set_dataframe() : Pandas DataFrame to Google sheets

Python Google sheets
Inserting Pandas DataFrame to google sheets by using set_dataframe() with options to copy index

Creating Google sheets

Create one folder inside your google drive, give the name as python-test.
Give Share authorization ( Share with people and group ) to Google API service account you are using.
Google API authorization and Share at Part 1 here
Creating one new File my_sheet1 ( google sheet ) inside python-test directory.
import pygsheets
path='G:\\My drive\\testing\\google-sheet\\creds1.json'
gc=pygsheets.authorize(service_account_file=path)
sh=gc.create('my_sheet1',folder_name='python-test')

set_dataframe

set_dataframe(df, start, copy_index=False, copy_head=True, 
	extend=False, fit=False, escape_formulae=False, **kwargs)
Pandas DataFrame
OptionsDetails
dfPandas DataFrame
startStaring Point tuple ( row, column). It is (1,1) in below example
copy_indexDefault False, Copy Index data from DataFrame
copy_headTrue ( default ), column headers of Pandas DataFrame is copied ( see example below ).
extendFalse( default) If required add columns and rows ( see example )
fitFalse(default), Resize to fit all data in DataFrame if required ( see example)
escape_formulaeHow to handle formula ( value with = or +- sign ) ( see example below )
nanReplace NaN with input value ( see example below )
Create Pandas DataFrame and then store the same inside our google sheets my_sheets1. Here we have used one blank google sheet and given permission to service account before running this code.

DataFrame to Google sheets
import pygsheets
path='G:\\My drive\\testing\\google-sheet\\creds1.json'
gc=pygsheets.authorize(service_account_file=path)
sh=gc.open('my_gsheets1') # open the existing file 
wk1=sh[0] # first worksheet 
#wk1.clear() # to remove all data from sheet
import pandas as pd # using pandas library 
my_dict={
	'NAME':['Ravi','Raju','Alex'],
	'ID':[1,2,3],'MATH':[30,40,50],
	'ENGLISH':[20,30,60]
	} # Create dictionary 
df = pd.DataFrame(data=my_dict) # Create DataFrame using dictionary
wk1.set_dataframe(df,(1,1)) # Place DataFrame from row 1 column 1 
print(wk1.get_row(4,include_tailing_empty=False)) # for testing 
Output ( for last line )
['Alex', '3', '50', '60']

copy_index

Let us add the DataFrame with index.
wk1.set_dataframe(df,(1,1),copy_index=True) #
set_dataframe() with copy_index=True

copy_head

We can remove the headers by using copy_head option with False
wk1.set_dataframe(df,(1,1),copy_index=True, copy_head=False) 
set_dataframe() with copy_head=False

extend

If DataFrame has more rows and columns than the sheet , then it will generate error saying exceeds grid limits. Max rows: 3, max columns: 5.

However we can use extend option with True ( default is False ) to expand the sheet to match the DataFrame rows and columns. We are reducing the default rows and columns of the sheet by using resize()
wk1.resize(2,2)
wk1.set_dataframe(df,(1,1),copy_index=True, extend=True)
set_dataframe() with extend=Ture

fit

Our sheet has 10 rows and 10 columns and our DataFrame has 5 rows and 4 columns. We can shrink our sheet to exactly match the size of our DataFrame and don't want to leave any blank cell outside the DataFrame by using fit option.
wk1.resize(10,10) # resize rows and columns of sheet
wk1.set_dataframe(df,(1,1),copy_index=True, fit=True) 
set_dataframe() with fit=Ture

escape_formulae

We will change the input DataFrame to include a formula like this '=40+5'. Default value for escape_Formulae is False.
my_dict={
	'NAME':['Ravi','Raju','Alex'],
	'ID':[1,2,3],'MATH':[30, '=40+5',50],
	'ENGLISH':[20,30,60]
	} # Create dictionary 
df = pd.DataFrame(data=my_dict) # Create DataFrame using dictionary
wk1.set_dataframe(df,(1,1), escape_formulae=False)
set_dataframe() with escape_formulae=False

Let us change the escape_formulae option to True
wk1.set_dataframe(df,(1,1), escape_formulae=True)
set_dataframe() with escape_formulae=True

nan

We can replace the NaN with values. Here we are using Numpy to add some NaN values to our DataFrame. Check the np.NaN data
import pandas as pd # using pandas library 
import numpy as np
my_dict={'NAME':['Ravi','Raju','Alex',None,'King',None],
         'ID':[1,2,np.NaN,4,5,6],
         'MATH':[80,40,70,70,82,30],
         'ENGLISH':[81,70,40,50,np.NaN,30]}
df = pd.DataFrame(data=my_dict) # Create DataFrame using dictionary
wk1.set_dataframe(df,(1,1))
set_dataframe() with nan

We will add nan option with * value.
wk1.set_dataframe(df,(1,1), nan='*') 
set_dataframe() with nan=*
Pygsheets and google API authorization get_as_df()
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