Скачать или смотреть How to Concatenate Rows of Two DataFrames with Different Columns in Python Pandas

Discover a simple method to concatenate two pandas DataFrames with different columns by using Python, transforming your data efficiently.
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How to Concatenate Rows of Two DataFrames with Different Columns in Python Pandas

Python's powerful data manipulation library, Pandas, often handles various data structures, like DataFrames. As Analysts and Data Scientists work with data, they frequently encounter situations where they need to combine data from different sources or DataFrames with different columns. Today, we will dive into an interesting problem: how to concatenate the rows of two DataFrames when not all columns are the same.

The Problem

Let's say we have two DataFrames, df1 and df2, with the following structures:

DataFrame 1 (df1)

timestampaccount_idorder_idUSDCAD2022-01-01usdcad11233123142022-01-02usdcad12313213252022-01-03usdcad1234124236DataFrame 2 (df2)

timestampaccount_idcurrencybalance2021-12-21usdcadUSD22021-12-21usdcadCAD3The goal is to add a new row to df1 at index 0, using the balance values from df2 based on currency. After this operation, our final DataFrame (df) should look like this:

timestampaccount_idorder_idUSDCAD000232022-01-01usdcad11233123142022-01-02usdcad12313213252022-01-03usdcad1234124236The Solution

To achieve this in a seamless and effective manner, we can utilize a couple of Pandas operations. Here's a step-by-step guide to solve the problem.

Step 1: Set the Index

First, we will set the index of df2 to the currency column. This action will help us later when accessing the balance values easily.

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Step 2: Transpose the DataFrame

Now, we transpose the DataFrame so that the rows become columns. This step will allow us to combine the DataFrames properly.

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Step 3: Append and Fill Missing Values

We will now append the transposed df2 to df1. To help manage any missing data that might occur, we will use the ignore_index=True parameter and fill any resulting NaN values with 0.

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Final Output

To see the result, simply print the combined DataFrame:

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The resulting DataFrame should look like this:

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

Conclusion

Combining DataFrames in Pandas, when they have different columns, is a common yet critical data manipulation task for data professionals. By following these organized steps, you can efficiently create a cohesive DataFrame that integrates your data seamlessly.

Whether you are a beginner or an expert, mastering these small but powerful operations can significantly boost your data wrangling skills. Happy coding!