§ ­-PhuEãóx—ddlmZddlmZmZmZerddlmZedd¬¦«ZGd„dee¦«Z d S) é)Ú annotations)Ú TYPE_CHECKINGÚGenericÚTypeVar)ÚExprÚExprTr)Úboundcó¤—eZdZd*d„Zd+d„Zddd œd,d„Zddœd-d„Zd.d/d„Zd0d„Zd1d„Z ddœd2d„Z d.d3d „Z d4d"„Z d5d6d$„Z d5d6d%„Zd.d7d'„Zd+d(„Zd+d)„ZdS)8ÚExprStringNamespaceÚexprrÚreturnÚNonecó—||_dS©N)Ú_expr)Úselfr s úQ/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/narwhals/expr_str.pyÚ__init__zExprStringNamespace.__init__ s €ØˆŒ ˆ ˆ ócó>‡—‰j ˆfd„¦«S)uReturn the length of each string as the number of characters. Returns: A new expression. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"words": ["foo", "345", None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(words_len=nw.col("words").str.len_chars()) ┌─────────────────────┠| Narwhals DataFrame | |---------------------| |shape: (3, 2) | |┌───────┬───────────â”| |│ words ┆ words_len │| |│ --- ┆ --- │| |│ str ┆ u32 │| |â•žâ•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•¡| |│ foo ┆ 3 │| |│ 345 ┆ 3 │| |│ null ┆ null │| |└───────┴───────────┘| └─────────────────────┘ cóf•—‰j |¦«j ¦«Sr)rÚ_to_compliant_exprÚstrÚ len_chars©Úplxrs €rúz/ExprStringNamespace.len_chars..+s&ø€˜œ ×5Ò5°cÑ:Ô:Ô>×HÒHÑJÔJ€r©rÚ_with_elementwise_op©rs`rrzExprStringNamespace.len_charss+ø€ð6Œz×.Ò.Ø JÐ JÐ JÐ Jñ ô ð rFé©ÚliteralÚnÚpatternrÚvaluer#Úboolr$ÚintcóN‡‡‡‡‡—‰j ˆˆˆˆˆfd„¦«S)uReplace first matching regex/literal substring with a new string value. Arguments: pattern: A valid regular expression pattern. value: String that will replace the matched substring. literal: Treat `pattern` as a literal string. n: Number of matches to replace. Returns: A new expression. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"foo": ["123abc", "abc abc123"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(replaced=nw.col("foo").str.replace("abc", "")) ┌──────────────────────┠| Narwhals DataFrame | |----------------------| | foo replaced| |0 123abc 123| |1 abc abc123 abc123| └──────────────────────┘ cóp•—‰j |¦«j ‰‰‰‰¬¦«S)Nr")rrrÚreplace)rr#r$r%rr&s €€€€€rrz-ExprStringNamespace.replace..Ks9ø€˜œ ×5Ò5°cÑ:Ô:Ô>×FÒFؘ¨°1ðGñô€rr)rr%r&r#r$s`````rr+zExprStringNamespace.replace.sKøøøøø€ð8Œz×.Ò.ð ð ð ð ð ð ð ð ñ ô ð r©r#cóJ‡‡‡‡—‰j ˆˆˆˆfd„¦«S)uÖReplace all matching regex/literal substring with a new string value. Arguments: pattern: A valid regular expression pattern. value: String that will replace the matched substring. literal: Treat `pattern` as a literal string. Returns: A new expression. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"foo": ["123abc", "abc abc123"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(replaced=nw.col("foo").str.replace_all("abc", "")) ┌──────────────────────┠| Narwhals DataFrame | |----------------------| | foo replaced| |0 123abc 123| |1 abc abc123 123| └──────────────────────┘ cón•—‰j |¦«j ‰‰‰¬¦«S©Nr,)rrrÚ replace_all)rr#r%rr&s €€€€rrz1ExprStringNamespace.replace_all..js7ø€˜œ ×5Ò5°cÑ:Ô:Ô>×JÒJؘ¨ðKñô€rr)rr%r&r#s````rr0zExprStringNamespace.replace_allPsEøøøø€ð2Œz×.Ò.ð ð ð ð ð ð ð ñ ô ð rNÚ charactersú str | NonecóB‡‡—‰j ˆˆfd„¦«S)aERemove leading and trailing characters. Arguments: characters: The set of characters to be removed. All combinations of this set of characters will be stripped from the start and end of the string. If set to None (default), all leading and trailing whitespace is removed instead. Returns: A new expression. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"fruits": ["apple", "\nmango"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(stripped=nw.col("fruits").str.strip_chars()).to_dict( ... as_series=False ... ) {'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']} cóh•—‰j |¦«j ‰¦«Sr)rrrÚ strip_chars)rr1rs €€rrz1ExprStringNamespace.strip_chars..†s(ø€˜œ ×5Ò5°cÑ:Ô:Ô>×JÒJÈ:ÑVÔV€rr)rr1s``rr5zExprStringNamespace.strip_charsos/øø€ð,Œz×.Ò.Ø VÐ VÐ VÐ VÐ Vñ ô ð rÚprefixcóB‡‡—‰j ˆˆfd„¦«S)u3Check if string values start with a substring. Arguments: prefix: prefix substring Returns: A new expression. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"fruits": ["apple", "mango", None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(has_prefix=nw.col("fruits").str.starts_with("app")) ┌───────────────────┠|Narwhals DataFrame | |-------------------| | fruits has_prefix| |0 apple True| |1 mango False| |2 None None| └───────────────────┘ cóh•—‰j |¦«j ‰¦«Sr)rrrÚ starts_with)rr6rs €€rrz1ExprStringNamespace.starts_with..¢s(ø€˜œ ×5Ò5°cÑ:Ô:Ô>×JÒJÈ6ÑRÔR€rr)rr6s``rr9zExprStringNamespace.starts_with‰s/øø€ð0Œz×.Ò.Ø RÐ RÐ RÐ RÐ Rñ ô ð rÚsuffixcóB‡‡—‰j ˆˆfd„¦«S)u/Check if string values end with a substring. Arguments: suffix: suffix substring Returns: A new expression. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"fruits": ["apple", "mango", None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(has_suffix=nw.col("fruits").str.ends_with("ngo")) ┌───────────────────┠|Narwhals DataFrame | |-------------------| | fruits has_suffix| |0 apple False| |1 mango True| |2 None None| └───────────────────┘ cóh•—‰j |¦«j ‰¦«Sr)rrrÚ ends_with)rrr:s €€rrz/ExprStringNamespace.ends_with..¾s(ø€˜œ ×5Ò5°cÑ:Ô:Ô>×HÒHÈÑPÔP€rr)rr:s``rr=zExprStringNamespace.ends_with¥s/øø€ð0Œz×.Ò.Ø PÐ PÐ PÐ PÐ Pñ ô ð rcóF‡‡‡—‰j ˆˆˆfd„¦«S)a[Check if string contains a substring that matches a pattern. Arguments: pattern: A Character sequence or valid regular expression pattern. literal: If True, treats the pattern as a literal string. If False, assumes the pattern is a regular expression. Returns: A new expression. Examples: >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"pets": ["cat", "dog", "rabbit and parrot"]}) >>> df = nw.from_native(df_native) >>> df.with_columns( ... default_match=nw.col("pets").str.contains("cat|parrot"), ... case_insensitive_match=nw.col("pets").str.contains("cat|(?i)parrot"), ... ).to_native() pyarrow.Table pets: string default_match: bool case_insensitive_match: bool ---- pets: [["cat","dog","rabbit and parrot"]] default_match: [[true,false,true]] case_insensitive_match: [[true,false,true]] cól•—‰j |¦«j ‰‰¬¦«Sr/)rrrÚcontains)rr#r%rs €€€rrz.ExprStringNamespace.contains..ßs5ø€˜œ ×5Ò5°cÑ:Ô:Ô>×GÒGØ ðHñô€rr)rr%r#s```rr@zExprStringNamespace.containsÁs?øøø€ð:Œz×.Ò.ð ð ð ð ð ð ñ ô ð rÚoffsetÚlengthú int | NonecóF‡‡‡—‰j ˆˆˆfd„¦«S)uÂCreate subslices of the string values of an expression. Arguments: offset: Start index. Negative indexing is supported. length: Length of the slice. If set to `None` (default), the slice is taken to the end of the string. Returns: A new expression. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"s": ["pear", None, "papaya"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(s_sliced=nw.col("s").str.slice(4, length=3)) ┌──────────────────┠|Narwhals DataFrame| |------------------| | s s_sliced| |0 pear | |1 None None| |2 papaya ya| └──────────────────┘ cól•—‰j |¦«j ‰‰¬¦«S©N)rArB©rrrÚslice)rrBrArs €€€rrz+ExprStringNamespace.slice..ÿs5ø€˜œ ×5Ò5°cÑ:Ô:Ô>×DÒDØ fðEñô€rr)rrArBs```rrHzExprStringNamespace.sliceäs?øøø€ð4Œz×.Ò.ð ð ð ð ð ð ñ ô ð rÚbycóB‡‡—‰j ˆˆfd„¦«S)u:Split the string values of an expression by a substring. Arguments: by: Substring to split by. Returns: A new expression. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"s": ["foo bar", "foo_bar"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(nw.col("s").str.split("_").alias("s_split")) ┌────────────────────────────┠| Narwhals DataFrame | |----------------------------| |shape: (2, 2) | |┌─────────┬────────────────â”| |│ s ┆ s_split │| |│ --- ┆ --- │| |│ str ┆ list[str] │| |â•žâ•â•â•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡| |│ foo bar ┆ ["foo bar"] │| |│ foo_bar ┆ ["foo", "bar"] │| |└─────────┴────────────────┘| └────────────────────────────┘ cój•—‰j |¦«j ‰¬¦«S)N)rI)rrrÚsplit)rrIrs €€rrz+ExprStringNamespace.split.."s+ø€˜œ ×5Ò5°cÑ:Ô:Ô>×DÒDÈÐDÑKÔK€rr)rrIs``rrLzExprStringNamespace.splits/øø€ð:Œz×.Ò.Ø KÐ KÐ KÐ KÐ Kñ ô ð récóB‡‡—‰j ˆˆfd„¦«S)aDTake the first n elements of each string. Arguments: n: Number of elements to take. Negative indexing is **not** supported. Returns: A new expression. Notes: If the length of the string has fewer than `n` characters, the full string is returned. Examples: >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"lyrics": ["taata", "taatatata", "zukkyun"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(lyrics_head=nw.col("lyrics").str.head()).to_native() pyarrow.Table lyrics: string lyrics_head: string ---- lyrics: [["taata","taatatata","zukkyun"]] lyrics_head: [["taata","taata","zukky"]] cój•—‰j |¦«j d‰¦«S)NrrG©rr$rs €€rrz*ExprStringNamespace.head..?s*ø€˜œ ×5Ò5°cÑ:Ô:Ô>×DÒDÀQÈÑJÔJ€rr©rr$s``rÚheadzExprStringNamespace.head%s/øø€ð2Œz×.Ò.Ø JÐ JÐ JÐ JÐ Jñ ô ð rcóB‡‡—‰j ˆˆfd„¦«S)aCTake the last n elements of each string. Arguments: n: Number of elements to take. Negative indexing is **not** supported. Returns: A new expression. Notes: If the length of the string has fewer than `n` characters, the full string is returned. Examples: >>> import pyarrow as pa >>> import narwhals as nw >>> df_native = pa.table({"lyrics": ["taata", "taatatata", "zukkyun"]}) >>> df = nw.from_native(df_native) >>> df.with_columns(lyrics_tail=nw.col("lyrics").str.tail()).to_native() pyarrow.Table lyrics: string lyrics_tail: string ---- lyrics: [["taata","taatatata","zukkyun"]] lyrics_tail: [["taata","atata","kkyun"]] cón•—‰j |¦«j ‰ d¬¦«SrFrGrPs €€rrz*ExprStringNamespace.tail..\s7ø€˜œ ×5Ò5°cÑ:Ô:Ô>×DÒDØr $ðEñô€rrrQs``rÚtailzExprStringNamespace.tailBs9øø€ð2Œz×.Ò.ð ð ð ð ð ñ ô ð rÚformatcóB‡‡—‰j ˆˆfd„¦«S)uCConvert to Datetime dtype. Notes: - pandas defaults to nanosecond time unit, Polars to microsecond. Prior to pandas 2.0, nanoseconds were the only time unit supported in pandas, with no ability to set any other one. The ability to set the time unit in pandas, if the version permits, will arrive. - timezone-aware strings are all converted to and parsed as UTC. Warning: As different backends auto-infer format in different ways, if `format=None` there is no guarantee that the result will be equal. Arguments: format: Format to use for conversion. If set to None (default), the format is inferred from the data. Returns: A new expression. Examples: >>> import polars as pl >>> import narwhals as nw >>> df_native = pl.DataFrame({"a": ["2020-01-01", "2020-01-02"]}) >>> df = nw.from_native(df_native) >>> df.select(nw.col("a").str.to_datetime(format="%Y-%m-%d")) ┌───────────────────────┠| Narwhals DataFrame | |-----------------------| |shape: (2, 1) | |┌─────────────────────â”| |│ a │| |│ --- │| |│ datetime[μs] │| |â•žâ•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•â•¡| |│ 2020-01-01 00:00:00 │| |│ 2020-01-02 00:00:00 │| |└─────────────────────┘| └───────────────────────┘ cój•—‰j |¦«j ‰¬¦«S)N)rV)rrrÚ to_datetime)rrVrs €€rrz1ExprStringNamespace.to_datetime..‹s,ø€˜œ ×5Ò5°cÑ:Ô:Ô>×JÒJÐRXÐJÑYÔY€rr)rrVs``rrYzExprStringNamespace.to_datetimeas0øø€ðRŒz×.Ò.Ø YÐ YÐ YÐ YÐ Yñ ô ð rcó>‡—‰j ˆfd„¦«S)uÐTransform string to uppercase variant. Returns: A new expression. Notes: The PyArrow backend will convert 'ß' to 'ẞ' instead of 'SS'. For more info see [the related issue](https://github.com/apache/arrow/issues/34599). There may be other unicode-edge-case-related variations across implementations. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"fruits": ["apple", None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(upper_col=nw.col("fruits").str.to_uppercase()) ┌──────────────────┠|Narwhals DataFrame| |------------------| | fruits upper_col| |0 apple APPLE| |1 None None| └──────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚ to_uppercasers €rrz2ExprStringNamespace.to_uppercase..¨ó&ø€˜œ ×5Ò5°cÑ:Ô:Ô>×KÒKÑMÔM€rrr s`rr\z ExprStringNamespace.to_uppercaseŽs+ø€ð2Œz×.Ò.Ø MÐ MÐ MÐ Mñ ô ð rcó>‡—‰j ˆfd„¦«S)u·Transform string to lowercase variant. Returns: A new expression. Examples: >>> import pandas as pd >>> import narwhals as nw >>> df_native = pd.DataFrame({"fruits": ["APPLE", None]}) >>> df = nw.from_native(df_native) >>> df.with_columns(lower_col=nw.col("fruits").str.to_lowercase()) ┌──────────────────┠|Narwhals DataFrame| |------------------| | fruits lower_col| |0 APPLE apple| |1 None None| └──────────────────┘ cóf•—‰j |¦«j ¦«Sr)rrrÚ to_lowercasers €rrz2ExprStringNamespace.to_lowercase..Àr]rrr s`rr`z ExprStringNamespace.to_lowercase«s+ø€ð(Œz×.Ò.Ø MÐ MÐ MÐ Mñ ô ð r)r rr r)r r) r%rr&rr#r'r$r(r r)r%rr&rr#r'r rr)r1r2r r)r6rr r)r:rr r)r%rr#r'r r)rAr(rBrCr r)rIrr r)rM)r$r(r r)rVr2r r)Ú__name__Ú __module__Ú __qualname__rrr+r0r5r9r=r@rHrLrRrUrYr\r`©rrr r s‘€€€€€ððððð ð ð ð ð@ ð ð ð ð ð4 ð ð ð ð8 ð ð ð ð89>ð! ð! ð! ð! ð! ð! ðF ð ð ð ð ð@ ð ð ð ðB ð ð ð ð ð: ð ð ð ð ð>+ ð+ ð+ ð+ ð+ ðZ ð ð ð ð: ð ð ð ð ð rr N) Ú __future__rÚtypingrrrÚ narwhals.exprrrr rdrrúrhs¥ðØ"Ð"Ð"Ð"Ð"Ð"à2Ð2Ð2Ð2Ð2Ð2Ð2Ð2Ð2Ð2àð#Ø"Ð"Ð"Ð"Ð"Ð"àˆ˜vÐ&Ñ&Ô&€ðv ðv ðv ðv ðv ˜' %œ.ñv ôv ðv ðv ðv r