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Folds

Polars provides many expressions to perform computations across columns, like sum_horizontal, mean_horizontal, and min_horizontal. However, these are just special cases of a general algorithm called a fold, and Polars provides a general mechanism for you to compute custom folds for when the specialised versions of Polars are not enough.

Folds computed with the function fold operate on the full columns for maximum speed. They utilize the data layout very efficiently and often have vectorized execution.

Basic example

As a first example, we will reimplement sum_horizontal with the function fold:

fold 

import operator
import polars as pl

df = pl.DataFrame(
    {
        "label": ["foo", "bar", "spam"],
        "a": [1, 2, 3],
        "b": [10, 20, 30],
    }
)

result = df.select(
    pl.fold(
        acc=pl.lit(0),
        function=operator.add,
        exprs=pl.col("a", "b"),
    ).alias("sum_fold"),
    pl.sum_horizontal(pl.col("a", "b")).alias("sum_horz"),
)

print(result)

fold_exprs 

use polars::prelude::*;
// Contribute the Rust translation of the Python example by opening a PR.


shape: (3, 2)
┌──────────┬──────────┐
│ sum_fold ┆ sum_horz │
│ ---      ┆ ---      │
│ i64      ┆ i64      │
╞══════════╪══════════╡
│ 11       ┆ 11       │
│ 22       ┆ 22       │
│ 33       ┆ 33       │
└──────────┴──────────┘

The function fold expects a function f as the parameter function and f should accept two arguments. The first argument is the accumulated result, which we initialise as zero, and the second argument takes the successive values of the expressions listed in the parameter exprs. In our case, they're the two columns “a” and “b”.

The snippet below includes a third explicit expression that represents what the function fold is doing above:

fold 

acc = pl.lit(0)
f = operator.add

result = df.select(
    f(f(acc, pl.col("a")), pl.col("b")),
    pl.fold(acc=acc, function=f, exprs=pl.col("a", "b")).alias("sum_fold"),
)

print(result)

fold_exprs 

// Contribute the Rust translation of the Python example by opening a PR.


shape: (3, 2)
┌─────────┬──────────┐
│ literal ┆ sum_fold │
│ ---     ┆ ---      │
│ i64     ┆ i64      │
╞═════════╪══════════╡
│ 11      ┆ 11       │
│ 22      ┆ 22       │
│ 33      ┆ 33       │
└─────────┴──────────┘
fold in Python

Most programming languages include a higher-order function that implements the algorithm that the function fold in Polars implements. The Polars fold is very similar to Python's functools.reduce. You can learn more about the power of functools.reduce in this article.

The initial value acc

The initial value chosen for the accumulator acc is typically, but not always, the identity element of the operation you want to apply. For example, if we wanted to multiply across the columns, we would not get the correct result if our accumulator was set to zero:

fold 

result = df.select(
    pl.fold(
        acc=pl.lit(0),
        function=operator.mul,
        exprs=pl.col("a", "b"),
    ).alias("prod"),
)

print(result)

fold_exprs 

// Contribute the Rust translation of the Python example by opening a PR.


shape: (3, 1)
┌──────┐
│ prod │
│ ---  │
│ i64  │
╞══════╡
│ 0    │
│ 0    │
│ 0    │
└──────┘

To fix this, the accumulator acc should be set to 1:

fold 

result = df.select(
    pl.fold(
        acc=pl.lit(1),
        function=operator.mul,
        exprs=pl.col("a", "b"),
    ).alias("prod"),
)

print(result)

fold_exprs 

// Contribute the Rust translation of the Python example by opening a PR.


shape: (3, 1)
┌──────┐
│ prod │
│ ---  │
│ i64  │
╞══════╡
│ 10   │
│ 40   │
│ 90   │
└──────┘

Conditional

In the case where you'd want to apply a condition/predicate across all columns in a dataframe, a fold can be a very concise way to express this.

fold 

df = pl.DataFrame(
    {
        "a": [1, 2, 3],
        "b": [0, 1, 2],
    }
)

result = df.filter(
    pl.fold(
        acc=pl.lit(True),
        function=lambda acc, x: acc & x,
        exprs=pl.all() > 1,
    )
)
print(result)

fold_exprs 

// Contribute the Rust translation of the Python example by opening a PR.


shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 3   ┆ 2   │
└─────┴─────┘

The snippet above filters all rows where all columns are greater than 1.

Folds and string data

Folds could be used to concatenate string data. However, due to the materialization of intermediate columns, this operation will have squared complexity.

Therefore, we recommend using the function concat_str for this:

concat_str 

df = pl.DataFrame(
    {
        "a": ["a", "b", "c"],
        "b": [1, 2, 3],
    }
)

result = df.select(pl.concat_str(["a", "b"]))
print(result)

concat_str · Available on feature concat_str 

let df = df!(
    "a" => ["a", "b", "c"],
    "b" => [1, 2, 3],
)?;

let result = df
    .lazy()
    .select([concat_str([col("a"), col("b")], "", false)])
    .collect()?;
println!("{:?}", result);


shape: (3, 1)
┌─────┐
│ a   │
│ --- │
│ str │
╞═════╡
│ a1  │
│ b2  │
│ c3  │
└─────┘