pandas.Series.str.replace¶
-
Series.str.
replace
(pat, repl, n=-1, case=None, flags=0)[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
Returns: replaced : Series/Index of objects
Notes
When pat is a compiled regex, all flags should be included in the compiled regex. Use of case or flags with a compiled regex will raise an error.
Examples
When repl is a string, every pat is replaced as with
str.replace()
. NaN value(s) in the Series are left as is.>>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f', 'b') 0 boo 1 buz 2 NaN dtype: object
When repl is a callable, it is called on every pat using
re.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<one>\w+) (?P<two>\w+) (?P<three>\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