Скачать или смотреть How to Fill NULL Values in a DataFrame Using Another DataFrame in Pandas

Discover effective methods to fill `NULL` values in DataFrames using Pandas. Learn how to seamlessly map data across different DataFrames for cleaner data analysis.
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How to Fill NULL Values in a DataFrame Using Another DataFrame in Pandas

Working with data in Pandas often means dealing with missing values. These gaps in your data can lead to challenges, especially when you want to analyze trends or perform calculations. A common scenario is when you have one DataFrame containing NULL values, and another DataFrame containing relevant information that can help fill those gaps. In this guide, we'll explore a straightforward approach to achieve this using the powerful libraries available in Python.

The Problem

Suppose you have the following data structure in your DataFrame (df):

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As you can see, the sprint column has some NULL values. This "missing" data can hamper insights and reporting.

You also possess another DataFrame (df2) that provides sprint date ranges:

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Your Mission

The task is to fill in the NULL values in df by mapping the relevant sprint from df2. Since the shapes of both DataFrames differ, we need a systematic approach to achieve this.

The Solution

We can solve this problem in a few steps. Here's how to go about it:

Step 1: Convert Columns to Datetime Format

Before proceeding, ensure that your date columns are in a compatible format. This is essential for accurate comparison and mapping.

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Step 2: Melt and Resample the DataFrame

Next, we will melt df2 to create a new DataFrame that gives us a continuous date range for each sprint. We will also employ forward filling (ffill) to populate missing values.

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Step 3: Create a Mapping Dictionary

By creating a mapping dictionary, we can easily lookup values from the melted DataFrame and apply those back to our original DataFrame.

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Step 4: Map the Values Back to the Original DataFrame

Finally, we will fill in the NULL values in df using the dictionary we created.

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Final Output

After following the steps outlined above, your updated DataFrame (df1) will look like this:

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The NULL values in sprint have been successfully filled based on the corresponding date ranges from df2.

Conclusion

Pandas is a powerful tool for data manipulation, and mapping values across DataFrames is a crucial skill in data analysis. By following these steps, you can elegantly handle missing data and ensure that your datasets are both clean and informative.

Ready to Tackle More Data Challenges?

If you have any other questions or need assistance with advanced data manipulation techniques, feel free to ask! Happy coding!