pandas.core.groupby.SeriesGroupBy.sample#

SeriesGroupBy.sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]#

Return a random sample of items from each group.

You can use random_state for reproducibility.

Parameters:

nint, optional: Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
fracfloat, optional: Fraction of items to return. Cannot be used with n.
replacebool, default False: Allow or disallow sampling of the same row more than once.
weightslist-like, optional: Default None results in equal probability weighting. If passed a list-like then values must have the same length as the underlying DataFrame or Series object and will be used as sampling probabilities after normalization within each group. Values must be non-negative with at least one positive element within each group.
random_stateint, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional: If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. Default None results in sampling with the current state of np.random.

Changed in version 1.4.0: np.random.Generator objects now accepted

Returns:

Series or DataFrame: A new object of same type as caller containing items randomly sampled within each group from the caller object.

See also

DataFrame.sample: Generate random samples from a DataFrame object.
Series.sample: Generate random samples from a Series object.
numpy.random.choice: Generate a random sample from a given 1-D numpy array.

Examples

>>> df = pd.DataFrame(
...     {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)}
... )
>>> df
       a  b
0    red  0
1    red  1
2   blue  2
3   blue  3
4  black  4
5  black  5

Select one row at random for each distinct value in column a. The random_state argument can be used to guarantee reproducibility:

>>> df.groupby("a").sample(n=1, random_state=1)
       a  b
4  black  4
2   blue  2
1    red  1

Set frac to sample fixed proportions rather than counts:

>>> df.groupby("a")["b"].sample(frac=0.5, random_state=2)
5    5
2    2
0    0
Name: b, dtype: int64

Control sample probabilities within groups by setting weights:

>>> df.groupby("a").sample(
...     n=1,
...     weights=[1, 1, 1, 0, 0, 1],
...     random_state=1,
... )
       a  b
5  black  5
2   blue  2
0    red  0