pandas.DataFrame.aggregate¶
-
DataFrame.
aggregate
(self, func, axis=0, *args, **kwargs)[source]¶ Aggregate using one or more operations over the specified axis.
New in version 0.20.0.
- Parameters
- funcfunction, str, list or dict
Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.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.
- 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.
- *args
Positional arguments to pass to func.
- **kwargs
Keyword arguments to pass to func.
- Returns
- scalar, Series or DataFrame
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 scalar, Series or DataFrame.
- 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.
See also
DataFrame.apply
Perform any type of operations.
DataFrame.transform
Perform transformation type operations.
core.groupby.GroupBy
Perform operations over groups.
core.resample.Resampler
Perform operations over resampled bins.
core.window.Rolling
Perform operations over rolling window.
core.window.Expanding
Perform operations over expanding window.
core.window.EWM
Perform operation over exponential weighted window.
Notes
agg is an alias for aggregate. Use the alias.
A passed user-defined-function will be passed a Series for evaluation.
Examples
>>> 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