// esm
import pl from 'nodejs-polars';

// require
const pl = require('nodejs-polars'); 

> const fooSeries = pl.Series("foo", [1, 2, 3])
> fooSeries.sum()
6

// a lot operations support both positional and named arguments
// you can see the full specs in the docs or the type definitions
> fooSeries.sort(true)
> fooSeries.sort({descending: true})
shape: (3,)
Series: 'foo' [f64]
[
        3
        2
        1
]
> fooSeries.toArray()
[1, 2, 3]

// Series are 'Iterables' so you can use javascript iterable syntax on them
> [...fooSeries]
[1, 2, 3]

> fooSeries[0]
1

>const df = pl.DataFrame(
...   {
...     A: [1, 2, 3, 4, 5],
...     fruits: ["banana", "banana", "apple", "apple", "banana"],
...     B: [5, 4, 3, 2, 1],
...     cars: ["beetle", "audi", "beetle", "beetle", "beetle"],
...   }
... )
> df.sort("fruits").select(
...     "fruits",
...     "cars",
...     pl.lit("fruits").alias("literal_string_fruits"),
...     pl.col("B").filter(pl.col("cars").eq(pl.lit("beetle"))).sum(),
...     pl.col("A").filter(pl.col("B").gt(2)).sum().over("cars").alias("sum_A_by_cars"),
...     pl.col("A").sum().over("fruits").alias("sum_A_by_fruits"),
...     pl.col("A").reverse().over("fruits").flatten().alias("rev_A_by_fruits")
...   )
shape: (5, 8)
┌──────────┬──────────┬──────────────┬─────┬─────────────┬─────────────┬─────────────┐
│ fruits   ┆ cars     ┆ literal_stri ┆ B   ┆ sum_A_by_ca ┆ sum_A_by_fr ┆ rev_A_by_fr │
│ ---      ┆ ---      ┆ ng_fruits    ┆ --- ┆ rs          ┆ uits        ┆ uits        │
│ str      ┆ str      ┆ ---          ┆ i64 ┆ ---         ┆ ---         ┆ ---         │
│          ┆          ┆ str          ┆     ┆ i64         ┆ i64         ┆ i64         │
╞══════════╪══════════╪══════════════╪═════╪═════════════╪═════════════╪═════════════╡
│ "apple"  ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 7           ┆ 4           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "apple"  ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 7           ┆ 3           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana" ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 8           ┆ 5           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana" ┆ "audi"   ┆ "fruits"     ┆ 11  ┆ 2           ┆ 8           ┆ 2           │
├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ "banana" ┆ "beetle" ┆ "fruits"     ┆ 11  ┆ 4           ┆ 8           ┆ 1           │
└──────────┴──────────┴──────────────┴─────┴─────────────┴─────────────┴─────────────┘

> df["cars"] // or df.getColumn("cars")
shape: (5,)
Series: 'cars' [str]
[
        "beetle"
        "beetle"
        "beetle"
        "audi"
        "beetle"
]