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This article is about how Pandas implements grouped data. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

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This operation is often performed in the daily lives of data scientists and analysts. Pandas provides a basic function to perform data grouping, namely Groupby.

Groupby operations include splitting objects according to specific conditions, applying functions, and then combining the results.

Let's look at the loan forecast dataset again, and suppose I want to look at the average amount of loans to people from different property sectors, such as rural, semi-urban and urban. Take the time to understand the problem statement and think about how to solve it.

Well, Pandas's groupby can solve this problem very effectively. First, the data is divided according to the attribute area. Second, we apply the mean () function to each category. Finally, we combine them and print them as new data frames.

# Import dataset import pandas as pddf = pd.read_csv ('.. / Data/loan_train.csv') df.head () # average income of men and women df.groupby (['Gender']) [[' ApplicantIncome']] .mean () # property areas with different average loan amounts, such as cities Rural df.groupby (['Property_Area']) [[' LoanAmount']] .mean () # compare the loan status of different educational backgrounds df.groupby (['Education']) [[' Loan_Status']] .count () Thank you for reading! This is the end of the article on "how to achieve grouped data in Pandas". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it for more people to see!

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