How can I display full (non-truncated) dataframe information in HTML when converting from Pandas dataframe to HTML?

Displaying Full Dataframe Information in HTML using Pandas

So you've converted your Pandas dataframe to HTML using the DataFrame.to_html function, but you noticed that the output in your HTML file is truncated. 😬 Don't worry, it's a common issue and we've got you covered! 👍

Understanding the Problem

Let's start by understanding the problem you're facing. When you convert a Pandas dataframe to HTML, by default, the text elements in each cell are truncated to fit within the cell width. This is fine for screen-friendly viewing but not ideal when you want to display the complete tabular data in an HTML file. 😱

The Solution

To display the complete non-truncated text data in the HTML output, we need to adjust the cell width to accommodate the full content of each cell. By setting a larger column width, we can ensure that the HTML table shows the complete data for each element. 📊

Step-by-step Guide

Now, let's dive into the steps to achieve this. 💪

Step 1: Import the necessary libraries

import pandas as pd

Step 2: Create your dataframe

# Example dataframe
data = {'TEXT': ['The film was an excellent effort in deconstructing the complex social sentiments that prevailed during this period.']}
df = pd.DataFrame(data)

Step 3: Adjust the column width parameter

# Set the column width parameter to a larger value (e.g., 1000)
html_output = df.to_html(col_width=1000)

Step 4: Save the HTML output to a file

# Save the HTML output to a file
with open('output.html', 'w') as f:
    f.write(html_output)

By setting a larger value for col_width, you allow the HTML table to expand and show the complete content of each cell in your dataframe. 🎉

Call-to-Action

Now that you know how to display full dataframe information in HTML, why not give it a try? Convert your Pandas dataframe to HTML using the DataFrame.to_html function with the adjusted col_width parameter, and see the magical transformation happen on your own data 🪄.

Remember to share this post with your friends or colleagues in need of this solution! ✉️

Let us know in the comments if you found this guide helpful or if you have any other questions. Happy coding! 😊✔️