forked from pandas-dev/pandas
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathv0.20.0.txt
1343 lines (953 loc) · 58.2 KB
/
v0.20.0.txt
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
.. _whatsnew_0200:
v0.20.0 (April ??, 2017)
------------------------
This is a major release from 0.19 and includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
users upgrade to this version.
Highlights include:
- The ``.ix`` indexer has been deprecated, see :ref:`here <whatsnew_0200.api_breaking.deprecate_ix>`
- ``Panel`` has been deprecated, see :ref:`here <whatsnew_0200.api_breaking.deprecate_panel>`
- Improved user API when accessing levels in ``.groupby()``, see :ref:`here <whatsnew_0200.enhancements.groupby_access>`
- Improved support for UInt64 dtypes, see :ref:`here <whatsnew_0200.enhancements.uint64_support>`
- A new orient for JSON serialization, ``orient='table'``, that uses the Table Schema spec, see :ref:`here <whatsnew_0200.enhancements.table_schema>`
- Window Binary Corr/Cov operations return a MultiIndexed ``DataFrame`` rather than a ``Panel``, as ``Panel`` is now deprecated, see :ref:`here <whatsnew_0200.api_breaking.rolling_pairwise>`
- Support for S3 handling now uses ``s3fs``, see :ref:`here <whatsnew_0200.api_breaking.s3>`
- Google BigQuery support now uses the ``pandas-gbq`` library, see :ref:`here <whatsnew_0200.api_breaking.gbq>`
- Switched the test framework to use `pytest <http://doc.pytest.org/en/latest>`__ (:issue:`13097`)
Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating.
.. contents:: What's new in v0.20.0
:local:
:backlinks: none
.. _whatsnew_0200.enhancements:
New features
~~~~~~~~~~~~
.. _whatsnew_0200.enhancements.dataio_dtype:
``dtype`` keyword for data IO
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The ``dtype`` keyword argument in the :func:`read_csv` function for specifying the types of parsed columns is now supported with the ``'python'`` engine (:issue:`14295`). See the :ref:`io docs <io.dtypes>` for more information.
.. ipython:: python
:suppress:
from pandas.compat import StringIO
.. ipython:: python
data = "a,b\n1,2\n3,4"
pd.read_csv(StringIO(data), engine='python').dtypes
pd.read_csv(StringIO(data), engine='python', dtype={'a':'float64', 'b':'object'}).dtypes
The ``dtype`` keyword argument is also now supported in the :func:`read_fwf` function for parsing
fixed-width text files, and :func:`read_excel` for parsing Excel files.
.. ipython:: python
data = "a b\n1 2\n3 4"
pd.read_fwf(StringIO(data)).dtypes
pd.read_fwf(StringIO(data), dtype={'a':'float64', 'b':'object'}).dtypes
.. _whatsnew_0120.enhancements.datetime_origin:
``.to_datetime()`` has gained an ``origin`` parameter
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
:func:`to_datetime` has gained a new parameter, ``origin``, to define a reference date
from where to compute the resulting ``DatetimeIndex``. (:issue:`11276`, :issue:`11745`)
Start with 1960-01-01 as the starting date
.. ipython:: python
pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
The default is set at ``origin='unix'``, which defaults to ``1970-01-01 00:00:00``.
Commonly called 'unix epoch' or POSIX time.
.. ipython:: python
pd.to_datetime([1, 2, 3], unit='D')
.. _whatsnew_0200.enhancements.errors:
pandas errors
^^^^^^^^^^^^^
We are adding a standard public location for all pandas exceptions & warnings ``pandas.errors``. (:issue:`14800`). Previously
these exceptions & warnings could be imported from ``pandas.core.common`` or ``pandas.io.common``. These exceptions and warnings
will be removed from the ``*.common`` locations in a future release. (:issue:`15541`)
The following are now part of this API:
.. code-block:: python
['DtypeWarning',
'EmptyDataError',
'OutOfBoundsDatetime',
'ParserError',
'ParserWarning',
'PerformanceWarning',
'UnsortedIndexError',
'UnsupportedFunctionCall']
.. _whatsnew_0200.enhancements.groupby_access:
Groupby Enhancements
^^^^^^^^^^^^^^^^^^^^
Strings passed to ``DataFrame.groupby()`` as the ``by`` parameter may now reference either column names or index level names (:issue:`5677`)
.. ipython:: python
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3],
'B': np.arange(8)},
index=index)
df
df.groupby(['second', 'A']).sum()
.. _whatsnew_0200.enhancements.compressed_urls:
Better support for compressed URLs in ``read_csv``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The compression code was refactored (:issue:`12688`). As a result, reading
dataframes from URLs in :func:`read_csv` or :func:`read_table` now supports
additional compression methods: ``xz``, ``bz2``, and ``zip`` (:issue:`14570`).
Previously, only ``gzip`` compression was supported. By default, compression of
URLs and paths are now both inferred using their file extensions. Additionally,
support for bz2 compression in the python 2 c-engine improved (:issue:`14874`).
.. ipython:: python
url = 'https://github.com/{repo}/raw/{branch}/{path}'.format(
repo = 'pandas-dev/pandas',
branch = 'master',
path = 'pandas/tests/io/parser/data/salaries.csv.bz2',
)
df = pd.read_table(url, compression='infer') # default, infer compression
df = pd.read_table(url, compression='bz2') # explicitly specify compression
df.head(2)
.. _whatsnew_0200.enhancements.pickle_compression:
Pickle file I/O now supports compression
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
:func:`read_pickle`, :meth:`DataFame.to_pickle` and :meth:`Series.to_pickle`
can now read from and write to compressed pickle files. Compression methods
can be an explicit parameter or be inferred from the file extension.
See :ref:`the docs here <io.pickle.compression>`
.. ipython:: python
df = pd.DataFrame({
'A': np.random.randn(1000),
'B': 'foo',
'C': pd.date_range('20130101', periods=1000, freq='s')})
Using an explicit compression type
.. ipython:: python
df.to_pickle("data.pkl.compress", compression="gzip")
rt = pd.read_pickle("data.pkl.compress", compression="gzip")
rt
Inferring compression type from the extension
.. ipython:: python
df.to_pickle("data.pkl.xz", compression="infer")
rt = pd.read_pickle("data.pkl.xz", compression="infer")
rt
The default is to 'infer
.. ipython:: python
df.to_pickle("data.pkl.gz")
rt = pd.read_pickle("data.pkl.gz")
rt
df["A"].to_pickle("s1.pkl.bz2")
rt = pd.read_pickle("s1.pkl.bz2")
rt
.. ipython:: python
:suppress:
import os
os.remove("data.pkl.compress")
os.remove("data.pkl.xz")
os.remove("data.pkl.gz")
os.remove("s1.pkl.bz2")
.. _whatsnew_0200.enhancements.uint64_support:
UInt64 Support Improved
^^^^^^^^^^^^^^^^^^^^^^^
Pandas has significantly improved support for operations involving unsigned,
or purely non-negative, integers. Previously, handling these integers would
result in improper rounding or data-type casting, leading to incorrect results.
Notably, a new numerical index, ``UInt64Index``, has been created (:issue:`14937`)
.. ipython:: python
idx = pd.UInt64Index([1, 2, 3])
df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)
df.index
- Bug in converting object elements of array-like objects to unsigned 64-bit integers (:issue:`4471`, :issue:`14982`)
- Bug in ``Series.unique()`` in which unsigned 64-bit integers were causing overflow (:issue:`14721`)
- Bug in ``DataFrame`` construction in which unsigned 64-bit integer elements were being converted to objects (:issue:`14881`)
- Bug in ``pd.read_csv()`` in which unsigned 64-bit integer elements were being improperly converted to the wrong data types (:issue:`14983`)
- Bug in ``pd.unique()`` in which unsigned 64-bit integers were causing overflow (:issue:`14915`)
- Bug in ``pd.value_counts()`` in which unsigned 64-bit integers were being erroneously truncated in the output (:issue:`14934`)
.. _whatsnew_0200.enhancements.groupy_categorical:
GroupBy on Categoricals
^^^^^^^^^^^^^^^^^^^^^^^
In previous versions, ``.groupby(..., sort=False)`` would fail with a ``ValueError`` when grouping on a categorical series with some categories not appearing in the data. (:issue:`13179`)
.. ipython:: python
chromosomes = np.r_[np.arange(1, 23).astype(str), ['X', 'Y']]
df = pd.DataFrame({
'A': np.random.randint(100),
'B': np.random.randint(100),
'C': np.random.randint(100),
'chromosomes': pd.Categorical(np.random.choice(chromosomes, 100),
categories=chromosomes,
ordered=True)})
df
Previous Behavior:
.. code-block:: ipython
In [3]: df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum()
---------------------------------------------------------------------------
ValueError: items in new_categories are not the same as in old categories
New Behavior:
.. ipython:: python
df[df.chromosomes != '1'].groupby('chromosomes', sort=False).sum()
.. _whatsnew_0200.enhancements.table_schema:
Table Schema Output
^^^^^^^^^^^^^^^^^^^
The new orient ``'table'`` for :meth:`DataFrame.to_json`
will generate a `Table Schema`_ compatible string representation of
the data.
.. ipython:: python
df = pd.DataFrame(
{'A': [1, 2, 3],
'B': ['a', 'b', 'c'],
'C': pd.date_range('2016-01-01', freq='d', periods=3),
}, index=pd.Index(range(3), name='idx'))
df
df.to_json(orient='table')
See :ref:`IO: Table Schema for more<io.table_schema>`.
Additionally, the repr for ``DataFrame`` and ``Series`` can now publish
this JSON Table schema representation of the Series or DataFrame if you are
using IPython (or another frontend like `nteract`_ using the Jupyter messaging
protocol).
This gives frontends like the Jupyter notebook and `nteract`_
more flexiblity in how they display pandas objects, since they have
more information about the data.
You must enable this by setting the ``display.html.table_schema`` option to True.
.. _Table Schema: http://specs.frictionlessdata.io/json-table-schema/
.. _nteract: http://nteract.io/
.. _whatsnew_0200.enhancements.scipy_sparse:
SciPy sparse matrix from/to SparseDataFrame
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Pandas now supports creating sparse dataframes directly from ``scipy.sparse.spmatrix`` instances.
See the :ref:`documentation <sparse.scipysparse>` for more information. (:issue:`4343`)
All sparse formats are supported, but matrices that are not in :mod:`COOrdinate <scipy.sparse>` format will be converted, copying data as needed.
.. ipython:: python
from scipy.sparse import csr_matrix
arr = np.random.random(size=(1000, 5))
arr[arr < .9] = 0
sp_arr = csr_matrix(arr)
sp_arr
sdf = pd.SparseDataFrame(sp_arr)
sdf
To convert a ``SparseDataFrame`` back to sparse SciPy matrix in COO format, you can use:
.. ipython:: python
sdf.to_coo()
.. _whatsnew_0200.enhancements.other:
Other Enhancements
^^^^^^^^^^^^^^^^^^
- ``DataFrame.rolling()`` now accepts the parameter ``closed='right'|'left'|'both'|'neither'`` to choose the rolling window endpoint closedness. See the :ref:`documentation <stats.rolling_window.endpoints>` (:issue:`13965`)
- Integration with the ``feather-format``, including a new top-level ``pd.read_feather()`` and ``DataFrame.to_feather()`` method, see :ref:`here <io.feather>`.
- ``Series.str.replace()`` now accepts a callable, as replacement, which is passed to ``re.sub`` (:issue:`15055`)
- ``Series.str.replace()`` now accepts a compiled regular expression as a pattern (:issue:`15446`)
- ``Series.sort_index`` accepts parameters ``kind`` and ``na_position`` (:issue:`13589`, :issue:`14444`)
- ``DataFrame`` has gained a ``nunique()`` method to count the distinct values over an axis (:issue:`14336`).
- ``DataFrame`` has gained a ``melt()`` method, equivalent to ``pd.melt()``, for unpivoting from a wide to long format (:issue:`12640`).
- ``DataFrame.groupby()`` has gained a ``.nunique()`` method to count the distinct values for all columns within each group (:issue:`14336`, :issue:`15197`).
- ``pd.read_excel()`` now preserves sheet order when using ``sheetname=None`` (:issue:`9930`)
- Multiple offset aliases with decimal points are now supported (e.g. '0.5min' is parsed as '30s') (:issue:`8419`)
- ``.isnull()`` and ``.notnull()`` have been added to ``Index`` object to make them more consistent with the ``Series`` API (:issue:`15300`)
- New ``UnsortedIndexError`` (subclass of ``KeyError``) raised when indexing/slicing into an
unsorted MultiIndex (:issue:`11897`). This allows differentiation between errors due to lack
of sorting or an incorrect key. See :ref:`here <advanced.unsorted>`
- ``MultiIndex`` has gained a ``.to_frame()`` method to convert to a ``DataFrame`` (:issue:`12397`)
- ``pd.cut`` and ``pd.qcut`` now support datetime64 and timedelta64 dtypes (:issue:`14714`, :issue:`14798`)
- ``pd.qcut`` has gained the ``duplicates='raise'|'drop'`` option to control whether to raise on duplicated edges (:issue:`7751`)
- ``Series`` provides a ``to_excel`` method to output Excel files (:issue:`8825`)
- The ``usecols`` argument in ``pd.read_csv()`` now accepts a callable function as a value (:issue:`14154`)
- The ``skiprows`` argument in ``pd.read_csv()`` now accepts a callable function as a value (:issue:`10882`)
- The ``nrows`` and ``chunksize`` arguments in ``pd.read_csv()`` are supported if both are passed (:issue:`6774`, :issue:`15755`)
- ``pd.DataFrame.plot`` now prints a title above each subplot if ``suplots=True`` and ``title`` is a list of strings (:issue:`14753`)
- ``pd.Series.interpolate`` now supports timedelta as an index type with ``method='time'`` (:issue:`6424`)
- ``Timedelta.isoformat`` method added for formatting Timedeltas as an `ISO 8601 duration`_. See the :ref:`Timedelta docs <timedeltas.isoformat>` (:issue:`15136`)
- ``.select_dtypes()`` now allows the string 'datetimetz' to generically select datetimes with tz (:issue:`14910`)
- The ``.to_latex()`` method will now accept ``multicolumn`` and ``multirow`` arguments to use the accompanying LaTeX enhancements
- ``pd.merge_asof()`` gained the option ``direction='backward'|'forward'|'nearest'`` (:issue:`14887`)
- ``Series/DataFrame.asfreq()`` have gained a ``fill_value`` parameter, to fill missing values (:issue:`3715`).
- ``Series/DataFrame.resample.asfreq`` have gained a ``fill_value`` parameter, to fill missing values during resampling (:issue:`3715`).
- ``pandas.tools.hashing`` has gained a ``hash_tuples`` routine, and ``hash_pandas_object`` has gained the ability to hash a ``MultiIndex`` (:issue:`15224`)
- ``Series/DataFrame.squeeze()`` have gained the ``axis`` parameter. (:issue:`15339`)
- ``DataFrame.to_excel()`` has a new ``freeze_panes`` parameter to turn on Freeze Panes when exporting to Excel (:issue:`15160`)
- ``pd.read_html()`` will parse multiple header rows, creating a multiindex header. (:issue:`13434`).
- HTML table output skips ``colspan`` or ``rowspan`` attribute if equal to 1. (:issue:`15403`)
- ``pd.TimedeltaIndex`` now has a custom datetick formatter specifically designed for nanosecond level precision (:issue:`8711`)
- ``pd.types.concat.union_categoricals`` gained the ``ignore_ordered`` argument to allow ignoring the ordered attribute of unioned categoricals (:issue:`13410`). See the :ref:`categorical union docs <categorical.union>` for more information.
- ``pd.DataFrame.to_latex`` and ``pd.DataFrame.to_string`` now allow optional header aliases. (:issue:`15536`)
- Re-enable the ``parse_dates`` keyword of ``read_excel`` to parse string columns as dates (:issue:`14326`)
- Added ``.empty`` property to subclasses of ``Index``. (:issue:`15270`)
- Enabled floor division for ``Timedelta`` and ``TimedeltaIndex`` (:issue:`15828`)
- ``pandas.io.json.json_normalize()`` gained the option ``errors='ignore'|'raise'``; the default is ``errors='raise'`` which is backward compatible. (:issue:`14583`)
- ``pandas.io.json.json_normalize()`` with an empty ``list`` will return an empty ``DataFrame`` (:issue:`15534`)
- ``pandas.io.json.json_normalize()`` has gained a ``sep`` option that accepts ``str`` to separate joined fields; the default is ".", which is backward compatible. (:issue:`14883`)
- :func:`MultiIndex.remove_unused_levels` has been added to facilitate :ref:`removing unused levels <advanced.shown_levels>`. (:issue:`15694`)
- ``pd.read_csv()`` will now raise a ``ParserError`` error whenever any parsing error occurs (:issue:`15913`, :issue:`15925`)
- ``pd.read_csv()`` now supports the ``error_bad_lines`` and ``warn_bad_lines`` arguments for the Python parser (:issue:`15925`)
.. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations
.. _whatsnew_0200.api_breaking:
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _whatsnew_0200.api_breaking.deprecate_ix:
Deprecate .ix
^^^^^^^^^^^^^
The ``.ix`` indexer is deprecated, in favor of the more strict ``.iloc`` and ``.loc`` indexers. ``.ix`` offers a lot of magic on the inference of what the user wants to do. To wit, ``.ix`` can decide to index *positionally* OR via *labels*, depending on the data type of the index. This has caused quite a bit of user confusion over the years. The full indexing documentation are :ref:`here <indexing>`. (:issue:`14218`)
The recommended methods of indexing are:
- ``.loc`` if you want to *label* index
- ``.iloc`` if you want to *positionally* index.
Using ``.ix`` will now show a ``DeprecationWarning`` with a link to some examples of how to convert code :ref:`here <indexing.deprecate_ix>`.
.. ipython:: python
df = pd.DataFrame({'A': [1, 2, 3],
'B': [4, 5, 6]},
index=list('abc'))
df
Previous Behavior, where you wish to get the 0th and the 2nd elements from the index in the 'A' column.
.. code-block:: ipython
In [3]: df.ix[[0, 2], 'A']
Out[3]:
a 1
c 3
Name: A, dtype: int64
Using ``.loc``. Here we will select the appropriate indexes from the index, then use *label* indexing.
.. ipython:: python
df.loc[df.index[[0, 2]], 'A']
Using ``.iloc``. Here we will get the location of the 'A' column, then use *positional* indexing to select things.
.. ipython:: python
df.iloc[[0, 2], df.columns.get_loc('A')]
.. _whatsnew_0200.api_breaking.deprecate_panel:
Deprecate Panel
^^^^^^^^^^^^^^^
``Panel`` is deprecated and will be removed in a future version. The recommended way to represent 3-D data are
with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas
provides a :meth:`~Panel.to_xarray` method to automate this conversion. See the documentation :ref:`Deprecate Panel <dsintro.deprecate_panel>`. (:issue:`13563`).
.. ipython:: python
:okwarning:
p = tm.makePanel()
p
Convert to a MultiIndex DataFrame
.. ipython:: python
p.to_frame()
Convert to an xarray DataArray
.. ipython:: python
p.to_xarray()
.. _whatsnew.api_breaking.io_compat:
Possible incompat for HDF5 formats for pandas < 0.13.0
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``pd.TimeSeries`` was deprecated officially in 0.17.0, though has only been an alias since 0.13.0. It has
been dropped in favor of ``pd.Series``. (:issue:`15098`).
This *may* cause HDF5 files that were created in prior versions to become unreadable if ``pd.TimeSeries``
was used. This is most likely to be for pandas < 0.13.0. If you find yourself in this situation.
You can use a recent prior version of pandas to read in your HDF5 files,
then write them out again after applying the procedure below.
.. code-block:: ipython
In [2]: s = pd.TimeSeries([1,2,3], index=pd.date_range('20130101', periods=3))
In [3]: s
Out[3]:
2013-01-01 1
2013-01-02 2
2013-01-03 3
Freq: D, dtype: int64
In [4]: type(s)
Out[4]: pandas.core.series.TimeSeries
In [5]: s = pd.Series(s)
In [6]: s
Out[6]:
2013-01-01 1
2013-01-02 2
2013-01-03 3
Freq: D, dtype: int64
In [7]: type(s)
Out[7]: pandas.core.series.Series
.. _whatsnew_0200.api_breaking.index_map:
Map on Index types now return other Index types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
``map`` on an ``Index`` now returns an ``Index``, not a numpy array (:issue:`12766`)
.. ipython:: python
idx = Index([1, 2])
idx
mi = MultiIndex.from_tuples([(1, 2), (2, 4)])
mi
Previous Behavior:
.. code-block:: ipython
In [5]: idx.map(lambda x: x * 2)
Out[5]: array([2, 4])
In [6]: idx.map(lambda x: (x, x * 2))
Out[6]: array([(1, 2), (2, 4)], dtype=object)
In [7]: mi.map(lambda x: x)
Out[7]: array([(1, 2), (2, 4)], dtype=object)
In [8]: mi.map(lambda x: x[0])
Out[8]: array([1, 2])
New Behavior:
.. ipython:: python
idx.map(lambda x: x * 2)
idx.map(lambda x: (x, x * 2))
mi.map(lambda x: x)
mi.map(lambda x: x[0])
``map`` on a ``Series`` with ``datetime64`` values may return ``int64`` dtypes rather than ``int32``
.. ipython:: python
s = Series(date_range('2011-01-02T00:00', '2011-01-02T02:00', freq='H').tz_localize('Asia/Tokyo'))
s
Previous Behavior:
.. code-block:: ipython
In [9]: s.map(lambda x: x.hour)
Out[9]:
0 0
1 1
2 2
dtype: int32
New Behavior:
.. ipython:: python
s.map(lambda x: x.hour)
.. _whatsnew_0200.api_breaking.index_dt_field:
Accessing datetime fields of Index now return Index
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The datetime-related attributes (see :ref:`here <timeseries.components>`
for an overview) of ``DatetimeIndex``, ``PeriodIndex`` and ``TimedeltaIndex`` previously
returned numpy arrays. They will now return a new ``Index`` object, except
in the case of a boolean field, where the result will stil be a boolean ndarray. (:issue:`15022`)
Previous behaviour:
.. code-block:: ipython
In [1]: idx = pd.date_range("2015-01-01", periods=5, freq='10H')
In [2]: idx.hour
Out[2]: array([ 0, 10, 20, 6, 16], dtype=int32)
New Behavior:
.. ipython:: python
idx = pd.date_range("2015-01-01", periods=5, freq='10H')
idx.hour
This has the advantage that specific ``Index`` methods are still available on the
result. On the other hand, this might have backward incompatibilities: e.g.
compared to numpy arrays, ``Index`` objects are not mutable. To get the original
ndarray, you can always convert explicitly using ``np.asarray(idx.hour)``.
.. _whatsnew_0200.api_breaking.unique:
pd.unique will now be consistent with extension types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In prior versions, using ``Series.unique()`` and ``pd.unique(Series)`` on ``Categorical`` and tz-aware
datatypes would yield different return types. These are now made consistent. (:issue:`15903`)
- Datetime tz-aware
Previous behaviour:
.. code-block:: ipython
# Series
In [5]: pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]).unique()
Out[5]: array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')], dtype=object)
In [6]: pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]))
Out[6]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]')
# Index
In [7]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]).unique()
Out[7]: DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
In [8]: pd.unique([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')])
Out[8]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]')
New Behavior:
.. ipython:: python
# Series, returns an array of Timestamp tz-aware
pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]).unique()
pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]))
# Index, returns a DatetimeIndex
pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]).unique()
pd.unique(pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
pd.Timestamp('20160101', tz='US/Eastern')]))
- Categoricals
Previous behaviour:
.. code-block:: ipython
In [1]: pd.Series(pd.Categorical(list('baabc'))).unique()
Out[1]:
[b, a, c]
Categories (3, object): [b, a, c]
In [2]: pd.unique(pd.Series(pd.Categorical(list('baabc'))))
Out[2]: array(['b', 'a', 'c'], dtype=object)
New Behavior:
.. ipython:: python
# returns a Categorical
pd.Series(pd.Categorical(list('baabc'))).unique()
pd.unique(pd.Series(pd.Categorical(list('baabc'))).unique())
.. _whatsnew_0200.api_breaking.s3:
S3 File Handling
^^^^^^^^^^^^^^^^
pandas now uses `s3fs <http://s3fs.readthedocs.io/>`_ for handling S3 connections. This shouldn't break
any code. However, since ``s3fs`` is not a required dependency, you will need to install it separately, like ``boto``
in prior versions of pandas. (:issue:`11915`).
.. _whatsnew_0200.api_breaking.partial_string_indexing:
Partial String Indexing Changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
:ref:`DatetimeIndex Partial String Indexing <timeseries.partialindexing>` now works as exact match, provided that string resolution coincides with index resolution, including a case when both are seconds (:issue:`14826`). See :ref:`Slice vs. Exact Match <timeseries.slice_vs_exact_match>` for details.
.. ipython:: python
df = DataFrame({'a': [1, 2, 3]}, DatetimeIndex(['2011-12-31 23:59:59',
'2012-01-01 00:00:00',
'2012-01-01 00:00:01']))
Previous Behavior:
.. code-block:: ipython
In [4]: df['2011-12-31 23:59:59']
Out[4]:
a
2011-12-31 23:59:59 1
In [5]: df['a']['2011-12-31 23:59:59']
Out[5]:
2011-12-31 23:59:59 1
Name: a, dtype: int64
New Behavior:
.. code-block:: ipython
In [4]: df['2011-12-31 23:59:59']
KeyError: '2011-12-31 23:59:59'
In [5]: df['a']['2011-12-31 23:59:59']
Out[5]: 1
.. _whatsnew_0200.api_breaking.concat_dtypes:
Concat of different float dtypes will not automatically upcast
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Previously, ``concat`` of multiple objects with different ``float`` dtypes would automatically upcast results to a dtype of ``float64``.
Now the smallest acceptable dtype will be used (:issue:`13247`)
.. ipython:: python
df1 = pd.DataFrame(np.array([1.0], dtype=np.float32, ndmin=2))
df1.dtypes
.. ipython:: python
df2 = pd.DataFrame(np.array([np.nan], dtype=np.float32, ndmin=2))
df2.dtypes
Previous Behavior:
.. code-block:: ipython
In [7]: pd.concat([df1,df2]).dtypes
Out[7]:
0 float64
dtype: object
New Behavior:
.. ipython:: python
pd.concat([df1,df2]).dtypes
.. _whatsnew_0200.api_breaking.gbq:
Pandas Google BigQuery support has moved
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pandas has split off Google BigQuery support into a separate package ``pandas-gbq``. You can ``conda install pandas-gbq -c conda-forge`` or
``pip install pandas-gbq`` to get it. The functionality of :func:`read_gbq` and :meth:`DataFrame.to_gbq` remain the same with the
currently released version of ``pandas-gbq=0.1.4``. Documentation is now hosted `here <https://pandas-gbq.readthedocs.io/>`__ (:issue:`15347`)
.. _whatsnew_0200.api_breaking.memory_usage:
Memory Usage for Index is more Accurate
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In previous versions, showing ``.memory_usage()`` on a pandas structure that has an index, would only include actual index values and not include structures that facilitated fast indexing. This will generally be different for ``Index`` and ``MultiIndex`` and less-so for other index types. (:issue:`15237`)
Previous Behavior:
.. code-block:: ipython
In [8]: index = Index(['foo', 'bar', 'baz'])
In [9]: index.memory_usage(deep=True)
Out[9]: 180
In [10]: index.get_loc('foo')
Out[10]: 0
In [11]: index.memory_usage(deep=True)
Out[11]: 180
New Behavior:
.. code-block:: ipython
In [8]: index = Index(['foo', 'bar', 'baz'])
In [9]: index.memory_usage(deep=True)
Out[9]: 180
In [10]: index.get_loc('foo')
Out[10]: 0
In [11]: index.memory_usage(deep=True)
Out[11]: 260
.. _whatsnew_0200.api_breaking.extensions:
Extension Modules Moved
^^^^^^^^^^^^^^^^^^^^^^^
Some formerly public c/c++/cython extension modules have been moved and/or renamed. These are all removed from the public API.
If indicated, a deprecation warning will be issued if you reference that module. (:issue:`12588`)
.. csv-table::
:header: "Previous Location", "New Location", "Deprecated"
:widths: 30, 30, 4
"pandas.lib", "pandas._libs.lib", "X"
"pandas.tslib", "pandas._libs.tslib", "X"
"pandas._join", "pandas._libs.join", ""
"pandas._period", "pandas._libs.period", ""
"pandas.msgpack", "pandas.io.msgpack", ""
"pandas.index", "pandas._libs.index", ""
"pandas.algos", "pandas._libs.algos", ""
"pandas.hashtable", "pandas._libs.hashtable", ""
"pandas.json", "pandas.io.json.libjson", "X"
"pandas.parser", "pandas.io.libparsers", "X"
"pandas.io.sas.saslib", "pandas.io.sas.libsas", ""
"pandas._testing", "pandas.util.libtesting", ""
"pandas._sparse", "pandas.sparse.libsparse", ""
"pandas._hash", "pandas.tools.libhash", ""
"pandas._window", "pandas.core.libwindow", ""
.. _whatsnew_0200.api_breaking.sort_index:
DataFrame.sort_index changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In certain cases, calling ``.sort_index()`` on a MultiIndexed DataFrame would return the *same* DataFrame without seeming to sort.
This would happen with a ``lexsorted``, but non-monotonic levels. (:issue:`15622`, :issue:`15687`, :issue:`14015`, :issue:`13431`)
This is UNCHANGED between versions, but showing for illustration purposes:
.. ipython:: python
df = DataFrame(np.arange(6), columns=['value'], index=MultiIndex.from_product([list('BA'), range(3)]))
df
.. ipython:: python
df.index.is_lexsorted()
df.index.is_monotonic
Sorting works as expected
.. ipython:: python
df.sort_index()
.. ipython:: python
df.sort_index().index.is_lexsorted()
df.sort_index().index.is_monotonic
However, this example, which has a non-monotonic 2nd level,
doesn't behave as desired.
.. ipython:: python
df = pd.DataFrame(
{'value': [1, 2, 3, 4]},
index=pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]]))
df
Previous Behavior:
.. code-block:: python
In [11]: df.sort_index()
Out[11]:
value
a bb 1
aa 2
b bb 3
aa 4
In [14]: df.sort_index().index.is_lexsorted()
Out[14]: True
In [15]: df.sort_index().index.is_monotonic
Out[15]: False
New Behavior:
.. ipython:: python
df.sort_index()
df.sort_index().index.is_lexsorted()
df.sort_index().index.is_monotonic
.. _whatsnew_0200.api_breaking.groupby_describe:
Groupby Describe Formatting
^^^^^^^^^^^^^^^^^^^^^^^^^^^
The output formatting of ``groupby.describe()`` now labels the ``describe()`` metrics in the columns instead of the index.
This format is consistent with ``groupby.agg()`` when applying multiple functions at once. (:issue:`4792`)
Previous Behavior:
.. code-block:: ipython
In [1]: df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})
In [2]: df.groupby('A').describe()
Out[2]:
B
A
1 count 2.000000
mean 1.500000
std 0.707107
min 1.000000
25% 1.250000
50% 1.500000
75% 1.750000
max 2.000000
2 count 2.000000
mean 3.500000
std 0.707107
min 3.000000
25% 3.250000
50% 3.500000
75% 3.750000
max 4.000000
In [3]: df.groupby('A').agg([np.mean, np.std, np.min, np.max])
Out[3]:
B
mean std amin amax
A
1 1.5 0.707107 1 2
2 3.5 0.707107 3 4
New Behavior:
.. ipython:: python
df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})
df.groupby('A').describe()
df.groupby('A').agg([np.mean, np.std, np.min, np.max])
.. _whatsnew_0200.api_breaking.rolling_pairwise:
Window Binary Corr/Cov operations return a MultiIndex DataFrame
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A binary window operation, like ``.corr()`` or ``.cov()``, when operating on a ``.rolling(..)``, ``.expanding(..)``, or ``.ewm(..)`` object,
will now return a 2-level ``MultiIndexed DataFrame`` rather than a ``Panel``, as ``Panel`` is now deprecated,
see :ref:`here <whatsnew_0200.api_breaking.deprecate_panel>`. These are equivalent in function,
but a MultiIndexed ``DataFrame`` enjoys more support in pandas.
See the section on :ref:`Windowed Binary Operations <stats.moments.binary>` for more information. (:issue:`15677`)
.. ipython:: python
np.random.seed(1234)
df = pd.DataFrame(np.random.rand(100, 2),
columns=pd.Index(['A', 'B'], name='bar'),
index=pd.date_range('20160101',
periods=100, freq='D', name='foo'))
df.tail()
Old Behavior:
.. code-block:: ipython
In [2]: df.rolling(12).corr()
Out[2]:
<class 'pandas.core.panel.Panel'>
Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)
Items axis: 2016-01-01 00:00:00 to 2016-04-09 00:00:00
Major_axis axis: A to B
Minor_axis axis: A to B
New Behavior:
.. ipython:: python
res = df.rolling(12).corr()
res.tail()
Retrieving a correlation matrix for a cross-section
.. ipython:: python
df.rolling(12).corr().loc['2016-04-07']
.. _whatsnew_0200.api_breaking.hdfstore_where:
HDFStore where string comparison
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In previous versions most types could be compared to string column in a ``HDFStore``
usually resulting in an invalid comparsion. These comparisions will now raise a
``TypeError`` (:issue:`15492`)
New Behavior:
.. code-block:: ipython
In [15]: df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']})