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TST: GH38718 Tests for casting an interval range from float to int
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2020-12-27T13:55:43Z
2020-12-30T03:27:32Z
2020-12-30T03:27:23Z
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xrefs #38697 , #15832 - [x] closes #38718 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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TST: GH30999 Add match=msg to all pytest.raises in tests/reductions and add an error message to nanmedian
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2020-12-27T14:36:43Z
2020-12-30T19:58:59Z
2020-12-30T19:58:55Z
MEMBER
null
This pull request partially addresses xref #30999 to remove bare `pytest.raises` by adding `match=msg`. It doesn't close that issue as I have only addressed test modules in the pandas/tests/reductions directory. When going through the tests I found one case where the error message was the empty string. I decided that wasn't correct so I modified the `TypeError` to inherit the error message from the `ValueError` it was raising from. This made the error message similar to other messages in the same test. - [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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2020-12-27T14:50:15Z
2021-01-20T21:29:11Z
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MEMBER
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Tracking issue for the 1.2.1 release. https://github.com/pandas-dev/pandas/milestone/81 Currently scheduled for January 18, 2021 (date flexible on severity of regressions) List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression
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At random intervals I keep getting the following error. > \.vscode\extensions\ms-python.python-2020.12.424452561\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydevd_bundle\pydevd_resolver.py:193: FutureWarning: _AXIS_NUMBERS has been deprecated. > attr = getattr(var, name) > \.vscode\extensions\ms-python.python-2020.12.424452561\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydevd_bundle\pydevd_resolver.py:193: FutureWarning: _AXIS_NAMES has been deprecated. > attr = getattr(var, name) This seems to be refering to how I am using a dataframe in pandas. Though, due to the delays, I am not sure what specifically. I have found only one other refernce to this issue on the internet [https://intellij-support.jetbrains.com/hc/en-us/community/posts/360009512560-PyDev-FutureWarning-Django-console](url) Saying they believe it occurs on `df.columns` I'm running my project though a conda environment with: > Version Build Channel > absl-py 0.9.0 pypi_0 pypi > astor 0.8.1 pypi_0 pypi > astunparse 1.6.3 pypi_0 pypi > beautifulsoup4 4.9.1 py37_0 > brotlipy 0.7.0 py37he774522_1000 > bzip2 1.0.8 he774522_0 > ca-certificates 2020.7.22 0 > cachetools 4.1.1 pypi_0 pypi > certifi 2020.6.20 py37_0 > cffi 1.14.3 py37h7a1dbc1_0 > chardet 3.0.4 py37_1003 > conda 4.8.5 py37_0 > conda-build 3.18.11 py37_0 > conda-package-handling 1.6.1 py37h62dcd97_0 > console_shortcut 0.1.1 4 > cryptography 3.1.1 py37h7a1dbc1_0 > filelock 3.0.12 py_0 > gast 0.2.2 pypi_0 pypi > glob2 0.7 py_0 > google-auth 1.18.0 pypi_0 pypi > google-auth-oauthlib 0.4.1 pypi_0 pypi > google-pasta 0.2.0 pypi_0 pypi > grpcio 1.30.0 pypi_0 pypi > h5py 2.10.0 pypi_0 pypi > idna 2.10 py_0 > importlib-metadata 1.7.0 pypi_0 pypi > jinja2 2.11.2 py_0 > keras-applications 1.0.8 pypi_0 pypi > keras-preprocessing 1.1.2 pypi_0 pypi > libarchive 3.4.2 h5e25573_0 > libiconv 1.15 h1df5818_7 > liblief 0.10.1 ha925a31_0 > libxml2 2.9.10 h464c3ec_1 > lz4-c 1.9.2 h62dcd97_1 > markdown 3.2.2 pypi_0 pypi > markupsafe 1.1.1 py37hfa6e2cd_1 > menuinst 1.4.16 py37he774522_0 > numpy 1.19.0 pypi_0 pypi > oauthlib 3.1.0 pypi_0 pypi > openssl 1.1.1h he774522_0 > opt-einsum 3.2.1 pypi_0 pypi > pip 20.2.3 py37_0 > pkginfo 1.5.0.1 py37_0 > powershell_shortcut 0.0.1 3 > protobuf 3.12.2 pypi_0 pypi > psutil 5.7.2 py37he774522_0 > py-lief 0.10.1 py37ha925a31_0 > pyasn1 0.4.8 pypi_0 pypi > pyasn1-modules 0.2.8 pypi_0 pypi > pycosat 0.6.3 py37he774522_0 > pycparser 2.20 py_2 > pyopenssl 19.1.0 py_1 > pysocks 1.7.1 py37_1 > python 3.7.7 h81c818b_4 > python-libarchive-c 2.9 py_0 > pytz 2020.1 py_0 > pywin32 227 py37he774522_1 > pyyaml 5.3.1 py37he774522_1 > requests 2.24.0 py_0 > requests-oauthlib 1.3.0 pypi_0 pypi > rsa 4.6 pypi_0 pypi > ruamel_yaml 0.15.87 py37he774522_1 > scipy 1.4.1 pypi_0 pypi > setuptools 49.6.0 py37_1 > six 1.15.0 py_0 > soupsieve 2.0.1 py_0 > sqlite 3.33.0 h2a8f88b_0 > style 1.1.0 pypi_0 pypi > tensorboard 1.15.0 pypi_0 pypi > tensorboard-plugin-wit 1.7.0 pypi_0 pypi > tensorflow 2.2.0 pypi_0 pypi > tensorflow-estimator 1.15.1 pypi_0 pypi > tensorflow-gpu 1.15.0 pypi_0 pypi > termcolor 1.1.0 pypi_0 pypi > tqdm 4.49.0 py_0 > update 0.0.1 pypi_0 pypi > urllib3 1.25.10 py_0 > vc 14.1 h0510ff6_4 > vs2015_runtime 14.16.27012 hf0eaf9b_3 > werkzeug 1.0.1 pypi_0 pypi > wheel 0.35.1 py_0 > win_inet_pton 1.1.0 py37_0 > wincertstore 0.2 py37_0 > wrapt 1.12.1 pypi_0 pypi > xz 5.2.5 h62dcd97_0 > yaml 0.2.5 he774522_0 > zipp 3.1.0 pypi_0 pypi > zlib 1.2.11 h62dcd97_4 > zstd 1.4.5 h04227a9_0 Anyone got any ideas?
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MDExOlB1bGxSZXF1ZXN0NTQ1ODMzNjg1
38,723
BUG: inconsistency between frame.any/all with dt64 vs dt64tz
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5
2020-12-27T17:12:34Z
2020-12-30T15:45:14Z
2020-12-30T13:23:25Z
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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TST: GH30999 Add match=msg to all but two pytest.raises in tests/io
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2
2020-12-27T17:20:11Z
2020-12-30T11:08:25Z
2020-12-28T14:25:22Z
MEMBER
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This pull request partially addresses xref #30999 to remove bare `pytest.raises` by adding `match=msg`. It doesn't close that issue as I have only addressed test modules in the pandas/tests/io/ directory. I thought this was going to be relatively small because there were only 25 such instances, but it turned out some of them were in methods that were reused several times, so this ended up larger than I expected. There were a couple of cases where I found the structure of the tests quite confusing, with an internal function definition and two nested asserts. This structure also made it difficult to add the assertion about the error message. I restructured the tests to be, in my opinion, more straightforward. There's one file, pandas/tests/io/test_sql.py, with two instances of `pytest.raises` in tests which were skipped on my machine and which I couldn't figure out how to run, so those are left as is. - [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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38,725
pandas.DataFrame.plot arguments 'xlabel' & 'ylabel' not working.
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1
2020-12-27T18:03:23Z
2020-12-27T18:29:01Z
2020-12-27T18:26:27Z
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Hello everyone. I was rehearsing some exercises with pandas.DataFrame.plot. I stumbled upon one (apparent) issue regarding '_xlabel_' and '_ylabel_' arguments. I followed instructions provided at [https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html](this site), as that is my current working Pandas version. If my understanding is correct, those arguments are to display some _str_ inserted by user, at _xlabel_ & _ylabel_ respectively, right? What the documentation says is the following: - xlabel: Name to use for the xlabel on x-axis --> ERROR SHOWN: 'Line2D' object has no property 'xlabel' - ylabel: Name to use for the ylabel on Y-axis --> ERROR SHOWN: 'Line2D' object has no property 'yabel' Currently, I am using the following lines of code AFTER df.plot() has been used, to obtain the desired result; however I'd love to know if the previously mentioned behaviour is an actual issue, or some missunderstanding on the current documentation: `plt.ylabel(ylabel=my_ylabel, fontsize=tz) plt.xlabel(xlabel=my_xlabel, fontsize=tz)` Thank you in advance.
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DOC: chat-ops
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2
2020-12-27T18:06:20Z
2020-12-30T15:07:16Z
2020-12-30T15:07:16Z
MEMBER
null
xref https://github.com/pandas-dev/pandas/pull/38444#issuecomment-749711369 There are some GitHub Actions workflows which can be triggered via comments: 1. If you comment `@github-actions pre-commit` on a pull request, it will trigger a workflow which will autofix formatting errors (e.g. from `black` or `isort`) 2. `@meeseeksdev backport ` will trigger a workflow which will backport a given change to a branch (e.g. `@meeseeksdev backport 1.2.x`) 3. If you comment `take` on an issue, it will trigger a workflow which will assign the issue to you These should be documented somewhere in the docs - I think `doc/source/development/contributing.rst` might be a sensible location. `take` is already documented in there, but the other two aren't
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775,086,717
MDExOlB1bGxSZXF1ZXN0NTQ1ODQyMzc5
38,727
BUG: pd.read_json May Not Maintain Numeric String Index
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4
2020-12-27T18:29:22Z
2021-01-04T23:29:00Z
2021-01-04T23:29:00Z
CONTRIBUTOR
null
- [x] closes #28556 - [x] tests added / passed - [ ] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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38,728
REGR: to_csv created corrupt ZIP files when chunksize<rows
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3
2020-12-27T18:51:54Z
2021-02-08T00:43:21Z
2020-12-29T00:05:07Z
CONTRIBUTOR
null
- [x] closes #38714 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry When `ZipFile`'s `write` is called multiple times, it will create multiple files within the zip file (with the same filename). Edit: This also happens independently of `chunksize` as https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/writers.pyx#L14 calls `writerows` multiple times.
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MDU6SXNzdWU3NzUwOTMyMzU=
38,729
BUG: DataFrame.where with EA other
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0
2020-12-27T19:17:30Z
2020-12-29T14:30:43Z
2020-12-29T14:30:43Z
MEMBER
null
``` df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) arr = pd.array([7, pd.NA, 9]) mask = np.ones(df.shape, dtype=bool) >>> result = df.where(mask, arr) AttributeError: 'IntegerArray' object has no attribute 'reshape' ``` we can kludge around this by using `np.reshape(arr, shape)` instead of `arr.reshape(shape)`, but that'll cast to object, which is not great. Non-kludge solution: 2D EAs
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BUG: casting column in place doesn't change data type
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2020-12-27T19:50:29Z
2020-12-27T19:50:29Z
NONE
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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. - [ ] (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 >>> a = pd.DataFrame({'a': [b'blah']}) >>> a.dtypes a object dtype: object >>> a.a = a.a.astype(np.dtype("S8")) >>> a.dtypes a object dtype: object >>> a['b'] = a.a.astype(np.dtype("S8")) >>> a.dtypes a object b |S8 dtype: object ``` #### Problem description Naively it seems like this should behave similarly to other data types. Trying the same example and casting int64 -> int32 works. #### Expected Output ```python >>> a = pd.DataFrame({'a': [b'blah']}) >>> a.dtypes a object dtype: object >>> a.a = a.a.astype(np.dtype("S8")) >>> a.dtypes a |S8 dtype: object ``` #### Output of ``pd.show_versions()`` INSTALLED VERSIONS ------------------ commit : None python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.6.6-300.fc32.x86_64 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 0.25.3 numpy : 1.18.4 pytz : 2020.4 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.6.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.4.1 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.12.0 pandas_datareader: 0.8.0 bs4 : None bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.4.1 matplotlib : 3.2.2 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : 3.5.2 xarray : None xlrd : 1.2.0 xlwt : 1.1.2 xlsxwriter : None
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The link already exists in the other direction, so want to make sure readers who come across the latter understand the built-in option. - [ ] ~~closes #xxxx~~ - [ ] ~~tests added / passed~~ - [ ] ~~passes `black pandas`~~ - [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~ - [ ] ~~whatsnew entry~~
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- [x] closes #33321 - [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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- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry @jorisvandenbossche thoughts on how/where to handle the skipna kwarg?
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Backport PR #38344 on branch 1.2.x (CI: Move sql builds from Travis to Github actions)
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Backport PR #38344: CI: Move sql builds from Travis to Github actions
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DOC: make shared includes for describing the datasets in the Getting Started tutorials
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2
2020-12-27T21:50:53Z
2020-12-31T00:11:06Z
2020-12-30T13:55:26Z
MEMBER
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Just a refactor; the net result should be the same. - [ ] ~~closes #xxxx~~ - [x] tests added / passed - [ ] ~~passes `black pandas`~~ - [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~ - [ ] ~~whatsnew entry~~
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38,736
BUG: Change of behavior between 1.1.5 and 1.2.0 in plot functions
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5
2020-12-27T22:23:09Z
2021-08-14T22:34:04Z
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. --- **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 from pandas import Timestamp import matplotlib.pyplot as plt a= {'Argentina': {Timestamp('2020-11-17 00:00:00'): 56.0}, 'Brazil': {Timestamp('2020-11-17 00:00:00'): 103.0}, 'Guatemala': {Timestamp('2020-11-17 00:00:00'): 99.0}, 'Ireland': {Timestamp('2020-11-17 00:00:00'): 53.0}, 'Italy': {Timestamp('2020-11-17 00:00:00'): 19.0}, 'Spain': {Timestamp('2020-11-17 00:00:00'): 49.0}, 'Sweden': {Timestamp('2020-11-17 00:00:00'): 42.0}, 'US': {Timestamp('2020-11-17 00:00:00'): 86.0}, 'United Kingdom': {Timestamp('2020-11-17 00:00:00'): 32.0}} b = {'Val': {'Afghanistan_confirmed': 50886}, 'Country': {'Afghanistan_confirmed': 'Afghanistan'}, 'Population': {'Afghanistan_confirmed': 37.171922}, 'Ratio': {'Afghanistan_confirmed': 1368.9364784527418}} doubling_df = pd.DataFrame().from_dict(a) df = pd.DataFrame().from_dict(b) _ = doubling_df.plot() _ = df.sort_values(by='Ratio', ascending=False)[:1]['Ratio'].plot.bar(color='blue') ``` #### Stack Trace <details> Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/matplotlib/axis.py", line 1523, in convert_units ret = self.converter.convert(x, self.units, self) File "/usr/local/lib/python3.8/site-packages/matplotlib/category.py", line 61, in convert unit.update(values) File "/usr/local/lib/python3.8/site-packages/matplotlib/category.py", line 211, in update cbook._check_isinstance((str, bytes), value=val) File "/usr/local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py", line 2246, in _check_isinstance raise TypeError( TypeError: 'value' must be an instance of str or bytes, not a datetime.datetime The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/pablo/bug_report.py", line 22, in <module> _ = df.sort_values(by='Ratio', ascending=False)[:1]['Ratio'].plot.bar(color='blue') File "/usr/local/lib/python3.8/site-packages/pandas/plotting/_core.py", line 1113, in bar return self(kind="bar", x=x, y=y, **kwargs) File "/usr/local/lib/python3.8/site-packages/pandas/plotting/_core.py", line 955, in __call__ return plot_backend.plot(data, kind=kind, **kwargs) File "/usr/local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/__init__.py", line 61, in plot plot_obj.generate() File "/usr/local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py", line 280, in generate self._make_plot() File "/usr/local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py", line 1433, in _make_plot ax.xaxis.update_units(self.ax_index) File "/usr/local/lib/python3.8/site-packages/matplotlib/axis.py", line 1466, in update_units default = self.converter.default_units(data, self) File "/usr/local/lib/python3.8/site-packages/matplotlib/category.py", line 107, in default_units axis.set_units(UnitData(data)) File "/usr/local/lib/python3.8/site-packages/matplotlib/axis.py", line 1541, in set_units self.callbacks.process('units') File "/usr/local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py", line 229, in process self.exception_handler(exc) File "/usr/local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py", line 81, in _exception_printer raise exc File "/usr/local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py", line 224, in process func(*args, **kwargs) File "/usr/local/lib/python3.8/site-packages/matplotlib/lines.py", line 648, in recache_always self.recache(always=True) File "/usr/local/lib/python3.8/site-packages/matplotlib/lines.py", line 652, in recache xconv = self.convert_xunits(self._xorig) File "/usr/local/lib/python3.8/site-packages/matplotlib/artist.py", line 175, in convert_xunits return ax.xaxis.convert_units(x) File "/usr/local/lib/python3.8/site-packages/matplotlib/axis.py", line 1525, in convert_units raise munits.ConversionError('Failed to convert value(s) to axis ' matplotlib.units.ConversionError: Failed to convert value(s) to axis units: array([datetime.datetime(2020, 11, 17, 0, 0)], dtype=object) </details> #### Problem description There was some change in behavior between pandas 1.1.5 and 1.2.0 where the code above crashes with the stack trace detailed. The solution for 1.2.0 to work is to add a ```plt.close('all')``` between the two plot functions, but this change of behavior is not documented in the release guide so it seems to be a bug. This happens only when running this in a headless python setup (in the real code I use ```get_figure``` to save the images) - when running in a Jupyter notebook this code works as expected. #### Expected Output #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 python : 3.8.7.final.0 python-bits : 64 OS : Linux OS-release : 4.19.121-linuxkit Version : #1 SMP Tue Dec 1 17:50:32 UTC 2020 machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.0 numpy : 1.19.4 pytz : 2020.5 dateutil : 2.8.1 pip : 20.3.3 setuptools : 51.1.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 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 pyxlsb : None s3fs : None scipy : 1.5.4 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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775,129,329
MDExOlB1bGxSZXF1ZXN0NTQ1ODcyNTc0
38,737
BUG/REG: RollingGroupby MultiIndex levels dropped
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7
2020-12-27T23:44:21Z
2020-12-29T19:35:36Z
2020-12-29T18:56:08Z
MEMBER
null
- [x] closes #38523 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry This was accidentally caused by https://github.com/pandas-dev/pandas/pull/37661 where I thought https://github.com/pandas-dev/pandas/issues/37641 was a bug when it was actually the correct behavior. Therefore, I had to change the behavior of some prior tests.
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38,738
WIP/REF: setitem_with_indexer always use same path for 2D
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2020-12-28T00:07:50Z
2021-01-16T03:43:21Z
2021-01-16T03:43:21Z
MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry @phofl I'm hoping you're interested in picking up the torch to get this across the finish line. There are two main places where I think this still needs work 1) being systematic about whether/when we issue a SettingWithCopyWarning 2) perf, as operating column-wise is going to take a toll. We need to operate block-wise.
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1
2020-12-28T02:20:14Z
2020-12-29T00:08:24Z
2020-12-28T15:19:51Z
MEMBER
null
Wasn't quite a duplicate — had a missing `<tr>`. Also updated the `.gitignore` to more accurately only exclude HTML files from `doc/source/_static/`, since other files are checked in. - [ ] ~~closes #xxxx~~ - [ ] tests added / passed - [ ] ~~passes `black pandas`~~ - [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~ - [ ] ~~whatsnew entry~~
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3
2020-12-28T02:57:52Z
2020-12-28T20:12:52Z
2020-12-28T19:51:44Z
CONTRIBUTOR
null
- [x] closes #38722 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Users that do e.g. `[getattr(pd.DataFrame, x) for x in dir(pd.DataFrame())]` or `inspect.getmembers(pd.DataFrame())` currently get an unfriendly deprecation warning. This fixes that by adding `_AXIS_NAMES` & `_AXIS_NUMBERS` to `_hidden_attrs`. @JSunRae, is it possible for you to add this to your pandas source code and see if this fixes your problem? xref: #33637
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BUG: IntervalIndex, PeriodIndex, DatetimeIndex symmetric_difference with Categorical
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2020-12-28T03:36:04Z
2020-12-28T23:19:58Z
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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
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CLN: NDFrame._where
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2020-12-28T04:10:29Z
2020-12-29T15:58:08Z
2020-12-29T14:30:43Z
MEMBER
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- [ ] closes #38729 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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BUG: IntervalIndex intersections are not intersections in many cases
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0
2020-12-28T05:26:06Z
2020-12-31T01:50:22Z
2020-12-31T01:50:22Z
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 # Your code here import pandas._testing as tm idx_unique = tm.makeIntervalIndex(k=5) idx_non_unique = idx_unique[[0, 0, 1, 2, 3, 4]] assert idx_unique.intersection(idx_non_unique).equals(idx_non_unique) assert idx_non_unique.intersection(idx_unique).equals(idx_non_unique) # As an implementation note: assert idx_non_unique._intersection_non_unique(idx_unique).equals(idx_non_unique) ``` All of these should probably fail #### Problem description Intersections on `IntervalIndexes` should not repeat duplicated elements, similar to all other `Index` types. I came across this while writing tests for #38654 For example, if we defined a test: ```python def check_intersection_commutative(left, right): assert left.intersection(right).equals(right.intersection(left)) @pytest.mark.parametrize("index_maker", tm.index_subclass_makers_generator()) def test_intersection_uniqueness(index_maker): if index_make is tm.makeMultiIndex: pytest.pass() # This index maker is bugged ind_unique = index_maker(k=5) ind_non_unique = idx_unique[[0, 0, 1, 2, 3, 4]] check_intersection_commutative(ind_unique, ind_non_unique) assert idx_unique.intersection(idx_non_unique).is_unique ``` Only `IntervalIndex` would fail (not including the bugged `makeMultiIndex`). #### Expected Output All of the assertions in the original example should fail. #### Output of ``pd.show_versions()`` <details> <summary> dev environment </summary> ``` INSTALLED VERSIONS ------------------ commit : 9f1a41dee0e9167361d9f435b4c3d499d01e415a python : 3.8.5.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.0.dev0+210.g9f1a41dee0.dirty numpy : 1.18.5 pytz : 2020.1 dateutil : 2.8.1 pip : 20.1.1 setuptools : 49.2.0.post20200712 Cython : 0.29.21 pytest : 5.4.3 hypothesis : 5.20.3 sphinx : 3.1.1 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 : 0.4.1 gcsfs : 0.6.2 matplotlib : 3.2.2 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.4 pandas_gbq : None pyarrow : 0.17.1 pyxlsb : None s3fs : 0.4.2 scipy : 1.5.1 sqlalchemy : 1.3.18 tables : 3.6.1 tabulate : 0.8.7 xarray : 0.16.0 xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.50.1 ``` </details> <details> <summary> 1.1.5 </summary> ``` INSTALLED VERSIONS ------------------ commit : b5958ee1999e9aead1938c0bba2b674378807b3d python : 3.8.6.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.5 numpy : 1.19.4 pytz : 2020.1 dateutil : 2.8.1 pip : 20.3.3 setuptools : 51.0.0 Cython : 0.29.21 pytest : 6.2.1 hypothesis : None sphinx : 3.3.1 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 : 0.8.5 fastparquet : 0.4.1 gcsfs : None matplotlib : 3.3.3 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 2.0.0 pytables : None pyxlsb : None s3fs : 0.4.2 scipy : 1.5.4 sqlalchemy : 1.3.18 tables : 3.6.1 tabulate : 0.8.7 xarray : 0.16.2 xlrd : 1.2.0 xlwt : None numba : 0.52.0 ``` </details>
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REF/POC: Share groupby/series algos (rank)
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8
2020-12-28T05:40:50Z
2021-01-01T00:21:34Z
2021-01-01T00:08:22Z
MEMBER
null
- [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` Right now there are 3 rank algorithms which are extremely similar, but have slight differences. This seems to cause some maintenance pain. First, inconsistencies between implementations allows more bug potential (like #32593, only a bug for `rank_2d`). For issues like #32859 which affect all 3, a fix would have to be applied in 3 places and testing added separately for `Series`, `DataFrame`, and `GroupBy`. This pr attempts to mitigate these issues by combining the implementations `group_rank` and `rank_1d` (which as an additional bonus gives the enhancement of object support for `GroupBy.rank()` (#38278)). Is this kind of refactor/deduplication helpful? If so, similar logic can probably be applied elsewhere. The diff here makes the changes look more complicated than they are because `rank_1d` is essentially replaced by `group_rank`. The original `group_rank` implementation is only slightly changed to allow for optional labels and object support. Benchmarks look ok: <details> ``` before after ratio [9f1a41de] [5cd81d25] <master> <ref/rank> 7.87±0.1ms 7.77±0.1ms 0.99 frame_methods.Rank.time_rank('float') 2.61±0.1ms 2.55±0.1ms 0.97 frame_methods.Rank.time_rank('int') 57.9±3ms 59.0±6ms 1.02 frame_methods.Rank.time_rank('object') 2.67±0.04ms 2.58±0.08ms 0.97 frame_methods.Rank.time_rank('uint') 10.8±0.4ms 9.45±0.3ms ~0.87 series_methods.Rank.time_rank('float') 7.39±0.2ms 6.79±1ms 0.92 series_methods.Rank.time_rank('int') 52.9±4ms 47.7±2ms ~0.90 series_methods.Rank.time_rank('object') 7.32±1ms 6.58±0.3ms ~0.90 series_methods.Rank.time_rank('uint') 314±3μs 352±40μs ~1.12 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct') 316±8μs 322±10μs 1.02 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'transformation') 421±10μs 475±60μs ~1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'direct') 409±10μs 482±60μs ~1.18 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'transformation') 505±3μs 420±10μs ~0.83 groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'direct') - 510±20μs 410±3μs 0.80 groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'transformation') 411±20μs 441±60μs 1.07 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct') 486±30μs 509±10μs 1.05 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'transformation') 470±50μs 424±9μs ~0.90 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'direct') 409±10μs 505±60μs ~1.23 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'transformation') 404±7μs 470±60μs ~1.16 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct') 407±7μs 412±9μs 1.01 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation') ``` </details>
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[WIP] Test (and more fixes) for duplicate indices with concat
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2020-12-28T07:16:22Z
2021-05-24T17:27:49Z
2021-05-24T17:27:34Z
CONTRIBUTOR
null
- [ ] closes #31308, #36263 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Follow up to #38654, currently WIP. It's a work in progress because it turns out union and intersection act differently for different index types. `intersection` seems mostly normal, except for `IntervalIndex` #38743), while `union` is sometimes a set union (for `Index`) and sometimes keeps duplicates (for many `Index` subclasses, though order matters sometimes). This effects `concat` in `get_objs_combined_axis`. Once I figured out where the problems were coming from, I figured I could maybe side step this by adding checks to `get_objs_combined_axis` for equality and uniqueness. All checks pass `reshape` on my machine, but I'm done working on this for the day, so I'll rely on CI to do the checks on the rest of the codebase. ------------------------ This test could be expanded with: * More types of indexes (there is no `object` index currently) * Checks for commutativity, i.e. results should have set-equal columns regardless of order * Interactions with other arguments (`sort`, `axis`?) TODO: - [ ] Move test for `concat` where duplicates don't error - [ ] Decide whether error should be `DuplicateLabelErrors` - [ ] Have error report which duplicates are used - [ ] Check that error reports which duplicates are used
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ENH: support downcasting of nullable EAs in pd.to_numeric
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2
2020-12-28T07:22:31Z
2020-12-30T14:06:49Z
2020-12-30T13:59:01Z
MEMBER
null
- [x] closes #33013 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Picking up #33435. In `pd.to_numeric` when encountering an `IntegerArray` and `FloatingArray` (or `Series` built from them) we downcast `_data` then reconstruct the array using the downcast `_data` and the `_mask` from the original array.
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2020-12-28T07:33:34Z
2020-12-30T12:14:18Z
2020-12-28T14:48:02Z
CONTRIBUTOR
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<!-- 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> <summary><em>add path</em></summary> ```Shell /home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin ``` </details> <details> <summary><em>pip install pre-commit</em></summary> ```Shell Collecting pre-commit Downloading pre_commit-2.9.3-py2.py3-none-any.whl (184 kB) Collecting cfgv>=2.0.0 Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB) Collecting identify>=1.0.0 Downloading identify-1.5.10-py2.py3-none-any.whl (97 kB) Collecting nodeenv>=0.11.1 Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB) Collecting pyyaml>=5.1 Using cached PyYAML-5.3.1-cp39-cp39-linux_x86_64.whl Collecting toml Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB) Collecting virtualenv>=20.0.8 Downloading virtualenv-20.2.2-py2.py3-none-any.whl (5.7 MB) Collecting appdirs<2,>=1.4.3 Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB) Collecting distlib<1,>=0.3.1 Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB) Collecting filelock<4,>=3.0.0 Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB) Collecting six<2,>=1.9.0 Using cached six-1.15.0-py2.py3-none-any.whl (10 kB) Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.5.10 nodeenv-1.5.0 pre-commit-2.9.3 pyyaml-5.3.1 six-1.15.0 toml-0.10.2 virtualenv-20.2.2 ``` ### stderr: ```Shell WARNING: You are using pip version 20.3.1; however, version 20.3.3 is available. You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.1/x64/bin/python -m pip install --upgrade pip' command. ``` </details> <details> <summary><em>pre-commit autoupdate || (exit 0);</em></summary> ```Shell Updating https://github.com/python/black ... already up to date. Updating https://gitlab.com/pycqa/flake8 ... already up to date. Updating https://github.com/PyCQA/isort ... already up to date. Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade. already up to date. Updating https://github.com/pre-commit/pygrep-hooks ... already up to date. Updating https://github.com/asottile/yesqa ... already up to date. Updating https://github.com/pre-commit/pre-commit-hooks ... 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[INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for local. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/asottile/yesqa. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... [INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks. [INFO] Once installed this environment will be reused. [INFO] This may take a few minutes... black..................................................................................................Passed flake8.................................................................................................Passed flake8 (cython)........................................................................................Passed flake8 (cython template)...............................................................................Passed isort..................................................................................................Passed pyupgrade..............................................................................................Passed rst ``code`` is two backticks..........................................................................Passed rst directives end with two colons.....................................................................Passed rst ``inline code`` next to normal text................................................................Passed Generate pip dependency from conda.....................................................................Passed flake8-rst.............................................................................................Passed Check for non-standard imports.........................................................................Passed Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed Check for non-standard imports in test suite...........................................................Passed Check for incorrect code block or IPython directives...................................................Passed Check for use of not concatenated strings..............................................................Passed Check for strings with wrong placed spaces.............................................................Passed Check for import of private attributes across modules..................................................Passed Check for use of private functions across modules......................................................Passed Check for inconsistent use of pandas namespace in tests................................................Passed Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed Check for use of foo.__class__ instead of type(foo)....................................................Passed Check for use of comment-based annotation syntax and missing error codes...............................Passed Check code for instances of os.remove..................................................................Passed Strip unnecessary `# noqa`s............................................................................Passed Fix End of Files.......................................................................................Passed Trim Trailing Whitespace...............................................................................Passed ``` </details> </details> ## Changed files <details> <summary>Changed file: </summary> - .pre-commit-config.yaml </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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Revert "TST: Bare pytest raises" in base extension tests
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2020-12-28T07:46:04Z
2020-12-28T15:35:00Z
2020-12-28T14:59:02Z
MEMBER
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Reverts pandas-dev/pandas#38576, see discussion there. We should not add detailed error message asserts, as those tests are also used by external packages, that might have different error messages.
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BUG: TypeError: bad operand type for unary -: 'FloatingArray'
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6
2020-12-28T11:28:29Z
2021-02-28T08:38:16Z
2021-02-28T08:38:16Z
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 import pandas as pd x = pd.Series([1, 2, 3], dtype=pd.Float32Dtype()) -x ``` #### Problem description Should it generate an error? `TypeError: bad operand type for unary -: 'FloatingArray'` #### Expected Output ```python -1 * x # like -pd.Series([1, 2, 3]) ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 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 126 Stepping 5, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_Europe.1252 pandas : 1.2.0 numpy : 1.19.3 pytz : 2020.4 dateutil : 2.8.1 pip : 20.2.2 setuptools : 49.2.1 Cython : 0.29.14 pytest : 6.0.1 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 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.0 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.3.13 tables : None tabulate : 0.8.7 xarray : None xlrd : 1.2.0 xlwt : None numba : 0.51.1 </details>
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38,750
Iloc truncates single-column dataframe with extension arrays
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2020-12-28T13:42:00Z
2021-02-11T01:42:14Z
2021-02-11T01:42:13Z
NONE
null
Added a test to ensure .iloc does not truncate data for a single-column dataframe with an extension array, as discussed in GH30263. - [ ] closes #30263 - [x ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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38,751
BUG: FloatingArray constructor with input of all missing values: np.nan vs. pd.NA
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2020-12-28T17:13:07Z
2021-08-14T22:34:56Z
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Not sure this is really an issue but maybe(or not?) a slight inconsistency. The following throws: ```python In [18]: import numpy as np ...: import pandas as pd ...: ...: arr = pd.array([pd.NA, pd.NA], dtype="float") ...: ser = pd.Series(arr) ...: pd.to_numeric(ser, downcast="float") ...: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-18-c7fb0ba8f6eb> in <module> 2 import pandas as pd 3 ----> 4 arr = pd.array([pd.NA, pd.NA], dtype="float") 5 ser = pd.Series(arr) 6 pd.to_numeric(ser, downcast="float") ~/repos/pandas/pandas/core/construction.py in array(data, dtype, copy) 344 return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy) 345 --> 346 result = PandasArray._from_sequence(data, dtype=dtype, copy=copy) 347 return result 348 ~/repos/pandas/pandas/core/arrays/numpy_.py in _from_sequence(cls, scalars, dtype, copy) 178 dtype = dtype._dtype 179 --> 180 result = np.asarray(scalars, dtype=dtype) 181 if copy and result is scalars: 182 result = result.copy() ~/anaconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order) 81 82 """ ---> 83 return array(a, dtype, copy=False, order=order) 84 85 TypeError: float() argument must be a string or a number, not 'NAType' ``` but with `np.nan` it runs fine: ``` python In [19]: import numpy as np ...: import pandas as pd ...: ...: arr = pd.array([np.nan, np.nan], dtype="float") ...: ser = pd.Series(arr) ...: pd.to_numeric(ser, downcast="float") ...: Out[19]: 0 NaN 1 NaN dtype: float32 ```
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38,752
BUG:Wrong sum in groupby rolling due to precision issues
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11
2020-12-28T17:38:15Z
2021-08-14T22:38:41Z
2021-08-14T22:38:41Z
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. --- #### Code Sample, a copy-pastable example ```python import numpy as np import math import pandas as pd id1 = pd.date_range('2019-10-01', '2020-01-08', freq='2H').to_frame(name='stamp').reset_index(drop=True) id2 = pd.date_range('2019-10-01', '2020-01-08').to_frame(name='stamp').reset_index(drop=True) id1['id'] = 1 a = 2.5 id1['value'] = (math.e + a) ** (((id1.index + 10) % 40) - 20) id2['id'] = 2 id2['value'] = np.nan id2.loc[id2.stamp == '2019-11-15', 'value'] = 10. ** -13 df = pd.concat([id1, id2], ignore_index=True) df.sort_values('stamp', inplace=True) roll_sum = df.groupby('id').rolling('93D', on='stamp')['value'].sum().reset_index() print(roll_sum[roll_sum.id == 2].iloc[-1].value) roll_sum_2 = df[df.id == 2].groupby('id').rolling('93D', on='stamp')['value'].sum().reset_index() print(roll_sum_2[roll_sum_2.id == 2].iloc[-1].value) ``` #### Problem description In the code above i show that last values in rollings are not equal: - if i count a sum of `value` using rolling on full dataframe, i obtain `0.015625000000100003` - If i filter a dataframe and keep only `id=2`, i obtain `1e-13`. It seems that the aggregation in the second group depends on the first group. If i replace `a` on `0`, i will get `2.980242e-08` in a first rolling. And if i replace `a` on `1`, i will get a correct answer `1e-13` #### Expected Output `1e-13` #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 python : 3.7.6.final.0 python-bits : 64 OS : Linux OS-release : 5.3.13-300.fc31.x86_64 Version : #1 SMP Mon Nov 25 17:25:25 UTC 2019 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.0 numpy : 1.17.4 pytz : 2019.2 dateutil : 2.8.0 pip : 19.1.1 setuptools : 50.3.0 Cython : None pytest : 5.4.1 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.5.0 html5lib : None pymysql : None psycopg2 : 2.8.4 (dt dec pq3 ext lo64) jinja2 : 2.10.1 IPython : None pandas_datareader: None bs4 : 4.9.0 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.2.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : 1.3.11 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None ``` </details>
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38,753
REGR: pd.read_csv segfaults with 1.2 (has worked since before pandas 1.0)
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9
2020-12-28T18:37:15Z
2020-12-30T18:41:00Z
2020-12-30T18:41:00Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example Wish I could give more information, but this is all I have (not sure I can share the input data): ``` Fatal Python error: Segmentation fault Current thread 0x00007f658531a740 (most recent call first): File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 2056 in read File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1052 in read File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 463 in _read File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 605 in read_csv ``` Once I added the pin `pandas<1.2` everything works again. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 python : 3.7.9.final.0 python-bits : 64 OS : Linux OS-release : 4.4.0-1020-aws Version : #29-Ubuntu SMP Wed Jun 14 15:54:52 UTC 2017 machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.0 numpy : 1.19.1 pytz : 2020.5 dateutil : 2.8.1 pip : 20.2.2 setuptools : 49.6.0 Cython : None pytest : 6.2.1 hypothesis : None sphinx : 3.4.1 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 : 2.11.2 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 : 1.5.4 sqlalchemy : 1.3.22 tables : None tabulate : 0.8.7 xarray : 0.16.2 xlrd : None xlwt : None numba : None None </details>
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DOC: chat-ops
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5
2020-12-28T19:11:48Z
2020-12-30T15:07:21Z
2020-12-30T15:07:16Z
CONTRIBUTOR
null
- [x] closes #38726 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] Added documentation for `@githubactions pre-commit` - [x] Added documentation for `@meeseeksdev backport`
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38,755
Backport PR #38740 on branch 1.2.x (DEPR: Hide deprecated attrs _AXIS_NAMES & _AXIS_NUMBERS)
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0
2020-12-28T19:53:07Z
2020-12-28T21:20:04Z
2020-12-28T21:20:03Z
NONE
null
Backport PR #38740: DEPR: Hide deprecated attrs _AXIS_NAMES & _AXIS_NUMBERS
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38,756
TST: use frame_or_series for some tz_localize/tz_convert tests
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2020-12-28T19:58:36Z
2020-12-28T23:27:16Z
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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: implement nested_data_to_arrays
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0
2020-12-28T20:37:16Z
2020-12-29T00:05:42Z
2020-12-29T00:02:36Z
MEMBER
null
In the next step, I plan to use this in the relevant case for ndarray, which we currently treat differently from EA. Categorical edits are unrelated, just sneaking in.
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775,583,838
MDExOlB1bGxSZXF1ZXN0NTQ2MjM1NDk4
38,758
CLN: simplify tm.decompress_file
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2
2020-12-28T20:56:22Z
2020-12-28T22:23:12Z
2020-12-28T22:23:04Z
CONTRIBUTOR
null
Could re-use `get_handle` but I'm not sure whether the testing code should depend on the to-be tested code?
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775,585,226
MDExOlB1bGxSZXF1ZXN0NTQ2MjM2NjA4
38,759
BUG: float-like string, trailing 0 truncation
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4
2020-12-28T21:01:04Z
2020-12-29T03:20:56Z
2020-12-29T03:18:49Z
MEMBER
null
- [x] closes #38708 - [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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MDU6SXNzdWU3NzU1OTE5NDE=
38,760
pandas.Series.to_json() incorrectly localizes tz-naive datetimes to UTC
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3
2020-12-28T21:21:49Z
2021-08-14T22:39:37Z
null
NONE
null
Seemingly related but not identical to #12997 ## How to reproduce the bug Checked with pandas 0.24.2, 1.1.5 and 1.2.0. ``` import pandas as pd index = pd.date_range( start='2020-12-28 00:00:00', end='2020-12-28 02:00:00', freq='1H', ) a = pd.Series( data=range(3), index=index, ) a.to_json(date_format='iso') ``` ## Output ``` '{"2020-12-28T00:00:00.000Z":0,"2020-12-28T01:00:00.000Z":1,"2020-12-28T02:00:00.000Z":2}' ``` (Notice the "Z" indicating UTC) ## Expected output ``` '{"2020-12-28T00:00:00.000":0,"2020-12-28T01:00:00.000":1,"2020-12-28T02:00:00.000":2}' ``` (without "Z")
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MDExOlB1bGxSZXF1ZXN0NTQ2MjQzNjM1
38,761
CI: print build version
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2
2020-12-28T21:27:36Z
2020-12-29T01:57:33Z
2020-12-28T23:12:15Z
MEMBER
null
xref #38344 followup
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38,762
Sample ExtensionArray.take implementation doesn't correctly handle scalars
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2020-12-28T22:24:46Z
2020-12-29T14:01:31Z
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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 The sample at https://github.com/pandas-dev/pandas/blob/6210077d32a9e9675526ea896e6d1f9189629d4a/pandas/core/arrays/base.py#L1031-L1047 is buggy since it doesn't handle scalars properly ```python class MyDtype(ExtensionDtype): name = "name" class MyArray(ExtensionArray): dtype = MyDtype() def __init__(self, data): self._data = data @classmethod def _from_sequence(cls, scalars, *, dtype=None, copy=False): return cls(np.array(scalars)) def __getitem__(self, item): return self._data[item] def __len__(self): return len(self._data) def take(self, indices, allow_fill=False, fill_value=None): from pandas.core.algorithms import take # If the ExtensionArray is backed by an ndarray, then # just pass that here instead of coercing to object. data = self.astype(object) if allow_fill and fill_value is None: fill_value = self.dtype.na_value # fill value should always be translated from the scalar # type for the array, to the physical storage type for # the data, before passing to take. result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill) return self._from_sequence(result, dtype=self.dtype) a = MyArray._from_sequence([1, 2, 3]) result = a.take(0) assert result == 1 ``` #### Problem description #### Expected Output `.take(0)` should return the scalar `1`, rather than trying to wrap it in a new `MyArray`. #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details>
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Backport PR #38761 on branch 1.2.x (CI: print build version)
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2020-12-28T23:12:44Z
2020-12-28T23:59:57Z
2020-12-28T23:59:57Z
NONE
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Backport PR #38761: CI: print build version
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BUG: Series construction with mismatched dt64 data vs td64 dtype
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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 Fixing this for DataFrame is much harder, so punting for now.
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- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry xref https://github.com/pandas-dev/pandas/pull/38416/files#r549438292
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2020-12-29T15:57:32Z
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Ran `brew upgrade` yesterday and ended up bumping from 3.9.0 to 3.9.1, after which I got build failures ``` LooseVersion(python_target) < "10.9" File "/usr/local/Cellar/python@3.9/3.9.1_2/Frameworks/Python.framework/Versions/3.9/lib/python3.9/distutils/version.py", line 306, in __init__ self.parse(vstring) File "/usr/local/Cellar/python@3.9/3.9.1_2/Frameworks/Python.framework/Versions/3.9/lib/python3.9/distutils/version.py", line 314, in parse components = [x for x in self.component_re.split(vstring) TypeError: expected string or bytes-like object ``` No idea why this just started breaking now.
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Backport PR #38728: REGR: to_csv created corrupt ZIP files when chunksize<rows
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2020-12-29T02:31:39Z
2020-12-29T18:43:13Z
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* Inlined a helper method * Added `const`s * Removed unneeded comments/code
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Backport PR #38759: BUG: float-like string, trailing 0 truncation
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2020-12-29T03:46:04Z
2020-12-29T22:00:30Z
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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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2020-12-29T04:53:05Z
2021-01-03T19:39:08Z
2021-01-03T19:39:08Z
MEMBER
null
This will help ensure consistency between the examples. Similar to https://github.com/pandas-dev/pandas/pull/38735, but focusing on shared content in a different folder. Particularly interested to see if the build fails in the same way. - [ ] ~~closes #xxxx~~ - [ ] tests added / passed - [ ] ~~passes `black pandas`~~ - [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~ - [ ] ~~whatsnew entry~~
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BUG: Added test cases to check loc on multiindex with NaNs #29751
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1
2020-12-29T05:05:16Z
2021-01-07T13:58:12Z
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CONTRIBUTOR
null
- [x] closes #29751 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Added test cases to check loc on multiindex containing NaN values using `np.nan`, `pd.NA`, and `None`
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38,773
API: idx.get_indexer(keys) fails if idx is non-unique, even if keys in idx are unique
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2020-12-29T06:46:31Z
2021-08-14T22:41:29Z
null
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example ```python import pandas as pd pd.Index(["a", "a", "b", "c"]).get_indexer(pd.Index(["b", "c"])) # InvalidIndexError: Reindexing only valid with uniquely valued Index objects ``` Came across this here: https://github.com/pandas-dev/pandas/pull/38745#discussion_r549577521 #### Problem description It makes sense to me why this would error: ```python pd.Index(["a", "a", "b", "c"]).get_indexer(pd.Index(["a", "c"])) ``` There isn't a unique solution. For the case presented at the top, there is a unique solution. #### Expected Output ```python pd.Index(["a", "a", "b", "c"]).get_indexer(pd.Index(["b", "c"])) # array([2, 3]) ``` Note, this is the behaviour of `pd.Index._get_indexer` on `master` #### Output of ``pd.show_versions()`` <details> <summary> master </summary> ```python INSTALLED VERSIONS ------------------ commit : 7f912a4009da963b5eacdcebb638c38eec06e7a7 python : 3.8.5.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.0.dev0+228.g7f912a4009 numpy : 1.18.5 pytz : 2020.1 dateutil : 2.8.1 pip : 20.1.1 setuptools : 49.2.0.post20200712 Cython : 0.29.21 pytest : 5.4.3 hypothesis : 5.20.3 sphinx : 3.1.1 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 : 0.4.1 gcsfs : 0.6.2 matplotlib : 3.2.2 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.4 pandas_gbq : None pyarrow : 0.17.1 pyxlsb : None s3fs : 0.4.2 scipy : 1.5.1 sqlalchemy : 1.3.18 tables : 3.6.1 tabulate : 0.8.7 xarray : 0.16.0 xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.50.1 ``` </details> <details> <summary> 1.2.0 </summary> `pd.show_versions()` is failing with: `ImportError: Can't determine version for numba`, so here's `conda list`: ``` $ conda list # packages in environment at /Users/isaac/miniconda3/envs/pandas-1.2: # # Name Version Build Channel appnope 0.1.2 py38hecd8cb5_1001 backcall 0.2.0 py_0 blas 1.0 mkl ca-certificates 2020.12.8 hecd8cb5_0 certifi 2020.12.5 py38hecd8cb5_0 decorator 4.4.2 py_0 intel-openmp 2019.4 233 ipython 7.19.0 py38h01d92e1_0 ipython_genutils 0.2.0 pyhd3eb1b0_1 jedi 0.17.0 py38_0 libcxx 11.0.0 h4c3b8ed_1 conda-forge libedit 3.1.20191231 h1de35cc_1 libffi 3.3 hb1e8313_2 mkl 2019.4 233 mkl-service 2.3.0 py38h9ed2024_0 mkl_fft 1.2.0 py38hc64f4ea_0 mkl_random 1.1.1 py38h959d312_0 ncurses 6.2 h0a44026_1 numpy 1.19.2 py38h456fd55_0 numpy-base 1.19.2 py38hcfb5961_0 openssl 1.1.1i h9ed2024_0 pandas 1.2.0 py38he9f00de_0 conda-forge parso 0.8.1 pyhd3eb1b0_0 pexpect 4.8.0 pyhd3eb1b0_3 pickleshare 0.7.5 pyhd3eb1b0_1003 pip 20.3.3 py38hecd8cb5_0 prompt-toolkit 3.0.8 py_0 ptyprocess 0.6.0 pyhd3eb1b0_2 pygments 2.7.3 pyhd3eb1b0_0 python 3.8.5 h26836e1_1 python-dateutil 2.8.1 py_0 python_abi 3.8 1_cp38 conda-forge pytz 2020.4 pyhd3eb1b0_0 readline 8.0 h1de35cc_0 setuptools 51.0.0 py38hecd8cb5_2 six 1.15.0 py38hecd8cb5_0 sqlite 3.33.0 hffcf06c_0 tk 8.6.10 hb0a8c7a_0 traitlets 5.0.5 py_0 wcwidth 0.2.5 py_0 wheel 0.36.2 pyhd3eb1b0_0 xz 5.2.5 h1de35cc_0 zlib 1.2.11 h1de35cc_3 ``` </details>
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BUG: sem() with level raised ValueError in pandas 1.2
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2020-12-30T23:38:49Z
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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. --- **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 import numpy as np idx = pd.MultiIndex.from_arrays([ [str(i) for i in range(100)], np.random.choice(['A', 'B'], size=(100,)) ], names=['a', 'b']) data_dict = dict((str(i), np.random.rand(100)) for i in range(10)) data_dict['string'] = [str(i) for i in range(100)] data_dict['bool'] = np.random.choice([True, False], (100,)) data = pd.DataFrame(data_dict, index=idx) data.sem(level=1) ``` #### Problem description Unexpected exception raised: ``` Traceback (most recent call last): File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-8-cd9abe134148>", line 1, in <module> data.sem(level=1) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 10925, in sem return NDFrame.sem(self, axis, skipna, level, ddof, numeric_only, **kwargs) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 10665, in sem return self._stat_function_ddof( File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 10655, in _stat_function_ddof return self._agg_by_level( File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 10552, in _agg_by_level return getattr(grouped, name)(**kwargs) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 1612, in sem result.iloc[:, cols] = result.iloc[:, cols] / np.sqrt( File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 691, in __setitem__ iloc._setitem_with_indexer(indexer, value, self.name) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1636, in _setitem_with_indexer self._setitem_single_block(indexer, value, name) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/indexing.py", line 1860, in _setitem_single_block self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 568, in setitem return self.apply("setitem", indexer=indexer, value=value) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 427, in apply applied = getattr(b, f)(**kwargs) File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 1025, in setitem values[indexer] = value ValueError: shape mismatch: value array of shape (2,12) could not be broadcast to indexing result of shape (11,2) ``` #### Expected Output Correct aggregation results shall be returned. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 python : 3.8.3.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : zh_CN.UTF-8 pandas : 1.2.0 numpy : 1.19.1 pytz : 2020.4 dateutil : 2.8.1 pip : 20.2.2 setuptools : 49.6.0.post20200814 Cython : 0.29.21 pytest : 6.0.1 hypothesis : None sphinx : 3.2.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.17.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : 0.4.2 gcsfs : None matplotlib : 3.2.2 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 1.0.1 pyxlsb : None s3fs : None scipy : 1.5.0 sqlalchemy : 1.3.18 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : 0.51.2 </details>
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MDU6SXNzdWU3NzU4MDc3MDE=
38,775
Jinja2 error when trying to render a Pandas DataFrame to html from within a PyInstaller distribution
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2020-12-29T10:02:56Z
2020-12-29T12:48:09Z
2020-12-29T12:07:00Z
NONE
null
- [x] I have checked that this issue has not already been reported. Yes - [x] I have confirmed this bug exists on the latest version of pandas. Yes - [x] (optional) I have confirmed this bug exists on the master branch of pandas. Yes --- **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 pandas-io-formats-style.py line 133 loader = jinja2.PackageLoader("pandas", "io/formats/templates") env = jinja2.Environment(loader=loader, trim_blocks=True) template = env.get_template("html.tpl") ```python # Your code here replaced the above lines with the following import sys if getattr(sys, 'frozen', False): # we are running in a bundle bundle_dir = sys._MEIPASS loader = jinja2.FileSystemLoader(bundle_dir) else: loader = jinja2.PackageLoader("pandas", "io/formats/templates") env = jinja2.Environment(loader=loader, trim_blocks=True) template = env.get_template("html.tpl") #### Problem description When trying to package projects which include pandas and jinja2 with pyinstaller and running the produced exe we are getting the following error: File "lib\site-packages\pandas\io\formats\style.py", line 62, in <module> File "lib\site-packages\pandas\io\formats\style.py", line 135, in Styler File "lib\site-packages\jinja2\environment.py", line 883, in get_template File "lib\site-packages\jinja2\environment.py", line 857, in _load_template File "lib\site-packages\jinja2\loaders.py", line 115, in load File "lib\site-packages\jinja2\loaders.py", line 248, in get_source jinja2.exceptions.TemplateNotFound: html.tpl #### Expected Output To run after compilation #### Notes If the code is replaced in pandas-io-formats-style.py line 133 as suggested above the problem is resolved. See StackOverflow link below [https://stackoverflow.com/questions/31259673/unable-to-include-jinja2-template-to-pyinstaller-distribution](URL)
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MDExOlB1bGxSZXF1ZXN0NTQ2NDQzNzQ0
38,776
CI,STYLE: add spell check?
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2020-12-29T11:20:53Z
2020-12-29T17:03:13Z
2020-12-29T17:03:09Z
MEMBER
null
IIRC it was suggested in a comment some time ago to use a spell checker, so I thought I'd make a little PR showing how `codespell` would work (for now just applied to `pandas/core`). Is this something we'd want? False positives can be ignored in `setup.cfg` in the `[codespell]` section
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38,777
Backport PR #38766 on branch 1.2.x (BLD: fix build failure py3.9.1 on OSX)
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2020-12-29T14:06:02Z
2020-12-29T15:35:02Z
2020-12-29T15:35:02Z
NONE
null
Backport PR #38766: BLD: fix build failure py3.9.1 on OSX
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BUG: sum vs groupby.sum errors
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10
2020-12-29T14:41:19Z
2021-01-03T23:25:09Z
2021-01-03T23:25:09Z
NONE
null
[data_source.zip](https://github.com/pandas-dev/pandas/files/5750979/data_source.zip) The attached data source is a subset of a larger set. I included just enough rows to show the error (when the datasource is larger, the difference of sums grows larger too). #### Code Sample, a copy-pastable example ``` import pandas as pd import numpy as np pd.options.display.float_format = '{:.5f}'.format df = pd.read_csv("data_source.csv", index_col = 0) df.reset_index(inplace = True) print ("Regular sum: %s\n" % df["rul_c"].sum()) print ("Regular sum on filtered column: %s\n" % df[df["cont"] == 20]["rul_c"].sum()) print ("GroupBy sum:\n%s" % df.groupby("cont")["rul_c"].sum()) ``` #### Problem description I know about floating point math and small associated "errors" but the "cont" column has an unique, not-null value, and this means all sums above should be the same (about 30880496049.43). However the groupby.sum is quite different from the expected result .45165. The larger the dataset, the larger the difference. ``` Regular sum: 30880496049.429993 Regular sum on filtered column: 30880496049.429993 GroupBy sum: cont 20 30880496049.45165 Name: rul_c, dtype: float64 ``` #### Expected Output All sums should be 30880496049.429993 #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 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 : None LOCALE : Romanian_Romania.1252 pandas : 1.2.0 numpy : 1.19.4 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.3 setuptools : 47.1.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.3.4 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 : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.5.2 sqlalchemy : None tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : None numba : None </details>
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MDU6SXNzdWU3NzU5MjY3OTQ=
38,779
QST: Can I use pandas to do all jobs done by MS Office word/excel in work?
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1
2020-12-29T14:41:25Z
2020-12-29T17:35:47Z
2020-12-29T17:35:39Z
NONE
null
Hi, I use Linux as my working environment exclusively. So, I can't access the native MS Office supplied for macOS/Windows. But I sometimes really need to manipulate and process MS Office documents, especially DOCX and XLSX files. Though there are some free and open source office suites, e.g. LibreOffice, but none of them can completely compatible with the MS Office. So, I want to know whether I can use pandas to do any possible job done by MS Office word/excel in work programmatically? Any hints/notes/comments will be highly appreciated. Regards, HY
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775,935,515
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38,780
Center rolling window for time offset
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14
2020-12-29T14:59:14Z
2021-04-13T12:11:23Z
2021-04-09T16:28:12Z
CONTRIBUTOR
null
- [x] closes #20012 - [x] tests added / passed - [x] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Added center functionality for `VariableWindowIndexer`. Note - I am unsure if the NotImplementedError in lines 1966-1969 in rolling.py still correctly raises an error for offset based windows. Finalizes previous PR https://github.com/pandas-dev/pandas/pull/36097
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DOC: Undocumented change in .isin behavior from 1.1.5 to 1.2.0
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9
2020-12-29T15:40:50Z
2021-01-17T15:36:01Z
2021-01-17T15:36:01Z
NONE
null
#### Location of the documentation https://pandas.pydata.org/docs/whatsnew/v1.2.0.html #### Documentation problem There seems to be an undocumented change with how `isin` works. pandas 1.1.5: ```python import pandas as pd pd.Series([0]).isin(['0']) # 0 True # dtype: bool pd.Series([1]).isin(['1']) # 0 True # dtype: bool pd.Series([1.1]).isin(['1.1']) # 0 True # dtype: bool ``` But in pandas 1.2.0 (and 1.2.0rc): ```python import pandas as pd pd.Series([0]).isin(['0']) # 0 False # dtype: bool pd.Series([1]).isin(['1']) # 0 False # dtype: bool pd.Series([1.1]).isin(['1.1']) # 0 False # dtype: bool ``` But the release notes mentions nothing about it. I have not posted this issue as an bug, because I think the newer implementation is the better implementation. However I propose adding the change to the release notes for other people like me where the change broke tests in a pipeline. #### Suggested fix for documentation Add a line mentioning that `isin` no longer returns true when comparing floats to a string of the float.
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38,782
BUG: inspect.getmembers(Series)
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5
2020-12-29T16:33:01Z
2021-02-08T17:23:38Z
2021-02-07T16:44:02Z
CONTRIBUTOR
null
Make `inspect.getmembers(Series)` work, previously raised an `AbstractMethodError`. xref: #38740
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Backport PR #38737 on branch 1.2.x (BUG/REG: RollingGroupby MultiIndex levels dropped)
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2020-12-29T18:56:53Z
2020-12-31T14:38:28Z
2020-12-31T14:38:28Z
NONE
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Backport PR #38737: BUG/REG: RollingGroupby MultiIndex levels dropped
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38,785
BUG: Series.apply with DatetimeTZDtype passes DatetimeIndex to UDF
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2020-12-29T19:03:54Z
2021-01-04T21:16:22Z
2021-01-04T21:16:22Z
CONTRIBUTOR
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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. --- #### Problem description Calling `.apply` on a `Series` of `DatetimeTZDtype` passes the data as a `DatetimeIndex` to the user-supplied function, instead of each value of the `Series` individually. #### Code Sample, a copy-pastable example ```python import pandas as pd def f(val): print(f"Function received: {val}\n") return val s = pd.Series([pd.Timestamp.utcnow()]) new_s = s.apply(f) print(new_s) ``` #### Actual Output ``` Function received: DatetimeIndex(['2020-12-29 18:48:48.688326+00:00'], dtype='datetime64[ns, UTC]', freq=None) 0 2020-12-29 18:48:48.688326+00:00 dtype: datetime64[ns, UTC] ``` Interestingly, if `f` returns `None`, then `apply` will first pass the `Series` as a `DatetimeIndex`, then it passes the actual `Timestamp`. The return value of that `apply` is still a length-1 `Series` (whose one value is `None`), not a length-2 `Series` with two `None` values. #### Expected Output ``` Function received: 2020-12-29 18:48:48.688326+00:00 0 2020-12-29 18:48:48.688326+00:00 dtype: datetime64[ns, UTC] ``` **NOTE: If the `Series` is created with `pd.Timestamp.now()` (not UTC), then we get the expected behavior.** #### Why this is a problem Users write their `apply` functions with the expectation that it will receive the values of the `Series` one by one. This leads to errors when operations are applied to the received value that do not work on a `DatetimeIndex`, but would work on a `Timestamp`. Additionally, users do not expect different behavior from the `apply` function based on whether their data has time zone information or not. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 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 9, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.2.0 numpy : 1.19.1 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.2 setuptools : 49.6.0.post20200814 Cython : None pytest : 6.0.1 hypothesis : None sphinx : 3.3.0 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 : 4.9.3 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>
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COMPAT: a xfailed test in `tests/scalar/timedelta/test_arithmetic.py` has passed on Apple M1 machine
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xref #31992 The exact test is `pandas/tests/scalar/timedelta/test_arithmetic.py::TestTimedeltaMultiplicationDivision::test_td_div_nan[nan1]` This test should only pass on py39. I built numpy from source since `pip install numpy` doesn't work on Apple M1 machine yet. It might be specific for numpy dev build for arm64. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 5a6a0f7d2c7c16d6379fc3f903152d99058d6ec5 python : 3.8.2.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_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.0.dev0+232.g5a6a0f7d2.dirty numpy : 1.21.0.dev0+384.g717df4e93 pytz : 2020.5 dateutil : 2.8.1 pip : 20.3.3 setuptools : 41.2.0 Cython : 0.29.21 pytest : 6.2.1 hypothesis : 5.43.4 </details>
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38,787
API: Preferred MultiIndex result for groupby().rolling() for an object with a MultiIndex
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8
2020-12-29T20:08:08Z
2021-04-05T16:22:13Z
2021-04-05T16:22:13Z
MEMBER
null
`groupby().rolling()` in master currently constructs the resulting `MultiIndex` manually by inserting `groupby` keys as the first level(s) and then the original object's `Index` as the second level(s). However, `groupby().rolling()` behaves similarly to `groupby().transform()` (i.e. maintains the original shape), so should the resulting index align with results of `groupby().transform()`? ``` In [4]: pd.__version__ Out[4]: '1.3.0.dev0+238.g1d196295c' In [6]: df = pd.DataFrame({'a': [1], 'b': [2]}) In [7]: df Out[7]: a b 0 1 2 # Example of the original DataFrame having a regular Index In [8]: df.groupby('a').rolling(1).sum() Out[8]: a b a 1 0 1.0 2.0 In [16]: df.groupby('a').transform(lambda x: np.sum(x)) Out[16]: b 0 2 In [9]: df = pd.DataFrame({'a': [1], 'b': [2]}, index=pd.MultiIndex.from_tuples([('idx1', 'idx2')], names=['label1', 'label2'])) In [9]: df Out[9]: a b label1 label2 idx1 idx2 1 2 # Examples of the original DataFrame having a MultiIndex In [10]: df.groupby('label1').rolling(1).sum() Out[10]: a b label1 label1 label2 idx1 idx1 idx2 1.0 2.0 In [20]: df.groupby('label1').transform(lambda x: np.sum(x)) Out[20]: a b label1 label2 idx1 idx2 1 2 In [11]: df.groupby('a').rolling(1).sum() Out[11]: a b a label1 label2 1 idx1 idx2 1.0 2.0 In [21]: df.groupby('a').transform(lambda x: np.sum(x)) Out[21]: b label1 label2 idx1 idx2 2 In [12]: df.groupby(['label1', 'a']).rolling(1).sum() Out[12]: b label1 a label1 label2 idx1 1 idx1 idx2 2.0 In [22]: df.groupby(['label1', 'a']).transform(lambda x: np.sum(x)) Out[22]: b label1 label2 idx1 idx2 2 ``` As shown, when the original object as a `MultiIndex`, there is consistency of the resulting `MultiIndex` for the `groupby().rolling()` result but can lead to redundancy. There is lack of consistency of the resulting `MultiIndex` for the `groupby().transform()` result but looks more convenient. IMO I prefer the consistent result we have today in `groupby().rolling()` but open to thoughts.
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MDU6SXNzdWU3NzYwODE4MDQ=
38,788
BUG: read_excel throws FileNotFoundError with s3fs objects
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3
2020-12-29T20:53:12Z
2020-12-30T21:12:30Z
2020-12-30T21:12:30Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample I get a FileNotFoundError when running the following code: ```python import pandas as pd import s3fs fs = s3fs.S3FileSystem() with fs.open('s3://bucket_name/filename.xlsx') as f: pd.read_excel(f) # NOTE: pd.ExcelFile(f) throws same error ``` ```python-traceback Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/home/airflow/GVIP/venv/lib/python3.8/site-packages/pandas/util/_decorators.py", line 299, in wrapper return func(*args, **kwargs) File "/home/airflow/GVIP/venv/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 336, in read_excel io = ExcelFile(io, storage_options=storage_options, engine=engine) File "/home/airflow/GVIP/venv/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 1062, in __init__ ext = inspect_excel_format( File "/home/airflow/GVIP/venv/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 938, in inspect_excel_format with get_handle( File "/home/airflow/GVIP/venv/lib/python3.8/site-packages/pandas/io/common.py", line 648, in get_handle handle = open(handle, ioargs.mode) FileNotFoundError: [Errno 2] No such file or directory: '<File-like object S3FileSystem, bucket_name/filename.xlsx>' ``` #### Problem description I should be able to read in the File-like object from s3fs when using `pd.read_excel` or `pd.ExcelFile`. Pandas 1.1.x allows for this, but it looks like changes to `pd.io.common.get_handle` in 1.2 have made this impossible. The simple workaround for this is to just use the s3 URI instead of using `s3fs` to open it first, but to my knowledge, the ability to use `read_excel` with an s3fs object was not intended to be deprecated in 1.2. #### My noob guess on what's going wrong I'm new to contributing to open source projects, so I don't know exactly how to fix this, but it looks like the issue is that the `pd.io.common.get_handle` method in 1.2 thinks the s3fs object is a file handle rather than a file-like buffer. To solve this, I would think something similar to the [`need_text_wrapping` boolean option](https://github.com/pandas-dev/pandas/blob/1.1.x/pandas/io/common.py#L503) from the `get_handle` method in 1.1.x needs to be added to 1.2's `get_handle` in order to tell pandas that the s3fs object needs a TextIOWrapper rather than treating it like a local file handle. If someone could give me a little guidance on how to fix this, I'd be happy to give my first open-source contribution a go, but if that's not really how this works, I understand. #### Expected Output <class 'pandas.core.frame.DataFrame'> #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 3e89b4c4b1580aa890023fc550774e63d499da25 python : 3.8.0.final.0 python-bits : 64 OS : Linux OS-release : 4.4.0-197-generic Version : #229-Ubuntu SMP Wed Nov 25 11:05:42 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.0 numpy : 1.19.4 pytz : 2020.4 dateutil : 2.8.1 pip : 20.3.3 setuptools : 41.2.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : 0.8.5 fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : None odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : 0.5.2 scipy : 1.5.4 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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BUG: Fix precise_xstrtod segfault on long exponent
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4
2020-12-29T21:00:46Z
2020-12-30T18:54:26Z
2020-12-30T18:41:00Z
MEMBER
null
- [x] closes #38753 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry @snowman2 can you check if this branch returns behavior to what you had before 1.2?
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38,790
Backport PR #38649 on branch 1.2.x (BUG: Fix regression for groupby.indices in case of unused categories)
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0
2020-12-29T23:16:53Z
2020-12-30T11:11:20Z
2020-12-30T11:11:20Z
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Backport PR #38649: BUG: Fix regression for groupby.indices in case of unused categories
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38,791
BUG: Categorical with non-nano dt64
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2020-12-30T01:49:07Z
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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 Fixes the flaky xfails in test_drop_duplicates
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BUG: DataFrame(dt64data, dtype=td64) corner cases
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REF: implement array_algos.putmask
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BUG: read_csv (c engine, round_trip precision) platform inconsistency with large exponents
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2020-12-30T02:41:40Z
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On OS X ```python data = io.StringIO("data\n10E999") pd.read_csv(data, engine="c", float_precision="round_trip") ``` gives ``` data 0 inf ``` But on Linux this gives ``` data 0 10E999 ``` Is this expected for any reason?
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BUG: pd.testing.makeMultiIndex ignores k parameter
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3
2020-12-30T03:04:47Z
2021-09-23T22:23:57Z
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. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python assert len(pd.testing.makeMultiIndex(k=10)) == 4 ``` https://github.com/pandas-dev/pandas/blob/0976c4ca0276a333346619c5a9e3c92631ec7006/pandas/_testing/__init__.py#L491-L492 #### Problem description All other `testing.make*Index` methods take a parameter `k` which lets you specify the length. `makeMultiIndex` explicitly takes this parameter but does nothing with it. #### Expected Output ```python assert len(pd.testing.makeMultiIndex(k=10)) == 10 ```
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38,796
BUG: GH38672 SeriesGroupBy.value_counts for categorical
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4
2020-12-30T03:06:51Z
2020-12-31T22:09:33Z
2020-12-31T16:41:17Z
CONTRIBUTOR
null
Unobserved categories in Series were being dropped in value_counts, which was inconsistent with Series.value_counts - [x] closes #38672 - [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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API/ ENH: Unambiguous indexing should be allowed, even if duplicates are present
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2020-12-30T05:27:56Z
2021-08-14T22:45:44Z
null
CONTRIBUTOR
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### Is your feature request related to a problem? While working on #38745, I was expecting this to be allowed: ```python import pandas as pd result = pd.concat( [ pd.Series(0, index=[0, 0, 1, 2]), pd.Series(1, index=[1, 2]), ], join="inner", ) expected = pd.DataFrame({0: [0, 0], 1: [1, 1]}, index=[1, 2]) pd.testing.assert_frame_equal(result, expected) ``` Because the intersection of those indices is well defined. However, it turns out this does not work – `pd.concat` errors – and has not worked recently (v1.1.5). This is related to allowing `.get_indexer` when result is uniquely specified (#38773), but I'll go into that more below. ### Describe the solution you'd like I would like this to work. I think this would also require these to work (they currently error): ```python pd.Series(np.arange(4), index=[0, 0, 1, 2]).reindex([1, 2]) pd.Index([0, 0, 1, 2]).get_index([1, 2]) ``` This could coincide with better error messages when the results are ambiguous, e.g. `DuplicateLabelErrors` where the duplicates are explicitly named. ### API breaking implications I think this is a subtle change to the API, which should not break much. It mostly will turn some errors into working cases, but those cases should have unambiguous results. I'd divide the behaviour changes into indexing and API functions. These indexing functions would change: * `Index.get_indexer` * `obj.reindex` * `obj.reindex_like` These API functions would change, though any function which relied on returning results from the intersection of indices could be affected. * `pd.concat(..., join="inner")` * `pd.crosstab` ### Describe alternatives you've considered * There could be no change, and continue to error. Maybe it's better to just encourage unique indices in all cases. But sometimes duplicates do happen. * There could be some changes (e.g. only change this for `pd.concat`, make it handle the indices) It could continue to be an error. It was an error in 1.1.5, and is currently. ### Additional context The exact behavior of combining indices with set operations seems to have a long history here, so I cannot provide a full context. I will go into detail about a bit of history I've come across while looking into this issue. #### `join="inner"` (Specific to `concat`) It is a bit strange that the `concat(..., join="inner")` gets the intersection of the indices, when an inner join would be a bit different. [It's documented](https://pandas.pydata.org/docs/user_guide/merging.html#concatenating-objects) that `concat` uses set operations on the "other axis", so I don't think this is a huge issue. the value `"inner"` does suggest different behaviour, where duplicates would always be allowed. I think this has been a source of confusion on `concat`s handling of duplicates (#36263). With set operations, there's no way to handle duplicates (unless we take some other definition of set (https://github.com/pandas-dev/pandas/issues/6963#issuecomment-573556670)). With joins, you'll be taking the cross product (which is often undesirable #36290). #### Performance There should only be a performance penalty when: * There are duplicated indices, so we need to check if they would be in the result * This may happen multiple times. In concat it would happen once when intersecting indices, and again with `get_index`. Is this so bad? Alternatively, there could be an "unsafe `get_indexer/ reindex` where the user promises the passed index has a unique solution, and no check occurs. * Checks if indexes are unique, which are cached. Wouldn't be necessary to check when `allows_duplicate_labels=False`. #### `.reindex`, `.get_index` Ultimately, I think this is an issue about what operations are unambiguous when there are duplicate values on the indices. Because of this, there may need to be other changes in behaviour this request consistent with the rest of library and reasonable to implement. For instance: ```python s1 = pd.Series(0, index=[0, 0, 1, 2]) s2 = pd.Series(1, index=[1, 2]) shared_indices = s1.index.intersection(s2.index) s1.reindex(shared_indices) # This currently errors ``` This currently errors, since the behaviour follows this logic (stated in #28257): > ... `.reindex` should allow targets to be duplicated but not the sources. However, currently in allows the source to be duplicated but not the targets, which is exactly the wrong behaviour. This is a bit similar to this statement (#8849): > I propose that when a series has a duplicate index, the method reindex should always raise an exception, because when a series with a duplicate index is to be conformed to a new index, the intended behaviour is always ambiguous. I think neither is quite right. I think that `reindex`, `get_index`, etc. should allow any operation where the result is uniquely defined. If the source has duplicates, and the target includes those duplicates, the result is ambiguous. If the source has duplicates, but the target does not contain them, the result is uniquely specified. ```python # ambiguous, should not be allowed pd.Index([0, 0, 1, 2]).get_index([0, 1]) # unambiguous, should be allowed pd.Index([0, 0, 1, 2]).get_index([1, 2]) ``` #### Additional example with `pd.crosstab` Current behaviour of `pd.crosstab` ```python pd.crosstab( pd.Series(np.arange(4), index=[0, 1, 1, 2]), pd.Series(np.arange(2), index=[0, 2]), ) # ValueError: cannot reindex from a duplicate axis pd.crosstab( pd.Series(np.arange(4), index=[0, 1, 1, 2]), pd.Series(np.arange(2), index=[3, 4]), ) # Empty DataFrame # Columns: [] # Index: [] ``` This proposal would also allow the first example to work, resulting in: ``` col_0 0 1 row_0 0 1 0 3 0 1 ```
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38,798
BUG: Series constructor with nullable unsigned integer dtype fails with large number
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2020-12-30T06:22:36Z
2021-06-08T16:47:24Z
null
MEMBER
null
xref #38746 The following runs fine: ``` python In [30]: s = pd.Series([np.iinfo(np.uint64).max-1, 1], dtype="uint64") In [31]: s Out[31]: 0 18446744073709551614 1 1 dtype: uint64 ``` but with the equivalent extension dtype the constructor throws: ```python In [29]: s = pd.Series([np.iinfo(np.uint64).max-1, 1], dtype="UInt64") --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ~/repos/pandas/pandas/core/arrays/integer.py in safe_cast(values, dtype, copy) 100 try: --> 101 return values.astype(dtype, casting="safe", copy=copy) 102 except TypeError as err: TypeError: Cannot cast array data from dtype('float64') to dtype('uint64') according to the rule 'safe' The above exception was the direct cause of the following exception: TypeError Traceback (most recent call last) <ipython-input-29-7fde9ec5cff9> in <module> ----> 1 s = pd.Series([np.iinfo(np.uint64).max-1, 1], dtype="UInt64") ~/repos/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath) 339 data = data.copy() 340 else: --> 341 data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True) 342 343 data = SingleBlockManager.from_array(data, index) ~/repos/pandas/pandas/core/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure) 485 486 if dtype is not None: --> 487 subarr = _try_cast(data, dtype, copy, raise_cast_failure) 488 else: 489 subarr = maybe_convert_platform(data) ~/repos/pandas/pandas/core/construction.py in _try_cast(arr, dtype, copy, raise_cast_failure) 594 # SparseDtype does not 595 array_type = dtype.construct_array_type()._from_sequence --> 596 subarr = array_type(arr, dtype=dtype, copy=copy) 597 return subarr 598 ~/repos/pandas/pandas/core/arrays/integer.py in _from_sequence(cls, scalars, dtype, copy) 307 cls, scalars, *, dtype=None, copy: bool = False 308 ) -> "IntegerArray": --> 309 values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy) 310 return IntegerArray(values, mask) 311 ~/repos/pandas/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy) 204 values = safe_cast(values, dtype, copy=False) 205 else: --> 206 values = safe_cast(values, dtype, copy=False) 207 208 return values, mask ~/repos/pandas/pandas/core/arrays/integer.py in safe_cast(values, dtype, copy) 106 return casted 107 --> 108 raise TypeError( 109 f"cannot safely cast non-equivalent {values.dtype} to {np.dtype(dtype)}" 110 ) from err TypeError: cannot safely cast non-equivalent float64 to uint64 ```
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776,343,445
MDExOlB1bGxSZXF1ZXN0NTQ2ODU2ODk4
38,799
TST: Add hook for Disallow bare pytest.raises
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4
2020-12-30T08:51:07Z
2021-01-05T16:56:39Z
2021-01-03T21:05:46Z
MEMBER
null
xref #30999 Adding a hook with lots of excluded folders (for now), this can be made stricter as we go along, there's not that much left
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TST: GH30999 Add match=msg to all pytest.raises in pandas/tests/reshape
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2
2020-12-30T10:21:53Z
2020-12-30T13:33:42Z
2020-12-30T13:33:38Z
MEMBER
null
This pull request partially addresses xref #30999 to remove bare pytest.raises by adding match=msg. It doesn't close that issue as I have only addressed test modules in the pandas/tests/reshape/ directory. This one was super simple. Fixed 5 bare pytest.raises and didn't do anything complicated or controversial. - [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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BUG: pandas 1.2.0 and Pyarrow [0.16.0, 1.0.0) are incompatible for some column types
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2
2020-12-30T10:32:34Z
2021-01-05T12:23:29Z
2021-01-05T12:23:29Z
CONTRIBUTOR
null
#35259 added optional importing from pyarrow. The currently listed minimum version of pyarrow is 0.15.1, and the logic of said PR guards against importing attributes from `pyarrow.compute` because it is not available in 0.15.1 Problem: pyarrow added the compute module in [0.16.0](https://github.com/apache/arrow/commit/27dded680e84f1a628de1bddff1f4eb62fbc5887#diff-3d08757408024228c4443730cc3536ab39c9436cd2e4cb63e5da34c69c18962f) but attributes imported by pandas are not available in that module until [1.0.0](https://github.com/apache/arrow/commit/dcd17bf36e0f7b18e8b8f466ed2cb3eb396955d8#diff-3d08757408024228c4443730cc3536ab39c9436cd2e4cb63e5da34c69c18962f) Therefore: with pandas 1.2.0 and merging a DataFrame (with an Array String column?) : | Pyarrow Version | Behavior | | -------------: | ----- | | not installed | works | | < 0.15.1 | not supported | | [0.15.1, 0.16.0). | works (pyarrow.compute.equal not used?) | | [0.16.0, 1.0.0) | ArgumentError | | [1.0.0, 2.0.0 (latest)] | works (using pyarrow.compute.equal) | Adding another try/except around the comparison functions in pandas [string_arrow.py](https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/string_arrow.py#L39) will change the table of functionality to: | Pyarrow Version | Behavior | | -------------: | ----- | | not installed | works | | < 0.15.1 | not supported | | [0.15.1, 1.0.0). | works (pyarrow.compute.equal not used?) | | [1.0.0, 2.0.0 (latest)] | works (using pyarrow.compute.equal) |
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CI/STYLE extend codespell beyond pandas/core
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12
2020-12-30T10:45:50Z
2021-03-11T19:03:30Z
2021-03-11T19:03:30Z
MEMBER
null
There is task left here: 1. apply the following diff ```diff diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d78c2bacc..bcb9202d4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -173,4 +173,4 @@ repos: hooks: - id: codespell types_or: [python, rst, markdown] files: ^(pandas|doc)/ - exclude: ^pandas/tests/ ``` , run `pre-commit run codespell --all-files`, and fixup the mistakes You _may_ need you to add new words to ignore in `setup.cfg`.
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38,803
BUG: avoid attribute error with pyarrow >=0.16.0 and <1.0.0
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16
2020-12-30T10:47:20Z
2021-01-05T13:23:45Z
2021-01-05T12:23:29Z
CONTRIBUTOR
null
Problem: The minimum pyarrow [listed](https://pandas.pydata.org/docs/dev/whatsnew/v1.2.0.html#increased-minimum-versions-for-dependencies) as an optional dependency is 0.15.1. Pyarrow added the compute module that is imported in the change here with pyarrow [0.16.0](https://github.com/apache/arrow/commit/27dded680e84f1a628de1bddff1f4eb62fbc5887#diff-3d08757408024228c4443730cc3536ab39c9436cd2e4cb63e5da34c69c18962f) but attributes imported by pandas are not available in that module until [1.0.0](https://github.com/apache/arrow/commit/dcd17bf36e0f7b18e8b8f466ed2cb3eb396955d8#diff-3d08757408024228c4443730cc3536ab39c9436cd2e4cb63e5da34c69c18962f) thus anyone using pyarrow [0.16.0, 1.0.0) will get an Attribute error. This PR adds as try/expect around accessing the attributes such that they are only accessed if available (i.e. pyarrow >=1.0.0) - [X] closes #38801 - [X] tests added / passed - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [X] whatsnew entry
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TST: GH30999 add match=msg to all pytest.raises in pandas/tests/window
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1
2020-12-30T11:27:00Z
2020-12-30T13:47:34Z
2020-12-30T13:47:31Z
MEMBER
null
This pull request partially addresses xref #30999 to remove bare `pytest.raises` by adding `match=msg`. It doesn't close that issue as I have only addressed test modules in the pandas/tests/window/ directory. I have added `match=msg` to 3 instances of `pytest.raises` and converted another to a `tm.external_error_raised`. Nothing complicated or controversial, imo. - [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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TST: GH30999 add match=msg to pytest.raises in modules with one simple instance each
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1
2020-12-30T11:46:47Z
2020-12-30T13:30:18Z
2020-12-30T13:30:14Z
MEMBER
null
This pull request partially addresses xref #30999 to remove bare pytest.raises by adding match=msg. It doesn't close that issue as I have only addressed the following three modules: pandas/tests/dtypes/test_inference.py pandas/tests/libs/test_hashtable.py pandas/tests/resample/test_resampler_grouper.py They are scattered around the test directory but each of them only had one instance of a bare pytest.raises which could be fixed with a simple addition of match=msg or change to tm.external_error_raised - [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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DOC: fix sphinx directive error in 1.2.1 release notes
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38,807
Backport PR #38723 on branch 1.2.x (BUG: inconsistency between frame.any/all with dt64 vs dt64tz)
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2020-12-30T13:24:07Z
2020-12-30T14:45:19Z
2020-12-30T14:45:19Z
NONE
null
Backport PR #38723: BUG: inconsistency between frame.any/all with dt64 vs dt64tz
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38,808
CLN: Add typing for dtype argument in codebase
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13
2020-12-30T13:28:45Z
2021-01-29T02:04:26Z
2021-01-29T02:04:26Z
CONTRIBUTOR
null
Follow up from #37546 and #38680 Add typing for the `dtype` argument throughout the pandas codebase. Part of the code might need an update after adding the type since the mypy type checks can fail. In #38680 it is already done for `io/sql.py`, from a directory search I found that the list of files below still have untyped dtype arguments. Would be great if people can work on a few files at a time in separate PRs and mention this issue. I will already start with the remaining files in the io directory - [x] io/parsers.py - [x] io/excel/_base.py - [x] io/json/_json.py - [x] io/pytables.py - [x] core/arrays/datetimelike.py - [x] core/arrays/base.py - [x] core/arrays/boolean.py - [x] core/arrays/categorical.py - [x] core/arrays/integer.py - [x] core/arrays/interval.py - [x] core/arrays/masked.py - [x] core/arrays/numpy_.py - [x] core/arrays/period.py - [x] core/arrays/sparse/array.py - [x] core/arrays/string_.py - [x] core/arrays/string_arrow.py - [x] core/arrays/timedeltas.py - [x] core/indexes/category.py - [x] core/indexes/datetimes.py - [x] core/indexes/interval.py - [x] core/indexes/numeric.py - [x] core/indexes/period.py - [x] core/indexes/range.py - [x] core/internals/block.py - [x] core/internals/managers.py - [x] core/reshape/reshape.py - [x] core/strings/object_array.py - [x] core/base.py - [x] core/common.py - [x] core/frame.py - [x] core/generic.py - [x] core/series.py
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MDU6SXNzdWU3NzY0NjQzNzk=
38,809
ERR: better error message with .rolling().apply(func using non-numpy things, raw=True)
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5
2020-12-30T13:47:03Z
2021-04-11T17:23:44Z
null
CONTRIBUTOR
null
we could use a better error message rather than `AttributeError` e.g. https://github.com/pandas-dev/pandas/pull/38804#discussion_r550196167 when using `df.rolling(...).apply(f, raw=True)` when `f` is using non-numpy functions e.g. `.iloc`
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38,810
Improve error message in Series.any and Series.all, when bool_only is passed and not level
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1
2020-12-30T14:21:32Z
2021-08-14T22:47:16Z
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MEMBER
null
When working on xref #30999 I came across TestSeriesReductions.test_all_any_params in pandas/tests/reductions/test_reductions.py It showed that calling the following code: ``` s = Series([False, False, True, True, False, True], index=[0, 0, 1, 1, 2, 2]) s.any(bool_only=True) ``` would raise a `NotImplementedError` with the error message "Series.any does not implement numeric_only", and equivalent for calling `s.all` the same way. This error message is confusing because the user didn't pass `numeric_only`, they passed `bool_only`. So the messages need to change to "Series.any does not implement bool_only" and "Series.all does not implement bool_only". > what actually needs to happen is we pass bool_only thru, but this increases the scope of this PR greatly. -- @jreback #38720
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776,484,253
MDExOlB1bGxSZXF1ZXN0NTQ2OTY3ODIx
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CI,STYLE: narrow down list of ignored words
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4
2020-12-30T14:32:14Z
2020-12-30T16:12:03Z
2020-12-30T16:04:06Z
CONTRIBUTOR
null
I have used `grep` to check if the words in the list exist in the code. Only `ba` can be deleted from the list.
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38,812
BUG: test_contains checks wrong non-missing element
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2020-12-30T14:33:15Z
2021-08-14T22:48:41Z
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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. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Problem description The `test_contains` test for `ExtensionArray` objects has the following lines: ```python # first elements are non-missing assert data[0] in data assert data_missing[0] in data_missing ``` However the first element in `data_missing` IS missing, and this test gives an error if `__getitem__` returns the physical representation of the NA object instead of `pd.NA`, as this needs not to return `True` for `__contains__` (unless I am missing something). Either way, the comment makes clear that this line intended to access a non NA object. #### Expected Output The lines should be changed to: ```python # first and second elements are respectively non-missing assert data[0] in data assert data_missing[1] in data_missing ```
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7
2020-12-30T14:40:03Z
2021-08-07T16:30:10Z
null
CONTRIBUTOR
null
we *should* very rarely be setting / getting options (except to test that functionaily explicity), and instead use `pd.option_context`, e.g. this should be changed: https://github.com/pandas-dev/pandas/pull/31569/files#diff-5974844fa02c9b81f3477198023bd02503bf82da1e3d15df6dfd62cdca48666bR242 pandas/tests/io/formats/test_format.py is a hotbed for incorrect usages like this. it *may* be possible to add a pre-commit hook to check for this in all but a small handful of files.
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CLN: Add typing for dtype argument in io directory (GH38808)
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1
2020-12-30T15:25:05Z
2020-12-30T21:24:26Z
2020-12-30T21:24:22Z
CONTRIBUTOR
null
incremental PR for issue #38808 - [ ] 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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38,815
TST: dropping of nuisance columns for groupby ops
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2
2020-12-30T15:39:48Z
2021-10-05T00:30:11Z
2021-10-05T00:30:11Z
MEMBER
null
xref #38774 As far as I can tell, for groupby we only have two tests for dropping nuisance columns and these only involve sum/mean. A more robust and parameterized test over all the groupby ops that drop nuisance columns should be added.
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776,515,407
MDExOlB1bGxSZXF1ZXN0NTQ2OTkxNjU1
38,816
REGR: groupby.sem with nuisance columns
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3
2020-12-30T15:40:27Z
2020-12-31T13:33:07Z
2020-12-30T23:38:49Z
MEMBER
null
- [x] closes #38774 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry We were using the same integer locs in the result of `std` and `count` when computing the quotient for `sem`. However, `std` will drop nuisance columns and `count` will not. I could only find two tests involving nuisance columns, and these only test sum/mean. Created #38815 for a followup.
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776,517,501
MDExOlB1bGxSZXF1ZXN0NTQ2OTkzMTkz
38,817
MAINT: regex char class improve
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1
2020-12-30T15:44:33Z
2020-12-30T18:42:07Z
2020-12-30T18:42:03Z
CONTRIBUTOR
null
* remove superfluous regex character classes from the codebase (those that contain a single character incur the overhead of a class with none of the advantages of a class) * for more details, see similar change in NumPy: https://github.com/numpy/numpy/pull/18083 * check performed with some simple [scraping code](https://github.com/tylerjereddy/regex-improve)
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776,522,411
MDExOlB1bGxSZXF1ZXN0NTQ2OTk3MTIx
38,818
TST/CLN: Remove duplicate abc tests
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1
2020-12-30T15:55:30Z
2020-12-30T18:43:34Z
2020-12-30T18:43:16Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry From #38588, this test is a proper subset of the tests immediately below them.
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