Mapping a Python dict to a Polars series

Question

In Pandas we can use the map function to map a dict to a series to create another series with the mapped values. More generally speaking, I believe it invokes the index operator of the argument, i.e. [].

import pandas as pd

dic = { 1: 'a', 2: 'b', 3: 'c' }

pd.Series([1, 2, 3, 4]).map(dic) # returns ["a", "b", "c", NaN]

I haven't found a way to do so directly in Polars, but have found a few alternatives. Would any of these be the recommended way to do so, or is there a better way?

import polars as pl

dic = { 1: 'a', 2: 'b', 3: 'c' }

# Approach 1 - map_elements
pl.Series([1, 2, 3, 4]).map_elements(lambda v: dic.get(v, None)) # returns ["a", "b", "c", null]

# Approach 2 - left join
(
    pl.Series([1, 2, 3, 4])
    .alias('key')
    .to_frame()
    .join(
        pl.DataFrame({
            'key': list(dic.keys()),
            'value': list(dic.values()),
        }),
        on='key', how='left',
    )['value']
) # returns ["a", "b", "c", null]

# Approach 3 - to pandas and back
pl.from_pandas(pl.Series([1, 2, 3, 4]).to_pandas().map(dic)) # returns ["a", "b", "c", null]

I saw this answer on mapping a dict of expressions but since its chains when/then/otherwise it might not work well for huge dicts.

Answer 1

Update 2024-07-09

Polars has dedicated replace and replace_strict expressions.

Old answer

Mapping a python dictionary over a polars Series should always be considered an anti-pattern. This will be terribly slow and what you want is semantically equal to a join.

Use joins. They are heavily optimized, multithreaded and don't use python.

Example

import polars as pl

dic = { 1: 'a', 2: 'b', 3: 'c' }

mapper = pl.DataFrame({
    "keys": list(dic.keys()),
    "values": list(dic.values())
})

pl.Series([1, 2, 3, 4]).to_frame("keys").join(mapper, on="keys", how="left").to_series(1)

Series: 'values' [str]
[
    "a"
    "b"
    "c"
    null
]