Скачать или смотреть How to Combine DataFrames and Calculate the Sum in Python Pandas

Learn how to combine two DataFrames in Python using Pandas and sum values based on a common key. Get step-by-step instructions here!
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Combining DataFrames and Summing Values in Python Pandas

Handling data in multiple DataFrames is a common task in data analysis, especially when you want to aggregate information. A common challenge that beginners face is how to combine these DataFrames and calculate sums based on a primary key. In this guide, we'll walk through a practical example that demonstrates how to achieve this using Python's powerful Pandas library.

The Problem: Combining DataFrames

Let’s say you have two DataFrames containing information about charges incurred by various entities. Each DataFrame has a common key, id, that links entries from both DataFrames. Here's what we have:

DataFrames

DataFrame 1 (df1):

idNameCharge1A1001A1002B2002B2005C3006D400DataFrame 2 (df2):

idNameCharge1A1001A1002B2008X200The goal is to combine these two DataFrames based on the id field and then calculate the total charges for each unique combination of id and Name.

The Solution: Using Pandas to Combine and Sum

To achieve the desired result, we’ll use the Pandas library to concatenate the DataFrames and then group by the common key to compute the sum. Here's a step-by-step explanation of how to do this.

Step 1: Import Pandas

First, ensure you have the Pandas library imported. If you haven't installed it yet, you can do so using pip:

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Next, import it into your Python script or notebook:

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Step 2: Create the DataFrames

Create the two DataFrames using the provided data:

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Step 3: Combine the DataFrames

Now, concatenate the two DataFrames:

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Step 4: Group and Calculate the Sum

Next, use the groupby method to aggregate charges by id and Name and sum the charges:

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Final Output

After executing the above steps, the resulting DataFrame will look like this:

idNameCharge1A4002B6005C3006D4008X200Your code should produce the final output as follows:

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Conclusion

By following the steps outlined above, you can easily combine multiple DataFrames in Pandas and calculate the sum based on shared keys. This technique is incredibly useful in data analysis, allowing you to efficiently aggregate and process data.

If you're ever faced with the challenge of managing multiple DataFrames, remember that Pandas offers powerful tools to streamline your workflow. Happy coding!