def from_dicts(dicts: Sequence[Dict[str, Any]]) -> DataFrame:
"""
Construct a DataFrame from a sequence of dictionaries.
Parameters
----------
dicts
Sequence with dictionaries mapping column name to value
Returns
-------
DataFrame
Examples
--------
>>> data = [{"a": 1, "b": 4}, {"a": 2, "b": 5}, {"a": 3, "b": 6}]
>>> df = pl.from_dicts(data)
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 4 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 6 │
└─────┴─────┘
"""
return DataFrame._from_dicts(dicts)
def from_dicts(dicts: Sequence[dict[str, Any]],
infer_schema_length: int | None = 50) -> DataFrame:
"""
Construct a DataFrame from a sequence of dictionaries.
Parameters
----------
dicts
Sequence with dictionaries mapping column name to value
infer_schema_length
How many dictionaries/rows to scan to determine the data types
if set to `None` all rows are scanned. This will be slow.
Returns
-------
DataFrame
Examples
--------
>>> data = [{"a": 1, "b": 4}, {"a": 2, "b": 5}, {"a": 3, "b": 6}]
>>> df = pl.from_dicts(data)
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 4 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 6 │
└─────┴─────┘
"""
return DataFrame._from_dicts(dicts, infer_schema_length)
