Скачать или смотреть Transforming a Pandas DataFrame: How to Pivot and Concatenate Index with Columns

A comprehensive guide on how to pivot and concatenate the index with columns in a Pandas DataFrame for cleaner data transformation.
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Transforming a Pandas DataFrame: How to Pivot and Concatenate Index with Columns

When working with data in Python, especially using the powerful library Pandas, you may encounter scenarios where you need to reshape your DataFrame for better analysis. One common operation is pivoting and concatenating the index with columns. In this guide, we’ll explore how to take a DataFrame indexed by weeks and transform it into a clearer format that makes it easier to analyze revenues, costs, and margins over different weeks for various names.

The Problem at Hand

Imagine having a DataFrame structured like this:

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The goal is to pivot this DataFrame so that for each person, we can see their revenue, cost, and margin for each week, all aligned in a single row. This means we want to expand the index into multiple columns, each suffixed with the week number. The desired output should look like this:

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The Solution: How to Pivot and Concatenate

To achieve this transformation, we will use a few simple steps in Pandas. Below is a breakdown of the process:

Step 1: Set the Index

First, we prepare our DataFrame by setting the Name column as a part of the index. This allows us to work with multiple indices when we perform the pivot operation.

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Step 2: Unstack the DataFrame

Next, we will unstack the DataFrame, which pivots the innermost index (in this case, the weeks) into columns. This is where the magic happens that transforms the rows into columns format.

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Step 3: Rename the Columns

After unstacking, the columns will still have a multi-level index. We need to flatten these into a single-level index and rename them appropriately by combining the original column name with the week number.

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Step 4: Reset the Index

Finally, we reset the index to turn the index back into regular columns, giving us the desired format.

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Example Code

Combining all the steps leads us to the complete code snippet:

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Conclusion

By following these steps, we can efficiently pivot and concatenate our DataFrame’s index with columns, making our data ready for analysis. This technique is particularly useful when working with time series data, as it provides clarity and ease of comparing metrics across different periods. Happy coding with Pandas!