class pyspark.sql.Row[source]

A row in DataFrame. The fields in it can be accessed:

  • like attributes (row.key)

  • like dictionary values (row[key])

key in row will search through row keys.

Row can be used to create a row object by using named arguments. It is not allowed to omit a named argument to represent that the value is None or missing. This should be explicitly set to None in this case.

Changed in version 3.0.0: Rows created from named arguments no longer have field names sorted alphabetically and will be ordered in the position as entered.


>>> row = Row(name="Alice", age=11)
>>> row
Row(name='Alice', age=11)
>>> row['name'], row['age']
('Alice', 11)
>>>, row.age
('Alice', 11)
>>> 'name' in row
>>> 'wrong_key' in row

Row also can be used to create another Row like class, then it could be used to create Row objects, such as

>>> Person = Row("name", "age")
>>> Person
<Row('name', 'age')>
>>> 'name' in Person
>>> 'wrong_key' in Person
>>> Person("Alice", 11)
Row(name='Alice', age=11)

This form can also be used to create rows as tuple values, i.e. with unnamed fields.

>>> row1 = Row("Alice", 11)
>>> row2 = Row(name="Alice", age=11)
>>> row1 == row2



Return as a dict

count(value, /)

Return number of occurrences of value.

index(value[, start, stop])

Return first index of value.