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754,775,262
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38,219
Update Dockerfile to use conda-forge compilers
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5
2020-12-01T22:29:33Z
2021-01-24T05:32:54Z
2020-12-02T12:09:14Z
CONTRIBUTOR
null
Additionally I switched to the miniforge3 image as this bases its installation on conda-forge and I also have used `mamba` to install the environment as this massively saves time. - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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38,220
BUG: DataFrame.drop raising TypeError for string label with non-unique MultiIndex and no level provided
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2
2020-12-01T22:49:48Z
2020-12-15T18:46:47Z
2020-12-14T23:55:16Z
MEMBER
null
- [x] closes #36293 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry As far as I know, isin needs a level input when only a string is given for a MultiIndex. If this should not change, we have to set level=0 in this case.
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38,221
BUG: Pip 20.3 is causing Linux py38 np dev pipeline to fail
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3
2020-12-01T23:24:22Z
2021-05-25T08:09:06Z
2021-05-25T08:09:06Z
MEMBER
null
The first pipeline to fail was 2a115d5d750c65765913c1940ac4c85f4ab51df9 which also was the first pipeline using pip 20.3 This seems to be a problem with scipy version. @jreback @jbrockmendel @jorisvandenbossche Last pipeline to work: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=49130&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=71ac627c-7b38-59b1-014b-81753001ec5e First fo fail: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=49159&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=71ac627c-7b38-59b1-014b-81753001ec5e&l=333
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MDU6SXNzdWU3NTQ4MTc0MjU=
38,222
API: make tm.assert_foo_equal kwargs keyword-only for rarely-used keywords
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2
2020-12-02T00:00:30Z
2020-12-15T16:19:33Z
2020-12-15T01:37:38Z
MEMBER
null
I expect this would need a deprecation cycle cc @jorisvandenbossche ?
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38,223
TST: tighten testing assertions
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0
2020-12-02T01:21:51Z
2020-12-02T14:48:19Z
2020-12-02T12:09:00Z
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MDU6SXNzdWU3NTQ4Njg5MTM=
38,224
API: bool(pd.NA)
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9
2020-12-02T02:06:07Z
2021-06-11T12:40:53Z
null
CONTRIBUTOR
null
xref https://github.com/pandas-dev/pandas/pull/38102#discussion_r532446692 for `np.nan` we define `bool(np.nan)` ``` In [189]: 1 or np.nan Out[189]: 1 In [190]: np.nan or 1 Out[190]: nan ``` however we don't do this for `pd.NA` ``` >>> pd.NA or 1 Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pandas/_libs/missing.pyx", line 360, in pandas._libs.missing.NAType.__bool__ raise TypeError("boolean value of NA is ambiguous") TypeError: boolean value of NA is ambiguous ``` I think this is pretty odd and not very useful.
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38,225
ENH: context-manager for chunksize/iterator-reader
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4
2020-12-02T02:54:26Z
2020-12-04T20:40:23Z
2020-12-04T18:58:48Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Allows (and encourages) the following use: ```py import pandas as pd filename = "pandas/tests/io/data/csv/iris.csv" chunksize = 2 with pd.read_csv(filename, chunksize=chunksize) as reader: for chunk in reader: # risky code that might raise ``` Same can be done for `read_json/sas` (I think these are all methods that support `chunksize`). If this PR should make it into 1.2, I can quickly add the changes for json/sas as well. Are there more places to promote this new context manager?
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38,226
Rewrite dict literal for files in pandas/tests/frame/test_indexing and test_where
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2020-12-02T03:40:20Z
2020-12-05T10:01:46Z
2020-12-05T10:01:46Z
CONTRIBUTOR
null
- [ ] closes #38138 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry identify unnecessary dict call - rewrite as a literal Rewrites as dictionary literals for the following files in two spots: - pandas/tests/frame/test_indexing - pandas/tests/frame/test_where based on @jreback comments: https://github.com/pandas-dev/pandas/pull/38207#issuecomment-736960208
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38,227
BUG: values are dropped when grouping on a variables with NaNs
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3
2020-12-02T04:02:55Z
2020-12-04T23:45:03Z
2020-12-04T23:45:03Z
NONE
null
Hello there, Consider this simple example ``` pd.__version__ Out[158]: '1.1.3' df = pd.DataFrame({'col': [1,2,3], 'group' : ['a',np.NaN,'b']}) df Out[160]: col group 0 1 a 1 2 NaN 2 3 b df.groupby('group').apply(lambda x: x) Out[161]: col group 0 1.0 a 1 NaN NaN 2 3.0 b ``` There are a few things that are puzzling. 1. I thought `groupby` would drop the NA groups by default 2. Why is the variable `col` set to missing? This looks like a bug to me, unless I am missing something (apologies if this is the case) What do you think? Thanks!
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CI: pin pip to 20.2 on numpy-dev build
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2
2020-12-02T08:45:21Z
2020-12-02T12:02:39Z
2020-12-02T10:08:14Z
MEMBER
null
See https://github.com/pandas-dev/pandas/issues/38221
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ENH: Apply function on a column easily, maintaining fluent interface
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2020-12-02T08:51:32Z
2021-08-14T17:07:46Z
2021-08-14T17:07:46Z
CONTRIBUTOR
null
I really like the pandas fluent / method chaining interface but it is not always convenient to use. I often end up writing code like so: ```python import pandas as pd import datetime as dt df = pd.DataFrame([["05SEP2014", "a"]], columns=["date", "other_col"]) def fix_date_a(df): df['date'] = df["date"].apply(lambda x: dt.datetime.strptime(x, "%d%b%Y")) return df # of course this example could have been vectorized much better like so def fix_date_b(): df['date'] = pd.to_datetime(df['date'], format='%d%b%Y') return df ``` This is ok, but I would much rather write this in a fluent style. As far as I am aware this is only possible with `.assign` and `.pipe`, and would give something like so: ```python from functools import partial def element_wise_date_parser(x): return dt.datetime.strptime(x, "%d%b%Y") vectorized_date_parser = partial(pd.to_datetime, format='%d%b%Y') def fix_date_c(): return df.assign(date=lambda x: x["date"].apply(element_wise_date_parser)) def fix_date_d(): return df.assign(date=lambda x: vectorized_date_parser(x["date"])) ``` I find that syntax not so easy to read and write. And as I find myself needing to perform an operation on a single column of a dataframe quite often, I would like to have a better way to do that. I propose add an apply_to method for the element wise which I made an example monkeypatch implementation: ```python def apply_to(self, column_name, function): return self.assign(**{column_name: lambda x: x[column_name].apply(function)}) pd.DataFrame.apply_to = apply_to def fix_date_d(df): return df.apply_to("date", element_wise_date_parser) # not sure what a column wise function could be named, lets say operate_on def operate_on(self, column_name, function): return self.assign(**{column_name: lambda x: function(x[column_name])}) pd.DataFrame.operate_on= operate_on def fix_date_e(df): return df.operate_on("date", vectorized_date_parser) ``` With these examples I find it much is easier to in-place modify a column without breaking the fluent interface. Of course adding even more methods to the already broad dataframe API is not free so am not 100% sure this is a good idea. But I wanted to put it up here anyway as often see myself and others cluttering code with unnecessary intermediate dataframes and or repeatedly reassigning `df` due to not knowing how to keep the fluent interface. And sprinkling around "assign with lambdas" everywhere is also not that appealing
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38,230
Backport PR #38228 on branch 1.1.x (CI: pin pip to 20.2 on numpy-dev build)
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0
2020-12-02T10:15:59Z
2020-12-02T11:16:48Z
2020-12-02T11:16:48Z
NONE
null
Backport PR #38228: CI: pin pip to 20.2 on numpy-dev build
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Backport PR #38209 on branch 1.1.x (CI/TST: fix CI with numpy dev for changed error message / dev version)
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0
2020-12-02T10:16:10Z
2020-12-02T11:38:41Z
2020-12-02T11:38:41Z
NONE
null
Backport PR #38209: CI/TST: fix CI with numpy dev for changed error message / dev version
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TST: Assert msg with pytest raises in pandas/tests/extension/base
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2020-12-02T11:45:09Z
2020-12-12T23:00:28Z
2020-12-12T23:00:24Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry This PR adds messages to the bare pytest raises in reduce.py and getitem.py in pandas/test/extension/base. This PR references #30999.
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BUG: Behaviour of DataFrame.xs when key is not in the index
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1
2020-12-02T14:25:27Z
2020-12-02T19:20:55Z
2020-12-02T19:20:55Z
NONE
null
As from title. Currently calling df.xs(key) throws KeyError if key is not in the index. I would advocate for it returning an empty dataframe.
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755,332,283
MDU6SXNzdWU3NTUzMzIyODM=
38,234
BUG: Minor HTML formatting error in Styler.render(): rowspan attribute should use quotes
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2
2020-12-02T14:41:16Z
2020-12-23T14:38:03Z
2020-12-23T14:38:02Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python df = pd.DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]]) s = Styler(df, uuid="_", cell_ids=False) assert '<th id="T__level0_row0" class="row_heading level0 row0" rowspan="2">l0</th>' in s.render() ``` #### Problem description This issue is very similar to #35706. The ``colspan`` attribute has been fixed in #36223 but the ``rowspan`` attribute hasn't been fixed yet. Fixing this would improve HTML correctness and make the ``Styler.render()`` behavior more consistent. Compare [line 330](https://github.com/attack68/pandas/blob/42f600e2acc0cd905c078ab77b473f0537699cc5/pandas/io/formats/style.py#L330) (colspan attribute) and [line 376](https://github.com/attack68/pandas/blob/42f600e2acc0cd905c078ab77b473f0537699cc5/pandas/io/formats/style.py#L376) (rowspan attribute). #### Expected Output The ``rowspan`` attribute uses quotes and the assert statement doesn't fail. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : db08276bc116c438d3fdee492026f8223584c477 python : 3.8.5.final.0 python-bits : 64 OS : Linux OS-release : 4.19.76-linuxkit Version : #1 SMP Tue May 26 11:42:35 UTC 2020 machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.3 numpy : 1.19.2 pytz : 2020.1 dateutil : 2.8.1 pip : 20.1.1 setuptools : 50.3.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.18.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.2 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.5.2 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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MDExOlB1bGxSZXF1ZXN0NTMxMDc1OTcw
38,235
REF: casting in _python_agg_general
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1
2020-12-02T14:59:53Z
2020-12-02T23:59:59Z
2020-12-02T23:33:28Z
MEMBER
null
Consolidate two for-loops into one. Instead of using maybe_cast_result in two places, use it only once, immediately after the function call being wrapped. Replace those two usages with the less-invasive `maybe_downcast_to_dtype`
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38,236
TYP: handle mypy ignore in pandas/_testing
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1
2020-12-02T15:12:33Z
2020-12-19T02:22:44Z
2020-12-19T02:22:44Z
MEMBER
null
- [ ] xref #37715 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Handle mypy ignore comments in ``pandas/_testing.py``, function ``write_to_compressed``.
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REF: consolidate result-casting in _cython_operation
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2
2020-12-02T15:17:11Z
2020-12-02T23:48:48Z
2020-12-02T23:35:19Z
MEMBER
null
Orthogonal to #38235. After this and 38235, we'll be down to only two usages of maybe_cast_result, only one of which is sketchy.
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TST: revert too-ambitious part of #38223
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2020-12-02T17:17:27Z
2021-03-28T16:23:33Z
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- [ ] xref #37715 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Handle mypy ignore comments in ``pandas/core/base.py``, class ``SelectionMixin``. There were issues with attributes ``obj`` and ``exclusions`` not defined in the mixin, but defined in the child classes. I wish I made ``obj`` of type ``FrameOrSeries`` rather than ``Any``. This is the type used in the child classes, but this results in multiple mypy errors of this kind. ``` pandas\core\window\rolling.py:197: error: FrameOrSeries? has no attribute "ndim" [attr-defined] ``` If I go with ``FrameOrSeriesUnion``, then it is necessary to go and do casting where required to DataFrame or Series. This is necessary since, for example, some kwargs for either of the types are irrelevant.
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2020-12-02T17:44:13Z
2021-08-14T17:09:13Z
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``` import pandas as pd from pandas.core.dtypes.cast import find_common_type left = pd.Index([1, 2, 3, 4]) right = pd.CategoricalIndex([1, None], categories=left) dtype = find_common_type([left.dtype, right.dtype]) >>> right.astype(dtype) ValueError: Cannot convert float NaN to integer ``` xref #37930; I expect this would also be relevant for #19371
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TST: setitem in presence of duplicate cols
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2020-12-02T17:50:01Z
2020-12-03T07:55:24Z
2020-12-02T23:37:07Z
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- [ ] closes #23239 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Add tests required to close the issue. The tests are based on the example in the issue.
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TST: string representation of IntervalIndex
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1
2020-12-02T17:52:12Z
2020-12-03T14:23:38Z
2020-12-03T14:23:34Z
MEMBER
null
- [x] closes #32553 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Picking up #36628
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38,243
BUG: Freq argument lost using `pd.infer_freq`
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2020-12-02T17:58:13Z
2021-08-14T17:10:21Z
2021-08-14T17:10:21Z
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 # reference, works: 2 normal days. i0 = pd.date_range('2020-09-24', '2020-09-26', freq='H', closed='left', tz='Europe/Berlin') pd.infer_freq(i0) # 'H' (i0.freq == '<Hour>') # problem, does not work: summertime to wintertime changeover. i1 = pd.date_range('2020-10-24', '2020-10-26', freq='H', closed='left', tz='Europe/Berlin') pd.infer_freq(i1) # '' <-- !!!!! (even though i1.freq == '<Hour>') # for completeness, works: wintertime to summertime changeover i2 = pd.date_range('2020-03-28', '2020-03-30', freq='H', closed='left', tz='Europe/Berlin') pd.infer_freq(i2) # 'H' (i2.freq == '<Hour>') ``` #### Problem description Even though `i1` has the frequency attribute correctly set, `pd.infer_freq(i1)` returns an empty string. #### Expected Output `'H'`, just like when using `i0` or `i2`. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : db08276bc116c438d3fdee492026f8223584c477 python : 3.8.5.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : en LOCALE : de_DE.cp1252 pandas : 1.1.3 numpy : 1.19.2 pytz : 2020.4 dateutil : 2.8.1 pip : 20.3 setuptools : 50.3.2.post20201202 Cython : None pytest : 6.1.2 hypothesis : None sphinx : 3.2.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.19.0 pandas_datareader: None bs4 : 4.9.1 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 pytables : None pyxlsb : None s3fs : None scipy : 1.5.2 sqlalchemy : 1.3.20 tables : None tabulate : None xarray : None xlrd : 1.2.0 xlwt : None numba : None </details>
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REGR: unstack on 'int' dtype prevent fillna to work
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3
2020-12-02T18:14:43Z
2020-12-03T11:11:12Z
2020-12-02T23:30:04Z
MEMBER
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- [ ] closes #37115 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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38,245
BUG: CategoricalDtype.__repr__ with RangeIndex categories
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2020-12-03T16:01:44Z
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MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry The problem was introduced a couple weeks ago, so shouldn't need a whatsnew.
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BUG: Allow numeric ExtensionDtypes in DataFrame.select_dtypes
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- [x] closes #35340 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Picking up #35341 (original PR addressed all comments - here I merged master + added whatsnew note) cc @simonjayhawkins @jreback @jbrockmendel
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38,247
BUG: Limited available colors
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5
2020-12-02T19:05:31Z
2021-03-22T20:24:55Z
2020-12-12T23:31:27Z
CONTRIBUTOR
null
(GH37967) - [x] closes #37967 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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PERF: timeseries comparison ops
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4
2020-12-02T19:20:27Z
2020-12-03T15:51:22Z
2020-12-03T14:19:36Z
MEMBER
null
- [x] closes #37462 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Locally this gets me to within 3-4% of 1.1.4 on the asv mentioned in #37462
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Fix C408 Errors in core/strings, core/arrays, core/dtypes...
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3
2020-12-02T19:36:25Z
2020-12-09T10:17:50Z
2020-12-09T10:17:49Z
NONE
null
...core/computation, core/indexes, core/ops, io/pytables.py, tests/dtypes, tests/io/generate_legacy_storage_files.py, tests/frame/methods/test_to_records.py, tests/groupby/transform/test_transform.py - [X] xref #[38138](https://github.com/pandas-dev/pandas/issues/38138) - [X] tests added / passed - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Fix dict call for pandas/tests/util/test_assert_series_equal.py
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2020-12-02T20:26:16Z
2020-12-03T21:26:41Z
2020-12-03T21:26:25Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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38,251
ENH: implement _should_compare/_is_comparable_dtype for all Index subclasses
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1
2020-12-02T22:27:22Z
2020-12-04T15:20:27Z
2020-12-04T14:20:41Z
MEMBER
null
Fixes some problems in get_indexer_non_unique that AFAICT are not actually causing any problems. Categorical kludge is needed because of #38240.
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38,252
CI: delete unused import in groupby/groupby.py
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2020-12-03T00:15:45Z
2020-12-03T02:35:19Z
2020-12-03T02:24:55Z
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Fixes a `flake8` bug on current master: ``` (pandas-dev) andrewwieteska@Andrews-MacBook-Pro pandas % flake8 pandas/core/groupby/groupby.py pandas/core/groupby/groupby.py:53:1: F401 'pandas.core.dtypes.cast.maybe_cast_result' imported but unused ```
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38,253
REF: simplify _cython_operation return
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BUG: incorrect EA casting in groubpy.agg
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2020-12-03T01:38:03Z
2021-01-24T22:02:34Z
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry The existing code is implicitly assuming that _from_sequence is strict, so that maybe_cast_to_extension_array will only return an EA when correct. Since that assumption is untrue, the current code will return incorrect results, as in the test this adds. If we just removed L729-L741 in groupby.ops, we would have 12ish test failures. A few of those would be for float64 result failing to cast back to Float64, and the rest would be for `ndarray[Decimal objects]` failing to cast back to DecimalArray. Until `_from_sequence` is reliable, I would rather remove these few lines and return correct-but-suboptimally-casted results than have these kludges. cc @jorisvandenbossche
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TST: split unfocused groupby test
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BUG: pandas to_json with orient "table" returns wrong schema & data string
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2020-12-10T10:16:14Z
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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. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python test = pd.DataFrame([[1,2,3],[4,5,6]], columns=[1, 2, 3]) s = test.to_json(orient="table") print(s) # wrong string : # '{"schema":{"fields":[{"name":"index","type":"integer"},{"name":1,"type":"integer"},{"name":2,"type":"integer"},{"name":3,"type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"1":1,"2":2,"3":3},{"index":1,"1":4,"2":5,"3":6}]}' pd.read_json(s, orient="table") #ValueError: Cannot convert non-finite values (NA or inf) to integer ``` #### Problem description When the initial columns are integers, the schema dict returns correct names (that unquoted integers), but the data dict identifies columns as string (quoted integers). Therefore, any loaded dataframe from this json format will return a dataframe full of empty (NaN) values or fail with an exception (I don't know which triggers which ; this minimal example here will trigger an exception ; my original dataset with multiindexes [in stackoverflow](https://stackoverflow.com/questions/65115871/pandas-read-json-with-orient-table) returned an empty dataframe... #### Expected Output This output for pandas.to_json(orient="table") could be read (though it is losing the "int" label key and transforming it to strings) : '{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"1","type":"integer"},{"name":"2","type":"integer"},{"name":"3","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.0.20"},"data":[{"index":0,"1":1,"2":2,"3":3},{"index":1,"1":4,"2":5,"3":6}]}' #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.6.3.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel byteorder : little LC_ALL : None LANG : fr LOCALE : None.None pandas : 1.1.4 numpy : 1.18.4 pytz : 2017.2 dateutil : 2.8.1 pip : 20.2.4 setuptools : 36.6.0 Cython : 0.27.2 pytest : 3.2.3 hypothesis : None sphinx : 1.6.5 blosc : 1.5.1 feather : 0.4.0 xlsxwriter : 1.0.2 lxml.etree : 4.1.0 html5lib : 0.9999999 pymysql : None psycopg2 : None jinja2 : 2.9.6 IPython : 6.2.1 pandas_datareader: None bs4 : 4.6.0 bottleneck : 1.2.1 fsspec : None fastparquet : None gcsfs : None matplotlib : 2.2.2 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 0.7.1 pytables : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.1.14 tables : None tabulate : 0.8.5 xarray : 0.9.6 xlrd : 1.1.0 xlwt : None numba : 0.35.0 </details>
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BUG: groupby.apply on the NaN group drops values with original axes return
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2
2020-12-03T08:22:13Z
2020-12-08T00:39:56Z
2020-12-04T23:45:03Z
CONTRIBUTOR
null
- [x] closes #38227 - [x] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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TST: mark all excel tests as slow
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2020-12-03T08:52:05Z
2020-12-08T03:49:09Z
2020-12-08T03:48:59Z
MEMBER
null
- [ ] xref #38091 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Marked all excel-related tests as slow. There are a lot of time for setup and teardown for every test. Overall, moving these tests to "slow" category may probably save about 5 minutes of test time for "not slow" pipelines.
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Backport PR #38244: REGR: unstack on 'int' dtype prevent fillna to work
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2020-12-03T11:10:30Z
2020-12-03T12:02:33Z
2020-12-03T12:02:28Z
MEMBER
null
Backport PR #38244
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C408 fix
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1
2020-12-03T11:45:08Z
2020-12-04T14:23:41Z
2020-12-04T14:23:37Z
CONTRIBUTOR
null
- [x] xref #38138 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Rewrote dict calls to dict literals in the following files ./pandas/tests/io/json/test_ujson.py ./pandas/tests/io/json/test_normalize.py Also changed list calls to literals in line 730 of the following file pandas/tests/io/json/test_ujson.py
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DOC: pandas.DataFrame.mask
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2020-12-03T12:30:11Z
2020-12-03T15:38:56Z
2020-12-03T15:29:54Z
NONE
null
#### Location of the documentation https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mask.html#pandas-dataframe-mask #### Documentation problem The first line of docstring states: >> Replace values where the condition is True. However, `where` replaces the values for which the `cond` is False. e.g. ``` zeros = pd.DataFrame(np.zeros(5)) zeros.where(zeros == 0.0, "REPLACED") >>> 0.0 0.0 0.0 0.0 0.0 zeros.where(zeros != 0.0, "REPLACED") >>> REPLACED REPLACED REPLACED REPLACED REPLACED #### Suggested fix for documentation I think documentation should say "Replace values where the condition is False." The rest of the `where` documentation should also be changed to reflect the actual behaviour of `where`.
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38,262
BUG: to_hdf and HDFStore for subclasses
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1
2020-12-03T14:34:39Z
2020-12-19T02:19:07Z
2020-12-19T02:19:00Z
MEMBER
null
- [ ] closes #33748 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Fix the bug that the subclasses of a dataframe and a series cannot be saved ``to_hdf``. The tests were added. When testing I found that ``assert_frame_equal`` does not work on subclasses as well - probably need to fix it in a separate PR. For now in the tests I compare values only.
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2020-12-03T15:40:40Z
2020-12-03T22:58:47Z
2020-12-03T22:58:33Z
CONTRIBUTOR
null
- [ ] xref #38138 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Identify unnecessary dict call - rewrite as a literal in `./pandas/tests/io/pytables/test_store.py`, `./pandas/core/arrays/interval.py`
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REF: implement Groupby idxmin, idxmax without fallback
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2020-12-03T16:53:39Z
2020-12-03T21:39:58Z
2020-12-03T21:39:58Z
CONTRIBUTOR
null
- [ ] xref #38138 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Identify unnecessary dict call - rewrite as a literal in `pandas\core\indexes\numeric.py`
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BUG: Inserting array of same size with Series.loc raises ValueError
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6
2020-12-03T17:33:05Z
2020-12-06T00:28:19Z
2020-12-05T17:14:06Z
CONTRIBUTOR
null
closes #37748 closes #37486 closes #38271 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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MDU6SXNzdWU3NTY0MDQyNTY=
38,267
BUG: Inconsistent behavior of `.replace()` in Int64 series with `<NA>`.
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5
2020-12-03T17:35:06Z
2020-12-28T14:55:46Z
2020-12-28T14:55:46Z
NONE
null
### Code Sample This code does work as expected (0 value is replaced by `<NA>` in an Int64 series). ```python >>> import pandas as pd >>> s = pd.Series([0, 1]).astype("Int64") >>> s.replace(0, pd.NA) 0 <NA> 1 1 dtype: Int64 ``` In contrast, when having `<NA>` values in the series `.replace()` does not work as expected (0 is **not** replaced by `<NA>`). ```python >>> import pandas as pd >>> s = pd.Series([0, None]).astype("Int64") >>> s.replace(0, pd.NA) 0 0 1 <NA> dtype: object ``` However, the code does work if we add `.fillna()` before replacing: ```python >>> import pandas as pd >>> s = pd.Series([0, None]).astype("Int64") >>> s.fillna(0).replace(0, pd.NA) 0 <NA> 1 <NA> dtype: Int64 ``` ### Problem description When applying the replace on a Int64 series with or without `<NA>` I expect consistent behavior. Also the result type of `.replace()` seems to be inconsistent based on the usage of `.fillna()` on the series (without `fillna()` the type changes to `object`). ### Expected behavior I would expect that `s.replace(0, pd.NA)` and `s.fillna(0).replace(0, pd.NA)` on an Int64 series will **always** lead to the same result. ### Output of pd.show_versions() <details> <summary>Details</summary> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.0.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-53-generic Version : #59~18.04.1-Ubuntu SMP Wed Oct 21 12:14:56 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.4 numpy : 1.19.2 pytz : 2020.4 dateutil : 2.8.1 pip : 20.3 setuptools : 50.3.2.post20201201 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 pytables : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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MDExOlB1bGxSZXF1ZXN0NTMxOTY5MjM1
38,268
BUG: name retention in Index.difference corner cases
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2
2020-12-03T17:58:15Z
2020-12-04T02:08:54Z
2020-12-04T02:01:12Z
MEMBER
null
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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38,269
BUG: _metadata does not persist through groupby
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2
2020-12-03T18:24:26Z
2020-12-12T16:52:36Z
2020-12-09T04:56:18Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python class MyDataFrame(DataFrame): _metadata = ['my_metadata'] data = MyDataFrame({'test': [1,1,1,2,2,2]}) data.my_metadata = 2 for group_id, group_data in data.groupby('test'): assert group_data.my_metadata == 2 ``` #### Problem description As per documentation [here](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#define-original-properties), it seems that any derivatives of a DataFrame should inherit the metadata properties assigned. This is true with slicing operations, but is not true with groupby(). It would be more consistent if the property persisted for slicing and groupby(). #### Expected Output Should not raise an assertion error. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.5.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-56-generic Version : #62-Ubuntu SMP Mon Nov 23 19:20:19 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.4 numpy : 1.19.4 pytz : 2019.3 dateutil : 2.7.3 pip : 20.0.2 setuptools : 45.2.0 Cython : 0.29.21 pytest : 6.1.2 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 : 2.10.1 IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.5.4 sqlalchemy : None tables : None tabulate : 0.8.6 xarray : 0.14.1 xlrd : None xlwt : None numba : 0.51.2 </details>
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38,270
BUG: Missing SettingwithCopyWarning with MultiIndex DataFrame
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2020-12-03T18:39:18Z
2021-08-14T17:12:59Z
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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. #### Code Sample, a copy-pastable example ```python """Missing SettingwithCopyWarning.""" import pandas as pd df = pd.DataFrame( [["Alice", 10]], columns=pd.MultiIndex.from_product([["person"], ["first_name", "age"]]), ) df.person.age = 11 print(df) ``` #### Problem description Value `11` is not assigned. Output is ``` person first_name age 0 Alice 10 ``` No SettingwithCopyWarning is shown. #### Expected Output Value `11` is assigned. Output is ``` person first_name age 0 Alice 11 ``` Or: SettingwithCopyWarning is shown. #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.3.18-lp152.44-default Version : #1 SMP Wed Sep 30 18:51:43 UTC 2020 (914f31e) machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.4 numpy : 1.19.4 pytz : 2020.4 dateutil : 2.8.1 pip : 20.3.1 setuptools : 50.3.2 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.3.7 lxml.etree : 4.6.2 html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.5.4 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : 1.2.0 xlwt : None ``` </details>
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BUG: setting object dtype Series row with an Extension Array
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2020-12-03T18:41:21Z
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On 1.2 master: ``` python In [11]: import numpy as np ...: import pandas as pd ...: ...: ser = pd.Series(1, index=list("abcde"), dtype="object") ...: ...: expected = pd.array([0,0,3,0,0]) ...: ser.loc["a"] = expected ...: result = ser[0] In [12]: ser Out[12]: a 0 b 0 c 3 d 0 e 0 dtype: Int64 ``` I expect: ``` python In [14]: ser Out[14]: a [0, 0, 3, 0, 0] b 1 c 1 d 1 e 1 dtype: object ```
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BUG: DataFrame.apply with axis=1 and EA dtype
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- [x] closes #36785 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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test groupby.indices for multiple groupby and mix of types
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Creates tests for GH26859 - [X] closes #26859 - [X] tests added / passed - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Does this warrant an entry in whatsnew?
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BUG: pandas.dataframe.drop_duplicates ignore_index parameter
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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. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import unittest import pandas as pd class Test(unittest.TestCase): def testError(self): cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'], 'Price': [22000,25000,27000,35000] } df = pd.DataFrame(cars, columns = ['Brand', 'Price']) dataError = df.drop_duplicates(ignore_index='error') print("Should give ValueError: \n"+ " ValueError: For argument ignore_index expected type bool, received type str.") if __name__ == '__main__' : unittest.main() ``` #### Problem description Hi! So I am doing white testing for school and found a tiny bug in drop_duplicates. When using the boolean parameter "ignore_index" it does not check if it is boolean or not. The "inplace" parameter is checked using the function validate_bool_kwarg(--). The "ignore_index" should be checked the same way. I have never reported a bug and I could fix it myself but I am a bit afraid to mess things up. Thank you! #### Expected Output ValueError: For argument "ignore_index" expected type bool, received type str. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6 python : 3.8.5.final.0 python-bits : 64 OS : Linux OS-release : 4.19.104-microsoft-standard Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.2 numpy : 1.19.2 pytz : 2020.1 dateutil : 2.8.1 pip : 20.0.2 setuptools : 45.2.0 Cython : None pytest : None hypothesis : None sphinx : 3.2.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.10.1 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 pytables : None pyxlsb : None s3fs : None scipy : 1.5.2 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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CLN: assorted cleanups
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2020-12-03T22:00:40Z
2020-12-04T14:22:42Z
2020-12-04T14:22:38Z
CONTRIBUTOR
null
- [ ] xref #38138 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Removed the dict calls for the files: `./pandas/core/arrays/floating.py` `./pandas/core/arrays/numpy_.py` `./pandas/core/arrays/interval.py`
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BUG: Validate drop_duplicates ignore_index argument for bool
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2020-12-03T22:36:46Z
2020-12-04T23:46:21Z
2020-12-04T14:24:58Z
MEMBER
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- [x] closes #38274 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Saw this on the tracker. I think we should validate this, some inputs could lead to weird results. The last drop duplicates entry in whatsnew was also under reshaping, so put it there
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BUG: DataError: No numeric types to aggregate on pandas.core.groupby.GroupBy.rank¶
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NONE
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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. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd df = pd.DataFrame({'id': ['1', '1', '2', '3'], 'title': ['title1', 'title2', 'title3', 'title4']}) # df['id'] = df['id'].astype('int') # df.dtypes df.groupby('id')['title'].rank(method = "first") ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] For a simple dataframe, I cannot rank a grouped dataframe on non-numeric data type. For example, I want to group by ID and rank a column. My aim here is to identify duplicates in one column by ranking it. This is the problem I get: ``` df.groupby('id')['title'].rank(method = "first") --------------------------------------------------------------------------- DataError Traceback (most recent call last) ~/projects/dentaway-marketing/linkedin-jobs-scraper/process_linkedin_jobs_data.py in ----> 276 df.groupby('id')['title'].rank(method = "first") ~/anaconda3/envs/dev/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in rank(self, method, ascending, na_option, pct, axis) 2299 msg = "na_option must be one of 'keep', 'top', or 'bottom'" 2300 raise ValueError(msg) -> 2301 return self._cython_transform( 2302 "rank", 2303 numeric_only=False, ~/anaconda3/envs/dev/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in _cython_transform(self, how, numeric_only, **kwargs) 970 971 if len(output) == 0: --> 972 raise DataError("No numeric types to aggregate") 973 974 return self._wrap_transformed_output(output) DataError: No numeric types to aggregate ``` #### Expected Output The expected output should be a ranked series. #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] pandas is installed with pip. INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.3.final.0 python-bits : 64 OS : Darwin OS-release : 20.1.0 Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.1.4 numpy : 1.18.2 pytz : 2020.1 dateutil : 2.8.1 pip : 20.1.1 setuptools : 49.2.0.post20200714 Cython : 0.29.21 pytest : 5.4.3 hypothesis : None sphinx : 3.1.2 blosc : None feather : None xlsxwriter : 1.2.9 lxml.etree : 4.5.2 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.16.1 pandas_datareader: None bs4 : 4.9.1 bottleneck : 1.3.2 fsspec : 0.7.4 fastparquet : None gcsfs : None matplotlib : 3.2.2 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.4 pandas_gbq : None pyarrow : 2.0.0 pytables : None pyxlsb : None s3fs : None scipy : 1.5.0 sqlalchemy : 1.3.18 tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.50.1 </details>
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MDExOlB1bGxSZXF1ZXN0NTMyMTgwMjI3
38,279
BUG: isin numeric vs string
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2020-12-03T23:16:56Z
2020-12-07T17:28:41Z
2020-12-04T14:25:42Z
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Using the new hashtables! ``` In [3]: arr = np.arange(10**7).astype(np.int32) In [4]: %timeit isin(arr, arr[:10]) 88.8 ms ± 497 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master 42.7 ms ± 385 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- PR ```
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38,280
pandas/core/strings/accesory.py Unnecessary dict call - rewrite as a literal #38138
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1
2020-12-04T00:52:36Z
2020-12-04T17:57:30Z
2020-12-04T14:21:39Z
CONTRIBUTOR
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xref #38138 File modified: pandas/core/strings/accesory.py Changed unnecessary dict calls for flake8 (C408) standard: - Rewrite dict() as {} - [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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38,281
DOC: add to str.endswith / startswith that tuples are accepted for multiple value testing
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2
2020-12-04T01:33:03Z
2020-12-04T02:23:44Z
null
CONTRIBUTOR
null
#### Location of the documentation https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.endswith.html https://pandas.pydata.org/docs/reference/api/pandas.Series.str.startswith.html #### Documentation problem Right now there is no indication that these methods accept a tuple for testing on multiple character sequences. #### Suggested fix for documentation Add in the documentation that tuples are accepted as values.
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BUG: IntervalIndex.union with mismatched dtypes both empty
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2020-12-04T01:44:54Z
2020-12-05T17:31:01Z
2020-12-05T17:14:41Z
MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Ever so slowly ironing out the wrinkles between the setops.
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38,283
REF: de-duplicate intersection methods
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2020-12-04T03:42:10Z
2020-12-04T15:14:59Z
2020-12-04T14:19:59Z
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De-duplicating the PeriodIndex, IntervalIndex, and MultiIndex methods is blocked by #38251
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TST: fixturize unfocused test
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2020-12-04T04:31:04Z
2020-12-04T15:16:05Z
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MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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REF: avoid catching all exceptions in libreduction
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2020-12-04T04:33:41Z
2020-12-17T15:43:21Z
2020-12-17T13:53:36Z
MEMBER
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Most of the libreduction edits are from #35417
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BUG: GroupBy.first does not skip missing values in string-valued columns
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4
2020-12-04T07:18:02Z
2020-12-07T17:56:54Z
2020-12-06T18:46:41Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Create a DataFrame with numbers and missing entries, first() on groupby works as expected, # skipping the missing leading entry in column b >>> df2=pd.DataFrame({"a": [4,4,4,4,4,4], "b": [None,3,None,3,3,3]}) >>> df2.groupby("a").first() b a 4 3.0 # Create a similar DataFrame, but with strings and missing entries, first() on groupby # does not work as expected, it doesn't skip the missing leading entry df2=pd.DataFrame({"a": [4,4,4,4,4,4], "b": [None,"foo",None,"foo","foo","foo"]}) >>> df2.groupby("a").first() b a 4 None ``` #### Problem description One utility of the GroupBy.first() function is its coalescing behavior--it skips missing values within groups. This behavior does not seem to happen when a column has string values, which creates two problems: 1) type-dependent behavior of an otherwise generic method, and 2) I'm not able to find a workaround to get the behavior I'd like (returning the first non-missing string in a group. #### Expected Output I would expect the output of the second example above to be "foo", not None. If the behavior above is the expected behavior of Pandas, then please provide the correct code to use in order to extract the first non-missing string in a groupby column. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.5.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.4 numpy : 1.19.4 pytz : 2020.4 dateutil : 2.8.1 pip : 20.2.3 setuptools : 50.2.0 Cython : None pytest : 6.1.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.19.0 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 pytables : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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757,009,837
MDU6SXNzdWU3NTcwMDk4Mzc=
38,287
DOC: Out-of-date list of sort algorithms
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3
2020-12-04T10:58:24Z
2020-12-30T13:49:06Z
2020-12-30T13:49:06Z
CONTRIBUTOR
null
#### Location of the documentation e.g. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.sort_values.html, https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_index.html #### Documentation problem List of values for `kind` should be updated to reflect changes in NumPy: https://numpy.org/doc/stable/reference/generated/numpy.sort.html Specifically, since NumPy 1.15.0, the list has been `{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}` #### Suggested fix for documentation Full list of options is provided. Also, the statement that "‘mergesort’ is the only stable algorithm" is now incorrect. I can do this, when I get time. But I'm more than happy for someone else to do it if they want.
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38,288
ENH: I wish I could use pandas to compare Two Excels and Create a difference Report where I can filter out differences alone
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1
2020-12-04T14:00:49Z
2020-12-04T15:58:50Z
2020-12-04T15:58:37Z
NONE
null
#### 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 [...]"] #### 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] #### API breaking implications [this should provide a description of how this feature will affect the API] #### Describe alternatives you've considered [this should provide a description of any alternative solutions or features you've considered] #### Additional context [add any other context, code examples, or references to existing implementations about the feature request here] ```python # Your code here, if applicable ```
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MDExOlB1bGxSZXF1ZXN0NTMyNTU3MTM3
38,289
CLN: remove internal usage of integer_array()
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10
2020-12-04T14:08:21Z
2020-12-12T08:50:42Z
2020-12-11T22:49:23Z
MEMBER
null
The `integer_array()` function stems from a time that `pd.array(..)` didn't exist yet. But since now `pd.array` can do this, IMO we shouldn't be using that outside of the integer module (eg I didn't add a `floating_array(..)`). Only in `tests/arrays/integer/tst_construction.py` I still left the usage of `integer_array()` as is (but probably could also change it there). Could in principle remove the function altogether, since it is just a small wrapper around the private `arrays.integer.coerce_to_array`
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MDExOlB1bGxSZXF1ZXN0NTMyNTcxNTky
38,290
REF: move common arithmetic ops from Integer/FloatingArray to NumericArray
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1
2020-12-04T14:32:41Z
2020-12-05T09:23:06Z
2020-12-04T23:30:52Z
MEMBER
null
xref https://github.com/pandas-dev/pandas/issues/38110
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757,259,479
MDExOlB1bGxSZXF1ZXN0NTMyNjY1MTQ3
38,291
ENH: use correct dtype in groupby cython ops when it is known (without try/except)
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7
2020-12-04T17:09:05Z
2020-12-08T16:07:35Z
2020-12-08T10:22:11Z
MEMBER
null
Addresses part of #37494. And xref https://github.com/pandas-dev/pandas/pull/38162/files#r536191377 cc @jorisvandenbossche
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38,292
BUG: read_csv raises IndexError when index_col is passed
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0
2020-12-04T18:23:59Z
2020-12-13T20:01:38Z
2020-12-13T20:01:38Z
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. --- #### Code Sample, a copy-pastable example ```python import io import pandas as pd pd.read_csv(io.StringIO('a0, a1, a2\n b0, b1, b2\n'), header=[0, 1], index_col=0) ``` #### Problem description This is a legal, if empty, csv. Note that variations such as ```pd.read_csv(io.StringIO('a0, a1, a2\n'), header=[0], index_col=0)```, ```pd.read_csv(io.StringIO('a0, a1, a2\n b0, b1, b2\n'), header=[0, 1])```, and ```pd.read_csv(io.StringIO('a0, a1, a2\n b0, b1, b2\ndata,data'), header=[0, 1], index_col=0)``` work fine. #### Expected Output ``` Empty DataFrame Columns: [( a1, b1), ( a2, b2)] Index: [] ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.9.0.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.4 numpy : 1.19.4 pytz : 2020.4 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 : 7.19.0 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 pytables : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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38,293
BUG: read_json does not respect chunksize
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15
2020-12-04T18:37:19Z
2020-12-23T16:32:40Z
2020-12-23T16:32:23Z
CONTRIBUTOR
null
- [X] closes #36791 - [X] closes #34548 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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38,294
ENH: set the first code value in Categorical
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12
2020-12-04T18:37:23Z
2020-12-06T02:10:52Z
2020-12-05T11:07:17Z
NONE
null
#### Is your feature request related to a problem? ```python import pandas as pd from pandas.api.types import CategoricalDtype df = pd.DataFrame(data={'col': ['small', 'medium', 'large']}) ordered = CategoricalDtype(categories=['small', 'medium', 'large'], ordered=True) df.col = df.col.astype(ordered) df.col.cat.codes # 0 0 # 1 1 # 2 2 # dtype: int8 ``` I often wish I can set the beginning underline code value in a Categorical. Right, no matter ordered or unordered, pandas will always begin the first code value as 0. Sometimes, I want it to begin with 1. like ``` ordered = CategoricalDtype(categories=['small', 'medium', 'large'], start=1, ordered=True) ``` Currently, there is no alternative I can think of other than manually storing the modified codes in a new column but this will not affect the underline coding. I really wish this feature could be added.
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38,295
Backport PR #38272 on branch 1.1.x (BUG: DataFrame.apply with axis=1 and EA dtype)
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0
2020-12-04T19:04:18Z
2020-12-04T20:03:20Z
2020-12-04T20:03:20Z
NONE
null
Backport PR #38272: BUG: DataFrame.apply with axis=1 and EA dtype
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38,296
BUG: DataFrame.transform(dict) drops columns not mentioned in the dict
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6
2020-12-04T19:54:38Z
2020-12-05T19:54:06Z
2020-12-05T18:54:59Z
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. I may have a dataframe with 100 columns, of which I want to transform 3. `DataFrame.transform()` accepts a dict with column -> function mappings, therefore my intuition is to provide 3 mappings for the 3 columns I want transformed. However, upon providing such a mapping dict, the function drops the non mentioned 97 columns. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # create a dataframe with 100 columns d = {'col%d' % i: [0] for i in range(100)} df = pandas.DataFrame(d) # transform 3 arbitrary columns with a function func42 = lambda x: 42 df.transform({ 'col10': func42, 'col20': func42, 'col42': func42 }) ``` #### Problem description The transformation lost the 97 columns for which there was no transformation mapping provided. The natural intuition is to preserve all columns that were not mentioned in the transformation. #### Expected Output A dataframe containing all 100 columns: the 3 transformed columns with the rest of the 97 columns untouched. The current implementation returns 3 columns. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.7.8.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-123-generic Version : #126~16.04.1-Ubuntu SMP Wed Oct 21 13:48:05 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.1.4 numpy : 1.19.4 pytz : 2020.4 dateutil : 2.8.1 pip : 20.3 setuptools : 49.6.0.post20201009 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 : 5.8.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.5.3 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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38,297
DOC: Pandas Pg 47 label/61 doc merge() link redirects to first page
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#### Location of the documentation Page 47 [this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"] **Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/). #### Documentation problem merge() link is broken and links back to the first page [this should provide a description of what documentation you believe needs to be fixed/improved] #### Suggested fix for documentation [this should explain the suggested fix and **why** it's better than the existing documentation]
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38,298
New dict call fixings #38138
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1
2020-12-04T21:20:04Z
2020-12-04T23:27:19Z
2020-12-04T23:27:16Z
CONTRIBUTOR
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xref #38138 File modified: pandas/core/strings/accesory.py Changed unnecessary dict calls for flake8 (C408) standard: Rewrite dict() as {} - [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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38,299
BUG: find_common_type([interval[f8], interval[i8]]) returns object
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2020-12-04T22:23:58Z
2020-12-12T23:21:45Z
2020-12-12T23:21:45Z
MEMBER
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I would expect it to return interval[f8], analogous to what we'd get with find_common_type([f8, i8])
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ENH: Make pandas API more consistent
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2020-12-04T22:51:30Z
2020-12-09T16:20:30Z
2020-12-09T16:20:30Z
NONE
null
#### Is your feature request related to a problem? The pandas DataFrame uses both arguments of `inplace` and `copy` (`DataFrame.astype` and `Series.astype`) in different methods to indicate change in place. It would be great if it can be unified to use `inplace` in all methods. #### Describe the solution you'd like Always use the argument `inplace` (instead of `copy`) to indicate change in place. #### API breaking implications Some methods such as `DataFrame.astype` and `Series.astype` will be affected as they argument `copy` is changed to `inplace`. #### Describe alternatives you've considered #### Additional context
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38,301
BUG: Fix typo in melt value_name futurewarning
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2020-12-04T22:57:54Z
2020-12-05T01:57:12Z
2020-12-05T01:57:04Z
CONTRIBUTOR
null
- [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` Just removing the duplicated 'i' in "resultiing" of the futurewarning in melt about using `value_name` with the same name as a column the in the original dataframe. Filing an issue or a whatsnew entry for such a minor change seemed excessive.
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BUG: Clean intersection and fix resulting names when MultiIndex are equal
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- [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` Names were wrong when MultiIndexes where equal and unique with different names. Additional cleanups cc @jbrockmendel
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BUG: Series.loc setitem with Series raises ValueError
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2020-12-05T01:33:26Z
2021-01-28T02:37:06Z
2021-01-28T02:37:06Z
CONTRIBUTOR
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd ser = pd.Series([0, 0, 0], dtype=object) ser.loc[0] = pd.Series([42]) # ValueError: No axis named 1 for object type Series ``` #### Problem description <details> ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~/sources/official.clone/pandas/pandas/core/generic.py in _get_axis_number(cls, axis) 456 try: --> 457 return cls._AXIS_TO_AXIS_NUMBER[axis] 458 except KeyError: KeyError: 1 During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-24-7c4b5d72ca25> in <module> ----> 1 ser.loc[0] = pd.Series([0]) ~/sources/official.clone/pandas/pandas/core/indexing.py in __setitem__(self, key, value) 689 690 iloc = self if self.name == "iloc" else self.obj.iloc --> 691 iloc._setitem_with_indexer(indexer, value, self.name) 692 693 def _validate_key(self, key, axis: int): ~/sources/official.clone/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value, name) 1634 self._setitem_with_indexer_split_path(indexer, value, name) 1635 else: -> 1636 self._setitem_single_block(indexer, value, name) 1637 1638 def _setitem_with_indexer_split_path(self, indexer, value, name: str): ~/sources/official.clone/pandas/pandas/core/indexing.py in _setitem_single_block(self, indexer, value, name) 1848 # setting for extensionarrays that store dicts. Need to decide 1849 # if it's worth supporting that. -> 1850 value = self._align_series(indexer, Series(value)) 1851 1852 elif isinstance(value, ABCDataFrame) and name != "iloc": ~/sources/official.clone/pandas/pandas/core/indexing.py in _align_series(self, indexer, ser, multiindex_indexer) 2018 2019 elif is_scalar(indexer): -> 2020 ax = self.obj._get_axis(1) 2021 2022 if ser.index.equals(ax): ~/sources/official.clone/pandas/pandas/core/generic.py in _get_axis(self, axis) 467 @final 468 def _get_axis(self, axis: Axis) -> Index: --> 469 axis_number = self._get_axis_number(axis) 470 assert axis_number in {0, 1} 471 return self.index if axis_number == 0 else self.columns ~/sources/official.clone/pandas/pandas/core/generic.py in _get_axis_number(cls, axis) 457 return cls._AXIS_TO_AXIS_NUMBER[axis] 458 except KeyError: --> 459 raise ValueError(f"No axis named {axis} for object type {cls.__name__}") 460 461 @final ValueError: No axis named 1 for object type Series ``` </details> #### Expected Output The same as with ```python ser.iloc[0] = pd.Series([42]) ``` ``` 0 0 42 dtype: int64 1 0 2 0 dtype: object ``` #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 03e58585036c83ca3d4c86d7d3d7ede955c15130 python : 3.7.3.final.0 python-bits : 64 OS : Linux OS-release : 5.8.0-0.bpo.2-amd64 Version : #1 SMP Debian 5.8.10-1~bpo10+1 (2020-09-26) machine : x86_64 processor : byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.0.dev0+1465.g03e585850 numpy : 1.19.4 pytz : 2020.4 dateutil : 2.8.1 pip : 20.2.4 setuptools : 40.8.0 Cython : 0.29.21 pytest : None 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 : 2.11.2 IPython : 7.15.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : None odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.5.3 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None``` </details>
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BUG: IntervalIndex cannot be saved to HDF file
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1
2020-12-05T02:36:23Z
2020-12-14T13:26:47Z
null
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. --- #### Code Sample, a copy-pastable example ```python import pandas as pd data = pd.DataFrame({"a": [1]}) data.index = pd.IntervalIndex.from_arrays( [0.5], [0.9], ) data.to_hdf('test.hdf', 'df') # fails (as index) data.reset_index().to_hdf('test.hdf', 'df') # fails (as column) ``` #### Problem description Pytables doesn't seems to know IntervalIndex. If pytables doesn't support IntervalIndex, pandas should either convert the IntervalIndex into two columns (and then merge them while reading) or throw an error early in `to_hdf`.
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TYP/BUG: fix passing incorrect type to interpolate_1d
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3
2020-12-05T03:50:01Z
2020-12-12T17:26:25Z
2020-12-07T19:30:58Z
MEMBER
null
use np.putmask in a few places, as it is appreciably more performant than `__setitem__`
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REF: implement Index._get_indexer
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2020-12-05T04:03:56Z
2020-12-08T21:17:04Z
2020-12-08T21:13:47Z
MEMBER
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De-duplicate all the boilerplate in Index.get_indexer.
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2020-12-05T04:08:30Z
2020-12-05T19:26:16Z
2020-12-05T17:48:43Z
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757,534,922
MDExOlB1bGxSZXF1ZXN0NTMyODk0Mjkw
38,310
TST: avoid skipping in frame arith test
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0
2020-12-05T04:11:33Z
2020-12-07T15:34:02Z
2020-12-07T13:35:33Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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38,311
DOC: Wrong output in the example of Timedelta.isoformat
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13
2020-12-05T04:46:56Z
2020-12-19T11:06:37Z
2020-12-19T10:23:41Z
CONTRIBUTOR
null
#### Location of the documentation [`pandas.Timedelta.isoformat`](https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.isoformat.html#pandas.Timedelta.isoformat) #### Documentation problem The output on an example in the docs has the wrong output(`Timedelta.isoformat` works fine) ![Screenshot from 2020-12-04 16-40-36](https://user-images.githubusercontent.com/47276342/101234113-ce80fa00-36e2-11eb-8e2c-9623f41a69ae.png) While it should've been `'P0DT1H0M10S'`
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MDU6SXNzdWU3NTc1NDUyNDY=
38,312
PERF: Period constructor parses input twice
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1
2020-12-05T05:22:16Z
2021-08-14T21:57:35Z
null
MEMBER
null
#38175 added correct handling of nanoseconds in the Period constructor but the code path as is parses the input twice. We should integrate the nanoseconds code into `parse_time_string` so this happens only once. xref https://github.com/pandas-dev/pandas/pull/38175/files#r536100941 It might be worth adding a nanosecond Period constructor benchmark too.
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38,313
REF: simplify casting of scalars to array
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2
2020-12-05T05:49:53Z
2020-12-07T20:53:24Z
2020-12-07T19:23:24Z
MEMBER
null
cast_scalar_to_array is only used in two places, and in each of them there is a better option available.
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38,314
BUG: Sliced subclassed Series returns full data result when custom method is applied
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2020-12-05T10:31:34Z
2020-12-06T08:55:20Z
2020-12-06T08:55:20Z
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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 class _Subseries(pd.Series): _metadata = ['custom_method'] @property def _constructor(self): return _Subseries @property def _constructor_expanddim(self): return _Subseries def custom_method(self, value): return self + value s = _Subseries(range(5)) s = s.iloc[-3:] s.custom_method(1) ``` #### Problem description The above code returns: 0&nbsp;&nbsp;&nbsp;1 1&nbsp;&nbsp;&nbsp;2 2&nbsp;&nbsp;&nbsp;3 3&nbsp;&nbsp;&nbsp;4 4&nbsp;&nbsp;&nbsp;5 A similar method, such as .add(), on a sliced Series will return the result of that method on the sliced data and not on the original full data. I would expect this to be the same in a subclassed version of a Series. #### Expected Output 2&nbsp;&nbsp;&nbsp;3 3&nbsp;&nbsp;&nbsp;4 4&nbsp;&nbsp;&nbsp;5 #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.9.0.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_Belgium.1252 pandas : 1.1.4 numpy : 1.19.4 pytz : 2020.4 dateutil : 2.8.1 pip : 20.3.1 setuptools : 49.6.0.post20201009 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 pytables : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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38,315
API: add EA._from_scalars / stricter casting of result values back to EA dtype
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19
2020-12-05T13:09:44Z
2021-05-24T17:59:00Z
null
MEMBER
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Closes #33254 Closes #31108 This adds a ``ExtensionArray._from_scalars`` function which is more strict than the existing `_from_sequence` constructor: in theory, it should only accept a sequence of scalars of its own type (i.e. the type you get with `arr[0]`, plus NAs), or arrays with the appropriate dtype. This can then be used in a context where we try to create an EA of the original dtype (in an attempt to preserve the original dtype), but we don't want to *coerce* any value to that dtype. **Note**: this is a WIP PR, which mostly adds *dummy* implementations of this method, just to mimic the desired behaviour for now, to be able to explore how this would work. If this turns out to be working, we will need to make better implementations for `_from_scalars` for our own EAs. Apart from that, this needs tests, docs, etc. But let's first discuss semantics. * [ ] Add tests for example case from https://github.com/pandas-dev/pandas/issues/31108, period case from https://github.com/pandas-dev/pandas/pull/38254, test case for categorical with non-categories * [ ] Reopen https://github.com/pandas-dev/pandas/pull/30282 once this is merged
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38,316
BUG: IntervalIndex.astype("category") doesn't preserve exact interval dtype in categories
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2020-12-05T15:10:20Z
2021-08-14T21:58:29Z
null
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Somewhere in the conversion, before factorizing, we convert the interval array/index to a object-dtype numpy array of Interval objects, and so afterwards *infer* the IntervalDtype again when creating the categories. Example consequence is that if you have uint64 intervals, they get inferred as int64 afterwards: ```python In [29]: index = pd.IntervalIndex.from_breaks(np.arange(5, dtype="uint64")) In [30]: index Out[30]: IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4]], closed='right', dtype='interval[uint64]') # <---- unsigned ints In [31]: pd.CategoricalIndex(index) Out[31]: CategoricalIndex([(0, 1], (1, 2], (2, 3], (3, 4]], categories=[(0, 1], (1, 2], (2, 3], (3, 4]], ordered=False, dtype='category') In [33]: pd.CategoricalIndex(index).dtype.categories Out[33]: IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4]], closed='right', dtype='interval[int64]') # <---- no longer uint64 ```
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38,317
DEPR: xlwt for writing excel files
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2020-12-05T17:04:46Z
2021-01-02T14:15:03Z
2020-12-08T10:11:55Z
MEMBER
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- [x] closes #26552 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Deprecates both specifying `engine="xlwt"` and the option `io.excel.xls.writer`. Note that a warning is emitted when a user sets the `io.excel.xls.writer` option, but not via the context manager. Whatsnew now reads: ![image](https://user-images.githubusercontent.com/45562402/101409318-bd371980-38ab-11eb-80f4-f64afc259438.png)
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DOC: 1.1.5 release date
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0
2020-12-05T17:52:30Z
2020-12-07T09:48:56Z
2020-12-07T09:48:52Z
MEMBER
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