maxframe.dataframe.Series.aggregate

Series.aggregate(func=None, axis=0, **kw)

Aggregate using one or more operations over the specified axis.

Parameters:

• df (DataFrame, Series) – Object to aggregate.

• func (list or dict) – Function to use for aggregating the data.

• axis ({0 or ‘index’, 1 or ‘columns’}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

• kw – Keyword arguments to pass to func.

Returns:

The return can be:

• scalar : when Series.agg is called with single function

• Series : when DataFrame.agg is called with a single function

• DataFrame : when DataFrame.agg is called with several functions

Return type:

scalar, Series or DataFrame

Examples

>>> import maxframe.dataframe as md
>>> df = md.DataFrame([[1, 2, 3],
...            [4, 5, 6],
...            [7, 8, 9],
...            [np.nan, np.nan, np.nan]],
...           columns=['A', 'B', 'C']).execute()

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min']).execute()
        A     B     C
min   1.0   2.0   3.0
sum  12.0  15.0  18.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']}).execute()
        A    B
max   NaN  8.0
min   1.0  2.0
sum  12.0  NaN

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean')).execute()
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

>>> s = md.Series([1, 2, 3, 4])
>>> s.agg('min').execute()
1

>>> s.agg(['min', 'max']).execute()
max    4
min    1