How to Fill NaN Values in a DataFrame Based on Another DataFrame in Python

Discover how to efficiently fill NaN values in a DataFrame using values from another DataFrame based on matching criteria in Python's pandas library.
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Filling NaN Values in a DataFrame Using Another DataFrame in Python

Handling missing data is a common challenge faced by data analysts and data scientists. One common scenario involves filling NaN values in one DataFrame based on the values from another DataFrame. In this guide, we will explore a practical example using Python's pandas library to update NaN values effectively. So let’s dive in!

Understanding the Problem

Imagine you have two DataFrames: df1 and df2. In df1, you have several columns, including a type column filled with NaN values. You want to fill in these missing values using the weakness column by referencing the data in df2, which contains corresponding values in its type column.

Here's a snapshot of our DataFrame structures:

DataFrame 1: df1

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DataFrame 2: df2

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In df1, the type values for some rows are missing (NaN) and need to be filled using the corresponding values from df2 based on a matching weakness entry.

The Solution

To solve this problem, we'll create a mapping dictionary from df2 and then use that to fill NaN values in the type column of df1. Below are the steps and the Python code to achieve this.

Step 1: Create a Dictionary for Mapping

We will create a dictionary from df2, where the keys will be the weakness values, and the values will be the corresponding type values. This is easily accomplished using the zip function combined with dict().

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

Step 2: Fill NaN Values Using the Mapping

Next, we will utilize the map() function to reference our mapping dictionary and fill the NaN values in the type column of df1. The following line of code will accomplish this:

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Result

After executing the above code, you can print the updated type values in df1:

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This will give you an output similar to the following:

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Conclusion

By following these steps, we can effectively fill in NaN values based on a relationship between two DataFrames in pandas. This approach is efficient and leverages the powerful mapping features of pandas to handle missing data accurately.

Now you can apply this method to your own data cleaning challenges in Python! Happy coding!