pandas.Series.str.replace — pandas 0.24.2 documentation (original) (raw)

Series.str. replace(pat, repl, n=-1, case=None, flags=0, regex=True)[source]¶

Replace occurrences of pattern/regex in the Series/Index with some other string. Equivalent to str.replace() orre.sub().

Parameters:	pat : string or compiled regex String can be a character sequence or regular expression. New in version 0.20.0: pat also accepts a compiled regex. repl : string or callable Replacement string or a callable. The callable is passed the regex match object and must return a replacement string to be used. See re.sub(). New in version 0.20.0: repl also accepts a callable. n : int, default -1 (all) Number of replacements to make from start case : boolean, default None If True, case sensitive (the default if pat is a string) Set to False for case insensitive Cannot be set if pat is a compiled regex flags : int, default 0 (no flags) re module flags, e.g. re.IGNORECASE Cannot be set if pat is a compiled regex regex : boolean, default True If True, assumes the passed-in pattern is a regular expression. If False, treats the pattern as a literal string Cannot be set to False if pat is a compiled regex or repl is a callable. New in version 0.23.0.
Returns:	Series or Index of object A copy of the object with all matching occurrences of pat replaced byrepl.
Raises:	ValueError if regex is False and repl is a callable or pat is a compiled regex if pat is a compiled regex and case or flags is set

Parameters:

pat : string or compiled regex String can be a character sequence or regular expression. New in version 0.20.0: pat also accepts a compiled regex. repl : string or callable Replacement string or a callable. The callable is passed the regex match object and must return a replacement string to be used. See re.sub(). New in version 0.20.0: repl also accepts a callable. n : int, default -1 (all) Number of replacements to make from start case : boolean, default None If True, case sensitive (the default if pat is a string) Set to False for case insensitive Cannot be set if pat is a compiled regex flags : int, default 0 (no flags) re module flags, e.g. re.IGNORECASE Cannot be set if pat is a compiled regex regex : boolean, default True If True, assumes the passed-in pattern is a regular expression. If False, treats the pattern as a literal string Cannot be set to False if pat is a compiled regex or repl is a callable. New in version 0.23.0.

Returns:

Series or Index of object A copy of the object with all matching occurrences of pat replaced byrepl.

Raises:

ValueError if regex is False and repl is a callable or pat is a compiled regex if pat is a compiled regex and case or flags is set

Notes

When pat is a compiled regex, all flags should be included in the compiled regex. Use of case, flags, or regex=False with a compiled regex will raise an error.

Examples

When pat is a string and regex is True (the default), the given patis compiled as a regex. When repl is a string, it replaces matching regex patterns as with re.sub(). NaN value(s) in the Series are left as is:

pd.Series(['foo', 'fuz', np.nan]).str.replace('f.', 'ba', regex=True) 0 bao 1 baz 2 NaN dtype: object

When pat is a string and regex is False, every pat is replaced withrepl as with str.replace():

pd.Series(['f.o', 'fuz', np.nan]).str.replace('f.', 'ba', regex=False) 0 bao 1 fuz 2 NaN dtype: object

When repl is a callable, it is called on every pat usingre.sub(). The callable should expect one positional argument (a regex object) and return a string.

To get the idea:

pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr) 0 <_sre.SRE_Match object; span=(0, 1), match='f'>oo 1 <_sre.SRE_Match object; span=(0, 1), match='f'>uz 2 NaN dtype: object

Reverse every lowercase alphabetic word:

repl = lambda m: m.group(0)[::-1] pd.Series(['foo 123', 'bar baz', np.nan]).str.replace(r'[a-z]+', repl) 0 oof 123 1 rab zab 2 NaN dtype: object

Using regex groups (extract second group and swap case):

pat = r"(?P\w+) (?P\w+) (?P\w+)" repl = lambda m: m.group('two').swapcase() pd.Series(['One Two Three', 'Foo Bar Baz']).str.replace(pat, repl) 0 tWO 1 bAR dtype: object

Using a compiled regex with flags

regex_pat = re.compile(r'FUZ', flags=re.IGNORECASE) pd.Series(['foo', 'fuz', np.nan]).str.replace(regex_pat, 'bar') 0 foo 1 bar 2 NaN dtype: object