pandas: merge (join) two data frames on multiple columns

📝 Title: A Guide to Merging Data Frames on Multiple Columns in Pandas

👋 Hey there, tech enthusiasts! 💻 Welcome back to our tech blog. Today, we're diving into the exciting world of data manipulation with pandas. 🐼 In this article, we'll explore how to merge (join) two data frames on multiple columns. 🔄 So, if you've ever encountered the pesky KeyError 😱 when trying to merge data frames, keep reading, because we've got you covered!

🔍 Understanding the problem

The error message you encountered, KeyError: '[B_1, c2]', suggests that there might be an issue with the column names you're using for the merge operation. Let's examine the syntax of your merge statement:

new_df = pd.merge(A_df, B_df,  how='left', left_on='[A_c1,c2]', right_on = '[B_c1,c2]')

It seems like you're passing a string containing multiple column names enclosed in square brackets ([]). However, pandas expects individual column names to be passed as separate arguments. That's why you're seeing the KeyError for '[B_1, c2]'.

✅ The solution

To perform the merge correctly, you need to separate the column names in the left_on and right_on parameters, like this:

new_df = pd.merge(A_df, B_df,  how='left', left_on=['A_c1', 'c2'], right_on=['B_c1', 'c2'])

By providing a list of column names, you're explicitly specifying which columns should be used for the merge operation. This helps pandas find the correct columns in both data frames and perform the join accordingly.

🛠️ Example

Here's a complete example to help you visualize the merge process:

import pandas as pd

A_df = pd.DataFrame({'A_c1': [1, 2, 3], 'c2': ['a', 'b', 'c'], 'other_data': [10, 20, 30]})
B_df = pd.DataFrame({'B_c1': [2, 1, 3], 'c2': ['b', 'a', 'c'], 'extra_data': ['foo', 'bar', 'baz']})

new_df = pd.merge(A_df, B_df,  how='left', left_on=['A_c1', 'c2'], right_on=['B_c1', 'c2'])

In this example, we have two data frames: A_df and B_df. We want to merge them based on the columns A_c1 and c2 in A_df, and the columns B_c1 and c2 in B_df. By specifying the correct column names in the left_on and right_on parameters, pandas will perform the merge operation smoothly.

📣 Time to take action!

Now that you're equipped with the knowledge of merging data frames on multiple columns in pandas, it's time to put it into practice. 🚀 Combine your data frames like a pro and unlock the power of data manipulation!

✨ Share your thoughts!

We would love to hear your experience with merging data frames in pandas. Have you encountered any interesting use cases or encountered different challenges? Share your stories and insights in the comments section below. Let's keep the conversation going!

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That's all for now, folks! Happy coding and pandas manipulating! 🐼💻✨