def from_records(
data: Sequence[Sequence[Any]],
columns: Sequence[str] | None = None,
orient: Literal["col", "row"] | None = None,
) -> DataFrame:
"""
Construct a DataFrame from a numpy ndarray or sequence of sequences.
Note that this is slower than creating from columnar memory.
Parameters
----------
data : numpy ndarray or Sequence of sequences
Two-dimensional data represented as numpy ndarray or sequence of sequences.
columns : Sequence of str, default None
Column labels to use for resulting DataFrame. Must match data dimensions.
If not specified, columns will be named `column_0`, `column_1`, etc.
orient : {'col', 'row'}, default None
Whether to interpret two-dimensional data as columns or as rows. If None,
the orientation is inferred by matching the columns and data dimensions. If
this does not yield conclusive results, column orientation is used.
Returns
-------
DataFrame
Examples
--------
>>> data = [[1, 2, 3], [4, 5, 6]]
>>> df = pl.from_records(data, columns=["a", "b"])
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 4 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 6 │
└─────┴─────┘
"""
if _NUMPY_AVAILABLE and isinstance(data, np.ndarray):
warnings.warn(
"using `from_records` with a numpy ndarray is deprecated, "
"use `from_numpy` instead",
DeprecationWarning,
)
return DataFrame._from_numpy(data, columns=columns, orient=orient)
else:
return DataFrame._from_records(data, columns=columns, orient=orient)
def from_numpy(
data: np.ndarray,
columns: Sequence[str] | None = None,
orient: Literal["col", "row"] | None = None,
) -> DataFrame:
"""
Construct a DataFrame from a numpy ndarray.
Note that this is slower than creating from columnar memory.
Parameters
----------
data : numpy ndarray
Two-dimensional data represented as a numpy ndarray.
columns : Sequence of str, default None
Column labels to use for resulting DataFrame. Must match data dimensions.
If not specified, columns will be named `column_0`, `column_1`, etc.
orient : {'col', 'row'}, default None
Whether to interpret two-dimensional data as columns or as rows. If None,
the orientation is inferred by matching the columns and data dimensions. If
this does not yield conclusive results, column orientation is used.
Returns
-------
DataFrame
Examples
--------
>>> import numpy as np
>>> data = np.array([[1, 2, 3], [4, 5, 6]])
>>> df = pl.from_numpy(data, columns=["a", "b"], orient="col")
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 4 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 6 │
└─────┴─────┘
"""
if not _NUMPY_AVAILABLE:
raise ImportError("'numpy' is required when using from_numpy().")
return DataFrame._from_numpy(data, columns=columns, orient=orient)
