nxcals.api.extraction.data.builders.DataFrame.join

DataFrame.join(other: DataFrame, on: Optional[Union[str, List[str], Column, List[Column]]] = None, how: Optional[str] = None) → DataFrame

Joins with another DataFrame, using the given join expression.

New in version 1.3.0.

Parameters:

  • other (DataFrame) – Right side of the join

  • on (str, list or Column, optional) – a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. If on is a string or a list of strings indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join.

  • how (str, optional) – default inner. Must be one of: inner, cross, outer, full, fullouter, full_outer, left, leftouter, left_outer, right, rightouter, right_outer, semi, leftsemi, left_semi, anti, leftanti and left_anti.

Examples

The following performs a full outer join between df1 and df2.

>>> from pyspark.sql.functions import desc
>>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height)                 .sort(desc("name")).collect()
[Row(name='Bob', height=85), Row(name='Alice', height=None), Row(name=None, height=80)]

>>> df.join(df2, 'name', 'outer').select('name', 'height').sort(desc("name")).collect()
[Row(name='Tom', height=80), Row(name='Bob', height=85), Row(name='Alice', height=None)]

>>> cond = [df.name == df3.name, df.age == df3.age]
>>> df.join(df3, cond, 'outer').select(df.name, df3.age).collect()
[Row(name='Alice', age=2), Row(name='Bob', age=5)]

>>> df.join(df2, 'name').select(df.name, df2.height).collect()
[Row(name='Bob', height=85)]

>>> df.join(df4, ['name', 'age']).select(df.name, df.age).collect()
[Row(name='Bob', age=5)]