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Learn how to efficiently merge two Data Frames in Python Pandas using specific columns for a LEFT JOIN operation, handling unmatched rows effectively.
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How to Merge Two Data Frames in Python Pandas Based on Multiple Columns

If you're working with data in Python Pandas, you may encounter a situation where you need to merge two DataFrames based on certain columns. For instance, suppose you have two DataFrames, df1 and df2, and you want to perform a LEFT JOIN on these DataFrames using specific columns from the second DataFrame. This article will guide you through the process, illustrating the steps needed to achieve your desired result.

Understanding the DataFrames

Let's take a closer look at the two DataFrames we'll be working with:

DataFrame 1: df1

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

DataFrame 2: df2

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Key Points

df1 contains more rows than df2.

All values in both DataFrames are integers.

The goal is to merge df1 with df2 such that:

If the ID from df1 matches ID1 from df2, it merges the rows.

If there's no match with ID1, it checks against ID2.

If there's still no match, it checks against ID3.

In case of no matches at all, fill the resulting merge with 123456.

Step-by-Step Solution

Step 1: Preparing DataFrame for Merging

First, we need to transform df2 to make it easier to work with. We'll stack df2 to get a DataFrame (df2a) that consolidates all the IDs into a single column.

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

Step 2: Merging DataFrames

Next, we perform a LEFT JOIN of df1 with the newly created df2a using the ID column.

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

At this point, if there are any NaN values (which means no match was found), we will still need to bring in data from the original df2.

Step 3: Joining the Original DataFrame

To complete our merge, we join the original df2 based on the level from the stacked DataFrame.

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

Step 4: Handling Unmatched Rows

After performing the joins and merging, we can fill any NaN values that represent no match with the value 123456.

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

Step 5: Cleaning Up the DataFrame

Lastly, we will drop any intermediate columns that were only needed for merging.

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

Complete Code Example

Here's how the complete process looks in code:

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

Result

After running this code, you would get an output similar to this:

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

Conclusion

Merging DataFrames in Python Pandas can initially seem challenging, especially with multiple conditions. However, by following the steps outlined in this article, you can effectively merge datasets and handle unmatched rows elegantly.

This method is not only efficient but also adaptable to different datasets, ensuring that your data integrity remains intact even when some items don’t have a direct match. Happy coding!