Loading and Editing Excel Pivot Table in Python

What will you learn?

In this tutorial, you will learn how to efficiently load and edit an Excel Pivot table using Python. We will guide you through the process step by step, covering the manipulation of data stored in an Excel Pivot table, making necessary edits or updates, and saving the modified file while preserving its structure.

Introduction to Problem and Solution

Managing data within an Excel Pivot table is a common requirement, and Python provides a powerful solution for this task. By utilizing libraries such as pandas for data manipulation and openpyxl for working with Excel files, we can seamlessly handle loading existing Excel files containing Pivot tables, making modifications to the data within these tables, and saving the updated file without losing any formatting.


# Importing necessary libraries
import pandas as pd

# Load your excel file (replace 'file.xlsx' with your actual filename)
excel_file = pd.ExcelFile('file.xlsx')

# Parse pivot tables from the loaded excel file
pivot_tables = excel_file.parse(sheet_name=None)

# Work with pivot tables as needed...

# Save the updated excel file (replace 'updated_file.xlsx' with desired filename)
writer = pd.ExcelWriter('updated_file.xlsx')
for sheet_name, df in pivot_tables.items():
    df.to_excel(writer, sheet_name=sheet_name)

# Copyright PHD


  1. Import Libraries: Begin by importing pandas library essential for handling data frames.
  2. Load Excel File: Use pd.ExcelFile() to load an existing Excel file into your script.
  3. Parse Pivot Tables: Extract all sheets containing pivot tables from the loaded Excel workbook using .parse().
  4. Modify Data: Apply necessary modifications within each DataFrame corresponding to a specific pivot table.
  5. Save Changes: Utilize pd.ExcelWriter() along with .to_excel() method to save changes back into a new Excel file after editing.
  1. How do I install pandas?

  2. You can install pandas using pip:

  3. pip install pandas
  4. # Copyright PHD
  5. Can I handle multiple pivot tables from one excel file?

  6. Yes! Loop through each extracted sheet from parsed pivot tables dictionary and work on them individually.

  7. What if my pivot table has complex calculations?

  8. Pandas offers capabilities for complex calculations like groupby functions on dataframes.

  9. Is openpyxl mandatory for working with excel files in pandas?

  10. No! While openpyxl provides advanced features when directly working with .xlsx files at lower levels compared to Pandas methods like .to_excel(), it’s not mandatory unless dealing extensively at that level.

  11. How do I know if my changes were saved successfully?

  12. Successful write operations feedback messages indicate that your updated dataframe(s) were saved back into a new/existing spreadsheet using .save() method on your writer object.

  13. Can I format my output before saving it back?

  14. Absolutely! You have complete control over formatting your final output before writing/saving it back into another spreadsheet.


This comprehensive guide demonstrated effective techniques for loading and editing content within an existing Excel Pivot Table structure using Python. By leveraging popular libraries such as Pandas and others, you can seamlessly manage tasks related to modifying Pivot table data.

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