-
Notifications
You must be signed in to change notification settings - Fork 0
/
__init__.py
3758 lines (3177 loc) · 118 KB
/
__init__.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
from __future__ import annotations
import textwrap
import numexpr
import numpy as np
import pandas as pd
import regex
import ujson
from collections import defaultdict
nested_dict = lambda: defaultdict(nested_dict)
data = nested_dict()
import operator
from functools import reduce
import codecs
from functools import wraps
import re
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
cast,
)
import warnings
import pandas._libs.lib as lib
import unicodedata
from pandas._typing import (
DtypeObj,
F,
Scalar,
)
from pandas.util._decorators import (
Appender,
deprecate_nonkeyword_arguments,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
ensure_object,
is_bool_dtype,
is_categorical_dtype,
is_integer,
is_list_like,
is_object_dtype,
is_re,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCIndex,
ABCMultiIndex,
ABCSeries,
)
from pandas.core.dtypes.missing import isna
from pandas.core.base import NoNewAttributesMixin
from pandas.core.construction import extract_array
if TYPE_CHECKING:
from pandas import (
DataFrame,
Index,
Series,
)
_shared_docs: dict[str, str] = {}
_cpython_optimized_encoders = (
"utf-8",
"utf8",
"latin-1",
"latin1",
"iso-8859-1",
"mbcs",
"ascii",
)
_cpython_optimized_decoders = _cpython_optimized_encoders + ("utf-16", "utf-32")
def forbid_nonstring_types(
forbidden: list[str] | None, name: str | None = None
) -> Callable[[F], F]:
"""
Decorator to forbid specific types for a method of StringMethods.
For calling `.str.{method}` on a Series or Index, it is necessary to first
initialize the :class:`StringMethods` object, and then call the method.
However, different methods allow different input types, and so this can not
be checked during :meth:`StringMethods.__init__`, but must be done on a
per-method basis. This decorator exists to facilitate this process, and
make it explicit which (inferred) types are disallowed by the method.
:meth:`StringMethods.__init__` allows the *union* of types its different
methods allow (after skipping NaNs; see :meth:`StringMethods._validate`),
namely: ['string', 'empty', 'bytes', 'mixed', 'mixed-integer'].
The default string types ['string', 'empty'] are allowed for all methods.
For the additional types ['bytes', 'mixed', 'mixed-integer'], each method
then needs to forbid the types it is not intended for.
Parameters
----------
forbidden : list-of-str or None
List of forbidden non-string types, may be one or more of
`['bytes', 'mixed', 'mixed-integer']`.
name : str, default None
Name of the method to use in the error message. By default, this is
None, in which case the name from the method being wrapped will be
copied. However, for working with further wrappers (like _pat_wrapper
and _noarg_wrapper), it is necessary to specify the name.
Returns
-------
func : wrapper
The method to which the decorator is applied, with an added check that
enforces the inferred type to not be in the list of forbidden types.
Raises
------
TypeError
If the inferred type of the underlying data is in `forbidden`.
"""
# deal with None
forbidden = [] if forbidden is None else forbidden
allowed_types = {"string", "empty", "bytes", "mixed", "mixed-integer"} - set(
forbidden
)
allowed_types |= {"bytes"}
def _forbid_nonstring_types(func: F) -> F:
func_name = func.__name__ if name is None else name
@wraps(func)
def wrapper(self, *args, **kwargs):
if self._inferred_dtype not in allowed_types:
msg = (
f"Cannot use .str.{func_name} with values of "
f"inferred dtype '{self._inferred_dtype}'."
)
raise TypeError(msg)
return func(self, *args, **kwargs)
wrapper.__name__ = func_name
return cast(F, wrapper)
return _forbid_nonstring_types
def _map_and_wrap(name, docstring):
@forbid_nonstring_types(["bytesb"], name=name)
def wrapper(self):
# result = getattr(self._data.array, f"_str_{name}")()
result = getattr(np.char.array(self._data.array), f"{name}")()
return self._wrap_result(result)
wrapper.__doc__ = docstring
return wrapper
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 = pd.Series(["A_Str_Series"])
>>> s
0 A_Str_Series
dtype: object
>>> s.str.split("_")
0 [A, Str, Series]
dtype: object
>>> s.str.replace("_", "")
0 AStrSeries
dtype: object
"""
# Note: see the docstring in pandas.core.strings.__init__
# for an explanation of the implementation.
# TODO: Dispatch all the methods
# Currently the following are not dispatched to the array
# * cat
# * extractall
def __init__(self, data) -> None:
from pandas.core.arrays.string_ import StringDtype
self._inferred_dtype = self._validate(data)
self._is_categorical = is_categorical_dtype(data.dtype)
self._is_string = isinstance(data.dtype, StringDtype)
self._is_string = False
self._data = data
self._index = self._name = None
if isinstance(data, ABCSeries):
self._index = data.index
self._name = data.name
# ._values.categories works for both Series/Index
self._parent = data._values.categories if self._is_categorical else data
# save orig to blow up categoricals to the right type
self._orig = data
self._freeze()
def __str__(self):
print(self._orig)
return ""
def __repr__(self):
print(self._orig)
return ""
@staticmethod
def _validate(data):
"""
Auxiliary function for StringMethods, infers and checks dtype of data.
This is a "first line of defence" at the creation of the StringMethods-
object, and just checks that the dtype is in the
*union* of the allowed types over all string methods below; this
restriction is then refined on a per-method basis using the decorator
@forbid_nonstring_types (more info in the corresponding docstring).
This really should exclude all series/index with any non-string values,
but that isn't practical for performance reasons until we have a str
dtype (GH 9343 / 13877)
Parameters
----------
data : The content of the Series
Returns
-------
dtype : inferred dtype of data
"""
if isinstance(data, ABCMultiIndex):
raise AttributeError(
"Can only use .str accessor with Index, not MultiIndex"
)
# see _libs/lib.pyx for list of inferred types
allowed_types = ["string", "empty", "bytes", "mixed", "mixed-integer"]
data = extract_array(data)
values = getattr(data, "categories", data) # categorical / normal
inferred_dtype = lib.infer_dtype(values, skipna=True)
if inferred_dtype not in allowed_types:
raise AttributeError("Can only use .str accessor with string values!")
return inferred_dtype
def __getitem__(self, key):
result = self._data.array._str_getitem(key)
return self._wrap_result(result)
def __iter__(self):
warnings.warn(
"Columnar iteration over characters will be deprecated in future releases.",
FutureWarning,
stacklevel=find_stack_level(),
)
i = 0
g = self.get(i)
while g.notna().any():
yield g
i += 1
g = self.get(i)
def _wrap_result(
self,
result,
name=None,
expand: bool | None = None,
fill_value=np.nan,
returns_string=True,
returns_bool: bool = False,
):
from pandas import (
Index,
MultiIndex,
)
if not hasattr(result, "ndim") or not hasattr(result, "dtype"):
if isinstance(result, ABCDataFrame):
result = result.__finalize__(self._orig, name="bytes")
return result
assert result.ndim < 3
# We can be wrapping a string / object / categorical result, in which
# case we'll want to return the same dtype as the input.
# Or we can be wrapping a numeric output, in which case we don't want
# to return a StringArray.
# Ideally the array method returns the right array type.
if expand is None:
# infer from ndim if expand is not specified
expand = result.ndim != 1
elif (
expand is True
and is_object_dtype(result)
and not isinstance(self._orig, ABCIndex)
):
# required when expand=True is explicitly specified
# not needed when inferred
def cons_row(x):
if is_list_like(x):
return x
else:
return [x]
result = [cons_row(x) for x in result]
if result and not self._is_string:
# propagate nan values to match longest sequence (GH 18450)
max_len = max(len(x) for x in result)
result = [
x * max_len if len(x) == 0 or x[0] is np.nan else 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, ABCIndex):
# 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)
out = MultiIndex.from_tuples(result, names=name)
if out.nlevels == 1:
# We had all tuples of length-one, which are
# better represented as a regular Index.
out = out.get_level_values(0)
return out
else:
return Index._with_infer(result, name=name)
else:
index = self._orig.index
# This is a mess.
dtype: DtypeObj | str | None
vdtype = getattr(result, "dtype", None)
if self._is_string:
if is_bool_dtype(vdtype):
dtype = result.dtype
elif returns_string:
dtype = self._orig.dtype
else:
dtype = vdtype
else:
dtype = vdtype
if expand:
cons = self._orig._constructor_expanddim
result = cons(result, columns=name, index=index, dtype=dtype)
else:
# Must be a Series
cons = self._orig._constructor
result = cons(result, name=name, index=index, dtype=dtype)
result = result.__finalize__(self._orig, method="str")
if name is not None and result.ndim == 1:
# __finalize__ might copy over the original name, but we may
# want the new name (e.g. str.extract).
result.name = name
return result
def _get_series_list(self, others):
"""
Auxiliary function for :meth:`str.cat`. Turn potentially mixed input
into a list of Series (elements without an index must match the length
of the calling Series/Index).
Parameters
----------
others : Series, DataFrame, np.ndarray, list-like or list-like of
Objects that are either Series, Index or np.ndarray (1-dim).
Returns
-------
list of Series
Others transformed into list of Series.
"""
from pandas import (
DataFrame,
Series,
)
# self._orig is either Series or Index
idx = self._orig if isinstance(self._orig, ABCIndex) else self._orig.index
# Generally speaking, all objects without an index inherit the index
# `idx` of the calling Series/Index - i.e. must have matching length.
# Objects with an index (i.e. Series/Index/DataFrame) keep their own.
if isinstance(others, ABCSeries):
return [others]
elif isinstance(others, ABCIndex):
return [Series(others._values, index=idx, dtype=others.dtype)]
elif isinstance(others, ABCDataFrame):
return [others[x] for x in others]
elif isinstance(others, np.ndarray) and others.ndim == 2:
others = DataFrame(others, index=idx)
return [others[x] for x in others]
elif is_list_like(others, allow_sets=False):
others = list(others) # ensure iterators do not get read twice etc
# in case of list-like `others`, all elements must be
# either Series/Index/np.ndarray (1-dim)...
if all(
isinstance(x, (ABCSeries, ABCIndex))
or (isinstance(x, np.ndarray) and x.ndim == 1)
for x in others
):
los: list[Series] = []
while others: # iterate through list and append each element
los = los + self._get_series_list(others.pop(0))
return los
# ... or just strings
elif all(not is_list_like(x) for x in others):
return [Series(others, index=idx)]
raise TypeError(
"others must be Series, Index, DataFrame, np.ndarray "
"or list-like (either containing only strings or "
"containing only objects of type Series/Index/"
"np.ndarray[1-dim])"
)
@forbid_nonstring_types(["bytes", "mixed", "mixed-integer"])
def cat(
self, others=None, sep=None, na_rep=None, join="left"
) -> str | Series | Index:
"""
Concatenate strings in the Series/Index with given separator.
If `others` is specified, this function concatenates the Series/Index
and elements of `others` element-wise.
If `others` is not passed, then all values in the Series/Index are
concatenated into a single string with a given `sep`.
Parameters
----------
others : Series, Index, DataFrame, np.ndarray or list-like
Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and
other list-likes of strings must have the same length as the
calling Series/Index, with the exception of indexed objects (i.e.
Series/Index/DataFrame) if `join` is not None.
If others is a list-like that contains a combination of Series,
Index or np.ndarray (1-dim), then all elements will be unpacked and
must satisfy the above criteria individually.
If others is None, the method returns the concatenation of all
strings in the calling Series/Index.
sep : str, default ''
The separator between the different elements/columns. By default
the empty string `''` is used.
na_rep : str or None, default None
Representation that is inserted for all missing values:
- If `na_rep` is None, and `others` is None, missing values in the
Series/Index are omitted from the result.
- If `na_rep` is None, and `others` is not None, a row containing a
missing value in any of the columns (before concatenation) will
have a missing value in the result.
join : {'left', 'right', 'outer', 'inner'}, default 'left'
Determines the join-style between the calling Series/Index and any
Series/Index/DataFrame in `others` (objects without an index need
to match the length of the calling Series/Index). To disable
alignment, use `.values` on any Series/Index/DataFrame in `others`.
.. versionadded:: 0.23.0
.. versionchanged:: 1.0.0
Changed default of `join` from None to `'left'`.
Returns
-------
str, Series or Index
If `others` is None, `str` is returned, otherwise a `Series/Index`
(same type as caller) of objects is returned.
See Also
--------
split : Split each string in the Series/Index.
join : Join lists contained as elements in the Series/Index.
Examples
--------
When not passing `others`, all values are concatenated into a single
string:
>>> s = pd.Series(['a', 'b', np.nan, 'd'])
>>> s.str.cat(sep=' ')
'a b d'
By default, NA values in the Series are ignored. Using `na_rep`, they
can be given a representation:
>>> s.str.cat(sep=' ', na_rep='?')
'a b ? d'
If `others` is specified, corresponding values are concatenated with
the separator. Result will be a Series of strings.
>>> s.str.cat(['A', 'B', 'C', 'D'], sep=',')
0 a,A
1 b,B
2 NaN
3 d,D
dtype: object
Missing values will remain missing in the result, but can again be
represented using `na_rep`
>>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')
0 a,A
1 b,B
2 -,C
3 d,D
dtype: object
If `sep` is not specified, the values are concatenated without
separation.
>>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')
0 aA
1 bB
2 -C
3 dD
dtype: object
Series with different indexes can be aligned before concatenation. The
`join`-keyword works as in other methods.
>>> t = pd.Series(['d', 'a', 'e', 'c'], index=[3, 0, 4, 2])
>>> s.str.cat(t, join='left', na_rep='-')
0 aa
1 b-
2 -c
3 dd
dtype: object
>>>
>>> s.str.cat(t, join='outer', na_rep='-')
0 aa
1 b-
2 -c
3 dd
4 -e
dtype: object
>>>
>>> s.str.cat(t, join='inner', na_rep='-')
0 aa
2 -c
3 dd
dtype: object
>>>
>>> s.str.cat(t, join='right', na_rep='-')
3 dd
0 aa
4 -e
2 -c
dtype: object
For more examples, see :ref:`here <text.concatenate>`.
"""
# TODO: dispatch
from pandas import (
Index,
Series,
concat,
)
if isinstance(others, str):
raise ValueError("Did you mean to supply a `sep` keyword?")
if sep is None:
sep = b""
if isinstance(self._orig, ABCIndex):
data = Series(self._orig, index=self._orig, dtype=self._orig.dtype)
else: # Series
data = self._orig
# concatenate Series/Index with itself if no "others"
if others is None:
# error: Incompatible types in assignment (expression has type
# "ndarray", variable has type "Series")
data = ensure_object(data) # type: ignore[assignment]
na_mask = isna(data)
if na_rep is None and na_mask.any():
return sep.join(data[~na_mask])
elif na_rep is not None and na_mask.any():
return sep.join(np.where(na_mask, na_rep, data))
else:
return sep.join(data)
try:
# turn anything in "others" into lists of Series
others = self._get_series_list(others)
except ValueError as err: # do not catch TypeError raised by _get_series_list
raise ValueError(
"If `others` contains arrays or lists (or other "
"list-likes without an index), these must all be "
"of the same length as the calling Series/Index."
) from err
# align if required
if any(not data.index.equals(x.index) for x in others):
# Need to add keys for uniqueness in case of duplicate columns
others = concat(
others,
axis=1,
join=(join if join == "inner" else "outer"),
keys=range(len(others)),
sort=False,
copy=False,
)
data, others = data.align(others, join=join)
others = [others[x] for x in others] # again list of Series
all_cols = [ensure_object(x) for x in [data] + others]
na_masks = np.array([isna(x) for x in all_cols])
union_mask = np.logical_or.reduce(na_masks, axis=0)
if na_rep is None and union_mask.any():
# no na_rep means NaNs for all rows where any column has a NaN
# only necessary if there are actually any NaNs
result = np.empty(len(data), dtype=object)
np.putmask(result, union_mask, np.nan)
not_masked = ~union_mask
result[not_masked] = cat_safe([x[not_masked] for x in all_cols], sep)
elif na_rep is not None and union_mask.any():
# fill NaNs with na_rep in case there are actually any NaNs
all_cols = [
np.where(nm, na_rep, col) for nm, col in zip(na_masks, all_cols)
]
result = cat_safe(all_cols, sep)
else:
# no NaNs - can just concatenate
result = cat_safe(all_cols, sep)
out: Index | Series
if isinstance(self._orig, ABCIndex):
# add dtype for case that result is all-NA
out = Index(result, dtype=object, name=self._orig.name)
else: # Series
if is_categorical_dtype(self._orig.dtype):
# We need to infer the new categories.
dtype = None
else:
dtype = self._orig.dtype
res_ser = Series(
result, dtype=dtype, index=data.index, name=self._orig.name
)
out = res_ser.__finalize__(self._orig, method="str_cat")
return out
_shared_docs[
"str_split"
] = r"""
Split strings around given separator/delimiter.
Splits the string in the Series/Index from the %(side)s,
at the specified delimiter string.
Parameters
----------
pat : str%(pat_regex)s, optional
%(pat_description)s.
If not specified, split on whitespace.
n : int, default -1 (all)
Limit number of splits in output.
``None``, 0 and -1 will be interpreted as return all splits.
expand : bool, default False
Expand the split strings into separate columns.
- If ``True``, return DataFrame/MultiIndex expanding dimensionality.
- If ``False``, return Series/Index, containing lists of strings.
%(regex_argument)s
Returns
-------
Series, Index, DataFrame or MultiIndex
Type matches caller unless ``expand=True`` (see Notes).
%(raises_split)s
See Also
--------
Series.str.split : Split strings around given separator/delimiter.
Series.str.rsplit : Splits string around given separator/delimiter,
starting from the right.
Series.str.join : Join lists contained as elements in the Series/Index
with passed delimiter.
str.split : Standard library version for split.
str.rsplit : Standard library version for rsplit.
Notes
-----
The handling of the `n` keyword depends on the number of found splits:
- If found splits > `n`, make first `n` splits only
- If found splits <= `n`, make all splits
- If for a certain row the number of found splits < `n`,
append `None` for padding up to `n` if ``expand=True``
If using ``expand=True``, Series and Index callers return DataFrame and
MultiIndex objects, respectively.
%(regex_pat_note)s
Examples
--------
>>> s = pd.Series(
... [
... "this is a regular sentence",
... "https://docs.python.org/3/tutorial/index.html",
... np.nan
... ]
... )
>>> s
0 this is a regular sentence
1 https://docs.python.org/3/tutorial/index.html
2 NaN
dtype: object
In the default setting, the string is split by whitespace.
>>> s.str.split()
0 [this, is, a, regular, sentence]
1 [https://docs.python.org/3/tutorial/index.html]
2 NaN
dtype: object
Without the `n` parameter, the outputs of `rsplit` and `split`
are identical.
>>> s.str.rsplit()
0 [this, is, a, regular, sentence]
1 [https://docs.python.org/3/tutorial/index.html]
2 NaN
dtype: object
The `n` parameter can be used to limit the number of splits on the
delimiter. The outputs of `split` and `rsplit` are different.
>>> s.str.split(n=2)
0 [this, is, a regular sentence]
1 [https://docs.python.org/3/tutorial/index.html]
2 NaN
dtype: object
>>> s.str.rsplit(n=2)
0 [this is a, regular, sentence]
1 [https://docs.python.org/3/tutorial/index.html]
2 NaN
dtype: object
The `pat` parameter can be used to split by other characters.
>>> s.str.split(pat="/")
0 [this is a regular sentence]
1 [https:, , docs.python.org, 3, tutorial, index...
2 NaN
dtype: object
When using ``expand=True``, the split elements will expand out into
separate columns. If NaN is present, it is propagated throughout
the columns during the split.
>>> s.str.split(expand=True)
0 1 2 3 4
0 this is a regular sentence
1 https://docs.python.org/3/tutorial/index.html None None None None
2 NaN NaN NaN NaN NaN
For slightly more complex use cases like splitting the html document name
from a url, a combination of parameter settings can be used.
>>> s.str.rsplit("/", n=1, expand=True)
0 1
0 this is a regular sentence None
1 https://docs.python.org/3/tutorial index.html
2 NaN NaN
%(regex_examples)s"""
@Appender(
_shared_docs["str_split"]
% {
"side": "beginning",
"pat_regex": " or compiled regex",
"pat_description": "String or regular expression to split on",
"regex_argument": """
regex : bool, default None
Determines if the passed-in pattern is a regular expression:
- If ``True``, assumes the passed-in pattern is a regular expression
- If ``False``, treats the pattern as a literal string.
- If ``None`` and `pat` length is 1, treats `pat` as a literal string.
- If ``None`` and `pat` length is not 1, treats `pat` as a regular expression.
- Cannot be set to False if `pat` is a compiled regex
.. versionadded:: 1.4.0
""",
"raises_split": """
Raises
------
ValueError
* if `regex` is False and `pat` is a compiled regex
""",
"regex_pat_note": """
Use of `regex =False` with a `pat` as a compiled regex will raise an error.
""",
"method": "split",
"regex_examples": r"""
Remember to escape special characters when explicitly using regular expressions.
>>> s = pd.Series(["foo and bar plus baz"])
>>> s.str.split(r"and|plus", expand=True)
0 1 2
0 foo bar baz
Regular expressions can be used to handle urls or file names.
When `pat` is a string and ``regex=None`` (the default), the given `pat` is compiled
as a regex only if ``len(pat) != 1``.
>>> s = pd.Series(['foojpgbar.jpg'])
>>> s.str.split(r".", expand=True)
0 1
0 foojpgbar jpg
>>> s.str.split(r"\.jpg", expand=True)
0 1
0 foojpgbar
When ``regex=True``, `pat` is interpreted as a regex
>>> s.str.split(r"\.jpg", regex=True, expand=True)
0 1
0 foojpgbar
A compiled regex can be passed as `pat`
>>> import re
>>> s.str.split(re.compile(r"\.jpg"), expand=True)
0 1
0 foojpgbar
When ``regex=False``, `pat` is interpreted as the string itself
>>> s.str.split(r"\.jpg", regex=False, expand=True)
0
0 foojpgbar.jpg
""",
}
)
@deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "pat"])
@forbid_nonstring_types(["bytesb"])
def split(
self,
pat: str | re.Pattern | None = None,
n=-1,
expand=False,
*,
regex: bool | None = None,
):
if regex is False and is_re(pat):
raise ValueError(
"Cannot use a compiled regex as replacement pattern with regex=False"
)
if is_re(pat):
regex = True
result = self._data.array._str_split(pat, n, expand, regex)
return self._wrap_result(result, returns_string=expand, expand=expand)
@Appender(
_shared_docs["str_split"]
% {
"side": "end",
"pat_regex": "",
"pat_description": "String to split on",
"regex_argument": "",
"raises_split": "",
"regex_pat_note": "",
"method": "rsplit",
"regex_examples": "",
}
)
@deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "pat"])
@forbid_nonstring_types(["bytesb"])
def rsplit(self, pat=None, n=-1, expand=False):
result = self._data.array._str_rsplit(pat, n=n)
return self._wrap_result(result, expand=expand, returns_string=expand)
_shared_docs[
"str_partition"
] = """
Split the string at the %(side)s occurrence of `sep`.
This method splits the string at the %(side)s occurrence of `sep`,
and returns 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
----------
sep : str, default whitespace
String to split on.
expand : bool, default True
If True, return DataFrame/MultiIndex expanding dimensionality.
If False, return Series/Index.
Returns
-------
DataFrame/MultiIndex or Series/Index of objects
See Also
--------
%(also)s
Series.str.split : Split strings around given separators.
str.partition : Standard library version.
Examples
--------
>>> s = pd.Series(['Linda van der Berg', 'George Pitt-Rivers'])
>>> s
0 Linda van der Berg
1 George Pitt-Rivers
dtype: object
>>> s.str.partition()
0 1 2
0 Linda van der Berg
1 George Pitt-Rivers
To partition by the last space instead of the first one:
>>> s.str.rpartition()
0 1 2
0 Linda van der Berg
1 George Pitt-Rivers
To partition by something different than a space:
>>> s.str.partition('-')
0 1 2
0 Linda van der Berg
1 George Pitt - Rivers
To return a Series containing tuples instead of a DataFrame:
>>> s.str.partition('-', expand=False)
0 (Linda van der Berg, , )
1 (George Pitt, -, Rivers)
dtype: object
Also available on indices:
>>> idx = pd.Index(['X 123', 'Y 999'])
>>> idx
Index(['X 123', 'Y 999'], dtype='object')
Which will create a MultiIndex:
>>> idx.str.partition()
MultiIndex([('X', ' ', '123'),
('Y', ' ', '999')],
)
Or an index with tuples with ``expand=False``:
>>> idx.str.partition(expand=False)
Index([('X', ' ', '123'), ('Y', ' ', '999')], dtype='object')
"""
@Appender(
_shared_docs["str_partition"]
% {
"side": "first",
"return": "3 elements containing the string itself, followed by two "
"empty strings",