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.
Creates a new map column from an even number of input columns or column references. The input columns are grouped into key-value pairs to form a map. For instance, the input (key1, value1, key2, value2, ...) would produce a map that associates key1 with value1, key2 with value2, and so on. The function supports grouping columns as a list as well.
Syntax
from pyspark.sql import functions as sf
sf.create_map(*cols)
Parameters
| Parameter | Type | Description |
|---|---|---|
cols |
pyspark.sql.Column or str |
The input column names or Column objects grouped into key-value pairs. These can also be expressed as a list of columns. |
Returns
pyspark.sql.Column: A new Column of Map type, where each value is a map formed from the corresponding key-value pairs provided in the input arguments.
Examples
Example 1: Basic usage of create_map function.
from pyspark.sql import functions as sf
df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
df.select(sf.create_map('name', 'age')).show()
+--------------+
|map(name, age)|
+--------------+
| {Alice -> 2}|
| {Bob -> 5}|
+--------------+
Example 2: Usage of create_map function with a list of columns.
from pyspark.sql import functions as sf
df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
df.select(sf.create_map([df.name, df.age])).show()
+--------------+
|map(name, age)|
+--------------+
| {Alice -> 2}|
| {Bob -> 5}|
+--------------+
Example 3: Usage of create_map function with more than one key-value pair.
from pyspark.sql import functions as sf
df = spark.createDataFrame([("Alice", 2, "female"),
("Bob", 5, "male")], ("name", "age", "gender"))
df.select(sf.create_map(sf.lit('name'), df['name'],
sf.lit('gender'), df['gender'])).show(truncate=False)
+---------------------------------+
|map(name, name, gender, gender) |
+---------------------------------+
|{name -> Alice, gender -> female}|
|{name -> Bob, gender -> male} |
+---------------------------------+
Example 4: Usage of create_map function with values of different types.
from pyspark.sql import functions as sf
df = spark.createDataFrame([("Alice", 2, 22.2),
("Bob", 5, 36.1)], ("name", "age", "weight"))
df.select(sf.create_map(sf.lit('age'), df['age'],
sf.lit('weight'), df['weight'])).show(truncate=False)
+-----------------------------+
|map(age, age, weight, weight)|
+-----------------------------+
|{age -> 2.0, weight -> 22.2} |
|{age -> 5.0, weight -> 36.1} |
+-----------------------------+