Handling Missing Values in Pandas DataFrames: Filling Gaps with NaN

A step-by-step guide on how to manage missing values in Pandas DataFrames, using a practical example of filling time intervals with `NaN` in Python.
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This video is based on the question https://stackoverflow.com/q/75317421/ asked by the user 'NoLimitLondon' ( https://stackoverflow.com/u/14909710/ ) and on the answer https://stackoverflow.com/a/75317534/ provided by the user 'BrokenBenchmark' ( https://stackoverflow.com/u/17769815/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Handling Missing Values in Pandas DataFrames: Filling Gaps with NaN

In data analysis, dealing with missing values is a common challenge. Let's explore a specific problem where we need to mark specific times in a DataFrame, even when there are no corresponding values.

The Problem

Consider a scenario where you're working with a financial dataset organized by date and time. You want to extract the last recorded value at 15:30:00.0 for every day. However, there's an issue—in some cases, there may be no data for that time.

Example DataFrame

Here is a simplified version of what your DataFrame looks like:

DateTimeOpenHighLowLast2023-01-1315:30:00.040164024.254014.754017.752023-01-1615:30:00.0N/AN/AN/AN/A2023-01-1715:30:00.040114014.254003.754010.00As shown in the table, on 2023-01-16, there are no values recorded for the time 15:30:00.0. You want to fill this gap with NaN to maintain a clear representation of your data.

The Solution

To tackle this, we can use Pandas' powerful data manipulation capabilities. Here’s how you can achieve the desired result:

Step 1: Create a Base DataFrame

First, we need to create a base DataFrame that includes all unique dates in our original DataFrame alongside the specified time of 15:30:00.0.

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

Step 2: Extract Last Values

Next, extract the last values for the time from your original DataFrame.

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

Step 3: Combine DataFrames

Now, you can combine the base DataFrame with the output from the previous step. Using .combine_first(), you can fill missing entries.

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

Step 4: Display the Final Result

Now, display your final DataFrame:

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

The expected output should look like this:

DateTimeLast2023-01-1315:30:00.04017.752023-01-1615:30:00.0NaN2023-01-1715:30:00.04010.00Conclusion

By using the above method, you can effectively manage missing data in your DataFrames. Filling gaps with NaN not only indicates absent data but also preserves the integrity of your dataset, ensuring that your analyses are meaningful and complete.

If you often find yourself dealing with time-series data in your analyses, mastering these techniques will make your data handling much more robust.

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