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41,920
PERF: fix regression in reductions for boolean/integer data (assume no missing values)
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3
2021-06-10T07:13:31Z
2021-06-11T10:48:02Z
2021-06-10T11:20:57Z
MEMBER
null
Address performance regression in https://github.com/pandas-dev/pandas/pull/41711/files#r647515492 (https://pandas.pydata.org/speed/pandas/#series_methods.All.time_all?python=3.8&Cython=0.29.21&p-N=1000000&p-case='fast'&p-dtype='bool'&commits=9231b495-44237a58)
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41,921
BUG: Scalar assignment to empty dataframe with loc. Closes GH41891
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5
2021-06-10T09:49:12Z
2021-08-17T02:21:55Z
2021-08-17T02:21:55Z
CONTRIBUTOR
null
This weakens a check introduced in 7bbeb79297532d209d67f7bdc61394ddee3acbb1 in PR #5227 addressing issue #5226 ("New appending behavior doesn't work on an empty DataFrame") which caused ValueErrors for `df[:, 1] = 1` if df is an empty DataFrame. With this commit no error is thrown and the column is added to the dataframe (which remains empty). See #41891 for why this makes sense. - [x] closes #41891 - [x] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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41,922
DOC: add contributors to 1.3 release notes
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2
2021-06-10T10:31:18Z
2021-06-11T10:14:06Z
2021-06-11T10:14:01Z
MEMBER
null
I think we should do this for the rc. We will be publishing the docs at https://pandas.pydata.org/pandas-docs/version/1.3.0/ and linked from the rc announcement. (It's the same url for the actual release and the rc docs are replaced) we haven't yet released 1.2.5 so the 1.2.5 tag does not yet exist. used 1.2.4 for now.
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Revert "PERF: reductions"
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2021-06-10T11:24:34Z
2021-06-10T11:24:46Z
2021-06-10T11:24:39Z
MEMBER
null
Reverts pandas-dev/pandas#41911
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2021-06-10T11:35:06Z
2021-06-12T07:30:56Z
2021-06-11T10:07:15Z
MEMBER
null
Retake of https://github.com/pandas-dev/pandas/pull/41911
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DOC: fix header level in 1.3 release notes
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DOC: start v1.4.0 release notes
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NOT TO BE MERGED till after we branch at 1.3.0rc0
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41,927
BUG: Timezones are not preserved in Series.mode() output
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2021-08-21T02:25:12Z
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 datetime times = pd.Series([pd.Timestamp(datetime.datetime.now(), tz='US/Central') for n in range(5)]) times.dtype # datetime64[ns, US/Central] == pd.DatetimeTZDtype(tz='US/Central') mode_result = times.mode() mode_result.dtype # dtype('<M8[ns]') == np.dtype('datetime64[ns]') ``` #### Problem description The Timestamps are normalized to (I think) UTC time, and the original timezone and offset are lost. `Series.median()` does preserve timezones, so I assumed this is just an unhandled edge case. I'm working around this using `scipy.stats.mode`, which does preserve timezones. #### Expected Output ```python times.mode().dtype == pd.DatetimeTZDtype(tz='US/Central') ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.8.7.final.0 python-bits : 64 OS : Darwin OS-release : 20.5.0 Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.2.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 20.3.4 setuptools : 49.2.1 Cython : 0.29.14 pytest : 6.2.4 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.24.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 2.0.0 pyxlsb : None s3fs : None scipy : 1.6.1 sqlalchemy : 1.3.23 tables : None tabulate : 0.8.9 xarray : None xlrd : None xlwt : None numba : None </details>
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41,928
CLN: remove references of Travis
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REF: de-duplicate IntervalIndex._intersection
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made possible by #41863
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917,651,513
MDU6SXNzdWU5MTc2NTE1MTM=
41,930
BUG: pd.Grouper cannot be reused in some cases
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1
2021-06-10T18:06:00Z
2021-07-01T21:49:35Z
null
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- [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 numpy as np import pandas as pd np.random.seed(1) # Create one dataframe dr = pd.date_range("2021-01-01", "2021-08-30") ind = np.random.randint(0,5,len(dr)) something = np.random.random(len(ind)) df = pd.DataFrame({"date":dr, "ind":ind, "something":something}) # Define what to group on GROUP_ON = ["ind", pd.Grouper(key="date", freq="2W")] # Copy used if you uncomment lines below GROUP_ON_COPY = ["ind", pd.Grouper(key="date", freq="2W")] # Comment either of the two lines below, and it works df.groupby(GROUP_ON).mean().reset_index().groupby(GROUP_ON).mean() df.groupby(GROUP_ON).mean().reset_index() # Use a different groupby in the second case, and it works #df.groupby(GROUP_ON).mean().reset_index().groupby(GROUP_ON).sum() #df.groupby(GROUP_ON_COPY).mean().reset_index() # Bonus points: do only one groupby first, and it works # df.groupby(GROUP_ON).mean().reset_index() # df.groupby(GROUP_ON).mean().reset_index() ``` #### Problem description In some cases it seems that pd.Grouper cannot be reused. I was not able to figure out exactly why, or if the output can sometimes corrupt. It leads to very painful debugging. Tested with master branch, and with python 3.8 and 3.9 #### Expected Output pd.Grouper should be able to be used any number of times, or should raise explicit error upon any reuse. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.final.0 python-bits : 64 OS : Linux OS-release : 4.4.0-19041-Microsoft Version : #488-Microsoft Mon Sep 01 13:43:00 PST 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.4 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.2 setuptools : 57.0.0 Cython : 0.29.23 pytest : 6.2.4 hypothesis : None sphinx : 4.0.2 blosc : None feather : None xlsxwriter : 1.4.3 lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.24.1 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : 2021.06.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.3 sqlalchemy : None tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : None </details>
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BUG/REF: use sorted_rank_1d for rank_2d
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3
2021-06-10T19:15:10Z
2021-06-25T17:41:05Z
2021-06-25T17:36:29Z
MEMBER
null
- [x] closes #19560 (All the inconsistencies are addressed, but could be left open if we want to remove object support) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry Built on #41916 There is a slight slowdown in 3 benchmarks, due to use of `lexsort` instead of `argsort` to sort with both the data and mask so that `na_option` can be properly handled. I am not sure if this can be avoided (though as a plus there is also potential perf improvement for users running code in parallel since the ranking is in a `nogil` block now). Benchmarks: <details> ``` [499ef8c0] [d678bbf0] <master> <ref/rank_2d_dedup> 7.14±0.1ms 7.04±0.4ms 0.99 categoricals.Rank.time_rank_int 7.85±0.3ms 7.34±0.2ms 0.93 categoricals.Rank.time_rank_int_cat 7.30±0.2ms 6.95±0.6ms 0.95 categoricals.Rank.time_rank_int_cat_ordered 121±3ms 122±10ms 1.01 categoricals.Rank.time_rank_string 8.66±1ms 8.13±1ms 0.94 categoricals.Rank.time_rank_string_cat 6.37±1ms 6.11±0.4ms 0.96 categoricals.Rank.time_rank_string_cat_ordered 9.34±2ms 10.1±1ms 1.08 frame_methods.Rank.time_rank('float') 3.81±1ms 4.16±0.4ms 1.09 frame_methods.Rank.time_rank('int') 59.0±20ms 48.0±0.9ms ~0.81 frame_methods.Rank.time_rank('object') 2.75±0.4ms 3.53±0.7ms ~1.28 frame_methods.Rank.time_rank('uint') 1.06±0.04ms 1.01±0.04ms 0.95 groupby.RankWithTies.time_rank_ties('datetime64', 'average') 1.13±0.06ms 1.02±0.04ms ~0.90 groupby.RankWithTies.time_rank_ties('datetime64', 'dense') 1.19±0.1ms 1.04±0.03ms ~0.87 groupby.RankWithTies.time_rank_ties('datetime64', 'first') 1.12±0.04ms 1.04±0.06ms 0.93 groupby.RankWithTies.time_rank_ties('datetime64', 'max') 1.03±0.05ms 1.03±0.04ms 1.00 groupby.RankWithTies.time_rank_ties('datetime64', 'min') 1.22±0.1ms 1.10±0.07ms ~0.90 groupby.RankWithTies.time_rank_ties('float32', 'average') 1.13±0.09ms 1.12±0.04ms 0.99 groupby.RankWithTies.time_rank_ties('float32', 'dense') 1.15±0.1ms 1.05±0.05ms 0.91 groupby.RankWithTies.time_rank_ties('float32', 'first') - 1.19±0.05ms 1.00±0.03ms 0.84 groupby.RankWithTies.time_rank_ties('float32', 'max') 1.18±0.04ms 1.13±0.08ms 0.96 groupby.RankWithTies.time_rank_ties('float32', 'min') 1.25±0.1ms 1.17±0.03ms 0.94 groupby.RankWithTies.time_rank_ties('float64', 'average') 1.45±0.5ms 990±10μs ~0.68 groupby.RankWithTies.time_rank_ties('float64', 'dense') 1.21±0.2ms 1.11±0.08ms 0.92 groupby.RankWithTies.time_rank_ties('float64', 'first') 1.18±0.05ms 1.15±0.07ms 0.97 groupby.RankWithTies.time_rank_ties('float64', 'max') 1.32±0.3ms 963±40μs ~0.73 groupby.RankWithTies.time_rank_ties('float64', 'min') 1.03±0.1ms 971±80μs 0.94 groupby.RankWithTies.time_rank_ties('int64', 'average') 1.02±0.06ms 1.02±0.06ms 1.01 groupby.RankWithTies.time_rank_ties('int64', 'dense') 1.13±0.08ms 1.01±0.04ms ~0.89 groupby.RankWithTies.time_rank_ties('int64', 'first') 1.24±0.1ms 959±20μs ~0.77 groupby.RankWithTies.time_rank_ties('int64', 'max') 1.14±0.06ms 979±50μs ~0.86 groupby.RankWithTies.time_rank_ties('int64', 'min') 10.9±0.6ms 9.80±0.4ms ~0.90 series_methods.Rank.time_rank('float') 7.18±0.3ms 7.15±0.6ms 1.00 series_methods.Rank.time_rank('int') 53.7±1ms 49.6±4ms 0.92 series_methods.Rank.time_rank('object') 7.64±0.2ms 7.78±0.6ms 1.02 series_methods.Rank.time_rank('uint') 9.96±0.2ms 11.7±0.3ms ~1.17 stat_ops.Rank.time_average_old('DataFrame', False) + 9.89±0.2ms 11.9±0.6ms 1.20 stat_ops.Rank.time_average_old('DataFrame', True) 13.5±0.9ms 12.6±0.8ms 0.93 stat_ops.Rank.time_average_old('Series', False) 12.8±1ms 11.9±0.6ms 0.93 stat_ops.Rank.time_average_old('Series', True) + 9.84±0.3ms 11.7±0.4ms 1.19 stat_ops.Rank.time_rank('DataFrame', False) + 8.53±0.3ms 11.7±0.8ms 1.38 stat_ops.Rank.time_rank('DataFrame', True) 14.0±3ms 12.2±1ms ~0.87 stat_ops.Rank.time_rank('Series', False) 13.0±0.9ms 12.1±1ms 0.93 stat_ops.Rank.time_rank('Series', True) ``` </details>
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TST: Add tests for numeric_only default behaviour (GH41351)
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- [x] closes #41351 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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BUG: CategoricalIndex.get_loc(np.nan)
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2021-06-10T21:13:41Z
2021-06-16T00:22:58Z
2021-06-16T00:11:35Z
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- [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NjY3NDYwNjIw
41,934
BUG: IntervalIndex.get_indexer with categorical both have nans
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2021-06-10T21:58:36Z
2021-06-17T18:50:02Z
2021-06-17T17:52:17Z
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- [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry Started with moving a fastpath from IntervalIndex to the base class. In the process found a bug in the IntervalIndex version, which is now fixed.
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CI: py 3.10 build failing
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6
2021-06-10T23:17:19Z
2021-11-01T23:46:25Z
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MEMBER
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@seberg this build is using numpy 1.22dev, looks like a bunch of the failures are raising in `np.iinfo(np.int64).max` ``` return np.iinfo(np.int64).max _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <[AttributeError("'iinfo' object has no attribute 'kind'") raised in repr()] iinfo object at 0x7f986b9aa830> int_type = <class 'numpy.int64'> def __init__(self, int_type): try: self.dtype = numeric.dtype(int_type) except TypeError: > self.dtype = numeric.dtype(type(int_type)) E TypeError: 'numpy.dtype[bool_]' object is not callable /opt/hostedtoolcache/Python/3.10.0-beta.2/x64/lib/python3.10/site-packages/numpy/core/getlimits.py:518: TypeError ```
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CLN: Remove useless Validation functions
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2021-06-10T23:24:06Z
2021-06-16T15:32:45Z
2021-06-15T23:50:03Z
MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry Not sure what the point of these were.
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CI: intermittent segfaults on `macOS py37_macos` run
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5
2021-06-11T01:02:31Z
2021-06-11T23:56:04Z
2021-06-11T23:56:04Z
MEMBER
null
See https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61339&view=logs&j=b516280a-8c75-541b-8ba0-ae13561718a7&t=a9217cb1-24f6-5625-717d-72085a70e1d3 and https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61324&view=logs&j=b516280a-8c75-541b-8ba0-ae13561718a7&t=a9217cb1-24f6-5625-717d-72085a70e1d3 for some examples on 2 different runs. I've only noticed it on this run, will look to try to see when it started (unless anyone knows :)
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918,098,404
MDExOlB1bGxSZXF1ZXN0NjY3NTY2MjU4
41,938
TYP/BUG: fix CI
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3
2021-06-11T01:04:35Z
2021-06-11T02:33:17Z
2021-06-11T02:33:10Z
CONTRIBUTOR
null
Fix bug introduced in https://github.com/pandas-dev/pandas/pull/41903 Mypy doesn't seem to like generic types with default values?
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918,141,827
MDExOlB1bGxSZXF1ZXN0NjY3NjAzNzY0
41,939
Use from future import annotations
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2
2021-06-11T02:30:22Z
2021-06-11T03:04:24Z
2021-06-11T02:39:56Z
NONE
null
- [X] closes #xxxx - [ ] no tests added - [ ] pre-commit hook, no python files added - [ ] No docstrings added; docs not rebuilt. _**Where do pre-commit hooks get added to “What’s New"?**_ Implementation: * Name set to always-use-future-annotations (rather than always-use-from-future-annotations) * We filter with` types: [python]` [rather than](https://pre-commit.com/#filtering-files-with-types) `files: "\\.(py)$”` * No minimum pre-commit version. What else is needed here for a merge?
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918,147,571
MDU6SXNzdWU5MTgxNDc1NzE=
41,940
TST: Xfails in Python 3.10 CI
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10
2021-06-11T02:41:54Z
2021-10-16T18:47:09Z
null
MEMBER
null
A list of failures for pandas on Python 3.10. Note: Some of these are flaky. - [x] ``TestUnique.test_first_nan_kept`` <Details> ________________________ TestUnique.test_first_nan_kept ________________________ self = <pandas.tests.test_algos.TestUnique object at 0x7fe611cc19c0> def test_first_nan_kept(self): # GH 22295 # create different nans from bit-patterns: bits_for_nan1 = 0xFFF8000000000001 bits_for_nan2 = 0x7FF8000000000001 NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0] NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0] assert NAN1 != NAN1 assert NAN2 != NAN2 for el_type in [np.float64, object]: a = np.array([NAN1, NAN2], dtype=el_type) result = pd.unique(a) > assert result.size == 1 E assert 2 == 1 E + where 2 = array([nan, nan], dtype=object).size pandas/tests/test_algos.py:798: AssertionError </Details> - [x] ``TestIsin.test_different_nans`` <Details> _________________________ TestIsin.test_different_nans _________________________ self = <pandas.tests.test_algos.TestIsin object at 0x7fe611cc2b30> def test_different_nans(self): # GH 22160 # all nans are handled as equivalent comps = [float("nan")] values = [float("nan")] assert comps[0] is not values[0] # different nan-objects # as list of python-objects: result = algos.isin(comps, values) tm.assert_numpy_array_equal(np.array([True]), result) # as object-array: result = algos.isin( np.asarray(comps, dtype=object), np.asarray(values, dtype=object) ) > tm.assert_numpy_array_equal(np.array([True]), result) pandas/tests/test_algos.py:1007: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ left = array([ True]), right = array([False]), err_msg = None def _raise(left, right, err_msg): if err_msg is None: if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shapes are different", left.shape, right.shape ) diff = 0 for left_arr, right_arr in zip(left, right): # count up differences if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan): diff += 1 diff = diff * 100.0 / left.size msg = f"{obj} values are different ({np.round(diff, 5)} %)" > raise_assert_detail(obj, msg, left, right, index_values=index_values) E AssertionError: numpy array are different E E numpy array values are different (100.0 %) E [left]: [True] E [right]: [False] pandas/_testing/asserters.py:726: AssertionError </Details> - [x] ``TestIsin.test_different_nan_objects`` <Details> _____________________ TestIsin.test_different_nan_objects ______________________ self = <pandas.tests.test_algos.TestIsin object at 0x7fe61167ff40> def test_different_nan_objects(self): # GH 22119 comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object) vals = np.array([float("nan")], dtype=object) expected = np.array([False, False, True]) result = algos.isin(comps, vals) > tm.assert_numpy_array_equal(expected, result) pandas/tests/test_algos.py:1039: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ left = array([False, False, True]), right = array([False, False, False]) err_msg = None def _raise(left, right, err_msg): if err_msg is None: if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shapes are different", left.shape, right.shape ) diff = 0 for left_arr, right_arr in zip(left, right): # count up differences if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan): diff += 1 diff = diff * 100.0 / left.size msg = f"{obj} values are different ({np.round(diff, 5)} %)" > raise_assert_detail(obj, msg, left, right, index_values=index_values) E AssertionError: numpy array are different E E numpy array values are different (33.33333 %) E [left]: [False, False, True] E [right]: [False, False, False] pandas/_testing/asserters.py:726: AssertionError </Details> - [x] ``test_hash_equal`` <Details> _________________________ test_hash_equal[a2-b2-True] __________________________ a = Sparse[float64, nan], b = Sparse[float64, nan], expected = True @pytest.mark.parametrize( "a, b, expected", [ (SparseDtype(float, 0.0), SparseDtype(np.dtype("float"), 0.0), True), (SparseDtype(int, 0), SparseDtype(int, 0), True), (SparseDtype(float, float("nan")), SparseDtype(float, np.nan), True), (SparseDtype(float, 0), SparseDtype(float, np.nan), False), (SparseDtype(int, 0.0), SparseDtype(float, 0.0), False), ], ) def test_hash_equal(a, b, expected): result = a == b assert result is expected result = hash(a) == hash(b) > assert result is expected E assert False is True pandas/tests/arrays/sparse/test_dtype.py:141: AssertionError </Details> - [x] ``TestReshaping.test_stack`` <Details> ____________________ TestReshaping.test_stack[nan-columns0] ____________________ self = <pandas.tests.extension.test_sparse.TestReshaping object at 0x7fe5fc4a6d10> data = [0.6069529775354311, 0.6532416455715819, nan, 0.45409596635270144, 0.10948849972327801, nan, 0.08987488776518548, 0.19...66, 67, 69, 70, 72, 73, 75, 76, 78, 79, 81, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99], dtype=int32) columns = ['A', 'B'] @pytest.mark.parametrize( "columns", [ ["A", "B"], pd.MultiIndex.from_tuples( [("A", "a"), ("A", "b")], names=["outer", "inner"] ), ], ) def test_stack(self, data, columns): df = pd.DataFrame({"A": data[:5], "B": data[:5]}) df.columns = columns result = df.stack() expected = df.astype(object).stack() # we need a second astype(object), in case the constructor inferred # object -> specialized, as is done for period. expected = expected.astype(object) if isinstance(expected, pd.Series): assert result.dtype == df.iloc[:, 0].dtype else: assert all(result.dtypes == df.iloc[:, 0].dtype) result = result.astype(object) > self.assert_equal(result, expected) pandas/tests/extension/base/reshaping.py:270: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cls = <class 'pandas.tests.extension.test_sparse.TestReshaping'> left = 0 A 0.606953 B 0.606953 1 A 0.653242 B 0.653242 3 A 0.454096 B 0.454096 4 A 0.109488 B 0.109488 dtype: Sparse[object, nan] right = 0 A 0.606953 B 0.606953 1 A 0.653242 B 0.653242 3 A 0.454096 B 0.454096 4 A 0.109488 B 0.109488 dtype: object kwargs = {} @classmethod def assert_equal(cls, left, right, **kwargs): > return tm.assert_equal(left, right, **kwargs) E AssertionError: Attributes of Series are different E E Attribute "dtype" are different E [left]: Sparse[object, nan] E [right]: object pandas/tests/extension/base/base.py:9: AssertionError ____________________ TestReshaping.test_stack[nan-columns1] ____________________ self = <pandas.tests.extension.test_sparse.TestReshaping object at 0x7fe5fbea8a30> data = [0.08642533151098042, 0.31948867044255314, nan, 0.7649119528014422, 0.8562776453950798, nan, 0.0026917407695283835, 0....66, 67, 69, 70, 72, 73, 75, 76, 78, 79, 81, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99], dtype=int32) columns = MultiIndex([('A', 'a'), ('A', 'b')], names=['outer', 'inner']) @pytest.mark.parametrize( "columns", [ ["A", "B"], pd.MultiIndex.from_tuples( [("A", "a"), ("A", "b")], names=["outer", "inner"] ), ], ) def test_stack(self, data, columns): df = pd.DataFrame({"A": data[:5], "B": data[:5]}) df.columns = columns result = df.stack() expected = df.astype(object).stack() # we need a second astype(object), in case the constructor inferred # object -> specialized, as is done for period. expected = expected.astype(object) if isinstance(expected, pd.Series): assert result.dtype == df.iloc[:, 0].dtype else: assert all(result.dtypes == df.iloc[:, 0].dtype) result = result.astype(object) > self.assert_equal(result, expected) pandas/tests/extension/base/reshaping.py:270: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cls = <class 'pandas.tests.extension.test_sparse.TestReshaping'> left = outer A inner 0 a 0.086425 b 0.086425 1 a 0.319489 b 0.319489 3 a 0.764912 b 0.764912 4 a 0.856278 b 0.856278 right = outer A inner 0 a 0.086425 b 0.086425 1 a 0.319489 b 0.319489 3 a 0.764912 b 0.764912 4 a 0.856278 b 0.856278 kwargs = {} @classmethod def assert_equal(cls, left, right, **kwargs): > return tm.assert_equal(left, right, **kwargs) E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="A") are different E E Attribute "dtype" are different E [left]: Sparse[object, nan] E [right]: object pandas/tests/extension/base/base.py:9: AssertionError </Details> - [x] ``TestDataFrameSelectReindex.test_reindex_nan`` <Details> _________________ TestDataFrameSelectReindex.test_reindex_nan __________________ self = <pandas.tests.frame.methods.test_reindex.TestDataFrameSelectReindex object at 0x7fe5f9d03f40> def test_reindex_nan(self): df = DataFrame( [[1, 2], [3, 5], [7, 11], [9, 23]], index=[2, np.nan, 1, 5], columns=["joe", "jim"], ) i, j = [np.nan, 5, 5, np.nan, 1, 2, np.nan], [1, 3, 3, 1, 2, 0, 1] tm.assert_frame_equal(df.reindex(i), df.iloc[j]) df.index = df.index.astype("object") > tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False) E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="joe") are different E E Attribute "dtype" are different E [left]: float64 E [right]: int64 pandas/tests/frame/methods/test_reindex.py:472: AssertionError </Details> - [x] ``TestIndex.test_isin_nan_common_object`` <Details> _____________ TestIndex.test_isin_nan_common_object[float0-float1] _____________ self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f6a4d930> nulls_fixture = nan, nulls_fixture2 = nan def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2): # Test cartesian product of null fixtures and ensure that we don't # mangle the various types (save a corner case with PyPy) # all nans are the same if ( isinstance(nulls_fixture, float) and isinstance(nulls_fixture2, float) and math.isnan(nulls_fixture) and math.isnan(nulls_fixture2) ): > tm.assert_numpy_array_equal( Index(["a", nulls_fixture]).isin([nulls_fixture2]), np.array([False, True]), ) pandas/tests/indexes/test_base.py:1006: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ left = array([False, False]), right = array([False, True]), err_msg = None def _raise(left, right, err_msg): if err_msg is None: if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shapes are different", left.shape, right.shape ) diff = 0 for left_arr, right_arr in zip(left, right): # count up differences if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan): diff += 1 diff = diff * 100.0 / left.size msg = f"{obj} values are different ({np.round(diff, 5)} %)" > raise_assert_detail(obj, msg, left, right, index_values=index_values) E AssertionError: numpy array are different E E numpy array values are different (50.0 %) E [left]: [False, False] E [right]: [False, True] pandas/_testing/asserters.py:726: AssertionError _____________ TestIndex.test_isin_nan_common_object[float1-float0] _____________ self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f5e186a0> nulls_fixture = nan, nulls_fixture2 = nan def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2): # Test cartesian product of null fixtures and ensure that we don't # mangle the various types (save a corner case with PyPy) # all nans are the same if ( isinstance(nulls_fixture, float) and isinstance(nulls_fixture2, float) and math.isnan(nulls_fixture) and math.isnan(nulls_fixture2) ): > tm.assert_numpy_array_equal( Index(["a", nulls_fixture]).isin([nulls_fixture2]), np.array([False, True]), ) pandas/tests/indexes/test_base.py:1006: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ left = array([False, False]), right = array([False, True]), err_msg = None def _raise(left, right, err_msg): if err_msg is None: if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shapes are different", left.shape, right.shape ) diff = 0 for left_arr, right_arr in zip(left, right): # count up differences if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan): diff += 1 diff = diff * 100.0 / left.size msg = f"{obj} values are different ({np.round(diff, 5)} %)" > raise_assert_detail(obj, msg, left, right, index_values=index_values) E AssertionError: numpy array are different E E numpy array values are different (50.0 %) E [left]: [False, False] E [right]: [False, True] pandas/_testing/asserters.py:726: AssertionError _____________ TestIndex.test_isin_nan_common_object[float1-float1] _____________ self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f6ab3eb0> nulls_fixture = nan, nulls_fixture2 = nan def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2): # Test cartesian product of null fixtures and ensure that we don't # mangle the various types (save a corner case with PyPy) # all nans are the same if ( isinstance(nulls_fixture, float) and isinstance(nulls_fixture2, float) and math.isnan(nulls_fixture) and math.isnan(nulls_fixture2) ): > tm.assert_numpy_array_equal( Index(["a", nulls_fixture]).isin([nulls_fixture2]), np.array([False, True]), ) pandas/tests/indexes/test_base.py:1006: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ left = array([False, False]), right = array([False, True]), err_msg = None def _raise(left, right, err_msg): if err_msg is None: if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shapes are different", left.shape, right.shape ) diff = 0 for left_arr, right_arr in zip(left, right): # count up differences if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan): diff += 1 diff = diff * 100.0 / left.size msg = f"{obj} values are different ({np.round(diff, 5)} %)" > raise_assert_detail(obj, msg, left, right, index_values=index_values) E AssertionError: numpy array are different E E numpy array values are different (50.0 %) E [left]: [False, False] E [right]: [False, True] pandas/_testing/asserters.py:726: AssertionError </Details> - [ ] ``TestUltraJSONTests.test_invalid_double_precision`` <Details> _____________ TestUltraJSONTests.test_invalid_double_precision[9] ______________ self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d780> invalid_val = '9' @pytest.mark.parametrize("invalid_val", [20, -1, "9", None]) def test_invalid_double_precision(self, invalid_val): double_input = 30.12345678901234567890 expected_exception = ValueError if isinstance(invalid_val, int) else TypeError msg = ( r"Invalid value '.*' for option 'double_precision', max is '15'|" r"an integer is required \(got type " ) with pytest.raises(expected_exception, match=msg): > ujson.encode(double_input, double_precision=invalid_val) E TypeError: 'str' object cannot be interpreted as an integer pandas/tests/io/json/test_ujson.py:260: TypeError During handling of the above exception, another exception occurred: self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d780> invalid_val = '9' @pytest.mark.parametrize("invalid_val", [20, -1, "9", None]) def test_invalid_double_precision(self, invalid_val): double_input = 30.12345678901234567890 expected_exception = ValueError if isinstance(invalid_val, int) else TypeError msg = ( r"Invalid value '.*' for option 'double_precision', max is '15'|" r"an integer is required \(got type " ) > with pytest.raises(expected_exception, match=msg): E AssertionError: Regex pattern "Invalid value '.*' for option 'double_precision', max is '15'|an integer is required \\(got type " does not match "'str' object cannot be interpreted as an integer". pandas/tests/io/json/test_ujson.py:259: AssertionError ____________ TestUltraJSONTests.test_invalid_double_precision[None] ____________ self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d990> invalid_val = None @pytest.mark.parametrize("invalid_val", [20, -1, "9", None]) def test_invalid_double_precision(self, invalid_val): double_input = 30.12345678901234567890 expected_exception = ValueError if isinstance(invalid_val, int) else TypeError msg = ( r"Invalid value '.*' for option 'double_precision', max is '15'|" r"an integer is required \(got type " ) with pytest.raises(expected_exception, match=msg): > ujson.encode(double_input, double_precision=invalid_val) E TypeError: 'NoneType' object cannot be interpreted as an integer pandas/tests/io/json/test_ujson.py:260: TypeError During handling of the above exception, another exception occurred: self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d990> invalid_val = None @pytest.mark.parametrize("invalid_val", [20, -1, "9", None]) def test_invalid_double_precision(self, invalid_val): double_input = 30.12345678901234567890 expected_exception = ValueError if isinstance(invalid_val, int) else TypeError msg = ( r"Invalid value '.*' for option 'double_precision', max is '15'|" r"an integer is required \(got type " ) > with pytest.raises(expected_exception, match=msg): E AssertionError: Regex pattern "Invalid value '.*' for option 'double_precision', max is '15'|an integer is required \\(got type " does not match "'NoneType' object cannot be interpreted as an integer". pandas/tests/io/json/test_ujson.py:259: AssertionError </Details> - [x] ``TestGetDummies.test_get_dummies_basic_drop_first_NA`` <Details> __________ TestGetDummies.test_get_dummies_basic_drop_first_NA[dense] __________ self = <pandas.tests.reshape.test_get_dummies.TestGetDummies object at 0x7fe5e7b1d7b0> sparse = False def test_get_dummies_basic_drop_first_NA(self, sparse): # Test NA handling together with drop_first s_NA = ["a", "b", np.nan] res = get_dummies(s_NA, drop_first=True, sparse=sparse) exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8) if sparse: exp = exp.apply(SparseArray, fill_value=0) tm.assert_frame_equal(res, exp) res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse) exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex( ["b", np.nan], axis=1 ) if sparse: exp_na = exp_na.apply(SparseArray, fill_value=0) > tm.assert_frame_equal(res_na, exp_na) E AssertionError pandas/tests/reshape/test_get_dummies.py:485: AssertionError </Details> - [x] ``test_reindex_nan`` <Details> _______________________________ test_reindex_nan _______________________________ def test_reindex_nan(): ts = Series([2, 3, 5, 7], index=[1, 4, np.nan, 8]) i, j = [np.nan, 1, np.nan, 8, 4, np.nan], [2, 0, 2, 3, 1, 2] tm.assert_series_equal(ts.reindex(i), ts.iloc[j]) ts.index = ts.index.astype("object") # reindex coerces index.dtype to float, loc/iloc doesn't > tm.assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False) E AssertionError: Attributes of Series are different E E Attribute "dtype" are different E [left]: float64 E [right]: int64 </Details> - [x] ``test_map_missing_mixed`` <Details> _________________ test_map_missing_mixed[vals0-mapping0-exp0] __________________ [gw1] linux -- Python 3.10.0 /opt/hostedtoolcache/Python/3.10.0-beta.2/x64/bin/python vals = ['a', 'b', 'c'], mapping = {nan: 'not NaN'} exp = [nan, nan, nan, 'not NaN'] @pytest.mark.parametrize( "vals,mapping,exp", [ (list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]), (list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3), (list(range(3)), {0: 42}, [42] + [np.nan] * 3), ], ) def test_map_missing_mixed(vals, mapping, exp): # GH20495 s = Series(vals + [np.nan]) result = s.map(mapping) > tm.assert_series_equal(result, Series(exp)) pandas/tests/apply/test_series_apply.py:821: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal cpdef assert_almost_equal(a, b, _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > raise_assert_detail(obj, msg, lobj, robj, index_values=index_values) E AssertionError: Series are different E E Series values are different (25.0 %) E [index]: [0, 1, 2, 3] E [left]: [nan, nan, nan, nan] E [right]: [nan, nan, nan, not NaN] pandas/_libs/testing.pyx:168: AssertionError </Details> NOTE: This list is not complete since there are also some failing JSON tests that freeze pytest. Will look at those soon.
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2021-06-11T02:53:40Z
2021-08-17T02:29:26Z
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NONE
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- [X] closes #41901 - [ ] no tests added - [ ] pre-commit hook, no python files added - [ ] No docstrings added; docs not rebuilt. _**Where do pre-commit hooks get added to “What’s New"?**_ Implementation: * Name set to always-use-future-annotations (rather than always-use-from-future-annotations) * We filter with` types: [python]` [rather than](https://pre-commit.com/#filtering-files-with-types) `files: "\\.(py)$”` * No minimum pre-commit version. What else is needed here for a merge?
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BUG: initial prompt for reading .xlsx files misleadingly refers to xlrd, not openpyxl
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2021-06-11T04:50:13Z
2021-06-14T22:48:42Z
2021-06-14T22:48:42Z
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 pd.read_excel("foo.xlsx") ``` Example (empty) excel file: https://github.com/pandas-dev/pandas/files/6635871/foo.xlsx #### Problem description Running the code example emits an error message that could be very helpful: ``` ImportError: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd. ``` However, as of version 2, the xlrd library only supports xls files, not xlsx (https://github.com/pandas-dev/pandas/issues/38410). This instruction is thus misleading: running `pip install xlrd` and then rerunning the code still fails: ``` ValueError: Your version of xlrd is 2.0.1. In xlrd >= 2.0, only the xls format is supported. Install openpyxl instead. ``` The correct/final resolution is thus to run `pip install openpyxl`. It's a bit of papercut to go though several steps here, especially as I suspect xlsx files are more common than xls these days (at least, it's been ~15-20 years since .xlsx was released according to https://en.wikipedia.org/wiki/Office_Open_XML). #### Expected Output It would be nice if the original error message made it clearer what was going on. For instance, options might be: 1. detect the file type/extension and reference the appropriate library (i.e. for .xlsx `ImportError: Missing optional dependency 'openpyxl'. Install openpyxl for .xlsx Excel support. Use pip or conda to install openpyxl.`) 2. mention both libraries so the human can make the decision `ImportError: Missing optional dependency 'xlrd' (for xls) 'openpyxl' (for xlsx). Install the appropriate one of these for Excel support. Use pip or conda to install xlrd or openpyxl.`) 2. suggest installing `xlrd>=1.0.0,<2.0.0` (I personally feel that 1 would be the nicest.) #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.8.7.final.0 python-bits : 64 OS : Darwin OS-release : 20.2.0 Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:40:21 PST 2020; root:xnu-7195.60.75~1/RELEASE_ARM64_T8101 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : en_AU.UTF-8 LOCALE : en_AU.UTF-8 pandas : 1.2.4 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 20.2.3 setuptools : 49.2.1 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 fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details> After `pip install xlrd`, it reports: xlrd: 2.0.1.
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MDU6SXNzdWU5MTgyNTQxMTc=
41,943
API: Make 'D' & offsets.Day always operate as calendar day instead of 24 Hour
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2021-06-11T05:43:20Z
2021-06-11T05:43:20Z
null
MEMBER
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From 2021-06-09 dev call, new discussion of https://github.com/pandas-dev/pandas/issues/22864 **Problem**: Frequency string `'D'` and `pd.offsets.Day` can mean 24 hours or calendar day (23/24/25H) depending on the operation **Original Settled Solution**: Deprecate all behavior where Frequency string `'D'` and `pd.offsets.Day` is a fixed 24 hours in favor of `"24H"`. A private `_Day` offset would be used where appropriate internally and swapped out once the deprecation is enforced. (Note: I lost steam last time catching all the warnings issued in the testing suite given the above solution touches datetimes, timedeltas, offsets, methods, etc) **Other Solutions** 1. Implement a new offset, e.g. `"'DayDST'"/pd.offsets.CalendarDay"`, that users can migrate to. 2. Deprecate `Tick`s (`Day` is a subclass) all together since they are redundant with `Timedelta`s cc @pandas-dev/pandas-core
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918,468,260
MDExOlB1bGxSZXF1ZXN0NjY3ODk0NzY5
41,944
[ArrayManager] Enable pytables IO by falling back to BlockManager
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3
2021-06-11T09:03:05Z
2021-06-11T11:52:12Z
2021-06-11T11:20:52Z
MEMBER
null
This doesn't "fix" anything, but by falling back for now to the BlockManager-specific implementation of `to_hdf` writer, this avoids failures when using ArrayManager, which should make it easier to test it.
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41,945
TYP: make numpy.typing available from pandas._typing
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2021-06-11T09:51:13Z
2021-07-02T19:12:48Z
2021-06-25T22:16:43Z
MEMBER
null
xref https://github.com/pandas-dev/pandas/issues/41807#issuecomment-859372500 this does not update the `__array__` to `to_numpy` methods of EA, see https://github.com/pandas-dev/pandas/pull/41185#issuecomment-852469918 but does need a couple of ignores till that is done. (we are removing 3 anyway, so a net reduction in ignores :smile:, and reducing false positives for users)
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ENH: `sparse_columns` and `sparse_index` added to `Styler.to_html`
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11
2021-06-11T09:53:35Z
2021-07-29T14:59:15Z
2021-07-28T23:01:05Z
CONTRIBUTOR
null
Checks an item off the list in #41693
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918,581,165
MDU6SXNzdWU5MTg1ODExNjU=
41,947
ENH: Give name to index column as argument when using `df.reset_index`
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2021-06-11T10:45:20Z
2021-08-21T02:26:14Z
2021-08-21T02:26:14Z
CONTRIBUTOR
null
#### Is your feature request related to a problem? When resetting the index of a `DataFrame`, and when needing to rename the "index" column to something more useful, it would be nice to do it directly with the call to`reset_index` (and not afterwards with `df.rename`). #### Describe the solution you'd like `DataFrame.reset_index` should get a new parameter `name` that renames the "index" column after resetting. #### API breaking implications I don't see any obvious implications.
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CI: use pyarrow 0.17.1 instead of 0.17.0 for py37_macos build
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6
2021-06-11T11:30:36Z
2021-06-12T08:31:36Z
2021-06-11T23:56:04Z
MEMBER
null
Closes #41937
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MDU6SXNzdWU5MTg2NDU1Mzc=
41,949
BUG: unpickle of Timestamp should not generate deprecation warning
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1
2021-06-11T11:48:36Z
2021-06-24T13:07:20Z
2021-06-21T13:18:36Z
MEMBER
null
Currently we give a deprecation warning when unpickling: ``` In [10]: idx = pd.date_range("2012", periods=3, freq="D") In [11]: res = pickle.dumps(idx[0]) In [12]: pickle.loads(res) /home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version. #!/home/joris/miniconda3/envs/dev/bin/python Out[12]: Timestamp('2012-01-01 00:00:00', freq='D') ``` We should ensure that unpickling a Timestamp does not generate a warning, only if the user afterwards accesses the freq as normal it should warn.
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DOC: fix link to pickle in msgpack section
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2
2021-06-11T12:22:09Z
2021-06-11T14:37:41Z
2021-06-11T14:33:27Z
MEMBER
null
That failed the doc build. In addition also removed the msgpack row in the table at the top of the page, since those functions don't exist anymore.
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DataFrame.melt fails if there are duplicate value_vars
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4
2021-06-11T12:33:10Z
2021-06-15T23:34:24Z
2021-06-15T23:34:24Z
NONE
null
DataFrame.columns.get_indexer fails if there are duplicate column names. https://github.com/pandas-dev/pandas/blob/05552d354a430f17a45b5fda8a1fd417f4c60c2e/pandas/core/reshape/melt.py#L109 This is really unfortunate -- melting should handle such cases id value value id1 2 3 should go to id1 2 id1 3
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MDExOlB1bGxSZXF1ZXN0NjY4MjUyNzMy
41,952
BUG: take nans correctly into consideration in complex and tuple
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14
2021-06-11T15:06:58Z
2021-06-16T22:15:53Z
2021-06-16T22:15:37Z
CONTRIBUTOR
null
- [x] closes #41836 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry NaNs as Float are already handled correctly (all nans are the same equavalency class), this PR adds similar functionality for complex and tuple. It doesn't handle `frozenset` (see also discussion in #41836) because it would require to duplicate a lot of code/internal details from setobject.
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918,952,566
MDU6SXNzdWU5MTg5NTI1NjY=
41,953
BUG: nan-objects lookup fails in Python3.10
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0
2021-06-11T16:24:03Z
2021-06-17T19:05:36Z
2021-06-17T19:05:36Z
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. --- Due to this change in CPython: https://github.com/python/cpython/commit/a07da09ad5bd7d234ccd084a3a0933c290d1b592 the following assumption is no longer true: https://github.com/pandas-dev/pandas/blob/059c8bac51e47d6eaaa3e36d6a293a22312925e6/pandas/_libs/src/klib/khash_python.h#L183-L187 Here is a failing example: ```python _____________________ TestIsin.test_different_nan_objects ______________________ self = <pandas.tests.test_algos.TestIsin object at 0x7f9ba1fe7250> def test_different_nan_objects(self): # GH 22119 comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object) vals = np.array([float("nan")], dtype=object) expected = np.array([False, False, True]) result = algos.isin(comps, vals) > tm.assert_numpy_array_equal(expected, result) pandas/tests/test_algos.py:1039: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ left = array([False, False, True]), right = array([False, False, False]) ```
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DOC: more cleanup of 1.3 release notes
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1
2021-06-11T16:32:25Z
2021-06-15T13:11:10Z
2021-06-15T13:11:02Z
MEMBER
null
and fixes https://github.com/pandas-dev/pandas/pull/41908#issuecomment-858571515
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41,955
CLN: Replace FrameOrSeriesUnion annotation by DataFrame | Series
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15
2021-06-11T18:16:38Z
2021-07-04T17:47:26Z
2021-07-04T10:06:54Z
CONTRIBUTOR
null
- [x] closes #40876 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NjY4NTA5MTA4
41,956
REF: avoid accessing ._engine in Series/DataFrame
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1
2021-06-11T19:54:49Z
2021-06-17T18:51:40Z
2021-06-17T17:39:52Z
MEMBER
null
sits on top of #41846
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RLS: 1.4/2.0
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11
2021-06-11T19:59:45Z
2021-11-11T14:29:24Z
null
MEMBER
null
Tracking issue for the 1.4 release. (it's possible we make this 2.0 at some point - not determined yet) https://github.com/pandas-dev/pandas/milestone/86 Currently scheduled for December 31, 2021 (with a release candidate around 2 weeks before) List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression
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REGR: Series[dt64/td64].astype(string)
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2
2021-06-11T23:15:36Z
2021-06-15T21:43:58Z
2021-06-15T21:04:41Z
MEMBER
null
- [x] closes #41409 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry This surfaces another bug in `StringArray.__getitem__` that I haven't looked into yet.
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REF: avoid accessing index._engine in set_with_engine
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2021-06-11T23:29:00Z
2021-06-16T00:24:39Z
2021-06-16T00:05:21Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry orthogonal but related to #41956
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CLN: assert_index_equal & assert_class_equal
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2021-06-11T23:40:55Z
2021-06-13T10:06:53Z
2021-06-13T09:18:37Z
CONTRIBUTOR
null
Minor clean-up.
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Debug failing numpy
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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CI: enable auto-cancel for GitHub Actions
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Backport PR #41924: PERF: fix regression in reductions for boolean/integer data
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2021-06-12T07:29:50Z
2021-06-12T13:20:56Z
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Backport PR #41924
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41,964
Bug in melt raising Error with dup columns as value vars
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2021-06-18T10:43:11Z
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- [x] closes #41951 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry @simonjayhawkins I think this changed in May last year, so probably not woth backporting?
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BUG: dropna('rows') changes index type even when no row was dropped
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1
2021-06-12T12:22:58Z
2021-07-01T20:33:27Z
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after dropna('rows'), the dataframe index changes from RangeIndex to Int64Index even when nothing changed in the dataframe. reset_index after dropna will fix it, but it should be avoided in the first place. #### Code Sample, a copy-pastable example ```python df = pd.DataFrame ({'a':range(1000)}) df.info() df = df.dropna ('rows') print ('\nafter dropna\n') df.info() ``` output: ``` <class 'pandas.core.frame.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 a 1000 non-null int64 dtypes: int64(1) memory usage: 7.9 KB after dropna <class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 0 to 999 Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 a 1000 non-null int64 dtypes: int64(1) memory usage: 15.6 KB ``` #### Problem description changing from RangeIndex to Int64Index increases the dataframe size, for no good reason. #### Output of ``pd.show_versions()`` <details> commit : db08276bc116c438d3fdee492026f8223584c477 python : 3.8.5.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_Israel.1252 pandas : 1.1.3 numpy : 1.19.2 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.4 setuptools : 50.3.1.post20201107 Cython : 0.29.21 pytest : 6.1.1 hypothesis : None sphinx : 3.2.1 blosc : None feather : None xlsxwriter : 1.3.7 lxml.etree : 4.6.1 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.10.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.8.3 fastparquet : None gcsfs : None matplotlib : 3.3.2 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : 0.15.1 pytables : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.3.20 tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.51.2 </details>
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41,966
CI: activate azure pipelines/github actions on 1.3.x
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3
2021-06-12T13:08:38Z
2021-06-13T16:54:02Z
2021-06-13T07:50:54Z
MEMBER
null
This is milestoned 1.4 since the config is on master. (and we don't want the autobackport)
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41,967
ENH: Modify .any() to return Null if all values are null
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5
2021-06-12T14:20:59Z
2021-10-04T01:37:37Z
2021-10-04T01:37:37Z
NONE
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#### Is your feature request related to a problem? For sparce data it is particuarly helpful to preserve null values when doing reduction operations. I end up using this (see below) wrapper function to achieve the intended result. ``` def any_nan(x): if pd.isnull(x).all(): return pd.NA else: return x.any(skipna=True) output = df.column_to_operate_on.apply(any_nan) ``` #### Describe the solution you'd like The ask is to add a configuration parameter to. `.any()` and `.all()` that optionally preserves null values when returning the array. Skipna doesn't seem to have to correct functionality as it will return false, if all the values of the array are null. Ideally, with the toggleable parameter, `.any()` would return `np.nan/pd.NA` if all values in the array are null. #### API breaking implications This shouldn't change the api. It will just add an optional parameter #### Describe alternatives you've considered `pd.NA`
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41,968
DOC: 1.3.0 release notes addition and doc fix
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2021-06-12T17:43:34Z
2021-06-16T13:22:17Z
2021-06-15T19:44:39Z
CONTRIBUTOR
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BUG: stacklevel for multidimensional indexing warning #31479
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2021-06-12T17:59:57Z
2021-06-16T00:30:28Z
2021-06-15T23:48:02Z
MEMBER
null
- [x] closes #31479 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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41,970
ENH: nullables use Kleene logic for any/all reductions
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4
2021-06-12T19:19:37Z
2021-09-21T20:59:42Z
2021-09-21T14:09:19Z
MEMBER
null
- [x] xref #41967 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry This gives consistency for nullable dtype treatment across `.any/.all` and the groupby versions which already used Kleene logic. Diff looks big, but this is just moving `BooleanArray.any` and `BooleanArray.all` to `BaseMaskedArray`
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919,648,387
MDExOlB1bGxSZXF1ZXN0NjY4OTQxMTE0
41,971
CI: mark window online test slow
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7
2021-06-12T19:43:59Z
2021-06-15T04:52:36Z
2021-06-15T01:12:49Z
MEMBER
null
Seems like the new online tests added increase test runtime a lot, making failures like https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61437&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a and https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61437&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a more common. Slowest durations showing this are below, have marked these as slow as a potential solution ``` ============================ slowest 30 durations ============================= 99.91s call pandas/tests/resample/test_base.py::test_resample_interpolate[period_range-pi-_index_start1-_index_end1] 47.56s call pandas/tests/resample/test_period_index.py::TestPeriodIndex::test_weekly_upsample[end-D-FRI] 47.36s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-True-obj1] 45.55s teardown pandas/tests/window/test_rolling.py::test_rolling_zero_window 30.25s call pandas/tests/tslibs/test_timezones.py::test_cache_keys_are_distinct_for_pytz_vs_dateutil[America/Argentina/San_Luis] 28.37s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-True-obj0] 23.03s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-True-obj1] 23.00s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-True-obj1] 20.95s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-True-obj0] 20.30s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-True-obj0] 19.94s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-False-obj1] 19.81s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-False-obj0] 19.37s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-False-obj1] 19.25s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-False-obj0] 18.13s call pandas/tests/test_sorting.py::TestSorting::test_int64_overflow_moar 18.10s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-False-obj0] 18.07s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-False-obj1] 17.17s call pandas/tests/window/test_numba.py::TestEngine::test_numba_vs_cython_apply[True-False-False-False-True] 17.16s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-True-obj1] 16.84s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-True-False-True-obj1] 16.75s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-True-obj0] 16.48s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-True-True-True-obj1] 16.47s setup pandas/tests/io/test_fsspec.py::test_from_s3_csv 16.36s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-False-True-True-obj1] 15.93s call pandas/tests/io/test_compression.py::test_with_missing_lzma 15.83s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-False-False-True-obj1] 15.13s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-False-obj1] 14.46s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-True-True-True-obj0] 14.14s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-False-obj0] 14.00s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-False-True-False-obj0] ```
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919,653,115
MDExOlB1bGxSZXF1ZXN0NjY4OTQ0OTA4
41,972
PERF: indexing on UInt64Index with int64s
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5
2021-06-12T20:08:10Z
2021-06-15T21:44:19Z
2021-06-15T21:05:57Z
MEMBER
null
- [x] closes #41873 - [ ] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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41,973
BUG: numpy.char funcs error with string array citing non-string array.
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2021-06-12T20:54:05Z
2021-07-01T20:39:14Z
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CONTRIBUTOR
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- [x] I have checked that this issue has not already been reported - **_briefly_** - [ ] 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. --- ```python s = Series(["a", "b"]) np.char.find(s, "b") # or np.capitalize(s) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-9-a924fee0248d> in <module> ----> 1 np.char.capitalize(s) <__array_function__ internals> in capitalize(*args, **kwargs) ~/envPandas38/lib/python3.8/site-packages/numpy/core/defchararray.py in capitalize(a) 414 """ 415 a_arr = numpy.asarray(a) --> 416 return _vec_string(a_arr, a_arr.dtype, 'capitalize') 417 418 TypeError: string operation on non-string array ``` #### Problem description This fails even though `s` is composed of strings and even things like `s.str` `s.astype(str)`, `dtype=str` make no difference. #### Expected Output numpy string functions should work <details> pandas master as of 12 jun 21. numpy 1.20.3 </details>
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41,974
BUG: UInt64Index.where with int64 value
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2021-06-16T00:26:00Z
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API: loc indexing with nested sequence raise on missing
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2021-06-12T21:35:55Z
2021-06-13T22:37:22Z
2021-06-13T22:37:22Z
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I think the current behavior is left over from the long long ago. ``` mi = pd.MultiIndex.from_product([range(3), ["A", "B"]]) ser = pd.Series(len(mi), index=mi) >>> ser.loc[1, ["A", "B", "not a key"]] 1 A 6 B 6 dtype: int64 >>> ser.loc[[99999], ["A", "B"]] Series([], dtype: int64) >>> ser.loc[[99999]] KeyError: '[99999] not in index' ``` I think we should deprecate this and eventually raise like we would with any other missing label.
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REF: De-duplicate MultiIndex._get_indexer
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2021-06-18T02:23:24Z
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xref #40169
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BUG: PeriodIndex inconsistent deserialization with HDF5 - PyTables
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- [x] I have checked that this issue has not already been reported. There was a issue 5 years ago mentioned that `.to_hdf()` acts inconsistently across Python2 & 3 on `PeriodIndex` for fixed format https://github.com/pandas-dev/pandas/issues/16781 - [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. The bug exists, but behavior is different - see next comment --- I noticed that the deserialization of a pandas Series/DataFrame with `PeriodIndex` from HDF5 file is inconsistent when using PyTables format: The retrieved series/df index will be converted to `Int64Index` instead of `PeriodIndex`: See code below for example ```python import pandas as pd store = pd.HDFStore('test.h5') series = pd.Series(index=pd.date_range(start='2015-01', end='2016-01', freq='M'), data=0).to_period('M') df = pd.DataFrame(index=pd.date_range(start='2015-01', end='2016-01', freq='M'), data=0, columns=['a']).to_period('M') store.put('/a/a', series, format='table') store.put('/a/b', df, format='table') ``` ```python store.select('/a/a') ``` Output: ``` 540 0 541 0 542 0 543 0 544 0 545 0 546 0 547 0 548 0 549 0 550 0 551 0 dtype: int64 ``` ```python store.select('/a/b').index ``` Output: ``` Int64Index([540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551], dtype='int64') ``` #### Problem description Inconsistent output with HDF5 file & PyTables format #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.1.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : English_United States.1252 pandas : 1.2.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 20.3.1 setuptools : 51.0.0.post20201207 Cython : None pytest : 6.2.4 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.4.3 lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.24.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 4.0.1 pyxlsb : None s3fs : None scipy : 1.6.3 sqlalchemy : None tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : None </details>
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Backport PR #41977 on branch 1.3.x (BLD: ignore multiple types of file in wheel)
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2021-06-13T05:39:23Z
2021-06-13T07:38:13Z
2021-06-13T07:38:13Z
NONE
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Backport PR #41977: BLD: ignore multiple types of file in wheel
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REF: Refactor assert_index_equal
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3
2021-06-13T09:44:51Z
2021-06-18T05:22:27Z
2021-06-18T01:46:49Z
CONTRIBUTOR
null
There are currently some issues in `_check_dtypes` in `assert_index_equal` related to #41153: * `assert_attr_equal("dtype", left, right, obj=obj)` currently isn't run if `check_categorical` is False (even if the indexes are not CategoricalIndexes) * `assert_class_equal` with `exact='equiv'` should not be restricted to accept equivalence of `Int64Index` and `RangeIndex`, but should accept equivalence of all subclasses of `NumericIndex` (in preparation for #41153). So, comparing numeric indexes with `assert_index_equal(idx1, idx2, exact="equiv")` we should for otherwise equal content have: * RangeIndex and Int64Index should pass, because they're both numeric indexes and have the same dtype * Int64Index and Float64Index should fail, because even though they're both numeric indexes, they have different dtypes * Int64Index and NumericIndex[int64] should pass, because they're both numeric indexes and have the same dtype * RangeIndex and NumericIndex[int64] should pass, because they're both numeric indexes and have the same dtype * Float64Index and NumericIndex[int64] should fail, because even though they're both numeric indexes, they have different dtypes Conversely, with `exact=True` class comparison should be strict, so: * RangeIndex and Int64Index should fail, because they're different classes, even though they have the same dtype * Int64Index and NumericIndex[int64] should fail, because they're different classes, even though they have the same dtype * etc. This refactoring does not change behaviour in master, but will make #41153 easier.
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BLD: Update MANIFEST.in
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2021-06-13T11:25:41Z
2021-06-13T16:39:18Z
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MEMBER
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xref https://github.com/pandas-dev/pandas/pull/41977#issuecomment-860171367
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TST: fix xpass for M1 Mac
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3
2021-06-13T14:41:44Z
2021-06-18T02:23:08Z
2021-06-15T23:33:23Z
MEMBER
null
xref #38921 xref https://github.com/numpy/numpy/issues/19146
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Backport PR #41981 on branch 1.3.x (BLD: Update MANIFEST.in)
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1
2021-06-13T16:39:43Z
2021-06-13T16:51:58Z
2021-06-13T16:51:58Z
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Backport PR #41981: BLD: Update MANIFEST.in
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2021-06-13T19:26:09Z
2021-06-13T19:26:09Z
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Backport PR #41966: CI: activate azure pipelines/github actions on 1.3.x
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DOC: update array-like parameters if scalars accepted
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1
2021-06-13T17:53:39Z
2021-07-19T12:00:54Z
2021-07-14T23:47:06Z
CONTRIBUTOR
null
- [x] based on discussion in #41807 - [x] tests added / passed - N/A since only doc changes - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - N/A since only doc changes - [x] whatsnew entry - Not needed Based on discussion, I went through all docstrings that had the regex `":.*array-like"` and added a scalar as an accepted argument where appropriate.
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STYLE Investigate restructuredtext_lint as pre-commit hook
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2
2021-06-13T18:50:51Z
2021-07-16T11:32:15Z
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MEMBER
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pytest have such a hook to check rst files https://github.com/pytest-dev/pytest/blob/main/.pre-commit-config.yaml : ```yaml - id: rst name: rst entry: rst-lint --encoding utf-8 files: ^(RELEASING.rst|README.rst|TIDELIFT.rst)$ language: python additional_dependencies: [pygments, restructuredtext_lint] ``` Would this be worth including in pandas? Investigations would be welcome
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TST: Un-xfail tests on numpy-dev
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2021-06-13T19:05:06Z
2021-06-17T11:36:01Z
2021-06-14T16:31:45Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry Makes 3.8 npdev green.
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BUG: nan-objects lookup fails with Python3.10
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4
2021-06-13T20:13:41Z
2021-06-17T19:06:14Z
2021-06-17T19:05:36Z
CONTRIBUTOR
null
- [x] closes #41953 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them This builds upon #41952, which should be integrated first. We no longer can assume that the CPython's hash function for elements with NaN (float, complex, tuple) will do the right thing (see https://github.com/python/cpython/commit/a07da09ad5bd7d234ccd084a3a0933c290d1b592) and thus need to replace the default CPython's hash-function with our own, similar to the way we handle equality-operator. We need to use old CPython hash functions for `float` and `complex`, because we would like to keep the equivalency `int(k)==float(k)==complex(k, 0.0)`. We have more freedom with tuple's hash-function, but still use CPython's implementation, so we get old hash-values.
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DEPS: drop Python 3.7 and NumPy 1.17 from tests (#41678)
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10
2021-06-13T22:22:56Z
2021-08-05T10:22:23Z
2021-07-04T21:44:01Z
MEMBER
null
closes #41678 Most of the new minimum support versions were released between Oct 14, 2019, Python 3.8's initial release date, and June/July 2020.
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CI: troubleshoot py310 build
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8
2021-06-14T01:52:48Z
2021-06-15T16:28:52Z
2021-06-15T13:17:09Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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STYLE: Added from __future__ import annotations to required files
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2021-06-14T05:25:32Z
2021-07-16T09:06:12Z
2021-07-16T09:06:11Z
NONE
null
- [X] closes #41901 - [ ] No tests added - [X] All linting passed (used `isort` to add imports) - What's New: Built a [script](https://gist.github.com/aacosta13/eb1cb4b22ece354c60f0e7aab90137b0) using `isort` to add `from __future__ import annotations` to necessary files as referenced by issue #41901.
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⬆️ UPGRADE: Autoupdate pre-commit config
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2021-06-14T07:07:40Z
2021-06-14T09:18:19Z
2021-06-14T08:03:18Z
CONTRIBUTOR
null
<!-- START pr-commits --> <!-- END pr-commits --> ## Base PullRequest default branch (https://github.com/pandas-dev/pandas/tree/master) ## Command results <details> <summary>Details: </summary> </details> ## Changed files <details> <summary>Changed file: </summary> </details> <hr> [:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action)
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ENH: `Styler.apply(map)_index` made compatible with `Styler.to_latex`
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0
2021-06-14T07:30:25Z
2021-08-12T17:02:45Z
2021-08-12T15:52:05Z
CONTRIBUTOR
null
This is a follow-on to #41893. It makes the new `Styler.apply_index` and `Styler.applymap_index` methods compatible with `Styler.to_latex`. ![Screen Shot 2021-06-14 at 09 24 46](https://user-images.githubusercontent.com/24256554/121854567-a22de200-ccf2-11eb-820a-cc905c7aac3a.png)
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41,994
BUG: apply_index styles not universally applied across LaTeX multirows
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2021-06-14T07:42:17Z
2021-10-26T18:35:26Z
null
CONTRIBUTOR
null
PR #41993 makes `Styler.applymap_index` and `Styler.apply_index` compatible with `to_latex`. But, with MultiIndexes this introduces a bug: cell coloring is not universal for multirow, albeit multicols are OK. ``` df = pd.DataFrame({"A": [0, 1, 2], "B": [-0.61, -1.22, -2.22], "C": ["ab", "cd", "de"]}) cidx = pd.MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) ridx = pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) df.index, df.columns = ridx, cidx styler = df.style func = lambda v: 'cellcolor:{red}; color:{white}; bfseries:--rwrap;' if "A" in v or "Z" in v or "c" in v else None print(styler.applymap_header(func, axis="columns").applymap_header(func).to_latex(hrules=True)) ``` ``` \begin{tabular}{llrrl} \toprule {} & {} & \multicolumn{2}{r}{\cellcolor{red} \color{white} \bfseries{Z}} & {Y} \\ {} & {} & {a} & {b} & {\cellcolor{red} \color{white} \bfseries{c}} \\ \midrule \multirow[c]{2}{*}{\cellcolor{red} \color{white} \bfseries{A}} & a & 0 & -0.610000 & ab \\ & b & 1 & -1.220000 & cd \\ B & \cellcolor{red} \color{white} \bfseries{c} & 2 & -2.220000 & de \\ \bottomrule \end{tabular} ``` ![Screen Shot 2021-06-14 at 09 37 21](https://user-images.githubusercontent.com/24256554/121855896-259c0300-ccf4-11eb-8c8b-7fa463429273.png) Even if the code is amended to print the styles in problem cells, albeit with a blank display value the problem is not solved because the text is overwritten: ``` \begin{tabular}{llrrl} \toprule {} & {} & \multicolumn{2}{r}{\cellcolor{red} \color{white} \bfseries{Z}} & {Y} \\ {} & {} & {a} & {b} & {\cellcolor{red} \color{white} \bfseries{c}} \\ \midrule \multirow[c]{2}{*}{\cellcolor{red} \color{white} \bfseries{A}} & a & 0 & -0.610000 & ab \\ \cellcolor{red} \color{white} \bfseries{} & b & 1 & -1.220000 & cd \\ B & \cellcolor{red} \color{white} \bfseries{c} & 2 & -2.220000 & de \\ \bottomrule \end{tabular} ``` ![Screen Shot 2021-06-14 at 09 39 03](https://user-images.githubusercontent.com/24256554/121856087-63009080-ccf4-11eb-9571-e75cebb7e4e2.png) One solution is to apply the multirow last so that it overwrites previous rendered cells, using a negative rowspan. ``` \begin{tabular}{llrrl} \toprule {} & {} & \multicolumn{2}{r}{\cellcolor{red} \color{white} \bfseries{Z}} & {Y} \\ {} & {} & {a} & {b} & {\cellcolor{red} \color{white} \bfseries{c}} \\ \midrule \cellcolor{red} \color{white} \bfseries{} & a & 0 & -0.610000 & ab \\ \multirow[c]{-2}{*}{\cellcolor{red} \color{white} \bfseries{A}} & b & 1 & -1.220000 & cd \\ B & \cellcolor{red} \color{white} \bfseries{c} & 2 & -2.220000 & de \\ \bottomrule \end{tabular} ``` ![Screen Shot 2021-06-14 at 09 41 13](https://user-images.githubusercontent.com/24256554/121856334-af4bd080-ccf4-11eb-81b5-75e430f4cfeb.png) Albeit this might be quite difficult to code and have other repercussions.
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ENH: `Styler.apply(map)_index` made compatible with `Styler.to_excel`
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3
2021-06-14T09:01:41Z
2021-10-19T05:31:41Z
2021-10-19T00:44:24Z
CONTRIBUTOR
null
This is a follow-on to #41893. It makes the new `Styler.apply_index` and `Styler.applymap_index` methods compatible with `Styler.to_excel`. If the methods are not used the output reverts to the old format. ![Screen Shot 2021-06-14 at 10 58 47](https://user-images.githubusercontent.com/24256554/121866799-93016100-ccff-11eb-8bc8-124fd768e550.png) **MultiIndex Case** ```python midx = pd.MultiIndex.from_product([['ix', 'jy'], [0, 1], ['x3', 'z4']]) df = pd.DataFrame([np.arange(8)], columns=midx) def highlight_x(s): return ["background-color: yellow;" if 'x' in v else "" for v in s] df.style.apply_index(highlight_x, axis="columns", level=[0, 2]).to_excel("mi_test.xlsx") ``` ![Screen Shot 2021-08-12 at 09 24 15](https://user-images.githubusercontent.com/24256554/129155325-1fe07a25-3f82-4063-8eba-b31ba725ce56.png) # NOTE on TESTS The tests for styler_to_excel were broken and were xfailed out of production in 2019. Since they dont work I have replaced them with some other, cleaner, tests and add tests relevant to this PR also. Closes #25351
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ENH: Gather `dtype` and `convertors` processing in one place
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2021-06-14T09:22:04Z
2021-06-16T16:58:02Z
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#### Is your feature request related to a problem? [this should provide a description of what the problem is, e.g. "I wish I could use pandas to do [...]"] There are many places where `dtype` and `converters` can be modified #### Describe the solution you'd like [this should provide a description of the feature request, e.g. "`DataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature] This should gather all the `dtype` and `convertors` processing in one place. #### API breaking implications [this should provide a description of how this feature will affect the API] The changes are absolutely internal. #### Describe alternatives you've considered [this should provide a description of any alternative solutions or features you've considered] It is maintainers' idea. #### Additional context [add any other context, code examples, or references to existing implementations about the feature request here] See discussion in #41664
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41,997
REGR: Behavior change with empty apply in pandas 1.3.0rc1
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15
2021-06-14T10:26:05Z
2021-11-19T22:42:12Z
null
CONTRIBUTOR
null
#### Problem description The following (toy) snippet worked with 1.2: ```python df = pd.DataFrame(columns=["a", "b"]) df["a"] = df.apply(lambda x: x["a"], axis=1) ``` With 1.3 it fails with `ValueError: Columns must be same length as key` Technically this is correct - the apply on an empty frame returns the empty frame so things do not really match. #### Expected Output It still works? Just reporting it here if this is an unintended change. Maybe I missed it, but I did not see this mentioned in the changelog. The fix is to only call apply when the frame is not empty I guess? I stumbled upon this one when running our test suite.
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41,998
REGR: 1.3 behavior change with groupby and asindex=False
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7
2021-06-14T10:39:23Z
2021-06-28T14:27:50Z
2021-06-28T14:27:50Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example ```python pd.DataFrame(columns=["date", "crew_member_id"]) .groupby(["date", "crew_member_id"], as_index=False) .sum() ``` #### Problem description With 1.3rc1 this fails with `ValueError: Length of values (0) does not match length of index (1)` while 1.2 returns and empty frame. #### Expected Output If this is the expected behavior fine. Just wanted to ensure that it is not an accidental regression.
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REGR: 1.3 behavior change with groupby, apply and side effect
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10
2021-06-14T11:21:04Z
2021-11-19T22:54:06Z
null
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example ```python def apply_func(df): del df["MEMBER_ID"] return df.head(1) df = pd.DataFrame.from_dict( { "MONTH": {0: "April", 1: "April", 2: "April"}, "MEMBER_ID": {0: "Member A", 1: "Member A", 2: "Member B"}, "ACTIVITY_CATEGORY": { 0: "Activity 1", 1: "Days off at homebase", 2: "Activity 1", }, "FTE": {0: 1.0, 1: 1.0, 2: 0.75}, "FTE_OFF_DAYS": {0: 5, 1: 5, 2: 10}, } ) df.groupby(["MONTH", "FTE"], observed=True)[ ["MEMBER_ID", "FTE_OFF_DAYS", "ACTIVITY_CATEGORY"] ].apply(apply_func) ``` #### Problem description Deleting the row passed to `apply_func` in combination with the call to `head` causes an exception in 1.3rc1. When `apply_func` is called the second time the `MEMBER_ID` column does not exist anymore. Doing a `df = df.copy()` in `apply_func` fixes the problem. #### Expected Output While one could argue that the code is a bit bogus (and should be possibly rewritten), I wonder if this change happened on purpose.
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42,000
ENH: Clarify error message when reindexing on an axis containing duplicates
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2021-06-14T12:56:50Z
2021-06-16T09:34:33Z
2021-06-16T09:34:33Z
CONTRIBUTOR
null
#### Is your feature request related to a problem? Let's consider the following snippet: ```python pd.DataFrame(index=[pd.to_datetime("2019"), pd.to_datetime("2019"), pd.to_datetime("2020")]).asfreq("M") ``` This will raise an exception as the to be resampled dataframe has an axis with duplicates. The problem: I stumpbled at least twice on this problem and never understood what the error message wanted to tell me on first sight. When seeing just the message (wihtout looking at the backtrace) I always thought this is about providing duplicate axis names or something like this. ```python ValueError: cannot reindex from a duplicate axis ``` #### Describe the solution you'd like Maybe the exception message could be clarified. The comment two lines above the exception always saves me: ```python # trying to reindex on an axis with duplicates ``` So maybe something like this could help? ```python raise ValueError("Cannot reindex on an axis with duplicate values") ``` #### API breaking implications None I guess. The message would additionally need to be changed in some tests and in the docs. #### Additional context If there is interest I could draft a PR.
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BUG: `DataFrame.apply` forces consistent dtypes with `result_type="broadcast"`
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- [x] I have checked that this issue has not already been reported. - [ ] 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. --- The documentation on `result_type` for `DataFrame.apply` states >‘broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained. It doesn't mention enforcing dtypes, yet: ``` >>> df = pd.DataFrame([[1, 2], [3, 4]], index=["A", "B"], columns=["X", "Y"]) >>> df.apply(lambda s: [1, 2], axis=1, result_type="broadcast") X Y A 1 2 B 1 2 >>> df.apply(lambda s: ["s", "t"], axis=1, result_type="broadcast") # ValueError: invalid literal for int() with base 10: 's' >>> df.apply(lambda s: [3.999, 2], axis=1, result_type="broadcast") X Y A 3 2 B 3 2 >>> df.apply(lambda s: [3+1j, 2+2j], axis=1, result_type="broadcast") # TypeError: can't convert complex to int ``` This seems like a bug to me. Can anyone confirm?
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BUG/TYP: 1.3.0 index.values returns ExtensionArray | ndarray instead of ndarray
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2021-06-14T15:23:48Z
2021-06-15T15:49:02Z
2021-06-15T15:49:02Z
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. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- Probably an intended change, but I didn't find anything related to it in the whatsnew. ```py import pandas as pd df = pd.DataFrame({"a": [1]}) reveal_type(df.values) reveal_type(df.index.values) ``` pyright with 1.2.4 ```sh info: Type of "df.values" is "ndarray" info: Type of "df.index.values" is "ndarray" ``` pyright with 1.3.0.rc1 ```sh info: Type of "df.values" is "ndarray" info: Type of "df.index.values" is "ExtensionArray | ndarray" ```
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BUG: 1.3.0rc1 pd.util.hash_array on Index fails to access _values_for_factorize
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2021-06-14T15:39:49Z
2021-06-15T18:00:01Z
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CONTRIBUTOR
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- [X] I have checked that this issue has not already been reported. - [ ] I have confirmed this bug exists on the latest version of pandas. (does not exist in 1.2.4, only seen in 1.3.0rc1 and master) - [X] (optional) I have confirmed this bug exists on the master branch of pandas. (confirmed on 0b68d87a443) --- #### Code Sample, a copy-pastable example ```python In [1]: import pandas as pd In [2]: pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00'], tz='Europe/Berlin')) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-2-389898c5af02> in <module> ----> 1 pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00'], tz='Europe/Berlin')) ~/working_dir/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize) 287 elif not isinstance(vals, np.ndarray): 288 # i.e. ExtensionArray --> 289 vals, _ = vals._values_for_factorize() 290 291 return _hash_ndarray(vals, encoding, hash_key, categorize) AttributeError: 'DatetimeIndex' object has no attribute '_values_for_factorize' In [3]: pd.util.hash_array(pd.Index([1,2,3])) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-3-e66efa244441> in <module> ----> 1 pd.util.hash_array(pd.Index([1,2,3])) ~/working_dir/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize) 287 elif not isinstance(vals, np.ndarray): 288 # i.e. ExtensionArray --> 289 vals, _ = vals._values_for_factorize() 290 291 return _hash_ndarray(vals, encoding, hash_key, categorize) AttributeError: 'Int64Index' object has no attribute '_values_for_factorize' In [4]: pd.util.hash_array(pd.RangeIndex(1,3)) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-4-af0900aae979> in <module> ----> 1 pd.util.hash_array(pd.RangeIndex(1,3)) ~/working_dir/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize) 287 elif not isinstance(vals, np.ndarray): 288 # i.e. ExtensionArray --> 289 vals, _ = vals._values_for_factorize() 290 291 return _hash_ndarray(vals, encoding, hash_key, categorize) AttributeError: 'RangeIndex' object has no attribute '_values_for_factorize' ``` #### Problem description This issue looks similar to https://github.com/pandas-dev/pandas/issues/41817, but that is specifically for DateTimeIndex with tz defined, while this seems to happen for any Index instance. #### Expected Output A hash of the input index, as in pandas < 1.3.0. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0 python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.10.28-1rodete1-amd64 Version : #1 SMP Debian 5.10.28-1rodete1 (2021-04-30) machine : x86_64 processor : byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.0rc1 numpy : 1.19.5 pytz : 2021.1 dateutil : 2.8.1 pip : 20.2.1 setuptools : 49.2.1 Cython : 0.29.22 pytest : 6.2.2 hypothesis : 6.4.0 sphinx : 3.5.1 blosc : 1.10.2 feather : None xlsxwriter : 1.3.7 lxml.etree : 4.6.2 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.21.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.8.7 fastparquet : 0.5.0 gcsfs : 0.7.2 matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.6 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.5.2 scipy : 1.6.1 sqlalchemy : 1.3.23 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.17.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.52.0 </details>
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Backport PR #41987 on branch 1.3.x (TST: Un-xfail tests on numpy-dev)
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2021-06-14T16:32:22Z
2021-06-15T00:39:50Z
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Backport PR #41987: TST: Un-xfail tests on numpy-dev
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BUG: `DataFrame.apply` labelling index and columns asymmetrically if axis=0/1.
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2021-08-21T02:31:13Z
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CONTRIBUTOR
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- [ ] I have checked that this issue has not already been reported. - [ ] 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. --- Currently `DataFrame.apply` works asymmetrically. ```python >>> df = pd.DataFrame([[1,2], [3,4]], index=["a", "b"], columns=["x", "y"]) >>> df.apply(lambda s: ["one", "two"], axis=0) x y a one one b two two >>> df.apply(lambda s: pd.Series(["one", "two"], index=["c", "d"]), axis=0) x y c one one d two two >>> df.apply(lambda s: ["one", "two", "three"], axis=0) x y 0 one one 1 two two 2 three three ``` Along axis=0, the native index is maintained, if the shapes match and it is not overwritten by the function result object. When shapes don't match the created index is the default RangeIndex. ```python >>> df.apply(lambda s: ["one", "two"], axis=1, result_type="expand") 0 1 a one two b one two >>> df.apply(lambda s: pd.Series(["one", "two"], index=["c", "d"]), axis=1, result_type="expand") c d a one two b one two >>> df.apply(lambda s: ["one", "two", "three"], axis=1, result_type="expand") 0 1 2 a one two three b one two three ``` Along axis=1 (with result_type=expand), the native columns are overwritten by a default RangeIndex (even if it has same shape) unless overwritten by the function result object index. #### Expected Output Would anyone object to a PR that looks to unify the two cases and apply the columns index where the shapes were the same? Currently I use the workaround: ``` df.apply(func, axis=1, result_type="expand") -> df.T.apply(func, axis=0).T ``` ### Bug 1 Documentation states that for `result_type` arguments - `These only act when axis=1 (columns):`. This is not true since: ```python >>> df = pd.DataFrame([[1, 2], [3, 4]], index=["a", "b"], columns=["a", "b"]) >>> df.apply(lambda s: [1,1,1], axis=0, result_type=None) # or "expand" a b 0 1 1 1 1 1 2 1 1 >>> df.apply(lambda s: [1,1,1], axis=0, result_type="broadcast") ValueError: cannot broadcast result >>> df.apply(lambda s: [1,1,1], axis=0, result_type="reduce") a [1, 1, 1] b [1, 1, 1] dtype: object ```
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42,006
DEP: move pkg_resources back inline in function (delayed import)
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8
2021-06-14T18:36:09Z
2021-06-15T23:53:08Z
2021-06-15T23:52:54Z
MEMBER
null
xref https://github.com/pandas-dev/pandas/issues/40169#issuecomment-860900794, fixup of #41503
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ENH: Clarify error message when reindexing fails (#42000)
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4
2021-06-14T18:41:30Z
2021-06-16T09:34:44Z
2021-06-16T09:34:33Z
CONTRIBUTOR
null
- [x] closes #42000 - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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CLN: Verify non-ndarray is an ExtensionArray in hash_array GH42003
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2021-06-14T20:30:07Z
2021-08-17T02:32:31Z
2021-08-17T02:32:31Z
CONTRIBUTOR
null
Closes #42003
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DOC: Add New Community Tutorial
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0
2021-06-15T01:07:38Z
2021-07-11T19:47:14Z
2021-07-11T19:47:14Z
CONTRIBUTOR
null
#### Location of the documentation I'm proposing the addition of another community tutorial to the [Community tutorials](https://pandas.pydata.org/docs/getting_started/tutorials.html) page. #### Documentation problem Not a problem, but an opportunity for improvement. #### Suggested fix for documentation I recently built a [pandas workshop](https://github.com/stefmolin/pandas-workshop) for the Open Data Science Conference (ODSC) Europe event aimed at new users of pandas. It focuses on real-world datasets (making it easier for beginners to understand), includes detailed environment setup instructions (and binder), and provides notebooks and exercises to practice the concepts. I think it would make a great addition to the [Community tutorials](https://pandas.pydata.org/docs/getting_started/tutorials.html) page -- the more learning resources the better 😃 I'm happy to make a PR.
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Backport PR #41971 on branch 1.3.x (CI: mark window online test slow)
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Backport PR #41971: CI: mark window online test slow
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42,011
Update frame.py
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2021-06-15T05:26:10Z
2021-06-15T06:59:28Z
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If not all levels are removed, the index is not reset to default integer values. import pandas as pd x = pd.Series(range(3), index=pd.MultiIndex.from_arrays([['a', 'b', 'c'], [1, 2, 3]], names=['letters', 'numbers'])) print(x.reset_index('numbers', drop=True)) - [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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42,012
ENH/PERF: optional mask in nullable dtypes
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2021-06-15T05:45:44Z
2021-06-20T01:41:59Z
2021-06-20T01:41:59Z
MEMBER
null
- [x] closes #30435 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry Benchmarks: <details> ``` before after ratio [e3d57c98] [ea9f1a89] <master> <perf/optional_mask~1> + 985±70ns 2.42±0.04μs 2.45 array.IntegerArray.time_constructor + 1.11±0.08μs 2.66±0.1μs 2.38 array.BooleanArray.time_constructor + 11.5±0.06μs 15.3±0.3μs 1.34 series_methods.NanOps.time_func('argmax', 1000, 'boolean') + 12.6±0.4μs 16.3±0.6μs 1.29 boolean.TimeLogicalOps.time_and_array + 25.9±1μs 32.4±3μs 1.25 boolean.TimeLogicalOps.time_or_scalar + 13.3±0.5μs 16.2±0.6μs 1.22 boolean.TimeLogicalOps.time_or_array + 27.5±1μs 33.3±2μs 1.21 boolean.TimeLogicalOps.time_and_scalar + 14.5±0.4μs 17.4±0.4μs 1.20 boolean.TimeLogicalOps.time_xor_array + 34.8±0.6μs 41.8±2μs 1.20 series_methods.Isna.time_isna_no_nans('Int64') + 29.2±0.4μs 34.7±1μs 1.19 boolean.TimeLogicalOps.time_xor_scalar + 27.2±0.3μs 31.0±3μs 1.14 array.IntegerArray.time_from_integer_array - 66.9±0.6μs 58.6±0.7μs 0.88 series_methods.NanOps.time_func('skew', 1000, 'Int64') - 78.0±2μs 66.9±1μs 0.86 series_methods.NanOps.time_func('std', 1000, 'boolean') - 67.4±0.9μs 57.6±0.8μs 0.85 series_methods.NanOps.time_func('kurt', 1000, 'boolean') - 66.9±0.8μs 56.9±0.7μs 0.85 series_methods.NanOps.time_func('kurt', 1000, 'Int64') - 67.6±2μs 57.4±0.6μs 0.85 series_methods.NanOps.time_func('var', 1000, 'Int64') - 10.8±0.2μs 9.12±0.5μs 0.85 series_methods.NanOps.time_func('sum', 1000, 'boolean') - 75.4±5μs 63.5±1μs 0.84 series_methods.NanOps.time_func('std', 1000, 'int32') - 5000 4016 0.80 array.NullableArrayMemory.track_array_size('Float32') - 150±20μs 119±0.6μs 0.80 series_methods.NanOps.time_func('sem', 1000, 'int8') - 8.81±0.2μs 6.92±0.2μs 0.79 series_methods.NanOps.time_func('max', 1000, 'boolean') - 6.25±0.06μs 4.83±0.1μs 0.77 series_methods.All.time_all(1000, 'slow', 'boolean') - 9.78±0.08μs 7.44±0.2μs 0.76 series_methods.NanOps.time_func('prod', 1000, 'Int64') - 1.40±0.02ms 1.03±0.08ms 0.73 series_methods.NanOps.time_func('prod', 1000000, 'boolean') - 9.44±0.1μs 6.83±0.1μs 0.72 series_methods.NanOps.time_func('min', 1000, 'Int64') - 12.9±0.2μs 9.12±2μs 0.70 series_methods.NanOps.time_func('mean', 1000, 'Int64') - 4.26±0.06ms 2.97±0.4ms 0.70 series_methods.NanOps.time_func('std', 1000000, 'boolean') - 170±10μs 108±8μs 0.64 algos.isin.IsIn.time_isin('boolean') - 10.2±0.3μs 6.50±0.1μs 0.63 series_methods.NanOps.time_func('max', 1000, 'Int64') - 4.14±0.2ms 2.60±0.2ms 0.63 series_methods.NanOps.time_func('var', 1000000, 'boolean') - 14.4±0.2μs 8.83±0.1μs 0.61 series_methods.NanOps.time_func('mean', 1000, 'boolean') - 6.27±1ms 3.70±0.1ms 0.59 series_methods.NanOps.time_func('std', 1000000, 'Int64') - 1.01±0.01ms 557±10μs 0.55 series_methods.NanOps.time_func('sum', 1000000, 'boolean') - 1.27±0.04ms 696±10μs 0.55 series_methods.NanOps.time_func('prod', 1000000, 'Int64') - 2000 1016 0.51 array.NullableArrayMemory.track_array_size('Int8') - 2000 1016 0.51 array.NullableArrayMemory.track_array_size('boolean') - 116±5μs 58.2±2μs 0.50 algos.isin.IsIn.time_isin_mismatched_dtype('boolean') - 925±400μs 338±6μs 0.37 series_methods.NanOps.time_func('sum', 1000000, 'Int64') - 1.03±0.06ms 371±30μs 0.36 series_methods.NanOps.time_func('mean', 1000000, 'Int64') - 1.95±0.2ms 329±5μs 0.17 series_methods.NanOps.time_func('max', 1000000, 'Int64') - 1.98±0.2ms 324±3μs 0.16 series_methods.NanOps.time_func('min', 1000000, 'Int64') - 454±7μs 69.6±1μs 0.15 series_methods.All.time_all(1000000, 'slow', 'boolean') - 454±10μs 64.4±2μs 0.14 series_methods.Any.time_any(1000000, 'slow', 'boolean') - 430±6μs 43.7±2μs 0.10 series_methods.Any.time_any(1000000, 'fast', 'boolean') - 431±7μs 42.2±1μs 0.10 series_methods.All.time_all(1000000, 'fast', 'boolean') - 505±4μs 42.4±3μs 0.08 series_methods.NanOps.time_func('min', 1000000, 'boolean') - 496±10μs 13.6±0.6μs 0.03 series_methods.NanOps.time_func('max', 1000000, 'boolean') ``` </details> - The decrease in the `isna` benchmark was somewhat surprising, but happens because of the following: ``` In [12]: mask = np.zeros(100000, dtype=np.bool_) In [13]: %timeit mask.copy() # what happens in master 2 µs ± 19.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) In [14]: %timeit np.zeros(100000, dtype=np.bool_) # essentially what happens in this pr 2.7 µs ± 182 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) ``` - The `argmax` performance decrease is caused by the `isna` slowdown because there are a bunch of `isna.any()` calls. I think this decrease could be prevented (and performance increased overall) by adding `argmax` to our masked reductions (or avoiding the repeated `isna.any()` some other way), but that seems better left for a different pr. - The `TimeLogicalOps` benchmark slowdown is occurring because of the `mask.any()` check added in the `BooleanArray` constructor - this slowdown could be avoided by more aggressively figuring out when `mask` can (or won't) be None, but that would complicate the logic somewhat, so would plan to leave as a potential followup. - Finally, the slowdown in `IntegerArray` and `BooleanArray` is also due to this added `mask.any()` check. This could be mitigated by determining earlier when missing values are not possible in the `coerce_to_array` functions, but that adds reasonable complexity to already complex functions.
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MDExOlB1bGxSZXF1ZXN0NjcwMTI0OTEz
42,013
Updated the docstring for reset_index in frame.py
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2021-06-15T07:03:13Z
2021-11-06T02:56:26Z
2021-11-06T02:56:25Z
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If not all levels are removed, the index is not reset to default integer values. import pandas as pd x = pd.Series(range(3), index=pd.MultiIndex.from_arrays([['a', 'b', 'c'], [1, 2, 3]], names=['letters', 'numbers'])) print(x.reset_index('numbers', drop=True)) - [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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42,014
BUG: `Styler.apply` consistently manages Series return objects aligning labels.
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2021-06-15T07:22:49Z
2021-08-20T05:31:08Z
2021-08-19T21:41:49Z
CONTRIBUTOR
null
- [x] closes #13657 This issue is due to the asymmetric application of `DataFrame.apply` (#42005). **The bug** is that when using `Styler.apply` the treatment of Series return objects is applied for `axis=0` and overwritten for `axis=1`. ```python df = pd.DataFrame([[1,2],[3,4]], index=["X", "Y"], columns=["X", "Y"]) s = df.style def series_ret(s): return pd.Series(["color:red;", "color:blue;"], index=["Y", "X"]) # note X-Y order ``` | Command | Current Behaviour | New Behaviour | | ------------ | ------------------------- | ---------------------- | | `s.apply(series_ret, axis=0)` | ![][image5] | ![][image5] | | `s.apply(series_ret, axis=1)` | ![][image6] | ![][image7] | Also the application has been expanded to **allow** (for axis=0 | 1) Series return objects that are **not** the same size, or (for axis=None) DataFrame return objects that are **not** the same size, if they contain only relevant index labels. This allows for more flexible user defined functions. Potentially it also increases performance by not having to loop over labels that no longer exist. ```python def series_ret(s): return pd.Series(["color:red;"], index=["Y"]) # note only returning 1 element def array_ret(s): return ["color:red;"] def ser_no_idx_ret(s): return pd.Series(["color:red;", "color:blue;"]) # note returning a Series of right shape with no index ``` | Command | Current Behaviour | New Behaviour | | ------------ | ------------------------- | ---------------------- | | `s.apply(series_ret, axis=0)` | `ValueError: Function returned the wrong shape.` | ![][image1] | | `s.apply(series_ret, axis=1)` | `ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements` | ![][image2] | | `s.apply(array_ret, axis=0)` | `ValueError: Function returned the wrong shape.` | `ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` | | `s.apply(array_ret, axis=1)` |`ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements` |`ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` | | `s.apply(ser_no_idx_ret, axis=0)` | `KeyError: 0` | `ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` | | `s.apply(ser_no_idx_ret, axis=1)` | ![][image6] |`ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` | Note all errors do report the shapes, I just didn't include in the table for brevity. ### Edge Cases The edge case here is that if a user has a DataFrame with a default range index (or a DataFrame with some indexes the same as those in default, e.g. 0 or 1) then even returning arrays of the wrong size may work, since `DataFrame.apply` assigns them to a RangeIndex. To be consistent with the above this should raise, but it is impossible to hook into the return value of the user-defined function and determine if it is a Series (where non-matching shapes are OK) or list-like (where matching shapes should be enforced) ```python df = pd.DataFrame([[1,2],[3,4]]) s = df.style ``` | Command | Current Behaviour | New Behaviour | | ------------ | ------------------------- | ---------------------- | | `s.apply(array_ret, axis=0)` | `ValueError: Function returned the wrong shape.` | ![][image3] | | `s.apply(array_ret, axis=1)` | `ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements` | ![][image4] | ### ASV Benchmarks unchanged. [image1]: https://user-images.githubusercontent.com/24256554/122002332-e59b5580-cdb1-11eb-85c2-993c7e20bdfc.png [image2]: https://user-images.githubusercontent.com/24256554/122002344-e9c77300-cdb1-11eb-8a01-f50ec660b300.png [image3]: https://user-images.githubusercontent.com/24256554/122004954-57c16980-cdb5-11eb-8ba4-ec14b1a6b06f.png [image4]: https://user-images.githubusercontent.com/24256554/122005013-69a30c80-cdb5-11eb-900c-0d4be813cb58.png [image5]: https://user-images.githubusercontent.com/24256554/122006522-3497b980-cdb7-11eb-892e-f34c80e3c7ff.png [image6]: https://user-images.githubusercontent.com/24256554/122006532-36fa1380-cdb7-11eb-8b08-f89d6991536c.png [image7]: https://user-images.githubusercontent.com/24256554/122006816-93f5c980-cdb7-11eb-8aff-09b658c594d9.png
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42,015
DOC: pandas.Series.str.isspace has wrong documentation
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1
2021-06-15T07:24:21Z
2021-06-15T22:40:31Z
2021-06-15T22:40:31Z
CONTRIBUTOR
null
#### Location of the documentation [pandas.Series.str.isspace](https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.str.isspace.html) #### Documentation problem > Check whether all characters in each string are alphanumeric. > > This is equivalent to running the Python string method str.isalnum() for each element of the Series/Index. If a string has zero characters, False is returned for that check. Seems like it was somehow copied from `pandas.Series.str.isalnum`. That does not seem to match the function. #### Suggested fix for documentation Not copy the doc of `pandas.Series.str.isalnum` but rather provide a specific one.
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42,016
BUG: groupby with dropna=False and 'unique' aggregate creates duplicated index
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2021-06-15T11:06:26Z
2021-08-21T02:32:13Z
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. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python df_list = [[1, 1, 1, 'A'], [1, None, 1, 'A'], [1, None, 2, 'A'], [1, None, 3, 'A']] df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c", 'partner']) # with 'unique', groupby result contains duplicates partners =\ df_dropna.groupby(['a', 'b', 'c'], dropna=False)\ .agg({'partner': ['nunique', 'unique']}) partners.index.is_unique # when using another aggregate function than 'unique', it is working as expected partners =\ df_dropna.groupby(['a', 'b', 'c'], dropna=False)\ .agg({'partner': ['nunique', 'sum']}) partners.index.is_unique ``` #### Problem description When using 'unique' and another aggregate function in case of a groupby on key containing NA values, the result index contains duplicates but it shouldn't: ``` partner nunique unique a b c 1 1.0 1 1 [A] NaN 1 1 [A] 2 1 [A] 3 1 [A] 1 1 [A] 2 1 [A] 3 1 [A] ``` #### Expected Output Groupby result index shouldn't contain duplicates as below: ``` partner nunique unique a b c 1 1.0 1 1 [A] NaN 1 1 [A] 2 1 [A] 3 1 [A] ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.8.10.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19042 machine : AMD64 processor : AMD64 Family 23 Model 96 Stepping 1, AuthenticAMD byteorder : little LC_ALL : None LANG : en LOCALE : English_United Kingdom.1252 pandas : 1.2.4 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.2 setuptools : 49.6.0.post20210108 Cython : None pytest : 6.2.2 hypothesis : None sphinx : 4.0.2 blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.2 html5lib : None pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 3.0.1 IPython : 7.24.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.2 numexpr : None odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : 1.3.22 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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DOC: Fix wrongly associated docstring of Series.str.isspace
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3
2021-06-15T11:11:25Z
2021-06-16T11:09:48Z
2021-06-15T22:40:31Z
CONTRIBUTOR
null
cc @simonjayhawkins - [ ] closes #42015 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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Backport PR #41954 on branch 1.3.x (DOC: more cleanup of 1.3 release notes)
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Backport PR #41954: DOC: more cleanup of 1.3 release notes
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Backport PR #41990: CI: troubleshoot py310 build
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