DataFrameGroupBy.agg详解

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本文介绍了 Pandas 库中 DataFrameGroupBy 类的 agg 方法的使用方式及参数说明。该方法允许用户通过字符串、函数、字典或函数列表进行聚合操作，支持多种输入组合形式，并展示了多个实例，包括单个聚合、多个聚合、选择列进行聚合以及不同列应用不同的聚合函数。

DataFrameGroupBy. agg ( arg, *args, **kwargs ) [source]

Aggregate using callable, string, dict, or list of string/callables

Parameters:

Parameters:	func : callable, string, dictionary, or list of string/callables Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names. Accepted Combinations are: string function name function list of functions dict of column names -> functions (or list of functions)
Returns:	aggregated : DataFrame

func : callable, string, dictionary, or list of string/callables

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names.

Accepted Combinations are:

string function name
function
list of functions
dict of column names -> functions (or list of functions)

Returns:

aggregated : DataFrame

See also

pandas.DataFrame.groupby.apply, pandas.DataFrame.groupby.transform, pandas.DataFrame.aggregate

Notes

Numpy functions mean/median/prod/sum/std/var are special cased so the default behavior is applying the function along axis=0 (e.g., np.mean(arr_2d, axis=0)) as opposed to mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).

agg is an alias for aggregate. Use the alias.

Examples

  >>> df = pd.DataFrame({'A': [1, 1, 2, 2],
...                    'B': [1, 2, 3, 4],
...                    'C': np.random.randn(4)})

 

  >>> df
   A  B         C
0  1  1  0.362838
1  1  2  0.227877
2  2  3  1.267767
3  2  4 -0.562860

 

The aggregation is for each column.

  >>> df.groupby('A').agg('min')
   B         C
A
1  1  0.227877
2  3 -0.562860

 

Multiple aggregations

  >>> df.groupby('A').agg(['min', 'max'])
    B             C
  min max       min       max
A
1   1   2  0.227877  0.362838
2   3   4 -0.562860  1.267767

 

Select a column for aggregation

  >>> df.groupby('A').B.agg(['min', 'max'])
   min  max
A
1    1    2
2    3    4

 

Different aggregations per column

  >>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
    B             C
  min max       sum
A
1   1   2  0.590716
2   3   4  0.704907
 