pyspark.sql.DataFrame.sample¶

DataFrame.sample(withReplacement: Union[float, bool, None] = None, fraction: Union[int, float, None] = None, seed: Optional[int] = None) → pyspark.sql.dataframe.DataFrame[source]¶

Returns a sampled subset of this DataFrame.

New in version 1.3.0.

Parameters

withReplacementbool, optional: Sample with replacement or not (default False).
fractionfloat, optional: Fraction of rows to generate, range [0.0, 1.0].
seedint, optional: Seed for sampling (default a random seed).

Notes

This is not guaranteed to provide exactly the fraction specified of the total count of the given DataFrame.

fraction is required and, withReplacement and seed are optional.

Examples

>>> df = spark.range(10)
>>> df.sample(0.5, 3).count()
7
>>> df.sample(fraction=0.5, seed=3).count()
7
>>> df.sample(withReplacement=True, fraction=0.5, seed=3).count()
1
>>> df.sample(1.0).count()
10
>>> df.sample(fraction=1.0).count()
10
>>> df.sample(False, fraction=1.0).count()
10

pyspark.sql.DataFrame.sameSemantics pyspark.sql.DataFrame.sampleBy