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https://api.github.com/repos/pandas-dev/pandas/issues/39519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39519/events | https://github.com/pandas-dev/pandas/issues/39519 | 798,007,292 | MDU6SXNzdWU3OTgwMDcyOTI= | 39,519 | BUG: Binary operations (__and__, __or__, etc.) do not take parameter `axis` and fail | {
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} | 7 | 2021-02-01T06:19:24Z | 2021-04-15T07:41:01Z | 2021-04-15T07:41:01Z | 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
# Your code here
import pandas as pd
import numpy as np
df = pd.DataFrame([1, 1])
a = np.array([0, 1])
df.__and__(a, axis=0)
TypeError: new_method() got an unexpected keyword argument 'axis'
```
#### Problem description
This worked in pandas 1.1.5
#### Expected Output
```python
df.__and__(a, axis=0)
0
0 0
1 1
```
#### Output of ``pd.show_versions()``
<details>
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39520 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39520/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39520/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39520/events | https://github.com/pandas-dev/pandas/issues/39520 | 798,031,483 | MDU6SXNzdWU3OTgwMzE0ODM= | 39,520 | BUG: AttributeError: type object 'object' has no attribute 'dtype' with numpy 1.20.x and pandas versions 1.0.4 and earlier | {
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} | 29 | 2021-02-01T06:54:40Z | 2021-10-15T04:00:36Z | 2021-10-15T04:00:36Z | NONE | null | root@548977c7dc-62l72:/app# pip list | grep pandas
pandas 1.0.3
In ipython ，i try initializing df
`
In [1]: import pandas as pd
In [2]: pd.DataFrame([],columns=['a','b','c'])
AttributeErrorTraceback (most recent call last)
<ipython-input-2-ea4921e768b2> in <module>
----> 1 pd.DataFrame([],columns=['clicks', 'uclicks', 'impressions'])
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
488 mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
489 else:
--> 490 mgr = init_dict({}, index, columns, dtype=dtype)
491 else:
492 try:
/usr/local/lib/python3.8/site-packages/pandas/core/internals/construction.py in init_dict(data, index, columns, dtype)
237 else:
238 nan_dtype = dtype
--> 239 val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype)
240 arrays.loc[missing] = [val] * missing.sum()
241
/usr/local/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in construct_1d_arraylike_from_scalar(value, length, dtype)
1438 else:
1439 if not isinstance(dtype, (np.dtype, type(np.dtype))):
-> 1440 dtype = dtype.dtype
1441
1442 if length and is_integer_dtype(dtype) and isna(value):
AttributeError: type object 'object' has no attribute 'dtype'
`
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https://api.github.com/repos/pandas-dev/pandas/issues/39521 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39521/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39521/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39521/events | https://github.com/pandas-dev/pandas/pull/39521 | 798,057,121 | MDExOlB1bGxSZXF1ZXN0NTY0OTM0NjAy | 39,521 | ⬆️ UPGRADE: Autoupdate pre-commit config | {
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} | [] | closed | false | null | [] | null | 1 | 2021-02-01T07:29:54Z | 2021-02-01T10:54:13Z | 2021-02-01T10:24:09Z | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.10.0-py2.py3-none-any.whl (185 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.1-py2.py3-none-any.whl (7.2 MB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-1.5.13-py2.py3-none-any.whl (97 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.5.13 nodeenv-1.5.0 pre-commit-2.10.0 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.1
```
### stderr:
```Shell
WARNING: You are using pip version 20.3.4; however, version 21.0.1 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.1/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/python/black ... 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 v2.7.4 -> v2.8.0.
Updating https://github.com/pre-commit/pygrep-hooks ... already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
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/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
updating v0.1.6 -> v0.1.7.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://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 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...
[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 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/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/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black..................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
pyupgrade..............................................................................................Failed
- hook id: pyupgrade
- exit code: 1
- files were modified by this hook
Rewriting pandas/plotting/_core.py
Rewriting pandas/io/excel/_base.py
Rewriting pandas/core/algorithms.py
Rewriting pandas/core/indexes/datetimes.py
Rewriting pandas/core/reshape/melt.py
Rewriting pandas/plotting/_matplotlib/core.py
Rewriting pandas/core/reshape/merge.py
Rewriting pandas/tests/extension/arrow/test_timestamp.py
Rewriting pandas/core/reshape/reshape.py
Rewriting pandas/core/resample.py
Rewriting pandas/core/arrays/string_arrow.py
Rewriting pandas/io/stata.py
Rewriting pandas/core/indexes/multi.py
Rewriting pandas/core/tools/datetimes.py
Rewriting pandas/core/internals/managers.py
Rewriting pandas/core/frame.py
Rewriting pandas/core/arrays/timedeltas.py
Rewriting pandas/core/computation/ops.py
Rewriting pandas/core/dtypes/base.py
Rewriting pandas/core/dtypes/dtypes.py
Rewriting pandas/io/excel/_openpyxl.py
Rewriting pandas/core/arrays/base.py
Rewriting pandas/core/apply.py
Rewriting pandas/tests/extension/decimal/array.py
Rewriting pandas/core/missing.py
Rewriting pandas/core/generic.py
Rewriting pandas/core/sorting.py
Rewriting pandas/io/formats/format.py
Rewriting pandas/io/sas/sasreader.py
Rewriting pandas/core/arrays/interval.py
Rewriting pandas/core/internals/array_manager.py
Rewriting pandas/core/arrays/masked.py
Rewriting pandas/core/window/rolling.py
Rewriting pandas/core/arrays/datetimes.py
Rewriting pandas/io/json/_normalize.py
Rewriting pandas/core/groupby/grouper.py
Rewriting pandas/io/formats/csvs.py
Rewriting pandas/core/groupby/groupby.py
Rewriting pandas/io/pytables.py
Rewriting pandas/plotting/_matplotlib/timeseries.py
Rewriting pandas/core/series.py
Rewriting pandas/plotting/_matplotlib/style.py
Rewriting pandas/core/construction.py
Rewriting pandas/core/indexes/base.py
Rewriting pandas/core/aggregation.py
Rewriting pandas/core/internals/blocks.py
Rewriting pandas/core/internals/ops.py
Rewriting pandas/core/ops/__init__.py
Rewriting pandas/core/groupby/ops.py
Rewriting pandas/core/reshape/concat.py
Rewriting pandas/tests/extension/json/array.py
Rewriting pandas/core/arrays/_mixins.py
Rewriting pandas/core/indexes/period.py
Rewriting pandas/core/arrays/categorical.py
Rewriting pandas/io/formats/info.py
Rewriting pandas/io/parquet.py
Rewriting pandas/core/arrays/string_.py
Rewriting pandas/core/indexes/range.py
Rewriting pandas/_testing/__init__.py
Rewriting pandas/core/arrays/floating.py
Rewriting pandas/core/arrays/datetimelike.py
Rewriting pandas/core/computation/scope.py
Rewriting pandas/tests/extension/arrow/arrays.py
Rewriting pandas/plotting/_matplotlib/__init__.py
Rewriting pandas/tests/extension/list/array.py
Rewriting pandas/core/computation/align.py
Rewriting pandas/core/dtypes/cast.py
Rewriting pandas/core/reshape/pivot.py
Rewriting pandas/plotting/_matplotlib/tools.py
Rewriting pandas/core/arrays/sparse/dtype.py
Rewriting pandas/core/window/ewm.py
Rewriting pandas/core/internals/concat.py
Rewriting pandas/core/internals/construction.py
Rewriting pandas/core/arrays/numeric.py
Rewriting pandas/core/arrays/boolean.py
Rewriting pandas/plotting/_matplotlib/misc.py
Rewriting pandas/core/arrays/period.py
Rewriting pandas/tests/plotting/common.py
Rewriting pandas/io/common.py
Rewriting pandas/core/arrays/numpy_.py
Rewriting pandas/core/arrays/integer.py
Rewriting pandas/core/indexing.py
Rewriting pandas/core/arrays/sparse/array.py
Rewriting pandas/io/formats/style.py
Rewriting pandas/core/describe.py
Rewriting pandas/compat/pickle_compat.py
Rewriting pandas/io/gbq.py
Rewriting pandas/core/computation/pytables.py
Rewriting pandas/core/groupby/generic.py
Rewriting pandas/core/indexes/interval.py
Rewriting pandas/io/orc.py
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Generate pip dependency from conda.....................................................................Passed
flake8-rst.............................................................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of private functions across modules......................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
codespell..............................................................................................Passed
no-string-hints........................................................................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed 92 files: </summary>
- .pre-commit-config.yaml
- pandas/_testing/__init__.py
- pandas/compat/pickle_compat.py
- pandas/core/aggregation.py
- pandas/core/algorithms.py
- pandas/core/apply.py
- pandas/core/arrays/_mixins.py
- pandas/core/arrays/base.py
- pandas/core/arrays/boolean.py
- pandas/core/arrays/categorical.py
- pandas/core/arrays/datetimelike.py
- pandas/core/arrays/datetimes.py
- pandas/core/arrays/floating.py
- pandas/core/arrays/integer.py
- pandas/core/arrays/interval.py
- pandas/core/arrays/masked.py
- pandas/core/arrays/numeric.py
- pandas/core/arrays/numpy_.py
- pandas/core/arrays/period.py
- pandas/core/arrays/sparse/array.py
- pandas/core/arrays/sparse/dtype.py
- pandas/core/arrays/string_.py
- pandas/core/arrays/string_arrow.py
- pandas/core/arrays/timedeltas.py
- pandas/core/computation/align.py
- pandas/core/computation/ops.py
- pandas/core/computation/pytables.py
- pandas/core/computation/scope.py
- pandas/core/construction.py
- pandas/core/describe.py
- pandas/core/dtypes/base.py
- pandas/core/dtypes/cast.py
- pandas/core/dtypes/dtypes.py
- pandas/core/frame.py
- pandas/core/generic.py
- pandas/core/groupby/generic.py
- pandas/core/groupby/groupby.py
- pandas/core/groupby/grouper.py
- pandas/core/groupby/ops.py
- pandas/core/indexes/base.py
- pandas/core/indexes/datetimes.py
- pandas/core/indexes/interval.py
- pandas/core/indexes/multi.py
- pandas/core/indexes/period.py
- pandas/core/indexes/range.py
- pandas/core/indexing.py
- pandas/core/internals/array_manager.py
- pandas/core/internals/blocks.py
- pandas/core/internals/concat.py
- pandas/core/internals/construction.py
- pandas/core/internals/managers.py
- pandas/core/internals/ops.py
- pandas/core/missing.py
- pandas/core/ops/__init__.py
- pandas/core/resample.py
- pandas/core/reshape/concat.py
- pandas/core/reshape/melt.py
- pandas/core/reshape/merge.py
- pandas/core/reshape/pivot.py
- pandas/core/reshape/reshape.py
- pandas/core/series.py
- pandas/core/sorting.py
- pandas/core/tools/datetimes.py
- pandas/core/window/ewm.py
- pandas/core/window/rolling.py
- pandas/io/common.py
- pandas/io/excel/_base.py
- pandas/io/excel/_openpyxl.py
- pandas/io/formats/csvs.py
- pandas/io/formats/format.py
- pandas/io/formats/info.py
- pandas/io/formats/style.py
- pandas/io/gbq.py
- pandas/io/json/_normalize.py
- pandas/io/orc.py
- pandas/io/parquet.py
- pandas/io/pytables.py
- pandas/io/sas/sasreader.py
- pandas/io/stata.py
- pandas/plotting/_core.py
- pandas/plotting/_matplotlib/__init__.py
- pandas/plotting/_matplotlib/core.py
- pandas/plotting/_matplotlib/misc.py
- pandas/plotting/_matplotlib/style.py
- pandas/plotting/_matplotlib/timeseries.py
- pandas/plotting/_matplotlib/tools.py
- pandas/tests/extension/arrow/arrays.py
- pandas/tests/extension/arrow/test_timestamp.py
- pandas/tests/extension/decimal/array.py
- pandas/tests/extension/json/array.py
- pandas/tests/extension/list/array.py
- pandas/tests/plotting/common.py
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39522 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39522/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39522/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39522/events | https://github.com/pandas-dev/pandas/issues/39522 | 798,099,282 | MDU6SXNzdWU3OTgwOTkyODI= | 39,522 | BUG: Wrong legend when combining multiple plots on the same axes | {
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] | open | false | null | [] | null | 6 | 2021-02-01T08:22:52Z | 2021-04-09T07:39:37Z | null | NONE | null | The code
```python
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame([{'x': 1, 'a': 1, 'b': 1}, {'x': 2, 'a': 2, 'b': 3}])
ax = plt.gca()
df.plot('x', 'a', yerr=0.1, ax=ax, label='with errors')
df.plot('x', 'b', ax=ax, label='without errors')
```
results in the following image on my platform:
<img width="372" alt="download" src="https://user-images.githubusercontent.com/5444873/106432400-f5ec0d00-646e-11eb-8c85-768c6091cb15.png">
Note that the labels in the legend are swapped!
I obviously expected the legend to show the correct labels.
<details>
<pre>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 51.3.3
Cython : 0.29.21
pytest : 6.2.2
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</pre>
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] | null | 1 | 2021-02-01T10:27:53Z | 2021-02-03T18:08:05Z | 2021-02-03T18:08:05Z | MEMBER | null | Issue here is:
1. in `.pre-commit-config.yaml`, change the `ref` of `pyupgrade` from v2.7.4 to v2.9.0
2. run `pre-commit run pyupgrade --all-files`
3. run `pre-commit run flake8 --all-files` - there will be lots of errors from unused imports, so you should remove them (or you could look into using [autoflake](https://github.com/myint/autoflake) to do it for you)
4. if both `pre-commit run flake8 --all-files` and `pre-commit run pyupgrade --all-files` both pass, then open a pull request :rocket:
---
xref #39521 | {
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} | 0 | 2021-02-01T10:30:38Z | 2021-02-03T13:14:26Z | 2021-02-03T13:05:41Z | MEMBER | null | Precursor to #39501, in which the previous version of `no-string-hints` was removing the string literals even for `Literal["foo"]` | {
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] | closed | false | null | [] | null | 2 | 2021-02-01T11:06:59Z | 2021-02-02T08:15:19Z | 2021-02-02T08:15:19Z | 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.
---
The groupby - apply functionality breaks for string columns if within an apply a new column is assigned and a choice of columns is returned. Short example:
```python
import numpy as np
import pandas as pd
data = pd.DataFrame()
data["foo"] = np.repeat(["person", "woman"], 3)
data["bar"] = np.repeat(["camera", "TV"], 3)
data["number"] = np.repeat([99,88], 3)
data["id"] = np.arange(6)
```
```python
foo bar number id
0 person camera 99 0
1 person camera 99 1
2 person camera 99 2
3 woman TV 88 3
4 woman TV 88 4
5 woman TV 88 5
```
Let's define a test function which simply adds another column and returns the original DataFrame when applied:
```python
def testfunc1(df):
df["added"] = "bazinga"
return df
data.groupby("bar").apply(testfunc1)
```
```python
foo bar number id added
0 person camera 99 0 bazinga
1 person camera 99 1 bazinga
2 person camera 99 2 bazinga
3 woman TV 88 3 bazinga
4 woman TV 88 4 bazinga
5 woman TV 88 5 bazinga
```
Behaves just like it should, this is the expected output. However, redefining this test function to return a specific set of columns returns this unexpected output:
```python
def testfunc2(df):
df["added"] = "bazinga"
return df[["foo", "bar", "number", "id", "added"]]
data.groupby("bar").apply(testfunc2)
```
```python
foo bar number id added
0 woman TV 99 0 bazinga
1 woman TV 99 1 bazinga
2 woman TV 99 2 bazinga
3 woman TV 88 3 bazinga
4 woman TV 88 4 bazinga
5 woman TV 88 5 bazinga
```
All information in string columns is overwritten with one specific value, while the behaviour for numeric columns carries the original values just as expected.
This was run on pandas version 1.2.1
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} | 3 | 2021-02-01T11:49:21Z | 2021-02-01T13:48:52Z | 2021-02-01T13:39:11Z | MEMBER | null | failures with numpy 1.20
https://github.com/pandas-dev/pandas/pull/39517/checks?check_run_id=1803471267
xref #39513 and https://github.com/pandas-dev/pandas/pull/38379#discussion_r567753143
conda-forge should pick up 1.19.5 with these changes. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39527 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39527/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39527/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39527/events | https://github.com/pandas-dev/pandas/issues/39527 | 798,313,632 | MDU6SXNzdWU3OTgzMTM2MzI= | 39,527 | DOC: Example for plotting scatterplot matrices is incomplete | {
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} | 3 | 2021-02-01T12:41:10Z | 2021-02-04T10:14:04Z | 2021-02-04T10:13:52Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#scatter-matrix-plot
#### Documentation problem
In the above-mentioned documentation, the following code is suggested to build/display a scatterplot matrix:
```
df = pd.DataFrame(np.random.randn(1000, 4), columns=["a", "b", "c", "d"])
scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal="kde");
```
This code indeed builds something, and returns a numpy array, but it does not display the graphics.
If one does not have a look at the source code or at one a the very few other docs on the subject, like:
https://stackoverflow.com/questions/7941207/is-there-a-function-to-make-scatterplot-matrices-in-matplotlib
https://stackoverflow.com/questions/34347145/pandas-plot-doesnt-show
Or if one is a newbie in Python/DataScience/Pandas and does not know about matplotlib. One will never know how to get the graphics to display. And neither the numpy array returned nor the docs/proposed-completions proposed by most IDE will give you any hint of the problem and/or its solution :{
#### Suggested fix for documentation
- Suggested correction to the doc: add the fact that to display the graph you need both 1/ to import an external module & 2/ to call a method of this module
```
import matplotlib.pyplot as plt, plt.show()
```
- Why? Because documentation examples should be self sufficient and provide MVE.
PS: I've not looked at the other type of graphs' example, but the problem might be the same for their MVE. | {
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} | 10 | 2021-02-01T13:16:35Z | 2021-02-07T16:24:23Z | 2021-02-07T16:24:23Z | 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
```python
from openpyxl import load_workbook
from pandas import read_excel
wb = load_workbook("testfile.xlsx")
read_excel(wb, engine="openpyxl")
```
#### Problem description
In pandas 1.1.5, the above code completes with no problems.
In pandas 1.2.1, it causes the following exception:
```
Traceback (most recent call last):
File "c:/Users/akaijanaho/scratch/pandas-openpyxl-bug/bug.py", line 5, in <module>
read_excel(wb, engine="openpyxl")
File "C:\Users\akaijanaho\scratch\pandas-openpyxl-bug\venv\lib\site-packages\pandas\util\_decorators.py", line 299, in wrapper
return func(*args, **kwargs)
File "C:\Users\akaijanaho\scratch\pandas-openpyxl-bug\venv\lib\site-packages\pandas\io\excel\_base.py", line 336, in read_excel
io = ExcelFile(io, storage_options=storage_options, engine=engine)
File "C:\Users\akaijanaho\scratch\pandas-openpyxl-bug\venv\lib\site-packages\pandas\io\excel\_base.py", line 1057, in __init__
ext = inspect_excel_format(
File "C:\Users\akaijanaho\scratch\pandas-openpyxl-bug\venv\lib\site-packages\pandas\io\excel\_base.py", line 938, in inspect_excel_format
with get_handle(
File "C:\Users\akaijanaho\scratch\pandas-openpyxl-bug\venv\lib\site-packages\pandas\io\common.py", line 558, in get_handle
ioargs = _get_filepath_or_buffer(
File "C:\Users\akaijanaho\scratch\pandas-openpyxl-bug\venv\lib\site-packages\pandas\io\common.py", line 371, in _get_filepath_or_buffer
raise ValueError(msg)
ValueError: Invalid file path or buffer object type: <class 'openpyxl.workbook.workbook.Workbook'>
```
The documentation does not specify Workbook as an acceptable value type for io, but supporting it seems reasonable and accords with the 1.1.5 behavior.
In my use case, we mainly parse an Excel file with openpyxl but use pandas with a specific sub-problem. We would like to reuse the same Workbook instead of having pandas re-read the file.
</details>
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I would like to be able to use pd.merge to replace or update the columns in the left dataset if non-key columns with the same names are being merged from the right dataset.
#### Describe the solution you'd like
Add an keyword extra parameter, `duplicate_columns` to specify how common non-key variables should be handled, with the following options:
- `keep_both` The default option should be to preserve the current behaviour, i.e. create two columns with left and right suffixes.
- `replace` Keep only the columns merged from right.
- `update_left` coalesce(right, left) for each column
#### API breaking implications
None that I can see if the default behaviour is unchanged.
#### Describe alternatives you've considered
The simplest way I know to do this at present is to drop any common columns from left before merging, or keep both columns and coalesce afterwards. However, these both seem to me as though they must be very common use cases, and so perhaps it might be a good idea to allow doing this within pd.merge directly?
If this is a bad idea and there's already a very easy way to do this with pd.merge, please let me know.
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`Styler` has a mixture of function placements, e.g. outer scope, or as class methods or inner scope of class methods.
This PR has moved two outer scope functions to inner scope of class methods, where they are only used / relevant for that section, to be more consistent with the rest of the class.
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and there are datetime.now(timezone.utc) objects in the table it causes the following error (Without orient table it works though.):
`---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-122-4a9c9a4c89de> in <module>
----> 1 x = current_opportunites.to_json(orient="table",index=False)
/opt/conda/lib/python3.8/site-packages/pandas/core/generic.py in to_json(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options)
2463 indent = indent or 0
2464
-> 2465 return json.to_json(
2466 path_or_buf=path_or_buf,
2467 obj=self,
/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py in to_json(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options)
83 raise NotImplementedError("'obj' should be a Series or a DataFrame")
84
---> 85 s = writer(
86 obj,
87 orient=orient,
/opt/conda/lib/python3.8/site-packages/pandas/io/json/_json.py in __init__(self, obj, orient, date_format, double_precision, ensure_ascii, date_unit, index, default_handler, indent)
249 raise ValueError(msg)
250
--> 251 self.schema = build_table_schema(obj, index=self.index)
252
253 # NotImplemented on a column MultiIndex
/opt/conda/lib/python3.8/site-packages/pandas/io/json/_table_schema.py in build_table_schema(data, index, primary_key, version)
261 if data.ndim > 1:
262 for column, s in data.items():
--> 263 fields.append(convert_pandas_type_to_json_field(s))
264 else:
265 fields.append(convert_pandas_type_to_json_field(data))
/opt/conda/lib/python3.8/site-packages/pandas/io/json/_table_schema.py in convert_pandas_type_to_json_field(arr)
122 field["freq"] = dtype.freq.freqstr
123 elif is_datetime64tz_dtype(dtype):
--> 124 field["tz"] = dtype.tz.zone
125 return field
126
AttributeError: 'datetime.timezone' object has no attribute 'zone'`
https://github.com/pandas-dev/pandas/blob/d3788525ee368db0b6565b72ab4eeba9140fd1d4/pandas/io/json/_table_schema.py#L123
Will have a deeper look later.
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} | 10 | 2021-02-01T17:55:27Z | 2021-02-03T18:26:21Z | 2021-02-03T18:08:05Z | CONTRIBUTOR | null | - [x] closes #39523
- [ ] 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
------
This PR upgrades `pyupgrade` and also includes style/formatting changes required after upgrading `pyupgrade`, namely, removing unused imports (`flake8`), and running `black` and `isort`.
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} | 10 | 2021-02-01T18:44:18Z | 2021-02-09T13:15:17Z | 2021-02-09T12:49:36Z | MEMBER | null | xref indexing work item of https://github.com/pandas-dev/pandas/issues/39146
This covers setitem with a boolean dataframe (like ``df[df < 0] = 2``), but specifically fixes the case where the dataframe is not aligned (eg `df[df[:-1] < 0] = 2` in the tests).
`DataFrame.fillna` infers object dtype columns getting filled for a proper dtype, but with ArrayManager internals I propose that `fillna` always preserves the dtype.
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} | 2 | 2021-02-01T19:07:44Z | 2021-02-02T12:54:29Z | 2021-02-01T21:06:45Z | CONTRIBUTOR | null | so the confluence of some pyarrow pinning and release of numpy 1.20 are causing pain rn. several PRs have addressed most of these. I think 38_locale is the remaining problem, needs to pin pyarrow.
We need to get to green on master & 1.2.2 before any other PRs.
cc @pandas-dev/pandas-core | {
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} | 2 | 2021-02-01T19:11:40Z | 2021-02-02T03:15:34Z | 2021-02-02T03:15:15Z | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
I tried making a dummy dataframe
```
python
column_names = ["a", "b", "c"]
df = pd.DataFrame(columns = column_names)
```
#### Problem description
I am not able to create an empty df with columns.
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} | 3 | 2021-02-01T19:18:01Z | 2021-02-01T21:07:59Z | 2021-02-01T21:06:45Z | MEMBER | null | - [x] closes #39541
- [ ] 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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} | 8 | 2021-02-01T22:43:57Z | 2021-02-08T16:37:47Z | 2021-02-08T15:14:25Z | MEMBER | null | - [x] closes #39181
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This is on top of #39486 | {
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} | 5 | 2021-02-01T23:12:21Z | 2021-09-14T12:56:52Z | 2021-09-14T12:56:52Z | 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 numpy as np
import pandas as pd
td = pd.Series([pd.Timedelta(days=i) for i in range(3)])
try:
# same as td.mask([False, False, True])
td.where([False, False, True], np.nan)
except TypeError as err:
print(err)
#> The DTypes <class 'numpy.dtype[float16]'> and <class 'numpy.dtype[timedelta64]'> do not have a common DType. For example they cannot be stored in a single array unless the dtype is `object`.
# gives same result as above on numpy < 1.20
print(td.where([False, False, True], pd.NaT))
#> 0 NaT
#> 1 NaT
#> 2 2 days
#> dtype: timedelta64[ns]
# also ok
print(td.where([False, False, True], 10.0))
#> 0 10.0
#> 1 10.0
#> 2 2 days 00:00:00
#> dtype: object
td.where([False, False, True], np.nan, inplace=True) # ok
print(td)
#> 0 NaT
#> 1 NaT
#> 2 2 days
#> dtype: timedelta64[ns]
```
<sup>Created on 2021-02-02 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>
#### Problem description
With numpy 1.20.0, `mask` on a Series of dtype timedelta64 raises a `TypeError`. The code above shows that the issue comes from the internals of `where` and happens only when a `np.nan` is passed as replacement value.
I noticed that `inplace=True` avoids the issue.
#### Expected Output
`td.mask(...)` should return the same as `where(..., pd.NaT)`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.7-3-MANJARO
Version : #1 SMP PREEMPT Fri Jan 15 21:11:34 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.20.0
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : 6.2.2
hypothesis : 6.1.1
sphinx : 3.4.3
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 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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} | 2 | 2021-02-02T01:59:29Z | 2021-03-19T14:22:49Z | 2021-03-19T14:22:49Z | CONTRIBUTOR | null | The database builds: https://github.com/pandas-dev/pandas/pull/39457/checks?check_run_id=1797892949
take the longest. we should just have this run the database tests only. (so add a marker for the appropriate tests / just run the sql tests entirely). | {
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```
2021-02-02T04:33:27.6929603Z =========================== short test summary info ============================
2021-02-02T04:33:27.6930740Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_pairwise_with_self[pairwise_frames0-<lambda>3]
2021-02-02T04:33:27.6931802Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_pairwise_with_self[pairwise_frames3-<lambda>3]
2021-02-02T04:33:27.6932849Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_pairwise_with_self[pairwise_frames4-<lambda>3]
2021-02-02T04:33:27.6933888Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_pairwise_with_self[pairwise_frames6-<lambda>3]
2021-02-02T04:33:27.6935070Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_pairwise_with_self[pairwise_frames7-<lambda>3]
2021-02-02T04:33:27.6936130Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_no_pairwise_with_self[pairwise_frames1-<lambda>3]
2021-02-02T04:33:27.6937383Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_no_pairwise_with_self[pairwise_frames2-<lambda>3]
2021-02-02T04:33:27.6938466Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_no_pairwise_with_self[pairwise_frames3-<lambda>3]
2021-02-02T04:33:27.6939538Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_no_pairwise_with_self[pairwise_frames5-<lambda>3]
2021-02-02T04:33:27.6940613Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_no_pairwise_with_self[pairwise_frames6-<lambda>3]
2021-02-02T04:33:27.6941921Z FAILED ../../venv/lib/python3.7/site-packages/pandas/tests/window/test_pairwise.py::TestPairwise::test_no_pairwise_with_self[pairwise_frames7-<lambda>3]
2021-02-02T04:33:27.6943014Z = 11 failed, 137465 passed, 18326 skipped, 965 xfailed, 1 xpassed, 8 warnings in 745.50s (0:12:25) =
```
cc @mroeschke
test_pairwise_with_self was updated in 1.2. (#36657) I will assume that this will also fail on 1.2.x for now. will open PR to test shortly.
I'll also add blocker for now since if tests fail wheels aren't uploaded. | {
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} | 0 | 2021-02-02T10:52:53Z | 2021-02-05T20:24:03Z | 2021-02-05T20:24:03Z | 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 numpy as np
import pandas
from pandas.testing import assert_frame_equal
data = {
"col1": [np.nan, 1, 2]
}
pd_df = pandas.DataFrame(data)
pd_rolled = pd_df.rolling(window=2, min_periods=None)
res1 = pd_rolled.sum()
pd_rolled.count()
res2 = pd_rolled.sum()
assert_frame_equal(res1, res2) # AssertionError
```
<details><summary>Output</summary>
```
AssertionError: DataFrame.iloc[:, 0] (column name="col1") are different
DataFrame.iloc[:, 0] (column name="col1") values are different (66.66667 %)
[index]: [0, 1, 2]
[left]: [nan, nan, 3.0]
[right]: [0.0, 1.0, 3.0]
```
</details>
#### Problem description
Two sequential calls of `.sum` on the rolling object produces different results if we call `.count` between them and `min_periods=None`. The default behavior of `Rolling.sum` if `min_periods` is None is to consider `min_periods` to be equal to the window size. Currently, `Rolling.count` behaves differently, and considers `min_periods` to be 0 if it is None. #36649 brought a warning that this behavior is deprecated and also refactored `.count` implementation. Right after giving a warning, it modifies the original value of `min_periods` of the rolling object, so the future calls of `.sum` and other operations give incorrect results.
https://github.com/pandas-dev/pandas/blob/5e02681cb9edc6f8d9e5397e9bafcf940a9cd9dc/pandas/core/window/rolling.py#L2138-L2149
#### Expected Output
`Rolling.count` should not modify `min_periods` attribute of the rolling object, or if it is, revert back the original value of `min_periods` after performing `count`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-50-generic
Version : #54-Ubuntu SMP Mon May 6 18:46:08 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : 0.13.2
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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] | closed | false | null | [] | null | 2 | 2021-02-02T11:16:57Z | 2021-06-26T00:52:33Z | 2021-06-26T00:52:33Z | NONE | null | - [ yes] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ yes] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
Currently I am working on a project about the extension array, while I have some problems related to the _reduce.
First I define my extension array.
```python
d4 = MyArray(Farr(np.array([[3,4,1,2],[3,5,2,1]])))
df4 = pd.DataFrame(d4, dtype = MyDtype())
```
it looks like this
0 1 2 3
0 Sfixed(value=3) Sfixed(value=4) Sfixed(value=1) Sfixed(value=2)
1 Sfixed(value=3) Sfixed(value=5) Sfixed(value=2) Sfixed(value=1)
where Sfixed is my own object.
Then I put it into DataFrame, then I call
df4.max(), it would not call the _reduce in my EA.
while run df4[0].max(), it would call the _reduce in my EA, this is correct.
My question is how to enable df.max() or df.std(), or df.abs() call my _reduce in EA?
Thanks! | {
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} | 3 | 2021-02-02T12:21:14Z | 2021-02-23T02:25:47Z | 2021-02-23T02:25:47Z | CONTRIBUTOR | null | The issue has been reported in #8772 which has been closed asking for an example (https://github.com/pandas-dev/pandas/issues/8772#issuecomment-523820768).
Here is the example
```python
import pandas
for tz in [None, "UTC", "CET"]:
index = pandas.date_range("2018", "2019", tz=tz, closed="left", freq='H')
print(f"tz = {tz}, index freq = {index.freq}, inferred index freq = {pandas.infer_freq(index)}")
```
#### Problem description
`pandas.infer_freq` can infer the frequency when the index has no tz or tz=="UTC". But for tz=="CET", it returns None.
This is due to a special handling in https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/frequencies.py#L198 to cater for timezone DST in frequencies beyond the hour (day, month,...). However for regular hourly frequencies, the delta which is constant when considering real time/UTC will now show multiple deltas (0 for the DST in october, 2H for the DST in march, 1H for the rest).
Probably needs two paths, one for business frequencies (days, months, ...) and one for hour and below frequencies. Not sure about frequencies between H and D (like 6H) but probably to be considered as business frequencies.
#### Expected Output
```
# output
# tz = None, index freq = <Hour>, inferred index freq = H
# tz = UTC, index freq = <Hour>, inferred index freq = H
# tz = CET, index freq = <Hour>, inferred index freq = None
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : French_Belgium.1252
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : 0.29.17
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.2
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.52.0
</details>
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} | 7 | 2021-02-02T14:15:10Z | 2021-02-07T18:15:57Z | 2021-02-03T03:26:56Z | CONTRIBUTOR | null | Timed pd.unique vs np.unique with 1_000 and 100_000 sequences lengths. In first case np.unique was faster, whereas in the second case pd.unique won.
Hence, I propose to make the doc a bit more accurate.
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} | 8 | 2021-02-02T14:48:00Z | 2021-03-31T08:33:10Z | 2021-03-30T21:18:08Z | CONTRIBUTOR | null | Added an example for infer_freq function. The initial index is built using a fixed number of periods to avoid encoding frequency in this index explicitly.
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} | 6 | 2021-02-02T15:27:10Z | 2021-02-06T18:06:39Z | 2021-02-06T16:48:43Z | NONE | null | I'm trying to execute a simple left join on a single column, as follows:
```python
data2016.join(expected_runs, how='left', on='STATE')
```
However, it fails with the message:
```
ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat
```
Looking at the STATE column in each table, it shows that both are object columns:
<img width="394" alt="Screen Shot 2021-02-02 at 9 24 57 AM" src="https://user-images.githubusercontent.com/81476/106621793-8a6e7200-6538-11eb-8955-0efeac1e3ba5.png">
Note that using `merge` works as follows:
pd.merge(data2016, expected_runs, how='left', on='STATE')
Here is the full error trace:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-58-bbf0335fbf11> in <module>
----> 1 data2016.join(expected_runs, how='left', on='STATE')
~/anaconda3/envs/baseball/lib/python3.8/site-packages/pandas/core/frame.py in join(self, other, on, how, lsuffix, rsuffix, sort)
8105 5 K5 A5 NaN
8106 """
-> 8107 return self._join_compat(
8108 other, on=on, how=how, lsuffix=lsuffix, rsuffix=rsuffix, sort=sort
8109 )
~/anaconda3/envs/baseball/lib/python3.8/site-packages/pandas/core/frame.py in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)
8130 sort=sort,
8131 )
-> 8132 return merge(
8133 self,
8134 other,
~/anaconda3/envs/baseball/lib/python3.8/site-packages/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
72 validate=None,
73 ) -> "DataFrame":
---> 74 op = _MergeOperation(
75 left,
76 right,
~/anaconda3/envs/baseball/lib/python3.8/site-packages/pandas/core/reshape/merge.py in __init__(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)
670 # validate the merge keys dtypes. We may need to coerce
671 # to avoid incompatible dtypes
--> 672 self._maybe_coerce_merge_keys()
673
674 # If argument passed to validate,
~/anaconda3/envs/baseball/lib/python3.8/site-packages/pandas/core/reshape/merge.py in _maybe_coerce_merge_keys(self)
1191 inferred_right in string_types and inferred_left not in string_types
1192 ):
-> 1193 raise ValueError(msg)
1194
1195 # datetimelikes must match exactly
ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:40:21 PST 2020; root:xnu-7195.60.75~1/RELEASE_ARM64_T8101
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.2
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2021-02-02T18:21:27Z | 2021-02-17T16:33:46Z | 2021-02-03T13:48:01Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
The code style guide reads
>
> Imports (aim for absolute)
> ==========================
>
> In Python 3, absolute imports are recommended. Using absolute imports, doing something
> like ``import string`` will import the string module rather than ``string.py``
> in the same directory. As much as possible, you should try to write out
> absolute imports that show the whole import chain from top-level pandas.
>
> Explicit relative imports are also supported in Python 3 but it is not
> recommended to use them. Implicit relative imports should never be used
> and are removed in Python 3.
>
> For example:
>
> ::
>
> # preferred
> import pandas.core.common as com
>
> # not preferred
> from .common import test_base
>
> # wrong
> from common import test_base
yet this isn't checked (nor followed).
So I've made a little package which does this - it's pretty simple and light ([link to source](https://github.com/MarcoGorelli/abs-imports/blob/main/abs_imports.py)), just standard Python libraries (`ast.parse`). Timing on my laptop:
```console
$ time pre-commit run abs-imports --all-files
abs-imports..............................................................Passed
real 0m1.102s
user 0m10.050s
sys 0m0.373s
```
I asked about doing this on the gitter channel and got the response
> in general we go for absolute imports, the exception being in places where that creates circular imports (generally init files)
I didn't find that this created any circular imports (at least, running `./test_fast.sh` worked fine), but `__init__` files can always be excluded if necessary (`exclude: __init__\.py$`) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39562 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39562/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39562/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39562/events | https://github.com/pandas-dev/pandas/issues/39562 | 799,607,228 | MDU6SXNzdWU3OTk2MDcyMjg= | 39,562 | pandas 1.2.0 - drop_duplicates() | {
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] | closed | false | null | [] | null | 2 | 2021-02-02T19:29:40Z | 2021-02-03T02:30:39Z | 2021-02-03T02:30:39Z | NONE | null | Hi everyone, I was using the .drop_duplicates() function to remove duplicate values from the .csv file, but unfortunately, it removed some non-duplicate values from my table. So what would you recommend me to save non-duplicate values while dropping duplicates from the .csv file using drop_duplicates() function?
one small part of my code:
table = pd.read_csv(csv_path, low_memory=False, names=['timestamp', 'data'])
table = table.drop_duplicates()
table = table.sort_values(by=['timestamp'])
table.to_csv(csv_path, index=False)
Thank you!
```
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} | 0 | 2021-02-02T20:09:27Z | 2021-02-05T14:57:51Z | 2021-02-05T14:57:51Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
Currently the `styler` has the ability to `set_table_styles` provided they are characterised with a css-selector and list of attribute value pairs, e.g.
```
styler.set_table_styles([{'selector': 'th', 'props': [('color', 'red'), ('font-size', '1em'), ('font-weight', 'bold')]}])
```
Even the legacy docs describe this as a bit of a pain, and for someone used to css, they will be typically looking for a css-style solution:
```
styler.set_table_styles([{'selector': 'th', 'props': 'color:red;font-size:1em;font-weight:bold;'}])
```
#### Describe the solution you'd like
optionally use the input argument valid CSS format above.
#### API breaking implications
none if backwards compatible.
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} | 3 | 2021-02-02T20:13:16Z | 2021-02-05T20:07:59Z | 2021-02-05T14:57:51Z | CONTRIBUTOR | null | - [x] closes #39563
- [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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] | closed | false | null | [] | null | 4 | 2021-02-02T21:10:10Z | 2021-08-15T04:31:42Z | 2021-08-15T04:31:42Z | NONE | null | I wish `fnmatch` was one of the str accessors.
regex (in `str.match` for instance) are sometimes complicated and unintuitive. Shell wildcards, although limited, are easy.
If I want to match all SEED channel codes (max 3 characters) for example that end with 'Z' in a column named `cha_code`, I could do:
```python
from fnmatch import fnmatch
df.cha_code.apply(fnmatch, args=('??Z', ))
```
or
```python
df.cha_code.apply(fnmatch, args=('*Z', ))
```
If there was an `str.fnmatch` accessor method:
```python
df.cha_code.str.fnmatch('*Z')
```
This is of course a simple example that can be handled with `endswith` but the idea is clear - I think.
This does not break anything else as far as I can think.
Another option is to add this functionality to `str.match` but as `pat` is already expecting a regex I am not sure how to avoid confusion. May be add a `fnmatch: bool` kwarg to switch between regex and fnmatch style patterns. | {
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] | open | false | null | [] | null | 5 | 2021-02-02T21:20:26Z | 2021-09-02T15:09:19Z | null | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version 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
```python
DataFrame(["foo", "bar", "baz"]).astype(bytes)
```
As discussed in #39484 we do not want to get numpy string dtypes here. This should have dtype object
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} | 2 | 2021-02-02T22:34:42Z | 2021-02-02T23:41:21Z | 2021-02-02T23:33:56Z | 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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- [ ] tests added / passed
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- [ ] whatsnew entry
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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.
---
df = pd.DataFrame({'a': [1,2,3]})
dtype = pd.api.types.CategoricalDtype([1,2])
df['b'] =df.a.astype(dtype)
df.groupby('b',dropna=False).size()
#### Problem description
observations for categorical variables with na values are dropped from groupby() even if dropna=False is specified.
#### Expected Output
b
nan 1
1 1
2 1
#### Actual output
b
1 1
2 1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.1
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
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 : 3.0.0
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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- [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
AFAICT several of the existing tests are just wrong, xref #39122 cc @jorisvandenbossche
Fixes (but havent yet added a test for) https://github.com/pandas-dev/pandas/issues/39037#issuecomment-756748197
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} | 2 | 2021-02-03T04:51:24Z | 2021-02-04T15:13:36Z | 2021-02-04T15:12:47Z | 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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} | 19 | 2021-02-03T09:57:49Z | 2021-07-20T08:49:27Z | 2021-02-07T17:28:01Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame()
with pd.ExcelWriter('test.xlsx', mode='w', engine='openpyxl') as writer:
df.to_excel(writer)
with pd.ExcelWriter('test.xlsx', mode='a', engine='openpyxl') as writer:
df.to_excel(writer)
```
#### Problem description
Appending to excel file with pandas>=1.2 produces file which Excel reports as corrupted and opens only after repair, forcing you to first open and repair file before passing it to someone else.
Looking at file with 7-Zip File Manager shows that after writing file with mode='a', some .xml files (app.xml, core.xml, theme1.xml, sheet1.xml) inside archive are duplicated (somehow). After Excel rapars file duplicates are gone, everything else looks the same.
With pandas 1.1.5 and same versions of other packages everything works as expected.
#### Expected Output
File which could be opened by Excel without any warnings.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Russian_Russia.1251
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
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 : 3.0.6
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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] | open | false | null | [] | null | 5 | 2021-02-03T12:24:51Z | 2021-08-15T04:27:28Z | null | MEMBER | null | The default for new repositories is now "main", so renaming might make contributing less confusing to newcomers.
scikit-learn have made this move (see [issue](https://github.com/scikit-learn/scikit-learn/issues/17595) and [PR](https://github.com/scikit-learn/scikit-learn/pull/19237/files)), and from https://github.com/github/renaming it doesn't look too disruptive:
> Renaming a branch will:
>
> - Re-target any open pull requests
> - Update any draft releases based on the branch
> - Move any branch protection rules that explicitly reference the old name
> - Update the branch used to build GitHub Pages, if applicable
> - Show a notice to repository contributors, maintainers, and admins on the repository homepage with instructions to update local copies of the repository
> - Show a notice to contributors who `git push` to the old branch
> - Redirect web requests for the old branch name to the new branch name
> - Return a "Moved Permanently" response in API requests for the old branch name
>
From the penultimate bullet point above, it seems that existing links in the docs wouldn't break even if we didn't update them, but I think the
```
push:
branches: [master]
```
part of the CI workflows would still need renaming. | {
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] | open | false | null | [] | null | 4 | 2021-02-03T14:19:53Z | 2021-05-26T00:08:19Z | null | MEMBER | null | xref indexing work item of #39146
This actually started as trying to get the tests passing for `tests/frame/indexing`, but while doing that I needed to make decisions on what the behaviour should be for setitem / loc / iloc assignment (so related other ongoing discussions about this).
Very much a draft, but a good way to see what we actually want / the consequences of those choices.
cc @jbrockmendel | {
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} | 6 | 2021-02-03T14:23:16Z | 2021-10-07T16:41:59Z | 2021-04-25T17:54:03Z | NONE | null | I'm trying to get the left edge from a Series of Intervals. Unexpectedly, the result is a Series of dtype category. I'm also getting a FutureWarning.
```python
>>> import pandas as pd
>>> s = pd.Series(pd.cut([1, 2, 3], 2))
>>> s
0 (0.998, 2.0]
1 (0.998, 2.0]
2 (2.0, 3.0]
dtype: category
Categories (2, interval[float64]): [(0.998, 2.0] < (2.0, 3.0]]
>>> s.apply(lambda x: x.left)
C:\Users\My.Name\.virtualenvs\MyRepo-nagZr6A2\lib\site-packages\pandas\io\formats\format.py:1403: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this wi
ll return a view on self.
for val, m in zip(values.ravel(), mask.ravel())
0 0.998
1 0.998
2 2.000
dtype: category
Categories (2, float64): [0.998 < 2.000]
```
Interestingly, the individual cells have objects with the correct dtype:
```python
>>> result = s.apply(lambda x: x.left)
>>> type(result[0])
<class 'numpy.float64'>
```
However, operations you would expect from a Series of floats to work do not work:
```python
>>> result + 1
[...]
TypeError: unsupported operand type(s) for +: 'Categorical' and 'int'
```
<details><summary>pd.show_versions()</summary>
INSTALLED VERSIONS<br>
------------------<br>
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa<br>
python : 3.8.7.final.0 <br>
python-bits : 64 <br>
OS : Windows <br>
OS-release : 10<br>
Version : 10.0.19041<br>
machine : AMD64<br>
processor : AMD64 Family 23 Model 96 Stepping 1, AuthenticAMD<br>
byteorder : little<br>
LC_ALL : None<br>
LANG : None<br>
LOCALE : de_DE.cp1252<br>
pandas : 1.2.1<br>
numpy : 1.20.0<br>
pytz : 2021.1<br>
dateutil : 2.8.1<br>
pip : 20.3.3<br>
setuptools : 51.1.2<br>
Cython : None<br>
pytest : 6.2.2<br>
hypothesis : None<br>
sphinx : None<br>
blosc : None<br>
feather : None<br>
xlsxwriter : None<br>
lxml.etree : None<br>
html5lib : None<br>
pymysql : None<br>
psycopg2 : None<br>
jinja2 : 2.11.3<br>
IPython : 7.20.0<br>
pandas_datareader: None<br>
bs4 : None<br>
bottleneck : None<br>
fsspec : None<br>
fastparquet : None<br>
gcsfs : None<br>
matplotlib : None<br>
numexpr : None<br>
odfpy : None<br>
openpyxl : 3.0.6<br>
pandas_gbq : None<br>
pyarrow : None<br>
pyxlsb : None<br>
s3fs : None<br>
scipy : 1.6.0<br>
sqlalchemy : 1.3.23<br>
tables : None<br>
tabulate : 0.8.7<br>
xarray : None<br>
xlrd : None<br>
xlwt : None<br>
numba : None<br>
</details>
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] | closed | false | null | [] | null | 3 | 2021-02-03T16:28:06Z | 2021-02-03T21:23:54Z | 2021-02-03T20:15:03Z | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
### Code Sample
**Bash**
```git
docker build --tag pandas-parfaitg-env .
docker run -it --rm -v path-to-pandas-yourname:/home/pandas-parfaitg pandas-parfaitg-env
cd /home
ls
# pandas pandas-parfaitg
cat > sandbox.py
# see script below
python sandbox.py
```
**Python** (sandbox.py)
```python
import sys
sys.path.remove('/home/pandas')
sys.path.append('/home/pandas-parfaitg')
import pandas as pd
```
### Problem Description
Currently, I am working on a pandas ENH PR and use a Docker container for my development environment, following exact instructions in [contributing docs](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#using-a-docker-container). Dockerfile builds image and runs container perfectly fine. However, after several weeks where above routine worked great, until yesterday evening after a `git fetch upstream` and `git merge upstream/master`, my development pandas `sandbox.py` code now errors out with the following message:
```python
Traceback (most recent call last):
File "sandbox.py", line 5, in <module>
import pandas as pd
File "/home/pandas-parfaitg/pandas/__init__.py", line 51, in <module>
from pandas.core.api import (
File "/home/pandas-parfaitg/pandas/core/api.py", line 31, in <module>
from pandas.core.groupby import Grouper, NamedAgg
File "/home/pandas-parfaitg/pandas/core/groupby/__init__.py", line 1, in <module>
from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy
File "/home/pandas-parfaitg/pandas/core/groupby/generic.py", line 58, in <module>
from pandas.core.aggregation import (
File "/home/pandas-parfaitg/pandas/core/aggregation.py", line 41, in <module>
from pandas.core.indexes.api import Index
File "/home/pandas-parfaitg/pandas/core/indexes/api.py", line 24, in <module>
from pandas.core.indexes.period import PeriodIndex
File "/home/pandas-parfaitg/pandas/core/indexes/period.py", line 66, in <module>
class PeriodIndex(DatetimeIndexOpsMixin):
File "/home/pandas-parfaitg/pandas/core/indexes/extension.py", line 112, in wrapper
meth = inherit_from_data(name, delegate, cache=cache, wrap=wrap)
File "/home/pandas-parfaitg/pandas/core/indexes/extension.py", line 43, in inherit_from_data
attr = getattr(delegate, name)
AttributeError: type object 'PeriodArray' has no attribute 'start_time'
````
If `sandbox.py` simply contains `import pandas` (the Docker build version), no error occurs. Does Dockerfile use last stable pandas build?
A few [commits have been merged yesterday evening](https://github.com/pandas-dev/pandas/commits/master/pandas/core) in the `pandas/core` directory. Is this somehow related? I tried many `git` synches to be current with latest version to no avail. Is my forked repo or local branch the issue?
#### Expected Output
No error
#### Output of ``pd.show_versions()``
Unable to run since I am cannot `import pandas as pd` from `pandas/parfaitg`
<details>
...
</details>
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} | 1 | 2021-02-03T18:11:38Z | 2021-02-04T02:33:52Z | 2021-02-04T01:59:27Z | 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
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/39583 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39583/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39583/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39583/events | https://github.com/pandas-dev/pandas/issues/39583 | 800,692,281 | MDU6SXNzdWU4MDA2OTIyODE= | 39,583 | BUG: Support keywords in pd.Timestamp.isoformat(), ref.#26131 is stale. | {
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} | 6 | 2021-02-03T20:55:54Z | 2021-02-03T21:25:03Z | 2021-02-03T21:24:58Z | 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.
```python
import pandas as pd
now=pd.Timestamp.utcnow()
now.isoformat(timespec="milliseconds")
print(now)
```
#### Problem description
The isoformat is just to follow the ISO format 8601, but it is not detecting any keywords.
#### Expected Output
"2020-02-03 21:54:34:024"
#### Output of ``pd.show_versions()``
</details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.2.final.0
python-bits : 32
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : es_ES.cp1252
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.1
setuptools : 51.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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
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] | open | false | null | [] | null | 23 | 2021-02-03T21:49:03Z | 2021-10-12T22:15:40Z | null | MEMBER | null | Currently, setitem-like operations (i.e. operations that change values in an existing series or dataframe such as `__setitem__` and `.loc`/`.iloc` setitem, or filling methods like `fillna`) first try to update in place, but if there is a dtype mismatch, pandas will upcast to a common dtype (typically object dtype).
For example, setting a string into an integer Series upcasts to object:
```python
>>> s = pd.Series([1, 2, 3])
>>> s.loc[1] = "B"
>>> s
0 1
1 B
2 3
dtype: object
```
or doing a `fillna` with an invalid fill value also upcasts instead of raising an error:
```python
>>> s = pd.Series(["2020-01-01", "NaT"], dtype="datetime64[ns]")
>>> s
0 2020-01-01
1 NaT
dtype: datetime64[ns]
>>> s.fillna(1)
0 2020-01-01 00:00:00
1 1
dtype: object
```
----
My **general proposal** would be that in some future (eg pandas 2.0 + after a deprecation), such inherently inplace operation should have the guarantee to either happen in place or either error, and thus never change the dtype of the original Series/DataFrame.
This is similar to eg numpy's behaviour where setitem never changes the dtype. Showing the first example from above in equivalent numpy code:
```python
>>> arr = np.array([1, 2, 3])
>>> arr[1] = "B"
...
ValueError: invalid literal for int() with base 10: 'B'
```
Apart from that, I also think this is the cleaner behaviour with less surprises. If a user specifically wants to allow mixed types in a column, they can manually cast to `object` dtype first.
On the other hand, this is quite a big change in how we generally are permissive right now and easily upcast, and such a change will certainly impact quite some user code (but, it's perfectly possible to do this with proper deprecation warnings in advance warning for the specific cases where it will error in the future AFAIK).
There are certainly some more details that need to discussed as well if we want this (which exact values are regarded as compatible with the dtype, eg setting a float in an integer column, should that error or silently round the float?). But what are people's thoughts on the general idea?
cc @pandas-dev/pandas-core
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] | open | false | null | [] | null | 39 | 2021-02-03T22:59:57Z | 2021-03-31T21:18:00Z | null | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
- See [this question](https://stackoverflow.com/questions/65927344/accessing-dataframe-with-multiindex-with-date-intermittently-failing) and [this question](https://stackoverflow.com/questions/65937092/read-pickle-failing-stochastically).
#### Question about pandas
I have a dataframe with a multiindex from which I am attempting access a row from. However, it is seemingly failing stochastically on around 1/10th of runs. I see this behaviour both locally and on prod. The dataframe can be recreated with the following:
```python
from datetime import timedelta, date
import pandas as pd
import pytz
from pandas import Timestamp
utc = pytz.UTC
data = {
"date": [
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).replace(minute=59, second=59, microsecond=999999),
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).date(),
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).date(),
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).date() + timedelta(days=1),
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).date() + timedelta(days=1),
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).date() + timedelta(days=2),
Timestamp("2020-06-03 15:00:00").replace(tzinfo=utc).date() + timedelta(days=2),
],
"a": ["alpha", "alpha", "beta", "alpha", "beta", "alpha", "beta"],
"b": [100, 100, 100, 100, 100, 100, 100],
"c": [100, 100, 100, 100, 100, 100, 100],
"d": [0, 0, 0, 0, 0, 0, 0],
"e": [100, 100, 100, 100, 100, 100, 100],
"f": [0, 0, 0, 0, 0, 0, 0],
"g": [0, 0, 0, 0, 0, 0, 0],
"h": ["A", "B", "C", "D", "E", "F", "G"],
}
df = pd.DataFrame(data)
breakdown = df.groupby(["date", "a"]).sum()
done = breakdown.loc[date(2020, 6, 3), "beta"]
```
I do not know if it is my incorrect usage that is causing this behaviour or a bug.
I originally encountered the issue on `pandas` `1.1.4`(*) with the error:
```
TypeError: '<' not supported between instances of 'int' and 'slice'
```
and on `1.2.1`(**) I see the same intermitent errors but with the error message:
```
KeyError: 'beta'
```
#### Version info
(*)
```
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 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
```
(**)
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.17
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
```
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} | 10 | 2021-02-03T23:40:06Z | 2021-02-12T03:01:12Z | 2021-02-07T16:24:23Z | MEMBER | null | - [x] closes #39528
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/39587 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39587/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39587/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39587/events | https://github.com/pandas-dev/pandas/pull/39587 | 800,813,494 | MDExOlB1bGxSZXF1ZXN0NTY3MjM5NTc3 | 39,587 | REGR: do not try to infer the excel type from a workbook object | {
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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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] | closed | false | null | [] | null | 2 | 2021-02-04T01:41:32Z | 2021-02-08T05:59:50Z | 2021-02-08T05:59:50Z | NONE | null | #### Question about pandas
For `Series.eq`, In the doc it says "Equivalent to `series == other`" but it seems not true.
Let me show a simple example.
```python
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> a.eq(b)
a True
b False
c False
d False
e False
dtype: bool
>>> a == b
Traceback (most recent call last):
...
ValueError: Can only compare identically-labeled Series objects
```
Series comparison with `eq` API works, whereas `==` raise an Exception.
Is it intended behavior ??
Thanks :) | {
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} | 1 | 2021-02-04T01:52:04Z | 2021-02-04T15:51:47Z | 2021-02-04T15:50:12Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Followup from #39482 - removed tests are duplicates of `test_readers.test_bad_sheetname_raises`
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] | closed | false | null | [] | null | 3 | 2021-02-04T03:08:17Z | 2021-02-04T03:46:13Z | 2021-02-04T03:37:00Z | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
(I created a venv, and installed pandas version 1.2.1, but did not clone/build from source, not sure if that counts or not).
---
A csv with MultiIndexed columns, when read in appends a .1, .2, etc. unnecessarily.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
# creating a multi-Indexed csv
cols = [
("Left", "Tier 2", "Column 1"),
("Left", "Tier 2", "Column 2"),
("Left", "Tier 2", "Total"),
("Right", "Tier 2", "Total")]
df = pd.DataFrame(np.arange(20).reshape((5,4)))
df.columns = pd.MultiIndex.from_tuples(cols)
print(df.head())
df.to_csv("test.csv")
read_df = pd.read_csv("./test.csv", header=[0,1], index_col=0)
print(read_df.head())
# output
# the original dataframe
Left Right
Tier 2 Tier 2
Column 1 Column 2 Total Total
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
4 16 17 18 19
# dataframe after being read back in from read_csv
Left Right
Tier 2 Tier 2.1 Tier 2.2 Tier 2
NaN Column 1 Column 2 Total Total
0.0 0 1 2 3
1.0 4 5 6 7
2.0 8 9 10 11
3.0 12 13 14 15
```
#### Problem description
In the example, the function read_csv appends a .1, .2 etc. to levels in a multi-indexed column. This is unnecessary, since the columns are uniquely identified by ("Left, "Tier 2", "Column 1"), ("Left", "Tier 2", "Column 2"), so in theory there should be no column name overlap. This is additionally undesirable, as one of the main benefits to having a multi-index is the ability to access subsets of columns easily.
#### Expected Output
The level names should not be mangled. Specifically, the expected output should be
```python
Left Right
Tier 2 Tier 2
Column 1 Column 2 Total Total
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
4 16 17 18 19
```
And
```python
print(read_df.columns.values)
```
```python
array([("Left", "Tier 2", "Column 1"),
("Left", "Tier 2", "Column 2"),
("Left", "Tier 2", "Total"),
("Right", "Tier 2", "Total")])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Nov 10 00:07:31 PST 2020; root:xnu-4903.278.51~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
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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} | 1 | 2021-02-04T06:40:51Z | 2021-02-05T17:49:23Z | 2021-02-05T14:26:14Z | MEMBER | 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
Also I think I stumbled on some bugs along the way; added whatsnew and modified the existing tests where necessary.
```
$ asv_bench % asv continuous -f 1.1 upstream/master HEAD -b rolling.Pairwise.time_groupby
before after ratio
[f79468b8] [6a1e8592]
<master> <ref/groupby_rolling_pairwise>
- 299±50ms 178±20ms 0.59 rolling.Pairwise.time_groupby(1000, 'corr', True)
- 272±7ms 157±20ms 0.58 rolling.Pairwise.time_groupby(1000, 'cov', True)
- 262±1ms 146±4ms 0.56 rolling.Pairwise.time_groupby(None, 'cov', True)
- 303±10ms 158±6ms 0.52 rolling.Pairwise.time_groupby(10, 'cov', True)
- 279±20ms 139±0.7ms 0.50 rolling.Pairwise.time_groupby(None, 'corr', True)
- 315±10ms 149±10ms 0.47 rolling.Pairwise.time_groupby(10, 'corr', True)
- 137±2ms 34.0±0.8ms 0.25 rolling.Pairwise.time_groupby(10, 'cov', False)
- 127±5ms 28.2±1ms 0.22 rolling.Pairwise.time_groupby(None, 'cov', False)
- 134±5ms 28.1±0.4ms 0.21 rolling.Pairwise.time_groupby(1000, 'cov', False)
- 135±2ms 27.7±1ms 0.21 rolling.Pairwise.time_groupby(None, 'corr', False)
- 178±10ms 33.7±2ms 0.19 rolling.Pairwise.time_groupby(10, 'corr', False)
- 159±20ms 29.9±2ms 0.19 rolling.Pairwise.time_groupby(1000, 'corr', False)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
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} | 7 | 2021-02-04T07:05:23Z | 2021-02-09T07:17:39Z | 2021-02-07T16:42:08Z | CONTRIBUTOR | null | - [x] closes #37615
- [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
Until now the upper 32bits of PyObject_Hash aren't taken into account at all. Because for my built-in objects the hash function is very simple (e.g. integers) many "normal" series would have all hashes being 0, which would lead to O(n^2) running times.
Thus for 64bit builds, we need to mangle the upper 32bit into the resulting 32bit-hash.
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} | 1 | 2021-02-04T07:31:12Z | 2021-03-29T15:55:19Z | 2021-03-29T15:55:19Z | CONTRIBUTOR | null | #### Location of the documentation
`ecosystem`, + `user_guide/index.rst + style.ipynb + visualization.rst`
possibly others.
#### Documentation problem and proposed changes.
**High Level**
Scientific data visualization is not just restricted to charting. Tabular data visualization is important and `Styler` has some really good features for dealing with this - albeit `Styler` is not a recognised scientific term.
Currently the `User Guide` has left navigation links `Visualization` and `Styling`, and I would like to rename them `Chart Visualization` and `Table Visualization` and put them next to each other in the list. Also with some cross-refs.
The `Api` page has a `Style` navigation link which is probably best left as is due to API naming.
**Low Level**
a) I will revisit the `style.ipynb` page and update it with many of the new features, and possibly provide more of a continuous real-case example, rather than using numpy.random repeatedly to demonstrate the features. This isn't completely necessary but maybe works well.
b) I will revisit the function doc strings to ensure consistency on the API page (there is some inconsistency currently) and add examples where missing to the API page.
<br>
<br>
If you feel this is warranted or have any comments on the items please share before I get started.
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} | 1 | 2021-02-04T08:06:08Z | 2021-02-22T23:20:50Z | 2021-02-22T23:20:50Z | CONTRIBUTOR | null | #### Location of the documentation
URL: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#optional-dependencies
File in GitHub: https://github.com/pandas-dev/pandas/blob/master/doc/source/getting_started/install.rst
#### Documentation problem
The list of optional dependencies is currently just listed in alphabetical order. However, I think it would be much more logical to group packages by topic (PyTables **reads/writes hdf5** files, xlrd **reads Excel** files). Then you can compare/contrast which packages you should install for *your* usecase, e.g. should I install openpyxl or xlrd to read my excel files; I should research more about them.
Additionally, it might be helpful to include more detailed information about how they're different, but I am not as familiar with the minute differences between them. That will have to be for another time.
#### Suggested fix for documentation
I am willing to work on this.
I could either a) add a fourth column to the dependency table listing which group it is a part of (read/write HTML, read/write Excel), or b) split it into multiple tables with a minor header for each group.
Are there any issues with making this change, and which of those two actions should I prefer? | {
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} | 3 | 2021-02-04T08:09:29Z | 2021-03-23T20:30:31Z | 2021-03-23T20:30:31Z | 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
>>> df1 = pd.DataFrame([1, 2, 3, 4], columns=['w'])
>>> df2 = pd.DataFrame([1, 2, 3, 4], columns=['w'])
>>> c1 = df1['w'].where(df1['w'] < 4, 4)
>>> c2 = df2['w'].where(df2['w'] < 5)
>>> c1 *= 2
>>> c2 *= 2
>>> assert (c1 == c2).all()
>>> assert (df1['w'] == df2['w']).all() # df2 got changed, so assertion fails
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AssertionError
>>>
>>> df1
w
0 1
1 2
2 3
3 4
>>> df2
w
0 2
1 4
2 6
3 8
>>>
```
#### Problem description
The behavior is inconsistent: in case `.where` is used to select all elements in the series, a reference to the original data is returned instead of a copy. This way it is possible to modify the original dataframe/series - but only when all elements are selected.
In the example code, c1 is identical to c2, however changing c2 (which is created by selecting all elements) changes the parent dataframe.
Prior to 1.1.4 `.where` always returned a copy: checked 0.25.1, 1.1.0, 1.1.1, 1.1.2, 1.1.3 and all of them created a copy. This has been changed in 1.1.4, and 1.1.5 as well as 1.2.1 still has this behaviour.
#### Expected Output
There is no assertion, i.e. `.where` returns a copy consistently.
Please note that the documentation is unclear on this, it might worth explicitly mentioning whether or not copying is expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.20.13-1.el7.elrepo.x86_64
Version : #1 SMP Wed Feb 27 10:02:05 EST 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 47.3.1.post20201218
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.30.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : 0.10.1
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 2 | 2021-02-04T10:39:54Z | 2021-02-04T13:14:23Z | 2021-02-04T13:13:58Z | NONE | null | When plotting a line chart with a defined color per line and a defined linestyle per line, using the plot function in a pandas DataFrame an invalid RGBA argument exception is thrown. When one defines either color or linestyle the code works, but it looks like you can't do both at the same time. Further more, this behavior only seems to occur when using one or more 'o' style lines. How come?
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(1000, 4), index=pd.date_range("1/1/2000", periods=1000), columns=list("ABCD"))
df = df.cumsum()
style = ['-'] * 3 + ['o']
colors = [
[.6, .6, .6],
[0, .7, .1],
[1, .5, .1],
[0.66, 0.55, 0.84],
]
params = dict(
color=colors,
style=style,
)
ax = df.plot(**params)
```
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} | 6 | 2021-02-04T17:49:07Z | 2021-04-04T20:03:22Z | 2021-04-04T20:03:22Z | NONE | null | - [ ] closes #38482
- [ ] 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: added warning in docs to show -1 will be returned for not found values, with a suitable example.
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] | closed | false | null | [] | null | 3 | 2021-02-04T20:42:07Z | 2021-08-15T04:35:17Z | 2021-08-15T04:35:17Z | 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
>>> pd.read_excel('~/testDatetimeNanos.xlsx')
TestDate
0 2020-09-24 01:00:00
1 2020-09-09 16:26:00
2 2020-09-09 16:26:00
>>> pd.read_excel('~/testDatetimeNanos.xlsx', engine='openpyxl')
TestDate
0 2020-09-24 01:00:00.000003
1 2020-09-09 16:25:59.999998
2 2020-09-09 16:26:00.000000
```
[testDatetimeNanos.xlsx](https://github.com/pandas-dev/pandas/files/5928342/testDatetimeNanos.xlsx)
#### Problem description
_Problem also described in [this Stack Overflow post](https://stackoverflow.com/questions/66037692/how-to-make-pandas-read-excel-with-engine-openpyxl-behave-like-it-did-with-xlr)._
The default behavior of the read_excel function seems to have changed when using the openpyxl engine from how it used to behave when using the xlrd engine. It looks like now datetimes are being represented with nanoseconds and in some cases appear to differ by ~2-3 nanoseconds from the expected datetime value as seen in Excel.
#### Expected Output
The expected output is reflected in the first dataframe shown above, where the datetimes do not include nanoseconds, as this has been the behavior previously for xlsx files using xlrd.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.6.12.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.230-0.1.ac.223.84.332.metal1.x86_64
Version : #1 SMP Tue Sep 8 15:48:58 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.0
pip : 21.0.1
setuptools : 43.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 0.9.6
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 0 | 2021-02-04T21:38:16Z | 2021-02-12T01:52:56Z | 2021-02-12T01:52:47Z | 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
@TomAugspurger i know you dont like adding default methods to EA, but i dont see any other way here to avoid special-casing Categorical (which is the goal here) | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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I could set the index as a column, and then hide the remaining index. This is not ideal, because I'd like to keep the pandas dataframe index formatting (i.e. bolding). Currently, it is possible to left align the columns within dataframes, just not the index.
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} | 0 | 2021-02-05T03:22:35Z | 2021-02-15T22:14:02Z | 2021-02-15T22:14:02Z | MEMBER | null | PR #39486 fixed an issue with openpyxl but only works for openpyxl >= 3.0.0. We should bump the minimum version with pandas 1.3 and remove the version-checking that was added in that PR. | {
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} | 3 | 2021-02-05T03:28:52Z | 2021-02-07T12:23:43Z | 2021-02-05T20:24:03Z | MEMBER | null | - [x] closes #39554
- [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 | 2021-02-05T03:32:07Z | 2021-02-07T21:57:11Z | 2021-02-07T17:28:01Z | CONTRIBUTOR | null | - [x] closes #39576
- [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
Tentatively ready: I added a working test but I don't have MS Excel to make sure it is happy now. Waiting for someone to test it https://github.com/pandas-dev/pandas/issues/39576#issuecomment-774102850 | {
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} | 5 | 2021-02-05T04:50:25Z | 2021-02-12T07:12:55Z | 2021-02-12T01:24:39Z | 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
I _think_ this should work for arbitrary EAs, but we only have tests for sparse, dt64tz, and this adds Period (and some untested cases for dt64tz)
Part of the idea is to get the code out of the Block method for ArrayManager compat. | {
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] | closed | false | null | [] | null | 5 | 2021-02-05T07:09:21Z | 2021-06-12T00:35:20Z | 2021-06-12T00:35:20Z | 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
# Your code here
import pandas
import numpy as np
df = pandas.DataFrame(
{
"int_col": [-5, 2, 7, 16],
"float_col": [np.NaN, -9.4, 10.1, np.NaN],
"str_col": ["a", np.NaN, "c", "d"],
"bool_col": [False, True, True, False],
}
)
df
int_col float_col bool_col
0 -5 NaN False
1 2 -9.4 True
2 7 10.1 True
3 16 NaN False
# pandas 1.1.5 behavior:
df.max(axis=0, numeric_only=True)
int_col 16.0
float_col 10.1
bool_col 1.0
dtype: float64
# pandas 1.2.1 behavior:
df.max(axis=0, numeric_only=True)
int_col 16
float_col 10.1
bool_col True
dtype: object
# the difference is in last two lines
bool_col 1.0
dtype: float64
# and
bool_col True
dtype: object
```
#### Problem description
The same relates to `min`.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 1 | 2021-02-05T08:04:27Z | 2021-02-05T08:53:13Z | 2021-02-05T08:53:02Z | CONTRIBUTOR | null | Fix typo.
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- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
```python
import pandas
df = pandas.DataFrame({"a": [1, 2], "b": [3, 4]})
print(df.agg({})) # ValueError: No objects to concatenate
```
<details><summary>Traceback</summary>
```
Traceback (most recent call last):
File "../exp.py", line 4, in <module>
print(df.agg({}))
File "/localdisk/dchigare/repos/pandas/pandas/core/frame.py", line 7689, in aggregate
result, how = self._aggregate(func, axis, *args, **kwargs)
File "/localdisk/dchigare/repos/pandas/pandas/core/frame.py", line 7723, in _aggregate
result, how = op.agg()
File "/localdisk/dchigare/repos/pandas/pandas/core/apply.py", line 177, in agg
return self.agg_dict_like(_axis), True
File "/localdisk/dchigare/repos/pandas/pandas/core/apply.py", line 374, in agg_dict_like
result = concat({k: results[k] for k in keys_to_use}, axis=axis)
File "/localdisk/dchigare/repos/pandas/pandas/core/reshape/concat.py", line 288, in concat
op = _Concatenator(
File "/localdisk/dchigare/repos/pandas/pandas/core/reshape/concat.py", line 345, in __init__
raise ValueError("No objects to concatenate")
ValueError: No objects to concatenate
```
</details>
Since #37227, aggregation with empty dictionary started to raise `ValueError` (previously it return an empty frame) which comes from `.concat` when trying to concat results:
https://github.com/pandas-dev/pandas/blob/cb1486c8ef53db474248a0f9b81937b487388431/pandas/core/apply.py#L374
Since `results` are empty, it's logical that it raise exception. But the logic that was touched by #37277 doesn't touch that `.concat` part, so the behavior should stay the same - return an empty frame.
Previously it goes to the `concat` branch if `any(cond1(x) for x in agg_dict)` and now it's `all(cond2(x) for x in agg_dict)`. `cond1` and `cond2` was written in that way so the condition with `any` and `all` are technically the same. But the difference between `any` and `all` is that they give the different results on an empty sequences:
```python
>>> all(x for x in [])
True
>>> any(x for x in [])
False
```
So after `any` was changed to `all` it gets to the `concat` branch even if `results` are empty and gets an error on `.concat`.
So the question is, is it desired behavior that now aggregation raises exception on empty dict? And if it is, should it catch these cases in [`agg_dict_line`](https://github.com/pandas-dev/pandas/blob/cb1486c8ef53db474248a0f9b81937b487388431/pandas/core/apply.py#L279) and give a nicer error?
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https://api.github.com/repos/pandas-dev/pandas/issues/39610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39610/events | https://github.com/pandas-dev/pandas/issues/39610 | 802,024,686 | MDU6SXNzdWU4MDIwMjQ2ODY= | 39,610 | BUG: Grouping by frequency doesn't preserve order within each group | {
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] | open | false | null | [] | null | 0 | 2021-02-05T09:58:50Z | 2021-08-15T17:28:18Z | 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
A = pd.DataFrame(list(zip([
'2018-08-30 18:01:37.774447+00:00',
'2018-08-30 16:07:41.125230+00:00',
'2019-08-30 13:08:16.467217+00:00',
'2016-08-30 13:12:43.530383+00:00',
'2018-08-30 12:13:26.028439+00:00'
],
[
'2018-05-30 18:01:37.774447+00:00',
'2018-05-30 12:01:37.774447+00:00',
'2018-05-30 11:01:37.774447+00:00',
'2018-09-30 13:13:26.028439+00:00',
'2018-09-30 12:13:26.028439+00:00'
], [1,1,1,2,2])), columns=['datetime', 'datetime2', 'grp'])
A['datetime'] = pd.to_datetime(A['datetime'], utc=True)
A['datetime2'] = pd.to_datetime(A['datetime2'], utc=True)
A = A.sort_values('datetime')
print(list(A.groupby('grp'))[0]) # this one is sorted correctly
# (1,
# datetime datetime2 grp
# 1 2018-08-30 16:07:41.125230+00:00 2018-05-30 12:01:37.774447+00:00 1
# 0 2018-08-30 18:01:37.774447+00:00 2018-05-30 18:01:37.774447+00:00 1
# 2 2019-08-30 13:08:16.467217+00:00 2018-05-30 11:01:37.774447+00:00 1)
print(list(A.groupby(pd.Grouper(key='datetime2', freq='1d')))[0]) # this one is not sorted
# (Timestamp('2018-05-30 00:00:00+0000', tz='UTC', freq='D'),
# datetime datetime2 grp
# 2 2019-08-30 13:08:16.467217+00:00 2018-05-30 11:01:37.774447+00:00 1
# 1 2018-08-30 16:07:41.125230+00:00 2018-05-30 12:01:37.774447+00:00 1
# 0 2018-08-30 18:01:37.774447+00:00 2018-05-30 18:01:37.774447+00:00 1)
```
#### Problem description
Pandas docs for groupby (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) says:
> Groupby preserves the order of rows within each group.
This works when grouping by column values, but when doing it by frequency this doesn't hold, as we can see in the example above.
Also, tests for this behaviour don't include dates: https://github.com/nickeubank/pandas/blob/3b49fad4558a6d4f3db1c1d360a7048d1b633211/pandas/tests/test_groupby.py#L5439
#### Expected Output
The groups should preserve order when grouping by frequency.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-130-generic
Version : #134-Ubuntu SMP Tue Jan 5 20:46:26 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.0
pip : 20.1.1
setuptools : 47.1.1
Cython : 0.29.21
pytest : 6.1.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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] | closed | false | null | [] | null | 1 | 2021-02-05T12:05:35Z | 2021-02-10T17:59:14Z | 2021-02-10T17:59:14Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39612/events | https://github.com/pandas-dev/pandas/pull/39612 | 802,117,374 | MDExOlB1bGxSZXF1ZXN0NTY4MzE0Mzg4 | 39,612 | [ArrayManager] REF: Implement concat with reindexing | {
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} | 35 | 2021-02-05T12:15:00Z | 2021-04-12T10:56:14Z | 2021-04-12T10:56:11Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146 (the concat work item)
This PR implements `concat` for DataFrames using the ArrayManager, and all tests in `pandas/tests/reshape/` already pass.
Summary of the changes:
* In `internals/concat.py`, I added a `concatenate_block_managers` equivalent for ArrayManagers. This function rather simply reindexes the managers and then concats the arrays (axis=0) or combines them in a single manager (axis=1).
* The main problem with that simple approach is that when reindexing the ArrayManager with the given indexer, we do not yet know the resulting dtype for when a new all-NA column needs to be added (i.e. the common dtype we would find when actually concatting). And we can't make this eg an all-NaN object array, as that would influence the `find_common_type` logic.
* The way I tried to solve this at the moment is by adding a dummy "proxy" object to signal such an all-NA array (the `NullArrayProxy` class), only tracking its length. And when actually concatting, we can replace this with an actual all-NA array after we determined the resulting dtype. This proxy object only lives inside the concatting code (it only gets created when we will call `concat_compat` and inside this function they always get replaced), so the user will never see this.
* An alternative could be to first determine what the resulting dtype will be in `internals/concat.py` so we can pass this to the `reindex` functionality, and after reindexing do the actual concatenation. But IMO that will be more complex; the concat / find_common_type logic gets involved twice (before and after reindexing), and getting those dtypes for which we want to know the common type is also quite tricky before actually reindexing the manager.
Basically, this replaces the 600 lines of code of `internals/concat.py` with 25 lines of `concatenate_array_managers` + the `NullArrayProxy` and `_array_from_proxy` code in `dtypes/concat.py` (but I am quite likely still missing some corner cases!)
Note: I did not explicitly test `copy=True/False` behaviour for concat along the columns with this new code.
cc @jreback @jbrockmendel | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39613/events | https://github.com/pandas-dev/pandas/issues/39613 | 802,214,573 | MDU6SXNzdWU4MDIyMTQ1NzM= | 39,613 | BUG: Inconsistency between Period.ordinal and Timestamp.fromordinal() | {
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] | closed | false | null | [] | null | 2 | 2021-02-05T14:36:27Z | 2021-02-06T16:39:51Z | 2021-02-06T16:39:51Z | 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
ordinaldate = pd.Period(pd.Timestamp('2020-12-31'), freq='s').ordinal
pd.Timestamp.fromordinal(ordinaldate)
```
#### Problem description
The code snippet raises the error message:
*** ValueError: year 4406314 is out of range
#### Expected Output
Timestamp('2020-12-31 00:00:00')
Maybe I completely misunderstand the concept of Period and Timestamp, but I would expect that when I convert from a Period to an ordinal date, and subsequently convert back to a Timestamp, I should get the initial Timestamp.
I am specifically interested in the inverse operation of the conversion to ordinal in class PeriodConverter(dates.DateConverter)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : 3.4.3
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 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2021-02-05T14:56:36Z | 2021-04-16T00:46:28Z | 2021-04-16T00:46:28Z | NONE | null | - [ x] 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
I noticed that indexing with a reversed list works but not assigning. The error message is not helpful. Is this intended?
```python
import pandas as pd
s = pd.Series([1, 2, 3])
mask = [True, False, False]
s.loc[reversed(mask)]
>>> 2 3
>>> dtype: int64
# But assigning does not work
s.loc[reversed(mask)] = 5
>>> ---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-912755dd04b5> in <module>
7
8 # But assigning does not work
----> 9 s.loc[reversed(mask)] = 5
~/miniconda3/envs/who/lib/python3.9/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
686 else:
687 key = com.apply_if_callable(key, self.obj)
--> 688 indexer = self._get_setitem_indexer(key)
689 self._has_valid_setitem_indexer(key)
690
~/miniconda3/envs/who/lib/python3.9/site-packages/pandas/core/indexing.py in _get_setitem_indexer(self, key)
634
635 try:
--> 636 return self._convert_to_indexer(key, axis=0, is_setter=True)
637 except TypeError as e:
638
~/miniconda3/envs/who/lib/python3.9/site-packages/pandas/core/indexing.py in _convert_to_indexer(self, key, axis, is_setter)
1210 else:
1211 # When setting, missing keys are not allowed, even with .loc:
-> 1212 return self._get_listlike_indexer(key, axis, raise_missing=True)[1]
1213 else:
1214 try:
~/miniconda3/envs/who/lib/python3.9/site-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1264 keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
1265
-> 1266 self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
1267 return keyarr, indexer
1268
~/miniconda3/envs/who/lib/python3.9/site-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1306 if missing == len(indexer):
1307 axis_name = self.obj._get_axis_name(axis)
-> 1308 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
1309
1310 ax = self.obj._get_axis(axis)
KeyError: "None of [Index([False, False, True], dtype='object')] are in the [index]"
```
pd.show_version() outputs this:
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
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 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.6
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
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https://api.github.com/repos/pandas-dev/pandas/issues/39615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39615/events | https://github.com/pandas-dev/pandas/pull/39615 | 802,316,993 | MDExOlB1bGxSZXF1ZXN0NTY4NDgwMzkx | 39,615 | BUG: fix Categorical.astype for dtype=np.int32 argument | {
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} | 16 | 2021-02-05T16:49:37Z | 2021-02-08T14:21:27Z | 2021-02-08T13:43:34Z | MEMBER | null | - [x] closes #39402
- [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
Fixing a bug I introduced in #37355 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39616/events | https://github.com/pandas-dev/pandas/issues/39616 | 802,357,730 | MDU6SXNzdWU4MDIzNTc3MzA= | 39,616 | BUG: astyping Categorical to nullable integer dtype | {
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] | open | false | null | [] | null | 4 | 2021-02-05T17:49:36Z | 2021-02-12T22:49:05Z | null | MEMBER | null | This astyping op works for `int64` but throws for `Int64`. It should work the same for both
``` python
In [16]: import numpy as np
...: import pandas as pd
...:
...: dtype = pd.CategoricalDtype([str(i) for i in range(5)])
...: arr = pd.Categorical.from_codes(np.random.randint(5, size=20), dtype=dtype)
...: arr
Out[16]:
['4', '1', '0', '1', '4', ..., '3', '0', '1', '1', '0']
Length: 20
Categories (5, object): ['0', '1', '2', '3', '4']
In [17]: arr.astype("int64")
Out[17]: array([4, 1, 0, 1, 4, 0, 1, 0, 3, 0, 3, 0, 1, 4, 2, 3, 0, 1, 1, 0])
In [18]: arr.astype("Int64")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-440524b22c6b> in <module>
----> 1 arr.astype("Int64")
~/repos/pandas/pandas/core/arrays/categorical.py in astype(self, dtype, copy)
456 # TODO: consolidate with ndarray case?
457 elif is_extension_array_dtype(dtype):
--> 458 result = array(self, dtype=dtype, copy=copy)
459
460 elif is_integer_dtype(dtype) and self.isna().any():
~/repos/pandas/pandas/core/construction.py in array(data, dtype, copy)
292 if is_extension_array_dtype(dtype):
293 cls = cast(ExtensionDtype, dtype).construct_array_type()
--> 294 return cls._from_sequence(data, dtype=dtype, copy=copy)
295
296 if dtype is None:
~/repos/pandas/pandas/core/arrays/integer.py in _from_sequence(cls, scalars, dtype, copy)
306 cls, scalars, *, dtype: Optional[Dtype] = None, copy: bool = False
307 ) -> IntegerArray:
--> 308 values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy)
309 return IntegerArray(values, mask)
310
~/repos/pandas/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy)
170 "mixed-integer-float",
171 ]:
--> 172 raise TypeError(f"{values.dtype} cannot be converted to an IntegerDtype")
173
174 elif is_bool_dtype(values) and is_integer_dtype(dtype):
TypeError: object cannot be converted to an IntegerDtype
``` | {
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] | closed | false | null | [] | null | 4 | 2021-02-05T19:49:07Z | 2021-02-13T19:59:40Z | 2021-02-13T19:46:31Z | NONE | null | #### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'x': [1,2,3]}, dtype='uint64')
df = df.query('x > 1')
print(df)
```
#### Problem description
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-44-168c7ec0f5d8> in <module>
1 import pandas as pd
2 df = pd.DataFrame({'x': [1,2,3]}, dtype='uint64')
----> 3 df = df.query('x > 1')
4 print(df)
~\.conda\envs\digital-activity\lib\site-packages\pandas\core\frame.py in query(self, expr, inplace, **kwargs)
3464 kwargs["level"] = kwargs.pop("level", 0) + 1
3465 kwargs["target"] = None
-> 3466 res = self.eval(expr, **kwargs)
3467
3468 try:
~\.conda\envs\digital-activity\lib\site-packages\pandas\core\frame.py in eval(self, expr, inplace, **kwargs)
3594 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
3595
-> 3596 return _eval(expr, inplace=inplace, **kwargs)
3597
3598 def select_dtypes(self, include=None, exclude=None) -> DataFrame:
~\.conda\envs\digital-activity\lib\site-packages\pandas\core\computation\eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
345 eng = ENGINES[engine]
346 eng_inst = eng(parsed_expr)
--> 347 ret = eng_inst.evaluate()
348
349 if parsed_expr.assigner is None:
~\.conda\envs\digital-activity\lib\site-packages\pandas\core\computation\engines.py in evaluate(self)
71
72 # make sure no names in resolvers and locals/globals clash
---> 73 res = self._evaluate()
74 return reconstruct_object(
75 self.result_type, res, self.aligned_axes, self.expr.terms.return_type
~\.conda\envs\digital-activity\lib\site-packages\pandas\core\computation\engines.py in _evaluate(self)
112 scope = env.full_scope
113 _check_ne_builtin_clash(self.expr)
--> 114 return ne.evaluate(s, local_dict=scope)
115
116
~\.conda\envs\digital-activity\lib\site-packages\numexpr\necompiler.py in evaluate(ex, local_dict, global_dict, out, order, casting, **kwargs)
834 _numexpr_last = dict(ex=compiled_ex, argnames=names, kwargs=kwargs)
835 with evaluate_lock:
--> 836 return compiled_ex(*arguments, **kwargs)
837
838
TypeError: Iterator operand 1 dtype could not be cast from dtype('uint64') to dtype('int64') according to the rule 'safe'
```
Failure when using an unsigned dtype should work (and worked in earlier versions, for example 1.0.3).
#### Expected Output
x
1 2
2 3
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 2012ServerR2
Version : 6.3.9600
machine : AMD64
processor : Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0.1
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 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 2 | 2021-02-05T22:52:09Z | 2021-02-08T15:05:15Z | 2021-02-08T13:50:34Z | MEMBER | null | Handle the scalar-qs case early on in DataFrame.quantile, hugely simplifies BlockManager.quantile and to a lesser extent Block.quantile | {
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