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Modified examples and See Also
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pandas/core/strings.py

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@@ -2210,7 +2210,7 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
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Split strings around given separator/delimiter.
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Splits the string in the Series/Index from the %(side)s,
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at the specified delimiter string.Equivalent to :meth:`str.%(method)s`.
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at the specified delimiter string. Equivalent to :meth:`str.%(method)s`.
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Parameters
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----------
@@ -2245,93 +2245,87 @@ def cat(self, others=None, sep=None, na_rep=None, join=None):
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See Also
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--------
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%(also)s
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Series.str.split : Split strings around given separator/delimiter.
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Series.str.rsplit : Splits string around given separator/delimiter, starting from
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the right.
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Series.str.join : Join lists contained as elements in the Series/Index
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with passed delimiter.
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str.split : Standard library version for split.
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str.rsplit : Standard library version for rsplit.
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Examples
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--------
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>>> s = pd.Series(["this is good text", "but this is even better"])
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>>> s = pd.Series(["this is a regular sentence", "this,is,comma,separated,text", np.nan])
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By default, split and rsplit will return an object of the same size
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having lists containing the split elements
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having lists containing the split elements.
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Parameter `n` can be used to limit the number of splits on the delimiter. If delimiter is
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not specified, string is split on whitespace.
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>>> s.str.split()
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0 [this, is, good, text]
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1 [but, this, is, even, better]
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>>> s.str.split(n=2)
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0 [this, is, a regular sentence]
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1 [this,is,comma,separated,text]
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2 NaN
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dtype: object
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>>> s.str.rsplit()
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0 [this, is, good, text]
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1 [but, this, is, even, better]
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>>> s.str.rsplit(n=2)
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0 [this is a, regular, sentence]
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1 [this,is,comma,separated,text]
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2 NaN
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dtype: object
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>>> s.str.split("random")
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0 [this is good text]
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1 [but this is even better]
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>>> s.str.split(",", n=2)
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0 [this is a regular sentence]
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1 [this, is, comma,separated,text]
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2 NaN
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dtype: object
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>>> s.str.rsplit("random")
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0 [this is good text]
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1 [but this is even better]
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>>> s.str.rsplit(",", n=2)
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0 [this is a regular sentence]
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1 [this,is,comma, separated, text]
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2 NaN
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dtype: object
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When using ``expand=True``, the split and rsplit elements will
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expand out into separate columns.
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For Series object, output return type is DataFrame.
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>>> s.str.split(expand=True)
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0 1 2 3 4
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0 this is good text None
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1 but this is even better
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>>> s.str.split(" is ", expand=True)
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0 1
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0 this good text
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1 but this even better
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Parameter `n` can be used to limit the number of splits in the output.
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>>> s.str.split("is", n=1)
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0 [th, is good text]
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1 [but th, is even better]
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dtype: object
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>>> s.str.rsplit("is", n=1)
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0 [this , good text]
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1 [but this , even better]
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dtype: object
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If NaN is present, it is propagated throughout the columns
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during the split.
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>>> s = pd.Series(["this is good text", "but this is even better", np.nan])
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>>> s.str.split(n=3, expand=True)
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0 1 2 3
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0 this is good text
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1 but this is even better
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2 NaN NaN NaN NaN
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>>> s.str.rsplit(n=3, expand=True)
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0 1 2 3
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0 this is good text
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1 but this is even better
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2 NaN NaN NaN NaN
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>>> s.str.split(n=2, expand=True)
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0 1 2
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0 this is a regular sentence
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1 this,is,comma,separated,text None None
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2 NaN NaN NaN
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>>> s.str.rsplit(n=2, expand=True)
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0 1 2
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0 this is a regular sentence
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1 this,is,comma,separated,text None None
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2 NaN NaN NaN
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>>> s.str.split(",", n=2, expand=True)
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0 1 2
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0 this is a regular sentence None None
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1 this is comma,separated,text
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2 NaN NaN NaN
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>>> s.str.rsplit(",", n=2, expand=True)
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0 1 2
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0 this is a regular sentence None None
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1 this,is,comma separated text
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2 NaN NaN NaN
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""")
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@Appender(_shared_docs['str_split'] % {
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'side': 'beginning',
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'method': 'split',
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'also': 'rsplit : Splits string at the last occurrence of delimiter'
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'method': 'split'
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})
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def split(self, pat=None, n=-1, expand=False):
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result = str_split(self._data, pat, n=n)
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return self._wrap_result(result, expand=expand)
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@Appender(_shared_docs['str_split'] % {
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'side': 'end',
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'method': 'rsplit',
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'also': 'split : Splits string at the first occurrence of delimiter'
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'method': 'rsplit'
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})
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def rsplit(self, pat=None, n=-1, expand=False):
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result = str_rsplit(self._data, pat, n=n)

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