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Applies a function to every key-value pair in a map and returns a map with the results of those applications as the new keys for the pairs. Supports Spark Connect.
For the corresponding Databricks SQL function, see transform_keys function.
Syntax
from pyspark.databricks.sql import functions as dbf
dbf.transform_keys(col=<col>, f=<f>)
Parameters
| Parameter | Type | Description |
|---|---|---|
col |
pyspark.sql.Column or str |
Name of column or expression. |
f |
function |
A binary function. |
Returns
pyspark.sql.Column: a new map of entries where new keys were calculated by applying given function to each key value argument.
Examples
from pyspark.databricks.sql import functions as dbf
df = spark.createDataFrame([(1, {"foo": -2.0, "bar": 2.0})], ("id", "data"))
row = df.select(dbf.transform_keys(
"data", lambda k, _: dbf.upper(k)).alias("data_upper")
).head()
sorted(row["data_upper"].items())
[('BAR', 2.0), ('FOO', -2.0)]