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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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https://api.github.com/repos/pandas-dev/pandas/issues/32918 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32918/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32918/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32918/events | https://github.com/pandas-dev/pandas/issues/32918 | 585,868,854 | MDU6SXNzdWU1ODU4Njg4NTQ= | 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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} | 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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} | 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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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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```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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} | 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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- [ ] 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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} | 1 | 2020-03-23T13:43:27Z | 2020-03-24T17:47:31Z | 2020-03-24T17:04:29Z | CONTRIBUTOR | null | - [ ] ref #30999
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
https://github.com/pandas-dev/pandas/issues/30999
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] | closed | false | null | [] | null | 1 | 2020-03-23T14:10:16Z | 2020-08-25T12:43:48Z | 2020-08-25T12:43:30Z | NONE | null | #### 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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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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} | 5 | 2020-03-23T15:57:17Z | 2020-03-29T16:34:50Z | 2020-03-29T16:08:42Z | MEMBER | null | - [ ] 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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... | open | false | null | [] | null | 2 | 2020-03-23T16:24:55Z | 2021-07-07T13:48:35Z | null | NONE | null | #### 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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} | 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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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/32936 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32936/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32936/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32936/events | https://github.com/pandas-dev/pandas/issues/32936 | 586,347,958 | 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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#### 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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} | 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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- [ ] passes `black pandas`
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cc @jbrockmendel
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```
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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```
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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```
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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} | 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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} | 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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} | 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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} | 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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} | 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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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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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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] | closed | false | null | [] | null | 4 | 2020-03-23T22:23:07Z | 2020-03-24T15:31:38Z | 2020-03-24T14:58:02Z | NONE | null | #### 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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} | 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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} | 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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} | 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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} | 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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>>> 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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} | 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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} | 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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} | 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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```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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} | 1 | 2020-03-24T14:19:17Z | 2020-04-07T00:23:49Z | 2020-04-07T00:23:21Z | CONTRIBUTOR | null | - [x] closes #32664
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```
- [ ] 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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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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] | closed | false | null | [] | null | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/32989 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32989/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32989/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32989/events | https://github.com/pandas-dev/pandas/issues/32989 | 587,315,121 | MDU6SXNzdWU1ODczMTUxMjE= | 32,989 | Int64 Series input to numpy.polyfit() fails | {
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"description": "pandas objects compatabi... | open | false | null | [] | null | 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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} | 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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} | 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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} | 5 | 2020-03-24T23:55:10Z | 2020-04-24T22:25:24Z | 2020-04-24T22:25:24Z | MEMBER | null | #### 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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"gi... | null | 9 | 2020-03-25T00:36:38Z | 2021-08-07T22:38:43Z | 2021-08-07T22:38:43Z | NONE | null | #### 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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- [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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} | 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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"default"... | closed | false | null | [] | null | 1 | 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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```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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- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977. | {
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} | 1 | 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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"description": "Related ... | open | false | null | [] | null | 13 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/33002 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/33002/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/33002/comments | https://api.github.com/repos/pandas-dev/pandas/issues/33002/events | https://github.com/pandas-dev/pandas/issues/33002 | 587,543,160 | MDU6SXNzdWU1ODc1NDMxNjA= | 33,002 | Timestamps with different same tzinfo but different tzinfo type (datetime.timezone vs pytz.UTC) | {
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} | [] | closed | false | null | [] | null | 1 | 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
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```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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} | 5 | 2020-03-25T10:39:02Z | 2020-03-25T23:54:48Z | 2020-03-25T23:44:41Z | MEMBER | null | - [ ] 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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"color": "f3aff... | closed | false | null | [] | null | 16 | 2020-03-25T10:46:26Z | 2021-07-22T02:32:55Z | 2020-11-26T19:12:14Z | CONTRIBUTOR | null | - [ ] closes #32556
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 2 | 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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} | 2 | 2020-03-25T11:46:48Z | 2020-03-31T03:18:04Z | 2020-03-30T22:41:55Z | CONTRIBUTOR | null | - [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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```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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} | 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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} | 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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} | 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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} | 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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} | 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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