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} | This pull request includes a change to the `_get_cleaned_column_resolvers` method in the `pandas/core/generic.py` file. The change ensures that the correct data type is used when creating a `Series` object.
Data type handling improvement:
* [`pandas/core/generic.py`](diffhunk://#diff-1a2e3df0db7dd8bddc2ec4bff9de8a7a55e328e6c32e2cecde761dc9549fcd46L606-R608): Modified the `_get_cleaned_column_resolvers` method to use the correct data type (`dtype`) as iteration of `dtypes` when creating a `Series` object by including `dtypes` in the `zip` function.
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} | - [ ] closes #60004 ? (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- I'm not sure if you have how you implement into the code base so I just gave use case examples
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
I am new to adding features and working with anything this extensive. I didn't realize to_markdown() was a function so I made my own. It also doesn't require tabulate and is around 40% fasters in my tests. Looking forward to any feedback.
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- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Amendment to #59996. The current stub has some imprecise typing.
### Feature Description
- inequalities do not cover comparisons of NaTType with itself.
- addition/subtraction does not cover `pd.Period` and numpy types.
- dunder methods should be marked with positional-only parameters, even if the class supports keyword usage.
### Alternative Solutions
N/A
### Additional Context
_No response_ | null | {
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} | - marked arguments in dunder methods positional only
(important for subclassing and protocols)
- added `Period`, `np.datetime64` and `np.timedelta64` as compatible for `__add__` and `__sub__`
- added `np.datetime64` and `np.timedelta64` as compatible with `__floordiv__` and `__truediv__`
- reworked comparisons:
- removed protocol and use plain definition
- changed return type from `bool` to `Literal[False]`
- added `Self` to comparable types, so that `pd.NaT > pd.NaT` is considered valid
- [x] closes #60003 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
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- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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<!--pre-commit.ci end-->
Note: pyupgrade 3.17 needs to be coordinated with an upgrade in mypy & pyright | null | {
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} | - Issue: The skipna was not properly handled for BaseMaskedArray
- Fix: Added mask for NA values
- Test: Added test to series/test_reductions since the test uses
- [X] closes #59965
- [X] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). | null | {
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} | - [ ] closes #59931 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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} | - [x] closes #59207 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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https://api.github.com/repos/pandas-dev/pandas/issues/59987 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59987/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59987/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59987/events | https://github.com/pandas-dev/pandas/pull/59987 | 2,569,446,341 | PR_kwDOAA0YD859xDGj | 59,987 | Fix: Change None values to NaN in combine_first method for better han… | {
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} | ### Description
This pull request addresses issue #58977 by improving the handling of missing data in the `combine_first` method. The changes ensure that `None` values are replaced with `NaN`, which allows for better integration with other Pandas functions that expect `NaN` values.
### Related Issue
- Fixes #58977
### Checklist
- [x] Tests added and passed if fixing a bug or adding a new feature
- [x] All code checks passed
- [ ] Added type annotations to new arguments/methods/functions (if applicable)
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature | {
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https://api.github.com/repos/pandas-dev/pandas/issues/59985 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59985/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59985/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59985/events | https://github.com/pandas-dev/pandas/pull/59985 | 2,568,852,430 | PR_kwDOAA0YD859vRrP | 59,985 | Programming Language :: Python :: 3.13 added to pyproject.toml | {
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} | - [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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https://api.github.com/repos/pandas-dev/pandas/issues/59978 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59978/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59978/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59978/events | https://github.com/pandas-dev/pandas/issues/59978 | 2,568,377,394 | I_kwDOAA0YD86ZFlQy | 59,978 | DOC: Pandas 2.2.3 pyproject.toml missing "Python 3.13" in classifiers -- but Pandas supports this Python version | {
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- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
This does not directly impact Pandas behavior, so Docs is the best fit for issue from what I can see. On searching docs, this is the closest ref: [https://pandas.pydata.org/docs/dev/getting_started/install.html](https://pandas.pydata.org/docs/dev/getting_started/install.html)<br>
I also searched:
https://pandas.pydata.org/docs/dev/search.html?q=3.13
### Documentation problem
Pandas 2.2.3 `pyproject.toml` file does not include `Python 3.13` in the list of classifiers.<br>
Although Pandas 2.2.3 is broadly compatible with `Python 3.13`, that fact is not documented in `pyproject.toml` causing Pandas version support to be shown incorrectly on on PyPi and other python sources.<br>
The Pandas release announcement on 20 Sept 2024 [https://pandas.pydata.org/docs/whatsnew/v2.2.3.html](https://pandas.pydata.org/docs/whatsnew/v2.2.3.html) states: *"Pandas 2.2.3 is the first version of pandas that is generally compatible with the upcoming Python 3.13, and both wheels for free-threaded and normal `Python 3.13` will be uploaded for this release."* In addition, I have tested Pandas 2.2.3 with `Python 3.13.0rc3` and not found issues.
No documentation on current active docs reference Python 3.13: https://pandas.pydata.org/docs/dev/search.html?q=3.13<br>
*Impact -- People do not know Pandas is compatible with Python 3.13**
- on PyPi -- Pandas 2.2.3 does not show Python 3.13 Programming Language Classifier.
- on pyreadiness.org for Python 3.13 - Pandas does not show up as compatible with 3.13 [https://pyreadiness.org/3.13/](https://pyreadiness.org/3.13/)
### Suggested fix for documentation
**Suggested fix:**
- in [pandas main branch](https://github.com/pandas-dev/pandas/tree/main)<br>
- Add the following line to `pandas/pyproject.toml` in the `classifiers` block: <br> ```'Programming Language :: Python :: 3.13',```<br>
**Why it's better:**
Adding this change would populate the Programming Language section of the Classifiers on PyPi, which would then flow through to other sites like pyreadiness.org. | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/59977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59977/events | https://github.com/pandas-dev/pandas/issues/59977 | 2,568,282,267 | I_kwDOAA0YD86ZFOCb | 59,977 | ENH: Add support for generating 4-4-5 but by month not just quarter or year | {
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] | open | false | null | [] | null | 0 | 2024-10-05T19:23:03 | 2024-10-05T19:23:03 | null | NONE | null | null | null | ### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Currently, you can generate quarters that fit the 4-4-5 fiscal calendar as is common in the retail world. [https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.FY5253Quarter.html](url)
How can I generate a datetime index by month (generating a "future" dataframe for forecasting purposes) that matches the input data which is monthly & follows that 4-4-5 calendar?
For example, input data covers these time periods:
`['2020-07-05 00:00:00', '2020-08-02 00:00:00', '2020-08-30 00:00:00',
'2020-10-04 00:00:00', '2020-11-01 00:00:00', '2020-11-29 00:00:00',
'2021-01-03 00:00:00', '2021-01-31 00:00:00', '2021-02-28 00:00:00',
'2021-04-04 00:00:00', '2021-05-02 00:00:00', '2021-05-30 00:00:00',
'2021-07-04 00:00:00', '2021-08-01 00:00:00', '2021-08-29 00:00:00',
'2021-10-03 00:00:00', '2021-10-31 00:00:00', '2021-11-28 00:00:00',
'2022-01-02 00:00:00', '2022-01-30 00:00:00', '2022-02-27 00:00:00',
'2022-04-03 00:00:00', '2022-05-01 00:00:00', '2022-05-29 00:00:00',
'2022-07-03 00:00:00', '2022-07-31 00:00:00', '2022-08-28 00:00:00',
'2022-10-02 00:00:00', '2022-10-30 00:00:00', '2022-11-27 00:00:00',
'2023-01-01 00:00:00']`
I can generate the quarters that match up with this frequency by using the following code:
`yoffset = pd.offsets.FY5253Quarter(weekday=6, startingMonth=8, variation="last")
yindex = pd.date_range('2020-08-30', periods=12, freq=yoffset)
yindex`
which in turn generates the correct output:
`DatetimeIndex(['2020-08-30', '2020-11-29', '2021-02-28', '2021-05-30',
'2021-08-29', '2021-11-28', '2022-02-27', '2022-05-29',
'2022-08-28', '2022-11-27', '2023-02-26', '2023-05-28'],
dtype='datetime64[ns]', freq='REQ-L-AUG-SUN-1')`
How can I resample these quarterly periods to generate an equivalent 4-4-5 monthly frequency that follows the same ruleset?
### Feature Description
pd.offsets.FY5253Month(weekday=x, startingMonth=y, variation="z)
### Alternative Solutions
Not aware of any and currently stuck
### Additional Context
_No response_ | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/59976 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59976/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59976/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59976/events | https://github.com/pandas-dev/pandas/issues/59976 | 2,568,026,073 | I_kwDOAA0YD86ZEPfZ | 59,976 | DOC: Improve docs in the dt namespace | {
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] | open | false | null | [] | null | 1 | 2024-10-05T13:08:12 | 2024-10-07T19:51:45 | null | MEMBER | null | null | null | Ref: https://github.com/pandas-dev/pandas/pull/59878#discussion_r1788584918
E.g. https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.dt.to_period.html
We currently copy the docstring from the underlying array method, which is at times either vague or incorrect as in the example above. We could instead override the docstring with custom value in `pandas/core/accessor.py` in the `_create_delegator_method` function. We would need a place to store the docstrings, and it would be a significant effort to add them all.
Possible approach: Create a new subdirectory, `pandas/core/docstrings/` for storing docstrings for purposes like this, and add `series_dt.py` there. It would contain a dictionary mapping the method name to the docstring. | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/59973 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59973/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59973/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59973/events | https://github.com/pandas-dev/pandas/pull/59973 | 2,567,581,009 | PR_kwDOAA0YD859rYky | 59,973 | Shiny new feature | {
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} | - [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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https://api.github.com/repos/pandas-dev/pandas/issues/59972 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59972/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59972/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59972/events | https://github.com/pandas-dev/pandas/pull/59972 | 2,567,554,253 | PR_kwDOAA0YD859rTGm | 59,972 | DOC: Fix title capitalization in documentation files (#32550) | {
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} | Corrected title capitalization in various .rst files to match the standard of capitalizing only the first word, unless a term like DataFrame or Series is involved.
Ran the script to find and
correct heading issues in the following files:
- doc/source/user_guide/timedeltas.rst
- doc/source/whatsnew/v0.7.0.rst
- doc/source/whatsnew/v0.23.4.rst
- (… and so on)
Fixes part of issue #32550.
- [ ] closes #32550
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} | ### Issue Description
When calling `DataFrame.query()` on a DataFrame with duplicate column names, an unexpected `TypeError` is raised due to a change introduced in pandas 2.2.1. The root cause is the `self.dtypes[k]` returning a Series for duplicate column names instead of a single value, leading to a failure in the query evaluation.
### Changes Made
- Fixed an issue where `Dataframe.query()` would throw an unexpected error
- The error was caused by `self.dtypes[k]`
- Adjusted the behavior to match the behavior prior to pandas version
- Added tests to ensure that `Dataframe.query()` works as expected
## PR Checklist
- [x] closes #59950
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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] | open | false | null | [] | null | 1 | 2024-10-04T11:24:31 | 2024-10-07T15:37:39 | null | NONE | null | null | null | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
s1 = pd.Series({"a": 0.0, "b": 1, "c": 1, "d": 0})
s2 = pd.Series({"a": 0.0, "b": 2, "c": 2, "d": 2})
s3 = s1/s2
#display(s3)
s4 = s1.convert_dtypes()/s2.convert_dtypes()
#display(s4)
s5 = pd.Series([None,0.5,0.5,0]).convert_dtypes()
#display(s5)
s3.mean(skipna=True), s4.mean(skipna=True), s5.mean(skipna=True)
```
### Issue Description
Following #59961, I understand that series/dataframes of FloatingArrays cointaing `np.NaN` values are possible and meant to exists. These very same dataframes/series, however, fail to skip `NaN` values when asked to. The above examples outputs:
```
(np.float64(0.3333333333333333), <NA>, np.float64(0.3333333333333333))
```
### Expected Behavior
```
>>> s4.mean(skipna=True)
np.float64(0.3333333333333333)
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 139def2145b83d40364235c6297e1833eab7bb05
python : 3.12.3
python-bits : 64
OS : Linux
OS-release : 6.8.0-41-generic
Version : #41-Ubuntu SMP PREEMPT_DYNAMIC Fri Aug 2 20:41:06 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 3.0.0.dev0+1545.g139def2145
numpy : 2.2.0.dev0+git20240930.3ee9e6a
dateutil : 2.9.0.post0
pip : 24.0
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pytz : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/59963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59963/events | https://github.com/pandas-dev/pandas/pull/59963 | 2,565,996,942 | PR_kwDOAA0YD859mho8 | 59,963 | Fix API: astype method fails to raise errors for category data type | {
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} | - [x] closes #59899 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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] | open | false | null | [] | null | 1 | 2024-10-04T06:25:07 | 2024-10-05T13:39:13 | null | NONE | null | null | null | ### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
We have sparse data with many null values, and while reading it using Pandas with PyArrow, it doesn't consume much memory because of pandas internal compression logic. However, during concatenation, NumPy allocates memory that isn't actually used, causing our Python script to fail due to memory allocation issues. Can you provide an option to disable NumPy memory allocation when concatenating DataFrames along axis=1?
### Feature Description
pd.concat(df_list,axis=1,numpy_allocation=False)
### Alternative Solutions
Atleast can you provide how can we change C++ script internally and use it for our purpose
### Additional Context
Please let me know if i am wrong. | null | {
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} | Fixes the following issues from #59595
-i "pandas.Series.sparse.fill_value SA01" \
-i "pandas.Series.str.match RT03" \
- [x] Partially addresses #59595
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
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- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
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- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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] | open | false | null | [] | null | 2 | 2024-10-03T13:59:42 | 2024-10-06T18:30:15 | null | NONE | null | null | null | ### Feature Type
- [ ] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
### Problem
I am currently reading this book called Hands On Data Analysis using Pandas by Stefie Molin. There are two formats of data, wide and long format data. The author uses pandas and matplotlib to plot a wide format data while uses seaborn package for the long format data. I tried searching in the web and it seems to be the custom. I tried asking gpt as well, and I can plot the long format data without seaborn too but it seems that I have to pivot the dataset. Is there a way around it .
Wide Data Frame Sample
date | TMAX | TMIN | TOBS
-- | -- | -- | --
2018-10-28 | 8.3 | 5.0 | 7.2
2018-10-04 | 22.8 | 11.7 | 11.7
2018-10-20 | 15.0 | -0.6 | 10.6
2018-10-24 | 16.7 | 4.4 | 6.7
2018-10-23 | 15.6 | -1.1 | 10.0
Long Data Frame Sample
date | datatype | value
-- | -- | --
2018-10-01 | TMAX | 21.1
2018-10-01 | TMIN | 8.9
2018-10-01 | TOBS | 13.9
2018-10-02 | TMAX | 23.9
2018-10-02 | TMIN | 13.9
2018-10-02 | TOBS | 17.2
Long Data Frame after pivoting
plot command for wide df
`ax = wide_df.plot(
x='date',
y=['TMAX', 'TMIN', 'TOBS'],
figsize=(15, 5),
title='Temperature in NYC in October 2018'
)
`
plot command for long df after pivot
`ax=long_df.pivot(index='date',columns='datatype',values='value')`
and apply a similar command as above
plot command for long_df with seaborn
`ax=sns.lineplot(data=long_df,x='date',y='value',hue='datatype')`
Why isn't there a hue parameter or something similar in pandas for a long data format? My question can also be framed this way,
" Why is pandas not enough for plotting? Why do I need external packages like matplotlib and seaborn to plot pandas data structure?"
Forgive me for my ignorance but I really want to know why cann't the features available in pandas and seaborn be available in pandas.
### Feature Description
Lets start with a hue feature in pandas for a long data format
### Alternative Solutions
we might have to pivot the table if we have to plot without using seaborn if we just need to use pandas
### Additional Context
_No response_ | null | {
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({'a': range(3), 'b': range(3), 'c': range(3)}).rename(columns={'b': 'a'})
print(df.query('c == 1'))
```
### Issue Description
Since pandas 2.2.1 this throws an unexpected error:
`TypeError: dtype 'a int64
a int64
dtype: object' not understood`
This is because DataFrame.query() calls DataFrame.eval() which in turn calls DataFrame._get_cleaned_column_resolvers().
The dict comprehension in DataFrame._get_cleaned_column_resolvers() was changed in version 2.2.1.
version 2.2.0
```
return {
clean_column_name(k): Series(
v, copy=False, index=self.index, name=k
).__finalize__(self)
for k, v in zip(self.columns, self._iter_column_arrays())
if not isinstance(k, int)
}
```
version 2.2.1
```
return {
clean_column_name(k): Series(
v, copy=False, index=self.index, name=k, dtype=self.dtypes[k]
).__finalize__(self)
for k, v in zip(self.columns, self._iter_column_arrays())
if not isinstance(k, int)
}
```
since the dtypes are now checked when the Series are created, this introduces the error described above, since for a duplicate
column name self.dtypes[k] returns a Series instead of single value.
### Expected Behavior
1) I would expect either the behavior prior to v2.2.1 where the above example would return:
```
>>> df.query('c == 1')
a a c
1 1 1 1
```
moreover, calling query() on column 'a' also works:
```
>>> df.query('a == 1')
a a c
1 1 1 1
```
or
2) If above behavior is unwanted, I would except better error handling, smt like:
```
>>> df.query('c == 1')
DuplicateColumnError: DataFrame.query() is not supported for DataFrames with duplicate column names
```
### Installed Versions
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.11.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-165-generic
Version : #182-Ubuntu SMP Mon Oct 2 19:43:28 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.1.1
pip : 23.0
Cython : None
pytest : 8.2.0
hypothesis : 6.100.4
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.2.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : 0.19.2
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.27
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None | null | {
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- [X] closes #59884 (Replace xxxx with the GitHub issue number)
- [X] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
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- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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https://api.github.com/repos/pandas-dev/pandas/issues/59947 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59947/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59947/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59947/events | https://github.com/pandas-dev/pandas/pull/59947 | 2,562,806,464 | PR_kwDOAA0YD859b4oy | 59,947 | Update hist.py | {
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Reduces these lines of code:
```
ax=ds.hist()
ax.set(
xlabel=ds.name,
)
```
to
```
ds.hist()
```
---
- [NA] closes #xxxx (Replace xxxx with the GitHub issue number)
- [NA] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
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- [NA] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [NA] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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- [x] closes #59207 (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
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- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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https://api.github.com/repos/pandas-dev/pandas/issues/59935 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59935/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59935/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59935/events | https://github.com/pandas-dev/pandas/pull/59935 | 2,560,968,766 | PR_kwDOAA0YD859WH1R | 59,935 | Handling conversion of empty categorical with dtype_backend='pyarrow' | {
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- [x] Tests added and passed
- [x] Closes #59934
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] | open | false | null | [] | null | 0 | 2024-10-02T08:22:10 | 2024-10-02T21:04:49 | null | NONE | null | null | null | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
# example 1
ser1 = pd.Series(pd.Categorical([None] * 5))
converted1 = ser1.convert_dtypes(dtype_backend="pyarrow")
# example 2
ser2 = pd.Series(pd.Categorical([None] * 5, categories=["S1", "S2"]))
converted2 = ser2.convert_dtypes(dtype_backend="pyarrow")
```
### Issue Description
Trying to convert categorical series with empty categories results in `pyarrow.lib.ArrowNotImplementedError: NumPyConverter doesn't implement <null> conversion. `.
Trying to convert categorical series with non-empty categories returns null[pyarrow] dtype.
This is inconsistent with regular behavior when converting categoricals - if series is not empty, convert_dtypes call returns categorical dtype (essentially ignoring the requested pyarrow backend conversion).
I encountered this issue when trying to read files (like parquet, spss, ...) where some categorical variables are empty...
### Expected Behavior
Returning categorical dtype with propagated categories, and not raising errors.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 00855f81bd84cc6ed9ae42c5f66916b2208dbe04
python : 3.12.0
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 3.0.0.dev0+1536.g00855f81bd
numpy : 1.26.4
dateutil : 2.9.0.post0
pip : 24.2
Cython : 3.0.11
sphinx : 8.0.2
IPython : 8.27.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : 1.4.0
fastparquet : 2024.5.0
fsspec : 2024.9.0
html5lib : 1.1
hypothesis : 6.112.2
gcsfs : 2024.9.0post1
jinja2 : 3.1.4
lxml.etree : 5.3.0
matplotlib : 3.9.2
numba : 0.60.0
numexpr : 2.10.1
odfpy : None
openpyxl : 3.1.5
psycopg2 : 2.9.9
pymysql : 1.4.6
pyarrow : 17.0.0
pyreadstat : 1.2.7
pytest : 8.3.3
python-calamine : None
pytz : 2024.2
pyxlsb : 1.0.10
s3fs : 2024.9.0
scipy : 1.14.1
sqlalchemy : 2.0.35
tables : 3.10.1
tabulate : 0.9.0
xarray : 2024.9.0
xlrd : 2.0.1
xlsxwriter : 3.2.0
zstandard : 0.23.0
tzdata : 2024.2
qtpy : None
pyqt5 : None
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https://api.github.com/repos/pandas-dev/pandas/issues/59933 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59933/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59933/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59933/events | https://github.com/pandas-dev/pandas/issues/59933 | 2,560,390,330 | I_kwDOAA0YD86YnHS6 | 59,933 | BUG: locset with Series as column key fails inconsistently | {
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] | open | false | null | [] | null | 2 | 2024-10-01T23:40:51 | 2024-10-05T22:50:57 | null | NONE | null | null | null | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=list("ABC"))
>>> df.loc[:, pd.Series(['A', 'C'])] = pd.Series([10, 20, 30]) # succeeds
>>> df
A B C
0 10 2 20
1 10 5 20
>>> df.loc[:, pd.Series(['A', 'B', 'C'])] = pd.Series([10, 20, 30]) # fails
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/blocks.py:1429, in Block.setitem(self, indexer, value, using_cow)
1428 try:
-> 1429 values[indexer] = casted
1430 except (TypeError, ValueError) as err:
ValueError: shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (2,3)
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
Cell In[5], line 1
----> 1 df.loc[:, pd.Series(['A', 'B', 'C'])] = pd.Series([10, 20, 30])
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/indexing.py:911, in _LocationIndexer.__setitem__(self, key, value)
908 self._has_valid_setitem_indexer(key)
910 iloc = self if self.name == "iloc" else self.obj.iloc
--> 911 iloc._setitem_with_indexer(indexer, value, self.name)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/indexing.py:1944, in _iLocIndexer._setitem_with_indexer(self, indexer, value, name)
1942 self._setitem_with_indexer_split_path(indexer, value, name)
1943 else:
-> 1944 self._setitem_single_block(indexer, value, name)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/indexing.py:2218, in _iLocIndexer._setitem_single_block(self, indexer, value, name)
2215 self.obj._check_is_chained_assignment_possible()
2217 # actually do the set
-> 2218 self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
2219 self.obj._maybe_update_cacher(clear=True, inplace=True)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/managers.py:415, in BaseBlockManager.setitem(self, indexer, value, warn)
411 # No need to split if we either set all columns or on a single block
412 # manager
413 self = self.copy()
--> 415 return self.apply("setitem", indexer=indexer, value=value)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/managers.py:363, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
361 applied = b.apply(f, **kwargs)
362 else:
--> 363 applied = getattr(b, f)(**kwargs)
364 result_blocks = extend_blocks(applied, result_blocks)
366 out = type(self).from_blocks(result_blocks, self.axes)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/blocks.py:1432, in Block.setitem(self, indexer, value, using_cow)
1430 except (TypeError, ValueError) as err:
1431 if is_list_like(casted):
-> 1432 raise ValueError(
1433 "setting an array element with a sequence."
1434 ) from err
1435 raise
1436 return self
ValueError: setting an array element with a sequence.
>>> In [7]: df.loc[:, pd.Series(['A'])] = pd.Series([10, 20, 30]) # Fails
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/blocks.py:1429, in Block.setitem(self, indexer, value, using_cow)
1428 try:
-> 1429 values[indexer] = casted
1430 except (TypeError, ValueError) as err:
ValueError: shape mismatch: value array of shape (2,) could not be broadcast to indexing result of shape (2,1)
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
Cell In[7], line 1
----> 1 df.loc[:, pd.Series(['A'])] = pd.Series([10, 20, 30])
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/indexing.py:911, in _LocationIndexer.__setitem__(self, key, value)
908 self._has_valid_setitem_indexer(key)
910 iloc = self if self.name == "iloc" else self.obj.iloc
--> 911 iloc._setitem_with_indexer(indexer, value, self.name)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/indexing.py:1944, in _iLocIndexer._setitem_with_indexer(self, indexer, value, name)
1942 self._setitem_with_indexer_split_path(indexer, value, name)
1943 else:
-> 1944 self._setitem_single_block(indexer, value, name)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/indexing.py:2218, in _iLocIndexer._setitem_single_block(self, indexer, value, name)
2215 self.obj._check_is_chained_assignment_possible()
2217 # actually do the set
-> 2218 self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
2219 self.obj._maybe_update_cacher(clear=True, inplace=True)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/managers.py:415, in BaseBlockManager.setitem(self, indexer, value, warn)
411 # No need to split if we either set all columns or on a single block
412 # manager
413 self = self.copy()
--> 415 return self.apply("setitem", indexer=indexer, value=value)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/managers.py:363, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
361 applied = b.apply(f, **kwargs)
362 else:
--> 363 applied = getattr(b, f)(**kwargs)
364 result_blocks = extend_blocks(applied, result_blocks)
366 out = type(self).from_blocks(result_blocks, self.axes)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/blocks.py:1432, in Block.setitem(self, indexer, value, using_cow)
1430 except (TypeError, ValueError) as err:
1431 if is_list_like(casted):
-> 1432 raise ValueError(
1433 "setting an array element with a sequence."
1434 ) from err
1435 raise
1436 return self
ValueError: setting an array element with a sequence
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=list("ABC"))
>>> df.loc[:, pd.Series(['A', 'B', 'C'])] = pd.Series([10, 20, 30]) # Succeeds
>>> df
A B C
0 10 20 30
1 10 20 30
2 10 20 30
```
### Issue Description
It seems that we can only provide as many column indexers in the Series key for the column as are rows, but all of the above examples should succeed.
### Expected Behavior
Columns that are indexed should be set with the corresponding value (determined by position).
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.14.final.0
python-bits : 64
OS : Darwin
OS-release : 23.6.0
Version : Darwin Kernel Version 23.6.0: Mon Jul 29 21:13:04 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 72.1.0
pip : 24.2
Cython : None
pytest : 7.4.4
hypothesis : None
sphinx : 5.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.27.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.1
gcsfs : None
matplotlib : 3.9.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/59932 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59932/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59932/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59932/events | https://github.com/pandas-dev/pandas/issues/59932 | 2,559,779,205 | I_kwDOAA0YD86YkyGF | 59,932 | BUG: np.nan_to_num change pandas DataFrame in place when copy = True | {
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] | open | false | null | [] | null | 1 | 2024-10-01T17:07:38 | 2024-10-01T21:03:57 | null | NONE | null | null | null | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
x = pd.DataFrame([np.nan])
np.nan_to_num(x, copy=True)
print(x)
```
### Issue Description
Result x is changed to 0.0 even when nan_to_num is called with copy=True.
This issue is originally reported to numpy [here](https://github.com/numpy/numpy/issues/27487). Looks like pandas ignores `copy=True` NumPy passes?
### Expected Behavior
x should not be changed when copy=True.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.11.8
python-bits : 64
OS : Linux
OS-release : 5.10.0-smp-1100.466.602.1
Version : #1 [v5.10.0-1100.466.602.1] SMP @1708156698
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 2.1.1
pytz : 2024.1
dateutil : 2.8.1
pip : None
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : 5.3.0
matplotlib : None
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlsxwriter : None
zstandard : None
tzdata : None
qtpy : None
pyqt5 : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/59931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59931/events | https://github.com/pandas-dev/pandas/issues/59931 | 2,559,117,094 | I_kwDOAA0YD86YiQcm | 59,931 | ENH: Add support to customize time format and more detailed error handling information for to_time | {
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] | open | false | null | [] | null | 1 | 2024-10-01T12:44:14 | 2024-10-07T06:36:15 | null | NONE | null | null | null | ### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
**Path:** pandas/core/tools/times.py
**Func:** to_time
Though to_time is able to handle several common time formats, I still wish to enhance its versatility and adaptability by customizing time format. By the way, customized error handling infos are also of use (though simple).
It's my pleasure if you agree with my changes and I'd love to make PR!
### Feature Description
I've modified **times.py** for the above enhancements, and I've made a test demo to show its new feature in **times_test.py**.
1. Improved compatibility: Allows users to customize the time format, which improves the versatility and adaptability of the function.
2. Enhanced error handling: Provide more detailed error information for easy debugging and problem location.
### Alternative Solutions
The source code are as follows:
1. new times.py
``` python
# modfied times.py
from __future__ import annotations
from datetime import datetime, time
from typing import TYPE_CHECKING, List, Union
import numpy as np
from pandas._libs.lib import is_list_like
from pandas.core.dtypes.generic import ABCIndex, ABCSeries
from pandas.core.dtypes.missing import notna
if TYPE_CHECKING:
from pandas._typing import DateTimeErrorChoices
def to_time(
arg,
format: Union[str, List[str], None] = None,
infer_time_format: bool = False,
errors: DateTimeErrorChoices = "raise",
custom_formats: List[str] = None,
):
"""
Parse time strings to time objects using fixed strptime formats ("%H:%M",
"%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
"%I%M%S%p") and additional custom formats.
Use infer_time_format if all the strings are in the same format to speed
up conversion.
Parameters
----------
arg : string in time format, datetime.time, list, tuple, 1-d array, Series
format : str or list of str, default None
Format(s) used to convert arg into a time object. If None, fixed
formats are used.
infer_time_format: bool, default False
Infer the time format based on the first non-NaN element. If all
strings are in the same format, this will speed up conversion.
errors : {'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as None
custom_formats : list of str, default None
Additional custom time formats to use.
Returns
-------
datetime.time or list of datetime.time
"""
if errors not in ("raise", "coerce"):
raise ValueError("errors must be one of 'raise', or 'coerce'.")
def _convert_listlike(arg, format):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype="O")
elif getattr(arg, "ndim", 1) > 1:
raise TypeError(
"arg must be a string, datetime, list, tuple, 1-d array, or Series"
)
arg = np.asarray(arg, dtype="O")
if infer_time_format and format is None:
format = _guess_time_format_for_array(arg)
times = []
if format is not None:
if isinstance(format, list):
for element in arg:
for fmt in format:
try:
times.append(datetime.strptime(element, fmt).time())
break
except (ValueError, TypeError):
continue
else:
if errors == "raise":
msg = (
f"Cannot convert {element} to a time with given "f"formats {format}")
raise ValueError(msg)
times.append(None)
else:
for element in arg:
try:
times.append(datetime.strptime(element, format).time())
except (ValueError, TypeError) as err:
if errors == "raise":
msg = (f"Cannot convert {element} to a time withgiven "f"format {format}")
raise ValueError(msg) from err
times.append(None)
else:
formats = _time_formats + (custom_formats or [])
for element in arg:
time_object = None
try:
time_object = time.fromisoformat(element)
except (ValueError, TypeError):
for time_format in formats:
try:
time_object = datetime.strptime(element, time_format).time()
break
except (ValueError, TypeError):
continue
if time_object is not None:
times.append(time_object)
elif errors == "raise":
raise ValueError(f"Cannot convert arg {arg} to a time")
else:
times.append(None)
return times
if arg is None:
return arg
elif isinstance(arg, time):
return arg
elif isinstance(arg, ABCSeries):
values = _convert_listlike(arg._values, format)
return arg._constructor(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndex):
return _convert_listlike(arg, format)
elif is_list_like(arg):
return _convert_listlike(arg, format)
return _convert_listlike(np.array([arg]), format)[0]
# Fixed time formats for time parsing
_time_formats = [
"%H:%M",
"%H%M",
"%I:%M%p",
"%I%M%p",
"%H:%M:%S",
"%H%M%S",
"%I:%M:%S%p",
"%I%M%S%p",
]
def _guess_time_format_for_array(arr):
# Try to guess the format based on the first non-NaN element
non_nan_elements = notna(arr).nonzero()[0]
if len(non_nan_elements):
element = arr[non_nan_elements[0]]
for time_format in _time_formats:
try:
datetime.strptime(element, time_format)
return time_format
except ValueError:
pass
return None
```
2. test demo (under the same directory of times.py)
``` python
# times_test.py
from datetime import time
import pandas as pd
import numpy as np
from times import to_time
# Test code
def test_to_time():
"""
- Tests the `to_time` function with various input formats
and demonstrates how it handles invalid inputs.
- return: None
"""
# Single time string
single_time_str = "12:30"
print("Single time string:", to_time(single_time_str))
# Time object
time_obj = time(12, 30)
print("Time object:", to_time(time_obj))
# List of time strings
time_list = ["12:30", "13:45"]
print("List of time strings:", to_time(time_list))
# NumPy array of time strings
time_array = np.array(["12:30", "13:45"])
print("NumPy array of time strings:", to_time(time_array))
# Pandas Series of time strings
time_series = pd.Series(["12:30", "13:45"])
print("Pandas Series of time strings:", to_time(time_series))
# Error handling
invalid_list = ["12:30", "invalid"]
try:
print("Invalid list:", to_time(invalid_list))
except ValueError as e:
print("Error:", e)
# Error handling (coerce)
invalid_list_coerce = ["12:30", "invalid"]
print("Invalid list (coerce):", to_time(invalid_list_coerce, errors="coerce"))
# Custom format
custom_format = ["%H:%M:%S.%f"]
custom_time_list = ["12:30:45.123456", "13:45:00.987654"]
print("Custom time list:", to_time(custom_time_list, custom_formats=custom_format))
# Inferred time list
inferred_time_list = ["12:30", "13:45"]
print("Inferred time list:", to_time(inferred_time_list, infer_time_format=True))
# ISO 8601 time list
iso_time_list = ["12:30:45", "13:45:00"]
print("ISO 8601 time list:", to_time(iso_time_list))
test_to_time()
```
3. result for times_test.py
``` bash
Single time string: 12:30:00
Time object: 12:30:00
List of time strings: [datetime.time(12, 30), datetime.time(13, 45)]
NumPy array of time strings: [datetime.time(12, 30), datetime.time(13, 45)]
Pandas Series of time strings: 0 12:30:00
1 13:45:00
dtype: object
Error: Cannot convert arg ['12:30' 'invalid'] to a time
Invalid list (coerce): [datetime.time(12, 30), None]
Custom time list: [datetime.time(12, 30, 45, 123456), datetime.time(13, 45, 0, 987654)]
Inferred time list: [datetime.time(12, 30), datetime.time(13, 45)]
ISO 8601 time list: [datetime.time(12, 30, 45), datetime.time(13, 45)]
```
### Additional Context
_No response_ | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/59922 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59922/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59922/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59922/events | https://github.com/pandas-dev/pandas/issues/59922 | 2,555,190,802 | I_kwDOAA0YD86YTR4S | 59,922 | BUG: cannot filter an empty DataFrame by a non-numeric condition | {
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] | open | false | null | [] | null | 4 | 2024-09-29T22:20:53 | 2024-10-08T05:10:53 | null | NONE | null | null | null | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
f = pd.DataFrame()
# this works as expected, returning an empty frame
f[f.index < 5]
# this fails
f[f.index > pd.Timestamp("2024-10-10")]
```
### Issue Description
filtering an empty `DataFrame` by a non-numeric filter fails with
> TypeError: '>' not supported between instances of 'numpy.ndarray' and 'Timestamp'
### Expected Behavior
an empty frame should be returned, just like with a numeric condition
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.12.6
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 2.2.3
numpy : 2.1.1
pytz : 2024.1
dateutil : 2.8.2
pip : 24.2
Cython : None
sphinx : None
IPython : 8.27.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.9.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : 5.3.0
matplotlib : 3.9.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : 0.23.0
tzdata : 2024.2
qtpy : 2.4.1
pyqt5 : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/59916 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/59916/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/59916/comments | https://api.github.com/repos/pandas-dev/pandas/issues/59916/events | https://github.com/pandas-dev/pandas/pull/59916 | 2,554,474,168 | PR_kwDOAA0YD859AOU- | 59,916 | ENH: Be able to add lines for all index levels, not just visible ones [fix #59877] | {
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} | Per discussion in #59877, add an `-invisible` suffix that can be added to the index selection in the `clines` option to `Styler.to_latex()`, allowing hidden indices to be used when deciding where to place lines between rows.
This approach does not break compatibility with existing code, at the expense of starting a potential combinatoric explosion of options being combined into this one string. There may be a more elegant and/or expressive way of achieving the same result, at the cost of breaking compatibility (or needing to maintain two interfaces until the previous one is deprecated).
- [x] closes #xxxx (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions. [No new arguments, methods, or functions added, outside of tests.]
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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- [x] xref #55178
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame(
[[1]],
index=pd.MultiIndex.from_frame(
pd.DataFrame(
{
"idx0": pd.Series("a", dtype=pd.StringDtype()),
"idx1": pd.Series(100, dtype=pd.Int64Dtype())
}
)
),
)
df.loc[("a", 100)].name
```
```
('a', np.int64(100))
```
```python
def foo(row): return row.name[1].item()
foo(df.loc[("a", 100)])
```
```
100
```
```python
df.apply(foo, axis=1)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/google/home/shobs/code/bigframes1/venv/lib/python3.12/site-packages/pandas/core/frame.py", line 10374, in apply
return op.apply().__finalize__(self, method="apply")
^^^^^^^^^^
File "/usr/local/google/home/shobs/code/bigframes1/venv/lib/python3.12/site-packages/pandas/core/apply.py", line 916, in apply
return self.apply_standard()
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/google/home/shobs/code/bigframes1/venv/lib/python3.12/site-packages/pandas/core/apply.py", line 1063, in apply_standard
results, res_index = self.apply_series_generator()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/google/home/shobs/code/bigframes1/venv/lib/python3.12/site-packages/pandas/core/apply.py", line 1081, in apply_series_generator
results[i] = self.func(v, *self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<stdin>", line 1, in foo
AttributeError: 'int' object has no attribute 'item'
```
### Issue Description
I ran into this issue where I have a series processing udf, which works fine if I pass a dataframe row to it, but breaks when I `dataframe.apply` it with `axis=1`. It seems the numpy value in the index is being lost in the latter.
### Expected Behavior
A user defined function should have the same behavior, whether a dataframe row is passed to it directly or via `DataFrame.apply`.
### Installed Versions
<details>
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.12.0b4
python-bits : 64
OS : Linux
OS-release : 6.9.10-1rodete4-amd64
Version : #1 SMP PREEMPT_DYNAMIC Debian 6.9.10-1rodete4 (2024-08-02)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 2.1.1
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 23.1.2
Cython : None
sphinx : None
IPython : 8.27.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.9.0
html5lib : None
hypothesis : None
gcsfs : 2024.9.0post1
jinja2 : None
lxml.etree : None
matplotlib : 3.9.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.23.2
psycopg2 : None
pymysql : None
pyarrow : 17.0.0
pyreadstat : None
pytest : 8.3.3
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : 2.0.35
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None
</details>
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import io
data = """date,id
20/12/2025,a
,b
31/12/2020,c
"""
df = pd.read_csv(io.StringIO(data), parse_dates=["date"], dayfirst=True, dtype_backend="pyarrow")
df.dtypes
# date string[pyarrow_numpy]
# id large_string[pyarrow]
```
### Issue Description
dtype for the date column is not a date dtype as a result of a date parsing failure because of the missing value on the second row
### Expected Behavior
dtype for the date column should be `timestamp[ns][pyarrow]`
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : d9cdd2ee5a58015ef6f4d15c7226110c9aab8140
python : 3.12.5.final.0
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 154 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United Kingdom.1252
pandas : 2.2.2
numpy : 2.0.1
pytz : 2024.1
dateutil : 2.9.0
setuptools : 72.1.0
pip : 24.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.3.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.26.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.9.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : 0.23.0
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
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annotations_creators: [] language:
- en language_creators:
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- afl-3.0
- artistic-2.0 multilinguality:
- other-my-multilinguality pretty_name: pandas issues dataset size_categories: [] source_datasets:
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