Modifying an Existing xlsx File with Python

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In this article, we will explore how to modify an existing Excel file (.xlsx) using Python. We’ll use the popular libraries Pandas and openpyxl to achieve this task.

Introduction

Python is a versatile language that can be used for various data manipulation tasks, including working with Excel files. The aim of this article is to provide a step-by-step guide on how to modify an existing xlsx file using Python.

Requirements

Before we dive into the code, let’s make sure you have the necessary libraries installed:

pandas (imported as pd)
openpyxl
python-openxml

You can install these libraries using pip:

pip install pandas openpyxl python-openxml

Loading an Existing Excel File

The first step is to load the existing Excel file into a Pandas DataFrame. We’ll use the pd.read_excel() function for this purpose.

Code

import pandas as pd

# Load the existing Excel file
file_origin = 'existing_file.xlsx'
df = pd.read_excel(file_origin)

Data Filtering and Modification

After loading the data, we need to filter or modify it according to our requirements. In this case, we want to add a new column with some values.

Code

# Create a new DataFrame with the desired data
data_filtered = pd.DataFrame([date, date, date, date], index=[2, 3, 4, 5])

# Print the original and filtered DataFrames
print("Original DataFrame:")
print(df)
print("\nFiltered DataFrame:")
print(data_filtered)

Loading an Existing xlsx File with openpyxl

Now that we have the data, let’s load the existing Excel file using openpyxl. We’ll use the load_workbook() function for this purpose.

Code

from openpyxl import load_workbook

# Load the existing Excel file
file_origin = 'existing_file.xlsx'
book = load_workbook(file_origin)

Creating an ExcelWriter Object

Next, we need to create an ExcelWriter object. We’ll use this object to write our filtered data into a new Excel file.

Code

from openpyxl import Workbook

# Create an ExcelWriter object
file_modif = 'modified_file.xlsx'
writer = pd.ExcelWriter(file_modif, engine='openpyxl', datetime_format='dd/mm/yyyy hh:mm:ss', date_format='dd/mm/yyyy')

# Set the writer.book attribute to match the original book
writer.book = book

# Set the writer.sheets attribute to access the sheet by title
writer.sheets = dict((ws.title, ws) for ws in book.worksheets)

# Write the filtered data into a new Excel file
data_filtered.to_excel(writer, sheet_name="PCA pour intégration", index=False, startrow=2, startcol=5, header=False, verbose=True)

Writing and Saving the Modified File

Finally, we can write and save our modified file.

Code

# Write and save the modified file
writer.save()

Conclusion

In this article, we explored how to modify an existing xlsx file using Python. We used Pandas for data filtering and modification, openpyxl for loading and writing Excel files.

We walked through each step of the process, providing code examples and explanations where necessary. By following these steps, you should be able to modify your own Excel files using Python.

Troubleshooting

When working with Excel files in Python, there are several potential issues that can arise.

Error Messages

openpyxl.utils.get_column_letter() is not defined: This error occurs when the column letter used in the to_excel() function does not match the actual column letter in the sheet.
ExcelWriter object uses the wrong engine: Make sure to use the correct engine for your Excel file, which can be either ‘openpyxl’ or ‘xlsxwriter’.
Incorrect date format: Verify that the date format used in the ExcelWriter object matches the actual date format in the Excel file.

Best Practices

When working with Excel files in Python, keep the following best practices in mind:

Use try-except blocks to catch and handle any errors that may occur during data manipulation.
Verify the accuracy of your data before writing it back to an Excel file.
Be mindful of the memory requirements when working with large datasets.

By following these tips and techniques, you can efficiently and accurately work with Excel files in Python.

Last modified on 2024-05-28