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32,917
A strange bug in version 1.0.3
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2020-03-23T01:06:45Z
2020-09-01T22:25:29Z
2020-09-01T22:25:29Z
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```python # Your code here import pandas as pd ``` #### Problem description When I import pandas 1.03 in pycharm, an error message was returned. This error did not occur when I lowered the version of the package to 0.23.4. In addition, this error no occur in spyder . Traceback (most recent call last): File "E:/coherenceestimation/code/venv/ROC_classifier.py", line 8, in <module> import pandas as pd File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\__init__.py", line 124, in <module> from pandas.core.computation.api import eval File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\core\computation\api.py", line 3, in <module> from pandas.core.computation.eval import eval File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\core\computation\eval.py", line 14, in <module> from pandas.core.computation.engines import _engines File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\core\computation\engines.py", line 9, in <module> from pandas.core.computation.ops import _mathops, _reductions File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\core\computation\ops.py", line 17, in <module> from pandas.core.computation.scope import _DEFAULT_GLOBALS File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\core\computation\scope.py", line 17, in <module> from pandas.compat.chainmap import DeepChainMap File "D:\Users\ThinkPad\Anaconda3\envs\MeachineLing\lib\site-packages\pandas\compat\chainmap.py", line 1, in <module> from typing import ChainMap, MutableMapping, TypeVar, cast ImportError: cannot import name 'ChainMap'
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32,918
memory regression in grouping by categorical variables
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9
2020-03-23T01:35:55Z
2020-05-20T00:51:41Z
2020-05-20T00:51:41Z
NONE
null
There seems to be a regression when grouping by categorical columns. One year old version 0.24.2 was able to complete the queryn, while 1.0.3 is hitting MemoryError. `memory_usage(deep=True)` reports size of data frame to be **524 MB**, while my machine has **125 GB** so memory should not be an issue. ## Input ```py import pandas as pd import numpy as np def randChar(f, numGrp, N) : things = [f%x for x in range(numGrp)] return [things[x] for x in np.random.choice(numGrp, N)] def randFloat(numGrp, N) : things = [round(100*np.random.random(),4) for x in range(numGrp)] return [things[x] for x in np.random.choice(numGrp, N)] N = int(1e7) K = 100 x = pd.DataFrame({ 'id1' : randChar("id%03d", K, N), # large groups (char) 'id2' : randChar("id%03d", K, N), # large groups (char) 'id3' : randChar("id%010d", N//K, N), # small groups (char) 'id4' : np.random.choice(K, N), # large groups (int) 'id5' : np.random.choice(K, N), # large groups (int) 'id6' : np.random.choice(N//K, N), # small groups (int) 'v1' : np.random.choice(5, N), # int in range [1,5] 'v2' : np.random.choice(5, N), # int in range [1,5] 'v3' : randFloat(100,N) # numeric e.g. 23.5749 }) x['id1'] = x['id1'].astype('category') x['id2'] = x['id2'].astype('category') x['id3'] = x['id3'].astype('category') import os import gc import timeit print(pd.__version__) gc.collect() t_start = timeit.default_timer() ans = x.groupby(['id1','id2','id3','id4','id5','id6']).agg({'v3':'sum', 'v1':'count'}) ans.reset_index(inplace=True) print(ans.shape, flush=True) t = timeit.default_timer() - t_start print(t) ``` ## Output ### 1.0.3 ```py print(pd.__version__) #1.0.3 gc.collect() #0 t_start = timeit.default_timer() ans = x.groupby(['id1','id2','id3','id4','id5','id6']).agg({'v3':'sum', 'v1' :'count'}) #Traceback (most recent call last): # File "<stdin>", line 1, in <module> # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/groupby/generic.py", line 928, in aggregate # result, how = self._aggregate(func, *args, **kwargs) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/base.py", line 419, in _aggregate # result = _agg(arg, _agg_1dim) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/base.py", line 386, in _agg # result[fname] = func(fname, agg_how) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/base.py", line 370, in _agg_1dim # return colg.aggregate(how) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/groupby/generic.py", line 247, in aggregate # return getattr(self, func)(*args, **kwargs) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/groupby/groupby.py", line 1371, in f # return self._cython_agg_general(alias, alt=npfunc, **kwargs) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/groupby/groupby.py", line 909, in _cython_agg_general # return self._wrap_aggregated_output(output) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/groupby/generic.py", line 386, in _wrap_aggregated_output # return self._reindex_output(result)._convert(datetime=True) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/groupby/groupby.py", line 2483, in _reindex_output # levels_list, names=self.grouper.names # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/indexes/multi.py", line 552, in from_product # codes = cartesian_product(codes) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/reshape/util.py", line 58, in cartesian_product # for i, x in enumerate(X) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #pandas/core/reshape/util.py", line 58, in <listcomp> # for i, x in enumerate(X) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #numpy/core/fromnumeric.py", line 445, in repeat # return _wrapfunc(a, 'repeat', repeats, axis=axis) # File "/home/jan/git/db-benchmark/pandas/py-pandas/lib/python3.6/site-packages/ #numpy/core/fromnumeric.py", line 51, in _wrapfunc # return getattr(obj, method)(*args, **kwds) #MemoryError ``` ### 0.24.2 ```py print(pd.__version__) #0.24.2 gc.collect() #0 t_start = timeit.default_timer() ans = x.groupby(['id1','id2','id3','id4','id5','id6']).agg({'v3':'sum', 'v1':'count'}) ans.reset_index(inplace=True) print(ans.shape, flush=True) #(10000000, 8) t = timeit.default_timer() - t_start print(t) #9.52401043381542 ```
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32,919
CLN: Remove unused is_datetimelike arg
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2020-03-23T01:39:39Z
2020-03-24T19:56:57Z
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REF: misplaced to_datetime, date_range tests
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TYP: Annotate groupby/ops.py
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6
2020-03-23T01:57:20Z
2021-03-30T20:16:48Z
2020-04-27T00:18:49Z
MEMBER
null
Adding a few type annotations to this file. I think the `values` arguments here should be `ndarrays` but am not positive.
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585,877,545
MDU6SXNzdWU1ODU4Nzc1NDU=
32,922
DEPR: Index.is_mixed
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1
2020-03-23T02:11:01Z
2020-04-05T19:53:13Z
2020-04-05T19:53:13Z
MEMBER
null
We only use it in one place, and removing it there doesn't break anything. Moreover, it behaves surprisingly: ``` >>> pd.Index(['a', np.nan, 'b']).is_mixed() True >>> Index([0, 'a', 1, 'b', 2, 'c']).is_mixed() False ```
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585,976,568
MDU6SXNzdWU1ODU5NzY1Njg=
32,923
Proposal: Rename bool() to to_bool()
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3
2020-03-23T06:57:33Z
2021-07-30T04:10:53Z
null
CONTRIBUTOR
null
https://github.com/pandas-dev/pandas/blob/f78b1cc46268e8cee8f5ccdacc41e667a6b92ef7/pandas/core/generic.py#L1371 This method conflicts with python's native `bool`. Wondering if it would be okay to rename this to `to_bool`. I can get the needed changes done, but wanted to make sure it's okay before sending out a PR.
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ImportError: cannot import name 'rolling_cov
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1
2020-03-23T09:57:44Z
2020-03-23T10:38:08Z
2020-03-23T10:37:46Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python from pandas import read_json, to_datetime, concat, rolling_cov ``` #### Problem description Missing export in Pandas 1.0.3 #### Expected Output #### Output of ``pd.show_versions()`` <details> commit : None python : 3.8.2.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.0.3 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 41.2.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : 4.8.2 bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.2.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None </details>
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586,093,702
MDExOlB1bGxSZXF1ZXN0MzkyMjkwNjky
32,925
DOC: Fix formatting in documentation
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1
2020-03-23T10:19:38Z
2020-03-23T11:10:44Z
2020-03-23T11:10:29Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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586,105,974
MDExOlB1bGxSZXF1ZXN0MzkyMzAwODA0
32,926
Renamed `validate_string_concatenation.py` to `validate_unwanted_patterns.py`
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0
2020-03-23T10:38:22Z
2020-03-23T12:06:39Z
2020-03-23T11:13:29Z
MEMBER
null
Follow up for #30755
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32,927
Added file paths to ignore in linter scripts/validate_unwanted_patterns.py
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1
2020-03-23T12:53:59Z
2020-03-29T16:36:14Z
2020-03-29T16:09:28Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry --- While running ```./ci/code_checks.sh lint``` on my local machine I got the output of: ``` Check for use of not concatenated strings ./asv_bench/env/40d026129f63c107b1ac48bf9ad6964c/lib/python3.6/_sitebuiltins.py:99:String unnecessarily split in two by black. Please merge them manually.. ./asv_bench/env/40d026129f63c107b1ac48bf9ad6964c/lib/python3.6/inspect.py:2768:String unnecessarily split in two by black. Please merge them manually.. ./asv_bench/env/40d026129f63c107b1ac48bf9ad6964c/lib/python3.6/inspect.py:2890:String unnecessarily split in two by black. Please merge them manually.. .... ``` This fixes it.
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32,928
Backport PR #32827 on branch 1.0.x (DOC: Fixed contributors for bugfix releases)
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0
2020-03-23T13:42:17Z
2020-03-23T14:14:06Z
2020-03-23T14:14:06Z
NONE
null
Backport PR #32827: DOC: Fixed contributors for bugfix releases
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32,929
TST: bare pytest raises in tests/scalar
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1
2020-03-23T13:43:27Z
2020-03-24T17:47:31Z
2020-03-24T17:04:29Z
CONTRIBUTOR
null
- [ ] ref #30999 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry https://github.com/pandas-dev/pandas/issues/30999
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MDU6SXNzdWU1ODYyMzg2MzQ=
32,930
pd.read_csv()/dtype and index_col combo
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1
2020-03-23T14:10:16Z
2020-08-25T12:43:48Z
2020-08-25T12:43:30Z
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#### Code Sample, a copy-pastable example if possible ```python data=pd.read_csv(file, dtype={'donor':str}, index_col='donor') # dtype of the index IS NOT str # to fix: data= pd.read_csv(file, dtype={'donor': str}) data.set_index('donor', drop=True, inplace=True) ``` #### Problem description during pd.read_csv(), when I set a dtype to one column and set the same column as index, the index dtype is lost #### Expected Output When both dtype and index_col are used with the same column, I want the index in dtype specificied in the pd.read_csv() call, i.e. in my example str #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.6.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.0.1 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.0 pip : 20.0.2 setuptools : 45.2.0 Cython : None pytest : 5.0.1 hypothesis : 4.27.0 sphinx : 2.1.2 blosc : None feather : None xlsxwriter : None lxml.etree : 4.4.1 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.1 IPython : 7.13.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.4.1 matplotlib : 3.1.3 numexpr : None odfpy : None openpyxl : 3.0.0 pandas_gbq : None pyarrow : None pytables : None pytest : 5.0.1 pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.3.13 tables : None tabulate : None xarray : 0.12.3 xlrd : 1.2.0 xlwt : None xlsxwriter : None numba : None </details>
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pd.NA in object dtype
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2020-03-23T15:23:14Z
2020-05-31T18:32:01Z
null
MEMBER
null
extract from https://github.com/pandas-dev/pandas/issues/32075#issuecomment-596735782 If we want to handle pd.NA in object dtype better, we will need to start using masks as well, and not rely on numpy behaviour. For example, also this is wrong: ``` In [9]: pd.Series([1, pd.NA], dtype=object) >= 1 Out[9]: 0 True 1 False dtype: bool ``` Related issues: * https://github.com/pandas-dev/pandas/issues/32075 (replace not working) * https://github.com/pandas-dev/pandas/issues/33065 (wrong truncated repr) * https://github.com/pandas-dev/pandas/issues/33066 (wrong comparison operations) * https://github.com/pandas-dev/pandas/issues/34497 (indexing with duplicates)
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REF: "bare_pytest_raises" to use the ast module
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2020-03-23T15:57:17Z
2020-03-29T16:34:50Z
2020-03-29T16:08:42Z
MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry --- There is also a performance boost: ``` In [1]: import os In [2]: from scripts.validate_unwanted_patterns import bare_pytest_raises, main In [3]: SOURCE_PATH = "pandas/tests/" In [4]: %timeit main(function=bare_pytest_raises, source_path=SOURCE_PATH, output_format="{source_path}:{line_number}:{msg}.") 12.6 s ± 76.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # Master 4.33 s ± 27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # PR ```
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32,933
pandas to_sql(if_exists='replace') should not commit after the drop query
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2020-03-23T16:24:55Z
2021-07-07T13:48:35Z
null
NONE
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#### Code Sample, a copy-pastable example if possible it is hard to generate a reproducible code for this problem as it would need parallel processes reading from a table, and another process that updates that table frequently. #### Problem description https://github.com/pandas-dev/pandas/blob/d3e54851b5ab2f2c99c22b4bf5c49cea41141072/pandas/io/sql.py#L646 https://github.com/pandas-dev/pandas/blob/d3e54851b5ab2f2c99c22b4bf5c49cea41141072/pandas/io/sql.py#L647 The issue is that to_sql with (if_exists='replace') carries the two instructions separately and commits them serially into the database. This causes 'table not found error' if multiple other processes are reading from the table as well. The commit needs to happen after the second instruction only and not into the drop query.
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32,934
Incorrect dtype for concat with empty float and datetime columns
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3
2020-03-23T16:26:05Z
2021-07-30T04:16:51Z
null
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python In [40]: a = pd.DataFrame({"A": pd.array(['2000'], dtype='datetime64[ns]')}) In [41]: b = pd.DataFrame({"A": pd.array([1.0], dtype='float64')}) In [42]: pd.concat([a.iloc[:0], b.iloc[:0]])['A'].dtype Out[42]: dtype('<M8[ns]') ``` #### Problem description The return dtype of concatentating a float64 and datetime64[ns] should be object dtype. That's typically the case when both are non-empty ```python In [45]: pd.concat([a, b])['A'].dtype Out[45]: dtype('O') ``` But when either is empty, we get incorrect results ```python In [42]: pd.concat([a.iloc[:0], b.iloc[:0]])['A'].dtype Out[42]: dtype('<M8[ns]') In [43]: pd.concat([a.iloc[:0], b])['A'].dtype Out[43]: dtype('float64') In [44]: pd.concat([a, b.iloc[:0]])['A'].dtype Out[44]: dtype('<M8[ns]') ``` All of those should be object dtype.
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DOC: Fixed examples in pandas/tseries
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1
2020-03-23T16:31:47Z
2020-03-29T09:24:16Z
2020-03-27T21:08:04Z
MEMBER
null
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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MDU6SXNzdWU1ODYzNDc5NTg=
32,936
Pandas 1.0.3 on Windows 7 64bit throws error "DLL load failed while importing aggregations" on import. Version 1.0.1 works fine.
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2020-03-23T16:39:48Z
2020-05-30T09:46:35Z
2020-03-23T18:45:45Z
NONE
null
_TLDR; 2 DLLs are missing that were included in 1.0.1_ #### Code Sample, a copy-pastable example if possible ```python import pandas as pd ``` #### Problem description I'm using Windows 7, German 64bit with a fresh installation of Python 3.8.2 64bit. I updated pip, created a fresh virtual environment with "py -3 -m venv venv" and activated it. This installation is in a restricted area with no direct, unfiltered internet access. I installed Pandas 1.0.3 from a via "python -m pip download pandas" download made on an unrestricted similar Windows7 machine. I moved the download folder behind a firewall and installed Pandas like this: "python -m pip install pandas --no-index --find-links /path/to/downloads": Pandas installs fine. It crashes on import with the error below (The last sentence says in English: The requested Module was not found) As recommended by this stack overflow thread (https://stackoverflow.com/questions/60763529/unable-to-import-pandas-pandas-libs-window-aggregations) going back to 1.0.1 fixes the problem. (More below, after the error) Error: (venv) C:\my_program>python Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AM D64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import pandas as pd Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\my_program\venv\lib\site-packages\pandas\__init__.py", line 5 5, in <module> from pandas.core.api import ( File "C:\my_program\venv\lib\site-packages\pandas\core\api.py", line 2 9, in <module> from pandas.core.groupby import Grouper, NamedAgg File "C:\my_program\venv\lib\site-packages\pandas\core\groupby\__init_ _.py", line 1, in <module> from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGr oupBy File "C:\my_program\venv\lib\site-packages\pandas\core\groupby\generic .py", line 60, in <module> from pandas.core.frame import DataFrame File "C:\my_program\venv\lib\site-packages\pandas\core\frame.py", line 124, in <module> from pandas.core.series import Series File "C:\my_program\venv\lib\site-packages\pandas\core\series.py", lin e 4572, in <module> Series._add_series_or_dataframe_operations() File "C:\my_program\venv\lib\site-packages\pandas\core\generic.py", li ne 10349, in _add_series_or_dataframe_operations from pandas.core.window import EWM, Expanding, Rolling, Window File "C:\my_program\venv\lib\site-packages\pandas\core\window\__init__ .py", line 1, in <module> from pandas.core.window.ewm import EWM # noqa:F401 File "C:\my_program\venv\lib\site-packages\pandas\core\window\ewm.py", line 5, in <module> import pandas._libs.window.aggregations as window_aggregations ImportError: DLL load failed while importing aggregations: Das angegebene Modul wurde nicht gefunden. I compared the installation of version 1.0.1 to 1.0.3; The 1.0.3 installs these files in "\venv\Lib\site-packages\pandas\_libs\window\" ``` __init__.py aggregations.cp38-win_amd64.pyd indexers.cp38-win_amd64.pyd ``` The 1.0.1 version also installs these two DLLs in the same folder. ``` concrt140.dll msvcp140.dll ``` When I **manually place these DLLs** into the above folder of the 1.0.3 version, the import works **without an error**. #### Expected Output No error message #### Output of ``pd.show_versions()`` Not available, since pd did not import
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MDExOlB1bGxSZXF1ZXN0MzkyNTA3NzU2
32,937
Fixing scatter plot size (#32904)
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2
2020-03-23T16:55:53Z
2020-03-28T22:04:48Z
2020-03-28T22:04:48Z
CONTRIBUTOR
null
This fixes the marker size in scatter plots (see https://github.com/pandas-dev/pandas/issues/32904). - [x] closes #32904 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzkyNTEzNjQ1
32,938
REF: misplaced DTI.shift tests
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2020-03-23T17:05:26Z
2020-03-24T19:51:16Z
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pandas/core/ops/: replace .format() with f-strings
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2020-03-23T17:20:29Z
2020-03-25T00:05:55Z
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- https://github.com/pandas-dev/pandas/issues/29547 - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REF: collect casting portion of Block.setitem
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0
2020-03-23T17:40:55Z
2020-03-25T00:40:59Z
2020-03-25T00:10:41Z
MEMBER
null
Block.setitem is sometimes inplace and sometimes casts/copies. The idea here is to isolate the casting cases and eventually separate them into their own method, so we can be more careful about when we call the inplace vs not-inplace setters.
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2020-03-23T18:22:26Z
2020-03-23T18:23:04Z
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MEMBER
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- [x] ref https://github.com/pandas-dev/pandas/pull/32932#issuecomment-602704059 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry --- cc @jbrockmendel
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2020-03-23T18:23:25Z
2021-05-05T12:14:43Z
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- [x] ref https://github.com/pandas-dev/pandas/pull/32932#issuecomment-602704059 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry --- cc @jbrockmendel
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2020-03-23T18:46:54Z
2020-03-25T19:06:10Z
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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DOC: Fix capitalization among headings in documentation files (#32550)
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2020-03-23T18:53:00Z
2020-04-07T18:35:04Z
2020-03-31T17:26:03Z
CONTRIBUTOR
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Files updated: ``` doc/source/development/contributing_docstring.rst doc/source/development/developer.rst doc/source/development/extending.rst doc/source/development/maintaining.rst doc/source/development/meeting.rst ```
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2020-03-23T19:13:07Z
2020-03-24T13:24:51Z
2020-03-23T19:27:41Z
CONTRIBUTOR
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Files updated: ``` doc/source/development/policies.rst doc/source/development/roadmap.rst doc/source/ecosystem.rst doc/source/getting_started/10min.rst doc/source/getting_started/basics.rst ```
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2020-03-23T19:21:37Z
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CONTRIBUTOR
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Files updated: ``` doc/source/getting_started/comparison/comparison_with_sas.rst doc/source/getting_started/comparison/comparison_with_sql.rst doc/source/getting_started/comparison/comparison_with_stata.rst doc/source/getting_started/intro_tutorials/01_table_oriented.rst doc/source/getting_started/tutorials.rst ```
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REF: .values -> ._values
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1
2020-03-23T20:18:05Z
2020-03-24T19:51:37Z
2020-03-24T19:41:35Z
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586,501,412
MDExOlB1bGxSZXF1ZXN0MzkyNjI1MjQy
32,948
REF: misplaced DataFrame.where tests
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2020-03-23T20:34:57Z
2020-03-24T19:51:55Z
2020-03-24T19:38:44Z
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586,504,064
MDExOlB1bGxSZXF1ZXN0MzkyNjI3NDQ3
32,949
CLN: remove unnecessary Series._convert calls
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4
2020-03-23T20:39:42Z
2020-04-10T17:23:12Z
2020-04-10T16:18:07Z
MEMBER
null
cc @WillAyd my confidence on the correctness here is only "pretty sure", so could use an extra set of eyes.
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586,506,185
MDExOlB1bGxSZXF1ZXN0MzkyNjI5MjIx
32,950
REF: move mixed-dtype frame_apply check outside of _reduce try/except
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4
2020-03-23T20:43:15Z
2020-03-30T23:08:05Z
2020-03-30T22:43:04Z
MEMBER
null
cc @jorisvandenbossche this is the same branch I linked the other day. Some variant of #32867 may make this irrelevant, but I don't think getting this improvement in the interim will make that any more difficult.
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32,951
CI: Exception ignored in: 'pandas._libs.tslibs.util.get_c_string_buf_and_size'
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2
2020-03-23T21:13:15Z
2020-04-28T19:40:32Z
2020-04-28T19:40:32Z
MEMBER
null
Seen in the logs lately: ``` Exception ignored in: 'pandas._libs.tslibs.util.get_c_string_buf_and_size' UnicodeEncodeError: 'utf-8' codec can't encode character '\ud83d' in position 0: surrogates not allowed ``` Possibly related to #32701?
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586,524,568
MDExOlB1bGxSZXF1ZXN0MzkyNjQ0MjIy
32,952
CLN: Remove GroupByError exception
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1
2020-03-23T21:18:17Z
2020-03-24T19:56:45Z
2020-03-24T19:39:39Z
MEMBER
null
From what I can tell this exception isn't used / needed
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32,953
BUG: passing a non-dict mapping to pd.concat raises a TypeError
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1
2020-03-23T21:48:29Z
2020-03-26T08:55:54Z
2020-03-26T01:19:31Z
CONTRIBUTOR
null
closes #32954 closes #32863 - [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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32,954
Fixture `non_mapping_dict_subclass` is wrongly named
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0
2020-03-23T21:53:39Z
2020-03-26T01:19:31Z
2020-03-26T01:19:31Z
CONTRIBUTOR
null
#### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution.] In `conftest.py`, a fixture is defined with the name `non_mapping_dict_subclass`, which returns a minimal class implementing the `collections.abc.Mapping` base class by minimally wrapping a `dict`. Obviously, the intended name was `non_dict_mapping_subclass`, which also is the name of the class returned (casing apart). #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 1d13390e26bc7b1f5fb597bc5df1591f5d160e1f python : 3.6.10.final.0 python-bits : 64 OS : Darwin OS-release : 19.3.0 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : en_GB.UTF-8 pandas : 1.0.3 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.0.0.post20200309 Cython : 0.29.15 pytest : 5.4.1 hypothesis : 5.7.0 sphinx : 2.4.4 blosc : 1.8.3 feather : None xlsxwriter : 1.2.8 lxml.etree : 4.3.1 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.11.1 IPython : 7.13.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : 1.2.1 fastparquet : 0.3.3 gcsfs : None lxml.etree : 4.3.1 matplotlib : 3.2.1 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.1 pandas_gbq : None pyarrow : 0.16.0 pytables : None pytest : 5.4.1 pyxlsb : None s3fs : 0.4.0 scipy : 1.4.1 sqlalchemy : 1.3.15 tables : 3.6.1 tabulate : 0.8.7 xarray : 0.15.0 xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.2.8 numba : 0.48.0 </details>
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32,955
CLN: Remove encoding specifier
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5
2020-03-23T22:02:10Z
2020-03-26T01:10:28Z
2020-03-26T01:09:27Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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32,956
CLN: update Appender to doc with case __doc__
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1
2020-03-23T22:07:14Z
2020-03-29T04:15:25Z
2020-03-27T21:14:04Z
CONTRIBUTOR
null
- [x] working on #31942 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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32,957
BUG: iloc on DataFrame[ea]
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3
2020-03-23T22:12:29Z
2020-04-10T20:51:26Z
2020-04-10T20:51:26Z
MEMBER
null
``` from pandas.tests.extension.decimal.test_decimal import * arr = data.__wrapped__() ser = pd.Series(arr) df = pd.DataFrame({"a": ser}) >>> df.iloc[:, :1] a 0 Decimal: 0.32737812402736643502265678762341849... 1 2 3 >>> df.iloc[:, :1].values array([[Decimal('0.32737812402736643502265678762341849505901336669921875')]], dtype=object) ``` Maybe slicing on the wrong axis?
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32,958
Subsequent calls to read_csv for newly created S3 files in the same directory throws an error
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4
2020-03-23T22:23:07Z
2020-03-24T15:31:38Z
2020-03-24T14:58:02Z
NONE
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#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import s3fs BUCKET = 'mattlbeck-mybucket' s3path1 = f's3://{BUCKET}/key/file1.csv' s3path2 = f's3://{BUCKET}/key/file2.csv' with s3fs.S3FileSystem().open(s3path1, 'w') as fh: fh.write('a,b,c\n1,2,3') pd.read_csv(s3path1) with s3fs.S3FileSystem().open(s3path2, 'w') as fh: fh.write('a,b,c\n1,2,3') pd.read_csv(s3path2) ``` Produces the trace summarised below: ``` Traceback (most recent call last): ... FileNotFoundError: mattlbeck-mybucket/key/file2.csv During handling of the above exception, another exception occurred: Traceback (most recent call last): ... botocore.exceptions.ClientError: An error occurred (AccessDenied) when calling the ListObjectsV2 operation: Access Denied During handling of the above exception, another exception occurred: Traceback (most recent call last): ... PermissionError: Access Denied ``` #### Problem description Subsequent calls to `read_csv` beyond the first call causes an error when the target is a file in S3 within the same directory as a file that has previously been read by `read_csv` The problem appears to be overly aggressive caching of S3 directory listings. But from reading the code I can't immediately see how the cache is persisting between the two calls - there is no obvious reuse of `S3FileSystem` instances. #### Expected Output Both calls to `read_csv` should resolve without error because both files exist at the time of the call. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : f20331d543c5d42e54dff29135146fc0e6798a84 python : 3.8.1.final.0 python-bits : 64 OS : Darwin OS-release : 19.3.0 Version : Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.1.0.dev0+945.gf20331d54 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.1.1.post20200323 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : 0.4.0 scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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BUG: df.iloc[:, :1] with EA column
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6
2020-03-23T22:30:23Z
2020-04-10T20:58:16Z
2020-04-10T20:51:26Z
MEMBER
null
- [x] closes #32957 - [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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32,960
Dataframe change alters original array used in creation
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11
2020-03-23T23:07:42Z
2021-03-31T01:03:21Z
2021-03-31T01:03:21Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python a = pd.array([0, 1, 2]) df1 = pd.DataFrame({"col1": ['a', 'b', 'c'], "col2": a}) df1.loc[0, 'col2'] = 3 print(a) ``` ```py <IntegerArray> [3, 1, 2] Length: 3, dtype: Int64 ``` #### Problem description Pandas master has this strange change in behaviour when df created from array changes the original array if the value in the related column is changed. Pandas 1.0.3 does not alter the original array. It seems that it does a copy somewhere while master does not do that anymore. I don't think the original array should be altered. Doing the same with list or np.array does not alter the original list/array either. #### Expected Output ``` <IntegerArray> [0, 1, 2] Length: 3, dtype: Int64 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : d3e54851b5ab2f2c99c22b4bf5c49cea41141072 python : 3.7.3.final.0 python-bits : 64 OS : Darwin OS-release : 19.3.0 Version : Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.0.dev0+944.gd3e54851b numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 41.6.0 Cython : 0.29.15 pytest : 5.4.1 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.8.4 (dt dec pq3 ext lo64) jinja2 : 2.11.1 IPython : 7.13.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None matplotlib : 3.1.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.3.15 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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586,583,737
MDExOlB1bGxSZXF1ZXN0MzkyNjkyODU3
32,961
CLN/STY: Nitpicks
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5
2020-03-23T23:25:29Z
2020-03-26T01:13:40Z
2020-03-26T01:11:22Z
MEMBER
null
Things I find myself doing in multiple branches
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586,589,400
MDExOlB1bGxSZXF1ZXN0MzkyNjk3Mjcx
32,962
TYP: ensure Block.putmask, Block.where get arrays, not Series/DataFrame
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2
2020-03-23T23:42:34Z
2020-03-26T01:07:01Z
2020-03-26T00:51:35Z
MEMBER
null
Avoids the `getattr(obj, "values", obj)` pattern (there's an issue for that)
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32,963
TST: Use indices fixture in tests/indexes/test_base.py
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13
2020-03-24T00:30:05Z
2020-03-29T19:51:58Z
2020-03-29T18:00:18Z
CONTRIBUTOR
null
part of #30914 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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2
2020-03-24T00:43:26Z
2020-03-26T01:15:28Z
2020-03-26T01:15:23Z
CONTRIBUTOR
null
part of #30914 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: Clean NDFrame.__finalize__ signature
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2020-03-24T01:58:06Z
2020-03-24T14:41:17Z
2020-03-24T14:41:13Z
MEMBER
null
`NDFrame.__finalize__` doesn't do anything with `method` or `kwargs` so removing them from the signature and all calls. Had to change a few tests which overrode this method with another that also included those arguments.
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CLN: Assorted core/generic.py cleanup
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2020-03-24T02:09:41Z
2020-03-25T01:05:20Z
2020-03-25T01:05:17Z
MEMBER
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A few mostly aesthetic cleanups
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6
2020-03-24T03:17:31Z
2020-07-16T01:51:19Z
2020-07-16T01:51:19Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python # Your code here from pandas import Timestamp Timestamp.min.tz_localize("Asia/Tokyo") ``` The example is picked directly from ```pandas/tests/scalar/timestamp/test_timezones.py::TestTimestampTZOperations::test_tz_localize_pushes_out_of_bounds``` #### Problem description Error message is empty. > yep, out of scope. but if you could raise an issue, that'll be great. _Originally posted by @simonjayhawkins in https://github.com/pandas-dev/pandas/pull/32929_ #### Expected Output Meaningful error message #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : bbfecaffa1887a86a801213fd3e1d6188f7d3456 python : 3.7.6.final.0 python-bits : 64 OS : Linux OS-release : 5.3.0-40-generic Version : #32-Ubuntu SMP Fri Jan 31 20:24:34 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.0.dev0+943.gbbfecaffa numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.0.0.post20200311 Cython : 0.29.15 pytest : 5.4.1 hypothesis : 5.6.0 sphinx : 2.4.4 blosc : None feather : None xlsxwriter : 1.2.8 lxml.etree : 4.5.0 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.11.1 IPython : 7.13.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : 1.3.2 fastparquet : 0.3.3 gcsfs : None matplotlib : 3.2.0 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.3 pandas_gbq : None pyarrow : 0.16.0 pytables : None pyxlsb : 1.0.6 s3fs : 0.4.0 scipy : 1.4.1 sqlalchemy : 1.3.15 tables : 3.6.1 tabulate : 0.8.6 xarray : 0.15.0 xlrd : 1.2.0 xlwt : 1.3.0 numba : 0.48.0 </details>
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CLN: move misplaced (and duplicated) Dataframe.__repr__ test
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REF: misplaces Series.where, Series.rename tests
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2020-03-24T03:55:42Z
2020-03-24T19:52:17Z
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586,829,328
MDExOlB1bGxSZXF1ZXN0MzkyODg0NjY5
32,970
Correct data type misspelling
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1
2020-03-24T09:48:21Z
2020-03-24T19:17:50Z
2020-03-24T18:24:06Z
CONTRIBUTOR
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586,845,124
MDU6SXNzdWU1ODY4NDUxMjQ=
32,971
import is failing in windows in 1.0.3 version
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2020-03-24T10:14:44Z
2020-03-24T12:34:46Z
2020-03-24T12:34:45Z
NONE
null
Import is failing in 1.0.3 version of panda in windows. It was working in 0.22 version >>> import pandas Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\__init__.py", line 55, in <module> from pandas.core.api import ( File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\api.py", line 29, in <module> from pandas.core.groupby import Grouper, NamedAgg File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\groupby\__init__.py", line 1, in <module> from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\groupby\generic.py", line 60, in <module> from pandas.core.frame import DataFrame File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\frame.py", line 124, in <module> from pandas.core.series import Series File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\series.py", line 4572, in <module> Series._add_series_or_dataframe_operations() File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\generic.py", line 10349, in _add_series_or_dataframe_operations from pandas.core.window import EWM, Expanding, Rolling, Window File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\window\__init__.py", line 1, in <module> from pandas.core.window.ewm import EWM # noqa:F401 File "C:\Users\Administrator\AppData\Local\Programs\Python\Python36-32\lib\site-packages\pandas\core\window\ewm.py", line 5, in <module> import pandas._libs.window.aggregations as window_aggregations ImportError: DLL load failed: The specified module could not be found. >>> exit() C:\Users\Administrator>pip uninstall pandas Found existing installation: pandas 1.0.3 Uninstalling pandas-1.0.3: Would remove: c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\pandas-1.0.3.dist-info\* c:\users\administrator\appdata\local\programs\python\python36-32\lib\site-packages\pandas\* Proceed (y/n)? y Successfully uninstalled pandas-1.0.3
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586,877,928
MDExOlB1bGxSZXF1ZXN0MzkyOTI0MTky
32,972
DOC: Change doc template to fix SA04 errors in docstrings #28792
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9
2020-03-24T11:07:58Z
2020-04-10T19:25:43Z
2020-04-10T19:25:36Z
CONTRIBUTOR
null
- [x] xref #28792 As requested in #32823 creating separate PR. Also all descriptions for methods starts with upper case and it looks a little out of place. Should it be casted to lowercase perhaps?
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MDExOlB1bGxSZXF1ZXN0MzkyOTQyNTM4
32,973
PERF: Using _Period for optimization
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10
2020-03-24T11:46:49Z
2020-03-31T06:38:10Z
2020-03-30T22:35:22Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry --- I did two bench marks one in a tight loop(```%timeit```), and one with asv. I don't see a significant performance change (but I'm not sure I read the output correctly), maybe worth just removing the "TODO" note. --- #### Benchmarks: ##### Timeit: ``` In [1]: import pandas as pd In [2]: idx = pd.period_range("2000-01-01", periods=3) In [3]: %timeit idx.get_loc(idx[1], "pad") 337 µs ± 15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # Master 338 µs ± 7.16 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # PR ``` ##### ASV (period.Indexing) ``` before after ratio [f20331d5] [f71bf172] <master> <PERF-index-_Period> 1.46±0.02ms 1.45±0.03ms 0.99 period.Indexing.time_align 8.57±0.2μs 8.51±0.2μs 0.99 period.Indexing.time_get_loc 512±30μs 506±3μs 0.99 period.Indexing.time_intersection 30.6±2μs 30.8±0.3μs 1.01 period.Indexing.time_series_loc 3.95±0.3μs 3.96±0.02μs 1.00 period.Indexing.time_shallow_copy 87.9±0.5μs 88.5±0.6μs 1.01 period.Indexing.time_unique ```
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32,974
TST: Rename indices fixture
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1
2020-03-24T11:58:36Z
2020-06-27T05:53:06Z
2020-06-27T00:15:17Z
CONTRIBUTOR
null
Following up on https://github.com/pandas-dev/pandas/pull/28865#discussion_r333052051 by @simonjayhawkins we should discuss if we want to rename the indices fixture. A singular name seems more reasonable to its use cases. The most obvious contender is `index`. Does any1 have objections? **EDIT:** In [another comment](https://github.com/pandas-dev/pandas/pull/32963#discussion_r397373641) jreback suggested to use `index_fixture` instead to make things more obvious. I agree that it would be a better fit
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CLN: Remove shebang from files that don't need it
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2020-03-24T12:12:35Z
2020-03-25T07:40:23Z
2020-03-24T23:56:08Z
MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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32,976
Categorical columns are slow in groupby operations
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2020-03-24T13:19:49Z
2020-05-26T12:22:35Z
null
NONE
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#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import random SIZE = 100000 GROUPS = 10000 # The larger, the more extreme the timing differences CARDINALITY = 10 CAT = pd.CategoricalDtype([i for i in range(CARDINALITY)]) df_int = pd.DataFrame({ 'group': [random.randint(0, GROUPS) for i in range(SIZE)], 'cat': [random.choice(CAT.categories) for i in range(SIZE)], }) df_cat = df_int.astype({'cat': CAT}) df_str = df_cat.astype({'cat': 'str'}) %timeit df_int.groupby('group').last() # 100 loops, best of 3: 5.51 ms per loop %timeit df_str.groupby('group').last() # 100 loops, best of 3: 11.4 ms per loop %timeit df_cat.groupby('group').last() # 1 loop, best of 3: 4.62 s per loop ``` #### Problem description We have a dataframe like the above in production and noticed a particular groupby call was drastically slowing down our pipeline. We were able to workaround the issue by converting categorical types to strings before the groupby operation. The issue occurs when categorical values are in the grouped frame (not when they're included in the groupby index). The problem scales with the number of groups. The larger the GROUPS value in the snippet above, the worse the timing differences are. We didn't expect using categories to cause these kind of performance issues. Is this expected, or could it be something worth optimizing for? #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] INSTALLED VERSIONS ------------------ commit : None python : 3.6.9.final.0 python-bits : 64 OS : Linux OS-release : 4.14.137+ machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 0.25.3 numpy : 1.18.2 pytz : 2018.9 dateutil : 2.8.1 pip : 19.3.1 setuptools : 46.0.0 Cython : 0.29.15 pytest : 3.6.4 hypothesis : None sphinx : 1.8.5 blosc : None feather : 0.4.0 xlsxwriter : None lxml.etree : 4.2.6 html5lib : 1.0.1 pymysql : None psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64) jinja2 : 2.11.1 IPython : 5.5.0 pandas_datareader: 0.7.4 bs4 : 4.6.3 bottleneck : 1.3.2 fastparquet : None gcsfs : None lxml.etree : 4.2.6 matplotlib : 3.2.1 numexpr : 2.7.1 odfpy : None openpyxl : 2.5.9 pandas_gbq : 0.11.0 pyarrow : 0.14.1 pytables : None s3fs : 0.4.0 scipy : 1.4.1 sqlalchemy : 1.3.15 tables : 3.4.4 xarray : 0.15.0 xlrd : 1.1.0 xlwt : 1.3.0 xlsxwriter : None </details>
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DOC: Mention default behaviour of index_col in readcsv
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1
2020-03-24T14:19:17Z
2020-04-07T00:23:49Z
2020-04-07T00:23:21Z
CONTRIBUTOR
null
- [x] closes #32664 - [ ] tests added / passed (Not needed) - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry (Not needed)
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DOC: Fix capitalization among headings in documentation files (#32550)
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2
2020-03-24T15:09:51Z
2020-04-07T18:29:24Z
2020-04-07T01:27:42Z
CONTRIBUTOR
null
Files updated: ``` - [ ] doc/source/development/policies.rst - [ ] doc/source/development/roadmap.rst - [ ] doc/source/ecosystem.rst - [ ] doc/source/getting_started/comparison/comparison_with_sas.rst - [ ] doc/source/getting_started/comparison/comparison_with_sql.rst ``` more files updated in different PRs: #32944, #32991
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587,035,963
MDExOlB1bGxSZXF1ZXN0MzkzMDUzMTQx
32,979
Add error message for tz_localize
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2020-03-24T15:15:46Z
2020-06-14T17:26:22Z
2020-06-14T17:26:22Z
CONTRIBUTOR
null
- [x] closes #32967 - [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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32,980
DOC: Fix examples in reshape
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2020-03-24T15:25:40Z
2020-03-29T09:23:40Z
2020-03-27T21:08:52Z
MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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32,981
TST: Move _get_cython_table_params into pandas/_testing.py
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2020-03-24T15:57:22Z
2020-03-24T19:35:43Z
2020-03-24T19:35:39Z
CONTRIBUTOR
null
Part of #30914 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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32,982
ImportError: Unable to import required dependencies. Most likely due to a circular import. Conflict with numpy
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2020-03-24T17:19:26Z
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<!-- Please describe the issue in detail here, and fill in the fields below --> I am working in python 3.8.1 to be specific. This error appears when runing script in conda environment and also in docker image. ### Reproducing code example: <!-- A short code example that reproduces the problem/missing feature. It should be self-contained, i.e., possible to run as-is via 'python myproblem.py' --> ``` conda create -n analzyer python=3.8.1 conda activate analyzer pip install -r requirements.txt python db_initiator.py ``` ``` # Requirements.txt numpy==1.18.2 # This package has conflict pandas==1.0.1 PyQt5==5.14.1 request==2019.4.13 requests==2.23.0 requests_oauthlib==1.3.0 pytest==5.3.5 psycopg2-binary==2.8.4 SQLAlchemy==1.3.15 ``` ``` # db_initiator.py from sqlalchemy import Column, Integer, String, ForeignKey ``` <!-- Remove these sections for a feature request --> ### Error message: ``` Traceback (most recent call last): File "/home/greg/_Local/git/TwitterAnalyzer/twitter_analyzer/analyzer/db_initiator.py", line 1, in <module> from sqlalchemy import Column, Integer, String, ForeignKey File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/sqlalchemy/__init__.py", line 8, in <module> from . import util as _util # noqa File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/sqlalchemy/util/__init__.py", line 9, in <module> from collections import defaultdict # noqa File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/collections/__init__.py", line 21, in <module> from operator import itemgetter as _itemgetter, eq as _eq File "/home/greg/_Local/git/TwitterAnalyzer/twitter_analyzer/analyzer/operator.py", line 6, in <module> import pandas as pd File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/pandas/__init__.py", line 16, in <module> raise ImportError( ImportError: Unable to import required dependencies: numpy: IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. - Try uninstalling and reinstalling numpy. - If you have already done that, then: 1. Check that you expected to use Python3.8 from "/home/greg/anaconda3/envs/analyzer/bin/python", and that you have no directories in your PATH or PYTHONPATH that can interfere with the Python and numpy version "1.18.2" you're trying to use. 2. If (1) looks fine, you can open a new issue at https://github.com/numpy/numpy/issues. Please include details on: - how you installed Python - how you installed numpy - your operating system - whether or not you have multiple versions of Python installed - if you built from source, your compiler versions and ideally a build log - If you're working with a numpy git repository, try `git clean -xdf` (removes all files not under version control) and rebuild numpy. Note: this error has many possible causes, so please don't comment on an existing issue about this - open a new one instead. Original error was: cannot import name 'namedtuple' from partially initialized module 'collections' (most likely due to a circular import) (/home/greg/anaconda3/envs/analyzer/lib/python3.8/collections/__init__.py) Process finished with exit code 1 ``` ### Numpy/Python version information: ``` numpy==1.18.2 pandas==1.0.1 ``` <!-- Output from 'import sys, numpy; print(numpy.__version__, sys.version)' --> I can not do this. Got error when importing numpy itself ``` import sys, numpy; print(numpy.__version__, sys.version) ``` ``` >>> import numpy Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/numpy/__init__.py", line 142, in <module> from . import core File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/numpy/core/__init__.py", line 24, in <module> from . import multiarray File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/numpy/core/multiarray.py", line 9, in <module> import functools File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/functools.py", line 17, in <module> from collections import namedtuple File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/collections/__init__.py", line 21, in <module> from operator import itemgetter as _itemgetter, eq as _eq File "/home/greg/_Local/git/TwitterAnalyzer/twitter_analyzer/analyzer/operator.py", line 6, in <module> import pandas as pd File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/pandas/__init__.py", line 22, in <module> from pandas.compat.numpy import ( File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/site-packages/pandas/compat/__init__.py", line 10, in <module> import platform File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/platform.py", line 117, in <module> import re File "/home/greg/anaconda3/envs/analyzer/lib/python3.8/re.py", line 313, in <module> @functools.lru_cache(_MAXCACHE) AttributeError: partially initialized module 'functools' has no attribute 'lru_cache' (most likely due to a circular import) ``` From my test, I know that conflict is when I install `pandas` and `numpy` only. Error is the same when importing numpy. ``` $ pip list Package Version --------------- ------------------- certifi 2019.11.28 numpy 1.18.2 pandas 1.0.3 pip 20.0.2 python-dateutil 2.8.1 pytz 2019.3 setuptools 46.1.1.post20200323 six 1.14.0 wheel 0.34.2 ```
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32,983
DOC: Fix orc link
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0
2020-03-24T16:29:52Z
2020-03-24T20:10:53Z
2020-03-24T20:10:36Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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587,160,294
MDExOlB1bGxSZXF1ZXN0MzkzMTU4MTEw
32,984
PERF: Enable %z in parsing datetime
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7
2020-03-24T17:48:46Z
2020-04-17T14:23:47Z
2020-03-29T15:54:50Z
CONTRIBUTOR
null
- [ ] closes #32792 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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587,250,533
MDExOlB1bGxSZXF1ZXN0MzkzMjMyMzM3
32,985
CLN: Remove NameResolutionError
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2
2020-03-24T20:12:36Z
2020-03-26T01:25:03Z
2020-03-26T01:20:03Z
MEMBER
null
Another small cleanup (this exception was removed in 0.14.0 according to release notes)
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MDExOlB1bGxSZXF1ZXN0MzkzMjM4MDg2
32,986
DOC: Modify validate_rst_title_capitalization.py script
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2
2020-03-24T20:25:36Z
2020-03-24T22:54:54Z
2020-03-24T22:54:54Z
CONTRIBUTOR
null
Regarding issue #32550. @datapythonista Modify script in order to not take into account words with 2 or more capital letters, which should all be proper words.
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MDExOlB1bGxSZXF1ZXN0MzkzMjQ1MDU0
32,987
CLN: Fix linting
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1
2020-03-24T20:41:32Z
2020-03-25T02:52:30Z
2020-03-25T02:51:24Z
MEMBER
null
I think this fixes the linting problem in the CI
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MDU6SXNzdWU1ODczMDg1MjY=
32,988
REGR: `replace` casts columns to `object`
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5
2020-03-24T22:06:12Z
2020-05-09T20:07:43Z
2020-05-09T20:07:43Z
NONE
null
#### Calling `df.replace` casts columns to `object` ```python pd.DataFrame(np.eye(2)).replace(to_replace=[None, -np.inf, np.inf], value=pd.NA).dtypes # 0 object # 1 object # dtype: object ``` #### Problem description I'd expect that the dtypes of the columns remain the same after replacing with pd.NA (especially considering no values are replaced in the above call). We do not get this issue if `to_replace` is any subset of `[None, -np.inf, np.inf]`. We get the same issue if `value` is instead `np.nan`. #### Expected Output ```python # 0 float64 # 1 float64 # dtype: object ``` #### Output of ``pd.show_versions()`` <details> Installed with `pip install -e .` on current master. Was also able to reproduce on `1.0.1`. INSTALLED VERSIONS ------------------ commit : bed9103e52ae412774c22b8e198f7b66708981c7 python : 3.7.0.final.0 python-bits : 64 OS : Darwin OS-release : 18.7.0 Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 0.26.0.dev0+2701.gbed9103e5 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.1.1.post20200323 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 7.13.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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32,989
Int64 Series input to numpy.polyfit() fails
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4
2020-03-24T22:21:22Z
2021-07-30T04:20:15Z
null
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import numpy as np import pandas as pd x = pd.Series([1,2,3]).astype('Int64') y = pd.Series([4,4,6]) np.polyfit(x,y,1) ``` #### Problem description The code raises from within Numpy: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<__array_function__ internals>", line 6, in polyfit File "C:\Anaconda3\lib\site-packages\numpy\lib\polynomial.py", line 609, in polyfit rcond = len(x)*finfo(x.dtype).eps File "C:\Anaconda3\lib\site-packages\numpy\core\getlimits.py", line 381, in __new__ raise ValueError("data type %r not inexact" % (dtype)) ValueError: data type <class 'numpy.object_'> not inexact ``` However, the same Series with ``dtype=int64`` do not raise. This happens for both ``pandas=0.24.0`` and ``pandas=1.0.3`` Since this dtype is documented as experimental I opened it as a pandas issue. Not sure if this is related to #29738 . #### Expected Output ``` array([1. , 2.66666667]) ``` #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Windows OS-release : 7 machine : AMD64 processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.0.3 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 45.2.0.post20200209 Cython : None pytest : 4.5.0 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.1 IPython : 7.13.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : 1.3.0 fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.2.0 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : 4.5.0 pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None </details> xref [https://github.com/mwaskom/seaborn/issues/1971](https://github.com/mwaskom/seaborn/issues/1971) cc @mojones who opened the linked seaborn issue
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32,990
BUG: Multiple unstack using row index level labels and multi level columns DataFrame
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5
2020-03-24T23:12:52Z
2020-03-26T22:46:31Z
2020-03-26T20:08:09Z
MEMBER
null
- [x] closes #24729 - [x] closes #28306 - [x] closes #32624 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry This should fix the issue with a long list of indices and unstack. The issue appeared with 6 indices for the first time.
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32,991
DOC: Fix capitalization among headings in documentation files (#32550)
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6
2020-03-24T23:16:27Z
2020-03-30T16:34:56Z
2020-03-30T16:32:42Z
CONTRIBUTOR
null
Following #32978 and #32944. Headers updated for the following files: ``` - [ ] doc/source/getting_started/comparison/comparison_with_stata.rst - [ ] doc/source/getting_started/intro_tutorials/01_table_oriented.rst - [ ] doc/source/getting_started/tutorials.rst - [ ] doc/source/reference/arrays.rst - [ ] doc/source/reference/window.rst - [ ] doc/source/user_guide/boolean.rst ```
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pd.NA TypeError in drop_duplicates with object dtype
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2020-03-24T23:55:10Z
2020-04-24T22:25:24Z
2020-04-24T22:25:24Z
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#### Code Sample, a copy-pastable example if possible ```python-traceback >>> pd.DataFrame([[1, pd.NA], [2, "a"]]).drop_duplicates() Traceback (most recent call last): ... File "/Users/williamayd/miniconda3/envs/sitka/lib/python3.8/site-packages/pandas/core/frame.py", line 4859, in f labels, shape = algorithms.factorize( File "/Users/williamayd/miniconda3/envs/sitka/lib/python3.8/site-packages/pandas/core/algorithms.py", line 629, in factorize codes, uniques = _factorize_array( File "/Users/williamayd/miniconda3/envs/sitka/lib/python3.8/site-packages/pandas/core/algorithms.py", line 478, in _factorize_array uniques, codes = table.factorize(values, na_sentinel=na_sentinel, na_value=na_value) File "pandas/_libs/hashtable_class_helper.pxi", line 1806, in pandas._libs.hashtable.PyObjectHashTable.factorize File "pandas/_libs/hashtable_class_helper.pxi", line 1728, in pandas._libs.hashtable.PyObjectHashTable._unique File "pandas/_libs/missing.pyx", line 360, in pandas._libs.missing.NAType.__bool__ TypeError: boolean value of NA is ambiguous ``` This same failure isn't present when using an extension type: ```python >>> df = pd.DataFrame([[1, pd.NA], [2, "a"]], columns=list("ab")) >>> df["b"] = df["b"].astype("string") >>> df.drop_duplicates() a b 0 1 <NA> 1 2 a ```
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df.drop_duplicates() not working as expected
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9
2020-03-25T00:36:38Z
2021-08-07T22:38:43Z
2021-08-07T22:38:43Z
NONE
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#### Code Sample, a copy-pastable example ```python df = pd.DataFrame([ ['Microsoft'], ['Microsoft\x00quq2a<ScRiPt>alert(1)</ScRiPt>srfe1'], ['../../../../../../../../../../../../../../../../etc/passwd\x00Microsoft'], ['..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\windows\\win.ini\x00Microsoft'], ['Microsoft\x00vp30o<script>alert(1)</script>vz075'], ['../../../../../../../../../../../../../../../../etc/passwdX\x00Microsoft'], ['..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\windows\\win.ini'], ['..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\windows\\win.iniX\x00Microsoft'], ['../../../../../../../../../../../../../../../../etc/passwd'], ['../../../../../../../../../../../../../../../../etc/passwdX'], ['..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\..\\windows\\win.iniX']]) df.drop_duplicates() ``` #### Problem description If you try running the code above, you see only rows `0, 2, 3, 5, and 7` are retained. However, the actual strings are all unique, and I would have expected `drop_duplicates()` to retain all rows. **It's looking like `drop_duplicates()` only compares strings up to non-printing characters, then everything else gets ignored in the comparisons**. Also, to note, I discovered this bug since I originally had these strings in a set and I didn't expect `drop_duplicates()` to do anything, but it did. I've verified on `pandas.__version__ == '1.0.3'` (installed via anaconda on linux in a new test environment), and I looked for other existing issues, but didn't find anything that seemed to match (although #11376 seems to be close/ in the same vein). #### Expected Output (github markdown is highlighting some of the rows red, for some reason, but this is unrelated to what should be shown.) ``` python >>> df 0 0 Microsoft 1 Microsoftquq2a<ScRiPt>alert(1)</ScRiPt>srfe1 2 ../../../../../../../../../../../../../../../.... 3 ..\..\..\..\..\..\..\..\..\..\..\..\..\..\..\.... 4 Microsoftvp30o<script>alert(1)</script>vz075 5 ../../../../../../../../../../../../../../../.... 6 ..\..\..\..\..\..\..\..\..\..\..\..\..\..\..\.... 7 ..\..\..\..\..\..\..\..\..\..\..\..\..\..\..\.... 8 ../../../../../../../../../../../../../../../.... 9 ../../../../../../../../../../../../../../../.... 10 ..\..\..\..\..\..\..\..\..\..\..\..\..\..\..\.... ``` #### Output of ``pd.show_versions()`` (ignoring rows with "None") <details> ``` INSTALLED VERSIONS ------------------ python : 3.8.2.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-88-generic machine : x86_64 processor : x86_64 byteorder : little LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.0.3 numpy : 1.18.1 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.1.1.post20200322 ``` </details>
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REF: collect DataFrame.drop, Series.drop tests
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BUG: DataFrame.diff(axis=1) with mixed (or EA) dtypes
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2020-03-25T01:31:54Z
2020-04-10T18:18:23Z
2020-04-10T16:05:53Z
MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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32,996
REF: collect TimedeltaIndex.delete tests
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0
2020-03-25T02:54:46Z
2020-03-26T00:40:29Z
2020-03-25T23:57:00Z
MEMBER
null
organize the tests left in test_indexing; i made that file a while back and had some pretty inconsistent ideas about what belonged there
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32,997
WIP/PERF: block-wise ops for frame-with-series axis=1
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2020-03-25T03:57:50Z
2021-11-20T23:23:13Z
2020-04-03T16:50:15Z
MEMBER
null
xref #32779 which does the same thing for frame-with-frame ops. This one is turning out to be the most difficult of the cases to get right. Posting this for comments, in large part because there are still two failing tests locally, and these may just be behaviors we want to change. ``` # test_scalar_na_logical_ops_corners s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)]) s[::2] = np.nan d = DataFrame({"A": s}) with pytest.raises(TypeError): d.__and__(s, axis="columns") # <-- when done block-wise, this does not raise ``` The other one is harder to post an example of because it uses fixtures making it a PITA to copy/paste from the test, but the upshot is that `op(frame[mixed-float-dtypes], series[float64])` downcasts to the original dtypes ATM (since each column is effectively operating against a scalar) but when done blockwise they all come back float64.
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32,998
df.pct_change() not working as expected
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2020-03-25T04:27:23Z
2020-03-25T05:00:33Z
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It appears that pct_change is handling signs wrongly! ```python df['% Change']=df['A'].pct_change().fillna(0)*100 ``` #### Problem description #### Current output from dataframe index | A | % Change 1 | -482.411 | 0.000000 2 | -357.073 | -25.981580 3 | -506.110 | 41.738524 #### Expected Output from the dataframe index | A | % Change 1 | -482.411 | 0.000000 2 | -357.073 | 25.981580 3 | -506.110 | -41.738524 #### Output of ``pd.show_versions()`` INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.0.1
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32,999
DOC: Fix PR01, PR02, RT03 on cumulative function
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2020-03-25T06:41:20Z
2020-03-25T06:44:50Z
2020-03-25T06:41:49Z
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- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Related to #27977.
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DOC: Fix PR01, PR02, RT03 on cumulative function
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2020-03-25T06:49:34Z
2020-03-25T23:49:35Z
2020-03-25T23:49:31Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Related to #27977.
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Idea: Make pd.Index.values not writable
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2020-03-25T07:38:07Z
2021-07-30T04:22:31Z
null
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd a = pd.DataFrame({"col1": [1, 2, 3]}, index=["a", "b", "c"]) b = a.copy() b.index.values[0] = "newval" assert a.index[0] == "newval" ``` #### Problem description `Index` objects are assumed to be immutable, but wrap a mutable object. In my experience, it's pretty common to just grab the `.values` from pandas object. This has caused surprising behaviour in my own code, and has been discussed here before https://github.com/pandas-dev/pandas/issues/19862. I would like to make sure that the values numpy array for index objects has it's`WRITABLE` flag set to false, to make `Index` object more immutable. Does this sound reasonable? If so, I can give a PR a shot. #### Expected Output ```python # This should have thrown an error b.index.values[0] = "newval" ``` #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] INSTALLED VERSIONS ------------------ commit : None python : 3.7.6.final.0 python-bits : 64 OS : Darwin OS-release : 19.3.0 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.0.3 numpy : 1.18.2 pytz : 2018.9 dateutil : 2.7.5 pip : 20.0.2 setuptools : 46.0.0 Cython : 0.29.14 pytest : 5.3.5 hypothesis : 5.6.0 sphinx : 2.4.4 blosc : None feather : None xlsxwriter : None lxml.etree : 4.4.1 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.1 IPython : 7.13.0 pandas_datareader: None bs4 : 4.8.2 bottleneck : None fastparquet : 0.3.3 gcsfs : None lxml.etree : 4.4.1 matplotlib : 3.2.0 numexpr : 2.7.1 odfpy : None openpyxl : 2.6.2 pandas_gbq : None pyarrow : 0.16.0 pytables : None pytest : 5.3.5 pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : 3.6.1 tabulate : 0.8.3 xarray : 0.15.0 xlrd : 1.2.0 xlwt : None xlsxwriter : None numba : 0.48.0 </details>
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33,002
Timestamps with different same tzinfo but different tzinfo type (datetime.timezone vs pytz.UTC)
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2020-03-25T08:59:45Z
2020-03-25T18:08:58Z
2020-03-25T18:08:57Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pytz import datetime import pandas as pd date = pd.Timestamp("13-12-2020",tzinfo=datetime.timezone.utc) date2 = pd.Timestamp("12-12-2020",tzinfo=pytz.UTC) date - date2 >>> Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pandas/_libs/tslibs/c_timestamp.pyx", line 300, in pandas._libs.tslibs.c_timestamp._Timestamp.__sub__ TypeError: Timestamp subtraction must have the same timezones or no timezones ``` #### Problem description Current's timezone comparison is not timzone object type sensitive. Even if two dates are UTC but different tzinfo object (e.g: datetime.timezone.utc and pytz.UTC) they will be considered different timezones. This could lead to unstabilitites since a Timestamp can have both obejct types as tzinfo #### Expected Output ```python import pytz import datetime import pandas as pd date = pd.Timestamp("13-12-2020",tzinfo=datetime.timezone.utc) date2 = pd.Timestamp("12-12-2020",tzinfo=pytz.UTC) date - date2 >>> Timedelta('1 days 00:00:00') ``` #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] INSTALLED VERSIONS ------------------ commit : None python : 3.6.9.final.0 python-bits : 64 OS : Linux OS-release : 4.4.0-18362-Microsoft machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.0.3 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 19.3.1 setuptools : 45.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.1 IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.2.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None </details>
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33,003
Extra factorization for inner join(documentation issue)
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2020-03-25T09:32:32Z
2020-03-31T03:04:18Z
2020-03-31T03:04:18Z
NONE
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#### Code Sample, a copy-pastable example if possible ```python # Your code here ``` #### Problem description Hi, I'm trying to understand the pandas source code for an **inner join** using pandas.merge. I've got a sense that the procedure is basically factorizing both the left keys and the right keys to values like [0,1,2,3...], and then join the table together based on the factorization result. The point that I'm confused about is that the keys from the two tables are first factorized at this point https://github.com/pandas-dev/pandas/blob/28e0f18a30451601e8ac37b3ed75777bf1ed8f10/pandas/core/reshape/merge.py#L1274. Then there is an optional factorization happening in this function: https://github.com/pandas-dev/pandas/blob/28e0f18a30451601e8ac37b3ed75777bf1ed8f10/pandas/core/reshape/merge.py#L1281. Also there is another mandatory factorization at this line: https://github.com/pandas-dev/pandas/blob/28e0f18a30451601e8ac37b3ed75777bf1ed8f10/pandas/core/reshape/merge.py#L1286. After the first factorization, I could already transform the keys to sth like [0,1,2,3..], what is the point to factorize it again? There are comments mentioning that it's flatting the keys and making it dense. I'm not sure what it means and could you tell me an easy example that could use the later factorizations? To clarify again, I'm only talking about the inner join case. Thank you sincerely for your help! #### Expected Output #### Output of ``pd.show_versions()``
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DOC: for pandas.read_json() using orient='table' add docu about schema spec
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2020-03-25T09:39:06Z
2020-03-27T06:39:53Z
2020-03-27T06:39:53Z
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Please provide documentation about what kind of type specifications are supported when using the `pandas.read_json()` function with `orient` = 'table'. Currently the [function documentation](https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.read_json.html#pandas.read_json) does not even mention 'table' under "The set of possible orients is". There is only a small example in the "Encoding with Table Schema" section.
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STY: Boolean values for bint variables
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2020-03-25T10:39:02Z
2020-03-25T23:54:48Z
2020-03-25T23:44:41Z
MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry --- ```asv``` benchmarks: to show that performance is not changed with the stylistic changes) ``` All benchmarks: before after ratio [28e0f18a] [6f1fd78d] <master> <STY-bint-boolean-not-int-join> 21.4±1ms 19.8±0.4ms 0.92 join_merge.Join.time_join_dataframe_index_multi(False) 24.9±0.3ms 23.6±0.3ms 0.95 join_merge.Join.time_join_dataframe_index_multi(True) 14.5±0.3ms 14.0±0.3ms 0.97 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(False) 17.6±0.6ms 16.7±0.5ms 0.95 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(True) 14.4±0.3ms 13.8±0.2ms 0.95 join_merge.Join.time_join_dataframe_index_single_key_bigger(False) 17.8±0.7ms 16.4±0.5ms 0.92 join_merge.Join.time_join_dataframe_index_single_key_bigger(True) 13.5±0.3ms 13.1±0.3ms 0.97 join_merge.Join.time_join_dataframe_index_single_key_small(False) 14.5±0.1ms 13.9±0.5ms 0.96 join_merge.Join.time_join_dataframe_index_single_key_small(True) 2.86±0.08s 2.94±0.05s 1.03 join_merge.JoinIndex.time_left_outer_join_index 433±70μs 428±50μs 0.99 join_merge.JoinNonUnique.time_join_non_unique_equal ```
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BUG: query uses numexpr even when 'compute.use_numexpr' is False
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2020-03-25T10:46:26Z
2021-07-22T02:32:55Z
2020-11-26T19:12:14Z
CONTRIBUTOR
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- [ ] closes #32556 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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33,007
min of Index does not have required axis= keyword for np.min
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2020-03-25T11:16:55Z
2020-03-25T11:28:07Z
2020-03-25T11:28:07Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python # Your code here > from pandas import DataFrame > import bumpy as np > df = DataFrame( [[10,20,30]], index=[0], columns=[1,2,3]) > df.columns.min() 1 > min(df.columns) 1 > np.min( df.columns ) TypeError: min() got an unexpected keyword argument 'axis' ``` #### Problem description The np.min (or max, ... etc) function failed probably because the .min method of Index does not have the axis keyword np.min should behave like min builtin #### Expected Output 1 #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None pandas: 0.23.4 pytest: 4.0.2 pip: 18.1 setuptools: 40.6.3 Cython: 0.29.2 numpy: 1.15.4 scipy: 1.1.0 pyarrow: None xarray: None IPython: 7.2.0 sphinx: 1.8.2 patsy: 0.5.1 dateutil: 2.7.5 pytz: 2018.7 blosc: None bottleneck: 1.2.1 tables: 3.4.4 numexpr: 2.6.8 feather: None matplotlib: 3.0.2 openpyxl: 3.0.0 xlrd: 1.2.0 xlwt: 1.3.0 xlsxwriter: 1.1.2 lxml: 4.2.5 bs4: 4.6.3 html5lib: 1.0.1 sqlalchemy: 1.2.15 pymysql: None psycopg2: None jinja2: 2.10 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None </details>
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STY: Boolean values for bint variables
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2020-03-26T15:40:16Z
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STY: Boolean values for bint variables
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33,010
add match to bare pyteset.raises() - arrays
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2020-03-25T11:46:48Z
2020-03-31T03:18:04Z
2020-03-30T22:41:55Z
CONTRIBUTOR
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- [x] ref #30999 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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pandas.errors.EmptyDataError: No columns to parse from file despite file being populated
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2020-08-13T07:26:31Z
2020-05-02T10:36:49Z
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#### Code Sample, a copy-pastable example if possible ```python df = pd.read_csv(filename, sep = r'\s{1,}') ``` #### Problem description I am trying to read in a txt as a pandas dataframe that I previous edited a column header in using `.replace()` and I get the error: ``` Traceback (most recent call last): File "dbcan_to_EC.py", line 26, in <module> EC_generator(directory) File "dbcan_to_EC.py", line 20, in EC_generator df = pd.read_csv(directory, sep = " ") File "/home/lamma/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 685, in parser_f return _read(filepath_or_buffer, kwds) File "/home/lamma/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 457, in _read parser = TextFileReader(fp_or_buf, **kwds) File "/home/lamma/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 895, in __init__ self._make_engine(self.engine) File "/home/lamma/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1135, in _make_engine self._engine = CParserWrapper(self.f, **self.options) File "/home/lamma/.local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1917, in __init__ self._reader = parsers.TextReader(src, **kwds) File "pandas/_libs/parsers.pyx", line 545, in pandas._libs.parsers.TextReader.__cinit__ pandas.errors.EmptyDataError: No columns to parse from file ``` #### Sample of the inut file ``` Gene_ID HMMER Hotpep DIAMOND #ofTools M32_00003 CBM16(54-167)+GH18(293-521) GH18(11)+CBM16(1) CBM16+GH18 3 M32_00048 GH87(65-567) GH87(2) GH87 3 M32_00053 GH13_30(36-384) GH13(4) GH13_30 3 M32_00083 CE4(60-180) CE4(33) CE4 3 M32_00093 GH16(86-322) GH16(2)+CBM11(2) GH16 3 M32_00101 GH15(232-611) GH15(2) GH15 3 M32_00106 GH114(62-231) GH114(2) GH114 3 M32_00164 GH3(61-286) GH3(15) GH3 3 M32_00173 GH1(30-487) GH1(2) GH1 3 M32_00174 GH2(358-783) GH2(6) GH2 3 ``` #### Output of ``pd.show_versions()`` INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Linux OS-release : 3.10.0-1062.1.1.el7.x86_64 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 : 0.25.1 numpy : 1.17.3 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.4.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None
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33,012
PERF: Performance regression with memory_usage(deep=True) on `object` columns
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9
2020-03-25T12:35:52Z
2020-05-05T16:04:39Z
2020-03-31T00:11:46Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import time import pandas as pd import numpy as np df = pd.DataFrame({ "a": np.empty(10000000), "b": np.empty(10000000), }) df["a"] = df["a"].astype("object") s = time.time() mem = df.memory_usage(deep=True) print("memory_usage(deep=True) took %.4fsecs" % (time.time() - s)) ``` #### Problem description Performance of `memory_usage(deep=True)` on `object` columns seems to have regressed significantly since v0.23.4. Once in v0.24.0, and another regression in v1.0.0 that remains in v1.0.3. **Output** v1.0.3 ``` memory_usage(deep=True) took 26.4566secs ``` v0.24.0 ``` memory_usage(deep=True) took 6.0479secs ``` v0.23.4 ``` memory_usage(deep=True) took 0.4633secs ``` Removing `df["a"] = df["a"].astype("object")` reverts it back to the expected magnitude of speed in v1.0.3: ``` memory_usage(deep=True) took 0.0024secs ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.6.5.final.0 python-bits : 64 OS : Linux OS-release : 3.16.0-77-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_AU.UTF-8 LOCALE : en_AU.UTF-8 pandas : 1.0.3 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.1.1 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None pytest : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None xlsxwriter : None numba : None </details>
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33,013
ENH: Support downcasting of nullable dtypes in to_numeric
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3
2020-03-25T12:50:15Z
2020-12-30T13:59:01Z
2020-12-30T13:59:01Z
MEMBER
null
This currently does not yet work: ``` In [5]: pd.to_numeric(pd.Series([1, 2, 3], dtype="Int64"), downcast='integer') --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-5-64a04efaaac0> in <module> ----> 1 pd.to_numeric(pd.Series([1, 2, 3], dtype="Int64"), downcast='integer') ~/scipy/pandas/pandas/core/tools/numeric.py in to_numeric(arg, errors, downcast) 179 for dtype in typecodes: 180 if np.dtype(dtype).itemsize <= values.dtype.itemsize: --> 181 values = maybe_downcast_to_dtype(values, dtype) 182 183 # successful conversion ~/scipy/pandas/pandas/core/dtypes/cast.py in maybe_downcast_to_dtype(result, dtype) 141 dtype = np.dtype(dtype) 142 --> 143 converted = maybe_downcast_numeric(result, dtype, do_round) 144 if converted is not result: 145 return converted ~/scipy/pandas/pandas/core/dtypes/cast.py in maybe_downcast_numeric(result, dtype, do_round) 234 return new_result 235 else: --> 236 if np.allclose(new_result, result, rtol=0): 237 return new_result 238 <__array_function__ internals> in allclose(*args, **kwargs) ~/miniconda3/envs/dev/lib/python3.7/site-packages/numpy/core/numeric.py in allclose(a, b, rtol, atol, equal_nan) 2169 2170 """ -> 2171 res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan)) 2172 return bool(res) 2173 <__array_function__ internals> in isclose(*args, **kwargs) ~/miniconda3/envs/dev/lib/python3.7/site-packages/numpy/core/numeric.py in isclose(a, b, rtol, atol, equal_nan) 2268 2269 xfin = isfinite(x) -> 2270 yfin = isfinite(y) 2271 if all(xfin) and all(yfin): 2272 return within_tol(x, y, atol, rtol) TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' ``` I suppose because under the hood a conversion to object dtype array is happening somewhere.
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587,688,673
MDU6SXNzdWU1ODc2ODg2NzM=
33,014
to_json should make separators configurable (similar to json.dump)
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7
2020-03-25T13:16:32Z
2020-11-16T10:42:20Z
null
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import numpy as np import pandas as pd print(pd.DataFrame([1]).to_json(indent=4)) ``` outputs ``` { "0":{ "0":1 } } ``` #### Problem description Any JSON prettyfier (including bare VS Code) wants to replace all `:` by `: `. So output of `to_json` should behave as `json.dump` does, at least when `indent is not None` (in which case the user clearly does not care about file size): > If specified, separators should be an (item_separator, key_separator) tuple. > **The default is `(', ', ': ')` if indent is None and `(',', ': ')` otherwise.** > To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace. https://docs.python.org/3/library/json.html#:~:text=To%20get%20the%20most%20compact%20JSON%20representation #### Expected Output ``` { "0": { "0": 1 } } ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.8.2.final.0 python-bits : 64 OS : Linux OS-release : 4.12.14-lp151.28.32-default machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.0.3 numpy : 1.18.2 pytz : 2019.3 dateutil : 2.8.1 pip : 20.0.2 setuptools : 46.1.1 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 1.2.8 lxml.etree : 4.5.0 html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : 4.8.0 bottleneck : None fastparquet : None gcsfs : None lxml.etree : 4.5.0 matplotlib : 3.2.1 numexpr : 2.7.1 odfpy : None openpyxl : 3.0.3 pandas_gbq : None pyarrow : None pytables : None pytest : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : 1.2.0 xlwt : None xlsxwriter : 1.2.8 numba : None </details>
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587,734,984
MDExOlB1bGxSZXF1ZXN0MzkzNjE5MTk5
33,015
Added RuntimeWarning to lib.fast_unique_multiple
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5
2020-03-25T14:25:39Z
2020-04-02T18:57:57Z
2020-04-02T14:25:03Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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33,016
DOC: Document axis for swaplevel, standardize elsewhere
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1
2020-03-25T14:47:00Z
2020-03-26T06:43:25Z
2020-03-26T03:18:26Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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