Скачать или смотреть How to Groupby Sum in Pandas for Custom Dataframe Outputs

Learn how to efficiently use `groupby` and `sum` functions in Pandas to customize your DataFrame as per specific requirements and conditions.
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Mastering Groupby and Sum in Pandas: A Step-by-Step Guide

When working with data, it’s common to aggregate or summarize information based on particular features or groups. One of the powerful tools in Python for such tasks is the Pandas library, which provides a straightforward way to manipulate, analyze, and visualize data. In this guide, we will explore how to use the groupby and sum functions in Pandas to both condense and customize a DataFrame, meeting specific conditions and requirements.

The Problem: Customizing a DataFrame

Imagine you have a DataFrame that summarizes transactions made by various customers across different types. Here’s a quick glance at what your data might look like:

IDTypeNumber_of_transactionsAmount1SuperMarket820002Hospital25001Education110001Travel3600............From this data, you want to create a new DataFrame that highlights the two types of spending for each customer along with the respective total amounts. The expected output DataFrame should look like this:

IDTop1AmountTop1Top2AmountTop21SuperMarket2100Education10002Hospital1700Education6003Hotel2200Education8004SuperMarket900NaNNaNIn this output, Top1 and Top2 represent the types where the highest and second-highest amounts were spent, respectively.

The Solution: Step-by-step Instructions

We can achieve this by performing a series of steps in Pandas. Here’s how you can go about it:

Step 1: Summarize the Amounts by ID and Type

First, we need to sum the Amount for each combination of ID and Type.

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This gives us a DataFrame (df1) where each row has a unique ID and Type, along with the summed Amount for that group.

Step 2: Identify Top Spending Types

Next, we want to extract the top two spending categories for each customer. We can do this using the nlargest function.

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This DataFrame (df2) now contains the top two types of spending for each ID along with their respective amounts.

Step 3: Assign Rank Numbers to Spending Types

To differentiate between the first and second top categories, we will assign a rank number to each type.

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This line adds a new column indicating whether the spending type is in position 1 or 2.

Step 4: Reshape the DataFrame

Now, we will pivot the DataFrame to create separate columns for Top1 and Top2 spending types.

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The result is a DataFrame (df3) organized in such a way that each ID has its top spending types and amounts side-by-side.

Final Output

When we print the final DataFrame (df3), you will see:

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Conclusion

Combining the groupby and sum functions in Pandas allows you to manipulate and summarize large datasets efficiently. By following the steps outlined in this guide, you can customize your DataFrame to fit your specific needs, making your data analysis tasks much easier. Whether you are working with financial data, customer records, or any other type of tabulated information, mastering these techniques is essential for any data analyst or scientist.

With this approach, you can confidently tackle more complex data challenges in your projects!