ga_campaing_costs.py

from apiclient.discovery import build
from oauth2client.service_account import ServiceAccountCredentials

import pandas as pd
import datetime

#SCOPES = ['https://www.googleapis.com/auth/analytics.readonly']
SCOPES = []
KEY_FILE_LOCATION = './path-to-your-private-key-file.json'
VIEW_ID = '123456789'
from_date = datetime.datetime(2017,12,1,0,0,0).strftime("%Y-%m-%d")


def initialize_analyticsreporting():
  """Initializes an Analytics Reporting API V4 service object.

  Returns:
    An authorized Analytics Reporting API V4 service object.
  """
  credentials = ServiceAccountCredentials.from_json_keyfile_name(
      KEY_FILE_LOCATION, SCOPES)

  # Build the service object.
  analytics = build('analyticsreporting', 'v4', credentials=credentials)

  return analytics



def get_report(analytics, query):
    """Queries the Analytics Reporting API V4, combines multiple pageSize
       together and returns a pandas dataframe.

    Args:
    analytics: An authorized Analytics Reporting API V4 service object.
    query: the json that describes the query
    Returns: a pandas dataframe.
    """
    nextPageToken = '0'
    responses = []
    while (nextPageToken != None):
        query['reportRequests'][0]['pageToken'] = nextPageToken
        result = analytics.reports().batchGet(
          body=query
        ).execute()
        responses.append(result)
        if 'nextPageToken' in result['reports'][0]: 
            nextPageToken = result['reports'][0]['nextPageToken']
        else:
            return responses
        
    return responses
    
# The columns from GA come with a ga: -prefix. This simply removes that where it exists.
def strip_ga_prefix(string):
    if string[:3] == 'ga:':
        return string[3:]
    else:
        return string


def get_dataframe(responses):
  """Parses the Analytics Reporting API V4 response to a pandas dataframe

  Args:
    response: An Analytics Reporting API V4 response.
  """
  rowlist = []
  for response in responses:
      for report in response.get('reports', []):
        columnHeader = report.get('columnHeader', {})
        dimensionHeaders = columnHeader.get('dimensions', [])
        metricHeaders = columnHeader.get('metricHeader', {}).get('metricHeaderEntries', [])

        for row in report.get('data', {}).get('rows', []):
          new_row = {}
          dimensions = row.get('dimensions', [])
          dateRangeValues = row.get('metrics', [])

          for header, dimension in zip(dimensionHeaders, dimensions):
            new_row[strip_ga_prefix(header)] = dimension

          for i, values in enumerate(dateRangeValues):
            for metricHeader, value in zip(metricHeaders, values.get('values')):
              new_row[strip_ga_prefix(metricHeader.get('name'))] = value

          rowlist.append(new_row)

  df = pd.DataFrame(rowlist)
  return df


query = {
    'reportRequests': [{
        'viewId' : VIEW_ID,
        'pageSize' : 10000,
        
        'dateRanges': [{
            'startDate' : from_date,
            'endDate'   : 'today'}],
            
        'dimensions' : [
            {'name' : 'ga:date'},
            {'name' : 'ga:source'},
            {'name' : 'ga:medium'},
            {'name' : 'ga:campaign'},
            {'name' : 'ga:keyword'},
            {'name' : 'ga:adGroup'}
            ],
          "orderBys": [
       {'fieldName' : 'ga:adCost', 'sortOrder': 'DESCENDING'},
  ],

        'metrics':[
            {'expression' : 'ga:adCost'},],
    }]
}


analytics = initialize_analyticsreporting()
response = get_report(analytics, query)
df = get_dataframe(response)