pyspark.sql.Row#

classpyspark.sql.Row(*args,**kwargs)[source]#

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

• like attributes (row.key)

• like dictionary values (row[key])

keyinrow 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 isNone 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 havefield names sorted alphabetically and will be ordered in the position asentered.

Examples

>>>frompyspark.sqlimportRow>>>row=Row(name="Alice",age=11)>>>rowRow(name='Alice', age=11)>>>row['name'],row['age']('Alice', 11)>>>row.name,row.age('Alice', 11)>>>'name'inrowTrue>>>'wrong_key'inrowFalse

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

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

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

>>>row1=Row("Alice",11)>>>row2=Row(name="Alice",age=11)>>>row1==row2True

Methods

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