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Learn how to effectively merge multiple pandas DataFrames in Python while maintaining data integrity, including filling missing values.
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How to Correctly Assign Data from One Pandas DataFrame to Another

Working with multiple Excel spreadsheets can often lead to discrepancies in data structure, making it challenging to consolidate your data effectively. If you've ever faced the issue of combining DataFrames in pandas, specifically when dealing with missing columns or misaligned data, this post is for you.

The Problem Statement

You might find yourself in a situation where you have numerous DataFrames corresponding to different years, each with slightly varying columns. What you ideally want is a single consolidated DataFrame that seamlessly integrates all the data, filling in any missing columns with predefined values, such as zeros.

In the code example below, we notice a frequent problem when attempting to assign one DataFrame's values to a predefined slice of another DataFrame. The expected outcome and the actual result differ, often leaving NaN values instead of valid data.

Example Scenario

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Upon trying to merge the above DataFrames, while following through similar approaches, the output often results in unexpected NaN values.

The Solution

To tackle this issue efficiently, pandas provides a straightforward function called pd.concat(). Below you’ll see how this function is the key to successfully merging your DataFrames.

Using pd.concat()

Basic Concatenation
Start with a simple concatenation, which aligns the data by columns.

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Filling NaN Values
Often while merging, you might encounter NaN values for missing data fields. To rectify this, you can chain a .fillna() method to replace NaNs with zeros (or any predefined value).

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Putting It All Together

Here’s how the complete solution looks:

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Expected Output

Now, your final output would correctly display:

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Conclusion

By using pd.concat() in conjunction with .fillna(), you can efficiently combine multiple DataFrames while ensuring that your final dataset is complete and devoid of NaN values. This approach not only saves you time and effort but also enhances the robustness of your data analysis processes.

With this knowledge, you're well-equipped to handle DataFrame consolidations in your Python projects. Happy coding!