ueshin opened a new pull request, #40527:
URL: https://github.com/apache/spark/pull/40527
### What changes were proposed in this pull request?
Fixes `createDataFrame` to respect inference and column names.
### Why are the changes needed?
Currently when a column name list is provided as a schema, the type
inference result is not taken care of.
As a result, `createDataFrame` from UDT objects with column name list
doesn't take the UDT type.
For example:
```py
>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(1.0, 1.0, Vectors.dense(0.0, 5.0)), (0.0,
2.0, Vectors.dense(1.0, 2.0))], ["label", "weight", "features"])
>>> df.printSchema()
root
|-- label: double (nullable = true)
|-- weight: double (nullable = true)
|-- features: struct (nullable = true)
| |-- type: byte (nullable = false)
| |-- size: integer (nullable = true)
| |-- indices: array (nullable = true)
| | |-- element: integer (containsNull = false)
| |-- values: array (nullable = true)
| | |-- element: double (containsNull = false)
```
, which should be:
```py
>>> df.printSchema()
root
|-- label: double (nullable = true)
|-- weight: double (nullable = true)
|-- features: vector (nullable = true)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added the related tests.
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