id
int64 | number
int64 | title
string | state
string | created_at
timestamp[s] | updated_at
timestamp[s] | closed_at
timestamp[s] | html_url
string | is_pull_request
bool | pull_request_url
string | pull_request_html_url
string | user_login
string | comments_count
int64 | body
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int64 | reactions_minus1
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,229,785,601
| 58,174
|
ENH: DtypeWarning message enhancement
|
closed
| 2024-04-07T14:02:00
| 2024-04-18T23:55:26
| 2024-04-18T23:55:25
|
https://github.com/pandas-dev/pandas/issues/58174
| true
| null | null |
VelizarVESSELINOV
| 2
|
### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
The current DtypeWarning message:
`DtypeWarning: Columns (15,19) have mixed types. Specify dtype option on import or set low_memory=False.`
Having a column name more than a column number would make sense.
Preferred warning message:
1. `DtypeWarning: Columns (15: Comment, 19: Description) have mixed types. Specify dtype option on import or set low_memory=False.`
2. `DtypeWarning: Columns (15, 19) have mixed types. Specify dtype option ({'Comment': str, 'Description': str}) on import or set low_memory=False.`
So the next user action can be easy:
```Python
read_csv(..., dtype={"Comment": str, "Description": str})
```
### Feature Description
Current code:
```Python
def _concatenate_chunks(chunks: list[dict[int, ArrayLike]]) -> dict:
"""
Concatenate chunks of data read with low_memory=True.
The tricky part is handling Categoricals, where different chunks
may have different inferred categories.
"""
names = list(chunks[0].keys())
warning_columns = []
result: dict = {}
for name in names:
arrs = [chunk.pop(name) for chunk in chunks]
# Check each arr for consistent types.
dtypes = {a.dtype for a in arrs}
non_cat_dtypes = {x for x in dtypes if not isinstance(x, CategoricalDtype)}
dtype = dtypes.pop()
if isinstance(dtype, CategoricalDtype):
result[name] = union_categoricals(arrs, sort_categories=False)
else:
result[name] = concat_compat(arrs)
if len(non_cat_dtypes) > 1 and result[name].dtype == np.dtype(object):
warning_columns.append(str(name))
if warning_columns:
warning_names = ",".join(warning_columns)
warning_message = " ".join(
[
f"Columns ({warning_names}) have mixed types. "
f"Specify dtype option on import or set low_memory=False."
]
)
warnings.warn(warning_message, DtypeWarning, stacklevel=find_stack_level())
return result
```
### Alternative Solutions
Replace columns number with column names
### Additional Context
_No response_
|
[
"Enhancement",
"IO CSV",
"Warnings"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"> 2\\. Specify dtype option ({'Comment': str, 'Description': str}) on import or set low_memory=False.\r\n\r\nI don't think we should do this because column names can be duplicated. I'm okay with option 1.",
"take"
] |
2,229,364,249
| 58,173
|
BUG: Inputting a masked 64-bit integer array from numpy gets mangled by a hidden float conversion
|
closed
| 2024-04-06T18:52:23
| 2024-06-03T18:08:37
| 2024-06-03T18:08:37
|
https://github.com/pandas-dev/pandas/issues/58173
| true
| null | null |
erykoff
| 4
|
### 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 numpy as np
import pandas as pd
data = np.array([2**52, 2**52+1, 2**53, 2**53+1, 1649900760361600113, 1649900760361609541, 0])
masked_data = np.ma.masked_array(data, mask=np.zeros(len(data), dtype=np.bool_))
s1 = pd.Series(data, dtype="Int64")
print(s1)
s2 = pd.Series(masked_data, dtype="Int64")
print(s2)
masked_data.mask[-1] = True
s3 = pd.Series(masked_data, dtype="Int64")
print(s3)
```
```
### Issue Description
When making a series or dataframe out of a masked 64-bit numpy array then it seems that it first gets converted to a 64-bit float which loses precision. In the example above the presence of a masked value means that the final converted Series does not match the initial converted Series. (This also happens when using an astropy MaskedColumn).
### Expected Behavior
Well, it should not change the datatype and round the large integer values. I expect `np.all(s3 == s1)` to return `True`.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a671b5a8bf5dd13fb19f0e88edc679bc9e15c673
python : 3.11.7.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Fri Mar 15 00:19:22 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T8112
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.4
numpy : 1.24.4
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.3.2
Cython : None
pytest : 7.4.4
hypothesis : 6.92.2
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.9
jinja2 : 3.1.2
IPython : 8.19.0
pandas_datareader : None
bs4 : 4.12.2
bottleneck : 1.3.7
dataframe-api-compat: None
fastparquet : 2023.10.1
fsspec : 2023.12.2
gcsfs : None
matplotlib : 3.8.2
numba : 0.58.1
numexpr : 2.8.8
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.2
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.4
sqlalchemy : 2.0.25
tables : 3.9.2
tabulate : 0.9.0
xarray : 2023.12.0
xlrd : None
zstandard : 0.22.0
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"This still happens on main and indeed seems to be an issue with the series constructor (the data sanitation in particular) and not with the numpy masked array constructor. I'll take a closer look.",
"take",
"This is a duplicate of #56566. And the issue seems to go further than that. When fixing your issue here in the array sanitising, Series.to_numpy() of the non-mangled ints fails similarly as the construction of the series prior to the fix. There might be more issues with such large ints and masked arrays in Series/DataFrames beyond that.",
"Closing as a duplicate of https://github.com/pandas-dev/pandas/issues/56566"
] |
2,229,302,192
| 58,172
|
ENH: add numeric_only to Dataframe.cum* methods
|
closed
| 2024-04-06T15:46:25
| 2024-04-11T15:24:01
| 2024-04-11T15:20:49
|
https://github.com/pandas-dev/pandas/pull/58172
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58172
|
https://github.com/pandas-dev/pandas/pull/58172
|
weikhor
| 1
|
- [x] closes #53072
- [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.
|
[
"Enhancement",
"Reduction Operations"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @weikhor "
] |
2,229,301,597
| 58,171
|
REF: de-duplicate tzinfo-awareness mismatch checks
|
closed
| 2024-04-06T15:44:29
| 2024-04-08T17:02:17
| 2024-04-08T16:44:54
|
https://github.com/pandas-dev/pandas/pull/58171
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58171
|
https://github.com/pandas-dev/pandas/pull/58171
|
jbrockmendel
| 1
|
- [ ] 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.
|
[
"Timezones",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jbrockmendel \r\n"
] |
2,229,060,633
| 58,170
|
BUG: fix aggregation when using udf with kwarg
|
closed
| 2024-04-06T03:56:11
| 2024-04-29T15:47:32
| 2024-04-29T15:47:32
|
https://github.com/pandas-dev/pandas/pull/58170
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58170
|
https://github.com/pandas-dev/pandas/pull/58170
|
nicholas-ys-tan
| 2
|
- [x] closes #58146
- [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).
- [ Not applicable ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ Pending review ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
closes https://github.com/pandas-dev/pandas/issues/39169
|
[
"Groupby",
"Apply"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"https://github.com/pandas-dev/pandas/issues/58146#issuecomment-2039750872\r\n\r\nNoted comment that the changes to agg will be breaking changes and I would need a workaround for #58132. This is fairly important as adding a `numeric_only` kwarg will more often push the code down `DataFrameGroupBy._aggregate_frame`. \r\n\r\nI was hoping to get your view on this PR as a temporary fix with minimalist code, it changes the path the code will take to use `GroupByApply.agg` as a list if there is more than one column to operate on - hence the aggregation done as loop of `SeriesGroupBy` if I understood the code correctly.",
"Could you add a whatsnew note to `v3.0.0.rst` (under `Bug FIx` in the groupby section)?"
] |
2,228,947,372
| 58,169
|
ENH: New proposed deprecation policy
|
closed
| 2024-04-05T23:37:37
| 2025-05-20T21:52:01
| 2024-06-13T01:19:53
|
https://github.com/pandas-dev/pandas/pull/58169
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58169
|
https://github.com/pandas-dev/pandas/pull/58169
|
Aloqeely
| 9
|
xref #54970
Use `DeprecationWarning` until the last minor version before a major release then we switch to `FutureWarning`
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"One thing to note is that when using `pytest.mark.filterwarnings` I have kept the ignore string as `\"ignore::DeprecationWarning\"` which we will need to change to `FutureWarning` once we change `Pandas40DeprecationWarning`, my reasoning for this is that you have to pass the module path (so `ignore::pandas.util._exceptions.Pandas40DeprecationWarning`) for aliased warnings, which didn't look pretty to me",
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"Gentle ping @lithomas1. I would like this to move forward so I don't have to update it every time a new deprecation is added",
"> Gentle ping @lithomas1. I would like this to move forward so I don't have to update it every time a new deprecation is added\r\n\r\nThanks for the reminder (and sorry that this slipped off my radar).\r\nWill take a look this evening.",
"One thing we also could consider doing is decoupling the test changes from this PR, and just introducing ``Pandas40DeprecationWarning``, and basic tests that it works as expected.\r\n\r\nThat way there's less chances of this creating conflicts and getting out of sync.",
"I think switching to Future warning only one release before the release that will break is too short. My motion on this is that it would be only 6 months or so, and sometimes less. Often end users report issues to projects through future warnings. This then requires downstream to make the changes and make a release before the next pandas, or face a break (unless strictly capping, which is not common). \n\nProducing a FutureWarning 2 releases prior to breaking things seems like a more balanced approach. ",
"> Producing a FutureWarning 2 releases prior to breaking things seems like a more balanced approach.\r\n\r\nThat would mean any deprecations released in the last 2 minor releases will immediately be FutureWarnings which means downstream libraries won't have *any* time to handle/suppress these warnings. Had we implemented your idea earlier, v2.1 deprecations would immediately be FutureWarning (there are a lot of them, see #50578), one of them being the cause of #54970 being opened in the first place.\r\n\r\nI guess if we think some deprecation needs attention by end users, we can leave it as a FutureWarning and postpone enforcing the deprecation to a later major release.",
"@Aloqeely we had a discussion about our deprecation policy at the bi-weekly developers meeting, and we think that a PDEP should be created that would formalize the policy and allow for greater discussion. Are you willing to write up a PDEP with a proposed policy?",
"Sure, I can give it a go.\r\nGoing to close this for now"
] |
2,228,848,493
| 58,168
|
No longer produce test.tar/test.zip after running test suite
|
closed
| 2024-04-05T21:45:59
| 2024-04-05T23:11:18
| 2024-04-05T23:11:11
|
https://github.com/pandas-dev/pandas/pull/58168
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58168
|
https://github.com/pandas-dev/pandas/pull/58168
|
Aloqeely
| 1
|
- [x] closes #58162 (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.
|
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @Aloqeely "
] |
2,228,760,655
| 58,167
|
DEPR: enforce Sparse deprecations
|
closed
| 2024-04-05T20:26:39
| 2024-04-06T00:13:37
| 2024-04-05T21:40:31
|
https://github.com/pandas-dev/pandas/pull/58167
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58167
|
https://github.com/pandas-dev/pandas/pull/58167
|
jbrockmendel
| 1
|
- [ ] 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.
|
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jbrockmendel "
] |
2,228,745,172
| 58,166
|
TST: Use temp_file fixture over ensure_clean
|
closed
| 2024-04-05T20:14:49
| 2024-04-09T16:32:00
| 2024-04-09T13:11:36
|
https://github.com/pandas-dev/pandas/pull/58166
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58166
|
https://github.com/pandas-dev/pandas/pull/58166
|
mroeschke
| 0
| null |
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,228,682,790
| 58,165
|
PERF: more realistic np datetime c benchmark
|
closed
| 2024-04-05T19:28:25
| 2024-05-31T18:41:54
| 2024-05-31T18:41:47
|
https://github.com/pandas-dev/pandas/pull/58165
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58165
|
https://github.com/pandas-dev/pandas/pull/58165
|
dontgoto
| 2
|
See my comments in #57951. This change in the benchmark input range has more coverage of the different logic branches in `np_datetime.c`. The benchmark should run about 1.8x as long (most likely a little bit machine dependent) after merging this.
Keeping this separate from my other PR #57988 that improves the `np_datetime.c`performance to distinguish the benchmark impacts.
@WillAyd
|
[
"Benchmark"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"Thanks @dontgoto "
] |
2,228,632,452
| 58,164
|
BUILD: C code coverage
|
open
| 2024-04-05T18:50:13
| 2024-04-07T13:39:32
| null |
https://github.com/pandas-dev/pandas/issues/58164
| true
| null | null |
tdhock
| 2
|
Hi, I am doing mutation testing of pandas with @agroce
I see five C source code files that are maintained by pandas, is that right?
```
_libs/src/datetime/date_conversions.c
_libs/src/datetime/pd_datetime.c
_libs/src/parser/tokenizer.c
_libs/src/parser/pd_parser.c
_libs/src/parser/io.c
```
I expected that pandas devs should be interested in the code coverage of these files, but on current coverage reports https://app.codecov.io/gh/pandas-dev/pandas/tree/main/pandas?search=&displayType=list there is no coverage computed for C files (only for py files).
How can the build/test scripts be modified so that C code coverage could be computed?
Or is there a reason why code coverage for C files is not interesting?
I searched the issue tracker for similar keywords, and the only related issue I saw is about coverage for Cython: https://github.com/pandas-dev/pandas/pull/54453
Thanks in advance.
|
[
"Enhancement",
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"> Or is there a reason why code coverage for C files is not interesting?\r\n\r\nNo there is not.\r\n\r\nRelated for cython #25600",
"Our data also suggests (though we're not sure) the code may be considerably less covered than much of the rest, perhaps due to the lack of visibility in part. That's because the mutation score here is a lot lower (~50% vs. 70%+) than for the rest of the code."
] |
2,228,560,462
| 58,163
|
DOC: Add notes to nullable types documentation about pd.NA column type
|
closed
| 2024-04-05T18:10:45
| 2024-06-06T21:01:13
| 2024-06-06T21:01:04
|
https://github.com/pandas-dev/pandas/pull/58163
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58163
|
https://github.com/pandas-dev/pandas/pull/58163
|
giormala
| 2
|
- [X] closes #49201
- [ ] [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.
|
[
"Docs",
"Missing-data",
"NA - MaskedArrays"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Should I change something else to be merged? Thanks!",
"Thanks @giormala!"
] |
2,228,560,276
| 58,162
|
test.tar file left over after running test suite
|
closed
| 2024-04-05T18:10:36
| 2024-04-05T23:11:12
| 2024-04-05T23:11:12
|
https://github.com/pandas-dev/pandas/issues/58162
| true
| null | null |
jbrockmendel
| 1
|
Just started noticing this in the last few days.
|
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Looks like `test_to_csv_warn_when_zip_tar_and_append_mode`"
] |
2,228,528,805
| 58,161
|
STY: Add flake8-slots and flake8-raise rules
|
closed
| 2024-04-05T17:48:52
| 2024-04-08T16:45:45
| 2024-04-08T16:45:42
|
https://github.com/pandas-dev/pandas/pull/58161
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58161
|
https://github.com/pandas-dev/pandas/pull/58161
|
mroeschke
| 1
|
Also moves `ICN001` to the main ruff linter
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Since things are passing here going to merge"
] |
2,228,494,923
| 58,160
|
BUG: Segfault when calling df.to_json(orient='records') and numpy.datetime64 being serialized
|
closed
| 2024-04-05T17:24:41
| 2024-07-08T17:57:29
| 2024-07-08T17:57:11
|
https://github.com/pandas-dev/pandas/issues/58160
| true
| null | null |
paddymul
| 5
|
### 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
Reproducing this bug has been very difficult. I have a test script that runs off of my library repo that gets it to fail, but when trying to make a reduced testcase it doesn't fail. Given that i'm filing this against an old version of pandas, I'll just describe the bug to start. I'm happy to work with you to produce a reduced test case.
This is the code that should cause a segfault
ts = pd.Timestamp('2020-02-21T00:00:00.000000000')
np_dt = pd.Series([ts]).mode().values[0]
sdf = pd.DataFrame({'a':[ts], 'b':[np_dt]}, index=['mode'])
sdf.to_json(orient='table', indent=2, default_handler=str)
My actual code in the library (with print instrumentation)
```python
def pd_to_obj(df:pd.DataFrame):
print("--------------")
print("here ser 86")
if len(df) > 4:
df = df[20:21]
print(df)
val = df.iloc[0][0]
print("val", val)
print("type(val)", type(val))
print("df.index", df.index)
print("len df", len(df))
serialized = df.to_json(orient='table')
print("here ser 88")
```
this fails at the `df.to_json` call. Here is the debugging info outputted
```
--------------
here ser 86
EventDate
mode 2020-02-21T00:00:00.000000000
val 2020-02-21T00:00:00.000000000
type(val) <class 'numpy.datetime64'>
df.index Index(['mode'], dtype='object')
len df 1
zsh: segmentation fault python crash_buckaroo.py
```
```
### Issue Description
Pandas 2.0.3 segafaults when executing `to_json(orient='records')`
### Expected Behavior
There shouldn't be a segfault
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 0f437949513225922d851e9581723d82120684a6
python : 3.10.14.final.0
python-bits : 64
OS : Darwin
OS-release : 22.6.0
Version : Darwin Kernel Version 22.6.0: Wed Oct 4 21:25:40 PDT 2023; root:xnu-8796.141.3.701.17~4/RELEASE_ARM64_T8103
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.0.3
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : 8.1.1
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.23.0
pandas_datareader: None
bs4 : 4.12.3
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.4
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.13.0
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"### System Information:\r\n OS: macOS-13.5-arm64-arm-64bit\r\n Architecture: ('64bit', '')\r\n Machine: arm64\r\n Processor: arm\r\n Byte order: little\r\nLocale Information:\r\n LC_ALL: (None, 'UTF-8')\r\n LANG: (None, 'UTF-8')\r\nPackage Versions:\r\n pandas: 3.0.0.dev0+680.gb8a4691647\r\n numpy: 1.24.2\r\n pytz: 2023.3\r\n dateutil: 2.8.2\r\n setuptools: 58.0.4\r\n pip: 24.0\r\n pytest: 8.1.1\r\n IPython: 8.18.1\r\n matplotlib: 3.6.3\r\n scipy: 1.10.1\r\n \r\nTestCase I used\r\nimport pandas as pd\r\n\r\n### Create a Pandas DataFrame\r\nts = pd.Timestamp('2020-02-21T00:00:00.000000000')\r\nnp_dt = pd.Series([ts]).mode().values[0]\r\nsdf = pd.DataFrame({'a': [ts], 'b': [np_dt]}, index=['mode'])\r\n\r\n### Print the DataFrame\r\nprint(\"DataFrame:\")\r\nprint(sdf)\r\n\r\n### Save the DataFrame as a JSON file\r\njson_filename = \"data.json\"\r\nsdf.to_json(json_filename, orient='table', indent=2, default_handler=str)\r\nprint(f\"DataFrame saved as JSON file: {json_filename}\")\r\n\r\n>python3 58160.py\r\nOutput:\r\nDataFrame:\r\n a b\r\nmode 2020-02-21 2020-02-21\r\nDataFrame saved as JSON file: data.json\r\n\r\ndata.json content\r\n{\r\n \"schema\":{\r\n \"fields\":[\r\n {\r\n \"name\":\"index\",\r\n \"type\":\"string\"\r\n },\r\n {\r\n \"name\":\"a\",\r\n \"type\":\"datetime\"\r\n },\r\n {\r\n \"name\":\"b\",\r\n \"type\":\"datetime\"\r\n }\r\n ],\r\n \"primaryKey\":[\r\n \"index\"\r\n ],\r\n \"pandas_version\":\"1.4.0\"\r\n },\r\n \"data\":[\r\n {\r\n \"index\":\"mode\",\r\n \"a\":\"2020-02-21T00:00:00.000\",\r\n \"b\":\"2020-02-21T00:00:00.000\"\r\n }\r\n ]\r\n}\r\nI am not able to see any problem with the above-mentioned configuration can you try it.\r\n\r\n",
"As I said in the bug. The approximate reproduction code doesn't cause a segfault. I also included the code in my application next to where the segfault happens, along with debugging print statements. I think I'm constructing a dataframe with the same values as the failing one, but the failing dataframe is the result of a series of summary functions being applied to a dataframe and all of the summary values are constructed into a single dataframe. The memory layout for the failing dataframe is probably different than the value equivalent dataframe. I don't know how to succinctly reproduce the failing dataframe.\r\n\r\nI'm happy to dig into this problem with whoever is interested. You might get an interesting test case out of it.",
"I created a pickle file with a simplified dataframe that can reproduce the segmentation fault [simplified_df.pckl](https://github.com/paddymul/buckaroo/raw/chore/reproduce-seg-fault/simplified_df.pckl)\r\n\r\nthe `value_counts' row is the problem. The value for `EventDate` is the result of calling `datetime_series.value_counts()` so I think it's a dataframe.\r\n\r\nHere is the test script\r\n```python\r\nimport pandas as pd\r\nsimplified_df = pd.read_pickle('simplified_df.pckl')\r\npd.show_versions()\r\nprint(\"simplified_df\")\r\nprint(simplified_df)\r\nprint(\"-\" * 80)\r\nprint(\"simplified_df.dtypes\")\r\nprint(simplified_df.dtypes)\r\nprint(\"-\" * 80)\r\nvc_bad_row = simplified_df['EventDate'].loc['value_counts']\r\nprint(\"vc_bad_row\")\r\nprint(vc_bad_row)\r\nprint(\"-\" * 80)\r\nprint(\"type(vc_bad_row)\")\r\nprint(type(vc_bad_row))\r\nprint(\"-\" * 80)\r\nprint(\"vc_bad_row.dtype\")\r\nprint(vc_bad_row.dtype)\r\nprint(\"-\" * 80)\r\nprint(\"before to_json\")\r\nsimplified_df.to_json(orient='table')\r\nprint(\"after to_json\")\r\n```\r\n\r\nwhich outputs the following on my machine\r\n\r\n\r\n```\r\npython crash_buckaroo_simple.py \r\npython crash_buckaroo_simple.py \r\n/Users/paddy/anaconda3/envs/buckaroo-dev-6/lib/python3.8/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\r\n warnings.warn(\"Setuptools is replacing distutils.\")\r\n\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit : 965ceca9fd796940050d6fc817707bba1c4f9bff\r\npython : 3.8.18.final.0\r\npython-bits : 64\r\nOS : Darwin\r\nOS-release : 22.6.0\r\nVersion : Darwin Kernel Version 22.6.0: Wed Oct 4 21:25:40 PDT 2023; root:xnu-8796.141.3.701.17~4/RELEASE_ARM64_T8103\r\nmachine : arm64\r\nprocessor : arm\r\nbyteorder : little\r\nLC_ALL : None\r\nLANG : en_US.UTF-8\r\nLOCALE : en_US.UTF-8\r\n\r\npandas : 2.0.2\r\nnumpy : 1.24.3\r\npytz : 2023.3\r\ndateutil : 2.8.2\r\nsetuptools : 68.2.2\r\npip : 23.3.1\r\nCython : None\r\npytest : 7.3.2\r\nhypothesis : None\r\nsphinx : 7.1.2\r\nblosc : None\r\nfeather : None\r\nxlsxwriter : None\r\nlxml.etree : None\r\nhtml5lib : None\r\npymysql : None\r\npsycopg2 : None\r\njinja2 : 3.1.2\r\nIPython : 8.12.2\r\npandas_datareader: None\r\nbs4 : 4.12.2\r\nbottleneck : 1.3.5\r\nbrotli : None\r\nfastparquet : None\r\nfsspec : None\r\ngcsfs : None\r\nmatplotlib : 3.7.5\r\nnumba : None\r\nnumexpr : 2.8.4\r\nodfpy : None\r\nopenpyxl : None\r\npandas_gbq : None\r\npyarrow : 14.0.2\r\npyreadstat : None\r\npyxlsb : None\r\ns3fs : None\r\nscipy : 1.10.1\r\nsnappy : None\r\nsqlalchemy : None\r\ntables : None\r\ntabulate : None\r\nxarray : None\r\nxlrd : None\r\nzstandard : None\r\ntzdata : 2023.3\r\nqtpy : None\r\npyqt5 : None\r\nsimplified_df\r\n index EventDate\r\nlength 1 1\r\nmin 0 NaN\r\nmax 0 NaN\r\nmean 0.0 0\r\nnan_count 0 0\r\nvalue_counts 0 1\r\nName: count, dtype: int64 EventDate\r\n2020-02-21 1\r\nName: count, dtype: ...\r\nmode 0 2020-02-21T00:00:00.000000000\r\n--------------------------------------------------------------------------------\r\nsimplified_df.dtypes\r\nindex object\r\nEventDate object\r\ndtype: object\r\n--------------------------------------------------------------------------------\r\nvc_bad_row\r\nEventDate\r\n2020-02-21 1\r\nName: count, dtype: int64\r\n--------------------------------------------------------------------------------\r\ntype(vc_bad_row)\r\n<class 'pandas.core.series.Series'>\r\n--------------------------------------------------------------------------------\r\nvc_bad_row.dtype\r\nint64\r\n--------------------------------------------------------------------------------\r\nbefore to_json\r\nzsh: segmentation fault python crash_buckaroo_simple.py\r\n```",
"I now have a minimally reproducible example\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nbad_df = pd.DataFrame({'column': { 'foo':8, 'bad_val': np.datetime64('2005-02-25')}})\r\nbad_df.to_json(orient='table')\r\n```",
"This looks to be fixed on main, so it appears to have been addressed since 2.0.3 so closing\r\n\r\n```python\r\n\r\nIn [1]: import pandas as pd\r\n ...: import numpy as np\r\n ...: bad_df = pd.DataFrame({'column': { 'foo':8, 'bad_val': np.datetime64('2005-02-25')}})\r\n ...: bad_df.to_json(orient='table')\r\nOut[1]: '{\"schema\":{\"fields\":[{\"name\":\"index\",\"type\":\"string\"},{\"name\":\"column\",\"type\":\"string\"}],\"primaryKey\":[\"index\"],\"pandas_version\":\"1.4.0\"},\"data\":[{\"index\":\"foo\",\"column\":8},{\"index\":\"bad_val\",\"column\":\"2005-02-25T00:00:00.000\"}]}'\r\n```"
] |
2,228,363,640
| 58,159
|
BUG: True cannot be cast to bool
|
closed
| 2024-04-05T15:58:31
| 2024-06-25T20:25:01
| 2024-06-25T20:25:01
|
https://github.com/pandas-dev/pandas/issues/58159
| true
| null | null |
NathanDWalsh
| 6
|
### 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
df = pd.DataFrame({"bool_column": [True]}, dtype=pd.BooleanDtype.name)
df.to_excel("test_pandas_bool.xlsx", index=False)
df2 = pd.read_excel("test_pandas_bool.xlsx", dtype={"bool_column": pd.BooleanDtype.name})
```
### Issue Description
When:
* Reading an Excel file with pd.read_excel function
* One of the Excel columns is a boolean type
* The dtype for that column is specificed as pd.BooleanDtype
The following error is raised:
* True cannot be cast to bool
### Expected Behavior
Bool literal should be read, no error should be raised.
### Installed Versions
<details>
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.11.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 2.2.1
numpy : 1.26.4
pytz : 2023.3
dateutil : 2.8.2
setuptools : 68.1.2
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
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 : None
pyarrow : 12.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
sqlalchemy : 2.0.20
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"IO Excel",
"NA - MaskedArrays"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take",
"\"Hi, @nwalsh82 I've found a patch that could potentially fix the issue. You can find the changes in the following link: [shiny-new-feature patch](https://github.com/pandas-dev/pandas/compare/main...aabhinavg:shiny-new-feature?expand=1). Could you please take a look and see if it addresses the problem? Let me know if you have any questions about it!\"\r\n",
"@aabhinavg Have you created a PR for this bug? If so, could you link your PR to this issue by following the [contribution guidelines](https://pandas.pydata.org/docs/dev/development/contributing.html#making-a-pull-request)?\r\n\r\nI believe that I've found @aabhinavg's [PR](https://github.com/pandas-dev/pandas/pull/58252) for this issue. It looks like it's closed because the PR went stale. Taking this issue unless @aabhinavg states that they are still interested in working on it. ",
"take",
"looks like similar issue as #45903 (but that may be looking at None -> Boolean whereas this is True -> boolean). If so, the PR #46374 was closed due to going stale, which I feel was close. @rmhowe425 \r\n\r\nThe main issue seems to be that `pd.BooleanArray._from_sequence_of_strings` is getting called with values that are not strings.",
"Also @yuanx749's comment https://github.com/pandas-dev/pandas/issues/45903#issuecomment-1910404766 of using `dtype_backed='numpy_nullable'` is a workaround, but it seems to only after the `dtype` evaluation if defined in the `read_excel` call."
] |
2,228,363,539
| 58,158
|
DEPR: PandasArray alias
|
closed
| 2024-04-05T15:58:26
| 2024-04-05T18:02:17
| 2024-04-05T17:01:56
|
https://github.com/pandas-dev/pandas/pull/58158
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58158
|
https://github.com/pandas-dev/pandas/pull/58158
|
jbrockmendel
| 1
|
- [ ] 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.
|
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jbrockmendel "
] |
2,228,359,859
| 58,157
|
DEPR: Categorical fastpath
|
closed
| 2024-04-05T15:56:26
| 2024-04-06T00:13:28
| 2024-04-05T19:45:43
|
https://github.com/pandas-dev/pandas/pull/58157
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58157
|
https://github.com/pandas-dev/pandas/pull/58157
|
jbrockmendel
| 2
|
- [ ] 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.
|
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Merge conflict otherwise LGTM",
"Thanks @jbrockmendel "
] |
2,228,143,166
| 58,156
|
BUG: Fix read_parquet not working with data type pyarrow list (#57411)
|
closed
| 2024-04-05T14:14:27
| 2024-05-08T16:58:52
| 2024-05-08T16:58:51
|
https://github.com/pandas-dev/pandas/pull/58156
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58156
|
https://github.com/pandas-dev/pandas/pull/58156
|
diogojarodrigues
| 3
|
- [x] closes #57411
- [x] [Tests added and passed]
- [x] All [code checks passed]
- [ ] Added [type annotations]
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
When writing a DataFrame to a Parquet file, if the data type of the columns of the DataFrame is a PyArrow list, an error is raised when reading it from the Parquet file.
The error occurs because when reading, pandas tries to use NumPy dtypes which do not support PyArrow lists (in the function pandas_dtype in the file ./pandas/core/dtypes/common.py).
To solve this issue, we add an if statement to check if the column's data type is a PyArrow list. If so, we return the data type without converting it into a NumPy dtype (which does not support PyArrow lists).
|
[
"Dtype Conversions",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the PR",
"Thanks for the feedback.\r\nI will try to make some changes and afterward I will update my pull request.",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,228,031,727
| 58,155
|
ENH: Add support for reading value labels from 108-format and prior Stata dta files
|
closed
| 2024-04-05T13:23:31
| 2024-04-09T16:55:47
| 2024-04-09T16:55:40
|
https://github.com/pandas-dev/pandas/pull/58155
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58155
|
https://github.com/pandas-dev/pandas/pull/58155
|
cmjcharlton
| 6
|
- [x] closes #58154
- [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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
This change extends support for reading value labels to Stata 108-format (Stata 6) and earlier dta files.
|
[
"IO Stata"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"cc @bashtage",
"One of the type checks fails because in various places the value table codes are expected to be floats (dict[float, str]) whereas in fact they are stored as ints (dict[int, str]). I think there are two options to make this work:\r\n\r\n1. Cast the codes to float when read and update the newly added type hints to match elsewhere, but this would break anything that relies on the existing behaviour\r\n2. Change the existing type hints to expect int keys, but this might not match the intent of the original author",
"As for typechecking, you can probably need to replace `self._value_label_dict[labname][val[i]]` with `self._value_label_dict[labname][int(val[i])]` since it will be hard to convince the type checker that `val` is a 1-d array of int which would be needed to let it know that `val[i]` was an int.\r\n",
"> As for typechecking, you can probably need to replace `self._value_label_dict[labname][val[i]]` with `self._value_label_dict[labname][int(val[i])]` since it will be hard to convince the type checker that `val` is a 1-d array of int which would be needed to let it know that `val[i]` was an int.\r\n\r\nIt seems to be complaining about a mismatch with lines like `def value_labels(self) -> dict[str, dict[float, str]]:` elsewhere in the file. I think I could make it happy by changing the type hints I added to match this, but then this wouldn't match the actual content of the data.",
"> Some comments on what it here. Overall I think the cyclomatic complexity has gotten too high and I think it would be better to pull these up as two private methods, one for reading 108+ and the other for older formats. Would let you avoid a long `if...else...` clause.\r\n\r\nI did wonder about splitting it into functions, but didn't want to change too many lines. I am happy to have a go at implementing this.",
"Thanks @cmjcharlton "
] |
2,227,963,945
| 58,154
|
ENH: Support reading value labels for Stata formats 108 (Stata 6) and earlier
|
closed
| 2024-04-05T12:50:45
| 2024-04-09T16:55:41
| 2024-04-09T16:55:41
|
https://github.com/pandas-dev/pandas/issues/58154
| true
| null | null |
cmjcharlton
| 0
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Currently Pandas supports reading value labels for data files saved in 111 (Stata 7 SE) and later formats. It would be nice if this could be extended to all supported format versions.
### Feature Description
This could be implemented by extending the function `_read_value_labels` in pandas/io/stata.py.
Value labels in the 108 format use the same structure as later versions, except that label names are restricted to 8 characters, plus a null terminator [1].
Values labels prior to the 108 format used a simple structure for each label containing a list of codes, followed by a list of 8 character strings corresponding to each code [2].
References:
[1] Description of the 108 .dta format, section 5.6 Value Labels ([dta_108.txt](https://github.com/pandas-dev/pandas/files/14884862/dta_108.txt))
[2] Description of the 105 .dta format, section 5.6 Value Labels ([dta_105.txt](https://github.com/pandas-dev/pandas/files/14884870/dta_105.txt))
### Alternative Solutions
Currently the only way to import these labels is to open the file in another piece of software that does support reading them, and then save them to a more recent version for which Pandas has value label support.
### Additional Context
_No response_
|
[
"Enhancement",
"IO Stata"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,227,284,966
| 58,153
|
BUG: pd.sort_values() not working on categorical column
|
open
| 2024-04-05T07:34:28
| 2024-04-06T17:27:29
| null |
https://github.com/pandas-dev/pandas/issues/58153
| true
| null | null |
BelaNeuendorf
| 2
|
### 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
df = pd.DataFrame([[1, 2, 'March'],[5, 6, 'Dec'],[3, 4, 'April']], columns=['a','b','month'])
df.month = df.month.astype('category')
custom_dict = {'March': 0, 'April': 1, 'Dec': 3}
df.sort_values(by=['month'], key=lambda x: x.map(custom_dict))
```
### Issue Description
the sorting is done alphabetically instead of by the provided dictionary.
Result:
a b month
2 3 4 April
1 5 6 Dec
0 1 2 March
### Expected Behavior
<html>
<body>
<!--StartFragment-->
| a | b | m
-- | -- | -- | --
1 | 2 | March
3 | 4 | April
5 | 6 | Dec
<!--EndFragment-->
</body>
</html>
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.12.2.final.0
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.22631
machine : AMD64
processor : AMD64 Family 25 Model 80 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 2.2.1
numpy : 1.26.4
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.22.2
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : 1.3.7
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.0
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"```\r\nimport pandas as pd\r\n\r\n# Create DataFrame\r\ndf = pd.DataFrame([[1, 2, 'March'],[5, 6, 'Dec'],[3, 4, 'April']], columns=['a','b','month'])\r\n\r\n# Define custom_dict\r\ncustom_dict = {'March': 0, 'April': 1, 'Dec': 3}\r\n\r\n# Convert 'month' column to Categorical with custom order\r\ndf['month'] = pd.Categorical(df['month'], categories=custom_dict.keys())\r\n\r\n# Sort DataFrame based on the custom order\r\ndf.sort_values(by='month', key=lambda x: x.map(custom_dict))\r\n\r\n```",
"To be more specific, `sort_values` doesn't respect the `key` parameter for categorical series.\r\n\r\nSorting does work as expected if the categorical is ordered using the `ordered` param:\r\n\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame([[1, 2, 'March'],[5, 6, 'Dec'],[3, 4, 'April']], columns=['a','b','month'])\r\ndf['month_cat_ordered'] = pd.Categorical(df.month, ['March', 'April', 'Dec'], ordered=True)\r\ndf.sort_values('month_cat_ordered')\r\n```\r\n| | a | b | month | month_cat_ordered | \r\n|---:|----:|----:|:--------|:--------------------| \r\n| 0 | 1 | 2 | March | March | \r\n| 2 | 3 | 4 | April | April | \r\n| 1 | 5 | 6 | Dec | Dec | \r\n\r\nMapping an unordered categorical as in the original example maps the old categories to the new categories but doesn't reorder the new categories. This is possible to implement this reordering in `Categorical.map` but would come at a performance cost and it isn't immediately clear it's worth it."
] |
2,226,233,843
| 58,152
|
BUG: pyarrow dictionary type ordered argument not respected
|
open
| 2024-04-04T18:26:38
| 2025-08-15T22:50:42
| null |
https://github.com/pandas-dev/pandas/issues/58152
| true
| null | null |
WillAyd
| 2
|
### 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
>>> dicttyp = pa.dictionary(pa.int8(), pa.string(), ordered=True)
>>> dicttyp.ordered
>>> pd.Series(["foo", "bar", "foo"], dtype=pd.ArrowDtype(dicttyp))
0 foo
1 bar
2 foo
dtype: dictionary<values=string, indices=int8, ordered=0>[pyarrow]
```
```
### Issue Description
The pyarrow type above is ordered, but that gets lost when forwarded to pandas
### Expected Behavior
order should be respected
### Installed Versions
main
|
[
"Bug",
"Upstream issue",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Looks to be a pyarrow bug\r\n\r\n```python\r\nIn [7]: pa.__version__\r\nOut[7]: '15.0.1'\r\n\r\nIn [8]: dicttyp\r\nOut[8]: DictionaryType(dictionary<values=string, indices=int8, ordered=1>)\r\n\r\nIn [9]: pa.array([\"foo\", \"bar\", \"foo\"], dicttyp).type\r\nOut[9]: DictionaryType(dictionary<values=string, indices=int8, ordered=0>)\r\n```",
"Very nice catch. Just opened https://github.com/apache/arrow/issues/41017\r\n"
] |
2,225,807,600
| 58,151
|
BUG: pd.isna handles np.nan inconsistently between numpy and extension / arrow types
|
closed
| 2024-04-04T15:22:23
| 2024-05-13T20:21:27
| 2024-05-13T19:57:57
|
https://github.com/pandas-dev/pandas/issues/58151
| true
| null | null |
WillAyd
| 4
|
### 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
import numpy as np
import pyarrow as pa
np_arr = pd.Series([0, np.nan], dtype=np.float64) / 0
pd_arr = pd.Series([0, np.nan], dtype=pd.Float64Dtype()) / 0
pa_arr = pd.Series([0, np.nan], dtype=pd.ArrowDtype(pa.float64())) / 0
```
```
>>> np_arr
0 NaN
1 NaN
dtype: float64
>>> pd.isna(np_arr)
0 True
1 True
dtype: bool
>>> pd_arr
0 NaN
1 <NA>
dtype: Float64
>>> pd.isna(pd_arr)
0 False
1 True
dtype: bool
>>> pa_arr
0 NaN
1 <NA>
dtype: double[pyarrow]
>>> pd.isna(pa_arr)
0 False
1 True
dtype: bool
```
### Issue Description
In the NumPy case, the NaN value resulting from the 0 / 0 division is caught by `pd.isna` but not in the pandas / pyarrow case
### Expected Behavior
I think all should be True @jorisvandenbossche
### Installed Versions
run on main
|
[
"Bug",
"Needs Triage"
] | 2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"There are several issues about this. We talked about it at the sprint ",
"agree with @jbrockmendel \r\n\r\nthis is a duplicate of a bunch of issues, so closing here",
"@phofl can you link to some of the issues you know about so readers can follow the discussion? I am not sure where to track this myself",
"#53887 and a bunch of issues mentioned there"
] |
2,225,765,421
| 58,150
|
Potential regression induced by "PERF: Series(dict) returns RangeIndex when possible"
|
closed
| 2024-04-04T15:03:47
| 2024-07-08T23:54:20
| 2024-07-08T23:54:19
|
https://github.com/pandas-dev/pandas/issues/58150
| true
| null | null |
DeaMariaLeon
| 2
|
PR #58118
If it is expected please ignore this issue.
Benchmark:
`series_methods.SeriesConstructor.time_constructor_dict` - no permutations
http://57.128.112.95:5000/compare/benchmarks/0660daf33c6478af8000779f25acbd4d...0660dd6fa43276cc8000cdfdb5c270dc
@mroeschke
<img width="698" alt="Screenshot 2024-04-04 at 17 00 28" src="https://github.com/pandas-dev/pandas/assets/11835246/9af1e6ae-f6d0-4a3c-aa67-e14620f51620">
|
[
"Benchmark"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the ping! I think the increase in time is due to inference done on the `dict.keys` to determine if the keys are integers. I am not sure if this can be avoided but can take a second look later",
"Closing as I didn't find a way to avoid this"
] |
2,225,513,739
| 58,149
|
BUG: Pandas does not recognise older missing value code for double when reading Stata files prior to 108 (Stata 6) format
|
closed
| 2024-04-04T13:24:56
| 2024-07-26T19:44:58
| 2024-07-26T19:44:58
|
https://github.com/pandas-dev/pandas/issues/58149
| true
| null | null |
cmjcharlton
| 0
|
### 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
# The problem occurs when reading an external file, so unfortunately this
# requires a data file linked to in the issue description
import pandas as pd
df = pd.read_stata("aidsdata.dta")
```
### Issue Description
Stata format 108 (corresponding to Stata 6) changed the code used to signify a missing value of type double (from 2^333 [1] to 2^1023 [2]). Pandas does not currently recognise this when reading these Stata file versions and therefore leaves the raw value in the DataFrame (note that the missing code for float types has not changed and therefore this works as expected):
```python
>>> df = pd.read_stata("aidsdata.dta")
>>> df[['gini', 'urbmf', 'miltpop', 'gaplit']][0:9]
gini urbmf miltpop gaplit
0 3.900000e-01 1.028946e+00 10.668921 26.999992
1 1.749801e+100 1.749801e+100 13.931355 26.199966
2 1.749801e+100 9.929842e-01 5.936691 0.800000
3 1.749801e+100 9.929842e-01 5.936691 0.800000
4 4.800000e-01 1.749801e+100 6.872657 NaN
5 4.800000e-01 1.749801e+100 6.872657 NaN
6 3.451000e-01 1.550366e+00 5.474547 26.399994
7 3.451000e-01 1.550366e+00 5.474547 26.399994
8 4.700000e-01 9.523810e-01 1.260353 NaN
```
The test file used above is \sg99\dta\aidsdata.dta contained in https://web.archive.org/web/19991011204243/http://lib.stat.cmu.edu/stata/STB/stb47v5.zip
References:
[1] Stata 1 reference manual, page 5 (https://www.statalist.org/forums/forum/general-stata-discussion/general/1638352-stata-1-reference-manual-now-available-to-anyone-who-wants-it)
[2] Description of the .dta file format 108, Representation of numbers ([dta_108.txt](https://github.com/pandas-dev/pandas/files/14871718/dta_108.txt))
### Expected Behavior
I would expect the data displayed to match that in Stata:
```
. dtaversion "aidsdata.dta"
(file "aidsdata.dta" is .dta-format 105 from Stata 5)
. use "aidsdata.dta"
(X-cntry data on HIV & Dtrmnts)
. list gini urbmf miltpop gaplit in 1/10
+-------------------------------------+
| gini urbmf miltpop gaplit |
|-------------------------------------|
1. | 0.390 1.029 10.66892 26.99999 |
2. | . . 13.93135 26.19997 |
3. | . 0.993 5.936691 .8000002 |
4. | . 0.993 5.936691 .8000002 |
5. | 0.480 . 6.872657 . |
|-------------------------------------|
6. | 0.480 . 6.872657 . |
7. | 0.345 1.550 5.474547 26.39999 |
8. | 0.345 1.550 5.474547 26.39999 |
9. | 0.470 0.952 1.260353 . |
10. | . 1.092 2.129809 14 |
+-------------------------------------+
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.12.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.2.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
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 : 3.8.3
numba : None
numexpr : 2.9.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"IO Stata",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,225,315,841
| 58,148
|
BUG: Fix Index.sort_values with natsort_key raises
|
closed
| 2024-04-04T12:09:08
| 2024-04-06T04:16:14
| 2024-04-05T16:50:15
|
https://github.com/pandas-dev/pandas/pull/58148
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58148
|
https://github.com/pandas-dev/pandas/pull/58148
|
yuanx749
| 2
|
- [x] closes #56081 (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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Index",
"Sorting"
] | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"An explanation of the fix for this case:\r\nIn L570, result is a tuple of tuple. Then it is converted to a MultiIndex in L580, which causes the error.\r\nhttps://github.com/pandas-dev/pandas/blob/f4232e7cdefcc33afe184891f833aec9c3c9bac2/pandas/core/sorting.py#L570-L580",
"Thanks @yuanx749 "
] |
2,224,855,025
| 58,147
|
BUG: Casting dtype of large datetime values raises an error inconsistent with resolution
|
closed
| 2024-04-04T08:35:19
| 2024-04-04T19:04:16
| 2024-04-04T17:33:57
|
https://github.com/pandas-dev/pandas/issues/58147
| true
| null | null |
amanlai
| 3
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [ ] 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
from datetime import datetime, UTC
lst = [datetime(3000,1,1, tzinfo=UTC), datetime(939,12,31, tzinfo=UTC)]
s = pd.Series(lst, dtype='datetime64[s]')
```
```
### Issue Description
The above code raises the following error:
```none
Traceback (most recent call last):
File "<pyshell#75>", line 1, in <module>
pd.Series(lst, dtype='datetime64[ns]')
File "C:\Python\Python312\Lib\site-packages\pandas\core\series.py", line 512, in __init__
data = sanitize_array(data, index, dtype, copy)
File "C:\Python\Python312\Lib\site-packages\pandas\core\construction.py", line 628, in sanitize_array
subarr = _try_cast(data, dtype, copy)
File "C:\Python\Python312\Lib\site-packages\pandas\core\construction.py", line 785, in _try_cast
return maybe_cast_to_datetime(arr, dtype)
File "C:\Python\Python312\Lib\site-packages\pandas\core\dtypes\cast.py", line 1216, in maybe_cast_to_datetime
dta = DatetimeArray._from_sequence(value, dtype=dtype)
File "C:\Python\Python312\Lib\site-packages\pandas\core\arrays\datetimes.py", line 297, in _from_sequence
return cls._from_sequence_not_strict(scalars, dtype=dtype, copy=copy)
File "C:\Python\Python312\Lib\site-packages\pandas\core\arrays\datetimes.py", line 339, in _from_sequence_not_strict
subarr, tz, inferred_freq = _sequence_to_dt64ns(
File "C:\Python\Python312\Lib\site-packages\pandas\core\arrays\datetimes.py", line 2226, in _sequence_to_dt64ns
data, inferred_tz = objects_to_datetime64ns(
File "C:\Python\Python312\Lib\site-packages\pandas\core\arrays\datetimes.py", line 2346, in objects_to_datetime64ns
result, tz_parsed = tslib.array_to_datetime(
File "tslib.pyx", line 403, in pandas._libs.tslib.array_to_datetime
File "tslib.pyx", line 552, in pandas._libs.tslib.array_to_datetime
File "tslib.pyx", line 487, in pandas._libs.tslib.array_to_datetime
File "conversion.pyx", line 767, in pandas._libs.tslibs.conversion.parse_pydatetime
File "np_datetime.pyx", line 231, in pandas._libs.tslibs.np_datetime.check_dts_bounds
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 3000-01-01 00:00:00, at position 0
```
The above code attempts to cast to `datetime[s]`, not `datetime[ns]`, so the error seems inconsistent.
Weirdly, if I cast each datetime into a POSIX timestamp and try again, the casting works without errors.
```python
lst1 = [x.timestamp() for x in lst]
s = pd.Series(lst1, dtype='datetime64[s]') # <--- OK
```
### Expected Behavior
Since the POSIX timestamp casting works fine, the expected behavior is the produce the following output:
```none
0 3000-01-01
1 939-12-31
dtype: datetime64[s]
```
At the minimum, the error message should not say `Out of bounds nanosecond timestamp: ` because the casting resolution is not nanosecond.
### Installed Versions
<details>
Python 3.12.0
pandas==2.1.1
numpy==1.26.0
Windows 10 64-bit
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"on main this raises as `ValueError: Cannot convert timezone-aware data to timezone-naive dtype. Use pd.Series(values).dt.tz_localize(None) instead.` which makes sense\r\n\r\n`s = pd.Series(lst, dtype='datetime64[s, UTC]')` works on main",
"@asishm `s = pd.Series(lst, dtype='datetime64[s, UTC]')` throws an `OverflowError: value too large` error but it works fine on main, so I guess it's a non-issue. Thanks for clarification.",
"Not sure when it was fixed, but `s = pd.Series(lst, dtype='datetime64[s, UTC]')` also works in 2.2.1"
] |
2,224,684,599
| 58,146
|
BUG: incorrect aggregation of dataframe when using UDF with kwarg
|
closed
| 2024-04-04T07:17:05
| 2024-04-05T13:01:37
| 2024-04-05T12:41:02
|
https://github.com/pandas-dev/pandas/issues/58146
| true
| null | null |
nicholas-ys-tan
| 3
|
### 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
import numpy as np
df = pd.DataFrame(
{
"A": [1, 2, 3, 4, 5],
"B": [10, 20, 30, 40, 50],
"groupby1": ["diamond", "diamond", "spade", "spade", "spade"]
}
)
def user_defined_aggfunc(df, addition=0):
return np.mean(df) + addition
out_df = df.groupby(by="groupby1").agg(func=user_defined_aggfunc, addition=5)
out_df
A B
groupby1
diamond 13.25 13.25
spade 27.00 27.00
```
### Issue Description
I am currently working on a PR to add `numeric_only` support to `DataFrameGroupBy.agg()` as it relates to some work on Geopandas I am currently trying to resolve. https://github.com/pandas-dev/pandas/issues/56946, https://github.com/pandas-dev/pandas/pull/58132
I noted there is an edge case that is currently not working as intended. This edge case occurs under the following conditions:
1. A single callable function is submitted as `func`
2. A `kwarg` is included
3. There is more than one column in the dataframe.
The first 2 conditions is what would lead it to use: `DataFrameGroupBy._aggregate_frame`
The third condition causes the bug in `DataFrameGroupBy._aggregate_frame` because the aggfunc would be applied to the entire dataframe, as opposed to column by column.
This has actually been touched upon in an existing test:
```python
def test_pass_args_kwargs_duplicate_columns(tsframe, as_index):
# go through _aggregate_frame with self.axis == 0 and duplicate columns
tsframe.columns = ["A", "B", "A", "C"]
gb = tsframe.groupby(lambda x: x.month, as_index=as_index)
res = gb.agg(np.percentile, 80, axis=0)
ex_data = {
1: tsframe[tsframe.index.month == 1].quantile(0.8),
2: tsframe[tsframe.index.month == 2].quantile(0.8),
}
expected = DataFrame(ex_data).T
if not as_index:
expected.insert(0, "index", [1, 2])
expected.index = Index(range(2))
tm.assert_frame_equal(res, expected)
```
Where the kwarg `axis=0` was fed into `agg()`. Three comments I have on this is that:
- it isn't necessarily obvious that this argument is required based on the documentation.
- not putting in that argument doesn't raise an error nor warning, but takes the 80th percentile in that group for all columns
- if using a UDF, such an argument would not be practical to be required in all cases
The root issue is how the aggfunc is applied in `GroupByDataFrame._aggregate_frame()` when there is more than one column. Without the `axis=0` kwarg, it applies the function on all columns to get a single value.
Below is a very braindead example of something that might work for `_aggregate_frame`:
```python
def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
if self._grouper.nkeys != 1:
raise AssertionError("Number of keys must be 1")
obj = self._obj_with_exclusions
result_index = self._grouper.result_index
out = self.obj._constructor([], index=obj.columns, columns=result_index)
for name, grp_df in self._grouper.get_iterator(obj):
aggregated_row_data = list()
for counter, _ in enumerate(grp_df.columns):
fres = func(grp_df.iloc[:,counter], *args, **kwargs)
aggregated_row_data.append(fres)
out[name] = aggregated_row_data
out = out.T
return out
```
There are a number of things that need to be accounted for:
- Need to consider that we can have duplicate column names, therefore we can not right out to the `result` `dict()` as dictionaries don't allow duplicates. Hence why the object is created to at the start and written to in the for loop
- If calling a dataframe[column_name], where there are duplicates names, all the data in the columns will show up and the function will be applied to all of them. Hence why iloc is used.
- To address wanting the function to be operated on only in the column-by-column, it's going through a for-loop.
This was just a naive approach to the issue but would like to confirm that this would be considered an issue before giving serious thought about a more appropriate fix.
### Expected Behavior
```
A B
groupby1
diamond 6.5 20.0
spade 9.0 45.0
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 6f39c4fa1fecec6fe447ec89bd1409d1427d1784
python : 3.10.14.final.0
python-bits : 64
OS : Linux
OS-release : 6.5.0-26-generic
Version : #26~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Mar 12 10:22:43 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 3.0.0.dev0+666.g6f39c4fa1f.dirty
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0
setuptools : 69.2.0
pip : 24.0
Cython : 3.0.10
pytest : 8.1.1
hypothesis : 6.100.0
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : 3.1.9
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : 1.4.6
psycopg2 : 2.9.9
jinja2 : 3.1.3
IPython : 8.22.2
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.3.8
fastparquet : 2024.2.0
fsspec : 2024.3.1
gcsfs : 2024.3.1
matplotlib : 3.8.3
numba : 0.59.1
numexpr : 2.9.0
odfpy : None
openpyxl : 3.1.2
pyarrow : 15.0.2
pyreadstat : 1.2.7
python-calamine : None
pyxlsb : 1.0.10
s3fs : 2024.3.1
scipy : 1.12.0
sqlalchemy : 2.0.29
tables : 3.9.2
tabulate : 0.9.0
xarray : 2024.3.0
xlrd : 2.0.1
zstandard : 0.22.0
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Groupby",
"Apply"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report - I believe this is a duplicate of #39169. Would you agree?\r\n\r\nI am optimistic that we will be able to get https://github.com/pandas-dev/pandas/pull/57706 in for 3.0.",
"> Thanks for the report - I believe this is a duplicate of #39169. Would you agree?\r\n> \r\n> I am optimistic that we will be able to get #57706 in for 3.0.\r\n\r\nYes, apologies, I hadn't picked up on the previous issue when I searched for past issues. Must not have had a close enough look.\r\n\r\n",
"Not a problem at all - with 3500+ issues, it can be quite difficult.\r\n\r\nAlso - the PR I linked to currently makes the changes to `agg` as breaking changes. I'm going to be looking at reworking it to instead introduce deprecations - so this bug will likely still exist in 3.0. All this is to say that you will need to work around this issue if we want to get #58132 into 3.0, and you shouldn't be waiting on #57706."
] |
2,224,338,386
| 58,145
|
REGR: from_records not initializing subclasses properly
|
closed
| 2024-04-04T02:52:22
| 2024-09-25T18:59:07
| 2024-09-25T18:59:07
|
https://github.com/pandas-dev/pandas/pull/58145
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58145
|
https://github.com/pandas-dev/pandas/pull/58145
|
phofl
| 2
|
- [ ] closes #57008 (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.
cc @jbrockmendel would appreciate thoughts, we don't have access to _constructor_from_mgr, so we need a workaround somehow.
|
[
"Regression",
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"Closing to clear the queue, but feel free to update and reopen anytime"
] |
2,224,047,667
| 58,144
|
BUG: df.where(series, axis=1) always results in TypeError
|
closed
| 2024-04-03T22:24:47
| 2024-04-03T22:42:35
| 2024-04-03T22:42:34
|
https://github.com/pandas-dev/pandas/issues/58144
| true
| null | null |
sfc-gh-rdurrani
| 2
|
### 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
df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=['col1', 'col2', 'col3'])
series = pd.Series([True, False, True])
df.where(series, axis=1)
```
```
### Issue Description
The above errors like so:
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[7], line 4
2 df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
3 series = pd.Series([True, False, True])
----> 4 df.where(series, axis=1)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/generic.py:10615, in NDFrame.where(self, cond, other, inplace, axis, level)
10609 warnings.warn(
10610 _chained_assignment_method_msg,
10611 ChainedAssignmentError,
10612 stacklevel=2,
10613 )
10614 other = common.apply_if_callable(other, self)
> 10615 return self._where(cond, other, inplace, axis, level)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/generic.py:10407, in NDFrame._where(self, cond, other, inplace, axis, level)
10404 return self._update_inplace(result)
10406 else:
> 10407 new_data = self._mgr.where(
10408 other=other,
10409 cond=cond,
10410 align=align,
10411 )
10412 result = self._constructor_from_mgr(new_data, axes=new_data.axes)
10413 return result.__finalize__(self)
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/base.py:190, in DataManager.where(self, other, cond, align)
187 align_keys = ["cond"]
188 other = extract_array(other, extract_numpy=True)
--> 190 return self.apply_with_block(
191 "where",
192 align_keys=align_keys,
193 other=other,
194 cond=cond,
195 using_cow=using_copy_on_write(),
196 )
File ~/.miniconda3/envs/snowpark/lib/python3.10/site-packages/pandas/core/internals/managers.py:349, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
346 kwargs[k] = obj.iloc[:, b.mgr_locs.indexer]._values
347 else:
348 # otherwise we have an ndarray
--> 349 kwargs[k] = obj[b.mgr_locs.indexer]
351 if callable(f):
352 applied = b.apply(f, **kwargs)
TypeError: 'float' object is not subscriptable
```
when it should work by transposing the Series and doing a positional join on labels.
### Expected Behavior
```python
col1 col2 col3
0 1 None 3
1 4 None 6
2 7 None 9
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a671b5a8bf5dd13fb19f0e88edc679bc9e15c673
python : 3.10.13.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Fri Mar 15 00:12:49 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.4
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 68.2.2
pip : 23.3.1
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 : 3.1.3
IPython : 8.22.2
pandas_datareader : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"With `mask`, the exception is instead: `'_NoDefault' object is not subscriptable`",
"closed: the error is because it seems that the axis argument only applies to other, and it fails here since other is a scalar."
] |
2,223,995,518
| 58,143
|
REF: Allow Index._with_infer to also return RangeIndex
|
closed
| 2024-04-03T21:50:42
| 2024-04-29T18:30:41
| 2024-04-29T18:30:37
|
https://github.com/pandas-dev/pandas/pull/58143
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58143
|
https://github.com/pandas-dev/pandas/pull/58143
|
mroeschke
| 1
|
Would supersede https://github.com/pandas-dev/pandas/pull/58117
I see 8 usages of `Index._with_infer` in the codebase. There are some ops like `groupby` and `CategoricalDtype` that would nicely return a `RangeIndex` result if possible
|
[
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"This may be too aggressive so closing for now"
] |
2,223,875,352
| 58,142
|
QST: loc returns matrix with one row when index is not strictly unique
|
open
| 2024-04-03T20:41:14
| 2024-04-03T20:44:36
| null |
https://github.com/pandas-dev/pandas/issues/58142
| true
| null | null |
Laubeee
| 0
|
### Research
- [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).
### Link to question on StackOverflow
https://stackoverflow.com/questions/78262738/why-does-pandas-loc-with-multiindex-return-a-matrix-with-single-row?noredirect=1#comment137985578_78262738
### Question about pandas
It seems when there exists at least one duplicate multi-index, ALL indices will return a DataFrame when queried with `.loc[]`, even if only one entry exists, resulting in a 1xN matrix. (I didn't test whether this is also applies to single-index). Is this wanted behaviour?
It does seem a bit strange and inconsistent with other functions, especially since you can still retrieve a Series when you query twice with `.loc[].loc[]`. On second glance it can also be seen to improve consistency by returning always a DataFrame for any index, regardless of the index that is used. While this has contributed to raising awarenss that the duplicate exist in one dataframe but not the other, the way there was long and tedious. I would prefer an INFO or WARN log over getting all confused why two similar dataframes return different objects when indexed in the same way.
code to reproduce (see also [SO-post](https://stackoverflow.com/questions/78262738/why-does-pandas-loc-with-multiindex-return-a-matrix-with-single-row?noredirect=1#comment137985578_78262738))
```python
import pandas as pd
from io import StringIO
file1 = StringIO("pdf_name;page;col1;col2\npdf1;0;val1;val2\npdf2;0;asdf;ffff")
file2 = StringIO("pdf_name;page;col1;col2\npdf1;0;;\npdf2;0;;\npdf2;0;;")
data1 = pd.read_csv(file1, sep=";", index_col=['pdf_name', 'page'])
data2 = pd.read_csv(file2, sep=";", index_col=['pdf_name', 'page'])
data2 = data2.sort_index() # data2.sort_index() # avoid performance warning
idx = data1.index[0]
print(idx) # ('pdf1', 0)
print("data1.loc[idx]", type(data1.loc[idx])) # data1.loc[idx] <class 'pandas.core.series.Series'>
print("data2.loc[idx]", type(data2.loc[idx])) # data2.loc[idx] <class 'pandas.core.frame.DataFrame'>
print("data2.loc[idx].shape", data2.loc[idx].shape) # (1, 2) -- single row
print("data2['col1'].loc[idx]", type(data2['col1'].loc[idx])) # data2['col1'].loc[idx] <class 'pandas.core.series.Series'>
```
|
[
"Usage Question",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,223,871,046
| 58,141
|
ENH: Consistent naming conventions for string dtype aliases
|
open
| 2024-04-03T20:39:48
| 2024-04-27T15:44:55
| null |
https://github.com/pandas-dev/pandas/issues/58141
| true
| null | null |
WillAyd
| 17
|
### Feature Type
- [X] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [X] Removing existing functionality in pandas
### Problem Description
Right now the string aliases for our types is inconsistent
```python
>>> import pandas as pd
>>> pd.Series(range(3), dtype="int8") # NumPy type
>>> pd.Series(range(3), dtype="Int8") # Pandas extension type
>>> pd.Series(range(3), dtype="int8[pyarrow]") # Arrow type
```
Strings have a similar inconsistency with "string", "string[pyarrow]" and "string[pyarrow_numpy]"
### Feature Description
I think we should create"int8[numpy]" and "int8[pandas]" aliases to stay consistent with pyarrow. This also has the advantage of decoupling "int8" from NumPy, so perhaps in the future we can allow the setting of the backend determine if NumPy or pyarrow types are returned
The pattern thus becomes "data_type[backend]", with the exception of "string[pyarrow_numpy]" which combines combines the backend and nullability semantics together. I am less sure what to do in that case - maybe even that should be called "string[pyarrow, numpy]" where the second argument is nullability?
In any case I am just hoping we can start to detach the logical type from the physical storage / nulllability semantics with a well defined pattern
@phofl
### Alternative Solutions
n/a
### Additional Context
_No response_
|
[
"Enhancement",
"API Design"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Meant to tag @jorisvandenbossche ",
"i like this idea, though as i mentioned at the sprint i think we should avoid \"backend\". maybe dtype \"family\"?",
"Maybe \"type provider\"?",
"Thinking through some more what I suggested above for `type_category[type_provider, nullability_provider]` won't always work as a pattern because there are still types that accept more arguments, e.g. datetime, pa.list, pa.dictionary, etc...\r\n\r\nI am wondering now if it is even worth trying to support string aliases or if we should push users towards using a more explicit dtype construction. This would be a change from where we are today but could be better in the long run (?)",
"As an exercise I tried to map out all of the types that pandas does today support (or reasonably could in the near term) and place in a hierarchy. Here is what I was able to come up with:\r\n\r\n\r\n\r\n\r\nTagging @pandas-dev/pandas-core in case this is of use to the larger team\r\n\r\ngraphviz used to build this:\r\n\r\n<details>\r\n\r\ndigraph type_graph {\r\n \r\n node [shape=box];\r\n \"type\"\r\n \r\n \"type\" -> \"scalar\"\r\n \"scalar\" -> \"numeric\"\r\n \r\n \"numeric\" -> \"integral\"\r\n \"integral\" -> \"signed\"\r\n subgraph cluster_signed {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.int8\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.int16\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.int32\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.int64\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.Int8Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.Int16Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.Int32Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.Int64Dtype\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.int8\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.int16\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.int32\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.int64\";\r\n \"np.int8\" -> \"np.int16\" -> \"np.int32\" -> \"np.int64\"\r\n \"pd.Int8Dtype\" -> \"pd.Int16Dtype\" -> \"pd.Int32Dtype\" -> \"pd.Int64Dtype\"\r\n \"pa.int8\" -> \"pa.int16\" -> \"pa.int32\" -> \"pa.int64\"\r\n }\r\n \"signed\" -> \"pd.Int8Dtype\" [arrowsize=0]\r\n \r\n \"integral\" -> \"unsigned\"\r\n subgraph cluster_unsigned {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.uint8\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.uint16\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.uint32\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.uint64\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.UInt8Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.UInt16Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.UInt32Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.UInt64Dtype\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.uint8\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.uint16\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.uint32\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.uint64\";\r\n \"np.uint8\" -> \"np.uint16\" -> \"np.uint32\" -> \"np.uint64\"\r\n \"pd.UInt8Dtype\" -> \"pd.UInt16Dtype\" -> \"pd.UInt32Dtype\" -> \"pd.UInt64Dtype\"\r\n \"pa.uint8\" -> \"pa.uint16\" -> \"pa.uint32\" -> \"pa.uint64\"\r\n }\r\n \"unsigned\" -> \"pd.UInt8Dtype\" [arrowsize=0]\r\n \r\n \"numeric\" -> \"floating point\"\r\n subgraph cluster_floating {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.float32\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.float64\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.Float32Dtype\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.Float64Dtype\"; \r\n node [fillcolor=\"lightgray\" style=filled] \"pa.float32\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.float64\";\r\n \"np.float32\" -> \"np.float64\"\r\n \"pd.Float32Dtype\" -> \"pd.Float64Dtype\"\r\n \"pa.float32\" -> \"pa.float64\"\r\n }\r\n \"floating point\" -> \"pd.Float32Dtype\" [arrowsize=0]\r\n\r\n \"numeric\" -> \"fixed point\"\r\n subgraph cluster_fixed {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.decimal128\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.decimal256\";\r\n \"pa.decimal128\" -> \"pa.decimal256\"\r\n }\r\n \"fixed point\" -> \"pa.decimal128\" [arrowsize=0]\r\n\r\n \"scalar\" -> \"boolean\"\r\n subgraph cluster_boolean {\r\n edge[style=invis]\r\n node[fillcolor=\"lightgreen\" style=filled] \"np.bool_\";\r\n node[fillcolor=\"lightblue\" style=filled] \"pd.BooleanDtype\";\r\n node[fillcolor=\"lightgray\" style=filled] \"pa.bool_\";\r\n }\r\n \"boolean\" -> \"pd.BooleanDtype\" [arrowsize=0]\r\n \r\n \"scalar\" -> \"temporal\"\r\n \"temporal\" -> \"date\"\r\n subgraph cluster_date {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.date32\"\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.date64\"\r\n \"pa.date32\" -> \"pa.date64\"\r\n }\r\n \"date\" -> \"pa.date32\" [arrowsize=0]\r\n \r\n \"temporal\" -> \"datetime\"\r\n subgraph cluster_timestamp {\r\n edge [style=invis]\r\n node [fillcolor=\"lightblue\" style=filled] \"datetime64[unit, tz]\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.timestamp(unit, tz)\";\r\n \"datetime64[unit, tz]\" -> \"pa.timestamp(unit, tz)\" [style=invis]\r\n }\r\n \"datetime\" -> \"datetime64[unit, tz]\" [arrowsize=0]\r\n \r\n \"temporal\" -> \"duration\"\r\n subgraph cluster_duration {\r\n edge [style=invis]\r\n node [fillcolor=\"lightblue\" style=filled] \"timedelta64[unit]\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.duration(unit)\";\r\n \"timedelta64[unit]\" -> \"pa.duration(unit)\" [style=invis]\r\n }\r\n \"duration\" -> \"timedelta64[unit]\" [arrowsize=0]\r\n\r\n \"temporal\" -> \"interval\"\r\n \"pa.month_day_nano_interval\" [fillcolor=\"lightgray\" style=filled]\r\n \"interval\" -> \"pa.month_day_nano_interval\"\r\n\r\n \"scalar\" -> \"binary\"\r\n subgraph cluster_binary {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.binary\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.large_binary\";\r\n \"pa.binary\" -> \"pa.large_binary\"\r\n }\r\n \"binary\" -> \"pa.binary\"\r\n \r\n \"binary\" -> \"string\"\r\n subgraph cluster_string {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgreen\" style=filled] \"object\";\r\n node [fillcolor=\"lightgreen\" style=filled] \"np.StringDType\";\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.StringDtype\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.string\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.large_string\";\r\n node [fillcolor=\"lightgray:lightgreen\" style=filled] \"string[pyarrow_numpy]\";\r\n \"object\" -> \"np.StringDType\"\r\n \"pa.string\" -> \"pa.large_string\"\r\n }\r\n \"string\" -> \"pa.string\" [arrowsize=0]\r\n \r\n \"scalar\" -> \"categorical\"\r\n subgraph cluster_categorical {\r\n edge [style=invis]\r\n node [fillcolor=\"lightblue\" style=filled] \"pd.CategoricalDtype\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.dictionary(index_type, value_type)\";\r\n \"pd.CategoricalDtype\" -> \"pa.dictionary(index_type, value_type)\" \r\n }\r\n \"categorical\" -> \"pd.CategoricalDtype\" [arrowsize=0]\r\n \r\n \"scalar\" -> \"sparse\"\r\n \"pd.SparseDtype(dtype)\" [fillcolor=\"lightblue\" style=filled];\r\n \"sparse\" -> \"pd.SparseDtype(dtype)\" [arrowsize=0]\r\n \r\n \"type\" -> \"aggregate\"\r\n \"aggregate\" -> \"list\"\r\n subgraph cluster_list {\r\n edge [style=invis]\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.list_(value_type)\";\r\n node [fillcolor=\"lightgray\" style=filled] \"pa.large_list(value_type)\";\r\n \"pa.list_(value_type)\" -> \"pa.large_list(value_type)\" \r\n }\r\n \"list\" -> \"pa.list_(value_type)\" [arrowsize=0]\r\n \r\n \"aggregate\" -> \"struct\"\r\n \"pa.struct(fields)\" [fillcolor=\"lightgray\" style=filled]\r\n \"struct\" -> \"pa.struct(fields)\" [arrowsize=0]\r\n\r\n \"aggregate\" -> \"dictionary\"\r\n \"dictionary\" -> \"pa.dictionary(index_type, value_type)\" [arrowsize=0]\r\n \"pa.map(index_type, value_type)\" [fillcolor=\"lightgray\" style=filled]\r\n \"dictionary\" -> \"pa.map(index_type, value_type)\" [arrowsize=0]\r\n}\r\n\r\n</details>",
"> I am wondering now if it is even worth trying to support string aliases or if we should push users towards using a more explicit dtype construction. This would be a change from where we are today but could be better in the long run (?)\r\n\r\nFrom a typing perspective, supporting all the different string versions of valid types for `dtype` are a PITA in `pandas-stubs`. So I'd be supportive of just having a class hierarchy to represent valid dtypes.\r\n\r\nHaving said that, if we are to deprecate the strings, we'd probably need a PDEP for that....\r\n",
"> I am wondering now if it is even worth trying to support string aliases or if we should push users towards using a more explicit dtype construction. \r\n\r\nI would be supportive of this as well. Especially for dtypes as strings that take parameters (timezone types, decimal types), it would be great to avoid string parsing to dtype object construction",
"> In any case I am just hoping we can start to detach the logical type from the physical storage / nulllability semantics with a well defined pattern\r\n\r\nTo your original point, I very much agree with this (at least for the physical storage, not necessarily for nullability semantics because I personally think we should move to just having one nullability semantic, but that's the topic for another PDEP)\r\n\r\nThis is a topic that I brought up last summer during the sprint, but never got around writing up publicly. The summary is that I would like to see us move to just having \"pandas\" dtypes, at least for the majority of the users that don't need to know the lower-level details. \r\nMost users just need to know they have eg a \"int64\" or \"string\" column, and don't have to care whether that is under the hood stored using a single numpy array, a combo of numpy arrays (our masked arrays) or a pyarrow array.\r\n\r\nThe current string aliases for non-default dtypes are I think mostly a band-aid to let people more easily specify those dtypes, and I fully agree those aren't very pretty. I do think it will be hard (or even desirable) to fully do away with string aliases though, at least for the default data types, because this is so widespread.\r\nBut IMO we should at least make the alternative to string aliases, construct dtypes programmatically, better supported and more consistent (eg so a user can just do `pd.Series(..., dtype=pd.int64())` or `pd.Series(..., dtype=pd.Int64Dtype())` and get the default int64 dtype based on their settings (which currently is the numpy dtype, but could also be a masked or pyarrow dtype based on their settings)).\r\n\r\n\r\n",
"So maybe then for each category in the type hierarchy above we have wrappers with signatures like:\r\n\r\n```python\r\nclass pd.int8(dtype_backend=\"pyarrow\"): ...\r\nclass pd.string(dtype_backend=\"pyarrow\", nullability=\"numpy\"): ...\r\nclass pd.datetime(dtype_backend=\"pyarrow\", unit=\"us\", tz=None): ...\r\nclass pd.list(value_type, dtype_backend=\"pyarrow\"): ...\r\nclass pd.categorical(key_type=\"infer\", value_type=\"infer\", dtype_backend=\"pandas\"): ...\r\n```\r\n\r\nI know @jbrockmendel prefers something besides `dtype_backend` but keeping that now for consistency with the I/O methods.\r\n\r\n> Having said that, if we are to deprecate the strings, we'd probably need a PDEP for that....\r\n\r\nI was thinking this as well",
"> The current string aliases for non-default dtypes are I think mostly a band-aid to let people more easily specify those dtypes, and I fully agree those aren't very pretty. I do think it will be hard (or even desirable) to fully do away with string aliases though, at least for the default data types, because this is so widespread.\r\n\r\nYea this would be a long process. I think what's hard about the string alias is that it only works for very basic types. It definitely has been and would continue to be relatively easy for users to just say \"int64\" and get a 64 bit integer irrespective of what that is backed by, but if the user wants to then create a list column they can't just do \"list\".\r\n\r\nI think users will end up with a frankenstein of string aliases alongside arguments like `dtype=pd.ArrowDtype(pa.list(pa.string()))` , which I find confusing",
"> Yea this would be a long process. I think what's hard about the string alias is that it only works for very basic types. It definitely has been and would continue to be relatively easy for users to just say \"int64\" and get a 64 bit integer irrespective of what that is backed by, but if the user wants to then create a list column they can't just do \"list\".\r\n> \r\n> I think users will end up with a frankenstein of string aliases alongside arguments like `dtype=pd.ArrowDtype(pa.list(pa.string()))` , which I find confusing\r\n\r\nI agree. One possibility to consider is to limit the number of string aliases to simple types `\"int\"`, `\"float\"`, `\"string\"`, `\"object\"`, `\"datetime\"`, `\"timedelta\"`, which default to something based on default backends, and even sizes (e.g., `\"int\"` means `\"int64\"`) as I guess that only a few of the strings are really used most often.\r\n",
"I found the notebook that I presented at the sprint last summer. It's a little bit off topic for the discussion just about string aliases, but I think it is relevant for the bigger picture (that we need to look at anyway if considering to move away from string aliases), so just dumping the content here (updated a little bit).\r\n\r\n---\r\n\r\nI like to have \"pandas data types\" with a consistent interface:\r\n\r\n- all data types are instances of our own DType class (hierarchy)\r\n- most users won't have to care about numpy vs arrow \r\n (and at least I think we should allow you to write code that is agnostic to it)\r\n- we might not want to follow all naming choices and implementation details of arrow\r\n\r\nFor example, for datetime-like data, we currently have:\r\n\r\n```python\r\n# current options\r\nser.astype(\"datetime64[ms]\") \r\n# vs\r\nser.astype(\"timestamp[us][pyarrow]\")\r\n\r\n# How to specify a \"datetime\" dtype being agnostic about the exact backend you are using?\r\n# -> should use a single name and will pick the default backend based on your settings\r\nser.astype(pd.datetime(\"ns\"))\r\n# or\r\nser.astype(pd.timestamp(\"ns\"))\r\n# for user that want's to be explicit\r\nser.astype(pd.datetime(\"ns\", backend=\"..\"))\r\n```\r\n\r\nAnother example, we currently have `pd.ArrowDtype(\"date64\")` or `\"date64[pyarrow]\"`, but if we want to enable a date dtype by default, users shouldn't need to know this is stored using pyarrow under the hood, so this could be `pd.date()` or `\"date\"`?\r\n\r\n---\r\n\r\n**Logical data types** vs physical data types:\r\n\r\n* For the \"string\" data type, we can store that with python objects or pyarrow strings (and now potentially also numpy's new string dtype), and then further for pyarrow, there are still 5 physical data types that all represent strings: string, large_string, string_view, dictionary[string], run_length_encoded[string] -> they all hold \"logical\" string values\r\n\r\nFor pandas, I think most users should care about logical data types, and not too much about the physical data type (and we can choose the best default, and advanced users can give hints which to use for performance optimizations)\r\n\r\n---\r\n\r\nAssuming we want a single pandas interface to all dtypes, we need to decide:\r\n\r\n- how to let users construct them: functional (`pd.string()` vs class constructors (`pd.StringDtype()`)\r\n- how to structure the DType classes: single class per logical type that are \"backend-parametrized\" (eg `pd.StringDtype(backend=\"arrow\")`, `pd.StringDtype(backend=\"numpy\")`) or separate classes based on the physical storage (what we have right now with eg `pd.Int64Dtype()` and `pd.ArrowDtype(pa.int64())`)\r\n\r\nEither we can use \"backend-parametrized\" classes or either hide classes a bit more and use dtype constructor factory functions:\r\n\r\n```python\r\npd.StringDtype(), pd.StringDtype(backend=\"arrow\"), pd.StringDtype(backend=\"numpy\")\r\nisinstance(dtype, pd.StringDtype)\r\n```\r\n\r\n-> but that means that choosing the approach of the current `StringDtype` with different backends instead of `ArrowDtype(\"string\")`\r\n\r\nor we could have different classes but then we definitely need the functional interface and dtype-checking helpers (because isinstance then doesn't work):\r\n\r\n```python\r\npd.string(), pd.string(backend=\"arrow\"), pd.string(backend=\"numpy\")\r\npd.api.types.is_string(..)\r\n```\r\n\r\n(and maybe `pd.string(backend=\"arrow\", storage=\"string_view\")` ?\r\n\r\nIn this case we are more free to keep whatever classes structure we want under the hood.\r\n\r\n\r\n",
"I forget the details, but remember finding Joris's presentation at the sprint compelling.",
"> Another example, we currently have `pd.ArrowDtype(\"date64\")` or `\"date64[pyarrow]\"`, but if we want to enable a date dtype by default, users shouldn't need to know this is stored using pyarrow under the hood, so this could be `pd.date()` or `\"date\"`?\r\n\r\nThis is an interesting example, but do we even need to support the pyarrow date64? I'm not really clear what advantages that has over date32. Per the hierarchy above I would just abstract this as `pd.date()` which under the hood would only use pyarrow's date32. It would be a suboptimal API if we had to do something like `pd.date(backend=\"pyarrow\", size=32)` but I'm not sure how likely that is.\r\n\r\nOutside of date types I do see that issue with strings where `dtype_backend=\"pyarrow\"` would leave it open to interpretation if you wanted pa.string(), pa.large_string(), or any of the other pyarrow string types you already mentioned.\r\n\r\n> Either we can use \"backend-parametrized\" classes or either hide classes a bit more and use dtype constructor factory functions:\r\n\r\nIn an ideal world I would be indifferent, but the problem with the class constructors is that they already exist (pd.StringDtype, pd.Int64Dtype, etc...). Repurposing them might only add to the confusion\r\n\r\nOverall though I agree with your sentiment of starting at a place where we think in terms of _logical data types_ foremost, which should cover the majority of use cases, and then giving some control over the physical data types via keyword arguments or options",
"> To your original point, I very much agree with this (at least for the physical storage, not necessarily for nullability semantics because I personally think we should move to just having one nullability semantic, but that's the topic for another PDEP)\r\n\r\nIs this in reference to how nulls are stored or how they are expressed to the end user? Storage-wise I feel like it would be a mistake to stray from the Arrow implementation",
"> > Either we can use \"backend-parametrized\" classes or either hide classes a bit more and use dtype constructor factory functions:\r\n> \r\n> In an ideal world I would be indifferent, but the problem with the class constructors is that they already exist (pd.StringDtype, pd.Int64Dtype, etc...). Repurposing them might only add to the confusion\r\n\r\nI think we already have both types somewhat, so we will need to clean this up to a certain extent whichever choice we make: \r\n\r\n- `pd.StringDtype(backend=\"python|\"pyarrow\")` is an example of using a single dtype class (that users can instantiate) as an interface to multiple implementations of the actual memory/compute (i.e. in this case we actually have multiple ExtensionArray classes that map to this single dtype depending on the backend) \r\n (although I know we also have `pd.ArrowDtype(pa.string())` which then uses a different DType class) \r\n- `np.dtype(\"int64\")` / `pd.Int64Dtype()` / `pd.ArrowDtype(pa.int64())` is essentially an example of a logical integer type where the entry point is a different class depending on which implementation you want (I know this wasn't necessarily designed together /grown historically, and I am mixing with also numpy dtypes, but it _is_ the current situation as how users face it) \r\n While we could decide to have a single `pd.Int64Dtype(backend=\"numpy\"|\"pyarrow\")` parametrized class (mimicking the string case above), or we could also decide that we are fine with having both `pd.Int64Dtype` (numpy based) and `pd.ArrowDtype` classes, but then I think we would need another entry point for users like `pd.int64()` factory function that can then create an instance of either of those classes (depending on your settings, or depending on a keyword you pass)\r\n\r\nSo while those class constructors indeed already exist, I think we have to repurpose or change the existing ones (and add new ones) to some extent anyway. And it is also not because we have those classes right now, that we can't decide we want to hide them more from the user by providing an alternative. I don't think there are already that many users that use `pd.Int64Dtype()` directly, and (_if we would prefer that interface_) there is certainly still room to start pushing a functional constructor interface.",
"> > To your original point, I very much agree with this (at least for the physical storage, not necessarily for nullability semantics because I personally think we should move to just having one nullability semantic, but that's the topic for another PDEP)\r\n> \r\n> Is this in reference to how nulls are stored or how they are expressed to the end user? Storage-wise I feel like it would be a mistake to stray from the Arrow implementation\r\n\r\nIn the first place to how they are expressed to the end user, because IMO that's the most important aspect (since we are talking about the user interface how dtypes are specified / presented). Personally I would also prefer to use a consistent implementation storage-wise, but that's more of an implementation detail that we could discuss/compromise per dtype."
] |
2,223,746,605
| 58,140
|
Backport PR #58138 on branch 2.2.x (BLD: Fix nightlies not building)
|
closed
| 2024-04-03T19:30:17
| 2024-04-03T22:02:48
| 2024-04-03T22:02:48
|
https://github.com/pandas-dev/pandas/pull/58140
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58140
|
https://github.com/pandas-dev/pandas/pull/58140
|
meeseeksmachine
| 0
|
Backport PR #58138: BLD: Fix nightlies not building
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,223,714,086
| 58,139
|
BUG: fixed to_numeric loss in precision when converting decimal type to integer #57213
|
closed
| 2024-04-03T19:11:25
| 2024-05-15T17:59:03
| 2024-05-15T17:59:02
|
https://github.com/pandas-dev/pandas/pull/58139
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58139
|
https://github.com/pandas-dev/pandas/pull/58139
|
Leventide
| 1
|
- [X] closes #57213
- [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. _(Not applicable)_
- [X] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Dtype Conversions"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,223,539,976
| 58,138
|
BLD: Fix nightlies not building
|
closed
| 2024-04-03T17:46:00
| 2024-04-03T19:30:43
| 2024-04-03T19:29:37
|
https://github.com/pandas-dev/pandas/pull/58138
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58138
|
https://github.com/pandas-dev/pandas/pull/58138
|
lithomas1
| 2
|
- [ ] 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.
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I forgot to delete the other build condition when removing the special build step for numpy 2.0 in my previous PR.",
"Thanks @lithomas1 "
] |
2,223,507,539
| 58,137
|
Backport PR #58100 on branch 2.2.x (MNT: fix compatibility with beautifulsoup4 4.13.0b2)
|
closed
| 2024-04-03T17:28:29
| 2024-04-03T19:29:53
| 2024-04-03T19:29:53
|
https://github.com/pandas-dev/pandas/pull/58137
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58137
|
https://github.com/pandas-dev/pandas/pull/58137
|
meeseeksmachine
| 0
|
Backport PR #58100: MNT: fix compatibility with beautifulsoup4 4.13.0b2
|
[
"IO HTML",
"Dependencies"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,223,425,252
| 58,136
|
ENH: Processing of .mask() for pd.NA #56844
|
closed
| 2024-04-03T16:49:45
| 2024-05-08T18:30:48
| 2024-05-08T17:00:07
|
https://github.com/pandas-dev/pandas/pull/58136
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58136
|
https://github.com/pandas-dev/pandas/pull/58136
|
mafaldam
| 3
|
- [ ] closes #56844
- [ ] [Tests added and passed]
- [ ] 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.
This commit addresses an issue where using NA in conjunction with the mask() method that resulted in unexpected behavior. The problem arose when comparing a Series containing NA as a condition within mask(), causing inconsistencies in the output. This fix ensures that NA behaves consistently as False in logical operations within mask().
|
[
"Missing-data"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen.",
"I am interested in continuing. Should I make another pull request with the merges? @mroeschke ",
"Yes that would be preferred"
] |
2,223,402,417
| 58,135
|
DOC: Add return value for Index.slice_locs when label not found #32680
|
closed
| 2024-04-03T16:38:26
| 2024-04-03T21:36:41
| 2024-04-03T21:36:34
|
https://github.com/pandas-dev/pandas/pull/58135
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58135
|
https://github.com/pandas-dev/pandas/pull/58135
|
KeiOshima
| 1
|
- [ ] Add return value for Index.slice_locs when label not found #32680
- [ ] [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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @KeiOshima "
] |
2,223,075,549
| 58,134
|
BUILD: Unable to install pandas on 32bit python due to mesons build error which requires x86_64. will not allow x86 (32 bit version of python 3.12)
|
closed
| 2024-04-03T14:18:53
| 2024-08-26T19:52:36
| 2024-08-26T19:52:36
|
https://github.com/pandas-dev/pandas/issues/58134
| true
| null | null |
BethDal
| 5
|
### Installation check
- [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas).
### Platform
Windows-10-10.0.19045-SP0
### Installation Method
pip install
### pandas Version
2.2.1
### Python Version
3.12
### Installation Logs
<details>
PS C:\Users\lineop\Documents\Python\1.BERTsuite_Nov2023\VcmMET3> py -m pip install pandas
Defaulting to user installation because normal site-packages is not writeable
Collecting pandas
Using cached pandas-2.2.1.tar.gz (4.4 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Installing backend dependencies ... done
**Preparing metadata (pyproject.toml) ... error**
error: subprocess-exited-with-error
× Preparing metadata (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [23 lines of output]
+ meson setup C:\Users\lineop.LI\AppData\Local\Temp\pip-install-o0ah6jbr\pandas_8176c5fd1a53489cadec284792bf22d6 C:\Users\lineop.LI\AppData\Local\Temp\pip-install-o0ah6jbr\pandas_8176c5fd1a53489cadec284792bf22d6\.mesonpy-1
alb6x3l\build -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --vsenv --native-file=C:\Users\lineop.LI\AppData\Local\Temp\pip-install-o0ah6jbr\pandas_8176c5fd1a53489cadec284792bf22d6\.mesonpy-1alb6x3l\build\meson-python-n
ative-file.ini
**The Meson build system**
Version: 1.2.1
Source dir: C:\Users\lineop.LI\AppData\Local\Temp\pip-install-o0ah6jbr\pandas_8176c5fd1a53489cadec284792bf22d6
Build dir: C:\Users\lineop.LI\AppData\Local\Temp\pip-install-o0ah6jbr\pandas_8176c5fd1a53489cadec284792bf22d6\.mesonpy-1alb6x3l\build
Build type: native build
Project name: pandas
Project version: 2.2.1
Activating VS 17.9.4
C compiler for the host machine: cl (msvc 19.39.33523 "Microsoft (R) C/C++ Optimizing Compiler Version 19.39.33523 for x64")
C linker for the host machine: link link 14.39.33523.0
C++ compiler for the host machine: cl (msvc 19.39.33523 "Microsoft (R) C/C++ Optimizing Compiler Version 19.39.33523 for x64")
C++ linker for the host machine: link link 14.39.33523.0
Cython compiler for the host machine: cython (cython 3.0.5)
**Host machine cpu family: x86_64
Host machine cpu: x86_64**
Program python found: YES (C:\Program Files (x86)\Python312-32\python.exe)
**Need python for x86_64, but found x86**
Run-time dependency python found: NO (tried sysconfig)
..\..\pandas\_libs\tslibs\meson.build:32:7: ERROR: Python dependency not found
A full log can be found at C:\Users\lineop.LI\AppData\Local\Temp\pip-install-o0ah6jbr\pandas_8176c5fd1a53489cadec284792bf22d6\.mesonpy-1alb6x3l\build\meson-logs\meson-log.txt
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
</details>
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"My application is to measure data from a I2C board that uses a .dll file compiled at 32-bit, so 32-bit python MUST be used.\r\nError when pip install pandas. Meson build requires python x86_64 but I have only x86 (32bit) installed in order to be comatible with my board. Not sure which back version of pandas is compatible with 32 bit. All version listed for windows are 64bit.\r\n\r\nNote: I am Running Pycharm behind a firewall, so not all python library websites are on my approved list. These can be added",
"So you are trying to build a 32 bit application on a 64 bit build system right? Technically meson allows for this, but I don't think that is supported in meson-python just yet. See https://github.com/mesonbuild/meson-python/issues/321",
"This is supported. What you have to do is ensure you are activating 32-bit MSVC, because Meson isn't going to do that automatically for you just because you're using a 32-bit Python (it's not a Python-specific build system, so that would be too magical). Here is how:\r\n\r\nhttps://github.com/numpy/numpy/blob/aa0cc043d1fe51e61024842b21905e11a2a4cee6/.github/workflows/wheels.yml#L107-L111\r\n\r\nand ensure you pass `--vsenv` as a `setup` argument to select MSVC, e.g.:\r\n\r\nhttps://github.com/numpy/numpy/blob/aa0cc043d1fe51e61024842b21905e11a2a4cee6/pyproject.toml#L180",
"Thanks to the hint above, opening \"x86 Native Tools Command Prompt for VS 2022\" to select the 32-bit MSVC compiler followed by `pip install pandas` worked.",
"Closing as a solution was provided"
] |
2,222,880,773
| 58,133
|
BUG: pandas groupby returns different results when one and two groups
|
closed
| 2024-04-03T13:03:27
| 2024-04-05T12:41:19
| 2024-04-05T12:41:19
|
https://github.com/pandas-dev/pandas/issues/58133
| true
| null | null |
ibigquant
| 2
|
### 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
# df = pd.DataFrame({"a": [1, 1, 1, 1], "b": [1, 2, 3, 4]})
df = pd.DataFrame({"a": [1, 2, 2, 1], "b": [1, 2, 3, 4]})
def foo(xf):
def bar(x):
# here just some sample code ..
return x['b'] + 1
return xf.apply(bar, axis=1)
result_series = df.groupby("a", group_keys=False).apply(foo)
result_series
```
### Issue Description
it returns dataframe when there's only one group
### Expected Behavior
always returns series
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.11.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.14.0-162.23.1.el9_1.x86_64
Version : #1 SMP PREEMPT_DYNAMIC Tue Apr 11 19:09:37 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : zh_CN.UTF-8
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 65.5.0
pip : 24.0
Cython : 3.0.9
pytest : 8.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.22.2
pandas_datareader : 0.10.0
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 : 3.8.3
numba : 0.59.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : 2.0.29
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I have run the example above and it returned the expected behavior.\r\n\r\nMy output:\r\n```\r\n0 2\r\n3 5\r\n1 3\r\n2 4\r\ndtype: int64\r\n```\r\n\r\nPandas version: 2.2.1",
"Thanks for the report, closing as a duplicate of #54992"
] |
2,222,687,792
| 58,132
|
API: add numeric_only support to groupby agg
|
closed
| 2024-04-03T11:34:15
| 2024-05-20T05:35:03
| 2024-05-20T05:35:03
|
https://github.com/pandas-dev/pandas/pull/58132
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58132
|
https://github.com/pandas-dev/pandas/pull/58132
|
nicholas-ys-tan
| 6
|
Currently in early phase seeking prelim feedback.
- [x] partially addresses #56946, relevant to #50538
- [ ] [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.
|
[
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Currently still not operating on the right numbers on test case `numeric_only True - 1_grouper_str - numpy_mean`.\r\n\r\nNeed to add test cases with expected data.",
"> Currently still not operating on the right numbers on test case `numeric_only True - 1_grouper_str - numpy_mean`.\r\n> \r\n> Need to add test cases with expected data.\r\n\r\nFailing may be an existing bug - documented here: https://github.com/pandas-dev/pandas/issues/58146\r\n\r\nAlso mangled up the ResamplerApply - need to rethink strategy",
"> Thanks for the PR! It looks like you are using `**kwargs` to pass `numeric_only` in the call to `agg`. These `kwargs` are meant for the user to pass through to the function and I do not think we should be allowing \"built-in\" arguments into them. Instead, `numeric_only` should be added to the signature of `agg`.\r\n> \r\n> ~In addition, it appears that there are various paths through `agg` that do not respect `numeric_only`, e.g. `df.groupby(...).agg(\"sum\", numeric_only=True)`.~ because `**kwargs` is passed through, this was wrong!\r\n\r\nThank you for this note, I had assumed that we did not want to explicitly take `numeric_only` as a `kwarg` based on this comment:\r\nhttps://github.com/pandas-dev/pandas/issues/49352#issuecomment-1304799900\r\n\r\nIf taking `numeric_only` as an arguement is the preferred method, this would probably greatly simplify things. One of my big struggles was the paths changing with and without `kwargs` but that can be more easily managed if we aren't feeding through `kwargs`.\r\n\r\nNote that this was a half-cooked draft PR because I ran into #39169 bug, and had submitted PR #58132, but you mentioned we will be deprecating before doing a more complete fix.",
"> Thank you for this note, I had assumed that we did not want to explicitly take `numeric_only` as a `kwarg` based on this comment:\r\n> [#49352 (comment)](https://github.com/pandas-dev/pandas/issues/49352#issuecomment-1304799900)\r\n\r\nThat was if we were to fix it in a patch version (1.5.2). For 3.0, I think we should prefer a new argument.\r\n\r\n> If taking `numeric_only` as an arguement is the preferred method, this would probably greatly simplify things.\r\n\r\nIt would also be more straightforward for users!\r\n\r\n> Note that this was a half-cooked draft PR because I ran into #39169 bug\r\n\r\nYes - I think I failed to notice it was in draft status when I first reviewed. My bad.\r\n",
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"Closing as much of this will be addressed with a different PR, a fundamental misunderstanding of whether `numeric_only` should be its own kwarg, and the changes here being breaking changes that require more performance assessment.\r\n\r\nThank you to @rhshadrach for your time and your feedback on this PR. Will keep the above in mind for any future contributions I make."
] |
2,222,643,023
| 58,131
|
BUG: read_csv file handle not closed after error
|
open
| 2024-04-03T11:15:04
| 2025-07-15T20:38:59
| null |
https://github.com/pandas-dev/pandas/issues/58131
| true
| null | null |
ravcio
| 6
|
### 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
pd.read_csv('2022_01_k.zip')
# ValueError: Multiple files found in ZIP file. Only one file per ZIP: ['k_d_01_2022.csv', 'k_d_t_01_2022.csv']
```
### Issue Description
Handle to 2022_01_k.zip is left open after failed read attempt (resource leak).
### Expected Behavior
Handle to file 2022_01_k.zip is closed. File can be renamed/deleted on Windows.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.11.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : Polish_Poland.1250
pandas : 2.2.1
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 69.2.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : 5.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.20.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.0
numba : None
numexpr : 2.8.7
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : 0.19.0
tzdata : 2023.3
qtpy : 2.4.1
pyqt5 : None
</details>
|
[
"Bug",
"IO CSV"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take",
"@jsjeon-um Feel free to ping me if you have questions or when you have a PR!",
"@jsjeon-um are you still working on it?\r\ncan i take it?",
"I'll take this.",
"I was not able to recreate this bug. I tried on both Windows 10/11 and Linux systems with multiple zip files. Looking over the relevant code, I didn't see anything glaringly incorrect. Can anyone else reproduce this?",
"> I was not able to recreate this bug. I tried on both Windows 10/11 and Linux systems with multiple zip files. Looking over the relevant code, I didn't see anything glaringly incorrect. Can anyone else reproduce this?\r\n\r\nThe bug should still exists as we still raise without first cleaning up opened file handlers: https://github.com/pandas-dev/pandas/blob/8a286fa16f3160e939b192cbe8e218992a84e6fc/pandas/io/common.py#L812\r\n\r\nYou probably need to enable ResoruceWarnings, for example, with `python -W default` "
] |
2,222,628,472
| 58,130
|
BUG: Integers with extreme values are incorrectly interpreted as missing when importing older versions (pre-version 8) of the Stata dta file format
|
closed
| 2024-04-03T11:08:13
| 2024-07-25T17:50:39
| 2024-07-25T17:50:38
|
https://github.com/pandas-dev/pandas/issues/58130
| true
| null | null |
cmjcharlton
| 0
|
### 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
# Unfortunately as this occurs when importing an external file I can't replicate it
# without a data file, so I have included this with the issue description
import pandas as pd
df = pd.read_stata("stata-intlimits-111.dta")
```
### Issue Description
Stata version 8 (corresponding to the dta formats 112 and 113) introduced multiple missing values codes [1]. To support this for integer values the valid limits were changed to accommodate reusing certain values to represent missing codes. These changed from [2] [3]:
byte
minimum nonmissing -128
maximum nonmissing +126
code for . +127
int
minimum nonmissing -32768
maximum nonmissing +32766
code for . +32767
long
minimum nonmissing -2,147,483,648
maximum nonmissing +2,147,483,646
code for . +2,147,483,647
to [4]:
byte
minimum nonmissing -127
maximum nonmissing +100
code for . +101
code for .a +102
code for .b +103
...
code for .z +127
int
minimum nonmissing -32767
maximum nonmissing +32740
code for . +32741
code for .a +32742
code for .b +32743
...
code for .z +32767
long
minimum nonmissing -2,147,483,647
maximum nonmissing +2,147,483,620
code for . +2,147,483,621
code for .a +2,147,483,622
code for .b +2,147,483,623
...
code for .z +2,147,483,647
When reading older format files into Stata the repurposed values are retained by promoting the variable type to the next largest (i.e. byte->int, int->long, long->double). Pandas on the other hand interprets the values as if they had the newer coding and converts them to missing:
```
>>> df = pd.read_stata("stata-intlimits-111.dta")
>>> df
index byte int long
0 1 0.0 0.0 0.0
1 2 NaN NaN NaN
2 3 NaN NaN NaN
3 4 NaN NaN NaN
>>> df.dtypes
index int16
byte float64
int float64
long float64
dtype: object
```
Most of the time this isn't a problem as the new .z missing value type corresponds to the old single missing value code, however if there are non-missing values that correspond to the new codes they will be incorrectly interpreted.
References:
[1] What's new in release 8.0, **What's new in data management** , section 20 (https://www.stata.com/help.cgi?whatsnew7to8)
[2] Stata 1 reference manual, page 4 (https://www.statalist.org/forums/forum/general-stata-discussion/general/1638352-stata-1-reference-manual-now-available-to-anyone-who-wants-it)
[3] Description of the .dta file format 110, representation of numbers ([dta_110.txt](https://github.com/pandas-dev/pandas/files/14850612/dta_110.txt))
[4] Description of the .dta file format 113, representation of numbers (https://www.stata.com/help.cgi?dta_113#numbers)
Test files:
[stata-intlimits.zip](https://github.com/pandas-dev/pandas/files/14850717/stata-intlimits.zip)
### Expected Behavior
I would expect the data to appear as it does in Stata (the following is output from Stata version 18):
```
. dtaversion "stata-intlimits-111.dta"
(file "C:\Users\edcmjc\stata-intlimits-111.dta" is .dta-format 111 from Stata 7)
. use "stata-intlimits-111.dta"
(Integer limits for 111 dta format)
. list
+-------------------------------------+
| index byte int long |
|-------------------------------------|
1. | 1 0 0 0 |
2. | 2 -128 -32768 -2147483648 |
3. | 3 126 32766 2147483646 |
4. | 4 . . . |
+-------------------------------------+
. describe
Contains data from stata-intlimits_111.dta
Observations: 4 Integer limits for 111 dta format
Variables: 4
-----------------------------------------------------------------------------------------------
Variable Storage Display Value
name type format label Variable label
-----------------------------------------------------------------------------------------------
index int %8.0g
byte int %8.0g byte variable
int long %8.0g int variable
long double %12.0g long variable
-----------------------------------------------------------------------------------------------
Sorted by:
Note: Dataset has changed since last saved.
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.12.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.2.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
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 : 3.8.3
numba : None
numexpr : 2.9.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,222,585,104
| 58,129
|
Convert result of group by agg to pyarrow if input is pyarrow
|
closed
| 2024-04-03T10:48:16
| 2024-08-10T11:09:01
| 2024-08-09T17:30:11
|
https://github.com/pandas-dev/pandas/pull/58129
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58129
|
https://github.com/pandas-dev/pandas/pull/58129
|
undermyumbrella1
| 13
|
- [ ] closes #53030 (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.
Root cause:
- `agg_series` always forces output dtype to be the same as input dtype, but depending on the lambda, the output dtype can be different
Fix:
- replace all NA with nan
- convert the `results' to respective pyarrow extension array, using pyarrow library methods
- pyarrow library methods is used instead of `maybe_convert_object`, as `maybe_convert_object` does not check for NA, and forces dtype to float if NA is present (NA is not float in pyarrow),
|
[
"Apply",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I have added the issue in the pr, the pr is still work in progress",
"Hi, I have completed the implementation, may i check why linux test if failing with `NameError: name 'pa' is not defined`, but it works for other os?",
"```\r\ndf = DataFrame({\"A\": [\"c1\", \"c2\", \"c3\"], \"B\": [100, 200, 255]})\r\ndf[\"B\"] = df[\"B\"].astype(\"bool[pyarrow]\")\r\ngb = df.groupby(\"A\")\r\n\r\nresult = gb.agg(lambda x: [1, 2, 3])\r\nprint(result[\"B\"].dtype)\r\n# list<item: int64>[pyarrow]\r\n\r\nresult = gb.agg(lambda x: [1, 2, \"a\"])\r\nprint(result[\"B\"].dtype)\r\n# object\r\n```\r\nThank you for the review. For this example, it is expected as that is how pyarrow represents these data structures. E.g homogenous int list and heterogenous object. Alternatively, what would be the expected dtype in this case?",
"Sorry for the delay in resolving the test cases. Originally the approach is to always caste it back to input dtype, even if the output dtype is different. Currenlty the approach is to let pyarow infer the dtype of the output. However this can result in some unexpected cases as mentioned above. May I check what would be suggested output dtype to fix this issue?\r\n\r\nFor windows run, it seems to be failing as it is unable to import pyarrow in files unrelated to the code change in the pr, not sure if i have to change some configs.",
"> May I check what would be suggested output dtype to fix this issue?\r\n\r\nI think this is the same problem as the discussion here: https://github.com/pandas-dev/pandas/pull/58258#discussion_r1566446371. This appears to me something that will need careful implementation on the EA side.",
"Noted, I have made the change according to the comments using `convert_dtypes` and a helper method to account for some unaccounted pyarrow dtypes. Although I am not sure if the change should be made here or in ` lib.maybe_convert_objects`. \r\n\r\nSome of the windows tests are failing due to unable to import pyarrow in other section of the code unrelated to this change. Not sure if this is an issue on my side.",
"Thank you for the review, I have updated the pr according to comments.",
"Thank you foe the review, I have made changes according to the pr comments.",
"I think merging main once more should resolve the CI issues.",
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"I plan to push this across the finish line.",
"Ah sorry, I’ll get to it this weekend\r\n\r\nOn Thu, 20 Jun 2024 at 4:49 AM, Richard Shadrach ***@***.***>\r\nwrote:\r\n\r\n> I plan to push this across the finish line.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/pandas-dev/pandas/pull/58129#issuecomment-2179439892>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A4UEHW5KQ6INWZS7EBRXLFLZIHVFVAVCNFSM6AAAAABFVB4M4WVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNZZGQZTSOBZGI>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,222,517,706
| 58,128
|
When excel cell with 0x005F_FFFF, pandas read excel with wrong output read value 0FFFF
|
closed
| 2024-04-03T10:17:19
| 2024-04-08T12:39:34
| 2024-04-08T12:39:24
|
https://github.com/pandas-dev/pandas/issues/58128
| true
| null | null |
Kane-W
| 7
|
pd.read_excel with output 0FFFF
however， 0x004F_FFFF, pd read the right value 0x004F_FFFF
|
[
"Bug",
"IO Excel",
"Upstream issue"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. Can you provide a reproducible example.",
"\r\n\r\n\r\n",
"Thanks @Kane-W - this is a good reproducible example. It would be helpful if you could make this easier for pandas developers to reproduce. This means providing the code as text rather than a screenshot (so we can copy & paste rather than having to transcribe the screenshot), and removing any need for external files (where possible). Can you create the Excel file by doing something like:\r\n\r\n```\r\ndf = pd.DataFrame(...)\r\ndf.to_excel(\"test.xlsx\")\r\n```",
"`import pandas as pd\r\n\r\nprint(pd.__version__)\r\n\r\nfile = \"./test.xlsx\"\r\nxls = pd.ExcelFile(file)\r\nprint(xls)\r\ndata = pd.read_excel(xls, \"test\", dtype=\"object\")\r\nprint(data)`\r\n\r\n[test.xlsx](https://github.com/pandas-dev/pandas/files/14897344/test.xlsx)",
"```py\r\nimport pandas as pd\r\nfrom io import BytesIO\r\n\r\nfo = BytesIO()\r\ndata = {'ITEM': ['a', 'b', 'c', 'd'], 'VALUE': ['0x005F_FFFF', '0x004F_FFFF', '0x005f_ffff', '0x004f_ffff']}\r\ndf = pd.DataFrame(data)\r\n\r\ndf.to_excel(fo, index=False)\r\nfo.seek(0)\r\n\r\nfor engine in ['openpyxl', 'calamine']:\r\n print(engine)\r\n print(pd.read_excel(fo, dtype=\"object\", engine=engine))\r\n print()\r\n```\r\n\r\noutputs:\r\n\r\n```\r\nopenpyxl\r\n ITEM VALUE\r\n0 a 0FFFF\r\n1 b 0x004F_FFFF\r\n2 c 0x005f_ffff\r\n3 d 0x004f_ffff\r\n\r\ncalamine\r\n ITEM VALUE\r\n0 a 0x005F_FFFF\r\n1 b 0x004F_FFFF\r\n2 c 0x005f_ffff\r\n3 d 0x004f_ffff\r\n```\r\n\r\nlooks like this is only with the openpyxl engine, calamine returns fine",
"Seems related https://foss.heptapod.net/openpyxl/openpyxl/-/issues/2099 and https://foss.heptapod.net/openpyxl/openpyxl/-/issues/1410\r\n\r\nChanging the above reproducer to\r\n\r\n`df.to_excel(..., engine='openpyxl')` doesn't cause this issue.",
"Thanks @asishm for the investigation - agreed. Closing as an upstream issue."
] |
2,221,744,946
| 58,127
|
Backport PR #58126: BLD: Build wheels with numpy 2.0rc1
|
closed
| 2024-04-03T02:04:55
| 2024-04-03T02:57:16
| 2024-04-03T02:57:16
|
https://github.com/pandas-dev/pandas/pull/58127
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58127
|
https://github.com/pandas-dev/pandas/pull/58127
|
lithomas1
| 0
|
- [ ] 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.
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,221,607,623
| 58,126
|
BLD: Build wheels with numpy 2.0rc1
|
closed
| 2024-04-02T23:15:29
| 2024-04-03T02:04:15
| 2024-04-03T00:23:49
|
https://github.com/pandas-dev/pandas/pull/58126
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58126
|
https://github.com/pandas-dev/pandas/pull/58126
|
lithomas1
| 2
|
- [ ] 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.
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @lithomas1 ",
"Owee, I'm MrMeeseeks, Look at me.\n\nThere seem to be a conflict, please backport manually. Here are approximate instructions:\n\n1. Checkout backport branch and update it.\n\n```\ngit checkout 2.2.x\ngit pull\n```\n\n2. Cherry pick the first parent branch of the this PR on top of the older branch:\n```\ngit cherry-pick -x -m1 9cd5e5546c36c54dc8c3fab3dc20b092f812362e\n```\n\n3. You will likely have some merge/cherry-pick conflict here, fix them and commit:\n\n```\ngit commit -am 'Backport PR #58126: BLD: Build wheels with numpy 2.0rc1'\n```\n\n4. Push to a named branch:\n\n```\ngit push YOURFORK 2.2.x:auto-backport-of-pr-58126-on-2.2.x\n```\n\n5. Create a PR against branch 2.2.x, I would have named this PR:\n\n> \"Backport PR #58126 on branch 2.2.x (BLD: Build wheels with numpy 2.0rc1)\"\n\nAnd apply the correct labels and milestones.\n\nCongratulations — you did some good work! Hopefully your backport PR will be tested by the continuous integration and merged soon!\n\nRemember to remove the `Still Needs Manual Backport` label once the PR gets merged.\n\nIf these instructions are inaccurate, feel free to [suggest an improvement](https://github.com/MeeseeksBox/MeeseeksDev).\n "
] |
2,221,531,195
| 58,125
|
CLN: enforce deprecation of the `kind` keyword on df.resample/ser.resample
|
closed
| 2024-04-02T21:59:07
| 2024-04-19T17:22:46
| 2024-04-19T17:22:39
|
https://github.com/pandas-dev/pandas/pull/58125
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58125
|
https://github.com/pandas-dev/pandas/pull/58125
|
natmokval
| 4
|
xref #55895
enforced deprecation of the kind keyword on df.resample/ser.resample
|
[
"Resample",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I enforced deprecation of the `kind` keyword on `df.resample/ser.resample`.\r\n@jbrockmendel, could you please take a look at this PR?",
"@ChadFulton any objection to moving forward with this part of the deprecation?",
"Thanks @jbrockmendel, this change makes a lot of sense to me!",
"Thanks @natmokval "
] |
2,221,425,517
| 58,124
|
BUG: Inconsistent `RollingGroupby` Behaviour
|
open
| 2024-04-02T20:52:41
| 2025-07-15T20:38:39
| null |
https://github.com/pandas-dev/pandas/issues/58124
| true
| null | null |
hsorsky
| 0
|
### 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 datetime
import pandas as pd
X = pd.DataFrame(
{
"groupby_col": [0, 0, 1, 0],
"agg_col": [1, 2, 3, 4],
"date": [
pd.Timestamp(datetime.date(2000, 1, 1)),
pd.Timestamp(datetime.date(2000, 1, 2)),
pd.Timestamp(datetime.date(2000, 1, 3)),
pd.Timestamp(datetime.date(2001, 1, 1)),
],
}
)
# ----------------------------------------------------------------------
print(
X.groupby(["groupby_col"])
.rolling(window="5D", on="date")[["agg_col"]]
.agg("sum")
)
print(
X.groupby(["groupby_col"])[["agg_col", "date"]]
.rolling(window="5D", on="date")
.agg("sum")
)
print(
X.groupby(["groupby_col"])[["agg_col", "date"]]
.rolling(window="5D", on="date")[["agg_col"]]
.agg("sum")
)
# ----------------------------------------------------------------------
print(
X.groupby(["groupby_col"], as_index=False)
.rolling(window="5D", on="date")[["agg_col"]]
.agg("sum")
)
print(
X.groupby(["groupby_col"], as_index=False)[["agg_col", "date"]]
.rolling(window="5D", on="date")
.agg("sum")
)
print(
X.groupby(["groupby_col"], as_index=False)[["agg_col", "date"]]
.rolling(window="5D", on="date")[["agg_col"]]
.agg("sum")
)
# ----------------------------------------------------------------------
print(
X.groupby(["groupby_col"], as_index=False, sort=False)
.rolling(window="5D", on="date")[["agg_col"]]
.agg("sum")
)
print(
X.groupby(["groupby_col"], as_index=False, sort=False)[["agg_col", "date"]]
.rolling(window="5D", on="date")
.agg("sum")
)
print(
X.groupby(["groupby_col"], as_index=False, sort=False)[["agg_col", "date"]]
.rolling(window="5D", on="date")[["agg_col"]]
.agg("sum")
)
```
### Issue Description
Behaviour is inconsistent depending on if we select columns on the `DataFrameGroupBy` vs on the `RollingGroupby`.
In the first example, behaviour is as expected and we get
```
agg_col
groupby_col date
0 2000-01-01 1.0
2000-01-02 3.0
2001-01-01 4.0
1 2000-01-03 3.0
```
In the second, we get
```
agg_col date
groupby_col
0 0 1.0 2000-01-01
1 3.0 2000-01-02
3 4.0 2001-01-01
1 2 3.0 2000-01-03
```
i.e. the original index is in there. I think I have seen a comment about this in another issue before, or in the docs but I can't seem to find it 🙃. Maybe related to https://github.com/pandas-dev/pandas/issues/56705?
The third example gives us the same as the first
```
agg_col
groupby_col date
0 2000-01-01 1.0
2000-01-02 3.0
2001-01-01 4.0
1 2000-01-03 3.0
```
---
How about if we try `as_index=False`?
Fourth example shows that this doesn't work as expected (it has no effect)
```
agg_col
groupby_col date
0 2000-01-01 1.0
2000-01-02 3.0
2001-01-01 4.0
1 2000-01-03 3.0
```
But if we select before the do `.rolling`, as in example five, we see that this does seem to work
```
groupby_col agg_col date
0 0 1.0 2000-01-01
1 0 3.0 2000-01-02
3 0 4.0 2001-01-01
2 1 3.0 2000-01-03
```
but im not sure if this is just a happy coincidence related to the weirdness from example two?
Example six shoes that selecting both pre and post `.rolling` is effectively the same as only selecting post `.rolling`
```
agg_col
groupby_col date
0 2000-01-01 1.0
2000-01-02 3.0
2001-01-01 4.0
1 2000-01-03 3.0
```
---
Throwing `sort` in there doesn't seem to do anything (as noted in other issues, e.g. https://github.com/pandas-dev/pandas/issues/50296, ), and exhibits the same behaviour as other examples wrt pre-rolling col selection, post-rolling col selection, both pre-and-post-rolling col selection, as shown by examples seven through nine.
### Expected Behavior
1. We would see consistent behaviour between pre-rolling col selection and post-rolling col selection
2. `as_index` would always work and if `False` return a `DataFrame` the `by` from the `groupby` are in the columns, and not the index, presumably leaving the resulting index as a (potentially) unsorted version of the original index
3. `sort` would work at all
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.11.final.0
python-bits : 64
OS : Darwin
OS-release : 22.4.0
Version : Darwin Kernel Version 22.4.0: Mon Mar 6 20:59:28 PST 2023; root:xnu-8796.101.5~3/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.3
pytz : 2021.1
dateutil : 2.8.2
setuptools : 69.1.0
pip : 23.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.13.2
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.2.0
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 2024.2.0
scipy : 1.11.4
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Window",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,221,344,392
| 58,123
|
ENH: adding jsonline output format
|
closed
| 2024-04-02T19:57:06
| 2024-04-28T12:21:49
| 2024-04-28T12:21:29
|
https://github.com/pandas-dev/pandas/issues/58123
| true
| null | null |
nathanneuro
| 3
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html
Seems like a simple but useful expansion to add a 'jsonlines' output format. I'm not sure it belongs as an 'orient' in the to_dict, but it is very similar to to_dict(orient='records'). The only difference is omitting the surrounding brackets [], and the commas, and using newlines instead of commas to separate each dict record.
### Feature Description
```
# I write out jsonlines like so, given a list of dicts.
import json
list_of_dicts = []
with open('target_file.jsonl', 'w') as f:
for d in list_of_dicts:
f.write(json.dumps(d) + '\n')
```
### Alternative Solutions
Alternative 3rd party package: https://jsonlines.readthedocs.io/en/latest/
(I haven't used it, so I don't know how well it works with edge cases and whatnot)
### Additional Context
_No response_
|
[
"Usage Question"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Maybe `DataFrame.to_json()` is what you're looking for?\r\nYou can pass `lines=True` and `orient='records'` to get your desired output",
"take",
"Agreed with @Aloqeely "
] |
2,221,272,843
| 58,122
|
DEPR: keyword-only arguments for DataFrame/Series statistical methods
|
closed
| 2024-04-02T19:19:01
| 2024-04-09T16:58:36
| 2024-04-09T16:58:28
|
https://github.com/pandas-dev/pandas/pull/58122
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58122
|
https://github.com/pandas-dev/pandas/pull/58122
|
Aloqeely
| 2
|
- [x] closes #57087 (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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Deprecate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"pre-commit.ci autofix",
"Thanks @Aloqeely "
] |
2,221,267,028
| 58,121
|
BUG: Timestamp.unit now reflects changes in components after Timestamp.replace
|
closed
| 2024-04-02T19:16:57
| 2024-08-09T17:29:38
| 2024-08-09T17:29:37
|
https://github.com/pandas-dev/pandas/pull/58121
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58121
|
https://github.com/pandas-dev/pandas/pull/58121
|
diogomsmiranda
| 8
|
- [x] closes #57749
- [x] [Tests added and passed]
- [x] All [code checks passed]
- [x] Added an entry in the latest `doc/source/whatsnew/v3.0.0.rst`
The logic behind this is having the `Timestamp.unit` set as the most precise unit changed on `Timestamp.replace` if one is more precise than the previous set.
|
[
"Non-Nano",
"Timestamp"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hey, I've made the requested changes.\r\n\r\ncc @jbrockmendel ",
"@jbrockmendel @mroeschke Hey!! Sorry for the ping but it's been a while since I last heard from you, is this change still of interest?\r\n\r\nLet me know If you need anything from me :)",
"@jbrockmendel I see you made a change on this files recently, does this still work as intended?\r\nI haven't heard from you in a while. Is this still on track to be merged?",
"Hey @mroeschke after speaking with him it is of my understanding that @jbrockmendel is taking some time mostly-off contributing for pandas, he indicated that you might be able to review this in the meantime? thanks in advance",
"Could you add a test like\r\n\r\n```python\r\nts = pd.Timestamp(year=2020, month=1, day=1, nanosecond=5)\r\nresult = ts.replace(nanosecond=0)\r\nassert result.unit == \"s\"\r\n```",
"@mroeschke In fact that logic was not yet implemented, thank you for noticing! I have attempted to solve the issue by registering if a replacement with 0 was made and if so add an extra check to recalculate the replacement resolution. The test now passes successfully.",
"@mroeschke Hey, I've added the test cases requested!! Had to change some things in the logic but I believe it's working as intended now.",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,221,142,044
| 58,120
|
BUG: to_datetime timestamp parsing in 2.x
|
closed
| 2024-04-02T18:16:17
| 2024-07-25T09:27:36
| 2024-07-25T09:25:14
|
https://github.com/pandas-dev/pandas/issues/58120
| true
| null | null |
Synse
| 2
|
### 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
Passing a unix timestamp as a string with `unit='s'` parses the wrong timestamp. Passing it as an integer works as expected.
```py3
Python 3.10.13 (main, Mar 21 2024, 17:51:02) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'2.2.1'
>>> pd.to_datetime(1582154775, unit='s') # correct
Timestamp('2020-02-19 23:26:15')
>>> pd.to_datetime('1582154775', unit='s') # incorrect, off by 23 seconds
<stdin>:1: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.
Timestamp('2020-02-19 23:25:52')
>>>
```
### Issue Description
Something changed with `pandas.to_datetime()` in pandas 2.x and timestamps are now parsing incorrectly if they're passed as a unix timestamp string and `units='s'`.
There's a deprecation warning about behavior changing in a future version but it appears to be broken in all 2.x releases (reproduced locally on 2.0.0 and 2.2.1).
### Expected Behavior
Unix timestamps should continue parse consistently when passed as a `str` or `int` until the future version that changes the default behavior.
This does work as expected in the 1.5.3 release:
```py3
Python 3.10.13 (main, Mar 21 2024, 17:51:02) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'1.5.3'
>>> pd.to_datetime(1582154775, unit='s') # correct
Timestamp('2020-02-19 23:26:15')
>>> pd.to_datetime('1582154775', unit='s') # correct
Timestamp('2020-02-19 23:26:15')
>>>
```
### Installed Versions
<details>
```py3
Python 3.10.13 (main, Mar 21 2024, 17:51:02) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
/home/codespace/.python/current/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-1019-azure
Version : #19~22.04.1-Ubuntu SMP Wed Jan 10 22:57:03 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 68.2.2
pip : 24.0
Cython : None
pytest : 8.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.22.2
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.3.1
gcsfs : None
matplotlib : 3.8.3
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
>>>
```
</details>
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"> 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.\n\n3.0.0 looks to be parsing timestamps correctly:\n\n```py3\nPython 3.10.13 (main, Mar 21 2024, 17:51:02) [GCC 9.4.0] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import pandas as pd\n>>> pd.__version__\n'3.0.0.dev0+666.g6f39c4fa1f'\n>>> pd.to_datetime(1582154775, unit='s') # correct\nTimestamp('2020-02-19 23:26:15')\n>>> pd.to_datetime('1582154775', unit='s') # correct\n<stdin>:1: FutureWarning: The behavior of 'to_datetime' with 'unit' when parsing strings is deprecated. In a future version, strings will be parsed as datetime strings, matching the behavior without a 'unit'. To retain the old behavior, explicitly cast ints or floats to numeric type before calling to_datetime.\nTimestamp('2020-02-19 23:26:15')\n>>> \n```",
"thanks @Synse - looks like this has \"fixed itself\"? if it's correct on `main`, then there's not much that can be done about previous versions which may have had an issue\r\n\r\nclosing then but thanks for the report"
] |
2,221,135,641
| 58,119
|
PERF: concat([Series, DataFrame], axis=0) returns RangeIndex columns when possible
|
closed
| 2024-04-02T18:13:03
| 2024-04-03T20:15:11
| 2024-04-03T20:14:07
|
https://github.com/pandas-dev/pandas/pull/58119
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58119
|
https://github.com/pandas-dev/pandas/pull/58119
|
mroeschke
| 1
|
Discovered in #57441
|
[
"Reshaping",
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Conflict but otherwise lgtm"
] |
2,221,050,346
| 58,118
|
PERF: Series(dict) returns RangeIndex when possible
|
closed
| 2024-04-02T17:24:14
| 2024-04-03T18:59:18
| 2024-04-03T18:42:13
|
https://github.com/pandas-dev/pandas/pull/58118
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58118
|
https://github.com/pandas-dev/pandas/pull/58118
|
mroeschke
| 1
|
Discovered in https://github.com/pandas-dev/pandas/pull/57441
|
[
"Index",
"Series"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @mroeschke "
] |
2,221,028,639
| 58,117
|
PERF: groupby returns a RangeIndex from groups when possible
|
closed
| 2024-04-02T17:11:16
| 2024-04-09T17:33:17
| 2024-04-09T17:33:12
|
https://github.com/pandas-dev/pandas/pull/58117
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58117
|
https://github.com/pandas-dev/pandas/pull/58117
|
mroeschke
| 1
|
Discovered in #57441
|
[
"Groupby",
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I think https://github.com/pandas-dev/pandas/pull/58143 is a more generic application that affects this so closing in favor of that PR"
] |
2,220,714,710
| 58,116
|
BUG: Setting values on `DataFrame` with multi-index `.loc(axis=0)` access adds columns
|
closed
| 2024-04-02T14:44:55
| 2024-04-18T18:32:46
| 2024-04-18T18:32:46
|
https://github.com/pandas-dev/pandas/issues/58116
| true
| null | null |
btschroer
| 6
|
### 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
dates = pd.date_range("2001-01-01", freq="D", periods=2)
ids = ["i1", "i2"]
index = pd.MultiIndex.from_product([dates, ids], names=["date", "identifier"])
frame = pd.DataFrame(0.0, index=index, columns=["A", "B"])
frame.loc(axis=0)["2001-01-01", ["i1", "i2"]] = None
frame
>>> A B i1 i2
date identifier
2001-01-01 i1 NaN NaN None None
i2 NaN NaN None None
2001-01-02 i1 0.0 0.0 NaN NaN
i2 0.0 0.0 NaN NaN
```
### Issue Description
Setting a multi-indexed `DataFrame` adds columns.
### Expected Behavior
No columns added, i.e. behaviour as outlined in https://pandas.pydata.org/docs/user_guide/advanced.html#advanced-indexing-with-hierarchical-index.
### Installed Versions
<details>
NSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.14.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:43 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0
setuptools : 69.2.0
pip : 24.0
Cython : None
pytest : 7.4.4
hypothesis : None
sphinx : 6.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.22.2
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 : 3.7.3
numba : 0.59.0
numexpr : 2.9.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : 3.9.2
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take",
"> take\r\n\r\n@eshaready What do you mean, by 'take'?",
"It means I assigned myself to the issue!",
"Great thanks a lot for looking into this.",
"Hey! It looks like for the behavior you want, you should be using the syntax \r\n`frame.loc[(\"2001-01-01\", \"i1\"):(\"2001-01-01\", \"i2\")] = None`\r\nI think this produces the behavior you're looking for, slicing multiple rows and assigning them to None. The syntax you used checks for i1 and i2 in columns, and since they aren't columns it adds them. ",
"Thanks a lot @eshaready for looking into this and suggesting a fix. I see a couple of caveats with the approach you suggested, maybe my example was a bit too oversimplified. Let me try to extend it a bit to maybe better bring over the point:\r\n\r\n```python\r\nimport pandas as pd\r\n\r\ndates = pd.date_range(\"2001-01-01\", freq=\"D\", periods=2)\r\nids = [\"i1\", \"i2\", \"i3\"]\r\nindex = pd.MultiIndex.from_product([dates, ids], names=[\"date\", \"identifier\"])\r\nframe = pd.DataFrame(0.0, index=index, columns=[\"A\", \"B\"])\r\nframe.loc(axis=0)[\"2001-01-01\", [\"i1\", \"i3\"]] = None\r\nframe\r\n>>> A B i1 i3\r\ndate identifier \r\n2001-01-01 i1 NaN NaN None None\r\n i2 0.0 0.0 NaN NaN\r\n i3 NaN NaN None None\r\n2001-01-02 i1 0.0 0.0 NaN NaN\r\n i2 0.0 0.0 NaN NaN\r\n i3 0.0 0.0 NaN NaN\r\n```\r\nIn this example I cannot 'slice' the second multilevel, since I do not want to modify rows where identifier is \"i2\".\r\n\r\nAlso I am using `.loc(axis=0)` which clearly indicates that I am selecting based on the index alone, so there must be no change in the original frame's columns effected by this call.\r\n\r\nThe documentation, https://pandas.pydata.org/docs/user_guide/advanced.html#advanced-indexing-with-hierarchical-index, also suggest that the syntax I am using is valid and should not modify the column schema of the frame:\r\n\r\n> Importantly, a list of tuples indexes several complete MultiIndex keys, whereas a tuple of lists refer to several values within a level:\r\n\r\n```python\r\nIn [48]: s = pd.Series(\r\n ....: [1, 2, 3, 4, 5, 6],\r\n ....: index=pd.MultiIndex.from_product([[\"A\", \"B\"], [\"c\", \"d\", \"e\"]]),\r\n ....: )\r\n ....: \r\n\r\nIn [49]: s.loc[[(\"A\", \"c\"), (\"B\", \"d\")]] # list of tuples\r\nOut[49]: \r\nA c 1\r\nB d 5\r\ndtype: int64\r\n\r\nIn [50]: s.loc[([\"A\", \"B\"], [\"c\", \"d\"])] # tuple of lists\r\nOut[50]: \r\nA c 1\r\n d 2\r\nB c 4\r\n d 5\r\ndtype: int64\r\n```\r\n"
] |
2,219,490,467
| 58,115
|
DOC: Remove deprecated methods from code_checks.sh
|
closed
| 2024-04-02T03:36:51
| 2024-04-17T22:37:29
| 2024-04-17T21:43:04
|
https://github.com/pandas-dev/pandas/pull/58115
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58115
|
https://github.com/pandas-dev/pandas/pull/58115
|
shriyakalakata
| 2
|
- [x] xref #58065
- [ ] [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.
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Could you fix the merge conflict?",
"Thanks @shriyakalakata "
] |
2,219,457,413
| 58,114
|
Add note for name field in groupby
|
closed
| 2024-04-02T02:53:32
| 2024-04-02T19:25:27
| 2024-04-02T18:18:58
|
https://github.com/pandas-dev/pandas/pull/58114
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58114
|
https://github.com/pandas-dev/pandas/pull/58114
|
shriyakalakata
| 1
|
- [x] closes #58024
- [N/A] [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).
- [N/A] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [N/A] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @shriyakalakata "
] |
2,219,164,622
| 58,113
|
ENH: function to concatenate columns with a separator
|
closed
| 2024-04-01T22:11:03
| 2024-08-24T19:47:00
| 2024-08-24T19:47:00
|
https://github.com/pandas-dev/pandas/issues/58113
| true
| null | null |
hutch3232
| 2
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
I am writing a script that will dynamically take a `list` of column names and I'd like to concatenate the `pandas.DataFrame` columns with a separator of my choice.
For now, I opted to go with this approach: https://stackoverflow.com/a/59618989/9244371
```python
df[['Code', 'Period']].astype(str).agg('_'.join, axis=1)
```
This definitely works, but in my opinion it is a little verbose/round-about. It also felt quite slow compared to (if I had fixed columns) doing:
```python
df['Code'].astype(str) + "_" + df['Period'].astype(str)
```
### Feature Description
`polars` has a function that accomplish what I would like `pandas` to have:
https://docs.pola.rs/py-polars/html/reference/expressions/api/polars.concat_str.html
### Alternative Solutions
Googling around it seems this is a very common question with an enormous number of possible solutions.
Here are a few pages of many:
https://stackoverflow.com/questions/33098383/merge-multiple-column-values-into-one-column-in-python-pandas
https://stackoverflow.com/questions/19377969/combine-two-columns-of-text-in-pandas-dataframe
https://stackoverflow.com/questions/59618562/concatenating-string-columns-in-pandas
### Additional Context
_No response_
|
[
"Enhancement",
"Needs Triage"
] | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take\r\n",
"Thanks for the issue, but it appears this hasn't gotten traction in a while so closing"
] |
2,219,032,611
| 58,112
|
Pixee branch
|
closed
| 2024-04-01T20:45:31
| 2024-04-01T20:47:25
| 2024-04-01T20:47:25
|
https://github.com/pandas-dev/pandas/pull/58112
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58112
|
https://github.com/pandas-dev/pandas/pull/58112
|
dsparr1010
| 1
|
- [ ] 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.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Please review https://github.com/pandas-dev/pandas/pull/58111#issuecomment-2030523217"
] |
2,219,015,504
| 58,111
|
Pixee branch
|
closed
| 2024-04-01T20:34:08
| 2024-04-01T20:43:09
| 2024-04-01T20:43:09
|
https://github.com/pandas-dev/pandas/pull/58111
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58111
|
https://github.com/pandas-dev/pandas/pull/58111
|
dsparr1010
| 1
|
- [ ] 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.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the PR, but it's unclear what this change achieves. I would recommend experimenting on your own fork first or opening a new PR clarifying what your PR achieves"
] |
2,218,862,168
| 58,110
|
DEPR: rename `startingMonth` to `starting_month` (argument in BQuarterBegin)
|
closed
| 2024-04-01T18:56:39
| 2024-05-31T18:47:31
| 2024-05-31T18:47:31
|
https://github.com/pandas-dev/pandas/pull/58110
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58110
|
https://github.com/pandas-dev/pandas/pull/58110
|
gboeker
| 4
|
- [x] closes #57794
- [ ] [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.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"You need to deprecate `startingMonth` first",
"Thanks @Aloqeely ! Could you explain to me how I can deprecate `startingMonth`?",
"Basically you should still allow `startingMonth`, but if it is used you should warn the user that it will be removed soon\r\nI think a check in `__init__` will do the job but I'm not sure, cc @jbrockmendel ",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,218,834,621
| 58,109
|
DEPR: rename startingMonth to starting_month (argument in BQuarterBegin)
|
closed
| 2024-04-01T18:37:14
| 2024-04-01T18:39:48
| 2024-04-01T18:37:30
|
https://github.com/pandas-dev/pandas/pull/58109
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58109
|
https://github.com/pandas-dev/pandas/pull/58109
|
gboeker
| 0
| null |
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,218,824,951
| 58,108
|
ENH: Series.map() that works with a dictionary of key-list pairs
|
closed
| 2024-04-01T18:31:01
| 2024-06-27T17:38:17
| 2024-06-27T17:38:17
|
https://github.com/pandas-dev/pandas/issues/58108
| true
| null | null |
kbmorales
| 2
|
### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Dictionaries are perfect for mapping, but I often have the situation where I need to reassign multiple values to a single key value. However, Series.map() does the opposite, by searching through the keys and assigning the values.
### Feature Description
I use this currently:
```
def flip_map(col, dct):
"""
Inverts a dictionary of key, value pairs where the values are lists
"""
exp_dct = pd.Series(dct).explode()
flip_dct = dict(zip(exp_dct,exp_dct.index))
return col.map(flip_dct).fillna(col)
```
### Alternative Solutions
I think the above using base python and Series.explode() should do it
### Additional Context
_No response_
|
[
"Enhancement",
"Needs Triage",
"Closing Candidate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request - I think there are many ways to modify your dictionary format before passing it to `Series.map` to get your desired result and I also don't think that dictionary format is particularly readable, so I'm not a fan of this\r\n\r\n`flip_map` is one way to achieve your desired result, but a nested loop will do the job and I think is more readable:\r\n```python\r\nflipped = {}\r\nfor key, value_list in dictionary.items():\r\n for item in value_list:\r\n flipped[item] = key\r\n```\r\nAnd if you want a 1 liner, you can achieve that with a dict comprehension:\r\n```python\r\nflipped = {item: key for key, value_list in dictionary.items() for item in value_list}\r\n```",
"Closing as it seems not many people are interested in this feature."
] |
2,218,807,050
| 58,107
|
BUG: Remove "Mean of empty slice" warning in nanmedian
|
closed
| 2024-04-01T18:21:54
| 2024-07-24T13:51:06
| 2024-04-01T22:40:49
|
https://github.com/pandas-dev/pandas/pull/58107
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58107
|
https://github.com/pandas-dev/pandas/pull/58107
|
sebastian-correa
| 2
|
This PR supresses a warning when calculating the `median` of a `pd.Series` that is full of `NA`s. The warning is just letting you know that `numpy` got an empty array but correctly returns `np.nan`.
The warning is issued as follows:
1. When the series has `NA`s and you try to calculate the `median`, `nanops.nanmedian` is called.
2. When the array is full of NAs, the `_mask` in `nanops.nanmedian.get_median` is all `False`, so `np.nanmedian(x[_mask])` gets an empty array.
3. `np.nanmedian` calls `numpy.lib.nanfunctions._nanmedian` which has a short-circuit path when the array is empty [[ref](https://github.com/numpy/numpy/blob/1c8b03bf2c87f081eea211a5061e423285c548af/numpy/lib/_nanfunctions_impl.py#L1215-L1216)].
4. This path calls `np.nanmean`. This creates an empty mask [[ref](https://github.com/numpy/numpy/blob/1c8b03bf2c87f081eea211a5061e423285c548af/numpy/lib/_nanfunctions_impl.py#L1033)] because the array is empty.
5. Then, the sum of the negated mask is used as the denominator of the `mean`, which causes the result to be `np.nan` (correct!).
6. The method issues a warning because the array was empty.
Another way to solve this would be to change `get_median` to explicitly return `np.nan` before the call to `np.nanmedian`, but that would involve tampering with the condition in the if, which has no comments so I'm unsure if changing it makes sense.
```python
def get_median(x, _mask=None):
if _mask is None:
_mask = notna(x)
else:
_mask = ~_mask
all_na = _mask.all()
if (not skipna and not all_na) or all_na:
return np.nan
with warnings.catch_warnings():
# Suppress RuntimeWarning about All-NaN slice
warnings.filterwarnings(
"ignore", "All-NaN slice encountered", RuntimeWarning
)
res = np.nanmedian(x[_mask])
return res
```
which simplifies to `if skipna: return np.nan` I think.
I haven't added tests nor ensured all of the below passed not added comments because I want to ensure this is the preferred solution. Otherwise, I can edit `get_median`. Let me know!
---
- [ ] [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.
|
[
"Warnings"
] | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
[
"pre-commit.ci autofix",
"Thanks @sebastian-correa "
] |
2,218,775,429
| 58,106
|
BUG: Timestamp.unit is now reflecting changes in components after Timestamp.replace
|
closed
| 2024-04-01T18:03:13
| 2024-04-01T21:20:51
| 2024-04-01T21:00:07
|
https://github.com/pandas-dev/pandas/pull/58106
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58106
|
https://github.com/pandas-dev/pandas/pull/58106
|
diogomsmiranda
| 0
|
Sorry for the inconvenience. Closed because of a hiccup with the commits. I'll open another requesting the same changes soon.
|
[
"Non-Nano"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,218,731,037
| 58,105
|
Backport PR #58087 on branch 2.2.x (BLD: Build wheels using numpy 2.0rc1)
|
closed
| 2024-04-01T17:37:14
| 2024-04-09T22:40:20
| 2024-04-09T22:40:19
|
https://github.com/pandas-dev/pandas/pull/58105
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58105
|
https://github.com/pandas-dev/pandas/pull/58105
|
meeseeksmachine
| 0
|
Backport PR #58087: BLD: Build wheels using numpy 2.0rc1
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,218,692,915
| 58,104
|
COMPAT/TST: Ensure that numpy 2 string dtype converts to object array in pandas 2.2.x
|
closed
| 2024-04-01T17:16:50
| 2024-04-10T11:48:26
| 2024-04-10T11:48:26
|
https://github.com/pandas-dev/pandas/issues/58104
| true
| null | null |
lithomas1
| 6
|
We should add a test for this, given that numpy 2.0 support is happening for pandas 2.2.2.
|
[
"Testing",
"Compat",
"Blocker"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Wouldn’t not-converting be nicer behavior?",
"> Wouldn’t not-converting be nicer behavior?\r\n\r\nI was hoping we could get @ngoldbaum 's numpy string array feature in in some form for 3.0, but that's probably too big of a change to get in for 2.2.2.\r\n\r\nThe main thing I want to avoid is a weird error happening somewhere in our internals if a numpy string array is used.",
"One big issue with just accepting stringdtype arrays is that stringdtype can’t support the buffer protocol, so a lot of cython code in pandas that expects an array type that supports the buffer protocol won’t work. My fork of pandas adding stringdtype support avoids low-level cython operations by calling numpy directly.",
"I would also support not coercing to object by default if possible (IMO it's OK postponing 2.2.2 extra to wait for compatibility)\r\n\r\nJust curious, in numpy 2.0, will `np.array([\"1\"])` default to the new string type?",
"> Just curious, in numpy 2.0, will np.array([\"1\"]) default to the new string type?\r\n\r\nNo, someone needs to explicitly pass e.g. `dtype=np.dtypes.StringDType()` or `dtype=\"T\"` somewhere to create a stringdtype array. Maybe in numpy 3.0 we'll change the default.\r\n\r\nIf there's interest in helping me out with getting my stringdtype changes upstreamed, I'd really appreciate it. My current latest work is [here](https://github.com/pandas-dev/pandas/compare/main...ngoldbaum:pandas:stringdtype). Currently it's based on the pandas 3.0 branch but backporting it wouldn't be hard. I was hoping to have a PR open already but between getting pulled into other projects and helping out with shipping numpy 2.0 I didn't quite make it in time.",
"I don't think we should back port this, that change is too large imo"
] |
2,218,617,723
| 58,103
|
[pre-commit.ci] pre-commit autoupdate
|
closed
| 2024-04-01T16:29:30
| 2024-04-01T20:45:23
| 2024-04-01T20:45:20
|
https://github.com/pandas-dev/pandas/pull/58103
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58103
|
https://github.com/pandas-dev/pandas/pull/58103
|
pre-commit-ci[bot]
| 0
|
<!--pre-commit.ci start-->
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.3.1 → v0.3.4](https://github.com/astral-sh/ruff-pre-commit/compare/v0.3.1...v0.3.4)
- [github.com/jendrikseipp/vulture: v2.10 → v2.11](https://github.com/jendrikseipp/vulture/compare/v2.10...v2.11)
- [github.com/pylint-dev/pylint: v3.0.1 → v3.1.0](https://github.com/pylint-dev/pylint/compare/v3.0.1...v3.1.0)
- [github.com/PyCQA/isort: 5.12.0 → 5.13.2](https://github.com/PyCQA/isort/compare/5.12.0...5.13.2)
- [github.com/asottile/pyupgrade: v3.15.0 → v3.15.2](https://github.com/asottile/pyupgrade/compare/v3.15.0...v3.15.2)
- [github.com/pre-commit/mirrors-clang-format: v17.0.6 → v18.1.2](https://github.com/pre-commit/mirrors-clang-format/compare/v17.0.6...v18.1.2)
<!--pre-commit.ci end-->
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,218,334,211
| 58,102
|
DOC: Fix docstring error for pandas.DataFrame.axes
|
closed
| 2024-04-01T13:59:02
| 2024-04-11T16:59:33
| 2024-04-11T16:59:32
|
https://github.com/pandas-dev/pandas/pull/58102
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58102
|
https://github.com/pandas-dev/pandas/pull/58102
|
v-lozko
| 0
|
- Fixes docstring error for pandas.DataFrame.axes method (https://github.com/pandas-dev/pandas/issues/58065).
- [ ] [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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,218,285,501
| 58,101
|
DOC: fix docstring for DataFrame.to_period, pandas.DataFrame.to_timestamp, pandas.DataFrame.tz_convert #58065
|
closed
| 2024-04-01T13:39:31
| 2024-04-01T18:54:51
| 2024-04-01T18:54:45
|
https://github.com/pandas-dev/pandas/pull/58101
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58101
|
https://github.com/pandas-dev/pandas/pull/58101
|
bergnerjonas
| 1
|
Fixes docstring errorrs for DataFrame and Series for methods `pandas.DataFrame.to_period` ,`pandas.DataFrame.to_timestamp` ,`pandas.DataFrame.tz_convert`. (issue #58065)
- [ ] [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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @bergnerjonas "
] |
2,218,162,756
| 58,100
|
MNT: fix compatibility with beautifulsoup4 4.13.0b2
|
closed
| 2024-04-01T12:26:18
| 2024-04-03T19:55:22
| 2024-04-01T17:08:54
|
https://github.com/pandas-dev/pandas/pull/58100
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58100
|
https://github.com/pandas-dev/pandas/pull/58100
|
neutrinoceros
| 6
|
Closes #58086
- [x] 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.
|
[
"IO HTML",
"Dependencies"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I'm not sure this change warrants a changelog entry, but I'll happily add one upon request.",
"Thanks @neutrinoceros (doesn't need a changelog entry)",
"Thanks for merging ! Would it be alright to backport this ?",
"Sure I supposed that would be OK",
"@Meeseeksdev backport 2.2.x",
"thanks !"
] |
2,217,927,325
| 58,099
|
BUG: fixed bug where None would appear as 'None' (#57702)
|
closed
| 2024-04-01T09:43:00
| 2024-05-15T17:58:28
| 2024-05-15T17:58:28
|
https://github.com/pandas-dev/pandas/pull/58099
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58099
|
https://github.com/pandas-dev/pandas/pull/58099
|
DuarteMarques510
| 4
|
- [X] closes #57702
- [X] added a test that proves this issue is resolved(pandas/tests/arrays/numpy_/test_numpy.py on line 127)
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"\r\nim trying to resolve the issues with the pipeline but there's a test that is bothering me (the only test failing in all of the tests)...\r\nmy objective with this issue is that a certain routine must be used if there are nones inside of the array given in pd.array\r\nbut the thing is that this test in this image is creating an array with 2 series, and raises an exception when it reaches the if (None not in scalars) because 'the truth value of a series is ambigous'",
"i understand the purpose of this test, but i see no need in filling the np.array with a series, as it's the only time in the whole test directory that it is done, why not just do a regular array? ",
"\r\nthe test mentioned above was changed to what is seen in the image above",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen."
] |
2,217,525,646
| 58,098
|
BUG: Problem with column header text alignment when printing df that contains emojis
|
open
| 2024-04-01T05:02:50
| 2024-04-19T16:15:45
| null |
https://github.com/pandas-dev/pandas/issues/58098
| true
| null | null |
AlanCPSC
| 7
|
# 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
### With Emojis
```
import pandas as pd
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
example = {'normal_col' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'text_col' : ['hello world'] * 10,
'emoji_col_A' : ['🟩 hello world'] * 10,
'emoji_col_B' : ['🟥 hello world'] * 10,
'emoji_col_C' : ['🟧 hello world'] * 10,
'emoji_col_D' : ['🟨 hello world'] * 10}
df = pd.DataFrame(example)
print(df)
```
```
normal_col text_col emoji_col_A emoji_col_B emoji_col_C emoji_col_D
0 1 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
1 2 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
2 3 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
3 4 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
4 5 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
5 6 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
6 7 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
7 8 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
8 9 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
9 10 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world
```
### Without Emojis
```
import pandas as pd
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
example = {'normal_col' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'text_col' : ['hello world'] * 10,
'emoji_col_A' : ['hello world'] * 10,
'emoji_col_B' : ['hello world'] * 10,
'emoji_col_C' : ['hello world'] * 10,
'emoji_col_D' : ['hello world'] * 10}
df = pd.DataFrame(example)
print(df)
```
```
normal_col text_col emoji_col_A emoji_col_B emoji_col_C emoji_col_D
0 1 hello world hello world hello world hello world hello world
1 2 hello world hello world hello world hello world hello world
2 3 hello world hello world hello world hello world hello world
3 4 hello world hello world hello world hello world hello world
4 5 hello world hello world hello world hello world hello world
5 6 hello world hello world hello world hello world hello world
6 7 hello world hello world hello world hello world hello world
7 8 hello world hello world hello world hello world hello world
8 9 hello world hello world hello world hello world hello world
9 10 hello world hello world hello world hello world hello world
```
# Issue Description
Dataframe with emojis has **MISALIGNED** header text when printed.
# Expected Behavior
Dataframe with emojis has **ALIGNED** header text when printed.
# Installed Versions
```
INSTALLED VERSIONS
------------------
commit : 0f437949513225922d851e9581723d82120684a6
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:31 PST 2024; root:xnu-10063.101.15~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 2.0.3
numpy : 1.22.4
pytz : 2022.7
dateutil : 2.8.2
setuptools : 52.0.0.post20210125
pip : 21.0.1
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.2
brotli :
fastparquet : None
fsspec : 2023.1.0
gcsfs : None
matplotlib : 3.3.4
numba : 0.53.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
snappy : None
sqlalchemy : 1.4.7
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : 1.9.0
pyqt5 : None
```
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hi,\r\n\r\nThis is my first time contributing to an open-source project and I would love to work on this issue.",
"take",
"@madhuramkumar Just checking in on the progress here. You doing okay, buddy?",
"Hi! Yes, I am still working on fixing it. Just for context, I am doing this with a partner as part of a class project in EECS 481 at the University of Michigan where we are required to contribute to an open-source project, so it might take a little bit longer for us to get accustomed to the project. We hope to have a PR in in the next week or so. Thank you!",
"@AlanCPSC We might have hit a roadblock and might need some additional assistance if possible. After some digging we think the issue stems from the _make_fixed_width function in the pandas/io/formats/format.py file. We attempted to edit this function to account for the emojis (identifying them using regex, and adding additional space if necessary) to change the max_len variable, and were able to change the alignment, but not in the way we intended. We wrote some unit tests in the tests/io/formats/test_format.py folder under the TestDataFrameFormatting class, and it seemed that our changes did not modify the alignment of the column header. We would gladly appreciate any help or guidance regarding whether we are working with the right function, or how to tackle the issue in general.\r\n\r\n`def _make_fixed_width(\r\n strings: list[str],\r\n justify: str = \"right\",\r\n minimum: int | None = None,\r\n adj: printing._TextAdjustment | None = None,\r\n) -> list[str]:\r\n if len(strings) == 0 or justify == \"all\":\r\n return strings\r\n\r\n if adj is None:\r\n adjustment = printing.get_adjustment()\r\n else:\r\n adjustment = adj\r\n\r\n # regex to identify emojis\r\n emoji_pattern = re.compile(\r\n \"[\"\r\n \"\\U0001F600-\\U0001F64F\" # emoticons\r\n \"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\r\n \"\\U0001F680-\\U0001F6FF\" # transport & map symbols\r\n \"\\U0001F780-\\U0001F7FF\" # Geometric Shapes Extended\r\n \"]+\", flags=re.UNICODE\r\n )\r\n\r\n # calc adjusted length with emojis\r\n def emoji_adjusted_length(s):\r\n non_emojis_len = adjustment.len(emoji_pattern.sub('', s)) # len w/o emojis\r\n emoji_count = len(emoji_pattern.findall(s)) # num emojis\r\n return non_emojis_len + 2 * emoji_count # emojis are length 2\r\n\r\n max_len = max((emoji_adjusted_length(x) for x in strings), default=0)\r\n\r\n if minimum is not None:\r\n max_len = max(minimum, max_len)\r\n\r\n conf_max = get_option(\"display.max_colwidth\")\r\n if conf_max is not None and max_len > conf_max:\r\n max_len = conf_max\r\n\r\n def just(x: str) -> str:\r\n if conf_max is not None:\r\n if (conf_max > 3) & (adjustment.len(x) > conf_max):\r\n x = x[: max_len - 3] + \"...\"\r\n return adjustment.justify([x], max_len, mode=justify)[0]\r\n\r\n result = [just(x) for x in strings]\r\n return result`\r\n \r\n \r\n \r\n ",
"Here is our test file:\r\n\r\n`def test_display_settings_with_emojis(self):\r\n # Define the data for DataFrame\r\n example = {\r\n 'normal_col': [1, 2, 3],\r\n 'text_col': ['hello world'] * 3,\r\n 'emoji_col_A': ['🟩 hello world'] * 3,\r\n 'emoji_col_B': ['🟥 hello world'] * 3,\r\n 'emoji_col_C': ['🟧 hello world'] * 3,\r\n 'emoji_col_D': ['🟨 hello world'] * 3\r\n }\r\n # Create DataFrame\r\n df = pd.DataFrame(example)\r\n output = repr(df)\r\n expected_output = \"\"\" normal_col text_col emoji_col_A emoji_col_B emoji_col_C emoji_col_D\r\n 0 1 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world\r\n 1 2 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world\r\n 2 3 hello world 🟩 hello world 🟥 hello world 🟧 hello world 🟨 hello world\"\"\"\r\n \r\n # Check if the actual output matches expected output\r\n assert output == expected_output\r\n\r\n # Reset options to defaults\r\n pd.reset_option('display.max_rows')\r\n pd.reset_option('display.max_columns')\r\n pd.reset_option('display.width')\r\n\r\n example2 = {\r\n 'normal_col': [1, 2, 3],\r\n 'text_col': ['hello world'] * 3,\r\n 'emoji_col_A': ['🙏🙏 hello world'] * 3,\r\n 'emoji_col_B': ['😭😭 hello world'] * 3,\r\n 'emoji_col_C': ['🥸🥸 hello world'] * 3,\r\n 'emoji_col_D': ['🚵🚵 hello world'] * 3\r\n }\r\n # Create DataFrame\r\n df = pd.DataFrame(example2)\r\n output = repr(df)\r\n expected_output2 = \"\"\" normal_col text_col emoji_col_A emoji_col_B emoji_col_C emoji_col_D\r\n 0 1 hello world 🙏🙏 hello world 😭😭 hello world 🥸🥸 hello world 🚵🚵 hello world\r\n 1 2 hello world 🙏🙏 hello world 😭😭 hello world 🥸🥸 hello world 🚵🚵 hello world\r\n 2 3 hello world 🙏🙏 hello world 😭😭 hello world 🥸🥸 hello world 🚵🚵 hello world\"\"\"\r\n # Check if the actual output matches expected output\r\n assert output == expected_output2\r\n\r\n # Reset options to defaults\r\n pd.reset_option('display.max_rows')\r\n pd.reset_option('display.max_columns')\r\n pd.reset_option('display.width')`",
"take"
] |
2,217,240,418
| 58,097
|
DOC: Fix docstring errors for pandas.DataFrame.unstack, pandas.DataFrame.value_counts and pandas.DataFrame.tz_localize
|
closed
| 2024-03-31T23:12:27
| 2024-04-01T17:11:51
| 2024-04-01T17:11:43
|
https://github.com/pandas-dev/pandas/pull/58097
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58097
|
https://github.com/pandas-dev/pandas/pull/58097
|
bergnerjonas
| 1
|
- Fixes docstring erros for following methods (https://github.com/pandas-dev/pandas/issues/58065):
`pandas.DataFrame.unstack`, `pandas.DataFrame.value_counts` and `pandas.DataFrame.tz_localize`
- [ ] [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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @bergnerjonas "
] |
2,217,226,508
| 58,096
|
Remove 'Not implemented for Series' from _num_doc in generic.py
|
closed
| 2024-03-31T22:27:34
| 2024-04-01T17:20:04
| 2024-04-01T17:19:57
|
https://github.com/pandas-dev/pandas/pull/58096
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58096
|
https://github.com/pandas-dev/pandas/pull/58096
|
kamelCased
| 1
|
Documentation fix in `generic.py`
line 11715: Removed 'Not implemented for Series' in _num_doc given that `numeric_only` is now implemented for Series in all the methods min, max, mean, median, skew, kurt
- [ ] 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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @kamelCased "
] |
2,217,164,400
| 58,095
|
DOC: read_sql_query docstring contraine wrong examples #58094
|
closed
| 2024-03-31T20:07:44
| 2024-04-02T01:41:40
| 2024-04-02T01:41:32
|
https://github.com/pandas-dev/pandas/pull/58095
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58095
|
https://github.com/pandas-dev/pandas/pull/58095
|
Tonow
| 1
|
- [x] closes #58094
- [ ] [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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @Tonow "
] |
2,217,163,147
| 58,094
|
DOC: read_sql_query docstring contraine wrong examples
|
closed
| 2024-03-31T20:04:10
| 2024-04-02T01:41:33
| 2024-04-02T01:41:33
|
https://github.com/pandas-dev/pandas/issues/58094
| true
| null | null |
Tonow
| 0
|
The issuse is a line : https://github.com/pandas-dev/pandas/blob/73fd026ce57377a6f791ec02463a72b506a9636c/pandas/io/sql.py#L477
The example is for the wrong function s/`read_sql_table`/`read_sql_query`
```
Examples
--------
>>> from sqlalchemy import create_engine # doctest: +SKIP
>>> engine = create_engine("sqlite:///database.db") # doctest: +SKIP
>>> with engine.connect() as conn, conn.begin(): # doctest: +SKIP
... data = pd.read_sql_table("data", conn) # doctest: +SKIP
```
Should be replace by somthing like
```
Examples
--------
>>> from sqlalchemy import create_engine # doctest: +SKIP
>>> engine = create_engine("sqlite:///database.db") # doctest: +SKIP
>>> sql_query = 'SELECT int_column FROM test_data # doctest: +SKIP
>>> with engine.connect() as conn, conn.begin(): # doctest: +SKIP
... data = pd.read_sql_query(sql_query, conn) # doctest: +SKIP
```
And this will modify https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_sql_query.html
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,217,037,176
| 58,093
|
Revert "BLD: Pin numpy on 2.2.x"
|
closed
| 2024-03-31T15:06:52
| 2024-04-03T17:28:30
| 2024-04-03T17:28:27
|
https://github.com/pandas-dev/pandas/pull/58093
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58093
|
https://github.com/pandas-dev/pandas/pull/58093
|
lithomas1
| 0
|
Reverts pandas-dev/pandas#56812
Waiting for #58087 to be merged and backported
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,216,881,710
| 58,092
|
BUG: PeriodIndex to DatetimeIndex fail or have wrong/no frequency
|
open
| 2024-03-31T10:09:07
| 2025-08-19T01:05:22
| null |
https://github.com/pandas-dev/pandas/issues/58092
| true
| null | null |
benHeid
| 1
|
### 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
pd.period_range("2020", periods=2, freq="M").to_timestamp(to_offset("ME"))
# Outputs DatetimeIndex(['2020-01-31', '2020-02-29'], dtype='datetime64[ns]', freq=None)
pd.period_range("2020", periods=3, freq="M").to_timestamp(to_offset("ME"))
# Outputs DatetimeIndex(['2020-01-31', '2020-02-29', '2020-03-31'], dtype='datetime64[ns]', freq='ME') Wrong frequency
pd.period_range("2020", periods=2, freq="M").to_timestamp("ME") # Fails
pd.period_range("2020", periods=3, freq="M").to_timestamp("ME") # Fails
```
### Issue Description
Related to #56213
### Expected Behavior
All of these four lines should return a datetindex with `2ME` as frequency.
I suppose that the the frequency term in to_timestamp is ignored and the frequency is directly inferred.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.13.final.0
python-bits : 64
OS : Darwin
OS-release : 23.2.0
Version : Darwin Kernel Version 23.2.0: Wed Nov 15 21:59:33 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T8112
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 : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : 3.0.8
pytest : 7.4.0
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.21.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.12.2
gcsfs : None
matplotlib : 3.8.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
sqlalchemy : 2.0.28
tables : None
tabulate : None
xarray : 2024.1.1
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Datetime",
"Frequency",
"freq retention"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take"
] |
2,216,730,815
| 58,091
|
DOC: Changed ndim to 1, modified doc as suggested. #57088
|
closed
| 2024-03-31T01:28:35
| 2024-04-01T17:32:08
| 2024-04-01T17:32:02
|
https://github.com/pandas-dev/pandas/pull/58091
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58091
|
https://github.com/pandas-dev/pandas/pull/58091
|
lamprinikourou
| 3
|
- [ ] closes #57088
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"/preview",
"Website preview of this PR available at: https://pandas.pydata.org/preview/pandas-dev/pandas/58091/",
"Thanks @lamprinikourou "
] |
2,216,715,546
| 58,090
|
CI: Remove deprecated methods from code_checks.sh
|
closed
| 2024-03-31T00:22:21
| 2024-04-01T17:27:31
| 2024-04-01T17:27:25
|
https://github.com/pandas-dev/pandas/pull/58090
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58090
|
https://github.com/pandas-dev/pandas/pull/58090
|
Aloqeely
| 1
|
- [ ] xref #58065
- [ ] [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.
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @Aloqeely "
] |
2,216,677,095
| 58,089
|
DEPR: Series setitem/getitem treating ints as positional
|
closed
| 2024-03-30T21:53:17
| 2024-04-29T22:46:03
| 2024-04-29T20:14:34
|
https://github.com/pandas-dev/pandas/pull/58089
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58089
|
https://github.com/pandas-dev/pandas/pull/58089
|
jbrockmendel
| 2
|
- [ ] 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.
|
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Looks like an error message needs updating in the npdev build",
"Thanks @jbrockmendel "
] |
2,216,668,448
| 58,088
|
BUG: Let `melt` name multiple variable columns for labels from a `MultiIndex`
|
closed
| 2024-03-30T21:20:09
| 2025-03-05T05:46:59
| 2024-05-25T20:41:02
|
https://github.com/pandas-dev/pandas/pull/58088
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58088
|
https://github.com/pandas-dev/pandas/pull/58088
|
rob-sil
| 6
|
- [x] closes #58033
- [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.
- [x] Added an entry in the latest `doc/source/whatsnew/v2.2.2.rst` file if fixing a bug or adding a new feature.
If there are fewer names provided in `var_name` than levels in the columns `MultiIndex`, then `melt` will just take the first several. I personally find this a bit strange, but it keeps with the previous behavior of `melt` with a `MultiIndex` where you could pass it a single name and it would only use one level of the `MultiIndex`.
|
[
"Reshaping",
"MultiIndex"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the PR! Sorry this hasn't been reviewed yet.\r\nGenerally LGTM. cc @mroeschke ",
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"Hi! Just checking in as this issue has been affecting my code. I wondered if anything needed to be done or if this will be merged in a later release?",
"Thanks @rob-sil ",
"Thanks for moving this forward!",
"my code works in jupyter notebook with 'var_names' parameter but not in py file, it gives the same error when running in py file. Interesting."
] |
2,216,590,838
| 58,087
|
BLD: Build wheels using numpy 2.0rc1
|
closed
| 2024-03-30T17:21:12
| 2024-04-02T02:45:12
| 2024-04-01T17:36:41
|
https://github.com/pandas-dev/pandas/pull/58087
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58087
|
https://github.com/pandas-dev/pandas/pull/58087
|
lithomas1
| 3
|
- [ ] closes #55519 (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.
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @lithomas1 ",
"@lithomas1 @mroeschke this doesn't look correct to me. I just checked [one CI log](https://github.com/pandas-dev/pandas/actions/runs/8492584989/job/23265753591) and it shows this _after_ the \"before build\" step (re-installs numpy 1.26.4):\r\n```\r\n Successfully installed Cython-3.0.9 meson-1.2.1 meson-python-0.13.1 numpy-1.26.4 packaging-24.0 pyproject-metadata-0.7.1 tomli-2.0.1 versioneer-0.29 wheel-0.43.0\r\n Installing build dependencies: finished with status 'done'\r\n```\r\n\r\nThis is due to build isolation. The `[build-requires]` section in `pyproject.toml` needs updating instead, it needs something like `\"numpy>=2.0.0rc1,<2.3\",` (or `<3` if you prefer) EDIT: in release branches, and `\"numpy>=2.0.0rc1` in `main`. I recommend copying what SciPy does (except change `2.0.0b1` to `2.0.0rc1` now):\r\nhttps://github.com/scipy/scipy/blob/2a8c4ca6edceba5daaac60bbd213a84e7c03c361/pyproject.toml#L28-L34",
"> @lithomas1 @mroeschke this doesn't look correct to me. I just checked [one CI log](https://github.com/pandas-dev/pandas/actions/runs/8492584989/job/23265753591) and it shows this _after_ the \"before build\" step (re-installs numpy 1.26.4):\r\n> \r\n> ```\r\n> Successfully installed Cython-3.0.9 meson-1.2.1 meson-python-0.13.1 numpy-1.26.4 packaging-24.0 pyproject-metadata-0.7.1 tomli-2.0.1 versioneer-0.29 wheel-0.43.0\r\n> Installing build dependencies: finished with status 'done'\r\n> ```\r\n> \r\n> This is due to build isolation. The `[build-requires]` section in `pyproject.toml` needs updating instead, it needs something like `\"numpy>=2.0.0rc1,<2.3\",` (or `<3` if you prefer) EDIT: in release branches, and `\"numpy>=2.0.0rc1` in `main`. I recommend copying what SciPy does (except change `2.0.0b1` to `2.0.0rc1` now): https://github.com/scipy/scipy/blob/2a8c4ca6edceba5daaac60bbd213a84e7c03c361/pyproject.toml#L28-L34\r\n\r\nNice catch, thanks!\r\n\r\nI was thinking in my head that editable installations would be messed up by the `build-requires`, but that obviously uses ``-no-build-isolation`` 🤦 .\r\n\r\nI'll put up another PR fixing this."
] |
2,216,550,242
| 58,086
|
BUG: beautifulsoup4 breaks pandas.read_html
|
closed
| 2024-03-30T15:56:55
| 2024-04-01T17:08:55
| 2024-04-01T17:08:55
|
https://github.com/pandas-dev/pandas/issues/58086
| true
| null | null |
neutrinoceros
| 1
|
### 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
from io import StringIO
table = """<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>col0</th>
<th>col1</th>
<th>col2</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>1.0</td>
<td>a</td>
</tr>
<tr>
<td>2</td>
<td>2.5</td>
<td>b</td>
</tr>
<tr>
<td>3</td>
<td>5.0</td>
<td>c</td>
</tr>
</tbody>
</table>
"""
buf = StringIO()
buf.write(table)
buf.seek(0)
pd.read_html(buf, flavor="bs4")
```
### Issue Description
`beautifuloup4` version `4.13.0b2` breaks this example, with the following exception being raised:
```python-traceback
Traceback (most recent call last):
File "/Users/clm/dev/astropy-project/coordinated/astropy/bugs/16251/t.py", line 34, in <module>
pd.read_html(buf, flavor="bs4")
File "/Users/clm/.pyenv/versions/astropy.dev/lib/python3.12/site-packages/pandas/io/html.py", line 1213, in read_html
return _parse(
^^^^^^^
File "/Users/clm/.pyenv/versions/astropy.dev/lib/python3.12/site-packages/pandas/io/html.py", line 972, in _parse
tables = p.parse_tables()
^^^^^^^^^^^^^^^^
File "/Users/clm/.pyenv/versions/astropy.dev/lib/python3.12/site-packages/pandas/io/html.py", line 242, in parse_tables
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/clm/.pyenv/versions/astropy.dev/lib/python3.12/site-packages/pandas/io/html.py", line 594, in _parse_tables
element_name = self._strainer.name
^^^^^^^^^^^^^^^^^^^
AttributeError: 'SoupStrainer' object has no attribute 'name'
```
I'm not sure whether it should be addressed in pandas or in bs4.
For context, this was discovered while testing astropy.
xref: https://github.com/astropy/astropy/issues/16251
### Expected Behavior
Not exception.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 4241ba5e1eb5b12adaff4e98df9ddad65ff94ddb
python : 3.12.2.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Fri Mar 15 00:12:49 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 3.0.0.dev0+644.g4241ba5e1
numpy : 1.26.0
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.1.0
pip : 24.0
Cython : None
pytest : 8.1.1
hypothesis : 6.98.9
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.22.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.0b2
bottleneck : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.8.3
numba : None
numexpr : None
odfpy : None
openpyxl : None
pyarrow : 15.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report. I don't immediately see anything in bs4's changelog that indicates `SoupStrainer.name` is deprecated/removed.\r\n\r\nThat said, seems like this should be an easy fix in pandas since it doesn't really use `SoupStrainer` anymore."
] |
2,216,480,869
| 58,085
|
ENH: Enable fillna(value=None)
|
closed
| 2024-03-30T14:14:53
| 2024-04-15T20:32:33
| 2024-04-15T17:22:40
|
https://github.com/pandas-dev/pandas/pull/58085
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58085
|
https://github.com/pandas-dev/pandas/pull/58085
|
rhshadrach
| 1
|
- [x] closes #57723 (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.
The one behavior introduced here I think is important to highlight is using `None` with non-object dtypes. In such a case we treat `None` as the NA value for the given dtype for NumPy float/datetime and pandas' EA dtypes. However for external EAs, by default we try to put `None` in the values and raise if this fails.
For object dtype, we replace any NA value with `None`.
cc @Dr-Irv
|
[
"Enhancement",
"Missing-data"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @rhshadrach "
] |
2,216,346,242
| 58,084
|
BUG/PERF: groupby.transform with unobserved categories
|
closed
| 2024-03-30T09:07:50
| 2024-05-08T22:34:09
| 2024-05-08T22:34:00
|
https://github.com/pandas-dev/pandas/pull/58084
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58084
|
https://github.com/pandas-dev/pandas/pull/58084
|
undermyumbrella1
| 12
|
- [x] closes #55326
- [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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Bug",
"Groupby",
"Performance",
"Categorical",
"Apply"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Is there an issue linked with this?",
"> Is there an issue linked with this?\r\n\r\nNo clue.\r\n@undermyumbrella1 I'd appreciate an explanation of what this change accomplishes. And please make sure all the code tests pass",
"this is a work in progress for issue #55326 , i have added the issue number",
"ok, the pr implementation is completed",
"HI thank you for the pr review, I have changed my implementation to temporarily set observed to true (and respective groupers), so that transform will return the correct result.\r\n\r\nI have initially tried to change the result of `getattr(self, func)(*args, **kwargs)`, by using grouped reduce to map each result block to `out_dtype` that was determined in `_cython_operation`. However this impl turned out to be way too complicated, as the `out_dtype`, `out_shape`, views of the original value block is determined by the entire nested sequence of method calls. Extracting this logic out proved to be complicated. \r\n",
"Thank you for the review, I have made the changes as requested",
"Thank you for the review, i have made the changes as suggested\r\n",
"Thanks for the changes @undermyumbrella1 - this is looking good! I have some minor refactor/style requests, but I'd like to get another eye here before any more work is done.\r\n\r\n@mroeschke - would you be able to take a look? In addition to the issue linked in the OP, this is fixing a regression caused by #55738:\r\n\r\n```\r\nN = 10**3\r\ndata = {\r\n \"a1\": Categorical(np.random.randint(100, size=N), categories=np.arange(N)),\r\n \"a2\": Categorical(np.random.randint(100, size=N), categories=np.arange(N)),\r\n \"b\": np.random.random(N),\r\n}\r\ndf = DataFrame(data)\r\n%timeit df.groupby([\"a1\", \"a2\"], observed=False)[\"b\"].transform(\"sum\")\r\n# 6.83 ms ± 27.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- main\r\n# 687 µs ± 16.3 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) <-- PR\r\n```\r\n\r\nWhile it's undesirable to swap out the grouper as is done here, I do not see any better way. There may be more efficient ways of computed the observed codes / result_index, but that can be readily built upon this later on.",
"Thank you for the review, I have updated the pr according to comments.",
"Thank you for the review, I have updated the pr according to comments.",
"Thank you for the review, I have updated the pr according to comments. Noted on force pushing ",
"Thanks @undermyumbrella1 - very nice!"
] |
2,216,118,389
| 58,083
|
CLN: move raising TypeError for `interpolate` with `object` dtype to Block
|
closed
| 2024-03-30T00:45:24
| 2024-04-01T20:50:04
| 2024-04-01T20:49:57
|
https://github.com/pandas-dev/pandas/pull/58083
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58083
|
https://github.com/pandas-dev/pandas/pull/58083
|
natmokval
| 1
|
xref #57820
moved raising `TypeError` for `interpolate` with object dtype from ``NDFrame`` to ``Block``
|
[
"Missing-data",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thanks @natmokval "
] |
2,216,062,901
| 58,082
|
Improve MultiIndex label rename checks
|
closed
| 2024-03-29T22:44:51
| 2024-05-26T09:11:24
| 2024-05-08T16:54:55
|
https://github.com/pandas-dev/pandas/pull/58082
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58082
|
https://github.com/pandas-dev/pandas/pull/58082
|
TabLand
| 4
|
- [x] closes #55169
- [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.
|
[
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@mroeschke - this is ready for your review",
"This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this.",
"Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen.",
"Hi @mroeschke,\r\nThe merge conflict was only against the `doc/source/whatsnew/v3.0.0.rst` file which I've fixed.\r\nAfter the merge, pandas is failing to compile on my dev environment due to what I'm assuming is an unrelated error:\r\n```\r\n[9/71] Compiling C object pandas/_libs/interval.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_interval.pyx.c.o ninja: build stopped: subcommand failed.\r\n```\r\nI'll rebuild my dev environment and update once I can confirm that tests are still passing"
] |
2,216,037,782
| 58,081
|
ENH: Implement round dunder methods for Timestamp and Timedelta
|
closed
| 2024-03-29T22:02:44
| 2024-04-08T13:36:39
| 2024-04-01T18:11:25
|
https://github.com/pandas-dev/pandas/pull/58081
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58081
|
https://github.com/pandas-dev/pandas/pull/58081
|
Aloqeely
| 6
|
- [x] closes #58071
- [ ] [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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"pre-commit.ci autofix",
"Makes sense, I apologize, I was too hasty to make a PR.\r\nShould I close this and wait until a decision is made?",
"I would recommend closing this PR until discussion in the issue has resolved",
"> I don't think `__round__` should be implemented for these objects. Since `round` is only well specified for numeric values, I think it's too opaque to apply for datelike objects.\r\n\r\n@mroeschke @MarcoGorelli @rhshadrach \r\n\r\nHello, I was the one that created the issue.\r\n\r\nThere is already an implementation of a round method for timedelta \r\n\r\npd.Timedelta.round('s') works. (https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.round.html)\r\nso round(pd.Timedelta) feel intuitive and just need to be added ?\r\n\r\n(I'm going on again because I still use the fix I made here https://github.com/pandas-dev/pandas/issues/58071 on my production server and I would be happy to not have to handle this manually but to see the library handle it).",
"> so round(pd.Timedelta) feel intuitive and just need to be added ?\r\n\r\nAs commented above (https://github.com/pandas-dev/pandas/pull/58081#discussion_r1545673887):\r\n\r\n> If this just happens to work due to an implementation detail in CPython, then it seems to me to be an abuse of the Python API and could break in the future.\r\n\r\nDo you disagree that making `round(pd.Timedelta)` work as it's implemented in this PR is an abuse of the Python API?\r\n\r\nAlternatively, if you are wanting the signature to be something like `def __round__(self, n=None):` and have the `n` mean seconds, then I am still of the opinion expressed in https://github.com/pandas-dev/pandas/issues/58071#issuecomment-2028743089 and quite negative on adding this. ",
"So because round(td, n=5) isn't really clear, you don't want to implement round(td).\r\nI understand your point of view.\r\n\r\nI disagree that it's a good enough reason to not do it.\r\nwith or without this feature, round(td, n=5) would crash.\r\nby doing this feature, hundreds/thousand of developers throughs ages would feel the library even more intuitive with round(td).\r\nBut the philosophical defense is understandable. It feels like the defense of a C++ dev rather than python dev but I understand.\r\n\r\nHave a nice day boys and thanks for this library (I use it every day at work and I love it !<3)\r\n"
] |
2,216,022,704
| 58,080
|
Backport PR #58075 on branch 2.2.x (DOC: whatsnew note for #57553)
|
closed
| 2024-03-29T21:40:25
| 2024-04-01T21:23:50
| 2024-04-01T21:23:48
|
https://github.com/pandas-dev/pandas/pull/58080
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58080
|
https://github.com/pandas-dev/pandas/pull/58080
|
meeseeksmachine
| 1
|
Backport PR #58075: DOC: whatsnew note for #57553
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"The PR has been backported https://github.com/pandas-dev/pandas/pull/58008"
] |
2,215,997,850
| 58,079
|
DOC: Update Categorical/CategoricalDtype methods' docstring to pass docstring validation
|
closed
| 2024-03-29T21:05:23
| 2024-04-29T18:54:20
| 2024-04-29T18:54:12
|
https://github.com/pandas-dev/pandas/pull/58079
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58079
|
https://github.com/pandas-dev/pandas/pull/58079
|
Aloqeely
| 1
|
- [x] closes #58064
- [ ] [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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @Aloqeely "
] |
2,215,950,924
| 58,078
|
BUG: Read hdfs:/// is broken since 2.1.0
|
open
| 2024-03-29T20:15:49
| 2025-08-01T22:53:39
| null |
https://github.com/pandas-dev/pandas/issues/58078
| true
| null | null |
yli7-bloomberg
| 0
|
### 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
filename ='hdfs:///dir/file1'
df = pd.read_parquet(filename) # This will fail, as pa_fs.FileSystem.from_uri(path) does not like hdfs:///, it wants hdfs://host_name/ instead. This will lead to error in bad URI format.
df = pd.read_parquet(filename, storage_options={}) # This will go through, as fsspec.core.url_to_fs understands hdfs:///
```
### Issue Description
https://github.com/pandas-dev/pandas/commit/0359f1706ebe69df627b58f95c4ae2cee0b5acaf?diff=split&w=0
The above change from 2.1.0, which uses pyarrow fs instead of fsspec seems introduced the problem above. And by reading the code, you can force it to the same old behavior by passing in empty storage_options
### Expected Behavior
file with hdfs:/// can be load properly.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : ba1cccd19da778f0c3a7d6a885685da16a072870
python : 3.9.18.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-150-generic
Version : #167~18.04.1-Ubuntu SMP Wed May 24 00:51:42 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 2.1.0
numpy : 1.26.1
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 63.4.3
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.34.0
pandas_datareader : None
bs4 : None
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : 2023.10.0
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2023.10.0
scipy : None
sqlalchemy : 1.4.46
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"IO Parquet",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,215,906,816
| 58,077
|
CLN: Remove unnecessary block in apply_str
|
closed
| 2024-03-29T19:30:21
| 2024-03-29T22:07:23
| 2024-03-29T21:39:18
|
https://github.com/pandas-dev/pandas/pull/58077
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58077
|
https://github.com/pandas-dev/pandas/pull/58077
|
rhshadrach
| 1
|
- [ ] 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.
Due to #57109, "axis" will not be an argument for groupby ops.
Hat tip to @jbrockmendel for finding this.
|
[
"Groupby",
"Clean",
"Apply"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @rhshadrach "
] |
2,215,864,273
| 58,076
|
DOC: docstring fix for pandas.DataFrame.where method
|
closed
| 2024-03-29T18:54:10
| 2024-04-01T17:53:10
| 2024-04-01T17:53:03
|
https://github.com/pandas-dev/pandas/pull/58076
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58076
|
https://github.com/pandas-dev/pandas/pull/58076
|
YashpalAhlawat
| 1
|
- [x] xref #58065 (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.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @YashpalAhlawat "
] |
2,215,823,368
| 58,075
|
DOC: whatsnew note for #57553
|
closed
| 2024-03-29T18:37:58
| 2024-03-29T21:42:48
| 2024-03-29T21:39:56
|
https://github.com/pandas-dev/pandas/pull/58075
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/58075
|
https://github.com/pandas-dev/pandas/pull/58075
|
jbrockmendel
| 1
|
- [ ] 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.
xref #58008
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jbrockmendel "
] |
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