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https://api.github.com/repos/pandas-dev/pandas/issues/42721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42721/events | https://github.com/pandas-dev/pandas/pull/42721 | 952,581,391 | MDExOlB1bGxSZXF1ZXN0Njk2NzU0MzQw | 42,721 | ⬆️ UPGRADE: Autoupdate pre-commit config | {
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<!-- 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 identify>=1.0.0
Downloading identify-2.2.11-py2.py3-none-any.whl (98 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.6.0-py2.py3-none-any.whl (5.3 MB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting platformdirs<3,>=2
Downloading platformdirs-2.1.0-py3-none-any.whl (13 kB)
Collecting backports.entry-points-selectable>=1.0.4
Downloading backports.entry_points_selectable-1.1.0-py2.py3-none-any.whl (6.2 kB)
Installing collected packages: six, platformdirs, filelock, distlib, backports.entry-points-selectable, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed backports.entry-points-selectable-1.1.0 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.11 nodeenv-1.6.0 platformdirs-2.1.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.6.0
```
### stderr:
```Shell
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.6/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
updating 21.6b0 -> 21.7b0.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/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.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.21.0 -> v2.23.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
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/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[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/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
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......................................................................................Passed
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 file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html
It would be nice to show the pandas version this documentation is related to.
The version string could be placed somewhere in the head, e.g. beside the horizontal menu bar.
The Python docs does this. There you can see everytime the current version.
Python also offers a drop down menu to select a specific docs version. I think this is overhead and not needed for pandas. | {
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] | closed | false | null | [] | null | 0 | "2021-07-26T12:20:47Z" | "2021-07-27T05:07:29Z" | "2021-07-27T05:07:29Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
I've asked on StackOverflow on this [thread](https://stackoverflow.com/questions/68528557/how-come-using-pandass-rolling-with-window-of-timestamp-does-not-return-the-num).
I wish to understand how does rolling works on time intervals given the timestamp is the same.
Let's say, I have 100 records on the same timestamp, for instance '2021-05-11 16:12:57', and my rolling windows period is '3s', shouldn't all the records having the same result as its having the same timestamp?
--- the same as of stackoverflow ---
Input:
```
ACCEL_X ACCEL_Y ACCEL_Z
DATETIME
2021-05-11 16:12:56 160 32 16392
2021-05-11 16:12:57 20 -192 16548
2021-05-11 16:12:57 128 224 16212
2021-05-11 16:12:57 -148 -132 16624
2021-05-11 16:12:57 -40 204 16132
2021-05-11 16:12:57 72 -132 16536
2021-05-11 16:12:57 220 48 16292
2021-05-11 16:12:57 -132 236 16332
2021-05-11 16:12:57 -232 -132 16628
2021-05-11 16:12:57 192 140 16172
2021-05-11 16:12:57 200 -96 16684
2021-05-11 16:12:57 0 64 16020
2021-05-11 16:12:57 -144 -24 16524
2021-05-11 16:12:57 -160 24 16336
2021-05-11 16:12:57 96 56 16252
2021-05-11 16:12:57 68 -44 16544
2021-05-11 16:12:57 12 76 16308
2021-05-11 16:12:57 -228 -132 16668
2021-05-11 16:12:57 72 -96 16244
2021-05-11 16:12:57 48 -96 16536
df = df.rolling('3s').mean()
df
```
returns
```
ACCEL_X ACCEL_Y ACCEL_Z
DATETIME
2021-05-11 16:12:56 160.000000 32.000000 16392.000000
2021-05-11 16:12:57 90.000000 -80.000000 16470.000000
2021-05-11 16:12:57 102.666667 21.333333 16384.000000
2021-05-11 16:12:57 40.000000 -17.000000 16444.000000
2021-05-11 16:12:57 24.000000 27.200000 16381.600000
2021-05-11 16:12:57 32.000000 0.666667 16407.333333
2021-05-11 16:12:57 58.857143 7.428571 16390.857143
2021-05-11 16:12:57 35.000000 36.000000 16383.500000
2021-05-11 16:12:57 5.333333 17.333333 16410.666667
2021-05-11 16:12:57 24.000000 29.600000 16386.800000
2021-05-11 16:12:57 40.000000 18.181818 16413.818182
2021-05-11 16:12:57 36.666667 22.000000 16381.000000
2021-05-11 16:12:57 22.769231 18.461538 16392.000000
2021-05-11 16:12:57 9.714286 18.857143 16388.000000
2021-05-11 16:12:57 15.466667 21.333333 16378.933333
2021-05-11 16:12:57 18.750000 17.250000 16389.250000
2021-05-11 16:12:57 18.352941 20.705882 16384.470588
2021-05-11 16:12:57 4.666667 12.222222 16400.222222
2021-05-11 16:12:57 8.210526 6.526316 16392.000000
2021-05-11 16:12:57 10.200000 1.400000 16399.200000
```
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} | 1 | "2021-07-26T14:26:49Z" | "2021-07-28T01:02:28Z" | "2021-07-28T01:02:24Z" | CONTRIBUTOR | null | - [x] closes #26779
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This PR adds a test for the following issue: #26779 | {
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} | 2 | "2021-07-26T14:42:12Z" | "2021-07-28T22:49:27Z" | "2021-07-28T22:49:27Z" | NONE | null | This question (I am not sure if this is a bug) is still on StackOverflow
https://stackoverflow.com/q/68526846/4865723
I was asked there to open an Issue about it here.
The result of `.agg(list, axis=1)` changed since pandas Version 1.3.0. The goal of my question is to understand what changed and why; and of course how to solve this.
#!/usr/bin/env python3
import pandas as pd
import numpy as np
print(pd.__version__)
df = pd.DataFrame(
{
'PERSON': ['Maya', 'Maya', 'Jim', 'Jim'],
'DAY': ['2016-01-14', '2016-01-14', '2016-02-21', '2016-02-21'],
'FOO': [12, 12, 9, 7],
'BAR': range(4)
}
)
print(df)
res = df.loc[:, ['FOO', 'BAR']].agg(list, axis=1)
print(res)
This is the result with the last pre-1.30 version of Pandas. The two selected columns are "joined" into a list.
1.2.5
PERSON DAY FOO BAR
0 Maya 2016-01-14 12 0
1 Maya 2016-01-14 12 1
2 Jim 2016-02-21 9 2
3 Jim 2016-02-21 7 3
0 [12, 0]
1 [12, 1]
2 [9, 2]
3 [7, 3]
dtype: object
>>>
But since pandas 1.3.0 the result is.
FOO BAR
0 12 0
1 12 1
2 9 2
3 7 3
I looked into the [changelog of pandas 1.3.0][1]. There is nothing about `agg()` but a lot about `apply()` and `transform()`. But I do not understand the details and I can see which one of this points is related to my situation.
I am sure the pandas devs have a good reason to change this behavior. When I understand the background of that decision I am maybe able to find a solution for it.
Sideinfo: In the final productive code I like to do things like this
df['NEW_COLUMN'] = df.loc[:, ['FOO', 'BAR']].agg(list, axis=1)
[1]: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.3.0.html# | {
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} | 0 | "2021-07-26T14:55:02Z" | "2021-07-26T22:41:05Z" | "2021-07-26T22:37:51Z" | 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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} | 1 | "2021-07-26T14:56:21Z" | "2021-07-26T18:48:09Z" | "2021-07-26T18:47:32Z" | MEMBER | null | move release note PR #42644
no backport required. | {
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} | 6 | "2021-07-26T16:07:36Z" | "2021-09-06T15:17:35Z" | "2021-09-06T15:17:35Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series(index=[pd.Timestamp(2021,7,26)], name=('A', 1))
s.groupby(s==s) # raises InvalidIndexError
```
#### Problem description
There are several things necessary to trigger this:
- The index has to consist of `pd.Timestamp`s. If you change the example to `s = pd.Series(index=[0,1], name=('A', 1))` everything is fine.
- The grouper, i.e. `groupby`'s `by` argument , has to be a `Series` named with a tuple (like you get if you select a `MultiIndex`-column from a `DataFrame`). If you change the example to `s.groupby((s==s).rename('foo'))` everything is fine. Same goes if you use a pure list or a function.
- The object to group must be a `Series`. If you change the example to `pd.DataFrame(s).groupby(s==s)` everything is fine.
- You need `pandas>=1.1.3`. If you downgrade to an older version - guess what - everything is fine.
Why this is a problem: The name of a grouper should not matter and it is just not to understand why a time series behaves differently than any other kind of `Series`.
#### Expected Output
Behavior should be as for non-time-series.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.1.3
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
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 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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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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} | 11 | "2021-07-26T16:38:50Z" | "2021-08-12T10:33:36Z" | "2021-08-12T10:33:36Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
```sh
pip install pandas==1.3.1
```
```python
import pandas as pd
```
#### Problem description
After upgrading from `v1.3.0` to patch fix `v1.3.1` we began receiving Import Errors on all of our scripts. We were able to resolve this by installing [Visual Studio C++ Build Tools](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) on our Windows Servers.
- 2019 Windows Server
- Python 3.7
### Output `` ImportError: DLL load failed``
<details>
```
import pandas._libs.window.aggregations as window_aggregations
ImportError: DLL load failed: The specified module could not be found.
```
</details>
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} | 1 | "2021-07-26T17:29:39Z" | "2021-07-27T22:57:10Z" | "2021-07-26T22:31:53Z" | 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
Oversight from my other PR syncing 3.9 builds | {
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} | 2 | "2021-07-26T19:45:00Z" | "2021-07-26T22:30:45Z" | "2021-07-26T22:29:04Z" | MEMBER | null | - [x] closes #42456
- [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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} | 5 | "2021-07-26T20:02:28Z" | "2021-08-25T00:39:35Z" | "2021-08-25T00:39:04Z" | MEMBER | null | @mroeschke looking at BaseWindow._apply we're spending a lot of time in apply_along_axis in our asvs (results posted below). IIUC whats happening is that homogeneous_func is passed to BlockManager.apply thereby iterating over Blocks, and then within that call it iterates over columns.
My intuition is that we should either iterate over blocks or columns but not both. Is there a reason to do both?
Not sure about UDFs, but for mean/sum/... it seems like we'd have to edit the cython functions to iterate over columns there, right?
```
from asv_bench.benchmarks.rolling import *
self = Engine()
self.setup("DataFrame", "float", sum, "cython", "mean")
%prun -s cumtime for n in range(1000): self.time_rolling_methods('DataFrame', 'float', sum, 'cython', 'mean')
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.325 0.325 {built-in method builtins.exec}
1 0.001 0.001 0.325 0.325 <string>:1(<module>)
1000 0.003 0.000 0.324 0.000 rolling.py:68(time_rolling_methods)
1000 0.002 0.000 0.302 0.000 rolling.py:1802(mean)
1000 0.002 0.000 0.299 0.000 rolling.py:1291(mean)
1000 0.002 0.000 0.295 0.000 rolling.py:479(_apply)
1000 0.007 0.000 0.291 0.000 rolling.py:408(_apply_blockwise)
1000 0.003 0.000 0.268 0.000 managers.py:276(apply)
1000 0.003 0.000 0.204 0.000 blocks.py:364(apply)
1000 0.002 0.000 0.165 0.000 rolling.py:425(hfunc)
1000 0.004 0.000 0.149 0.000 rolling.py:514(homogeneous_func)
14000/3000 0.013 0.000 0.140 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
1000 0.001 0.000 0.136 0.000 <__array_function__ internals>:2(apply_along_axis)
1000 0.015 0.000 0.134 0.000 shape_base.py:267(apply_along_axis)
1000 0.002 0.000 0.074 0.000 rolling.py:520(calc)
1000 0.010 0.000 0.058 0.000 managers.py:539(_combine)
1000 0.007 0.000 0.050 0.000 indexers.py:73(get_window_bounds)
# With ArrayManager so we only iterate over columns:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.198 0.198 {built-in method builtins.exec}
1 0.001 0.001 0.198 0.198 <string>:1(<module>)
1000 0.003 0.000 0.197 0.000 rolling.py:68(time_rolling_methods)
1000 0.002 0.000 0.174 0.000 rolling.py:1802(mean)
1000 0.002 0.000 0.171 0.000 rolling.py:1291(mean)
1000 0.002 0.000 0.167 0.000 rolling.py:479(_apply)
1000 0.007 0.000 0.163 0.000 rolling.py:408(_apply_blockwise)
1000 0.005 0.000 0.139 0.000 array_manager.py:194(apply)
1000 0.002 0.000 0.100 0.000 rolling.py:425(hfunc)
1000 0.004 0.000 0.086 0.000 rolling.py:514(homogeneous_func)
1000 0.002 0.000 0.071 0.000 rolling.py:520(calc)
1000 0.006 0.000 0.047 0.000 indexers.py:73(get_window_bounds)
2000 0.001 0.000 0.039 0.000 <__array_function__ internals>:2(clip)
7000/3000 0.003 0.000 0.038 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
2000 0.002 0.000 0.036 0.000 fromnumeric.py:2046(clip)
```
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] | open | false | null | [] | null | 0 | "2021-07-26T20:04:21Z" | "2021-08-21T20:09:46Z" | null | NONE | null | When dealing with regular series, one can do :
```python
import numpy as np
import pandas as pd
s = pd.Series(np.arange(10))
s.nsmallest(1) # returns a series contaning "0" as expected
```
When using an extension array (user-defined in my case), calling the `.nsmallest` method raises a TypeError (full message below):
```python
import numpy as np
import physipandas
from physipy import m
import pandas as pd
sq = pd.Series(np.arange(10)*m, dtype='physipy[m]')
sq.nsmallest(1)
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/5k/bf4syt7x1zjbhc6b28srzzym0000gn/T/ipykernel_72417/2048250381.py in <module>
4 import pandas as pd
5 sq = pd.Series(np.arange(10)*m, dtype='physipy[m]')
----> 6 sq.nsmallest(1)
/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py in nsmallest(self, n, keep)
3861 dtype: int64
3862 """
-> 3863 return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest()
3864
3865 @doc(
/opt/anaconda3/lib/python3.8/site-packages/pandas/core/algorithms.py in nsmallest(self)
1220
1221 def nsmallest(self):
-> 1222 return self.compute("nsmallest")
1223
1224 @staticmethod
/opt/anaconda3/lib/python3.8/site-packages/pandas/core/algorithms.py in compute(self, method)
1253 dtype = self.obj.dtype
1254 if not self.is_valid_dtype_n_method(dtype):
-> 1255 raise TypeError(f"Cannot use method '{method}' with dtype {dtype}")
1256
1257 if n <= 0:
TypeError: Cannot use method 'nsmallest' with dtype physipy[m]
```
This seems to happen because the extension dtype is not "registered" to `is_valid_dtype_n_method`.
Would it be feasable to support `nsmallest`/`nlargest` for extensions ?
pandas version : 1.3.0. | {
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} | 0 | "2021-07-26T20:04:27Z" | "2021-08-10T18:59:43Z" | "2021-08-10T18:59:43Z" | MEMBER | null | In BaseWindow._apply we call mgr.apply with ignore_failures=True, which causes us to silently drop columns for which the function is invalid. We have deprecated this pattern for DataFrame reductions and GroupBy reductions, should deprecate it here too. | {
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] | open | false | null | [] | null | 3 | "2021-07-26T20:22:44Z" | "2021-08-21T20:10:19Z" | 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 numpy as np
import pandas as pd
import numpy.ma as ma
# create a masked, structured array
a = np.ma.array([(1, 2.2), (42, 5.5)],
dtype=[('a',int),('b',float)],
mask=[(True,False),(False,True)])
b = pd.DataFrame(a)
```
#### Problem description
This is somewhere between a bug report and a feature request? The above code returns this error:
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-40-a4c5236a3cd4> in <module>
----> 1 pd.DataFrame(a)
/usr/local/anaconda/lib/python3.8/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
636 # a masked array
637 else:
--> 638 data = sanitize_masked_array(data)
639 mgr = ndarray_to_mgr(
640 data,
/usr/local/anaconda/lib/python3.8/site-packages/pandas/core/construction.py in sanitize_masked_array(data)
452 """
453 mask = ma.getmaskarray(data)
--> 454 if mask.any():
455 data, fill_value = maybe_upcast(data, copy=True)
456 data.soften_mask() # set hardmask False if it was True
/usr/local/anaconda/lib/python3.8/site-packages/numpy/core/_methods.py in _any(a, axis, dtype, out, keepdims, where)
54 # Parsing keyword arguments is currently fairly slow, so avoid it for now
55 if where is True:
---> 56 return umr_any(a, axis, dtype, out, keepdims)
57 return umr_any(a, axis, dtype, out, keepdims, where=where)
58
TypeError: cannot perform reduce with flexible type
```
The [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=masked%20array#from-a-list-of-dataclasses) notes that:
> Alternatively, you may pass a numpy.MaskedArray as the data argument to the DataFrame constructor, and its masked entries will be considered missing.
The above works if `dtype` is omitted; all values are "upgraded" to floats.
#### Expected Output
Several possibilities:
* This is expected behavior. If so, it would be nice to have the documentation linked above note that masked structured NumPy arrays are not supported. The code should also raise the appropriate exception informing the user these arrays are not supported (e.g. `ValueError`?).
* This functionality is supported. If so, this appears to be a bug.
* Functionality is supported, but with extra steps. It seems as though this conversion is possible with these steps.
* Use [numpy.ma.MaskedArray.filled](https://numpy.org/devdocs/reference/generated/numpy.ma.MaskedArray.filled.html) and fill the NumPy masked array with "special" (e.g. 1e20, 99999) values based on data type.
* Convert the array to a `DataFrame`.
* Convert any integer series to the nullable int type `Int64`.
* Replace the same filled values to appropriate NULL values, e.g. `NaN`, `pd.NA`.
Options to address the latter case can be either supported in code or else documentation.
This issue was discussed on StackOverflow [here](https://stackoverflow.com/questions/68532200/creating-a-pandas-dataframe-from-a-numpy-masked-array/68534675#68534675) (my question).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.8.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 : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.21
tables : 3.6.1
tabulate : None
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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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
```
dti = pd.date_range('2016-01-01', periods=3).repeat(2)
ser = pd.Series(dti)
>>> ser.unique()
<stdin>:1: FutureWarning: Series.unique behavior with datetime64[ns] dtype is deprecated. In a future version this will return a <class 'pandas.core.arrays.datetimes.DatetimeArray'> instead of a np.ndarray
array(['2016-01-01T00:00:00.000000000', '2016-01-02T00:00:00.000000000',
'2016-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
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} | 3 | "2021-07-27T03:17:13Z" | "2021-07-28T18:06:19Z" | "2021-07-28T01:27:00Z" | MEMBER | null | This is preparatory to making get_cythonized_result operate blockwise, which should improve perf a decent amount | {
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} | 4 | "2021-07-27T05:38:16Z" | "2021-11-12T14:57:18Z" | "2021-11-12T14:57:14Z" | CONTRIBUTOR | null | - [x] closes #42688
- [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
Null-like values are no longer mangled in `value_counts`.
This was overlooked in #22296, as decision was made not to mangle null-like values (np.nan, None, pd.NaT and so on).
It also has impact on `mode`, as it uses `value_counts` under the hood. | {
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Overall the docs should be able to support browser theme selected by user. Or the user should be able to switch the theme as per preference.
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
It would be nice to support the dark mode with toggle button on top header.
#### Suggested fix for documentation
https://github.com/MrDogeBro/sphinx_rtd_dark_mode (there are several sphinx plugin to do, this is just an example.)
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} | 5 | "2021-07-27T06:26:35Z" | "2021-08-27T13:51:09Z" | "2021-08-27T13:51:09Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Docker image file
```Dockerfile
FROM golang:1.16
RUN apt-get update \
&& DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
python3 \
python3-pip
COPY requirements.txt /requirements.txt
RUN python3 -m pip install -r /requirements.txt
```
Requirments file
```
pandas == 1.3.1
```
Command to run
```sh
docker build --file Dockerfile --tag test-image:latest .
```
#### Problem description
We have been using a similar docker image(retracted the unrelated parts) with pandas version `1.3.0` so far with no problems.
When the version changed to `1.3.1` we are getting compile errors, and since the only variable that changed is pandas version, I have to assume it's problem with that.
Dropping the version back to `1.3.0` fixes the problem and everything works correctly.
#### Expected Output
Expected no output except the fact that install was a success
Actually got
```
---RETRACTED A LOT OF COPYING OPERATIONS---
#8 187.7 copying pandas/_libs/tslibs/src/datetime/np_datetime_strings.h -> build/lib.linux-x86_64-3.7/pandas/_libs/tslibs/src/datetime
#8 187.7 UPDATING build/lib.linux-x86_64-3.7/pandas/_version.py
#8 187.7 set build/lib.linux-x86_64-3.7/pandas/_version.py to '1.3.1'
#8 187.7 running build_ext
#8 187.7 building 'pandas._libs.algos' extension
#8 187.7 creating build/temp.linux-x86_64-3.7
#8 187.7 creating build/temp.linux-x86_64-3.7/pandas
#8 187.7 creating build/temp.linux-x86_64-3.7/pandas/_libs#8 187.7 x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -W
#8 187.7 x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNPY_NO_DEPRECATED_API=0 -I./pandas/_libs -Ipandas/_libs/src/klib -I/tmp/pip-build-env-ailjqcbp/lib/python3.7/site-packages/numpy/core/include -I/usr/include/python3.7m -c pandas/_libs/algos.c -o build/temp.linux-x86_64-3.7/pandas/_libs/algos.o
#8 187.7 pandas/_libs/algos.c:42:10: fatal error: Python.h: No such file or directory
#8 187.7 #include "Python.h"
#8 187.7 ^~~~~~~~~~
#8 187.7 compilation terminated.
#8 187.7 error: command 'x86_64-linux-gnu-gcc' failed with exit status 1
#8 187.7
#8 187.7 ----------------------------------------
#8 187.9 Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-quz7o69n/pandas/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-ygkh2jru/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-install-quz7o69n/pandas/
------
executor failed running [/bin/sh -c python3 -m pip install -r /requirements.txt]: exit code: 1
````
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} | 4 | "2021-07-27T06:30:20Z" | "2021-07-28T01:03:56Z" | "2021-07-28T01:03:52Z" | CONTRIBUTOR | null | …with a non-unique datetime index.
- [ ] closes GH#27994 pandas-dev/pandas/issues/27994
- [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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} | 4 | "2021-07-27T09:22:31Z" | "2021-11-18T21:38:16Z" | 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({"datetime": pd.date_range("1/1/1", periods=20, freq="H", tz="UTC")})
df.set_index("datetime", inplace=True)
df.index.freq --> `None`
```
#### Problem description
This behavior is new in `1.3.0` and `1.3.1`.
In `1.2.5`, the output was, as expected, `H`
#### Expected Output
```
'H'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.4.0
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 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 13 | "2021-07-27T12:05:42Z" | "2021-10-26T10:02:58Z" | "2021-10-24T14:45:42Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
tmp = pd.DataFrame(
{'col_name': [1,2,3,4]}
)
import yaml
dumped = yaml.dump(tmp) # Fails starting with pandas==1.3.0
print(dumped)
```
#### Problem description
Another package, [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning), has the option to save all given hyperparameters. They yaml all given parameters, which resulted in the kind of ugly output shown in the expected output for dataframes. Starting with `pandas==1.3.0` this breaks because of the MWE shown above.
If this is expected behaviour from pandas I am sorry, then the issue should go to pytorch-lightning probably.
#### Expected Output
<details>
!!python/object:pandas.core.frame.DataFrame
_flags:
allows_duplicate_labels: true
_metadata: []
_mgr: !!python/object/new:pandas.core.internals.managers.BlockManager
state: !!python/tuple
- &id004
- !!python/object/apply:pandas.core.indexes.base._new_Index
- &id002 !!python/name:pandas.core.indexes.base.Index ''
- data: !!python/object/apply:numpy.core.multiarray._reconstruct
args:
- &id001 !!python/name:numpy.ndarray ''
- !!python/tuple
- 0
- !!binary |
Yg==
state: !!python/tuple
- 1
- !!python/tuple
- 1
- &id003 !!python/object/apply:numpy.dtype
args:
- O8
- false
- true
state: !!python/tuple
- 3
- '|'
- null
- null
- null
- -1
- -1
- 63
- false
- - col_name
name: null
- !!python/object/apply:pandas.core.indexes.base._new_Index
- !!python/name:pandas.core.indexes.range.RangeIndex ''
- name: null
start: 0
step: 1
stop: 4
- - &id005 !!python/object/apply:numpy.core.multiarray._reconstruct
args:
- *id001
- !!python/tuple
- 0
- !!binary |
Yg==
state: !!python/tuple
- 1
- !!python/tuple
- 1
- 4
- !!python/object/apply:numpy.dtype
args:
- i8
- false
- true
state: !!python/tuple
- 3
- <
- null
- null
- null
- -1
- -1
- 0
- false
- !!binary |
AQAAAAAAAAACAAAAAAAAAAMAAAAAAAAABAAAAAAAAAA=
- - !!python/object/apply:pandas.core.indexes.base._new_Index
- *id002
- data: !!python/object/apply:numpy.core.multiarray._reconstruct
args:
- *id001
- !!python/tuple
- 0
- !!binary |
Yg==
state: !!python/tuple
- 1
- !!python/tuple
- 1
- *id003
- false
- - col_name
name: null
- 0.14.1:
axes: *id004
blocks:
- mgr_locs: !!python/object/apply:builtins.slice
- 0
- 1
- 1
values: *id005
_typ: dataframe
attrs: {}
</details>
#### Output of ``pd.show_versions()``
`pandas 1.2.5`:
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.2.5
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
`pandas 1.3.1`:
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
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] | open | false | null | [] | null | 0 | "2021-07-27T14:37:54Z" | "2021-08-21T20:12:11Z" | 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
# Prepare data, df_multi is multiple symbols
df_multi = pd.DataFrame(
{'time': [1,2,1,2],
'symbol': ['a','a','b','b'],
'value1': [10, 12, 20, 21],
'value2': [20, 22, 40, 41]
}
).set_index(['time', 'symbol'])
df_single = df_multi[df_multi.index.get_level_values('symbol') == 'a'].copy()
res_multi = df_multi.groupby(level = 'symbol').apply(lambda x: x['value1']) # we are applying trivial function here, but goal is here can be complex calculation
print(res_multi)
res_single = df_single.groupby(level = 'symbol').apply(lambda x: x['value1'])
print(res_single)
```
#### Problem description
res_multi and res_single have inconsistent shapes.
Output in res_multi is correct, output in res_single have index level[0] in columns instead on in index.
Output seems to be consistent, after calling something like (but I am not sure how general it is..): res_single.swapaxis(level = 1).unstack()
So something like this is possible fix.
(I believe it should be handled in pandas, because otherwise all downstream code have to handle single group case separately.)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.13.5-xanmod1
Version : #0~git20210725.ec5a2ac SMP PREEMPT Sun Jul 25 17:24:47 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: 0.10.0
bs4 : None
bottleneck : 1.3.2
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 : 1.7.0
sqlalchemy : 1.4.19
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 4 | "2021-07-27T14:57:55Z" | "2021-08-04T02:11:19Z" | "2021-08-04T02:11:19Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df_ok = pd.DataFrame(dict(a=[np.nan, -1, 1], b=[np.nan, -1, 1]))
df_bug = pd.DataFrame(dict(a=[pd.NA, -1, 1], b=[pd.NA, -1, 1]))
```
#### Problem description
`df_ok.style.highlight_min()` works correctly while `df_bug.style.highlight_min()` returns the following error:
```
TypeError: boolean value of NA is ambiguous
```
#### Fix
For `highlight_min`, the error comes from https://github.com/pandas-dev/pandas/blob/v1.3.1/pandas/io/formats/style.py#L2367. Specifically, from the following line:
```
np.where(data == np.nanmin(data.to_numpy()), props, "")
```
I'm assuming this could be fixed by replacing it with:
```
np.where(data == data.min(skipna=True), props, "")
```
or by making sure ```to_numpy``` converts `pd.NA` to `np.nan`?
Happy to implement a fix.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.3.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 : en_US.UTF-8
LANG : en_GB.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
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 : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-07-27T15:20:48Z" | "2021-08-21T20:12:54Z" | 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.
---
#### Code Sample, a copy-pastable example
```python
s1 = pd.Series([pd.NA, -1], dtype='Int64')
s2 = pd.Series([pd.NA, -1]) # the dtype is now object
```
#### Problem description
`s1.fillna(np.nan)` and `s2.fillna(np.nan)` return different series. Specifically, the first one keeps the `pd.NA` cell untouched and does not replace it with `np.nan`. Replacing with any other value that `np.nan` does not trigger this bug.
#### Expected Output
```
> s2.fillna(np.nan)
0 NaN
1 -1.0
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-210-generic
Version : #242-Ubuntu SMP Fri Apr 16 09:57:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.2
setuptools : 50.3.0
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-07-27T18:00:17Z" | "2021-07-28T01:10:28Z" | "2021-07-28T01:09:32Z" | CONTRIBUTOR | null | The following commit fixes the doctest in _parse_latex_table_styles.
- [X] closes #42674
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} | 3 | "2021-07-27T19:21:00Z" | "2021-08-07T23:15:05Z" | "2021-08-07T23:15:05Z" | 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
def rolling_test(window, df=None, center=True):
if df is None:
df = pd.DataFrame(
{"x": 1},
index=[pd.Timestamp("20130101 09:00:01"),
pd.Timestamp("20130101 09:00:02")
]
)
df["right"] = df.rolling(window, closed="right", center=center).x.sum()
df["both"] = df.rolling(window, closed="both", center=center).x.sum()
df["left"] = df.rolling(window, closed="left", center=center).x.sum()
df["neither"] = df.rolling(window, closed="neither", center=center).x.sum()
return df
rolling_test('1T')
```
#### Problem description
When `center=True` and the offset is large enough to contain all of the rows, the first output row of rolling aggregates is wrong most of the time. This can happen for all possible values of the `closed` parameter. For the sample code the observed wrong results are `1` when `closed` is `right` or `both` and `NaN` when `closed` is `left` or `neither`. Here are some examples:
```
x right both left neither
2013-01-01 09:00:01 1 1.0 1.0 NaN NaN
2013-01-01 09:00:02 1 2.0 2.0 2.0 2.0
x right both left neither
2013-01-01 09:00:01 1 2.0 2.0 2.0 NaN
2013-01-01 09:00:02 1 2.0 2.0 2.0 2.0
x right both left neither
2013-01-01 09:00:01 1 1.0 2.0 2.0 2.0
2013-01-01 09:00:02 1 2.0 2.0 2.0 2.0
```
I have tested with `sum` and `count` aggregates and the problem seems to be that the first row is taken by itself. The same behavior is present on 1.3.1 and on master (b8d750fb90c68d4af25af89442aba001483adffa).
#### Expected Output
The code sample uses an offset of `1T` and the data only has two seconds worth of data points so any aggregate should include all the rows. Sometimes I get the correct result:
```
x right both left neither
2013-01-01 09:00:01 1 2.0 2.0 2.0 2.0
2013-01-01 09:00:02 1 2.0 2.0 2.0 2.0
```
Even if I were wrong about the expected result, there is the fact that the result vary from one moment to the next.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b8d750fb90c68d4af25af89442aba001483adffa
python : 3.9.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu May 6 00:48:39 PDT 2021; root:xnu-6153.141.33~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+305.gb8d750fb90
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 54.2.0
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 : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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```
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.12.0, library is 1.12.1
```
followed by a segfault.
This is likely an upstream issue, but parking this here for now. | {
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} | 12 | "2021-07-27T22:01:58Z" | "2021-07-29T20:26:49Z" | "2021-07-29T20:26:49Z" | 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.
---
#### Code Sample
```python
import pandas as pd
data = {"column_a": [5, 10, 15], "column_b": ["five", "ten", "fifteen"]}
index = [0, 1, 2]
frame = pd.DataFrame(data, index=index)
frame.drop(tuple([0, 1]))
```
#### Problem description
DataFrame.drop (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html) should take a parameter 'labels' of 'single label or list-like' for the 'Index or column labels to drop.'
According to documentation for api.types.is_list_like (https://pandas.pydata.org/docs/reference/api/pandas.api.types.is_list_like.html) "Objects that are considered list-like are for example Python lists, tuples, sets, NumPy arrays, and Pandas Series."
We should be able to drop using a Tuple of Index labels. However, this leads to a 'KeyError' indicating the the entire tuple is not found as a value in the Index.
#### Expected Output
I expect this should return a DataFrame with the dropped Indexes - the same as if dropping with a List, Set, Array or Series of [0, 1].
```python
frame.drop(tuple([0, 1])) == frame.drop([0, 1])
```
```
expected_data = {"column_a": [15], "column_b": ["fifteen"]}
expected_index = [2]
expected_output = pd.DataFrame(data, index=index)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 71f01a885f51544b4d9f388162d0476686061175
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Apr 12 20:57:45 PDT 2021; root:xnu-6153.141.28.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+310.g71f01a885f
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.14.4
sphinx : 4.1.2
blosc : 1.10.4
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 : 2021.05.0
scipy : 1.7.0
sqlalchemy : 1.4.22
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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} | 1 | "2021-07-27T23:19:33Z" | "2021-07-28T00:25:02Z" | "2021-07-28T00:06:46Z" | MEMBER | null | Some perf-neutral cleanups to simplify an eventual `nancorr` perf improvement.
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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
DO NOT MERGE.
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} | 5 | "2021-07-28T01:34:22Z" | "2021-07-31T06:38:46Z" | "2021-07-31T06:38:46Z" | CONTRIBUTOR | null | # tl;dr
Fix the numerous doctests that are giving errors in the style.py file:
```
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.apply
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.applymap
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.background_gradient
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.hide_columns
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.hide_index
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.highlight_between
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.highlight_quantile
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.pipe
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.set_properties
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.set_table_attributes
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.set_table_styles
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.set_td_classes
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.set_tooltips
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.text_gradient
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.to_latex
FAILED pandas/io/formats/style.py::pandas.io.formats.style.Styler.where
======================================================================== 16 failed, 1 passed, 1 warning in 0.76s ========================================================================
```
# Detailed instructions
Each of the doctests failed for different reasons. You can see the reason for each of them by running the command below:
```
python -m pytest --doctest-modules pandas/io/formats/style.py
```
In general, the above errors can be fixed with things like:
* Fixing a typo (a missing comma, a misspelled variable name...)
* Adding an object that hasn't been defined (like, if ``df`` is used, but
no sample dataset ``df`` has been first defined)
* Fixing the expected output, when it's wrong
* In exceptional cases, examples shouldn't run, since they can't work.
For example, a function that connects to a private webservice. In
such cases, we can add ``# doctest: +SKIP`` at the end of the lines
that should not run
* In some cases, as it is not possible to display the result as it may
involve the styling of a table, it will be necessary to skip the
test (ensuring that it is happening correctly) or use the tag ``# doctest: +ELLIPSIS``
# Observation
In some cases it'll be necessary to use ``# doctest: +SKIP`` and later in another use or in this same issue, add images for some results that cannot be displayed, such as stylizations and color addition (this was dealt with in another [issue](https://github.com/pandas-dev/pandas/issues/42674#issuecomment-887067302)). | {
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} | 3 | "2021-07-28T01:46:20Z" | "2021-08-05T11:52:43Z" | "2021-08-05T11:52:43Z" | CONTRIBUTOR | null | #### Location of the documentation
[`.to_hdf()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_hdf.html)
#### Documentation problem
They keyword argument default is `complevel=None`. The documentation says **complevel {0-9}, optional**. It seems like this parameter is passed to pytables directly, however, a little bit of direction from pandas would be nice for users here.
#### Suggested fix for documentation
Could include a pointer to [this documentation](http://www.pytables.org/usersguide/optimization.html#compression-issues) from pytables, but more would be nice. | {
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} | 1 | "2021-07-28T01:58:54Z" | "2021-07-31T01:25:02Z" | "2021-07-31T00:59:44Z" | MEMBER | null | - [x] xref #40956
- [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
Benchmarks:
```
before after ratio
[e64fcfa7] [c9a99585]
<master> <nancorr_perf>
52.3M 52.1M 1.00 stat_ops.Correlation.peakmem_corr_wide('pearson')
- 307±5μs 217±10μs 0.71 stat_ops.Correlation.time_corr('pearson')
132±10μs 138±10μs 1.05 stat_ops.Correlation.time_corr_series('pearson')
- 7.74±0.6ms 2.80±0.2ms 0.36 stat_ops.Correlation.time_corr_wide('pearson')
9.04±0.7ms 8.22±0.8ms 0.91 stat_ops.Correlation.time_corr_wide_nans('pearson')
3.92±0.2ms 4.00±0.3ms 1.02 stat_ops.Correlation.time_corrwith_cols('pearson')
6.99±0.2ms 7.62±0.5ms 1.09 stat_ops.Correlation.time_corrwith_rows('pearson')
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42762/events | https://github.com/pandas-dev/pandas/pull/42762 | 954,436,068 | MDExOlB1bGxSZXF1ZXN0Njk4MzMyMDEw | 42,762 | REG: DataFrame.agg where func returns lists and axis=1 | {
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} | 3 | "2021-07-28T03:25:00Z" | "2021-10-19T03:46:09Z" | "2021-07-28T22:49:27Z" | MEMBER | null | - [x] closes #42727
- [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 reverts #40428. Once agg/apply paths are sorted out so that agg doesn't fall back to apply, we can redo #40428. See https://github.com/pandas-dev/pandas/issues/42727#issuecomment-887191683 for more details. | {
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] | open | false | null | [] | null | 3 | "2021-07-28T03:43:51Z" | "2021-08-21T20:15:32Z" | null | 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# Example-1:
s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
freq='A',
periods=2))
print(s)
# 2012 1
# 2013 2
# Freq: A-DEC, dtype: int64
print(s.resample('Q', kind='period', convention='start').count())
# 2012Q1 1.0
# 2012Q2 NaN
# 2012Q3 NaN
# 2012Q4 NaN
# 2013Q1 2.0
# 2013Q2 NaN
# 2013Q3 NaN
# 2013Q4 NaN
# Example-2: the result is all NaN, but the data is actually grouped
idx = pd.period_range(start='1/10/2000', periods=2, freq='D')
series = pd.Series(range(2,4), index=idx)
print(series.resample('12H', convention='end').count())
# 2000-01-10 12:00 NaN
# 2000-01-11 00:00 NaN
# 2000-01-11 12:00 NaN
print(series.resample('12H', convention='start').count())
# 2000-01-10 00:00 2.0
# 2000-01-10 12:00 NaN
# 2000-01-11 00:00 3.0
# 2000-01-11 12:00 NaN
# Freq: 12H, dtype: float64
for idx, ss in rs:
if ss.empty == False: print(ss)
# 2000-01-10 2
# Freq: D, dtype: int64
# 2000-01-11 3
# Freq: D, dtype: int64
```
#### Problem description
Try to up-resample a time series with `PeriodIndex` as the index of the series, and then count the number of records in each time-group. However, the output is not the "count", but it seems to be the only record's value in the group.
In the second example with `convention='end'`, the result is all `NaN`. If switches to 'convention='start'', then the result is similar to that of the Example-1. In addition, from the grouped details, we can see that the data has been correctly grouped, while the aggregate functions (e.g., `count`) behave unexpected.
#### Expected Output
The number of samples for each group (call the `count()` method).
As a comparison of Example-1, I make another example, which tries to up-sample a time series with `Timestamp` as index. It behaves as expected.
```python
s = pd.Series([1, 2], index=pd.date_range('2012-01-01',
freq='A',
periods=2))
display(s)
# 2012-12-31 1
# 2013-12-31 2
# Freq: A-DEC, dtype: int64
display(s.resample('Q', convention='start').count())
# 2012-12-31 1
# 2013-03-31 0
# 2013-06-30 0
# 2013-09-30 0
# 2013-12-31 1
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.10.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 : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.1
setuptools : 49.6.0.post20210108
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 : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 1 | "2021-07-28T04:06:36Z" | "2021-08-06T13:25:15Z" | "2021-08-05T23:42:08Z" | CONTRIBUTOR | 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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} | 3 | "2021-07-28T06:21:32Z" | "2021-08-01T15:52:08Z" | "2021-08-01T15:52:08Z" | CONTRIBUTOR | null | Fix the following doctest in style_render.py file:
```
pandas/io/formats/style_render.py F
=================================== FAILURES ===================================
________ [doctest] pandas.io.formats.style_render.StylerRenderer.format ________
647
648 >>> df = pd.DataFrame([[np.nan, 1.0, 'A'], [2.0, np.nan, 3.0]])
649 >>> df.style.format(na_rep='MISS', precision=3) # doctest: +SKIP
650 0 1 2
651 0 MISS 1.000 A
652 1 2.000 MISS 3.000
653
654 Using a ``formatter`` specification on consistent column dtypes
655
656 >>> df.style.format('{:.2f}', na_rep='MISS', subset=[0,1])
Expected:
0 1 2
0 MISS 1.00 A
1 2.00 MISS 3.000000
Got:
<pandas.io.formats.style.Styler object at 0x7f77b214ea30>
/home/aneesh/Desktop/Side Projects/ops/pandas/pandas/io/formats/style_render.py:656: DocTestFailure
- generated xml file: /home/aneesh/Desktop/Side Projects/ops/pandas/test-data.xml -
============================= slowest 30 durations =============================
1.14s call pandas/io/formats/style_render.py::pandas.io.formats.style_render.StylerRenderer.format
(2 durations < 0.005s hidden. Use -vv to show these durations.)
=========================== short test summary info ============================
FAILED pandas/io/formats/style_render.py::pandas.io.formats.style_render.StylerRenderer.format
============================== 1 failed in 1.70s ===============================
```
---
## OBSERVATION
The mentioned test returns a Styler object which will render properly in (for instance, a notebook), but will fail the doctest as it will simply return the object.
## SOLUTION
Add images of output rendered, in the doctest wherever required. (The approach is mentioned https://github.com/pandas-dev/pandas/issues/42674#issuecomment-887075744, for the documentation of [background_gradient()](https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.background_gradient.html)) or add a ```# doctest: +SKIP``` if necessary.
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} | 0 | "2021-07-28T07:43:37Z" | "2021-07-28T15:18:14Z" | "2021-07-28T11:09:04Z" | CONTRIBUTOR | null | `styler_render.py` was prematurely added to doctesting in #42700
We should revert this before the necessary devleopment is done to make the file ready for doctesting (that PR only fixed one example)
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} | 1 | "2021-07-28T08:55:49Z" | "2021-08-11T13:26:05Z" | "2021-08-11T13:26:05Z" | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example
```python
from dataclasses import dataclass
import pandas as pd
@dataclass()
class MyObject:
a: "str"
b: "str"
c: "str"
@dataclass()
class MyObject2:
a: "str"
_b: "str"
c: "str"
o1 = MyObject(a='a', b='b', c='c')
o2 = MyObject2(a='a', _b='b', c='c')
df = pd.DataFrame({"objects":[o1, o2]})
print(df.to_json())
print(pd._libs.json.dumps(o1))
print(pd._libs.json.dumps(o2))
```
Output:
```
{"objects":{"0":{"a":"a","c":"c"},"1":{"a":"a"}}}
{"a":"a","c":"c"}
{"a":"a"}
```
#### Problem description
While the dataclasses have three attributes depending on the naming only one or two show up in the JSON dump.
#### Expected Output
```
{"objects":{"0":{"a":"a","b"="b","c":"c"},"1":{"a":"a","_b"="b","c":"c"}}}
{"a":"a","b"="b","c":"c"}
{"a":"a","_b"="b","c":"c"}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.1.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 : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
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 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : 2021.07.0
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
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```
pandas/core/reshape/tile.py:421: error: No overload variant of "searchsorted" of "ndarray" matches argument types "Any", "str" [call-overload]
pandas/core/reshape/tile.py:421: note: Possible overload variants:
pandas/core/reshape/tile.py:421: note: def searchsorted(self, v: Union[int, float, complex, str, bytes, generic], side: Union[Literal['left'], Literal['right']] = ..., sorter: Union[_SupportsArray[dtype[Union[bool_, integer[Any]]]], Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]], Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]], Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]]], Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]]]], int, Sequence[int], Sequence[Sequence[int]], Sequence[Sequence[Sequence[int]]], Sequence[Sequence[Sequence[Sequence[int]]]], None] = ...) -> signedinteger[Any]
pandas/core/reshape/tile.py:421: note: def searchsorted(self, v: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], Union[Union[_SupportsArray[dtype[Any]], Sequence[_SupportsArray[dtype[Any]]], Sequence[Sequence[_SupportsArray[dtype[Any]]]], Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]], Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[Any]]]]]]], Union[bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]]]], side: Union[Literal['left'], Literal['right']] = ..., sorter: Union[_SupportsArray[dtype[Union[bool_, integer[Any]]]], Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]], Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]], Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]]], Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]]]], int, Sequence[int], Sequence[Sequence[int]], Sequence[Sequence[Sequence[int]]], Sequence[Sequence[Sequence[Sequence[int]]]], None] = ...) -> ndarray[Any, dtype[signedinteger[Any]]]
pandas/core/groupby/groupby.py:2949: error: Incompatible types in assignment (expression has type "Union[ExtensionArray, ndarray[Any, Any]]", variable has type "ndarray[Any, Any]") [assignment]
pandas/core/groupby/groupby.py:2984: error: Incompatible types in assignment (expression has type "Union[ExtensionArray, ndarray[Any, Any]]", target has type "ndarray[Any, Any]") [assignment]
```
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} | 1 | "2021-07-28T14:22:40Z" | "2021-07-28T22:53:57Z" | "2021-07-28T22:53:54Z" | CONTRIBUTOR | null | - [X] closes #42769
- [N/A] 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
- [N/A] whatsnew entry
Should allow CI to proceed. | {
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} | 2 | "2021-07-28T15:34:38Z" | "2021-07-31T00:52:08Z" | "2021-07-31T00:52:08Z" | 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
lev_0 = [1, 2]
lev_1 = [1, 1]
idx = pd.MultiIndex.from_product([lev_0, lev_1])
df = pd.DataFrame(data={'a': range(0, len(idx))}, index=idx)
# Can't drop the first row use the code below, return the df itself.
df.drop(index=df.index[0])
# Also can’t drop the rows when passing a list-like index.
df.drop(index=df.index[0:1])
# When dropping by a single level, it works properly.
df.drop(index=1, level=0)
```
#### Problem description
When the DataFrame is MultiIndexed, and there's duplicated value in any level, DataFrame.drop() can't drop a row by an index tuple. I've tried swapping the levels' order, and there's still the same problem.
#### Expected Output
Drop the correct row by index.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.5.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 : 1.3.1
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
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 : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 13 | "2021-07-28T15:40:25Z" | "2021-09-06T15:18:28Z" | "2021-09-06T15:17:35Z" | CONTRIBUTOR | null | …a tuple-named grouper. (#42731)
- [x] closes #42731
- [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
Skipped any "What's New" for now, since it's not decided which version will have it. Will update as soon as this is decided. | {
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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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} | 0 | "2021-07-28T16:04:37Z" | "2021-07-29T00:56:33Z" | "2021-07-28T23:00:46Z" | MEMBER | null | Also small bump in extract_array | {
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} | 1 | "2021-07-28T16:05:35Z" | "2021-08-05T11:52:48Z" | "2021-08-05T11:52:43Z" | CONTRIBUTOR | null | - [x] closes #42760
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} | 6 | "2021-07-28T17:23:23Z" | "2021-08-07T23:31:20Z" | "2021-08-07T23:15:06Z" | CONTRIBUTOR | null | - [x] closes #42753
- [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
---
Allow for cases in which the end bound of the variable window for the first row extends past the last row, that is, cases in which the offset is large enough to cover all the rows. | {
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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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- [X] closes #42766
Following this commit, all the doctests in style_render.py file passed.
Output:
```
python -m pytest --doctest-modules pandas/io/formats/style_render.py
========================================================= test session starts =========================================================
platform linux -- Python 3.8.5, pytest-6.2.4, py-1.10.0, pluggy-0.13.1
rootdir: /home/aneesh/Desktop/Side Projects/ops/pandas, configfile: pyproject.toml
plugins: forked-1.3.0, asyncio-0.15.1, anyio-3.3.0, cov-2.12.1, instafail-0.4.2, hypothesis-6.14.3, xdist-2.3.0
collected 3 items
pandas/io/formats/style_render.py ...
--------------------------- generated xml file: /home/aneesh/Desktop/Side Projects/ops/pandas/test-data.xml ---------------------------
======================================================== slowest 30 durations =========================================================
1.06s call pandas/io/formats/style_render.py::pandas.io.formats.style_render.StylerRenderer.format
(8 durations < 0.005s hidden. Use -vv to show these durations.)
========================================================== 3 passed in 1.15s ==========================================================
```
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#### Question about pandas
**Note**: The program can run only 4 times in the for loop(like 1-5) and then the error comes out
```python
#
import requests
import pandas as pd
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.164 Safari/537.36'}
screen = requests.get('https://finviz.com/screener.ashx?v=111&f=cap_microunder,exch_nasd', headers = header).text
tables = pd.read_html(screen)
tables = tables[-2]
tables.columns = tables.iloc[0]
tables = tables[1:]
for i in range(1, 62):
num = i * 20 + 1
screen = requests.get(f'https://finviz.com/screener.ashx?v=111&f=cap_microunder,exch_nasd&r={num}', headers = header).text
tables1 = pd.read_html(screen)
tables1 = tables1[-2]
tables1.columns = tables1.iloc[0]
tables1 = tables1[1:]
tables = pd.concat([tables, tables1], ignore_index=True)
print(tables)
tables.to_csv('table.csv')
```
### Traceback
```python
0 No. Ticker Company ... Price Change Volume
0 1 AACG ATA Creativity Global ... 3.26 5.81% 74743
1 2 AAME Atlantic American Corporation ... 4.05 -0.74% 13064
2 3 AAOI Applied Optoelectronics, Inc. ... 7.50 4.02% 300084
3 4 AATC Autoscope Technologies Corporation ... 6.68 5.67% 23572
4 5 ABEO Abeona Therapeutics Inc. ... 1.24 -0.80% 1644640
5 6 ABGI ABG Acquisition Corp. I ... 9.73 -0.10% 1102
6 7 ABIO ARCA biopharma, Inc. ... 3.02 1.34% 127806
7 8 ABUS Arbutus Biopharma Corporation ... 2.74 1.44% 557042
8 9 ACER Acer Therapeutics Inc. ... 2.40 1.69% 72867
9 10 ACET Adicet Bio, Inc. ... 7.46 -0.40% 60257
10 11 ACEV ACE Convergence Acquisition Corp. ... 9.86 0.00% 227069
11 12 ACHL Achilles Therapeutics plc ... 6.02 0.17% 3129
12 13 ACHV Achieve Life Sciences, Inc. ... 7.26 0.12% 125431
13 14 ACKIU Ackrell SPAC Partners I Co. ... 10.21 -0.51% 20429
14 15 ACNB ACNB Corporation ... 28.18 -0.04% 3847
15 16 ACOR Acorda Therapeutics, Inc. ... 3.89 -1.74% 314330
16 17 ACRX AcelRx Pharmaceuticals, Inc. ... 1.19 0.00% 491255
17 18 ACST Acasti Pharma Inc. ... 0.47 3.67% 3735616
18 19 ACTG Acacia Research Corporation ... 5.72 0.88% 96306
19 20 ACXP Acurx Pharmaceuticals, Inc. ... 5.19 1.21% 72454
20 21 ADES Advanced Emissions Solutions, Inc. ... 7.64 2.41% 94783
21 22 ADIL Adial Pharmaceuticals, Inc. ... 2.38 2.14% 114676
22 23 ADMA ADMA Biologics, Inc. ... 1.60 -3.03% 724842
23 24 ADMP Adamis Pharmaceuticals Corporation ... 1.03 -0.48% 770632
24 25 ADMS Adamas Pharmaceuticals, Inc. ... 4.86 1.25% 41785
25 26 ADOC Edoc Acquisition Corp. ... 10.05 -0.20% 101
26 27 ADTX Aditxt, Inc. ... 2.15 -0.46% 245755
27 28 ADVM Adverum Biotechnologies, Inc. ... 2.32 -0.22% 1007877
28 29 ADXN Addex Therapeutics Ltd ... 9.00 -5.76% 1218
29 30 ADXS Advaxis, Inc. ... 0.43 3.98% 1963730
30 31 AEAC Authentic Equity Acquisition Corp. ... 9.66 -0.21% 2
31 32 AEHL Antelope Enterprise Holdings Limited ... 2.93 4.08% 329894
32 33 AEHR Aehr Test Systems ... 5.90 5.73% 1501288
33 34 AEI Alset EHome International Inc. ... 2.17 -36.22% 10833029
34 35 AEMD Aethlon Medical, Inc. ... 5.31 9.98% 376869
35 36 AESE Allied Esports Entertainment Inc. ... 2.05 3.02% 150842
36 37 AEY ADDvantage Technologies Group, Inc. ... 2.23 0.00% 8484
37 38 AEYE AudioEye, Inc. ... 13.95 3.68% 34198
38 39 AEZS Aeterna Zentaris Inc. ... 0.77 1.25% 1025815
39 40 AFBI Affinity Bancshares, Inc. ... 13.24 0.00% 1592
[40 rows x 11 columns]
0 No. Ticker ... Change Volume
0 1 AACG ... 5.81% 74743
1 2 AAME ... -0.74% 13064
2 3 AAOI ... 4.02% 300084
3 4 AATC ... 5.67% 23572
4 5 ABEO ... -0.80% 1644640
5 6 ABGI ... -0.10% 1102
6 7 ABIO ... 1.34% 127806
7 8 ABUS ... 1.44% 557042
8 9 ACER ... 1.69% 72867
9 10 ACET ... -0.40% 60257
10 11 ACEV ... 0.00% 227069
11 12 ACHL ... 0.17% 3129
12 13 ACHV ... 0.12% 125431
13 14 ACKIU ... -0.51% 20429
14 15 ACNB ... -0.04% 3847
15 16 ACOR ... -1.74% 314330
16 17 ACRX ... 0.00% 491255
17 18 ACST ... 3.67% 3735616
18 19 ACTG ... 0.88% 96306
19 20 ACXP ... 1.21% 72454
20 21 ADES ... 2.41% 94783
21 22 ADIL ... 2.14% 114676
22 23 ADMA ... -3.03% 724842
23 24 ADMP ... -0.48% 770632
24 25 ADMS ... 1.25% 41785
25 26 ADOC ... -0.20% 101
26 27 ADTX ... -0.46% 245755
27 28 ADVM ... -0.22% 1007877
28 29 ADXN ... -5.76% 1218
29 30 ADXS ... 3.98% 1963730
30 31 AEAC ... -0.21% 2
31 32 AEHL ... 4.08% 329894
32 33 AEHR ... 5.73% 1501288
33 34 AEI ... -36.22% 10833029
34 35 AEMD ... 9.98% 376869
35 36 AESE ... 3.02% 150842
36 37 AEY ... 0.00% 8484
37 38 AEYE ... 3.68% 34198
38 39 AEZS ... 1.25% 1025815
39 40 AFBI ... 0.00% 1592
40 41 AFCG ... 0.52% 15971
41 42 AGFS ... 2.62% 52066
42 43 AGGR ... -0.10% 16350
43 44 AGMH ... -0.55% 69710
44 45 AGRI ... 0.34% 249721
45 46 AGRX ... 8.02% 502187
46 47 AGTC ... 0.00% 194981
47 48 AHAC ... -0.21% 28010
48 49 AHPI ... -6.10% 594069
49 50 AIH ... 11.89% 172789
50 51 AIHS ... 0.28% 798443
51 52 AIKI ... 1.85% 529251
52 53 AIRG ... 2.19% 13836
53 54 AIRT ... 2.38% 6816
54 55 AKIC ... 0.00% 0
55 56 AKTX ... 2.52% 69737
56 57 ALAC ... -0.09% 4935
57 58 ALCO ... -0.38% 15543
58 59 ALF ... -2.78% 900340
59 60 ALIM ... -0.69% 6687
[60 rows x 11 columns]
0 No. Ticker Company ... Price Change Volume
0 1 AACG ATA Creativity Global ... 3.26 5.81% 74743
1 2 AAME Atlantic American Corporation ... 4.05 -0.74% 13064
2 3 AAOI Applied Optoelectronics, Inc. ... 7.50 4.02% 300084
3 4 AATC Autoscope Technologies Corporation ... 6.68 5.67% 23572
4 5 ABEO Abeona Therapeutics Inc. ... 1.24 -0.80% 1644640
.. .. ... ... ... ... ... ...
75 76 ANPC AnPac Bio-Medical Science Co., Ltd. ... 3.80 0.80% 33758
76 77 ANTE AirNet Technology Inc. ... 2.36 4.42% 237799
77 78 ANY Sphere 3D Corp. ... 2.99 5.28% 2170342
78 79 APDN Applied DNA Sciences, Inc. ... 5.20 -1.70% 275280
79 80 APEN Apollo Endosurgery, Inc. ... 7.94 2.58% 8045
[80 rows x 11 columns]
0 No. Ticker Company ... Price Change Volume
0 1 AACG ATA Creativity Global ... 3.26 5.81% 74743
1 2 AAME Atlantic American Corporation ... 4.05 -0.74% 13064
2 3 AAOI Applied Optoelectronics, Inc. ... 7.50 4.02% 300084
3 4 AATC Autoscope Technologies Corporation ... 6.68 5.67% 23572
4 5 ABEO Abeona Therapeutics Inc. ... 1.24 -0.80% 1644640
.. ... ... ... ... ... ... ...
95 96 ARTL Artelo Biosciences, Inc. ... 1.02 -0.97% 2794669
96 97 ARTW Art's-Way Manufacturing Co., Inc. ... 3.60 2.27% 24353
97 98 ARYD ARYA Sciences Acquisition Corp IV ... 10.16 -0.26% 2014
98 99 ASLN ASLAN Pharmaceuticals Limited ... 2.87 6.30% 218557
99 100 ASMB Assembly Biosciences, Inc. ... 3.51 2.03% 206015
[100 rows x 11 columns]
Traceback (most recent call last):
File "C:\Users\yanli\.spyder-py3\temp.py", line 20, in <module>
tables1 = pd.read_html(screen)
File "C:\Non-system\Anaconda\lib\site-packages\pandas\util\_decorators.py", line 296, in wrapper
return func(*args, **kwargs)
File "C:\Non-system\Anaconda\lib\site-packages\pandas\io\html.py", line 1086, in read_html
return _parse(
File "C:\Non-system\Anaconda\lib\site-packages\pandas\io\html.py", line 917, in _parse
raise retained
File "C:\Non-system\Anaconda\lib\site-packages\pandas\io\html.py", line 898, in _parse
tables = p.parse_tables()
File "C:\Non-system\Anaconda\lib\site-packages\pandas\io\html.py", line 217, in parse_tables
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
File "C:\Non-system\Anaconda\lib\site-packages\pandas\io\html.py", line 547, in _parse_tables
raise ValueError("No tables found")
ValueError: No tables found
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} | 0 | "2021-07-28T18:39:58Z" | "2021-08-05T15:24:26Z" | "2021-08-05T11:51:50Z" | 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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} | 2 | "2021-07-28T18:57:16Z" | "2021-08-13T22:54:16Z" | "2021-08-13T22:54:16Z" | 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.
Hi, I stumbled upon this while trying to fix another bug.
Is there a reason for the multiple check below (Lines 3536-3537)?
Should be a quick fix.
https://github.com/pandas-dev/pandas/blob/8924277fa3dbe775f46e679ab8bd97b293e465ea/pandas/core/indexes/multi.py#L3533-L3540 | {
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} | 5 | "2021-07-28T19:07:55Z" | "2021-08-13T22:53:56Z" | "2021-08-13T22:53:50Z" | NONE | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
#### What's new
`generate_fake_dataframe` aims to facilitate the creation of dummy DataFrames with a specific configuration and datatypes.
Current solutions like `makeDataFrame` or `makeMixedDataFrame` are not versatile enough and I think there is a need for something better.
The idea is that with a short function call like `pd.generate_fake_dataframe(1000, "iiifccd")` you can generate a DataFrame with 1000 rows and, for example, 4 columns of integers, 1 of floats, 2 categorical columns and 1 with dates in that specific order. Other params like `seed` to control randomization, or `intervals` to control the values among which data is sampled let us easily generate a DataFrame that is suited to our needs.
#### Checklist:
* I have followed the documentation as thoroughly as I could and added a long docstring with description, parameters, examples , etc.
* I have added a couple of tests.
#### Some concerns
* I put the function inside a new file called `generate_fake_dataframe.py` in `pandas/core/` as I was unsure about where was the right place to put it.
* I am quite unsure about the suitability of the name of the function and/or its parameters.
* I have run linting tests, but could not run the other ones since the environment setup via docker defined [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_environment.html#creating-an-environment-using-docker) is not working for me. However, since I am not touching anything already written and I am only adding stuff nothing should break. My own tests are passing of course.
That's it. I hope moderators find this function useful and after the necessary changes can be somehow included in the library. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42783 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42783/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42783/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42783/events | https://github.com/pandas-dev/pandas/pull/42783 | 955,170,551 | MDExOlB1bGxSZXF1ZXN0Njk4OTU5NzE5 | 42,783 | TST: Fix doctests in ``style.py`` | {
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} | 7 | "2021-07-28T19:29:59Z" | "2021-07-31T17:33:18Z" | "2021-07-31T06:38:46Z" | CONTRIBUTOR | null | The following commits fix the doctests of the ``style.py``, add some redirects to the User Guide, and some expected results.
- [x] closes #42759
- [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 | "2021-07-28T19:59:06Z" | "2021-08-21T18:20:20Z" | null | NONE | null | #### Is your feature request related to a problem?
I wish I could use the Pandas `DataFrame.join` function to perform left semi joins: selecting each row from the left DataFrame that has at least one matching row in the right DataFrame.
#### Describe the solution you'd like
I would like to be able to pass `leftsemi` to the `how` parameter in the `DataDrame.join` function to perform this type of join.
#### Additional context
For example: in SQL this might look like
```SQL
SELECT *
FROM table1
WHERE EXISTS
(SELECT 1
FROM table2
WHERE table1.id = table2.id)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/42785 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42785/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42785/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42785/events | https://github.com/pandas-dev/pandas/issues/42785 | 955,210,497 | MDU6SXNzdWU5NTUyMTA0OTc= | 42,785 | BUG: Multiindex Categorical type lost after div operation | {
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] | open | false | null | [] | null | 0 | "2021-07-28T20:29:52Z" | "2021-08-21T20:19:40Z" | 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
V0_cat_idx = pd.CategoricalIndex([10, 20, 30],
categories=[10, 20, 30],
ordered=True,
name="V0")
V1_cat_idx = pd.CategoricalIndex(["B", "A"],
categories=["B", "A"],
ordered=True,
name="V1")
V2_cat_idx = pd.CategoricalIndex(["X", "Y"],
categories=["X", "Y"],
name="V2")
cat_midx = pd.MultiIndex.from_product([V0_cat_idx, V1_cat_idx, V2_cat_idx])
myseries = pd.Series(range(12), index=cat_midx)
norm = myseries.sum(level=["V1", "V2"])
myseries_norm = myseries.div(norm)
assert isinstance(myseries_norm.index.levels[0], pd.CategoricalIndex) # Failed
assert isinstance(myseries_norm.index.levels[1], pd.CategoricalIndex) # Failed
assert isinstance(myseries_norm.index.levels[2], pd.CategoricalIndex) # OK
```
#### Problem description
If we now look at the index type of `myseries_norm`, we notice that we have lost the `CategoricalIndex` type. However, the div operation is carried out on two Series with compatible multiindex consisting of `CategoricalIndex` types.
The problem arises after `div` operation but also with `__mul__`.
Last info : if I change
```
cat_midx = pd.MultiIndex.from_product([V0_cat_idx, V1_cat_idx, V2_cat_idx])
```
by putting `V0_cat_idx` level at the rightmost position :
```
cat_midx = pd.MultiIndex.from_product([V1_cat_idx, V2_cat_idx, V0_cat_idx])
```
The categorical indexes seem to be preserved...
#### Expected Output
assert isinstance(myseries_norm.index.levels[0], pd.CategoricalIndex) # OK
assert isinstance(myseries_norm.index.levels[1], pd.CategoricalIndex) # OK
assert isinstance(myseries_norm.index.levels[2], pd.CategoricalIndex) # OK
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-63-generic
Version : #71~20.04.1-Ubuntu SMP Thu Jul 15 17:46:08 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.3.1
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 8 | "2021-07-28T23:05:15Z" | "2021-08-02T09:35:06Z" | "2021-07-31T01:01:48Z" | CONTRIBUTOR | null | - [X] closes #42430
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This PR contains a 1-line patch for issue #42430, plus a regression test to ensure the issue does not recur.
Before this change, `ExtensionBlock. getitem_block_index()` would request 2D slices of a block's underlying extension array, even though the `__getitem__()` method of `ExtensionArray` is (currently) only supposed to support 1D slicing. After the change, `getitem_block_index()` requests 1D slices. The change also fixes some linter/type checking errors. | {
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Fixes mypy issue without using an ignore. cc @Dr-Irv | {
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} | 4 | "2021-07-29T02:51:11Z" | "2021-07-29T22:47:41Z" | "2021-07-29T21:53:14Z" | MEMBER | null | - [x] closes #42798
- [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
Fixes three doctest failures on master.
One is from #42569, where name is no longer preserved when passing a named Index into `.loc`. We want to preserve the name when passed an Index (this was documented), but certainly not a boolean Series mask. Not certain if there are cases outside of Index where it should be preserved. cc @jbrockmendel | {
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} | 0 | "2021-07-29T08:42:35Z" | "2021-07-29T23:30:52Z" | "2021-07-29T23:30:52Z" | NONE | null | Seeing this warning while using
```
pyfolio.create_returns_tear_sheet(stock_rets)
```
```
.../lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:418: MatplotlibDeprecationWarning:
The is_last_row function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_last_row() instead.
if ax.is_last_row():
.../lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:418: MatplotlibDeprecationWarning:
The is_last_row function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use ax.get_subplotspec().is_last_row() instead.
if ax.is_last_row():
```
change tools.py:418 as suggested did fix the warning.
```
except IndexError:
# if gridspec is used, ax.rowNum and ax.colNum may different
# from layout shape. in this case, use last_row logic
for ax in axarr:
if ax.get_subplotspecis_last_row():
continue
if sharex or _has_externally_shared_axis(ax, "x"):
_remove_labels_from_axis(ax.xaxis)
````
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} | 2 | "2021-07-29T08:44:50Z" | "2021-07-30T12:32:32Z" | "2021-07-30T12:32:32Z" | 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 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
"Realistic" version when this could happen:
```python
import pandas as pd
from io import StringIO
file_data = StringIO("A,B,C,B,D")
# Import csv file (duplicated columns are assigned ".1")
df = pd.read_csv(file_data)
# Some logic which accidentally, makes columns not unique anymore
df.columns = [col[0] for col in df.columns]
# Some slicing logic or even reassigning the the columns
# It does not matter what type it is (list etc.)
df[df.columns]
```
Minimal version:
```python
import pandas as pd
df = pd.DataFrame(columns=["A", "B", "C", "B", "D"])
df[df.columns]
```
Results in:
| A | B | B | C | B | B | D |
|-----|-----|-----|-----|-----|-----|-----|
#### Problem description
Duplicated rows double unexpectedly and when having a lot of rows this can go undetected easily. I've only realized this, when exporting to parquet.
#### Expected Output
I would like pandas to raise an error if there are duplicated columns, because other edge case exist there (df[col] returning a DataFrame). Even on csv import, when duplicated columns are silently assigned an ".1" should raise an error.
Of course the quick fix for the problem at hand would be to stop the doubling.
I currently havent looked into why this is happening.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.7.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 : None.None
pandas : 1.3.1
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.6.0.post20200923
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : None
numba : 0.51.2+0.g9d5709615.dirty
</details>
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- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
```
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} | 1 | "2021-07-29T11:02:46Z" | "2021-07-29T23:23:18Z" | "2021-07-29T23:23:18Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from datetime import datetime
df = pd.DataFrame({"OOB": [datetime(1,1,1), datetime(9999,9,9)]})
```
#### Actual output
```
>>> df["OOB"].clip(lower=pd.Timestamp.min, upper=pd.Timestamp.max)
0 2262-04-11 23:47:16.854775807
1 1677-09-21 00:12:43.145224193
Name: OOB, dtype: object
```
#### Problem description
The problem is that the `datetime(1,1,1)` ended up being clipped to `pd.Timestamp.max` i.e the lowest value became the highest and vice-versa.
I suspect that this is to do with the interaction between datetime objects and Timestamps. The last version where this was functioning as I expected was `1.2.5`.
Notes:
- I couldn't use the equivalent datetime to pd.Timestamp.{min,max} as the lower and upper bound because even then I would get an OOB error when trying to `pd.to_timestamp` the `Series`. Obviously there are kind of gross ways to end up with a `Series` that has been clipped to min/max.
#### Expected Output
This is what I expected to happen (and what happened in previous versions.
```
>>> df["OOB"].clip(lower=pd.Timestamp.min, upper=pd.Timestamp.max)
0 1677-09-21 00:12:43.145224193
1 2262-04-11 23:47:16.854775807
Name: OOB, dtype: object
```
Here is what happens if I clip using datetimes...
```
>>> df["OOB"].clip(lower=pd.Timestamp.min.to_pydatetime(), upper=pd.Timestamp.max.to_pydatetime())
sys:1: UserWarning: Discarding nonzero nanoseconds in conversion
0 1677-09-21 00:12:43.145224
1 2262-04-11 23:47:16.854775
```
I get the expected output this way however, I fail in my ultimate goal of obtaining a clipped `Series[Timestamp]` and just get a `Series[datetime]`.
```
>>> pd.to_datetime(df["OOB"].clip(lower=pd.Timestamp.min.to_pydatetime(), upper=pd.Timestamp.max.to_pydatetime()))
...
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1677-09-21 00:12:43
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.17
tables : None
tabulate : 0.8.9
xarray : 0.18.2
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 3 | "2021-07-29T11:56:37Z" | "2021-07-31T18:59:00Z" | null | NONE | null | #### Is your feature request related to a problem?
The problem that occurred to me is due to inconsistent types outputted by `for values, match_df in df.groupby(names)` - if names is a list of length >1, then `values` can be a tuple. Otherwise it's an element type, which can be e.g. string.
I had a bug where I iterating over `values` resulted in going over characters of a string, together with `zip` it just discarded most of the field.
#### Describe the solution you'd like
Whenever df.groupby is called with a list, output a tuple of values found. Specifically when called with a list of length 1, output a tuple of length 1 instead of unwrapped element.
#### API breaking implications
This would change how existing code behaves.
#### Describe alternatives you've considered
Leave things as they are. Unfortunately it means people need to write special case handling ifs around groupby to get consistent types outputted.
#### Additional context
```python
import pandas as pd
df = pd.DataFrame(columns=['a','b','c'], index=['x','y'])
df.loc['y'] = pd.Series({'a':1, 'b':5, 'c':2})
print(df)
values, _ = next(iter(df.groupby(['a', 'b'])))
print(type(values)) # <class 'tuple'>
values, _ = next(iter(df.groupby(['a'])))
print(type(values)) # <class 'int'> # IMHO should be 'tuple'!
values, _ = next(iter(df.groupby('a')))
print(type(values)) # <class 'int'>
```
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} | 3 | "2021-07-29T16:53:36Z" | "2021-07-29T23:27:49Z" | "2021-07-29T23:23:18Z" | MEMBER | null | - [x] closes #42794
- [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 | "2021-07-29T17:21:38Z" | "2021-08-08T01:46:19Z" | "2021-08-08T01:45:23Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 7 | "2021-07-29T17:45:35Z" | "2021-07-29T21:53:14Z" | "2021-07-29T21:53:14Z" | MEMBER | null | The next doctests are failing on master, and are breaking the CI. Didn't check what caused the problem, would be good to understand (I guess we merged a PR that wasn't updated). In any case, we should update the output, so the CI is green again:
```
_______________ [doctest] pandas.core.construction.extract_array _______________
398 Categories (3, object): ['a', 'b', 'c']
399
400 Other objects like lists, arrays, and DataFrames are just passed through.
401
402 >>> extract_array([1, 2, 3])
403 [1, 2, 3]
404
405 For an ndarray-backed Series / Index a PandasArray is returned.
406
407 >>> extract_array(pd.Series([1, 2, 3]))
Expected:
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
Got:
array([1, 2, 3])
/home/runner/work/pandas/pandas/pandas/core/construction.py:407: DocTestFailure
_______________ [doctest] pandas.core.indexing.IndexingMixin.loc _______________
356 Alignable boolean Series:
357
358 >>> df.loc[pd.Series([False, True, False],
359 ... index=['viper', 'sidewinder', 'cobra'])]
360 max_speed shield
361 sidewinder 7 8
362
363 Index (same behavior as ``df.reindex``)
364
365 >>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
Differences (unified diff with -expected +actual):
@@ -1,4 +1,3 @@
max_speed shield
-foo
cobra 1 2
viper 4 5
/home/runner/work/pandas/pandas/pandas/core/indexing.py:365: DocTestFailure
__________________ [doctest] pandas.core.indexing._LocIndexer __________________
1043 Alignable boolean Series:
1044
1045 >>> df.loc[pd.Series([False, True, False],
1046 ... index=['viper', 'sidewinder', 'cobra'])]
1047 max_speed shield
1048 sidewinder 7 8
1049
1050 Index (same behavior as ``df.reindex``)
1051
1052 >>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
Differences (unified diff with -expected +actual):
@@ -1,4 +1,3 @@
max_speed shield
-foo
cobra 1 2
viper 4 5
/home/runner/work/pandas/pandas/pandas/core/indexing.py:1052: DocTestFailure
```
The next command should report the above errors, and should return clean after the fixes: `./ci/code_checks.sh doctests` | {
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} | 3 | "2021-07-29T18:56:52Z" | "2021-08-08T12:38:30Z" | "2021-08-07T23:14:31Z" | CONTRIBUTOR | null | - [x] closes #42537
BUG:
In 1.3.0/1.3.1 `set_sticky` did not handle well the names of column/index headers.
This overall bug fix makes sure those cells are well handled.
**Should be backported to 1.3.2**
Before any Fixes | After 2 of 2 fixes
--- | ---
**Index / No Names** |
 | 
**Index + Names**
 |
**MultIndex / No Names**
 | 
**MultIndex + Names**
 | 
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} | 0 | "2021-07-29T20:23:54Z" | "2021-09-07T22:40:36Z" | "2021-09-07T22:40:36Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
int1 = pd.Series([1, 2], dtype='int64')
int2 = pd.Series([pd.NA, 4], dtype='Int64')
pd.concat([int1, int2])
```
```
0 1
1 2
0 <NA>
1 4
dtype: Int64
```
```python
bool1 = pd.Series([True, False], dtype='bool')
bool2 = pd.Series([pd.NA, True], dtype='boolean')
pd.concat([bool1, bool2])
```
```
0 True
1 False
0 <NA>
1 True
dtype: object
```
#### Problem description
When running `concat` with two series, one of `int64` dtype and one of `Int64` dtype containing `pd.NA`, the resulting series has a dtype of `Int64`, which makes sense as this type is able to contain data from both. However, performing this same test on series with types of `bool` and `boolean`, results in a series with a dtype of `object`. It seems for consistency with the other behavior the series that results from concat should be `boolean` and not `object`.
#### Expected Output
The series resulting from running concat with a `bool` series and a `boolean` series, should be a series with a `boolean` dtype instead of `object`.
```
0 True
1 False
0 <NA>
1 True
dtype: boolean
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Jan 12 22:04:47 PST 2021; root:xnu-4903.278.56~1/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 : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 40.8.0
Cython : None
pytest : 5.2.0
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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Some cleanups
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- [ ] tests added / passed
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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.
#### Please note
I have reported this problem for PyYAML as https://github.com/yaml/pyyaml/issues/541, but one of the maintainers says that this cannot be fixed on their side, but is caused by the way Pandas implements `reduce`.
#### Code Sample, a copy-pastable example
Code:
```python
import pandas as pd
import numpy as np
import yaml
x = pd.DataFrame({
'a': np.array([1.0, 2.0])
})
yaml.dump(x)
```
Result:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/__init__.py", line 290, in dump
return dump_all([data], stream, Dumper=Dumper, **kwds)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/__init__.py", line 278, in dump_all
dumper.represent(data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 27, in represent
node = self.represent_data(data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 52, in represent_data
node = self.yaml_multi_representers[data_type](self, data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 342, in represent_object
return self.represent_mapping(
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 52, in represent_data
node = self.yaml_multi_representers[data_type](self, data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 346, in represent_object
return self.represent_sequence(tag+function_name, args)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 92, in represent_sequence
node_item = self.represent_data(item)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 286, in represent_tuple
return self.represent_sequence('tag:yaml.org,2002:python/tuple', data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 92, in represent_sequence
node_item = self.represent_data(item)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 52, in represent_data
node = self.yaml_multi_representers[data_type](self, data)
File "/home/ca/.conda/envs/std/lib/python3.8/site-packages/yaml/representer.py", line 331, in represent_object
if function.__name__ == '__newobj__':
AttributeError: 'functools.partial' object has no attribute '__name__'
```
Python 3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)
PyYAML 5.4.1
Pandas 1.3.1
NumPy 1.21.1
#### Problem description
This prevents dataframes from being saved as YAML.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-17-amd64
Version : #1 SMP Debian 4.19.194-3 (2021-07-18)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.1
setuptools : 49.6.0.post20210108
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 : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.19.0
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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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.
---
**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 to reproduce
```
import pandas as pd
pd.Timedelta((pd.Timedelta.max - pd.Timedelta(microseconds=1)).to_pytimedelta()) # works
pd.Timedelta(pd.Timedelta.max.to_pytimedelta()) # fails
```
#### Problem description
I'd expect `to_pytimedelta` for any Pandas Timedelta to convert to a python Timedelta which won't overflow Pandas Timdelta.
Instead, I get this error:
```
>>> pd.Timedelta(pd.Timedelta.max.to_pytimedelta())
Traceback (most recent call last):
File "pandas/_libs/tslibs/timedeltas.pyx", line 186, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds
OverflowError: Python int too large to convert to C long
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timedeltas.pyx", line 1301, in pandas._libs.tslibs.timedeltas.Timedelta.__new__
File "pandas/_libs/tslibs/timedeltas.pyx", line 306, in pandas._libs.tslibs.timedeltas.convert_to_timedelta64
File "pandas/_libs/tslibs/timedeltas.pyx", line 189, in pandas._libs.tslibs.timedeltas.delta_to_nanoseconds
pandas._libs.tslibs.conversion.OutOfBoundsTimedelta: Python int too large to convert to C long
>>>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Feb 10 21:08:45 PST 2020; root:xnu-4903.278.28~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42808 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42808/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42808/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42808/events | https://github.com/pandas-dev/pandas/issues/42808 | 956,287,826 | MDU6SXNzdWU5NTYyODc4MjY= | 42,808 | BUG: read_csv converts NaN to 1.0 in certain circumstances | {
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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
import numpy as np
a = pd.DataFrame([np.nan, True, False])
a.to_csv('test.csv',index=False)
b = pd.read_csv('test.csv',dtype='float')
print(b)
```
Output:
```
0
0 1.0
1 1.0
2 0.0
```
#### Problem description
When reading a csv file containing NaNs mixed with booleans, and when told to convert to a float, pandas converts NaN to 1.0.
#### Expected Output
I'd expect NaN to be read as NaN. It should behave the same as when I do this:
```python
print(a.astype('float'))
```
Output:
```
0
0 NaN
1 1.0
2 0.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.232-123.381.amzn1.x86_64
Version : #1 SMP Wed May 19 18:00:09 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 : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : 2021.04.0
scipy : 1.6.3
sqlalchemy : 1.4.11
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 5 | "2021-07-30T00:06:20Z" | "2021-08-12T16:57:10Z" | "2021-08-06T04:42:10Z" | CONTRIBUTOR | null | Fixed 2 of the 4 GL02 errors generated by pandas_validation_errors.ipynb
The remaining two errors are from pandas.Timestamp.isoweekday and will be tackled next.
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} | 1 | "2021-07-30T05:59:57Z" | "2021-08-17T06:26:16Z" | "2021-08-17T06:26:16Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.DataFrame([[pd.Timestamp("2021-01-01")]], index=[1, 2, 3])
```
This will throw an exception:
```
Traceback (most recent call last):
File "/tmp/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 1770, in create_block_manager_from_arrays
mgr = BlockManager(blocks, axes)
File "/tmp/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 910, in __init__
self._verify_integrity()
File "/tmp/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 917, in _verify_integrity
raise construction_error(tot_items, block.shape[1:], self.axes)
ValueError: Shape of passed values is (1, 1), indices imply (3, 1)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/tmp/venv/lib/python3.9/site-packages/pandas/core/frame.py", line 702, in __init__
mgr = arrays_to_mgr(
File "/tmp/venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 135, in arrays_to_mgr
return create_block_manager_from_arrays(
File "/tmp/venv/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 1772, in create_block_manager_from_arrays
raise construction_error(len(arrays), arrays[0].shape, axes, e)
ValueError: Shape of passed values is (1, 1), indices imply (3, 1)
```
The same exception will also be raised when the `data` argument is a list-of-dicts but not when `data` is a dict-of-lists:
```python
pd.DataFrame([{"x": pd.Timestamp("2021-01-01"}], index=[1, 2, 3]) # this will fail due to the same exception
pd.DataFrame({"x": [pd.Timestamp("2021-01-01")]}, index=[1, 2, 3]) # this format is ok
```
#### Problem description
From what I can tell, the `DataFrame` constructor supports supplying a length-1 list and an index of length greater than 1 as long as the list doesn't contain a value of type `Timestamp` / `datetime64`. Raising an error only for these types is therefore inconsistent and could lead to user confusion. It is also a change from the behaviour in pandas 1.2 where it was possible to supply a length-1 list containing a `Timestamp`.
```
>>> pd.__version__
'1.3.1'
>>> pd.DataFrame([[7]], index=[1, 2, 3])
0
1 7
2 7
3 7
>>> pd.DataFrame([["abc"]], index=[1, 2, 3])
0
1 abc
2 abc
3 abc
>>> pd.DataFrame([[object()]], index=[1, 2, 3])
0
1 <object object at 0x111a16e20>
2 <object object at 0x111a16e20>
3 <object object at 0x111a16e20>
```
In Pandas 1.2.5:
```
>>> import pandas as pd
>>> pd.__version__
'1.2.5'
>>> pd.DataFrame([[pd.Timestamp("2021-01-01")]], index=[1, 2, 3])
0
1 2021-01-01
2 2021-01-01
3 2021-01-01
```
#### Expected Output
The length-1 list should be repeated to the length of the index:
```
>>> pd.DataFrame([[pd.Timestamp("2021-01-01")]], index=[1, 2, 3])
0
1 2021-01-01
2 2021-01-01
3 2021-01-01
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.4.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_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
#### Notes
This appear to be caused by the code going through the [`elif isinstance(data, ABCExtensionArray)` branch of `sanitize_array`](https://github.com/pandas-dev/pandas/blob/5f15f5c85e272f5a63279ff0daacf10da8a131d0/pandas/core/construction.py#L545-L553) which causes it to return early before `_sanitize_ndim` is called. Removing that early return appears to fix the problem and it didn't cause any tests to fail when I ran `./test_fast.sh`. If this change makes sense I'd be happy to put up a PR. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42811 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42811/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42811/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42811/events | https://github.com/pandas-dev/pandas/pull/42811 | 956,511,402 | MDExOlB1bGxSZXF1ZXN0NzAwMDk1MDg1 | 42,811 | Backport PR #42690 on branch 1.3.x (REGR: ValueError raised when both prefix and names are set to None) | {
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} | 0 | "2021-07-30T08:08:09Z" | "2021-07-30T10:50:44Z" | "2021-07-30T10:50:44Z" | NONE | null | Backport PR #42690: REGR: ValueError raised when both prefix and names are set to None | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42812/events | https://github.com/pandas-dev/pandas/pull/42812 | 956,512,736 | MDExOlB1bGxSZXF1ZXN0NzAwMDk2MjEz | 42,812 | fix import error for version under python3.8 | {
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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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https://api.github.com/repos/pandas-dev/pandas/issues/42813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42813/events | https://github.com/pandas-dev/pandas/issues/42813 | 956,537,413 | MDU6SXNzdWU5NTY1Mzc0MTM= | 42,813 | rollback and rollforward both of them gives same response for month end business day | {
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] | open | false | null | [] | null | 2 | "2021-07-30T08:41:45Z" | "2021-07-30T11:46:49Z" | null | NONE | null | When the date we pass is the last business day of the month in rollback and rollforward both of them returns the same date we passed , shouldn't it give the previous month last business day for rollback and the passed date for rollforward .
eg.: 30th july 2021 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42814/events | https://github.com/pandas-dev/pandas/issues/42814 | 956,553,488 | MDU6SXNzdWU5NTY1NTM0ODg= | 42,814 | BUG: Posix / pytest (actions-39-numpydev.yaml, ...) checks failing on master | {
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} | 2 | "2021-07-30T09:02:32Z" | "2021-07-30T18:57:10Z" | "2021-07-30T18:56:57Z" | 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.
---
Posix Checks are failing on master affecting all pr's. If you take a look at any recent pr's eg #42690 #42705 #42809 and #42701 you can see that they had 24/25 checks successful and it is the same check failing for all of them. Needs discussion
```python
=================================== FAILURES ===================================
___________________________________ test_agg ___________________________________
[gw1] linux -- Python 3.9.5 /usr/share/miniconda/envs/pandas-dev/bin/python
self = <pandas.core.groupby.generic.SeriesGroupBy object at 0x7f1fdee50760>
how = 'group_var', cython_dtype = dtype('float64'), aggregate = True
numeric_only = False, needs_counts = True, needs_values = True, needs_2d = True
needs_nullable = False, min_count = None, needs_mask = False
needs_ngroups = False, result_is_index = False, pre_processing = None
post_processing = <function GroupBy.std.<locals>.<lambda> at 0x7f1fdee52040>
fill_value = None, kwargs = {'ddof': 1}
grouper = <pandas.core.groupby.ops.BinGrouper object at 0x7f1fdee4e430>
ids = array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), _ = array([0, 1, 2, 3, 4])
output = {}
blk_func = <function GroupBy._get_cythonized_result.<locals>.blk_func at 0x7f1fdee52310>
error_msg = 'Cannot cast DatetimeArray to dtype float64', idx = 0
@final
def _get_cythonized_result(
self,
how: str,
cython_dtype: np.dtype,
aggregate: bool = False,
numeric_only: bool | lib.NoDefault = lib.no_default,
needs_counts: bool = False,
needs_values: bool = False,
needs_2d: bool = False,
needs_nullable: bool = False,
min_count: int | None = None,
needs_mask: bool = False,
needs_ngroups: bool = False,
result_is_index: bool = False,
pre_processing=None,
post_processing=None,
fill_value=None,
**kwargs,
):
"""
Get result for Cythonized functions.
Parameters
----------
how : str, Cythonized function name to be called
cython_dtype : np.dtype
Type of the array that will be modified by the Cython call.
aggregate : bool, default False
Whether the result should be aggregated to match the number of
groups
numeric_only : bool, default True
Whether only numeric datatypes should be computed
needs_counts : bool, default False
Whether the counts should be a part of the Cython call
needs_values : bool, default False
Whether the values should be a part of the Cython call
signature
needs_2d : bool, default False
Whether the values and result of the Cython call signature
are 2-dimensional.
min_count : int, default None
When not None, min_count for the Cython call
needs_mask : bool, default False
Whether boolean mask needs to be part of the Cython call
signature
needs_ngroups : bool, default False
Whether number of groups is part of the Cython call signature
needs_nullable : bool, default False
Whether a bool specifying if the input is nullable is part
of the Cython call signature
result_is_index : bool, default False
Whether the result of the Cython operation is an index of
values to be retrieved, instead of the actual values themselves
pre_processing : function, default None
Function to be applied to `values` prior to passing to Cython.
Function should return a tuple where the first element is the
values to be passed to Cython and the second element is an optional
type which the values should be converted to after being returned
by the Cython operation. This function is also responsible for
raising a TypeError if the values have an invalid type. Raises
if `needs_values` is False.
post_processing : function, default None
Function to be applied to result of Cython function. Should accept
an array of values as the first argument and type inferences as its
second argument, i.e. the signature should be
(ndarray, Type). If `needs_nullable=True`, a third argument should be
`nullable`, to allow for processing specific to nullable values.
fill_value : any, default None
The scalar value to use for newly introduced missing values.
**kwargs : dict
Extra arguments to be passed back to Cython funcs
Returns
-------
`Series` or `DataFrame` with filled values
"""
numeric_only = self._resolve_numeric_only(numeric_only)
if result_is_index and aggregate:
raise ValueError("'result_is_index' and 'aggregate' cannot both be True!")
if post_processing and not callable(post_processing):
raise ValueError("'post_processing' must be a callable!")
if pre_processing:
if not callable(pre_processing):
raise ValueError("'pre_processing' must be a callable!")
if not needs_values:
raise ValueError(
"Cannot use 'pre_processing' without specifying 'needs_values'!"
)
grouper = self.grouper
ids, _, ngroups = grouper.group_info
output: dict[base.OutputKey, ArrayLike] = {}
base_func = getattr(libgroupby, how)
base_func = partial(base_func, labels=ids)
if needs_ngroups:
base_func = partial(base_func, ngroups=ngroups)
if min_count is not None:
base_func = partial(base_func, min_count=min_count)
def blk_func(values: ArrayLike) -> ArrayLike:
if aggregate:
result_sz = ngroups
else:
result_sz = len(values)
result: ArrayLike
result = np.zeros(result_sz, dtype=cython_dtype)
if needs_2d:
result = result.reshape((-1, 1))
func = partial(base_func, out=result)
inferences = None
if needs_counts:
counts = np.zeros(self.ngroups, dtype=np.int64)
func = partial(func, counts=counts)
if needs_values:
vals = values
if pre_processing:
vals, inferences = pre_processing(vals)
vals = vals.astype(cython_dtype, copy=False)
if needs_2d:
vals = vals.reshape((-1, 1))
func = partial(func, values=vals)
if needs_mask:
mask = isna(values).view(np.uint8)
func = partial(func, mask=mask)
if needs_nullable:
is_nullable = isinstance(values, BaseMaskedArray)
func = partial(func, nullable=is_nullable)
func(**kwargs) # Call func to modify indexer values in place
if needs_2d:
result = result.reshape(-1)
if result_is_index:
result = algorithms.take_nd(values, result, fill_value=fill_value)
if post_processing:
pp_kwargs = {}
if needs_nullable:
pp_kwargs["nullable"] = isinstance(values, BaseMaskedArray)
result = post_processing(result, inferences, **pp_kwargs)
return result
error_msg = ""
for idx, obj in enumerate(self._iterate_slices()):
values = obj._values
if numeric_only and not is_numeric_dtype(values.dtype):
continue
try:
> result = blk_func(values)
pandas/core/groupby/groupby.py:2972:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
values = <DatetimeArray>
['2005-01-01 00:00:00', '2005-01-02 00:00:00', '2005-01-03 00:00:00',
'2005-01-04 00:00:00', '2005-01...1-07 00:00:00', '2005-01-08 00:00:00', '2005-01-09 00:00:00',
'2005-01-10 00:00:00']
Length: 10, dtype: datetime64[ns]
def blk_func(values: ArrayLike) -> ArrayLike:
if aggregate:
result_sz = ngroups
else:
result_sz = len(values)
result: ArrayLike
result = np.zeros(result_sz, dtype=cython_dtype)
if needs_2d:
result = result.reshape((-1, 1))
func = partial(base_func, out=result)
inferences = None
if needs_counts:
counts = np.zeros(self.ngroups, dtype=np.int64)
func = partial(func, counts=counts)
if needs_values:
vals = values
if pre_processing:
vals, inferences = pre_processing(vals)
> vals = vals.astype(cython_dtype, copy=False)
pandas/core/groupby/groupby.py:2934:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <DatetimeArray>
['2005-01-01 00:00:00', '2005-01-02 00:00:00', '2005-01-03 00:00:00',
'2005-01-04 00:00:00', '2005-01...1-07 00:00:00', '2005-01-08 00:00:00', '2005-01-09 00:00:00',
'2005-01-10 00:00:00']
Length: 10, dtype: datetime64[ns]
dtype = dtype('float64'), copy = False
def astype(self, dtype, copy: bool = True):
# We handle
# --> datetime
# --> period
# DatetimeLikeArrayMixin Super handles the rest.
dtype = pandas_dtype(dtype)
if is_dtype_equal(dtype, self.dtype):
if copy:
return self.copy()
return self
elif is_datetime64_ns_dtype(dtype):
return astype_dt64_to_dt64tz(self, dtype, copy, via_utc=False)
elif self.tz is None and is_datetime64_dtype(dtype) and dtype != self.dtype:
# unit conversion e.g. datetime64[s]
return self._ndarray.astype(dtype)
elif is_period_dtype(dtype):
return self.to_period(freq=dtype.freq)
> return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy)
pandas/core/arrays/datetimes.py:651:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <DatetimeArray>
['2005-01-01 00:00:00', '2005-01-02 00:00:00', '2005-01-03 00:00:00',
'2005-01-04 00:00:00', '2005-01...1-07 00:00:00', '2005-01-08 00:00:00', '2005-01-09 00:00:00',
'2005-01-10 00:00:00']
Length: 10, dtype: datetime64[ns]
dtype = dtype('float64'), copy = False
def astype(self, dtype, copy: bool = True):
# Some notes on cases we don't have to handle here in the base class:
# 1. PeriodArray.astype handles period -> period
# 2. DatetimeArray.astype handles conversion between tz.
# 3. DatetimeArray.astype handles datetime -> period
dtype = pandas_dtype(dtype)
if is_object_dtype(dtype):
return self._box_values(self.asi8.ravel()).reshape(self.shape)
elif is_string_dtype(dtype) and not is_categorical_dtype(dtype):
if is_extension_array_dtype(dtype):
arr_cls = dtype.construct_array_type()
return arr_cls._from_sequence(self, dtype=dtype, copy=copy)
else:
return self._format_native_types()
elif is_integer_dtype(dtype):
# we deliberately ignore int32 vs. int64 here.
# See https://github.com/pandas-dev/pandas/issues/24381 for more.
level = find_stack_level()
warnings.warn(
f"casting {self.dtype} values to int64 with .astype(...) is "
"deprecated and will raise in a future version. "
"Use .view(...) instead.",
FutureWarning,
stacklevel=level,
)
values = self.asi8
if is_unsigned_integer_dtype(dtype):
# Again, we ignore int32 vs. int64
values = values.view("uint64")
if copy:
values = values.copy()
return values
elif (
is_datetime_or_timedelta_dtype(dtype)
and not is_dtype_equal(self.dtype, dtype)
) or is_float_dtype(dtype):
# disallow conversion between datetime/timedelta,
# and conversions for any datetimelike to float
msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
> raise TypeError(msg)
E TypeError: Cannot cast DatetimeArray to dtype float64
pandas/core/arrays/datetimelike.py:424: TypeError
During handling of the above exception, another exception occurred:
def test_agg():
# test with all three Resampler apis and TimeGrouper
np.random.seed(1234)
index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
index.name = "date"
df = DataFrame(np.random.rand(10, 2), columns=list("AB"), index=index)
df_col = df.reset_index()
df_mult = df_col.copy()
df_mult.index = pd.MultiIndex.from_arrays(
[range(10), df.index], names=["index", "date"]
)
r = df.resample("2D")
cases = [
r,
df_col.resample("2D", on="date"),
df_mult.resample("2D", level="date"),
df.groupby(pd.Grouper(freq="2D")),
]
a_mean = r["A"].mean()
a_std = r["A"].std()
a_sum = r["A"].sum()
b_mean = r["B"].mean()
b_std = r["B"].std()
b_sum = r["B"].sum()
expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
for t in cases:
> result = t.aggregate([np.mean, np.std])
pandas/tests/resample/test_resample_api.py:352:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/core/resample.py:331: in aggregate
result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
pandas/core/apply.py:164: in agg
return self.agg_list_like()
pandas/core/apply.py:355: in agg_list_like
new_res = colg.aggregate(arg)
pandas/core/groupby/generic.py:248: in aggregate
ret = self._aggregate_multiple_funcs(func)
pandas/core/groupby/generic.py:302: in _aggregate_multiple_funcs
results[key] = self.aggregate(func)
pandas/core/groupby/generic.py:258: in aggregate
return getattr(self, cyfunc)()
pandas/core/groupby/groupby.py:1728: in std
return self._get_cythonized_result(
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} | 1 | "2021-07-30T09:14:55Z" | "2021-07-30T09:32:36Z" | "2021-07-30T09:32:29Z" | NONE | null | Hope someone can help me out here
I’m trying to create a separate data frame and have it read files based on data I have in a separate data frame
For example
Here’s my code so far
```
Import pandas as pd
Df = pd.read_excel(aaa.xlsx)
# df would look something like this
Company | excel_name
A | apple.xslx
G | Google.xlsx
M | Microsoft.xlsx
Etc…..
Df_names = df.company.unique()
Filenames = df.excel_names.unique()
For company in df_names:
Vars()[comapny] = pd.DataFrame()
```
This so far creates the empty data frames for me but how can I get each dataframe to pd.read_excel(excel_names) in the data frame above in contrast to its company name.
Data frame A would read Apple.xlsx
Dataframe G = read_excel(google.xlsx)
M = read_excel(microsoft.xslx) etc….
The data is variable week by week so it could be different companies in week 2 than week 1 and because of that I'm trying o avoid writing any hard code.
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42816 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42816/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42816/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42816/events | https://github.com/pandas-dev/pandas/issues/42816 | 956,648,720 | MDU6SXNzdWU5NTY2NDg3MjA= | 42,816 | BUG: nlargest raises TypeError "No matching signature found" on Float64Dtype Series, versions >1.3.0 | {
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} | 4 | "2021-07-30T11:11:10Z" | "2021-08-10T21:45:16Z" | "2021-08-10T21:45:16Z" | NONE | null | - [yes] I have checked that this issue has not already been reported.
- [yes] I have confirmed this bug exists on the latest version of pandas.
#### Code Sample, a copy-pastable example
```python
# this works:
pandas.Series(numpy.random.random(10)).nlargest(5)
# this works on pandas 1.2.5 but fails on 1.3.0 and 1.3.1. All Float64DType Series seem to have the same issue
pandas.Series(numpy.random.random(10)).astype('Float64').nlargest(5)
```
#### Software/hardware
python version: 3.8.10
Ubuntu 20.04
(Intel Tiger Lake CPU)
#### Stack trace
```
TypeError Traceback (most recent call last)
/tmp/ipykernel_213893/2592114079.py in <module>
----> 1 pandas.Series(numpy.random.random(10)).astype('Float64').nlargest(5)
~/.pyenv/versions/3.8.10/envs/myvenv/lib/python3.8/site-packages/pandas/core/series.py in nlargest(self, n, keep)
3764 dtype: int64
3765 """
-> 3766 return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()
3767
3768 def nsmallest(self, n: int = 5, keep: str = "first") -> Series:
~/.pyenv/versions/3.8.10/envs/myvenv/lib/python3.8/site-packages/pandas/core/algorithms.py in nlargest(self)
1217
1218 def nlargest(self):
-> 1219 return self.compute("nlargest")
1220
1221 def nsmallest(self):
~/.pyenv/versions/3.8.10/envs/myvenv/lib/python3.8/site-packages/pandas/core/algorithms.py in compute(self, method)
1285 # arr passed into kth_smallest must be contiguous. We copy
1286 # here because kth_smallest will modify its input
-> 1287 kth_val = algos.kth_smallest(arr.copy(order="C"), n - 1)
1288 (ns,) = np.nonzero(arr <= kth_val)
1289 inds = ns[arr[ns].argsort(kind="mergesort")]
~/.pyenv/versions/3.8.10/envs/myvenv/lib/python3.8/site-packages/pandas/_libs/algos.pyx in pandas._libs.algos.__pyx_fused_cpdef()
TypeError: No matching signature found
```
Thanks! | {
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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
# this works
pandas.cut(pandas.Series(numpy.arange(10)), pandas.Series([3, 7]), include_lowest=True)
# this fails with IndexError - stack trace below
pandas.cut(pandas.Series(numpy.arange(10)).astype('Float64'), pandas.Series([3, 7]), include_lowest=True)
```
#### Software used
tried pandas 1.2.0-1.3.1 (same error in each case)
python 3.8.10
ubuntu 20.04
#### Stack trace
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/tmp/ipykernel_220282/2616585682.py in <module>
----> 1 pandas.cut(pandas.Series(numpy.arange(10)).astype('Float64'), pandas.Series([3, 7]), include_lowest=True)
~/.pyenv/versions/3.8.10/envs/testvenv/lib/python3.8/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,
~/.pyenv/versions/3.8.10/envs/testvenv/lib/python3.8/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)
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
From a quick look at line 410 in pdb it seems that `ids` is a numpy array, while `x==bins[0]` returns a pandas.Series of dtype `boolean` i.e. nullable, which cannot be used to index `ids`.
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} | 15 | "2021-07-30T13:03:07Z" | "2021-09-08T09:08:37Z" | "2021-09-08T09:08:37Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
x = pd.Series(["a", "b", "c"])
y = pd.Series([1, 2, 3])
pd.DataFrame(y, x)
>>> 0
>>> a NaN
>>> b NaN
>>> c NaN
```
```python
pd.DataFrame(x, y)
>>> 0
>>> 1 b
>>> 2 c
>>> 3 NaN
```
```python
pd.DataFrame(x.values, y.values)
>>> 0
>>> 1 a
>>> 2 b
>>> 3 c
```
#### Problem description
I would expect `pd.Series` objects to be valid inputs for the `DataFrame` constructor.
If this is not the case a warning (or even raising an error) would be nice...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en
LOCALE : German_Austria.1252
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.1
setuptools : 49.6.0.post20210108
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 : None
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 : 1.7.0
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 8 | "2021-07-30T14:43:59Z" | "2021-10-04T19:13:49Z" | "2021-10-04T19:13:41Z" | CONTRIBUTOR | null | - [x] closes #33205
- [x] closes #21892
- [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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} | 4 | "2021-07-30T16:31:03Z" | "2021-07-30T19:20:45Z" | "2021-07-30T18:56:57Z" | MEMBER | null | - [x] closes #42814
- [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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