web_analytics_codes.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b134f5a",
   "metadata": {},
   "source": [
    "## Section 1: Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f3d3c570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9693e59",
   "metadata": {},
   "source": [
    "## Section 2: Generating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4ea2fa8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that converts a into a line and stores the values accordingly\n",
    "def generate_data_dict(line):\n",
    "    \n",
    "    line = line.rstrip(\"\\n\") # convert the row into a line by stripping over a new-line character\n",
    "    data_rows = line.split(\",\") # split the values in the row by comma chacater\n",
    "    \n",
    "    # Return the value for each feature in a dictionary format\n",
    "    return {'Source': data_rows[0], # the first value in a row - origin/source\n",
    "            'Platform': data_rows[1], # the second value in a row - platform\n",
    "            'Clickstream': data_rows[2:], # the remaining values in a row - pages visited\n",
    "            '# of pages visited': len(data_rows[2:]),}   # the count of the pages visited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8d335dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list that wil store the values for each visit\n",
    "visitor_data_list = []\n",
    "\n",
    "with open('clickstream_data.csv', 'r') as file: # open the dataset file\n",
    "    \n",
    "    rows = file.readlines() # read each row in the dataset\n",
    "    \n",
    "    for row in rows: # iterate through the rows\n",
    "        \n",
    "        data_dict = generate_data_dict(row) # store the values for each row intoa dictionary\n",
    "        visitor_data_list.append(data_dict) # add the values into a list\n",
    "\n",
    "file.close() # close the dataset file\n",
    "\n",
    "# Convert the visit values into a dataframe\n",
    "visitor_data_df = pd.DataFrame(data=visitor_data_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f403c94",
   "metadata": {},
   "source": [
    "## Section 3: Analyzing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fd3214eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "purchase_success_status_list = [] # create a list to visits that ended up in success\n",
    "drop_out_status_list = [] # create a list to visits that dropped out\n",
    "single_page_status_list = [] # create a list to visits that visited only one page\n",
    "\n",
    "# iterate through the indices of the dataframe\n",
    "for index in visitor_data_df.index:\n",
    "\n",
    "    clickstream_list = visitor_data_df.loc[index]['Clickstream'] # seperate the clickstream value, i.e., pages visited\n",
    "\n",
    "    if 'purchase_success' in clickstream_list: # if the visit was successful, i.e., contains 'purhcase_success' page\n",
    "        purchase_success_status_list.append(1)  # add 1 if YES\n",
    "    else:  \n",
    "        purchase_success_status_list.append(0) # add 0 if NO\n",
    "        \n",
    "    if 'purchase_start' in clickstream_list and 'purchase_success' not in clickstream_list: # if the visit was dropped out, i.e., entrance into purchasing page without a success\n",
    "        drop_out_status_list.append(1) # add 1 if YES\n",
    "    else:  \n",
    "        drop_out_status_list.append(0) # add 0 if NO\n",
    "\n",
    "    if len(clickstream_list) == 1: # if the visit contains only 1 page. i.e., a visitor bounced after a single page visit\n",
    "        single_page_status_list.append(1) # add 1 if YES\n",
    "    else:  \n",
    "        single_page_status_list.append(0) # add 0 if NO\n",
    "    \n",
    "# add the respective list values into the dataframe\n",
    "visitor_data_df['Conversion'] = purchase_success_status_list\n",
    "visitor_data_df['Drop-out'] = drop_out_status_list\n",
    "visitor_data_df['Bounce'] = single_page_status_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d2a370",
   "metadata": {},
   "source": [
    "### Section 3.1: Analyzing the sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4853822a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupyby all the values by source, summing the numeric values only\n",
    "source_group_df = visitor_data_df.groupby('Source').sum(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8b6cdd02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list that stores the total number of visits per source\n",
    "number_of_visits_per_source = list(visitor_data_df.groupby('Source').count()['Platform'])\n",
    "\n",
    "# Add the total visits to the grouped by dataframe\n",
    "source_group_df['Total Visits'] = number_of_visits_per_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0f31f12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that returns a ratio for a given metric given numerator, denominator, and dataframe\n",
    "def generate_ratio(numerator, denominator, dataframe):\n",
    "    \n",
    "    ratios = [] # Create a list to store the ratio values\n",
    "    \n",
    "    for index in dataframe.index: # For each index\n",
    "\n",
    "        numerator_value = dataframe.at[index, numerator] # Take the numeric from that index\n",
    "        denominator_value = dataframe.at[index, denominator] # Take the denominator from that index\n",
    "\n",
    "        ratio = numerator_value / denominator_value # Calculate the ratio\n",
    "\n",
    "        ratios.append(round(ratio,2)) # Round the ratio into two decimal points\n",
    "        \n",
    "    return ratios # Return the ratios list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4c2050f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the list for columns whose ratios will be calculated\n",
    "list_of_columns_for_ratio_calculation  = ['Conversion', 'Drop-out', 'Bounce']\n",
    "\n",
    "# Iterate through the columns list\n",
    "for column in list_of_columns_for_ratio_calculation:\n",
    "    \n",
    "    # Formulate the column name by adding ratio in the front\n",
    "    column_name = column + ' rate'\n",
    "    \n",
    "    # Generate the ratios and add to the dataframe under the formulated column name\n",
    "    source_group_df[column_name] = generate_ratio(column, 'Total Visits', source_group_df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "36f1914b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># of pages visited</th>\n",
       "      <th>Conversion</th>\n",
       "      <th>Drop-out</th>\n",
       "      <th>Bounce</th>\n",
       "      <th>Total Visits</th>\n",
       "      <th>Conversion rate</th>\n",
       "      <th>Drop-out rate</th>\n",
       "      <th>Bounce rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>direct</th>\n",
       "      <td>47724</td>\n",
       "      <td>4275</td>\n",
       "      <td>1191</td>\n",
       "      <td>1287</td>\n",
       "      <td>13500</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>facebook_advert</th>\n",
       "      <td>19334</td>\n",
       "      <td>93</td>\n",
       "      <td>2728</td>\n",
       "      <td>4262</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>facebook_share</th>\n",
       "      <td>177253</td>\n",
       "      <td>6657</td>\n",
       "      <td>12345</td>\n",
       "      <td>8125</td>\n",
       "      <td>51300</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linkedin_advert</th>\n",
       "      <td>8292</td>\n",
       "      <td>504</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linkedin_share</th>\n",
       "      <td>71015</td>\n",
       "      <td>2546</td>\n",
       "      <td>4913</td>\n",
       "      <td>4239</td>\n",
       "      <td>21200</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partner_advert</th>\n",
       "      <td>19034</td>\n",
       "      <td>545</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search</th>\n",
       "      <td>188925</td>\n",
       "      <td>8720</td>\n",
       "      <td>12127</td>\n",
       "      <td>10537</td>\n",
       "      <td>57400</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 # of pages visited  Conversion  Drop-out  Bounce  \\\n",
       "Source                                                              \n",
       "direct                        47724        4275      1191    1287   \n",
       "facebook_advert               19334          93      2728    4262   \n",
       "facebook_share               177253        6657     12345    8125   \n",
       "linkedin_advert                8292         504       896       0   \n",
       "linkedin_share                71015        2546      4913    4239   \n",
       "partner_advert                19034         545      3000       0   \n",
       "search                       188925        8720     12127   10537   \n",
       "\n",
       "                 Total Visits  Conversion rate  Drop-out rate  Bounce rate  \n",
       "Source                                                                      \n",
       "direct                  13500             0.32           0.09         0.10  \n",
       "facebook_advert         10000             0.01           0.27         0.43  \n",
       "facebook_share          51300             0.13           0.24         0.16  \n",
       "linkedin_advert          2000             0.25           0.45         0.00  \n",
       "linkedin_share          21200             0.12           0.23         0.20  \n",
       "partner_advert           5000             0.11           0.60         0.00  \n",
       "search                  57400             0.15           0.21         0.18  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the source stats\n",
    "source_group_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "31bfb779",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># of pages visited</th>\n",
       "      <th>Conversion</th>\n",
       "      <th>Drop-out</th>\n",
       "      <th>Bounce</th>\n",
       "      <th>Total Visits</th>\n",
       "      <th>Conversion rate</th>\n",
       "      <th>Drop-out rate</th>\n",
       "      <th>Bounce rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>facebook_advert</th>\n",
       "      <td>19334</td>\n",
       "      <td>93</td>\n",
       "      <td>2728</td>\n",
       "      <td>4262</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linkedin_advert</th>\n",
       "      <td>8292</td>\n",
       "      <td>504</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partner_advert</th>\n",
       "      <td>19034</td>\n",
       "      <td>545</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
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      ],
      "text/plain": [
       "                 # of pages visited  Conversion  Drop-out  Bounce  \\\n",
       "Source                                                              \n",
       "facebook_advert               19334          93      2728    4262   \n",
       "linkedin_advert                8292         504       896       0   \n",
       "partner_advert                19034         545      3000       0   \n",
       "\n",
       "                 Total Visits  Conversion rate  Drop-out rate  Bounce rate  \n",
       "Source                                                                      \n",
       "facebook_advert         10000             0.01           0.27         0.43  \n",
       "linkedin_advert          2000             0.25           0.45         0.00  \n",
       "partner_advert           5000             0.11           0.60         0.00  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the source stats for only three advertisement campaigns\n",
    "source_group_df.loc[['facebook_advert','linkedin_advert','partner_advert']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82dae74",
   "metadata": {},
   "source": [
    "### Section 3.2: Analyzing the platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efc0328a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupyby all the values by platform, summing the numeric values only\n",
    "platform_group_df = visitor_data_df.groupby('Platform').sum(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e7934aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list that stores the total number of visits per platform\n",
    "number_of_visits_per_platform = list(visitor_data_df.groupby('Platform').count()['Source'])\n",
    "\n",
    "# Add the total visits to the grouped by dataframe\n",
    "platform_group_df['Total visits'] = number_of_visits_per_platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "51d717ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the columns list\n",
    "for column in list_of_columns_for_ratio_calculation:\n",
    "    \n",
    "    # Formulate the column name by adding ratio in the front\n",
    "    column_name = column + ' rate'\n",
    "    \n",
    "    # Generate the ratios and add to the dataframe under the formulated column name\n",
    "    platform_group_df[column_name] = generate_ratio(column, 'Total visits', platform_group_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8c3e29f5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># of pages visited</th>\n",
       "      <th>Conversion</th>\n",
       "      <th>Drop-out</th>\n",
       "      <th>Bounce</th>\n",
       "      <th>Total visits</th>\n",
       "      <th>Conversion rate</th>\n",
       "      <th>Drop-out rate</th>\n",
       "      <th>Bounce rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>android</th>\n",
       "      <td>148686</td>\n",
       "      <td>6642</td>\n",
       "      <td>10003</td>\n",
       "      <td>8003</td>\n",
       "      <td>44500</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ios</th>\n",
       "      <td>145639</td>\n",
       "      <td>5649</td>\n",
       "      <td>10969</td>\n",
       "      <td>8001</td>\n",
       "      <td>44500</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mac</th>\n",
       "      <td>94268</td>\n",
       "      <td>4481</td>\n",
       "      <td>6296</td>\n",
       "      <td>4497</td>\n",
       "      <td>28000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>48723</td>\n",
       "      <td>1976</td>\n",
       "      <td>3704</td>\n",
       "      <td>3341</td>\n",
       "      <td>15400</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windows</th>\n",
       "      <td>94261</td>\n",
       "      <td>4592</td>\n",
       "      <td>6228</td>\n",
       "      <td>4608</td>\n",
       "      <td>28000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          # of pages visited  Conversion  Drop-out  Bounce  Total visits  \\\n",
       "Platform                                                                   \n",
       "android               148686        6642     10003    8003         44500   \n",
       "ios                   145639        5649     10969    8001         44500   \n",
       "mac                    94268        4481      6296    4497         28000   \n",
       "unknown                48723        1976      3704    3341         15400   \n",
       "windows                94261        4592      6228    4608         28000   \n",
       "\n",
       "          Conversion rate  Drop-out rate  Bounce rate  \n",
       "Platform                                               \n",
       "android              0.15           0.22         0.18  \n",
       "ios                  0.13           0.25         0.18  \n",
       "mac                  0.16           0.22         0.16  \n",
       "unknown              0.13           0.24         0.22  \n",
       "windows              0.16           0.22         0.16  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the platform stats\n",
    "platform_group_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a47bf19f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of mobile visitors are 89000\n",
      "The total number of desktop visitors are 56000\n"
     ]
    }
   ],
   "source": [
    "# Group the data r=frame by mobile visitors, summing up the values\n",
    "mobile_visitors_count = platform_group_df.loc[['android', 'ios']]['Total visits'].sum()\n",
    "\n",
    "# Group the data r=frame by desktop visitors, summing up the values\n",
    "desktop_visitors_count = platform_group_df.loc[['windows', 'mac']]['Total visits'].sum()\n",
    "\n",
    "print(f\"The total number of mobile visitors are {mobile_visitors_count}\")\n",
    "print(f\"The total number of desktop visitors are {desktop_visitors_count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc570a6",
   "metadata": {},
   "source": [
    "### Section 3.3: Analyzing successful transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "97f43381",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store only successful visits from the dataframe\n",
    "successful_visits = visitor_data_df[visitor_data_df['Conversion'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "57e6da2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that returns the blog visits\n",
    "def return_blog_visits(blog_name, dataframe):\n",
    "    \n",
    "    blog_visits = 0 # Create the variable counting the blog visits\n",
    "    \n",
    "    for index in dataframe.index: # Iterate through the indices of dataframe\n",
    "        \n",
    "        clickstream = dataframe.at[index, 'Clickstream'] # Obtain the clickstream for each index, or visit\n",
    "        \n",
    "        if blog_name in clickstream: # Check if the blog name is present in the clickstream\n",
    "            \n",
    "            blog_visits += 1 # Add 1 to the count if YES\n",
    "            \n",
    "    return blog_visits # Return the blog visits count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "91e1bd6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of 23340 succesful purchases, Blog 1 page has been opened 5567 times , which has a ration of 0.24\n",
      "Out of 23340 succesful purchases, Blog 2 page has been opened 4470 times , which has a ration of 0.19\n"
     ]
    }
   ],
   "source": [
    "successful_purhcases_count = len(successful_visits) # Number of successful visits\n",
    "\n",
    "blog_1_returns = return_blog_visits('blog_1', successful_visits) # Blog 1 visit count\n",
    "blog_2_returns = return_blog_visits('blog_2', successful_visits) # Blog 2 visit count\n",
    "\n",
    "print(f\"Out of {successful_purhcases_count} succesful purchases, Blog 1 page has been opened {blog_1_returns} times\",\n",
    "      f\", which has a ration of {round(blog_1_returns/successful_purhcases_count,2)}\")\n",
    "\n",
    "print(f\"Out of {successful_purhcases_count} succesful purchases, Blog 2 page has been opened {blog_2_returns} times\",\n",
    "      f\", which has a ration of {round(blog_2_returns/successful_purhcases_count,2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a60987",
   "metadata": {},
   "source": [
    "### Section 3.4: Analyzing the pages causing struggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b1684f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "pages_bounced = [] # Create the variable to store the pages bounced\n",
    "\n",
    "for index in visitor_data_df.index: # iterate through the indices of dataframe\n",
    "    \n",
    "    clickstream = visitor_data_df.at[index, 'Clickstream'] # Obtain the clickstream for each index, or visit\n",
    "    \n",
    "    if len(clickstream) == 1: # Check if the lenght of the clickstream is 1, i.e., it is visited only one time\n",
    "        \n",
    "        page_bounced = clickstream[0] # If YES, take the page name\n",
    "        \n",
    "        pages_bounced.append(page_bounced) # Add the page name into the list\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        pages_bounced.append(r\"N\\A\") # if NO, add 'N\\A', meaning the visit was not bounced\n",
    "            \n",
    "visitor_data_df['Bounced page'] = pages_bounced # Add the bounced page names into the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7e7e57d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slice the visits in which bounces occured: 'purchase_success', 'contact_us', 'N\\A' are unfiltered as they cannot be considered bounced\n",
    "visitor_data_exited_df = visitor_data_df[(visitor_data_df['Bounced page'] != \"purchase_success\") &\n",
    "                                        (visitor_data_df['Bounced page'] != \"contact_us\") &\n",
    "                                        (visitor_data_df['Bounced page'] != \"N\\A\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d419570c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that returns the bounce rate for each page by platform\n",
    "def count_pages_per_platform(platform, dataframe):\n",
    "    \n",
    "    filtered_df = dataframe[dataframe['Platform'] == platform] # filter for the platform put in\n",
    "    grouped_df = filtered_df.groupby('Bounced page').count() # group by the bounced page, counting the values\n",
    "    \n",
    "    bounce_rates = [] # create a list to store bounce rates\n",
    "\n",
    "    for index in grouped_df.index: # iterate through the indices of the dataframe\n",
    "\n",
    "        bounce = grouped_df.at[index, 'Bounce'] # get the bounce count\n",
    "        total_visits = platform_group_df['Total visits'].loc[platform] # get the total visits\n",
    "\n",
    "        bounce_rate = round(bounce/total_visits, 2) # calculate the bounce rate\n",
    "\n",
    "        bounce_rates.append(bounce_rate) # add the bounce rate to the list\n",
    "    \n",
    "    grouped_df['Bounce rate'] = bounce_rates # add the bounce rates list to the dataframe\n",
    "    \n",
    "    return grouped_df.sort_values(by='Bounce rate',ascending=False)['Bounce rate'] # return bounce rate values of the grouped datafraem, sorted by bounce rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ab20a8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The IOS platform has exit page counts as the below:\n",
      "Bounced page\n",
      "blog_1                 0.06\n",
      "home                   0.06\n",
      "special_advert_deal    0.02\n",
      "blog_2                 0.01\n",
      "Name: Bounce rate, dtype: float64\n",
      "\n",
      "\n",
      "The ANDROID platform has exit page counts as the below:\n",
      "Bounced page\n",
      "blog_1                 0.06\n",
      "home                   0.06\n",
      "special_advert_deal    0.02\n",
      "blog_2                 0.01\n",
      "Name: Bounce rate, dtype: float64\n",
      "\n",
      "\n",
      "The WINDOWS platform has exit page counts as the below:\n",
      "Bounced page\n",
      "home                   0.07\n",
      "blog_1                 0.03\n",
      "special_advert_deal    0.03\n",
      "blog_2                 0.00\n",
      "Name: Bounce rate, dtype: float64\n",
      "\n",
      "\n",
      "The MAC platform has exit page counts as the below:\n",
      "Bounced page\n",
      "home                   0.07\n",
      "blog_1                 0.03\n",
      "special_advert_deal    0.03\n",
      "blog_2                 0.00\n",
      "Name: Bounce rate, dtype: float64\n",
      "\n",
      "\n",
      "The UNKNOWN platform has exit page counts as the below:\n",
      "Bounced page\n",
      "blog_1                 0.06\n",
      "special_advert_deal    0.06\n",
      "home                   0.04\n",
      "blog_2                 0.01\n",
      "Name: Bounce rate, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Obtain the unique platfomr names in a list\n",
    "platforms = list(visitor_data_exited_df['Platform'].unique())\n",
    "\n",
    "for platform in platforms: # iterate through each platform\n",
    "    \n",
    "    # print the stats\n",
    "    print(f\"The {platform.upper()} platform has exit page counts as the below:\")\n",
    "    print(count_pages_per_platform(platform, visitor_data_exited_df))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a30771da",
   "metadata": {},
   "source": [
    "### Section 4: Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "346f0977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Codes for Figure 1:\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,6))\n",
    "\n",
    "colors = ['darkturquoise',\n",
    "'slategrey',\n",
    "'silver',\n",
    "'lightgrey']\n",
    "\n",
    "sources = list(source_group_df.index)\n",
    "\n",
    "visitors = np.array(list(source_group_df['Total Visits']))\n",
    "converted_visitors = np.array(list(source_group_df['Conversion']))\n",
    "bounced_visitors = np.array(list(source_group_df['Bounce']))\n",
    "dropped_out_visitors = np.array(list(source_group_df['Drop-out']))\n",
    "\n",
    "other_visitors = []\n",
    "\n",
    "for index in range(0,len(visitors)):\n",
    "    \n",
    "    other_visitor = visitors[index] - (converted_visitors[index] + bounced_visitors[index] + dropped_out_visitors[index])\n",
    "    other_visitors.append(other_visitor)\n",
    "\n",
    "other_visitors =  np.array(other_visitors)\n",
    "\n",
    "plt.bar(sources, other_visitors , color=colors[0])\n",
    "plt.bar(sources, converted_visitors , bottom= other_visitors, color=colors[1])\n",
    "plt.bar(sources, dropped_out_visitors, bottom=other_visitors+converted_visitors, color=colors[2])\n",
    "plt.bar(sources, bounced_visitors, bottom=other_visitors+converted_visitors+dropped_out_visitors, color=colors[3])\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.xlabel(\"Source\")\n",
    "plt.ylabel(\"Visitors\")\n",
    "\n",
    "plt.legend([\"Other visitors\", \"Conversion\",\"Drop-out\",\"Bounce\"])\n",
    "\n",
    "plt.title(\"Figure 1: Visitors of Different Types per Each Source\\n\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e087e1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Codes for Figure 2:\n",
    "\n",
    "conversion_rates = list(source_group_df['Conversion rate'])\n",
    "sources = list(source_group_df.index)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.title(\"Figure 2: Conversion Rates per Source\", fontsize=14)\n",
    "\n",
    "# Assign colors to the bars based on their index\n",
    "colors = ['#343434' if i == 0 else\n",
    "          '#1877F2' if i  in [1, 2] else\n",
    "          '#0077B7' if i in [3, 4] else\n",
    "          '#2977C1' if i == 5 else \n",
    "          '#117F97' for i in range(len(sources))]\n",
    "\n",
    "ax.bar(sources, conversion_rates, width=0.5, edgecolor='black', color=colors)\n",
    "\n",
    "ax.set_xlabel(\"Sources\", fontsize=12)\n",
    "ax.set_ylabel(\"Conversion Rates (%)\", fontsize=12)\n",
    "\n",
    "ax.set_ylim(0,0.5)\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1d0642e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.15, 0.3)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Codes for Figure 3:\n",
    "\n",
    "bounce_rates = list(platform_group_df['Bounce rate'])\n",
    "drop_out_rates = list(platform_group_df['Drop-out rate'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,6))\n",
    "\n",
    "platforms = list(platform_group_df.index)\n",
    "\n",
    "plt.plot(platforms , bounce_rates , linewidth=2.8 , color = 'darkcyan', label='Bounce rate', marker='o')\n",
    "plt.plot(platforms , drop_out_rates , linewidth=2.8 , color = 'olive', label='Drop-out rate', marker='o')\n",
    "\n",
    "ax.set_title(\"Figure 3: Bounce and Drop-out Rates per Platform\\n\", fontsize=14)\n",
    "\n",
    "ax.set_xlabel(\"Platform\")\n",
    "ax.set_ylabel(\"Rate\")\n",
    "\n",
    "plt.grid(axis='both')\n",
    "\n",
    "ax.set_xticklabels(platforms)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.ylim([0.15,0.3])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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