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get_monthly_ga4_update_for_github.py
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#!/usr/bin/env python
# Copyright 2021 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Google Analytics Data API sample application demonstrating the batch creation
of multiple reports.
See https://developers.google.com/analytics/devguides/reporting/data/v1/rest/v1beta/properties/batchRunReports
for more information.
"""
# [START analyticsdata_run_batch_report]
# Starting point using Google's code to develop our own reporting code
import datetime as dt
import pandas as pd
import numpy as np
from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import (
BatchRunReportsRequest, # which report request do we run?
DateRange,
Dimension,
Metric,
RunReportRequest, # this is the other request?
)
import pdb
#
# see if old analytics file is there and load it:
mypath='/home/flbahr/webdata/'
myfile='ga4_monthly_analytics_new.csv'
mynow=dt.datetime.now()
try:
olddataframe=pd.read_csv(mypath+myfile)
lastmonth=olddataframe['month'].iloc[-1]
# need to worry about january and year change
if mynow.month < lastmonth:
mystart=dt.datetime(mynow.year-1,lastmonth,1)
else:
mystart=dt.datetime(mynow.year,lastmonth,1)
nodataframe=0
except:
mystart=mynow-dt.timedelta(days=31)
nodataframe=1
#
# get last value and use that for start point
#
syear=str(mystart.year)
smon=str(mystart.month)
sday=str(mystart.day)
#
# CeNCOOS property ID
property_id=put your property ID here
# create google data api client?
# set path to credentials
credentials_json_path="put your credential file here" # put in correct json file name
client=BetaAnalyticsDataClient()
# example code from google
#dimension=[Dimension(name="city")], # example don't want this
#metrics=[Metric(name="activeUsers")],
#date_ranges=[DateRange(start_date="YYYY-MM-DD", end_date="today")],
mydaterange=[DateRange(start_date=syear+"-"+smon+"-"+sday, end_date="today")]
mydimensions=[Dimension(name="month"),
Dimension(name="year")] # this will output only a month data
mymetrics=[
Metric(name="totalUsers"),
Metric(name="newUsers"),
Metric(name="sessions"),
Metric(name="bounceRate"),
Metric(name="averageSessionDuration"),
Metric(name="active28DayUsers"),
Metric(name="activeUsers"),
Metric(name="ScreenPageViewsPerSession"),
Metric(name="screenPageViews")]
#
# Make the request from Google
# use the metrics above and the daterange above
#
requests=RunReportRequest(
property=f"properties/{property_id}",
dimensions=mydimensions,
metrics=mymetrics,
date_ranges=mydaterange,
)
response=client.run_report(requests)
dim_len=len(response.dimension_headers)
metric_len=len(response.metric_headers)
all_data=[]
for row in response.rows:
row_data={}
for i in range(0,dim_len):
row_data.update({response.dimension_headers[i].name: row.dimension_values[i].value})
for i in range(0, metric_len):
row_data.update({response.metric_headers[i].name: row.metric_values[i].value})
all_data.append(row_data)
newdataframe=pd.DataFrame(all_data)
x=newdataframe.sort_values('month').copy(deep=True)
spv=x['screenPageViews']
sess=x['sessions']
nuser=x['newUsers']
totu=x['totalUsers']
# sessions does not equate to number of users....
notnewu=totu.astype(int)-nuser.astype(int) # number of returing users
pctnu=nuser.astype(int)/sess.astype(int)
avgsess_per_user=sess.astype(int)/totu.astype(int)
avg_not_newsessions=avgsess_per_user*notnewu
newsess=sess.astype(int)-avg_not_newsessions
avg_percent_newsessions=newsess/sess.astype(int)
#
# okay need to append to old data frame and possibly replace the last element
#
out_df=x[['month','year','sessions','totalUsers']].copy(deep=True)
out_df['sessions']=out_df['sessions'].astype(str)
out_df['totalUsers']=out_df['totalUsers'].astype(str)
# this isn't what we really want...
# need to find location of last month in olddataframe and update
# need to append the rest of out_df to olddataframe
#
# okay we need to find the index in the new dataframe of the last month in the old dataframe
x=x.reset_index()
thelastmonth=olddataframe['month'].iloc[-1]
thelastyear=olddataframe['year'].iloc[-1]
# okay now find the index of this location in the new dataframe
therowineed=x[(x['month'].astype(int)==thelastmonth)&(x['year'].astype(int)==thelastyear)].index.to_numpy()
therow=therowineed[0]
# now replace the last values in the olddataframe with the new values
olddataframe.iloc[-1,2]=x['sessions'][therow] # sessions is column 2 and -1 is last row (columns start with 0)
olddataframe.iloc[-1,3]=x['totalUsers'][therow] # totalUsers is column 3 and -1 is last row
#
# rows to drop
#
rows_to_remove=np.arange(0,therow+1)
out_df=out_df.reset_index()
out_df=out_df.drop(rows_to_remove)
# now merge dataframes
olddataframe=pd.concat([olddataframe,out_df],ignore_index=True)
olddataframe.drop(columns='index')
#
#
daysinmonth=np.array([31,28,31,30,31,30,31,31,30,31,30,31])
newdf=olddataframe.copy(deep=True)
months=newdf['month'].astype(int)-1
dpm=daysinmonth[months]
newdf['days']=dpm
newdf['month']=newdf['month'].astype(str)
newdf['month']=newdf['month'].str.zfill(2)
newdf['year']=newdf['year'].astype(str)
newdf['days']=newdf['days'].astype(str)
newdf['date']=newdf['year']+'-'+newdf['month']+'-15'
#
# now print out to a csv file!
olddataframe.to_csv('/home/flbahr/webdata/ga4_monthly_analytics_new.csv',index=False)
newdf=newdf.drop(columns=['month','year'])
newdf=newdf[['date','sessions','totalUsers']]
newdf=newdf.rename(columns={'totalUsers':'users'})
newdf=newdf[:-1]
newdf.to_csv('/home/flbahr/webdata/ga4_monthly_forwebplot.csv',index=False)