import numpy as np
from pandas.compat import zip
from pandas.core.dtypes.generic import ABCSeries, ABCIndex
from pandas.core.dtypes.missing import isnull, notnull
from pandas.core.dtypes.common import (
is_bool_dtype,
is_categorical_dtype,
is_object_dtype,
is_string_like,
is_list_like,
is_scalar,
is_integer,
is_re)
from pandas.core.common import _values_from_object
from pandas.core.algorithms import take_1d
import pandas.compat as compat
from pandas.core.base import AccessorProperty, NoNewAttributesMixin
from pandas.util._decorators import Appender
import re
import pandas._libs.lib as lib
import warnings
import textwrap
import codecs
_cpython_optimized_encoders = (
"utf-8", "utf8", "latin-1", "latin1", "iso-8859-1", "mbcs", "ascii"
)
_cpython_optimized_decoders = _cpython_optimized_encoders + (
"utf-16", "utf-32"
)
_shared_docs = dict()
def _get_array_list(arr, others):
from pandas.core.series import Series
if len(others) and isinstance(_values_from_object(others)[0],
(list, np.ndarray, Series)):
arrays = [arr] + list(others)
else:
arrays = [arr, others]
return [np.asarray(x, dtype=object) for x in arrays]
def str_cat(arr, others=None, sep=None, na_rep=None):
"""
Concatenate strings in the Series/Index with given separator.
Parameters
----------
others : list-like, or list of list-likes
If None, returns str concatenating strings of the Series
sep : string or None, default None
na_rep : string or None, default None
If None, NA in the series are ignored.
Returns
-------
concat : Series/Index of objects or str
Examples
--------
When ``na_rep`` is `None` (default behavior), NaN value(s)
in the Series are ignored.
>>> Series(['a','b',np.nan,'c']).str.cat(sep=' ')
'a b c'
>>> Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?')
'a b ? c'
If ``others`` is specified, corresponding values are
concatenated with the separator. Result will be a Series of strings.
>>> Series(['a', 'b', 'c']).str.cat(['A', 'B', 'C'], sep=',')
0 a,A
1 b,B
2 c,C
dtype: object
Otherwise, strings in the Series are concatenated. Result will be a string.
>>> Series(['a', 'b', 'c']).str.cat(sep=',')
'a,b,c'
Also, you can pass a list of list-likes.
>>> Series(['a', 'b']).str.cat([['x', 'y'], ['1', '2']], sep=',')
0 a,x,1
1 b,y,2
dtype: object
"""
if sep is None:
sep = ''
if others is not None:
arrays = _get_array_list(arr, others)
n = _length_check(arrays)
masks = np.array([isnull(x) for x in arrays])
cats = None
if na_rep is None:
na_mask = np.logical_or.reduce(masks, axis=0)
result = np.empty(n, dtype=object)
np.putmask(result, na_mask, np.nan)
notmask = ~na_mask
tuples = zip(*[x[notmask] for x in arrays])
cats = [sep.join(tup) for tup in tuples]
result[notmask] = cats
else:
for i, x in enumerate(arrays):
x = np.where(masks[i], na_rep, x)
if cats is None:
cats = x
else:
cats = cats + sep + x
result = cats
return result
else:
arr = np.asarray(arr, dtype=object)
mask = isnull(arr)
if na_rep is None and mask.any():
if sep == '':
na_rep = ''
else:
return sep.join(arr[notnull(arr)])
return sep.join(np.where(mask, na_rep, arr))
def _length_check(others):
n = None
for x in others:
try:
if n is None:
n = len(x)
elif len(x) != n:
raise ValueError('All arrays must be same length')
except TypeError:
raise ValueError("Did you mean to supply a `sep` keyword?")
return n
def _na_map(f, arr, na_result=np.nan, dtype=object):
# should really _check_ for NA
return _map(f, arr, na_mask=True, na_value=na_result, dtype=dtype)
def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object):
if not len(arr):
return np.ndarray(0, dtype=dtype)
if isinstance(arr, ABCSeries):
arr = arr.values
if not isinstance(arr, np.ndarray):
arr = np.asarray(arr, dtype=object)
if na_mask:
mask = isnull(arr)
try:
convert = not all(mask)
result = lib.map_infer_mask(arr, f, mask.view(np.uint8), convert)
except (TypeError, AttributeError) as e:
# Reraise the exception if callable `f` got wrong number of args.
# The user may want to be warned by this, instead of getting NaN
if compat.PY2:
p_err = r'takes (no|(exactly|at (least|most)) ?\d+) arguments?'
else:
p_err = (r'((takes)|(missing)) (?(2)from \d+ to )?\d+ '
r'(?(3)required )positional arguments?')
if len(e.args) >= 1 and re.search(p_err, e.args[0]):
raise e
def g(x):
try:
return f(x)
except (TypeError, AttributeError):
return na_value
return _map(g, arr, dtype=dtype)
if na_value is not np.nan:
np.putmask(result, mask, na_value)
if result.dtype == object:
result = lib.maybe_convert_objects(result)
return result
else:
return lib.map_infer(arr, f)
def str_count(arr, pat, flags=0):
"""
Count occurrences of pattern in each string of the Series/Index.
Parameters
----------
pat : string, valid regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
counts : Series/Index of integer values
"""
regex = re.compile(pat, flags=flags)
f = lambda x: len(regex.findall(x))
return _na_map(f, arr, dtype=int)
def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True):
"""
Return boolean Series/``array`` whether given pattern/regex is
contained in each string in the Series/Index.
Parameters
----------
pat : string
Character sequence or regular expression
case : boolean, default True
If True, case sensitive
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
na : default NaN, fill value for missing values.
regex : bool, default True
If True use re.search, otherwise use Python in operator
Returns
-------
contained : Series/array of boolean values
See Also
--------
match : analogous, but stricter, relying on re.match instead of re.search
"""
if regex:
if not case:
flags |= re.IGNORECASE
regex = re.compile(pat, flags=flags)
if regex.groups > 0:
warnings.warn("This pattern has match groups. To actually get the"
" groups, use str.extract.", UserWarning,
stacklevel=3)
f = lambda x: bool(regex.search(x))
else:
if case:
f = lambda x: pat in x
else:
upper_pat = pat.upper()
f = lambda x: upper_pat in x
uppered = _na_map(lambda x: x.upper(), arr)
return _na_map(f, uppered, na, dtype=bool)
return _na_map(f, arr, na, dtype=bool)
def str_startswith(arr, pat, na=np.nan):
"""
Return boolean Series/``array`` indicating whether each string in the
Series/Index starts with passed pattern. Equivalent to
:meth:`str.startswith`.
Parameters
----------
pat : string
Character sequence
na : bool, default NaN
Returns
-------
startswith : Series/array of boolean values
"""
f = lambda x: x.startswith(pat)
return _na_map(f, arr, na, dtype=bool)
def str_endswith(arr, pat, na=np.nan):
"""
Return boolean Series indicating whether each string in the
Series/Index ends with passed pattern. Equivalent to
:meth:`str.endswith`.
Parameters
----------
pat : string
Character sequence
na : bool, default NaN
Returns
-------
endswith : Series/array of boolean values
"""
f = lambda x: x.endswith(pat)
return _na_map(f, arr, na, dtype=bool)
def str_replace(arr, pat, repl, n=-1, case=None, flags=0):
"""
Replace occurrences of pattern/regex in the Series/Index with
some other string. Equivalent to :meth:`str.replace` or
:func:`re.sub`.
Parameters
----------
pat : string or compiled regex
String can be a character sequence or regular expression.
.. versionadded:: 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 :func:`re.sub`.
.. versionadded:: 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
:meth:`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
:func:`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
"""
# Check whether repl is valid (GH 13438, GH 15055)
if not (is_string_like(repl) or callable(repl)):
raise TypeError("repl must be a string or callable")
is_compiled_re = is_re(pat)
if is_compiled_re:
if (case is not None) or (flags != 0):
raise ValueError("case and flags cannot be set"
" when pat is a compiled regex")
else:
# not a compiled regex
# set default case
if case is None:
case = True
# add case flag, if provided
if case is False:
flags |= re.IGNORECASE
use_re = is_compiled_re or len(pat) > 1 or flags or callable(repl)
if use_re:
n = n if n >= 0 else 0
regex = re.compile(pat, flags=flags)
f = lambda x: regex.sub(repl=repl, string=x, count=n)
else:
f = lambda x: x.replace(pat, repl, n)
return _na_map(f, arr)
def str_repeat(arr, repeats):
"""
Duplicate each string in the Series/Index by indicated number
of times.
Parameters
----------
repeats : int or array
Same value for all (int) or different value per (array)
Returns
-------
repeated : Series/Index of objects
"""
if is_scalar(repeats):
def rep(x):
try:
return compat.binary_type.__mul__(x, repeats)
except TypeError:
return compat.text_type.__mul__(x, repeats)
return _na_map(rep, arr)
else:
def rep(x, r):
try:
return compat.binary_type.__mul__(x, r)
except TypeError:
return compat.text_type.__mul__(x, r)
repeats = np.asarray(repeats, dtype=object)
result = lib.vec_binop(_values_from_object(arr), repeats, rep)
return result
def str_match(arr, pat, case=True, flags=0, na=np.nan, as_indexer=None):
"""
Determine if each string matches a regular expression.
Parameters
----------
pat : string
Character sequence or regular expression
case : boolean, default True
If True, case sensitive
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
na : default NaN, fill value for missing values.
as_indexer : DEPRECATED
Returns
-------
Series/array of boolean values
See Also
--------
contains : analogous, but less strict, relying on re.search instead of
re.match
extract : extract matched groups
"""
if not case:
flags |= re.IGNORECASE
regex = re.compile(pat, flags=flags)
if (as_indexer is False) and (regex.groups > 0):
raise ValueError("as_indexer=False with a pattern with groups is no "
"longer supported. Use '.str.extract(pat)' instead")
elif as_indexer is not None:
# Previously, this keyword was used for changing the default but
# deprecated behaviour. This keyword is now no longer needed.
warnings.warn("'as_indexer' keyword was specified but is ignored "
"(match now returns a boolean indexer by default), "
"and will be removed in a future version.",
FutureWarning, stacklevel=3)
dtype = bool
f = lambda x: bool(regex.match(x))
return _na_map(f, arr, na, dtype=dtype)
def _get_single_group_name(rx):
try:
return list(rx.groupindex.keys()).pop()
except IndexError:
return None
def _groups_or_na_fun(regex):
"""Used in both extract_noexpand and extract_frame"""
if regex.groups == 0:
raise ValueError("pattern contains no capture groups")
empty_row = [np.nan] * regex.groups
def f(x):
if not isinstance(x, compat.string_types):
return empty_row
m = regex.search(x)
if m:
return [np.nan if item is None else item for item in m.groups()]
else:
return empty_row
return f
def _str_extract_noexpand(arr, pat, flags=0):
"""
Find groups in each string in the Series using passed regular
expression. This function is called from
str_extract(expand=False), and can return Series, DataFrame, or
Index.
"""
from pandas import DataFrame, Index
regex = re.compile(pat, flags=flags)
groups_or_na = _groups_or_na_fun(regex)
if regex.groups == 1:
result = np.array([groups_or_na(val)[0] for val in arr], dtype=object)
name = _get_single_group_name(regex)
else:
if isinstance(arr, Index):
raise ValueError("only one regex group is supported with Index")
name = None
names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
columns = [names.get(1 + i, i) for i in range(regex.groups)]
if arr.empty:
result = DataFrame(columns=columns, dtype=object)
else:
result = DataFrame(
[groups_or_na(val) for val in arr],
columns=columns,
index=arr.index,
dtype=object)
return result, name
def _str_extract_frame(arr, pat, flags=0):
"""
For each subject string in the Series, extract groups from the
first match of regular expression pat. This function is called from
str_extract(expand=True), and always returns a DataFrame.
"""
from pandas import DataFrame
regex = re.compile(pat, flags=flags)
groups_or_na = _groups_or_na_fun(regex)
names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
columns = [names.get(1 + i, i) for i in range(regex.groups)]
if len(arr) == 0:
return DataFrame(columns=columns, dtype=object)
try:
result_index = arr.index
except AttributeError:
result_index = None
return DataFrame(
[groups_or_na(val) for val in arr],
columns=columns,
index=result_index,
dtype=object)
def str_extract(arr, pat, flags=0, expand=None):
"""
For each subject string in the Series, extract groups from the
first match of regular expression pat.
.. versionadded:: 0.13.0
Parameters
----------
pat : string
Regular expression pattern with capturing groups
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
.. versionadded:: 0.18.0
expand : bool, default False
* If True, return DataFrame.
* If False, return Series/Index/DataFrame.
Returns
-------
DataFrame with one row for each subject string, and one column for
each group. Any capture group names in regular expression pat will
be used for column names; otherwise capture group numbers will be
used. The dtype of each result column is always object, even when
no match is found. If expand=False and pat has only one capture group,
then return a Series (if subject is a Series) or Index (if subject
is an Index).
See Also
--------
extractall : returns all matches (not just the first match)
Examples
--------
A pattern with two groups will return a DataFrame with two columns.
Non-matches will be NaN.
>>> s = Series(['a1', 'b2', 'c3'])
>>> s.str.extract('([ab])(\d)')
0 1
0 a 1
1 b 2
2 NaN NaN
A pattern may contain optional groups.
>>> s.str.extract('([ab])?(\d)')
0 1
0 a 1
1 b 2
2 NaN 3
Named groups will become column names in the result.
>>> s.str.extract('(?P<letter>[ab])(?P<digit>\d)')
letter digit
0 a 1
1 b 2
2 NaN NaN
A pattern with one group will return a DataFrame with one column
if expand=True.
>>> s.str.extract('[ab](\d)', expand=True)
0
0 1
1 2
2 NaN
A pattern with one group will return a Series if expand=False.
>>> s.str.extract('[ab](\d)', expand=False)
0 1
1 2
2 NaN
dtype: object
"""
if expand is None:
warnings.warn(
"currently extract(expand=None) " +
"means expand=False (return Index/Series/DataFrame) " +
"but in a future version of pandas this will be changed " +
"to expand=True (return DataFrame)",
FutureWarning,
stacklevel=3)
expand = False
if not isinstance(expand, bool):
raise ValueError("expand must be True or False")
if expand:
return _str_extract_frame(arr._orig, pat, flags=flags)
else:
result, name = _str_extract_noexpand(arr._data, pat, flags=flags)
return arr._wrap_result(result, name=name, expand=expand)
def str_extractall(arr, pat, flags=0):
"""
For each subject string in the Series, extract groups from all
matches of regular expression pat. When each subject string in the
Series has exactly one match, extractall(pat).xs(0, level='match')
is the same as extract(pat).
.. versionadded:: 0.18.0
Parameters
----------
pat : string
Regular expression pattern with capturing groups
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
A DataFrame with one row for each match, and one column for each
group. Its rows have a MultiIndex with first levels that come from
the subject Series. The last level is named 'match' and indicates
the order in the subject. Any capture group names in regular
expression pat will be used for column names; otherwise capture
group numbers will be used.
See Also
--------
extract : returns first match only (not all matches)
Examples
--------
A pattern with one group will return a DataFrame with one column.
Indices with no matches will not appear in the result.
>>> s = Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
>>> s.str.extractall("[ab](\d)")
0
match
A 0 1
1 2
B 0 1
Capture group names are used for column names of the result.
>>> s.str.extractall("[ab](?P<digit>\d)")
digit
match
A 0 1
1 2
B 0 1
A pattern with two groups will return a DataFrame with two columns.
>>> s.str.extractall("(?P<letter>[ab])(?P<digit>\d)")
letter digit
match
A 0 a 1
1 a 2
B 0 b 1
Optional groups that do not match are NaN in the result.
>>> s.str.extractall("(?P<letter>[ab])?(?P<digit>\d)")
letter digit
match
A 0 a 1
1 a 2
B 0 b 1
C 0 NaN 1
"""
regex = re.compile(pat, flags=flags)
# the regex must contain capture groups.
if regex.groups == 0:
raise ValueError("pattern contains no capture groups")
if isinstance(arr, ABCIndex):
arr = arr.to_series().reset_index(drop=True)
names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
columns = [names.get(1 + i, i) for i in range(regex.groups)]
match_list = []
index_list = []
is_mi = arr.index.nlevels > 1
for subject_key, subject in arr.iteritems():
if isinstance(subject, compat.string_types):
if not is_mi:
subject_key = (subject_key, )
for match_i, match_tuple in enumerate(regex.findall(subject)):
if isinstance(match_tuple, compat.string_types):
match_tuple = (match_tuple,)
na_tuple = [np.NaN if group == "" else group
for group in match_tuple]
match_list.append(na_tuple)
result_key = tuple(subject_key + (match_i, ))
index_list.append(result_key)
if 0 < len(index_list):
from pandas import MultiIndex
index = MultiIndex.from_tuples(
index_list, names=arr.index.names + ["match"])
else:
index = None
result = arr._constructor_expanddim(match_list, index=index,
columns=columns)
return result
def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a frame of
dummy/indicator variables.
Parameters
----------
sep : string, default "|"
String to split on.
Returns
-------
dummies : DataFrame
Examples
--------
>>> Series(['a|b', 'a', 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 1 0 0
2 1 0 1
>>> Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 0 0 0
2 1 0 1
See Also
--------
pandas.get_dummies
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - set([""]))
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
def str_join(arr, sep):
"""
Join lists contained as elements in the Series/Index with
passed delimiter. Equivalent to :meth:`str.join`.
Parameters
----------
sep : string
Delimiter
Returns
-------
joined : Series/Index of objects
"""
return _na_map(sep.join, arr)
def str_findall(arr, pat, flags=0):
"""
Find all occurrences of pattern or regular expression in the
Series/Index. Equivalent to :func:`re.findall`.
Parameters
----------
pat : string
Pattern or regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
matches : Series/Index of lists
See Also
--------
extractall : returns DataFrame with one column per capture group
"""
regex = re.compile(pat, flags=flags)
return _na_map(regex.findall, arr)
def str_find(arr, sub, start=0, end=None, side='left'):
"""
Return indexes in each strings in the Series/Index where the
substring is fully contained between [start:end]. Return -1 on failure.
Parameters
----------
sub : str
Substring being searched
start : int
Left edge index
end : int
Right edge index
side : {'left', 'right'}, default 'left'
Specifies a starting side, equivalent to ``find`` or ``rfind``
Returns
-------
found : Series/Index of integer values
"""
if not isinstance(sub, compat.string_types):
msg = 'expected a string object, not {0}'
raise TypeError(msg.format(type(sub).__name__))
if side == 'left':
method = 'find'
elif side == 'right':
method = 'rfind'
else: # pragma: no cover
raise ValueError('Invalid side')
if end is None:
f = lambda x: getattr(x, method)(sub, start)
else:
f = lambda x: getattr(x, method)(sub, start, end)
return _na_map(f, arr, dtype=int)
def str_index(arr, sub, start=0, end=None, side='left'):
if not isinstance(sub, compat.string_types):
msg = 'expected a string object, not {0}'
raise TypeError(msg.format(type(sub).__name__))
if side == 'left':
method = 'index'
elif side == 'right':
method = 'rindex'
else: # pragma: no cover
raise ValueError('Invalid side')
if end is None:
f = lambda x: getattr(x, method)(sub, start)
else:
f = lambda x: getattr(x, method)(sub, start, end)
return _na_map(f, arr, dtype=int)
def str_pad(arr, width, side='left', fillchar=' '):
"""
Pad strings in the Series/Index with an additional character to
specified side.
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be filled
with spaces
side : {'left', 'right', 'both'}, default 'left'
fillchar : str
Additional character for filling, default is whitespace
Returns
-------
padded : Series/Index of objects
"""
if not isinstance(fillchar, compat.string_types):
msg = 'fillchar must be a character, not {0}'
raise TypeError(msg.format(type(fillchar).__name__))
if len(fillchar) != 1:
raise TypeError('fillchar must be a character, not str')
if not is_integer(width):
msg = 'width must be of integer type, not {0}'
raise TypeError(msg.format(type(width).__name__))
if side == 'left':
f = lambda x: x.rjust(width, fillchar)
elif side == 'right':
f = lambda x: x.ljust(width, fillchar)
elif side == 'both':
f = lambda x: x.center(width, fillchar)
else: # pragma: no cover
raise ValueError('Invalid side')
return _na_map(f, arr)
def str_split(arr, pat=None, n=None):
"""
Split each string (a la re.split) in the Series/Index by given
pattern, propagating NA values. Equivalent to :meth:`str.split`.
Parameters
----------
pat : string, default None
String or regular expression to split on. If None, splits on whitespace
n : int, default -1 (all)
None, 0 and -1 will be interpreted as return all splits
expand : bool, default False
* If True, return DataFrame/MultiIndex expanding dimensionality.
* If False, return Series/Index.
.. versionadded:: 0.16.1
return_type : deprecated, use `expand`
Returns
-------
split : Series/Index or DataFrame/MultiIndex of objects
"""
if pat is None:
if n is None or n == 0:
n = -1
f = lambda x: x.split(pat, n)
else:
if len(pat) == 1:
if n is None or n == 0:
n = -1
f = lambda x: x.split(pat, n)
else:
if n is None or n == -1:
n = 0
regex = re.compile(pat)
f = lambda x: regex.split(x, maxsplit=n)
res = _na_map(f, arr)
return res
def str_rsplit(arr, pat=None, n=None):
"""
Split each string in the Series/Index by the given delimiter
string, starting at the end of the string and working to the front.
Equivalent to :meth:`str.rsplit`.
.. versionadded:: 0.16.2
Parameters
----------
pat : string, default None
Separator to split on. If None, splits on whitespace
n : int, default -1 (all)
None, 0 and -1 will be interpreted as return all splits
expand : bool, default False
* If True, return DataFrame/MultiIndex expanding dimensionality.
* If False, return Series/Index.
Returns
-------
split : Series/Index or DataFrame/MultiIndex of objects
"""
if n is None or n == 0:
n = -1
f = lambda x: x.rsplit(pat, n)
res = _na_map(f, arr)
return res
def str_slice(arr, start=None, stop=None, step=None):
"""
Slice substrings from each element in the Series/Index
Parameters
----------
start : int or None
stop : int or None
step : int or None
Returns
-------
sliced : Series/Index of objects
"""
obj = slice(start, stop, step)
f = lambda x: x[obj]
return _na_map(f, arr)
def str_slice_replace(arr, start=None, stop=None, repl=None):
"""
Replace a slice of each string in the Series/Index with another
string.
Parameters
----------
start : int or None
stop : int or None
repl : str or None
String for replacement
Returns
-------
replaced : Series/Index of objects
"""
if repl is None:
repl = ''
def f(x):
if x[start:stop] == '':
local_stop = start
else:
local_stop = stop
y = ''
if start is not None:
y += x[:start]
y += repl
if stop is not None:
y += x[local_stop:]
return y
return _na_map(f, arr)
def str_strip(arr, to_strip=None, side='both'):
"""
Strip whitespace (including newlines) from each string in the
Series/Index.
Parameters
----------
to_strip : str or unicode
side : {'left', 'right', 'both'}, default 'both'
Returns
-------
stripped : Series/Index of objects
"""
if side == 'both':
f = lambda x: x.strip(to_strip)
elif side == 'left':
f = lambda x: x.lstrip(to_strip)
elif side == 'right':
f = lambda x: x.rstrip(to_strip)
else: # pragma: no cover
raise ValueError('Invalid side')
return _na_map(f, arr)
def str_wrap(arr, width, **kwargs):
r"""
Wrap long strings in the Series/Index to be formatted in
paragraphs with length less than a given width.
This method has the same keyword parameters and defaults as
:class:`textwrap.TextWrapper`.
Parameters
----------
width : int
Maximum line-width
expand_tabs : bool, optional
If true, tab characters will be expanded to spaces (default: True)
replace_whitespace : bool, optional
If true, each whitespace character (as defined by string.whitespace)
remaining after tab expansion will be replaced by a single space
(default: True)
drop_whitespace : bool, optional
If true, whitespace that, after wrapping, happens to end up at the
beginning or end of a line is dropped (default: True)
break_long_words : bool, optional
If true, then words longer than width will be broken in order to ensure
that no lines are longer than width. If it is false, long words will
not be broken, and some lines may be longer than width. (default: True)
break_on_hyphens : bool, optional
If true, wrapping will occur preferably on whitespace and right after
hyphens in compound words, as it is customary in English. If false,
only whitespaces will be considered as potentially good places for line
breaks, but you need to set break_long_words to false if you want truly
insecable words. (default: True)
Returns
-------
wrapped : Series/Index of objects
Notes
-----
Internally, this method uses a :class:`textwrap.TextWrapper` instance with
default settings. To achieve behavior matching R's stringr library str_wrap
function, use the arguments:
- expand_tabs = False
- replace_whitespace = True
- drop_whitespace = True
- break_long_words = False
- break_on_hyphens = False
Examples
--------
>>> s = pd.Series(['line to be wrapped', 'another line to be wrapped'])
>>> s.str.wrap(12)
0 line to be\nwrapped
1 another line\nto be\nwrapped
"""
kwargs['width'] = width
tw = textwrap.TextWrapper(**kwargs)
return _na_map(lambda s: '\n'.join(tw.wrap(s)), arr)
def str_translate(arr, table, deletechars=None):
"""
Map all characters in the string through the given mapping table.
Equivalent to standard :meth:`str.translate`. Note that the optional
argument deletechars is only valid if you are using python 2. For python 3,
character deletion should be specified via the table argument.
Parameters
----------
table : dict (python 3), str or None (python 2)
In python 3, table is a mapping of Unicode ordinals to Unicode
ordinals, strings, or None. Unmapped characters are left untouched.
Characters mapped to None are deleted. :meth:`str.maketrans` is a
helper function for making translation tables.
In python 2, table is either a string of length 256 or None. If the
table argument is None, no translation is applied and the operation
simply removes the characters in deletechars. :func:`string.maketrans`
is a helper function for making translation tables.
deletechars : str, optional (python 2)
A string of characters to delete. This argument is only valid
in python 2.
Returns
-------
translated : Series/Index of objects
"""
if deletechars is None:
f = lambda x: x.translate(table)
else:
from pandas import compat
if compat.PY3:
raise ValueError("deletechars is not a valid argument for "
"str.translate in python 3. You should simply "
"specify character deletions in the table "
"argument")
f = lambda x: x.translate(table, deletechars)
return _na_map(f, arr)
def str_get(arr, i):
"""
Extract element from lists, tuples, or strings in each element in the
Series/Index.
Parameters
----------
i : int
Integer index (location)
Returns
-------
items : Series/Index of objects
"""
f = lambda x: x[i] if len(x) > i else np.nan
return _na_map(f, arr)
def str_decode(arr, encoding, errors="strict"):
"""
Decode character string in the Series/Index using indicated encoding.
Equivalent to :meth:`str.decode` in python2 and :meth:`bytes.decode` in
python3.
Parameters
----------
encoding : str
errors : str, optional
Returns
-------
decoded : Series/Index of objects
"""
if encoding in _cpython_optimized_decoders:
# CPython optimized implementation
f = lambda x: x.decode(encoding, errors)
else:
decoder = codecs.getdecoder(encoding)
f = lambda x: decoder(x, errors)[0]
return _na_map(f, arr)
def str_encode(arr, encoding, errors="strict"):
"""
Encode character string in the Series/Index using indicated encoding.
Equivalent to :meth:`str.encode`.
Parameters
----------
encoding : str
errors : str, optional
Returns
-------
encoded : Series/Index of objects
"""
if encoding in _cpython_optimized_encoders:
# CPython optimized implementation
f = lambda x: x.encode(encoding, errors)
else:
encoder = codecs.getencoder(encoding)
f = lambda x: encoder(x, errors)[0]
return _na_map(f, arr)
def _noarg_wrapper(f, docstring=None, **kargs):
def wrapper(self):
result = _na_map(f, self._data, **kargs)
return self._wrap_result(result)
wrapper.__name__ = f.__name__
if docstring is not None:
wrapper.__doc__ = docstring
else:
raise ValueError('Provide docstring')
return wrapper
def _pat_wrapper(f, flags=False, na=False, **kwargs):
def wrapper1(self, pat):
result = f(self._data, pat)
return self._wrap_result(result)
def wrapper2(self, pat, flags=0, **kwargs):
result = f(self._data, pat, flags=flags, **kwargs)
return self._wrap_result(result)
def wrapper3(self, pat, na=np.nan):
result = f(self._data, pat, na=na)
return self._wrap_result(result)
wrapper = wrapper3 if na else wrapper2 if flags else wrapper1
wrapper.__name__ = f.__name__
if f.__doc__:
wrapper.__doc__ = f.__doc__
return wrapper
def copy(source):
"Copy a docstring from another source function (if present)"
def do_copy(target):
if source.__doc__:
target.__doc__ = source.__doc__
return target
return do_copy
class StringMethods(NoNewAttributesMixin):
"""
Vectorized string functions for Series and Index. NAs stay NA unless
handled otherwise by a particular method. Patterned after Python's string
methods, with some inspiration from R's stringr package.
Examples
--------
>>> s.str.split('_')
>>> s.str.replace('_', '')
"""
def __init__(self, data):
self._is_categorical = is_categorical_dtype(data)
self._data = data.cat.categories if self._is_categorical else data
# save orig to blow up categoricals to the right type
self._orig = data
self._freeze()
def __getitem__(self, key):
if isinstance(key, slice):
return self.slice(start=key.start, stop=key.stop, step=key.step)
else:
return self.get(key)
def __iter__(self):
i = 0
g = self.get(i)
while g.notnull().any():
yield g
i += 1
g = self.get(i)
def _wrap_result(self, result, use_codes=True,
name=None, expand=None):
from pandas.core.index import Index, MultiIndex
# for category, we do the stuff on the categories, so blow it up
# to the full series again
# But for some operations, we have to do the stuff on the full values,
# so make it possible to skip this step as the method already did this
# before the transformation...
if use_codes and self._is_categorical:
result = take_1d(result, self._orig.cat.codes)
if not hasattr(result, 'ndim') or not hasattr(result, 'dtype'):
return result
assert result.ndim < 3
if expand is None:
# infer from ndim if expand is not specified
expand = False if result.ndim == 1 else True
elif expand is True and not isinstance(self._orig, Index):
# required when expand=True is explicitly specified
# not needed when infered
def cons_row(x):
if is_list_like(x):
return x
else:
return [x]
result = [cons_row(x) for x in result]
if not isinstance(expand, bool):
raise ValueError("expand must be True or False")
if expand is False:
# if expand is False, result should have the same name
# as the original otherwise specified
if name is None:
name = getattr(result, 'name', None)
if name is None:
# do not use logical or, _orig may be a DataFrame
# which has "name" column
name = self._orig.name
# Wait until we are sure result is a Series or Index before
# checking attributes (GH 12180)
if isinstance(self._orig, Index):
# if result is a boolean np.array, return the np.array
# instead of wrapping it into a boolean Index (GH 8875)
if is_bool_dtype(result):
return result
if expand:
result = list(result)
return MultiIndex.from_tuples(result, names=name)
else:
return Index(result, name=name)
else:
index = self._orig.index
if expand:
cons = self._orig._constructor_expanddim
return cons(result, columns=name, index=index)
else:
# Must be a Series
cons = self._orig._constructor
return cons(result, name=name, index=index)
@copy(str_cat)
def cat(self, others=None, sep=None, na_rep=None):
data = self._orig if self._is_categorical else self._data
result = str_cat(data, others=others, sep=sep, na_rep=na_rep)
return self._wrap_result(result, use_codes=(not self._is_categorical))
@copy(str_split)
def split(self, pat=None, n=-1, expand=False):
result = str_split(self._data, pat, n=n)
return self._wrap_result(result, expand=expand)
@copy(str_rsplit)
def rsplit(self, pat=None, n=-1, expand=False):
result = str_rsplit(self._data, pat, n=n)
return self._wrap_result(result, expand=expand)
_shared_docs['str_partition'] = ("""
Split the string at the %(side)s occurrence of `sep`, and return 3 elements
containing the part before the separator, the separator itself,
and the part after the separator.
If the separator is not found, return %(return)s.
Parameters
----------
pat : string, default whitespace
String to split on.
expand : bool, default True
* If True, return DataFrame/MultiIndex expanding dimensionality.
* If False, return Series/Index.
Returns
-------
split : DataFrame/MultiIndex or Series/Index of objects
See Also
--------
%(also)s
Examples
--------
>>> s = Series(['A_B_C', 'D_E_F', 'X'])
0 A_B_C
1 D_E_F
2 X
dtype: object
>>> s.str.partition('_')
0 1 2
0 A _ B_C
1 D _ E_F
2 X
>>> s.str.rpartition('_')
0 1 2
0 A_B _ C
1 D_E _ F
2 X
""")
@Appender(_shared_docs['str_partition'] % {
'side': 'first',
'return': '3 elements containing the string itself, followed by two '
'empty strings',
'also': 'rpartition : Split the string at the last occurrence of `sep`'
})
def partition(self, pat=' ', expand=True):
f = lambda x: x.partition(pat)
result = _na_map(f, self._data)
return self._wrap_result(result, expand=expand)
@Appender(_shared_docs['str_partition'] % {
'side': 'last',
'return': '3 elements containing two empty strings, followed by the '
'string itself',
'also': 'partition : Split the string at the first occurrence of `sep`'
})
def rpartition(self, pat=' ', expand=True):
f = lambda x: x.rpartition(pat)
result = _na_map(f, self._data)
return self._wrap_result(result, expand=expand)
@copy(str_get)
def get(self, i):
result = str_get(self._data, i)
return self._wrap_result(result)
@copy(str_join)
def join(self, sep):
result = str_join(self._data, sep)
return self._wrap_result(result)
@copy(str_contains)
def contains(self, pat, case=True, flags=0, na=np.nan, regex=True):
result = str_contains(self._data, pat, case=case, flags=flags, na=na,
regex=regex)
return self._wrap_result(result)
@copy(str_match)
def match(self, pat, case=True, flags=0, na=np.nan, as_indexer=None):
result = str_match(self._data, pat, case=case, flags=flags, na=na,
as_indexer=as_indexer)
return self._wrap_result(result)
@copy(str_replace)
def replace(self, pat, repl, n=-1, case=None, flags=0):
result = str_replace(self._data, pat, repl, n=n, case=case,
flags=flags)
return self._wrap_result(result)
@copy(str_repeat)
def repeat(self, repeats):
result = str_repeat(self._data, repeats)
return self._wrap_result(result)
@copy(str_pad)
def pad(self, width, side='left', fillchar=' '):
result = str_pad(self._data, width, side=side, fillchar=fillchar)
return self._wrap_result(result)
_shared_docs['str_pad'] = ("""
Filling %(side)s side of strings in the Series/Index with an
additional character. Equivalent to :meth:`str.%(method)s`.
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be filled
with ``fillchar``
fillchar : str
Additional character for filling, default is whitespace
Returns
-------
filled : Series/Index of objects
""")
@Appender(_shared_docs['str_pad'] % dict(side='left and right',
method='center'))
def center(self, width, fillchar=' '):
return self.pad(width, side='both', fillchar=fillchar)
@Appender(_shared_docs['str_pad'] % dict(side='right', method='ljust'))
def ljust(self, width, fillchar=' '):
return self.pad(width, side='right', fillchar=fillchar)
@Appender(_shared_docs['str_pad'] % dict(side='left', method='rjust'))
def rjust(self, width, fillchar=' '):
return self.pad(width, side='left', fillchar=fillchar)
def zfill(self, width):
"""
Filling left side of strings in the Series/Index with 0.
Equivalent to :meth:`str.zfill`.
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be
filled with 0
Returns
-------
filled : Series/Index of objects
"""
result = str_pad(self._data, width, side='left', fillchar='0')
return self._wrap_result(result)
@copy(str_slice)
def slice(self, start=None, stop=None, step=None):
result = str_slice(self._data, start, stop, step)
return self._wrap_result(result)
@copy(str_slice_replace)
def slice_replace(self, start=None, stop=None, repl=None):
result = str_slice_replace(self._data, start, stop, repl)
return self._wrap_result(result)
@copy(str_decode)
def decode(self, encoding, errors="strict"):
result = str_decode(self._data, encoding, errors)
return self._wrap_result(result)
@copy(str_encode)
def encode(self, encoding, errors="strict"):
result = str_encode(self._data, encoding, errors)
return self._wrap_result(result)
_shared_docs['str_strip'] = ("""
Strip whitespace (including newlines) from each string in the
Series/Index from %(side)s. Equivalent to :meth:`str.%(method)s`.
Returns
-------
stripped : Series/Index of objects
""")
@Appender(_shared_docs['str_strip'] % dict(side='left and right sides',
method='strip'))
def strip(self, to_strip=None):
result = str_strip(self._data, to_strip, side='both')
return self._wrap_result(result)
@Appender(_shared_docs['str_strip'] % dict(side='left side',
method='lstrip'))
def lstrip(self, to_strip=None):
result = str_strip(self._data, to_strip, side='left')
return self._wrap_result(result)
@Appender(_shared_docs['str_strip'] % dict(side='right side',
method='rstrip'))
def rstrip(self, to_strip=None):
result = str_strip(self._data, to_strip, side='right')
return self._wrap_result(result)
@copy(str_wrap)
def wrap(self, width, **kwargs):
result = str_wrap(self._data, width, **kwargs)
return self._wrap_result(result)
@copy(str_get_dummies)
def get_dummies(self, sep='|'):
# we need to cast to Series of strings as only that has all
# methods available for making the dummies...
data = self._orig.astype(str) if self._is_categorical else self._data
result, name = str_get_dummies(data, sep)
return self._wrap_result(result, use_codes=(not self._is_categorical),
name=name, expand=True)
@copy(str_translate)
def translate(self, table, deletechars=None):
result = str_translate(self._data, table, deletechars)
return self._wrap_result(result)
count = _pat_wrapper(str_count, flags=True)
startswith = _pat_wrapper(str_startswith, na=True)
endswith = _pat_wrapper(str_endswith, na=True)
findall = _pat_wrapper(str_findall, flags=True)
@copy(str_extract)
def extract(self, pat, flags=0, expand=None):
return str_extract(self, pat, flags=flags, expand=expand)
@copy(str_extractall)
def extractall(self, pat, flags=0):
return str_extractall(self._orig, pat, flags=flags)
_shared_docs['find'] = ("""
Return %(side)s indexes in each strings in the Series/Index
where the substring is fully contained between [start:end].
Return -1 on failure. Equivalent to standard :meth:`str.%(method)s`.
Parameters
----------
sub : str
Substring being searched
start : int
Left edge index
end : int
Right edge index
Returns
-------
found : Series/Index of integer values
See Also
--------
%(also)s
""")
@Appender(_shared_docs['find'] %
dict(side='lowest', method='find',
also='rfind : Return highest indexes in each strings'))
def find(self, sub, start=0, end=None):
result = str_find(self._data, sub, start=start, end=end, side='left')
return self._wrap_result(result)
@Appender(_shared_docs['find'] %
dict(side='highest', method='rfind',
also='find : Return lowest indexes in each strings'))
def rfind(self, sub, start=0, end=None):
result = str_find(self._data, sub, start=start, end=end, side='right')
return self._wrap_result(result)
def normalize(self, form):
"""Return the Unicode normal form for the strings in the Series/Index.
For more information on the forms, see the
:func:`unicodedata.normalize`.
Parameters
----------
form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
Unicode form
Returns
-------
normalized : Series/Index of objects
"""
import unicodedata
f = lambda x: unicodedata.normalize(form, compat.u_safe(x))
result = _na_map(f, self._data)
return self._wrap_result(result)
_shared_docs['index'] = ("""
Return %(side)s indexes in each strings where the substring is
fully contained between [start:end]. This is the same as
``str.%(similar)s`` except instead of returning -1, it raises a ValueError
when the substring is not found. Equivalent to standard ``str.%(method)s``.
Parameters
----------
sub : str
Substring being searched
start : int
Left edge index
end : int
Right edge index
Returns
-------
found : Series/Index of objects
See Also
--------
%(also)s
""")
@Appender(_shared_docs['index'] %
dict(side='lowest', similar='find', method='index',
also='rindex : Return highest indexes in each strings'))
def index(self, sub, start=0, end=None):
result = str_index(self._data, sub, start=start, end=end, side='left')
return self._wrap_result(result)
@Appender(_shared_docs['index'] %
dict(side='highest', similar='rfind', method='rindex',
also='index : Return lowest indexes in each strings'))
def rindex(self, sub, start=0, end=None):
result = str_index(self._data, sub, start=start, end=end, side='right')
return self._wrap_result(result)
_shared_docs['len'] = ("""
Compute length of each string in the Series/Index.
Returns
-------
lengths : Series/Index of integer values
""")
len = _noarg_wrapper(len, docstring=_shared_docs['len'], dtype=int)
_shared_docs['casemethods'] = ("""
Convert strings in the Series/Index to %(type)s.
Equivalent to :meth:`str.%(method)s`.
Returns
-------
converted : Series/Index of objects
""")
_shared_docs['lower'] = dict(type='lowercase', method='lower')
_shared_docs['upper'] = dict(type='uppercase', method='upper')
_shared_docs['title'] = dict(type='titlecase', method='title')
_shared_docs['capitalize'] = dict(type='be capitalized',
method='capitalize')
_shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase')
lower = _noarg_wrapper(lambda x: x.lower(),
docstring=_shared_docs['casemethods'] %
_shared_docs['lower'])
upper = _noarg_wrapper(lambda x: x.upper(),
docstring=_shared_docs['casemethods'] %
_shared_docs['upper'])
title = _noarg_wrapper(lambda x: x.title(),
docstring=_shared_docs['casemethods'] %
_shared_docs['title'])
capitalize = _noarg_wrapper(lambda x: x.capitalize(),
docstring=_shared_docs['casemethods'] %
_shared_docs['capitalize'])
swapcase = _noarg_wrapper(lambda x: x.swapcase(),
docstring=_shared_docs['casemethods'] %
_shared_docs['swapcase'])
_shared_docs['ismethods'] = ("""
Check whether all characters in each string in the Series/Index
are %(type)s. Equivalent to :meth:`str.%(method)s`.
Returns
-------
is : Series/array of boolean values
""")
_shared_docs['isalnum'] = dict(type='alphanumeric', method='isalnum')
_shared_docs['isalpha'] = dict(type='alphabetic', method='isalpha')
_shared_docs['isdigit'] = dict(type='digits', method='isdigit')
_shared_docs['isspace'] = dict(type='whitespace', method='isspace')
_shared_docs['islower'] = dict(type='lowercase', method='islower')
_shared_docs['isupper'] = dict(type='uppercase', method='isupper')
_shared_docs['istitle'] = dict(type='titlecase', method='istitle')
_shared_docs['isnumeric'] = dict(type='numeric', method='isnumeric')
_shared_docs['isdecimal'] = dict(type='decimal', method='isdecimal')
isalnum = _noarg_wrapper(lambda x: x.isalnum(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isalnum'])
isalpha = _noarg_wrapper(lambda x: x.isalpha(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isalpha'])
isdigit = _noarg_wrapper(lambda x: x.isdigit(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isdigit'])
isspace = _noarg_wrapper(lambda x: x.isspace(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isspace'])
islower = _noarg_wrapper(lambda x: x.islower(),
docstring=_shared_docs['ismethods'] %
_shared_docs['islower'])
isupper = _noarg_wrapper(lambda x: x.isupper(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isupper'])
istitle = _noarg_wrapper(lambda x: x.istitle(),
docstring=_shared_docs['ismethods'] %
_shared_docs['istitle'])
isnumeric = _noarg_wrapper(lambda x: compat.u_safe(x).isnumeric(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isnumeric'])
isdecimal = _noarg_wrapper(lambda x: compat.u_safe(x).isdecimal(),
docstring=_shared_docs['ismethods'] %
_shared_docs['isdecimal'])
class StringAccessorMixin(object):
""" Mixin to add a `.str` acessor to the class."""
# string methods
def _make_str_accessor(self):
from pandas.core.index import Index
if (isinstance(self, ABCSeries) and
not ((is_categorical_dtype(self.dtype) and
is_object_dtype(self.values.categories)) or
(is_object_dtype(self.dtype)))):
# it's neither a string series not a categorical series with
# strings inside the categories.
# this really should exclude all series with any non-string values
# (instead of test for object dtype), but that isn't practical for
# performance reasons until we have a str dtype (GH 9343)
raise AttributeError("Can only use .str accessor with string "
"values, which use np.object_ dtype in "
"pandas")
elif isinstance(self, Index):
# can't use ABCIndex to exclude non-str
# see scc/inferrence.pyx which can contain string values
allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
if self.inferred_type not in allowed_types:
message = ("Can only use .str accessor with string values "
"(i.e. inferred_type is 'string', 'unicode' or "
"'mixed')")
raise AttributeError(message)
if self.nlevels > 1:
message = ("Can only use .str accessor with Index, not "
"MultiIndex")
raise AttributeError(message)
return StringMethods(self)
str = AccessorProperty(StringMethods, _make_str_accessor)
def _dir_additions(self):
return set()
def _dir_deletions(self):
try:
getattr(self, 'str')
except AttributeError:
return set(['str'])
return set()