pyspark.sql.DataFrame.sampleBy

DataFrame.sampleBy(col: ColumnOrName, fractions: Dict[Any, float], seed: Optional[int] = None) → DataFrame

Returns a stratified sample without replacement based on the fraction given on each stratum.

Parameters

colColumn or str

column that defines strata

Added sampling by a column of Column

fractionsdict

sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.

seedint, optional

random seed

Returns

a new DataFrame that represents the stratified sample

Examples

>>> from pyspark.sql.functions import col
>>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
>>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
>>> sampled.groupBy("key").count().orderBy("key").show()
+---+-----+
|key|count|
+---+-----+
|  0|    3|
|  1|    6|
+---+-----+
>>> dataset.sampleBy(col("key"), fractions={2: 1.0}, seed=0).count()
33

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