DataFrame.agg(func, axis=0, *args, **kwargs)
[source]
Aggregate using one or more operations over the specified axis.
New in version 0.20.0.
Parameters: |
func : function, string, dictionary, or list of string/functions 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:
axis : {0 or ‘index’, 1 or ‘columns’}, default 0
*args Positional arguments to pass to **kwargs Keyword arguments to pass to |
---|---|
Returns: |
|
See also
DataFrame.apply
DataFrame.transform
pandas.core.groupby.GroupBy
pandas.core.resample.Resampler
pandas.core.window.Rolling
pandas.core.window.Expanding
pandas.core.window.EWM
agg
is an alias for aggregate
. Use the alias.
A passed user-defined-function will be passed a Series for evaluation.
The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy
aggregation functions (mean
, median
, prod
, sum
, std
, var
), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d)
as opposed to numpy.mean(arr_2d,
axis=0)
.
agg
is an alias for aggregate
. Use the alias.
>>> df = pd.DataFrame([[1, 2, 3], ... [4, 5, 6], ... [7, 8, 9], ... [np.nan, np.nan, np.nan]], ... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min']) A B C sum 12.0 15.0 18.0 min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']}) A B max NaN 8.0 min 1.0 2.0 sum 12.0 NaN
Aggregate over the columns.
>>> df.agg("mean", axis="columns") 0 2.0 1 5.0 2 8.0 3 NaN dtype: float64
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http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.agg.html