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Performance regression in timeseries.SortIndex.time_sort_index
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| 2020-06-03T09:04:43Z
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|
CONTRIBUTOR
| null |
```python
import pandas as pd
import numpy as np
N = 10 ** 5
idx = pd.date_range(start="1/1/2000", periods=N, freq="s")
s = pd.Series(np.random.randn(N), index=idx)
%timeit s.sort_index()
```
```
# 1.0.2
108 µs ± 8.27 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
# master
225 µs ± 8.36 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
According to https://pandas.pydata.org/speed/pandas/index.html#timeseries.SortIndex.time_sort_index?p-monotonic=True&commits=f683473a156f032a64a1d7edcebde21c42a8702d-085860a49f3a87aa4e24b3115b50b85c4b3c5676, the first slow commit is https://github.com/pandas-dev/pandas/pull/33755, which just bumps Cython in numpydev... So probably not actually that commit.
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Performance regression in timeseries.DatetimeIndex.time_get
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| 2
| 2020-05-01T13:35:09Z
| 2020-07-02T21:05:05Z
| 2020-07-02T21:05:05Z
|
CONTRIBUTOR
| null |
```python
import pandas as pd
import numpy as np
N = 100000
index = pd.date_range(start="2000", periods=N, freq="s", tz="US/Eastern")
%timeit index[0]
```
```
# pandas 1.0.2
12 µs ± 292 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
# master
25.9 µs ± 935 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
https://pandas.pydata.org/speed/pandas/index.html#timeseries.DatetimeIndex.time_get?p-index_type=%27tz_aware%27&commits=428791c5e01453ff6979b43d37c39c7315c0aaa2-1e5ff233c559ecb2f14fdb9c8d9537db3a02ce1d points to somewhere in `428791c5..1e5ff233`
```
* 1e5ff233c5 - ENH: allow passing freq=None to DatetimeIndex/TimedeltaIndex (#33635) (6 days ago) <jbrockmendel>
* 0907d9ece2 - BUG: DTI/TDI/PI.where accepting incorrectly-typed NaTs (#33715) (6 days ago) <jbrockmendel>
* ee1736a325 - REF: use cached inferred_type when calling lib.infer_dtype(index) (#33537) (6 days ago) <jbrockmendel>
* 23e91a7f3f - TST: groupby-reindex on DTI (#33638) (6 days ago) <CloseChoice>
* cb71376385 - BUG: support skew function for custom BaseIndexer rolling windows (#33745) (6 days ago) <Alex Kirko>
* e008a0a7ac - DOC: Remove ambiguity in fill_value documentation (#33788) (6 days ago) <Bharat Raghunathan>
* e82c0c69fd - CLN: Pass numpy args as kwargs (#33789) (6 days ago) <Daniel Saxton>
* a7741e3ff8 - PERF: op(frame, series) when series is not EA (#33600) (6 days ago) <jbrockmendel>
* ad4465edbd - CLN: remove unused PeriodEngine methods (#33796) (6 days ago) <jbrockmendel>
* 3c09d22f7f - BUG: arg validation in DTA/TDA/PA.take, DTI/TDI/PI.where (#33685) (6 days ago) <jbrockmendel>
* c6a1638bcd - BUG: Series[listlike_of_ints] incorrect on MultiIndex (#33539) (6 days ago) <jbrockmendel>
* 17dc6b0959 - REF: implement test_astype (#33734) (6 days ago) <jbrockmendel>
* c4ebf21e7e - REF: Implement NDArrayBackedExtensionArray (#33660) (6 days ago) <jbrockmendel>
* c60882429e - REF: remove need to override get_indexer_non_unique in DatetimeIndexOpsMixin (#33792) (6 days ago) <jbrockmendel>
* 0163b7941f - CLN: avoid getattr(obj, "values", obj) (#33776) (6 days ago) <jbrockmendel>
* 8c1df8d1b4 - BUG: Setting DTI/TDI freq affecting other indexes viewing the same data (#33552) (6 days ago) <jbrockmendel>
* 77a0f19c53 - ENH: Implement IntegerArray.sum (#33538) (6 days ago) <Daniel Saxton>
* f49269f5bf - ENH: Implement StringArray.min / max (#33351) (6 days ago) <Daniel Saxton>
* 31875eb3d8 - BUG: MonthOffset.name (#33757) (7 days ago) <jbrockmendel>
* 92afd5c2c0 - BUG: freq not retained on apply_index (#33779) (7 days ago) <jbrockmendel>
* ea09d504c9 - TST: prepare for freq-checking in tests.io (#33711) (7 days ago) <jbrockmendel>
* 2b6b89280b - TST: pd.NA TypeError in drop_duplicates with object dtype (#33751) (7 days ago) <Simon Hawkins>
* a585b63394 - TST: more accurate freq attr in `expected`s (#33773) (7 days ago) <jbrockmendel>
* f1fe03134d - CLN: simplify DTA.__getitem__ (#33714) (7 days ago) <jbrockmendel>
* 68132f3e0a - REGR: disallow mean of period column again (#33758) (7 days ago) <Joris Van den Bossche>
* 3c8593d0f3 - REF: simplify try_cast (#33764) (7 days ago) <jbrockmendel>
* 70175beb5b - REF: misplaced sort_index tests (#33774) (7 days ago) <jbrockmendel>
* 3df51fcc72 - DOC: data_columns can take bool (#33768) (7 days ago) <Kilian Lieret>
* 930a7f8373 - TYP/CLN: rogue type comment not caught by code checks (#33763) (7 days ago) <Simon Hawkins>
* 1a82659ec6 - TYP: construction (#33725) (7 days ago) <jbrockmendel>
```
(cc @jbrockmendel)
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Performance regression in gil.ParallelDatetimeFields.time_period_to_datetime
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| 5
| 2020-05-01T13:47:36Z
| 2020-07-09T22:08:17Z
| 2020-07-09T22:08:17Z
|
CONTRIBUTOR
| null |
```python
import pandas as pd
import numpy as np
from pandas._testing import test_parallel
N = 10 ** 6
dti = pd.date_range("1900-01-01", periods=N, freq="T")
period = dti.to_period("D")
@test_parallel(num_threads=2)
def run(period):
period.to_timestamp()
%timeit run(period)
```
```
# 1.0.2
96.8 ms ± 1.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
# master
129 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
https://pandas.pydata.org/speed/pandas/index.html#gil.ParallelDatetimeFields.time_period_to_datetime?commits=d106b81ce532bc71ec6cced944ddb751a4b0e5a3-577de1c6b5cda7f5ae0e4832c2bc3f97ca186e9b points to https://github.com/pandas-dev/pandas/compare/d106b81ce532bc71ec6cced944ddb751a4b0e5a3...577de1c6b5cda7f5ae0e4832c2bc3f97ca186e9b, perhaps https://github.com/pandas-dev/pandas/pull/33491 or https://github.com/pandas-dev/pandas/pull/33047 (cc @jbrockmendel)
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Performance regression in replace.ReplaceDict.time_replace_series
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| 2020-05-01T15:11:41Z
| 2020-07-15T12:27:22Z
| 2020-07-15T12:27:22Z
|
CONTRIBUTOR
| null |
Setup
```python
import pandas as pd
import numpy as np
inplace = False
N = 10 ** 5
start_value = 10 ** 5
to_rep = dict(enumerate(np.arange(N) + start_value))
s = pd.Series(np.random.randint(N, size=10 ** 3))
%timeit s.replace(to_rep, inplace=inplace)
```
```
# 1.0.2
2.48 s ± 53.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# master
5.77 s ± 117 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
https://pandas.pydata.org/speed/pandas/index.html#replace.ReplaceDict.time_replace_series?p-inplace=False&commits=acb525a79fd3496a57b93fcfdb86be3de28a1815-aa8e869d76878f07dff065f947c99b5663342087 points to
```
aa8e869d76 BLD: recursive inclusion of DLLs in package data (#33246)
610568c146 REF: remove replace_list kludge (#33445)
df68cceeb3 REF: .dot tests (#33214)
8d299e4ce1 REF: call pandas_dtype up-front in Index.__new__ (#33407)
e7cbe6dc1a BUG: df.iloc[:, :1] with EA column (#32959)
31ea45f8a3 BUG: Add test to ensure, that bug will not occur again. #33058 (#33072)
f334fcc681 Properly handle missing attributes in query/eval strings (#32408)
12ffb23a72 DOC: Change doc template to fix SA04 errors in docstrings #28792 (#32972)
7f276c8ba1 DOC: Fixed examples in `pandas/core/groupby/` (#33230)
982b4aadef CI: fix lint issue (#33461)
6bc8a49f25 DOC: Fix heading capitalization in doc/source/whatsnew - part2 (#32550) (#33403)
47de449681 API/TST: Call __finalize__ in more places (#33379)
916d1f3786 DOC: Fix EX01 in DataFrame.drop_duplicates (#33283)
4fc8c2515e DOC: Fix heading capitalization in doc/source/whatsnew - part3 (#32550) (#33429)
ef9b9387c8 BUG: Fix bug when concatenating Index of strings (#33436)
40fd73ab8f Update citation webpage (#33311)
3cca07c8a5 BUG: Fix replacing in `string` series with NA (pandas-dev#32621) (#32890)
a2cdd50427 INT: provide helpers for accessing the values of DataFrame columns (#33252)
efa85af76d PERF: improve IntegerArray fast constructor (#33359)
716689a9bf REGR: Fix construction of PeriodIndex from strings (#33304)
4a74463d02 Updated headers for files in doc/source/whatsnew (#33376)
542ef40bb4 Updated headers for files in doc/source/whatsnew (#33378)
12b0d4523a TYP: F used in decorators to _typing (#33456)
d72116b0e7 BUG/PLT: Multiple plots with different cmap, colorbars legends use first cmap (#33392)
2a68c12509 BUG: `weights` is not working for multiple columns in df.plot.hist (#33440)
b5f6e59cb4 BUG: Fix ValueError when grouping by read-only category (#33410) (#33446)
0c69615ad5 DEP: Bump min version of dateutil to 2.7.3 (#33363)
be86b6583f CLN: remove unnecessary Series._convert calls (#32949)
4334482c34 BUG/API: prohibit dtype-changing IntervalArray.__setitem__ (#32782)
6e3537dbab CLN: Static types in `pandas/_lib/lib.pyx` (#33329)
54f9b03cf6 BUG/REF: unstack with EA dtypes (#33356)
496c982b4e REF: reshape.concat operate on arrays, not SingleBlockManagers (#33125)
d72dc24e62 CLN: avoid accessing private functions (#33427)
a142ad7bb2 BUG: DataFrame.diff(axis=1) with mixed (or EA) dtypes (#32995)
185a654e3d BUG: scalar indexing on 2D DTA/TDA/PA (#33342)
5f2cdf8e1d REF: call _block_shape from EABlock.make_block (#33308)
5d0faa8ca5 BUG: Series.__getitem__ with MultiIndex and leading integer level (#33404)
fe42954e08 BUG: Timedelta == ndarray[td64] (#33441)
991f784a72 STY: Using __all__; removed "noqa" comment (#33143)
fbae09e11a CLN/TYP: redundant casts and unused ignores (#33453)
78d8af1fe3 Bump cython for asv environment (#33454)
```
perhaps one of based on the commit message. Not sure.
* 610568c146 REF: remove replace_list kludge (#33445)
* 3cca07c8a5 BUG: Fix replacing in `string` series with NA (pandas-dev#32621) (#32890)
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Performance regression in reshape.Cut.time_qcut_timedelta
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| 3
| 2020-05-01T15:21:00Z
| 2020-06-24T22:37:00Z
| 2020-06-24T22:37:00Z
|
CONTRIBUTOR
| null |
Setup
```python
import numpy as np
import pandas as pd
N = 10 ** 5
bins = 1000
timedelta_series = pd.Series(
np.random.randint(N, size=N), dtype="timedelta64[ns]"
)
%timeit pd.qcut(timedelta_series, bins)
```
```
# 1.0.2
57.7 ms ± 1.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
# master
139 ms ± 2.97 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
https://pandas.pydata.org/speed/pandas/index.html#rolling.Methods.time_rolling?p-constructor=%27Series%27&p-window=10&p-dtype=%27float%27&p-method=%27sum%27&commits=265b8420121a66ed18329c7a90d5381aeda5454f-ad4ad22c6804e6925c4eb82f51b974c03c3036a8 points to https://github.com/pandas-dev/pandas/commit/d04b965f98fa53f22cc41f7e4d081d763d2856a1 (cc @mabelvj)
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Unstable time_get_loc benchmark
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| 0
| 2020-05-01T15:25:10Z
| 2020-05-01T15:25:10Z
| null |
CONTRIBUTOR
| null |
This benchmark regularly jumps between 200 and 800 ms: https://pandas.pydata.org/speed/pandas/index.html#indexing_engines.NumericEngineIndexing.time_get_loc?p-engine_and_dtype=%28%3Cclass+%27pandas._libs.index.Int16Engine%27%3E%2C+%3Cclass+%27numpy.int16%27%3E%29&p-index_type=%27monotonic_incr%27&commits=d04b965f98fa53f22cc41f7e4d081d763d2856a1
I don't immediately see any issues with it, but haven't looked closely. This issue can be closed when we determine the cause and make the benchmark timings stable.
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Performance regression in Index.union
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| 2020-05-01T15:33:25Z
| 2020-07-07T20:15:44Z
| 2020-07-07T20:15:43Z
|
CONTRIBUTOR
| null |
Setup
```python
import numpy as np
import pandas as pd
dtype = "datetime"
method = "union"
N = 10 ** 5
left = pd.date_range("1/1/2000", periods=N, freq="T")
right = left[:-1]
%timeit left.union(right)
```
```
# 1.0.2
161 µs ± 7.46 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
# master
296 µs ± 5.58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
https://pandas.pydata.org/speed/pandas/index.html#index_object.SetOperations.time_operation?p-dtype=%27datetime%27&p-method=%27union%27&commits=428791c5e01453ff6979b43d37c39c7315c0aaa2-1e5ff233c559ecb2f14fdb9c8d9537db3a02ce1d points to somewhere in https://github.com/pandas-dev/pandas/compare/428791c5e01453ff6979b43d37c39c7315c0aaa2...1e5ff233c559ecb2f14fdb9c8d9537db3a02ce1d
Maybe c60882429e? (cc @jbrockmendel) Haven't looked closely yet.
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Performance regression in DataFrame[bool_indexer]
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| 1
| 2020-05-01T15:45:02Z
| 2020-06-01T01:42:30Z
| 2020-06-01T01:42:30Z
|
CONTRIBUTOR
| null |
```python
import numpy as np
import pandas as pd
idx_dupe = np.array(range(30)) * 99
df = pd.DataFrame(np.random.randn(10000, 5))
bool_indexer = [True] * 5000 + [False] * 5000
%timeit df[np.array(bool_indexer)]
```
```
# 1.0.2
2.58 ms ± 108 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# master
5.47 ms ± 192 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
Note that this only affects the case where the indexer is a *list*. Performance looks fine when an ndarray is passed.
https://pandas.pydata.org/speed/pandas/index.html#indexing.DataFrameNumericIndexing.time_bool_indexer?commits=80d37adcc3d9bfbbe17e8aa626d6b5873465ca98-4f89c261f624305fc7bae6c43ae862663994be34 points to somewhere in 80d37adc..4f89c261, which has a bunch of commits.
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MDExOlB1bGxSZXF1ZXN0NDEyMTg2Mzg0
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30999 fix bare pytest raises
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| 1
| 2020-05-01T15:55:15Z
| 2020-05-01T19:30:35Z
| 2020-05-01T19:30:31Z
|
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Rethink Inlining?
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[] | null | 1
| 2020-05-01T17:15:42Z
| 2020-05-07T20:56:34Z
| null |
MEMBER
| null |
ref https://github.com/pandas-dev/pandas/pull/33886#discussion_r418637034
We use the `inline` keyword pretty liberally in our Cython extensions, with the thought that this probably improves performance. However, running GCC with `-Winline` as a flag produces a ton of warnings that we aren't actually inlining things. Some of these _may_ even do more harm than good (just a hunch, I have no expertise with inline)
I've attached warnings from a clean build on master. Not sure all of these are necessarily even just from pandas but may be worth a look for anyone interested
[warnings.log.zip](https://github.com/pandas-dev/pandas/files/4565443/warnings.log.zip)
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TST/REF: Move static methods out of consistency classes
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| 2
| 2020-05-01T17:53:19Z
| 2020-05-02T15:33:19Z
| 2020-05-02T15:33:14Z
|
MEMBER
| null |
In order to keep such tests refactor PR more readable, in this PR, I only move those methods which are actually staticmethods out of huge consistency classes to be independent tests, so no code changes! And after this PR, the big consistency classes are broken down to much smaller.
This one will pave the way for replacing some other final fixtures & staticmethods out of classes
cc @jreback
|
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MDU6SXNzdWU2MTA5MjQ5NjM=
| 33,928
|
BUG: Local test fail due to change of botocore error handling
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[] | null | 0
| 2020-05-01T19:26:56Z
| 2020-06-05T18:51:43Z
| null |
NONE
| null |
Running
```
pytest pandas/tests/io/parser/test_network.py::TestS3::test_write_s3_csv_fails
```
or
```
pytest pandas/tests/io/parser/test_network.py::TestS3::test_write_s3_parquet_fails
```
will fail
```
FAILED pandas/tests/io/parser/test_network.py::TestS3::test_write_s3_csv_fails - botocore.errorfactory.NoSuchBucket: An error occurred (NoSuchBucket) when calling the PutObject operation: The specified ...
FAILED pandas/tests/io/parser/test_network.py::TestS3::test_write_s3_parquet_fails - botocore.errorfactory.NoSuchBucket: An error occurred (NoSuchBucket) when calling the PutObject operation: The specif...
```
With python 3.8 (linux), s3fs 0.4.2
I believe the issue comes from a change of error handling in botocore (used by s3fs).
Output of pd.show_versions():
<details>
INSTALLED VERSIONS
------------------
commit : 1c88e6aff94cc9183909b7c110f554df42509073
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-28-generic
Version : #32-Ubuntu SMP Wed Apr 22 17:40:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1446.g1c88e6aff.dirty
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3.post20200325
Cython : 0.29.17
pytest : 5.4.1
hypothesis : 5.10.4
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDEyMjcxODgw
| 33,929
|
BUG: Fix small memory access errors in ujson objToJSON.c
|
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| 5
| 2020-05-01T19:42:04Z
| 2020-05-02T16:34:48Z
| 2020-05-02T16:34:44Z
|
CONTRIBUTOR
| null |
These were found (and validated fixed) using valgrind. It is likely
that none of these changes should create any issues (except maybe
on a debug build of python?). But silencing valgrind warnings is
good on its own, to ease possible future debugging using valgrind.
----
NOTE: I removed skipping of `test_astype_generic_timestamp_no_frequency`, this is just out of curiosity, I would be very surprised if it made a difference. So this should probably be only merged after undoing the test change.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Inconsistent name of binary operations between Series and ndarray, list, tuple
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| 0
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| 2020-10-07T15:00:01Z
| 2020-10-07T15:00:01Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```
import numpy as np
import pandas as pd
s1 = pd.Series([1, 2, 3, 4, 5], name = "s1")
nparray = np.arange(1, len(s1) + 1)
s2 = s1 / nparray
print("s2.name = ", s2.name)
s3 = s1.div(nparray)
print("s3.name = ", s3.name)
numbers_list = [1.0, 2.0, 3.0, 4.0, 5.0]
s4 = s1 / numbers_list
print("s4.name = ", s4.name)
s5 = s1.div(numbers_list)
print("s5.name = ", s5.name)
numbers_tuple = (1.0, 2.0, 3.0, 4.0, 5.0)
s6 = s1 / numbers_tuple
print("s6.name = ", s6.name)
s7 = s1.div(numbers_tuple)
print("s7.name = ", s7.name)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Depending on how binary operation is done, e.g. arithmetic operation or function call resulting `name` is different.
- In case of function call it is preserved because `Series` object with no `name` is constructed from ndarray, list or tuple https://github.com/pandas-dev/pandas/blob/master/pandas/core/ops/__init__.py#L476. Call for `get_op_result_name` with arguments of original `Series` object and constructed `Series` object with no name leads to name being set to `None`.
- In case of arithmetic operation no `Series` object is created in `_arith_method_SERIES` and call to `get_op_result_name` to arguments of `Series` and non-ABCSeries, non-ABCIndexClass leads to `name` of `Series` operand being preserved.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1
setuptools : 41.2.0
Cython : 0.29.17
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : 0.13.1
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
INSTALLED VERSIONS
------------------
commit : 81093ba8214476e979ea55765d7ddb609f729ac9
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
Version : #28-Ubuntu SMP Wed Dec 18 05:37:46 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1453.g81093ba82
numpy : 1.17.3
pytz : 2019.2
dateutil : 2.7.3
pip : 18.1
setuptools : 41.1.0
Cython : 0.29.17
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : 0.46.0
</details>
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MDU6SXNzdWU2MTA5NTUyMjQ=
| 33,931
|
BUG: hash of Timestamp on fold=1 create a Segfault
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| 2020-05-01T20:33:51Z
| 2021-04-28T17:36:20Z
| null |
MEMBER
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
america_chicago = "dateutil//usr/share/zoneinfo/America/Chicago"
transition_1 = pd.Timestamp(year=2013, month=11, day=3, hour=1, minute=0, tz=america_chicago)
transition_2 = pd.Timestamp(year=2013, month=11, day=3, hour=1, minute=0, fold=1, tz=america_chicago)
print(transition_1, transition_2)
print(hash(transition_1))
print(hash(transition_2))
```
```bash
$ python3 -q -X faulthandler hash_bug.py
```
```python-traceback
2013-11-03 01:00:00-05:00 2013-11-03 01:00:00-06:00
780959649129526403
Fatal Python error: Segmentation fault
Current thread 0x00007f908caf1740 (most recent call first):
File "/usr/lib/python3.8/site-packages/dateutil/tz/tz.py", line 1814 in _datetime_to_timestamp
File "/usr/lib/python3.8/site-packages/dateutil/tz/tz.py", line 717 in _find_last_transition
File "/usr/lib/python3.8/site-packages/dateutil/tz/tz.py", line 809 in _resolve_ambiguous_time
File "/usr/lib/python3.8/site-packages/dateutil/tz/tz.py", line 739 in _find_ttinfo
File "/usr/lib/python3.8/site-packages/dateutil/tz/tz.py", line 828 in utcoffset
File "t.py", line 8 in <module>
[1] 2761996 segmentation fault (core dumped) python3 -q -X faulthandler t.py
```
#### Problem description
It should return a correct hash value, and it should not Segfault.
This create issue when using a manipulating a Timestamp with a dictionary or a set.
#### Expected Output
I would have expected the same behavior than datetime in Python:
```python
import datetime as dt
from dateutil.tz import gettz
america_chicago = gettz("America/Chicago")
transition_1 = dt.datetime(year=2013, month=11, day=3, hour=1, minute=0, tzinfo=america_chicago)
transition_2 = dt.datetime(year=2013, month=11, day=3, hour=1, minute=0, fold=1, tzinfo=america_chicago)
print(transition_1, transition_2)
print(hash(transition_1))
print(hash(transition_2))
```
```
2013-11-03 01:00:00-05:00 2013-11-03 01:00:00-06:00
780959649129526403
780959649129526403
```
So it seems that this bug is coming from pandas.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 1c88e6aff94cc9183909b7c110f554df42509073
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13-arch2-1
Version : #1 SMP PREEMPT Mon, 30 Mar 2020 20:42:41 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1446.g1c88e6aff
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.16
pytest : 5.4.1
hypothesis : None
sphinx : 3.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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TST: tighten xfails in test_strings
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| 1
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| 2020-05-02T16:21:19Z
| 2020-05-02T15:29:54Z
|
MEMBER
| null |
cc @TomAugspurger
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PERF: Datetimelike lookups
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| 3
| 2020-05-01T21:26:37Z
| 2020-05-04T13:26:01Z
| 2020-05-02T16:27:25Z
|
MEMBER
| null |
cc @TomAugspurger this should address at least some of the regressions you posted about this morning.
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|
BUG: pytest with cProfile of test_numba segfault
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| 2020-05-01T22:35:07Z
| 2020-05-02T04:11:43Z
| 2020-05-02T04:11:42Z
|
MEMBER
| null |
```
python3 -m cProfile -o stats2.dat -m pytest pandas/tests/groupby/aggregate/test_numba.py
[...]
collected 66 items
pandas/tests/groupby/aggregate/test_numba.py ...Segmentation fault: 11
```
cc @mroeschke
|
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MDExOlB1bGxSZXF1ZXN0NDEyMzg0NTQz
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TYP: annotations in nanops
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| 1
| 2020-05-02T02:04:45Z
| 2020-05-06T20:39:59Z
| 2020-05-06T19:24:14Z
|
MEMBER
| null |
tightening types a little bit.
Some of the other functions in this file have inaccurate return types, saving that for a separate pass
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MDExOlB1bGxSZXF1ZXN0NDEyMzg1OTcz
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REF: move bits of offsets to liboffsets, de-privatize
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| 3
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| 2020-05-02T17:58:49Z
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|
MEMBER
| null |
xref #32942, #33394
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MDExOlB1bGxSZXF1ZXN0NDEyMzg4NjMy
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PERF: use fastpaths for is_period_dtype checks
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| 1
| 2020-05-02T02:45:12Z
| 2020-05-02T14:57:19Z
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|
MEMBER
| null |
This gets most of the is_period_dtype and needs_i8_conversion checks. There are a few left in reshape.merge where i need to track down what type of objects we're working with.
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MDExOlB1bGxSZXF1ZXN0NDEyMzkxMjcx
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TST: raise InvalidIndexError with IntervalIndex.get_value and get_loc
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| 1
| 2020-05-02T03:13:12Z
| 2020-05-10T01:54:45Z
| 2020-05-09T20:22:04Z
|
CONTRIBUTOR
| null |
ensure InvalidIndexError is raised when non-scalar is passed as key to
get_loc and get_value methods for IntervalIndex
- [X] closes #31117
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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TST: add pattern for nearest
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| 2020-05-06T17:58:24Z
| 2020-05-06T17:58:20Z
|
CONTRIBUTOR
| null |
- [ ] closes #33895
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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API: how strict should the equals() method be?
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[] | null | 8
| 2020-05-02T08:36:29Z
| 2021-11-01T23:27:42Z
| null |
MEMBER
| null |
While adding an `equals()` method to ExtensionArray (https://github.com/pandas-dev/pandas/issues/27081, https://github.com/pandas-dev/pandas/pull/30652), some questions come up about how strict the method should be:
1. Other array-likes are not equivalent, even if they are all equal?
2. But *subclasses* are equivalent, when they are all equal?
3. Objects with different dtype are not equivalent (eg int8 vs int16), even if all values are equal?
And it seems that right now, we are somewhat inconsistent with this in pandas regarding being strict on the data type.
Series is strict about the dtype, while Index is not:
```python
>>> pd.Series([1, 2, 3], dtype="int64").equals(pd.Series([1, 2, 3], dtype="int32"))
False
>>> pd.Index([1, 2, 3], dtype="int64").equals(pd.Index([1, 2, 3], dtype="int32"))
True
```
For Index, this not even gives True for different integer dtypes as above, but also for float/int, object/int (both examples give False for Series):
```python
>>> pd.Index([1, 2, 3], dtype="int64").equals(pd.Index([1, 2, 3], dtype="object"))
True
>>> pd.Index([1, 2, 3], dtype="int64").equals(pd.Index([1, 2, 3], dtype="float64"))
True
```
Index and Series *are* consistent when it comes to not being equal with other array-likes:
```python
# all those cases return False
pd.Series([1, 2, 3]).equals(np.array([1, 2, 3]))
pd.Index([1, 2, 3]).equals(np.array([1, 2, 3]))
pd.Series([1, 2, 3]).equals(pd.Index([1, 2, 3]))
pd.Index([1, 2, 3]).equals(pd.Series([1, 2, 3]))
pd.Series([1, 2, 3]).equals([1, 2, 3])
pd.Index([1, 2, 3]).equals([1, 2, 3])
```
Both Index and Series also seem to allow subclasses:
```python
class MySeries(pd.Series):
pass
>>> pd.Series([1, 2, 3]).equals(MySeries([1, 2, 3]))
True
```
So in the end, I think the main discussion point is: should the dtype be exactly the same, or should only the values be equal?
For DataFrame, it shares the implementation with Series so follows that behaviour (except that for DataFrame there are some additional rules about how column names need to compare equal).
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API/BUG: Series.argmin/max with all-NaN data returns -1 ?
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| 2020-05-02T12:35:43Z
| 2021-08-07T05:37:40Z
| null |
MEMBER
| null |
Ran into this while looking at https://github.com/pandas-dev/pandas/pull/27801.
Both numpy and pandas raise an error for empty arrays/Series in `argmin` and `argmax`:
```python
>>> pd.Series([], dtype=float).argmin()
...
ValueError: attempt to get argmin of an empty sequence
>>> np.array([], dtype=float).argmin()
...
ValueError: attempt to get argmin of an empty sequence
```
However, when having all NaN data, we see a different behaviour:
```python
>>> pd.Series([np.nan, np.nan], dtype=float).argmin()
-1
>>> np.array([np.nan, np.nan], dtype=float).argmin()
0
```
Does somebody have an explanation of *why* this would return -1 ?
In principle, in pandas, `argmin` has a `skipna=True` keyword, so for pandas I would expect that an all-NaN Series behaves the same as an empty Series.
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API: argmin/argmax behaviour for nullable dtypes with skipna=False
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| 2020-05-02T15:35:29Z
| 2021-08-07T05:38:48Z
| null |
MEMBER
| null |
Now we are adding ExtensionArray.argmin/argmax (#24382 / #27801), we also need to decide on its behaviour for nullable / masked arrays.
For the default of `skipna=True`, I think the behaviour is clear (we skip those values, and calculate the argmin/argmax of the remaining values), but we need to decide on the behaviour in case of `skipna=False` in presence of missing values.
I don't think it makes much sense to follow the current behaviour for Series with NaNs:
```
In [25]: pd.Series([1, 2, 3, np.nan]).argmin()
Out[25]: 0
In [26]: pd.Series([1, 2, 3, np.nan]).argmin(skipna=False)
Out[26]: -1
```
(the -1 is discussed separately in https://github.com/pandas-dev/pandas/issues/33941 as well)
We also can't really compare with numpy, as there NaNs are regarded as the largest values (which follows somewhat from the sorting behaviour with NaNs).
For other reductions, the logic is: once there is an NA present, the result is also an NA with `skipna=False` (with the logic here that the with NAs, the min/max is not known, and thus also not the location).
So something like:
```python
>>> pd.Series([1, 2, 3, pd.NA], dtype="Int64").argmin(skipna=False)
<NA>
```
However, this also means that `argmin`/`argmax` return something that is not an integer, and since those methods are typically used to get a value that can be used to index, that might also be annoying.
An alternative could also be to raise an error (similar as for empty arrays).
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| 6
| 2020-05-02T16:41:45Z
| 2020-05-10T15:45:59Z
| 2020-05-10T15:45:53Z
|
MEMBER
| null |
This PR fixturizes the last constants (`base_functions` and `no_nan_functions`) in ConsistencyBase, and take corresponding tests out of class.
After this one, only constants in `Base` class needs to be fixed (if `Base` is also wanted to be cleaned)
cc @jreback
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TYP: annotate core.algorithms
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PERF: use fastpath for is_categorical_dtype calls
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| 1
| 2020-05-02T18:48:49Z
| 2020-05-04T17:01:58Z
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|
MEMBER
| null |
This doesn't get all of them
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MDU6SXNzdWU2MTEyNTQ0MTg=
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CondaMemoryError When Creating Development Environment with 1 GB Memory
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| 2020-05-02T19:06:43Z
| 2021-08-07T05:39:39Z
| null |
MEMBER
| null |
This is on a rather limited set of hardware (only 1 GB of memory available) but was surprised when building an environment that running a
```sh
conda env create -f environment.yml
```
throws the following error:
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CondaMemoryError: The conda process ran out of memory. Increase system memory and/or try again.
```
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BUG: CategoricalIndex.__contains__ incorrect NaTs
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| 1
| 2020-05-02T20:35:34Z
| 2020-05-05T17:19:15Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
MDU6SXNzdWU2MTEyOTMxNzg=
| 33,948
|
CI: Move Python 3.9 Build to Conda
|
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| 0
| 2020-05-02T23:12:34Z
| 2020-11-25T10:59:45Z
| 2020-11-14T02:27:33Z
|
CONTRIBUTOR
| null |
Once Python 3.9 is released (Oct 2020) as a follow up from https://github.com/pandas-dev/pandas/pull/33505 we can move the builds to conda and to run on Azure.
Note: Use numpy wheels when available current installs from source.
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MDExOlB1bGxSZXF1ZXN0NDEyNTI0NjU3
| 33,949
|
WIP: BUG: Setting DataFrame values via iloc aligns when arguments are lists
|
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| 2020-05-02T23:57:30Z
| 2020-10-11T13:22:15Z
| 2020-05-25T21:14:21Z
|
MEMBER
| null |
- [x] closes #22046
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR currently fails two tests on Windows due to int32 vs int64, but I cant' duplicate on my machine.
````
pandas\tests\indexing\test_indexing.py:588
test_astype_assignment
df = df_orig.copy()
df.iloc[:, 0:2] = df.iloc[:, 0:2].astype(np.int64)
expected = DataFrame(
[[1, 2, "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG")
)
> tm.assert_frame_equal(df, expected)
E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="A") are different
E
E Attribute "dtype" are different
E [left]: int32
E [right]: int64
````
Within these test, the change made causes the setting in iloc to be done here:
https://github.com/pandas-dev/pandas/blob/c6ad13d57c8157cd175a294f06c427f8a239dcea/pandas/core/indexing.py#L1722-L1724
as opposed to the usual path here:
https://github.com/pandas-dev/pandas/blob/c6ad13d57c8157cd175a294f06c427f8a239dcea/pandas/core/indexing.py#L1695-L1705
My best guess is that this is a bug in the former path that is being uncovered by this change, but I am not sure how to fix. Any suggestions or help is much appreciated.
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MDU6SXNzdWU2MTEzMTg2NDM=
| 33,950
|
BUG: pd.NA ignored in DataFrame.to_html(na_rep)
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| 2020-05-03T02:44:53Z
| 2021-08-07T05:40:15Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
A short example adjusted from the one in issue #29975 (which appeared to be the closest bug to this one, unless I missed something)
```python
>>> df = pd.DataFrame({'a': [1, pd.NA, 2]})
>>> df
>>> 'zzzz' in df.to_csv(na_rep='zzzz')
True
>>> 'zzzz' in df.to_html(na_rep='zzzz')
False
```
#### Problem description
pd.NA seems to be ignored by the `na_rep` in `to_html()`. In the HTML, pd.NA shows up as `<NA>`.
The `na_rep` works fine for pd.NA in `to_csv()` and works fine for NaNs in `to_html()`:
```python
>>> df = pd.DataFrame({'a': [1, float('nan'), 2]})
>>> 'zzzz' in df.to_csv(na_rep='zzzz')
True
>>> 'zzzz' in df.to_html(na_rep='zzzz')
True
```
#### Expected Output
I'd expect pd.NA to be represented by the `na_rep` in the output of `to_html()`.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-7642-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDEyNTQxNjQ3
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|
CLN: remove unnecessary ABC classes
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| 611,364,038
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MDU6SXNzdWU2MTEzNjQwMzg=
| 33,952
|
BUG: adding a value not in the Categories does not raise a ValueError on a Series when adding the value to a new index
|
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[] | null | 8
| 2020-05-03T08:47:38Z
| 2020-05-08T10:51:41Z
| 2020-05-08T10:51:23Z
|
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
s = pd.Series(['a' , 'b'], dtype='category')
>>>
0 a
1 b
dtype: category
Categories (2, object): [a, b]
# this is expected, cannot assign a value not in the category
s.loc[1] = 'c'
>>>
ValueError: Cannot setitem on a Categorical with a new category, set the categories first
# this is NOT expected. not only it assigns a value not the in the category, but it also implicitly
# change the dtype
s.loc[2] = 'c'
>>>
0 a
1 b
2 c
dtype: object
```
#### Problem description
Assigning a value not in the category to an index already in the Series show a different behavior than when the index is not already in the Series.
This looks like an inconsistent behavior that is hard to explain.
The expected behavior is that assigning a value not in a category will always raise a ValueError Exception whether the assignement is in an existing index or in a new index.
#### Expected Output
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: 0.8.1
bs4 : None
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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|
DOC: Update numpy website url
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| 0
| 2020-05-03T13:31:39Z
| 2020-05-26T09:37:20Z
| 2020-05-05T09:06:18Z
|
MEMBER
| null |
When clicking on the `See Also` item of https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.argsort.html
We go to the old numpy url (docs.scipy.org), and a warning is shown to the user. We should update the target url of our numpy links.
Sphinx builds automatically these links, I guess the url is in `doc/source/conf.py`.
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|
DOC: update conf.py to use new numpy doc url
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| 5
| 2020-05-03T13:50:10Z
| 2020-05-05T11:30:45Z
| 2020-05-05T09:06:18Z
|
CONTRIBUTOR
| null |
- [X] closes #33953
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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|
BUG: Not properly checking if post_processing function is callable or not in _get_cythonized_result() function in class GroupBy
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| 6
| 2020-05-03T16:30:59Z
| 2020-07-07T20:55:43Z
| 2020-07-07T20:55:43Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Problem description
While looking at the code, I found this line which I think is a bug. In the current master branch, pandas/core/groupby/groupby.py file, in the _get_cythonized_result() function :
```
if post_processing:
if not callable(pre_processing):
raise ValueError("'post_processing' must be a callable!")
```
While checking if post_processing is callable or not, we are actually passing pre_processing object instead of post_processing. This looks like a bug.
#### Expected Code
```
if post_processing:
if not callable(post_processing):
raise ValueError("'post_processing' must be a callable!")
```
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BUG: throwing error in interpolate depending on dtype of column names
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| 3
| 2020-05-03T18:47:40Z
| 2020-06-02T22:42:50Z
| 2020-06-02T22:42:50Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas. (on 862db6421256, last commit where build works as of 20:42 2020-05-03).
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df
Out[6]:
A B C
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 NaN NaN
4 NaN 8.0 NaN
5 5.0 10.0 30.0
df.interpolate(method='ffill', axis=1)
```
Throws an error `ValueError: Index column must be numeric or datetime type when using ffill method other than linear. Try setting a numeric or datetime index column before interpolating.` But the following code works:
```python
df.columns = [1, 2, 3]
df
Out[9]:
1 2 3
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 NaN NaN
4 NaN 8.0 NaN
5 5.0 10.0 30.0
df.interpolate(method='ffill', axis=1)
Out[10]:
1 2 3
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 6.0 9.0
4 4.0 8.0 9.0
5 5.0 10.0 30.0
```
#### Problem description
Throwing an error should not depend on the dtype of column names.
#### Expected Output
```python
A B C
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 6.0 9.0
4 4.0 8.0 9.0
5 5.0 10.0 30.0
```
#### Output of ``pd.show_versions()``
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 862db6421256cb7a00ae3e88a4a6999347b76271
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-generic
Version : #44~18.04.2-Ubuntu SMP Thu Apr 23 14:27:18 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1463.g862db6421
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3.post20200325
Cython : 0.29.17
pytest : 5.4.1
hypothesis : 5.10.4
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
```
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CLN: remove ABCSparseArray
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CLN: remove ABCDateOffset
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fix bfill, ffill and pad when calling with df.interpolate with column…
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| 6
| 2020-05-03T23:38:37Z
| 2020-06-04T11:25:02Z
| 2020-06-02T22:42:51Z
|
MEMBER
| null |
… dtype string (#33956)
- [x] closes #33956
- [x] closes #12918
- [x] closes #29146
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: commit 14eda5865 breaks pandas
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| 3
| 2020-05-03T23:50:55Z
| 2020-05-06T19:40:33Z
| 2020-05-04T07:50:42Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
From commit 14eda5865 onwards an ImportError is thrown:
```python
ImportError: cannot import name 'BusinessMixin' from 'pandas._libs.tslibs.offsets' (/home/tobias/programming/python/pandas_tests/pandas/pandas/_libs/tslibs/offsets.cpython-38-x86_64-linux-gnu.so)
```
#### Code Sample, a copy-pastable example
```python
import pandas as pd
```
</details>
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MDExOlB1bGxSZXF1ZXN0NDEyNjg4NDY4
| 33,961
|
PERF: 30% faster is_period_object checks
|
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| 0
| 2020-05-04T00:03:10Z
| 2020-05-04T15:33:44Z
| 2020-05-04T15:29:50Z
|
MEMBER
| null |
Doing an appropriate isinstance check occurs all in C-space.
```
In [1]: import pandas as pd
In [2]: from pandas._libs.lib import *
In [3]: obj = pd.Period(2004, "A")
In [4]: %timeit is_scalar(obj)
78.4 ns ± 0.761 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
101 ns ± 1.74 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
In [5]: %timeit is_period(obj)
54.1 ns ± 0.548 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
80.7 ns ± 1.14 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
In [6]: %timeit is_period([])
50.1 ns ± 1.02 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
340 ns ± 26.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
In [7]: %timeit is_scalar([])
65.6 ns ± 0.421 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
67.1 ns ± 0.293 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
In [8]: obj2 = object()
In [9]: %timeit is_scalar(obj2)
544 ns ± 6.97 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- PR
782 ns ± 6.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
```
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MDExOlB1bGxSZXF1ZXN0NDEyNzQyNzY0
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Add nrows to read json.
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| 17
| 2020-05-04T05:19:02Z
| 2020-06-04T20:45:50Z
| 2020-06-04T20:44:21Z
|
CONTRIBUTOR
| null |
- [x] closes #33916
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Add the nrows to read_json parameter that returns only the required number of json from the line delimited json
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MDU6SXNzdWU2MTE2NDUwNzY=
| 33,963
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BUG: memory_usage(deep=True) of dtype='string' is wrong
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| 2020-05-04T06:57:54Z
| 2020-05-05T14:18:58Z
| 2020-05-05T14:18:58Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [4]: s = pd.Series(['a','b','c'],dtype='object')
...: print(s.memory_usage())
...: print(s.memory_usage(deep=True))
152
326
In [5]: s = pd.Series(['a','b','c'],dtype='string')
...: print(s.memory_usage())
...: print(s.memory_usage(deep=True))
152
152
```
#### Problem description
memory_usage(deep=True) for new 'string' dtype is not 'deep' size.
#### Expected Output
In [3]: s = pd.Series(['a','b','c'],dtype='string')
...: print(s.memory_usage())
...: print(s.memory_usage(deep=True))
152
326
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 20.1
setuptools : 40.8.0
Cython : 0.29.16
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : 0.2.2
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : 0.8.5
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.2
numba : None
</details>
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REGR: string indexing on PeriodIndex raises KeyError on master (not yet released)
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| 2020-06-14T16:39:59Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1466.ga3477c769'
>>>
>>> index = pd.period_range(start="2000", periods=20, freq="B")
>>> series = pd.Series(range(20), index=index)
>>>
>>> series.index
PeriodIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
'2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
'2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
'2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
'2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28'],
dtype='period[B]', freq='B')
>>>
>>> series.loc["2000-01-14"]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\indexing.py", line 871, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\simon\pandas\pandas\core\indexing.py", line 1102, in _getitem_axis
return self._get_label(key, axis=axis)
File "C:\Users\simon\pandas\pandas\core\indexing.py", line 1051, in _get_label
return self.obj.xs(label, axis=axis)
File "C:\Users\simon\pandas\pandas\core\generic.py", line 3512, in xs
loc = self.index.get_loc(key)
File "C:\Users\simon\pandas\pandas\core\indexes\period.py", line 519, in get_loc
raise KeyError(key)
KeyError: '2000-01-14'
>>>
```
#### Problem description
regression in #31172 (i.e. not yet released)
9a211aae9f710db23c9113aea0251e2758904755 is the first bad commit
commit 9a211aae9f710db23c9113aea0251e2758904755
Author: jbrockmendel <jbrockmendel@gmail.com>
Date: Sat Jan 25 08:07:15 2020 -0800
BUG: inconsistency between PeriodIndex.get_value vs get_loc (#31172)
cc @jbrockmendel
#### Expected Output
```
>>> import pandas as pd
>>>
>>> pd.__version__
'1.0.3'
>>>
>>> index = pd.period_range(start="2000", periods=20, freq="B")
>>> series = pd.Series(range(20), index=index)
>>>
>>> series.index
PeriodIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',
'2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',
'2000-01-13', '2000-01-14', '2000-01-17', '2000-01-18',
'2000-01-19', '2000-01-20', '2000-01-21', '2000-01-24',
'2000-01-25', '2000-01-26', '2000-01-27', '2000-01-28'],
dtype='period[B]', freq='B')
>>>
>>> series.loc["2000-01-14"]
9
>>>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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| 33,965
|
Feature Request: Add Direct Support to Plotly Express as a submodule in pandas.DataFrame.iplot
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| 2020-05-06T14:17:02Z
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|
NONE
| null |
Could support to Plotly Express be directly added within pandas as a iplot submodule?:
https://plotly.com/python/plotly-express/
Thanks!
|
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MDU6SXNzdWU2MTE3OTk4MTA=
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|
Feature Request: Create a Global Setting for Enabling numba engine
|
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| 6
| 2020-05-04T11:25:42Z
| 2020-07-15T12:28:04Z
| 2020-07-15T12:28:04Z
|
NONE
| null |
Would it be possible to create a default pandas global setting to enable numba engine whenever possible?
In pandas.DataFrame.apply, .transform, etc.
Thanks!
|
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pandas.read_csv outputs two data frames in Jupiter Notebook
|
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| 2020-05-04T12:30:34Z
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NONE
| null |
### My code:
First cell:
```python
import pandas as pd
test = pd.read_csv("dump.csv")
```
Second cell:
```python
test
```
Output:
<img width="1114" alt="Screenshot 2020-05-04 at 13 42 52" src="https://user-images.githubusercontent.com/10774221/80962334-284bd980-8e0d-11ea-9d3e-456a112770d4.png">
and
```python
type(test)
```
Output:
<img width="1113" alt="Screenshot 2020-05-04 at 13 43 45" src="https://user-images.githubusercontent.com/10774221/80962402-47e30200-8e0d-11ea-9a56-4bf387b37257.png">
It seems like the command imports two dataframes but only in Jupyter Notebook.
I tested it in Spyder and there everything works fine.
### Setup:
The file I am reading: [sp500.csv.zip](https://github.com/pandas-dev/pandas/files/4574412/sp500.csv.zip)
```python
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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CI: Bump numpydev URL
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| 19
| 2020-05-04T13:02:42Z
| 2020-05-19T11:02:37Z
| 2020-05-11T21:42:29Z
|
CONTRIBUTOR
| null |
NumPy and scipy are uploading their wheels to a new URL.
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Fix expected dtype in test_reindex_with_same_tz
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| 1
| 2020-05-04T14:02:12Z
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| 2020-05-05T14:18:01Z
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/33845
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DOC: start 1.0.4
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| 9
| 2020-05-04T15:21:33Z
| 2020-05-05T12:37:49Z
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|
MEMBER
| null |
NOTE: opened against 1.0.x branch as to not commit to release at this stage.
I think could backport a few PRs moving the whatsnews and only after a release remove from 1.1 whatsnew on master. This gives us the chance to easily abandon the release at any time.
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MDU6SXNzdWU2MTE5Njk3MzM=
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|
Supporting arm64, aarch64, and other platforms.
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[] | null | 17
| 2020-05-04T15:36:30Z
| 2021-08-07T05:42:41Z
| null |
CONTRIBUTOR
| null |
There are some desires / efforts to support other platforms like arm64 / aarch64 (https://github.com/pandas-dev/pandas/pull/30641). I might have missed it, but I don't know that we've had a real discussion on if / how we are going to support those.
I'm not against adding support for either of these, but we should do it deliberately. I think that us uploading wheels / conda packages implies some kind of official blessing that we support those platforms, and will fix bugs specific to those platforms when they arise. These platform specific issues are extremely hard for community-maintained open-source projects to manage (we've had enough issues with 32-bit Windows / Linux). Contributors and maintainers don't have access to hardware and are stuck trying to debug things through CI.
Let's use this issue to discuss
1. Do we want to support these two platforms? (I think yes to both)
2. What are the requirements for us to add a platform?
- Freely available CI infrastructure
- NumPy supports it (any other deps? Cython? Arrow at some point?)
- Some level of popularity? We drop Python versions based on popularity, but this seems hard to measure for platforms we don't already officially support.
3. We need a person / team who will champion platform-specific issues. We can ping that person or team when CI / packaging issues arise with that platform.
I think we can discuss this more on our monthly dev call next week.
cc @pandas-dev/pandas-core & @odidev and @ossdev07 in particular.
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Backport PR #33566: CI: Fix jedi deprecation warning for 0.17.0 on IPython
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Backport PR #33080 on branch 1.0.x (CI troubleshoot azure)
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NONE
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Backport PR #33080: CI troubleshoot azure
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REF: use is_foo_object checks instead of _typ checks
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MEMBER
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Backport PR #33309 on branch 1.0.x (DOC: include Offset.__call__ to autosummary to fix sphinx warning)
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NONE
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Backport PR #33309: DOC: include Offset.__call__ to autosummary to fix sphinx warning
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Backport PR #31146 on branch 1.0.x (Remove possibly illegal test data)
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| 0
| 2020-05-04T19:57:28Z
| 2020-05-04T20:59:49Z
| 2020-05-04T20:59:49Z
|
NONE
| null |
Backport PR #31146: Remove possibly illegal test data
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MDExOlB1bGxSZXF1ZXN0NDEzMTU1OTk0
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DOC: whatsnew for 33717
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| 0
| 2020-05-04T20:02:42Z
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MEMBER
| null |
xref #33717
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MDExOlB1bGxSZXF1ZXN0NDEzMTU3Nzkw
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REF: simplify Timedelta arithmetic methods
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| 1
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| 2020-05-06T14:11:03Z
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MEMBER
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In particular this avoids the "_typ" checks which go through python-space
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MDExOlB1bGxSZXF1ZXN0NDEzMTg3MDgx
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PERF: make _Tick into a cdef class
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MEMBER
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The actual "PERF" part is that in the follow-up we can cimport `is_tick_object` which will be appreciably faster than the `getattr(other, "_typ", None) == "dateoffset" and hasattr(other, "delta")` checks we now do
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MDU6SXNzdWU2MTIyMDI1OTA=
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BUG: Series.update() raises ValueError if dtype="string"
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| 0
| 2020-05-04T22:12:54Z
| 2020-05-05T16:27:25Z
| 2020-05-05T16:27:25Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
a = pd.Series(["a", None, "c"], dtype="string")
b = pd.Series([None, "b", None], dtype="string")
a.update(b)
```
results in:
```python-traceback
Traceback (most recent call last):
File "<ipython-input-15-b9da8f25067a>", line 1, in <module>
a.update(b)
File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\series.py", line 2810, in update
self._data = self._data.putmask(mask=mask, new=other, inplace=True)
File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\internals\managers.py", line 564, in putmask
return self.apply("putmask", **kwargs)
File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\internals\managers.py", line 442, in apply
applied = getattr(b, f)(**kwargs)
File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\internals\blocks.py", line 1676, in putmask
new_values[mask] = new
File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\arrays\string_.py", line 248, in __setitem__
super().__setitem__(key, value)
File "C:\tools\anaconda3\envs\Simple\lib\site-packages\pandas\core\arrays\numpy_.py", line 252, in __setitem__
self._ndarray[key] = value
ValueError: NumPy boolean array indexing assignment cannot assign 3 input values to the 1 output values where the mask is true
```
#### Problem description
The example works if I leave off the `dtype="string"` (resulting in the implicit dtype `object`).
IMO update should work for all dtypes, not only the "old" ones.
`a = pd.Series([1, None, 3], dtype="Int16")` etc. also raises ValueError, while the same with `dtype="float64"`works.
It looks as if update doesn't work with the new nullable dtypes (the ones with `pd.NA`).
#### Expected Output
The expected result is that `a.update(b)` updates `a` without raising an exception, not only for `object` and `float64`, but also for `string` and `Int16` etc..
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 ..., GenuineIntel
...
pandas : 1.0.3
numpy : 1.18.1
...
</details>
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[] | null | 3
| 2020-05-04T22:50:05Z
| 2020-06-13T18:27:39Z
| 2020-06-13T18:27:38Z
|
NONE
| null |
I am using the below code to merger the multiple CSV files in the folder. The code is working fine for smaller dataset but for bigger data.
As the total number of rows is 51163632 but as a result, in big_frame I am getting only 20883611 rows.
Is there a limit of pandas dataframe to store the values?
big_frame = pd.DataFrame()
filename= []
num_comments=0
for file in os.listdir():
print(file)
if file.endswith('.CSV'):
filename.append(file)
num_lines = sum(1 for line in open(file, encoding="utf8"))
#print(num_lines)
num_comments = num_comments + num_lines
print(num_comments)
try:
#df = pd.read_csv(file, error_bad_lines=False, sep=',', header=None, encoding='utf-8')
df = pd.read_csv(file, error_bad_lines=False, delimiter=',', header=None, encoding='utf-8')
except:
continue
big_frame = big_frame.append(df, ignore_index=True)
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| 5
| 2020-05-04T22:51:16Z
| 2020-06-26T07:19:51Z
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CONTRIBUTOR
| null |
- [x] closes #33476
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 2020-05-30T16:30:29Z
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- [x] closes #33980
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDEzMjcyNjM5
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BUG: fix memory_usage method with deep of StringArray
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- [ ] closes #33963
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MTIyODUwNDk=
| 33,986
|
QST: Should list-likes with pd.NA have an inferred nullable dtype if possible?
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|
MEMBER
| null |
It looks like in each of these cases the dtype is inferred as object, but does it make sense to try to use a nullable extension type when possible?
```python
[ins] In [1]: pd.Series([1, 2, pd.NA]).dtype
Out[1]: dtype('O')
[ins] In [2]: pd.Series([True, False, pd.NA]).dtype
Out[2]: dtype('O')
[ins] In [3]: pd.Series(["a", "b", pd.NA]).dtype
Out[3]: dtype('O')
```
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BUG:Cannot write as xlsx to GCS
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| 1
| 2020-05-05T02:37:41Z
| 2020-11-13T13:39:29Z
| 2020-11-13T13:39:29Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
output_path = f"gs://my_bucket/incidents/prediction/{ds}_incidents_result"
[...]
export_app.to_parquet(f"{output_path}.parquet")
export_app.to_excel(f"{output_path}.xlsx")
```
#### Problem description
I want to write the DF as a `.parquet` and a `.xlsx` file to a GCloud Storage bucket.
I launch the job in a K8S pod and I finally got the error message :
>textPayload: "[Errno 2] No such file or directory: 'gs://my_bucket/incidents/prediction/2020-04-29_incidents_result.xlsx'
Next I change the `to_excel` -> `to_csv` and everything works as expected.
Do the `to_excel` can handle a path as `gs://...` ? Because it's the only issue I can see with that.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.138+
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : 0.6.1
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : 1.3.0
xlsxwriter : None
numba : None
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Issue33955 Properly checking whether post_processing is callable or not in Class GroupBy
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CONTRIBUTOR
| null |
- [x ] closes #33955
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MDExOlB1bGxSZXF1ZXN0NDEzMzczNjk5
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DOC: Add plotting examples and fix broken examples
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| 6
| 2020-05-05T08:24:00Z
| 2020-05-06T07:49:55Z
| 2020-05-06T07:49:46Z
|
CONTRIBUTOR
| null |
This PR improves the `pandas.plotting._misc.py` module docs by:
1. Adding example plots where they were missing from docstrings.
2. Fixing examples that were not formatted to render with
```
.. plot::
:context: close-figs
```
3. Simplifying the syntax of some oddly-written examples.
In some cases, I replicated the examples found in the [User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#plotting-tools).
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CI: test_unsupported_other fails on pyarrow 0.17
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| 0
| 2020-05-05T08:31:25Z
| 2020-05-05T09:43:56Z
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/33300#issuecomment-623690451, https://github.com/pandas-dev/pandas/pull/33422#discussion_r406240082
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BUG: Segmentation fault when importing pandas
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| 4
| 2020-05-05T08:47:05Z
| 2020-05-06T19:39:58Z
| 2020-05-05T13:41:22Z
|
NONE
| null |
- [ x ] I have checked that this issue has not already been reported.
- [ x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
I have just downloaded fresh miniconda (python 3, 64 bit) script.
Instalation of miniconda went well.
Than I installed pandas:
```
(base) [user@server ~]$ conda install pandas
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /home/user/miniconda3
added / updated specs:
- pandas
The following packages will be downloaded:
package | build
---------------------------|-----------------
blas-1.0 | mkl 6 KB
certifi-2020.4.5.1 | py37_0 155 KB
conda-4.8.3 | py37_0 2.8 MB
intel-openmp-2020.0 | 166 756 KB
libgfortran-ng-7.3.0 | hdf63c60_0 1006 KB
mkl-2020.0 | 166 128.9 MB
mkl-service-2.3.0 | py37he904b0f_0 218 KB
mkl_fft-1.0.15 | py37ha843d7b_0 154 KB
mkl_random-1.1.0 | py37hd6b4f25_0 321 KB
numpy-1.18.1 | py37h4f9e942_0 5 KB
numpy-base-1.18.1 | py37hde5b4d6_1 4.2 MB
openssl-1.1.1g | h7b6447c_0 2.5 MB
pandas-1.0.3 | py37h0573a6f_0 8.6 MB
python-dateutil-2.8.1 | py_0 224 KB
pytz-2020.1 | py_0 184 KB
------------------------------------------------------------
Total: 149.9 MB
The following NEW packages will be INSTALLED:
blas pkgs/main/linux-64::blas-1.0-mkl
intel-openmp pkgs/main/linux-64::intel-openmp-2020.0-166
libgfortran-ng pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0
mkl pkgs/main/linux-64::mkl-2020.0-166
mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0
mkl_fft pkgs/main/linux-64::mkl_fft-1.0.15-py37ha843d7b_0
mkl_random pkgs/main/linux-64::mkl_random-1.1.0-py37hd6b4f25_0
numpy pkgs/main/linux-64::numpy-1.18.1-py37h4f9e942_0
numpy-base pkgs/main/linux-64::numpy-base-1.18.1-py37hde5b4d6_1
pandas pkgs/main/linux-64::pandas-1.0.3-py37h0573a6f_0
python-dateutil pkgs/main/noarch::python-dateutil-2.8.1-py_0
pytz pkgs/main/noarch::pytz-2020.1-py_0
The following packages will be SUPERSEDED by a higher-priority channel:
ca-certificates conda-forge::ca-certificates-2020.4.5~ --> pkgs/main::ca-certificates-2020.1.1-0
certifi conda-forge::certifi-2020.4.5.1-py37h~ --> pkgs/main::certifi-2020.4.5.1-py37_0
conda conda-forge::conda-4.8.3-py37hc8dfbb8~ --> pkgs/main::conda-4.8.3-py37_0
openssl conda-forge::openssl-1.1.1g-h516909a_0 --> pkgs/main::openssl-1.1.1g-h7b6447c_0
Proceed ([y]/n)? y
Downloading and Extracting Packages
mkl-service-2.3.0 | 218 KB | ######################################################################################################################################################################################## | 100%
pytz-2020.1 | 184 KB | ######################################################################################################################################################################################## | 100%
mkl_random-1.1.0 | 321 KB | ######################################################################################################################################################################################## | 100%
openssl-1.1.1g | 2.5 MB | ######################################################################################################################################################################################## | 100%
blas-1.0 | 6 KB | ######################################################################################################################################################################################## | 100%
numpy-1.18.1 | 5 KB | ######################################################################################################################################################################################## | 100%
mkl_fft-1.0.15 | 154 KB | ######################################################################################################################################################################################## | 100%
conda-4.8.3 | 2.8 MB | ######################################################################################################################################################################################## | 100%
numpy-base-1.18.1 | 4.2 MB | ######################################################################################################################################################################################## | 100%
pandas-1.0.3 | 8.6 MB | ######################################################################################################################################################################################## | 100%
certifi-2020.4.5.1 | 155 KB | ######################################################################################################################################################################################## | 100%
libgfortran-ng-7.3.0 | 1006 KB | ######################################################################################################################################################################################## | 100%
python-dateutil-2.8. | 224 KB | ######################################################################################################################################################################################## | 100%
mkl-2020.0 | 128.9 MB | ######################################################################################################################################################################################## | 100%
intel-openmp-2020.0 | 756 KB | ######################################################################################################################################################################################## | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(base) [user@server ~]$
```
Now if I try to import pandas I get the Segmentation Fault error.
```(base) [user@server ~]$ python
Python 3.7.6 (default, Jan 8 2020, 19:59:22)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
Segmentation fault
(base) [user@server ~]$
```
uname -a shows:
```
(base) [user@server ~]$ uname -a
Linux server 3.10.0-327.10.1.el7.x86_64 #1 SMP Sat Jan 23 04:54:55 EST 2016 x86_64 x86_64 x86_64 GNU/Linux
```
#### Problem description
I cannot import pandas.
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| 2020-05-05T08:54:05Z
| 2020-06-05T18:51:13Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas (1.0.3).
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> pd.Timedelta(1 / 128, "seconds").resolution_string
'N'
>>> pd.Timedelta(seconds=1 / 128).resolution_string
'U'
```
#### Problem description
The resolution of `Timedelta` differs when created with `value` and `unit`, versus with keyword arguments. When tested with seconds as units, the former will round to nanoseconds, while the latter rounds to microseconds.
#### Expected Output
They should both have the same resolution, nanoseconds making the most sense to me:
```python
>>> pd.Timedelta(seconds=1 / 128).resolution_string
'N'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
Backport PR #33241 on branch 1.0.x (tostring->tobytes)
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NONE
| null |
Backport PR #33241: tostring->tobytes
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CI/DEP: Use numba.extending.is_jitted for numba version > 0.49.0
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| 2020-05-05T10:39:05Z
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MEMBER
| null |
xref #33687
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BLD: recursive inclusion of DLLs in package data (#33246)
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MEMBER
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xref #33246
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Backport PR #33954 on branch 1.0.x (DOC: update conf.py to use new numpy doc url)
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| 2020-05-05T11:30:04Z
| 2020-05-05T12:27:15Z
| 2020-05-05T12:27:15Z
|
NONE
| null |
Backport PR #33954: DOC: update conf.py to use new numpy doc url
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BUG:
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[] | null | 8
| 2020-05-05T12:15:50Z
| 2020-07-07T17:52:48Z
| 2020-05-19T21:14:42Z
|
NONE
| null |
- [O ] I have checked that this issue has not already been reported.
- [O ] I have confirmed this bug exists on the latest version of pandas.
- [O ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas
```
#### Problem description
I cannot import pandas because of the Attribute error. AttributeError: partially initialized module 'pandas' has no attribute 'plotting' (most likely due to a circular import)
#### Expected Output
No error.
#### Output of ``pd.show_versions()``
Couldn't do it because it could not be imported. O a,
<details>
I didn't get the error when I installed version 1.0.1.
Below is the error message.
import pandas
File "C:\Users\user\app\venv\lib\site-packages\pandas\__init__.py", line 55, in <module>
from pandas.core.api import (
File "C:\Users\user\app\venv\lib\site-packages\pandas\core\api.py", line 29, in <module>
from pandas.core.groupby import Grouper, NamedAgg
File "C:\Users\user\app\venv\lib\site-packages\pandas\core\groupby\__init__.py", line 1, in <module>
from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy
File "C:\Users\user\app\venv\lib\site-packages\pandas\core\groupby\generic.py", line 60, in <module>
from pandas.core.frame import DataFrame
File "C:\Users\user\app\venv\lib\site-packages\pandas\core\frame.py", line 124, in <module>
from pandas.core.series import Series
File "C:\Users\user\app\venv\lib\site-packages\pandas\core\series.py", line 122, in <module>
class Series(base.IndexOpsMixin, generic.NDFrame):
File "C:\Users\user\app\venv\lib\site-packages\pandas\core\series.py", line 4562, in Series
plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
AttributeError: partially initialized module 'pandas' has no attribute 'plotting' (most likely due to a circular import)
</details>
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Backport PR #33462 on branch 1.0.x (BUG: None converted to NaN after …
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| 0
| 2020-05-05T12:16:39Z
| 2020-05-05T12:55:24Z
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|
MEMBER
| null |
…groupby first and last)
xref #33462
fixes regression in 1.0.2 (#32800)
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Failing test_missing_required_dependency in pandas-wheels
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| 2
| 2020-05-05T13:07:38Z
| 2020-10-27T12:49:45Z
| 2020-10-27T12:49:45Z
|
CONTRIBUTOR
| null |
The test `test_missing_required_dependency` is failing in MacPython/pandas-wheels. https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=34824&view=logs&j=c0130b29-789d-5a3c-6978-10796a508a7f&t=e120bc6c-1f5e-5a41-8f0a-1d992cd2fbfb.
This has caused issues in the past. It's low priority so for now I'm just going to skip it over there.
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Backport PR #33761 on branch 1.0.x (REGR: fix DataFrame reduction wit…
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MEMBER
| null |
…h EA columns and numeric_only=True)
xref #33761
fixes regression in 1.0.0 (#33256)
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Backport PR #33629 on branch 1.0.x (BUG: Fix Categorical use_inf_as_n…
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…a bug)
xref #33629
fixed regression in 1.0.0 (#33594)
# NOTE: code changes in #33629 not really suitable for backport.
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BUG: read_csv() wrong reading if comment-character included in na_value
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| 2020-05-05T15:24:16Z
| 2020-06-05T20:36:18Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from io import StringIO
dataframe_file = (
"# this is a comment\n"
"1,2,3,4\n"
"1,2,3,4#inline comment\n"
"1,2#,3,4\n"
"1,2,#N/A,4\n"
)
dataframe = pd.read_csv(StringIO(dataframe_file), comment="#", na_values="#N/A")
print(dataframe)
```
Output:
```
1 2 3 4
0 1 2 3.0 4.0
1 1 2 NaN NaN
2 1 2 NaN NaN
```
#### Problem description
On the last row, it should read the 3rd value (`#N/A`) as NA-Value (`NaN`) and not as a comment.
But because the value `#N/A` starts with the comment-character `#` the rest of the line is interpreted as comment and the last value is not read.
#### Expected Output
```
1 2 3 4
0 1 2 3.0 4.0
1 1 2 NaN NaN
2 1 2 NaN 4.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.8-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.17
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0
</details>
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…header)
xref #32870
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MEMBER
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…gorical value with self)
xref #33292
fixes regression in 1.0.0 #33288
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xref #33157
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MDExOlB1bGxSZXF1ZXN0NDEzNjIxNDEw
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CLN: remove unused out-of-bounds handling
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MEMBER
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We only have one test that goes through the OOB handling code, and that re-raises within the OOB handling. We're better off being strict here like everywhere else rather than having spotty support.
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CLN: avoid _typ checks in Period ops
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CLN: avoid getattr checks in liboffsets
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| 5
| 2020-05-05T20:09:23Z
| 2020-05-09T21:43:48Z
| 2020-05-09T20:20:33Z
|
MEMBER
| null |
The existing cimport of pd.NA into tslibs.nattype is a circular dependency (its not actually clear to me why it doesnt raise). I'm finding that its presence is making other refactoring around tslibs.offsets more difficult. This PR moves the import of pd.NA to tslib, breaking the circular dependency.
This _does_ change some behavior, but none that is tested, so we should discuss what places we want to recognize pd.NA. In particular, `pd.Timedelta(pd.NA)` returns `NaT` in master and raises `ValueError` in this PR. `pd.Timestamp(pd.NA)` and `pd.Period(pd.NA)` raise TypeError and ValueError, respectively, in both master and this PR.
cc @jorisvandenbossche what is your preferred behavior?
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BUG: ValueError: Given date string not likely a datetime when grouping by PeriodIndex level
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| 2
| 2020-05-05T20:12:28Z
| 2020-05-26T09:28:10Z
| 2020-05-19T08:43:05Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```pytb
In [10]: t = pd.Series([1], index=pd.PeriodIndex(['2000'], name="A", freq="D"))
In [11]: t.groupby(level="A").size()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/period.py in get_loc(self, key, method, tolerance)
593 try:
--> 594 return self._engine.get_loc(key)
595 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: 'A'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-11-ca2eac2f77ff> in <module>
----> 1 t.groupby("A").size()
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/series.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed)
1685 group_keys=group_keys,
1686 squeeze=squeeze,
-> 1687 observed=observed,
1688 )
1689
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated)
407 sort=sort,
408 observed=observed,
--> 409 mutated=self.mutated,
410 )
411
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in get_grouper(obj, key, axis, level, sort, observed, mutated, validate)
588
589 elif is_in_axis(gpr): # df.groupby('name')
--> 590 if gpr in obj:
591 if validate:
592 obj._check_label_or_level_ambiguity(gpr, axis=axis)
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/generic.py in __contains__(self, key)
1848 def __contains__(self, key) -> bool_t:
1849 """True if the key is in the info axis"""
-> 1850 return key in self._info_axis
1851
1852 @property
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/period.py in __contains__(self, key)
384 else:
385 try:
--> 386 self.get_loc(key)
387 return True
388 except (TypeError, KeyError):
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/indexes/period.py in get_loc(self, key, method, tolerance)
598
599 try:
--> 600 asdt, parsed, reso = parse_time_string(key, self.freq)
601 key = asdt
602 except TypeError:
pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_time_string()
pandas/_libs/tslibs/parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso()
ValueError: Given date string not likely a datetime.
```
#### Problem description
With a regular Index, we see the following
```python
In [13]: s = pd.Series([1], index=pd.Index(['a'], name='A'))
In [14]: s.groupby(level="A").size()
Out[14]:
A
a 1
dtype: int64
```
This seems to only affect PeriodIndex. DatetimeIndex works fine.
#### Expected Output
```python
A
2000-01-01 1
Freq: D, dtype: int64
```
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Add fix to raise error when category value 'x' is not predefined but is assigned through df.loc[..]=x
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CONTRIBUTOR
| null |
- [X] closes #25383
- [X] tests passed, new test added
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 4
| 2020-05-05T21:19:29Z
| 2020-07-11T16:02:05Z
| 2020-05-27T12:43:34Z
|
MEMBER
| null |
- [x] closes #32579
- [x] closes #21090
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This makes any groupby function that uses _GroupBy._make_wrapper to act on self._obj_with_exclusions rather than self._selected_obj, in parallel with the cython paths. Also similar to the cython paths, the grouping columns are added back onto the result in _wrap_applied_output. Similar remarks apply to nunique, which does not go through the _make_wrapper.
After finishing this, I found PR #29131. This PR does not change the behavior of calling apply() itself, only the previous code paths that would internally go through apply. Also, the change made there does not have any impact when as_index is False.
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MDExOlB1bGxSZXF1ZXN0NDEzNzc5NTAy
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|
ENH: Add compression to stata exporters
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| 2020-05-05T22:13:55Z
| 2020-07-28T14:41:35Z
| 2020-05-12T16:10:36Z
|
CONTRIBUTOR
| null |
Add standard compression optons to stata exporters
closes #26599
- [X] closes #26599
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU2MTI5MzMwNDg=
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Update setting data pointers for Cython 3
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| 34
| 2020-05-05T22:26:12Z
| 2021-09-11T20:17:37Z
| 2021-09-11T02:40:37Z
|
CONTRIBUTOR
| null |
Need to update the following locations
- [x] _libs/window/aggregations.pyx: `bufarr.data`
- [ ] _libs/reduction.pyx: `chunk.data`
---
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=34888&view=logs&j=33ccdd52-c922-5ef2-8209-78215e36d994&t=046ffbec-62c2-54e3-e88a-7745f4292bb6&l=457
just started failing.
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage
if anyone has insights
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MDExOlB1bGxSZXF1ZXN0NDEzODE5MjIw
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|
REF: update iNaT imports
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MDExOlB1bGxSZXF1ZXN0NDEzODIyNDA4
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CLN: remove normalize_datetime
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MEMBER
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No longer necessary following #34006
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