A collection of Datasets.

Constructors¶

Collection(data, ...)

Initialize self. See help(type(self)) for accurate signature.

Config¶

set_scope(key: str | Iterable[str] | None = None) → Self

set_scope_by_index(idx: int | Iterable[int] | None = None) → Self

Delegated¶

set_sorted(column: str, *, descending: bool = False) → Self

approx_n_unique() → Self

bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], ...) → Self

cast(dtypes, ...) → Self

clear(n: int = 0) → Self

clone() → Self

collect_model_blueprint(...) → dict[str, str]

collect_schema() → dict[str, Schema]

corr(**kwargs: Any) → Self

count() → Self

describe(...) → Self

drop(*columns, ...) → Self

drop_in_place(...) → dict[str, TypeAliasForwardRef('polars.Series')]

drop_nans(...) → Self

drop_nulls(...) → Self

equals(other: polars.DataFrame, *, ...) → dict[str, bool]

estimated_size(unit: SizeUnit = 'b') → dict[str, int | float]

explode(columns, ...) → Self

extend(other: polars.DataFrame) → Self

fill_nan(value: Expr | int | float | None) → Self

fill_null(value: Any | Expr | None = None, ...) → Self

filter(*predicates, ...) → Self

fold(operation) → dict[str, TypeAliasForwardRef('polars.Series')]

gather_every(n: int, offset: int = 0) → Self

get_column(name: str, *, ...) → dict[str, polars.Series | Any]

get_column_index(name: str) → dict[str, int]

get_columns() → dict[str, list[TypeAliasForwardRef('polars.Series')]]

glimpse(*, ...) → dict[str, str | None]

hash_rows(...) → dict[str, TypeAliasForwardRef('polars.Series')]

head(n: int = 5) → Self

hstack(columns: list[polars.Series] | polars.DataFrame, ...) → Self

insert_column(index: int, column: IntoExprColumn) → Self

interpolate() → Self

is_duplicated() → dict[str, TypeAliasForwardRef('polars.Series')]

is_empty() → dict[str, bool]

is_unique() → dict[str, TypeAliasForwardRef('polars.Series')]

item(row: int | None = None, ...) → dict[str, Any]

iter_columns() → dict[str, Iterator[polars.Series]]

iter_rows(...) → dict[str, Iterator[tuple[Any, ...]] | Iterator[dict[str, Any]]]

iter_slices(...) → dict[str, Iterator[polars.DataFrame]]

limit(n: int = 5) → Self

map_columns(column_names, ...) → Self

map_rows(function: Callable[[tuple[Any, ...]], Any], ...) → Self

match_to_schema(schema: SchemaDict | Schema, *, ...) → Self

max() → Self

max_horizontal() → dict[str, TypeAliasForwardRef('polars.Series')]

mean() → Self

mean_horizontal(...) → dict[str, TypeAliasForwardRef('polars.Series')]

median() → Self

melt(...) → Self

merge_sorted(other: Dataset[U] | polars.DataFrame, key: str) → Self

min() → Self

min_horizontal() → dict[str, TypeAliasForwardRef('polars.Series')]

n_chunks(...) → dict[str, int | list[int]]

n_unique(...) → dict[str, int]

null_count() → Self

partition_by(...) → dict[str, list[polars.DataFrame] | dict[tuple[Any, ...], polars.DataFrame]]

pipe(function, ...) → dict[str, T]

pivot(on, ...) → Self

product() → Self

quantile(quantile: float, ...) → Self

rechunk() → Self

remove(*predicates, ...) → Self

rename(mapping, ...) → Self

replace_column(index: int, column: polars.Series) → Self

reverse() → Self

row(...) → dict[str, tuple[Any, ...] | dict[str, Any]]

rows(...) → dict[str, list[tuple[Any, ...]] | list[dict[str, Any]]]

rows_by_key(key, ...) → dict[str, dict[Any, Any]]

sample(n: int | polars.Series | None = None, *, ...) → Self

select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs) → Self

select_seq(*exprs: IntoExpr | Iterable[IntoExpr], ...) → Self

shift(n: int = 1, *, fill_value: IntoExpr | None = None) → Self

shrink_to_fit(*, in_place: bool = False) → Self

skim(...) → dict[str, Collection]

slice(offset: int, length: int | None = None) → Self

sort(by: IntoExpr | Iterable[IntoExpr], *more_by, ...) → Self

sql(query: str, *, table_name: str = 'self') → Self

std(ddof: int = 1) → Self

sum() → Self

sum_horizontal(...) → dict[str, TypeAliasForwardRef('polars.Series')]

tail(n: int = 5) → Self

top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], ...) → Self

transpose(*, include_header: bool = False, ...) → Self

unique(...) → Self

unnest(columns, ...) → Self

unpivot(...) → Self

unstack(*, step: int, ...) → Self

update(other: polars.DataFrame, ...) → Self

upsample(time_column: str, *, every: str | timedelta, ...) → Self

var(ddof: int = 1) → Self

vstack(other: Dataset[VFM] | polars.DataFrame, *, ...) → Self

with_columns(*exprs: IntoExpr | Iterable[IntoExpr], ...) → Self

with_columns_seq(*exprs: IntoExpr | Iterable[IntoExpr], ...) → Self

with_name(name: str | None) → Self

with_row_count(name: str = 'row_nr', offset: int = 0) → Self

with_row_index(name: str = 'index', offset: int = 0) → Self

without_model() → dict[str, Dataset[Any]]

to_arrow(*, ...) → dict[str, pa.Table]

to_dataframe() → dict[str, TypeAliasForwardRef('polars.DataFrame')]

to_dummies(...) → Self

to_init_repr(n: int = 1000) → dict[str, str]

to_jax(...) → dict[str, jax.Array | dict[str, jax.Array]]

to_lazyframe() → dict[str, TypeAliasForwardRef('polars.LazyFrame')]

to_numpy(*, ...) → dict[str, np.ndarray[Any, Any]]

to_pandas(*, ...) → dict[str, pd.DataFrame]

to_polars() → dict[str, TypeAliasForwardRef('polars.DataFrame')]

to_series(...) → dict[str, TypeAliasForwardRef('polars.Series')]

to_struct(...) → dict[str, TypeAliasForwardRef('polars.Series')]

to_torch(...) → dict[str, torch.Tensor | dict[str, torch.Tensor] | PolarsDataset]

Methods¶

lazy() → LazyCollection

items() → ItemsView[str, _DST]

keys() → KeysView[str]

values() → ValuesView[_DST]

concat(*, with_key_column: bool | str = False, ...) → _DST

Concatenate all items into a single Dataset/LazyDataset.

group_by(*by, ...) → _CollectionGroupBy

join(other, ...) → Self

Properties¶

property columns : dict[str, Any]

group: Delegated

property dtypes : dict[str, Any]

group: Delegated

property flags : dict[str, Any]

group: Delegated

property height : dict[str, Any]

group: Delegated

property model : dict[str, Any]

group: Delegated

property plot : dict[str, Any]

group: Delegated

property schema : dict[str, Any]

group: Delegated

property shape : dict[str, Any]

group: Delegated

property style : dict[str, Any]

group: Delegated

property vcol : dict[str, Any]

group: Delegated

property width : dict[str, Any]

group: Delegated

Export¶

to_dict(*, to_polars: bool = False) → dict