polars.Expr.str.replace#

Expr.str.replace( pattern: str | Expr, value: str | Expr, *, literal: bool = False, n: int = 1, ) → Expr[source]#

Replace first matching regex/literal substring with a new string value.

Parameters:

pattern: A valid regular expression pattern, compatible with the regex crate.
value: String that will replace the matched substring.
literal: Treat pattern as a literal string.
n: Number of matches to replace.

See also

replace_all

Notes

To modify regular expression behaviour (such as case-sensitivity) with flags, use the inline (?iLmsuxU) syntax. See the regex crate’s section on grouping and flags for additional information about the use of inline expression modifiers.
The dollar sign ($) is a special character related to capture groups; if you want to replace some target pattern with characters that include a literal $ you should escape it by doubling it up as $$, or set literal=True if you do not need a full regular expression pattern match. Otherwise, you will be referencing a (potentially non-existent) capture group.

In the example below we need to double up $ (to represent a literal dollar sign, and then refer to the capture group using $n or ${n}, hence the three consecutive $ characters in the replacement value:
```
>>> df = pl.DataFrame({"cost": ["#12.34", "#56.78"]})
>>> df.with_columns(
...     cost_usd=pl.col("cost").str.replace(r"#(\d+)", "$$${1}")
... )
shape: (2, 2)
┌────────┬──────────┐
│ cost   ┆ cost_usd │
│ ---    ┆ ---      │
│ str    ┆ str      │
╞════════╪══════════╡
│ #12.34 ┆ $12.34   │
│ #56.78 ┆ $56.78   │
└────────┴──────────┘
```

Examples

>>> df = pl.DataFrame({"id": [1, 2], "text": ["123abc", "abc456"]})
>>> df.with_columns(pl.col("text").str.replace(r"abc\b", "ABC"))
shape: (2, 2)
┌─────┬────────┐
│ id  ┆ text   │
│ --- ┆ ---    │
│ i64 ┆ str    │
╞═════╪════════╡
│ 1   ┆ 123ABC │
│ 2   ┆ abc456 │
└─────┴────────┘

Capture groups are supported. Use $1 or ${1} in the value string to refer to the first capture group in the pattern, $2 or ${2} to refer to the second capture group, and so on. You can also use named capture groups.

>>> df = pl.DataFrame({"word": ["hat", "hut"]})
>>> df.with_columns(
...     positional=pl.col.word.str.replace("h(.)t", "b${1}d"),
...     named=pl.col.word.str.replace("h(?<vowel>.)t", "b${vowel}d"),
... )
shape: (2, 3)
┌──────┬────────────┬───────┐
│ word ┆ positional ┆ named │
│ ---  ┆ ---        ┆ ---   │
│ str  ┆ str        ┆ str   │
╞══════╪════════════╪═══════╡
│ hat  ┆ bad        ┆ bad   │
│ hut  ┆ bud        ┆ bud   │
└──────┴────────────┴───────┘

Apply case-insensitive string replacement using the (?i) flag.

>>> df = pl.DataFrame(
...     {
...         "city": "Philadelphia",
...         "season": ["Spring", "Summer", "Autumn", "Winter"],
...         "weather": ["Rainy", "Sunny", "Cloudy", "Snowy"],
...     }
... )
>>> df.with_columns(
...     pl.col("weather").str.replace(r"(?i)foggy|rainy|cloudy|snowy", "Sunny")
... )
shape: (4, 3)
┌──────────────┬────────┬─────────┐
│ city         ┆ season ┆ weather │
│ ---          ┆ ---    ┆ ---     │
│ str          ┆ str    ┆ str     │
╞══════════════╪════════╪═════════╡
│ Philadelphia ┆ Spring ┆ Sunny   │
│ Philadelphia ┆ Summer ┆ Sunny   │
│ Philadelphia ┆ Autumn ┆ Sunny   │
│ Philadelphia ┆ Winter ┆ Sunny   │
└──────────────┴────────┴─────────┘