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} | 4 | "2021-09-05T20:12:02Z" | "2021-09-08T13:04:13Z" | "2021-09-07T21:59:33Z" | 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 (yes, on version 1.3.2).
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
The following works fine on 1.2.5 and now fails (starting with 1.3.0). I waited for it to be fixed by 1.3.1/1.3.2 but it seems like this one went unnoticed until now.
#### Code Sample, a copy-pastable example
```python
from pandas import DataFrame
from numpy import zeros
# (Leaving out the dtype doesn't change things)
frame = DataFrame(columns=["x", "P"], dtype=object)
data = {"x": zeros((2, )), "P": zeros((2, 2))}
frame.append(data, ignore_index=True)
frame.loc[0] = data
```
#### Problem description
No exception, just the inserted data.
#### Expected Output
None.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-81-generic
Version : #91-Ubuntu SMP Thu Jul 15 19:09:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 57.0.0
Cython : None
pytest : 6.2.5
hypothesis : 6.17.4
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Goes towards #43161 and #41648
This is WIP since 41648 is not finished yet, but it is getting close.
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} | 8 | "2021-09-06T00:54:25Z" | "2021-09-09T11:37:26Z" | "2021-09-09T11:37:26Z" | MEMBER | 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.
---
#### Code Sample, a copy-pastable example
```python
ser = pd.Series([1.0, 2.0], dtype="float32")
val = {1: 1.01}
ser.fillna(val)
```
#### Problem description
The above code raises with
`TypeError: Cannot cast array data from dtype('float64') to dtype('float32') according to the rule 'safe'`
#### Expected Output
```
0 1.0
1 2.0
dtype: float32
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 343ac2a4179fdd1cb24633e9b95cbec33371c14d
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.4.0.dev0+599.g343ac2a417.dirty
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.1
setuptools : 49.6.0.post20210108
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.14.3
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.05.0
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 1 | "2021-09-06T16:18:02Z" | "2021-09-06T18:56:48Z" | "2021-09-06T18:37:29Z" | CONTRIBUTOR | null | xref #43236 | {
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} | 5 | "2021-09-06T17:15:26Z" | "2021-09-12T15:07:29Z" | "2021-09-10T13:45:49Z" | MEMBER | null | - [ ] closes #43242
- [ ] tests added / passed
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- [ ] whatsnew entry
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} | 4 | "2021-09-06T18:35:34Z" | "2021-09-09T13:51:16Z" | "2021-09-09T13:51:01Z" | MEMBER | null | - [x] closes #43223
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@jbrockmendel Any idea why we used ``maybe_indices_to_slice`` instead of take like in loc?
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`UnicodeEncodeError: 'utf-8' codec can't encode character '\ud83d' in position 84: surrogates not allowed`
(I believe that \ud83d is a smily face character...) | {
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- [ ] tests added / passed
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} | 1 | "2021-09-06T19:46:28Z" | "2021-09-06T23:50:57Z" | "2021-09-06T23:50:50Z" | CONTRIBUTOR | null | - [ ] closes #43401
- [ ] tests added / passed
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} | 1 | "2021-09-06T21:36:17Z" | "2021-09-12T16:56:08Z" | "2021-09-12T16:55:59Z" | CONTRIBUTOR | null | * engine for xlsx : xlsxwriter instead of openpyxl
* correction to pass the doctest
- [x] closes #43359
- [x] tests added / passed : script/validate_docstrings.py detected 3 errors before my pull request. It's now resolved
- [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 : black pandas didn't change anything
- [x] whatsnew entry : one word changed in docstring
Script validate_docstings report : [validate_docstrings.txt](https://github.com/pandas-dev/pandas/files/7117843/validate_docstrings.txt)
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} | 1 | "2021-09-06T21:51:55Z" | "2021-09-07T14:12:00Z" | "2021-09-07T14:11:56Z" | CONTRIBUTOR | null | - [x] closes #36973
- [x] tests added / passed
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- [ ] 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
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} | 6 | "2021-09-07T06:22:20Z" | "2021-09-11T14:51:04Z" | "2021-09-11T14:50:50Z" | CONTRIBUTOR | null | - [x] closes #24306
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/43437 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43437/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43437/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43437/events | https://github.com/pandas-dev/pandas/issues/43437 | 989,687,901 | MDU6SXNzdWU5ODk2ODc5MDE= | 43,437 | ENH: Handle for current DataFrame in pd.query expression | {
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] | open | false | null | [] | null | 0 | "2021-09-07T07:39:13Z" | "2021-09-14T11:32:55Z" | null | NONE | null | #### Is your feature request related to a problem?
I use `pd.query` and `pd.eval` a lot. However, sometimes I find myself in situations where I would like to filter an unnamed DataFrame with `pd.query` and it would be very handy if there was a handle for the DataFrame available in the query expression.
#### Describe the solution you'd like
For sake of an example, consider this DataFrame:
```python
>>> df = pd.DataFrame(
data=np.arange(20).reshape(5, 4),
columns=pd.MultiIndex.from_product([['A', 'B'], ['x', 'y']]))
>>> df
A B
x y x y
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
4 16 17 18 19
```
In order to get all `y` columns with values above `10`, I would like to use something like
```python
df.loc[:, (slice(None), 'y')].query('(PLACEHOLDER > 10).any(1)')
```
The placeholder could be a symbol like `#` or simply nothing (i.e. one would write `df.loc[:, (slice(None), 'y')].query('(> 10).any(1)')`), but this would be harder to implement I guess.
#### API breaking implications
I don't think that this would break anything as it is just the addition of a feature.
#### Describe alternatives you've considered
I know that what I wanted to do in my example can be achieved in multiple other ways. However, most of them are neither as concise nor as flexible as simply being able to access the current DataFrame in the query expression.
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] | closed | false | null | [] | null | 1 | "2021-09-07T09:13:05Z" | "2021-09-07T09:53:29Z" | "2021-09-07T09:53:29Z" | NONE | null | Please reopen #20007
There was no valid reason for closing.
The "closer" just wrote about a workaround (not a solution) with `fillna()`.
Please let us reopen the discussion on the original Issue. | {
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} | 4 | "2021-09-07T09:24:10Z" | "2021-09-10T00:24:10Z" | "2021-09-10T00:24:10Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import os
import pandas
import tempfile
import traceback
with tempfile.SpooledTemporaryFile() as f:
f.write(b'abcd')
f.seek(0)
try:
result = pandas.read_csv(f)
print(result)
except Exception as e:
print(e)
print(traceback.format_exc())
```
### Issue Description
This worked in pandas 1.1.5 but is no longer working in version >=1.2.0.
### Expected Behavior
According to the `read_csv` description I would expect this to work.
filepath_or_bufferstr, path object or file-like object
> Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.csv.
> If you want to pass in a path object, pandas accepts any os.PathLike.
> By file-like object, we refer to objects with a read() method, such as a file handle (e.g. via builtin open function) or StringIO.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 20.1.1
setuptools : 47.1.0
IPython : 7.27.0
</details>
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} | 2 | "2021-09-07T09:41:48Z" | "2021-09-29T13:19:36Z" | null | CONTRIBUTOR | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.ExcelWriter.html?highlight=excelwriter#pandas.ExcelWriter
### Documentation problem
There is no use-case specified for `engine_kwargs`, as a result of which I was confused and made #42214. There, my test was overwritten by #42292 that uses the 'correct' use of `engine_kwargs`. How I did it first (incorrect):
```
import pandas as pd
with pd.ExcelWriter("file.xlsx",engine="xlsxwriter",engine_kwargs={"strings_to_formulas":False}) as writer:
pd.DataFrame(["=1+1"]).to_excel(writer)
```
where it should have been:
```
import pandas as pd
with pd.ExcelWriter("file.xlsx",engine="xlsxwriter",engine_kwargs={"options":{"strings_to_formulas":False}}) as writer:
pd.DataFrame(["=1+1"]).to_excel(writer)
```
However, since only `xlsxwriter` actually passes the `engine_kwargs` through to the `Workbook` class, this is unintuitive without documentation.
### Suggested fix for documentation
I will submit a PR | {
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} | 6 | "2021-09-07T09:56:19Z" | "2021-09-08T23:53:56Z" | "2021-09-08T23:53:49Z" | CONTRIBUTOR | null | - [ ] 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
The contributing page has comments on style, but does not link to setting up pre-commits yourself. It would be nice to be guided there | {
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} | 1 | "2021-09-07T09:59:59Z" | "2021-09-29T13:19:30Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
from datetime import datetime
with pd.ExcelWriter("file.xlsx",engine="openpyxl",engine_kwarg={"iso_dates":True}) as writer:
pd.DataFrame([datetime(1996,20,10)]).to_excel(writer)
```
### Issue Description
the arguments are silently not passed through to the openpyxl [`Workbook`](https://openpyxl.readthedocs.io/en/stable/api/openpyxl.workbook.workbook.html) class.
### Expected Behavior
This should either raise or pass through to the engine
### Installed Versions
<details>
/home/kees/miniconda3/envs/pd-doc/lib/python3.8/site-packages/fastparquet/dataframe.py:5: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.NumericIndex with the appropriate dtype instead.
from pandas import (
INSTALLED VERSIONS
------------------
commit : 73c473dce9f07bba96993c42843320e116cf94ea
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-59-generic
Version : #66~20.04.1-Ubuntu SMP Thu Jun 17 11:14:10 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+613.g73c473dce9.dirty
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.18.0
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.7.1
gcsfs : 2021.05.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.05.0
scipy : 1.7.1
sqlalchemy : 1.4.23
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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This bug is split from #43366, but with more clear intent and added test. | {
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} | 7 | "2021-09-07T15:46:38Z" | "2021-11-18T21:22:51Z" | null | CONTRIBUTOR | null | - [x] closes #43442
- [x] closes #43440
- [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 the revised version of #42214. In fact, in xlsxwriter, all kwargs are passed like so:
```python
ExcelWriter("path_to_file.xlsx",engine="xlsxwriter",engine_kwargs={"options":{"nan_inf_to_errors":True}})
```
which then becomes internally:
```python
from xlsxwriter import Workbook
Workbook("path_to_file.xlsx",options={"nan_inf_to_errors":True})
```
so my initial fix was wrong in that sense, because I made `engine_kwargs` able to be passed in like this:
```python
ExcelWriter("path_to_file.xlsx",engine="xlsxwriter",engine_kwargs={"nan_inf_to_errors":True})
```
and the resulted internals:
```python
Workbook("pat_to_file.xlsx",{"nan_inf_to_errors":True})
```
so #42214 was actually wrong (we want users to actually pass in **kw**args.
However, there was still the issue of #43442, where the following engines *do* have some options that were silently ignored:
* *openpyxl* can set `iso_dates` and `write_only`
* *xlwt* can set `style_compression` (not sure why anybody would want this, but people may)
* *xlsxwriter* has many `options`
so I added tests for that in `engine_kwargs`. | {
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} | 0 | "2021-09-07T16:13:28Z" | "2021-09-07T23:08:28Z" | "2021-09-07T22:59:53Z" | 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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https://api.github.com/repos/pandas-dev/pandas/issues/43447 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43447/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43447/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43447/events | https://github.com/pandas-dev/pandas/pull/43447 | 990,169,249 | MDExOlB1bGxSZXF1ZXN0NzI4Nzk4Njcy | 43,447 | REGR: SpooledTemporaryFile support in read_csv | {
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} | 8 | "2021-09-07T16:47:48Z" | "2021-09-10T00:24:31Z" | "2021-09-10T00:24:11Z" | CONTRIBUTOR | null | - [ ] closes #43439
- [ ] 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
Probably should throw a warning, if we cannot wrap the handle with `io.TextIOWrapper`.
To avoid the try-except, we could check whether the handle has the `readable` attribute, but there might be other attribute errors that might occur inside `TextIOWrapper`. | {
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [x] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame(
{
"col1": [1, 1, 2, 2, 3, 3],
"col2": ["a", "a", "b", "b", "c", "c"],
"col3": [8, 8, 8, 8, 8, 8],
}
)
df.col1 = df.col1.astype("category")
df.groupby(["col1", "col2"]).col3.first()
```
Output:
```
col1 col2 col3
1 a 8.0
1 b NaN
1 c NaN
2 a NaN
2 b 8.0
2 c NaN
3 a NaN
3 b NaN
3 c 8.0
```
### Issue Description
Combinations that don't exist in the original dataframe are created after the `groupby`, for instance: `col1=1 and col2='b'`.
### Expected Behavior
```python
import pandas as pd
df = pd.DataFrame(
{
"col1": [1, 1, 2, 2, 3, 3],
"col2": ["a", "a", "b", "b", "c", "c"],
"col3": [8, 8, 8, 8, 8, 8],
}
)
df.groupby(["col1", "col2"]).col3.first()
```
Output when `col1` isn't a category:
```
col1 col2 col3
1 a 8
2 b 8
3 c 8
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #1151-Microsoft Thu Jul 22 21:05:00 PST 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-09-07T20:42:18Z" | "2021-09-08T07:25:12Z" | "2021-09-07T21:59:33Z" | MEMBER | null | - [x] closes #43422
- [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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} | 3 | "2021-09-07T21:33:58Z" | "2021-09-07T23:00:56Z" | "2021-09-07T22:43:31Z" | NONE | null | ###
- [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas).
### Platform
Linux-4.15.0-144-generic-x86_64-with-glibc2.27
### Installation Method
pip install
### pandas Version
latest
### Python Version
3.8
### Installation Logs
Files on https://pypi.org/project/pandas/#files appear to have a duplicated platform tag e.g.
```pandas-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl```
instead of
```pandas-1.3.2-cp38-cp38-manylinux_2_17_x86_64.whl```
So pip installs are not able to match and will attempt to build from source. | {
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] | open | false | null | [] | null | 0 | "2021-09-07T21:50:11Z" | "2021-09-30T23:31:53Z" | null | NONE | null | #### Is your feature request related to a problem?
Yes, it would be much easier to set comprehension and return sets of things
```python
a = pd.Index([1,1,2,3,4])
b = pd.Index([2,3,5])
c = pd.Index([3,5,6])
a.intersection(b).intersection(c)
# Int64Index([3], dtype='int64')
```
#### Describe the solution you'd like
It would be nice if this could be done:
```python
a.toset() & b.toset() & c.toset()
```
For example, what if there are a list of `N` indexes:
```python
indexes = [a, b, c, ..., N]
set.intersection(*map(lambda x: x.toset(), indexes))
set.union(*map(lambda x: x.toset(), indexes))
```
Yes you could do `set(x)` but sometimes the expression is much more complex and having nested parentheses makes it more confusing to keep track of everything. Same reason why `.tolist()` exists I suspect.
#### API breaking implications
Should not break API at all.
#### Describe alternatives you've considered
Parentheses but as mentioned nested parentheses gets confusing for complex one liners
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Look to use `is_*_dtype` instead of `isinstance` when implementing `_get_common_dtype` for EA's. | {
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} | 7 | "2021-09-08T02:17:12Z" | "2021-09-09T12:55:24Z" | "2021-09-09T11:37:26Z" | MEMBER | null | - [x] closes #43424
- [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
For now have put whatsnew in 1.3.3, but might be better for 1.4 since regression is later (worked in 1.1.5) and the `can_hold_element` change causes subtle indexing behavior differences (but that are now more consistent between int and float) | {
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} | 2 | "2021-09-08T03:04:14Z" | "2021-11-06T21:36:18Z" | "2021-11-06T21:36:18Z" | NONE | null | test to check numeric precision after .groupby and .agg
- [X] closes #33234
- [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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} | 1 | "2021-09-08T07:13:57Z" | "2021-09-11T21:12:14Z" | null | NONE | null | #### Is your feature request related to a problem?
I wish I could use [`pandas.testing.assert_frame_equal`](https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_frame_equal.html) to determine which of my `datetime64[ns]` columns are not equal when comparing two dataframes. Currently the output is `AssertionError: numpy array are different` without indicating which columns are different.
#### Describe the solution you'd like
When two dataframes with `datetime64[ns]` columns are compared using [`pandas.testing.assert_frame_equal`](https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_frame_equal.html) and there are different values, the output should also have the column name that has this difference, e.g. `AssertionError: DataFrame.iloc[:, 1] (column name="b") are different`.
#### Additional context
Minimal reproducible code below. If you change one of the `datetime` values to an integer, the dtype of the column will be `object`, in which case, the desired behaviour is shown.
```python
import pandas as pd
from pandas.testing import assert_frame_equal
df_left = pd.DataFrame({'a': [1, 2], 'b': [
datetime(2021, 1, 1),
# 1,
datetime(2021, 1, 2)
]})
df_right = pd.DataFrame({'a': [1, 2], 'b': [
datetime(2021, 1, 1),
# 1,
datetime(2021, 1, 3)
]})
assert_frame_equal(left=df_left, right=df_right)
```
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- [ ] whatsnew entry
The `if_sheet_exists` parameter works also in write mode of openpyxl. e.g.:
```python
import pandas as pd
with pd.ExcelWriter("f.xlsx",engine="openpyxl") as writer:
pd.DataFrame([1,2]).to_excel(writer)
pd.DataFrame([3,4]).to_excel(writer)
```
will raise an error, because the default `if_sheet_exists` is set to "error". This could be fixed by this simple docs change, or changing the default of `if_sheet_exists` to "overlay". However, if that is the case, it could also very well be implemented for other engines. | {
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} | 3 | "2021-09-08T14:05:33Z" | "2021-09-29T13:07:25Z" | "2021-09-29T12:58:50Z" | CONTRIBUTOR | null | - [ ] closes #41241
- [ ] 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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I would say this should raise, since expectations can be pretty ambiguous here and is confusing to me.
Also the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.isin.html) right now states that the `values` parameter should be "set or list-like"
Code example:
```python
values = list("ABCD")
s = pd.Series(values)
df = pd.DataFrame({"Column": values})
print(s)
0 A
1 B
2 C
3 D
dtype: object
print(df)
Column
0 A
1 B
2 C
3 D
```
```python
# comparing DataFrame to Series, this is okay (right?) and I understand the result
df.isin(s)
Column
0 True
1 True
2 True
3 True
# comparing a series to df, what do we expect here? Should this raise? Why do you want to compare a Series to a DataFrame?
s.isin(df)
0 False
1 False
2 False
3 False
dtype: bool
```
Proposal: when passing anything else than "set or list-like" should raise in `Series.isin` | {
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} | 2 | "2021-09-08T16:57:15Z" | "2021-09-10T09:07:23Z" | null | CONTRIBUTOR | null | Currently if Python is built without lzma, following https://github.com/pandas-dev/pandas/pull/27882 a warning is going to be shown to users at import time,
```
lib/python3.9/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma
module. Your installed Python is incomplete. Attempting to use lzma compression will result in a
RuntimeError.
```
I don't believe this is helpful. While lzma is part of stdlib, CPython [builds perfectly fine](https://github.com/python/cpython/blob/2ec9428e3516b6e7c6583687aa3b39fb96925577/setup.py#L1736-L1740) without lzma, and most of the test suite [gracefully handles](https://github.com/python/cpython/blob/6564656495d456a1bcc1aaa06abfc696209f37b2/Lib/test/test_shutil.py#L1584) the fact that lzma might be missing.
Of course the `RuntimeError` is unavoidable, but I think the warning about "Your installed Python is incomplete" at import time is confusing to users. Particularly that in my, possibly biased experience, opening xz compressed files is not that common: users would most often either read plain text/csv for readability (possibly gzip compressed) or switch to parquet with compression if they care about size/performance. So most users should not be affected by this even if they don't have lzma.
For instance in [Pyodide](https://github.com/pyodide/pyodide) this warning makes users wonder what is wrong with their install https://github.com/pyodide/pyodide/issues/1821 :) We will likely include lzma eventually, but so far there haven't been much requests for it (and every extra module has a download size penalty). | {
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Some jobs, though, dont have the option to re-run in isolation. Is there a way of getting a button in github that re-runs just failed tests. I imagine this would improve the performance by avoiding recalculating the majority of the jobs which have already passed?
| {
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- [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas).
### Platform
mac 10.15.7
### Installation Method
pip install
### pandas Version
1.3.2
### Python Version
3.8
### Installation Logs
Hi,
im want to use pandas for a simple python project.
needed to import pd in a python module, in the simplest way and without install more requirements such as bottleneck.
im doing:
`import pandas as pd
`
and i tried to set
`pd.set_option("compute.use_bottleneck", False)
`
after the import, but it still throw error
`ImportError: Can't determine version for bottleneck
`
appreciate your help using pandas.
full log:
`Traceback (most recent call last):
File "/Users/xxx/github/xx/linb_kpi/visualize.py", line 5, in <module>
import pandas as pd
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/__init__.py", line 50, in <module>
from pandas.core.api import (
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/core/api.py", line 29, in <module>
from pandas.core.arrays import Categorical
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/core/arrays/__init__.py", line 6, in <module>
from pandas.core.arrays.boolean import BooleanArray
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/core/arrays/boolean.py", line 36, in <module>
from pandas.core.arrays.masked import (
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/core/arrays/masked.py", line 46, in <module>
from pandas.core import (
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/core/nanops.py", line 59, in <module>
bn = import_optional_dependency("bottleneck", errors="warn")
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/compat/_optional.py", line 131, in import_optional_dependency
version = get_version(module_to_get)
File "/Users/xx/github/commonvenv-new/lib/python3.8/site-packages/pandas/compat/_optional.py", line 60, in get_version
raise ImportError(f"Can't determine version for {module.__name__}")
ImportError: Can't determine version for bottleneck` | {
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} | 1 | "2021-09-08T18:41:34Z" | "2021-09-09T11:59:53Z" | "2021-09-09T11:59:53Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
pd.options.styler.sparse.columns=False
midx = MultiIndex.from_product([[1, 2],["a", "a", "b"]], names=["zero", "one"])
df = DataFrame(9, index=midx, columns=midx)
styler = df.style.hide_columns((1, "a"))
styler
```
### Issue Description
### Expected Behavior
Columns are hidden in the header row
### Installed Versions
<details>
Replace this line with the output of pd.show_versions()
</details>
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} | 0 | "2021-09-08T19:09:28Z" | "2021-09-08T21:47:00Z" | "2021-09-08T21:24:54Z" | CONTRIBUTOR | null | - [x] closes #43464
- [x] tests added / passed
- [x] whatsnew entry
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} | 0 | "2021-09-08T20:26:00Z" | "2021-09-08T22:34:28Z" | "2021-09-08T21:25:42Z" | 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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} | 26 | "2021-09-08T22:28:28Z" | "2021-09-18T02:32:24Z" | "2021-09-18T02:31:52Z" | CONTRIBUTOR | null | Make group_mean compatible with NaT
## Background
NaT is the datetime equivalent of NaN and is set to be the lowest possible 64-bit integer -(2**63). Previously, we could not support this value in any groupby.mean() calculations which lead to #43132.
## Implementation
On a high level, we slightly modify the `group_mean` to not count NaT values. To do so, we introduce the `is_datetimelike` parameter to the function call (already present in other functions, e.g., `group_cumsum`).
## Tests
This PR adds integration and unit tests for the new functionality. In contrast to other tests in classes, I've tried to keep an individual test's scope as small as possible.
## Notes
Additionally, I've taken the liberty to:
* Add a docstring for the group_mean algorithm.
- [x] closes #43132
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} | 30 | "2021-09-09T03:19:52Z" | "2021-09-10T15:23:11Z" | "2021-09-10T12:39:28Z" | CONTRIBUTOR | null | Trying an experiment for Windows CI where `PYTEST_WORKERS` is `auto` rather than 2.
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} | 2 | "2021-09-09T03:39:40Z" | "2021-09-09T21:46:44Z" | "2021-09-09T21:18:42Z" | MEMBER | null | Nearly 10x
```
import pandas as pd
import numpy as np
np.random.seed(536445246)
arr = np.random.randn(10**5, 5)
df = pd.DataFrame(arr, columns=["A", "B", "C", "D", "E"])
gb = df.groupby(df.index % 7)
qs = np.arange(0, 1, 0.1)
%timeit res = gb.quantile(qs)
771 ms ± 34 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
81.2 ms ± 8.63 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43470 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43470/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43470/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43470/events | https://github.com/pandas-dev/pandas/pull/43470 | 991,791,606 | MDExOlB1bGxSZXF1ZXN0NzMwMjA3MzY4 | 43,470 | BUG: `Styler` hidden elements sparse none | {
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} | 1 | "2021-09-09T05:40:54Z" | "2021-09-12T17:37:49Z" | "2021-09-09T11:59:53Z" | CONTRIBUTOR | null | - [x] closes #43464
- [x] tests added / passed
- [x] whatsnew entry
sorry, missed that this also applied to index. more generic fix here, | {
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] | closed | false | null | [] | null | 0 | "2021-09-09T06:45:31Z" | "2021-09-21T14:09:54Z" | "2021-09-21T14:09:54Z" | CONTRIBUTOR | null | Currently styler only allows `multicol_align` in `{"r", "l", "c", "naive-l", "naive-r"}`
Simple pipe characters around multicol_alignment allow dividers to be drawn, e.g. `|r` or `|r|` or `r|` etc..
```
\begin{tabular}{lllrrrrrr}
\toprule
{} & {} & {} & \multicolumn{3}{|r|}{1} & \multicolumn{3}{|r|}{2} \\
{} & {} & {} & \multicolumn{2}{r|}{a} & {b} & \multicolumn{2}{|l}{a} & {b} \\
{} & {} & {} & {0} & {0} & {0} & {0} & {0} & {0} \\
\midrule
\multirow[t]{3}{*}{1} & \multirow[t]{2}{*}{a} & 0 & -1 & -1 & -1 & -1 & -1 & -1 \\
& & 0 & -1 & -1 & -1 & -1 & -1 & -1 \\
& b & 0 & -1 & -1 & -1 & -1 & -1 & -1 \\ \cline{1-3}
\multirow[t]{3}{*}{2} & \multirow[t]{2}{*}{a} & 0 & -1 & -1 & -1 & -1 & -1 & -1 \\
& & 0 & -1 & -1 & -1 & -1 & -1 & -1 \\
& b & 0 & -1 & -1 & -1 & -1 & -1 & -1 \\
\bottomrule
\end{tabular}
```
Also note the cline (this one might be harder to implement)
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] | closed | false | null | [] | null | 4 | "2021-09-09T08:34:57Z" | "2021-09-30T23:38:03Z" | "2021-09-30T23:38:03Z" | NONE | null | I find myself often wanting to rename columns with a prefix or suffix **inplace** and want to avoid creating copies, so I end up with code as:
`df.rename(columns={col: add_prefix_or_suffix_func_prototype(col) for col in df.columns}, inplace=True)`
It would be a simple feature addition if the `add_prefix` and `add_suffix` would expose this `kwarg` (**inplace**) to simplify the code to:
`df.add_prefix('prefix', **inplace**=True) # same for add_suffix` | {
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
pd.test()
```
### Issue Description
```console
bug:
FAILED pandas/tests/config/test_localization.py::test_encoding_detected - LookupError: unknown encoding: C
FAILED pandas/tests/indexes/numeric/test_numeric.py::TestUInt64Index::test_constructor_does_not_cast_to_float
FAILED pandas/tests/io/test_clipboard.py::test_raw_roundtrip[\U0001f44d...] - pandas.io.clipboard.PyperclipException:
FAILED pandas/tests/io/test_clipboard.py::test_raw_roundtrip[\u03a9\u0153\u2211\xb4...] - pandas.io.clipboard.PyperclipException:
FAILED pandas/tests/io/test_clipboard.py::test_raw_roundtrip[abcd...] - pandas.io.clipboard.PyperclipException:
```
### Expected Behavior
```console
detail:
_____________________________________________________________________________________________ test_encoding_detected ______________________________________________________________________________________________
def test_encoding_detected():
system_locale = os.environ.get("LC_ALL")
system_encoding = system_locale.split(".")[-1] if system_locale else "utf-8"
> assert (
codecs.lookup(pd.options.display.encoding).name
== codecs.lookup(system_encoding).name
)
E LookupError: unknown encoding: C
pandas/tests/config/test_localization.py:106: LookupError
_____________________________________________________________________________ TestUInt64Index.test_constructor_does_not_cast_to_float _____________________________________________________________________________
[XPASS(strict)] https://github.com/numpy/numpy/issues/19146
________________________________________________________________________________________ test_raw_roundtrip[\U0001f44d...] ________________________________________________________________________________________
data = '👍...'
@pytest.mark.single
@pytest.mark.clipboard
@pytest.mark.parametrize("data", ["\U0001f44d...", "Ωœ∑´...", "abcd..."])
def test_raw_roundtrip(data):
# PR #25040 wide unicode wasn't copied correctly on PY3 on windows
> clipboard_set(data)
pandas/tests/io/test_clipboard.py:312:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/io/clipboard/__init__.py:633: in lazy_load_stub_copy
return copy(text)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <pandas.io.clipboard.init_no_clipboard.<locals>.ClipboardUnavailable object at 0xfffee2be8940>, args = ('👍...',), kwargs = {}
def __call__(self, *args, **kwargs):
> raise PyperclipException(EXCEPT_MSG)
E pandas.io.clipboard.PyperclipException:
E Pyperclip could not find a copy/paste mechanism for your system.
E For more information, please visit
E https://pyperclip.readthedocs.io/en/latest/#not-implemented-error
pandas/io/clipboard/__init__.py:294: PyperclipException
__________________________________________________________________________________ test_raw_roundtrip[\u03a9\u0153\u2211\xb4...] __________________________________________________________________________________
data = 'Ωœ∑´...'
@pytest.mark.single
@pytest.mark.clipboard
@pytest.mark.parametrize("data", ["\U0001f44d...", "Ωœ∑´...", "abcd..."])
def test_raw_roundtrip(data):
# PR #25040 wide unicode wasn't copied correctly on PY3 on windows
> clipboard_set(data)
pandas/tests/io/test_clipboard.py:312:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/io/clipboard/__init__.py:633: in lazy_load_stub_copy
return copy(text)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <pandas.io.clipboard.init_no_clipboard.<locals>.ClipboardUnavailable object at 0xfffee2afa640>, args = ('Ωœ∑´...',), kwargs = {}
def __call__(self, *args, **kwargs):
> raise PyperclipException(EXCEPT_MSG)
E pandas.io.clipboard.PyperclipException:
E Pyperclip could not find a copy/paste mechanism for your system.
E For more information, please visit
E https://pyperclip.readthedocs.io/en/latest/#not-implemented-error
pandas/io/clipboard/__init__.py:294: PyperclipException
___________________________________________________________________________________________ test_raw_roundtrip[abcd...] ___________________________________________________________________________________________
data = 'abcd...'
@pytest.mark.single
@pytest.mark.clipboard
@pytest.mark.parametrize("data", ["\U0001f44d...", "Ωœ∑´...", "abcd..."])
def test_raw_roundtrip(data):
# PR #25040 wide unicode wasn't copied correctly on PY3 on windows
> clipboard_set(data)
pandas/tests/io/test_clipboard.py:312:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/io/clipboard/__init__.py:633: in lazy_load_stub_copy
return copy(text)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <pandas.io.clipboard.init_no_clipboard.<locals>.ClipboardUnavailable object at 0xfffee2b84880>, args = ('abcd...',), kwargs = {}
def __call__(self, *args, **kwargs):
> raise PyperclipException(EXCEPT_MSG)
E pandas.io.clipboard.PyperclipException:
E Pyperclip could not find a copy/paste mechanism for your system.
E For more information, please visit
E https://pyperclip.readthedocs.io/en/latest/#not-implemented-error
pandas/io/clipboard/__init__.py:294: PyperclipException
================================================================================================ warnings summary =================================================================================================
../../../home/all_spack_env/spack/opt/spack/linux-centos8-aarch64/gcc-8.4.1/python-3.8.8-aosgvy74ce4zaoqnbj7obuyvg7csguo7/lib/python3.8/site-packages/_pytest/config/__init__.py:1114
/home/all_spack_env/spack/opt/spack/linux-centos8-aarch64/gcc-8.4.1/python-3.8.8-aosgvy74ce4zaoqnbj7obuyvg7csguo7/lib/python3.8/site-packages/_pytest/config/__init__.py:1114: PytestAssertRewriteWarning: Module already imported so cannot be rewritten: hypothesis
self._mark_plugins_for_rewrite(hook)
pandas/tests/apply/test_invalid_arg.py::test_transform_wont_agg_series[func1]
/root/9/pandas-1.3.2/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in sqrt
result = getattr(ufunc, method)(*inputs, **kwargs)
pandas/tests/scalar/timedelta/test_arithmetic.py::TestTimedeltaMultiplicationDivision::test_td_div_nan[nan1]
/root/9/pandas-1.3.2/pandas/tests/scalar/timedelta/test_arithmetic.py:445: RuntimeWarning: invalid value encountered in double_scalars
result = td // nan
-- Docs: https://docs.pytest.org/en/stable/warnings.html
----------------------------------------------------------------------------- generated xml file: /root/9/pandas-1.3.2/test-data.xml --------
```
### Installed Versions
<details>
pd.show_versions()
1.3.2
</details>
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} | 1 | "2021-09-09T16:07:01Z" | "2021-09-10T14:09:32Z" | "2021-09-10T14:09:32Z" | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df1 = pd.DataFrame(pd.NA, index=[0, 1], columns=['A', 'A', 'B'])
df1.fillna({'A': 0})
df2 = pd.DataFrame(pd.NA, index=[0, 1], columns=['A', 'A'])
df2.fillna({'A': 0})
```
### Issue Description
For dataframes with duplicate column names `.fillna` fails to fill missing values, if a `dict` is passed as the `value` parameter
- **Example 1** (`df1`). When multiple, but not all, columns share a name, the `SettingWithCopyWarning` is thrown.
- **Example 2** (`df2`). When all column names are identical, no error is thrown, nor is missing values replaced.
### Expected Behavior
Expecting missing values in columns of the same name to be filled according to the parsed `dict`.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.8.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 : Danish_Denmark.1252
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
a = pd.Series(['A', 'B'], dtype='category')
b = pd.Series(['C', 'D'], dtype='category')
print(a.dtypes, b.dtypes, pd.concat((a,b)).dtypes)
```
### Issue Description
The output of the codes above is:
`category category object`
### Expected Behavior
I expect the output to be:
`category category category`
Concatenation of two objects of the same type should give an object of that type. The current behavior gave me some unexpected results that took me a while to figure out.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.120+
Version : #1 SMP Wed Aug 18 10:20:32 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.06.1
fastparquet : None
gcsfs : 2021.06.0
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : 2021.06.1
scipy : 1.6.3
sqlalchemy : 1.4.18
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
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numba : 0.53.1
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} | 2 | "2021-09-09T17:17:18Z" | "2021-09-10T21:39:32Z" | "2021-09-10T21:38:19Z" | MEMBER | null | We get a decent-but-not-whopping perf boost, with a much bigger perf boost in the follow-up that de-duplicates an argsort call that we're currently doing repeatedly inside the cython function.
More importantly, we're getting close to having everything operate blockwise, at which point we can get rid of _get_cythonized_result and send everything through the _cython_operation path.
```
import pandas as pd
import numpy as np
np.random.seed(23446365)
arr = np.random.randn(10**5, 10)
mask = arr < -1
arr[mask] = np.nan
df = pd.DataFrame(arr)
gb = df.groupby(df.index % 7)
%timeit res = gb.pad()
28.7 ms ± 113 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
24 ms ± 1.06 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
``` | {
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} | 3 | "2021-09-09T17:51:57Z" | "2021-11-14T03:08:55Z" | "2021-11-14T03:08:55Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({'cat':list('aabbc'),
'v':range(5)}, dtype=object)
df.groupby('cat').boxplot(subplots=False, column='v')
```
results in the following error:
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/src/pandas/pandas/plotting/_core.py", line 604, in boxplot_frame_groupby
return plot_backend.boxplot_frame_groupby(
File "/src/pandas/pandas/plotting/_matplotlib/boxplot.py", line 485, in boxplot_frame_groupby
ret = df.boxplot(
File "/src/pandas/pandas/plotting/_core.py", line 511, in boxplot_frame
return plot_backend.boxplot_frame(
File "/Users/ian/src/pandas/pandas/plotting/_matplotlib/boxplot.py", line 414, in boxplot_frame
ax = boxplot(
File "/src/pandas/pandas/plotting/_matplotlib/boxplot.py", line 391, in boxplot
data = data[columns]
File "/src/pandas/pandas/core/frame.py", line 3504, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/src/pandas/pandas/core/indexes/multi.py", line 2559, in _get_indexer_strict
return super()._get_indexer_strict(key, axis_name)
File "/src/pandas/pandas/core/indexes/base.py", line 5510, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/src/pandas/pandas/core/indexes/multi.py", line 2579, in _raise_if_missing
return super()._raise_if_missing(key, indexer, axis_name)
File "/src/pandas/pandas/core/indexes/base.py", line 5570, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [MultiIndex([('a', 'v'),\n ('b', 'v'),\n ('c', 'v')],\n )] are in the [columns]"
```
### Issue Description
This might be related to #16748
### Expected Behavior
The same as:
```python
import pandas as pd
df = pd.DataFrame({'cat':list('aabbc'),
'v':range(5)})
df.groupby('cat').boxplot(subplots=False, column='v')
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a951998314f91e90f9a32f534119fbb46c8858d8
python : 3.9.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:31 PDT 2021; root:xnu-7195.141.2~5/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.4.0.dev0+634.ga951998314
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 52.0.0.post20210125
Cython : 0.29.24
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 : 3.4.2
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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intersphinx inventory 'https://www.statsmodels.org/devel/objects.inv' not fetchable due to <class 'requests.exceptions.ConnectionError'>: HTTPSConnectionPool(host='www.statsmodels.org', port=443): Max retries exceeded with url: /devel/objects.inv (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fcc923600a0>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution'))
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} | 0 | "2021-09-09T20:40:47Z" | "2021-09-14T21:36:46Z" | "2021-09-14T21:36:46Z" | CONTRIBUTOR | null | quick note to record a todo:
change the level arg in Styler.set_sticky to accept level names, akin to Styler.hide_index, for consistency and expanded usage. | {
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] | open | false | null | [] | null | 0 | "2021-09-09T22:15:09Z" | "2021-09-30T23:38:39Z" | null | MEMBER | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
ser = pd.Series(range(5))
sers = [ser + n for n in range(4)]
keys = ["A", "B", "C"]
>>> pd.concat(sers, keys=keys, axis=1)
A B C
0 0 1 2
1 1 2 3
2 2 3 4
3 3 4 5
4 4 5 6
```
```
### Issue Description
I expected the pd.concat below to raise. It kind of makes sense as a reindex or analogue to the DataFrame constructor, but seems more likely to me to be a footgun. In particular, I think the _lack_ of raising may be hiding a bug in the groupby code.
Is not-raising an intentional decision? If so, thoughts on either deprecating or issuing a warning?
### Expected Behavior
N/A
### Installed Versions
N/A | {
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] | open | false | null | [] | null | 0 | "2021-09-09T22:34:37Z" | "2021-09-30T23:39:18Z" | null | MEMBER | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({"date": [pd.NaT], "value": [11]})
grouper = pd.Grouper(freq="M", key="date")
>>> gb = df.groupby(grouper)
[...]
File "pandas/core/resample.py", line 1878, in _get_timestamp_range_edges
first = first.normalize()
AttributeError: 'NaTType' object has no attribute 'normalize'
```
```
### Issue Description
Example derived from test_timegrouper_apply_return_type_series
### Expected Behavior
Either not raise or raise at construction of the Grouper object with a useful exception message.
### Installed Versions
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} | 4 | "2021-09-10T00:01:18Z" | "2021-09-10T21:39:06Z" | "2021-09-10T21:36:35Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
cc @mroeschke im pretty sure this is slightly more correct than the status quo, but i could use some help in coming up with test cases. Two main cases to cover:
1) `res = df.groupby(more_than_one_column).transform(..., engine="numba", i_dont_know_off_the_top_the_calling_convention)`
I'm pretty sure in this case `res.index.names` will be wrong in master.
2) Cases with BinGrouper where there is a length mismatch (also an issue with non-numba paths)
```
import pandas as pd
df = pd.DataFrame(
{
"Buyer": "Carl Carl Carl Carl Joe Carl".split(),
"Quantity": [18, 3, 5, 1, 9, 3],
"Date": [
pd.Timestamp(2013, 9, 1, 13, 0),
pd.Timestamp(2013, 9, 1, 13, 5),
pd.Timestamp(2013, 10, 1, 20, 0),
pd.Timestamp(2013, 10, 3, 10, 0),
pd.NaT,
pd.Timestamp(2013, 9, 2, 14, 0),
],
}
)
gb = df.groupby(pd.Grouper(key="Date", freq="5D"))
gb.transform(lambda x: x, engine="numba", ...) # <- pretty sure this raises
```
Or are these not reachable for some reason? | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-09-10T04:41:55Z" | "2021-09-10T14:13:39Z" | "2021-09-10T14:09:32Z" | MEMBER | null | - [x] closes #43476
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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- [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).
### Link to question on StackOverflow
I am trying to run a machine learning code in Python. I get the following error: UnicodeDecodeError: 'ascii' codec can't decode byte 0xac in position 6: ordinal not in range(128)
### Question about pandas
_No response_ | {
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- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
from scipy.spatial import distance
df = pd.DataFrame(np.ones([5,3])).corr(distance.jaccard)
print(df)
0 1 2
0 1.0 0.0 0.0
1 0.0 1.0 0.0
2 0.0 0.0 1.0
```
### Issue Description
pd.DataFrame.corr assume diagnoal line should always be filled with one (one vector with itself). However, for many distance measures, 0 is the result when comparing two identical vectors. I think, we should allow users to specify whether to use 1 for default value on the diagnoal line, or let the underlying custom function to decide
### Expected Behavior
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.2
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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- [x] closes #43461
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry: I'm not sure if a what's new entry is necessairy, and if yes, under which section of v1.4.0 should I put it?
cc @TomAugspurger
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] | open | false | null | [] | null | 1 | "2021-09-10T15:05:22Z" | "2021-09-30T23:39:37Z" | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
If you do `value_counts` on a categorical there's no way to suppress unobserved categories.
#### Describe the solution you'd like
Add an observed keyword to value_counts.
#### API breaking implications
None, unless we want to revisit the default in light of #35967.
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} | 2 | "2021-09-10T15:54:54Z" | "2021-09-10T21:39:53Z" | "2021-09-10T21:37:42Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
Inching towards getting rid of redundant index-like attributes | {
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} | 11 | "2021-09-10T17:27:44Z" | "2021-10-16T19:07:42Z" | null | MEMBER | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
frame = pd.DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
frame[["b"]].groupby(frame["a"]).mean()
```
### Issue Description
the code sample was raising `DataError: No numeric types to aggregate` in 1.2.5 but in 1.3.x this now issues a warning that it will raise in the future. (and also appears that the stacklevel is incorrect)
on master
```
>>> frame[["b"]].groupby(frame["a"]).mean()
sys:1: FutureWarning: Dropping invalid columns in DataFrameGroupBy.mean is deprecated. In a future version, a TypeError will be raised. Before calling .mean, select only columns which should be valid for the function.
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4]
```
The change to a empty dataframe was in #41706 with the warning being added in #43154
cc @jbrockmendel @Dr-Irv
### Expected Behavior
same as 1.2.5
```
>>> frame[["b"]].groupby(frame["a"]).mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/simon/miniconda3/envs/pandas-1.2.5/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1496, in mean
return self._cython_agg_general(
File "/home/simon/miniconda3/envs/pandas-1.2.5/lib/python3.9/site-packages/pandas/core/groupby/generic.py", line 1015, in _cython_agg_general
agg_mgr = self._cython_agg_blocks(
File "/home/simon/miniconda3/envs/pandas-1.2.5/lib/python3.9/site-packages/pandas/core/groupby/generic.py", line 1121, in _cython_agg_blocks
raise DataError("No numeric types to aggregate")
pandas.core.base.DataError: No numeric types to aggregate
```
### Installed Versions
pd.show_versions() | {
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} | 1 | "2021-09-10T19:25:51Z" | "2021-09-10T19:45:51Z" | "2021-09-10T19:45:33Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
test = pd.DataFrame([1, 2, 3])
test_a = test
test_b = test
test_a['year'] = 1990
test_b['year'] = 2000
print(test_a)
```
### Issue Description
I am very surprised by this, I thought it might be some element sticking on the memory on an interactive session but it does the same if executing the script on a console.
Basically, if I duplicate a data frame and try to assign values at the same time that I create a column, if I do this in sequence the values in the first data frame are replaced by those assigned to the second data frame.
In the reproducible example this would print a data frame `test_a` with 2000s... while I assigned 1990s. I don't see how assigning a value in the second data frame could affect a different object...
### Expected Behavior
In the above code, `test_a['year']` should be filled with 1990, not 2000
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-7620-generic
Version : #21~1626191760~21.04~55de9c3-Ubuntu SMP Tue Jul 20 22:18:55 UTC
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.23
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.5
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.2
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : None
</details>
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
#!/usr/bin/env python3
import pandas as pd
# 1.2.5 and 1.3.2
print(pd.__version__)
df = pd.DataFrame(data={'VAR': ['A', 'B'], 'X': range(2)})
df.VAR = df.VAR.astype('category')
df.X = df.X.astype('int8')
# Name: VAR, dtype: category
# Categories (2, object): ['A', 'B']
print(df.VAR)
# VAR category
# X int8
# dtype: object
print(df.dtypes)
def foo(row):
# doing nothing
return row
for arg in [None, 'reduce', 'expand', 'broadcast']:
print(f'\n{arg}')
print(df.apply(foo, axis=1, result_type='reduce').dtypes)
```
### Issue Description
When I "iterate" on row level via `DataFrame.apply(foo, axis=1)` the `dtype` of the columns are changing.
Here in the MWE a `category` becomes `object` and an `int8` becomes `int64'.
The argument `result_type=` seems to have no effect. The MWE checks each possible option for it.
This is the output of the MWE
```
1.3.2
0 A
1 B
Name: VAR, dtype: category
Categories (2, object): ['A', 'B']
VAR category
X int8
dtype: object
None
VAR object
X int64
dtype: object
reduce
VAR object
X int64
dtype: object
expand
VAR object
X int64
dtype: object
broadcast
VAR object
X int64
dtype: object
```
### Expected Behavior
Just keep the `dtype`. Do not convert it without users permission. or minimally throw a warning if there is a good reason for it.
Possible related #42001
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.2
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.4
setuptools : 57.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.13
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
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} | 6 | "2021-09-10T20:35:29Z" | "2021-10-03T16:44:08Z" | "2021-10-03T16:43:57Z" | CONTRIBUTOR | null | closes #43394
xref #40245
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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} | 3 | "2021-09-10T21:47:44Z" | "2021-09-13T11:42:15Z" | "2021-09-11T00:17:41Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
cc @bashtage per comment https://github.com/pandas-dev/pandas/pull/37468#discussion_r706100798 I'm taking your word for it that the change this makes in the crosstab test is correct. Does this represent a bugfix? | {
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} | 6 | "2021-09-11T02:44:19Z" | "2021-09-21T20:04:43Z" | "2021-09-15T01:37:14Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
This is in the grey area between a bugfix and an API change. Towards the bugfix side, it fixes some truly strange behavior:
1) Concatenating ignores the dtypes of all-NaN columns (see test_append_dtypes)
```
import numpy as np
import pandas as pd
import pandas._testing as tm
df1 = pd.DataFrame({"bar": pd.Timestamp("20130101")}, index=range(1))
df2 = pd.DataFrame({"bar": np.nan}, index=range(1, 2))
df3 = df2.astype(object)
result = df1.append(df2)
expected = pd.DataFrame(
{"bar": pd.Series([pd.Timestamp("20130101"), np.nan], dtype="M8[ns]")}
)
tm.assert_frame_equal(result, expected)
result2 = df1.append(df3)
tm.assert_frame_equal(result2, expected)
```
2) Concatenating _sometimes_ ignores the dtype on empty (see test_join_append_timedeltas)
```
df = pd.DataFrame(columns=["d", "t"]) # <- object dtype
df2 = pd.DataFrame({"d": [pd.Timestamp(2013, 11, 5, 5, 56)], "t": [pd.Timedelta(seconds=22500)]})
result = df.append(df2, ignore_index=True) # <- *not* object dtype
assert (result.dtypes == df2.dtypes).all()
```
The change in indexing code/tests is the least-invasive I could make it.
cc @jorisvandenbossche, this moves us towards having less values-dependent behavior, also makes the BM behavior more like the AM behavior in several affected cases.
Lots of cleanup becomes available after this, leaving for follow-up.
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} | 0 | "2021-09-11T04:25:46Z" | "2021-09-11T17:15:38Z" | "2021-09-11T14:57:32Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
I'm increasingly sure that we should use result_index instead of all of the various near-identical alternatives in these files. The hard part is finding test cases where the distinction is relevant. This gets two of them. | {
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} | 1 | "2021-09-11T04:43:14Z" | "2021-09-12T15:11:21Z" | "2021-09-12T15:07:31Z" | MEMBER | null | Inspired by idea from @jbrockmendel in https://github.com/pandas-dev/pandas/pull/43489#discussion_r705868510.
Didn't end up seeing a huge perf improvement though, for example results on a wide frame:
```
arr = np.random.randn(10 ** 5, 100)
df = pd.DataFrame(arr)
gb = df.groupby(df.index % 3)
gb.quantile(0.5)
# this pr: 1.18 s ± 9.58 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# master: 1.55 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Based on a quick profile, almost all time is spent in `lexsort` on master or with this pr - seems lexsorting the entire block at once has comparable performance to going column by column (I guess more improvement would show up on a _really_ wide frame).
Regardless, though, I think moving this out of cython is an improvement (since gives no perf benefit for them to be in cython when just numpy functions, also saves ~80kb on compiled code). | {
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- [ ] tests added / passed
- [✔ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-09-11T17:14:40Z" | "2021-09-12T15:11:00Z" | "2021-09-12T14:59:41Z" | MEMBER | null | A start to typing sparse stuff, tried to do this without touching the cython code, hopefully some cleanups possible once typing somewhat specified.
cc @jbrockmendel, one annoying piece going on here is the abstract base class `SparseIndex` - it functions entirely like an interface, and so has nothing but abstract methods. Currently, in `cython` only `__init__` is defined for the base class, so the current typing with multiple methods in the base class is a bit of a white lie. We could just add all the methods to the base class and have them raise `NotImplementedError`, but not sure if that's just unnecessary code bloat since it works as is. | {
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} | 2 | "2021-09-11T17:27:30Z" | "2021-09-12T18:18:34Z" | "2021-09-12T16:25:46Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-09-11T17:42:29Z" | "2021-09-20T14:34:42Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import backports.zoneinfo
import datetime
import pandas
res = pandas.to_datetime(
(datetime.datetime(
1960, 5, 2, 0, 0,
tzinfo=backports.zoneinfo.ZoneInfo(key='America/New_York')),
),
errors='coerce')
repr(res)
```
### Issue Description
`pandas.to_datetime` allows the creation of objects with a broken method `__repr__()`. The code example above triggers
```
AttributeError: 'NoneType' object has no attribute 'total_seconds'
```
The issue was discovered when investigating this: https://github.com/rpy2/rpy2/issues/823#issuecomment-917444202
### Expected Behavior
Completion of `repr()` and a string return instead of an exception raised.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.19.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : 0.29.21
pytest : 6.2.4
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.26.0
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 : 4.0.1
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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} | 1 | "2021-09-11T19:25:00Z" | "2021-09-12T15:16:08Z" | "2021-09-12T15:08:35Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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Follow-ups include the big perf bump discussed in #43478
```
import pandas as pd
import numpy as np
np.random.seed(23446365)
arr = np.random.randn(10**5, 10)
mask = arr < -1
arr[mask] = np.nan
df = pd.DataFrame(arr)
gb = df.groupby(df.index % 7)
%timeit res = gb.pad()
29.8 ms ± 193 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
14.6 ms ± 2.41 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
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