Web Traffic Analysis: Extracting and Visualizing Google Analytics Data

Tracking website traffic is essential for understanding user behavior, optimizing marketing campaigns, and improving website performance. With the Google Analytics API, we can extract data and visualize insights to make informed decisions.

Getting Google Analytics API Access

1. Enable the Google Analytics API in Google Cloud Console.
2. Create credentials and download the credentials.json file.
3. Install required Python libraries:

   pip install google-auth google-auth-oauthlib google-auth-httplib2 googleapiclient pandas matplotlib

Fetching Website Traffic Data

Using the Google Analytics API, we can retrieve session data for a given timeframe.

1. Authenticate and Connect to the API

from googleapiclient.discovery import build
from google.oauth2 import service_account

SCOPES = ['https://www.googleapis.com/auth/analytics.readonly']
KEY_FILE_LOCATION = 'credentials.json'
VIEW_ID = 'YOUR_VIEW_ID'

# Authenticate
credentials = service_account.Credentials.from_service_account_file(
    KEY_FILE_LOCATION, scopes=SCOPES)

analytics = build('analyticsreporting', 'v4', credentials=credentials)

2. Fetch Data from Google Analytics

def get_report():
    return analytics.reports().batchGet(
        body={
            'reportRequests': [
                {
                    'viewId': VIEW_ID,
                    'dateRanges': [{'startDate': '7daysAgo', 'endDate': 'today'}],
                    'metrics': [{'expression': 'ga:sessions'}, {'expression': 'ga:pageviews'}],
                    'dimensions': [{'name': 'ga:date'}]
                }
            ]
        }
    ).execute()

data = get_report()

3. Process and Visualize the Data

import pandas as pd
import matplotlib.pyplot as plt

# Extract relevant data
rows = data['reports'][0]['data']['rows']
dates = [row['dimensions'][0] for row in rows]
sessions = [int(row['metrics'][0]['values'][0]) for row in rows]

# Convert to DataFrame
df = pd.DataFrame({'Date': dates, 'Sessions': sessions})
df['Date'] = pd.to_datetime(df['Date'])

# Plot the data
plt.figure(figsize=(10,5))
plt.plot(df['Date'], df['Sessions'], marker='o', linestyle='-')
plt.title('Website Sessions Over Time')
plt.xlabel('Date')
plt.ylabel('Sessions')
plt.xticks(rotation=45)
plt.show()

Conclusion

Using the Google Analytics API and Python, we can automate web traffic analysis and visualize user behavior trends. These insights help optimize marketing strategies and improve website performance.