def arrow_to_pydf(data: pa.Table,
columns: Optional[Sequence[str]] = None,
rechunk: bool = True) -> "PyDataFrame":
"""
Construct a PyDataFrame from an Arrow Table.
"""
if columns is not None:
try:
data = data.rename_columns(columns)
except pa.lib.ArrowInvalid as e:
raise ValueError(
"Dimensions of columns arg must match data dimensions.") from e
data_dict = {}
for i, column in enumerate(data):
# extract the name before casting
if column._name is None:
name = f"column_{i}"
else:
name = column._name
column = coerce_arrow(column)
data_dict[name] = column
batches = pa.table(data_dict).to_batches()
pydf = PyDataFrame.from_arrow_record_batches(batches)
if rechunk:
pydf = pydf.rechunk()
return pydf
def arrow_to_pyseries(name: str, values: pa.Array) -> "PySeries":
"""
Construct a PySeries from an Arrow array.
"""
array = coerce_arrow(values)
return PySeries.from_arrow(name, array)