pynapple.TsdFrame.groupby_apply

TsdFrame.groupby_apply(by, func, input_key=None, **func_kwargs)

Apply a function to each group in a grouped pynapple object.

Parameters:

• by (str or list of str) – Metadata name(s) to group by.

• func (function) – Function to apply to each group.

• input_key (str or None, optional) – Input key that the grouped object will be passed as. If None, the grouped object will be passed as the first positional argument.

• **func_kwargs (optional) – Additional keyword arguments to pass to the function. Any required positional arguments that are not the grouped object should be passed as keyword arguments.

Returns:

Dictionary of results from applying the function to each group, where the keys are the group names and the values are the results.

Return type:

dict

Examples

>>> import pynapple as nap
>>> import numpy as np
>>> metadata = {"l1": [1, 2, 2], "l2": ["x", "x", "y"]}
>>> tsdframe = nap.TsdFrame(t=np.arange(5), d=np.ones((5, 3)), metadata=metadata)
>>> print(tsdframe)
Time (s)    0         1         2
----------  --------  --------  --------
0.0         1.0       1.0       1.0
1.0         1.0       1.0       1.0
2.0         1.0       1.0       1.0
3.0         1.0       1.0       1.0
4.0         1.0       1.0       1.0
Metadata
--------    --------  --------  --------
l1          1         2         2
l2          x         x         y

dtype: float64, shape: (5, 3)

Apply a numpy function:

>>> tsdframe.groupby_apply("l1", np.sum)
{1: 5.0, 2: 10.0}

Apply a custom function:

>>> tsdframe.groupby_apply("l1", lambda x: x.shape)
{1: (5, 1), 2: (5, 2)}

Apply a function with additional arguments:

>>> tsdframe.groupby_apply("l1", np.sum, axis=0)
{1: array([5.]), 2: array([5., 5.])}