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https://api.github.com/repos/pandas-dev/pandas/issues/42120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42120/events | https://github.com/pandas-dev/pandas/issues/42120 | 925,393,429 | MDU6SXNzdWU5MjUzOTM0Mjk= | 42,120 | DOC share docs between Series.swaplevel and DataFrame.swaplevel | {
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} | 2 | "2021-06-19T13:20:56Z" | "2021-06-26T18:32:04Z" | "2021-06-26T18:32:04Z" | MEMBER | null | Lots of duplication here, these docs could be somewhat shared | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42121 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42121/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42121/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42121/events | https://github.com/pandas-dev/pandas/issues/42121 | 925,393,994 | MDU6SXNzdWU5MjUzOTM5OTQ= | 42,121 | STYLE move spacing conventions in cython casting to pre-commit | {
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```
### LINTING ###
if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
# Check that cython casting is of the form `<type>obj` as opposed to `<type> obj`;
# it doesn't make a difference, but we want to be internally consistent.
# Note: this grep pattern is (intended to be) equivalent to the python
# regex r'(?<![ ->])> '
MSG='Linting .pyx code for spacing conventions in casting' ; echo $MSG
invgrep -r -E --include '*.pyx' --include '*.pxi.in' '[a-zA-Z0-9*]> ' pandas/_libs
RET=$(($RET + $?)) ; echo $MSG "DONE"
# readability/casting: Warnings about C casting instead of C++ casting
# runtime/int: Warnings about using C number types instead of C++ ones
# build/include_subdir: Warnings about prefacing included header files with directory
fi
```
It could be a `pygrep` check | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42122 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42122/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42122/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42122/events | https://github.com/pandas-dev/pandas/issues/42122 | 925,394,823 | MDU6SXNzdWU5MjUzOTQ4MjM= | 42,122 | STYLE move blocklisted imports check to pre-commit | {
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```
MSG='Check import. No warnings, and blocklist some optional dependencies' ; echo $MSG
python -W error -c "
import sys
import pandas
blocklist = {'bs4', 'gcsfs', 'html5lib', 'http', 'ipython', 'jinja2', 'hypothesis',
'lxml', 'matplotlib', 'openpyxl', 'py', 'pytest', 's3fs', 'scipy',
'tables', 'urllib.request', 'xlrd', 'xlsxwriter', 'xlwt'}
# GH#28227 for some of these check for top-level modules, while others are
# more specific (e.g. urllib.request)
import_mods = set(m.split('.')[0] for m in sys.modules) | set(sys.modules)
mods = blocklist & import_mods
if mods:
sys.stderr.write('err: pandas should not import: {}\n'.format(', '.join(mods)))
sys.exit(len(mods))
"
RET=$(($RET + $?)) ; echo $MSG "DONE"
```
it could be made to be its own Python hook, with no additional dependencies (so an existing environment will be reused) | {
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} | 3 | "2021-06-19T13:34:27Z" | "2021-08-07T21:58:49Z" | "2021-08-07T21:58:38Z" | MEMBER | null | These checks from `ci/code_checks.sh` should be moved to pre-commit:
```
### PATTERNS ###
if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
# Check for the following code in the extension array base tests: `tm.assert_frame_equal` and `tm.assert_series_equal`
MSG='Check for invalid EA testing' ; echo $MSG
invgrep -r -E --include '*.py' --exclude base.py 'tm.assert_(series|frame)_equal' pandas/tests/extension/base
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for deprecated messages without sphinx directive' ; echo $MSG
invgrep -R --include="*.py" --include="*.pyx" -E "(DEPRECATED|DEPRECATE|Deprecated)(:|,|\.)" pandas
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for backticks incorrectly rendering because of missing spaces' ; echo $MSG
invgrep -R --include="*.rst" -E "[a-zA-Z0-9]\`\`?[a-zA-Z0-9]" doc/source/
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for unnecessary random seeds in asv benchmarks' ; echo $MSG
invgrep -R --exclude pandas_vb_common.py -E 'np.random.seed' asv_bench/benchmarks/
RET=$(($RET + $?)) ; echo $MSG "DONE"
fi
```
some might fit in the `unwanted patterns` hook, others might need their own pygrep hook (no additional dependencies are necessary here though so the same existing virtualenvs will be reused)
- [ ] check for invalid EA testing
- [ ] deprecated messages without sphinx directive
- [ ] backticks incorrectly rendering because of missing spaces
- [ ] unnecessary random seeds in asv benchmarks | {
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] | null | 7 | "2021-06-19T13:36:40Z" | "2021-08-23T16:54:48Z" | "2021-08-23T16:54:48Z" | MEMBER | null | https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.reorder_levels.html?highlight=reorder_levels#pandas.DataFrame.reorder_levels
No examples! Can we add at least one? | {
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Align has no examples - can one be added? | {
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] | null | 1 | "2021-06-19T16:38:00Z" | "2021-07-11T09:17:04Z" | "2021-07-11T09:17:04Z" | MEMBER | null | The examples show the series ones: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_markdown.html | {
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] | closed | false | null | [] | null | 2 | "2021-06-19T17:02:43Z" | "2021-08-21T02:40:48Z" | "2021-08-21T02:40:48Z" | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pipenv install pandaas
```
#### Problem description
After running lots of minutes, I still got this errors!!
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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] | closed | false | null | [] | null | 4 | "2021-06-19T17:20:36Z" | "2021-06-21T22:12:41Z" | "2021-06-21T19:33:44Z" | NONE | null | #### Location of the documentation
https://pandas.pydata.org/docs/user_guide/style.html
#### Documentation problem
The docs include sample code to demonstrate how a .style.set_table_styles method can be chained using the overwrite=False arg.
```
html = html.set_table_styles({
'B': [dict(selector='', props=[('color', 'green')])],
'C': [dict(selector='td', props=[('color', 'red')])],
}, overwrite=False)
html
```
Copying and pasting the sample produces an error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-93-53e5b3797780> in <module>
2 'A': [ {'selector':'', 'props': [('color', 'red')]}],
3 'B': [ {'selector':'td', 'props': ['color: blue']}]
----> 4 }, overwrite=False)
TypeError: set_table_styles() got an unexpected keyword argument 'overwrite'
```
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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
- [ ] whatsnew entry
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} | 3 | "2021-06-19T20:06:16Z" | "2021-06-22T00:40:26Z" | "2021-06-22T00:40:09Z" | CONTRIBUTOR | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
`pandas/tests/arrays/sparse/test_dtype.py test_hash_equal` fails on Python3.10 (see #41940). This is due to the fact, that since Python3.10 different nan-objects have different hashes.
In #41988 a hash function with the old pre-Python3.10 behavior was introduced. This function is used in ExtensionDtype to keep the correct behavior also with Python3.10.
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} | 1 | "2021-06-20T02:01:45Z" | "2021-08-21T02:41:35Z" | null | MEMBER | null | From https://github.com/pandas-dev/pandas/pull/41970#discussion_r654770580, right now constructing nullable integer (and floating) arrays from boolean data has the following behavior:
```
In [2]: pd.Series([True, False, True, True], dtype='Int64')
Out[2]:
0 1
1 0
2 1
3 1
dtype: Int64
In [3]: pd.Series([True, False, True, pd.NA], dtype='Int64')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
TypeError: object cannot be converted to an IntegerDtype
```
This seems inconsistent, if so, which behavior is correct? | {
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} | 2 | "2021-06-20T04:01:12Z" | "2021-06-21T15:00:30Z" | "2021-06-21T13:18:36Z" | MEMBER | null | - [x] closes #41949
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [Y] I have confirmed this bug exists on the latest version of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
data = [['tom', 20], ['mike', 30], ['julie', float('nan')]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])
print(df) # can see that there is a NaN value in the dataframe
print(df.info()) # says that there are 3 non-null counts for column 'Age' instead of 2
print(df.isnull()) # says that the age value in the third row is not null
# below does the same process but with np.nan instead of float('nan'), and the same result is produced
import numpy as np
data = [['tom', 20], ['mike', 30], ['julie', np.nan]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])
print(df) # can see that there is a NaN value in the dataframe
print(df.info()) # says that there are 3 non-null counts for column 'Age' instead of 2
print(df.isnull()) # says that the age value in the third row is not null
```
#### Problem description
df.info() should output 2 non-null count for column 'Age' since a NaN value has been deliberately placed for one of the elements however it outputs 3 non-null count for column 'Age'.
Furthermore, df.isnull() method should output "True" for the third element in column 'Age' however it outputs "False".
#### Expected Output
df.info():
Data columns (total 2 columns):
Column Non-Null Count Dtype
0 Name 3 non-null object
1 Age **2** non-null float64
dtypes: float64(1), object(1)
AND
df.isnull():
Name Age
0 False False
1 False False
2 False **TRUE**
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
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 21:46:41 PDT 2021; root:xnu-7195.101.2~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 57.0.0
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 : 7.24.1
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 : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-06-20T07:05:11Z" | "2021-08-17T14:12:54Z" | "2021-08-17T13:11:53Z" | CONTRIBUTOR | null | This PR deprecates the method `Styler.render()` in favour of `Styler.to_html()`, which has been recently introduced and produces the same result under the default case of no arguments.
Additionally this PR has added a test to a new file `pandas/tests/io/formats/style/test_deprecated.py`, and it has moved some of the previous Styler deprecation tests into this file.
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} | 2 | "2021-06-20T07:21:42Z" | "2021-06-21T14:20:52Z" | "2021-06-21T14:20:52Z" | MEMBER | null | Check this note from the flake8 docs:
>
> Following the recommended settings for Python’s configparser, Flake8 does not support inline comments for any of the keys. So while this is fine:
>
> [flake8]
> per-file-ignores =
> # imported but unused
> __init__.py: F401
>
> this is not:
>
> [flake8]
> per-file-ignores =
> __init__.py: F401 # imported but unused
>
However, that's exactly what we do:
https://github.com/pandas-dev/pandas/blob/98e22297bb66f81ff4c1d4f1c3277cfe8c6b9ce7/setup.cfg#L71-L104
In each case, the comment should be moved to be on its own line, on the line before the code
Else, we risk accidentally putting something in the comments that'll mess with how flake8 works | {
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} | 2 | "2021-06-20T07:41:39Z" | "2021-08-09T07:59:48Z" | "2021-08-08T23:33:43Z" | MEMBER | null | the alias means you can run `pre-commit run flake8-cython --all-files` | {
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} | 4 | "2021-06-20T10:27:06Z" | "2021-10-31T00:47:11Z" | null | CONTRIBUTOR | null | - [ ] closes #23478
- [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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} | 14 | "2021-06-20T13:05:41Z" | "2021-07-11T09:17:05Z" | "2021-07-11T09:17:04Z" | CONTRIBUTOR | null | - [ ] closes #42126
- [ ] 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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] | closed | false | null | [] | null | 2 | "2021-06-20T13:42:48Z" | "2021-08-26T17:04:55Z" | "2021-08-26T17:04:55Z" | 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 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
df = pd.DataFrame(
{
"x": [1 + 2j, 4 - 3j, 10 - 1j, 4 + 5j, 5 - 8j],
"y": [3, 4, 5, 6, 7],
"z": [0.1, 0.2, 0.3, 0.4, 0.5],
}
)
df.set_index(["x", "y"], inplace=True)
```
#### Problem description
If creating a MultiIndex, the imaginary part of complex numbers gets dropped.
Update: I did find a workaround that seems to be pretty usable, so not sure how important it is to fix this issue. But, I will leave it here and you can decide. The workaround is the explicitly define the 'x' complex variable as a Series with dtype='object'. That makes the MultiIndex work as expected, with the imag part of the complex number kept.
Here is the workaround code:
df = pd.DataFrame(
{
"x": pd.Series([1 + 2j, 4 - 3j, 10 - 1j, 4 + 5j, 5 - 8j], dtype="object"),
"y": [3, 4, 5, 6, 7],
"z": [0.1, 0.2, 0.3, 0.4, 0.5],
}
)
df.set_index(["x", "y"], inplace=True)
#### Expected Output
The expected output is shown below, with both x and y included in a MultiIndex, and x maintaining its imaginary part.
df.index
MultiIndex([( 1.0+2j, 3), ( 4.0-3j, 4), (10.0-1j, 5), ( 4.0+5j, 6), ( 5.0-8j, 7)], names=['x', 'y'])
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.2.0
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
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 : 1.6.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.18.2
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-06-20T14:24:22Z" | "2021-06-21T14:20:56Z" | "2021-06-21T14:20:52Z" | CONTRIBUTOR | null | - [x] closes #42141
Hi @MarcoGorelli, how does this look?
Let me know if this needs any changes.
Thanks | {
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] | null | 2 | "2021-06-20T16:13:59Z" | "2021-06-21T18:35:51Z" | null | MEMBER | null | Looking at the fillna code, it looks like a mask is generated and then the fill value is then put in using putmask. Wouldn't it be faster if we skipped the mask generation and just put in nan values directly by looping over in Cython.
https://github.com/pandas-dev/pandas/blob/98e22297bb66f81ff4c1d4f1c3277cfe8c6b9ce7/pandas/core/internals/blocks.py#L430-L470
I can take this issue up if this is the correct approach | {
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} | 1 | "2021-06-20T17:06:22Z" | "2021-06-26T18:32:05Z" | "2021-06-26T18:32:05Z" | CONTRIBUTOR | null | - [ ] closes #42120
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] tests added / passed
- [ ] whatsnew entry
This commit
- adds examples to the Series.swaplevel docstring
- reuses the same template in Series.swaplevel and DataFrame.swaplevel docstrings
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- [ ] tests added / passed
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} | 0 | "2021-06-20T23:03:40Z" | "2021-06-25T01:37:00Z" | "2021-06-25T01:28:57Z" | 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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} | 0 | "2021-06-21T07:14:28Z" | "2021-06-22T15:15:12Z" | "2021-06-22T15:15:06Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.10-py2.py3-none-any.whl (98 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.7-py2.py3-none-any.whl (7.2 MB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.10 nodeenv-1.6.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.4.7
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
updating 21.5b2 -> 21.6b0.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
updating v2.0.0 -> v2.1.0.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
updating 5.8.0 -> 5.9.0.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.18.3 -> v2.19.4.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
updating v1.8.0 -> v1.9.0.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Failed
- hook id: codespell
- exit code: 65
pandas/core/missing.py:401: covert ==> convert
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Failed
- hook id: pyupgrade
- exit code: 1
- files were modified by this hook
Rewriting pandas/core/arrays/sparse/scipy_sparse.py
Rewriting pandas/tests/frame/test_arithmetic.py
Rewriting pandas/tests/indexing/test_categorical.py
Rewriting pandas/core/reshape/melt.py
Rewriting pandas/core/arrays/sparse/array.py
Rewriting pandas/io/sql.py
Rewriting pandas/tests/frame/methods/test_describe.py
Rewriting pandas/io/formats/format.py
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed 9 files: </summary>
- .pre-commit-config.yaml
- pandas/core/arrays/sparse/array.py
- pandas/core/arrays/sparse/scipy_sparse.py
- pandas/core/reshape/melt.py
- pandas/io/formats/format.py
- pandas/io/sql.py
- pandas/tests/frame/methods/test_describe.py
- pandas/tests/frame/test_arithmetic.py
- pandas/tests/indexing/test_categorical.py
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42152 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42152/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42152/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42152/events | https://github.com/pandas-dev/pandas/issues/42152 | 926,058,739 | MDU6SXNzdWU5MjYwNTg3Mzk= | 42,152 | BUG: Index lost when serializing to json | {
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- [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.
---
When serializing a pandas `DataFrame` to json the index is usally serialized by use of the `primary_key` in the json. In some cases this does not work. For example:
```python
import pandas as pd
df = pd.DataFrame({'ind': ['TIME', 'TIME'], 'id': [1, 1], 'data': [0.1, 0.2]})
df.set_index(['ind', 'id'], inplace=True)
js = df.to_json(orient='table')
new = pd.read_json(js, orient='table')
```
The json string `js`:
```
{"schema":{"fields":[{"name":"ind","type":"string"},{"name":"id","type":"integer"},{"name":"data","type":"number"}],"pandas_version":"0.20.0"},"data":[{"ind":"TIME","id":1,"data":0.1},{"ind":"TIME","id":1,"data":0.2}]}
```
The `new` data frame created by reading the json doesn't have the same index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-74-generic
Version : #83-Ubuntu SMP Sat May 8 02:35:39 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2019.3
dateutil : 2.7.3
pip : 21.1.1
setuptools : 45.2.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42153 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42153/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42153/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42153/events | https://github.com/pandas-dev/pandas/pull/42153 | 926,194,796 | MDExOlB1bGxSZXF1ZXN0Njc0NTMzODU4 | 42,153 | Backport PR #41685 on branch 1.3.x (CI: add sdist release workflow) | {
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} | 4 | "2021-06-21T14:03:26Z" | "2021-06-21T16:07:29Z" | "2021-06-21T16:07:13Z" | CONTRIBUTOR | null | - [ ] 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
This is a regeression in #41952. Checking the identity first avoids long comparison of e.g. tuples - checking the id first is also how `PyObject_RichCompareBool(a, b, Py_EQ)` works.
Here are results from asv:
```
before after ratio
[1ff6970c] [b8285515]
- 11.0±0.1ms 7.09±0.06ms 0.64 algos.isin.IsInWithLongTupples.time_isin
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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} | 2 | "2021-06-21T17:06:45Z" | "2021-06-21T22:38:52Z" | "2021-06-21T18:28:18Z" | MEMBER | null | - [x] closes #42099
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-06-21T17:17:39Z" | "2021-08-05T02:23:06Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> df = pd.DataFrame( { 'foo':['a','b','c'], 'bar':[1,3,2] } )
>>> df.iloc[1,0] = None
>>> df
foo bar
0 a 1
1 None 3
2 c 2
>>> df['foo'] == None
0 False
1 False
2 False
Name: foo, dtype: bool
>>> df['foo'].apply( lambda x: x == None )
0 False
1 True
2 False
Name: foo, dtype: bool
>>>
```
#### Problem description
One expects the `==` operator to distribute/vectorize over pandas columns. In this case, it appears to do so, returning a bool for each value in the column, but those results are not what one would expect. Indeed, one can use an apply (as shown above) to verify that the answer from the vectorized version is not correct.
#### Expected Output
That `df['foo'] == None` should match `df['foo'].apply( lambda x: x == None )`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon May 3 20:41:19 PDT 2021; root:xnu-4903.278.68~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.17
pytest : 6.0.1
hypothesis : 5.24.0
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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} | 6 | "2021-06-21T17:55:57Z" | "2021-06-25T21:06:01Z" | "2021-06-25T17:38:24Z" | MEMBER | null | - [x] closes #38581
- [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
Picking up #38594. Left in 1.3 for now since all comments were addressed except small typing issues and seems like a nice enhancement, but let me know if it should be moved. | {
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} | 4 | "2021-06-21T19:10:30Z" | "2021-06-22T00:41:43Z" | "2021-06-22T00:34:48Z" | MEMBER | null | Seeing lots of CI timeouts - this test sometimes takes > 5 min for the python parser, guessing dependent on memory availability.
AFAICT the data size is only so large to test the warning given by the c parser and low memory case, so shrank size for other cases. | {
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} | 0 | "2021-06-21T19:22:02Z" | "2021-06-21T19:22:26Z" | null | MEMBER | null | From https://github.com/pandas-dev/pandas/pull/42085#discussion_r655520599, for groupby any/all we have bool coercing logic in https://github.com/pandas-dev/pandas/blob/6ca875787b60b5d45ad0136ff501f5d523719a3a/pandas/core/groupby/groupby.py#L1521-L1526
Would be nice to share this with handling in https://github.com/pandas-dev/pandas/blob/6ca875787b60b5d45ad0136ff501f5d523719a3a/pandas/core/nanops.py#L534-L539
though complicated by shape requirement differences and the complicated nature of `_get_values` | {
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] | open | false | null | [] | null | 7 | "2021-06-21T19:36:59Z" | "2021-08-21T02:43:44Z" | null | NONE | null | #### Is your feature request related to a problem?
I wish to take the AND, OR, XOR, and NOT of dataframes. Of course I could do this manually but an inbuilt way would be far cleaner and elegant.
#### Possible Solution
Example 1:
A = {'a': 1 2 3}
B = {'a': 1 3 4}
pandas.xor(A,B, on = "a")
{'a': 2 4}
Example 2:
A.or( B, on= "a")
{'a': 1 2 3 4}
Example 3:
A.and(B,on='a')
{'a': 1 3}
#### API breaking implications
It will not affect the API it is just a convenience feature.
#### Additional context
Typically when comparing data from 2 sources, fields will not correlate and need to be cleaned through basic and, or, nor, and not, and xor operations. This would speed up greatly those tasks.
```python
Code to come later wanting to hear you're thoughts on implementation first.
```
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} | 6 | "2021-06-21T19:55:23Z" | "2021-07-15T04:21:32Z" | "2021-07-15T04:21:23Z" | CONTRIBUTOR | null | - [x] closes #41876
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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.html#code-standards) for how to run them
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} | 8 | "2021-06-21T20:32:48Z" | "2021-07-11T09:19:51Z" | "2021-07-11T09:19:51Z" | CONTRIBUTOR | null | - [x] closes #[42103](https://github.com/pandas-dev/pandas/issues/42103)
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I didn't add anything to the whatsnew file, as it seems it's not always necessary depending on the change?
Error message for a failed assert_produces_warning(match=". . .") now looks like e.g.:
```
Did not see warning 'DtypeWarning' matching 'Warning'. The emitted warning messages are [DtypeWarning('This is not a match.'), DtypeWarning('Another unmatched warning.')]
```
Thanks for any feedback if I've done something wrong here. | {
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} | 0 | "2021-06-22T00:34:58Z" | "2021-06-22T08:42:19Z" | "2021-06-22T08:42:18Z" | NONE | null | Backport PR #42174: TST/CI: speed up chunksize test | {
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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
- [ ] whatsnew entry
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} | 3 | "2021-06-22T07:16:17Z" | "2021-07-28T01:01:42Z" | "2021-07-28T01:01:42Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
`pandas.cut` fails for a `pandas.Series` with non-unique index as input.
```python
In [1]: import pandas as pd
...:
...: s = pd.Series([0, 1, 2, 3, 0], index=[0, 1, 2, 3, 0])
...:
...: cut = pd.cut(s, [0, 2, 4])
...: cut
Out[1]:
0 NaN
1 (0.0, 2.0]
2 (0.0, 2.0]
3 (2.0, 4.0]
0 NaN
dtype: category
Categories (2, interval[int64]): [(0, 2] < (2, 4]]
In [2]: cut = pd.cut(s, [0, 2, 4], include_lowest=True)
...: cut
/home/mlondschien/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/reshape/tile.py:410: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted a
s an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
ids[x == bins[0]] = 1
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3079 try:
-> 3080 return self._engine.get_loc(casted_key)
3081 except KeyError as err:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine._get_loc_duplicates()
pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._maybe_get_bool_indexer()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine._unpack_bool_indexer()
KeyError: 4
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
<ipython-input-5-00a66777dca1> in <module>
----> 1 cut = pd.cut(s, [0, 2, 4], include_lowest=True)
2 cut
~/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/reshape/tile.py in cut(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)
271 raise ValueError("bins must increase monotonically.")
272
--> 273 fac, bins = _bins_to_cuts(
274 x,
275 bins,
~/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/reshape/tile.py in _bins_to_cuts(x, bins, right, labels, precision, include_lowest, dtype, duplicates, ordered)
408
409 if include_lowest:
--> 410 ids[x == bins[0]] = 1
411
412 na_mask = isna(x) | (ids == len(bins)) | (ids == 0)
~/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/series.py in __getitem__(self, key)
851
852 elif key_is_scalar:
--> 853 return self._get_value(key)
854
855 if is_hashable(key):
~/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/series.py in _get_value(self, label, takeable)
959
960 # Similar to Index.get_value, but we do not fall back to positional
--> 961 loc = self.index.get_loc(label)
962 return self.index._get_values_for_loc(self, loc, label)
963
~/miniforge3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3080 return self._engine.get_loc(casted_key)
3081 except KeyError as err:
-> 3082 raise KeyError(key) from err
3083
3084 if tolerance is not None:
KeyError: 4
In [3]: cut = pd.cut(s.to_numpy(), [0, 2, 4], include_lowest=True)
...: cut
Out[3]:
[(-0.001, 2.0], (-0.001, 2.0], (-0.001, 2.0], (2.0, 4.0], (-0.001, 2.0]]
Categories (2, interval[float64]): [(-0.001, 2.0] < (2.0, 4.0]]
```
<details>
```python
import pandas as pd
s = pd.Series([0, 1, 2, 3, 0], index=[0, 1, 2, 3, 0])
cut = pd.cut(s, [0, 2, 4])
cut
cut = pd.cut(s, [0, 2, 4], include_lowest=True)
cut = pd.cut(s.to_numpy(), [0, 2, 4], include_lowest=True)
cut
```
</details>
<details>
```
In [8]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-74-generic
Version : #83-Ubuntu SMP Sat May 8 02:35:39 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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- [ ] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-06-22T09:27:27Z" | "2021-06-27T21:40:48Z" | "2021-06-25T00:48:38Z" | CONTRIBUTOR | null | checks an items off #40675
The current mechanics are unstructured and the tests is not future proof (if new attributes are added tests will not pick these up and fail). This PR generalises the construction and future proofs the tests. No user facing cahnge.
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- [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
df1 = pd.DataFrame({"col1": [1, 2, 3], "col2": [2, 3, 4], "col_index": ['a', 'b', 'b']}).set_index("col_index")
df2 = pd.DataFrame({"col1": [1, 2, 3], "col2": [2, 3, 4], "col_index": ['a', 'b', 'c']}).set_index("col_index")
selected_index = df1.index.intersection(['a'])
print(df1.loc[selected_index])
print(df2.loc[selected_index])
```
#### Problem description
These two dataframes are almost the same, except the last index value. However, after we select the first row with index as "a" by `.loc[]` on both dataframes, the returned dataframes are different. And the index name of the result for `df2` is missing, which is unexpected.
```
>>> print(df1.loc[selected_index])
col1 col2
col_index
a 1 2
>>> print(df2.loc[selected_index])
col1 col2
a 1 2
```
#### Expected Output
The index name for the `df.loc[selected_index])` should be `col_index` as it is for `df1`.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.5
numpy : 1.18.5
pytz : 2019.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 52.0.0.post20210125
Cython : 0.29.14
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.16.1
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 : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [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
pd.Series(['a','b',None], dtype='category').astype('bool')
```
In 1.2.5 produces:
```python
0 True
1 True
2 True
dtype: bool
```
In 1.2.4 produces:
```python
0 True
1 True
2 NaN
dtype: object
```
#### Problem description
At first, I thought this was a bug, but the `astype` call producing `object` dtype only started showing up in 1.2.0, so I went back and checked 1.1.5, and 1.2.5 now matches what was on 1.1.5.
This makes me think that the behavior in 1.1.5 and 1.2.5 is the expected behavior, but I don't see a release note in 1.1.5 referencing this change back to the expected, so I just wanted to point this out incase it was an unknown regression/fix or a bug of some sort.
I do see that #41797 seems like a similar problem that also first showed up in 1.2.0 and was fixed in 1.2.5, so I'm wondering if they're related.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.5
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.06.0
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
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} | 3 | "2021-06-22T20:36:58Z" | "2021-08-03T17:15:14Z" | "2021-08-03T17:15:14Z" | CONTRIBUTOR | null | - [ ] closes #42101
- [ ] tests not added. Will update this PR with the tests.
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I'm asking since having this be unified would greatly simplify my work in #42147 | {
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} | 6 | "2021-06-23T01:11:54Z" | "2021-06-25T02:50:59Z" | "2021-06-24T12:51:22Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 4 | "2021-06-23T01:30:31Z" | "2021-06-24T13:09:03Z" | "2021-06-24T12:51:48Z" | MEMBER | null | - [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
Ref: https://github.com/pandas-dev/pandas/pull/39941#issuecomment-829604511 and the conversation preceding it. While I don't find @MarcoGorelli's suggestion as implemented here very clear, it at least makes it correct and I don't see a better way. | {
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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.html#code-standards) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/42195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42195/events | https://github.com/pandas-dev/pandas/pull/42195 | 927,781,000 | MDExOlB1bGxSZXF1ZXN0Njc1ODg1MDE4 | 42,195 | PERF: Try fast/slow paths only once in DataFrameGroupby.transform | {
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} | 9 | "2021-06-23T01:55:14Z" | "2021-07-08T16:15:02Z" | "2021-07-08T16:08:09Z" | MEMBER | null | - [x] closes #41598
- [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
Still need to run ASVs to measure performance, making sure CI passes first.
Edit: ASV results for 7b86fdb
```
before after ratio
[9f6a91ae] [7b86fdb9]
<groupby_transform_path~1> <groupby_transform_path>
- 23.8±0.07ms 21.4±2ms 0.90 groupby.Nth.time_series_nth_all('float32')
- 11.8±0.1ms 10.4±0.6ms 0.88 groupby.Nth.time_frame_nth('object')
- 27.1±0.1ms 23.3±0.1ms 0.86 groupby.Nth.time_series_nth_all('object')
- 19.4±0.2ms 16.5±0.2ms 0.85 groupby.Nth.time_groupby_nth_all('float64')
- 21.7±0.2ms 18.4±0.3ms 0.85 groupby.Nth.time_frame_nth_any('float32')
- 24.1±0.1ms 20.2±0.2ms 0.84 groupby.Nth.time_series_nth_any('float64')
- 24.0±0.2ms 20.1±0.05ms 0.84 groupby.Nth.time_series_nth_all('float64')
- 56.4±0.4ms 20.1±0.4ms 0.36 groupby.TransformEngine.time_dataframe_cython(False)
- 56.5±0.4ms 20.1±0.4ms 0.36 groupby.TransformEngine.time_dataframe_cython(True)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42196/events | https://github.com/pandas-dev/pandas/pull/42196 | 927,801,147 | MDExOlB1bGxSZXF1ZXN0Njc1OTAxODM0 | 42,196 | CI: Pin numpy | {
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Testing mypy fails with numpy 1.21.0, then will confirm mypy passes with < 1.21 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42197/events | https://github.com/pandas-dev/pandas/pull/42197 | 927,825,693 | MDExOlB1bGxSZXF1ZXN0Njc1OTIyMjU1 | 42,197 | PERF: IntervalArray.unique, IntervalIndex.intersection | {
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} | 2 | "2021-06-23T03:38:33Z" | "2021-06-25T16:03:14Z" | "2021-06-25T14:32:17Z" | 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
Partially address perf hit discussed in #41929
```
import numpy as np
from pandas import *
N = 10**5
left = np.append(np.arange(N), np.array(0))
right = np.append(np.arange(1, N + 1), np.array(1))
intv = IntervalIndex.from_arrays(left, right)
intv2 = IntervalIndex.from_arrays(left + 1, right + 1)
%timeit intv.intersection(intv2)
122 ms ± 2.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
263 ms ± 5.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
```
xref #36482 | {
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] | open | false | null | [] | null | 0 | "2021-06-23T04:24:59Z" | "2021-06-24T02:09:00Z" | null | MEMBER | null | In `combine`, current behavior tries to maintain extension types by casting back if possible through `maybe_cast_pointwise_result`. This can give some value dependent behavior with a (contrived) example like the following:
```
In [3]: ser = pd.Series([True, True], dtype="boolean")
In [4]: ser.combine(2, lambda x, y: int(x) * y)
Out[4]:
0 2
1 2
dtype: int64
In [5]: ser = pd.Series([False, False], dtype="boolean")
In [6]: ser.combine(2, lambda x, y: int(x) * y)
Out[6]:
0 False
1 False
dtype: boolean
```
Not sure if that is desired behavior on its own, but it presents an issue for fixing #42137. On current master object boolean data raises in `IntegerArray._from_sequence`, so `combine` fails to cast back:
```
In [2]: ser = pd.Series([1, pd.NA], dtype="Int64")
In [3]: ser.combine(2, lambda x, y: x < y)
Out[3]:
0 True
1 <NA>
dtype: object
```
However, fixing #42137 will allow casting back, changing this behavior to
```
In [5]: ser = pd.Series([1, pd.NA], dtype="Int64")
In [6]: ser.combine(2, lambda x, y: x < y)
Out[6]:
0 1
1 <NA>
dtype: Int64
```
which looks unexpected (and fails `pandas/tests/extension/test_integer.py::TestMethods::test_combine_le` section of tests).
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Probably not a bug in pandas per se, but it might be useful to be aware of this:
I noticed that the `python-dateutil` dependency on this github dependency graph page https://github.com/pandas-dev/pandas/network/dependencies points to https://github.com/clones/python-dateutil which is a strange 1-commit mirror repo from 2008, instead of the official repo https://github.com/dateutil/dateutil/
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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.html#code-standards) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/42202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42202/events | https://github.com/pandas-dev/pandas/issues/42202 | 928,840,782 | MDU6SXNzdWU5Mjg4NDA3ODI= | 42,202 | June 2021 Docs Meeting - CANCELLED | {
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- [ ] Remove from calendar | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42203/events | https://github.com/pandas-dev/pandas/issues/42203 | 928,886,905 | MDU6SXNzdWU5Mjg4ODY5MDU= | 42,203 | BUG: DataFrame.agg - why numpy.size doesn't work? | {
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] | open | false | null | [] | null | 7 | "2021-06-24T06:19:11Z" | "2021-08-30T18:35:36Z" | null | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]],
columns=['A', 'B', 'C'])
df.agg({'A':['mean','std','size']})
import numpy as np
#Somehow this just doesn't work with DF.agg but works with DFGroupby.agg
df.agg({'A':[np.mean,np.std,np.size]})
```
#### Problem description
Intuitively, I assumed **df.agg({'A':[np.mean,np.std,np.size]})** should work as **df.agg({'A':['mean','std','size']})** does, but it doesn't. I wonder why? Looked through docs like the below but still didn't get it:
- https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.agg.html
- https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.core.groupby.DataFrameGroupBy.agg.html
#### Expected Output
<html>
<body>
<!--StartFragment-->
A
--
4.0
3.0
4.0
<!--EndFragment-->
</body>
</html>
####
Output of ``*df.agg({'A':[np.mean,np.std,np.size]})``
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\base.py in _aggregate_multiple_funcs(self, arg, _axis)
553 try:
--> 554 return concat(results, keys=keys, axis=1, sort=False)
555 except TypeError:
~\anaconda3\lib\site-packages\pandas\core\reshape\concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
280 copy=copy,
--> 281 sort=sort,
282 )
~\anaconda3\lib\site-packages\pandas\core\reshape\concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
356 )
--> 357 raise TypeError(msg)
358
TypeError: cannot concatenate object of type '<class 'float'>'; only Series and DataFrame objs are valid
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-39-051b5cf01f85> in <module>
1 import numpy as np
----> 2 df.agg({'A':[np.mean,np.std,np.size]})
~\anaconda3\lib\site-packages\pandas\core\frame.py in aggregate(self, func, axis, *args, **kwargs)
6704 result = None
6705 try:
-> 6706 result, how = self._aggregate(func, axis=axis, *args, **kwargs)
6707 except TypeError:
6708 pass
~\anaconda3\lib\site-packages\pandas\core\frame.py in _aggregate(self, arg, axis, *args, **kwargs)
6718 result = result.T if result is not None else result
6719 return result, how
-> 6720 return super()._aggregate(arg, *args, **kwargs)
6721
6722 agg = aggregate
~\anaconda3\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
426
427 try:
--> 428 result = _agg(arg, _agg_1dim)
429 except SpecificationError:
430
~\anaconda3\lib\site-packages\pandas\core\base.py in _agg(arg, func)
393 result = {}
394 for fname, agg_how in arg.items():
--> 395 result[fname] = func(fname, agg_how)
396 return result
397
~\anaconda3\lib\site-packages\pandas\core\base.py in _agg_1dim(name, how, subset)
377 "nested dictionary is ambiguous in aggregation"
378 )
--> 379 return colg.aggregate(how)
380
381 def _agg_2dim(name, how):
~\anaconda3\lib\site-packages\pandas\core\series.py in aggregate(self, func, axis, *args, **kwargs)
3686 # Validate the axis parameter
3687 self._get_axis_number(axis)
-> 3688 result, how = self._aggregate(func, *args, **kwargs)
3689 if result is None:
3690
~\anaconda3\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
484 elif is_list_like(arg):
485 # we require a list, but not an 'str'
--> 486 return self._aggregate_multiple_funcs(arg, _axis=_axis), None
487 else:
488 result = None
~\anaconda3\lib\site-packages\pandas\core\base.py in _aggregate_multiple_funcs(self, arg, _axis)
562 result = Series(results, index=keys, name=self.name)
563 if is_nested_object(result):
--> 564 raise ValueError("cannot combine transform and aggregation operations")
565 return result
566
ValueError: cannot combine transform and aggregation operations
</details>
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} | 4 | "2021-06-24T09:46:35Z" | "2021-06-24T20:17:51Z" | null | NONE | null | In the docstrings for the head and tail functions (in pandas/core/generic.py) there is an absolute value or minus sign missing:
e.g. in tail docstring
```
For negative values of `n`, this function returns all rows except
the first `n` rows, equivalent to ``df[n:]``.
```
should read something like
```
For negative values of `n`, this function returns all rows except
the first `np.abs(n)` rows, equivalent to ``df[np.abs(n):]``.
```
or
```
For negative values of `n`, this function returns all rows except
the first `-n` rows, equivalent to ``df[-n:]``.
```
Similarly for head. | {
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} | 0 | "2021-06-24T12:51:56Z" | "2021-06-24T14:52:00Z" | "2021-06-24T14:52:00Z" | NONE | null | Backport PR #42193: DOC: Correct docstring for Series.apply | {
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} | 0 | "2021-06-24T14:57:09Z" | "2021-06-24T20:24:39Z" | "2021-06-24T20:22:39Z" | 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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} | 0 | "2021-06-24T16:35:25Z" | "2021-06-24T17:24:00Z" | "2021-06-24T17:23:24Z" | MEMBER | null | Not sure why the new failing azure build picked up the new version later, but looks like under same umbrella as #42026
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I would like to use `pd.cut` to sort values into bins that are half-open with the lower boundary being the closed end of the interval (i.e. [0, 5), so setting `right=False`) but still be able to include the upper bound of the last interval (i.e. have the last interval be closed, something like `include_highest=True` analogous to `include_lowest=True` for `right=True`). I encountered this also for infinite boundaries, where adding a small number is not an option (although as a workaround, one can `fillna` the result as the only remaining `na`s are those of the infinite right boundary).
I.e. while I can make the first interval closed for `pd.cut`:
```python
In [1]: import pandas as pd
In [2]: pd.cut(pd.Series([0, 1, 2, 3]), bins=[0, 1, 2, 3], include_lowest=True, retbins=False)
Out[2]:
0 (-0.001, 1.0]
1 (-0.001, 1.0]
2 (1.0, 2.0]
3 (2.0, 3.0]
dtype: category
Categories (3, interval[float64, right]): [(-0.001, 1.0] < (1.0, 2.0] < (2.0, 3.0]]
```
I can't do the same for `right=False` where `include_lowest=True` seems functionless:
```python
In [3]: pd.cut(pd.Series([0, 1, 2, 3]), bins=[0, 1, 2, 3], right=False, include_lowest=True, retbins=False)
Out[3]:
0 [0.0, 1.0)
1 [1.0, 2.0)
2 [2.0, 3.0)
3 NaN
dtype: category
Categories (3, interval[int64, left]): [[0, 1) < [1, 2) < [2, 3)]
```
(I would want the last value 3 to be in the final bin [2, 3).)
#### Describe the solution you'd like
Either, `include_lowest` could be changed to `final_interval_closed` or similar to work as `include_lowest` for `right=True` and `include_highest` for `right=False` (which would break API, see below). This would make the function work somewhat symmetrically for `right=True` and `right=False`. Alternatively, such a parameter could be added additionally, which would make `include_lowest` more or less obsolete though, as far as I can see. Or to make the API more symmetric one could add another parameter `include_highest`, which does nothing for `right=True` but makes the last interval closed on both ends for `right=False`.
#### API breaking implications
Changing the parameter `include_lowest` to `final_interval_closed` or similar would break the API. The alternative solutions (adding either `final_interval_closed` or `include_highest`) would add an additional parameter to the function `pd.cut` (and if the former would be added, potentially `include_lowest` could be deprecated down the line).
#### Describe alternatives you've considered
See three alternatives under **Describe the solution you'd like** | {
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- [ ] 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 - Included example for reorder_levels
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} | 8 | "2021-06-24T22:02:01Z" | "2021-09-05T20:47:41Z" | "2021-09-02T00:59:28Z" | CONTRIBUTOR | null | - [x] closes #29095
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - *I cannot find the linked page*
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The `engine_kwargs` argument was not passed properly and this was not tested. Also added some documentation. | {
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} | 4 | "2021-06-25T02:51:18Z" | "2021-07-04T21:38:47Z" | "2021-07-04T21:38:47Z" | MEMBER | null | - [x] closes #33469
- [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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- [X] As test, ran scripts/validate_docstring.py with outputs
Docstring for "pandas.core.generic.NDFrame.head" correct. :)
Docstring for "pandas.core.generic.NDFrame.tail" correct. :)
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