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- [ ] tests added / passed
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Still some deprecation warnings left. Anyone want to approve for me so we can get this merged ASAP? | {
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} | 1 | "2021-11-19T12:26:48Z" | "2021-11-19T15:02:40Z" | "2021-11-19T15:02:29Z" | CONTRIBUTOR | null | - [ ] closes #xxxx
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https://api.github.com/repos/pandas-dev/pandas/issues/44526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44526/events | https://github.com/pandas-dev/pandas/pull/44526 | 1,058,752,666 | PR_kwDOAA0YD84uyH5B | 44,526 | Fix Regression when using sum/cumsum on Groupby objects | {
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NOTE: This will reduce performance significantly. Hopefully someone points me to a better check for `nan` which can be done with released GIL. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/44527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44527/events | https://github.com/pandas-dev/pandas/pull/44527 | 1,058,792,153 | PR_kwDOAA0YD84uyQAh | 44,527 | Fix/26837 format array | {
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- [x] tests added / passed
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The basic strategy is to move `format_array` from pandas.io.formats.format to `ExtensionArray._format_array`. By default, we convert to ndarray and then use the existing formatting mechanism on the ndarray. | {
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] | open | false | null | [] | null | 2 | "2021-11-19T19:30:34Z" | "2021-11-21T08:55:28Z" | 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](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
from io import StringIO
csv_data = "x,y\n1,1\n1,2\n2,1\n1,9\n9,1"
csv_data2 = "x,y\n1,1\n1,2\n2,1\n1,3\n3,1"
pd.read_csv(StringIO(csv_data)).plot(kind='hexbin',x='x',y='y',\
xlim=[-0.5,10.5],ylim=[0,10.5],\
gridsize=[10,10],sharex=False)
pd.read_csv(StringIO(csv_data2)).plot(kind='hexbin',x='x',y='y',\
xlim=[-0.5,10.5],ylim=[0,10.5],\
gridsize=[10,10],sharex=False)
```
### Issue Description
csv_data gets plotted with big hexagons touching each other as expected. csv_data2 gets plotted with much smaller hexagons, probably because there is no data near the right xlim, upper ylim.
### Expected Behavior
I was expecting the same size of hexagon for those two datasets
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-18-cloud-amd64
Version : #1 SMP Debian 4.19.208-1 (2021-09-29)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.19.5
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3
setuptools : 58.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : 7.28.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 2021.10.1
fastparquet : None
gcsfs : 2021.10.1
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.26
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.54.1
</details>
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} | 4 | "2021-11-19T20:42:38Z" | "2021-11-20T22:54:11Z" | "2021-11-20T04:51:23Z" | 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
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} | 3 | "2021-11-19T21:47:37Z" | "2021-11-20T20:24:11Z" | "2021-11-20T16:19:43Z" | MEMBER | null | - [x] closes #42844
- [x] tests added / passed
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This restores the 1.2.x behavior.
Passing an array as join key is something we don't have much test support. We have 2 tests for this which were added ages ago. We could deprecate this. | {
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} | 3 | "2021-11-19T23:08:41Z" | "2021-11-20T21:22:49Z" | "2021-11-20T21:11:25Z" | MEMBER | null | - [x] closes #42540
read_json looked like it had to be used like pandas.io.json.read_json and json_normalize was missing alltogether. Maybe backporting as well?
Edit: Build Docs locally, look fine now | {
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- [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
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updates some other lines that are out of date.
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No longer needed following #44507 | {
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- [ ] tests added / passed
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Hi pandas maintainers! I wanted to field a question here to add [pandera](https://pandera.readthedocs.io/) to the pandas ecosystem docs page under "data validation and cleaning". Pandera is a schema specification, type annotation, and data testing tool for Pandas and dataframe-like libraries (Dask, Modin, and Koalas) and wanted to see if it would make sense to add to this page. | {
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- [x] tests added / passed
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This behaves like np.insert, so changed the docstring. Making this compatible with list.insert would be a breaking change
Nevertheless the else block in the base implementation had a bug, so fixed that one too
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- [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
Found this test was flaky in https://github.com/pandas-dev/pandas/runs/4275006116?check_suite_focus=true#step:7:51
Decided to parameterize the value instead of `n` so it appears in the logs and used `hypothesis` instead of 100 random values.
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- [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
To avoid CI flakiness from this test, only compare the checksum of the ZIP contents to avoid comparing the flaky last modified timestamp of the ZIP archive. IMO, the CI stability is worth the the trade off of being a little less strict here.
cc @jbrockmendel
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https://github.com/pandas-dev/pandas/pull/44546/checks?check_run_id=4275864618
https://github.com/pandas-dev/pandas/pull/44461/checks?check_run_id=4276518961
Change codecov patch builds to always pass but still report the % coverage of the PR:
https://docs.codecov.com/docs/commit-status#informational
IMO our code review process should ensure that new code has associated tests. | {
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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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