Understanding the Issue with Adding Images to Excel Files using pandas and xlsxwriter

As a data scientist, working with Excel files is a common task. When it comes to adding images to these files, things can get a bit more complicated. In this article, we’ll delve into the world of pandas, xlsxwriter, and image insertion to understand why our code isn’t working as expected.

Introduction

The question at hand revolves around using pandas’ to_excel() function along with xlsxwriter’s engine. The goal is to insert an image into a specific cell within an Excel file created by pandas. However, we’re facing a roadblock when it comes to accessing the worksheet after calling to_excel(). Let’s explore what’s happening behind the scenes and find out if there’s a way to achieve our desired outcome.

Background

When you call pandas.ExcelWriter() and then use its to_excel() method, pandas creates an Excel file with the specified sheet name. However, the engine used for writing the Excel file (in this case, xlsxwriter) only has read access to the worksheet after it’s been written.

The Role of xlsxwriter

The xlsxwriter engine is a powerful tool that allows you to customize the look and feel of your Excel files. It provides a range of features, including support for images, formatting, and more. When used with pandas’ to_excel() function, it writes the data to an Excel file using its built-in functionality.

However, this process can lead to some limitations when trying to access the worksheet later on.

The Problem

When you call writer.sheets['Sheet1'] after calling data_frame.to_excel(writer), pandas’ to_excel() function has already written the data to the Excel file. This means that the xlsxwriter engine only has read access to the worksheet, and we’re unable to make changes or insert images.

On the other hand, if you use workbook.get_worksheet_by_name('Sheet1'), which is essentially a lower-level interface to the xlsxwriter engine, you can successfully insert an image into the worksheet.

Explanation

So, why does this happen? The reason lies in how pandas’ ExcelWriter object interacts with the xlsxwriter engine. When you call to_excel(), it writes the data to the Excel file and then closes the writer object. However, the xlsxwriter engine doesn’t release its read lock on the worksheet until we explicitly do so.

The issue here is that when we try to access the worksheet using writer.sheets['Sheet1'], pandas’ ExcelWriter object has already closed the writer object, leaving us with only a read-only interface. This prevents us from making changes or inserting images into the worksheet.

Solution

To resolve this problem, we need to find a way to maintain read access to the worksheet after calling to_excel(). One approach is to use the lower-level interface provided by xlsxwriter, which allows us to work directly with the workbook and worksheets.

Here’s an updated code example that uses this approach:

import pandas as pd

data_frame = pd.DataFrame({'Fruits': ['Appple', 'Banana', 'Mango',
                           'Dragon Fruit', 'Musk melon', 'grapes'],
                           'Sales in kg': [20, 30, 15, 10, 50, 40]})

# Create a new workbook
workbook = pd.ExcelWriter("foo.xlsx")

# Insert the data into the workbook
data_frame.to_excel(workbook,
                    sheet_name="Sheet1",
                    index=False)

# Get the worksheet using the lower-level interface
worksheet = workbook.book.get_worksheet_by_name('Sheet1')

# Insert an image into the worksheet
worksheet.insert_image("D2", "pic.png")

By using this approach, we can maintain read access to the worksheet and insert images as needed.

Conclusion

Inserting images into Excel files can be a tricky task, especially when working with pandas and xlsxwriter. However, by understanding how these tools interact with each other and using the lower-level interface provided by xlsxwriter, we can overcome this challenge and create beautifully formatted Excel files with images.

In the next article, we’ll explore more advanced topics in data science, including data visualization using matplotlib and seaborn. Stay tuned for more tutorials and explanations on various data science tools and techniques!

Last modified on 2024-11-22