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[SPARK-40990][PYTHON] DataFrame creation from 2d NumPy array with arbitrary columns #38473

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[SPARK-40990][PYTHON] DataFrame creation from 2d NumPy array with arbitrary columns #38473

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7 changes: 6 additions & 1 deletion python/pyspark/sql/session.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1139,7 +1139,12 @@ def createDataFrame( # type: ignore[misc]
require_minimum_pandas_version()
if data.ndim not in [1, 2]:
raise ValueError("NumPy array input should be of 1 or 2 dimensions.")
column_names = ["value"] if data.ndim == 1 else ["_1", "_2"]

if data.ndim == 1 or data.shape[1] == 1:
column_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
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14 changes: 9 additions & 5 deletions python/pyspark/sql/tests/test_arrow.py
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Expand Up @@ -188,8 +188,10 @@ def create_np_arrs(self):
return (
[np.array([1, 2]).astype(t) for t in int_dtypes]
+ [np.array([0.1, 0.2]).astype(t) for t in float_dtypes]
+ [np.array([[1, 2], [3, 4]]).astype(t) for t in int_dtypes]
+ [np.array([[0.1, 0.2], [0.3, 0.4]]).astype(t) for t in float_dtypes]
+ [np.array([[1], [2]]).astype(t) for t in int_dtypes]
+ [np.array([[0.1], [0.2]]).astype(t) for t in float_dtypes]
+ [np.array([[1, 1, 1], [2, 2, 2]]).astype(t) for t in int_dtypes]
+ [np.array([[0.1, 0.1, 0.1], [0.2, 0.2, 0.2]]).astype(t) for t in float_dtypes]
)

def test_toPandas_fallback_enabled(self):
Expand Down Expand Up @@ -510,9 +512,11 @@ def test_schema_conversion_roundtrip(self):

def test_createDataFrame_with_ndarray(self):
dtypes = ["tinyint", "smallint", "int", "bigint", "float", "double"]
expected_dtypes = [[("value", t)] for t in dtypes] + [
[("_1", t), ("_2", t)] for t in dtypes
]
expected_dtypes = (
[[("value", t)] for t in dtypes]
+ [[("value", t)] for t in dtypes]
+ [[("_1", t), ("_2", t), ("_3", t)] for t in dtypes]
)
arrs = self.create_np_arrs

for arr, dtypes in zip(arrs, expected_dtypes):
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