# install libraries
!pip install --upgrade google-api-python-client

Enable API's

• Google Analytics Reporting API - Access report data in Google Analytics.
• Google Analytics API - The Analytics API provides access to Analytics configuration and report data.
• Google Analytics Data API - Accesses report data in Google Analytics.

#Load Libraries
from oauth2client.service_account import ServiceAccountCredentials
from apiclient.discovery import build
import httplib2
import pandas as pd

Create service credentials

#Rename your JSON key to client_secrets.json and save it to your working folder
credentials = ServiceAccountCredentials.from_json_keyfile_name('client_secrets.json', ['https://www.googleapis.com/auth/analytics.readonly'])

Create a service object

http = credentials.authorize(httplib2.Http())
service = build('analytics', 'v4', http=http, discoveryServiceUrl=('https://analyticsreporting.googleapis.com/$discovery/rest'))

Set pagePath Filter

regex='~^/4-ways-to-take-your-business-into-the-future/.*'

Make an api-call to Google Analytics

response = service.reports().batchGet(
    body={
        'reportRequests': [
            {
                'viewId': '60792143',
                'dateRanges': [{'startDate': '90daysAgo', 'endDate': 'today'}],
                'metrics': [{'expression': 'ga:sessions'}],
                'dimensions': [{"name": "ga:pagePath"}],
                "filtersExpression":f"ga:pagePath={regex}", 
                'orderBys': [{"fieldName": "ga:sessions", "sortOrder": "DESCENDING"}],
                'pageSize': 10000
            }]
    }
).execute()

Parsing The Report Data

#create two empty lists that will hold our dimentions and sessions data
dim = []
val = []
  
#Extract Data
for report in response.get('reports', []):
  
    columnHeader = report.get('columnHeader', {})
    dimensionHeaders = columnHeader.get('dimensions', [])
    metricHeaders = columnHeader.get('metricHeader', {}).get('metricHeaderEntries', [])
    rows = report.get('data', {}).get('rows', [])
  
    for row in rows:
  
        dimensions = row.get('dimensions', [])
        dateRangeValues = row.get('metrics', [])
  
        for header, dimension in zip(dimensionHeaders, dimensions):
            dim.append(dimension)
  
        for i, values in enumerate(dateRangeValues):
            for metricHeader, value in zip(metricHeaders, values.get('values')):
                val.append(int(value))

Create a DataFrame With Your Data

df = pd.DataFrame() 
df["Sessions"]=val
df["pagePath"]=dim
df=df[["pagePath","Sessions"]]

print(df)

                                         pagePath  Sessions
0  /4-ways-to-take-your-business-into-the-future/         2

Export to CSV

df.to_csv("page_by_session.csv")