zhengruifeng commented on code in PR #38979:
URL: https://github.com/apache/spark/pull/38979#discussion_r1043125777
##########
python/pyspark/sql/connect/_typing.py:
##########
@@ -26,8 +26,12 @@
import datetime
import decimal
+from numpy import ndarray as NDArray
+
from pyspark.sql.connect.column import Column
+ArrayLike = NDArray
Review Comment:
just copy it from pyspark
##########
python/pyspark/sql/connect/session.py:
##########
@@ -264,9 +304,76 @@ def createDataFrame(self, data: "pd.DataFrame") ->
"DataFrame":
"""
assert data is not None
- if len(data) == 0:
+ if isinstance(data, DataFrame):
+ raise TypeError("data is already a DataFrame")
+ if isinstance(data, Sized) and len(data) == 0:
raise ValueError("Input data cannot be empty")
- return DataFrame.withPlan(plan.LocalRelation(data), self)
+
+ struct: Optional[StructType] = None
+ column_names: List[str] = []
+
+ if isinstance(schema, StructType):
+ struct = schema
+ column_names = struct.names
+
+ elif isinstance(schema, str):
+ struct = _parse_datatype_string(schema) # type: ignore[assignment]
+ assert isinstance(struct, StructType)
+ column_names = struct.names
+
+ elif isinstance(schema, (list, tuple)):
+ # Must re-encode any unicode strings to be consistent with
StructField names
+ column_names = [x.encode("utf-8") if not isinstance(x, str) else x
for x in schema]
+
+ # Create the Pandas DataFrame
+ if isinstance(data, pd.DataFrame):
+ pdf = data
+
+ elif isinstance(data, np.ndarray):
+ # `data` of numpy.ndarray type will be converted to a pandas
DataFrame,
+ if data.ndim not in [1, 2]:
+ raise ValueError("NumPy array input should be of 1 or 2
dimensions.")
+
+ pdf = pd.DataFrame(data)
+
+ if len(column_names) == 0:
+ 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)]
+
+ else:
+ pdf = pd.DataFrame(list(data))
+
+ if len(column_names) == 0:
+ column_names = ["_%s" % i for i in range(1, pdf.shape[1] + 1)]
+
+ # Adjust the column names
+ if len(column_names) > 0:
+ pdf.columns = column_names
+
+ # Casting according to the input schema
+ if struct is not None:
+ for field in struct.fields:
+ name = field.name
+ dt = field.dataType
+
+ if isinstance(dt, ByteType):
Review Comment:
let me have a try
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