# => │ function │ Boolean(IsIn)           │
# => │ options  │ FunctionOptions { ... } │
# => ╰──────────┴─────────────────────────╯
```

and this new mask can be used to filter the dataframe

```nu
$df_1 | polars filter-with $mask_2
# => ╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
# => │ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │  word  │
# => ├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
# => │ 0 │     4 │    14 │    0.40 │    3.00 │ b     │ a      │ c     │ second │
# => │ 1 │     0 │    15 │    0.50 │    4.00 │ b     │ a      │ a     │ third  │
# => │ 2 │     6 │    16 │    0.60 │    5.00 │ b     │ a      │ a     │ second │
# => │ 3 │     7 │    17 │    0.70 │    6.00 │ b     │ c      │ a     │ third  │
# => │ 4 │     8 │    18 │    0.80 │    7.00 │ c     │ c      │ b     │ eight  │
# => │ 5 │     9 │    19 │    0.90 │    8.00 │ c     │ c      │ b     │ ninth  │
# => │ 6 │     0 │    10 │    0.00 │    9.00 │ c     │ c      │ b     │ ninth  │
# => ╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
```

Another operation that can be done with masks is setting or replacing a value
from a series. For example, we can change the value in the column `first` where
the value is equal to `a`

```nu
$df_1 | polars get first | polars set new --mask ($df_1.first =~ a)
# => ╭───┬────────╮
# => │ # │ string │
# => ├───┼────────┤
# => │ 0 │ new    │
# => │ 1 │ new    │
# => │ 2 │ new    │
# => │ 3 │ b      │
# => │ 4 │ b      │
# => │ 5 │ b      │
# => │ 6 │ b      │
# => │ 7 │ c      │
# => │ 8 │ c      │
# => │ 9 │ c      │
# => ╰───┴────────╯
```

## Series as Indices

Series can be also used as a way of filtering a dataframe by using them as a
list of indices. For example, let's say that we want to get rows 1, 4, and 6
from our original dataframe. With that in mind, we can use the next command to
extract that information

```nu
let indices_0 = [1 4 6] | polars into-df
$df_1 | polars take $indices_0
# => ╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
# => │ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │  word  │
# => ├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
# => │ 0 │     2 │    12 │    0.20 │    1.00 │ a     │ b      │ c     │ second │
# => │ 1 │     0 │    15 │    0.50 │    4.00 │ b     │ a      │ a     │ third  │
# => │ 2 │     7 │    17 │    0.70 │    6.00 │ b     │ c      │ a     │ third  │
# => ╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
```

The command [`polars take`](/commands/docs/polars_take.md) is very handy, especially if we mix it with other commands.
Let's say that we want to extract all rows for the first duplicated element for
column `first`. In order to do that, we can use the command `polars arg-unique` as
shown in the next example

```nu
let indices_1 = $df_1 | polars get first | polars arg-unique
$df_1 | polars take $indices_1
# => ╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮
# => │ # │ int_1 │ int_2 │ float_1 │ float_2 │ first │ second │ third │  word  │
# => ├───┼───────┼───────┼─────────┼─────────┼───────┼────────┼───────┼────────┤
# => │ 0 │     1 │    11 │    0.10 │    1.00 │ a     │ b      │ c     │ first  │
# => │ 1 │     4 │    14 │    0.40 │    3.00 │ b     │ a      │ c     │ second │
# => │ 2 │     8 │    18 │    0.80 │    7.00 │ c     │ c      │ b     │ eight  │
# => ╰───┴───────┴───────┴─────────┴─────────┴───────┴────────┴───────┴────────╯
```

Or what if we want to create a new sorted dataframe using a column in specific.
We can use the `arg-sort` to accomplish that. In the next example we
can sort the dataframe by the column `word`

::: tip
The same result could be accomplished using the command [`sort`](/commands/docs/sort.md)
:::

```nu
let indices_2 = $df_1 | polars get word | polars arg-sort
$df_1 | polars take $indices_2
# => ╭───┬───────┬───────┬─────────┬─────────┬───────┬────────┬───────┬────────╮