How to Append New Rows to a DataFrame in Python with Missing Values Filled with NaN

Learn the step-by-step process for appending new rows to a DataFrame in Python using Pandas, while handling missing values with NaN.
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How to Append New Rows to a DataFrame in Python with Missing Values Filled with NaN

Working with data in Python often involves using Pandas, a powerful library designed for data manipulation. One common challenge that data analysts face is how to append new rows to a DataFrame, particularly when dealing with dynamic datasets where columns (like dates) are constantly being added. In this guide, we'll explore how to efficiently append a new row to a DataFrame while filling missing values with NaN.

Understanding the Problem

Imagine you have a DataFrame representing daily attendance records. This DataFrame is structured with dates as columns and rows representing different entities. Each day, a new column is added, and now you wish to add a new record (or row) for a specific entity that was not present before.

For example, here's how the current DataFrame looks:

[[See Video to Reveal this Text or Code Snippet]]

Now, if we want to add a new entity T6 on, say, 4/8/2021, our new DataFrame should look like this:

[[See Video to Reveal this Text or Code Snippet]]

The missing values for previous dates should be filled with NaN, and where appropriate, we can have a default value of False.

Step-by-Step Solution

Step 1: Adding an Empty Row to the DataFrame

[[See Video to Reveal this Text or Code Snippet]]

After this step, your DataFrame now looks like this:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Adding New Column for the New Date

After appending the empty row, the next step is to add a new column for the new date. You can either fill in values directly or use a list:

[[See Video to Reveal this Text or Code Snippet]]

Putting it All Together

Now that we've gone through the two main steps, the complete code will look like this:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Manipulating DataFrames can initially seem daunting, but with a few straightforward commands, you can efficiently manage and append rows and columns. In this post, we learned how to add a new row to a DataFrame and fill missing values with NaN. By mastering these steps, you can ensure your data analysis remains organized and accurate even as your dataset evolves.

Happy coding with Pandas! If you have any questions or additional thoughts on handling DataFrames, feel free to leave a comment below.