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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=u'Bob', height=85), Row(name=u'Alice', height=None)]
>>> df.join(df2, 'name', 'outer').select('name', 'height').collect()
[Row(name=u'Tom', height=80), Row(name=u'Bob', height=85), Row(name=u'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=u'Alice', age=2), Row(name=u'Bob', age=5)]
>>> df.join(df2, 'name').select(df.name, df2.height).collect()
[Row(name=u'Bob', height=85)]
>>> df.join(df4, ['name', 'age']).select(df.name, df.age).collect()
[Row(name=u'Bob', age=5)]
New in version 1.3.
