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TST: Add test for KeyError with MultiIndex
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cc @jreback Added a test
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TST: Add test for missing fillvalue in categorical dtype
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cc @jreback Could not find a test for something similar with reindex.
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CLN: clean categorical indexes tests
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MEMBER
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Found this while searching for other tests. Deleted code fragement was probably for ix previously.
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BUG: loc.__getitem__[[na_value]] with CategoricalIndex containing NAs
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MEMBER
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- [ ] closes #xxxx
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REF: collect boilerplate in _datetimelike_compat
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MDExOlB1bGxSZXF1ZXN0NTE4MDQzMjg4
| 37,724
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REF: remove Categorical._validate_listlike, unbox_listlike
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| 2
| 2020-11-09T21:14:28Z
| 2020-11-10T03:43:13Z
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|
MEMBER
| null |
Getting closer to having these standardized.
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|
ENH: Disallow subsetting columns in groupby with axis=1
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| 0
| 2020-11-09T22:21:53Z
| 2020-11-11T02:36:21Z
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|
MEMBER
| null |
E.g.
```
df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3]})
g = df.groupby([0, 0, 1], axis=1)
g[['A', 'B']].sum()
```
Other than using `.size()`, I think just about any op will raise. This can be explicitly raised with a more helpful error message when a user subsets the columns.
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MDExOlB1bGxSZXF1ZXN0NTE4MDc4MzYz
| 37,726
|
TST: Add test for filling new rows through reindexing MultiIndex
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| 1
| 2020-11-09T22:29:15Z
| 2020-11-13T13:16:08Z
| 2020-11-13T05:11:10Z
|
MEMBER
| null |
- [x] closes #23693
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Issue was fixed previously
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ENH: Raise when subsetting columns on groupby with axis=1
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Bug in iloc aligned objects
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- [x] closes #22046
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If iloc should be purely position based, this PR fixes the bug described in the issue. One test depends on the wrong behavior, so I had to adjust it.
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For some of these its just breaking up big tests and giving them meaningful names.
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an attempt to clean setup.py
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API: consistently raise TypeError for invalid-typed fill_value
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MEMBER
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- [ ] closes #xxxx
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This makes us consistent about raising TypeError when passing a wrong-typed fill_value to e.g. `take`. The downside is that we changed `Categorical.take` from TypeError to ValueError back in 1.1.0 #33660, so theres a bit of whiplash.
The upside (besides this just being More Correct) is that we will be just about fully sharing setitem-like validator methods.
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TST: use default_axes=True where multiple plots
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MEMBER
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- [ ] closes #xxxx
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Use new kwarg ``default_axes`` in ``_check_plot_works`` (#37274), which allows one to eliminate catching warnings related to multiple subplots created.
There warnings are not related to the plotting function, but rather were caused by the ``_check_plot_works`` itself.
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| 2020-11-10T06:05:22Z
| 2020-11-26T03:57:10Z
| 2020-11-10T23:40:31Z
|
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Parametrize some tests in ``pandas/tests/plotting/test_frame.py``.
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MDExOlB1bGxSZXF1ZXN0NTE4MjYyODQz
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BUG: read_html - file path cannot be pathlib.Path type
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| 5
| 2020-11-10T06:47:19Z
| 2020-11-11T19:20:38Z
| 2020-11-11T02:29:56Z
|
CONTRIBUTOR
| null |
Fix `read_html` TypeError when parsing `pathlib.Path` object.
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BUG: Output all columns even if subsetting columns after groupby
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| 2020-11-10T08:36:25Z
| 2021-08-23T04:04:26Z
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem
I would like create a dictionary from pandas dataframe using groupby, which the key is the group which is used in pandas groupby and the value is list of the groupby output.
```python
# create a dataframe
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [2, 3, 4], 'C': [3, 4, 5]})
```
When subsetting a single column after groupby, it works well,
```python
# get a single column after groupby
dict(df.groupby('A')['B'].apply(list))
# output
{1: [2], 2: [3], 3: [4]}
```
but when subsetting multiple columns, it outputs the list of dataframe.columns.
```python
# get multiple columns after groupby
dict(df.groupby('A')[['B', 'C']].apply(list))
# output
{1: ['B', 'C'], 2: ['B', 'C'], 3: ['B', 'C']}
```
I checked raw output when subsetting columns after groupby and found that it outputs all columns even if subsetting columns after groupby.
```python
# get raw output when subsetting columns after groupby
list(df.groupby('A')[['B', 'C']])
# output
[(1,
A B C
0 1 2 3),
(2,
A B C
1 2 3 4),
(3,
A B C
2 3 4 5)]
```
#### Expected Output
For 1st example:
```python
# get multiple columns after groupby
dict(df.groupby('A')[['B', 'C']].apply(list))
# output
{1: [2, 3], 2: [3, 4], 3: [4, 5]}
```
And for 2nd example:
```python
# get raw output when subsetting columns after groupby
list(df.groupby('A')[['B', 'C']])
# output
[(1,
B C
0 2 3),
(2,
B C
1 3 4),
(3,
B C
2 4 5)]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-29-generic
Version : #31-Ubuntu SMP Tue Jul 17 15:39:52 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
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 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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TYP: fix mypy ignored error in pandas/io/formats/latex.py
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| 2
| 2020-11-10T10:39:33Z
| 2020-11-10T15:22:54Z
| 2020-11-10T14:49:41Z
|
MEMBER
| null |
- [ ] xref #37715
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Handle mypy ignored error in ``pandas/io/formats/latex.py``.
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TYP: fix mypy ignored err in pandas/io/formats/format.py
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| 2020-12-03T07:57:38Z
| 2020-11-10T19:22:44Z
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| null |
- [ ] xref #37715
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Handle ignored mypy error in ``pandas/io/formats/format.py``
|
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MDExOlB1bGxSZXF1ZXN0NTE4NDA5MTUw
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[WIP] ci testing of fixes for numpy types
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| 2020-11-10T11:08:41Z
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xref #36092
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MDExOlB1bGxSZXF1ZXN0NTE4NDk1ODA1
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Backport PR #37661 on branch 1.1.x: BUG: RollingGroupby when groupby key is in the index
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Backport PR #37661
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BUG: rolling.apply() with engine="numba" causes memory leak.
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| 2020-11-14T18:36:46Z
| 2020-11-14T01:40:41Z
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NONE
| null |
Apologies if this is a duplicate, I searched the outstanding bugs and could find this one.
I running pandas version **1.1.4** and numba version **0.51.2**
If I use the 'numba' engine in the rolling.apply() the process leaks memory.
Take this example function:
```python
def rolling_mad(values):
@nb.njit(nogil=True)
def _mad(x):
return np.fabs(x - x.mean()).mean()
df = pd.DataFrame(values)
rolling = df.rolling(180, min_periods=1, center=True)
return rolling.apply(_mad, engine="numba", raw=True).values
```
This snippet demonstrates the issue:
```python
import numpy as np
import numba as nb
import pandas as pd
import resource
values = np.random.rand(1200)
for i in range(20):
print(f"Memory usage before call {resource.getrusage(resource.RUSAGE_SELF)[2] / 1024:.3f}KB")
m = rolling_mad(values)
m = None
print(f"Memory usage after call {resource.getrusage(resource.RUSAGE_SELF)[2] / 1024:.3f}KB")
```
If I use engine == 'numba' there is a leak
```
Memory usage before call 121.801MB
Memory usage after call 148.547MB
Memory usage before call 148.547MB
Memory usage after call 149.320MB
Memory usage before call 149.320MB
Memory usage after call 151.625MB
Memory usage before call 151.625MB
Memory usage after call 155.672MB
Memory usage before call 155.672MB
Memory usage after call 159.473MB
Memory usage before call 159.473MB
Memory usage after call 159.988MB
Memory usage before call 159.988MB
Memory usage after call 161.535MB
Memory usage before call 161.535MB
Memory usage after call 162.566MB
Memory usage before call 162.566MB
Memory usage after call 163.512MB
Memory usage before call 163.512MB
Memory usage after call 164.543MB
Memory usage before call 164.543MB
Memory usage after call 165.316MB
Memory usage before call 165.316MB
Memory usage after call 167.121MB
Memory usage before call 167.121MB
Memory usage after call 169.184MB
Memory usage before call 169.184MB
Memory usage after call 169.957MB
Memory usage before call 169.957MB
Memory usage after call 170.730MB
Memory usage before call 170.730MB
Memory usage after call 171.762MB
Memory usage before call 171.762MB
Memory usage after call 172.277MB
Memory usage before call 172.277MB
Memory usage after call 173.051MB
Memory usage before call 173.051MB
Memory usage after call 174.082MB
Memory usage before call 174.082MB
Memory usage after call 177.500MB
```
However, if I do not use engine="numba" in rolling.apply() there is no memory leak.
```
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
Memory usage before call 121.801MB
Memory usage after call 121.801MB
```
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BUG: rolling groupby does not respect min_periods when center=True
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| 2020-11-10T16:02:40Z
| 2020-11-10T19:18:01Z
| 2020-11-10T19:18:00Z
|
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
s = pd.Series(1, index=list(range(0, 5)) * 4)
s.groupby(s.index).rolling(window=3, center=True, min_periods=1).sum()
```
Outputs:
```
0 0 2.0
0 3.0
0 3.0
0 1.0
1 1 2.0
1 3.0
1 3.0
1 1.0
2 2 2.0
2 3.0
2 3.0
2 1.0
3 3 2.0
3 3.0
3 3.0
3 1.0
4 4 2.0
4 3.0
4 3.0
4 NaN
dtype: float64
```
#### Problem description
For the given input `s`, where every element is 1, the snippet above should return the same as counting the number of values in each window:
```python
s.groupby(s.index).rolling(window=3, center=True, min_periods=1).count()
```
The output above is inconsistent with versions of pandas prior to 1.1 and is inconsistent in the semantics of `min_periods`; the first element of each group has the same number of periods as the last, but its value is computed correctly. The last element of each group is also computed incorrectly (should be 2). Finally, the last element of the final group (4) should not be NA, but should again be 2.
Some similar issues have been reported since the changes to `.groupby().rolling()` made in pandas 1.1.0.
- #36040
- #37141 might cover the same bug, but it is not immediately clear (I would also very much prefer to have this fixed in the 1.1.x line rather than wait for 1.2 and need to exclude 1.1.x in my dependencies).
#### Expected Output
```
0 0 2.0
0 3.0
0 3.0
0 2.0
1 1 2.0
1 3.0
1 3.0
1 2.0
2 2 2.0
2 3.0
2 3.0
2 2.0
3 3 2.0
3 3.0
3 3.0
3 2.0
4 4 2.0
4 3.0
4 3.0
4 2.0
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
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 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTE4NjI1MDMx
| 37,744
|
DOC: add hvplot
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| 2
| 2020-11-10T16:50:25Z
| 2020-11-17T22:37:44Z
| 2020-11-17T22:37:28Z
|
CONTRIBUTOR
| null |
- [x] closes #33730
- [NA] tests added / passed
- [NA] passes `black pandas`
- [NA] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [NA?] whatsnew entry
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TYP: follow-up to #37723
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| 2020-11-11T17:12:46Z
| 2020-11-11T17:12:32Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CI: troubleshoot windows builds
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| 15
| 2020-11-10T18:08:19Z
| 2020-11-18T16:27:54Z
| 2020-11-18T13:55:20Z
|
MEMBER
| null |
Windows builds are still hitting what look like OOM errors. Trying splitting these into smaller pieces; its fragile.
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CLN: remove unused min_count argument in libgroupby.group_nth
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BUG: ValueError is mistakenly raised if a numpy array is assigned to a pd.Series of dtype=object and both have the same length
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| 2020-12-05T17:14:04Z
| 2020-12-05T17:14:04Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
pd.__version__ # '1.1.3'
pdseries = pd.Series(index=[1,2,3,4], dtype=object)
pdseries.loc[1] = np.zeros(100) # this works fine
pdseries.loc[3] = np.zeros(4) # this raises a value error because len(pdseries)==len(np.zeros(4))
```
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 2878, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-40-460230264bf1>", line 1, in <module>
pdseries.loc[3] = np.zeros(4)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/indexing.py", line 670, in __setitem__
iloc._setitem_with_indexer(indexer, value)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/indexing.py", line 1802, in _setitem_with_indexer
self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 534, in setitem
return self.apply("setitem", indexer=indexer, value=value)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 406, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/daniel/.conda/envs/production_system/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 887, in setitem
values = values.astype(arr_value.dtype, copy=False)
ValueError: setting an array element with a sequence.
#### Problem description
It is possible to assign (numpy) arrays to elements of pandas.Series ofd type=object. Unfortunately, in case the array is of the same size as the Series a ValueError is raised.
How can one avoid this error?
#### Expected Output
The interesting thing is that the assignment takes place as expected:
In[42]: pdseries
Out[42]:
1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
2 NaN
3 [0.0, 0.0, 0.0, 0.0]
4 NaN
One might argue that a warning could be useful but an error is misleading and tricky to debug.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.3
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 : 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 : 5.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: Bug in loc did not change dtype when complete column was assigned
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| 2020-11-11T02:30:15Z
| 2020-12-09T17:44:07Z
| 2020-11-11T20:27:02Z
|
CONTRIBUTOR
| null |
The monthly dev meeting is Wednesday November 11th (tomorrow), at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time. The US had a DST transition since the last meeting, so your local time may be different.
Video Call: https://zoom.us/j/942410248?pwd=T2l2Qi9vaC82Z294ZEtFczYxMVM2dz09
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
FYI, I likely won't be able to attend tomorrow.
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TST/REF: fixturize test_drop_duplicates
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| 2020-11-12T02:19:02Z
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MEMBER
| null |
The datetimelike de-duplication is unrelated, just didnt seem to merit its own PR.
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DOC: troubleshoot docbuild
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TYP: Extend typing to allow passing dates to to_datetime
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CONTRIBUTOR
| null |
Updates the typing to reflect what is already possible.
Otherwise, Pycharm is pretty unhappy when a date is pased.
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: Series.groupby() fails in pandas 1.1.4 when index has tuple name.
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| 2020-11-14T03:31:46Z
| 2020-11-14T03:31:46Z
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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
print("Test 1")
a = pd.Series([1,2,3,4], index=[1,1,2,2], name=("a", "a"))
a.index.name = ("b", "b")
print(a)
print(a.index)
print(a.groupby(level=0).last())
print("Test 2")
a = pd.Series([1,2,3,4], index=[2,3,4,5], name=("a", "a"))
b = pd.Series([1,1,2,2], index=[2,3,4,5], name=("b", "b"))
a.index = b.reindex(a.index)
print(a)
print(a.index)
print(a.groupby(level=0).last())
```
#### Problem description
In pandas 1.1.2 this works fine. While it crashes in pandas 1.1.4. The problem seems related to the tuple index names. The output is:
```
Test 1
(b, b)
1 1
1 2
2 3
2 4
Name: (a, a), dtype: int64
Int64Index([1, 1, 2, 2], dtype='int64', name=('b', 'b'))
Traceback (most recent call last):
File "testcase.py", line 8, in <module>
print(a.groupby(level=0).last())
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/series.py", line 1735, in groupby
return SeriesGroupBy(
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 525, in __init__
grouper, exclusions, obj = get_grouper(
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/groupby/grouper.py", line 773, in get_grouper
if is_in_obj(gpr): # df.groupby(df['name'])
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/groupby/grouper.py", line 765, in is_in_obj
return gpr is obj[gpr.name]
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/series.py", line 888, in __getitem__
result = self._get_value(key)
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/series.py", line 989, in _get_value
loc = self.index.get_loc(label)
File "/home/burk/.local/share/virtualenvs/timeseries-Gj3kjmJv/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2895, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 96, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 118, in pandas._libs.index.IndexEngine._get_loc_duplicates
TypeError: only integer scalar arrays can be converted to a scalar index
```
#### Expected Output
This is the output in 1.1.2:
```
Test 1
(b, b)
1 1
1 2
2 3
2 4
Name: (a, a), dtype: int64
Int64Index([1, 1, 2, 2], dtype='int64', name=('b', 'b'))
(b, b)
1 2
2 4
Name: (a, a), dtype: int64
Test 2
(b, b)
1 1
1 2
2 3
2 4
Name: (a, a), dtype: int64
Int64Index([1, 1, 2, 2], dtype='int64', name=('b', 'b'))
(b, b)
1 2
2 4
Name: (a, a), dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-26-generic
Version : #27-Ubuntu SMP Wed Oct 21 22:29:16 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.1.1
setuptools : 44.0.0
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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|
Revert "DOC: test organization"
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| 2020-11-11T11:49:41Z
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MEMBER
| null |
Reverts pandas-dev/pandas#37637
(CI is failing because of this)
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BUG: csv newline \n \r\n in Window with opened file
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| 10
| 2020-11-11T09:38:26Z
| 2020-11-16T11:54:33Z
| 2020-11-16T11:54:33Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
Related issue: #22497, the issues looks related, but it is 2 years old and does not have any update.
- [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
https://github.com/noklam/fsspec_pandas_bug_demo # if you need to download the data
```python
# Your code here
import pandas as pd
import fsspec
from fsspec.implementations.local import LocalFileSystem
fs = LocalFileSystem()
df = pd.read_csv('iris.csv')
with fs.open('iris_fsspec.csv', 'w') as f:
df.to_csv(f)
df.to_csv('iris_classic_pandas.csv')
```
#### Problem description
The output csv behavior is inconsistent
#### Expected Output
Expected to have identical output.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
OS: Window 10
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : English_Hong Kong SAR.1252
pandas : 1.1.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.3.1.post20200515
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.6.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.17
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
BUG: read_fwf() does not obey skip_blank_lines
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| 0
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| 2020-11-14T20:13:01Z
| 2020-11-14T20:13:01Z
|
NONE
| null |
The `read_fwf()` function does not obey the `skip_blank_lines` argument:
```python
data = io.StringIO("""\
1 2 3
4 5 6
""")
d = pandas.read_fwf(data, header=None, colspec=[(0, 2), (2, 4), (4, 6)])
```
results in:
```
0 1 2
0 1 2 3
1 4 5 6
```
while
```python
data = io.StringIO("""\
1 2 3
4 5 6
""")
d = pandas.read_fwf(data, header=None, colspec=[(0, 2), (2, 4), (4, 6)])
```
results in
```
0 1 2
0 1.0 2.0 3.0
1 NaN NaN NaN
2 4.0 5.0 6.0
```
The `skip_blank_lines` argument defaults to `True`, but also explicitly setting it results in the same behavior. The `read_fwf()` function documentation says that it takes all arguments supported by `TextFileReader` thus this is unexpected.
I have tested this with Pandas 1.1.3 as distributed by conda, however, the relevant code does not seem to have changed on master.
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ENH: option to plot data points on top of boxplot
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| 2
| 2020-11-11T13:54:41Z
| 2021-02-07T15:31:57Z
| 2021-02-07T15:31:16Z
|
NONE
| null |
#### Is your feature request related to a problem?
My longing for this feature comes from many years of using Minitab, [which has a convenient option to show individual data on boxplots](https://support.minitab.com/en-us/minitab/18/help-and-how-to/graphs/how-to/general-graph-options/display-options/boxplot/). As a result, I often find myself wanting to be able to get a better visual idea of the data distribution. The boxplot would look something like:
#### Describe the solution you'd like
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10,2))
df.boxplot(plot_points=True)
```
#### API breaking implications
The above implementation adds an optional parameter to boxplot function.
#### Describe alternatives you've considered
What I'm after can be accomplished by other means, for example:
https://stackoverflow.com/questions/29779079/adding-a-scatter-of-points-to-a-boxplot-using-matplotlib
I find some of these alternative methods a bit clumsy, especially when trying to interactively inspect data.
I've looked for some solution along these lines (layer a scatter plot on top of a boxplot), but haven't been able to figure out a path that works.
```
ax = df.plot(kind='box')
# This doesn't work. It requires x and y parameters to be passed. I haven't figured out a way to make categorical scatter plots.
df.plot(kind='scatter')
```
I've found a way to do it using Seaborn:
```
import seaborn as sns
df = pd.DataFrame(np.random.randn(10, 3))
df2 = df.melt()
sns.boxplot(x='variable', y='value', data=df2)
sns.stripplot(x='variable', y='value', data=df2)
```
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MDExOlB1bGxSZXF1ZXN0NTE5Mjc4OTUz
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DOC: test organization
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| 3
| 2020-11-11T15:38:45Z
| 2020-11-14T03:00:21Z
| 2020-11-14T02:56:11Z
|
MEMBER
| null |
Retry of #37637, reverted by #37756
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MDExOlB1bGxSZXF1ZXN0NTE5MzA2MjEw
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| 13
| 2020-11-11T16:22:50Z
| 2021-05-25T09:34:03Z
| 2021-05-25T09:29:09Z
|
MEMBER
| null |
- [x] closes #20438
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Not quite sure if conversion to numpy array is necessary, but ``is_dtype_bool`` does not accept lists
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BUG: clean the figure windows created by tests
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| 1
| 2020-11-11T18:30:43Z
| 2020-11-12T16:23:53Z
| 2020-11-12T16:23:38Z
|
CONTRIBUTOR
| null |
- [x] closes #35080
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
fixed: FAILED pandas/tests/plotting/test_datetimelike.py::TestTSPlot::test_ts_plot_with_tz['UTC']
reference:
- https://github.com/pandas-dev/pandas/issues/35080#issuecomment-652988132 @StefRe as co-author (have already marked as the co-author in my forked repo, however, I'm not sure whether as well as right here)
- https://stackoverflow.com/a/8228808/7069618
- https://stackoverflow.com/a/33343289/7069618
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BUG: DataFrame.at setter of categorical DF overwrites entire row
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| 8
| 2020-11-11T19:42:40Z
| 2020-11-26T18:08:12Z
| 2020-11-26T18:08:12Z
|
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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
On a DataFrame with categorical dtype, `df.at[x,y] = v` sets all non-initialized values in row `x`.
#### Code Sample, a copy-pastable example
```
$ python
Python 3.8.0 (default, Oct 28 2019, 16:14:01)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
```
```python
>>> import pandas as pd
>>> pd.__version__
'1.2.0.dev0+1137.g50b34a4a8'
>>> df = pd.DataFrame(index=range(3), columns=range(3), dtype=pd.CategoricalDtype(['foo', 'bar']))
>>> df.at[1,2] = 'foo'
>>> df
0 1 2
0 NaN NaN NaN
1 foo foo foo
2 NaN NaN NaN
```
It doesn't overwrite values that have been set with `df.loc`:
```python
>>> df = pd.DataFrame(index=range(3), columns=range(3), dtype=pd.CategoricalDtype(['foo', 'bar']))
>>> df.loc[1,1] = 'bar' # not necessary, just for demo
>>> df.at[1,2] = 'foo'
>>> df
0 1 2
0 NaN NaN NaN
1 foo bar foo
2 NaN NaN NaN
```
#### Problem description
`df.at[x, y] = v` on a categorical dtype should behave as with other dtypes, and the same as `df.loc[x, y] = v`.
#### Expected Output
The same as what happens with a DF initialized with `None`s:
```python
>>> df = pd.DataFrame([[None] * 3] * 3, index=range(3), columns=range(3), dtype=pd.CategoricalDtype(['foo', 'bar']))
>>> df.loc[1,1] = 'bar'
>>> df.at[1,2] = 'foo'
>>> df
0 1 2
0 NaN NaN NaN
1 NaN bar foo
2 NaN NaN NaN
```
Or as with `dtype=float`:
```python
>>> df = pd.DataFrame(index=range(3), columns=range(3), dtype=float)
>>> df.loc[1,1] = 1
>>> df.at[1,2] = 27
>>> df
0 1 2
0 NaN NaN NaN
1 NaN 1.0 27.0
2 NaN NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
<ul>
<li>------------------</li>
<li>commit : 50b34a4a8008b6da24c3dac6ae6d66536e463239</li>
<li>python : 3.8.0.final.0</li>
<li>python-bits : 64</li>
<li>OS : Linux</li>
<li>OS-release : 5.4.0-52-generic</li>
<li>Version : #57~18.04.1-Ubuntu SMP Thu Oct 15 14:04:49 UTC 2020</li>
<li>machine : x86_64</li>
<li>processor : x86_64</li>
<li>byteorder : little</li>
<li>LC_ALL : None</li>
<li>LANG : en_US.UTF-8</li>
<li>LOCALE : en_US.UTF-8</li>
<li></li>
<li>pandas : 1.2.0.dev0+1137.g50b34a4a8</li>
<li>numpy : 1.19.4</li>
<li>pytz : 2020.4</li>
<li>dateutil : 2.8.1</li>
<li>pip : 20.2.4</li>
<li>setuptools : 50.3.2</li>
<li>Cython : None</li>
<li>pytest : None</li>
<li>hypothesis : None</li>
<li>sphinx : None</li>
<li>blosc : None</li>
<li>feather : None</li>
<li>xlsxwriter : None</li>
<li>lxml.etree : None</li>
<li>html5lib : None</li>
<li>pymysql : None</li>
<li>psycopg2 : None</li>
<li>jinja2 : None</li>
<li>IPython : None</li>
<li>pandas_datareader: None</li>
<li>bs4 : None</li>
<li>bottleneck : None</li>
<li>fsspec : None</li>
<li>fastparquet : None</li>
<li>gcsfs : None</li>
<li>matplotlib : None</li>
<li>numexpr : None</li>
<li>odfpy : None</li>
<li>openpyxl : None</li>
<li>pandas_gbq : None</li>
<li>pyarrow : None</li>
<li>pyxlsb : None</li>
<li>s3fs : None</li>
<li>scipy : None</li>
<li>sqlalchemy : None</li>
<li>tables : None</li>
<li>tabulate : None</li>
<li>xarray : None</li>
<li>xlrd : None</li>
<li>xlwt : None</li>
<li>numba : None</li>
</ul>
</details>
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MDExOlB1bGxSZXF1ZXN0NTE5NDI3OTk1
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Bug in loc did not raise KeyError when missing combination with slice(None) was given
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| 7
| 2020-11-11T20:17:04Z
| 2020-11-21T23:42:22Z
| 2020-11-21T22:21:04Z
|
MEMBER
| null |
- [x] closes #19556
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Additional question: When an non existent key is given for ``get_locs`` it raises a ``KeyError``. When just a missing combination of keys is given (``(b,1)`` in the new test), while the individual keys exist, an empty array is returned. Is this the expected behavior?
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CI: migrate from travis-ci.org to travis-ci.com
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| 11
| 2020-11-11T20:25:23Z
| 2021-01-01T22:53:51Z
| 2021-01-01T22:53:50Z
|
MEMBER
| null |
See https://docs.travis-ci.com/user/migrate/open-source-repository-migration, if we want to keep the Travis CI builds, we need to migrate to the .com version before the end of the year.
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CI/TST: read_html test_banklist_url_positional_match failing with ResourceWarning on Travis
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| 2020-11-13T15:25:13Z
| 2020-11-12T10:25:17Z
|
MEMBER
| null |
Travis builds are recently failing, eg https://travis-ci.org/github/pandas-dev/pandas/jobs/742927007
```
=================================== FAILURES ===================================
_____________ TestReadHtml.test_banklist_url_positional_match[bs4] _____________
[gw0] linux -- Python 3.7.8 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.io.test_html.TestReadHtml object at 0x7f3091661050>
@tm.network
def test_banklist_url_positional_match(self):
url = "http://www.fdic.gov/bank/individual/failed/banklist.html"
# Passing match argument as positional should cause a FutureWarning.
with tm.assert_produces_warning(FutureWarning):
df1 = self.read_html(
> url, "First Federal Bank of Florida", attrs={"id": "table"}
)
pandas/tests/io/test_html.py:130:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f309168d4d0>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=18, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.10', 43648), raddr=('172.217.212.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/treebuilders/base.py', 38), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=16, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.10', 43650), raddr=('172.217.212.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/treebuilders/base.py', 38), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=43, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.10', 43962), raddr=('172.217.214.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/bs4/builder/_html5lib.py', 335), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=42, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.10', 46504), raddr=('74.125.124.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/bs4/builder/_html5lib.py', 335), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=41, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.10', 46502), raddr=('74.125.124.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/bs4/builder/_html5lib.py', 335), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=15, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.10', 43640), raddr=('172.217.212.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/bs4/builder/_html5lib.py', 335)]
../../../miniconda3/envs/pandas-dev/lib/python3.7/contextlib.py:119: AssertionError
```
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MDExOlB1bGxSZXF1ZXN0NTE5NDQ1MjAw
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CI: remove unused import
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MEMBER
| null |
xref #37703
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BUG: downsampling with last doesn't accept "min_count" keyword
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| 3
| 2020-11-11T21:11:02Z
| 2020-11-26T15:36:26Z
| 2020-11-26T15:36:26Z
|
NONE
| null |
I verified this on v 1.1.3. I am on miniconda, 1.1.4 is not available yet. There are ways to install probably but if I read documentation right, this feature should be around since 0.22
```
import numpy as np
import pandas as pd
index = pd.date_range(start='2020', freq='M', periods=6)
data = np.ones(6)
data[4:6] = np.nan
datetime = pd.Series(data, index)
period = datetime.to_period()
datetime
2020-01-31 1.0
2020-02-29 1.0
2020-03-31 1.0
2020-04-30 1.0
2020-05-31 NaN
2020-06-30 NaN
Freq: M, dtype: float64
```
```
datetime.resample('Q').sum(min_count=2)
2020-03-31 3.0
2020-06-30 NaN
Freq: Q-DEC, dtype: float64
```
```
datetime.resample('Q').last(min_count=3)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-bd5dfd934676> in <module>
----> 1 datetime.resample('Q').last(min_count=3)
~\miniconda3\lib\site-packages\pandas\core\resample.py in g(self, _method, *args, **kwargs)
933
934 def g(self, _method=method, *args, **kwargs):
--> 935 nv.validate_resampler_func(_method, args, kwargs)
936 return self._downsample(_method)
937
~\miniconda3\lib\site-packages\pandas\compat\numpy\function.py in validate_resampler_func(method, args, kwargs)
395 )
396 else:
--> 397 raise TypeError("too many arguments passed in")
398
399
TypeError: too many arguments passed in
```
#### Problem description
According to the documentation, .last() does accept the keyword "min_count", just like for example .sum()
where it works fine, see above
So I should not see the error above. The "min_count" is useful also for .last() if you have nans in your data and want to avoid the record that is not truly the last record in the segment.
Doc:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.resample.Resampler.last.html#pandas.core.resample.Resampler.last)
#### Expected Output
```
2020-03-31 1.0
2020-06-30 NaN
```
#### Output of ``pd.show_versions()``
See above - verified with pd 1.1.3. I started the issue on my laptop with older pandas but finished with 1.1.3. Error message and pd.show_versions() is up to date
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 8.1
Version : 6.3.9600
machine : AMD64
processor : Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
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 : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.49.1
</details>
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MDExOlB1bGxSZXF1ZXN0NTE5NDY0MzQ0
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CLN: removing imports from generic.py
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MDExOlB1bGxSZXF1ZXN0NTE5NDc1OTE0
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TST: Isin converted floats unnecessarily to int causing rounding issues
|
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| 2020-11-11T22:03:28Z
| 2020-12-30T09:10:11Z
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|
MEMBER
| null |
- [x] closes #19356
- [x] closes #21804
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTE5NDc2OTIx
| 37,771
|
Fix pivot index bug
|
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| 9
| 2020-11-11T22:05:50Z
| 2020-11-14T18:46:23Z
| 2020-11-14T02:21:09Z
|
CONTRIBUTOR
| null |
Previously, the variable "cols" was created solely to concatenate
the lists "index" and "columns" when the "values" parameter was
left as None. It did so in a way that it modified the variable
passed as "index" using list.extend(), affecting the caller's
namespace.
This change simply uses an anonymous list in place of the
variable "cols"
Had some trouble setting up the dev environment to build pandas from source,
so no tests checked or docs built. However, it's fairly minor change, so hoping
that it builds green regardless...
- [X] closes #37635
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTE5NDk0ODk1
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|
Bug in __getitem__ incorrectly dropped columns for empty Series as indexer
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MEMBER
| null |
- [x] closes #19290
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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TST/REF: collect Index setop, indexing tests
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|
API: should setitem-with-expansion _ever_ raise?
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| 2020-11-11T23:38:09Z
| 2021-08-14T05:05:49Z
| null |
MEMBER
| null |
ATM we are pretty inconsistent as to when setitem-with-expansion raises. This boils down to idiosyncratic casting rules for `Index.insert` mentioned in today's dev call.
Examples:
```
ri = pd.Index(range(6))
ci = pd.CategoricalIndex(["a", "a", "b", "b", "c", "a"])
dti = pd.date_range("2016-01-01", periods=6)
mi = pd.MultiIndex.from_arrays([ri, dti])
ser1 = pd.Series(range(6), index=ci)
ser2 = pd.Series(range(6), index=dti)
ser3 = pd.Series(range(6), index=mi)
>>> ser1.loc["d"] = 10
ValueError: 'fill_value=d' is not present in this Categorical's categories
>>> ser2.loc[4] = 10
TypeError: value should be a 'Timestamp' or 'NaT'. Got 'int' instead.
>>> ser2.loc["foo"] = 10 # <-- casts to object
>>> ser3.loc[dti[0], dti[0]] = 10
TypeError: int() argument must be a string, a bytes-like object or a number, not 'Timestamp'
>>> ser3.loc[3, 4] = 10
TypeError: value should be a 'Timestamp' or 'NaT'. Got 'int' instead.
>>> ser3.loc[3, "a"] = 10 # <-- casts level to object
```
I see two options:
1) use consistent casting rules for new item being inserted, so allow iff dtype can be retained.
2) always cast, never raise
Option 2 seems the more user-friendly, and is necessary for e.g. some of our `crosstab` tests which insert "All". AFAICT that is why DTI/TDI have a special case casting for strings and raising for everything else.
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CLN: _get_daily_rule
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Broken off from branch addressing the infer_freq bug mentioned on today's dev call.
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xref https://github.com/pandas-dev/pandas/issues/28385#issuecomment-572750633
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BUG: adding Timedelta to DatetimeIndex raising incorrectly
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REF: simplify Block.replace
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BUG: json_normalize cannot parse metadata fields list type
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| 2021-08-14T05:07:12Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example
```python
test_data = [
{"values": [1, 2, 3], "metadata": {"listdata": [1, 2]}}]
df = json_normalize(test_data,
record_path=["values"],
meta=[["metadata", "listdata"]])
print(df)
```
#### Problem description
It throws error ```ValueError: Length of values (6) does not match length of index (3)```
Changing listdata field from `[1,2]` to `{1,2}` helped but I can't control JSON I receive.
#### Expected Output
| 0 | metadata.listdata |
|---|-------------------|
| 1 | [1, 2] |
| 2 | [1, 2] |
| 3 | [1, 2] |
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.6.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-52-generic
Version : #57-Ubuntu SMP Thu Oct 15 10:57:00 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
</details>
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BUG: json_normalize generates ```TypeError: 'NoneType' object is not subscriptable``` as `metadata` object is not always present.
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| 2020-11-12T08:08:45Z
| 2021-03-24T15:27:01Z
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NONE
| null |
The example below always generate an error ```TypeError: 'NoneType' object is not subscriptable``` as `metadata` object is not always present.
```
file = [{'values': ['1', '2'], 'metadata': {'name': 'first_value'}},
{'values': ['3', '4'], 'metadata': None}]
df = json_normalize(file,
record_path='values',
meta=[['metadata', 'name']],
errors='ignore')
```
The `metadata` object is dynamic and nested, so I am concerned why it doesn't generate something like this:
| 0 | metadata.name |
|---|---------------|
| 1 | first_value |
| 2 | first_value |
| 3 | nan |
| 4 | nan |
as I set ```errors='ignore' ```
@WillAyd Could you please explain the best way to handle this?
_Originally posted by @sann05 in https://github.com/pandas-dev/pandas/issues/21830#issuecomment-725439522_
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| 81
| 2020-11-12T10:09:04Z
| 2021-01-19T10:29:12Z
| 2021-01-19T10:28:50Z
|
MEMBER
| null |
Tracking issue for the 1.2 release. https://github.com/pandas-dev/pandas/milestone/73
Planning a release candidate for sometime towards the end of the month, 1-2 weeks before the final release.
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression (includes regressions that may be included in 1.1.5)
List of open blockers: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3Ablocker+
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MDU6SXNzdWU3NDE1MzkxNjc=
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ENH: date_format or date_unit in to_dict
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[] | null | 2
| 2020-11-12T11:56:45Z
| 2021-08-14T05:09:23Z
| null |
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).
---
#### Question about pandas
Is there a way to use `to_json` but get the results as a dictionary instead of a string?
I have an ETL pipeline and Pandas gets me 99% of the way there, which is awesome, but I am encountering a snap.
The `to_json` method returns the data in the format I need, but the values are strings. Since I need to add additional information to pass on to our internal REST API, I need the ability to further manipulate the JSON. For small amounts of data, this is fine, I can use the dumps/loads methods, but for large datasets, the conversion between string <-> dict is having lots of overhead. Is there a way on `to_json` to get the response back as a dictionary instead of a string?
I've looked at the following:
- `to_records`
- `to_dict`
and a few others, but they do not have the additional options that `to_json` has.
```python
import json
json_data = data.to_json(orient='records',
date_format='epoch')
dict_json = json.loads(json_data)
dict_json[0]['other keys'] = {'some large' : 'amount of 3rd party information'}
....
```
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MDExOlB1bGxSZXF1ZXN0NTE5ODM2MzYx
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CI/TST: use https to avoid ResourceWarning in html tests
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| 4
| 2020-11-12T12:00:25Z
| 2020-11-13T16:31:05Z
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|
MEMBER
| null |
See #36467
(no idea if this would actually fix anything, just mimicking what @alimcmaster1 did in https://github.com/pandas-dev/pandas/pull/36480)
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Fix regression for loc and __setitem__ when one-dimensional tuple was given for MultiIndex
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| 6
| 2020-11-12T13:06:14Z
| 2020-11-14T19:06:11Z
| 2020-11-14T16:51:58Z
|
MEMBER
| null |
- [x] closes #37711
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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REF: implement Block._putmask_simple for non-casting putmask
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| 2020-11-12T16:19:02Z
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|
MEMBER
| null |
Much easier to reason about than Block.putmask.
|
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CLN: remove no-longer-reachable Block.replace code
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We recently changed NDFrame.replace such that Block.replace never gets a list for `to_replace`, so this chunk of code is never reachable.
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BUG: freq inference for tz-aware DatetimeIndex
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: remove unreachable in interpolate_with_fill
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Remove unnecessary casts in tokenizer.c
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| 2020-11-13T13:20:15Z
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MEMBER
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Some of these are visual clutter, some are actual bugs though in unlikely cases (see the format specifiers)
https://stackoverflow.com/questions/605845/do-i-cast-the-result-of-malloc
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QST: Rolling pd.Grouper over N days
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[] | null | 4
| 2020-11-12T21:04:59Z
| 2020-12-15T17:52:06Z
| 2020-12-15T17:52:05Z
|
NONE
| null |
Starting from a desired date in a dataframe, can we group and sum the last 30 days and compare the prior 30 day period? And it would have to be days since this calculation could be done at any point in a month.
The first option below isn't very elegant, but I think gets to the right place. The second option using pd.Grouper, but chops off values during the group and produces different results and groups to the beginning of the period.
Any suggestions? Is there another function to try? Thanks!
```python
data = pd.DataFrame({
'Timestamp': pd.date_range('8-11-2020', '10-12-20', freq='D'),
'Qty': [4, 8, 5, 0, 6, 1, 0, 5, 4, 8, 4, 4, 8, 2, 6, 5, 3, 2, 4, 8, 0, 4,
0, 9, 5, 3, 8, 2, 2, 4, 5, 7, 6, 7, 6, 5, 1, 6, 0, 1, 0, 5, 0, 2,
3, 6, 5, 2, 5, 8, 3, 7, 8, 6, 4, 5, 8, 7, 7, 5, 9, 6, 6]
})
df = data.groupby(pd.Grouper(key='Timestamp', freq='D')).sum()[['Qty']].sort_index()
df = df.rolling('30D').sum().pct_change(1, freq='30D')
df.loc['10-12-2020']['Qty']
# or...
data \
.groupby(pd.Grouper(key='Timestamp', freq='30D', label='right', closed='right')) \
.sum()[['Qty']].sort_index().pct_change()
```
Using grouper begins the grouping of the Timestamp column from the earliest date to the latest. I would want to do the opposite.
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|
[BUG]: Fix bug in MultiIndex.drop dropped nan when non existing key was given
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| 2020-11-12T21:39:08Z
| 2020-11-18T20:37:25Z
| 2020-11-18T18:35:06Z
|
MEMBER
| null |
- [x] closes #18853
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Additional question:
The doc says (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.drop.html) that ``drop`` raises when not all labels are found, but
```
pd.DataFrame(index=pd.MultiIndex.from_product([range(3), range(3)])).drop([1, 5], level=0)
```
does not raise, because ``1`` is found. Should we implement, that it raises every time when at least one of the keys is not found?
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|
getiterator deprecated in Python 3.9; failure to call pd.read_excel()
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[] | null | 10
| 2020-11-12T22:28:41Z
| 2021-06-23T01:30:09Z
| 2020-12-01T23:37:48Z
|
NONE
| null |
I've read here https://stackoverflow.com/a/64744395 that `getiterator` has been deprecated in Python 3.9.0. My script ran without issues until upgrading Python to 3.9, and now I get the following error:
```python
Traceback (most recent call last):
File "/Users/user/Documents/Programming/MyProject/MyProject.py", line 551, in <module>
df = pd.read_excel(excel_filename, excel_sheetname)
...
AttributeError: 'ElementTree' object has no attribute 'getiterator'
```
Running Python 3.9.0, Pandas 1.1.4
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| 2020-11-12T22:39:07Z
| 2020-11-15T17:39:37Z
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MEMBER
| null |
- [x] closes #18531
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The test for historical reasons :) failed after the change. Is this a problem?
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TYP: fix mypy ignored error in pandas/tests/io/test_fsspec.py
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| 1
| 2020-11-12T23:01:09Z
| 2020-11-13T05:55:48Z
| 2020-11-13T04:30:00Z
|
CONTRIBUTOR
| null |
- [ ] xref #37715
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Handle mypy ignored error in pandas/tests/io/test_fsspec.py
Is this decision correct?
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BUG: Bug in DataFrame.loc for setitem changed dtype when setting more than one column
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Could not find a reason why we should convert the dtype to object, if values is already an array.
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Discussed on the dev call yesterday.
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BUG: Error raised accessing tuple value in Series when index contains duplicates
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MEMBER
| null |
E.g.
```
s = pd.Series([1, 1], index=[(1, 1), (1, 1)])
s[(1, 1)]
```
raises `KeyError: (1, 1)`. Within `pandas._libs.index.IndexEngine._get_loc_duplicates`, we're using ndarray.searchsorted which interprets the tuple as an array-like of values to search for rather than a single tuple.
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|
REGR: SeriesGroupBy where index has a tuple name fails
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| 3
| 2020-11-12T23:52:40Z
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MEMBER
| null |
- [x] closes #37755
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The groupby code that calls `Series.__getitem__` catches `KeyError` and `TypeError`. If a tuple is fed into an `Index` that is not a `MultiIndex`, we were raising a `ValueError` but `KeyError` seems more appropriate here and also fixes the regression.
While working on this, I found that we raise a `KeyError` inappropriately if an index of tuples contains duplicates, however this exists back in 1.0.x and so is not part of the regression. I've opened #37800 for this.
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MDExOlB1bGxSZXF1ZXN0NTIwMjMwOTk2
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REF: avoid try/except in Block.where
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| 2020-11-13T16:20:31Z
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| 742,018,585
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MDExOlB1bGxSZXF1ZXN0NTIwMjMzMTgy
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BUG: skip_blank_lines ignored by read_fwf
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CONTRIBUTOR
| null |
- [x] closes #37758
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDU6SXNzdWU3NDIxMTkzODY=
| 37,804
|
ENH: Add 'end' option in resample's origin
|
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| 0
| 2020-11-13T04:10:10Z
| 2020-12-22T14:02:49Z
| 2020-12-22T14:02:49Z
|
CONTRIBUTOR
| null |
In some cases I need to calculate the time group from the end of time series instead of the beginning.
For example, if user uses `s.resample('7min', origin='end', closed='right')` , the following expected groups are (09:23:45, 09:30:45], (09:16:45, 09:23:45], (09:09:45, 09:16:45], ... , (08:20:45, 08:27:45]. And under normal cases, `closed` should be `right` when `origin` is `end` since last value should serve as the end value of the last group.
```python
>>>idx = pd.date_range('20200101 8:26:35', '20200101 9:31:58', freq='77s')
>>>idx
DatetimeIndex(['2020-01-01 08:26:35', '2020-01-01 08:27:52',
'2020-01-01 08:29:09', '2020-01-01 08:30:26',
'2020-01-01 08:31:43', '2020-01-01 08:33:00',
... ...
'2020-01-01 09:25:37', '2020-01-01 09:26:54',
'2020-01-01 09:28:11', '2020-01-01 09:29:28',
'2020-01-01 09:30:45'],
dtype='datetime64[ns]', freq='77S')
>>>data = np.ones(len(idx))
>>>s = pd.Series(data,index=idx)
>>>s
2020-01-01 08:26:35 1.0
2020-01-01 08:27:52 1.0
2020-01-01 08:29:09 1.0
.... ...
2020-01-01 09:23:03 1.0
2020-01-01 09:24:20 1.0
2020-01-01 09:25:37 1.0
2020-01-01 09:26:54 1.0
2020-01-01 09:28:11 1.0
2020-01-01 09:29:28 1.0
2020-01-01 09:30:45 1.0
Freq: 77S, dtype: float64
```
Thus the sum operation on this resampler shows:
```python
>>>s.resample('7min', origin='end', closed='right').sum()
2020-01-01 08:27:45 1.0
2020-01-01 08:34:45 6.0
2020-01-01 08:41:45 5.0
2020-01-01 08:48:45 6.0
2020-01-01 08:55:45 5.0
2020-01-01 09:02:45 6.0
2020-01-01 09:09:45 5.0
2020-01-01 09:16:45 6.0
2020-01-01 09:23:45 5.0
2020-01-01 09:30:45 6.0
Freq: 7T, dtype: float64
```
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MDExOlB1bGxSZXF1ZXN0NTIwMzIxNTA0
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|
ENH: Add end and end_day options for origin from resample
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CONTRIBUTOR
| null |
- [x] closes #37804
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
Backport PR #37780 on branch 1.1.x (BUG: adding Timedelta to DatetimeIndex raising incorrectly)
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| 2020-11-13T09:04:33Z
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NONE
| null |
Backport PR #37780: BUG: adding Timedelta to DatetimeIndex raising incorrectly
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MDExOlB1bGxSZXF1ZXN0NTIwMzMyNTE3
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PERF: avoid object array cast in pd.isna
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| 2020-11-26T16:50:53Z
| 2020-11-26T16:50:53Z
|
MEMBER
| null |
- [x] closes #18856
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
#18856 is a fairly old issue about avoiding an object cast in the `_isna` method in `core/dtypes/missing.py`. AFAICT we never hit that clause in the method in our current testing suite. Modulo CI turning something up we should just delete it
Update: this *does* get hit. Looking into the ideas in #18856
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DOC: capitalize Python as proper noun
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|
DOC add note about how to skip tests during CI
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| 2020-11-13T09:55:50Z
| 2020-11-13T10:01:46Z
| 2020-11-13T10:01:46Z
|
MEMBER
| null |
Tomorrow there will be a PyDataGlobal sprint, which'll probably involve many, many documentation-related pull requests, so this might help with not using up azure pipelines resources unnecessarily
EDIT
----
doesn't seem to be working :flushed:
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BUG: Slicing of Index with strings does not raise anymore when keys are not in Index
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MEMBER
| null |
The following
```
df2 = pd.DataFrame({"A": [1, 2, 3]}, index=['a', 'c', 'b'])
df2.loc['d':'e']
```
Should raise a ``KeyError``. This worked in the past (see #18531) but does not anymore. Question is if we should raise in ``slice_locs`` if start and end are equal or if we should do this after calling the function.
cc @jreback
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MDU6SXNzdWU3NDI0MTQ1NDY=
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|
BUG: rowLabels and colLabels args in pandas.plotting.table have no effect
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| 3
| 2020-11-13T12:27:25Z
| 2020-11-16T15:14:46Z
| 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.
- [ yes ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
# Your code here
import pandas as pd
from matplotlib import pyplot as plt
df = pd.DataFrame({'old_title':[42,73]})
fig,ax = plt.subplots(1,1)
pd.plotting.table(ax,df, colLabels=['new_title'], loc='center')
ax.axis('off')
plt.show()
```
#### Problem description
Setting `rowLabels `or `colLabels `in `pandas.plotting.table` has no effect. I've found that the problem is in `table() `function of **pandas/plotting/_misc.py** as it returns plot_backend.table with predefined rowLabels=None, colLabels=None:
```
return plot_backend.table(
ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
)
```
It obviously should just pass `rowLabels `and `colLabels ` as is, instead.
Just replace `rowLabels=None, colLabels=None` with `rowLabels=rowLabels, colLabels=colLabels` in line 26 of _misc.py, please.
#### Expected Output
Expected output is a table with column named 'new_title' instead of 'old_title'
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
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 : 7.19.0
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 : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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CI: The `set-env` and `add-path` commands are deprecated and will be disabled on November 16th.
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| 2
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| 2020-11-13T16:20:36Z
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MEMBER
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https://github.com/pandas-dev/pandas/actions/runs/361523610
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DOC: corrupted function signatures on stable
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| 5
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|
MEMBER
| null |
#### Location of the documentation
Stable (v1.1.4) docs.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_parquet.html
#### Documentation problem
On stable (v1.1.4) there are corrupted function signatures in docs for these functions at least.
- pandas.DataFrame.to_stata
- pandas.DataFrame.to_feather
- pandas.DataFrame.to_parquet
Instead of named arguments followed by kwargs there are only kwargs.
There is no such problem on dev docs.
For example, here https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_parquet.html?highlight=to_parquet#pandas.DataFrame.to_parquet
#### Suggested fix for documentation
I though that probably decorator ``deprecate_kwargs`` was causing problems (the functions concerned are decorated),
but there is no such problem on dev docs, so the reason is not clear to me.
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Backport PR #37812 on branch 1.1.x (CI: The `set-env` and `add-path` commands are deprecated and will be disabled on November 16th.)
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| 2020-11-13T16:21:17Z
| 2020-11-13T18:43:40Z
| 2020-11-13T18:43:40Z
|
NONE
| null |
Backport PR #37812: CI: The `set-env` and `add-path` commands are deprecated and will be disabled on November 16th.
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REF: define nanargminmax without values_for_argsort
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TYP: copy method of EAs
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| 2
| 2020-11-13T18:35:17Z
| 2020-11-15T11:36:16Z
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MEMBER
| null |
some overlap with #37816
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ENH: storage_options for to_excel
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| 5
| 2020-11-13T19:06:20Z
| 2020-11-14T18:47:34Z
| 2020-11-14T17:05:51Z
|
CONTRIBUTOR
| null |
- [ ] ref #33987 (follow up to #37639)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry: not added, already implied in whatsnew of #37639?
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"patch_url": "https://github.com/pandas-dev/pandas/pull/37818.patch",
"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/37818"
}
|
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