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ENH: __array_ufunc__ handle np.minimum.reduce
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- [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry np.mimum.reduce(frame) -> return Series instead of ndarray np.maximum.reduce(series[dt64]) -> return Timestamp instead of np.datetime64 np.maximum.reduce(series[td64]) -> return Timedelta instead of np.timedelta64 np.maximum.reduce(DTA) -> return Timestamp instead of np.datetime64 np.maximum.reduce(TDA) -> return Timedelta instead of np.timedelta64 np.maximum.reduce(DTI) -> return Timestamp instead of np.datetime64 np.maximum.reduce(TDI) -> return Timedelta instead of np.timedelta64 np.maximum.reduce(integer_array) works instead of raising NotImplementedError np.maximum.reduce(floating_array) works instead of raising NotImplementedError np.maximum.reduce(Series[Int]) works instead of raising NotImplementedError np.maximum.reduce(Series[Float]) works instead of raising NotImplementedError Ditto for np.minimum.reduce
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I_kwDOAA0YD8482I2b
43,924
BUG: `multicolumn` of `to_latex` does not work correctly when `index==False`
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"2021-10-09T07:57:23Z"
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null
### - [X] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd df = pd.DataFrame([[1,2,3,4]], columns=pd.MultiIndex.from_tuples([('1', 'a'), ('1', 'b'), ('2', 'a'), ('2', 'b'), ])) print(df.to_latex(index=False, multicolumn=True,)) \begin{tabular}{rrrr} \toprule 1 & \multicolumn{2}{l}{2} \\ a & b & a & b \\ \midrule 1 & 2 & 3 & 4 \\ \bottomrule \end{tabular} ``` ### Issue Description The top level of multicolumns are not located correctly. ### Expected Behavior \begin{tabular}{rrrr} \toprule \multicolumn{2}{l}{1} & \multicolumn{2}{l}{2} \\ a & b & a & b \\ \midrule 1 & 2 & 3 & 4 \\ \bottomrule \end{tabular} ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.8.10.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.1 numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 57.4.0 Cython : 0.29.24 pytest : 6.2.4 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.25.0 pandas_datareader: 0.10.0 bs4 : 4.9.3 bottleneck : None fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : None scipy : 1.7.0 sqlalchemy : None tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : None numba : 0.53.1 </details>
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PR_kwDOAA0YD84s8fmY
43,925
Add Zstandard compression support
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"2021-11-16T22:39:54Z"
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- [ ] closes #xxxx - [x] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Haven't found any discussion about Zstandard so far but I think it would be a valuable addition to the Pickle compression options.
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PR_kwDOAA0YD84s8gpp
43,926
BUG: Fix groupby nth with axis=1
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"2021-10-08T13:50:09Z"
"2021-10-13T23:00:22Z"
"2021-10-13T15:31:48Z"
CONTRIBUTOR
null
Fixes a bug that no one has reported and probably no one cares about. But overlaps slightly with #42947, so I thought that I would make it a separate PR. Current behaviour: `df.groupby(df.iloc[1], axis=1).nth(0)` crashes ungracefully.
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BUG: centered rolling on decreasing datetime-index inconsistency
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"2021-10-09T01:10:01Z"
"2021-10-09T01:10:01Z"
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null
### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np index = pd.date_range('2020', periods=4, freq='s') inc = pd.Series([1,1,1,1], index=index).rolling('3s', center=True).sum() dec = pd.Series([1,1,1,1], index=index[::-1]).rolling('3s', center=True).sum() assert np.all(inc.values == dec.values) >>> inc 2020-01-01 00:00:00 2.0 2020-01-01 00:00:01 3.0 2020-01-01 00:00:02 3.0 2020-01-01 00:00:03 2.0 Freq: S, dtype: float64 >>> dec 2020-01-01 00:00:03 NaN 2020-01-01 00:00:02 3.0 2020-01-01 00:00:01 3.0 2020-01-01 00:00:00 2.0 Freq: -1S, dtype: float64 ``` ### Issue Description the first value is a `NaN` when rolling over a decreasing index with `center=True`, but it shouldnt. This happens for ever value of `closed`. AFAIK, this comes from the cython implementation of `calculate_valiable_window_bounds`. I prepared a MR for this. ### Expected Behavior no `NaN` as first value. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.5.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-88-generic Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 57.0.0 Cython : None pytest : 6.2.5 hypothesis : 6.17.4 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 : 3.4.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 4.0.1 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : 0.54.0 </details>
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fixed rolling for a decreasing index, added a test for that
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CONTRIBUTOR
null
- [x] closes #43927 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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BUG: Fix the `Float` type overflow issue for statistical functions.
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python >>> import pandas as pd >>> df = pd.DataFrame([60000.0, 60000.0], dtype="float16") ``` ```Python /Users/username/opt/miniconda3/lib/python3.7/site-packages/numpy/core/_methods.py:47: RuntimeWarning: overflow encountered in reduce return umr_sum(a, axis, dtype, out, keepdims, initial, where) 0 inf dtype: float16 ``` ### Issue Description I have read the source code of pandas recently, it seems that when pandas implement DataFrame.mean() method, it calculate the tot = elements.sum() first, and then divide by the count_of_elements secondly to get the mean_value. This could cause a overflow error even if the result shouldn't be a number out of dtype range. ### Expected Behavior 1. The example should return 60000.0 2. For any Float dtype Series/DataFrame, the mean method should work and do not overflow. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.7.6.final.0 python-bits : 64 OS : Darwin OS-release : 18.7.0 Version : Darwin Kernel Version 18.7.0: Tue Jun 22 19:37:08 PDT 2021; root:xnu-4903.278.70~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.3 numpy : 1.19.2 pytz : 2021.1 dateutil : 2.8.1 pip : 20.0.2 setuptools : 45.2.0.post20200210 Cython : 0.29.22 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.21.0 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 : 3.0.0 pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : None tables : None tabulate : 0.8.9 xarray : None xlrd : None xlwt : None numba : 0.53.1 </details>
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ENH/POC/WIP: EA-backed Index
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Still TODO - [x] <s>288</s>15 not-yet-passing tests (with --skip-slow --skip-db) - [ ] flesh out tests/extension/base/ea_index.py - [x] <s>deprecation cycle</s> (decided we can probably just change) - [ ] optimize EAIndexEngine/NullableIndexEngine once #43870 is resolved - [x] EA.putmask implementation ATM is just a stub (and needs tests) - [ ] whatsnew cc @jorisvandenbossche @TomAugspurger xref https://github.com/pandas-dev/pandas/issues/39133
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BENCH : Added benchmark for indexing with .loc for sorted/unsorted DatetimeIndex
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- [x] closes #38690
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Raising ValueError when xlim and ylim contain non-int and non-float dtype elements #40889
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- [x] closes #40781 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry
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BUG: NumericIndex.insert
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"2021-10-09T00:18:48Z"
"2021-10-09T02:33:30Z"
"2021-10-09T02:33:30Z"
NONE
null
Backport PR #43910: TST: Skip leaky test on Python 3.10
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DOC: sphinx doc list item text has strange bold effect
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"2021-10-10T18:12:42Z"
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CONTRIBUTOR
null
### - [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/) ### Location of the documentation - https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.replace.html - https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.replace.html - and more ### Documentation problem As title. ![image](https://user-images.githubusercontent.com/25895405/136639776-5085c41f-d591-498b-a083-d1e19979914e.png) ### Suggested fix for documentation In using sphinx experience, the following could fix that. Note that `numeric` and `replaced` have the same indent level. ```diff - numeric: numeric values equal to `to_replace` will be - replaced with `value` + replaced with `value` ``` ``` - numeric: numeric values equal to `to_replace` will be replaced with `value` ``` My local test result. ![image](https://user-images.githubusercontent.com/25895405/136640134-543e756d-ea5b-4a03-bb12-e549e761f423.png)
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BUG: NumericIndex.append retain dtype
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MEMBER
null
- [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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BUG: fix Frame getitem when column keys are nested tuples
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CONTRIBUTOR
null
- [x] closes #43780 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Multi-column selection when the column labels are nested tuples, for example atomic labels of form `((1,),)` or MultiIndex having tuples at each level as reported in the issue raised error due to a condition in asarray_tuplesafe. A key list of form `[((1,))]` converts to `[[[1]]]` through the call to np.array, resulting in ndim>2. However, only the case ndim==2 was further handled, causing a multi-dimensional array to be returned and subsequently an error. This extends the ndim==2 condition for nested-tuple keys, but excludes multi-dimensional arrays so that errors are raised in accordance with [test_getitem_ndarray_3d](https://github.com/pandas-dev/pandas/blob/d30aeeba0c79fb8e4b651a8f528e87c3de8cb898/pandas/tests/indexing/test_indexing.py#L73).
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BUG: pandas.DataFrame.index.map() works differently if debugpy debugger is attached
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"2021-10-31T00:17:45Z"
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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd def map_func(dt): if dt.month <= 2: return dt.year + dt.month - 1 return dt.year + dt.month dict = { "Name": ["Tom", "Joseph"], "Date": ["2021-10-01", "2021-11-01"], } df = pd.DataFrame(dict) df = df.set_index("Date") df.index = pd.to_datetime(df.index) print(f"Result: {df.index.map(map_func)}") ``` ### Issue Description Running the attached program in debugpy debugger produces different results than if I run it plainly. Running it plainly with `/usr/bin/env /usr/bin/python3.9 test.py` produces output as expected: `Result: Int64Index([2031, 2032], dtype='int64', name='Date')` Running it in debugpy with: `cd $USER/test_error ; /usr/bin/env /usr/bin/python3.9 $USER/.vscode/extensions/ms-python.python-2021.10.1317843341/pythonFiles/lib/python/debugpy/launcher 37319 -- $USER/test_error/test.py` produces a crash on line 5. For some reason dt.month is the full index: `DatetimeIndex(['2021-10-01', '2021-11-01'], dtype='datetime64[ns]', name='Date', freq=None)` and not only a single date. Note that the debugpy command above is the auto generated command when using the VS code debugger (Run -> Start Debugging, or F5). Note that it also assumes that you are in the '$USER/test_error' directory. VS code version is 1.61.0 and Python extension version is v2021.10.1317843341. I'm posting it here, but I'm not sure if it's a pandas, debugger or python issue. ### Expected Behavior No crash when running in debugpy, or at least the same result ### Installed Versions INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.6.final.0 python-bits : 64 OS : Linux OS-release : 5.11.0-37-generic Version : #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.2.0 Cython : 0.29.24 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.2 IPython : None pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : None tables : 3.6.1 tabulate : None xarray : None xlrd : None xlwt : None numba : None None
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43,941
BUG: Series.plot.hist segfault for timedelta64
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"2021-10-09T11:22:06Z"
"2021-10-11T19:53:05Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd t = pd.to_datetime(["2021-01-01", "2021-01-03", "2021-01-04"]) print(t) dt = t.to_series().diff() print(dt) dt.plot.hist() ``` ### Issue Description I get this crash from the `dt.plot.hist()` call: ``` % python crash.py DatetimeIndex(['2021-01-01', '2021-01-03', '2021-01-04'], dtype='datetime64[ns]', freq=None) 2021-01-01 NaT 2021-01-03 2 days 2021-01-04 1 days dtype: timedelta64[ns] zsh: segmentation fault python crash.py ``` ### Expected Behavior No segfault. Give a plot or some informative exception. ### Installed Versions This is with Python 3.9 on MacOS installed via conda-forge. <details> % cat environment.yml name: cement channels: - conda-forge - nodefaults dependencies: - python==3.9 - jupyterlab==3.1 - pandas==1.3 - scikit-learn==1.0 - missingno - matplotlib - seaborn==0.11 - dtale==1.56 - pandas-profiling - sweetviz==2.1 - hcrystalball==0.1.10 - pip - pip: - flaml </details> <details> >>> pd.show_versions() INSTALLED VERSIONS ------------------ commit : f00ed8f47020034e752baf0250483053340971b0 python : 3.9.0.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.0 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.28.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : None tables : None tabulate : None xarray : 0.19.0 xlrd : 2.0.1 xlwt : None numba : 0.53.1 </details>
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DOC: Add examples for categorical functions
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"2021-10-09T17:00:32Z"
"2021-10-11T16:06:03Z"
"2021-10-11T16:05:59Z"
CONTRIBUTOR
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Adds simple examples to docstrings for `add_categories`, `remove_categories`, and `remove_unused_categories`.
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Fix multiindex loc nan
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- [x] closes #43814 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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BUG: rolling on centered datetimelike windows disrespect duplicate indices
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd index = pd.DatetimeIndex([ '2020-01-01', '2020-01-01', '2020-01-02', '2020-01-02', '2020-01-03', '2020-01-03', '2020-01-04', '2020-01-04', ]) s = pd.Series([1]*8, index=index, dtype=float) s.rolling('2D', center=True).sum() >> Out[6]: 2020-01-01 4.0 2020-01-01 4.0 2020-01-02 3.0 2020-01-02 4.0 2020-01-03 3.0 2020-01-03 4.0 2020-01-04 2.0 2020-01-04 2.0 dtype: float64 ``` ### Issue Description the right window bound is not respected for **centered** datetime windows. This differs somewhat from issue #20712 and does not change the behavior discussed there.. i prepared a PR for this. ### Expected Behavior ```python s.rolling('2D', center=True).sum() Out[4]: 2020-01-01 4.0 2020-01-01 4.0 2020-01-02 4.0 2020-01-02 4.0 2020-01-03 4.0 2020-01-03 4.0 2020-01-04 2.0 2020-01-04 2.0 dtype: float64 ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : d30aeeba0c79fb8e4b651a8f528e87c3de8cb898 python : 3.8.12.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-88-generic Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.4.0.dev0+857.gd30aeeba0c numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.2.0 Cython : 0.29.24 pytest : 6.2.5 hypothesis : 6.23.1 sphinx : 4.2.0 blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.28.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : 2021.10.0 fastparquet : 0.7.1 gcsfs : 2021.10.0 matplotlib : 3.4.3 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : 2021.10.0 scipy : 1.7.1 sqlalchemy : 1.4.25 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.18.2 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1 </details>
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[FIX] rolling to respect duplicate datetime indices on the right bound of centered windows
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…. - [x] closes #43944 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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ENH: Make it possible to convert types using astype inplace
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#### Is your feature request related to a problem? No, the feature is not related to a problem. #### Describe the solution you'd like It should be possible to perform the `astype()` operation directly into a dataframe passing an argument `inplace` as True. #### API breaking implications The API should not break once the default argument should be false, and the function will keep returning the same result as before. #### Describe alternatives you've considered Creating a new function exclusively for doing this kind of operation. #### Additional context ```python df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : [1.0, 2.0, 3.0]}) df.astype('float32', inplace=True) ```
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- [X] closes #43946 - [] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [X] This commit adds an argument to make it possible to convert the types of a dataframe inplace.
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ENH: Make it possible to change type inplace
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- [X] closes #43946 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [X] This commits adds the possibility of performing the operation of astype directly in a dataframe using the inplace parameter.
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Fix str dtype -> IntegerDtype conversions
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CONTRIBUTOR
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closes #25472 - [x] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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BUG: startswith function gives misleading results when passed a series #3485
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- [x] closes #3485 - [] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry ``` python import pandas d = pandas.DataFrame([['hi,hi 2'],['bye','bye 2']], columns=['one','two']) print(d.one.str.startswith(d.two)) ```
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TYP: Use Protocols for file-like objects in read/to_*
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"2021-11-17T02:11:42Z"
"2021-11-17T02:11:38Z"
CONTRIBUTOR
null
Fixes #41610, rebased on top of #43855. This PR does a few things: 1. break `FilePathOrBuffer` apart to not mix basic types and generics 2. use protocols instead of union of specific classes 3. define many fine-grained protocols for the to/read methods/functions I tested that the protocols are sufficient (need no additional attributes/methods) using mock classes with: - read_csv (python/c/pyarrow; w/wo compression) and to_csv (w/wo compression) - to_json and read_json (each w/wo compression) - to_pickle, read_pickle - to_excel (openpyxl/xlsxwriter) and read_excel (openpyxl) - to_stata and read_stata Future: use many overloads for `get_handle` to return the (wrapped) fine-grained protocols.
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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MEMBER
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``` import pandas as pd idx = pd.Index([4, 5, 6]) %timeit idx.insert(1, 19) 26.7 µs ± 299 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master 15.5 µs ± 185 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR ```
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ENH: Pandas Update Python - 3.10 Support
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NONE
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The *Python Version 3.10* is being released! I request you to update pandas to the 3.10 version of python
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DOC: cancel replace's doc list item text bold effect
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1
"2021-10-10T10:17:20Z"
"2021-10-10T18:12:46Z"
"2021-10-10T18:12:43Z"
CONTRIBUTOR
null
- [x] closes #43937 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] Cancel replace's doc list item text bold effect, detail could see below. From ![image](https://user-images.githubusercontent.com/25895405/136691591-6aae6597-99e7-4ae4-bca6-1e7380b1cad4.png) To ![image](https://user-images.githubusercontent.com/25895405/136691594-c2b14ad8-c761-49c9-9ba3-b1a472280df7.png)
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43,956
ENH: Rolling TimeSeries Interpolation Methods
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"2021-10-10T12:21:34Z"
"2021-10-31T00:19:35Z"
"2021-10-31T00:19:35Z"
NONE
null
I commonly deal with time-series data with gaps and would like Pandas to have the functionality to either apply a custom interpolation function which I can specify into .interpolate or have a dedicated time series data generator based on say the rolling N period values with some noise added to it for real-world applicability.
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DEPS/BLD: 3.9 builds breaking with missing Cython
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"2021-10-10T14:37:20Z"
"2021-10-31T00:20:04Z"
"2021-10-31T00:20:04Z"
CONTRIBUTOR
null
xref https://github.com/pandas-dev/pandas/pull/43952/checks?check_run_id=3849531985 as an exxample maybe something got released / renamed? looks like an issue with coverage ``` File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/plugin.py", line 126, in pytest_load_initial_conftests plugin = CovPlugin(options, early_config.pluginmanager) File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/plugin.py", line 175, in __init__ self.start(engine.DistMaster) File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/plugin.py", line 199, in start self.cov_controller.start() File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/engine.py", line 44, in ensure_topdir_wrapper return meth(self, *args, **kwargs) File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/pytest_cov/engine.py", line 267, in start self.cov.start() File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/coverage/control.py", line 532, in start self._init() File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/coverage/control.py", line 275, in _init self._plugins = Plugins.load_plugins(self.config.plugins, self.config, self._debug) File "/usr/share/miniconda/envs/pandas-dev/lib/python3.9/site-packages/coverage/plugin_support.py", line 41, in load_plugins __import__(module) ModuleNotFoundError: No module named 'Cython' ``` cc @pandas-dev/pandas-core
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CONTRIBUTOR
null
and in io/feather_format.py
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"2021-10-10T20:02:20Z"
"2021-10-12T22:20:29Z"
"2021-10-12T22:20:29Z"
CONTRIBUTOR
null
# Add `notin` convenience method to `DataFrame`/`Series` classes This may already have been discussed, but I couldn't find any existing issue/enhancement with this title. It's trivial to implement, but I think it might be syntactically helpful to write ```python df.notin(df1) ``` instead of ```python ~df.isin(df1) ``` especially in longer expressions where the boolean array is being used to slice a dataframe. #### Describe the solution you'd like Add a convenience function which returns the `not` value of `df.isin` #### API breaking implications No impact on API. #### Additional context This has already been done for `isna`/`notna`, and I find it very helpful—just borrowing the same idea. Copied from `frame.py`: ```python @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"]) def isna(self) -> DataFrame: result = self._constructor(self._mgr.isna(func=isna)) return result.__finalize__(self, method="isna") @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"]) def notna(self) -> DataFrame: return ~self.isna() ```
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ENH/Discussion: "event"/"per sample" weights support
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"2021-10-10T21:06:46Z"
"2021-10-16T04:08:20Z"
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NONE
null
#### Why this discussion Having looked around, I did to my surprise not find any real discussion on per sample weights for pandas DataFrames and therefore opening this thread to have a discussion on it. (*if I am mistaken and the discussion exists somewhere, I must have missed it and this can be gladly closed*) #### Per sample weights A datapoint can have a weight assigned to it reflecting the "count" of that point. Weighted data samples are not uncommon in data science (and many APIs in machine learning such as Scikit-learn, Keras, ...) and are an essential part of the data (as far as that a data point can be regarded as a tuple of two points: the data and the weight). A data weight changes many things fundamentally: plotting of a histogram or scatter plot (or others), calculation of quantities such as mean, std, ... Pandas, AFAIK, does not offer any support of dealing with weights and calculating e.g. a variance means that one needs to use a function from another library. #### What it could look like DataFrame could take an additional argument "weights" and have an attribute with the same name that is technically a Series and behaves like another column in the dataframe (broadcasting, index) but is not accessible over the normal indexing. A lot of the behavior is straight forward (and has his open questions): - any kind of slicing will always return a weighted dataframe as well (but what about concat? like two different columns) - plots will use weights (but what if weights can't be used?) - calculated quantities will use the weighted version (what if not available? Error?) - Other libraries (such as sklearn) could access them directly #### API breaking implications Since it would constitute a completely new feature, having errors if it can't be handled - or also other considerations of changed behavior - would *not break any existing code* (modulo user defined attribute "weight", but these users will likely want the new feature) #### Describe alternatives you've considered This is of course always achievable in other ways such as calling matplotlib, passing a weights argument explicitly etc. But this can be said about anything in pandas, given that it here to help with tabular data. In fact that would be the main reason to kickoff the discussion: if one has weighted data, pandas (AFAIK) does not support them at all, as it doesn't know about them (in the sense that many features cannot just be used). Adding them would make pandas a platform that can also handle weighted data. #### Discussion Needless to say that there are many cases that will need to be sorted out, needless to say that there are many fundamental objections and to be clear: this post is not saying we *should* introduce them. But it discusses it, since - weights are a common occurrence in data science - weights are an _essential_ part of a dataset as they change the meaning and a lot of functionality. They are not merely a specialized attribute that can be handled as meta-data but fundamentally part of a data point. - pandas makes analysis of (tabular) data easier by offering handles for it, yet (AFAIK) does not support weights at all, offering only a minimal help with weighted data - It doesn't break _any_ backwards compatibility and could be *gradually* introduced (with methods first erroring if weights are not yet supported). I am not clear on a side myself: It seems to me on the same time an unreasonably big change in so many places that will, realistically, not occur, yet at the same time _actually_ absolutely save (no break, not every feature needs to be supported, no conflict) and a fundamental property to data. What are your thoughts on this?
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ENH: Update isin docs with examples of ~ operator usage (#43959)
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"2021-10-10T21:42:10Z"
"2021-10-12T22:20:33Z"
"2021-10-12T22:20:29Z"
CONTRIBUTOR
null
- [x] closes #43959 Updated relevant docs to show usage of ~ to emulate `notin` operation.
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BUG: RangeIndex arithmetic result.name
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"2021-10-10T21:48:33Z"
"2021-10-11T22:21:06Z"
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MEMBER
null
- [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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Test that using a `tuple` or `list` for parameter `ascending` of `sort_index` is the same
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"2021-10-16T14:35:23Z"
"2021-10-16T14:35:19Z"
CONTRIBUTOR
null
- [x] closes #43884 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry I could not find a suitable place to add a `whatsnew` entry, if there is one I will push another commit.
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DOC: Fix Series nlargest and nsmallest doc strings (#43964)
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"2021-10-15T02:02:53Z"
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CONTRIBUTOR
null
- [x] closes #43937 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] Cancel nlargest and nsmallest's doc list item text bold effect, detail could see below. From ![Clip_20211011_094600](https://user-images.githubusercontent.com/25895405/136727253-30d20e21-ba1c-4be0-b1ad-87239b66d59e.png) To ![Clip_20211011_105541](https://user-images.githubusercontent.com/25895405/136727262-f28a75e0-48e8-478b-a460-7690181d97d0.png)
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BUG: Setting data of same dtype as existing column in shallow copy DF causes change to propagate to original DF
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python # Setting column data with different dtype on shallow copy causes original to be unchanged original = pd.DataFrame({'a': ['initial string data']}) shallow = original.copy(deep=False) shallow['a'] = 5 # Set column to integer dtype original['a'][0] >> 'initial string data' # Whereas if new data has same dtype as existing, original DF is updated too original = pd.DataFrame({'a': ['initial string data']}) shallow = original.copy(deep=False) shallow['a'] = 'new string data' # Set column to different str/object value original['a'][0] >> 'new string data' ``` ### Issue Description Setting data of same dtype as existing column in shallow DF copy causes change to propagate to original DF. However this does not happen if new data has different dtype. ### Expected Behavior Assigning column data in DF shallow copy should not also cause change in original DF (regardless of dtype) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : f2c8480af2f25efdbd803218b9d87980f416563e python : 3.9.1.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_Australia.1252 pandas : 1.2.3 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 20.2.3 setuptools : 49.2.1 Cython : 0.29.24 pytest : 6.2.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.25.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : 3.0.6 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.54.0 </details>
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CI: Add Cache task on Windows
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- [ ] closes #42580 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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Example for pandas.DataFrame.to_period docs
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CONTRIBUTOR
null
- [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry Doc improvement - added the example section for pandas.DataFrame.to_period
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FIX BUG: Timestamp __add__/__sub__ DateOffset with nanoseconds lost.
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CONTRIBUTOR
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- [x] closes #43892 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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PANDAS FAILED INSTALLATATION VIA PIP ON WINDOWS 11
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"2021-10-15T17:10:11Z"
"2021-10-12T14:27:12Z"
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La instalacion falla, para la ultima version de pandas, e incluso empieza a buscar otra version y tampoco la encuentra una que funcione todas caen. C:\Users\ufx>pip install pandas Collecting pandas Using cached pandas-1.3.3.tar.gz (4.7 MB) Installing build dependencies ... error ERROR: Command errored out with exit status 1: command: 'C:\Users\ufx\AppData\Local\Programs\Python\Python310\python.exe' 'C:\Users\ufx\AppData\Local\Temp\pip-standalone-pip-hwkn7bp9\__env_pip__.zip\pip' install --ignore-installed --no-user --prefix 'C:\Users\ufx\AppData\Local\Temp\pip-build-env-q28f4203\overlay' --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- 'setuptools>=51.0.0' wheel 'Cython>=0.29.21,<3' 'numpy==1.17.3; python_version=='"'"'3.7'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.18.3; python_version=='"'"'3.8'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.19.3; python_version>='"'"'3.9'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.19.2; python_version=='"'"'3.7'"'"' and platform_machine=='"'"'aarch64'"'"'' 'numpy==1.19.2; python_version=='"'"'3.8'"'"' and platform_machine=='"'"'aarch64'"'"'' 'numpy>=1.20.0; python_version=='"'"'3.8'"'"' and platform_machine=='"'"'arm64'"'"' and platform_system=='"'"'Darwin'"'"'' 'numpy>=1.20.0; python_version=='"'"'3.9'"'"' and platform_machine=='"'"'arm64'"'"' and platform_system=='"'"'Darwin'"'"'' cwd: None Complete output (839 lines): Ignoring numpy: markers 'python_version == "3.7" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment Ignoring numpy: markers 'python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment Ignoring numpy: markers 'python_version == "3.7" and platform_machine == "aarch64"' don't match your environment Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "aarch64"' don't match your environment Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment Ignoring numpy: markers 'python_version == "3.9" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment Collecting setuptools>=51.0.0 Using cached setuptools-58.2.0-py3-none-any.whl (946 kB) Collecting wheel Using cached wheel-0.37.0-py2.py3-none-any.whl (35 kB)
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TST: changed behavior of df.loc on multiindex
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"2021-10-11T13:37:35Z"
"2021-10-11T16:57:40Z"
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-added unittest for issue #43599 -error occurs due to different versions of pandas -made the requested changes
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STYLE ban np.testing
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"2021-10-11T22:29:44Z"
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MEMBER
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xref https://github.com/pandas-dev/pandas/pull/43674#discussion_r720831806 There used to be a linting rule for this, but I missed it when moving linting rules over to `pandas-dev-flaker` in https://github.com/pandas-dev/pandas/pull/40906 - sorry about that, I thought I'd checked that everything had been preserved Would be good to use this one from `pandas-dev-flaker` directly, but without a `--per-file-selects` option it'd be rather cumbersome (https://github.com/PyCQA/flake8/issues/344#issuecomment-940054283)
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BUG: `pandas.DataFrame.plot` results in inconsistent/incompatible `xticks` depending on the date span of the data
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"2021-10-16T04:09:45Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt # sample data dates1 = ['2021-08-26', '2021-08-27', '2021-08-30', '2021-08-31', '2021-09-01', '2021-09-02', '2021-09-03', '2021-09-07', '2021-09-08', '2021-09-09', '2021-09-10', '2021-09-13', '2021-09-14', '2021-09-15', '2021-09-16', '2021-09-17', '2021-09-20', '2021-09-21', '2021-09-22', '2021-09-23', '2021-09-24', '2021-09-27', '2021-09-28', '2021-09-29', '2021-09-30', '2021-10-01', '2021-10-04', '2021-10-05', '2021-10-06', '2021-10-07', '2021-10-08'] dates2 = ['2021-08-29', '2021-09-05', '2021-09-12', '2021-09-19', '2021-09-26'] np.random.seed(365) y1 = np.random.randn(len(dates1)).cumsum() y2 = np.random.randn(len(dates2)).cumsum() # dataframe with more than a month span df1 = pd.DataFrame({'date':pd.to_datetime(dates1), 'y1':y1}) df1.set_index('date', inplace=True) # dataframe with less than a month span df2 = pd.DataFrame({'date':pd.to_datetime(dates2), 'y2':y2}) df2.set_index('date', inplace=True) ``` ### Issue Description - See [SO: Plotting two pandas time-series on the same axes with matplotlib - unexpected behavior](https://stackoverflow.com/q/69516844/7758804) - Using `pandas.DataFrame.plot` to plot data with a date range span of more than one month with another data set where the date range span is less than a month, on `seconday_y`, produces unexpected results in how the API formats and plots the `xticks`, which results in an incorrect visualization in subplot 0. - In both cases, it is not clear what format the dates have been converted to for plotting. - If `dates2` spans at least a month, the issue doesn't occur. (e.g. `dates2 = ['2021-08-29', '2021-09-05', '2021-09-12', '2021-09-19', '2021-09-26', '2021-09-29']`). ```python fig, axs = plt.subplots(2, 2, figsize=[12, 12]) axs = axs.flat print('Note the difference in xticks depending on the date span') df1.plot(ax=axs[0], title='x-axis is incorrect when the dataframe with\nmore than a month of dates is plotted first') print(f'axs[0]: {axs[0].get_xticks()}') df2.plot(ax=axs[0], secondary_y=True) print(f'axs[0]: {axs[0].get_xticks()}') df2.plot(ax=axs[1], color='tab:orange', title='x-axis is correct when the dataframe with\nless than a month of dates is plotted first') print(f'axs[1]: {axs[1].get_xticks()}') df1.plot(ax=axs[1], color='tab:blue', secondary_y=True) print(f'axs[1]: {axs[1].get_xticks()}') df1.y1.plot(ax=axs[2], color='tab:blue', title='More than a month of data') print(f'axs[2]: {axs[2].get_xticks()}') df2.y2.plot(ax=axs[3], color='tab:orange', title='Less than a month of data') print(f'axs[3]: {axs[3].get_xticks()}') plt.tight_layout() ``` - Printed output ```python Note the difference in xticks depending on the date span axs[0]: [18871. 18878. 18885. 18892. 18901. 18908.] axs[0]: [ 2696 4175 6784 9393 12002 14611 17220 18908] axs[1]: [2696 2697 2700] axs[1]: [2696 2697 2701 2702] axs[2]: [18871. 18878. 18885. 18892. 18901. 18908.] axs[3]: [2696 2697 2700] ``` ![image](https://user-images.githubusercontent.com/12875240/136818534-607528f7-0d35-44b6-90db-71b4b64502be.png) ### Expected Behavior - Plotting directly with `matplotlib.pyplot.plot` produces the correct result ```python fig, axs = plt.subplots(2, 2, figsize=[20, 8], sharey=False, sharex=False) axs = axs.flatten() axs[0].plot(df1.index, df1.y1, marker='.', color='tab:blue') print(f'axs[0]: {axs[0].get_xticks()}') ax4 = axs[0].twinx() ax4.plot(df2.index, df2.y2, marker='.', color='tab:orange') print(f'ax4: {ax4.get_xticks()}') axs[1].plot(df2.index, df2.y2, marker='.', color='tab:orange') print(f'axs[1]: {axs[1].get_xticks()}') ax5 = axs[1].twinx() ax5.plot(df1.index, df1.y1, marker='.', color='tab:blue') print(f'ax5: {ax5.get_xticks()}') axs[2].plot(df1.index, df1.y1, marker='.', color='tab:blue') print(f'axs[2]: {axs[2].get_xticks()}') axs[3].plot(df2.index, df2.y2, marker='.', color='tab:orange') print(f'axs[3]: {axs[3].get_xticks()}') ``` - Print output ```python axs[0]: [18871. 18878. 18885. 18892. 18901. 18908.] ax4: [18871. 18878. 18885. 18892. 18901. 18908.] axs[1]: [18868. 18871. 18875. 18879. 18883. 18887. 18891. 18895.] ax5: [18871. 18878. 18885. 18892. 18901. 18908.] axs[2]: [18871. 18878. 18885. 18892. 18901. 18908.] axs[3]: [18868. 18871. 18875. 18879. 18883. 18887. 18891. 18895.] ``` ![image](https://user-images.githubusercontent.com/12875240/136818841-eb834721-9e54-48a6-9e5b-539291848c37.png) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19043 machine : AMD64 processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.3.3 numpy : 1.20.3 pytz : 2021.3 dateutil : 2.8.2 pip : 21.0.1 setuptools : 58.0.4 Cython : 0.29.24 pytest : 6.2.4 hypothesis : None sphinx : 4.2.0 blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.27.0 pandas_datareader: 0.10.0 bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : 2021.08.1 fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1 </details>
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- [x] closes #15354 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry - [x] update docu - [ ] docu example - [ ] add *versionadded* to docu if accepted
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BUG/TST: fixed so various methods call `__finalize__`, issue #28283
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### Progress towards #28283 After this PD, the following methods should propagate metadata using `__finalize__`: - `DataFrame.combine, DataFrame.combine_first` - `DataFrame.explode` - `DataFrame.isin` - `DataFrame.merge` - `DataFrame.melt` - `DataFrame.mode` - `DataFrame.quantile` <br/> - [ ] closes #xxxx - [x] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Contributors: - [Casper Lundberg](https://github.com/casperlundberg) - [Rickard Bemm](https://github.com/Rickos99) - [Simon Lundberg](https://github.com/SimonHotDog)
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ENH: Add unixtime accessor to Timestamp and DatetimeIndex objects
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#### Pain Point I feel that extracting unixtimes from date strings is less graceful than it could be, and relies on the user knowing more about the internal format of datetime objects than they should have to. Currently (at least as of version 1.3.3), a user needs to cast a Timestamp object as an integer to extract the unixtime (in nanoseconds) ``` import pandas as pd import numpy as np x = pd.to_datetime("2020") unixtime_sec = x.astype(np.int64) / 1e9 ``` This makes sense if you understand how a Timestamp object is storing its information internally. However, I would argue that this requires the end-user to understand an implementation detail that they shouldn't need to. I shouldn't need to care whether the time is represented internally by unixtime, TAI, GPS time etc. I should be able to ask for unixtime without relying on knowledge of the implementation details. I would also argue that the current approach also makes code harder to read. While other attributes of time can be obtained by asking for them directly, unixtime requires an more indirect request ``` hour_of_day = x.hour #Easy to read unixtime_ns = x.astype(np.int64) #Harder to read ``` To add to the confusion, the `DatetimeIndex` object requires the user needs to use `view` instead of `astype` (see https://github.com/pandas-dev/pandas/pull/38544). I don't understand quite why the interface for Timestamps and DatetimeIndices needs to be different . ``` x = pd.date_range("2020", "2021") unixtime_sec = x.view(np.int64) / 1e9 ``` #### Proposed solution Timestamps and DatetimeIndex objects should have a unixtime accessor consistent with the interface used to access hour of day, day of month, etc. ``` x = pd.to_datetime("2020") hour_of_day = x.hour # Currently exists day_of_month = x.day # Currently exists unixtime_sec = x.unixtime # This proposal x = pd.date_range("2020", "2021") unixtime_sec = x.unixtime # This proposal ``` The accessor should return a floating point number for a timestamp, or an iterable (e.g a Series) of floating point numbers for a DatetimeIndex. This float should represent the number of seconds elapsed from the epoch. The unit should be seconds, not nanoseconds, to be consistent with the definition of unixtime (https://pubs.opengroup.org/onlinepubs/9699919799/xrat/V4_xbd_chap04.html#tag_21_04_16). #### API breaking implications This will not break any other features of the API, although deprecating the astype() and view() accessors could be considered. If these accessors are deprecated, that would make changing the internal representation in a Timestamp object easier in future. #### Describe alternatives you've considered The current methods work, although they are unsatisfactory because 1. They require the end user have knowledge of what is an implementation detail 2. They result in code that is harder to read 3. It is cumbersome to write code that works for both Timestamps and DatetimeIndex objects
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CONTRIBUTOR
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just updating something that I missed on previous pr when arg name was changed.
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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BUG: DataFrame.drop() fails when `columns=` is given as tuple
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6
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"2021-10-21T02:03:44Z"
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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'D']) print(df.drop(columns=list(["A", "B"]))) # <-- Works print(df.drop(columns=np.array(["A", "B"]))) # <-- Works print(df.drop(columns=tuple(["A", "B"]))) # <-- Fails with KeyError ``` ### Issue Description The documentation says that the `columns=` argument of `DataFrame.drop()` can take a single label or list-like, but it fails when given a `tuple` with more than column name. This method seems to work when the exact same column labels are provided as a `list` or a `np.ndarray`. Just an observation -- a `tuple` with `len()==1` does seem to work successfully here. ### Expected Behavior For the above example, `df.drop(columns=("A", "B"))` should produce the same output as `columns=["A", "B"]` or `columns=np.array(["A", "B"])`, resulting in the following DataFrame: ``` C D 0 2 3 1 6 7 2 10 11 ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.11.final.0 python-bits : 64 OS : Darwin OS-release : 17.7.0 Version : Darwin Kernel Version 17.7.0: Fri Oct 30 13:34:27 PDT 2020; root:xnu-4570.71.82.8~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.0.1 setuptools : 58.0.4 Cython : None pytest : 6.2.5 hypothesis : None sphinx : 4.2.0 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.28.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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BUG: Precision loss when casting 19-digit integer to float
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"2021-10-11T20:43:28Z"
"2021-10-16T04:11:43Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python ls = [ 1234567890123456789, np.nan, 1234567890123337789, ] pd.Series(ls, dtype=np.float128) ``` ### Issue Description When a series is created from a list that contains np.nan values, its type is float. And if the list contains 19-digit integers the precision of the last few digits is lost. A similar issue seems to have been fixed in numpy a couple of years ago: https://github.com/numpy/numpy/issues/9006. Would you please suggest any workaround until this is resolved? Having a series with integer values and nans would work for me in this particular case. Thank you in advance. ### Expected Behavior The expected behaviour would be to keep the precision exactly. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.5.final.0 pandas : 1.3.3 numpy : 1.20.2 </details>
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BUG: Fix `skipna` default value in method signatures
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"2021-10-16T17:30:01Z"
"2021-10-16T17:28:54Z"
CONTRIBUTOR
null
Set `skipna` default values to `True` so that the value matches the purpose of the variable and its docstring. - [X] closes #34063 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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TYP: Fixup annotations.
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"2021-10-11T21:22:41Z"
"2021-10-13T07:45:40Z"
"2021-10-13T07:45:40Z"
CONTRIBUTOR
null
Both modified functions may return a MultiIndex; the return annotation has been updated to reflect that. Added type hint for `names`. Minor docstring tweak. - [ ] closes #xxxx - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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ENH: Polymorphic `pd.eq()` and friends
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#### Is your feature request related to a problem? I'm looking for a polymorphic way to test for equality. Right now, ff I want to compare two objects in pandas, I need to know the type so I can use `DataFrame.eq` or `Series.eq`, or if they're scalars then I need to use `x == y` or similar. It would be nice if there were a `pd.equals(x, y)` method that could handle scalars, series, dataframes, indexes, etc. Right now I'm rolling my own custom version of this code. I might be missing some obvious solution here, if so please point me there. #### Describe the solution you'd like Probably this would just involve some type checking and then dispatch to the proper methods. This should be elementwise, so you get back `bool | np.nan` data of the same shape. Of course, this also opens the door to a whole lot more questions, like probably then there should be a corresponding `pd.ne`, `pd.lt`, etc for other comparisons. #### API breaking implications Would add a lot more API surface area, and there are lots of little questions as to how to make this consistent with the rest of the API, e.g. is `nan == nan`? That's what `DataFrame.equals` uses, but not `DataFrame.eq`. #### Describe alternatives you've considered I can add this later if this idea get's any support.
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DOC: Update Indexing user guide
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"2021-10-15T15:44:03Z"
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CONTRIBUTOR
null
### - [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/) ### Location of the documentation https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-with-list-with-missing-labels-is-deprecated ### Documentation problem The `Warning` doesn't explicitly state a `KeyError` will be raised. The last example displays behaviour exhibited prior to deprecation; a KeyError is now raised. ### Suggested fix for documentation Using .loc or [] with a list with one or more missing labels will now raise a ``KeyError``—use ``.reindex`` instead. The last example (*with the warning*) will no longer be required.
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ENH: Refactor if-else with structural pattern matching (Python3.10)
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"2021-10-13T03:24:32Z"
"2021-10-11T22:06:03Z"
CONTRIBUTOR
null
#### Describe the solution you'd like Have there been any discussions on refactoring `if-else-isinstance` checks with the new `match-case` syntax (*pattern matching*)? It looks very useful, even if purely used as a `switch-case` stand in. This should simplify typechecks, of which there are quite a few in the codebase. #### API breaking implications Should not have any impact, as purely an internal refactor. #### Additional context Sample example of refactor—`between()`, taken from `series.py`: Current: ```python if inclusive == "both": lmask = self >= left rmask = self <= right elif inclusive == "left": lmask = self >= left rmask = self < right elif inclusive == "right": lmask = self > left rmask = self <= right elif inclusive == "neither": lmask = self > left rmask = self < right else: raise ValueError( "Inclusive has to be either string of 'both'," "'left', 'right', or 'neither'." ) ``` Refactored (*poorly, probably*): ```python match inclusive: case "both": lmask = self >= left rmask = self <= right case "left": lmask = self >= left rmask = self < right case "right": lmask = self > left rmask = self <= right case "neither": lmask = self > left rmask = self < right case _: raise ValueError( "Inclusive has to be either string of 'both'," "'left', 'right', or 'neither'." ) ```
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BUG: DataFrame.drop fails when columns argument given tuple
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CONTRIBUTOR
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- [x] closes #43978 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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BUG: DataFrame from ndarray[SeriesObjects] vs PandasArray[SeriesObjects] inconsistent
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MEMBER
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import numpy as np import pandas as pd ser = pd.Series([1, 2]) arr = np.array([None, None], dtype=object) arr[0] = ser arr[1] = ser * 2 parr = pd.array(arr) # <- PandasArray >>> pd.DataFrame(arr) 0 0 0 1 1 2 dtype: int64 1 0 2 1 4 dtype: int64 >>> pd.DataFrame(parr) 0 1 0 1 2 1 2 4 ``` ### Issue Description We un-nest nested data when it is in a PandasArray but not when it is in a ndarray/Index/Series. Aligning this behavior would allow us to simplify the DataFrame constructor. Only one test where we pass a PandasArray containing Series to DataFrame: test_apply_series_on_date_time_index_aware_series ### Expected Behavior ndarray vs PandasArray should not matter here. ### Installed Versions <details> Replace this line with the output of pd.show_versions() </details>
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REF: extract params used in DataFrame.__repr__
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"2021-10-11T22:58:31Z"
"2021-10-16T13:12:03Z"
"2021-10-14T23:33:23Z"
CONTRIBUTOR
null
`DataFrame.__repr__` used different parameters than `DataFrame.to_string` and I've got a case where I want to use the `__repr__` ones, but with a change to one of the parameters. This refactoring makes it possible to extract those parameters so we can do: ```python >>> params = pd.io.formats.format.get_frame_repr_params() >>> params[my_param] = new_value >>> df.to_string(**params) ```
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PERF: RangeIndex.insert
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"2021-10-11T23:50:46Z"
"2021-10-12T19:45:16Z"
"2021-10-12T19:32:17Z"
MEMBER
null
``` index = pd.Index(range(2)) %timeit index.insert(2, 2) 48.4 µs ± 2.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master 2.54 µs ± 70.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR ```
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"2021-10-12T19:31:54Z"
"2021-10-12T19:26:51Z"
MEMBER
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After this we're close to being able to change the default from "equiv" to True (not that we would since that would be an API change)
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ENH: Function to make categories the same across several Categoricals
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"2021-10-12T02:19:13Z"
"2021-10-12T03:01:40Z"
"2021-10-12T02:48:12Z"
CONTRIBUTOR
null
#### Motivation We often want to make vectorized comparisons between two Categoricals. However, this is not possible unless they have the same categories: ```python In [45]: A = pd.Categorical([10, 33, 24, 33, 10]) In [46]: A Out[46]: [10, 33, 24, 33, 10] Categories (3, int64): [10, 24, 33] In [47]: B = pd.Categorical([33, 21, 24, 33, 10]) In [48]: B Out[48]: [33, 21, 24, 33, 10] Categories (4, int64): [10, 21, 24, 33] In [49]: A == B --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-49-acc3d1f167fc> in <module> ----> 1 A == B ... 143 msg = "Categoricals can only be compared if 'categories' are the same." 144 if not self._categories_match_up_to_permutation(other): --> 145 raise TypeError(msg) 146 147 if not self.ordered and not self.categories.equals(other.categories): TypeError: Categoricals can only be compared if 'categories' are the same. ``` #### Proposed solution It would be useful to have a utility function which takes two or more Categoricals as input and re-encodes each of them using the union of their respective categories as the new categories. This is very similar to what `union_categoricals` does, except that outputs a concatenation of the input Categoricals. Here is a basic implementation which I have used, with apologies for my lack of knowledge of Pandas internals: ```python In [50]: def unify_categoricals(*categoricals): ...: all_categories = set(itertools.chain(*[c.categories for c in categoricals])) ...: for categorical in categoricals: ...: new_categories = all_categories.difference(set(categorical.categories)) ...: if new_categories: ...: categorical.add_categories(new_categories, inplace=True) ...: In [51]: unify_categoricals(A, B) <ipython-input-50-e69ac645c9fb>:6: FutureWarning: The `inplace` parameter in pandas.Categorical.add_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object. categorical.add_categories(new_categories, inplace=True) In [52]: A Out[52]: [10, 33, 24, 33, 10] Categories (4, int64): [10, 24, 33, 21] In [53]: B Out[53]: [33, 21, 24, 33, 10] Categories (4, int64): [10, 21, 24, 33] In [54]: A == B Out[54]: array([False, False, True, True, True]) ``` This could be modified to avoid inplace operations on the input Categoricals and extended to support ordered Categoricals as well. The name is probably too similar to `union_categoricals`; suggestions are appreciated. #### API breaking implications None #### Alternatives For one-off comparisons, you can get away with simply decoding the Categoricals: ```python In [55]: A.to_numpy() == B.to_numpy() Out[55]: array([False, False, True, True, True]) ``` There is only a real benefit if you need to make many comparisons (for my application, we persist DataFrames to Feather files and load them into memory for many different tasks, so it is useful to perform this conversion once before storage). Since this is somewhat of a niche use case it could be left to application code. There might also be some way to chop up the output of `union_categoricals` into multiple Categoricals which are the re-encoded versions of the inputs.
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CLN: use numpy quantile in qcut
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"2021-10-12T04:18:47Z"
"2021-10-12T19:31:13Z"
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MEMBER
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The quantile function in algos is a partial implementation of the numpy quantile and there are no pandas objects involved.
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43,992
REF: share more ExtensionIndex methods
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MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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43,993
BUG: TypeError when doing equality comparison with Extension Dtype
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"2021-10-13T02:08:11Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python s1 = pd.Series([1,2,3,4,5, None]) s2 = s1.astype('Int16') s1.dtype Out[13]: dtype('float64') s2.dtype Out[14]: Int16Dtype() # Equality using extension Dtype first works as expected s2.dtype == s1.dtype Out[15]: False # Equality using non-extension Dtype first raises TypeError s1.dtype == s2.dtype File "<ipython-input-16-8f0ca559fe44>", line 1, in <module> s1.dtype == s2.dtype TypeError: Cannot interpret 'Int16Dtype()' as a data type ``` ### Issue Description It appears that when doing a dtype comparison with a non-extension Dtype first and extension Dtype second causes TypeError, whereas doing the other way round works as expected ### Expected Behavior Dtype comparison behaviour should not depend on the order of the dtypes in the statement ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.1.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_Australia.1252 pandas : 1.3.3 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 20.2.3 setuptools : 49.2.1 Cython : 0.29.24 pytest : 6.2.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.25.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : 3.0.6 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.54.0 </details>
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43,994
ENH: add ignore_index to nlargest and nsmallest method
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"2021-10-12T08:33:16Z"
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CONTRIBUTOR
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#### Describe the solution you'd like Just like other methods where we select a subset or reorder, we have a `ignore_index` argument, to reset the index. ```python In [1]: import pandas as pd In [2]: df = pd.DataFrame({"A": range(10), "B": range(10, 20)}) In [3]: df.nlargest(n=2, columns="A") Out[3]: A B 9 9 19 8 8 18 In [4]: df.nlargest(n=2, columns="A", ignore_index=True) Out[3]: A B 0 9 19 1 8 18 ```
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MAINT: Correct small cast issues on Windows
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CONTRIBUTOR
null
Use correct types to avoid downcasts on Windows - [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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BUG: v1.3.0 Introduces Unexpected Retyping of Categorical Column with NaN inside
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python # Bug in v1.3.0 and later import pandas as pd import numpy as np print(pd.__version__) X = pd.DataFrame({ "a": pd.Categorical([np.nan], [1]), "b": [1] # comment this out and it will return a categorical in 1.3.0 }) mask = X["a"].isna() X.loc[mask, "a"] = np.array([1]) print(X["a"].dtype) # int, but should remain a category ``` ### Issue Description In the above reproducible example, using pandas v1.3.0 will return a int64 type. However, the column was initially a categorical type. The behaviour is very esoteric. In v1.2.5, this code works properly and keeps the categorical type. If the column "b" is removed, then it works correctly in v1.3.0. If the np.nan is replaced with a value such as 1 in the "a" column, this code works correctly. After performing a `git bisect`, the offending commit is 80227439db6b90985b9fbcde2a8f8ea85a4569b2. Its parent (4ecba519dc4d93f6524bde102c97b5ebb91aa3ae) returns a category. Something in 802274... introduces this bug. ### Expected Behavior The last print returns a category type, not an int64 which is the correct and expected behaviour. ```python # Works correctly in v1.2.5 import pandas as pd import numpy as np print(pd.__version__) X = pd.DataFrame({ "a": pd.Categorical([np.nan], [1]), "b": [1] }) mask = X["a"].isna() X.loc[mask, "a"] = np.array([1]) print(X["a"].dtype) # category ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 80227439db6b90985b9fbcde2a8f8ea85a4569b2 python : 3.9.7.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:27 PDT 2021; root:xnu-7195.141.2~5/RELEASE_ARM64_T8101 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.0.dev0+435.g80227439db numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.3 setuptools : 58.2.0 Cython : 0.29.24 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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BUG: rolling with uneven nanosecond window have wrong results
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd index = pd.date_range("2020", periods=4, freq="1ns") df = pd.Series([1, 1, 1, 1], index=index, dtype=float) result = df.rolling('3ns', closed='neither', center=True).sum() print(result) # 2020-01-01 00:00:00.000000000 1.0 # 2020-01-01 00:00:00.000000001 1.0 # 2020-01-01 00:00:00.000000002 1.0 # 2020-01-01 00:00:00.000000003 1.0 # Freq: N, dtype: float64 ``` ### Issue Description Rolling with smallest possible resolution (currently nanoseconds) **centered** windows and uneven nanoseconds produce wrong results. Eg. rolling with a centered `window='3ns'`, regardless of `closed`, should result in a forward facing and a backward facing window of `1.5ns`, which is effectively a fwd/bwd facing window of `1ns` with `closed='both'`. btw. thats exactly the code change in the followup PR will introduce ;) ### Expected Behavior ```py >>> result 2020-01-01 00:00:00.000000000 2.0 2020-01-01 00:00:00.000000001 3.0 2020-01-01 00:00:00.000000002 3.0 2020-01-01 00:00:00.000000003 2.0 Freq: N, dtype: float64 ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 075ec87643130074c9389a17a9d964cd33f779dc python : 3.8.12.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-88-generic Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.4.0.dev0+869.g075ec87643 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.2.0 Cython : 0.29.24 pytest : 6.2.5 hypothesis : 6.23.1 sphinx : 4.2.0 blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.28.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : 2021.10.0 fastparquet : 0.7.1 gcsfs : 2021.10.0 matplotlib : 3.4.3 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : 2021.10.0 scipy : 1.7.1 sqlalchemy : 1.4.25 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.18.2 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1 </details>
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BUG: Fix for rolling with uneven nanosecond windows have wrong results
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CONTRIBUTOR
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- [x] closes #43997 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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ENH: Default to `observed=True` in `DataFrame.groupby`
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#### Is your feature request related to a problem? The default behaviour of `pandas.DataFrame.groupby` is currently different depending on the type of the groupers (when one of the groupers is categorical, unobserved categories are added to the groupby by default. This behaviour can be overriden by setting the `observed` argument to ` False`). I feel like making the groupby API consistent by default and regardless of the underlying data type would provide a much better user experience. #### Describe the solution you'd like Default to `observed=False` in `pandas.DataFrame.groupby`. #### API breaking implications Would break backwards-compatibility. #### Describe alternatives you've considered So far the only option I can think of is to add `observed=True` to every groupby I write to make sure it will behave correctly no matter what kind of data gets passed to it.
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BUG: Index.union with both bools and ints, duplicates
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python idx = pd.Index([1, True, 0, False]) idx2 = idx[1:] >>> idx.union(idx2) Index([0, 0, 1, 1], dtype='object') ``` ### Issue Description Best guess is union_with_duplicates cc @phofl Breaks at least one test in #43930 ### Expected Behavior `Index([1, True, 0, False], dtype=object)`
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BUG: Inconsistent behaviour for DataFrame.value_counts and Series.value_counts on categoricals
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"2021-10-16T04:25:10Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd df = (pd.DataFrame({'gender' : ['male', 'female', 'male', 'female', 'male'], 'country': ['BR', 'BR', 'US', 'BR', 'US']}) .astype('category') ) df.value_counts() # drops all empty categories # gender country # female BR 2 # male US 2 # BR 1 # dtype: int64 # also compare (df .query("country != 'BR'") .value_counts() ) # drops non-observed categories # gender country # male US 2 # dtype: int64 (df .query("country != 'BR'") ['gender'] .value_counts() ) # shows 0 for non-observed catgories # male 2 # female 0 # Name: gender, dtype: int64 ``` ### Issue Description `DataFrame.value_counts()` drops any non observed categories while `Series.value_counts()` shows 0 for non-observed categories. There was some discussion about the zeroes for `Series.value_counts()` in this issue https://github.com/pandas-dev/pandas/issues/8559, based on that I checked behaviour in R which also keeps the zeros: ``` r gender <- factor(c('male', 'female', 'male', 'female', 'male')) country <- factor( c( 'BR', 'BR', 'US', 'BR', 'US') ) df <- data.frame(gender=gender, country=country) table(df) #> country #> gender BR US #> female 2 0 #> male 1 2 ``` ### Expected Behavior The following work-around gives the expected behaviour: ```py df.groupby("gender").country.value_counts() # gender # female BR 2 # US 0 # male US 2 # BR 1 # Name: country, dtype: int64 ``` This fails however for more than two categorical variables. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.7.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Thu Sep 16 20:58:47 PDT 2021; root:xnu-6153.141.40.1~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 7.27.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None 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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ENH: Support observed keyword argument in Categorical.value_counts
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- [x] closes #43498 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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[BUG] Fix DataFrameGroupBy.boxplot with subplots=False fails for object columns
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- [x] closes #43480 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry
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REF: de-duplicate MaskedArray tests
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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TST: Adding test to test_numeric, #37348
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CONTRIBUTOR
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- [x] closes #37348 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them This makes sure that comparing int to empty strings doesn't return an error.
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CLN: pragma no cover, post-array_ufunc
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry
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DOC: Added DataFrame in Parameters & Return description in the docstring
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"2021-10-18T15:57:24Z"
"2021-10-17T20:55:07Z"
CONTRIBUTOR
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- [x] closes #41676 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Hello! I have added the following information to the source code (docstring of to_datetime method): 1. In case when DataFrame is passed as a parameter, the to_datetime method expects the following column names (minimally "year", "month", "day"). 2. The method returns Series when the DataFrame is passed. I have created Docker Image & Python Environment, then tested my docstring update by using `validate_docstrings.py` script. Before testing my update, I have run the script on master branch. The script found 2 errors on master branch: ``` 2 Errors found: No extended summary found flake8 error: E999 SyntaxError: invalid syntax ``` Then, I switched to my [feature branch](https://github.com/abatomunkuev/pandas/tree/issue%2341676) and run the `validate_docstrings.py` script. Output of the script ``` ################################################################################ ######################## Docstring (pandas.to_datetime) ######################## ################################################################################ Convert argument to datetime. Parameters ---------- arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like The object to convert to a datetime. If the DataFrame is provided, the method expects minimally the following columns ( "year", "month", "day") in the DataFrame. errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception. - If 'coerce', then invalid parsing will be set as NaT. - If 'ignore', then invalid parsing will return the input. dayfirst : bool, default False Specify a date parse order if `arg` is str or its list-likes. If True, parses dates with the day first, eg 10/11/12 is parsed as 2012-11-10. .. warning:: dayfirst=True is not strict, but will prefer to parse with day first. If a delimited date string cannot be parsed in accordance with the given `dayfirst` option, e.g. ``to_datetime(['31-12-2021'])``, then a warning will be shown. yearfirst : bool, default False Specify a date parse order if `arg` is str or its list-likes. - If True parses dates with the year first, eg 10/11/12 is parsed as 2010-11-12. - If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil). .. warning:: yearfirst=True is not strict, but will prefer to parse with year first. utc : bool, default None Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well). format : str, default None The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse all the way up to nanoseconds. See strftime documentation for more information on choices: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior. exact : bool, True by default Behaves as: - If True, require an exact format match. - If False, allow the format to match anywhere in the target string. unit : str, default 'ns' The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the origin. Example, with unit='ms' and origin='unix' (the default), this would calculate the number of milliseconds to the unix epoch start. infer_datetime_format : bool, default False If True and no `format` is given, attempt to infer the format of the datetime strings based on the first non-NaN element, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x. origin : scalar, default 'unix' Define the reference date. The numeric values would be parsed as number of units (defined by `unit`) since this reference date. - If 'unix' (or POSIX) time; origin is set to 1970-01-01. - If 'julian', unit must be 'D', and origin is set to beginning of Julian Calendar. Julian day number 0 is assigned to the day starting at noon on January 1, 4713 BC. - If Timestamp convertible, origin is set to Timestamp identified by origin. cache : bool, default True If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. The cache is only used when there are at least 50 values. The presence of out-of-bounds values will render the cache unusable and may slow down parsing. .. versionchanged:: 0.25.0 - changed default value from False to True. Returns ------- datetime If parsing succeeded. Return type depends on input: - list-like: - DatetimeIndex, if timezone naive or aware with the same timezone - Index of object dtype, if timezone aware with mixed time offsets - Series: Series of datetime64 dtype - DataFrame: Series of datetime64 dtype - scalar: Timestamp In case when it is not possible to return designated types (e.g. when any element of input is before Timestamp.min or after Timestamp.max) return will have datetime.datetime type (or corresponding array/Series). See Also -------- DataFrame.astype : Cast argument to a specified dtype. to_timedelta : Convert argument to timedelta. convert_dtypes : Convert dtypes. Examples -------- Assembling a datetime from multiple columns of a DataFrame. The keys can be common abbreviations like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or plurals of the same >>> df = pd.DataFrame({'year': [2015, 2016], ... 'month': [2, 3], ... 'day': [4, 5]}) >>> pd.to_datetime(df) 0 2015-02-04 1 2016-03-05 dtype: datetime64[ns] If a date does not meet the `timestamp limitations <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html #timeseries-timestamp-limits>`_, passing errors='ignore' will return the original input instead of raising any exception. Passing errors='coerce' will force an out-of-bounds date to NaT, in addition to forcing non-dates (or non-parseable dates) to NaT. >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore') datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT Passing infer_datetime_format=True can often-times speedup a parsing if its not an ISO8601 format exactly, but in a regular format. >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000) >>> s.head() 0 3/11/2000 1 3/12/2000 2 3/13/2000 3 3/11/2000 4 3/12/2000 dtype: object >>> %timeit pd.to_datetime(s, infer_datetime_format=True) # doctest: +SKIP 100 loops, best of 3: 10.4 ms per loop >>> %timeit pd.to_datetime(s, infer_datetime_format=False) # doctest: +SKIP 1 loop, best of 3: 471 ms per loop Using a unix epoch time >>> pd.to_datetime(1490195805, unit='s') Timestamp('2017-03-22 15:16:45') >>> pd.to_datetime(1490195805433502912, unit='ns') Timestamp('2017-03-22 15:16:45.433502912') .. warning:: For float arg, precision rounding might happen. To prevent unexpected behavior use a fixed-width exact type. Using a non-unix epoch origin >>> pd.to_datetime([1, 2, 3], unit='D', ... origin=pd.Timestamp('1960-01-01')) DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None) In case input is list-like and the elements of input are of mixed timezones, return will have object type Index if utc=False. >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500']) Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object') >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'], ... utc=True) DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None) ################################################################################ ################################## Validation ################################## ################################################################################ 2 Errors found: No extended summary found flake8 error: E999 SyntaxError: invalid syntax ``` There are still some errors from master branch.
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BUG: sum() and mean() return wrong values when applied to integers as strings
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd ser = pd.Series(['1', '2']) ser.sum() # this returns 12 instead of 3 ser.mean() # this returns 6.0 instead of 1.5 ``` ### Issue Description The sum() and mean() methods of the Series and DataFrame return wrong values when applied to integers as strings. This is silently catastrophic since the user is left with apparently good but erroneous results. ### Expected Behavior ``` >>> import pandas as pd >>> ser = pd.Series(['1', '2']) >>> ser.sum() TypeError: ... ``` OR ``` >>> ser.sum() 3 ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.10.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : ja_JP.UTF-8 LOCALE : ja_JP.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.3 setuptools : 58.0.4 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.25.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : 1.4.20 tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : None </details>
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BUG: Inconsistency between behavior of cumsum when applied directly, and when applied within groupby.
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd df = pd.DataFrame({"A": [2, 1, 2, 2], "B": [3, 3, 4, 4], "E": [['10'], ['20'], ['30'], ['40']]}) df["A"].cumsum() df["E"].cumsum() df.groupby("B")["A"].cumsum() df.groupby("B")["E"].cumsum() ``` ### Issue Description Inconsistency between behavior of cumsum() when applied directly, and when applied within groupby(). When used directly on a column of a dataframe that contains lists, cumsum() progressively concatenates the lists. When used as part of groupby() on the same dataframe cumsum() throws "NotImplementedError: function is not implemented for this dtype: [how->cumsum,dtype->object]" ### Expected Behavior I'd expect cumsum() to progressively concatenate the lists of all rows within each group defined by groupby(), in a way similar to what it does with numeric values. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.2.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-147-generic Version : #151-Ubuntu SMP Fri Jun 18 19:21:19 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.19.2 pytz : 2020.1 dateutil : 2.8.1 pip : 20.1.1 setuptools : 46.1.3 Cython : 0.29.21 pytest : 6.2.2 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.2.9 lxml.etree : 4.5.2 html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.16.1 pandas_datareader: None bs4 : 4.9.1 bottleneck : 1.3.2 fsspec : None fastparquet : None gcsfs : None matplotlib : 3.2.2 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 0.16.0 pyxlsb : None s3fs : None scipy : 1.5.0 sqlalchemy : 1.3.18 tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : None numba : 0.53.1 </details>
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BUG: on groupby and sum between inf and float
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python python 3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import pandas as pd >>> pd.__version__ '1.3.3' >>> import numpy as np >>> df = pd.DataFrame({'A':[1,1], 'B':[np.inf,2]}) >>> df.sum() A 2.0 B inf dtype: float64 >>> df.groupby(['A']).agg(sum) B A 1 NaN >>> df.groupby(['A']).sum() B A 1 NaN ``` ### Issue Description when summing inf and a number from a groupby aggregation the result is NaN ### Expected Behavior when summing inf and a number from a groupby aggregation the result should be inf ### Installed Versions <details> pd.show_version() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/thomas/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/__init__.py", line 244, in __getattr__ raise AttributeError(f"module 'pandas' has no attribute '{name}'") AttributeError: module 'pandas' has no attribute 'show_version' </details>
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BUG: Multiple CSS selectors using set_table_styles and dict form of table_styles
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"2021-10-16T13:58:10Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [x] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd df = pd.DataFrame([range(5) for _ in range(5)], columns=list('abcde')) styled = df.style.set_table_styles({ 'b': [{'selector': 'th, td', 'props': [('border-left', '2px solid black')]}] }) # styled = df.style.set_table_styles({ # 'b': [{'selector': 'th', 'props': [('border-left', '2px solid black')]}, # {'selector': 'td', 'props': [('border-left', '2px solid black')]}] # }) html = styled.render() with open('css_style_bug.html', 'w') as f: f.write(html) ``` ### Issue Description This is a minor issue when styling a table for html display. When setting a table style for a specified column using `set_table_styles`, the column is only applied to the final selector tag if they are named in a comma seperated list. This seems to be similar to issue #34061. ### Expected Behavior I have commented out, in the above examples, a workaround which does the expected behaviour. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.10.final.0 python-bits : 64 OS : Linux OS-release : 5.4.72-microsoft-standard-WSL2 Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 20.0.2 setuptools : 44.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.2 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 : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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DOC: minor fixup of 1.3.4 release notes
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QST: Is Series in pandas size mutable?
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### - [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions. - [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com). ### Link to question on StackOverflow https://stackoverflow.com/questions/69555071/is-series-size-mutable ### Question about pandas >>>s a 4.0 b 4.0 c 8.0 d 9.0 f NaN dtype: float64`>>>s a 4.0 b 4.0 c 8.0 d 9.0 f NaN dtype: float64 >>>s.shape (5,) s.drop('f',inplace=True) >>> s a 4.0 b 4.0 c 8.0 d 9.0 dtype: float64 >>> s.size 4 >>> s['f']=9 >>> s a 4.0 b 4.0 c 8.0 d 9.0 f 9.0 dtype: float64 >>> s['b']=100 >>> s a 4.0 b 100.0 c 8.0 d 9.0 f 9.0 dtype: float64 >>> s['g']=200 >>> s.size 6 Please go through the above Python commands that imply that Pandas Series are also size mutable. The size of given series is initially 5 Then I delete one element s.drop('f',inplace=True), so the size of the Series will be 4. Then I again assign a new variable s['f']=9 and s['g']=200, then the size of the Series is 6 now. It means Series is also size mutable.
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BUG: Compare returns wrong results for nullable int type
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"2021-10-14T03:28:43Z"
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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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd df1 = pd.DataFrame({'a': [4, 4, 4], 'b': [1, 2, 3]}).astype({'a': 'float64', 'b': 'float64'}) # Nullable int 'Int64' gives wrong results. 'int64' works just fine. df2 = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]}).astype({'a': 'Int64', 'b': 'float64'}) df1.compare(df2, keep_shape=True) ``` ### Issue Description The reproducible example returns | | ('a', 'self') | ('a', 'other') | ('b', 'self') | ('b', 'other') | |---:|----------------:|-----------------:|----------------:|-----------------:| | 0 | nan | 1 | nan | nan | | 1 | nan | 2 | nan | nan | | 2 | nan | 3 | nan | nan | ### Expected Behavior We expect the reproducible example to return | | ('a', 'self') | ('a', 'other') | ('b', 'self') | ('b', 'other') | |---:|----------------:|-----------------:|----------------:|-----------------:| | 0 | 4 | 1 | nan | nan | | 1 | 4 | 2 | nan | nan | | 2 | 4 | 3 | nan | nan | ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.10.final.0 python-bits : 64 OS : Linux OS-release : 5.11.0-37-generic Version : #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_SG.UTF-8 LOCALE : en_SG.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.1.1 setuptools : 56.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : None pymysql : 0.8.1 psycopg2 : None jinja2 : 2.11.3 IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : 1.3.24 tables : None tabulate : 0.8.7 xarray : None xlrd : None xlwt : None numba : None </details>
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- [X] Related to #43996 - [X] tests added to `pandas/tests/indexing/test_iloc.py` - [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [X] During assignment to a .iloc or a .loc indexer, depending on whether the data frame is with mixed types or singular, the typing of the resulting assignment is not consistent. As explained in issue #43996, the update of v1.3.0 causes a change in dtype casting when assigning a series. The issue contains a minimal working example of this rather inconsistent behaviour. In short, the type of a column changes when the assignment happens on a data frame only containing one column. With multiple columns, assigning the same series retains the original type. This contribution includes a test case which performs an assignment operation on a single column data frame. Naturally, because of this inconsistency of typing in the current pandas code, this test case fails. Its aim is to serve the bug fix for this inconsistency.
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Backport PR #44012 on branch 1.3.x (DOC: minor fixup of 1.3.4 release notes)
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Backport PR #44012: DOC: minor fixup of 1.3.4 release notes
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This is my first time creating an issue on GitHub. I did not see one like it but if there is, please reference it. I see a lot of communications about adding upsert features to the to_sql method. I think this is a great idea. I would like to know if there is any possibility of adding functionality to call a stored procedure which takes care of the upsert. Something like: df.to_sql('mytable', schema='dbo', con=engine, index=False, method='mytable_upsert', stored_proc=True) I ask because I want to keep my data in a df to upload but I also would like to handle the upsert issues with a stored procedure for performance reasons. I am using the pymssql driver. Yes Matthew, I read #14553 and #29636 before creating this. These are very good efforts and I hope they are adopted. The difference with my request is that I would like to be able to call a stored procedure in the database which would handle inserting or updating rows inside the database itself which is faster than issuing a command in python to insert and then finding it exists and then issuing an update on the row. There are ways to do this, just not with a dataframe using to_sql.
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CONTRIBUTOR
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- [x] closes #40949 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry I went ahead and modified the `.github/workflows/ci.yml` so that the cheatsheets are copied to the docs build directory once the Sphinx site is built
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CONTRIBUTOR
null
`DatetimeIndex.std` is missing a doc-string. Add doc-string to `DatetimeArray.std` as `DatetimeIndex` is re-using `DatetimeArray.std`. There are a few more keywords, but they seem to be not implemented (numpy-compatiblity?) ```py pd.DatetimeIndex([1, 2]).std(out=[]) ValueError: the 'out' parameter is not supported in the pandas implementation of std() pd.DatetimeIndex([1, 2]).std(keepdims=True) ValueError: the 'keepdims' parameter is not supported in the pandas implementation of std() pd.DatetimeIndex([1, 2]).std(dtype=np.dtype("datetime64[ns]")) ValueError: the 'dtype' parameter is not supported in the pandas implementation of std() ``` For some reason, this doc-string is needed for #43828.
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4
"2021-10-13T23:22:51Z"
"2021-11-16T13:00:01Z"
"2021-11-16T00:55:59Z"
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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import io import pandas as pd fwf_data = ( "a b c d\n" "id8141 360.242940 149.910199 11950.7\n" "id1594 444.953632 166.985655 11788.4\n" "id1849 364.136849 183.628767 11806.2\n" "id1230 413.836124 184.375703 11916.8\n" "id1948 502.953953 173.237159 12468.3\n" "id8141 360.242940 149.910199 11950.7\n" "id1594 444.953632 166.985655 11788.4\n" "id1849 364.136849 183.628767 11806.2\n" ) df1 = pandas.read_fwf(filepath_or_buffer=io.StringIO(fwf_data), skiprows=[2, 5]) print(df1) '''Output a b c d 0 id8141 360.242940 149.910199 11950.7 1 id1849 364.136849 183.628767 11806.2 2 id1230 413.836124 184.375703 11916.8 3 id8141 360.242940 149.910199 11950.7 4 id1594 444.953632 166.985655 11788.4 5 id1849 364.136849 183.628767 11806.2 ''' df2 = pandas.read_fwf(filepath_or_buffer=io.StringIO(fwf_data), skiprows=[2, 5], nrows=7) print(df2) '''Output a b c d 0 id8141 360.242940 149.910199 11950.7 1 id1849 364.136849 183.628767 11806.2 2 id1230 413.836124 184.375703 11916.8 3 id1948 502.953953 173.237159 12468.3 4 id8141 360.242940 149.910199 11950.7 5 id1594 444.953632 166.985655 11788.4 6 id1849 364.136849 183.628767 11806.2 ''' ``` ### Issue Description For `df2`, not all specified lines are skipped. ### Expected Behavior ```python df2 = pandas.read_fwf(filepath_or_buffer=io.StringIO(fwf_data), skiprows=[2, 5], nrows=7) print(df2) '''Expected output a b c d 0 id8141 360.242940 149.910199 11950.7 1 id1849 364.136849 183.628767 11806.2 2 id1230 413.836124 184.375703 11916.8 3 id8141 360.242940 149.910199 11950.7 4 id1594 444.953632 166.985655 11788.4 ''' ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.10.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19042 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : English_United States.1252 pandas : 1.3.3 numpy : 1.21.1 pytz : 2020.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 57.4.0 Cython : None pytest : 6.2.4 hypothesis : None sphinx : 4.1.2 blosc : None feather : 0.4.1 xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.27.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.2.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : 0.15.0 pyarrow : 3.0.0 pyxlsb : None s3fs : 0.4.2 scipy : 1.7.0 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : None xarray : 0.19.0 xlrd : 2.0.1 xlwt : None numba : None </details>
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PR_kwDOAA0YD84tK-65
44,022
ENH: Ability to coerce floats when reading csv files GH2570
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2
"2021-10-14T00:02:58Z"
"2021-11-14T03:07:59Z"
"2021-11-14T03:07:58Z"
NONE
null
- [x] closes #2570 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry Ran the `io.csv.Read*` benchmarks which resulted in `BENCHMARKS NOT SIGNIFICANTLY CHANGED.` I believe that `Dict[Hashable, Tuple[Dtype, str]]` should be added to `DtypeArg` to fix the failing typing check. However, that results in 20 additional errors in io\sql.py and io\parsers\base_parser.py with the additional tuple type. Perhaps there should be a distinction and an additional `CSVDtypeArg`. Looking for advice on how best to approach fixing this.
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