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strings.py
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strings.py
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import numpy as np
from pandas.compat import zip
from pandas.core.common import isnull, _values_from_object
import pandas.compat as compat
import re
import pandas.lib as lib
import warnings
import textwrap
_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 arrays of strings with given separator
Parameters
----------
arr : list or array-like
others : list or array, or list of arrays
sep : string or None, default None
na_rep : string or None, default None
If None, an NA in any array will propagate
Returns
-------
concat : array
"""
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():
return np.nan
return sep.join(np.where(mask, na_rep, arr))
def _length_check(others):
n = None
for x in others:
if n is None:
n = len(x)
elif len(x) != n:
raise ValueError('All arrays must be same length')
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):
from pandas.core.series import Series
if not len(arr):
return np.ndarray(0, dtype=dtype)
if isinstance(arr, Series):
arr = arr.values
if not isinstance(arr, np.ndarray):
arr = np.asarray(arr, dtype=object)
if na_mask:
mask = isnull(arr)
try:
result = lib.map_infer_mask(arr, f, mask.view(np.uint8))
except (TypeError, AttributeError):
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
Parameters
----------
arr : list or array-like
pat : string, valid regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
counts : arrays
"""
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):
"""
Check whether given pattern is contained in each string in the array
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
-------
Series of boolean values
See Also
--------
match : analagous, 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)
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 array indicating whether each string starts with passed
pattern
Parameters
----------
pat : string
Character sequence
na : bool, default NaN
Returns
-------
startswith : array (boolean)
"""
f = lambda x: x.startswith(pat)
return _na_map(f, arr, na, dtype=bool)
def str_endswith(arr, pat, na=np.nan):
"""
Return boolean array indicating whether each string ends with passed
pattern
Parameters
----------
pat : string
Character sequence
na : bool, default NaN
Returns
-------
endswith : array (boolean)
"""
f = lambda x: x.endswith(pat)
return _na_map(f, arr, na, dtype=bool)
def str_replace(arr, pat, repl, n=-1, case=True, flags=0):
"""
Replace
Parameters
----------
pat : string
Character sequence or regular expression
repl : string
Replacement sequence
n : int, default -1 (all)
Number of replacements to make from start
case : boolean, default True
If True, case sensitive
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
replaced : array
"""
use_re = not case or len(pat) > 1 or flags
if use_re:
if not case:
flags |= re.IGNORECASE
regex = re.compile(pat, flags=flags)
n = n if n >= 0 else 0
def f(x):
return regex.sub(repl, 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 array by indicated number of times
Parameters
----------
repeats : int or array
Same value for all (int) or different value per (array)
Returns
-------
repeated : array
"""
if np.isscalar(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=False):
"""
Deprecated: Find groups in each string using passed regular expression.
If as_indexer=True, 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 : False, by default, gives deprecated behavior better achieved
using str_extract. True return boolean indexer.
Returns
-------
Series of boolean values
if as_indexer=True
Series of tuples
if as_indexer=False, default but deprecated
See Also
--------
contains : analagous, but less strict, relying on re.search instead of
re.match
extract : now preferred to the deprecated usage of match (as_indexer=False)
Notes
-----
To extract matched groups, which is the deprecated behavior of match, use
str.extract.
"""
if not case:
flags |= re.IGNORECASE
regex = re.compile(pat, flags=flags)
if (not as_indexer) and regex.groups > 0:
# Do this first, to make sure it happens even if the re.compile
# raises below.
warnings.warn("In future versions of pandas, match will change to"
" always return a bool indexer.", UserWarning)
if as_indexer and regex.groups > 0:
warnings.warn("This pattern has match groups. To actually get the"
" groups, use str.extract.", UserWarning)
# If not as_indexer and regex.groups == 0, this returns empty lists
# and is basically useless, so we will not warn.
if (not as_indexer) and regex.groups > 0:
dtype = object
def f(x):
m = regex.match(x)
if m:
return m.groups()
else:
return []
else:
# This is the new behavior of str_match.
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 str_extract(arr, pat, flags=0):
"""
Find groups in each string using passed regular expression
Parameters
----------
pat : string
Pattern or regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
extracted groups : Series (one group) or DataFrame (multiple groups)
Note that dtype of the result is always object, even when no match is
found and the result is a Series or DataFrame containing only NaN
values.
Examples
--------
A pattern with one group will return a Series. Non-matches will be NaN.
>>> Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
0 1
1 2
2 NaN
dtype: object
A pattern with more than one group will return a DataFrame.
>>> Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
0 1
0 a 1
1 b 2
2 NaN NaN
A pattern may contain optional groups.
>>> Series(['a1', 'b2', 'c3']).str.extract('([ab])?(\d)')
0 1
0 a 1
1 b 2
2 NaN 3
Named groups will become column names in the result.
>>> Series(['a1', 'b2', 'c3']).str.extract('(?P<letter>[ab])(?P<digit>\d)')
letter digit
0 a 1
1 b 2
2 NaN NaN
"""
from pandas.core.series import Series
from pandas.core.frame import DataFrame
regex = re.compile(pat, flags=flags)
# just to be safe, check this
if regex.groups == 0:
raise ValueError("This pattern contains no groups to capture.")
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
if regex.groups == 1:
result = Series([f(val)[0] for val in arr],
name=_get_single_group_name(regex),
index=arr.index, dtype=object)
else:
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([f(val) for val in arr],
columns=columns,
index=arr.index,
dtype=object)
return result
def str_get_dummies(arr, sep='|'):
"""
Split each string by sep and return a frame of dummy/indicator variables.
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
>>> pd.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 ``pd.get_dummies``.
"""
from pandas.core.frame import DataFrame
# TODO remove this hack?
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 DataFrame(dummies, arr.index, tags)
def str_join(arr, sep):
"""
Join lists contained as elements in array, a la str.join
Parameters
----------
sep : string
Delimiter
Returns
-------
joined : array
"""
return _na_map(sep.join, arr)
def str_findall(arr, pat, flags=0):
"""
Find all occurrences of pattern or regular expression
Parameters
----------
pat : string
Pattern or regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
matches : array
"""
regex = re.compile(pat, flags=flags)
return _na_map(regex.findall, arr)
def str_pad(arr, width, side='left'):
"""
Pad strings with whitespace
Parameters
----------
arr : list or array-like
width : int
Minimum width of resulting string; additional characters will be filled
with spaces
side : {'left', 'right', 'both'}, default 'left'
Returns
-------
padded : array
"""
if side == 'left':
f = lambda x: x.rjust(width)
elif side == 'right':
f = lambda x: x.ljust(width)
elif side == 'both':
f = lambda x: x.center(width)
else: # pragma: no cover
raise ValueError('Invalid side')
return _na_map(f, arr)
def str_center(arr, width):
"""
"Center" strings, filling left and right side with additional whitespace
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be filled
with spaces
Returns
-------
centered : array
"""
return str_pad(arr, width, side='both')
def str_split(arr, pat=None, n=None, return_type='series'):
"""
Split each string (a la re.split) in array by given pattern, propagating NA
values
Parameters
----------
pat : string, default None
String or regular expression to split on. If None, splits on whitespace
n : int, default None (all)
return_type : {'series', 'frame'}, default 'series
If frame, returns a DataFrame (elements are strings)
If series, returns an Series (elements are lists of strings).
Notes
-----
Both 0 and -1 will be interpreted as return all splits
Returns
-------
split : array
"""
from pandas.core.series import Series
from pandas.core.frame import DataFrame
if return_type not in ('series', 'frame'):
raise ValueError("return_type must be {'series', 'frame'}")
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)
if return_type == 'frame':
res = DataFrame((Series(x) for x in _na_map(f, arr)), index=arr.index)
else:
res = _na_map(f, arr)
return res
def str_slice(arr, start=None, stop=None, step=None):
"""
Slice substrings from each element in array
Parameters
----------
start : int or None
stop : int or None
step : int or None
Returns
-------
sliced : array
"""
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 with another string.
Parameters
----------
start : int or None
stop : int or None
repl : str or None
Returns
-------
replaced : array
"""
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):
"""
Strip whitespace (including newlines) from each string in the array
Parameters
----------
to_strip : str or unicode
Returns
-------
stripped : array
"""
return _na_map(lambda x: x.strip(to_strip), arr)
def str_lstrip(arr, to_strip=None):
"""
Strip whitespace (including newlines) from left side of each string in the
array
Parameters
----------
to_strip : str or unicode
Returns
-------
stripped : array
"""
return _na_map(lambda x: x.lstrip(to_strip), arr)
def str_rstrip(arr, to_strip=None):
"""
Strip whitespace (including newlines) from right side of each string in the
array
Parameters
----------
to_strip : str or unicode
Returns
-------
stripped : array
"""
return _na_map(lambda x: x.rstrip(to_strip), arr)
def str_wrap(arr, width, **kwargs):
"""
Wrap long strings to be formatted in paragraphs
Parameters
----------
Same keyword parameters and defaults as :class:`textwrap.TextWrapper`
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 : array
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_get(arr, i):
"""
Extract element from lists, tuples, or strings in each element in the array
Parameters
----------
i : int
Integer index (location)
Returns
-------
items : array
"""
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 to unicode using indicated encoding
Parameters
----------
encoding : string
errors : string
Returns
-------
decoded : array
"""
f = lambda x: x.decode(encoding, errors)
return _na_map(f, arr)
def str_encode(arr, encoding, errors="strict"):
"""
Encode character string to some other encoding using indicated encoding
Parameters
----------
encoding : string
errors : string
Returns
-------
encoded : array
"""
f = lambda x: x.encode(encoding, errors)
return _na_map(f, arr)
def _noarg_wrapper(f, docstring=None, **kargs):
def wrapper(self):
result = _na_map(f, self.series, **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.series, pat)
return self._wrap_result(result)
def wrapper2(self, pat, flags=0, **kwargs):
result = f(self.series, pat, flags=flags, **kwargs)
return self._wrap_result(result)
def wrapper3(self, pat, na=np.nan):
result = f(self.series, 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(object):
"""
Vectorized string functions for Series. 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, series):
self.series = series
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):
from pandas.core.series import Series
from pandas.core.frame import DataFrame
if not hasattr(result, 'ndim'):
return result
elif result.ndim == 1:
name = getattr(result, 'name', None)
return Series(result, index=self.series.index,
name=name or self.series.name)
else:
assert result.ndim < 3
return DataFrame(result, index=self.series.index)
@copy(str_cat)
def cat(self, others=None, sep=None, na_rep=None):
result = str_cat(self.series, others=others, sep=sep, na_rep=na_rep)
return self._wrap_result(result)
@copy(str_split)
def split(self, pat=None, n=-1, return_type='series'):
result = str_split(self.series, pat, n=n, return_type=return_type)
return self._wrap_result(result)
@copy(str_get)
def get(self, i):
result = str_get(self.series, i)
return self._wrap_result(result)
@copy(str_join)
def join(self, sep):
result = str_join(self.series, 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.series, 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=False):
result = str_match(self.series, 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=True, flags=0):
result = str_replace(self.series, pat, repl, n=n, case=case,
flags=flags)
return self._wrap_result(result)
@copy(str_repeat)
def repeat(self, repeats):
result = str_repeat(self.series, repeats)
return self._wrap_result(result)
@copy(str_pad)
def pad(self, width, side='left'):
result = str_pad(self.series, width, side=side)
return self._wrap_result(result)
@copy(str_center)
def center(self, width):
result = str_center(self.series, width)
return self._wrap_result(result)
@copy(str_slice)
def slice(self, start=None, stop=None, step=None):
result = str_slice(self.series, 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.series, start, stop, repl)
return self._wrap_result(result)
@copy(str_decode)