Interface StringNamespace

Vertically concat the values in the Series to a single string value.

Example

>>> df = pl.DataFrame({"foo": [1, null, 2]})
>>> df = df.select(pl.col("foo").str.concat("-"))
>>> df
shape: (1, 1)
┌──────────┐
│ foo      │
│ ---      │
│ str      │
╞══════════╡
│ 1-null-2 │
└──────────┘
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Check if strings in Series contain regex pattern.

Decodes a value using the provided encoding

Example

>>> df = pl.DataFrame({"strings": ["666f6f", "626172", null]})
>>> df.select(col("strings").str.decode("hex"))
shape: (3, 1)
┌─────────┐
│ strings │
│ ---     │
│ str     │
╞═════════╡
│ foo     │
├╌╌╌╌╌╌╌╌╌┤
│ bar     │
├╌╌╌╌╌╌╌╌╌┤
│ null    │
└─────────┘
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Encodes a value using the provided encoding

Example

>>> df = pl.DataFrame({"strings", ["foo", "bar", null]})
>>> df.select(col("strings").str.encode("hex"))
shape: (3, 1)
┌─────────┐
│ strings │
│ ---     │
│ str     │
╞═════════╡
│ 666f6f  │
├╌╌╌╌╌╌╌╌╌┤
│ 626172  │
├╌╌╌╌╌╌╌╌╌┤
│ null    │
└─────────┘
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Extract the target capture group from provided patterns.

Example

> df = pl.DataFrame({
...   'a': [
...       'http://vote.com/ballon_dor?candidate=messi&ref=polars',
...       'http://vote.com/ballon_dor?candidat=jorginho&ref=polars',
...       'http://vote.com/ballon_dor?candidate=ronaldo&ref=polars'
...   ]})
> df.select(pl.col('a').str.extract(/candidate=(\w+)/, 1))
shape: (3, 1)
┌─────────┐
│ a       │
│ ---     │
│ str     │
╞═════════╡
│ messi   │
├╌╌╌╌╌╌╌╌╌┤
│ null    │
├╌╌╌╌╌╌╌╌╌┤
│ ronaldo │
└─────────┘
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Parse string values as JSON. Throw errors if encounter invalid JSON strings.

Params

Not implemented ATM

Example

>>> df = pl.DataFrame( {json: ['{"a":1, "b": true}', null, '{"a":2, "b": false}']} )
>>> df.select(pl.col("json").str.jsonDecode())
shape: (3, 1)
┌─────────────┐
│ json        │
│ ---         │
│ struct[2]   │
╞═════════════╡
│ {1,true}    │
│ {null,null} │
│ {2,false}   │
└─────────────┘
See Also
----------
jsonPathMatch : Extract the first match of json string with provided JSONPath expression.
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Parse string values as JSON. Throw errors if encounter invalid JSON strings.

Params

Not implemented ATM

Deprecated

Since

0.8.4

Use

jsonDecode

Example

>>> df = pl.DataFrame( {json: ['{"a":1, "b": true}', null, '{"a":2, "b": false}']} )
>>> df.select(pl.col("json").str.jsonExtract())
shape: (3, 1)
┌─────────────┐
│ json        │
│ ---         │
│ struct[2]   │
╞═════════════╡
│ {1,true}    │
│ {null,null} │
│ {2,false}   │
└─────────────┘
See Also
----------
jsonPathMatch : Extract the first match of json string with provided JSONPath expression.
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Extract the first match of json string with provided JSONPath expression. Throw errors if encounter invalid json strings. All return value will be casted to Utf8 regardless of the original value.

See

https://goessner.net/articles/JsonPath/

Example

>>> df = pl.DataFrame({
...   'json_val': [
...     '{"a":"1"}',
...     null,
...     '{"a":2}',
...     '{"a":2.1}',
...     '{"a":true}'
...   ]
... })
>>> df.select(pl.col('json_val').str.jsonPathMatch('$.a')
shape: (5,)
Series: 'json_val' [str]
[
    "1"
    null
    "2"
    "2.1"
    "true"
]
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Get length of the string values in the Series.

Remove leading whitespace.

Add a trailing fillChar to a string until string length is reached. If string is longer or equal to given length no modifications will be done

Note

If a string longer than 1 character is provided only the first character will be used *

Example

> df = pl.DataFrame({
...   'foo': [
...       "a",
...       "b",
...       "LONG_WORD",
...       "cow"
...   ]})
> df.select(pl.col('foo').str.padEnd("_", 3)
shape: (4, 1)
┌──────────┐
│ a        │
│ -------- │
│ str      │
╞══════════╡
│ a__      │
├╌╌╌╌╌╌╌╌╌╌┤
│ b__      │
├╌╌╌╌╌╌╌╌╌╌┤
│ LONG_WORD│
├╌╌╌╌╌╌╌╌╌╌┤
│ cow      │
└──────────┘
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Add a leading fillChar to a string until string length is reached. If string is longer or equal to given length no modifications will be done

Note

If a string longer than 1 character is provided only the first character will be used

Example

> df = pl.DataFrame({
...   'foo': [
...       "a",
...       "b",
...       "LONG_WORD",
...       "cow"
...   ]})
> df.select(pl.col('foo').str.padStart("_", 3)
shape: (4, 1)
┌──────────┐
│ a        │
│ -------- │
│ str      │
╞══════════╡
│ __a      │
├╌╌╌╌╌╌╌╌╌╌┤
│ __b      │
├╌╌╌╌╌╌╌╌╌╌┤
│ LONG_WORD│
├╌╌╌╌╌╌╌╌╌╌┤
│ cow      │
└──────────┘
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Replace first regex match with a string value.

Replace all regex matches with a string value.

Remove trailing whitespace.

Create subslices of the string values of a Utf8 Series.

Split a string into substrings using the specified separator and return them as a Series.

Remove leading and trailing whitespace.

Parse a Series of dtype Utf8 to a Date/Datetime Series.

Modify the strings to their lowercase equivalent.

Modify the strings to their uppercase equivalent.

Add leading "0" to a string until string length is reached. If string is longer or equal to given length no modifications will be done

See

padStart *

Example

> df = pl.DataFrame({
...   'foo': [
...       "a",
...       "b",
...       "LONG_WORD",
...       "cow"
...   ]})
> df.select(pl.col('foo').str.justify(3)
shape: (4, 1)
┌──────────┐
│ a        │
│ -------- │
│ str      │
╞══════════╡
│ 00a      │
├╌╌╌╌╌╌╌╌╌╌┤
│ 00b      │
├╌╌╌╌╌╌╌╌╌╌┤
│ LONG_WORD│
├╌╌╌╌╌╌╌╌╌╌┤
│ cow      │
└──────────┘
Copy