A row in
DataFrame. The fields in it can be accessed:
like attributes (
like dictionary values (
key in rowwill 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.
>>> from pyspark.sql import Row >>> 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
Return as a dict
Return number of occurrences of value.
index(value[, start, stop])
Return first index of value.