Google Analytics response to Pandas Dataframe in Python

Question

Still a newbie to Python so please be gentle.

I'm trying to parse a Google Analytics Reporting API V4 response to a Pandas dataframe in Python, specifically using Repl if that makes any difference. I've tried a few commonly accepted answers from SO but none seem to be working. The code 'successfully' (no error msgs) executes but even basic print('Done') commands at the end of the script aren't showing up in the terminal.

Can anyone see what might be wrong with my code?

import pandas as pd
from pandas import json_normalize

response = {'reports':[{'columnHeader':{'dimensions':['ga:date','ga:sourceMedium','ga:landingPagePath','ga:deviceCategory','ga:browser'],'metricHeader':{'metricHeaderEntries':[{'name':'ga:sessions','type':'INTEGER'}]}},'data':{'rows':[{'dimensions':['20201126','(direct) / (none)','/test/page.html','desktop','Chrome'],'metrics':[{'values':['1']}]}],'totals':[{'values':['1000']}],'rowCount':100,'minimums':[{'values':['1']}],'maximums':[{'values':['10']}],'isDataGolden':True},'nextPageToken':'1'}]}

def parse_data(response):

  reports = response['reports'][0]
  columnHeader = reports['columnHeader']['dimensions']
  metricHeader = reports['columnHeader']['metricHeader']['metricHeaderEntries']

  columns = columnHeader
  for metric in metricHeader:
    columns.append(metric['name'])

  data = pd.json_normalize(reports['data']['rows'])
  data_dimensions = pd.DataFrame(data['dimensions'].tolist())
  data_metrics = pd.DataFrame(data['metrics'].tolist())
  data_metrics = data_metrics.applymap(lambda x: x['values'])
  data_metrics = pd.DataFrame(data_metrics[0].tolist())
  result = pd.concat([data_dimensions, data_metrics], axis=1, ignore_index=True)

  return result
  print(result)
  result.to_csv('result.csv')
  print('Done')

Cameron Riddell · Accepted Answer · 2021-06-29 18:42:58Z

5

It's because in a given function, any code that occurs after the return statement is not executed. Once return is reached, the function is done. If you put your print call and to_csv method before the return statement, they should run.

Try reorganizing your code as such:

def parse_data(response):

  reports = response['reports'][0]
  columnHeader = reports['columnHeader']['dimensions']
  metricHeader = reports['columnHeader']['metricHeader']['metricHeaderEntries']

  columns = columnHeader
  for metric in metricHeader:
    columns.append(metric['name'])

  data = pd.json_normalize(reports['data']['rows'])
  data_dimensions = pd.DataFrame(data['dimensions'].tolist())
  data_metrics = pd.DataFrame(data['metrics'].tolist())
  data_metrics = data_metrics.applymap(lambda x: x['values'])
  data_metrics = pd.DataFrame(data_metrics[0].tolist())
  result = pd.concat([data_dimensions, data_metrics], axis=1, ignore_index=True)

  print(result)
  result.to_csv('result.csv')
  print('Done')
  return result

edited Jun 29, 2021 at 18:42

answered Nov 25, 2020 at 21:51

Cameron Riddell

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2 Comments

PuGZoR Over a year ago

Good call out! Sadly that didn't seem to solve the issue. Same result. :(

wildlifehexagon Over a year ago

This code works if you specify pd.json_normalize rather than just json_normalize

Kirill Dolbnev · Accepted Answer · 2022-04-15 09:34:42Z

I would just modify the above function, in order to add colum names as well:

def parse_data(response):
reports = response['reports'][0]
columnHeader = reports['columnHeader']['dimensions']
metricHeader = reports['columnHeader']['metricHeader']['metricHeaderEntries']
#Get dimenssion names
dim_names = [columnHeader[n].split(":")[1] for n in range (len(columnHeader))]
#Get metric names
metric_names = [metricHeader[n]['name'].split(":")[1] for n in range (len(metricHeader))]
column_names = dim_names+metric_names


columns = columnHeader
for metric in metricHeader:
    columns.append(metric['name'])

data = pd.json_normalize(reports['data']['rows'])
data_dimensions = pd.DataFrame(data['dimensions'].tolist())
data_metrics = pd.DataFrame(data['metrics'].tolist())
data_metrics = data_metrics.applymap(lambda x: x['values'])
data_metrics = pd.DataFrame(data_metrics[0].tolist())
result = pd.concat([data_dimensions, data_metrics], axis=1, ignore_index=True)

# Assign columns names to DF
result.columns = column_names
print(result)
result.to_csv('result.csv')
print('Done')
return result

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