polars.from_dicts#

polars.from_dicts(dicts: Sequence[dict[str, Any]], infer_schema_length: int | None = 100, *, schema: SchemaDefinition | None = None, schema_overrides: SchemaDict | None = None) → DataFrame[source]#

Construct a DataFrame from a sequence of dictionaries. This operation clones data.

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 then ALL dicts are scanned; this will be slow.

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 a list of column names is supplied that does NOT match the names in the underlying data, the names given here will overwrite the actual fields in the order that they appear - however, in this case it is typically clearer to rename after loading the frame.

If you want to drop some of the fields found in the input dictionaries, a _partial_ schema can be declared, in which case omitted fields will not be loaded. Similarly you can extend the loaded frame with empty columns by adding them to the schema.

schema_overridesdict, default None

Support override of inferred types for one or more columns.

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   │
└─────┴─────┘

Declaring a partial schema will drop the omitted columns.

>>> df = pl.from_dicts(data, schema={"a": pl.Int32})
>>> df
shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ i32 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

Can also use the schema param to extend the loaded columns with one or more additional (empty) columns that are not present in the input dicts:

>>> pl.from_dicts(
...     data,
...     schema=["a", "b", "c", "d"],
...     schema_overrides={"c": pl.Float64, "d": pl.Utf8},
... )
shape: (3, 4)
┌─────┬─────┬──────┬──────┐
│ a   ┆ b   ┆ c    ┆ d    │
│ --- ┆ --- ┆ ---  ┆ ---  │
│ i64 ┆ i64 ┆ f64  ┆ str  │
╞═════╪═════╪══════╪══════╡
│ 1   ┆ 4   ┆ null ┆ null │
│ 2   ┆ 5   ┆ null ┆ null │
│ 3   ┆ 6   ┆ null ┆ null │
└─────┴─────┴──────┴──────┘