5 Best Ways to Write a Python Program to Export DataFrames into an Excel File with Multiple Sheets

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πŸ’‘ Problem Formulation: In data analysis, there is often a need to export complex datasets organized in DataFrames to Excel files for reporting or sharing purposes. A common requirement is to create multiple sheets within a single Excel file, each containing different subsets or transformations of the data. For example, one might want a DataFrame with sales data to be exported into an Excel file with separate sheets for each region or product category.

Method 1: Using pandas ExcelWriter

One popular method is utilizing the pandas library’s ExcelWriter context manager. This provides a way to write multiple DataFrames to different Excel sheets. It’s particularly useful when dealing with multiple sheets because it efficiently manages the writer’s object without you needing to explicitly close it, thanks to the context manager protocol.

Here’s an example:

import pandas as pd

# Create some example DataFrames
df1 = pd.DataFrame({'Data': [1, 2, 3]})
df2 = pd.DataFrame({'Data': [4, 5, 6]})

# Export to Excel with multiple sheets
with pd.ExcelWriter('output.xlsx') as writer:
    df1.to_excel(writer, sheet_name='Sheet1')
    df2.to_excel(writer, sheet_name='Sheet2')

Output: An Excel file named ‘output.xlsx’ with two sheets: ‘Sheet1’ and ‘Sheet2’.

The above snippet uses pandas’ ExcelWriter to create an Excel file, where each DataFrame is written to a separate sheet. The with construct ensures that the Excel file is properly saved and closed after the DataFrames are written.

Method 2: Writing Different DataFrames in Loop

When you have a dynamic or large number of DataFrames, iterating over them and writing each one to a separate sheet can be very effective. This method scales with the number of DataFrames and is flexible based on your data’s structure.

Here’s an example:

import pandas as pd

# Dictionary of DataFrames
dataframes = {'Sheet1': df1, 'Sheet2': df2, 'Sheet3': df3}

with pd.ExcelWriter('output.xlsx') as writer:
    for sheet_name, dataframe in dataframes.items():
        dataframe.to_excel(writer, sheet_name=sheet_name)

Output: An Excel file named ‘output.xlsx’ with sheets named ‘Sheet1’, ‘Sheet2’, and ‘Sheet3’.

This script creates an Excel file where a loop goes through a dictionary of DataFrames. Each key-value pair corresponds to a sheet name and a DataFrame, respectively, which neatly organizes the DataFrames into separate sheets.

Method 3: Specifying DataFrames’ Start Row and Column

For more control over the placement of DataFrames within each sheet, one can specify the starting row and column using the startrow and startcol parameters of the to_excel method. This is useful for adding DataFrames next to each other or for leaving space for headers and metadata.

Here’s an example:

with pd.ExcelWriter('output.xlsx') as writer:
    df1.to_excel(writer, sheet_name='Sheet1', startrow=0, startcol=0)
    df2.to_excel(writer, sheet_name='Sheet1', startrow=0, startcol=3)

Output: An Excel file named ‘output.xlsx’ with a single sheet ‘Sheet1’ where ‘df1’ starts at cell A1 and ‘df2’ starts at cell D1.

This method allows for precise positioning of DataFrames within an Excel sheet, providing the flexibility to format the spreadsheet as needed. The parameters startrow and startcol control where each DataFrame begins, without any data merge conflict.

Method 4: Appending DataFrames to Existing Excel Files

If there is a pre-existing Excel file to which DataFrames need to be appended as separate sheets, pandas’ ExcelWriter can be used with the mode set to ‘a’ for appending. This method is ideal for incrementally building up an Excel report over time.

Here’s an example:

with pd.ExcelWriter('output.xlsx', mode='a') as writer:
    df3.to_excel(writer, sheet_name='Sheet3')

Output: An Excel file named ‘output.xlsx’, appending ‘df3’ as a new sheet ‘Sheet3’ to the existing sheets.

This snippet demonstrates appending a new DataFrame to an existing Excel file as a new sheet. Here, ‘output.xlsx’ is first read in append mode and then the new DataFrame is appended without overwriting the existing data.

Bonus One-Liner Method 5: Direct DataFrame Export

For a quick export of a single DataFrame to an Excel file, one might opt for a direct to_excel method call without ExcelWriter. While not suitable for multiple sheets, this is the simplest way to write a DataFrame to Excel.

Here’s an example:

df1.to_excel('output.xlsx', sheet_name='Sheet1')

Output: A straightforward Excel file named ‘output.xlsx’ with a single sheet ‘Sheet1’ containing ‘df1’ data.

This method directly exports ‘df1’ into an Excel file under ‘Sheet1’, saving time and code lines when only a single sheet is needed.


  • Method 1: Using pandas ExcelWriter. Strengths: Flexibility and automatic resource management. Weaknesses: Slightly verbose for simple tasks.
  • Method 2: Writing Different DataFrames in Loop. Strengths: Great for dynamic DataFrame exports. Weaknesses: Requires set up of DataFrames or iteration structure.
  • Method 3: Specifying DataFrames’ Start Row and Column. Strengths: Enhanced control over DataFrame positioning. Weaknesses: Could be complex for beginners.
  • Method 4: Appending DataFrames to Existing Excel Files. Strengths: Good for incremental file updates. Weaknesses: Depends on file existence and can be slow for large files.
  • Bonus One-Liner Method 5: Direct DataFrame Export. Strengths: Simplicity and speed for single sheets. Weaknesses: Not suitable for multiple sheets scenarios.