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DOC: clean-up docstring str.replace (GH15056) #15210

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DOC: clean-up docstring str.replace (GH15056) #15210

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21 changes: 11 additions & 10 deletions pandas/core/strings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -318,7 +318,8 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0):
match object and must return a replacement string to be used.
See :func:`re.sub`.

.. versionadded:: 0.20.0
.. versionadded:: 0.20.0
`repl` also accepts a callable.

n : int, default -1 (all)
Number of replacements to make from start
Expand All @@ -333,22 +334,22 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0):

Examples
--------
When ``repl`` is a string, every ``pat`` is replaced as with
When `repl` is a string, every `pat` is replaced as with
:meth:`str.replace`. NaN value(s) in the Series are left as is.

>>> Series(['foo', 'fuz', np.nan]).str.replace('f', 'b')
>>> 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
When `repl` is a callable, it is called on every `pat` using
:func:`re.sub`. The callable should expect one positional argument
(a regex object) and return a string.

To get the idea:

>>> Series(['foo', 'fuz', np.nan]).str.replace('f', repr)
>>> 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
Expand All @@ -357,19 +358,19 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0):
Reverse every lowercase alphabetic word:

>>> repl = lambda m: m.group(0)[::-1]
>>> Series(['foo 123', 'bar baz', np.nan]).str.replace(r'[a-z]+', repl)
>>> 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:
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()
>>> Series(['Foo Bar Baz', np.nan]).str.replace(pat, repl)
0 bAR
1 NaN
>>> pd.Series(['One Two Three', 'Foo Bar Baz']).str.replace(pat, repl)
0 tWO
1 bAR
dtype: object
"""

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