narwhals.Expr.list
contains
contains(item: NonNestedLiteral) -> ExprT
Check if sublists contain the given item.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
item
|
NonNestedLiteral
|
Item that will be checked for membership. |
required |
Examples:
>>> import polars as pl
>>> import narwhals as nw
>>> df_native = pl.DataFrame({"a": [[1, 2], None, []]})
>>> df = nw.from_native(df_native)
>>> df.with_columns(a_contains_1=nw.col("a").list.contains(1))
┌────────────────────────────┐
| Narwhals DataFrame |
|----------------------------|
|shape: (3, 2) |
|┌───────────┬──────────────┐|
|│ a ┆ a_contains_1 │|
|│ --- ┆ --- │|
|│ list[i64] ┆ bool │|
|╞═══════════╪══════════════╡|
|│ [1, 2] ┆ true │|
|│ null ┆ null │|
|│ [] ┆ false │|
|└───────────┴──────────────┘|
└────────────────────────────┘
get
get(index: int) -> ExprT
Return the value by index in each list.
Negative indices are not accepted.
Returns:
Type | Description |
---|---|
ExprT
|
A new expression. |
Examples:
>>> import polars as pl
>>> import narwhals as nw
>>> df_native = pl.DataFrame({"a": [[1, 2], [3, 4, None], [None, 5]]})
>>> df = nw.from_native(df_native)
>>> df.with_columns(a_first=nw.col("a").list.get(0))
┌──────────────────────────┐
| Narwhals DataFrame |
|--------------------------|
|shape: (3, 2) |
|┌──────────────┬─────────┐|
|│ a ┆ a_first │|
|│ --- ┆ --- │|
|│ list[i64] ┆ i64 │|
|╞══════════════╪═════════╡|
|│ [1, 2] ┆ 1 │|
|│ [3, 4, null] ┆ 3 │|
|│ [null, 5] ┆ null │|
|└──────────────┴─────────┘|
└──────────────────────────┘
len
len() -> ExprT
Return the number of elements in each list.
Null values count towards the total.
Examples:
>>> import polars as pl
>>> import narwhals as nw
>>> df_native = pl.DataFrame({"a": [[1, 2], [3, 4, None], None, []]})
>>> df = nw.from_native(df_native)
>>> df.with_columns(a_len=nw.col("a").list.len())
┌────────────────────────┐
| Narwhals DataFrame |
|------------------------|
|shape: (4, 2) |
|┌──────────────┬───────┐|
|│ a ┆ a_len │|
|│ --- ┆ --- │|
|│ list[i64] ┆ u32 │|
|╞══════════════╪═══════╡|
|│ [1, 2] ┆ 2 │|
|│ [3, 4, null] ┆ 3 │|
|│ null ┆ null │|
|│ [] ┆ 0 │|
|└──────────────┴───────┘|
└────────────────────────┘
unique
unique() -> ExprT
Get the unique/distinct values in the list.
Null values are included in the result. The order of unique values is not guaranteed.
Examples:
>>> import polars as pl
>>> import narwhals as nw
>>> df_native = pl.DataFrame({"a": [[1, 1, 2], [3, 3, None], None, []]})
>>> df = nw.from_native(df_native)
>>> df.with_columns(a_unique=nw.col("a").list.unique())
┌────────────────────────────┐
| Narwhals DataFrame |
|----------------------------|
|shape: (4, 2) |
|┌──────────────┬───────────┐|
|│ a ┆ a_unique │|
|│ --- ┆ --- │|
|│ list[i64] ┆ list[i64] │|
|╞══════════════╪═══════════╡|
|│ [1, 1, 2] ┆ [1, 2] │|
|│ [3, 3, null] ┆ [null, 3] │|
|│ null ┆ null │|
|│ [] ┆ [] │|
|└──────────────┴───────────┘|
└────────────────────────────┘