polars.from_dict#

polars.from_dict( data: Mapping[str, Sequence[object] | Mapping[str, Sequence[object]] | Series], schema: SchemaDefinition | None = None, *, schema_overrides: SchemaDict | None = None, strict: bool = True, ) → DataFrame[source]#

Construct a DataFrame from a dictionary of sequences.

This operation clones data, unless you pass a {str: pl.Series,} dict.

Parameters:

datadict of sequences

Two-dimensional data represented as a dictionary. dict must contain Sequences.

schemaSequence of str, (str,DataType) pairs, or a {str:DataType,} dict

The DataFrame schema may be declared in several ways:

As a dict of {name:type} pairs; if type is None, it will be auto-inferred.
As a list of column names; in this case types are automatically inferred.
As a list of (name,type) pairs; this is equivalent to the dictionary form.

If you supply a list of column names that does not match the names in the underlying data, the names given here will overwrite them. The number of names given in the schema should match the underlying data dimensions.

schema_overridesdict, default None

Support type specification or override of one or more columns; note that any dtypes inferred from the columns param will be overridden.

strictbool, default True

Throw an error if any data value does not exactly match the given or inferred data type for that column. If set to False, values that do not match the data type are cast to that data type or, if casting is not possible, set to null instead.

Returns:

DataFrame

Examples

>>> df = pl.from_dict({"a": [1, 2], "b": [3, 4]})
>>> df
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 3   │
│ 2   ┆ 4   │
└─────┴─────┘