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BUG: Assigning back dataframe with dropna does not work, but works with inplace = True
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"2021-04-29T20:56:14Z"
"2021-04-29T20:56:03Z"
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- [X] I have checked that this issue has not already been reported. - [X ] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Your code here import pandas as pd import numpy as np test_df1 = pd.DataFrame({'a':[1,2,3,4],'b':['a','b','c','d'],'c':[np.nan,np.nan,np.nan,np.nan]}) test_df2 = pd.DataFrame({'a':[1,2,3,4],'b':[np.nan,np.nan,np.nan,np.nan],'c':['a','b','c','d']}) def drop_na_test1(df): df = df.dropna(axis=1,how='all') return df def drop_na_test2(df): df.dropna(axis=1,how='all',inplace=True) return df dfs_to_process = [test_df1,test_df2] for df in dfs_to_process: df = drop_na_test1(df) display(test_df1) display(test_df2) for df in dfs_to_process: df = drop_na_test2(df) display(test_df1) display(test_df2) ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] Assigning back dataframe with dropna() should yield same result as dropna(inplace=True). #### Expected Output Expect same result with df=df.dropna() as df.dropna(inplace=True) #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : English_United States.1252 pandas : 1.2.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1 setuptools : 56.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.1 numexpr : None odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.3 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : None
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BUG: rows containing missing values are being ignored by pivot_table
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3
"2021-04-29T19:12:36Z"
"2021-04-30T22:29:25Z"
"2021-04-30T17:17:29Z"
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample ```python import pandas as pd import numpy as np df = pd.DataFrame({'A': ['a', np.nan, 'a', 'a'], 'B': ['b', 'b', np.nan, 'b'], 'X': [5, 10, 15, 20]}) A B X 0 a b 5 1 NaN b 10 2 a NaN 15 3 a b 20 pd.pivot_table(df, index=['A', 'B'], values=['X'], aggfunc=np.sum) A B X 0 a b 25 ``` #### Problem description Certain rows are being ignored during aggregation by pivot_table method when the data contains any missing values in index columns. #### Expected Output ```python pd.pivot_table(df, index=['A', 'B'], values=['X'], aggfunc=np.sum) A B X 0 a b 25 1 NaN b 10 2 a NaN 15 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.2.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1 setuptools : 56.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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[ArrowStringArray] startswith/endswith using native pyarrow method
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``` [ 50.00%] ··· strings.Methods.time_startswith ok [ 50.00%] ··· ============== ========== dtype -------------- ---------- str 19.2±0ms string 16.0±0ms arrow_string 2.42±0ms ============== ========== [ 50.00%] ··· strings.Methods.time_endswith ok [ 50.00%] ··· ============== ========== dtype -------------- ---------- str 19.3±0ms string 15.0±0ms arrow_string 6.68±0ms ============== ========== ```
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5
"2021-04-29T20:09:15Z"
"2021-05-21T15:57:25Z"
"2021-05-21T15:57:21Z"
CONTRIBUTOR
null
- [x] closes #40904 - [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 - Already have one for the same bug, not sure if I need one for this.
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REF: maybe_apply_* in core.apply
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1
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MEMBER
null
- [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 Followup to #41067. cc @jbrockmendel
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ENH: The XLS_SIGNATURE is too restrictive
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4
"2021-04-29T20:35:43Z"
"2021-05-21T01:44:13Z"
"2021-05-21T01:44:13Z"
CONTRIBUTOR
null
Pandas 1.2.4 fails to open XLS files generated by Lotus 1-2-3 because they have a different header than the one expected in `XLS_SIGNATURE` (`io.excel._base` line 1001 and then 1050). I know I'm likely to be the only person in the world with this issue, but my boss still uses Lotus 1-2-3 🙄 I'm willing to submit a pull request with my fix if people are okay with it. The fix basically involves changing `XLS_SIGNATURE` to a list: ``` XLS_SIGNATURES = [ b"\xD0\xCF\x11\xE0\xA1\xB1\xA1\xE1", b"\t\x04\x06", ] ZIP_SIGNATURE = b"PK\x03\x04" PEEK_SIZE = max(map(len, XLS_SIGNATURES + [ZIP_SIGNATURE])) ``` and then later: ``` if any(peek.startswith(signature) for signature in XLS_SIGNATURES): return "xls" ``` I don't know if the second `XLS_SIGNATURES` value is "good," but it works. I can attach an XLS that was exported by 1-2-3 if it would be beneficial to others, or if they'd be able to help me find a better second value for `XLS_SIGNATURES`. I tried looking through the byte-code for anything similar to the "DOCFILE" XLS signature, but didn't see anything.
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DOC: Add hint for "Product space too large"
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14
"2021-04-29T21:41:44Z"
"2021-10-04T13:18:00Z"
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CONTRIBUTOR
null
Adds a note to the documentation for `DataFrame.groupby` suggesting that the cause of a `Product space too large to allocate arrays!` error may be from unexpected behavior when grouping on a combination of sparse categorical columns. - [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG: read_excel trailing blank rows and columns
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4
"2021-04-29T23:13:08Z"
"2021-05-06T04:00:43Z"
"2021-05-06T01:41:26Z"
CONTRIBUTOR
null
- [x] closes #41167 - [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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COMPAT: error massage for py310
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BUG: float period not supported in df.diff() since 1.1.5
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- [previous commit #36710](https://github.com/pandas-dev/pandas/pull/36710) ```python def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame: if not isinstance(periods, int): if not (is_float(periods) and periods.is_integer()): raise ValueError("periods must be an integer") periods = int(periods) bm_axis = self._get_block_manager_axis(axis) if bm_axis == 0 and periods != 0: return self - self.shift(periods, axis=axis) new_data = self._mgr.diff(n=periods, axis=bm_axis) return self._constructor(new_data).__finalize__(self, "diff") ``` If periods is float, then it can't pass `(is_float(periods) and periods.is_integer())`, and ValueError will always be raised So I assume it should be `or` here? ```python # suggested: # here str can't pass through, I'm not sure if str should be supported as well if not (is_float(periods) or is_integer(periods)): raise ValueError("periods must be an integer or float") ``` Besides, I think `.diff()` should support float for backward compatibility #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : f2c8480af2f25efdbd803218b9d87980f416563e python : 3.8.8.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-140-generic Version : #144-Ubuntu SMP Fri Mar 19 14:12:35 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.3 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : 0.29.22 pytest : 6.2.3 hypothesis : None sphinx : 3.5.3 blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.2 html5lib : 1.1 pymysql : 0.9.3 psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: 0.9.0 bs4 : 4.9.3 bottleneck : None fsspec : 0.8.5 fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : None tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : 0.53.1563e python : 3.8.8.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-140-generic Version : #144-Ubuntu SMP Fri Mar 19 14:12:35 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.3 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : 0.29.22 pytest : 6.2.3 hypothesis : None sphinx : 3.5.3 blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.2 html5lib : 1.1 pymysql : 0.9.3 psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: 0.9.0 bs4 : 4.9.3 bottleneck : None fsspec : 0.8.5 fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : None tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : None numba : 0.53.1 </details>
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`float()` raises TypeError in py310 ``` ________________ TestEnsureNumeric.test_non_convertable_values _________________ self = <pandas.tests.test_nanops.TestEnsureNumeric object at 0x7f7da1e029e0> def test_non_convertable_values(self): msg = "Could not convert foo to numeric" with pytest.raises(TypeError, match=msg): nanops._ensure_numeric("foo") # with the wrong type, python raises TypeError for us msg = "argument must be a string or a number" with pytest.raises(TypeError, match=msg): > nanops._ensure_numeric({}) pandas/tests/test_nanops.py:771: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ x = {} def _ensure_numeric(x): if isinstance(x, np.ndarray): if is_integer_dtype(x) or is_bool_dtype(x): x = x.astype(np.float64) elif is_object_dtype(x): try: x = x.astype(np.complex128) except (TypeError, ValueError): try: x = x.astype(np.float64) except (TypeError, ValueError) as err: # GH#29941 we get here with object arrays containing strs raise TypeError(f"Could not convert {x} to numeric") from err else: if not np.any(np.imag(x)): x = x.real elif not (is_float(x) or is_integer(x) or is_complex(x)): try: > x = float(x) E TypeError: float() argument must be a string or a real number, not 'dict' pandas/core/nanops.py:1600: TypeError ```
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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ENH: Add formatters parameter for the xlsxwriter engine
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"2021-04-30T06:25:46Z"
"2021-10-16T20:49:26Z"
"2021-10-16T20:49:26Z"
NONE
null
- [X] closes #30275 - [X] tests added / passed - [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry Currently pandas doesn't support different formats for columns of the type `date`/`datetime`. Giving the provision to do so using the `formatters` parameter as shown below: **Master** ``` from datetime import datetime df = pd.DataFrame( { "Col1": [1010.34, 2020.67, 3030.65,3210.65], "Col2": [ datetime(2014, 1, 31), datetime(2014, 1, 31), datetime(2014, 1, 31), datetime(2014, 1, 31) ], "Col3": [ datetime(2014, 1, 31), datetime(2014, 1, 31), datetime(2014, 1, 31), datetime(2014, 1, 31) ], "Col4": [ datetime(2014, 1, 31), datetime(2014, 1, 31), datetime(2014, 1, 31), datetime(2014, 1, 31)] }, ) writer = pd.ExcelWriter("pd_datetime.xlsx", engine='xlsxwriter', datetime_format='MM/DD/YYYY HH:MM:SS', date_format='MM/DD/YYYY') df.to_excel(writer, sheet_name='Sheet1') writer.save() ``` **Output of the file** ![image](https://user-images.githubusercontent.com/55484403/116774842-e9328180-aa7c-11eb-831e-de637cad0959.png) **Proposed change** ``` writer = pd.ExcelWriter( "pd_datetime.xlsx", engine="xlsxwriter", datetime_format="MM/DD/YYYY HH:MM:SS", date_format="MM/DD/YYYY", formatters={"Col2": "mmmm", "Col3": "YYYY"}, ) df.to_excel(writer, sheet_name="Sheet1") writer.save() ``` **Output of the file** ![image](https://user-images.githubusercontent.com/55484403/116774928-85f51f00-aa7d-11eb-9cdd-1a41c0562fad.png) The `date`/`datetime` columns that do not have a format specified in the `formatters` dict will take the format from the `date_format`/`datetime_format` parameters. PS- The proposed change is currently applicable **only** for the `date`/`datetime` types but can easily be extended for `int` and `float` as well.
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BUG: histogram plot ignores `xlabel` and `ylabel` arguments
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"2021-06-06T17:18:44Z"
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import numpy as np import pandas as pd data = pd.Series(np.random.random(10)) ##problem data.plot.hist(xlabel="My xlabel", ylabel="My ylabel") ``` Output graph: ![wrong](https://user-images.githubusercontent.com/6746316/116669303-b4e89380-a99e-11eb-9c80-e601b2516b87.jpg) #### Problem description The method accepts the keyword arguments but ignores them and overrides them with defaults (respectively <nothing> and "Frequency". While those defaults are sensible, specifying those keyword arguments should either be met with the intended output (custom x and y labels) or an error (of the type `NotImplementedError`) The documentation (for [hist](https://github.com/pandas-dev/pandas/blob/2cb96529396d93b46abab7bbc73a208e708c642e/pandas/plotting/_core.py#L1266) and [plot](https://github.com/pandas-dev/pandas/blob/2cb96529396d93b46abab7bbc73a208e708c642e/pandas/plotting/_core.py#L680)) seem to indicate this is supported (although a bit unclear) #### Expected Output The outputted graph should be the same as when produced with the following code: ```python import numpy as np import pandas as pd data = pd.Series(np.random.random(10)) ax = data.plot.hist() ax.set_xlabel("My xlabel") ax.set_ylabel("My ylabel") ``` Which is the following: ![fixed](https://user-images.githubusercontent.com/6746316/116672092-0d6d6000-a9a2-11eb-842f-44966ff83550.jpg) #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.final.0 python-bits : 64 OS : Linux OS-release : 5.11.0-7614-generic Version : #15~1618626693~20.10~ecb25cd-Ubuntu SMP Thu Apr 22 16:00:45 UTC machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.4 numpy : 1.19.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 52.0.0.post20210125 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.2 html5lib : None pymysql : 1.0.2 psycopg2 : None jinja2 : None IPython : 7.22.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.7 tables : None tabulate : 0.8.9 xarray : None xlrd : None xlwt : None numba : None ``` </details>
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Regression: concatenating `MultiIndex` with empty `RangeIndex` raises
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"2021-05-04T12:53:48Z"
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- As also reported here: https://github.com/dask/dask/issues/7610, PR https://github.com/pandas-dev/pandas/pull/38671/ introduced the following behaviour: > A reproducer with just pandas: > > ``` > df1 = pd.DataFrame([[1, 2]], columns=pd.MultiIndex.from_tuples([('B', 1), ('C', 1)])) > df2 = pd.DataFrame(index=[0], columns=pd.RangeIndex(0)) > > pd.concat([df1, df2]) > ``` > > What triggers the error here is the `columns=pd.RangeIndex(0)` for the empty `df2` (by default pandas creates a zero-length object dtype Index, which works fine, but if it's an empty RangeIndex, we now get this error). > > This is a regression in itself in pandas. But I am also wondering a bit where the empty RangeIndex is coming from (it seems that the groupby operation in dask results in some empty partitions) _Originally posted by @jorisvandenbossche in https://github.com/dask/dask/issues/7610#issuecomment-827708470_ IIUC the fix for this could possibly be as simple as replacing https://github.com/pandas-dev/pandas/blob/0acbff881c9273c2cda962fb263f2862a5e7be28/pandas/core/indexes/base.py#L2926-L2928 with ```python if isinstance(self, ABCMultiIndex) and not is_object_dtype( unpack_nested_dtype(other) ) and len(other) > 0: ```
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry Simplified kwargs unpacking in resample, by utilizing keyword-only arguments.
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Optimising Series.nunique for Nan values #40865
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"2021-04-30T14:08:32Z"
"2021-05-03T15:12:37Z"
"2021-05-03T15:12:33Z"
CONTRIBUTOR
null
- [ ] closes #40865
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Confusing error message when SQLAlchemy is not installed
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2
"2021-04-30T14:22:38Z"
"2021-08-20T02:44:31Z"
null
NONE
null
Trying to use read_sql_table on an instance without SQLAlchemy installed gives a confusing error message: > NotImplementedError: read_sql_table only supported for SQLAlchemy connectable. The issue is that `_is_sqlalchemy_connectable` returns false if SQLAlchemy is not installed. My suggestion is to raise an error stating that SQLAlchemy is required by trying to import it directly in read_sql_table or extending the error message to say that SQLAlchemy is required. I don't know the internals and what might go wrong, but one way would be to not catch the ImportError... https://github.com/pandas-dev/pandas/blob/49c779118c2d473c60565d9010e119bb62d06f74/pandas/io/sql.py#L308-L311
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BUG: Frozensets are not supported as indices.
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"2021-08-20T02:45:09Z"
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python df = DataFrame.from_dict({'key': [frozenset([1])], 'value': [2]}).set_index('key') k = frozenset([1]) k in df.index # True df.loc[[k]] # OK df.loc[k] # raises: KeyError: "None of [Int64Index([1], dtype='int64', name='key')] are in the [index]" df.value.loc[[k]] # OK df.value.loc[k] # raises: KeyError: "None of [Int64Index([1], dtype='int64', name='key')] are in the [index]" ``` #### Problem description Frozensets are hashable and could serve as indices. They are only partially supported in the current version: - set a frozenset as index: OK, - is frozenset in index: OK, - index with a collection of frozensets: OK, - index with a frozenset: KeyError. This applies to both Series and DataFrame. Full support would be nice. #### Expected Output #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.2.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.utf8 pandas : 1.2.4 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.21.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : 0.9.0 fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None ``` </details>
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41,239
CLN: remove Resampler._typ
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MEMBER
null
ATM `isinstance(resampler, ABCDataFrame)` can be True, which I wasn't aware of until recently. This strikes me as a grade-A footgun.
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CLN: no need to catch libgroupby validation ValueError
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG: New param [use_nullable_dtypes] of pd.read_parquet() can't handle empty parquet file
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"2021-09-29T12:58:50Z"
"2021-09-29T12:58:50Z"
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null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python df_pq = pd.read_parquet(x, use_nullable_dtypes = True) ``` #### Problem description Get error when add the new parameter **use_nullable_dtypes** to pd.read_parquet(). If remove it , everything go back to normal. OS: Ubuntu 16 Python: 3.8 A empty parquet file from spark causes the problem. Its schema is: Authors,AuthorId,int64 Authors,Rank,int32 Authors,NormalizedName,string Authors,DisplayName,string Authors,LastKnownAffiliationId,int64 Authors,PaperCount,int64 Authors,PaperFamilyCount,int64 Authors,CitationCount,int64 Authors,CreatedDate,date32[day] error msg: df_pq = pd.read_parquet(x,use_nullable_dtypes = True) File "/vjan/lib/python3.8/site-packages/pandas/io/parquet.py", line 459, in read_parquet return impl.read( File "/vjan/lib/python3.8/site-packages/pandas/io/parquet.py", line 221, in read return self.api.parquet.read_table( File "pyarrow/array.pxi", line 751, in pyarrow.lib._PandasConvertible.to_pandas File "pyarrow/table.pxi", line 1668, in pyarrow.lib.Table._to_pandas File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 792, in table_to_blockmanager blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes) File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 1133, in _table_to_blocks return [_reconstruct_block(item, columns, extension_columns) File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 1133, in <listcomp> return [_reconstruct_block(item, columns, extension_columns) File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 751, in _reconstruct_block pd_ext_arr = pandas_dtype.__from_arrow__(arr) File "/vjan/lib/python3.8/site-packages/pandas/core/arrays/integer.py", line 121, in __from_arrow__ return IntegerArray._concat_same_type(results) File "/vjan/lib/python3.8/site-packages/pandas/core/arrays/masked.py", line 271, in _concat_same_type data = np.concatenate([x._data for x in to_concat]) File "<__array_function__ internals>", line 5, in concatenate ValueError: need at least one array to concatenate #### Expected Output read the empty parquet file and generate an empty df #### Output of ``pd.show_versions()`` <details> 1.2.4 </details>
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REF: make libreduction behavior match _aggregate_series_pure_python
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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REF: remove unnecessary consolidate in GroupBy.describe
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<b>update</b> reverted most of this in favor of #41271
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"2021-05-12T09:19:02Z"
"2021-05-12T01:26:19Z"
MEMBER
null
- [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 Regression on master so no whatsnew. Restored behaviro of 1.2.4 cc @gfyoung
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REF: de-lazify Resampler init
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MEMBER
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AFAICT the only reason `Resampler.__init__` has a lazy component is bc we call `super().__init__(None)` in `GroupByMixin.__init__`. By avoiding that (and directly setting the one attribute we need that super call to set), we can de-lazify `Resampler.__init__`, which in turn allows simplifying a bunch of other 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.html#code-standards) for how to run them - [ ] whatsnew entry
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[ArrowStringArray] PERF: use pyarrow.compute.utf8_length if available
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``` [ 25.00%] ··· strings.Methods.time_len ok [ 25.00%] ··· ============== ========== dtype -------------- ---------- str 19.1±0ms string 6.82±0ms arrow_string 1.93±0ms ============== ========== ```
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MDExOlB1bGxSZXF1ZXN0NjI4NDI0MjY2
41,249
CLN: remove outdated arrow compat
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precursor of #41207
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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.html#code-standards) for how to run them - [ ] whatsnew entry
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"2021-09-06T19:48:58Z"
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CONTRIBUTOR
null
Minimal (hopefully) typing changes for `ExtensionArray.astype` .
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CONTRIBUTOR
null
can approve as quickly as possible to hide terrible coding.
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873,771,263
MDExOlB1bGxSZXF1ZXN0NjI4NDk2MTgw
41,254
CLN: Move stuff in tests.indexes.test_numeric.py to more logical locations
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4
"2021-05-01T21:19:03Z"
"2021-05-05T16:33:16Z"
"2021-05-04T12:59:25Z"
CONTRIBUTOR
null
This does four things to better organize the numeric index's tests: 1. Moves test method `test_index_groupby` from `tests.indexes.test_numeric.py::TestNumericIndex` to `tests.indexes.common.py::Base` and makes it use a fixture. This means this tests will be used for all index types. Also deletes `TestNumericIndex`, as its empty now. 2. Moves the `Numeric` class from `test_numeric.py` to `common.py` and renames it `NumericBase`. 3. Moves the `tests.indexes.numeric.py::TestArithmetic::test_arithmetic_explicit_conversions` tests to the new `common.py::NumericBase` and delete `test_numeric.py::TestArithmetic` class, as it's now empty. 4. Moves the file `tests.indexes.test_numeric.py` to `tests.indexes.numeric.test_numeric.py`.
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41,255
TYP: Simple type fixes for ExtensionArray
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1
"2021-05-01T23:16:21Z"
"2021-05-06T02:19:02Z"
"2021-05-06T01:39:35Z"
CONTRIBUTOR
null
Typing changes for the following methods in `ExtensionArray` - `__iter__` - `__contains__` - `fillna` - `transpose` - `ravel` - `__hash__`
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873,793,577
MDExOlB1bGxSZXF1ZXN0NjI4NTExMDAy
41,256
REF: avoid unnecessary casting in algorithms
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"2021-05-03T01:12:41Z"
"2021-05-02T23:16:56Z"
MEMBER
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Sparse annotations are unrelated, were figured out in the process of getting this working.
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Backport PR #40994: REGR: memory_map with non-UTF8 encoding
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7
"2021-05-01T23:58:40Z"
"2021-06-05T20:50:08Z"
"2021-05-25T13:22:36Z"
CONTRIBUTOR
null
Backport PR #40994
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TYP: Typing for ExtensionArray.__getitem__
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"2021-05-02T00:02:01Z"
"2021-09-08T22:21:42Z"
"2021-09-08T22:12:21Z"
CONTRIBUTOR
null
Changes for typing of `ExtensionArray.__getitem__()`. Required changes to `DateTimeLikeArrayMixin` for correct compatibility.
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- [X] add Todo marker for "wrong" example (description said `where` should be used but actually it isn't) - [ ] add actual example using `where` (beyond my skillset)
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"2021-05-02T01:33:00Z"
"2021-05-03T13:00:03Z"
"2021-05-03T12:46:14Z"
CONTRIBUTOR
null
Typing fixes for `ExtensionArray.unique()` and `ExtensionArray.repeat()`
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COMPAT: fix _bootlocale import error on py310
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The _bootlocale module has been removed from Python 3.10. https://github.com/python/cpython/commit/b62bdf71ea0cd52041d49691d8ae3dc645bd48e1
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REF: simplify libreduction
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG: RangeIndex.astype('category')
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3
"2021-05-02T10:46:15Z"
"2021-05-05T13:41:07Z"
"2021-05-05T12:39:13Z"
CONTRIBUTOR
null
There's currently inconsistent behaviour when converting `RangeIndex` to `CategoricalIndex` using different methods. This fixes that. ```python >>> ridx = pd.RangeIndex(5) >>> pd.CategoricalIndex(ridx).categories RangeIndex(start=0, stop=5, step=1) # both master and this PR >>> ridx.astype("category").categories Int64Index([0, 1, 2, 3, 4], dtype='int64') # master RangeIndex(start=0, stop=5, step=1) # this PR ``` In general, when supplying a Index subclass to `Categorical`/`CategoricalIndex`, the new categories should be of that type (unless the supplied index is a `CategoricalIndex` itself). Discovered working on tests for #41153.
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41,264
BUG: specifying fill_value in pandas.DataFrame.shift() messes with index of empty dataframes
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17
"2021-05-02T15:39:41Z"
"2021-10-31T14:58:27Z"
"2021-10-31T14:58:27Z"
NONE
null
- [ x] I have checked that this issue has not already been reported. - [ x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd # create empty df, and set a multi index empty_df = pd.DataFrame(columns=["a", "b", "c"]) multi_index_empty_df = empty_df.set_index(keys=["a", "b"]) # index is different when applying shift on grouping when specifying a fill value assert multi_index_empty_df.groupby(["a","b"]).shift(1).index.names == multi_index_empty_df.groupby(["a","b"]).shift(1, fill_value=0).index.names ``` #### Problem description I discovered this problem when a unit test failed on a function that performed a groupby and shift on an empty dataframe. Specifying `fill_value` in pandas.DataFrame.shift() should not alter the index that was set on a dataframe. This is not the case for empty dataframes with a multi-index, as the example above shows. #### Expected Output When executing: ```python multi_index_empty_df.groupby(["a","b"]).shift(1).index.names ``` I get the output: ``` FrozenList(['a', 'b']) ``` But when executing ```python multi_index_empty_df.groupby(["a","b"]).shift(1, fill_value=0).index.names ``` I should get the same output, but instead I get ``` FrozenList([None]) ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.1.final.0 python-bits : 64 OS : Darwin OS-release : 18.7.0 machine : x86_64 processor : i386 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.0.5 numpy : 1.19.0 pytz : 2018.7 dateutil : 2.7.5 pip : 18.1 setuptools : 40.6.3 Cython : 0.29.2 pytest : 4.0.2 hypothesis : None sphinx : 1.8.2 blosc : None feather : None xlsxwriter : 1.1.2 lxml.etree : 4.2.5 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.19.0 pandas_datareader: None bs4 : 4.6.3 bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.2.5 matplotlib : 3.2.2 numexpr : 2.6.8 odfpy : None openpyxl : 2.5.12 pandas_gbq : None pyarrow : None pytables : None pytest : 4.0.2 pyxlsb : None s3fs : None scipy : 1.5.1 sqlalchemy : 1.2.15 tables : 3.4.4 tabulate : None xarray : None xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.1.2 numba : 0.41.0 </details>
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REF: avoid unnecessary raise in DataFrameGroupBy._cython_agg_general
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"2021-05-03T16:51:38Z"
"2021-05-03T15:14:58Z"
MEMBER
null
Untangling these layered try/excepts is turning out to be an exceptional PITA, so splitting it into extra-small pieces.
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8
"2021-05-02T16:02:20Z"
"2021-07-27T18:03:50Z"
"2021-06-16T00:22:53Z"
CONTRIBUTOR
null
This closes #41158 (which is an alternative PR for similar functionality) Currently `hide_index()` and `hide_columns(subset)` have similar signatures but **different** operations. One hides the index whilst showing the data-rows, and the other hides data-columns whilst showing the column-headers row. ### This PR - adds the `subset` keyword to give: `hide_index(subset=None)`. - sets the default to `hide_columns(subset=None)`. When `subset` is None the function now operates to hide the entire index, or entire column headers row respectively. When `subset` is not None it operates to selectively hide the given rows or columns respectively, keeping the index or column headers row. <s>We also add the `show` keyword to allow the method to operate inversely.</s> @jreback I think you will prefer this over the `hide_values` and `hide_headers` methods suggested in #41158 since this re-uses the existing methods with minimal changes but tries to align their consistency. It is also fully backwards compatible.
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- [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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"2021-05-02T17:55:43Z"
"2021-05-03T18:30:08Z"
"2021-05-02T23:47:54Z"
MEMBER
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- [x] closes #40440 - [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 I'll adress the IntegerArray case from #40933 after this is merged
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1
"2021-05-02T18:38:41Z"
"2021-05-07T05:18:04Z"
"2021-05-06T23:32:02Z"
CONTRIBUTOR
null
- [x] closes #41143 The PR aims to make Styler compatible with non-unique indexes/columns, for the purpose of rendering all DataFrame types (even if no styling is applied) - [x] `Styler.format`: made FULLY compatible with some modifications to the loops, inc TESTS. - [x] <s>`Styler.apply` and `Styler.applymap`: made PARTIALLY compatible: - if `subset`s are unique then compatible, inc TESTS. - if `subset`s are non-unique slices will raise a not compatible `KeyError`, inc. TESTS</s> - [x] `Styler.apply` and `applymap` are NOT compatible. Raises KeyError. - [x] `Styler.set_table_styles`: made FULLY compatible and will style multiple rows/columns from a non-unique key, inc TESTS. - [x] `Styler.set_td_classes` uses `reindex` so is PARTIALLY compatible where `classes` has unique index/columns: now returns a `KeyError` in non-unique case, inc TESTS. - [x] `Styler.set_tooltips` uses `reindex` so is PARTIALLY compatible where `ttips` has unique index/columns: now returns a `KeyError` in non-unique case, inc TESTS. - [x] `Styler.hide_index` and `.hide_columns` are already FULLY compatible through existing code (inc TESTS) - [x] all the `built-in` styling functions use some version of `apply` or `applymap` so are captured by the above cases. I believe this is all relevant functionality reviewed.
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"2021-05-02T23:08:51Z"
CONTRIBUTOR
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follow up to #41083 to change test
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- make the exception-handling in SeriesGroupBy._cython_agg_general match that in DataFrameGroupBy._cython_agg_general - remove an unnecessary for loop - define array_func to match the pattern in DataFrameGroupBy._cython_agg_general in the hopes that we can share these methods before long
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CONTRIBUTOR
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I've made a (draft) PR in #41153 that implements an index class for all the normal numpy numeric dtypes (int64/32/16/8, uint64/32/16/8 an dfloat65/32). There was some discussion in that PR how the public API for this should be, so I'm opening this issue for discussing that. My plan is to get #41153 merged when its ready, possibly without any public-facing API changes. The public-facing changes would then come afterwards, and after there is an conclusion on how the API should be. ## Summary of the options As I see it the options for the API is: |Nr.|Option | Is backwards compat. | complexity | --- |--- | --- | --- |1.| Have a single, public `NumericIndex` class for all numeric index types | Yes | Easy | |2.|Have dtype-specific subclasses (`Int8Index`, `Int16Index`, `Int32Index`, etc), and those can still be backed by a single internal `NumIndex` |Yes|Easy| |3.| Use the existing, general-purpose `Index` class for these numeric numpy dtypes | No | Harder | |4.| Use the existing, numeric index classes for these numeric numpy dtypes, so e.g. `Int64Index` can take int32 etc. | No | Easy | Options 1. and 3. would mean that the existing `Int64Index`, `UInt64Index` and `Float64Index` would be deprecated and removed in pandas 2.0, because their functionality would bw covered by the other index classes. Option 2. would increase the number of numeric index classes. Options 4. would extend the functionality of the existing numeric index classes. So there are quite a few possibilities. Hopefully we can come to a common conclusion. @pandas-dev/pandas-core
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REF: clearer names in libreduction
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Allow to union `MultiIndex` with empty `RangeIndex`
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1
"2021-05-03T07:03:01Z"
"2021-06-02T14:38:08Z"
"2021-05-04T12:53:48Z"
CONTRIBUTOR
null
- [x] closes #41234 - [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 See also https://github.com/dask/dask/issues/7610 and https://github.com/JDASoftwareGroup/kartothek/issues/464. The regression seems to have been introduced in pandas-dev/pandas#38671.
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QST: Efficiently stylizing and appending new dataframes to existing excel
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"2021-05-03T07:42:50Z"
"2021-07-16T13:15:46Z"
"2021-07-16T13:15:46Z"
NONE
null
- [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). See my question [here](https://stackoverflow.com/questions/67336786/efficiently-stylizing-and-appending-new-dataframes-to-existing-csv) --- I believe that my stackoverflow question is explained pretty well. As such, I will mostly copy paste the relevant info here. I need to log important information in a human readable format for an app. The three biggest requirements are as follows: 1. The info should be logged in a single CSV/excel file. 2. A human should be able to read the CSV/excel file efficiently, which implies that important rows should be colored. 3. It should be fast and memory efficient I have found many ways of satisfying requirement 1, 2 and 3. But never all at the same time. For point 1, I can use [Append existing excel sheet with new dataframe using python pandas](https://stackoverflow.com/questions/38074678/append-existing-excel-sheet-with-new-dataframe-using-python-pandas/38075046#38075046) or [How to add pandas data to an existing csv file?](https://stackoverflow.com/questions/17530542/how-to-add-pandas-data-to-an-existing-csv-file) For point 2, I can use Color formatting excel file row in python If I want to accomplish point 1 and 2 at the same time, I can use a slightly modified version of [Append existing excel sheet with new dataframe using python pandas](https://stackoverflow.com/questions/38074678/append-existing-excel-sheet-with-new-dataframe-using-python-pandas/38075046#38075046) (by using` df.style.apply` right before the `to_excel()` call). But since the excel file is read at each iteration, the result is slow. Similarly, [How to add pandas data to an existing csv file?](https://stackoverflow.com/questions/17530542/how-to-add-pandas-data-to-an-existing-csv-file) satisfies point 1 and 3, but not point 2, as it would seem like colors do not work in the same way for `to_csv()` (at least not when saving to a file). `to_excel()`, on the other hand, colors the rows correctly but doesn't take the `mode='a'` argument which gives to `to_csv()` its efficiency. Here is a minimum reproducible example corresponding to point 1 and 2 at the same time. As you will see if you run the code, the time taken per iteration quickly increases. ```python from os.path import isfile import pandas as pd from openpyxl import load_workbook from time import time def highlight_idx(x, idx): if x.name == idx: return ['background-color: red'] * len(x) return [''] * len(x) def append_df_to_excel(filename, df, sheet_name='Sheet1', selected_idx=None, startrow=None, truncate_sheet=False, **to_excel_kwargs): # Excel file doesn't exist - saving and exiting if not isfile(filename): df.style.apply(lambda x: highlight_idx(x, selected_idx), axis=1).to_excel( filename, sheet_name=sheet_name, startrow=startrow if startrow is not None else 0, **to_excel_kwargs) return # ignore [engine] parameter if it was passed if 'engine' in to_excel_kwargs: to_excel_kwargs.pop('engine') writer = pd.ExcelWriter(filename, engine='openpyxl', mode='a') # try to open an existing workbook writer.book = load_workbook(filename) # get the last row in the existing Excel sheet # if it was not specified explicitly if startrow is None and sheet_name in writer.book.sheetnames: startrow = writer.book[sheet_name].max_row # truncate sheet if truncate_sheet and sheet_name in writer.book.sheetnames: # index of [sheet_name] sheet idx = writer.book.sheetnames.index(sheet_name) # remove [sheet_name] writer.book.remove(writer.book.worksheets[idx]) # create an empty sheet [sheet_name] using old index writer.book.create_sheet(sheet_name, idx) # copy existing sheets writer.sheets = {ws.title: ws for ws in writer.book.worksheets} if startrow is None: startrow = 0 # write out the new sheet df.style.apply(lambda x: highlight_idx(x, selected_idx), axis=1).to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs) # save the workbook writer.save() for i in range(100000): df = pd.DataFrame({'A': ['2', '3', '4'], 'B': ['5', '6', '7']}) start = time() append_df_to_excel("out.xlsx", df, selected_idx=1) print("time taken for iteration:", time() - start) ``` The above is just one of the many things I have attempted. But, as I said earlier, none of my attempts have managed to satisfy all 3 criteria so far. So much so that I'm starting to wonder if doing such a thing is even possible with pandas at the time? I have tried it with the latest pandas version (1.2.4). Other than that, the relevant libraries I'm using are jinja2==2.11.3 and openpyxl=3.0.7
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QST: Pandas describe() methods(with float16 and float32) are different
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"2021-08-20T02:47:33Z"
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- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions. - [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com). --- #### Question about pandas I'm trying to optimise my df dtypes to consume less memory. I noticed differences in the describe() method output on that df. Why those outputs are different? **Note**: If you'd still like to submit a question, please read [this guide]( https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question. ```python # Your code here, if applicable df = pd.DataFrame({'a':[11111,22222,3333]}) df['a'].describe() df['a'].astype('float16').describe() ```
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TST: fix casting extension tests for external users
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MEMBER
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cc @simonjayhawkins fixtures defined by pandas are not known when inheriting the tests as external package. There are a bunch of fixtures that external packages are expected to define in `pandas/tests/extension/conftest.py`, but I think for the string dtype here this is not worth it to expect downstream users to define it themselves (certainly given that it is still going to change)
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CI: Fix warning from SSL transport (again)
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1
"2021-05-03T12:19:53Z"
"2021-05-03T13:53:58Z"
"2021-05-03T13:49:46Z"
CONTRIBUTOR
null
Turns out that the issue was the comparison of the type. Loop has `WarningMessage`, but the type of warning is in `actual_warning.category`
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CI: Added unselected directories to doctest CI
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2
"2021-05-03T12:23:55Z"
"2021-05-03T13:53:19Z"
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MEMBER
null
Some of those do have docstrings with example to be validated, and some do not have. This only make sure that future files or changes to files in those directories will be checked. --- - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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[ArrowStringArray] PERF: isin using native pyarrow function if available
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MEMBER
null
Unlike MaskedArray, this returns a numpy bool array to be consistent with the EA interface and StringArray and also due to the fact that the returned boolean array has no null values to be consistent with the latest version of pyarrow. ``` [ 0.00%] ·· Benchmarking existing-py_home_simon_miniconda3_envs_pandas-dev_bin_python [ 4.17%] ··· algos.isin.IsIn.time_isin ok [ 4.17%] ··· ================== ========== dtype ------------------ ---------- int64 295±0μs uint64 348±0μs object 337±0μs Int64 785±0μs boolean 868±0μs bool 420±0μs datetime64[ns] 4.67±0ms category[object] 9.46±0ms category[int] 7.30±0ms str 535±0μs string 556±0μs arrow_string 330±0μs ================== ========== [ 8.33%] ··· algos.isin.IsIn.time_isin_categorical ok [ 8.33%] ··· ================== ========== dtype ------------------ ---------- int64 374±0μs uint64 507±0μs object 467±0μs Int64 633±0μs boolean 702±0μs bool 458±0μs datetime64[ns] 3.10±0ms category[object] 10.2±0ms category[int] 9.12±0ms str 598±0μs string 628±0μs arrow_string 404±0μs ================== ========== [ 12.50%] ··· algos.isin.IsIn.time_isin_empty ok [ 12.50%] ··· ================== ========== dtype ------------------ ---------- int64 275±0μs uint64 285±0μs object 390±0μs Int64 1.06±0ms boolean 1.14±0ms bool 280±0μs datetime64[ns] 295±0μs category[object] 4.17±0ms category[int] 3.49±0ms str 424±0μs string 718±0μs arrow_string 140±0μs ================== ========== [ 16.67%] ··· algos.isin.IsIn.time_isin_mismatched_dtype ok [ 16.67%] ··· ================== ========== dtype ------------------ ---------- int64 221±0μs uint64 216±0μs object 337±0μs Int64 337±0μs boolean 356±0μs bool 323±0μs datetime64[ns] 348±0μs category[object] 4.86±0ms category[int] 3.47±0ms str 586±0μs string 525±0μs arrow_string 224±0μs ================== ========== ```
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TYP Series and DataFrame currently type-check as hashable
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MEMBER
null
Noticed here #41184 From https://github.com/python/typeshed/issues/2148#issuecomment-520783318 it seems this can be resolved with ``` __hash__: None ```
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17
"2021-05-03T14:15:01Z"
"2021-06-29T12:31:30Z"
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MEMBER
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- [ ] closes #40013 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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DOC: Fix docs for io/json/*
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- [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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TYP: core.sorting
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MEMBER
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both mypy and i have a hard time with `labels, lshape = map(list, zip(*map(maybe_lift, labels, lshape)))`
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DOC: Fix docs in pandas/io/excel/*
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MEMBER
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- [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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REF: Avoid fallback in GroupBy._agg_general
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pre-emptively use non-cython path in explicit cases instead of try/except. Many of the affected cases involve empty DataFrames and I think the tested behavior is sketchy (e.g. the fallback paths ignore the `numeric_only` kwarg), so putting this in draft mode while I look at that. @WillAyd can i get your help with _wrap_applied_output here? As commented, the added case is pretty much a kludge and I think we can do better. Am I remembering correctly that you refactored a bunch of the wrapping code ~18 months ago?
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Bug in iloc.setitem orienting IntegerArray into the wrong direction
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MEMBER
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- [x] closes #40933 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them This is more or less a follow up of #39040, so don't think we need a whatsnew?
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"2021-05-04T16:27:25Z"
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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ENH: pandas.Series.apply method to work with missing values.
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"2021-05-03T20:06:05Z"
"2021-08-20T02:50:14Z"
"2021-08-20T02:50:13Z"
NONE
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It would be great if `pandas.Series.apply` had in-built handling of missing values. This could be achieved by simply adding a `na_action` parameter, exactly as `pandas.Series.map` to work in a similar way. In this respect NaN values could simply be ignored or an error raised if one was encountered. `pandas.Series.map` has basic missing value handling, but does NOT handle *args or **kwargs. The ideal world would be a modification to `apply` to provide both the missing value AND additional argument functionality. Alternatives could be a new function name that handles both cases, but I wouldn't recommend this due to redundancy. I am aware of easy solutions to this practically, including NaN handling within the custom function, dropping NaN values prior to calling `apply`, but this is an ease-of-use feature that I believe can significantly improve usability. See this use case standalone: ```python import pandas as pd from numpy import nan string2 = "love" x = pd.Series(['we','love',nan,'pandas','very','much']) def some_string_processing_function(s, another_string): return s[1]==another_string[1] # pd.Series.map doesn't work here because of keyword argument x.apply(some_string_processing_function, another_string=string2) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-116-95783a17b05f> in <module> ----> 1 x.apply(some_string_processing_function, another_string="love") ~\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds) 4136 else: 4137 values = self.astype(object)._values -> 4138 mapped = lib.map_infer(values, f, convert=convert_dtype) 4139 4140 if len(mapped) and isinstance(mapped[0], Series): pandas\_libs\lib.pyx in pandas._libs.lib.map_infer() ~\lib\site-packages\pandas\core\series.py in f(x) 4121 4122 def f(x): -> 4123 return func(x, *args, **kwds) 4124 4125 else: <ipython-input-115-4f94a2ec1809> in some_string_processing_function(s, another_string) 1 def some_string_processing_function(s, another_string): ----> 2 return s[1]==another_string[1] TypeError: 'float' object is not subscriptable ``` Thank you for your time.
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2
"2021-05-03T20:33:56Z"
"2021-06-02T15:14:09Z"
"2021-06-02T15:14:09Z"
MEMBER
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By default numeric_only=True. But when there are no numeric columns and we raise DataError, the fallback ignores numeric_only. So we end up getting wonky results. Below I think that gb1 raising is correct, gb3 should raise, gb2 and gb4 should return empty DataFrames. cc @jreback @jorisvandenbossche @TomAugspurger @WillAyd ``` dti = pd.date_range("2016-01-01", periods=3) ser = pd.Series(dti) df = ser.to_frame() gb1 = ser.groupby([0, 0, 0]) gb2 = df.groupby(gb1.grouper) gb3 = ser[:0].groupby([]) gb4 = df[:0].groupby([]) >>> gb1.prod() TypeError: 'DatetimeArray' does not implement reduction 'prod' >>> gb2.prod() # <-float64 0 0 NaN >>> gb3.prod() Series([], dtype: datetime64[ns]) >>> gb4.prod() # <- dt64 Empty DataFrame Columns: [0] Index: [] ```
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BUG: fix suppressed TypeErrors in Groupby.mean, median, var
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"2021-05-03T21:31:20Z"
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MEMBER
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These are raising TypeError because we aren't passing enough args, but then we're catching those TypeErrors and falling back. So this doesn't change behavior, just avoids an unnecessary fallback in some cases. No tests. Surfacing the exceptions would require ugly/invasive changes.
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"2021-05-04T18:10:17Z"
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MEMBER
null
pre-cursor cleanup to avoid duplicating validation
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REF: support object dtype in libgroupby.group_add
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DOC: Validator + converting array_like to array-like in docstrings
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"2021-06-19T22:37:10Z"
"2021-06-12T10:21:23Z"
CONTRIBUTOR
null
- [ ] closes #40560 (Pending feedback on decorator-generated docstrings) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry First try at any open source stuff! This one looked pretty doable, hopefully it's there's nothing wrong with it/let me know! I'm not sure whether *I* should be checking these boxes, or a reviewer should. I *believe* this fixes the issue since it adds the validator, though it's not a particularly clever validator. It technically is an added test, and it technically works - I re-added one of the entries and got a successful (correct) result for its failure... (amongst the infinite other validation failures 😢 ) I assume linting will run after the PR is submitted, but I'll look into running it locally, too. ``` ... None:None:ES01:pandas.IndexSlice:No extended summary foundNone:None:EX03:pandas.IndexSlice:flake8 error: E231 missing whitespace after ',' (4 times) .../pandas/pandas/core/indexes/multi.py:434:ES01:pandas.MultiIndex.from_arrays:No extended summary found .../pandas/pandas/core/indexes/multi.py:434:GL05:pandas.MultiIndex.from_arrays:Use 'array-like' rather than 'array_like' in docstrings. <<<--- Message for added issue. .../pandas/pandas/core/indexes/multi.py:500:ES01:pandas.MultiIndex.from_tuples:No extended summary found .../pandas/pandas/core/indexes/multi.py:567:ES01:pandas.MultiIndex.from_product:No extended summary found None:None:ES01:pandas.MultiIndex.names:No extended summary found ... ``` More on the validator: * I chose `GL` as the code after looking through [the numpy docstring validation guide](https://numpydoc.readthedocs.io/en/latest/validation.html) and the [pandas docstring validation guide](https://pandas.pydata.org/docs/dev/development/contributing_docstring.html) - I think it stands for "General" and that seems like the best categorization for this message. * I used `raw_doc` for this but there was another property like `clean_doc` as well. I could just iterate through each section, too. 🤷 * I didn't add any extra detail/context to the error message. The line and file are already given, so I think there's nothing extra that's needed. Using the validator to pick up any missed docstring changes: ``` $ time ./validate_docstrings.py 2>&1 | tee validate_docstrings.log ### (It takes ~5m to run) $ grep GL05 validate_docstrings.log .../pandas/pandas/core/frame.py:10220:GL05:pandas.DataFrame.resample:Use 'array-like' rather than 'array_like' in docstrings. .../pandas/pandas/core/series.py:5171:GL05:pandas.Series.resample:Use 'array-like' rather than 'array_like' in docstrings. ``` ----- Actually, looking into those two remaining results - those are docstrings generated by lines like: ```10220 @doc(NDFrame.resample, **_shared_doc_kwargs)``` Looking in more detail, I'm not exactly sure how this happens, but it seems like this decorator must be generating text with "array_like" in it. I kind of want to declare it out of scope for this PR, but I can dig into it more if needed. I suspect maybe the `Substitution` decorator tool can help, possibly. It seems kind of cheesy to add something that will substitute array_like for array-like in a docstring - eg, it might be the result of a function whose param is actually called `array_like`, which, I'm pretty sure they *do* exist. I'm open to feedback on that.
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CLN: remove unused filter_empty from BinGrouper
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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DEPR: pd.Grouper?
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I guess the point of Grouper is to allow for finer customization of groupby behavior? My _hope_ is that it is a historical artifact and we can now do anything we need without it. Any idea if this is the case?
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REF: raise more selectively in libreduction
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"2021-05-04T19:40:52Z"
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This allows slightly more cases to go through the cython path
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TYP: BaseWindowGroupby._grouper
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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CLN: more descriptive names, annotations in groupby
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Not sure if this is the desired way to address this. ``` ________________________ TestDataFrameRound.test_round _________________________ self = <pandas.tests.frame.methods.test_round.TestDataFrameRound object at 0x7fcdd089eb90> def test_round(self): # GH#2665 # Test that rounding an empty DataFrame does nothing df = DataFrame() tm.assert_frame_equal(df, df.round()) # Here's the test frame we'll be working with df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]}) # Default round to integer (i.e. decimals=0) expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]}) tm.assert_frame_equal(df.round(), expected_rounded) # Round with an integer decimals = 2 expected_rounded = DataFrame( {"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]} ) tm.assert_frame_equal(df.round(decimals), expected_rounded) # This should also work with np.round (since np.round dispatches to # df.round) tm.assert_frame_equal(np.round(df, decimals), expected_rounded) # Round with a list round_list = [1, 2] msg = "decimals must be an integer, a dict-like or a Series" with pytest.raises(TypeError, match=msg): df.round(round_list) # Round with a dictionary expected_rounded = DataFrame( {"col1": [1.1, 2.1, 3.1], "col2": [1.23, 2.23, 3.23]} ) round_dict = {"col1": 1, "col2": 2} tm.assert_frame_equal(df.round(round_dict), expected_rounded) # Incomplete dict expected_partially_rounded = DataFrame( {"col1": [1.123, 2.123, 3.123], "col2": [1.2, 2.2, 3.2]} ) partial_round_dict = {"col2": 1} tm.assert_frame_equal(df.round(partial_round_dict), expected_partially_rounded) # Dict with unknown elements wrong_round_dict = {"col3": 2, "col2": 1} tm.assert_frame_equal(df.round(wrong_round_dict), expected_partially_rounded) # float input to `decimals` non_int_round_dict = {"col1": 1, "col2": 0.5} msg = "integer argument expected, got float" with pytest.raises(TypeError, match=msg): > df.round(non_int_round_dict) pandas/tests/frame/methods/test_round.py:67: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = col1 col2 0 1.123 1.234 1 2.123 2.234 2 3.123 3.234 decimals = {'col1': 1, 'col2': 0.5}, args = (), kwargs = {} concat = <function concat at 0x7fce2c668e50> _dict_round = <function DataFrame.round.<locals>._dict_round at 0x7fcdd13728c0> def round( self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs ) -> DataFrame: """ Round a DataFrame to a variable number of decimal places. Parameters ---------- decimals : int, dict, Series Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if `decimals` is a dict-like, or in the index if `decimals` is a Series. Any columns not included in `decimals` will be left as is. Elements of `decimals` which are not columns of the input will be ignored. *args Additional keywords have no effect but might be accepted for compatibility with numpy. **kwargs Additional keywords have no effect but might be accepted for compatibility with numpy. Returns ------- DataFrame A DataFrame with the affected columns rounded to the specified number of decimal places. See Also -------- numpy.around : Round a numpy array to the given number of decimals. Series.round : Round a Series to the given number of decimals. Examples -------- >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)], ... columns=['dogs', 'cats']) >>> df dogs cats 0 0.21 0.32 1 0.01 0.67 2 0.66 0.03 3 0.21 0.18 By providing an integer each column is rounded to the same number of decimal places >>> df.round(1) dogs cats 0 0.2 0.3 1 0.0 0.7 2 0.7 0.0 3 0.2 0.2 With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value >>> df.round({'dogs': 1, 'cats': 0}) dogs cats 0 0.2 0.0 1 0.0 1.0 2 0.7 0.0 3 0.2 0.0 Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value >>> decimals = pd.Series([0, 1], index=['cats', 'dogs']) >>> df.round(decimals) dogs cats 0 0.2 0.0 1 0.0 1.0 2 0.7 0.0 3 0.2 0.0 """ from pandas.core.reshape.concat import concat def _dict_round(df, decimals): for col, vals in df.items(): try: yield _series_round(vals, decimals[col]) except KeyError: yield vals def _series_round(s, decimals): if is_integer_dtype(s) or is_float_dtype(s): return s.round(decimals) return s nv.validate_round(args, kwargs) if isinstance(decimals, (dict, Series)): if isinstance(decimals, Series): if not decimals.index.is_unique: raise ValueError("Index of decimals must be unique") > new_cols = list(_dict_round(self, decimals)) pandas/core/frame.py:9205: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ df = col1 col2 0 1.123 1.234 1 2.123 2.234 2 3.123 3.234 decimals = {'col1': 1, 'col2': 0.5} def _dict_round(df, decimals): for col, vals in df.items(): try: > yield _series_round(vals, decimals[col]) pandas/core/frame.py:9190: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ s = 0 1.234 1 2.234 2 3.234 Name: col2, dtype: float64, decimals = 0.5 def _series_round(s, decimals): if is_integer_dtype(s) or is_float_dtype(s): > return s.round(decimals) pandas/core/frame.py:9196: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = 0 1.234 1 2.234 2 3.234 Name: col2, dtype: float64 decimals = 0.5, args = (), kwargs = {} def round(self, decimals=0, *args, **kwargs) -> Series: """ Round each value in a Series to the given number of decimals. Parameters ---------- decimals : int, default 0 Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point. *args, **kwargs Additional arguments and keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- Series Rounded values of the Series. See Also -------- numpy.around : Round values of an np.array. DataFrame.round : Round values of a DataFrame. Examples -------- >>> s = pd.Series([0.1, 1.3, 2.7]) >>> s.round() 0 0.0 1 1.0 2 3.0 dtype: float64 """ nv.validate_round(args, kwargs) > result = self._values.round(decimals) E TypeError: 'float' object cannot be interpreted as an integer pandas/core/series.py:2361: TypeError During handling of the above exception, another exception occurred: self = <pandas.tests.frame.methods.test_round.TestDataFrameRound object at 0x7fcdd089eb90> > ??? E AssertionError: Regex pattern 'integer argument expected, got float' does not match "'float' object cannot be interpreted as an integer". pandas/tests/frame/methods/test_round.py:-1: AssertionError ```
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41,302
ASV: add benchmarks to test formatting func in Styler.
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1
"2021-05-04T10:43:44Z"
"2021-05-06T07:35:51Z"
"2021-05-04T12:44:09Z"
CONTRIBUTOR
null
The main time in rendering Styler is consumed by: - formatting values for display - adding cell styles via apply / applymap - adding cell styles via set_td_classes We have benchmarks for the latter 2 but not the first. Any future changes to the formatting function might be worth having benchmarks (I would use them :) ) Especially if we try to merge Styler formatting with the floatformatter for example.
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MDU6SXNzdWU4NzUzMzk4OTY=
41,303
BUG: DataFrame.mask does not mask NaT using None
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3
"2021-05-04T10:49:51Z"
"2021-05-04T11:52:47Z"
"2021-05-04T11:52:32Z"
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd import numpy as np df = pd.DataFrame({"dates": ["2020-07-07", np.datetime64("Nat")]}) df.mask(df.isna(), None) ``` #### Problem description `df.mask` does not replace `NaT` by `None`. ![image](https://user-images.githubusercontent.com/8948865/116993013-2bef9600-acd7-11eb-92e4-c8c00f024d26.png) #### Expected Output `df.mask` should properly mask `NaT`. #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.8.8.final.0 python-bits : 64 OS : Linux OS-release : 4.14.214-160.339.amzn2.x86_64 Version : #1 SMP Sun Jan 10 05:53:05 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.4 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : 0.29.22 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.6.2 html5lib : None pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.21.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.8.7 fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.5.2 scipy : 1.6.1 sqlalchemy : 1.3.23 tables : 3.6.1 tabulate : None xarray : 0.17.0 xlrd : 1.2.0 xlwt : None numba : 0.52.0 ``` </details>
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MDU6SXNzdWU4NzUzNDQ1MzY=
41,304
BUG: Incorrect parsing csv header line if no separator in the end
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- ```python import pandas as pd from io import StringIO data = """ "date_time";"T";"Po" "04.05.2021 09:00";"9.8";"759.0"; """ df = pd.read_csv(StringIO(data), sep=';',index_col=0) df['T'] df.head(1) ``` #### Problem description When there is no separator at the end of the header line, pandas seems like skip first column (T) and shows second column instead. ```python df['T'] df.loc[:,'T'] ``` **Output:** ``` 04.05.2021 09:00 759.0 Name: T, dtype: float64 ``` **Expected:** (as df.iloc[0,0] give 9.8): ``` Местное время в Омске / им. Д. М. Карбышева (аэропорт) 04.05.2021 09:00 9.8 Name: T, dtype: float64 ``` After I add ";" separator after "sss" bug disappeared, df.iloc[0,0] output is same in both case. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : b5958ee1999e9aead1938c0bba2b674378807b3d python : 3.6.8.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.21354 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.1.5 numpy : 1.17.4 pytz : 2018.9 dateutil : 2.8.1 pip : 19.0.3 setuptools : 42.0.1 Cython : 0.29.21 pytest : 6.2.3 hypothesis : None sphinx : 3.5.4 blosc : None feather : None xlsxwriter : None lxml.etree : 4.4.1 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10 IPython : 7.5.0 pandas_datareader: None bs4 : 4.7.1 bottleneck : 1.3.1 fsspec : 0.6.1 fastparquet : None gcsfs : 0.5.0 matplotlib : 3.1.2 numexpr : None odfpy : None openpyxl : 2.6.1 pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.2.1 sqlalchemy : 1.2.18 tables : None tabulate : None xarray : 0.14.1 xlrd : 1.2.0 xlwt : None numba : 0.48.0 </details>
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BUG: shared doc string decorator doesn't handle args defined with sets
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I was exploring using a shared docstring decorator today and came up with the issue that the pandas notation for parameter sets is the same as needed for string replacement. So for example: ``` @doc(param1="some_value") def test_doc_replacement(self, set_ars): """ Description of {param1} Parameters ---------- set_args : {'str1', 'str2_or', 'str3'} string values from a set. Returns ------- A KeyError!! """ pass ``` Yielded a KeyError, since its tries to find a key `"'str1', 'str2_or', 'str3'", in my docstring parameters. The way I'm attempting to get around it is: ``` @doc(param1="some_value", set_args="{'str1', 'str2_or', 'str3'}") def test_doc_replacement(self, set_ars): """ Description of {param1} Parameters ---------- set_args : {set_args} string values from a set. Returns ------- Hopefully the right display. """ pass ``` Incidentally anywhere in the docs that a `{}` appears seems to be a problem. So in my examples section I also had to try changing: `>>> df = DataFrame({'A': [1,2], 'B': [3,4]})` to `>>> df = DataFrame([[1,3],[2,4]], columns=["A", "B"])`
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[ArrowStringArray] CLN: assorted cleanup
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seperate commits for cleanups remove hasattr for str methods available in pyarrow 1.0.0 add comments to indicate pyarrow version a method is addded. de-duplicate fixture de-duplicate warning from str.contains with pattern with capture groups remove special casing StringDtype
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- [x] xref #37643 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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REF: share GroupBy.transform
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG: Using .loc raises SettingWithCopyWarning
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Your code here res = pd.DataFrame({"Method": ["Alpha1", "Beta1", "Beta2", "Alpha2", "Alpha3"], "Value": [5, 6, 7, 8, 10]}) df = res[res["Value"] <= 8] df.loc[df["Method"].str.contains("Alpha"), "Method"] = "Gamma" display(res) display(df) ``` #### Problem description In the code above, we are: 1. Creating a new dataframe `res` 2. Getting a copy of the dataframe where values are less than 8 and assigning that to `df` 3. Setting values in `df` using the `.loc` indexing Step 3 raises the `SettingWithCopyWarning`. I don't believe we are setting the value of a copy there as we are using the `.loc` indexing method, which should set values in `df`. The values are being set, but the warning is being raised. If I'm understand the warning correctly, it should not be raised. #### Expected Output No warning should be raised #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.7.9.final.0 python-bits : 64 OS : Linux OS-release : 5.8.0-50-generic Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.4 numpy : 1.19.1 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.2 setuptools : 49.6.0.post20200814 Cython : None pytest : 6.2.1 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.18.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.5.2 sqlalchemy : None tables : None tabulate : 0.8.7 xarray : None xlrd : None xlwt : None numba : 0.51.1 </details>
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CI: skip tests when only files in doc/web changes (github actions)
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- [x] closes #41101 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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ENH: df.to_csv() and pd.read_csv() defaults do not give back original data
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#### Is your feature request related to a problem? I expect when loading and saving CSV data that whatever defaults used for loading and saving can be used mindlessly if my data is simple enough. For example, in the below example, I would expect that ``new_df`` is equal to ``df``: ``` df = pd.DataFrame({'time (ms)': [1, 2, 3, 4]}) df.to_csv('test.csv') new_df = pd.read_csv('test.csv') ``` However, this is not so. The index is saved as the first column by default, but is not *read* as the first column by default when loading data with ``read_csv()``. #### Describe the solution you'd like I should not have to pass in additional arguments to get the simplest use case to work for loading and saving data. I suggest either not saving the index by default when converting to CSV (this seems less desirable), or setting the ``index_col=0`` by default when loading a CSV file. #### API breaking implications ``index_col`` would be set by default #### Describe alternatives you've considered Manually avoiding saving the index, and manually adding the ``index_col=0`` keyword argument when loading CSV data. Both have caused me endless frustration.
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[ArrowStringDtype] Make it already a StringDtype subclass
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@simonjayhawkins this will be redundant after https://github.com/pandas-dev/pandas/pull/39908, but in the meantime, ArrowStringDtype being a subclass would help for writing robust code related to `StringDtype` in downstream packages (for those testing against pandas master)
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CI: numpy doesn't build on python 3.10beta
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The CI is failing is only failing for the "Install dependencies" phase for ```python 3.10``` Saw in: - https://github.com/pandas-dev/pandas/pull/41308/checks?check_run_id=2501536827 - https://github.com/pandas-dev/pandas/pull/41307/checks?check_run_id=2501358707 - https://github.com/pandas-dev/pandas/pull/41269/checks?check_run_id=2498810271
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ENH: Incorporate inplace and subset parameters into pandas.DataFrame.apply
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I would love to see pandas.DataFrame.apply incorporate the inplace and subset parameters. Normal operations when inplace is not in use would still apply. Subset should only be used if inplace=True is provided. My lame examples of how the function would look if implemented. ``` import numpy as np import pandas as pd # Set the seed for a reproducible sample np.random.seed(0) df = pd.DataFrame(np.random.randn(10, 5), columns=list('ABCDE')) df.apply(lambda x: x + 2*x, inplace=True) # would apply x + 2*x to each column df.apply(lambda x: x + 2*x, inplace=True, subset=['A','B']) # would apply x + 2*x to each column, 'A' and 'B' ```
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CI: pin py310-dev version to alpha 7
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"2021-05-04T19:27:49Z"
"2021-05-04T19:27:24Z"
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xref #41313 Not sure if the syntax is correct. Let's see.
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BUG: Using df.groupby().rolling() when grouping by a MultiIndex level causes the MultiIndex to lose levels
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- [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- When grouping a DataFrame which has a MultiIndex using the `level` parameter for `groupby`, performing any `rolling` operation will cause all MultiIndex levels except the one it was grouped by to be lost. This bug has been present since at least 1.1.5. ```python import pandas as pd print(pd.__version__) df = pd.DataFrame({"idx1": [1] * 5 + [2] * 5, "idx2": range(10), "value": range(10)}) df = df.set_index(["idx1", "idx2"]) test = df.groupby(level=0).rolling(2).mean() print(test.index) ``` On version 1.2.4: ```python >>>1.2.4 >>>MultiIndex([(1,), (1,), (1,), (1,), (1,), (2,), (2,), (2,), (2,), (2,)], names=['idx1']) ``` On version 1.0.5: ```python >>>1.0.5 >>>MultiIndex([(1, 1, 0), (1, 1, 1), (1, 1, 2), (1, 1, 3), (1, 1, 4), (2, 2, 5), (2, 2, 6), (2, 2, 7), (2, 2, 8), (2, 2, 9)], names=['idx1', 'idx1', 'idx2']) ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.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_United States.1252 pandas : 1.2.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 54.1.2 Cython : None pytest : 6.2.4 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 : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : 1.4.13 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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REF: remove no-op casting
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"2021-05-05T14:50:22Z"
"2021-05-05T12:44:33Z"
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In _python_agg_general: ``` if self.grouper._filter_empty_groups: mask = counts.ravel() > 0 ``` It isn't entirely trivial, but we can show that we will always have `mask.all()`, as a result of which the remainder of this code chunk of code is a no-op. This PR removes it. As a consequence, we can remove `_filter_empty_groups` and `compressed`
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BUG: sort_values with ascending=None and key!=None raises exception
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd def do_nothing(value): return value df = pd.DataFrame( { "col1": [2, 51, 17], "col2": [5.5, 3.3, 4.4], "col3": ["a", "b", "c"], }, ) print(df) df1 = df.sort_values(by=["col1"], ascending=None, key=do_nothing) print(df1) ``` #### Problem description Output is ``` col1 col2 col3 0 2 5.5 a 1 51 3.3 b 2 17 4.4 c Traceback (most recent call last): File "sort_values_test9.py", line 14, in <module> df1 = df.sort_values(by=["col1"], ascending=None, key=do_nothing) File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/frame.py", line 5464, in sort_values indexer = nargsort( File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/sorting.py", line 354, in nargsort return nargsort( File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/sorting.py", line 368, in nargsort return items.argsort(ascending=ascending, kind=kind, na_position=na_position) File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/arrays/base.py", line 583, in argsort ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs) File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/compat/numpy/function.py", line 150, in validate_argsort_with_ascending validate_argsort_kind(args, kwargs, max_fname_arg_count=3) File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/compat/numpy/function.py", line 67, in __call__ validate_args_and_kwargs( File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/util/_validators.py", line 205, in validate_args_and_kwargs validate_kwargs(fname, kwargs, compat_args) File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/util/_validators.py", line 149, in validate_kwargs _check_for_default_values(fname, kwds, compat_args) File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/util/_validators.py", line 65, in _check_for_default_values raise ValueError( ValueError: the 'axis' parameter is not supported in the pandas implementation of argsort() ``` Function `validate_argsort_with_ascending` inserts a value for `ascending` argument in case it is `None`, but later this value is considered as a value of `axis` parameter for some reason. This leads to an exception instead of a normal operation. #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-72-generic Version : #80-Ubuntu SMP Mon Apr 12 17:35:00 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.4 numpy : 1.19.5 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : None pytest : 6.2.3 hypothesis : None sphinx : 3.5.4 blosc : None feather : 0.4.1 xlsxwriter : None lxml.etree : 4.6.3 html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.20.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : 0.8.5 fastparquet : None gcsfs : None matplotlib : 3.2.2 numexpr : 2.7.2 odfpy : None openpyxl : 3.0.7 pandas_gbq : 0.15.0 pyarrow : 1.0.0 pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.8 tables : 3.6.1 tabulate : None xarray : 0.17.0 xlrd : 2.0.1 xlwt : None numba : None </details>
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875,817,625
MDU6SXNzdWU4NzU4MTc2MjU=
41,319
BUG: pandas utctimetuple() fails on python3.9 (but not 37) with timezone aware timestamps
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[]
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"2021-05-04T20:38:24Z"
"2021-05-04T23:32:22Z"
"2021-05-04T21:26:05Z"
NONE
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- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ``` Python 3.9.2 (default, Feb 24 2021, 13:26:09) [Clang 12.0.0 (clang-1200.0.32.29)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import pandas as pd >>> ts = pd.Timestamp("2020-11-27 16:00:00-0500", tz="US/Eastern") >>> ts.utctimetuple() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pandas/_libs/tslibs/timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.__new__ TypeError: an integer is required >>> ts = pd.Timestamp("2020-11-27 16:00:00", tz="US/Eastern") >>> ts.utctimetuple() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pandas/_libs/tslibs/timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.__new__ TypeError: an integer is required >>> ts = pd.Timestamp("2020-11-27 16:00:00") >>> ts.utctimetuple() time.struct_time(tm_year=2020, tm_mon=11, tm_mday=27, tm_hour=16, tm_min=0, tm_sec=0, tm_wday=4, tm_yday=332, tm_isdst=0) >>> ``` #### Problem description This does not happen in python3.7 with whatever the latest pandas is for 37; it happens on 1.2.4 in python3.9. Pandas timestamps that are timezone aware fail `.utctimetuple()` however the python JSON encoder calls this function; for example I originally thought this was a flask issue: https://github.com/pallets/flask/issues/3993: ``` return jsonify(aod) File "/Users/tommycarpenter/Development/python-indexapi/.tox/py39/lib/python3.9/site-packages/flask/json/__init__.py", line 370, in jsonify dumps(data, indent=indent, separators=separators) + "\n", File "/Users/tommycarpenter/Development/python-indexapi/.tox/py39/lib/python3.9/site-packages/flask/json/__init__.py", line 211, in dumps rv = _json.dumps(obj, **kwargs) File "/usr/local/Cellar/[email protected]/3.9.2_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 234, in dumps return cls( File "/usr/local/Cellar/[email protected]/3.9.2_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "/usr/local/Cellar/[email protected]/3.9.2_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/encoder.py", line 257, in iterencode return _iterencode(o, 0) File "/Users/tommycarpenter/Development/python-indexapi/.tox/py39/lib/python3.9/site-packages/flask/json/__init__.py", line 91, in default return http_date(o.utctimetuple()) File "pandas/_libs/tslibs/timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.__new__ TypeError: an integer is required ``` #### Expected Output All three variants above work, and timezoning does not affect jsonifying pandas timestamps #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.2.final.0 python-bits : 64 OS : Darwin OS-release : 20.3.0 Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.1 setuptools : 53.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 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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