Github user mortada commented on the issue:
https://github.com/apache/spark/pull/15053
In other words, currently it is not possible for the user to follow the
examples in this docstring below. It's not clear what all these input variables
(`df`, `df2`, etc) are, and where you'd even find them
```
In [5]: DataFrame.join?
Signature: DataFrame.join(self, other, on=None, how=None)
Docstring:
Joins with another :class:`DataFrame`, using the given join expression.
:param other: Right side of the join
:param on: 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.
:param how: str, default 'inner'.
One of `inner`, `outer`, `left_outer`, `right_outer`, `leftsemi`.
The following performs a full outer join between ``df1`` and ``df2``.
>>> df.join(df2, df.name == df2.name, 'outer').select(df.name,
df2.height).collect()
[Row(name=None, height=80), Row(name='Bob', height=85), Row(name='Alice',
height=None)]
>>> df.join(df2, 'name', 'outer').select('name', 'height').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)]
.. versionadded:: 1.3
File: ~/code/spark/python/pyspark/sql/dataframe.py
Type: function
```
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