sample(self, n: 'int | None' = None, frac: 'float | None' = None, replace: 'bool' = False, weights: 'Sequence | Series | None' = None, random_state: 'RandomState | None' = None)
You can use random_state
for reproducibility.
Number of items to return for each group. Cannot be used with frac
and must be no larger than the smallest group unless :None:None:`replace` is True. Default is one if frac
is None.
Fraction of items to return. Cannot be used with n.
Allow or disallow sampling of the same row more than once.
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.
If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.
np.random.Generator objects now accepted
A new object of same type as caller containing items randomly sampled within each group from the caller object.
Return a random sample of items from each group.
DataFrame.sample
Generate random samples from a DataFrame object.
numpy.random.choice
Generate a random sample from a given 1-D numpy array.
>>> 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:
This example is valid syntax, but we were not able to check execution>>> df.groupby("a").sample(See :
... n=1,
... weights=[1, 1, 1, 0, 0, 1],
... random_state=1,
... ) a b 5 black 5 2 blue 2 0 red 0
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.sample.process_sampling_size
pandas.core.sample.preprocess_weights
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