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[SPARK-54568][PYTHON] Avoid unnecessary pandas conversion in create dataframe from ndarray #53280

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[SPARK-54568][PYTHON] Avoid unnecessary pandas conversion in create dataframe from ndarray #53280

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052fc95
test
zhengruifeng Dec 2, 2025
40fe3f6
simplify
zhengruifeng Dec 2, 2025
d0812c0
lint
zhengruifeng Dec 3, 2025
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40 changes: 21 additions & 19 deletions python/pyspark/sql/session.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,15 +52,13 @@
from pyspark.sql.types import (
AtomicType,
DataType,
StructField,
StructType,
VariantVal,
_make_type_verifier,
_infer_schema,
_has_nulltype,
_merge_type,
_create_converter,
_from_numpy_type,
)
from pyspark.errors.exceptions.captured import install_exception_handler
from pyspark.sql.utils import (
Expand Down Expand Up @@ -1565,32 +1563,36 @@ def createDataFrame( # type: ignore[misc]
has_pyarrow = False

if has_numpy and isinstance(data, np.ndarray):
# `data` of numpy.ndarray type will be converted to a pandas DataFrame,
# so pandas is required.
from pyspark.sql.pandas.utils import require_minimum_pandas_version
# `data` of numpy.ndarray type will be converted to an arrow Table,
# so pyarrow is required.
from pyspark.sql.pandas.utils import require_minimum_pyarrow_version

require_minimum_pandas_version()
require_minimum_pyarrow_version()
if data.ndim not in [1, 2]:
raise PySparkValueError(
errorClass="INVALID_NDARRAY_DIMENSION",
messageParameters={"dimensions": "1 or 2"},
)

if data.ndim == 1 or data.shape[1] == 1:
column_names = ["value"]
col_names: list[str] = []
if isinstance(schema, StructType):
col_names = schema.names
elif isinstance(schema, list):
col_names = schema
elif data.ndim == 1 or data.shape[1] == 1:
col_names = ["value"]
else:
column_names = ["_%s" % i for i in range(1, data.shape[1] + 1)]

if schema is None and not self._jconf.arrowPySparkEnabled():
# Construct `schema` from `np.dtype` of the input NumPy array
# TODO: Apply the logic below when self._jconf.arrowPySparkEnabled() is True
spark_type = _from_numpy_type(data.dtype)
if spark_type is not None:
schema = StructType(
[StructField(name, spark_type, nullable=True) for name in column_names]
)
col_names = [f"_{i + 1}" for i in range(0, data.shape[1])]

data = pd.DataFrame(data, columns=column_names)
if data.ndim == 1:
data = pa.Table.from_arrays(arrays=[pa.array(data)], names=col_names)
elif data.shape[1] == 1:
data = pa.Table.from_arrays(arrays=[pa.array(data.squeeze())], names=col_names)
else:
data = pa.Table.from_arrays(
arrays=[pa.array(data[::, i]) for i in range(0, data.shape[1])],
names=col_names,
)

if has_pandas and isinstance(data, pd.DataFrame):
# Create a DataFrame from pandas DataFrame.
Expand Down