pyspark.sql.Row¶

class pyspark.sql.Row¶

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.

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

Examples

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

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
True
>>> 'wrong_key' in Person
False
>>> 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
True

Methods

`asDict`([recursive])	Return as a dict
`count`(value, /)	Return number of occurrences of value.
`index`(value[, start, stop])	Return first index of value.

pyspark.sql.Observation

pyspark.sql.GroupedData