pyspark.sql.DataFrame.randomSplit¶

DataFrame.randomSplit(weights: List[float], seed: Optional[int] = None) → List[pyspark.sql.dataframe.DataFrame][source]¶

Randomly splits this DataFrame with the provided weights.

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

weightslist: list of doubles as weights with which to split the DataFrame. Weights will be normalized if they don’t sum up to 1.0.
seedint, optional: The seed for sampling.

Returns

list: List of DataFrames.

Examples

>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([
...     Row(age=10, height=80, name="Alice"),
...     Row(age=5, height=None, name="Bob"),
...     Row(age=None, height=None, name="Tom"),
...     Row(age=None, height=None, name=None),
... ])

>>> splits = df.randomSplit([1.0, 2.0], 24)
>>> splits[0].count()
2
>>> splits[1].count()
2

pyspark.sql.DataFrame.printSchema pyspark.sql.DataFrame.rdd