Скачать или смотреть How to Join Two Pandas DataFrame Columns Row-Wise

Discover an efficient method to combine two columns in a Pandas DataFrame row-wise, enhancing your data manipulation skills.
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Understanding the Problem: Joining DataFrame Columns

If you're working with a Pandas DataFrame in Python, you may encounter situations where you need to combine multiple columns into one. This is especially common in data analysis and manipulation tasks where clarity and organization are crucial.

For instance, consider the following DataFrame that contains information about vaccine recipients:

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In this case, you want to join the age and height columns into a new column, grouped by their respective vaccine. The end goal is to have a DataFrame that looks like this:

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

The Challenge

You've attempted to use various methods like pd.concat and df.merge, but none have provided the desired result. You might also have tried the apply function without success. Don't worry—there's a straightforward solution using a combination of Pandas functions.

The Solution: Stacking Your DataFrame

To achieve the desired DataFrame structure, you can follow these simple steps:

Step 1: Set the Index

First, you need to set the index of your DataFrame to the vaccine column:

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

This action will allow the other columns (age and height) to be stacked together, preserving their association with the vaccine type.

Step 2: Stack the DataFrame

Next, use the stack() method. This method will reshape your DataFrame, turning the columns into a single column indexed by the original index (which is now just vaccine):

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

Step 3: Drop Unnecessary Index Levels

When you stack the DataFrame, two index levels will be created. You'll want to remove the second level, which represents the original column names (age and height). You can do this with droplevel(1):

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Step 4: Convert to DataFrame

Convert the stacked result into a DataFrame with a meaningful column name, like new_col:

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Step 5: Reset the Index

Lastly, reset the index so that vaccine becomes a regular column again:

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Putting It All Together

When we compile all these steps into one complete line of code, it looks like this:

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

Running this code will give you the desired DataFrame:

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Conclusion

By following these steps, you can effectively combine multiple columns of a Pandas DataFrame into one, organized by another column. This is a powerful skill for any data enthusiast, allowing for more readable and manageable datasets.

Now that you know how to join two Pandas DataFrame columns, you can apply this method to various data manipulation tasks and enhance your data analysis capabilities effectively!