Write data to existing excel w/o replacing previous data

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Python

MS-Excel

Currently, I am exporting two data frames to a single excel file. Each data frame is placed on separate sheets. I am currently running a simulation and need 2,000 columns of each data frame (2,000 on each sheet). Every time I run my code, it creates a new excel file (what I originally intended). But to save time, I was wondering if it would be possible to write onto an existing excel file by adding new columns of data without writing over the existing data?

######### This is my original code ################
    import pandas as pd
    from pandas import ExcelWriter
    from pandas import ExcelFile
    from pandas import DataFrame
    from openpyxl import Workbook
    
    df1 = pd.DataFrame(returns1)
    df2 = pd.DataFrame(returns2)
    
    
    x1 = 'mypath/ '
    x2 = datetime.datetime.now().strftime('%B %d, %Y %H-%M-%S')
    x3 = '.xlsx'
    destination = x1 + x2 + x3
    writer = pd.ExcelWriter(destination, engine='xlsxwriter')
    
    df1.to_excel(writer, sheet_name= 'Returns 1', index=False)
    df2.to_excel(writer, sheet_name= 'Returns 2', index=False)
    writer.save()

What I have tried:

############ Update ##############

Got it to work! This is the final code! Does exactly what I wanted. I hope this will help others who run into the same problem I was having. 
    
    df1 = pd.DataFrame(returns1)
    df2 = pd.DataFrame(returns2)
    
    book = load_workbook('mypath.xlsx')
    writer = pd.ExcelWriter('mypath.xlsx', engine='openpyxl')
    writer.book = book
    writer.sheets = {ws.title: ws for ws in book.worksheets}
    

    df1.to_excel(writer, sheet_name='Returns1', startrow=0, startcol=writer.sheets['Returns1'].max_column, index=False)
    df2.to_excel(writer, sheet_name='Returns2', startrow=0, startcol=writer.sheets['Returns2'].max_column, index=False)


This is what I wanted the code to do... 
 A
100
120
119
225

Second Time
 A     B
100    98
120   100
119   105
125   111

Third Time
 A     B     C
100    98    106 
120   100    99
119   105    101
125   111    89

and so on...