ga4_python_report.py

# -*- coding: utf-8 -*-
"""
# Google Analytics 4 (GA4) Data in Pyton Using run_report
"""

import numpy as np
import pandas as pd
import os

from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import DateRange
from google.analytics.data_v1beta.types import Dimension
from google.analytics.data_v1beta.types import Metric
from google.analytics.data_v1beta.types import RunReportRequest
from google.analytics.data_v1beta.types import OrderBy

## Set up global variables

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = 'google_analytics_api_access_keys.json'
property_id = 'GA4_property_id'

client = BetaAnalyticsDataClient()

## Format Report - run_report method
def format_report(request):
    response = client.run_report(request)

    # Row index
    row_index_names = [header.name for header in response.dimension_headers]
    row_header = []
    for i in range(len(row_index_names)):
        row_header.append([row.dimension_values[i].value for row in response.rows])

    row_index_named = pd.MultiIndex.from_arrays(np.array(row_header), names = np.array(row_index_names))
    # Row flat data
    metric_names = [header.name for header in response.metric_headers]
    data_values = []
    for i in range(len(metric_names)):
        data_values.append([row.metric_values[i].value for row in response.rows])

    output = pd.DataFrame(data = np.transpose(np.array(data_values, dtype = 'f')),
                          index = row_index_named, columns = metric_names)
    return output

request = RunReportRequest(
        property='properties/'+property_id,
        dimensions=[Dimension(name="month"),
                    Dimension(name="sessionMedium")],
        metrics=[Metric(name="averageSessionDuration"),
                 Metric(name="activeUsers")],
        order_bys = [OrderBy(dimension = {'dimension_name': 'month'}),
                    OrderBy(dimension = {'dimension_name': 'sessionMedium'})],
        date_ranges=[DateRange(start_date="2022-06-01", end_date="today")],
    )


format_report(request)

output_df = format_report(request)

## Export to Excel
output_df.reset_index().to_excel('GA4_python_output.xlsx', sheet_name = 'GA4_report', engine = 'xlsxwriter')

## Export to CSV
output_df.to_csv('GA4_python_output.csv')

"""# GA4 Monthly Traffic Chart"""

from datetime import date
from datetime import timedelta
import matplotlib.pyplot as plt
import seaborn as sns

monhtly_users_pivot = pd.pivot_table(output_df,
                                     columns=['sessionMedium'],
                                     index=['month'],
                                     values=['activeUsers'],
                                     aggfunc = 'sum',
                                     fill_value=0).droplevel(0, axis=1)

monhtly_users_pivot

sns.set_theme()
monhtly_users_pivot.plot.bar(y = ['(none)', 'organic', 'referral', '(not set)'], stacked = True,
                             colormap = 'Dark2',
                             figsize=(7,5), title = 'Active Users by Month')
plt.legend(title = 'User Medium', bbox_to_anchor = (1.05, 0.5))
plt.title('Active Users by Month', fontsize = 15);

"""# GA4 Traffic Report"""

## Global variables
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = 'google_analytics_api_access_keys.json'
property_id = 'GA4_property_id'
client = BetaAnalyticsDataClient()

## Report dates
end_date = 'today' ## ("today" or "yyyy-mm-dd")
no_days = 90

def calc_start_date(end_date, no_days):
    if end_date == "today":
        start_date = date.today() - timedelta(days=no_days)
    else:
        start_date = date.fromisoformat(end_date) - timedelta(days=no_days)

    return start_date.strftime("%Y-%m-%d")

def produce_report (end_date, no_days, property_id = property_id, client = client):
    daily_traffic_request = RunReportRequest(
            property='properties/'+property_id,
            dimensions=[Dimension(name="date"),
                        Dimension(name="sessionMedium")],
            metrics=[Metric(name="activeUsers")],
            order_bys = [OrderBy(dimension = {'dimension_name': 'date'}),
                        OrderBy(dimension = {'dimension_name': 'sessionMedium'})],
            date_ranges=[DateRange(start_date=calc_start_date(end_date, no_days), end_date=end_date)],
        )

    page_users_request = RunReportRequest(
            property='properties/'+property_id,
            dimensions=[Dimension(name="pagePath")],
            metrics=[Metric(name="activeUsers")],
            order_bys = [OrderBy(metric = {'metric_name': 'activeUsers'}, desc = True)],
            date_ranges=[DateRange(start_date=calc_start_date(end_date, no_days), end_date=end_date)],
        )


    landing_page_users_request = RunReportRequest(
            property='properties/'+property_id,
            dimensions=[Dimension(name="landingPage")],
            metrics=[Metric(name="activeUsers")],
            order_bys = [OrderBy(metric = {'metric_name': 'activeUsers'}, desc = True)],
            date_ranges=[DateRange(start_date=calc_start_date(end_date, no_days), end_date=end_date)],
        )

    daily_traffic = format_report(daily_traffic_request).reset_index()
    active_users_pivot = pd.pivot_table(daily_traffic,
                                     columns=['sessionMedium'],
                                     index=['date'],
                                     values=['activeUsers'],
                                     aggfunc = 'sum',
                                     fill_value=0).droplevel(0, axis=1)
    active_users_pivot.index = active_users_pivot.index.str.slice(start=4)

    # Produce pie and line charts

    fig, (axs1, axs2) = plt.subplots(1,2, figsize = (14, 4), gridspec_kw={'width_ratios': [1, 2]})
    pie_data = daily_traffic.groupby(by = ['sessionMedium']).sum().sort_values(by = ['activeUsers'], ascending = False)
    pie_data.plot.pie(ax = axs1,
                      colormap = 'Dark2',
                      y = ['activeUsers'],
                      title = 'Active Users by Medium',
                      legend = False,
                      label = False,
                      startangle = 0,
                      autopct = lambda p:f'{p:.0f}%').set_ylabel('')

    active_users_pivot.plot.line(ax = axs2,
                                 colormap = 'Dark2',
                                 y = pie_data.index,
                                 title = 'Active Users by Day')


    axs2.legend(title = 'User Medium', bbox_to_anchor = (1.05, 0.6))

    plt.show();

    # Produce Top 10 pgaes output tables
    landing_table = format_report(landing_page_users_request)
    landing_table['activeUsers'] = landing_table['activeUsers'].astype('int')
    page_users_table = format_report(page_users_request)
    page_users_table['activeUsers'] = page_users_table['activeUsers'].astype('int')

    print('\nTop 10 Landing Pages')
    display(landing_table[0:10])
    print('\nTop 10 Visited Pages')
    display(page_users_table[0:10])

produce_report(end_date, no_days)