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Backport PR #39316 on branch 1.2.x ([DOC] Update PR link in 1.2.0 release notes)
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Backport PR #39316: [DOC] Update PR link in 1.2.0 release notes
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BUG: Series.plot.bar() raises TypeError on timedelta dtype
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| 2021-04-25T04:03:31Z
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'ts1': pd.to_datetime([0, 1, 2, 3], unit='s'), 'ts2': pd.to_datetime([1, 1, 5, 4], unit='s')})
diff = df['ts2']-df['ts1']
diff.plot.bar()
```
#### Problem description
Pandas uses `fillna()` to produce valid data points. Unforunately, the data type `timedelta64[ns]` doesn't support setting to integer `0`.
Output:
<details><pre>
TypeError Traceback (most recent call last)
<ipython-input-25-b19437dbe585> in <module>
2 df = pd.DataFrame({'ts1': pd.to_datetime([0, 1, 2, 3], unit='s'), 'ts2': pd.to_datetime([1, 1, 5, 4], unit='s')})
3 diff = df['ts2']-df['ts1']
----> 4 diff.plot.bar()
~/.local/lib/python3.9/site-packages/pandas/plotting/_core.py in bar(self, x, y, **kwargs)
1111 other axis represents a measured value.
1112 """
-> 1113 return self(kind="bar", x=x, y=y, **kwargs)
1114
1115 @Appender(
~/.local/lib/python3.9/site-packages/pandas/plotting/_core.py in __call__(self, *args, **kwargs)
953 data.columns = label_name
954
--> 955 return plot_backend.plot(data, kind=kind, **kwargs)
956
957 __call__.__doc__ = __doc__
~/.local/lib/python3.9/site-packages/pandas/plotting/_matplotlib/__init__.py in plot(data, kind, **kwargs)
59 kwargs["ax"] = getattr(ax, "left_ax", ax)
60 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 61 plot_obj.generate()
62 plot_obj.draw()
63 return plot_obj.result
~/.local/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py in generate(self)
278 self._compute_plot_data()
279 self._setup_subplots()
--> 280 self._make_plot()
281 self._add_table()
282 self._make_legend()
~/.local/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py in _make_plot(self)
1419 K = self.nseries
1420
-> 1421 for i, (label, y) in enumerate(self._iter_data(fillna=0)):
1422 ax = self._get_ax(i)
1423 kwds = self.kwds.copy()
~/.local/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py in _iter_data(self, data, keep_index, fillna)
256 data = self.data
257 if fillna is not None:
--> 258 data = data.fillna(fillna)
259
260 for col, values in data.items():
~/.local/lib/python3.9/site-packages/pandas/core/frame.py in fillna(self, value, method, axis, inplace, limit, downcast)
4457 downcast=None,
4458 ) -> Optional[DataFrame]:
-> 4459 return super().fillna(
4460 value=value,
4461 method=method,
~/.local/lib/python3.9/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
6424
6425 elif not is_list_like(value):
-> 6426 new_data = self._mgr.fillna(
6427 value=value, limit=limit, inplace=inplace, downcast=downcast
6428 )
~/.local/lib/python3.9/site-packages/pandas/core/internals/managers.py in fillna(self, value, limit, inplace, downcast)
619
620 def fillna(self, value, limit, inplace: bool, downcast) -> "BlockManager":
--> 621 return self.apply(
622 "fillna", value=value, limit=limit, inplace=inplace, downcast=downcast
623 )
~/.local/lib/python3.9/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
425 applied = b.apply(f, **kwargs)
426 else:
--> 427 applied = getattr(b, f)(**kwargs)
428 except (TypeError, NotImplementedError):
429 if not ignore_failures:
~/.local/lib/python3.9/site-packages/pandas/core/internals/blocks.py in fillna(self, value, **kwargs)
2478 if is_integer(value):
2479 # Deprecation GH#24694, GH#19233
-> 2480 raise TypeError(
2481 "Passing integers to fillna for timedelta64[ns] dtype is no "
2482 "longer supported. To obtain the old behavior, pass "
TypeError: Passing integers to fillna for timedelta64[ns] dtype is no longer supported. To obtain the old behavior, pass `pd.Timedelta(seconds=n)` instead.
</pre></details>
#### Expected Output
Bar plot.
#### Output of ``pd.show_versions()``
<details><pre>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.8-200.fc33.x86_64
Version : #1 SMP Sun Jan 17 19:53:01 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1.1
setuptools : 51.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</pre></details>
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TST: csv_dir_path fixture check the existence of the dir instead of the file
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| 2021-01-21T17:57:39Z
| 2021-08-15T03:38:40Z
| null |
MEMBER
| null |
Currently, all test date files are excluded in distribution. But we are using `csv_dir_path` fixture to check the existence of the directory instead of the test file itself. It caused many failures in #38852. I excluded the dir `pandas/tests/io/parser/data` in that PR. Maybe should check the file instead of the dir.
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Backport PR #39314 on branch 1.2.x (DOC: clarify in which version the excel engine default changed)
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| 2021-01-21T23:50:43Z
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NONE
| null |
Backport PR #39314: DOC: clarify in which version the excel engine default changed
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MDExOlB1bGxSZXF1ZXN0NTU5NDUwMDEw
| 39,323
|
add test for fillna with column
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| 2021-01-21T18:07:40Z
| 2021-02-11T00:27:01Z
| 2021-02-11T00:27:01Z
|
NONE
| null |
- [X] tests added / passed
I noticed there was no test for pd.Series.fillna using a column, so I added one.
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MDU6SXNzdWU3OTEzOTg1Mjc=
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BUG: Series.to_json returning a native python float from a column of type int64
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| 2021-01-21T18:34:41Z
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
example_df = pandas.DataFrame({'a': [1], 'b': [1.0]})
# this passes (DataFrame.to_dict is called)
assert type(example_df.to_dict('records')[0]['a']) is int
# this passes (Series.to_dict is called but on a Series with no floats)
assert type(example_df[['a']].iloc[0].to_dict()['a']) is int
# this fails
assert type(example_df.iloc[0].to_dict()['a']) is int
# this fails
assert type(example_df.apply(pandas.Series.to_dict, axis=1).iloc[0]['a']) is int
```
#### Problem description
I have found another issue which involves type conversion / handling with the `DataFrame`/`Series` `to_dict` function. There are a number of other issues in this area that I found, but I think this one is unique.
Related issues:
- https://github.com/pandas-dev/pandas/issues/24908: this is an issue where the output is not being converted to the native python data type
- https://github.com/pandas-dev/pandas/issues/25969: again, this is "numpy -> native" type conversion not happening
- https://github.com/pandas-dev/pandas/issues/27616: numpy.boolean being returned
In most/all the cases I could find, the issue was that `to_dict` was returning the underlying `numpy` type and not converting to the native. In this case, I am getting a native python type, just the wrong one. It only happens when both are true:
- Series.to_dict is called
- The Series in question contains an int64 and a float64 column
#### Expected Output
I would expect all 3 assert statements to pass, which is to say that I should get data back with int type for the 'a' column. Visually speaking:
```python
In [7]: example_df.to_dict('records')[0]
Out[7]: {'a': 1, 'b': 1.0}
In [8]: example_df.iloc[0].to_dict()
Out[8]: {'a': 1.0, 'b': 1.0}. # a should not be a float here
In [11]: type(example_df.iloc[0].to_dict()['a'])
Out[11]: float # this should be int
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3OTE0MDk4MzU=
| 39,325
|
BUG: Read from S3 fails after assuming an AWS IAM role
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| 2021-01-21T18:53:15Z
| 2021-08-15T03:40:55Z
| 2021-08-15T03:40:55Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
assume_iam_role(role)
pd.read_csv(filepath)
```
#### Problem description
Hey all, I am having an issue where after assuming an AWS IAM role, I still get a forbidden error when running `pd.read_csv`. This error only happens in a Jupyter notebook. When just running a Python file, everything works (maybe related to the notebook kernel?).
Additionally, if I re-import Pandas after I assume the IAM role, then I am able to read from AWS without error.
Unfortunately, I have to assume my IAM role through a specific command that is internal to my company, so I am unable to share exactly how that part is happening (though I think it is mostly just calling the AWS CLI).
Could someone help me understand why this is happening and/or suggest a better way to handle this issue rather than re-importing pandas? I assume something is happening in the `__init__.py`, but after looking through this repo, I am unsure how the read from S3 stuff is being handled at all.
#### Expected Output
`pd.read_csv` works without error after assuming an IAM role.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.209-160.339.amzn2.x86_64
Version : #1 SMP Wed Dec 16 22:44:04 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.3.3.post20210118
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.5.2
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NTU5NDgyMTQ1
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|
BUG: SeriesGroupBy.value_counts() raising error on an empty Series
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| 4
| 2021-01-21T19:09:19Z
| 2021-01-22T23:26:57Z
| 2021-01-22T23:26:52Z
|
CONTRIBUTOR
| null |
- [x] closes #39172
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
|
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MDU6SXNzdWU3OTE1MTc3Njg=
| 39,327
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BUG: pandas.DataFrame.apply gives different results between pandas versions 1.1.4 and 1.2.0
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[] | null | 5
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| 2021-01-23T00:04:05Z
| 2021-01-23T00:04:04Z
|
NONE
| null |
- [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
df2 =pd.DataFrame(dict(one=[str((i/10)*100)+"%" for i in range(5)],
two=[str((i/20)*100)+"%" for i in range(10,15)],
three=[str((i/30)*100)+"%" for i in range(20,25)]))
# the following two lines give the same result as the other in 1.1.4 but different results in 1.2.0
df2.apply(lambda series: series.str.replace("%","").astype(float)/100, axis=1)
df2.apply(lambda series: series.str.replace("%","").astype(float)/100, axis=0)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
The two versions of pandas (1.1.4 and 1.2.0) give different results when `axis=1`
#### Expected Output
They should be the same when `axis=1`
#### Output of ``pd.show_versions()``
<details>
even though my "pandas 1.2.0 environment" is python 3.8.5 while my "pandas 1.1.4 environment" is python 3.7.9, the problem is resolved if I revert the python 3.8.5 environment to pandas 1.1.4.
pandas 1.1.4 environment
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.11.1.el7.x86_64
Version : #1 SMP Mon Nov 30 13:05:31 EST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
pandas 1.2.0 environment
```
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.11.1.el7.x86_64
Version : #1 SMP Mon Nov 30 13:05:31 EST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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API: DatetimeIndex union vs join different behavior with mismatched tzs
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| 3
| 2021-01-21T22:19:34Z
| 2021-05-14T16:09:46Z
| 2021-05-14T16:09:46Z
|
MEMBER
| null |
For union when we have mismatched tzs or mismatched tzawareness, we cast to object.
For join when we have mismatched tzawareness we raise TypeError, with mismatched tzs we cast to UTC.
It would be convenient if these had the same behavior.
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REGR: Assertion error in 1.2.1 but not 1.0.5 with groupby, resample, min
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| 3
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|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from datetime import datetime
from math import nan
def dt(datestr):
return datetime.strptime(datestr, '%Y-%m-%d')
df = pd.DataFrame(data={'Date_reported': {0: dt('2020-07-23'), 1: dt('2020-07-24'), 2: dt('2020-07-25'), 3: dt('2020-07-26'), 4: dt('2020-07-27'), 5: dt('2020-07-28'), 6: dt('2020-07-29'), 7: dt('2020-07-30'), 8: dt('2020-07-31'), 9: dt('2020-08-01'), 10: dt('2020-08-02'), 11: dt('2020-08-03'), 12: dt('2020-08-04'), 13: dt('2020-08-05'), 14: dt('2020-08-06'), 15: dt('2020-07-23'), 16: dt('2020-07-24'), 17: dt('2020-07-25'), 18: dt('2020-07-26'), 19: dt('2020-07-27'), 20: dt('2020-07-28'), 21: dt('2020-07-29'), 22: dt('2020-07-30'), 23: dt('2020-07-31'), 24: dt('2020-08-01'), 25: dt('2020-08-02'), 26: dt('2020-08-03'), 27: dt('2020-08-04'), 28: dt('2020-08-05'), 29: dt('2020-08-06')}, 'ISO_3_CODE': {0: 'AFG', 1: 'AFG', 2: 'AFG', 3: 'AFG', 4: 'AFG', 5: 'AFG', 6: 'AFG', 7: 'AFG', 8: 'AFG', 9: 'AFG', 10: 'AFG', 11: 'AFG', 12: 'AFG', 13: 'AFG', 14: 'AFG', 15: 'H63', 16: 'H63', 17: 'H63', 18: 'H63', 19: 'H63', 20: 'H63', 21: 'H63', 22: 'H63', 23: 'H63', 24: 'H63', 25: 'H63', 26: 'H63', 27: 'H63', 28: 'H63', 29: 'H63'}, 'Cumulative_cases': {0: 35915, 1: 35981, 2: 36036, 3: 36157, 4: 36263, 5: 36368, 6: 36471, 7: 36542, 8: 36542, 9: 36710, 10: 36710, 11: 36710, 12: 36747, 13: 36829, 14: 36896, 15: 47194, 16: 47856, 17: 48448, 18: 48952, 19: 49388, 20: 49825, 21: 50409, 22: 51000, 23: 51380, 24: 51942, 25: 52265, 26: 52490, 27: 52771, 28: 53457, 29: 53877}, 'Regional_office': {0: 'ROAP', 1: 'ROAP', 2: 'ROAP', 3: 'ROAP', 4: 'ROAP', 5: 'ROAP', 6: 'ROAP', 7: 'ROAP', 8: 'ROAP', 9: 'ROAP', 10: 'ROAP', 11: 'ROAP', 12: 'ROAP', 13: 'ROAP', 14: 'ROAP', 15: nan, 16: nan, 17: nan, 18: nan, 19: nan, 20: nan, 21: nan, 22: nan, 23: nan, 24: nan, 25: nan, 26: nan, 27: nan, 28: nan, 29: nan}})
result = df.groupby(['ISO_3_CODE']).resample('W', on='Date_reported').min()
```
#### Problem description
When running it in 1.21 it fails with:
```
assert len(result._mgr.blocks) == 1
AssertionError
```
That exception is raised from another:
```
NotImplementedError: function is not implemented for this dtype: [how->min,dtype->object]
```
#### Expected Output
When running the above code in 1.05 it works without error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-40-generic
Version : #45~20.04.1-Ubuntu SMP Fri Jan 15 11:35:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTU5NjY5OTUx
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REF: de-duplicate algos.try_sort calls
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- [ ] whatsnew entry
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REF: Fix PeriodIndex._outer_indexer -> share remaining set methods
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PERF: DatetimeIndex.get_indexer with mismatched tz
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MEMBER
| null |
Avoid casting to object dtype.
```
In [2]: dti = pd.date_range("2016-01-01", periods=10000, tz="US/Pacific")
In [3]: dti2 = dti.tz_convert("UTC")
In [4]: %timeit dti.get_indexer(dti2)
127 ms ± 4.69 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
352 µs ± 10.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
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TYP: @final for more Index methods
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG: Can't save pyarrow.lib.Decimal128Scalar to_parquet
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| 2021-01-29T15:14:38Z
| 2021-01-25T12:22:02Z
|
NONE
| null |
- [ X] I have checked that this issue has not already been reported.
- [X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Converting value to pyarrow.lib.Decimal128Scalar works in pyarrow:
```python
>>> t = pyarrow.array([decimal.Decimal('2478463143.1140136719')], pyarrow.decimal128(38,10))[0]
>>> print(t)
2478463143.1140136719
>>>
```
However when attempting to write this value as one of the columns to parquet (**_to_parquet_**) the following happens:
> pyarrow.lib.ArrowInvalid: ('Could not convert 2478463143.1140136719 with type pyarrow.lib.Decimal128Scalar: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column X with type object')
#### Problem description
It should be possible to write this value to parquet since it is a valid decimal with precision and scale
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: pd.get_dummies() returns copy of DataFrame for DataFrame with single integer dtype column
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| 2021-08-15T03:42:38Z
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MEMBER
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"C": [1, 2, 3]})
pd.get_dummies(df)
```
#### Problem description
see https://github.com/pandas-dev/pandas/issues/39110#issuecomment-758726915
output is
```
C
0 1
1 2
2 3
```
#### Expected Output
Empty DataFrame
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 7ceeef50e8cf5bdb678021511d7aa3b518f40b84
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.3.0.dev0+634.g7ceeef50e8
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.4
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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| 9
| 2021-01-22T12:46:17Z
| 2021-01-22T14:57:17Z
| null |
NONE
| null |
#### Problem description
Pandas float formatters ignore the nullable floating extension types `Float64` which was introduced with 1.2.0.
In particular, this is extremely confusing when using `DataFrame.convert_dtypes` since the default is now with `convert_floating=True` which converts all float colums to `Float64` columns.
That is, the following does not produce the expected float formatting:
```python
import pandas as pd
df = pd.DataFrame([0.123456789, 1.123456789, 2.123456789], columns=["value"])
df = df.convert_dtypes()
print(df.to_string(formatters=["{:.2f}".format]))
```
gives
```
value
0 0.123457
1 1.123457
2 2.123457
```
I am not sure, whether this classifies as a bug or missing documentation (or both) however having not heard of this new nullable floating type `Float64` I did not notice this subtle difference at first. Also, I did not expect `DataFrame.convert_dtypes()` to result in a non-formatable `DataFrame`. So, if `Float64` is not expected to format as `float64`, the documentation should make this clear.
However, I believe the best would be that `Float64` formats just as the regular float dtype.
#### Expected Output
The formatting should be applied to the `Float64` dtype just as when using the `float` dtype such as
```python
import pandas as pd
df = pd.DataFrame([0.123456789, 1.123456789, 2.123456789], columns=["value"])
print(df.to_string(formatters=["{:.2f}".format]))
```
which gives
```
value
0 0.12
1 1.12
2 2.12
```
#### Output of ``pd.show_versions()``
<details>
```python
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.9-arch1-1
Version : #1 SMP PREEMPT Tue, 19 Jan 2021 22:06:06 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_DK.UTF-8
LOCALE : en_DK.UTF-8
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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MDU6SXNzdWU3OTE5OTQ4MTI=
| 39,337
|
BUG: IndexError occuring on duplicate axis does not fail gracefully.
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open
| false
| null |
[] | null | 0
| 2021-01-22T13:13:06Z
| 2021-08-15T03:45:03Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
import numpy as np
# pd.show_versions()
## Adjust N for your machine
# n = 10000000
df = pd.DataFrame(np.arange(n), columns='a'.split()).assign(**{'ts':pd.date_range(start='2020-01-01', freq='1S', periods=n)})
df['td'] = df.ts.dt.normalize()
df = df.set_index(['td', 'ts'])
# Missing index level from reset
d = df.reset_index(1).groupby(pd.Grouper(level=0)).a.apply(lambda x: (x.shift() - x ) < pd.Timedelta(minutes=1))
d
# The assignment, usage of values solves index error
df['x'] = d.cumsum()#.values
df
```
#### Problem description
Assigning in my case causes very slow climbing in memory and if the dataframe is large enough, OOM killing.
The dataframe is similar is width to the example here, the equivalent multi-index is unique.
Raising this stops using hunting for memory leaks or bugs etc.
#### Expected Output
The error is raised quickly without requiring disproportionate memory.
#### Output of ``pd.show_versions()``
```
pandas : 1.2.1
numpy : 1.19.5
```
</details>
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| 39,338
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BUG: replace fails with IndexError when regex parameter is passed a dictionary with multiple items
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| 8
| 2021-01-22T14:53:23Z
| 2021-03-25T01:51:54Z
| 2021-03-25T01:51:54Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
data = df = pd.DataFrame({ 'a_str' : ['A1','A2','A3'],
'b_int' : ['1,000','200','3'],
'c_str' : ['C1','C2','C3'],
'd_date' : ['2021-01-01','','2021-03-03']})
non_string_columns = ['b_int','d_date']
df[non_string_columns] = df[non_string_columns].replace(regex={'':np.nan,',':''})
```
```python-traceback
Traceback (most recent call last):
File "c:\Users\donder\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydevd_bundle\pydevd_vars.py", line 416, in evaluate_expression
compiled = compile(_expression_to_evaluate(expression), '<string>', 'eval')
File "<string>", line 1
df[non_string_columns] = df[non_string_columns].replace(regex={'':np.nan,',':''})
^
SyntaxError: invalid syntax
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "E:\Python\Python38\lib\site-packages\pandas\core\frame.py", line 4521, in replace
return super().replace(
File "E:\Python\Python38\lib\site-packages\pandas\core\generic.py", line 6842, in replace
return self.replace(
File "E:\Python\Python38\lib\site-packages\pandas\core\frame.py", line 4521, in replace
return super().replace(
File "E:\Python\Python38\lib\site-packages\pandas\core\generic.py", line 6891, in replace
new_data = self._mgr.replace_list(
File "E:\Python\Python38\lib\site-packages\pandas\core\internals\managers.py", line 664, in replace_list
bm = self.apply(
File "E:\Python\Python38\lib\site-packages\pandas\core\internals\managers.py", line 427, in apply
applied = getattr(b, f)(**kwargs)
File "E:\Python\Python38\lib\site-packages\pandas\core\internals\blocks.py", line 901, in _replace_list
result = blk._replace_coerce(
File "E:\Python\Python38\lib\site-packages\pandas\core\internals\blocks.py", line 1643, in _replace_coerce
return self._replace_regex(
File "E:\Python\Python38\lib\site-packages\pandas\core\internals\blocks.py", line 844, in _replace_regex
replace_regex(new_values, rx, value, mask)
File "E:\Python\Python38\lib\site-packages\pandas\core\array_algos\replace.py", line 133, in replace_regex
values[mask] = f(values[mask])
IndexError: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 2
```
#### Problem description
In the dataframe columns specified by the non_string_column variable, the above code should be replacing all the blanks with np.nan and removing all the commas.
If I change the replace to look like the following, the dataframe is correct.
```python
df[non_string_columns] = df[non_string_columns].replace(regex={'':np.nan})
df[non_string_columns] = df[non_string_columns].replace(regex={',':''})
```
#### Expected Output
I expected the dataframe to look like the following:
```python
a_str b_int c_str d_date
0 A1 1000 C1 2021-01-01
1 A2 200 C2 NaN
2 A3 3 C3 2021-03-03
```
I get this error whether I am using Python 3.8.3 or 3.9.1. Also, the numpy version under Python 3.9.1 is 1.19.5.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 0, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.1
numpy : 1.18.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
None
</details>
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BUG / Question: Cant loop over Full/Complete DataFrame Index
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[] | null | 2
| 2021-01-22T14:53:46Z
| 2021-01-22T19:02:07Z
| 2021-01-22T19:02:07Z
|
NONE
| null |
I have been looping over dataframes from selected indexes. Works fine for the most part, however, when trying to loop over the entire dataframe (rather than using .query() and then saving the queried index), I get KeyError: 4393
Why I think this is a bug is because I can make it work by doing:
```
dfDbi = dfD.head(4390) # dataframe has 4393 keys, tried .copy() but no luck with the for loop
dfDbi = dfDbi.query('OC<0').index #saving index to later loop through
# pseudocode
for x in dfDbi:
if dfD.loc[x] and dfD.loc[x+1] condition true:
(do something)
```
The above works. Changing dfD.head to (4391) or higher does not.
What I want to do is:
```
dfDi = dfD.index
for x in dfDi:
if dfD[x] and dfD.loc[x+1] condition true:
(do something)
```
If I have butchered my explanation, please let me know so I can try to further explain.
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BUG: pandas.DataFrame.update with subset of MultiIndex fails without exception
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| 2021-01-22T17:14:42Z
| 2021-08-15T03:46:23Z
| null |
NONE
| null |
- [Yes] I have checked that this issue has not already been reported.
- [Yes] I have confirmed this bug exists on the latest version of pandas.
- [No] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Reproducible example:
```python
import pandas as pd
import numpy as np
# Prepare the data. We want to update df with dd.
df = pd.DataFrame(np.random.rand(10, 2), columns=['col1', 'col2'])
df['index_1'] = ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'c']
df['index_2'] = ['a', 'b', 'c', 'a', 'b', 'c', 'a', 'b', 'c', 'd']
df.set_index(['index_1', 'index_2'], inplace=True)
df.sort_index(inplace=True)
df['d'] = 0.0
dd = df.reset_index(drop=False).loc[:, ['index_1', 'd']].drop_duplicates()
dd['d'] = np.arange(3)
dd.set_index(['index_1'], inplace=True)
df.update(dd)
print(df)
```
#### Problem description
Updating a dataframe with a subset of a multiindex doesn't work as expected (no update is done), but also no exceptions are thrown to alert the user that the update failed. [The documentation of pandas.DataFrame.update](https://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.DataFrame.update.html) isn't specific about this behavior either.
Overall, ***I think*** there are three issues here:
1. Update fails when proper index alignment would work
2. An exception should be thrown to alert the user indices can not be aligned
3. The documentation should be more specific about this.
#### Expected Output
The reproduction code outputs
outputs
```
col1 col2 d
index_1 index_2
a a 0.795218 0.212346 0.0
b 0.465080 0.577137 0.0
c 0.400045 0.659623 0.0
b a 0.243119 0.338171 0.0
b 0.204571 0.432486 0.0
c 0.981068 0.169303 0.0
c a 0.097898 0.944876 0.0
b 0.864037 0.664877 0.0
c 0.795955 0.558834 0.0
d 0.614118 0.556195 0.0
```
While the expected output is
```
col1 col2 d
index_1 index_2
a a 0.795218 0.212346 0
b 0.465080 0.577137 0
c 0.400045 0.659623 0
b a 0.243119 0.338171 1
b 0.204571 0.432486 1
c 0.981068 0.169303 1
c a 0.097898 0.944876 2
b 0.864037 0.664877 2
c 0.795955 0.558834 2
d 0.614118 0.556195 2
```
That is, I would expect update to behave as if we had done
```python
df['d'] = dd.reindex(df.index.get_level_values(0))['d'].values
```
With the caveat that col1, col2 are random and just there as fillers.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.17-200.fc31.x86_64
Version : #1 SMP Mon Apr 13 15:29:42 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 50.3.2
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 8.0.0.dev
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
#### Something extra
The issue of update failing for the case of 2 indices updated with 1 could be easily fixed by using the _level_ argument of reindex, as used in by pandas.DataFrame.reindex_like, which is used by update. However, this wouldn't work if we were updating 3 indices from 2, because level doesn't accept a list.
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MEMBER
| null |
- [x] closes #38604
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Have to check how many columns key references before raising (duplicates or MultiIndex may reference more than one column)
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Tried fixing the Mypy Errors in parsers
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TST: fix incorrect extension test
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REF: consolidate in CSV parser module
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| 2021-01-23T06:05:47Z
| 2021-05-01T20:33:11Z
| null |
MEMBER
| null |
Follow-up to #38930, mentioned in #39217 and #38370
We'd like to consolidate the parser classes (C, python and pyarrow). One way to go would be to move the logic common to all parsers into ParserBase and delegate parser-specific logic into methods on the subclasses that ParserBase calls
IMO this is best done after the initial pyarrow engine PR (#38370) is merged (so as to to kill all the birds with one stone) but ofc completely up to whoever is doing the work. Creating this issue to track the discussion(s) in either case
cc @jreback @phofl
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ENH: Please add immutable/hashable FrozenDataFrame
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| 2021-01-23T11:45:07Z
| 2021-08-15T03:52:08Z
| 2021-08-15T03:52:07Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to use (algorithm, DataFrame) tuples as dict keys to record results of various experiments on various datasets, but while we can make immutable algorithms, we cannot make immutable DataFrames.
There are a bajillion benefits we miss by not enforcing immutability.
For example in parallelism, we need to pickle data to share, which requires hashing.
Caching often requires hash comparison to be performant (functools.lru_cache)
Dict keys must be hashable. Immutable data programs are easier to reason about.
The code can be simpler which allows faster easier development.
Many bugs can be prevented, etc etc.
#### Describe the solution you'd like
It's a big ask, but a FrozenDataFrame (or whatever name the maintainers like for it) class would be handy because it could be hashable, and updates could return new FrozenDataFrame with pointers to updated data. Zero Copy operations would be preferred since this would be faster and save memory.
#### API breaking implications
FrozenDataFrame can be a subclass of DataFrame and thus this would be an addition, so there wouldn't be any requirement to change previous stuff. Perhaps the subclass can override methods which mutate data, with new methods which raise helpful error messages, or return new pointers to new FrozenDataFrame instances if possible.
Perhaps down the road if the API were stabilized and well tested, this new subclass could replace the pd.DataFrame in a major version change, if the maintainers wanted.
#### Describe alternatives you've considered
There are hacks around this but it seems like something Pandas community would use.
It seems the Pandas community already favors immutability in a lot of places.
immutables.Map can hold google/jax arrays but this loses a LOT of useful features.
#### Additional context
```python
algo: str = 'string representation of an algorithm'
candles: pd.DataFrame = get_candles("SPY", 5, "min")
key: tuple = (algo, candles)
value: float = backtest(algo, candles)
results: immutables.Map = immutables.Map()
results_with_backtest = R.assoc(key, value, results)
```
> ~/miniconda3/envs/py38/lib/python3.8/site-packages/pandas/core/generic.py in __hash__(self)
> 1666
> 1667 def __hash__(self):
> -> 1668 raise TypeError(
> 1669 f"{repr(type(self).__name__)} objects are mutable, "
> 1670 f"thus they cannot be hashed"
>
> TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed
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STYLE: cpplint (unintentionally?) skips pandas/_libs/src/ujson/
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| 2
| 2021-01-23T14:13:25Z
| 2021-01-26T22:37:11Z
| 2021-01-26T22:37:11Z
|
MEMBER
| null |
In code_checks.sh we have
```
cpplint --quiet --extensions=c,h --headers=h --recursive --filter=-readability/casting,-runtime/int,-build/include_subdir pandas/_libs/src/*.h pandas/_libs/src/parser pandas/_libs/ujson pandas/_libs/tslibs/src/datetime pandas/_libs/*.cpp
```
However, `pandas/_libs/ujson` doesn't exist.
I presume `cpplint` should be running on `pandas/_libs/src/ujson/` instead?
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small typing fixes
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| 2021-01-23T16:35:46Z
| 2021-03-16T11:59:29Z
| 2021-01-30T20:44:05Z
|
CONTRIBUTOR
| null |
Some very small fixes to typing declarations to fix some errors found by `pyright`
Should not affect running code at all. mypy tests passed locally.
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HackIllinois 2021 Open Source Fellowship mentoring
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| 2021-01-23T16:37:23Z
| 2021-03-13T04:42:44Z
| 2021-03-13T04:42:44Z
|
MEMBER
| null |
Just got this email:
> My name is Rishin and I am one of the Directors of HackIllinois- the largest hackathon in the Midwestern United States with a focus on Open Source initiatives. Today, I am reaching out to invite you or another member of the Pandas team to be a mentor for a new Open Source Fellowship program we're organizing from February 15th-March 5th, 2021!
>
> HackIllinois has long been an Open Source hackathon with the mission to introduce students to Open Source projects and the greater Open Source community and has attracted 1500+ applicants from around the world every year. This year, however, we're reevaluating the efficacy of the hackathon format in furthering this mission. Instead, we're hoping our new long-term program with asynchronous mentorship and weekly check-ins will increase retention amongst students, bringing new members to the Open Source community!
>
> We’d love to have somebody from the Pandas team share their experiences with the project and more this year from February 15th-March 5th, 2021 through our new program. As a mentor, you would guide a group of a few passionate and dedicated students through the process of contributing to an open-source project. This would include anything from navigating the codebase to making contributions of their own. We would be grateful to have you join us in bringing new members to the Open Source community!
>
> Please let me know if you’re interested in this event or know anybody else on the team who may be! If you have any questions, I’d be happy to hop on a quick call to discuss your potential involvement!
I'm going to offer to mentor contributions to the docs; reach out to ~~mentor@hackillinois.org~~ contact@hackillinois.org if interested in participating.
~~Will plan to close this issue after a few days, since there isn't an action item for the project really.~~
## TODOs
- [x] Express interest
- [x] Meet with organizers
- [x] Complete mentor registration
- [x] Collect list of potential projects
- Specify relative technical difficulty, time commitment, and depth of pandas knowledge required. Maybe other things?
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TST: fix xfailed tests for IntegerArray, FloatingArray
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| 2021-01-23T17:14:52Z
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BUG: read_hdf bad filtering in case of categorical string columns
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| 2021-01-26T20:58:57Z
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|
CONTRIBUTOR
| null |
- [x] closes #39189
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This bug happens when filtering on categorical string columns and choose a value which doesn't exist in the column.
Instead of returning an empty dataframe, we get some records.
It happens because of the usage in numpy `searchsorted(v, side="left")` that find indices where elements should be inserted to maintain order (and not 0 in case that the value doesn't exist), like was assumed in the code.
I changed it to first the for the value, and use `searchsorted` only if value exists, I also added a test for this specific use case.
I think in the long run, maybe we should refactor this area in the code since one function covers multiple use cases which makes it more complex to test.
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STYLE: Fix incorrect path to ujson directory
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| 22
| 2021-01-23T19:04:06Z
| 2021-01-27T16:44:13Z
| 2021-01-26T22:37:11Z
|
CONTRIBUTOR
| null |
- [x] closes #39347
I fixed the path to `ujson` directory so `cpplint` will run on `pandas/_libs/src/ujson/` instead of `pandas/_libs/ujson`, which doesn't exist.
But the title of the issue says `cpplint (unintentionally?) skips pandas/_libs/src/ujson/`, so I understand there's a chance the `pandas/_libs/src/ujson/` is getting *intentionally* skipped.
Assuming this was **unintentional**, I believe this small fix solves the issue.
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MDExOlB1bGxSZXF1ZXN0NTYwNDc1NDI5
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ENH: making mode stable/keeping original ordering
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| 7
| 2021-01-23T19:09:47Z
| 2021-01-27T19:04:33Z
| 2021-01-27T14:03:52Z
|
CONTRIBUTOR
| null |
- [x] closes #39007
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This is follow up of #39009 but for `mode`: now keys returned by `mode` are in the original order. This is also improvement of the code quality as `mode` now uses the same code path as `value_counts`.
Fixes a performance regression of #39009.
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TST: remove incorrect xfails
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| 2021-01-23T19:44:23Z
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MEMBER
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- [ ] closes #xxxx
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- [ ] whatsnew entry
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BUG: read_excel failing to check older xlrd versions properly
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| 9
| 2021-01-23T20:43:59Z
| 2021-02-01T17:38:07Z
| 2021-02-01T13:45:49Z
|
MEMBER
| null |
- [ ] closes #38955
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- [ ] whatsnew entry
Probably should be in 1.2.1 given that this occurred after the deprecation of the xlrd engine in 1.2.
cc @rhshadrach
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TST: Multiindex slicing with NaNs, unexpected results for #25154
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| 3
| 2021-01-23T21:41:51Z
| 2021-02-05T15:41:19Z
| 2021-02-05T15:41:15Z
|
CONTRIBUTOR
| null |
- [x] closes #25154
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Added test case for Multiindex slicing with NaNs.
Executed linting test based on the instructions provided here and 23 errors were reported, however none regarding the python test file modified.
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BUG: Series(list_of_tuples, dtype=PandasDtype(object))
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MEMBER
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG: Series.setitem raising ValueError when setting Series with scalar indexer
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MEMBER
| null |
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- [x] tests added / passed
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- [x] whatsnew entry
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ENH: `Styler.apply` with `axis=None` can accept ndarray
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CONTRIBUTOR
| null |
Currently `Styler.apply` with `axis=0` and `axis=1` permit functions which return array-like objects, provided they are of appropriate shape. With `axis=None` the return *must* be a `DataFrame`.
This means the following example:
```
def highlight_max(d):
"""Highlight the maximum in a Series or DataFrame bold-orange."""
css = 'background-color: orange; font-weight: bold;'
return np.where(d == np.nanmax(d.values), css, None)
df.style.apply(highlight_max, axis=0) # works
df.style.apply(highlight_max, axis=1) # works
df.style.apply(highlight_max, axis=None) # raises TypeError since return is not DataFrame
```
#### Describe the solution you'd like
The solution is a relatively simple change to code as part of the `Styler._apply` method that checks for `array-like` (or specifically `ndarray`) result instead of `DataFrame`, and constructs a `DataFrame` from that data if needs be, with appropriate index and cols.
#### API breaking implications
None since all previous behaviour is replicable.
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DOC: add option for skipping execution of code blocks
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| 3
| 2021-01-23T23:12:15Z
| 2021-02-11T03:31:14Z
| 2021-02-11T03:31:14Z
|
MEMBER
| null |
Came up as an idea at the documentation meeting (https://github.com/pandas-dev/pandas/issues/39183). This should make documentation builds faster for people who aren't working on the code samples.
```
$ time python make.py --single getting_started/comparison/comparison_with_spreadsheets.rst
…
real 0m22.119s
user 0m13.745s
sys 0m1.700s
$ time python make.py --no-ipython --single getting_started/comparison/comparison_with_spreadsheets.rst
…
real 0m17.481s
user 0m11.750s
sys 0m1.521s
```
Very open to alternatives names to `--no-ipython`.
- [ ] ~~closes #xxxx~~
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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TST: fix fillna test for PandasArray
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG: incorrect casting ints to Period in GroupBy.agg
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| 1
| 2021-01-23T23:44:17Z
| 2021-01-24T22:03:13Z
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|
MEMBER
| null |
- [x] closes #38254
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This is pretty much throwing in the towel on #38254.
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TST/WIP: stricter xfails
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DOC: parallelize the number of Sphinx jobs by default
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| 8
| 2021-01-24T00:21:41Z
| 2021-02-17T01:30:43Z
| 2021-02-17T01:30:40Z
|
MEMBER
| null |
I'm assuming most people building docs would want that to happen as quickly as possible, so this has Sphinx use the maximum parallelism possible by default. [Corresponding Sphinx documentation.](https://www.sphinx-doc.org/en/master/man/sphinx-build.html#cmdoption-sphinx-build-j)
- [ ] ~~closes #xxxx~~
- [x] tests ~~added~~ / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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MDExOlB1bGxSZXF1ZXN0NTYwNTIyMzUy
| 39,365
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REF: Unify _set_noconvert_dtype_columns for parsers
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| 2
| 2021-01-24T00:57:26Z
| 2021-01-27T18:36:35Z
| 2021-01-27T14:01:36Z
|
MEMBER
| null |
- [x] xref #39345
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
I unified the code which led to a bugfix. The test failed for the python parser case previously
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CI: parallelize the documentation build
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| 2021-01-24T01:00:48Z
| 2021-01-24T01:54:32Z
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|
MEMBER
| null |
Testing if this makes a difference in the build time, relative to `auto` (https://github.com/pandas-dev/pandas/pull/39364).
- [ ] ~~closes #xxxx~~
- [ ] tests ~~added /~~ passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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DOC: split Contributing page
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| 2021-01-24T01:27:21Z
| 2021-04-04T17:07:42Z
| 2021-04-04T17:07:42Z
|
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/development/contributing.html
#### Documentation problem
The page is long and covers a lot:
- Environment setup
- The mechanics of contributing to code and docs
- Code standards
#### Suggested fix for documentation
I suggest streamlining the primary Contributing page to be about the basic workflow, splitting out sub-pages for:
- Setting up an environment outside of Docker
- `Installing a C compiler`
- `Creating a Python environment`
- `Creating a Python environment (pip)`
- `Contributing to the code base`
- `Contributing to the documentation`
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|
WEB: decommission dev.pandas.io
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| 2021-01-24T01:42:27Z
| 2021-08-24T05:21:46Z
| null |
MEMBER
| null |
#### Location of the documentation
http://dev.pandas.io/
#### Documentation problem
Both http://dev.pandas.io/ and https://pandas.pydata.org/docs/dev/ exist, and it's confusing what the difference is / what each are for.
#### Suggested fix for documentation
- [x] Remove references to `dev.pandas.io` -> #39377
- [ ] Ensure all content from http://dev.pandas.io/pandas-blog/ is on https://pandas.pydata.org/community/blog/
- https://github.com/pandas-dev/pandas-blog/tree/master/content
- https://github.com/pandas-dev/pandas/tree/master/web/pandas/community/blog
- [ ] Redirect `dev.pandas.io`
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participate in Season of Docs?
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[] | null | 5
| 2021-01-24T01:47:05Z
| 2021-05-27T11:33:35Z
| 2021-05-27T11:33:35Z
|
MEMBER
| null |
Google has a [Season of Docs](https://developers.google.com/season-of-docs) program, in which the provide a stipend for people to contribute to documentation of an open source community. Does pandas want to [apply as an Organization](https://developers.google.com/season-of-docs/docs/admin-guide)?
This would also require folks to step up as [Mentors](https://developers.google.com/season-of-docs/docs/mentor-guide) — I am personally interested.
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TST: tighten PandasArray tests
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MEMBER
| null |
xfail instead of skip where appropriate
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ENH: add a method for piping self instead of the function return value
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[] | null | 2
| 2021-01-24T12:09:05Z
| 2021-08-15T03:53:00Z
| null |
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
Pandas has the `.pipe` method to pipe a function result to the next step in a ETL process. However, this method is awkward when calling functions where you want to pipe self and not the function output.
There are many situations where piping self is wanted:
* validation
* logging
* charting
* saving to file / database etc.
* inplace operations on a DataFrame/Series, e.g. changing index or columns
and probably other needs as well.
To use the above functionality in pipes in pandas now, you need to explicitly return the input dataframe even though that isolated doesn't make sense. So, instead of the natural validation function:
```python
def validate(df: pd.DataFrame) -> None:
... # validate something
```
You currently usually do:
```python
def validate(df: pd.DataFrame) -> pd.DataFrame:
... # validate something
return df
```
Returning a df indicates that the function returns somethings modified, even though that is not the case here, so this makes thing less clear in the piped functions.
Also, reusing external functions that don't return anything is more difficult in pandas currently, because you have to wrap them before using them.
Lastly, using lambdas in `.pipe` to work around the problem is problematic, because lambdas don't have a return type, so e.g.
```python
new_df = (df.pipe(manipulate_1)
.pipe(lambda x: x.plot.bar() and x)
.pipe(manipulate_2)
)
```
loses the type information in the second step.
#### Describe the solution you'd like
I propose adding a `.do` method that is implemented like
```python
def do(self: T, func: Union[Callable, Tuple[Callable, str]], *args, **kwargs) -> T:
self.pipe(func, *args, **kwargs)
return self
```
This means `.pipe` will be for when you want to pipe the function result and `.do` is for when you want to pipe self. Notice the different signature compared to `.pipe`.
This method is much more natural to use for all the steps listed above + it always understands the return type.
#### API breaking implications
This is a new method, so no breaking implications.
#### Describe alternatives you've considered
Alternatives - in addition to always add a `return df` inside functions that naturally would return None - may be using lambdas:
```python
new_df = (df.pipe(manipulate_1)
.pipe(lambda x: validate(x); x) # notice the semi-colon
.pipe(manipulate(2)
)
```
or to make a seperate user-created `.do` function:
```python
def do(obj: T, func, *args, **kwars) -> T:
func(obj, *args, **kwargs)
return obj
new_df = (df.pipe(manipulate_1)
.pipe(do, validate)
.pipe(manipulate_2)
)
```
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ERR: Improve error message for Series.loc.getitem with too many dimensions
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| 3
| 2021-01-24T12:19:08Z
| 2021-06-30T18:44:08Z
| 2021-01-28T00:59:52Z
|
MEMBER
| null |
- [x] closes #35349
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
tuple in tuple means we are indexing index and columns
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|
Warn on boolean frame indexer
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|
NONE
| null |
- [ ] closes #39004
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
- Is it ok to check index/column to allow things like df[df > 0] or should I just warn on DataFrame in general?
- Can I do the same thing for `setitem` as well?
Current version warns in 3 already existing tests:
- pandas/tests/frame/indexing/test_where.py::TestDataFrameIndexingWhere::test_where_datetime
- pandas/tests/frame/test_nonunique_indexes.py::TestDataFrameNonuniqueIndexes::test_getitem_boolean_frame_unaligned_with_duplicate_columns
- pandas/tests/indexing/test_indexing.py::TestMisc::test_partial_boolean_frame_indexing
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MDExOlB1bGxSZXF1ZXN0NTYwNTk4MjQx
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DOC: Clarify behavior for Series with dict-like data and index
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MEMBER
| null |
- [x] closes #34534
I think the previous description was wrong. We only reindex if the index is not None, not the opposite.
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Fixed code breakage for return_type = tuples for axis = 0
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NONE
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REGR: write compressed pickle files with protocol=5
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| 2
| 2021-01-24T16:40:49Z
| 2021-02-08T00:43:17Z
| 2021-01-26T23:31:50Z
|
CONTRIBUTOR
| null |
- [x] closes #39002
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I don't know why the change from #37056 fails in combination with `protocol=5` and compression. @ig248
An alternative fix would be to have `get_handle` insert a write buffer for all compressions (similar to #38728) to avoid if/else in format specific `to_*`
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DOC: remove references to dev.pandas.io
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| 2021-01-24T16:59:07Z
| 2021-03-02T01:57:22Z
| 2021-01-25T16:36:07Z
|
CONTRIBUTOR
| null |
- [ ] xref #39368
- [x] tests added / passed
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- [x] whatsnew entry
Suggested fix
- [x] Remove references to dev.pandas.io
- [ ] Ensure all content from http://dev.pandas.io/pandas-blog/ is on https://pandas.pydata.org/community/blog/
https://github.com/pandas-dev/pandas-blog/tree/master/content
https://github.com/pandas-dev/pandas/tree/master/web/pandas/community/blog
- [ ] Redirect dev.pandas.io
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BUG: Slicing DatetimeIndex with strings containing microseconds raising KeyError
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MEMBER
| null |
- [x] closes #33589
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Is there a reason not supporting milliseconds resolution when supporting microseconds?
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BUG: pd.read_clipboard() not working in WSL2
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| 2021-01-25T10:57:03Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pd.read_clipboard()
```
#### Problem description
I recently started using WSL2 on Windows 10 Pro / Ubuntu 20.04 / Python 3.8.5. In general, It works incredibly well today (unlike in 2018). Unfortunately, pd.read_clipboard() and df.to_clipboard() don't work. I believe the problem is pandas because I got the desired functionality to work using pyperclip directly.
#### Error Message
>>> pd.read_clipboard()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/mickeymouse/.local/lib/python3.8/site-packages/pandas/io/clipboards.py", line 38, in read_clipboard
text = clipboard_get()
File "/home/mickeymouse/.local/lib/python3.8/site-packages/pandas/io/clipboard/__init__.py", line 649, in lazy_load_stub_paste
return paste()
File "/home/mickeymouse/.local/lib/python3.8/site-packages/pandas/io/clipboard/__init__.py", line 287, in __call__
raise PyperclipException(EXCEPT_MSG)
pandas.io.clipboard.PyperclipException:
Pyperclip could not find a copy/paste mechanism for your system.
For more information, please visit
https://pyperclip.readthedocs.io/en/latest/introduction.html#not-implemented-error
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BUG: #35830 Restore index & slice by date to DatetimeIndex
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NONE
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#35830 DatetimeIndex has supported indexing by date for many many years. This was broken after 1.0.5. This attempts to restore support by converting a date to Timestamp in `get_loc` and `_validate_searchsorted_value`. This restores indexing slicing, and `asof` but `in` does not work. It seems the best solution would be to do the conversion in `DatetimeLikeArrayMixin._validate_scalar` but this breaks a test looking for an expected `FutureWarning` in `TestDataFrameIndexing::test_loc_setitem_datetime_coercion` warning a `Timestamp` can't be compared to a `date`
- [x] closes #35830
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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TST: tighten Decimal tests
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Make get_loc with nan for FloatIndex consistent with other index types
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| null |
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Have not added a whatsnew because I am not sure if 2 is expected here. You could also make a point for retruning nan. But to handle these cases consistently, we have to check for method here
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REF: in pandas.io.common integerate BytesIOWrapper into IOHandle
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| 1
| 2021-01-24T23:32:11Z
| 2021-08-04T23:46:18Z
| 2021-08-04T23:46:18Z
|
MEMBER
| null |
references:
- [pyarrow CSV engine basic PR](#38370)
- https://github.com/pandas-dev/pandas/pull/37639#issuecomment-742278895
- https://github.com/pandas-dev/pandas/pull/37639#issuecomment-742295452
- https://github.com/pandas-dev/pandas/pull/37639#issuecomment-742650316
pyarrow requires `BytesIO` not `StringIO` so in #38370 we add a class that returns a binary handle to reader input. This class (`BytesIOWrapper`) should be integrated into `IOHandle` to better align with the patterns we use elsewhere for file handles (#37639 is the PR that standardized things)
I originally intended to do this in #38370 but I got a little stuck and since that PR is sizeable already I think it's better to spin it off into a separate follow-on task
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CI: fix PandasArray test
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REF: move PandasDtype to dtypes.dtypes
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This will allow us to import it in e.g. core.construction without a circular import
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MDExOlB1bGxSZXF1ZXN0NTYwNzI1MzU0
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TST: remove test_error
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| 2021-01-26T00:16:54Z
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|
MEMBER
| null |
It makes no sense to me that we'd test unwanted behavior by default and then override it for working arrays.
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ENH: PandasArray.value_counts
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTYwNzUzMDQy
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PERF: Rolling.cov/corr
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| 1
| 2021-01-25T03:32:10Z
| 2021-01-25T17:50:21Z
| 2021-01-25T14:24:31Z
|
MEMBER
| null |
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Will attempt to improve the performance of `groupby.rolling` next.
```
before after ratio
[37b5800a] [ee9a56c1]
<master> <ref/rolling_cov_corr>
- 10.4±2ms 8.90±0.2ms 0.86 rolling.Pairwise.time_pairwise(10, 'cov', True)
- 10.2±1ms 8.52±0.1ms 0.83 rolling.Pairwise.time_pairwise(None, 'cov', True)
- 2.48±0.1ms 1.07±0.01ms 0.43 rolling.Pairwise.time_pairwise(1000, 'cov', False)
- 2.55±0.2ms 1.08±0.02ms 0.42 rolling.Pairwise.time_pairwise(10, 'cov', False)
- 2.33±0.04ms 918±6μs 0.39 rolling.Pairwise.time_pairwise(None, 'cov', False)
- 4.02±0.6ms 1.46±0.02ms 0.36 rolling.Pairwise.time_pairwise(10, 'corr', False)
- 4.08±0.4ms 1.42±0.1ms 0.35 rolling.Pairwise.time_pairwise(1000, 'corr', False)
- 3.69±0.07ms 1.17±0.03ms 0.32 rolling.Pairwise.time_pairwise(None, 'corr', False)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
Added a `ddof` argument to `corr` which was implicitly accepted (and used) by `kwargs` before; making it an explicit kwargs in this PR. Will document it after https://github.com/pandas-dev/pandas/pull/39219 is in.
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BUG: DataFrame to_dict method raise Out of bounds nanosecond timestamp
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| 2021-01-25T03:52:35Z
| 2021-10-01T17:31:00Z
| 2021-01-31T16:45:21Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
from datetime import datetime
series = pd.Series([datetime(year=4172, month=12, day=31)])
series.to_dict()
# raise pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime:
# Out of bounds nanosecond timestamp: 4172-12-31 00:00:00
d = {'d1': [datetime(year=4172, month=12, day=31)]}
df = pd.DataFrame(data=d)
df.to_dict(orient='records')
```
#### Problem description
The maximum default value of date in oracle database is 4172-12-31 00:00:00, so there is a lot of such data, we read the data by pandas + sqlalchemy and then to_dict() returns the result to the front-end display. But here an exception occurs.
I think the to_dict operation does not need to check the date range, because the date type itself can exist is the language level allows, to_dict after getting the python object, python are allowed, we do not need to add restrictions here.
https://github.com/pandas-dev/pandas/blob/37b5800afb335fe1d09dde9153ca32c48c39f73e/pandas/core/frame.py#L1632
#37648 and #37571 may have something to do with it
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 37b5800afb335fe1d09dde9153ca32c48c39f73e
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+529.g37b5800af
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 49.2.1
Cython : 0.29.21
pytest : 6.2.1
hypothesis : 6.0.3
sphinx : 3.4.3
blosc : 1.10.2
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : 0.5.0
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTYwODcxMjc5
| 39,390
|
CI: save site+docs builds as artifacts
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| 5
| 2021-01-25T07:51:43Z
| 2021-02-11T13:44:01Z
| 2021-02-11T13:43:56Z
|
MEMBER
| null |
Makes the downloadable for previewing locally. Not as good as having a URL to preview it, but better than nothing.
- [x] closes https://github.com/pandas-dev/pandas/issues/17921
- [ ] tests ~~added /~~ passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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| 39,391
|
⬆️ UPGRADE: Autoupdate pre-commit config
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| 0
| 2021-01-25T08:00:24Z
| 2021-01-25T14:34:15Z
| 2021-01-25T14:31:27Z
|
CONTRIBUTOR
| null |
<!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.9.3-py2.py3-none-any.whl (184 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-1.5.13-py2.py3-none-any.whl (97 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.0-py2.py3-none-any.whl (5.7 MB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.5.13 nodeenv-1.5.0 pre-commit-2.9.3 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.0
```
### stderr:
```Shell
WARNING: You are using pip version 20.3.1; however, version 21.0 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.1/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/python/black ... already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
updating v0.1.5 -> v0.1.6.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black..................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
pyupgrade..............................................................................................Passed
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Generate pip dependency from conda.....................................................................Passed
flake8-rst.............................................................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of private functions across modules......................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
codespell..............................................................................................Passed
no-string-hints........................................................................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action)
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MDExOlB1bGxSZXF1ZXN0NTYwODg5MjU3
| 39,392
|
CI: split benchmarks from other checks
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[] | null | 10
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| 2021-07-03T10:45:33Z
| 2021-07-03T10:45:32Z
|
MEMBER
| null |
That job was the longest-running, so this should parallelize the various code checks with the benchmarks to get done in less wall clock time.
This pull request builds on https://github.com/pandas-dev/pandas/pull/39745.
cc @fangchenli
- [ ] ~~closes #xxxx~~
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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ENH: `Styler.apply` accept ndarray return with `axis=None` for consistency
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CONTRIBUTOR
| null |
- [x] closes #39359
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Also minor edits to docs to extend information and show examples.
cc @TomAugspurger
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ENH: Fix support for matplotlib's constrained_layout (#25261)
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- [x] closes #25261
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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BUG: `Styler.apply` and `applymap` duplicate CSS on re-render.
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| 2021-01-26T14:15:14Z
| 2021-01-26T14:15:14Z
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CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame((np.random.rand(1, 1)*100))
s = df.style\
.applymap(lambda x: 'background-color: black;')\
.apply(lambda x: pd.DataFrame([['color: red;']]), axis=None)
print(s.render())
print(s.ctx)
print(s.render())
print(s.ctx)
...
<style type="text/css" >
#T_fda50_row0_col0{
background-color: black;
color: red;
}</style>
defaultdict(<class 'list'>, {(0, 0): ['background-color: black', 'color: red']})
<style type="text/css" >
#T_fda50_row0_col0{
background-color: black;
color: red;
background-color: black;
color: red;
}</style>
defaultdict(<class 'list'>, {(0, 0): ['background-color: black', 'color: red', 'background-color: black', 'color: red']})
```
#### Problem description
The private `_todo` list is not cleared after initial execution so repeats the execution on second render.
There does not seem to be any reason any reason to maintain the `_todo` list after it has been done and the `ctx` object has been updated with necessary css-styles.
#### Expected Output
Same on each render.
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MDExOlB1bGxSZXF1ZXN0NTYxMDMwODMz
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BUG: re-render CSS with `styler.apply` and `applymap` non-cleared ` _todo`
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| 2
| 2021-01-25T12:03:24Z
| 2021-01-26T17:01:17Z
| 2021-01-26T14:15:14Z
|
CONTRIBUTOR
| null |
- [ x] closes #39395
- [ x] tests added / passed
- [ x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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QST: When using df.loc[ ] in a for loop I get better performance overall than if I use df.loc() sequentially.
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[] | null | 4
| 2021-01-25T15:06:13Z
| 2021-08-15T04:00:42Z
| 2021-08-15T04:00:34Z
|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com): I am not a StackOverflow user.
---
## Question about pandas
When using df.loc[ ] in a for loop I get better performance overall then if I use df.loc() sequentially.
### Background
I have noticed the following behavior, which is more pronounced with larger DataFrames(). Currently, the "larger" DataFrame() we use is 75,000 rows with 2,100 columns. The DataFrame() is extended an additional 1,000 rows, and so is in total 76,000 rows long (see below). The columns are equally split between,
- int64, float64, and object (a string).
We are pre-loading the DataFrame() with a 1,000 extra rows, which are used for updates instead of resorting to more costly appends. The code I used to look at the performance of updating rows with a list of Series and df.loc[ ] as opposed to using a single row with df.loc[ ] is listed below.
### Behavior Updating Rows
Using update_multiple_rows() I find that,
- mean = 3.0 seconds per row
- maximum time to insert a row = 260 seconds at the first position 75001.
Using 2 repeated calls to update_one_row() I find that,
- For each call the time required for one row to be updated is 260 seconds.
```python
def main():
df = create_df() # Returns a large DataFrame().
test_rows = get_test_rows() # Gets 100 rows to update the DataFrame() with.
update_multiple_rows(test_rows, 75001)
update_one_row(test_rows[0], 75001)
update_one_row(test_rows[0], 75001)
def update_multiple_rows(df: DataFrame, rows: list, index_start: int):
number_of_rows = len(rows)
for i in range(index_start, number_of_rows + 1):
start = time()
df.loc[i] = new_row_data
elapsed = time() - start
def update_one_row(df: DataFrame, row: Series, index: int):
start = time()
df.loc[i] = new_row_data
elapsed = time() - start
```
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MDU6SXNzdWU3OTM0NzExMzc=
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|
BUG: `DateTimeIndex + DateTimeIndex.freq` giving incorrect result if timezone is set.
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[] | null | 2
| 2021-01-25T15:18:39Z
| 2021-01-25T19:02:58Z
| 2021-01-25T19:02:58Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
i = pd.date_range('2020-03-27', freq='D', periods=4, tz='Europe/Berlin')
i2 = i + i.freq
i2.freq = 'D' # ValueError: Inferred frequency None from passed values does not conform to passed frequency D
```
#### Problem description
`i` is as expected.
```
DatetimeIndex(['2020-03-27 00:00:00+01:00', '2020-03-28 00:00:00+01:00',
'2020-03-29 00:00:00+01:00', '2020-03-30 00:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D') #good
```
but `i2` has two issues:
```
DatetimeIndex(['2020-03-29 00:00:00+01:00', '2020-03-30 01:00:00+02:00',
'2020-03-31 00:00:00+02:00', '2020-04-01 00:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D')
#issue: 2020-03-30 timestamp not at midnight
#issue: i2 has attribute `freq='D'`, but pd.infer_freq returns None
```
#### Expected Output
`i2` to be
```
DatetimeIndex(['2020-03-29 00:00:00+01:00', '2020-03-30 00:00:00+02:00',
'2020-03-31 00:00:00+02:00', '2020-04-01 00:00:00+02:00'],
dtype='datetime64[ns, Europe/Berlin]', freq='D')
```
(`2020-03-30 00:00:00+02:00` is only change),
with `i2.freq` not returning `None`
#### Related
May be related to [#38243](https://github.com/pandas-dev/pandas/issues/38243)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3OTM1Njg4NTQ=
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|
TYP upgrade and pin mypy to 0.800
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| 2021-01-25T17:18:04Z
| 2021-01-26T20:46:42Z
| 2021-01-26T20:46:42Z
|
MEMBER
| null |
mypy 0.800 came out 3 days ago https://mypy-lang.blogspot.com/2021/01/mypy-0800-released.html , so it would be good to try upgrading
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QST: Styler puts all CSS ids on a single attribute which is not rendered
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| 2021-01-25T17:42:29Z
| 2021-02-08T11:42:31Z
| 2021-02-08T11:42:31Z
|
NONE
| null |
- [X ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com/questions/65834907/pandas-styler-css-limit-reached).
---
#### Question about pandas
I am trying to use the pandas dataframe styler to colour cells of the dataframe and present the result on a webpage.
The rendered CSS shows up as follows:
```
#T_394319f6_5c20_11eb_832b_04ed338ce712row0_col6,#T_394319f6_5c20_11eb_832b_04ed338ce712row0_col7,
...
#T_394319f6_5c20_11eb_832b_04ed338ce712row1_col6 { background-colour: red }
```
For large tables, the number of ids can exceed 4096. When processed on the web-browser, the id's after 4096 are ignored and the cells are not coloured. Resulting in partially coloured HTML tables.
I have verified my style function works by downloading the Excel file of the styled object, which is coloured correctly. It seems to be a limitation on various web-browsers (Chrome, Firefox, Edge).
I have also tried randomizing the exact hexcode used to colour the cells, which fixes the problem.
What can I do?
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ValueError when concatenating a length-one, reversed object
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| 6
| 2021-01-25T18:08:48Z
| 2021-01-29T04:24:47Z
| 2021-01-28T15:04:20Z
|
CONTRIBUTOR
| null |
The following code should return a new Series with a single value, but raises a ValueError:
```python
import pandas as pd
s = pd.Series([100])
pd.concat([s.iloc[::-1]])
```
The corresponding example with a DataFrame also raises a ValueError:
```python
import pandas as pd
df = pd.DataFrame([100])
pd.concat([df.iloc[::-1]])
```
The DataFrame version can be reproduced with multiple columns, but any of the following modifications will prevent the bug:
- create Series or DataFrame with len > 1
- concatenate more than one object
- remove the reversing operation `.iloc[::-1]`
- pass ignore_index=True to pd.concat
This bug appeared as an edge case in a function that usually handles DataFrames with multiple rows.
<details>
<summary>Traceback for the Series example</summary>
```
C:\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
296 )
297
--> 298 return op.get_result()
299
300
C:\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in get_result(self)
483
484 res = concat_compat(arrs, axis=0)
--> 485 result = cons(res, index=self.new_axes[0], name=name, dtype=res.dtype)
486 return result.__finalize__(self, method="concat")
487
C:\Anaconda3\lib\site-packages\pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
320 if len(index) != len(data):
321 raise ValueError(
--> 322 f"Length of passed values is {len(data)}, "
323 f"index implies {len(index)}."
324 )
ValueError: Length of passed values is 1, index implies 0.
```
</details>
<details>
<summary>Traceback for the DataFrame example</summary>
```
C:\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
296 )
297
--> 298 return op.get_result()
299
300
C:\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in get_result(self)
519
520 new_data = concatenate_block_managers(
--> 521 mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
522 )
523 if not self.copy:
C:\Anaconda3\lib\site-packages\pandas\core\internals\concat.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
87 blocks.append(b)
88
---> 89 return BlockManager(blocks, axes)
90
91
C:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in __init__(self, blocks, axes, do_integrity_check)
141
142 if do_integrity_check:
--> 143 self._verify_integrity()
144
145 # Populate known_consolidate, blknos, and blklocs lazily
C:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in _verify_integrity(self)
321 for block in self.blocks:
322 if block.shape[1:] != mgr_shape[1:]:
--> 323 raise construction_error(tot_items, block.shape[1:], self.axes)
324 if len(self.items) != tot_items:
325 raise AssertionError(
ValueError: Shape of passed values is (1, 1), indices imply (0, 1)
```
</details>
<details>
<summary>pd.show_versions()</summary>
```
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Canada.1252
pandas : 1.2.1
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.3.3.post20210118
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
```
</details>
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| 793,689,411
|
MDU6SXNzdWU3OTM2ODk0MTE=
| 39,402
|
BUG: astype from categorical to np.int32 conversion returns np.int64 in pandas 1.2.0 and 1.2.0
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| 2
| 2021-01-25T20:08:58Z
| 2021-02-08T13:43:34Z
| 2021-02-08T13:43:34Z
|
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
# numpy=1.18.5, pandas=1.2.x (has issue), pandas = 1.1.5 (no issue)
import pandas as pd
import numpy as pd
df = pd.DataFrame(data={'column1': [2.0, -1.0, 3.0]})
df.column1 = df.column1.astype('category')
df.column1 = df.column1.astype(np.int32)
assert df.column1.dtype.type == np.int32 # fails in pandas 1.2.x, returns np.int64
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
in pandas 1.1.5 astype correctly returning np.int32 as the column1 dtype.
This is expected as we are trying to convert this categorical col into np.int32.
Now it returns np.int64 in pandas 1.2.x
#### Expected Output
no assertion error
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.0
numpy : 1.18.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1.1
setuptools : 51.3.3.post20210118
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
pd.show_versions()
</details>
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MDExOlB1bGxSZXF1ZXN0NTYxMzQ2MTkx
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Clean up DataFrame.setitem behavior for duplicate columns
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| 4
| 2021-01-25T20:30:25Z
| 2021-01-29T21:03:02Z
| 2021-01-29T02:12:54Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
cc @jbrockmendel This cleans the edgy behavior up for duplicate columns.
Previous test was wrong.
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BUG: rolling().skew modifies original data column
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| 1
| 2021-01-25T21:25:35Z
| 2021-01-25T21:59:23Z
| 2021-01-25T21:59:23Z
|
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(np.random.random(100)*10, columns=['A'])
```
```python
A
0 5.076681
1 6.959102
2 9.989156
.. ...
97 3.475425
98 3.280561
99 6.966864
```
Apply skew to create new column and df['A'] is now modified unexpectedly
```python
df['skew'] = df['A'].rolling(10).skew()
```
```python
A skew
0 0.076681 NaN
1 1.959102 NaN
2 4.989156 NaN
.. ... ...
97 -1.524575 -0.482644
98 -1.719439 -0.114206
99 1.966864 -0.414480
```
#### Problem description
`rolling().skew` modifies original data column. This is an unexpected and problematic behaviour since it does not occur with other rolling window methods like `mean()`, `std()` and `'kurt'. A coder would reasonably expect that these methods can be applied in the same way, therefore it is likely to cause issues that are hard to debug.
It can be worked around by using using copy, but again, this is a deviation from the usage of other similar methods: `df['A'].copy().rolling(10).skew()`
#### Expected Output
```python
A skew
0 5.076681 NaN
1 6.959102 NaN
2 9.989156 NaN
.. ... ...
97 3.475425 -0.482644
98 3.280561 -0.114206
99 6.966864 -0.414480
A
0 5.076681
1 6.959102
2 9.989156
.. ...
97 3.475425
98 3.280561
99 6.966864
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Australia.1252
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTYxMzg2ODY3
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ENH: implement EA.delete
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| 6
| 2021-01-25T21:52:23Z
| 2021-01-26T21:58:07Z
| 2021-01-26T02:27:03Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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DOC: link to correct PR
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| 3
| 2021-01-25T22:50:40Z
| 2021-01-27T09:21:39Z
| 2021-01-26T23:32:35Z
|
CONTRIBUTOR
| null |
xref #39171, #18262, #35224
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TYP: Make mypy 0.800 compatible
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| 6
| 2021-01-25T23:28:47Z
| 2021-01-26T21:02:16Z
| 2021-01-26T20:46:42Z
|
MEMBER
| null |
- [x] closes #39399
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
|
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MDExOlB1bGxSZXF1ZXN0NTYxNDM0MTIz
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REF: implement Index._find_common_type_compat
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| 0
| 2021-01-25T23:48:33Z
| 2021-01-26T03:04:30Z
| 2021-01-26T02:28:03Z
|
MEMBER
| null |
Before long im going to need to flesh this out to handle complex dtype.
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|
ENH: recognize Decimal("NaN") in pd.isna
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| 11
| 2021-01-26T04:53:43Z
| 2021-03-02T03:40:42Z
| 2021-02-25T00:54:36Z
|
MEMBER
| null |
- [x] closes #23530
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Discussion in #23530 seems ambivalent on whether this is desirable, and I don't have a strong opinion on it in general. BUT tm.assert_foo_equal is incorrect with Decimal("NaN") ATM and id like to see that fixed.
xref #32206
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BUG: pd.testing.assert_frame_equal(..., check_exact=True) raises AssertionError when comparing numeric ExtensionDtypes
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| 1
| 2021-01-26T07:03:35Z
| 2021-01-28T02:34:12Z
| 2021-01-28T02:34:12Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
d = {"x": [1, 2]}
df1 = pd.DataFrame(data=d, dtype=pd.UInt32Dtype())
df2 = pd.DataFrame(data=d, dtype=pd.UInt32Dtype())
pd.testing.assert_frame_equal(df1, df2, check_exact=True)
```
#### Problem description
The above code raises the following exception:
```
AssertionError: ndarray Expected type <class 'numpy.ndarray'>, found <class 'pandas.core.arrays.integer.IntegerArray'> instead
```
Instead, the usage of `pd.testing.assert_frame_equal(..., check_exact=True)` should not raise an exception when comparing numeric `ExtensionDtype`s.
#### Expected Output
No exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Fri Oct 30 13:34:27 PDT 2020; root:xnu-4570.71.82.8~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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ERROR: file not found: pyarrow and to_parquet() not working with integer/float
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| 2
| 2021-01-26T08:32:52Z
| 2021-01-26T09:37:46Z
| 2021-01-26T09:35:01Z
|
NONE
| null |
Hi,
I am using pyarrow 1.0.0.
It failed to process integers and floats but worked with objects. When I run the following python script:
sample = pd.DataFrame({'a':[1, 2], 'b': [3, 4]})
sample.to_parquet('tmp.parquet')
I got:
ArrowTypeError: ('Did not pass numpy.dtype object', 'Conversion failed for column a with type int64')
When I run pytest pyarrow, I got file not found error:
============================= test session starts ==============================
platform linux -- Python 3.7.3, pytest-4.3.1, py-1.8.0, pluggy-0.9.0
rootdir: /home/ubuntu, inifile:
plugins: remotedata-0.3.1, openfiles-0.3.2, doctestplus-0.3.0, arraydiff-0.3
collecting ...
========================= no tests ran in 0.00 seconds =========================
ERROR: file not found: pyarrow
And the package is installed because when I run pip3.7 install --no-cache pyarrow, I got:
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: pyarrow in ./anaconda3/lib/python3.7/site-packages (1.0.0)
Requirement already satisfied: numpy>=1.14 in ./anaconda3/lib/python3.7/site-packages (from pyarrow) (1.20.0rc2)
Here is what I got if I run pd.show_versions():
INSTALLED VERSIONS
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1035-aws
Version : #37~18.04.1-Ubuntu SMP Wed Jan 6 22:31:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.20.0rc2
pytz : 2018.9
dateutil : 2.8.0
pip : 21.0
setuptools : 52.0.0
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : 1.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.1
tables : 3.5.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.43.1
Could anyone please help me with this? Thanks!
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MDExOlB1bGxSZXF1ZXN0NTYxODg4NzY1
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BUG: make Index.where behavior mirror Index.putmask behavior
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| 5
| 2021-01-26T16:08:21Z
| 2021-02-03T15:05:22Z
| 2021-02-03T13:34:33Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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|
BUG: Performance regression to_csv when formatting datatime index
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| 2021-01-26T17:21:56Z
| 2021-08-15T04:01:12Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
All variants give the same output. The issue is not the different execution time of the individual variants but the performance regression of the first variant. The other variants are added just to show that nothing changed here between versions `1.1.5` and `1.2.0` (versions `1.2.1` and `1.2.0rc0` give the same results as version `1.2.0`).
```python
import pandas as pd
print(pd.__version__)
n = 100_000
df = pd.DataFrame().assign(timestamp=pd.date_range('2000', periods=n, freq='S'), col1=1)
%timeit _ = df.set_index('timestamp').to_csv(date_format='%Y-%m-%d %H:%M:%S')
%timeit _ = df.set_index('timestamp').to_csv()
%timeit _ = df.set_index(df.timestamp.dt.strftime('%Y-%m-%d %H:%M:%S')).drop('timestamp',1).to_csv()
%timeit _ = df.to_csv(date_format='%Y-%m-%d %H:%M:%S', index=False)
%timeit _ = df.to_csv(index=False)
```
#### Problem description
Using a `date_format` for a datetime index in `to_csv` is almost 3 times slower in version `1.2.0` than in `1.1.5` (
see first row in test output: 2.39 s instead of 829 ms for 100,000 rows). For 1,000,000 rows the slowdown is 19 times.
```
1.1.5
829 ms ± 11.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
214 ms ± 1.12 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
987 ms ± 20.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.04 s ± 5.73 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
290 ms ± 18.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.2.0
2.39 s ± 38 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
214 ms ± 2.19 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
992 ms ± 19.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.04 s ± 4.72 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
270 ms ± 2.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
This might be related to #37484.
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.2.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : 0.29.14
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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update link to config.py in options user guide
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| 1
| 2021-01-26T17:32:18Z
| 2021-02-11T13:37:15Z
| 2021-02-11T13:37:11Z
|
CONTRIBUTOR
| null |
- `config.py` now `config_core.py` ?!
- maybe also [pandas/_config/config.py](https://github.com/pandas-dev/pandas/blob/master/pandas/_config/config.py)
- link in [`user_guide/options` ](https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html) changed.
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MDU6SXNzdWU3OTQ0NzQ1MjI=
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BLD: Enable PEP 517
|
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| 2021-01-26T18:47:16Z
| 2021-01-26T18:47:32Z
| null |
MEMBER
| null |
After #38852, we could enable PEP 517 by modifying PATH in setup.py or use non-vendored versioneer. See https://github.com/python-versioneer/python-versioneer/issues/193 for details.
|
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MDU6SXNzdWU3OTQ0NzY3NDk=
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|
BLD: optional numpy import
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| 2021-06-15T12:30:04Z
| null |
MEMBER
| null |
The added workflow pip installs pandas without the requirements pre-installed. Seems to pull in requirements OK. (I also wanted to test with other versions of setuptools locally though before approving this PR)
OT for the sdist build step we do `python setup.py clean --quiet`. This fails if numpy not installed.
_Originally posted by @simonjayhawkins in https://github.com/pandas-dev/pandas/pull/38852#r552479485_
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MDU6SXNzdWU3OTQ0ODM5Nzc=
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CI: add sdist test for PRs
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| 2021-01-26T19:02:23Z
| 2021-06-21T13:03:12Z
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|
MEMBER
| null |
In #38852, we talked about adding CI job to build and test sdist. So we could discover relevant errors sooner. xref https://github.com/pandas-dev/pandas/pull/38852#discussion_r552490251
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BUG: SettingWithCopyError raised unsystematically (?)
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.options.mode.chained_assignment = "raise"
def f(df):
res = df["a"]
is_two = df["a"] == 2
res.loc[is_two] = None
return res
df1 = pd.DataFrame(dict(a=range(3)))
df2 = df1.assign(b=pd.date_range("2020-01-01", "2020-01-03"))
f(df1) # no problem
f(df2) # SettingWithCopyError
```
#### Problem description
The function above selects a column `a`, finds the position of values that are equal to two and sets those values to None, then returns the `a` Series.
When a dataframe only contains the `a` column, there is no problem. When a datetime column is introduced, `SettingWithCopyError` is raised.
#### Expected Output
Because the function `f` does not access any column besides `a`, I expect it to behave identically for both dataframes.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.2
xlrd : None
xlwt : None
numba : None
</details>
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