DataFrame.aggregate(func, axis=0, *args, **kwargs)
[source]
Aggregate using callable, string, dict, or list of string/callables
New in version 0.20.0.
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:
|
---|---|
Returns: |
aggregated : DataFrame |
See also
pandas.DataFrame.apply
, pandas.DataFrame.transform
, pandas.DataFrame.groupby.aggregate
, pandas.DataFrame.resample.aggregate
, pandas.DataFrame.rolling.aggregate
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.
>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'], ... index=pd.date_range('1/1/2000', periods=10)) >>> df.iloc[3:7] = np.nan
Aggregate these functions across all columns
>>> df.agg(['sum', 'min']) A B C sum -0.182253 -0.614014 -2.909534 min -1.916563 -1.460076 -1.568297
Different aggregations per column
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']}) A B max NaN 1.514318 min -1.916563 -1.460076 sum -0.182253 NaN
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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.DataFrame.aggregate.html