नोट
इस पेज तक पहुँच के लिए प्रमाणन की आवश्यकता होती है. आप साइन इन करने या निर्देशिकाओं को बदलने का प्रयास कर सकते हैं.
इस पेज तक पहुँच के लिए प्रमाणन की आवश्यकता होती है. आप निर्देशिकाओं को बदलने का प्रयास कर सकते हैं.
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 Databricks Runtime 7.4: Rows created from named arguments no longer have field names sorted alphabetically and will be ordered in the position as entered.
Syntax
from pyspark.sql import Row
Row(tuple)
Parameters
| Parameter | Type | Description |
|---|---|---|
tuple |
tuple | The row elements |
Methods
| Method | Description |
|---|---|
asDict(recursive) |
Returns the Row as Dict[str, Any]. |
Examples
Using named arguments
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
Creating Row classes
Row can also be used to create another Row-like class, then it could be used to create Row objects:
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, with unnamed fields:
row1 = Row("Alice", 11)
row2 = Row(name="Alice", age=11)
row1 == row2
# True