Is there a way to vectorise over ragged arrays in polars-CodePudding

I have a column with lists of different length like below and want to make a parallel np.diff on each of the independent arrays.

import polars as pl
import numpy as np
np.random.seed(0)
ragged_arrays = [np.random.randint(10, size=np.random.choice(range(10))) for _ in range(5)]

df = pl.DataFrame({'values':ragged_arrays})
df

shape: (5, 1)
┌───────────────────┐
│ values            │
│ ---               │
│ object            │
╞═══════════════════╡
│ [0 3 3 7 9]       │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [5 2 4]           │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [6 8 8 1 6 7 7]   │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [1 5 9 8 9 4 3 0] │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [5 0 2]           │
└───────────────────┘

I have tried to simply apply np.diff like this:

df.select([
    np.diff(pl.col("values"))
])

But it gives me this error:

ValueError: diff requires input that is at least one dimensional

It looks like this type of vectorisation is not supported at the moment, but is there any workaround to achieve the same thing with polars? I want to avoid having to group arrays by length before running this.

CodePudding user response：

Note that you create a DataFrame of type Object this is almost never what you want. Polars does not know what to do with this dtype.

I adapted your example a bit to create a ragged array of dtype pl.List.

There is a special namespace expression.arr that gives you access to expressions especially designed for Series the List dtype.

As of polars>=0.13.8 this includes arr.diff.

np.random.seed(0)
ragged_arrays = [pl.Series(np.random.randint(10, size=np.random.choice(range(10)))) for _ in range(5)]

(pl.DataFrame({
    "values": ragged_arrays
}).with_columns([
    pl.col("values").arr.diff().alias("values_diff")
]))

This yields

shape: (5, 2)
┌───────────────┬───────────────────┐
│ values        ┆ values_diff       │
│ ---           ┆ ---               │
│ list [i64]    ┆ list [i64]        │
╞═══════════════╪═══════════════════╡
│ [0, 3, ... 9] ┆ [null, 3, ... 2]  │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [5, 2, 4]     ┆ [null, -3, 2]     │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [6, 8, ... 7] ┆ [null, 2, ... 0]  │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [1, 5, ... 0] ┆ [null, 4, ... -3] │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ [5, 0, 2]     ┆ [null, -5, 2]     │
└───────────────┴───────────────────┘