How can data be grouped in Python?

Python has the capability to group data using the pandas library. pandas is a powerful data analysis library that offers flexible and high-performance data structures for handling and analyzing data.

The general steps for grouping data using pandas are as follows:

1. Import the pandas library: When importing the pandas library in your code, it is commonly done using the alias pd.

import pandas as pd

1. Generate data by storing it in pandas data structures, with the most common being DataFrame.

data = pd.DataFrame({'列名1': [值1, 值2, ...],
                     '列名2': [值1, 值2, ...],
                     ...})

1. Grouped Data: Use the groupby function to group the data, specifying one or more column names as the grouping criteria.

grouped_data = data.groupby('列名')

1. Perform operations on grouped data: Various operations can be carried out on grouped data, such as calculating statistical measures and applying functions.

For example, you can calculate the average of each group.

grouped_data.mean()

Alternatively, you can use a custom function.

grouped_data.apply(自定义函数)

1. View Results: Check the outcome of the grouping operation.

print(grouped_data.mean())

This is just a brief overview of the basic steps in grouping data using pandas. Depending on specific needs, further processing and analysis of the group data may be necessary.