def sequence_to_pydf(
data: Sequence[Any],
columns: Optional[ColumnsType] = None,
orient: Optional[str] = None,
) -> "PyDataFrame":
"""
Construct a PyDataFrame from a sequence.
"""
data_series: List["PySeries"]
if len(data) == 0:
return dict_to_pydf({}, columns=columns)
elif isinstance(data[0], pli.Series):
series_names = [s.name for s in data]
columns, dtypes = _unpack_columns(columns or series_names, n_expected=len(data))
data_series = []
for i, s in enumerate(data):
if not s.name: # TODO: Replace by `if s.name is None` once allowed
s.rename(columns[i], in_place=True)
new_dtype = dtypes.get(columns[i])
if new_dtype and new_dtype != s.dtype:
s = s.cast(new_dtype)
data_series.append(s.inner())
elif isinstance(data[0], dict):
pydf = PyDataFrame.read_dicts(data)
if columns:
pydf = _post_apply_columns(pydf, columns)
return pydf
elif isinstance(data[0], Sequence) and not isinstance(data[0], str):
# Infer orientation
if orient is None and columns is not None:
orient = "col" if len(columns) == len(data) else "row"
if orient == "row":
pydf = PyDataFrame.read_rows(data)
if columns:
pydf = _post_apply_columns(pydf, columns)
return pydf
else:
columns, dtypes = _unpack_columns(columns, n_expected=len(data))
data_series = [
pli.Series(columns[i], data[i], dtypes.get(columns[i])).inner()
for i in range(len(data))
]
else:
columns, dtypes = _unpack_columns(columns, n_expected=1)
data_series = [pli.Series(columns[0], data, dtypes.get(columns[0])).inner()]
data_series = _handle_columns_arg(data_series, columns=columns)
return PyDataFrame(data_series)
def sequence_to_pydf(
data: Sequence[Any],
columns: Optional[Sequence[str]] = None,
orient: Optional[str] = None,
nullable: bool = True,
) -> "PyDataFrame":
"""
Construct a PyDataFrame from a sequence.
"""
data_series: List["PySeries"]
if len(data) == 0:
data_series = []
elif isinstance(data[0], pl.Series):
data_series = []
for i, s in enumerate(data):
if not s.name: # TODO: Replace by `if s.name is None` once allowed
s.rename(f"column_{i}", in_place=True)
data_series.append(s.inner())
elif isinstance(data[0], dict):
pydf = PyDataFrame.read_dicts(data)
if columns is not None:
pydf.set_column_names(columns)
return pydf
elif isinstance(data[0], Sequence) and not isinstance(data[0], str):
# Infer orientation
if orient is None and columns is not None:
orient = "col" if len(columns) == len(data) else "row"
if orient == "row":
pydf = PyDataFrame.read_rows(data)
if columns is not None:
pydf.set_column_names(columns)
return pydf
else:
data_series = [
pl.Series(f"column_{i}", data[i], nullable=nullable).inner()
for i in range(len(data))
]
else:
s = pl.Series("column_0", data, nullable=nullable).inner()
data_series = [s]
data_series = _handle_columns_arg(data_series,
columns=columns,
nullable=nullable)
return PyDataFrame(data_series)