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A step-by-step guide on how to use inner joins effectively to merge two dataframes in Python, removing unnecessary rows and retaining relevant data.
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How to Compare and Join Two Columns of DataFrames in Python

When working with data in Python, especially using libraries like Pandas, you may encounter situations where you need to compare and combine data from multiple DataFrames. This guide will address a common issue that arises when handling such operations—specifically, how to merge two DataFrames while excluding entries that do not match up.

The Problem

Imagine you have two DataFrames containing information about cryptocurrencies. The first DataFrame (df1) lists different coins and their quantities, while the second DataFrame (df2) contains the selling rates for these coins. You want to create a new DataFrame that includes only the coins available in both original DataFrames, effectively filtering out unwanted entries.

Here's how the two DataFrames are defined:

DataFrame 1 (df1)

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Output:

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DataFrame 2 (df2)

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Output:

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Expected Output

You want the output to look like this:

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In this output, the rows for FTM and LRC are successfully removed because they do not exist in the first DataFrame.

The Solution

To achieve the desired result, you need to perform an inner join on the two DataFrames. An inner join will only keep the rows that have equivalents in both DataFrames.

Step-by-Step Guide

Import Pandas: Ensure you have Pandas imported in your Python environment.

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Create the DataFrames: Define your two DataFrames df1 and df2.

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Perform the Inner Join: Use the merge function with the how='inner' argument. This way, you ensure that only the matching coins are retained.

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Drop Unnecessary Columns: After merging, you may find a duplicate column that you want to clean up.

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Final Output: The resulting full_df will include only the relevant data. You can print it to verify.

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Result

The output will be:

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Conclusion

Using an inner join is a powerful method for combining DataFrames in Python while eliminating extraneous data. By following these steps, you can easily compare and join columns from two DataFrames, retaining only the records that matter.

This approach will help you keep your data clean and relevant, ensuring that you can analyze it effectively. If you find yourself needing to perform similar operations, don't hesitate to use the methods demonstrated in this post!