Use of methods

Pandas2.2 DataFrame

Function application, GroupBy & window

method	describe
(func[, axis, raw, …])	Used to apply a function along the axis (row or column) of a DataFrame
(func[, na_action])	Used to apply a function to each element of a DataFrame
(func[, na_action])	Used to apply a function to each element in a DataFrame
(func, args, *kwargs)	Methods for implementing chain programming styles
([func, axis])	Used to perform data on DataFrameAggregation operation

()

()(or()) method is used to perform data of DataFrameAggregation operation. It can apply one or more aggregate functions along a specified axis (row or column) and is commonly used in statistical summary analysis.

Method signature

(func=None, axis=0)

Parameter description

parameter	type	describe
`func`	function, str, list or dict	The aggregate function to apply. It can be a function name string (such as`'sum'`), function objects (such as), function list, or specify a dictionary of different functions for each column.
`axis`	{0 or ‘index’, 1 or ‘columns’}, default: 0	Along which axis is the aggregated:`0`Indicates aggregation by column (default),`1`Indicates aggregation by row.

Return value

iffuncis a single aggregate function, then return aSeries。
iffuncis multiple aggregate functions or multiple columns aggregation separately, and a is returnedDataFrame。

Example

Example 1: Use a single aggregate function (such as 'mean')

import pandas as pd

df = ({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

result = ('mean')
print(result)

Output:

A 2.0
B 5.0
dtype: float64

Example 2: Use multiple aggregate functions (such as ['min', 'max'])

result = (['min', 'max'])
print(result)

Output:

A B
min 1 4
max 3 6

Example 3: Use different aggregation functions for different columns

result = ({
    'A': 'mean',
    'B': ['min', 'max']
})
print(result)

Output:

A B
mean 2.0 NaN
min NaN 4.0
max NaN 6.0

Example 4: Aggregate by row (axis=1)

result = ('sum', axis=1)
print(result)

Output:

0 5
1 7
2 9
dtype: int64

Summarize

agg()Supports multiple aggregation methods and is flexibly applicable to various statistical summary needs.
Different aggregate functions can be specified for different columns.
Commonly used in data analysis (andgroupby()More powerful when used together).

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