`str.replace_many` to take a dictionary that defines a replacement mapping.

[avhz]

`str.replace_many` to take a dictionary that defines a replacement mapping.

[stinodego]

Good suggestion - we can do the same 'trick' as we do in replace.

[Arengard]

just use

map = {
"a": "x",
"b": "y",
"c": "z",
}

df.with_columns(
polars.col("A").replace(map)
)

[avhz]

Is there a way that we can make this work with regex ?

I have tried something like:

import regex
import polars 

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

map = {
    regex.compile(r"[a-z]"): "alpha",
    regex.compile(r"[0-9]"): "digit",
}

df.with_columns(
    polars.col("x").replace(map)
)

For my personal use case, I need to replace a large number of regex patterns, and it's not very ergonomic to use two lists because it can be hard to keep track of what is replacing what.

Another possibility is a list of tuples:

map = [
    (r"[a-z]", "alpha"),
    (r"[0-9]", "digit"),
]

This (in my opinion) is nicer to follow than something like:

old = [r"[a-z]", r"[0-9]"]
new = ["alpha", "digit"]

[stinodego]

A dict is just going to be syntactic sugar. You can just define your map and then call str.replace_many(map.keys(), map.values()).

[avhz]

That gives me:

TypeError: cannot create expression literal for value of type dict_keys: 
...

Hint: Pass `allow_object=True` to accept any value and create a literal of type Object.

[stinodego]

Call list on each input then. I'm just saying: this is just some syntactic sugar that you can do yourself. You don't need us to take care of it. Though it would be nice if we did.

[avhz]

I must be missing something, as none of the following work for me:

## ============================================================================

import regex
import polars

## ============================================================================

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

## ============================================================================

map = {
    regex.compile(r"[a-z]"): "alpha",
    regex.compile(r"[0-9]"): "digit",
}

df.with_columns(polars.col("x").replace(map))
df.with_columns(polars.col("x").replace(map.keys(), map.values()))
df.with_columns(polars.col("x").replace(list(map.keys()), list(map.values())))

## ============================================================================

map = {
    r"[a-z]": "alpha",
    r"[0-9]": "digit",
}

df.with_columns(polars.col("x").str.replace_many(map))
df.with_columns(polars.col("x").str.replace_many(map.keys(), map.values()))
df.with_columns(polars.col("x").str.replace_many(list(map.keys()), list(map.values())))

## ============================================================================

All throw an exception except the third (when calling list()), which does not throw an exception, but also does not match the regex pattern, so I am left with the original dataframe.

[cmdlineluser]

There are a few different issues:

1. You're passing regex.compile() objects - which Polars does not understand.

Polars uses the Rust crate https://github.com/rust-lang/regex - so you must pass "strings" when using the regex functions.

2. .str.replace_many does not work with regular expressions. (perhaps a note could be added to the docs?)

It uses https://github.com/BurntSushi/aho-corasick which works with "literal strings" only.

It sounds like you may really be asking for:

• #11418