Pandas Series: aggregate() function

Last update on September 15 2022 12:55:44 (UTC/GMT +8 hours)

Aggregation with pandas series

The aggregate() function uses to one or more operations over the specified axis.

Syntax:

Series.aggregate(self, func, axis=0, *args, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
func	Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Series.apply. Accepted combinations are: function string function name list of functions and/or function names, e.g. [np.sum, 'mean'] dict of axis labels -> functions, function names or list of such.	unction, str, list or dict	Required
axis	Parameter needed for compatibility with DataFrame.	{0 or ‘index’}	Required
args	Positional arguments to pass to func.		Required
**kwds	Keyword arguments to pass to func.		Required

Returns: scalar, Series or DataFrame
The return can be:

Return scalar, Series or DataFrame.

Notes:agg is an alias for aggregate. Use the alias.
A passed user-defined-function will be passed a Series for evaluation.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 4, 6, 8, 10, 12])
s

Output:

0     2
1     4
2     6
3     8
4    10
5    12
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 4, 6, 8, 10, 12])
s.agg('min')

Output:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 4, 6, 8, 10, 12])
s.agg('min')
s.agg(['min', 'max'])

Output:

min     2
max    12
dtype: int64

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