user_id', 'product_id', 'event_type'], nrows =100000, chunksize=1000, iterator = True):\n",
    "    \n",
    "    for e, frame in chunck[chunck.event_type == 'cart'].groupby(['user_id', 'product_id']):\n",
    "       \n",
    "        if len(chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'cart')]) == 1: \n",
    "                \n",
    "                my_sum += 1\n",
    "                i += chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'purchase')].shape[0]\n",
    "\n",
    "#if at least a user put a product in the cart just once...  \n",
    "if my_sum != 0:  \n",
    "    \n",
    "    res = round(i/my_sum, 2) \n",
    "    print(\"The probability that a user boughts a product after adding it to the cart is \"+ str(res*100) +\"%\")\n",
    "    \n",
    "#...else...\n",
    "else:\n",
    "    print(\"Not even one user has added a product to the cart\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "This result shows that, on average, the user tends to buy the product directly or put it in the cart several times, while only 18 times on 100 he/she tends to put it in the cart and then buy it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ 1.4*\n",
    "#### *What's the average time an item stays in the cart before being removed?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, an item stays in the cart before being removed 2 second(s)\n"
     ]
    }
   ],
   "source": [
    "#Ex 1.4 \n",
    "\n",
    "#In order to solve this exercise, we consider the event 'remove a product from the cart' as the event 'buy a product after putting it in the cart'\n",
    "\n",
    "#initialize 2 empty numeric variables\n",
    "my_sum = 0\n",
    "time = 0\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['user_id', 'product_id', 'event_type', 'event_time'], nrows =100000, chunksize=1000, iterator = True, parse_dates = ['event_time'], date_parser = pd.to_datetime):\n",
    "\n",
    "#...for each couple (user, product) where the user bought the product...\n",
    "    for e, frame in chunck[chunck.event_type == 'purchase'].groupby(['user_id', 'product_id']):\n",
    "    \n",
    "#...increase my_sum of one and time of the seconds passed between the events 'the user put the product in the cart for the 1st time' and 'the user bought the product'\n",
    "        my_chunck = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1])]\n",
    "        chunck_cart = my_chunck[my_chunck.event_type == 'cart']\n",
    "        if len(chunck_cart) != 0:\n",
    "            \n",
    "            first_cart = min(chunck_cart.event_time).second\n",
    "            first_purchase = min(my_chunck[my_chunck.event_type == 'purchase'].event_time).second\n",
    "            if first_purchase >= first_cart:\n",
    "                time += first_purchase - first_cart\n",
    "                my_sum += 1\n",
    "\n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['user_id', 'product_id', 'event_type', 'event_time'], nrows =100000, chunksize=1000, iterator = True, parse_dates = ['event_time'], date_parser = pd.to_datetime):\n",
    "\n",
    "    for e, frame in chunck[chunck.event_type == 'purchase'].groupby(['user_id', 'product_id']):\n",
    "    \n",
    "        my_sum += 1\n",
    "        my_chunck = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1])]\n",
    "        chunck_cart = my_chunck[my_chunck.event_type == 'cart']\n",
    "        if len(chunck_cart) != 0:\n",
    "            \n",
    "            first_cart = min(chunck_cart.event_time).second\n",
    "            first_purchase = min(my_chunck[my_chunck.event_type == 'purchase'].event_time).second\n",
    "            if first_purchase >= first_cart:\n",
    "                time += first_purchase - first_cart\n",
    "                my_sum += 1\n",
    "        \n",
    "#if at least a user bought a product...\n",
    "if my_sum != 0:\n",
    "    \n",
    "    res = int(time/my_sum)\n",
    "    print(\"On average, an item stays in the cart before being removed \"+ str(res) +\" second(s)\")\n",
    "\n",
    "#...else...\n",
    "else:\n",
    "    \n",
    "    print(\"Not even one user has removed a product from the cart\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "This result shows that, on average, an item stays in the cart for just 2 seconds, and then it is almost immediately bought"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ 1.5*\n",
    "#### *How much time passes on average between the first view time and a purchase/addition to cart?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ex 1.5 \n",
    "\n",
    "#initialize 3 empty numeric variables\n",
    "my_sum = 0\n",
    "my_min = 0\n",
    "time = 0\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['user_id', 'product_id', 'event_type', 'event_time'], nrows = 100000, chunksize=1000, iterator = True, parse_dates = ['event_time'], date_parser = pd.to_datetime):\n",
    "\n",
    "#...for each couple (user, product) where the user put the product in the cart or bought it...\n",
    "    for e, frame in chunck[(chunck.event_type == 'cart') | (chunck.event_type == 'purchase')].groupby(['user_id', 'product_id']):\n",
    "\n",
    "#...if the user saw the product at least once...\n",
    "        my_chunck = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'view')]\n",
    "        if len(my_chunck) != 0:\n",
    "            \n",
    "            first_view = min(my_chunck.event_time).second\n",
    "            chunck_cart = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'cart')]\n",
    "            \n",
    "#...and if the user put the product in the cart at least once...            \n",
    "            if len(chunck_cart) != 0:\n",
    "                \n",
    "                first_cart = min(chunck_cart.event_time).second\n",
    "                if  first_view < first_cart:\n",
    "                    \n",
    "#...set my_min equals to the seconds at which the user put the product in the cart for the 1st time\n",
    "                    my_min = first_cart\n",
    "\n",
    "#...if the user bought the product at least once...\n",
    "            chunck_purchase = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'purchase')]\n",
    "            if len(chunck_purchase) != 0:\n",
    "                \n",
    "                first_purchase = min(chunck_purchase.event_time).second\n",
    "                if first_view < first_purchase:\n",
    "                    \n",
    "                    my_min1 = first_purchase\n",
    "                    \n",
    "#and if the user didn't put the product in the cart...\n",
    "                    if len(chunck_cart) == 0:\n",
    "\n",
    "#...set my_min equals to the seconds at which the user bought the product for the 1st time \n",
    "                        my_min = my_min1\n",
    "                    \n",
    "                    else:\n",
    "                        if my_min1 < my_min:\n",
    "                            my_min = my_min1\n",
    "            \n",
    "#incresase my_sum of one and time of the seconds at which the user put in the cart/bought the product for the 1st time\n",
    "            time += my_min - min(my_chunck.event_time).second\n",
    "            my_sum += 1\n",
    "        \n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['user_id', 'product_id', 'event_type', 'event_time'], nrows =10000, chunksize=1000, iterator = True, parse_dates = ['event_time'], date_parser = pd.to_datetime):\n",
    "\n",
    "        for e, frame in chunck[(chunck.event_type == 'cart') | (chunck.event_type == 'purchase')].groupby(['user_id', 'product_id']):\n",
    "            \n",
    "            my_chunck = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'view')]\n",
    "\n",
    "            if len(my_chunck) != 0:\n",
    "\n",
    "                first_view = min(my_chunck.event_time).second\n",
    "                chunck_cart = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'cart')]\n",
    "\n",
    "\n",
    "                if len(chunck_cart) != 0:\n",
    "                    \n",
    "                    first_cart = min(chunck_cart.event_time).second\n",
    "                    if  first_view < first_cart:\n",
    "                        \n",
    "                        my_min = first_cart\n",
    "\n",
    "                chunck_purchase = chunck[(chunck.user_id == e[0]) & (chunck.product_id == e[1]) & (chunck.event_type == 'purchase')]\n",
    "\n",
    "                if len(chunck_purchase) != 0:\n",
    "\n",
    "                    first_purchase = min(chunck_purchase.event_time).second\n",
    "                    if first_view < first_purchase:\n",
    "\n",
    "                        my_min1 = first_purchase\n",
    "\n",
    "                        if len(chunck_cart) == 0:\n",
    "\n",
    "                            my_min = my_min1\n",
    "\n",
    "                        else:\n",
    "                            if my_min1 < my_min:\n",
    "                                my_min = my_min1\n",
    "\n",
    "                time += my_min - min(my_chunck.event_time).second\n",
    "                my_sum += 1\n",
    "        \n",
    "#if at least a user purchased/added to the cart a product...\n",
    "if my_sum != 0:\n",
    "    \n",
    "    res = int(time/my_sum)\n",
    "    print(\"On average, \"+ str(res) +\" second(s) pass between the first view and a purchase/addition to cart\")\n",
    "\n",
    "#...else...\n",
    "else:\n",
    "    \n",
    "    print(\"Not even a user has added a product to the cart\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "This result shows that, on average, an item is bought or put in the cart just 9 seconds after having seen it for the first time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.  What are the categories of the most trending products overall? For each month visualize this information through a plot showing the number of sold products per category.\n",
    "    2.1 Plot the most visited subcategories\n",
    "    2.2 What are the 10 most sold products per category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df[['event_time', 'event_type', 'category_code']]  # Select only the columns that we plan to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's start finding the most trending products per category:\n",
    "- First we filter the dataframe such that we obtain only the purchase event_type\n",
    "- Then we group by category_code and count the latter\n",
    "- Finally we take only the five largest number which are the most trending products (products that have been sold the most) and plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purc = df_2[df_2['event_type'] == 'purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "result = df_purc.groupby('category_code').category_code.count().nlargest()\n",
    "result = result.compute()\n",
    "result = result.plot.bar(figsize=(10,6), color='m')\n",
    "plt.title('Overall most trending Products', fontsize=18)\n",
    "plt.xlabel('Category', fontsize=15)\n",
    "plt.ylabel('Number of sales per product', fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above plot we can conclude that the catagories of the most trending products are:**\n",
    "1. Electronics.smartphone\n",
    "2. Electronics.audio.headphone\n",
    "3. Electronics.video.tv\n",
    "4. Electronics.clocks\n",
    "5. Appliances.kitchen.washer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we plot  for each month the same information ( most trending products per category )\n",
    "- First the data is sorted by the index column 'event_time', in order to retrieve the dataset of October and November. This also allows for faster access, joins, groupby-apply operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_idx = df_purc.set_index('event_time')  \n",
    "df_idx = df_idx.persist()   # Persist data in memory.This allows future computations to be much faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_oct = df_idx['2019-10']  # By selecting 2019-10 and 2019-11 we filter the dataframe that correspond to the month October and November respectively  \n",
    "df_nov = df_idx['2019-11']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To plot Oct. most trending product \n",
    "- take df_oct dataframe, which is previously filtered by data_type == 'purchase'  \n",
    "- group by category_code and count the latter\n",
    "- plot the five largest numbers which correspond to Oct. most trending products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "result_oct = df_oct.groupby('category_code').category_code.count().nlargest()\n",
    "result_oct = result_oct.compute()\n",
    "result_oct = result_oct.plot.bar(figsize=(10,6))\n",
    "plt.title('Oct. most trending products', fontsize=18)\n",
    "plt.xlabel('Category', fontsize=15)\n",
    "plt.ylabel('Number of sales per product', fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above plot we can conclude that October's most trending products are the following catagories:**\n",
    "1. Electronics.smartphone\n",
    "2. Electronics.audio.headphone\n",
    "3. Electronics.video.tv\n",
    "4. Electronics.clocks\n",
    "5. Appliances.kitchen.washer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Same procedure to plot Nov. most trending product  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "result_nov = df_nov.groupby('category_code').category_code.count().nlargest()\n",
    "result_nov = result_nov.compute()\n",
    "result_nov = result_nov.plot.bar(figsize=(10,6), color='c')\n",
    "plt.title('Nov. most trending products', fontsize=18)\n",
    "plt.xlabel('Category', fontsize=15)\n",
    "plt.ylabel('Number of sales per product', fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**November's most trending products are the same as October's catagories:**\n",
    "1. Electronics.smartphone\n",
    "2. Electronics.audio.headphone\n",
    "3. Electronics.video.tv\n",
    "4. Electronics.clocks\n",
    "5. Appliances.kitchen.washer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Plot the most visited subcategories\n",
    "- filter the dataframe by 'view' event_type\n",
    "- group by category_code and count the latter\n",
    "- plot the five largest numbers which represent to the most visited subcatagories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df[['event_type', 'category_code']]\n",
    "df_2view = df_2[df_2.event_type == 'view']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = df_2view.groupby('category_code').category_code.count().nlargest()\n",
    "result = result.compute()\n",
    "result = result.plot.pie(figsize=(10,6))\n",
    "plt.title('Most viewd Products', fontsize=18)\n",
    "#plt.xlabel('Category', fontsize=15)\n",
    "#plt.ylabel('Number of views per category', fontsize=15)\n",
    "plt.legend(fontsize=10,  bbox_to_anchor=(1.5,1), loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above plot we can conclude that the most visited catagories are:**\n",
    "1. Electronics.smartphone\n",
    "2. Electronics.clocks \n",
    "3. Computers.notebook\n",
    "4. Electronics.video.tv\n",
    "5. Electronics.audio.headphone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 What are the 10 most sold products per category?\n",
    "\n",
    "- filter the dataframe by 'purchase' event_type\n",
    "- group by category_code, count the latter and sort  \n",
    "- plot the ten largest numbers which represent the most sold products per catagory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2purc = df_2[df_2.event_type == 'purchase']"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = df_2purc.groupby('category_code').category_code.count().nlargest(10)\n",
    "result = result.compute()\n",
    "result = result.plot.pie(figsize=(10,6))\n",
    "plt.title('The ten most sold Products', fontsize=18)\n",
    "#plt.xlabel('Category', fontsize=15)\n",
    "#plt.ylabel('Number of views per category', fontsize=15)\n",
    "plt.legend(fontsize=10, bbox_to_anchor=(1.6,1), loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ 3.1*\n",
    "#### *Write a function that asks the user a category in input and returns a plot indicating the average price of the products sold by the brand.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the category you are looking for? 2053013558920217191\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ex 3.1 \n",
    "\n",
    "def my_fun(x):\n",
    "    \n",
    "    my_dict = {}\n",
    "    my_dict['sums'] = x['price'].sum()\n",
    "    my_dict['counts'] = x['price'].count()\n",
    "    \n",
    "    return pd.Series(my_dict, index = ['sums', 'counts'])\n",
    "\n",
    "\n",
    "#define a function that takes as argument the category_id chosen by the user, that we'll call my_category\n",
    "def my_func(my_category = input(\"What is the category you are looking for? \")):\n",
    "    \n",
    "    plt.figure(figsize=(10,10))\n",
    "\n",
    "#inizialize an empty dataframe\n",
    "    data = pd.DataFrame()\n",
    "    \n",
    "#for each chunck of October's dataset...\n",
    "    for chunck in pd.read_csv('2019-Oct.csv', usecols = ['category_id', 'brand', 'price'], nrows = 100000,  chunksize = 1000, iterator = True):\n",
    "\n",
    "#...if my_category is present into the chunck...\n",
    "            chunck = chunck[chunck.category_id == int(my_category)]\n",
    "            if len(chunck) != 0:\n",
    "\n",
    "#...group by the brand, calculate the sum of the prices of the products belonging to my_category sold by the brand, calculate\n",
    "#the number of products belonging to my_category sold by the brand and append this new information to the dataframe \n",
    "                data = data.append(pd.DataFrame(chunck.groupby('brand').apply(my_fun)))\n",
    "\n",
    "#the same for each chunck of November's dataset\n",
    "    for chunck in pd.read_csv('2019-Nov.csv', usecols = ['category_id', 'brand', 'price'],nrows = 100000,  chunksize = 1000, iterator = True):\n",
    "            \n",
    "            chunck = chunck[chunck.category_id == int(my_category)]\n",
    "            if len(chunck) != 0:\n",
    "\n",
    "                data = data.append(pd.DataFrame(chunck.groupby('brand').apply(my_fun)))\n",
    "\n",
    "#repeat the same process for the dataframe filled with information...\n",
    "    data = pd.DataFrame(data.groupby('brand').sum())\n",
    "\n",
    "#and calculate the mean...\n",
    "    data['means'] = data['sums']/data['counts']\n",
    "\n",
    "#plot a plot bar\n",
    "    plt.bar(data.index, data['means'], color = \"purple\")\n",
    "    plt.title(\"Average price of the products with category_id\\n\"+ my_category +\"\\n\", fontsize = 15)\n",
    "    plt.xlabel(\"\\nBrands with products belonging to the category_id \"+ my_category, fontsize = 12)\n",
    "    plt.show()\n",
    "\n",
    "my_func()           \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "In order to solved this research question, we used as input from the user the category_id 2053013558920217191, for which we obtained that the brand with the highest average price of the products belonging to that category is Apple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ 3.2*\n",
    "#### *Find, for each category, the brand with the highest average price. Return all the results in ascending order by price.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      brands       prices\n",
      "categories                               \n",
      "2053013552695869677    huion   112.440000\n",
      "2053013552326770905  ariston   112.831667\n",
      "2053013552293216471  almacom   137.710000\n",
      "2053013552385491165   xiaomi   231.640000\n",
      "2053013552226107603    deton   462.610000\n",
      "2053013552351936731  samsung   582.158333\n",
      "2053013552259662037   daewoo  2548.020000\n"
     ]
    }
   ],
   "source": [
    "#Ex 3.2 \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def my_fun(x):\n",
    "    \n",
    "    my_dict = {}\n",
    "    \n",
    "    my_dict['sums'] = x['price'].sum()\n",
    "    my_dict['counts'] = x['price'].count()\n",
    "    \n",
    "    return pd.Series(my_dict, index = ['sums', 'counts'])\n",
    "\n",
    "#define an empty dataframe and an empty numeric variable\n",
    "data = pd.DataFrame(columns = ['categories', 'sums', 'counts'])\n",
    "i = 0\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['category_id', 'brand', 'price'], nrows = 100000, chunksize = 1000, iterator = True):\n",
    "    \n",
    "#...for each category belonging to the chunck...\n",
    "    for category_id, frame in chunck.groupby('category_id'):\n",
    "        \n",
    "        chunck = chunck[chunck.category_id == category_id]\n",
    "        if len(chunck) != 0:\n",
    "\n",
    "#...group by brand, calculate the sum of the prices of the products belonging to that category sold by the brand, calculate the\n",
    "#number of products belonging to that category sold by the brand and append this new information to the dataframe\n",
    "            arg = pd.DataFrame(chunck.groupby('brand').apply(my_fun))\n",
    "            my_len = len(arg)\n",
    "            data = data.append(arg)\n",
    "            for j in range(i, i+my_len):\n",
    "    \n",
    "#add information about the category we are referring to\n",
    "                data['categories'][j] = category_id\n",
    "            i = i + my_len\n",
    "            \n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['category_id', 'brand', 'price'], nrows = 100000, chunksize = 1000, iterator = True):\n",
    "    \n",
    "    for category_id, frame in chunck.groupby('category_id'):\n",
    "        \n",
    "        chunck = chunck[chunck.category_id == category_id]\n",
    "        if len(chunck) != 0:\n",
    "            \n",
    "            arg = pd.DataFrame(chunck.groupby('brand').apply(my_fun))\n",
    "            my_len = len(arg)\n",
    "            data = data.append(arg)\n",
    "            for j in range(i, i+my_len):\n",
    "                data['categories'][j] = category_id\n",
    "            i = i + my_len\n",
    "\n",
    "#calculate the means...\n",
    "data['means'] = data['sums']/data['counts']\n",
    "\n",
    "#...and initialize an empty dataframe where to put  the brand with the highest average price and the average price \n",
    "my_data = pd.DataFrame(columns = ['brands', 'prices'])\n",
    "my_data['brands'] = data.groupby('categories').means.idxmax()\n",
    "my_data['prices'] = data.groupby('categories').means.max()\n",
    "\n",
    "#sort the dataframe by the values of the variable 'prices'\n",
    "my_data.sort_values(by = 'prices', inplace = True)\n",
    "print(my_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "From this table we can see for different categories the brands with the highest average prices and the average prices. We might comment that these prices are very variable, depending on the category we are referring to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.  How much does each brand earn per month? Write a function that given the name of a brand in input returns, for each month, its profit. Is the average price of products of different brands significantly different?\n",
    "    4.1 Using the function you just created, find the top 3 brands that have suffered the biggest losses in earnings between one month and the next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_4 = df[['event_time', 'event_type', 'brand', 'price']]  # Select only the columns that we plan to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_4 = df_4[df_4.brand.notnull()]  # Remove unlabeled brands "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_4pur = df_4[df_4['event_type'] == 'purchase']  # Select only the 'perchase' event_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_idx = df_4pur.set_index('event_time')  # indexing 'time_event' (not a column anymore)\n",
    "df_idx = df_idx.persist()   # Persist data in memory.This allows future computations to be much faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_oct = df_idx['2019-10']  # By selecting 2019-10 and 2019-11 we filter the dataframe that correspond to the month October and November respectively  \n",
    "df_nov = df_idx['2019-11']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us define a function ‘brand_profit’ which takes as an input the brands name and returns its profit for the month of October and November.\n",
    "- define 'df_1', which filter the dataset of October based on the name of the brand (input of the function)\n",
    "- define 'df_2', filter Novembers dataset on the given brands name\n",
    "- 'oct_pro' yield the total sum of the purchased brand for the month of October\n",
    "- likewise, 'nov_pro' gives us the total purchased price of the given brand \n",
    "- the function returns for the given brand name a tuple containing its profit of October and November   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def brand_profit (df_oct, df_nov, brand_name):\n",
    "    df_1 = df_oct[df_oct.brand == brand_name].compute()\n",
    "    df_2 = df_nov[df_nov.brand == brand_name].compute()\n",
    "    oct_pro = df_1.price.sum()\n",
    "    nov_pro = df_2.price.sum()\n",
    "    return (int(oct_pro), int(nov_pro))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9194033, 11259865)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_profit(df_oct, df_nov, 'xiaomi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assessment of the difference between the average price of different brands \n",
    "- select the column of interest from the dataset (and we need all the event_type here, not only ‘purchase’) \n",
    "- groupby brand and compute the sum of price for each brand\n",
    "- likewise, groupby brand and count the number of products of the same brand\n",
    "- calculate the average price of products per brand and plot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the obtained plot we can infer that there is indeed a significant average price difference across different brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tot_sale = df_4[['brand', 'price']].groupby('brand').price.sum().compute()\n",
    "num_prod = df_4[['brand', 'price']].groupby('brand').brand.count().compute()\n",
    "avg_sale = tot_sale / num_prod\n",
    "avg_sale.plot(figsize=(20,10))\n",
    "plt.title(' Average price of products per brands', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 find the top 3 brands that have suffered the biggest losses in earnings\n",
    "- because the function returns a tuple of two elements, first element October’s profit and second element Novembers profit, we calculate the percentage loss only if October’s profit is greater than that’s of November\n",
    "- the result is appended to a list 'loss_perc'(initially empty ), to keep track of the correspondence between the unique brands and their profit loss, we append zero to the list when Octobers profit results lower than that of November\n",
    "- create a tuple of unique brand and percentage loss and sort the latter \n",
    "- this yield the names and the percentage loss of the brands that have suffered the biggest loss between October and November"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "unq_bra = df_4.brand.unique().compute()  # List of unique brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_perc = []\n",
    "for i in unq_bra:\n",
    "    profit = brand_profit(df_oct, df_nov, i)\n",
    "    if profit[0] > profit[1]:\n",
    "        x = ((profit[0] - profit[1]) * 100) / profit[0]\n",
    "        loss_perc.append(x)\n",
    "    else:\n",
    "        loss_perc.append(0)              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(100.0, 'zic'),\n",
       " (100.0, 'zenmart'),\n",
       " (100.0, 'zegan'),\n",
       " (100.0, 'zara'),\n",
       " (100.0, 'yuandong'),\n",
       " (100.0, 'yota'),\n",
       " (100.0, 'yokatta'),\n",
       " (100.0, 'yh'),\n",
       " (100.0, 'xxr'),\n",
       " (100.0, 'ww'),\n",
       " (100.0, 'wsp'),\n",
       " (100.0, 'worwo'),\n",
       " (100.0, 'wk'),\n",
       " (100.0, 'wilo'),\n",
       " (100.0, 'wess'),\n",
       " (100.0, 'wella'),\n",
       " (100.0, 'wangfeng'),\n",
       " (100.0, 'vokul'),\n",
       " (100.0, 'vip'),\n",
       " (100.0, 'vionne'),\n",
       " (100.0, 'vibe'),\n",
       " (100.0, 'venti'),\n",
       " (100.0, 'unikum'),\n",
       " (100.0, 'tuarex'),\n",
       " (100.0, 'trespass'),\n",
       " (100.0, 'toysmax'),\n",
       " (100.0, 'thorvik'),\n",
       " (100.0, 'telwin'),\n",
       " (100.0, 'tec'),\n",
       " (100.0, 'sunsuki'),\n",
       " (100.0, 'stikbot'),\n",
       " (100.0, 'sterntaler'),\n",
       " (100.0, 'steger'),\n",
       " (100.0, 'steelflex'),\n",
       " (100.0, 'steba'),\n",
       " (100.0, 'standard'),\n",
       " (100.0, 'sportbaby'),\n",
       " (100.0, 'specialized'),\n",
       " (100.0, 'sowell'),\n",
       " (100.0, 'soundstream'),\n",
       " (100.0, 'soundking'),\n",
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       " (0, 'work'),\n",
       " (0, 'witerra'),\n",
       " (0, 'wiseson'),\n",
       " (0, 'wingoffly'),\n",
       " (0, 'wingo'),\n",
       " (0, 'winda'),\n",
       " (0, 'wincars'),\n",
       " (0, 'wilson'),\n",
       " (0, 'willmark'),\n",
       " (0, 'whitewave'),\n",
       " (0, 'whirlpool'),\n",
       " (0, 'wethepeople'),\n",
       " (0, 'westone'),\n",
       " (0, 'westerndigital'),\n",
       " (0, 'wester'),\n",
       " (0, 'westa'),\n",
       " (0, 'wenyi'),\n",
       " (0, 'wenge'),\n",
       " (0, 'wellneo'),\n",
       " (0, 'wellberg'),\n",
       " (0, 'weina'),\n",
       " (0, 'weifeng'),\n",
       " (0, 'weider'),\n",
       " (0, 'weekend'),\n",
       " (0, 'weber'),\n",
       " (0, 'waterworld'),\n",
       " (0, 'waterpik'),\n",
       " (0, 'waspo'),\n",
       " (0, 'wallberry'),\n",
       " (0, 'walkmaxx'),\n",
       " (0, 'walfix'),\n",
       " (0, 'wacom'),\n",
       " (0, 'w-star'),\n",
       " (0, 'vulli'),\n",
       " (0, 'vt'),\n",
       " (0, 'voyager'),\n",
       " (0, 'vossen'),\n",
       " (0, 'voltmaster'),\n",
       " (0, 'voltman'),\n",
       " (0, 'volta'),\n",
       " (0, 'volkl'),\n",
       " (0, 'volcano'),\n",
       " (0, 'vobix'),\n",
       " (0, 'vmv'),\n",
       " (0, 'vlk'),\n",
       " (0, 'vladi'),\n",
       " (0, 'vivo'),\n",
       " (0, 'viviennesabo'),\n",
       " (0, 'vitek'),\n",
       " (0, 'vitebskiekovry'),\n",
       " (0, 'vitaluce'),\n",
       " (0, 'visavis'),\n",
       " (0, 'visage'),\n",
       " (0, 'vipe'),\n",
       " (0, 'vinzer'),\n",
       " (0, 'vihr'),\n",
       " (0, 'viewsonic'),\n",
       " (0, 'victorinox'),\n",
       " (0, 'victoriassecret'),\n",
       " (0, 'vichy'),\n",
       " (0, 'viatti'),\n",
       " (0, 'veston'),\n",
       " (0, 'vestel'),\n",
       " (0, 'ves'),\n",
       " (0, 'versale'),\n",
       " (0, 'versace'),\n",
       " (0, 'verossa'),\n",
       " (0, 'verloni'),\n",
       " (0, 'veritas'),\n",
       " (0, 'verdi'),\n",
       " (0, 'verbatim'),\n",
       " (0, 'venta'),\n",
       " (0, 'velvet-santiago'),\n",
       " (0, 'velvet'),\n",
       " (0, 'velo'),\n",
       " (0, 'vegas'),\n",
       " (0, 'vega'),\n",
       " (0, 'vectron'),\n",
       " (0, 'vdovichev'),\n",
       " (0, 'vaude'),\n",
       " (0, 'vasin'),\n",
       " (0, 'vasilisa'),\n",
       " (0, 'vasden'),\n",
       " (0, 'vardoven'),\n",
       " (0, 'vard'),\n",
       " (0, 'vans'),\n",
       " (0, 'valberg'),\n",
       " (0, 'vako'),\n",
       " (0, 'vaiper'),\n",
       " (0, 'uvelirnyetradicii'),\n",
       " (0, 'usborne'),\n",
       " (0, 'urgaz'),\n",
       " (0, 'uralsport'),\n",
       " (0, 'ural'),\n",
       " (0, 'uomo'),\n",
       " (0, 'uno'),\n",
       " (0, 'universal'),\n",
       " (0, 'unison'),\n",
       " (0, 'uniforce'),\n",
       " (0, 'umi'),\n",
       " (0, 'ubisoft'),\n",
       " (0, 'tyr'),\n",
       " (0, 'ty'),\n",
       " (0, 'tv-shop'),\n",
       " (0, 'tutu'),\n",
       " (0, 'tutti'),\n",
       " (0, 'turbosound'),\n",
       " (0, 'turboair'),\n",
       " (0, 'turbo'),\n",
       " (0, 'tunga'),\n",
       " (0, 'tuncmatik'),\n",
       " (0, 'tuka'),\n",
       " (0, 'tuffoni'),\n",
       " (0, 'tsv'),\n",
       " (0, 'tsubaki'),\n",
       " (0, 'trust'),\n",
       " (0, 'troyka'),\n",
       " (0, 'trollbeads'),\n",
       " (0, 'triumphnord'),\n",
       " (0, 'triumph'),\n",
       " (0, 'triumf'),\n",
       " (0, 'triton'),\n",
       " (0, 'tridevatoecarstvo'),\n",
       " (0, 'trichup'),\n",
       " (0, 'trendbeauty'),\n",
       " (0, 'trek'),\n",
       " (0, 'trefl'),\n",
       " (0, 'traxxas'),\n",
       " (0, 'travellight'),\n",
       " (0, 'traser'),\n",
       " (0, 'transcend'),\n",
       " (0, 'tramontina'),\n",
       " (0, 'tpmaster'),\n",
       " (0, 'tp-link'),\n",
       " (0, 'toyota'),\n",
       " (0, 'toyland'),\n",
       " (0, 'toway'),\n",
       " (0, 'touchbeauty'),\n",
       " (0, 'total'),\n",
       " (0, 'totaku'),\n",
       " ...]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proflos_bra = list(zip(loss_perc, unq_bra))\n",
    "proflos_bra.sort(reverse=True)\n",
    "proflos_bra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Taking into consideration that the percentage loss results 100% when there is no purchase event for that brand in the month of November, we conclude that the three brands that have suffered the most are\n",
    "1. Jonnesway 96.65% loss\n",
    "2. Sunfull 96.00% loss\n",
    "3. Evga 95.903% loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ 5*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# Ex 5 \n",
    "\n",
    "#inizialize an empty dataframe and a list filled with 7 zeros\n",
    "data = pd.DataFrame(columns = ['day', 'users'])\n",
    "l = [0, 0, 0, 0, 0, 0, 0]\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['event_time', 'user_id'], parse_dates = ['event_time'], date_parser = pd.to_datetime, nrows = 100000, chunksize = 1000, iterator = True):\n",
    "    \n",
    "#...for every day of the week...\n",
    "    for i in range(0, 7):\n",
    "\n",
    "#initialize an empty numeric variable\n",
    "        my_sum = 0\n",
    "        chunck_day = chunck[chunck.event_time.dt.dayofweek == i]\n",
    "    \n",
    "#if at least one user made an operation during the ith day of the week...\n",
    "        if len(chunck_day) != 0:\n",
    "        \n",
    "#...fill the ith position of the list with the number of operations made during the ith day of the week \n",
    "            l[i] += len(chunck_day)\n",
    "    \n",
    "#increase my_sum of the number of users who made any operation during the ith day of the week...\n",
    "            my_sum += chunck_day.groupby('user_id').user_id.unique().count()\n",
    "\n",
    "#...and append the new information to the dataframe\n",
    "        data = data.append(pd.DataFrame([[i, my_sum]], columns = ['day', 'users']))\n",
    "        \n",
    "#the same for each chunck of November's dataset \n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['event_time', 'user_id'], parse_dates = ['event_time'], date_parser = pd.to_datetime, nrows = 100000, chunksize = 1000, iterator = True):\n",
    "    \n",
    "    for i in range(0, 7):\n",
    "        \n",
    "        my_sum = 0\n",
    "        chunck_day = chunck[chunck.event_time.dt.dayofweek == i]\n",
    "        if len(chunck_day) != 0:\n",
    "            \n",
    "            l[i] += len(chunck_day)\n",
    "            my_sum += chunck_day.groupby('user_id').user_id.unique().count()\n",
    "        \n",
    "        data = data.append(pd.DataFrame([[i, my_sum]], columns = ['day', 'users']))\n",
    "        \n",
    "#repeat the same process for the dataframe filled with information...\n",
    "data = data.groupby('day').users.sum()\n",
    "\n",
    "for i in range(len(l)):\n",
    "    if l[i] == 0:\n",
    "        l[i] = 1\n",
    "\n",
    "#plot\n",
    "plt.plot((data.values/l)/24, color = 'purple')\n",
    "plt.title(\"Hourly average of visitors per day of the week\\n\")\n",
    "plt.xlabel(\"\\nDay of the week\")\n",
    "plt.xticks(range(0,6), [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "From this plot we can see that Tuesday and Friday were the two most active days of the week, while Monday, Thursday, Wednesday, Saturday and Sunday weren't active days at all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. The conversion rate of a product is given by the number of times a product has been bought over the number of times it has been visited. What's the conversion rate of your online store?\n",
    "    6.1 Find the overall conversion rate of your store\n",
    "    6.2 Plot the number of purchases of each category and show the conversion rate of each category in decreasing order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The conversion rate of the online store is calculated by\n",
    "- Select from the dataset only the column we are interested in\n",
    "- Select the list of the unique products present in the store\n",
    "- Check for each unique products the event that is the number of times the unique product appears in the column df_6['product_id'] and if the product has been purchased or viewed.  This yield for each unique product the number of times that has been purchased and viewed.\n",
    "- Find the conversion rate for each product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6 = df[['event_type', 'product_id', 'category_code']]  # Select only the columns that we plan to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unq_pro = df_6.product_id.unique().compute()  # List of unique products \n",
    "tot_pro = df_6.product_id.compute()  # List of all products present In the dataset of Oct and Nov (same product might appear multiple times)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pur = []\n",
    "for i in range (len(unq_pro)):\n",
    "    for j in range (len(tot_pro)):\n",
    "        if (tot_pro[j] == unq_pro[i]) & (df_6.event_type[j] == 'purchase'):\n",
    "            num_pur[i] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_view = []\n",
    "for i in range (len(unq_pro)):\n",
    "    for j in range (len(tot_pro)):\n",
    "        if (tot_pro[j] == products[i]) & (df_6.event_type[j] == 'purchase'):\n",
    "            num_view += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion_rate = (num_pur / num_view)\n",
    "conversion_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 Find the overall conversion rate of your store\n",
    "- filter the dataframe by 'purchase' event_type and count the unique 'product_id'\n",
    "- filter the dataframe by 'view' event_type and count the unique 'product_id'  \n",
    "- the overall conversion rate is given by the proportion of the total number of purchased products to viewd products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6purc = df_6[df_6.event_type == 'purchase']\n",
    "n_purc = df_6purc['product_id'].nunique().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6view = df_6[df_6.event_type == 'view']\n",
    "n_view = df_6view['product_id'].nunique().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3290666872909376"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Overall_conversion_rate = (n_purc / n_view)\n",
    "Overall_conversion_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 Plot the number of purchases of each category and show the conversion rate of each category in decreasing order\n",
    "- filter the dataframe by 'purchase' event_type\n",
    "- groupby 'category_code' and count the event_type ('purchase'), this gives the number of purchase per category\n",
    "- plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6purc = df_6[df_6.event_type == 'purchase']\n",
    "purc_cat = df_6purc.groupby('category_code').event_type.count().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "purc_cat.plot(figsize=(15,6), color='r')\n",
    "plt.title('Number of purchase per category', fontsize=18)\n",
    "plt.xlabel('Category', fontsize=15)\n",
    "plt.ylabel('Number of sales', fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**to obtain the conversion rate per category in decreasing order:**\n",
    "- filter the dataframe by 'purchase' event_type, groupby 'category_code' and count the unique 'product_id'\n",
    "- filter the dataframe by 'view' event_type, groupby 'category_code' and count the unique 'product_id'  \n",
    "- the conversion rate per category is given by the proportion of the total number of purchased products to viewd products per category\n",
    "- sort the conversion rate in descending order\n",
    "- plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6purc = df_6[df_6.event_type == 'purchase']\n",
    "purc_cat = df_6purc.groupby(['category_code'])['product_id'].nunique().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_code\n",
       "accessories.bag                  778\n",
       "accessories.wallet               229\n",
       "apparel.belt                      24\n",
       "apparel.costume                  179\n",
       "apparel.dress                     60\n",
       "                                ... \n",
       "country_yard.furniture.hammok      1\n",
       "apparel.shorts                     3\n",
       "appliances.kitchen.fryer           1\n",
       "construction.tools.screw           1\n",
       "apparel.jacket                     1\n",
       "Name: product_id, Length: 127, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purc_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6view = df_6[df_6.event_type == 'view']\n",
    "view_cat = df_6view.groupby(['category_code'])['product_id'].nunique().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_code\n",
       "accessories.bag             2479\n",
       "accessories.umbrella          37\n",
       "accessories.wallet           614\n",
       "apparel.belt                  87\n",
       "apparel.costume              461\n",
       "                            ... \n",
       "apparel.glove                124\n",
       "apparel.skirt                161\n",
       "apparel.jacket                11\n",
       "construction.tools.screw      12\n",
       "appliances.kitchen.fryer       1\n",
       "Name: product_id, Length: 129, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "convrate_cate = (purc_cat / view_cat)\n",
    "convrate_cate = convrate_cate.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_code\n",
       "appliances.kitchen.fryer           1.000000\n",
       "computers.ebooks                   0.848485\n",
       "auto.accessories.radar             0.780822\n",
       "electronics.smartphone             0.755132\n",
       "appliances.kitchen.meat_grinder    0.751852\n",
       "                                     ...   \n",
       "apparel.shoes.sandals              0.063804\n",
       "apparel.underwear                  0.050948\n",
       "apparel.shorts                     0.034483\n",
       "apparel.shoes.espadrilles               NaN\n",
       "country_yard.furniture.bench            NaN\n",
       "Name: product_id, Length: 129, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convrate_cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "convrate_cate.plot(figsize=(15,6), color='r')\n",
    "plt.title('Conversion rate per category', fontsize=18)\n",
    "plt.xlabel('Category', fontsize=15)\n",
    "plt.ylabel('Conversion rate', fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **confront purchase per category with conversion rate per category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "purc_cat.plot(label='purchase per category')\n",
    "(convrate_cate * 100).plot(label='conversion rate per category')  # conversion rate in percentage \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *RQ 7*\n",
    "#### *Prove that the pareto principle applies to your store.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pareto Principle can be applied to the store: the 20% of the users have spent 621530.65$ , which is close to the 80% of the earnings: 722243.05$\n"
     ]
    }
   ],
   "source": [
    "#Ex 7 \n",
    "\n",
    "#initialize 2 empty numeric variables and an empty dataframe\n",
    "earnings = 0\n",
    "buyers = 0\n",
    "earnings_per_user = pd.DataFrame()\n",
    "\n",
    "#for each chunck of October's dataset...\n",
    "for chunck in pd.read_csv('2019-Oct.csv', usecols = ['event_type', 'price', 'user_id'], nrows = 100000, chunksize = 1000, iterator = True):\n",
    "    \n",
    "    chunck_purchase = chunck[chunck.event_type == 'purchase']  \n",
    "    if len(chunck_purchase) != 0:\n",
    "        \n",
    "#...increase earnings of the prices of the products purchased...\n",
    "        earnings += chunck_purchase['price'].sum()\n",
    "    \n",
    "#...increase buyers of the number of user who purchased any product...\n",
    "        buyers += chunck_purchase['user_id'].count()\n",
    "    \n",
    "#group by user and append to the dataframe the prices of the products purchased by each user\n",
    "        earnings_per_user = earnings_per_user.append(pd.DataFrame(chunck_purchase.groupby('user_id').price.sum()))\n",
    "    \n",
    "#the same for each chunck of November's dataset\n",
    "for chunck in pd.read_csv('2019-Nov.csv', usecols = ['event_type', 'price', 'user_id'], nrows = 100000, chunksize = 1000, iterator = True):\n",
    "    \n",
    "    chunck_purchase = chunck[chunck.event_type == 'purchase']  \n",
    "    if len(chunck_purchase) != 0:\n",
    "        earnings += chunck_purchase['price'].sum()\n",
    "        buyers += chunck_purchase['user_id'].count()\n",
    "        earnings_per_user = earnings_per_user.append(pd.DataFrame(chunck_purchase.groupby('user_id').price.sum()))\n",
    "\n",
    "#calculate the 80% of the earnings\n",
    "earnings80 = round(earnings*.8, 2)\n",
    "\n",
    "#count the top 20% buyers\n",
    "buyers20 = int(buyers*.2)\n",
    "\n",
    "#group by user and calculate the prices of the products purchased by each user\n",
    "earnings_per_user = earnings_per_user.groupby('user_id').price.sum()\n",
    "top_earnings = round(earnings_per_user.sort_values(ascending = False).head(buyers20).sum(), 2)\n",
    "\n",
    "print(\"The Pareto Principle can be applied to the store: the 20% of the users have spent \"+ str(top_earnings) +\"$ , which is close to the 80% of the earnings: \"+ str(earnings80) +\"$\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment\n",
    "This result simply shows that the Pareto principle can be applied to the e-commerce store"
   ]
  }
 ],
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