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
string | labels
list | reactions_plus1
int64 | reactions_minus1
int64 | reactions_laugh
int64 | reactions_hooray
int64 | reactions_confused
int64 | reactions_heart
int64 | reactions_rocket
int64 | reactions_eyes
int64 | comments
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,203,946,807
| 57,974
|
BUG: Fixed ADBC to_sql creation of table when using public schema
|
closed
| 2024-03-23T16:19:24
| 2024-03-28T17:56:04
| 2024-03-28T17:55:54
|
https://github.com/pandas-dev/pandas/pull/57974
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57974
|
https://github.com/pandas-dev/pandas/pull/57974
|
shabab477
| 2
|
Problem: table on public schema being lost when tried to be created
Solution: used db_schema_name argument to specify schema name of adbc_ingest
- [x] closes #57539 (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.
|
[
"Bug",
"IO SQL",
"Arrow",
"Sprints"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Very nice PR. Can you also add a whatsnew note for version 2.2.2? Outside of that and @jorisvandenbossche comment this lgtm",
"Thanks @shabab477 "
] |
2,203,943,530
| 57,973
|
Changed the strings to make code simpler
|
closed
| 2024-03-23T16:10:53
| 2024-03-25T17:44:38
| 2024-03-25T17:44:31
|
https://github.com/pandas-dev/pandas/pull/57973
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57973
|
https://github.com/pandas-dev/pandas/pull/57973
|
igeni
| 1
|
Changed the strings to make code simpler
- [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).
|
[
"Benchmark"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @igeni "
] |
2,203,787,041
| 57,972
|
BUG: Wrong kurtosis outcome due to inadequate fix to previous issues
|
open
| 2024-03-23T09:16:28
| 2024-09-06T06:44:10
| null |
https://github.com/pandas-dev/pandas/issues/57972
| true
| null | null |
j7168908jx
| 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.
- [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 polars as pl
import pandas as pd
import numpy as np
import scipy.stats as st
data = np.array([-2.05191341e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, -4.10391103e-05, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00])
print(pl.Series(data).kurtosis())
print(pd.Series(data).kurt())
print(st.kurtosis(data))
```
### Issue Description
The output of `pandas` kurtosis function is incorrect.
After simple debugging I found a comment at `core/nanops.py` line 1360, in function `nankurt`,
saying to fix #18044 it manually zeros out values less than 1e-14, which is in any way improper.
This affects whatever data comes with not much variance but lots of data.
### Expected Behavior
Output of provided example:
```text
14.916104870028523
0.0
14.916104870028551
```
Expected output: roughly 14.9161 for unbiased (`pandas`'s default behaviour) is correct.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.0-1010-nvidia-lowlatency
Version : #10-Ubuntu SMP PREEMPT_DYNAMIC Wed Apr 26 00:40:27 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 65.5.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.22.1
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 : 3.8.3
numba : 0.59.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
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",
"Reduction Operations"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Good point. Reproducing your example, this does happen in your example. Trying to scale it up to larger input distributions alleviates the issue though.\r\n\r\nYour example is a sweet spot for this error, rescaling your distribution to be larger, the zeroing out stops happening very quickly due to the O(count^2) and O(count^3) terms in the numerator and denominator equations counteracting lifting the very small m4 and m2^2 above the e-14 threshold.\r\n\r\nDoing a check of the form (pseudocode)\r\n`count < 100 and abs(frexp(denominator) - frexp(numerator)) < 24`\r\nbefore doing the zeroing out should alleviate this issue, but I would like to hear someone else's opinion before putting in a PR.",
"Another note: the kurtosis fomulation then still deviates from the scipy implementation by 3, up until a distribution size of about 10x your example, using the same shape of your example.\r\n\r\nI was not able to iron out that instability, though.",
"> Another note: the kurtosis fomulation then still deviates from the scipy implementation by 3, up until a distribution size of about 10x your example, using the same shape of your example.\r\n> \r\n> I was not able to iron out that instability, though.\r\n\r\nDo you mean that the difference of their output is roughly 3? If you have not set `bias=False` in `scipy` or `polars`, the difference here will be roughly 3.",
"> Do you mean that the difference of their output is roughly 3?\r\n\r\nExactly \r\n\r\n> If you have not set `bias=False` in `scipy` or `polars`, the difference here will be roughly 3.\r\n\r\nI did not, so then that's also explained. Then I see no issues with my solution anymore.\r\n\r\n",
"Why not apply welford method for skew and kurt?"
] |
2,203,596,226
| 57,971
|
CLN: Enforce deprecation of argmin/max and idxmin/max with NA values
|
closed
| 2024-03-23T01:14:13
| 2024-03-27T18:05:06
| 2024-03-25T17:55:56
|
https://github.com/pandas-dev/pandas/pull/57971
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57971
|
https://github.com/pandas-dev/pandas/pull/57971
|
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.
Ref: #33941, #51276
This is complicated by #57745 - we still need a proper deprecation for groupby's idxmin/idxmax. For DataFrame with EAs and axis=1, we use groupby's implementation. So I'm leaving that deprecation in place for now, and we can enforce it after groupby's is deprecated and ready to be enforced.
|
[
"Missing-data",
"Clean",
"Reduction Operations"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @rhshadrach "
] |
2,203,416,361
| 57,970
|
Implement hash_join for merges
|
closed
| 2024-03-22T22:27:29
| 2024-03-24T00:41:14
| 2024-03-24T00:41:11
|
https://github.com/pandas-dev/pandas/pull/57970
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57970
|
https://github.com/pandas-dev/pandas/pull/57970
|
phofl
| 1
|
cc @mroeschke
Our abstraction in merges is bad, this makes it a little worse unfortunately. But it enables a potentially huge performance improvement for joins that could be hash joins. I am using "right" to make the decision, because left determines the result order, which means that we would have to sort after we are finished which gives the performance improvement back. using right makes this problem go away.
We get time complexity O(m+n) here over O(m*n) with a non-trivial factor as before
This makes adding semi joins pretty easy as well, which is nice next to the performance improvements here.
```
| Change | Before [38086f11] <backtest> | After [3b6b787e] <to> | Ratio | Benchmark (Parameter) |
|----------|--------------------------------|-------------------------|---------|-----------------------------------------------------------------------------|
| - | 193±4ms | 165±5ms | 0.85 | join_merge.I8Merge.time_i8merge('inner') |
| - | 7.76±0.2ms | 6.53±0.06ms | 0.84 | join_merge.Merge.time_merge_2intkey(False) |
| - | 1.03±0.02ms | 834±4μs | 0.81 | join_merge.Merge.time_merge_dataframe_integer_2key(False) |
| - | 485±2μs | 339±5μs | 0.7 | join_merge.Merge.time_merge_dataframe_integer_key(False) |
| - | 3.29±0.2ms | 2.29±0.07ms | 0.7 | join_merge.MergeDatetime.time_merge(('ms', 'ms'), 'Europe/Brussels', False) |
| - | 3.31±0.07ms | 2.27±0.07ms | 0.69 | join_merge.MergeDatetime.time_merge(('ms', 'ms'), None, False) |
| - | 2.79±0.09ms | 1.77±0.01ms | 0.63 | join_merge.MergeDatetime.time_merge(('ns', 'ms'), 'Europe/Brussels', False) |
| - | 2.89±0.04ms | 1.78±0.05ms | 0.62 | join_merge.MergeDatetime.time_merge(('ns', 'ms'), None, False) |
| - | 2.57±0.09ms | 1.56±0.03ms | 0.61 | join_merge.MergeDatetime.time_merge(('ns', 'ns'), None, False) |
| - | 1.97±0.05ms | 1.18±0.02ms | 0.6 | join_merge.MergeEA.time_merge('Float32', False) |
| - | 1.84±0.02ms | 1.10±0.03ms | 0.6 | join_merge.MergeEA.time_merge('UInt16', False) |
| - | 2.09±0.04ms | 1.24±0.01ms | 0.59 | join_merge.MergeEA.time_merge('UInt64', False) |
| - | 2.10±0.09ms | 1.22±0.01ms | 0.58 | join_merge.MergeEA.time_merge('Float64', False) |
| - | 2.09±0.08ms | 1.22±0.01ms | 0.58 | join_merge.MergeEA.time_merge('UInt32', False) |
| - | 2.70±0.1ms | 1.54±0.02ms | 0.57 | join_merge.MergeDatetime.time_merge(('ns', 'ns'), 'Europe/Brussels', False) |
| - | 1.72±0.02ms | 971±20μs | 0.57 | join_merge.MergeEA.time_merge('Int16', False) |
| - | 1.76±0.03ms | 973±10μs | 0.55 | join_merge.MergeEA.time_merge('Int32', False) |
| - | 1.94±0.09ms | 1.07±0.03ms | 0.55 | join_merge.MergeEA.time_merge('Int64', False) |
| - | 57.0±2ms | 21.7±0.3ms | 0.38 | join_merge.UniqueMerge.time_unique_merge(1000000) |
| - | 106±7ms | 34.1±0.3ms | 0.32 | join_merge.UniqueMerge.time_unique_merge(4000000) |
```
|
[
"Reshaping"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thx for your rewiew! merging to unblock follow up work but happy to address other comments in follow ups"
] |
2,203,392,498
| 57,969
|
WEB: Updating active/inactive core devs
|
closed
| 2024-03-22T22:02:25
| 2024-03-26T17:28:33
| 2024-03-26T17:28:26
|
https://github.com/pandas-dev/pandas/pull/57969
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57969
|
https://github.com/pandas-dev/pandas/pull/57969
|
datapythonista
| 6
|
I've checked with the devs who weren't active in pandas recently to see if they wished to become inactive, and few were happy to become inactive.
Besides to manage expectations of other core devs and the community, this is relevant for the decision making proposed in [PDEP-1](https://github.com/pandas-dev/pandas/pull/53576/files). With the current proposal, the number of maintainers to have a quorum is lowered from 12 to 11 after this PR, and it'll have an impact if more people become a maintainer.
@bashtage I couldn't get an answer from you via email (I sent two emails). If you'd like to remain active that's totally fine, just let me know.
|
[
"Admin",
"Web"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I'm updating the finance workgroup. Wes was the lead but he's becoming inactive, so I'm removing him from the workgroup. @mroeschke I'm adding you as leader of the workgroup, since I think you've been doing most of the work recently (at least approving expenses). If you want to have a discussion with the rest of the workgroup and there is a different outcome, please let me know:\r\n\r\nCC: @jreback @jorisvandenbossche @TomAugspurger ",
"SGTM. If anyone else wants a spot on the finance group I'm more than happy to step aside.",
"sgtm ",
"@TomAugspurger I personally don't think it's needed to have many people in the workgroups. I think having a couple of people to have a backup is good, or maybe 3 depending on the workgroup. So, seems reasonable that you step aside even if nobody new takes your spot, we already got Matt, Jeff and Joris.\r\n\r\nAlso, in the responsibilities we've got for the finance workgroup now we only have \"approve the expenses\". Not sure if it'd make sense to include requesting the grants and organizing the funds. Maybe in that case it could make sense if @phofl joins it?",
"> Maybe in that case it could make sense if @phofl joins it?\r\n\r\nsgtm",
"Thanks @datapythonista "
] |
2,203,317,094
| 57,968
|
BUG: #57954 encoding ignored for filelike
|
closed
| 2024-03-22T20:58:01
| 2024-03-28T23:30:31
| 2024-03-28T18:10:56
|
https://github.com/pandas-dev/pandas/pull/57968
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57968
|
https://github.com/pandas-dev/pandas/pull/57968
|
dontgoto
| 1
|
- [x] closes #57954
- [x] [Tests added and passed if fixing a bug or adding a new feature
- [x] All [code checks passed]
|
[
"Error Reporting",
"IO CSV"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @dontgoto "
] |
2,203,173,592
| 57,967
|
BUG: 7023 allow style when using error bars
|
closed
| 2024-03-22T19:27:02
| 2024-07-07T09:01:31
| 2024-07-05T17:23:14
|
https://github.com/pandas-dev/pandas/pull/57967
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57967
|
https://github.com/pandas-dev/pandas/pull/57967
|
thetestspecimen
| 13
|
- [x] closes #7023
- [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/v3.0.0.rst` file if fixing a bug or adding a new feature.
If the 'style' parameter is passed to the DataFrame's 'plot' method at the same time as error bar parameters 'yerr' or 'xerr' (or both), then the 'style' parameter is not applied to the plot. For example, something like this:
```ax = df.plot(xerr=err_x, yerr=err_y, style='or:')```
will result in the marker style, color and line style being ignored.
This PR passes the style parameter to the plot.
The comments in the bug report mention a work around not working with 'subplots'. As such, I have created scenario in the tests that confirm this fix will also work for subplots.
**Note:** This fix is identical to that previously provided by @GLeurquin (PR #40816), but it went stale and was closed. I have basically added the missing tests that were requested, and never provided.
|
[
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the PR! Sorry that this hasn't been reviewed yet. In the meantime can you fix the merge conflict?\r\ncc @datapythonista ",
"> Thanks for the PR! Sorry that this hasn't been reviewed yet. In the meantime can you fix the merge conflict? cc @datapythonista\r\n\r\nNo problem at all. \r\n\r\nI will take a look at your comments and get it up to date as soon as I get a minute (probably tomorrow).",
"Apparently 1 check fails in relation to ```Unit Tests / Numpy Dev (pull_request) ```, but I'm not sure it has anything to do with the code:\r\n\r\n```\r\n[2024-04-21T10:31:57.779Z] ['error'] There was an error running the uploader: Error uploading to [https://codecov.io:](https://codecov.io/) Error: There was an error fetching the storage URL during POST: 404 - {'detail': ErrorDetail(string='Unable to locate build via Github Actions API. Please upload with the Codecov repository upload token to resolve issue.', code='not_found')}\r\n[2024-04-21T10:31:57.779Z] ['info'] Codecov will exit with status code 0. If you are expecting a non-zero exit code, please pass in the `-Z` flag\r\n```\r\n\r\nIs there something you need me to do, or a way I can re-run the checks?",
"This is what I'm seeing: \r\n```\r\nFAILED pandas/tests/extension/test_sparse.py::TestSparseArray::test_isna_returns_copy[0-isna] - ValueError: assignment destination is read-only\r\nFAILED pandas/tests/extension/test_sparse.py::TestSparseArray::test_isna_returns_copy[0-notna] - ValueError: assignment destination is read-only\r\nFAILED pandas/tests/extension/test_sparse.py::TestSparseArray::test_isna_returns_copy[nan-isna] - ValueError: assignment destination is read-only\r\nFAILED pandas/tests/extension/test_sparse.py::TestSparseArray::test_isna_returns_copy[nan-notna] - ValueError: assignment destination is read-only\r\nFAILED pandas/tests/indexes/test_base.py::TestIndex::test_drop_by_str_label[string] - KeyError: \"['SOMEBIGLONGSTRING'] not found in axis\"\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestDatetimeArray::test_array_interface[D] - AssertionError\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestDatetimeArray::test_array_interface[B] - AssertionError\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestDatetimeArray::test_array_interface[W] - AssertionError\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestDatetimeArray::test_array_interface[ME] - AssertionError\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestDatetimeArray::test_array_interface[QE] - AssertionError\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestDatetimeArray::test_array_interface[YE] - AssertionError\r\nFAILED pandas/tests/arrays/test_datetimelike.py::TestTimedeltaArray::test_array_interface - AssertionError\r\n= 12 failed, 171032 passed, 25300 skipped, 892 xfailed, 78 xpassed in 718.59s (0:11:58) =\r\n```\r\nI don't know of a better way, but, you can merge with main if you want to re-run the checks",
"Update: this failure seems to be happening on all PRs, you have nothing to worry about!",
"> Update: this failure seems to be happening on all PRs, you have nothing to worry about!\r\n\r\nThanks for letting me know. I thought it was a bit strange. \r\n\r\nLet me know if you need me to force the checks to re-run (and when). ",
"I understand you requested my review but I don't know much about the plotting code to help review this, maybe @mroeschke can help",
"Unfortunately we don't have anyone on the core team who really understands the plotting code anymore. I can try looking later to give a high level review",
"> Unfortunately we don't have anyone on the core team who really understands the plotting code anymore. I can try looking later to give a high level review\r\n\r\nNo problem. The code change is very small, and was actually originally provided in PR #40816 . I have just added the tests that were missing.",
"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.",
"> 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.\r\n\r\nI will update this soon to keep it available.",
"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.",
"> 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.\r\n\r\n@mroeschke I have merged the main branch in and pushed it to my fork of pandas. I suspect you will need to reopen before it gets pulled across here?"
] |
2,203,008,274
| 57,966
|
CLN: Enforce verbose parameter deprecation in read_csv/read_table
|
closed
| 2024-03-22T17:47:06
| 2024-04-07T21:49:27
| 2024-03-22T19:07:49
|
https://github.com/pandas-dev/pandas/pull/57966
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57966
|
https://github.com/pandas-dev/pandas/pull/57966
|
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.
|
[
"IO CSV",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @lithomas1 ",
"I have noticed that there is a test in `pandas/tests/io/parser/test_unsupported.py` which still checks for the deprecation warning, will open a PR soon"
] |
2,202,685,023
| 57,965
|
BUG: Fix na_values dict not working on index column (#57547)
|
closed
| 2024-03-22T14:51:57
| 2024-04-09T22:44:43
| 2024-04-09T17:08:34
|
https://github.com/pandas-dev/pandas/pull/57965
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57965
|
https://github.com/pandas-dev/pandas/pull/57965
|
tomhoq
| 3
|
- [x] closes #57547
- [x] [Tests added and passed]
- [x] All [code checks passed]
- [ ] 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/v3.0.0.rst` file if fixing a bug or adding a new feature.
In the read_csv method, pandas allows having na_values set as a dict, which in such case lets you decide which values are null for each column. In the occurence of one of the columns being `None`, no null values are applied to the column and it remains as it was. This specific case is what is being tested in the issue #57547.
The problem was that in these particular conditions variables `col_na_values` and `col_na_fvalues` were not being set correctly causing a `TypeError`. All i had to do was correctly define these variables as empty sets in an `else` block.
On the python engine this same logic was not yet programmed. I implemented it, by adding an if statement, ensuring na_values are only applied if the column is not `None`.
|
[
"IO CSV"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@rhshadrach Would it be possible to ask someone to review this PR? Thank you",
"Thanks @tomhoq ",
"Thank you for the review"
] |
2,202,683,637
| 57,964
|
BUG: `testing.assert_frame_equal` ignores `check_names=False`
|
closed
| 2024-03-22T14:51:22
| 2024-03-24T11:07:50
| 2024-03-22T16:59:44
|
https://github.com/pandas-dev/pandas/issues/57964
| true
| null | null |
odros
| 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
left = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
# Note the second column is now 'c' instead of 'b'
right = pd.DataFrame({'a': [1, 2, 3], 'c': [4, 5, 6]})
pd.testing.assert_frame_equal(left, right, check_names=False)
```
### Issue Description
DataFrame assertion throws the following error despite the `check_names=False`:
```python
Traceback (most recent call last):
Cell In[36], line 4
pd.testing.assert_frame_equal(left, right, check_names=False)
File ~/venvs/base/lib/python3.10/site-packages/pandas/_testing/asserters.py:1240 in assert_frame_equal
assert_index_equal(
File ~/venvs/base/lib/python3.10/site-packages/pandas/_testing/asserters.py:326 in assert_index_equal
_testing.assert_almost_equal(
File testing.pyx:55 in pandas._libs.testing.assert_almost_equal
File testing.pyx:173 in pandas._libs.testing.assert_almost_equal
File ~/venvs/base/lib/python3.10/site-packages/pandas/_testing/asserters.py:614 in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.columns are different
DataFrame.columns values are different (50.0 %)
[left]: Index(['a', 'b'], dtype='object')
[right]: Index(['a', 'c'], dtype='object')
At positional index 1, first diff: b != c
```
### Expected Behavior
If I interpret [the documentation ](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.testing.assert_frame_equal.html#pandas.testing.assert_frame_equal)correctly, `check_names=False` should result in no DataFrame.column comparison taking place, which is not the case.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.12.final.0
python-bits : 64
OS : Darwin
OS-release : 22.6.0
Version : Darwin Kernel Version 22.6.0: Mon Feb 19 19:48:53 PST 2024; root:xnu-8796.141.3.704.6~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : de_DE.UTF-8
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 2.2.1
numpy : 1.23.5
pytz : 2023.3
dateutil : 2.8.2
setuptools : 69.0.2
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : 6.1.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.14.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.9.2
gcsfs : None
matplotlib : 3.7.2
numba : 0.58.0
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 14.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : 2.4.1
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report, but this is the expected behavior. `check_names` applies to comparing `pandas.Index.names`, so for your example, `check_names=False` would ignore comparing `left.columns.names == right.columns.names`. Closing as a usage question",
"My bad. Thank you for taking the time to actually explain; TIL indexes can have names."
] |
2,202,435,152
| 57,963
|
BUG: install pandas on embedded python 3.8 win32 successful, but failed to `import pandas`
|
closed
| 2024-03-22T12:54:08
| 2024-08-05T16:17:08
| 2024-03-22T23:01:50
|
https://github.com/pandas-dev/pandas/issues/57963
| true
| null | null |
l1t1
| 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
```python
pip install pandas
import pandas
or
import numpy
or
import duckdb
print(duckdb.sql("select 1 a").df())
```
### Issue Description
`pip install pandas` on embedded python 3.8 win32 successful, but failed to `import pandas`
import failed, it pops an error message box and exit.
numpy also failed.
even no import lines, other modules returns a dataframe, also failed.
### Expected Behavior
```python
import pandas
```
it works as normal
### Installed Versions
<details>
pandas-2.0.3
</details>
|
[
"Build",
"Needs Info",
"32bit"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Can you provide a traceback?",
"how to make a traceback? ",
"What's the error that you get when you do ``import pandas``?",
"Please use the newest version of pandas (which will also require a newer python version). We don't support 2.0.3 anymore",
"Python 3.8 is the latest version on Windows 7.\r\nof course, the os is also out of support. ",
"Interesting, I didn't know the windows python part. Thanks.\r\n\r\nThere is nothing we can do then. It is likely that it is somehow related to NumPy if it also failed, that needs to work first before worrying about pandas makes sense",
"install Numpy 1.23.5, it works flawlessly"
] |
2,202,022,620
| 57,962
|
"ValueError: Must have equal len keys and value when setting with an iterable" when updating an object type cell using .loc with a nd.array
|
open
| 2024-03-22T09:03:22
| 2025-06-07T08:14:57
| null |
https://github.com/pandas-dev/pandas/issues/57962
| true
| null | null |
isabelladegen
| 11
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [x] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({1: [1, 2, 3], 2: [np.zeros(3), np.ones(3), np.zeros(0)]})
print(df.dtypes)
df.loc[1, 2] = np.zeros(3)
```
### Issue Description
The loc assign throws a "ValueError: Must have equal len keys and value when setting with an iterable" despite dtypes being object for column 2. This started to happen when I updated Pandas from 1.4.4 -> 2.2.1. In 1.4.4 this syntax worked. This issue also happens when assigning multiple columns at the same time as soon as one of the new values is an iterable.
### Expected Behavior
The assign should set the second row to np.array of zeros.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.9.19.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:06 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T8103
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_GB.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 : 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 : 8.18.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.2.0
gcsfs : None
matplotlib : 3.8.1
numba : 0.59.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.11.3
sqlalchemy : 2.0.28
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
None
</details>
|
[
"Bug",
"Needs Triage"
] | 6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@isabelladegen The exact same problem appears on the main branch of pandas 2.2.2 if you do `df.loc[1, 2] = list(np.zeros(3))`. Please update your title that the problem happens with any iterable, not just a numpy array. Please also tick the checkbox that \"this bug exists on the main branch of pandas\".",
"Just ran into this today with 2.2.2. Is anyone aware of a workaround?",
"@onejgordon This is how I worked around it. Warning it is ugly. For the above toy example, instead of indexing the row and column(s) to update the cell like this:\r\n`df.loc[1, 2] = np.zeros(3)`\r\n\r\nI update the whole row like this: \r\n`df.loc[1] = {1:2, 2:np.zeros(3)}`",
"Thanks for your response @isabelladegen. \r\n\r\nIt appears one of the factors influencing my case was a non-unique index. Dropping duplicates from the index column resolved the error and I was able to set both arrays and individual cells with:\r\n\r\n`df.loc[3, 'arr'] = np.array([1, 2])`\r\n\r\nand \r\n\r\n`df.loc[[2, 4, 6], 'arr'] = np.array([1, 2])`",
"Using `df.at` instead of `df.loc` seems to do the job if you only want to set a single value:\r\n\r\n```py\r\ndf.at[1, 2] = np.zeros(3)\r\n```",
"I'm having the same problem with df.at, unfortunately. \r\n`df.at[row.Index,\"pmids_old_before\"] = results.get('idlist')` \r\n is generating the same error as \r\n`df.loc[row.Index,\"pmids_old_before\"] = results.get('idlist')`\r\n\r\nOddly, this was working fine for me until today, and I haven't changed anything in my coding environment, so I'm very puzzled about the break. Also, it still works on two dataframes before suddenly generating the error on the third that uses this function. I can't figure out any salient differences. The function creates and initializes this column with None values before trying to make this assignment, so it should be identical conditions for each dataframe. ",
"@AlastairKelly does your column `pmids_old_before` have `dtype=object`?",
"> Using `df.at` instead of `df.loc` seems to do the job if you only want to set a single value:\r\n> \r\n> ```python\r\n> df.at[1, 2] = np.zeros(3)\r\n> ```\r\n\r\n@saeub method worked for me, while having identical issue listed here. Ty.",
"Same issue in panda 2.2.2 and nothing works (.at or wrapping with a list)",
"> Same issue in panda 2.2.2 and nothing works (.at or wrapping with a list)\n\n\n@tcourat \nTry to convert column dtype to object. It helped me.\n```\n(Pdb) dataframe[field].dtype\nstring[python]\n(Pdb) dataframe[field] = dataframe[field].astype(str)\n(Pdb) dataframe.at[index, field] = [1,1]\n```",
"I ran into this problem using a mask, here is a pythonic workaround.\n\n```python\nimport numpy as np\nimport pandas as pd\n\ndf = pd.DataFrame({1: [1, 2, 3], 2: [np.zeros(3), np.NAN, np.zeros(3)]})\nprint(df.dtypes)\n\nmask = df[2].notna()\n# do some calculations for masked values\nsome_new_calculated_vecs = [np.ones(3), np.ones(3)]\n\nprint(f\"Length of data frame: {len(df)}\")\nprint(f\"Number of rows that are notna(): {sum(mask)}\")\nprint(f\"Mask indices: {df.index[mask].tolist()}\")\nprint(f\"Masked np.array: {df.loc[mask, 2].tolist()}\")\n\nprint(f\"Length of some_new_calculated_vecs: {len(some_new_calculated_vecs)}\")\n\n# this line will raise ValueError('Must have equal len keys and value when setting with an ndarray')\n# df.loc[mask,'some_new_calculated_vecs'] = some_new_calculated_vecs\n\n# this is a pythonic workaround\nworkaround = iter(some_new_calculated_vecs)\ndf['some_new_calculated_vecs'] = pd.Series(\n data=(next(workaround) if notna else np.NAN for notna in mask), \n index=df.index, \n dtype='object'\n)\nprint(df)\n```"
] |
2,201,709,766
| 57,961
|
DOC: fix closing sq. bracket in pandas.read_fwf example (#57959)
|
closed
| 2024-03-22T05:07:58
| 2024-03-22T14:33:38
| 2024-03-22T14:33:38
|
https://github.com/pandas-dev/pandas/pull/57961
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57961
|
https://github.com/pandas-dev/pandas/pull/57961
|
danlsn
| 1
|
- change closing square bracket in colspecs description to correct "]"
- [x] closes #57959
- [ ] [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 @danlsn "
] |
2,201,707,809
| 57,960
|
BUG: Inconsistent behavior while constructing a Series with large integers in a int64 masked array
|
closed
| 2024-03-22T05:05:41
| 2024-05-15T17:57:02
| 2024-05-15T17:57:01
|
https://github.com/pandas-dev/pandas/pull/57960
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57960
|
https://github.com/pandas-dev/pandas/pull/57960
|
parthi-siva
| 2
|
- [x] closes #56566
- [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.
|
[
"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.",
"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."
] |
2,201,701,811
| 57,959
|
DOC: Flipped square bracket in pandas.read_fwf documentation
|
closed
| 2024-03-22T04:58:45
| 2024-03-22T14:33:39
| 2024-03-22T14:33:39
|
https://github.com/pandas-dev/pandas/issues/57959
| true
| null | null |
danlsn
| 1
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_fwf.html
### Documentation problem
Under the Parameters for `pandas.read_fwf()`, specifically the param `colspecs`, the description is as below:
> **colspecs : _list of tuple (int, int) or ‘infer’. optional_ **
> A list of tuples giving the extents of the fixed-width fields of each line as half-open intervals **(i.e., [from, to[ ).** String value ‘infer’ can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data which are not being skipped via skiprows (default=’infer’).
Note that that closing square bracket in the example is also a left square bracket "[".
### Suggested fix for documentation
The suggested fix is simply to replace the "[" with the correct closing "]" square bracket.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take"
] |
2,201,665,768
| 57,958
|
BUG: Fix `is_unique` regression for slices of `Index`es
|
closed
| 2024-03-22T04:17:50
| 2024-08-06T01:06:38
| 2024-08-06T01:06:27
|
https://github.com/pandas-dev/pandas/pull/57958
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57958
|
https://github.com/pandas-dev/pandas/pull/57958
|
rob-sil
| 11
|
- [x] closes #57911
- [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/v3.0.0.rst` file if fixing a bug or adding a new feature.
Slicing a unique `Index` will always give another unique `Index`, so inheriting uniqueness flags is safe and efficient. However, the slice of a non-unique `Index` can end up with unique elements. Inheriting in the non-unique case caused the regression so this PR changes the code to just inherit when the base `Index` is marked as unique.
|
[
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"A note or question for reviewers: With this extra code, is pre-computing uniqueness and monotonicity for indexes still a performance boost?\r\n\r\nI'm still seeing gains for calling `is_monotonic_increasing` on a sliced `Index`, almost as large as in #51738. However, pre-computing slows down slicing when neither `is_unique` nor `is_monotonic_increasing` is called on the resulting `Index`. With no benefit, pre-computing is a performance decrease. I'd expect that slicing data frames and series is more common than calling `is_monotonic_increasing` on a sliced index, but is it significantly more common? For a full offset, I think it would have to be more than 500x as frequent.\r\n\r\nThis PR:\r\n```\r\nIn [1]: import pandas as pd\r\n\r\nIn [2]: df = pd.DataFrame(index=list(range(1_000_000)))\r\n\r\nIn [3]: df.index.is_monotonic_increasing\r\nOut[3]: True\r\n\r\nIn [4]: %timeit df.index[:].is_monotonic_increasing\r\n3.79 µs ± 19.1 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\r\n\r\nIn [5]: %timeit df[:]\r\n8.23 µs ± 63.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\r\n```\r\n\r\nWithout pre-computing:\r\n```\r\nIn [5]: %timeit df.index[:].is_monotonic_increasing\r\n1.28 ms ± 2.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\r\n\r\nIn [6]: %timeit df[:]\r\n4.75 µs ± 2.95 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\r\n```",
"But it only slows it down by a large margin if `is_unique` or `is_monotonic_increasing` were called on the original index, right?\r\nThoughts @mroeschke?",
"The pre-computing code should run anytime `Index` has generated its `_engine`, which can come from a couple other methods in addition to `is_unique` and `is_monotonic_increasing`. I think `DataFrame.loc` indirectly calls one of these indexer methods.\r\n\r\nEDIT: as a quick check, I raised in `_update_from_sliced` (where the pre-computing happens) and ran part of the test suite. Looks like it can run in other places too, like `DataFrame.join`.",
"I think `DataFrame.join` uses `is_monotonic_increasing` and `is_unique`, although I'm not sure if they're in a place to benefit from the pre-computation. On net, the code's impact on efficiency looks pretty complicated and might take awhile to fully count.\r\n\r\nTo avoid scope creep in this issue, how about I open a separate issue for a performance check and have this PR move forward with the bug fix?",
"> To avoid scope creep in this issue, how about I open a separate issue for a performance check and have this PR move forward with the bug fix?\r\n\r\nSure, sounds good to me.\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.",
"Sorry this is not stale, @WillAyd mind having a look? Initially looks good to me.\r\n(I just pinged because you got assigned to this, let me know if I should ping someone else)",
"> 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.\r\n\r\nUpdated for 3.0.0 now.",
"Is there anything else I should do to make this PR not stale any more?",
"> Is there anything else I should do to make this PR not stale any more?\r\n\r\nI removed the label. @mroeschke mind taking a look please?",
"Thanks @rob-sil "
] |
2,201,398,788
| 57,957
|
BUG: Groupby median on timedelta column with NaT returns odd value (#…
|
closed
| 2024-03-21T23:36:07
| 2024-04-10T12:09:36
| 2024-03-26T20:57:52
|
https://github.com/pandas-dev/pandas/pull/57957
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57957
|
https://github.com/pandas-dev/pandas/pull/57957
|
JJLLWW
| 6
|
…57926)
Handle NaT correctly in group_median_float64
- [x] closes #57926
- [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.
|
[
"Groupby"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @JJLLWW ",
"@WillAyd - why the backport? I didn't think this was a regression.",
"Ah OK - I thought we were doing bugfixes on that branch now. Didn't realize it was limited to regressions. Can just keep this on 3.x then",
"This might be just me, but I like to keep patch versions as small as possible since bugfixes can induce new bugs and would like patch upgrade to be as safe as possible for users. But in the past, I know others have been more inclined to backport more than I prefer - cc @phofl for any thoughts.",
"Agree with @rhshadrach \r\n\r\nwe backported fixes for new features that were introduced in the version before but generally not much else",
"> we backported fixes for new features that were introduced in the version before but generally not much else\r\n\r\nAh, I think that's where I remember a disagreement - thanks. And yea, I've come around to doing bug fixes for new features."
] |
2,201,334,363
| 57,956
|
BUG: IndexError with pandas.DataFrame.cumsum where dtype=timedelta64[ns]
|
closed
| 2024-03-21T22:56:33
| 2024-05-24T17:57:10
| 2024-03-28T00:13:59
|
https://github.com/pandas-dev/pandas/issues/57956
| true
| null | null |
omor1
| 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
# Example DataFrame from pandas.DataFrame.cumsum documentation
df = pd.DataFrame([[2.0, 1.0],
[3.0, np.nan],
[1.0, 0.0]],
columns=list('AB'))
# IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
df.astype('timedelta64[ns]').cumsum()
# These are fine
df.cumsum().astype('timedelta64[ns]')
df.apply(lambda s: s.astype('timedelta64[ns]').cumsum())
df.astype('timedelta64[ns]').apply(lambda s: s.cumsum())
```
### Issue Description
A cumulative sum along the columns of a `DataFrame` with `timedelta64[ns]` types results in an `IndexError`. Cumulative min and max work correctly, and product correctly results in a `TypeError`, since it's not supported for timedelta.
Cumulative sums work correctly for `Series` with `timedelta64[ns]` type, so this seems to be a bug in `DataFrame`.
This is perhaps related to #41720?
### Expected Behavior
`pandas.DataFrame.cumsum` on a `DataFrame` with `timedelta64[ns]` columns should not erroneously raise `IndexError` and instead compute the cumulative sum, with the same effect as `df.apply(lambda s: s.cumsum())`.
### Installed Versions
<details>
<summary>Installed Versions</summary>
```
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.12.2.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:45:48 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T8122
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : None
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.22.2
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 : 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>
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"The code seems to be taking an single list instead of numpy array below\r\n<img width=\"484\" alt=\"Screenshot 2024-03-22 at 12 30 21 AM\" src=\"https://github.com/pandas-dev/pandas/assets/58086649/9a1ac5d1-cc4e-4745-b9aa-17b60ea92ba0\">\r\n",
" original_list = func(y)\r\n result = [[x] for x in original_list]\r\n result = np.array(result)\r\n \r\n This might help?",
"The problem exists in `core\\array_algos\\datetimelike_accumulations.py`, `_cum_func`:\r\n```python\r\nresult = func(y)\r\nresult[mask] = iNaT\r\n```\r\nEssentially, what this is doing is performing `func` (`np.cumsum` in this case) on your whole data array, and by default `np.cumsum` computes the sum over a flattened array and returns it, but then when masking the NA values, it is expecting that the data array was in it's original shape and not flattened.\r\n\r\nTo fix this, you have to pass `axis` as 0 to `func` (again, `np.cumsum` in this case), so that it performs the `cumsum` along the given axis and not over the whole flattened array (it's the only `func` with `axis=None` by default, unlike `cummin` and `cummax` which have `axis=0` by default and work properly)",
"@phofl @mroeschke any comment on this based on #41720. I found result is 1D while mask is an array. Thus I tried \r\n\r\n` flattened_mask = mask.flatten() `\r\n ` result[flattened_mask] = iNaT`\r\n\r\n but ended up in error at \r\n\r\n` block = self.blocks[self.blknos[i]] `\r\n `values = block.iget(self.blklocs[i])`\r\n `bp = BlockPlacement(slice(0, len(values)))`\r\n \r\n in managers.py",
"take",
"Thanks @Aloqeely for helping me understand this."
] |
2,201,277,502
| 57,955
|
BUG: test
|
closed
| 2024-03-21T22:08:21
| 2024-03-21T22:08:36
| 2024-03-21T22:08:36
|
https://github.com/pandas-dev/pandas/issues/57955
| true
| null | null |
liaochris
| 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
test
```
### Issue Description
test
### Expected Behavior
test
### Installed Versions
<details>
Replace this line with the output of pd.show_versions()
</details>
test
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,201,110,829
| 57,954
|
BUG: encoding is **silently** ignored for `read_csv` on FileLike objects
|
closed
| 2024-03-21T20:24:07
| 2024-03-28T18:10:57
| 2024-03-28T18:10:57
|
https://github.com/pandas-dev/pandas/issues/57954
| true
| null | null |
JensHeinrich
| 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
```python
import pathlib
import pandas
import pytest
def test_encoding_is_ignored(
tmp_path: pathlib.Path,
):
_test_csv = tmp_path / "test.csv"
with open(_test_csv, "w", encoding="latin1") as f:
f.write("A,B\nÜ,Ä\n")
# works
_direct_read = pandas.read_csv(_test_csv, encoding="latin1")
with open(_test_csv, "r") as f:
# fails
_indirect_read = pandas.read_csv(f, encoding="latin1")
```
### Issue Description
If `read_csv` is used with a FileLike object, the encoding is correctly delegated to the object, but the encoding parameter is silently ignored.
### Expected Behavior
A warning should be created to make the user aware of this behavior, which might be expected if the encoding is given explicitly to the `open` but not if it is using its default.
An easy way would be a comparison of the FileType's encoding to the one `read_csv` would use, if the latter is not the default.
This checking for deviation of the default could also be done by setting `encoding=None` in the definition and setting the internal encoding like `encoding = encoding or "utf-8"`
### Installed Versions
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.12.1.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-26-generic
Version : #26-Ubuntu SMP PREEMPT_DYNAMIC Mon Jul 10 23:39:54 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.2.0
pip : 23.2.1
Cython : None
pytest : 8.1.1
hypothesis : 6.99.11
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.22.2
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 : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : 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",
"Error Reporting",
"IO CSV"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hm, I guess we could raise if we're passed a file-like object and encoding != None, when the given encoding doesn't match the encoding for the file-like object.\r\n\r\nPRs for this would be welcome.",
"take"
] |
2,200,889,629
| 57,953
|
BUG: pandas df.to_markdown() with period at end of field - is not displayed.
|
closed
| 2024-03-21T18:31:01
| 2024-03-24T17:33:19
| 2024-03-24T17:33:19
|
https://github.com/pandas-dev/pandas/issues/57953
| true
| null | null |
dwgillies
| 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
>>> df = pd.DataFrame({'a': ['1234.']})
>>> df
a
0 1234.
>>> df.to_markdown()
'| | a |\n|---:|-----:|\n| 0 | 1234 |'
^^ notice the 1234 above is lacking the '.' at the end of the string. When I print the dataframe
cell, the period appears:
>>> print(df['a'][0])
1234.
```
### Issue Description
When printing a string field that ends in a period, to_markdown() fails to print the period at the end of the string.
### Expected Behavior
```python
>>> df.to_markdown()
'| | a |\n|---:|-----:|\n| 0 | 1234. |'
```
### Installed Versions
```
>>> pd.show_versions()
/usr/lib/python3.9/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.9.16.final.0
python-bits : 64
OS : CYGWIN_NT-10.0-19045
OS-release : 3.5.0-1.x86_64
Version : 2024-02-01 11:02 UTC
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 2.2.0
numpy : 1.26.4
pytz : 2023.3
dateutil : 2.8.2
setuptools : 69.0.2
pip : 24.0
Cython : 0.29.33
pytest : None
hypothesis : None
sphinx : 4.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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 : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
```
### DEVELOPMENT VERSIONS
ALSO the bug appears on the latest development versions:
```
>>> pd.show_versions()
/usr/lib/python3.9/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
commit : d17723c6d447d37c0cb753a517df74705806f4a2
python : 3.9.16.final.0
python-bits : 64
OS : CYGWIN_NT-10.0-19045
OS-release : 3.5.0-1.x86_64
Version : 2024-02-01 11:02 UTC
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 3.0.0.dev0+578.gd17723c6d4
numpy : 1.26.4
pytz : 2023.3
dateutil : 2.8.2
setuptools : 69.0.2
pip : 24.0
Cython : 0.29.33
pytest : None
hypothesis : None
sphinx : 4.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
```
|
[
"Bug",
"IO Data",
"Usage Question",
"Closing Candidate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Explanation from `python-tabulate/README.md`:\r\n> For tabulate, anything which can be parsed as a number is a number. Even numbers represented as strings are aligned properly. This feature comes in handy when reading a mixed table of text and numbers from a file\r\n> To disable this feature use `disable_numparse=True`.\r\n\r\nSo to achieve your desired output:\r\n```\r\n>>> df.to_markdown(disable_numparse=True)\r\n'| | a |\\n|:---|:------|\\n| 0 | 1234. |'\r\n```\r\n",
"thanks @Aloqeely, closing here"
] |
2,200,686,559
| 57,952
|
Study on refactoring the series constructor
|
closed
| 2024-03-21T16:52:20
| 2024-04-11T17:18:34
| 2024-04-10T13:42:15
|
https://github.com/pandas-dev/pandas/pull/57952
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57952
|
https://github.com/pandas-dev/pandas/pull/57952
|
aureliobarbosa
| 0
|
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [X] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
This is an study on refactoring the series constructor, with emphasis on two points:
- clear separation of the tasks;
- and reduction of code duplication.
In particular, it should noticed that data manipulation on dictionaries and series data were duplicate and it was possible to eliminate the function `self._init_dict()` by reusing logic used to create series objects from other series.
Another interesting point is that, after refactoring, it became evident that the current code uses twice as much memory for manipulating dictionaries, as opposed to lists, with the same data set (tests were done locally with 10 million random numbers). In this case, it is possible to reduce the memory usage while creating the Series with dictionaries by up to 25% by avoiding the creation of a tuple from dictionary keys.
As mentioned above, this is an study and the main objective of this PR is to check if tests passes on all platforms.
Nevertheless, if any core member can take a look on it, I would be glad to receive a feedback.
If the community decide that any idea introduced here are useful for integration in the main branch, I would be glad to take them from this work and do separate PR with them.
P.S.: This branch diverged a little bit from main, but the only changes introduced so far were related to #57889. Those changes were incorporated on this PR.
Regards
EDIT: typo and P.S.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,200,577,446
| 57,951
|
Potential regression induced by "Refactored pandas_timedelta_to_timedeltastruct"
|
open
| 2024-03-21T16:01:40
| 2024-05-20T11:33:00
| null |
https://github.com/pandas-dev/pandas/issues/57951
| true
| null | null |
DeaMariaLeon
| 6
|
PR #55999
If it is expected please ignore this issue.
@WillAyd
"eb55bca5bf91541a5c4f6213b18824589415127b": {
"`tslibs.fields.TimeGetTimedeltaField.time_get_timedelta_field` (Python) with field='microseconds', size=1000000": "http://57.128.112.95:5000/compare/benchmarks/065fb93bf74c75d480006fe97e7b82d8...065fb94f7876798380009c8d16cc367e",
"`tslibs.fields.TimeGetTimedeltaField.time_get_timedelta_field` (Python) with field='seconds', size=1000000": "http://57.128.112.95:5000/compare/benchmarks/065fb93bf6bf7a7d8000621519222b60...065fb94f77ee7e3e800087d3ad953647",
"`tslibs.fields.TimeGetTimedeltaField.time_get_timedelta_field` (Python) with field='nanoseconds', size=1000000": "http://57.128.112.95:5000/compare/benchmarks/065fb93bf7e07ec98000d31ecf9914d4...065fb94f78fa779080005b333b82e55a"
}
<img width="720" alt="Screenshot 2024-03-21 at 17 00 27" src="https://github.com/pandas-dev/pandas/assets/11835246/92fa12ae-955b-4db5-a343-7756434f2c41">
|
[
"Performance",
"Regression",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I think the issue here is that we combined a few case statements into one:\r\n\r\n```c\r\n case NPY_FR_s:\r\n case NPY_FR_ms:\r\n case NPY_FR_us:\r\n case NPY_FR_ns: {\r\n npy_int64 per_sec;\r\n if (base == NPY_FR_s) {\r\n per_sec = 1;\r\n } else if (base == NPY_FR_ms) {\r\n per_sec = 1000;\r\n } else if (base == NPY_FR_us) {\r\n per_sec = 1000000;\r\n } else {\r\n per_sec = 1000000000;\r\n }\r\n\r\n ...\r\n```\r\n\r\nWhereas previously the code was copy/pasted and slightly tweaked for every case statement.\r\n\r\nThere are probably some tricks we can try like using a static lookup table instead of branches:\r\n\r\n```c\r\n case NPY_FR_s:\r\n case NPY_FR_ms:\r\n case NPY_FR_us:\r\n case NPY_FR_ns: {\r\n const npy_int64 sec_per_day = 86400;\r\n static npy_int64 per_secs[] = {\r\n 1, // NPY_FR_s\r\n 1000, // NPY_FR_ms\r\n 1000000, // NPY_FR_us\r\n 1000000000 // NPY_FR_ns\r\n };\r\n const npy_int64 per_sec = per_secs[base - NPY_FR_s];\r\n```\r\n\r\nbut I'm not really sure the tricks are worth it. And I think the code de-duplication is probably the increased timing here",
"> There are probably some tricks we can try like using a static lookup table instead of branches:\r\nI tried that, but it did not improve the situation on my machine. \r\n\r\nI did some other refactoring that brought down the benchmark's runtime by 20% on my machine for s/us/ns.\r\n\r\nThere are also code paths that the benchmark is not really testing (those I saw a similar improvement from my changes): s/ms/us/ns values so large that the \"day\" parsing part of the code gets triggered as well as negative offsets, both don't get checked because the benchmark runs only with random numbers between 0 and 10.",
"I now have a version of the date time conversion that is correct and tested and runs up to 2.5x faster than main and the previous version before the regression (at least on my machine, M2 ARM). Interestingly, I do not see large differences in runtimes between the pre regression commit and main. \r\n\r\nI would suggest changing the benchmark inputs to negative values that are at least on the order of one day to make most of the logic in the function actually show up in the runtime of the benchmark. What do you think? Is there a procedure to follow to update the history of benchmark runtimes?\r\n\r\nTo back that up, here are some of my benchmark results for all three versions, I will update my PR soon:\r\n\r\n| input range \\| version | main | main pre regression | fix |\r\n|------------------------|------|---------------------|-----|\r\n| +30±10ns | 4.9 | 4.8 | 3.9 |\r\n| -30±10ns | 7.9 | 7.2 | 5.2 |\r\n| ±30ns | 8.4 | 8.5 | 7.1 |\r\n| +4d±100s | 11.6 | 11.4 | 5.2 |\r\n| -4d±100s | 12.7 | 12.7 | 5.3 |\r\n| ±4d | 9.7 | 9.4 | 7.5 |\r\n\r\n(Runtimes are in ms, runs are for 10^6 random values)\r\n\r\n The current versions of the function have branch prediction and variable dependency issues that only show up when using larger ranges of inputs and negative values. The previously mentioned checks against the base unit size are of very low cost in reality since the unit size does not change (in the benchmark, but also in real world) and get branch predicted without failure.\r\n\r\nRandomly negative and positive inputs still do not do all too well in my version, but realistically timestamps would not have this property. ",
"@dontgoto thanks for the great research. If you have improvements to the benchmark I would just go ahead and submit it - we don't manage the history of changes to a benchmark itself that strictly",
"Possibly related?: https://github.com/pandas-dev/pandas/issues/57035",
"> Possibly related?: https://github.com/pandas-dev/pandas/issues/57035\n\nGood find, but the regression here should be unrelated to that plotting issue. Converting 1M timestamps takes some milliseconds, but the referenced regression is in the seconds range."
] |
2,200,566,841
| 57,950
|
Potential regression induced by "Cython guard against [c|m|re]alloc failures"
|
open
| 2024-03-21T15:57:38
| 2024-03-23T21:58:16
| null |
https://github.com/pandas-dev/pandas/issues/57950
| true
| null | null |
DeaMariaLeon
| 5
|
PR #57705
If it is expected please ignore this issue.
Please be aware that the server is slow to respond some times.
@WillAyd
commit "114a84d8a0eee8fb93a4d2d701a2a6e62ebcf6d2": {
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Float64', method='cummax', with_nans=True": "http://57.128.112.95:5000/compare/benchmarks/065fb944a14b793780003322ee823635...065fb8e49664747b8000f70e4ec84ae6",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Int64', method='cummin', with_nans=True": "http://57.128.112.95:5000/compare/benchmarks/065fb944ae8474e58000b3cec8275f57...065fb8e498ce7594800008071c2d8038",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Int64', method='cummin', with_nans=False": "http://57.128.112.95:5000/compare/benchmarks/065fb944b2b47a138000cb49180477b4...065fb8e499487a9e8000b44250789920",
"`algos.isin.IsInLongSeriesLookUpDominates.time_isin` (Python) with MaxNumber=1000, dtype='Float64', series_type='random_misses'": "http://57.128.112.95:5000/compare/benchmarks/065fb9407ebe716e8000ed2fe62f5b83...065fb8e1b287741080003a30c54d529f",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='int64', method='cummin', with_nans=False": "http://57.128.112.95:5000/compare/benchmarks/065fb94491a376d180006f30866e9cb7...065fb8e4939b78d480008873c990c076",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Int64', method='cummax', with_nans=False": "http://57.128.112.95:5000/compare/benchmarks/065fb944bc017c0780002137a5469d07...065fb8e49a7476d9800097b740074116",
"`algorithms.Factorize.time_factorize` (Python) with dtype='boolean', sort=True, unique=False": "http://57.128.112.95:5000/compare/benchmarks/065fb9400e25709d8000634602d20fa9...065fb8e14c1270388000c8005022c82f",
"`algos.isin.IsInLongSeriesLookUpDominates.time_isin` (Python) with MaxNumber=1000, dtype='float64', series_type='random_misses'": "http://57.128.112.95:5000/compare/benchmarks/065fb9406aa0734d8000d932d3e79d24...065fb8e1a1037615800024333e87bbf7",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Float64', method='cummin', with_nans=True": "http://57.128.112.95:5000/compare/benchmarks/065fb9449c0075d7800006671aaa142b...065fb8e4954a7e678000d0cf25e62d15",
"`groupby.GroupByCythonAggEaDtypes.time_frame_agg` (Python) with dtype='Int32', method='any'": "http://57.128.112.95:5000/compare/benchmarks/065fb945a1cb705f800027e9e412ca99...065fb8e4baf27ba58000e2dcb9c6c91c",
"`algos.isin.IsinWithArange.time_isin` (Python) with M=8000, dtype=<class 'numpy.float64'>, offset_factor=-2": "http://57.128.112.95:5000/compare/benchmarks/065fb940c55774118000016187a81f4d...065fb8e1f156773380009660ce668fd7",
"`algos.isin.IsInLongSeriesLookUpDominates.time_isin` (Python) with MaxNumber=1000, dtype='float64', series_type='monotone_misses'": "http://57.128.112.95:5000/compare/benchmarks/065fb9406baf7c048000e4b240c9b233...065fb8e1a200710f8000cf630f8dcec9",
"`groupby.GroupByCythonAggEaDtypes.time_frame_agg` (Python) with dtype='Float64', method='first'": "http://57.128.112.95:5000/compare/benchmarks/065fb94566c67d2f80005785dda0523a...065fb8e4acf773d98000663b6949b9a7",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='float64', method='cummax', with_nans=False": "http://57.128.112.95:5000/compare/benchmarks/065fb944863a73328000100ab11e015f...065fb8e491f073228000be1a0dcb0813",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Int64', method='cummax', with_nans=True": "http://57.128.112.95:5000/compare/benchmarks/065fb944b887736180002dd51b13dfc4...065fb8e499e779438000356086562483",
"`groupby.Cumulative.time_frame_transform` (Python) with dtype='Float64', method='cummax', with_nans=False": "http://57.128.112.95:5000/compare/benchmarks/065fb944a4cc74dd80007276e8078ead...065fb8e496fc7c4d80002e67128bc5cf"
}
<img width="721" alt="Screenshot 2024-03-21 at 16 53 24" src="https://github.com/pandas-dev/pandas/assets/11835246/3f80f028-2f17-423b-a850-e5c026715229">
|
[
"Performance",
"Closing Candidate"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hmm that's interesting. OK thanks - definitely unexpected. Will take a look later",
"I checked the generated code and it generates things like:\r\n\r\n```c\r\nif (unlikely(ptr == NULL)) {\r\n ... // raise MemoryError\r\n}\r\n```\r\n\r\nSo I don't think this can be any more efficient. I noticed also some of the benchmarks were not really regressions when compared to values a few months back:\r\n\r\n\r\n\r\n\r\n\r\nSo I don't think there is much to do here",
"@WillAyd - only question I have is how many times are these checks hit in the test? If it's an inner loop that's hit a few hundred times then a 2ms regression makes sense. But if they are just hit a few times per test run then perhaps more investigation is warranted. I'll come back and see if I can find out in a few days.",
"These all come after malloc calls. I don't believe any are done in a tight loop, but if they were the surprising part is that the malloc itself is where you would expect to see a bottleneck, not the subsequent call to check if malloc failed",
"Interesting - I'm not seeing any of the checks added in #57705 hit by \r\n\r\n> groupby.Cumulative.time_frame_transform (Python) with dtype='Float64', method='cummax', with_nans=True\r\n\r\nSame with\r\n\r\n> algos.isin.IsInLongSeriesLookUpDominates.time_isin (Python) with MaxNumber=1000, dtype='Float64', series_type='random_misses'"
] |
2,200,515,238
| 57,949
|
BUG: Unable to use CustomBusinessDays in a MultiIndex
|
open
| 2024-03-21T15:34:53
| 2025-07-15T21:02:10
| null |
https://github.com/pandas-dev/pandas/issues/57949
| true
| null | null |
cokelid
| 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
bus_day = pd.offsets.CustomBusinessDay(holidays=['2023-07-04']) # Mon-Fri except July 4th
dates = pd.date_range('2023-07-03', '2023-07-06', freq=bus_day)
df1 = pd.DataFrame({'col1': [300, 500, 600]}, index=dates)
print(df1.index.freq.holidays) # Can get freq from dataframe index
mi = pd.MultiIndex.from_product([dates, ('foo','bar')])
df2 = pd.DataFrame({'col1': [300, 301, 500, 501, 600, 601]}, index=mi)
print(df2.index.get_level_values(0).freq) # Gives None. Freq lost from dataframe multi-index
periods = pd.period_range('2023-07-03', '2023-07-06', freq=bus_day) # Try PeriodIndex?
# Gives: TypeError: CustomBusinessDay cannot be used with Period or PeriodDtype
```
### Issue Description
I can use a `CustomBusinessDay` frequency in a `DatetimeIndex` and retrieve the frequency from the `.index` of a dataframe.
If I use a `DatetimeIndex` in a `MultiIndex`, then I lose the `freq` from the level of the index.
I note the `freq` _does_ persist in a `PeriodIndex` within a `MultiIndex`, but you can't use a `CustomBusinessDay` in a `PeriodIndex` as shown.
### Expected Behavior
I would like the Datetime level of the MultiIndex to show the orginial freq:
`print(df2.index.get_level_values(0).freq)` would give `<CustomBusinessDay>`
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a671b5a8bf5dd13fb19f0e88edc679bc9e15c673
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.145.2-1.cm2
Version : #1 SMP Wed Jan 17 15:39:07 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.4
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 65.5.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
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 : None
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",
"Frequency",
"MultiIndex",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"This loss of information does happen with the DateTimeIndex and not with the PeriodIndex. Reason is that the `freq`is an attribute of each value in the PeriodIndex. With the DateTimeIndex, the freq, etc. get lost when calling `get_level_values()`, due to the shallow copying logic.\r\n\r\nThe DateTimeIndex is the only index type with this edge case.",
"Thanks. Is this the shallow copy you're referring to?\r\n[`return lev._shallow_copy(filled, name=name)`](https://github.com/pandas-dev/pandas/blob/4f145b3a04ac2e9167545a8a2a09d30856d9ce42/pandas/core/indexes/multi.py#L1726)\r\n\r\n"
] |
2,199,631,617
| 57,948
|
BUG: itemsize wrong for date32[day][pyarrow] dtype?
|
open
| 2024-03-21T09:25:48
| 2025-08-23T02:42:38
| null |
https://github.com/pandas-dev/pandas/issues/57948
| true
| null | null |
MarcoGorelli
| 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
```python
import pyarrow as pa
import pandas as pd
pd.ArrowDtype(pa.date32()) # date32[day][pyarrow]
pd.ArrowDtype(pa.date32()).itemsize # 8
```
### Issue Description
I think it should show `4`? `pa.date32()` is 32 bits, so 4 bytes
### Expected Behavior
pd.ArrowDtype(pa.date32()).itemsize # 4
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : b033ca94e7ae6e1320c9d65a8163bd0a6049f40a
python : 3.10.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.146.1-microsoft-standard-WSL2
Version : #1 SMP Thu Jan 11 04:09:03 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.1.0.dev0+761.gb033ca94e7
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.1.1
pip : 24.0
Cython : 3.0.8
pytest : 8.0.2
hypothesis : 6.98.15
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : 3.2.0
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : 1.4.6
psycopg2 : 2.9.9
jinja2 : 3.1.3
IPython : 8.22.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : 1.3.8
fastparquet : 2024.2.0
fsspec : 2024.2.0
gcsfs : 2024.2.0
matplotlib : 3.8.3
numba : 0.59.0
numexpr : 2.9.0
odfpy : None
openpyxl : 3.1.2
pyarrow : 15.0.2
pyreadstat : 1.2.6
python-calamine : None
pyxlsb : 1.0.10
s3fs : 2024.2.0
scipy : 1.12.0
sqlalchemy : 2.0.27
tables : 3.9.2
tabulate : 0.9.0
xarray : 2024.2.0
xlrd : 2.0.1
zstandard : 0.22.0
tzdata : 2024.1
qtpy : 2.4.1
pyqt5 : None
</details>
|
[
"Bug",
"good first issue",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Not sure what to do here, as there isn't a numpy dtype corresponding to date32\r\n\r\nhttps://github.com/pandas-dev/pandas/blob/77f9d7abee14888447a1f9942f7f6f4cdbcd927b/pandas/core/dtypes/dtypes.py#L2215-L2218\r\n\r\n",
"I think it's OK to override `ArrowDtype.itemsize` to handle this case separately",
"PyArrow data types of fixed width have a `bit_width` attribute that could be used here. That does raise for nested types, though at the moment we just return 8 from the numpy object dtype, which also doesn't necessarily makes sense.\r\n\r\n```\r\nIn [13]: pd.ArrowDtype(pa.list_(pa.int32())).itemsize\r\nOut[13]: 8\r\n```",
"take",
"take",
"I'm having a hard time trying to find what and where to find the relationship between numpy_dtype and the itemsize method. Going to the definition of itemsize method does not show an implementation of the method, so I'm not sure what it is doing. \r\n\r\n[pandas/pandas/core/dtypes/dtypes.py#L2213-L2216](https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/dtypes.py#L2213-L2216)\r\n\r\nAny help is appreciated",
"take"
] |
2,199,570,168
| 57,947
|
Backport PR #57764 on branch 2.2.x (BUG: PyArrow dtypes were not supported in the interchange protocol)
|
closed
| 2024-03-21T08:55:32
| 2024-03-21T16:37:58
| 2024-03-21T16:37:58
|
https://github.com/pandas-dev/pandas/pull/57947
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57947
|
https://github.com/pandas-dev/pandas/pull/57947
|
MarcoGorelli
| 0
|
#57764
|
[
"Arrow",
"Interchange"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,198,971,338
| 57,946
|
Clean up more Cython warning
|
closed
| 2024-03-21T02:27:51
| 2024-03-21T18:28:22
| 2024-03-21T16:16:39
|
https://github.com/pandas-dev/pandas/pull/57946
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57946
|
https://github.com/pandas-dev/pandas/pull/57946
|
WillAyd
| 1
|
Overlooked this in the recent PR. I think this is the last of Cython warnings.
`(warning: /home/pandas/pandas/_libs/parsers.pyx:1605:26: noexcept clause is ignored for function returning Python object)`
|
[
"Internals"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @WillAyd "
] |
2,198,924,511
| 57,945
|
BUILD: cross-compiling issue using crossenv
|
closed
| 2024-03-21T01:44:55
| 2024-03-26T13:46:48
| 2024-03-26T13:15:51
|
https://github.com/pandas-dev/pandas/issues/57945
| true
| null | null |
th0ma7
| 8
|
### Installation check
- [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas).
### Platform
Synology toolchain for any (x64, armv7, aarch64, ppc)
### Installation Method
Built from source
### pandas Version
2.0.3 and 2.2.1
### Python Version
3.11.8
### Installation Logs
<details>
Requirement already satisfied: pybind11==2.11.1 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages (2.11.1)
Requirement already satisfied: expandvars==0.12.0 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages (0.12.0)
Requirement already satisfied: python-dateutil==2.9.0.post0 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages (2.9.0.post0)
Requirement already satisfied: pytz==2024.1 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages (2024.1)
Collecting tzdata==2024.1
Downloading tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)
Requirement already satisfied: six>=1.5 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages (from python-dateutil==2.9.0.post0) (1.16.0)
Downloading tzdata-2024.1-py2.py3-none-any.whl (345 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 345.4/345.4 kB 3.9 MB/s eta 0:00:00
Installing collected packages: tzdata
Successfully installed tzdata-2024.1
Requirement already satisfied: pybind11==2.11.1 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/lib/python3.11/site-packages (2.11.1)
Requirement already satisfied: expandvars==0.12.0 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/lib/python3.11/site-packages (0.12.0)
Requirement already satisfied: python-dateutil==2.9.0.post0 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/lib/python3.11/site-packages (2.9.0.post0)
Requirement already satisfied: pytz==2024.1 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/lib/python3.11/site-packages (2024.1)
Collecting tzdata==2024.1
Using cached tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)
Requirement already satisfied: six>=1.5 in /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/lib/python3.11/site-packages (from python-dateutil==2.9.0.post0) (1.16.0)
Using cached tzdata-2024.1-py2.py3-none-any.whl (345 kB)
Installing collected packages: tzdata
Successfully installed tzdata-2024.1
===> Processing wheels of homeassistant
===> Downloading wheels from src/requirements-abi3.txt ...
===> pip download [pycryptodome], version [3.18.0]
INFO: File already exists [pycryptodome-3.18.0.tar.gz]
===> pip download [pycryptodomex], version [3.18.0]
INFO: File already exists [pycryptodomex-3.18.0.tar.gz]
===> Downloading wheels from src/requirements-crossenv.txt ...
===> pip download [pandas], version [2.2.1]
INFO: File already exists [pandas-2.2.1.tar.gz]
===> pip download [webrtcvad], version [2.0.10], URL: [git+https://github.com/wiseman/py-webrtcvad@3bd761332a9404f5c9276105070ee814c4428342#egg=webrtcvad==2.0.10]
WARNING: Skipping download URL - Downloaded at build time
===> Adding existing src/requirements-abi3.txt file as ABI-limited
===> Adding existing src/requirements-crossenv.txt file as cross-compiled (discarding any pure-python)
===> Adding existing src/requirements-pure.txt file as pure-python (discarding any cross-compiled)
===> Cross-compiling wheels
===> Python crossenv found: [/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/bin/activate]
===> pip crossenv found: [/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/pip]
===> [pandas] --config-settings=editable-verbose=true
make[3]: Entering directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'
===> _PYTHON_HOST_PLATFORM=x86_64-pc-linux-gnu /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/pip wheel --disable-pip-version-check --no-binary :all: --find-links /home/spksrc/py311-update/spksrc/spk/homeassistant/../../distrib/pip --cache-dir /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/pip --no-deps --wheel-dir /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/wheelhouse --no-index --config-settings=editable-verbose=true --no-build-isolation pandas==2.2.1
Looking in links: /home/spksrc/py311-update/spksrc/spk/homeassistant/../../distrib/pip
Processing /home/spksrc/py311-update/spksrc/distrib/pip/pandas-2.2.1.tar.gz
Preparing metadata (pyproject.toml): started
Preparing metadata (pyproject.toml): finished with status 'done'
ERROR: Exception:
Traceback (most recent call last):
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/cli/base_command.py", line 180, in exc_logging_wrapper
status = run_func(*args)
^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/cli/req_command.py", line 245, in wrapper
return func(self, options, args)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/commands/wheel.py", line 147, in run
requirement_set = resolver.resolve(reqs, check_supported_wheels=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/resolver.py", line 95, in resolve
result = self._result = resolver.resolve(
^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/resolvelib/resolvers.py", line 546, in resolve
state = resolution.resolve(requirements, max_rounds=max_rounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/resolvelib/resolvers.py", line 397, in resolve
self._add_to_criteria(self.state.criteria, r, parent=None)
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/resolvelib/resolvers.py", line 173, in _add_to_criteria
if not criterion.candidates:
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/resolvelib/structs.py", line 156, in __bool__
return bool(self._sequence)
^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py", line 155, in __bool__
return any(self)
^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py", line 143, in <genexpr>
return (c for c in iterator if id(c) not in self._incompatible_ids)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py", line 47, in _iter_built
candidate = func()
^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/factory.py", line 182, in _make_candidate_from_link
base: Optional[BaseCandidate] = self._make_base_candidate_from_link(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/factory.py", line 228, in _make_base_candidate_from_link
self._link_candidate_cache[link] = LinkCandidate(
^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/candidates.py", line 290, in __init__
super().__init__(
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/candidates.py", line 156, in __init__
self.dist = self._prepare()
^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/candidates.py", line 222, in _prepare
dist = self._prepare_distribution()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/candidates.py", line 301, in _prepare_distribution
return preparer.prepare_linked_requirement(self._ireq, parallel_builds=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/operations/prepare.py", line 525, in prepare_linked_requirement
return self._prepare_linked_requirement(req, parallel_builds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/operations/prepare.py", line 640, in _prepare_linked_requirement
dist = _get_prepared_distribution(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/operations/prepare.py", line 71, in _get_prepared_distribution
abstract_dist.prepare_distribution_metadata(
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/distributions/sdist.py", line 67, in prepare_distribution_metadata
self.req.prepare_metadata()
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/req/req_install.py", line 579, in prepare_metadata
self.metadata_directory = generate_metadata(
^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/operations/build/metadata.py", line 35, in generate_metadata
distinfo_dir = backend.prepare_metadata_for_build_wheel(metadata_dir)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_internal/utils/misc.py", line 766, in prepare_metadata_for_build_wheel
return super().prepare_metadata_for_build_wheel(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_impl.py", line 186, in prepare_metadata_for_build_wheel
return self._call_hook('prepare_metadata_for_build_wheel', {
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_impl.py", line 321, in _call_hook
raise BackendUnavailable(data.get('traceback', ''))
pip._vendor.pyproject_hooks._impl.BackendUnavailable: Traceback (most recent call last):
File "/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/build/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 77, in _build_backend
obj = import_module(mod_path)
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/spksrc/py311-update/spksrc/native/python311/work-native/install/usr/local/lib/python3.11/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1140, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'mesonpy'
make[3]: *** [../../mk/spksrc.wheel.mk:203: cross-compile-wheel-pandas] Error 2
make[3]: Leaving directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'
make[2]: *** [../../mk/spksrc.wheel.mk:144: build_wheel_target] Error 1
make[2]: Leaving directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'
make[1]: *** [../../mk/spksrc.spk.mk:719: build-arch-x64-7.1] Error 1
make[1]: Leaving directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'
make: *** [../../mk/spksrc.spk.mk:713: arch-x64-7.1] Error 2
</details>
|
[
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Associated PR (although code is in my local tree) https://github.com/SynoCommunity/spksrc/pull/6040",
"Can you provide your build command?\r\n\r\nLooking at the error, you are missing the ``meson/meson-python`` dependencies.\r\n\r\n",
"Indeed it was missing, thnx now added:\r\n```\r\nmake[3]: Entering directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'\r\n===> _PYTHON_HOST_PLATFORM=x86_64-pc-linux-gnu /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/pip wheel --disable-pip-version-check --no-binary :all: --find-links /home/spksrc/py311-update/spksrc/spk/homeassistant/../../distrib/pip --cache-dir /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/pip --no-deps --wheel-dir /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/wheelhouse --no-index --no-clean --config-settings=editable-verbose=true --no-build-isolation pandas==2.2.1\r\nLooking in links: /home/spksrc/py311-update/spksrc/spk/homeassistant/../../distrib/pip\r\nProcessing /home/spksrc/py311-update/spksrc/distrib/pip/pandas-2.2.1.tar.gz\r\n Preparing metadata (pyproject.toml): started\r\n Preparing metadata (pyproject.toml): finished with status 'error'\r\n error: subprocess-exited-with-error\r\n \r\n × Preparing metadata (pyproject.toml) did not run successfully.\r\n │ exit code: 1\r\n ╰─> [10 lines of output]\r\n + meson setup /tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9 /tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9/.mesonpy-y9035jpi -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --vsenv --native-file=/tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9/.mesonpy-y9035jpi/meson-python-native-file.ini\r\n The Meson build system\r\n Version: 1.4.0\r\n Source dir: /tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9\r\n Build dir: /tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9/.mesonpy-y9035jpi\r\n Build type: native build\r\n \r\n ../meson.build:5:13: ERROR: Command `/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/python /tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9/generate_version.py --print` failed with status 1.\r\n \r\n A full log can be found at /tmp/pip-wheel-8eh27d9c/pandas_598e5b7c95ba46e28cbfac20f70c52d9/.mesonpy-y9035jpi/meson-logs/meson-log.txt\r\n [end of output]\r\n \r\n note: This error originates from a subprocess, and is likely not a problem with pip.\r\nerror: metadata-generation-failed\r\n\r\n× Encountered error while generating package metadata.\r\n╰─> See above for output.\r\n\r\nnote: This is an issue with the package mentioned above, not pip.\r\nhint: See above for details.\r\n```\r\nSo from here I now added `--no-clean` to pip but still the `meson-log.txt` gets deleted. Actually the whole `.meson-*` is deleted from the temporary folder. Any arguments I can pass at pip build time so meson's files do not get deleted?",
"I was able to keep the log file by setting-up the build directory: `--config-settings=builddir=build`\r\n```\r\nBuild started at 2024-03-23T13:48:57.608601\r\nMain binary: /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/python\r\nBuild Options: -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md -Dvsenv=True --cross-file=/home/spksrc/py311-update/\r\nspksrc/spk/homeassistant/work-x64-7.1/tc_vars.meson --native-file=/tmp/pip-wheel-s1xttpye/pandas_2f0e7fd58d5b45ebaf8fb7d83\r\nedf1917/build/meson-python-native-file.ini\r\nPython system: Linux\r\nThe Meson build system\r\nVersion: 1.4.0\r\nSource dir: /tmp/pip-wheel-s1xttpye/pandas_2f0e7fd58d5b45ebaf8fb7d83edf1917\r\nBuild dir: /tmp/pip-wheel-s1xttpye/pandas_2f0e7fd58d5b45ebaf8fb7d83edf1917/build\r\nBuild type: cross build\r\nRunning command: /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/python /tmp/pip-wheel-s1xt\r\ntpye/pandas_2f0e7fd58d5b45ebaf8fb7d83edf1917/generate_version.py --print\r\n--- stdout ---\r\n\r\n--- stderr ---\r\nTraceback (most recent call last):\r\n File \"/tmp/pip-wheel-s1xttpye/pandas_2f0e7fd58d5b45ebaf8fb7d83edf1917/generate_version.py\", line 8, in <module>\r\n import versioneer\r\nModuleNotFoundError: No module named 'versioneer'\r\n\r\n\r\n\r\n../meson.build:5:13: ERROR: Command `/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/python\r\n /tmp/pip-wheel-s1xttpye/pandas_2f0e7fd58d5b45ebaf8fb7d83edf1917/generate_version.py --print` failed with status 1.\r\n```",
"Now added `versioneer` to my crossenv, with the following result (progress I guess):\r\n\r\nEDIT: Note that I found curious the `Visual Studio environment` message.\r\n\r\n```\r\n===> _PYTHON_HOST_PLATFORM=x86_64-pc-linux-gnu /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/pip wheel --disable-pip-version-check --no-binary :all: --find-links /home/spksrc/py311-update/spksrc/spk/homeassistant/../../distrib/pip --cache-dir /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/pip --no-deps --wheel-dir /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/wheelhouse --no-index --no-clean --config-settings=builddir=build --config-settings=editable-verbose=true --config-settings=setup-args=--cross-file=/home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/tc_vars.meson --no-build-isolation pandas==2.2.1\r\nLooking in links: /home/spksrc/py311-update/spksrc/spk/homeassistant/../../distrib/pip\r\nProcessing /home/spksrc/py311-update/spksrc/distrib/pip/pandas-2.2.1.tar.gz\r\n Preparing metadata (pyproject.toml): started\r\n Preparing metadata (pyproject.toml): finished with status 'error'\r\n error: subprocess-exited-with-error\r\n \r\n × Preparing metadata (pyproject.toml) did not run successfully.\r\n │ exit code: 1\r\n ╰─> [117 lines of output]\r\n + meson setup /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287 /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/build -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --vsenv --cross-file=/home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/tc_vars.meson --native-file=/tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/build/meson-python-native-file.ini\r\n The Meson build system\r\n Version: 1.4.0\r\n Source dir: /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287\r\n Build dir: /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/build\r\n Build type: cross build\r\n Project name: pandas\r\n Project version: 2.2.1\r\n C compiler for the host machine: /home/spksrc/py311-update/spksrc/toolchain/syno-x64-7.1/work/x86_64-pc-linux-gnu/bin/x86_64-pc-linux-gnu-gcc (gcc 8.5.0 \"x86_64-pc-linux-gnu-gcc (GCC) 8.5.0\")\r\n C linker for the host machine: /home/spksrc/py311-update/spksrc/toolchain/syno-x64-7.1/work/x86_64-pc-linux-gnu/bin/x86_64-pc-linux-gnu-gcc ld.bfd 2.30\r\n C++ compiler for the host machine: /home/spksrc/py311-update/spksrc/toolchain/syno-x64-7.1/work/x86_64-pc-linux-gnu/bin/x86_64-pc-linux-gnu-g++ (gcc 8.5.0 \"x86_64-pc-linux-gnu-g++ (GCC) 8.5.0\")\r\n C++ linker for the host machine: /home/spksrc/py311-update/spksrc/toolchain/syno-x64-7.1/work/x86_64-pc-linux-gnu/bin/x86_64-pc-linux-gnu-g++ ld.bfd 2.30\r\n Cython compiler for the host machine: cython (cython 0.29.37)\r\n C compiler for the build machine: cc (gcc 10.2.1 \"cc (Debian 10.2.1-6) 10.2.1 20210110\")\r\n C linker for the build machine: cc ld.bfd 2.35.2\r\n C++ compiler for the build machine: c++ (gcc 10.2.1 \"c++ (Debian 10.2.1-6) 10.2.1 20210110\")\r\n C++ linker for the build machine: c++ ld.bfd 2.35.2\r\n Cython compiler for the build machine: cython (cython 0.29.37)\r\n Build machine cpu family: x86_64\r\n Build machine cpu: x86_64\r\n Host machine cpu family: x86_64\r\n Host machine cpu: x86_64\r\n Target machine cpu family: x86_64\r\n Target machine cpu: x86_64\r\n Program python3 found: YES (/home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/python)\r\n Found pkg-config: NO\r\n Run-time dependency python found: YES 3.11\r\n Build targets in project: 53\r\n \r\n pandas 2.2.1\r\n \r\n User defined options\r\n Cross files : /home/spksrc/py311-update/spksrc/spk/homeassistant/work-x64-7.1/tc_vars.meson\r\n Native files: /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/build/meson-python-native-file.ini\r\n buildtype : release\r\n vsenv : true\r\n b_ndebug : if-release\r\n b_vscrt : md\r\n \r\n Found ninja-1.10.1 at /usr/bin/ninja\r\n \r\n Visual Studio environment is needed to run Ninja. It is recommended to use Meson wrapper:\r\n /home/spksrc/py311-update/spksrc/spk/python311/work-x64-7.1/crossenv/cross/bin/meson compile -C .\r\n + /usr/bin/ninja\r\n [1/151] Generating pandas/_libs/khash_primitive_helper_pxi with a custom command\r\n [2/151] Generating pandas/_libs/algos_common_helper_pxi with a custom command\r\n [3/151] Generating pandas/_libs/intervaltree_helper_pxi with a custom command\r\n [4/151] Generating pandas/_libs/sparse_op_helper_pxi with a custom command\r\n [5/151] Generating pandas/_libs/hashtable_class_helper_pxi with a custom command\r\n [6/151] Generating pandas/_libs/algos_take_helper_pxi with a custom command\r\n [7/151] Copying file pandas/__init__.py\r\n [8/151] Generating pandas/_libs/hashtable_func_helper_pxi with a custom command\r\n [9/151] Generating pandas/_libs/index_class_helper_pxi with a custom command\r\n [10/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/base.pyx\r\n [11/151] Compiling C object pandas/_libs/tslibs/parsing.cpython-311-x86_64-linux-gnu.so.p/.._src_parser_tokenizer.c.o\r\n [12/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/ccalendar.pyx\r\n [13/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/dtypes.pyx\r\n [14/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/np_datetime.pyx\r\n [15/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/nattype.pyx\r\n [16/151] Compiling C object pandas/_libs/tslibs/base.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_base.pyx.c.o\r\n [17/151] Linking target pandas/_libs/tslibs/base.cpython-311-x86_64-linux-gnu.so\r\n [18/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/conversion.pyx\r\n [19/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/fields.pyx\r\n [20/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/strptime.pyx\r\n [21/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/parsing.pyx\r\n [22/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/offsets.pyx\r\n [23/151] Compiling C object pandas/_libs/tslibs/ccalendar.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_ccalendar.pyx.c.o\r\n [24/151] Linking target pandas/_libs/tslibs/ccalendar.cpython-311-x86_64-linux-gnu.so\r\n [25/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/timestamps.pyx\r\n [26/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/timedeltas.pyx\r\n [27/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/timezones.pyx\r\n [28/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/period.pyx\r\n [29/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/tzconversion.pyx\r\n [30/151] Compiling C object pandas/_libs/tslibs/np_datetime.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_np_datetime.pyx.c.o\r\n [31/151] Linking target pandas/_libs/tslibs/np_datetime.cpython-311-x86_64-linux-gnu.so\r\n [32/151] Compiling C object pandas/_libs/tslibs/dtypes.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_dtypes.pyx.c.o\r\n [33/151] Linking target pandas/_libs/tslibs/dtypes.cpython-311-x86_64-linux-gnu.so\r\n [34/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/algos.pyx\r\n FAILED: pandas/_libs/algos.cpython-311-x86_64-linux-gnu.so.p/pandas/_libs/algos.pyx.c\r\n cython -M --fast-fail -3 --include-dir /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/build/pandas/_libs '-X always_allow_keywords=true' /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/algos.pyx -o pandas/_libs/algos.cpython-311-x86_64-linux-gnu.so.p/pandas/_libs/algos.pyx.c\r\n \r\n Error compiling Cython file:\r\n ------------------------------------------------------------\r\n ...\r\n # each incremental i value can be looked up in the lexsort_indexer\r\n # array that we sorted previously, which gives us the location of\r\n # that sorted value for retrieval back from the original\r\n # values / masked_vals arrays\r\n # TODO(cython3): de-duplicate once cython supports conditional nogil\r\n with gil(numeric_object_t is object):\r\n ^\r\n ------------------------------------------------------------\r\n \r\n /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/algos.pyx:1147:12: Expected ':', found '('\r\n [35/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/tslibs/vectorized.pyx\r\n [36/151] Compiling C object pandas/_libs/tslibs/nattype.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_nattype.pyx.c.o\r\n [37/151] Compiling Cython source /tmp/pip-wheel-cpma2d0r/pandas_1745047ec6b24424b3bcdf2679ac0287/pandas/_libs/arrays.pyx\r\n [38/151] Compiling C object pandas/_libs/tslibs/conversion.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_conversion.pyx.c.o\r\n pandas/_libs/tslibs/conversion.cpython-311-x86_64-linux-gnu.so.p/pandas/_libs/tslibs/conversion.pyx.c: In function ‘__pyx_pf_6pandas_5_libs_6tslibs_10conversion_cast_from_unit_vectorized.isra.48’:\r\n pandas/_libs/tslibs/conversion.cpython-311-x86_64-linux-gnu.so.p/pandas/_libs/tslibs/conversion.pyx.c:2398:79: warning: ‘__pyx_v_i’ may be used uninitialized in this function [-Wmaybe-uninitialized]\r\n __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\\\r\n ^~\r\n (is_list ? (PyErr_SetString(PyExc_IndexError, \"list index out of range\"), (PyObject*)NULL) :\\\r\n \r\n pandas/_libs/tslibs/conversion.cpython-311-x86_64-linux-gnu.so.p/pandas/_libs/tslibs/conversion.pyx.c:3853:14: note: ‘__pyx_v_i’ was declared here\r\n Py_ssize_t __pyx_v_i;\r\n ^~~~~~~~~\r\n [39/151] Compiling C object pandas/_libs/tslibs/timezones.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_timezones.pyx.c.o\r\n [40/151] Compiling C object pandas/_libs/tslibs/fields.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_fields.pyx.c.o\r\n [41/151] Compiling C object pandas/_libs/tslibs/parsing.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_parsing.pyx.c.o\r\n [42/151] Compiling C object pandas/_libs/tslibs/tzconversion.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_tzconversion.pyx.c.o\r\n [43/151] Compiling C object pandas/_libs/tslibs/strptime.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_strptime.pyx.c.o\r\n [44/151] Compiling C object pandas/_libs/tslibs/period.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_period.pyx.c.o\r\n [45/151] Compiling C object pandas/_libs/tslibs/timestamps.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_timestamps.pyx.c.o\r\n [46/151] Compiling C object pandas/_libs/tslibs/timedeltas.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_timedeltas.pyx.c.o\r\n [47/151] Compiling C object pandas/_libs/tslibs/offsets.cpython-311-x86_64-linux-gnu.so.p/meson-generated_pandas__libs_tslibs_offsets.pyx.c.o\r\n ninja: build stopped: subcommand failed.\r\n [end of output]\r\n \r\n note: This error originates from a subprocess, and is likely not a problem with pip.\r\nerror: metadata-generation-failed\r\n\r\n× Encountered error while generating package metadata.\r\n╰─> See above for output.\r\n\r\nnote: This is an issue with the package mentioned above, not pip.\r\nhint: See above for details.\r\nmake[3]: *** [../../mk/spksrc.wheel.mk:203: cross-compile-wheel-pandas] Error 1\r\nmake[3]: Leaving directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'\r\nmake[2]: *** [../../mk/spksrc.wheel.mk:144: build_wheel_target] Error 1\r\nmake[2]: Leaving directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'\r\nmake[1]: *** [../../mk/spksrc.spk.mk:719: build-arch-x64-7.1] Error 1\r\nmake[1]: Leaving directory '/home/spksrc/py311-update/spksrc/spk/homeassistant'\r\nmake: *** [../../mk/spksrc.spk.mk:713: arch-x64-7.1] Error 2\r\n```",
"You need Cython 3 to be able to compile pandas. It looks like you have Cython 0.29.37.",
"Going to close as I'm assuming the issue is fixed.\r\n\r\nFeel free to comment if you're experiencing any other issues and I'll reopen this.",
"Thnx for the help and I'll reopen if i need further assistance but i believe my concerns are now answered."
] |
2,198,903,694
| 57,944
|
BUG: read_csv() raises IndexError when dict supplied as dtype uses integer keys of same sign
|
open
| 2024-03-21T01:21:12
| 2025-07-15T20:41:22
| null |
https://github.com/pandas-dev/pandas/issues/57944
| true
| null | null |
EsmeMaxwell
| 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
import pandas as pd
import numpy as np
from pathlib import Path
# Create a simple df with a RangeIndex for the columns. N.B. The RangeIndex
# start and stop have the same sign.
range_idx = pd.RangeIndex(start=-10, stop=-5, step=1)
df = pd.DataFrame(np.random.randn(3, len(range_idx)), columns=range_idx)
csv_file = Path("./data/test_range_index.csv")
csv_file.parent.mkdir(exist_ok=True)
# Write df to a CSV along with row+col indices
df.to_csv(
csv_file,
header=True,
index=True,
)
# Create a dictionary to specify the dtypes
dtype_spec = {}
for v in range_idx:
dtype_spec[v] = "float64"
# dtype_spec[f"{v}"] = "float64" # * Alternatively, convert to str, which works
# Reload the CSV
df_reload = pd.read_csv(
csv_file,
header=0,
index_col=0,
float_precision='round_trip',
dtype=dtype_spec,
)
```
### Issue Description
This may in part be my misunderstanding of how dtypes should be specified and the implicit conversion of the column index to str.
Notwithstanding that, the problem is when reading a CSV using `read_csv()` with a header row that consists of integers of the same sign and specifying the dtype with a dictionary that has integer keys: it fails with an `IndexError: list index out of range`.
### Traceback
<details>
```python-traceback
IndexError Traceback (most recent call last)
Cell In[106], [line 1](vscode-notebook-cell:?execution_count=106&line=1)
----> [1](vscode-notebook-cell:?execution_count=106&line=1) df_reload = pd.read_csv(
[2](vscode-notebook-cell:?execution_count=106&line=2) csv_file,
[3](vscode-notebook-cell:?execution_count=106&line=3) header=0,
[4](vscode-notebook-cell:?execution_count=106&line=4) index_col=0,
[5](vscode-notebook-cell:?execution_count=106&line=5) float_precision='round_trip',
[6](vscode-notebook-cell:?execution_count=106&line=6) dtype=dtype_spec,
[7](vscode-notebook-cell:?execution_count=106&line=7) )
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1026](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1026), in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
[1013](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1013) kwds_defaults = _refine_defaults_read(
[1014](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1014) dialect,
[1015](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1015) delimiter,
(...)
[1022](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1022) dtype_backend=dtype_backend,
[1023](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1023) )
[1024](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1024) kwds.update(kwds_defaults)
-> [1026](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1026) return _read(filepath_or_buffer, kwds)
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:626](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:626), in _read(filepath_or_buffer, kwds)
[623](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:623) return parser
[625](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:625) with parser:
--> [626](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:626) return parser.read(nrows)
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1923](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1923), in TextFileReader.read(self, nrows)
[1916](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1916) nrows = validate_integer("nrows", nrows)
[1917](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1917) try:
[1918](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1918) # error: "ParserBase" has no attribute "read"
[1919](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1919) (
[1920](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1920) index,
[1921](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1921) columns,
[1922](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1922) col_dict,
-> [1923](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1923) ) = self._engine.read( # type: ignore[attr-defined]
[1924](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1924) nrows
[1925](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1925) )
[1926](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1926) except Exception:
[1927](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1927) self.close()
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:333](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:333), in CParserWrapper.read(self, nrows)
[330](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:330) data = {k: v for k, (i, v) in zip(names, data_tups)}
[332](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:332) names, date_data = self._do_date_conversions(names, data)
--> [333](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:333) index, column_names = self._make_index(date_data, alldata, names)
[335](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/c_parser_wrapper.py:335) return index, column_names, date_data
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:372](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:372), in ParserBase._make_index(self, data, alldata, columns, indexnamerow)
[370](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:370) elif not self._has_complex_date_col:
[371](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:371) simple_index = self._get_simple_index(alldata, columns)
--> [372](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:372) index = self._agg_index(simple_index)
[373](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:373) elif self._has_complex_date_col:
[374](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:374) if not self._name_processed:
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:489](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:489), in ParserBase._agg_index(self, index, try_parse_dates)
[484](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:484) if col_name is not None:
[485](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:485) col_na_values, col_na_fvalues = _get_na_values(
[486](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:486) col_name, self.na_values, self.na_fvalues, self.keep_default_na
[487](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:487) )
--> [489](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:489) clean_dtypes = self._clean_mapping(self.dtype)
[491](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:491) cast_type = None
[492](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:492) index_converter = False
File [~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:455](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:455), in ParserBase._clean_mapping(self, mapping)
[453](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:453) for col, v in mapping.items():
[454](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:454) if isinstance(col, int) and col not in self.orig_names:
--> [455](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:455) col = self.orig_names[col]
[456](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:456) clean[col] = v
[457](https://file+.vscode-resource.vscode-cdn.net/home/peterma/exio_dev/repo/df_file_interchange/pandas_github_issues/~/miniconda3/envs/exio-datamanager/lib/python3.11/site-packages/pandas/io/parsers/base_parser.py:457) if isinstance(mapping, defaultdict):
IndexError: list index out of range
```
</details>
### Further discussion
The `IndexError` does not get raised if the original `RangeIndex` has a start and stop of different signs.
Neither does it occur if the `dtype=` is not specified in `read_csv()`'s arguments or if the keys in the dictionary of the dtypes are converted to str.
In all successful cases, the columns are an `Index` that contain the str representation of the integers. Does this mean they should always be integers for the purposes of `read_csv()` and that any conversion back to integer has to happen later? If so, the docs might benefit from being a little bit clearer.
### Expected Behavior
That `read_csv()` would succeed when specifying the dtypes with a dict with integer keys (of the same sign).
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.11.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-101-generic
Version : #111~20.04.1-Ubuntu SMP Mon Mar 11 15:44:43 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.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 : 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 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : 2024.2.0
fsspec : 2024.3.1
gcsfs : None
matplotlib : None
numba : None
numexpr : 2.9.0
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 15.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.1
zstandard : 0.22.0
tzdata : 2024.1
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"IO CSV",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take"
] |
2,198,804,547
| 57,943
|
PERF: DataFrame(dict) returns RangeIndex columns when possible
|
closed
| 2024-03-20T23:52:43
| 2024-03-25T18:24:43
| 2024-03-25T18:24:40
|
https://github.com/pandas-dev/pandas/pull/57943
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57943
|
https://github.com/pandas-dev/pandas/pull/57943
|
mroeschke
| 1
|
Discovered in https://github.com/pandas-dev/pandas/pull/57441
Also removed a seemingly unused `_sort_msg`
|
[
"Performance",
"Index",
"Constructors"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Going to merge, but happy to follow up if needed"
] |
2,198,798,022
| 57,942
|
BUG: index duplicates keys with non ascii chars
|
open
| 2024-03-20T23:46:18
| 2024-04-02T21:55:40
| null |
https://github.com/pandas-dev/pandas/issues/57942
| true
| null | null |
aquirin
| 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
When creating a dataframe with an index containing non-ascii chars, pandas is merging different keys into a single key.
```python
import pandas as pd
car = "é".encode("latin1").decode('utf8', 'surrogateescape')
data = [(1,"a-"+car, "x-"+car), (2, "b-"+car, "y-"+car)]
df = pd.DataFrame(data, columns=["c1", "c2", "c3"]).set_index(["c2", "c3"]).reset_index()
print(list(df["c3"]))
```
returns the same two keys:
```
['x-\udce9', 'x-\udce9']
```
Expected behavior:
```
['x-\udce9', 'y-\udce9']
```
Note that when using ascii chars, the behavior is correct:
```
import pandas as pd
car = "0"
data = [(1,"a-"+car, "x-"+car), (2, "b-"+car, "y-"+car)]
df = pd.DataFrame(data, columns=["c1", "c2", "c3"]).set_index(["c2", "c3"]).reset_index()
print(list(df["c3"]))
```
returns two different keys:
```
['x-0', 'y-0']
```
Note that the behavior is correct with non-ascii char and using a single column in the index:
```
car = "é".encode("latin1").decode('utf8', 'surrogateescape')
data = [(1,"a-"+car, "x-"+car), (2, "b-"+car, "y-"+car)]
df = pd.DataFrame(data, columns=["c1", "c2", "c3"]).set_index(["c3"]).reset_index()
print(list(df["c3"]))
```
returns two different keys:
```
['x-\udce9', 'y-\udce9']
```
### Issue Description
Creating a multi-index with non-ascii characters will not keep unique indices. Instead, keys are merged.
### Expected Behavior
Creating a multi-index with non-ascii characters should keep unique keys.
### Installed Versions
```
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.12.1.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.105
Version : #25556 SMP Sat Aug 28 02:13:34 CST 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : None
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
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 : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : 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
```
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Interesting. This seems to happen due to the malformed unicode characters, somewhere in the MultIndex calls a search for duplicates (in e.g., `['x-\\udce9', 'y-\\udce9']`) in the index happens via a cython hash map, this duplicate check does not happen for a single level index.\r\n\r\nIn the end this filters out a false positive duplicate, leading to the `['x-\\udce9', 'x-\\udce9']` index values in your example.\r\n\r\nI don't think changing the hashmap implementation is a good idea, but one could at least throw an exception if the user wants to put malformed unicode into a MultiIndex.",
"take"
] |
2,198,717,234
| 57,941
|
Remove versioneer / setuptools
|
closed
| 2024-03-20T22:44:07
| 2025-04-21T02:18:11
| 2025-04-21T02:18:11
|
https://github.com/pandas-dev/pandas/pull/57941
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57941
|
https://github.com/pandas-dev/pandas/pull/57941
|
WillAyd
| 5
|
trying to follow the lead set by https://github.com/numpy/numpy/pull/24196
@lithomas1
|
[
"Build",
"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 since there hasn't been much activity here, but feel free to reopen if/when you have time to revisit",
"@lithomas1 we are dropping setuptools support for 3.0 right?",
"I don't think the setuptools stuff is maintained anymore. (I just forgot to remove it)\r\n\r\nI think my main objection was to the versioneer stuff.",
"Not actively working on this - let's clear the queue"
] |
2,198,684,582
| 57,940
|
Backport PR #57029 on branch 2.2.x (DOC: Add `DataFrame.to_numpy` method)
|
closed
| 2024-03-20T22:22:40
| 2024-03-21T03:05:31
| 2024-03-21T03:05:31
|
https://github.com/pandas-dev/pandas/pull/57940
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57940
|
https://github.com/pandas-dev/pandas/pull/57940
|
meeseeksmachine
| 2
|
Backport PR #57029: DOC: Add `DataFrame.to_numpy` method
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@datapythonista \r\n\r\nJust to confirm - changes to docs should be backported if we want them to appear in 2.2.x, since the docs would be built from the 2.2.x branch?",
"Correct. It's only the website that is updated for every commit, and it's not useful to backport things. If you want the docs (sphinx) to contain this in 2.2.x besides in 3.0 you should backport it."
] |
2,198,659,824
| 57,939
|
DOC: #38067 add missing holiday observance rules
|
closed
| 2024-03-20T22:04:10
| 2024-03-22T00:05:40
| 2024-03-21T16:22:47
|
https://github.com/pandas-dev/pandas/pull/57939
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57939
|
https://github.com/pandas-dev/pandas/pull/57939
|
dontgoto
| 5
|
- [x] closes #38067
Updated docs with missing functions.
`weekend_to_monday` and `next_monday` do indeed do the exact same thing, and I agree that `next_monday` should be deprecated. Any thoughts?
|
[
"Docs"
] | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @dontgoto \r\n\r\n> weekend_to_monday and next_monday do indeed do the exact same thing, and I agree that next_monday should be deprecated. Any thoughts?\r\n\r\nYeah that makes sense to deprecate one of them if they are redundant",
"\n> Yeah that makes sense to deprecate one of them if they are redundant\n\nCan I put in a deprecation notice for 4.0.0? Or something like that?\n",
"My two cents here - I think \"weekend_to_monday\" is more accurate and descriptive, BUT if that's the one we keep it would make the most sense to also change the current \"previous_friday\" to \"weekend_to_friday\". I think it's clearest to keep the before/after and next/previous pairs as matched as possible ... so whichever gets deprecated, the pair should be either:\r\n\r\n* weekend_to_monday and weekend_to_friday\r\n* next_monday and previous_friday\r\n\r\nI prefer the former but wouldn't care too much if folks prefer the latter.",
"> * weekend_to_monday and weekend_to_friday\n> \n> * next_monday and previous_friday\n\nI find the former function names make more sense , because in the end they only shift when on a weekend, the latter implies that weekdays are also shifted, but they're not.\n\nOn the other hand, the latter ones have been in the user guide since forever, the former ones not.\n",
"@dontgoto I agree on the \"makes more sense\" ... even though the others have been documented I think that can be got around by deprecating them for a while before they go away"
] |
2,198,618,612
| 57,938
|
BUG: #29049 make holiday support offsets of offsets
|
closed
| 2024-03-20T21:39:57
| 2024-03-28T00:51:29
| 2024-03-28T00:08:54
|
https://github.com/pandas-dev/pandas/pull/57938
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57938
|
https://github.com/pandas-dev/pandas/pull/57938
|
dontgoto
| 2
|
- [x] closes #29049 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/v3.0.0.rst` file if fixing a bug or adding a new feature.
The summing of offsets is generally not possible, so the bug is fixed by simply flattening the asymmetric list of lists of offsets that one might easily end up with when defining holidays in relation to each other.
In theory it is possible to sum consecutive `DataOffset(n, weekday=None)` in the offset list, but I did not want to introduce even more complexity for such little gain.
Allowing asymmetrical lists of lists and their element dtypes as parameters is not very pretty, but the only solution to this problem that does not involve a major refactoring of the offset classes to allow representation of lists of offsets in a single composite offset e.g. ["TUE(1)", 3]. Summing of composite offsets is not possible because additions between weekday and regular day shift offsets are not associative, not distributive, and not commutative.
Another option would be to just enhance the error message given to the end user when trying to chain offsets in the manner shown in the issue and let them do the aggregation to a simple list of offsets themselves.
|
[
"Error Reporting"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @dontgoto ",
"Thank you for the input @mroeschke "
] |
2,198,612,293
| 57,937
|
BUG: Fix bad datetime64[ns] array to str dtype conversion in Series ctor which causes odd read_csv behaviour
|
closed
| 2024-03-20T21:35:33
| 2024-08-09T17:29:02
| 2024-08-09T17:29:01
|
https://github.com/pandas-dev/pandas/pull/57937
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57937
|
https://github.com/pandas-dev/pandas/pull/57937
|
ruimamaral
| 1
|
- [x] closes #57512
- [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.
Dates were being converted to nanosecond timestamps by numpy's `ndarray.astype` when converting an `M8[ns]` array to `object` during `ensure_string_array`. By wrapping the array with a `DatetimeArray` EA, we make sure that the dates get nicely converted in `ensure_string_array`.
Also added a check to `ensure_string_array` since it was converting na values disregarding the `convert_na_value` argument which caused missing dates to come out as `NaN` instead of `NaT`.
|
[
"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."
] |
2,198,576,999
| 57,936
|
ENH: Rename internal `DataFrame._append()`
|
closed
| 2024-03-20T21:11:54
| 2025-08-21T20:35:57
| 2025-08-21T20:35:57
|
https://github.com/pandas-dev/pandas/issues/57936
| true
| null | null |
Dr-Irv
| 12
|
### Feature Type
- [ ] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [X] Removing existing functionality in pandas
### Problem Description
This comment on the python discussion is interesting:
https://discuss.python.org/t/name-suggestions-for-attributeerrors-and-possibly-nameerrors-should-not-include-names-with-single-leading-underscores/48588
Example code (from main):
```python
>>> pd.DataFrame.append()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'DataFrame' has no attribute 'append'. Did you mean: '_append'?
```
What is happening is that the python interpreter is suggesting people use `DataFrame._append()` since we removed `append()`
### Feature Description
I think if we renamed the internal `_append()` to something like `_internal_append_do_not_use()` then maybe the interpreter wouldn't do that suggestion.
### Alternative Solutions
Bring back `DataFrame.append()` ???
### Additional Context
_No response_
|
[
"Enhancement",
"API Design",
"good first issue"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Is the proposal to do this with all internal methods that are similar to removed external names? I'm pretty opposed here - I think we should be free to chose internal names however we see fit.",
"> Is the proposal to do this with all internal methods that are similar to removed external names? I'm pretty opposed here - I think we should be free to chose internal names however we see fit.\r\n\r\nNo, but in this case, since we deprecated `DataFrame.append()`, we don't want people using `DataFrame._append()` as a workaround so we need to hide it better.\r\n",
"~~I'd me somewhat +1 - see this SO answer -https://stackoverflow.com/a/76449334/2214597\r\nwhile it does have a disclaimer, it's still pretty heavily upvoted.~~\r\n\r\nNvm - this SO question is already mentioned in the Discourse thread",
"I would definitely wait until https://github.com/python/cpython/issues/116871 has a resolution upstream before preemptively acting here. ",
"Isn’t a fix in cpython going to take ages? I’d be fine with changing the name on our end",
"No opposition if this is a unique case, but I'd like to not play whack-a-mole with the CPython interpreter and our internal names. That seems like too much code churn to me.",
"> No opposition if this is a unique case, but I'd like to not play whack-a-mole with the CPython interpreter and our internal names. That seems like too much code churn to me.\r\n\r\nI think the issue here is that we used to have `DataFrame.append()`, and now CPython is suggesting `DataFrame._append()`, so people are using it. If we remove methods in the future, then we should be aware of the similarity of the removed name with any \"private\" methods.",
"I'd also be fine with changing the name internally. Agree with @jbrockmendel we will be waiting a long time for the fix to make its way through different versions of Python.\r\n\r\nThere will be some breakage if people have decided to use this, but the longer we wait the worse that handcuff becomes",
"I'm fine doing it in this case, but maintain my opposition if more cases start popping up.",
"@jbrockmendel, I just made a PR to resolve this issue. Can you have a look?",
"/assign-me",
"Hey man,I would Love to work on this.Could you assign this issue to me?"
] |
2,198,570,965
| 57,935
|
Fix tagging within Dockerfile
|
closed
| 2024-03-20T21:07:30
| 2024-03-20T23:21:16
| 2024-03-20T23:21:09
|
https://github.com/pandas-dev/pandas/pull/57935
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57935
|
https://github.com/pandas-dev/pandas/pull/57935
|
WillAyd
| 1
|
When the user within a docker container does not match the user on the host machine (this is the case by default on Linux, not sure of all OSes) you cannot use git to inspect the worktree:
```sh
root@6de0debf3870:/home/pandas# git log
fatal: detected dubious ownership in repository at '/home/pandas'
To add an exception for this directory, call:
git config --global --add safe.directory /home/pandas
```
This prevents builds within docker from being tagged with the appropriate git revision
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @WillAyd "
] |
2,198,257,490
| 57,934
|
ENH: Add split method to DataFrame for flexible row-based partitioning
|
open
| 2024-03-20T18:25:04
| 2024-03-21T00:08:00
| null |
https://github.com/pandas-dev/pandas/issues/57934
| true
| null | null |
gclopton
| 3
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Currently, pandas does not provide a direct, built-in method to split a DataFrame into multiple smaller DataFrames based on a number of specified rows. Users seeking to partition a DataFrame into chunks either have to relay on workarounds using loops and manual indexing. Adding such a feature can save users time in scenarios requiring a lot of processing of data into smaller segments, such as batch processing, cross-validation in machine learning, or dividing data for parallel processing.
### Feature Description
The .split() method that I am proposing for pandas DataFrames would allow users to divide a DataFrame into smaller DataFrames based on a specified number of rows, with flexible handling of any remainder rows. The method can be described in detail as follows:
DataFrame.split(n, remainder='first' or 'last' or None)
**Parameters:**
1. n (integer) Would represent number of rows each resulting dataframe should contain
2. remainder (string, optional): Specifies how to handle remainder rows that do not fit evenly into the split. It accepts the following values:
2.1. 'first': Include the remainder rows in the first split DataFrame.
2.2. 'last': Include the remainder rows in the last split DataFrame.
2.3. 'None': If the DataFrame cannot be evenly split, raise an error. This is the default behavior.
**Pseudocode Description:**
def split_dataframe(df, n, remainder=None):
1.) Get total number of rows in the dataframe
2.) Check for divisibility of remainder is None:
2.1) If length is not perfectly divisible by n, raise an error.
3.) If the remainder is 'first':
3.1) Calculate the number of rows for the first split to include the remainder.
3.2) Split the DataFrame accordingly, adjusting subsequent splits to have n rows.
4.) If the remainder is 'last':
4.1) Split the DataFrame into partitions of n rows, except for the last partition
4.2) Calculate and append the last partition separately to include any remainder.
5.) Else, if the dataframe is perfectly divisible ( or if the remainder is None and the divisibility check has passed):
5.1.) Split the DataFrame into equal parts of n rows.
6) Return the list of split DataFrames.
Example of usage:
Say that we have a DataFrame consisting of 100 rows. Then we could split this DataFrame into a list of 10 dataframes:
split_dfs = df.split(10)
In another case, we have a dataframe consisting of 99 rows. Then we could split the dataframe into 10 dataframes, where the first DataFrame consists of 9 rows and the rest of the DataFrames have 10 rows.
split_dfs = df.split(9, remainder='first')
In the other case, we could split the DataFrame into a list of 10 DataFrames, where the last DataFrame consists of the remaining 9 rows.
### Alternative Solutions
One approach could involve splitting a DataFrame into smaller chunks using `numpy.array_split`. This function can divide an array or DataFrame into a specified number of parts and handle any remainders by evenly distributing them across splits. However there are limitations to this approach:
1.) The result is a list of numpy arrays, not DataFrames. So you lose the DataFrame context and metadata (like column names).
2.) It requires an additional step to convert these arrays back into DataFrames, adding more complexity.
We could also do manual looping and slicing. The problem is that it requires boilerplate code, which is error prone and inefficient especially when working with larger groups of people. This approach also lacks the simplicity and ease of use that a built-in method would provide.
There are also third-party libraries and packages for splitting DataFrames, such as dask.DataFrame or more_itertools.chunked. Though dask.dataframe allows for processing data into chunks, you still would have too many modifications to implement the functionality that I've described for df.split. more_itertools.chunked can be used to split an iterable into smaller iterables of a specified size, which can be applied to DataFrames. The problem is you need to convert chunks back into DataFrames. So it would be much simpler to have a built in df.split method.
### Additional Context
Practical use Cases: I've encountered several scenarios in data preprocessing for machine learning where batch processing of DataFrame chunks was necessary. Implementing a custom solution each time has not been ideal. I've also run into several situations where I've needed to split DataFrame into individual DataFrames for subsequent calculations on the partitioned data.
|
[
"Enhancement",
"Indexing",
"Needs Discussion"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I would like to contribute to the implementation of this feature, pending approval of the feature request.\r\n\r\nIn addition to the initial proposal, I believe enhancing the function to include an option to split along either axis would significantly increase its utility.\r\n\r\nDataFrame.split(n, axis=1, remainder='first', or 'last' or None) where axis=0 (split along rows) is the default.",
"Thanks for the request - it seems to me pandas provides a number of ways to split up existing DataFrames that can be utilized, I don't see this feature provided any more utility that isn't already possible. For example, if `chunker` is any iterable (e.g. `[0, 0, 0, 1, 1, 1]`), then one can do:\r\n\r\n [chunk for _, chunk in df.groupby(chunker)]\r\n\r\nIn addition, since pandas does not support multiple processes or distributed computing, it seems to me most common operations would benefit from not splitting up a DataFrame.",
"Thank you for the feedback, especially regarding your suggestion on using df.groupby(chunker) as a solution.\r\n\r\nI want to clarify the intent behind proposing this method for splitting DataFrames into smaller chunks.\r\n\r\nThe primary motivation is to enhance pandas with additional flexibility for handling large datasets, which would include scenarios such as:\r\n\r\n1.) stepwise processing or algorithms requiring partitioned input, where splitting a DataFrame into smaller segments is necessary.\r\n2.) Chunk-wise data export/import, simplifying the management of large datasets.\r\n3.) Memory constraints, where processing an entire dataset at once is not feasible.\r\n4.) External parallelization, where despite pandas not supporting distributed computing, processing smaller, independent chunks in parallel using external tools can significantly speed up operations.\r\n\r\nThe suggested method of using df.groupby(chunker) is a great solution for segmenting a DataFrame based on predefined criteria but differs from a bit from what I was suggesting:\r\n\r\n1.) Uniformity and simplicity: The df.split() method aims to uniformly split a DataFrame into chunks based on a specified size, offering a straightforward solution when division based on size rather than data-driven groupings is important. This especially useful for tasks requiring equal-sized partions and for users seeking a simple, direct way to divide their dataframe.\r\n2.) Ease of use: For those aiming to divide DataFrame into smaller parts without complex grouping criteria, the 'df.split()' method simplifies the process by requiring only the desired chunk size as input and optionally how to handle remainders.\r\n\r\nI am thinking of something roughly along the lines of this:\r\n\r\n\r\n\r\n```Python\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndef split_dataframe(df, n, remainder=None):\r\n # Get number of rows in DataFrame\r\n total_rows = len(df)\r\n \r\n # Initialize list to hold the split DataFrames\r\n dfs = []\r\n \r\n # Calculate the number of DataFrames\r\n num_dfs = total_rows // n\r\n extra_rows = total_rows % n\r\n \r\n if remainder == 'first' and extra_rows > 0:\r\n # Size of first chunk is equal to remainder. Remaining chunks are same size.\r\n dfs.append(df.iloc[:n + extra_rows])\r\n start_idx = n + extra_rows\r\n for _ in range(1, num_dfs):\r\n dfs.append(df.iloc[start_idx:start_idx + n])\r\n start_idx += n\r\n elif remainder == 'last' and extra_rows > 0:\r\n # Last chunk consists of remaining rows. The other chunks are the same size.\r\n for i in range(num_dfs):\r\n dfs.append(df.iloc[i*n:(i+1)*n])\r\n # Add extra rows to the last chunk\r\n dfs.append(df.iloc[num_dfs*n:])\r\n elif remainder == 'spread':\r\n # Evenly spread extra rows across the first few chunks\r\n for i in range(num_dfs + (1 if extra_rows > 0 else 0)):\r\n size = n + (1 if i < extra_rows else 0)\r\n dfs.append(df.iloc[i*size:min((i+1)*size, total_rows)])\r\n else:\r\n if extra_rows > 0:\r\n raise ValueError(f\"DataFrame length is not perfectly divisible by {n}. Please specify the 'remainder' parameter.\")\r\n # If remainder is None and the DataFrame is perfectly divisible\r\n for i in range(num_dfs):\r\n dfs.append(df.iloc[i*n:(i+1)*n])\r\n \r\n return dfs\r\n\r\n\r\n# Example usage\r\ndf1 = pd.DataFrame(np.arange(100), columns=['Column'])\r\ndf2 = pd.DataFrame(np.arange(99), columns=['Column'])\r\ndfs_equal = split_dataframe(df1, 10) # Uses the default 'equal' distribution\r\ndfs_first = split_dataframe(df2, 10, remainder='first')\r\ndfs_last = split_dataframe(df2, 10, remainder='last')\r\ndfs_spread = split_dataframe(df2, 10, remainder='spread')\r\n```\r\n\r\n"
] |
2,198,204,091
| 57,933
|
REF: Clean up concat statefullness and validation
|
closed
| 2024-03-20T17:59:59
| 2024-03-20T21:00:47
| 2024-03-20T21:00:44
|
https://github.com/pandas-dev/pandas/pull/57933
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57933
|
https://github.com/pandas-dev/pandas/pull/57933
|
mroeschke
| 0
|
* Removed an additional pass over `objs` during validation
* Removed methods off of `_Concatenator` that did not rely on `self`
|
[
"Refactor",
"Reshaping"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,198,074,996
| 57,932
|
RFE: move away from deprecated `html5lib`
|
closed
| 2024-03-20T17:07:20
| 2024-08-27T16:56:51
| 2024-08-27T16:56:51
|
https://github.com/pandas-dev/pandas/issues/57932
| true
| null | null |
kloczek
| 3
|
### Feature Type
- [ ] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [X] Removing existing functionality in pandas
### Problem Description
**Is your feature request related to a problem? Please describe.**
It would be nice tu cut tail of some legacy modules decencies.
One of those modules is `html5lib`.
`pandas` only optionally depends on `html5lib` but I think that ATM even that optional dependency can be removed like it has been done ~2 years ago wit pip https://github.com/pypa/pip/pull/11259
### Feature Description
Cut tail of some legacy modules dependenies.
### Alternative Solutions
N/A
### Additional Context
_No response_
|
[
"IO HTML",
"Deprecate",
"Needs Info",
"Dependencies"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Can you elaborate more?\r\n\r\nHas Python's stdlib ``html.parser`` caught up to ``html5lib``?\r\n\r\nLooking at bs4's docs, it looks like html5lib is more lenient than the stdlib/lxml (which might be one reason people want to use it)\r\n\r\n<img width=\"822\" alt=\"image\" src=\"https://github.com/pandas-dev/pandas/assets/47963215/e48bb106-ef40-42a8-80a5-ec141e06befc\">",
"Can you clarify more clearly what is supposed to be done in this issue ?\r\n",
"Thanks for the suggestion, but it appears this suggestion hasn't gained much traction and html5lib still provides some utility for users so closing"
] |
2,197,946,051
| 57,931
|
DOC: Doc page for DataFrame.to_numpy missing only for 2.2
|
closed
| 2024-03-20T16:13:40
| 2024-03-21T03:05:37
| 2024-03-21T03:05:37
|
https://github.com/pandas-dev/pandas/issues/57931
| true
| null | null |
cjerdonek
| 4
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html (scroll to `to_numpy` below and see that there is no hyperlink)
### Documentation problem
There is no doc page for `DataFrame.to_numpy` for 2.2 (but there is for 2.1 and dev). One way to see this is to go to the page for 2.1, and then use the pull-down at the top to toggle to 2.2: https://pandas.pydata.org/pandas-docs/version/2.1/reference/api/pandas.DataFrame.to_numpy.html
It results in a 404. This also causes 404's e.g. on StackOverflow posts that link to the page.
### Suggested fix for documentation
It looks like this PR needs to be backported: https://github.com/pandas-dev/pandas/pull/57029
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take",
"I opened #57940 to do the backport, but I'm not sure if it's actually necessary.\r\n\r\n(I've asked another maintainer on there)\r\n\r\n@yukikitayama \r\nSorry for barging in here, I just wanted to do this personally since backporting is usually done with our automated backport bot - and is tricky/unnecessary to do by hand.\r\n\r\nIf you want, I can help you find another issue to work on.",
"> I opened #57940 to do the backport, but I'm not sure if it's actually necessary.\r\n> \r\n> (I've asked another maintainer on there)\r\n\r\nI will just say, as an end-user, it was confusing that the doc page was 404-ing when toggling the API page to the stable release. It made it look like the method was removed in 2.2.\r\n\r\n",
"@lithomas1 Sure, I will unassign me."
] |
2,197,677,069
| 57,930
|
BUG: pd.read_csv error when parse_dates used on index_col for dtype_backend="pyarrow"
|
closed
| 2024-03-20T14:26:22
| 2025-06-03T08:29:09
| 2025-06-03T08:29:08
|
https://github.com/pandas-dev/pandas/issues/57930
| true
| null | null |
MCRE-BE
| 12
|
### 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
df = pd.DataFrame({'a': [1], 'b': [datetime.datetime.now()]})
df.to_csv('test.csv', index=None)
a = pd.read_csv(
'test.csv',
parse_dates=['b'],
index_col="b",
dtype_backend="pyarrow",
engine="pyarrow",
)
pd.read_csv(
'test.csv',
parse_dates=['b'],
index_col="b",
)
```
the following fails :
```python
pd.read_csv(
'test.csv',
parse_dates=['b'],
index_col="b",
dtype_backend="pyarrow",
)
```
### Issue Description
returns
`ValueError: not all elements from date_cols are numpy arrays`
<details>
ValueError Traceback (most recent call last)
Cell In[78], [line 1](vscode-notebook-cell:?execution_count=78&line=1)
----> [1](vscode-notebook-cell:?execution_count=78&line=1) pd.read_csv(
[2](vscode-notebook-cell:?execution_count=78&line=2) 'test.csv',
[3](vscode-notebook-cell:?execution_count=78&line=3) parse_dates=['b'],
[4](vscode-notebook-cell:?execution_count=78&line=4) index_col="b",
[5](vscode-notebook-cell:?execution_count=78&line=5) dtype_backend="pyarrow",
[6](vscode-notebook-cell:?execution_count=78&line=6) )
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\readers.py:948](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:948), in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
[935](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:935) kwds_defaults = _refine_defaults_read(
[936](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:936) dialect,
[937](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:937) delimiter,
(...)
[944](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:944) dtype_backend=dtype_backend,
[945](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:945) )
[946](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:946) kwds.update(kwds_defaults)
--> [948](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:948) return _read(filepath_or_buffer, kwds)
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\readers.py:617](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:617), in _read(filepath_or_buffer, kwds)
[614](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:614) return parser
[616](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:616) with parser:
--> [617](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:617) return parser.read(nrows)
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\readers.py:1748](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1748), in TextFileReader.read(self, nrows)
[1741](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1741) nrows = validate_integer("nrows", nrows)
[1742](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1742) try:
[1743](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1743) # error: "ParserBase" has no attribute "read"
[1744](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1744) (
[1745](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1745) index,
[1746](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1746) columns,
[1747](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1747) col_dict,
-> [1748](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1748) ) = self._engine.read( # type: ignore[attr-defined]
[1749](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1749) nrows
[1750](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1750) )
[1751](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1751) except Exception:
[1752](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/readers.py:1752) self.close()
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\c_parser_wrapper.py:333](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/c_parser_wrapper.py:333), in CParserWrapper.read(self, nrows)
[330](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/c_parser_wrapper.py:330) data = {k: v for k, (i, v) in zip(names, data_tups)}
[332](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/c_parser_wrapper.py:332) names, date_data = self._do_date_conversions(names, data)
--> [333](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/c_parser_wrapper.py:333) index, column_names = self._make_index(date_data, alldata, names)
[335](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/c_parser_wrapper.py:335) return index, column_names, date_data
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\base_parser.py:371](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:371), in ParserBase._make_index(self, data, alldata, columns, indexnamerow)
[369](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:369) elif not self._has_complex_date_col:
[370](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:370) simple_index = self._get_simple_index(alldata, columns)
--> [371](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:371) index = self._agg_index(simple_index)
[372](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:372) elif self._has_complex_date_col:
[373](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:373) if not self._name_processed:
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\base_parser.py:468](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:468), in ParserBase._agg_index(self, index, try_parse_dates)
[466](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:466) for i, arr in enumerate(index):
[467](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:467) if try_parse_dates and self._should_parse_dates(i):
--> [468](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:468) arr = self._date_conv(
[469](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:469) arr,
[470](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:470) col=self.index_names[i] if self.index_names is not None else None,
[471](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:471) )
[473](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:473) if self.na_filter:
[474](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:474) col_na_values = self.na_values
File [c:\ProgramData\miniconda3\envs\DLL_Forecast\Lib\site-packages\pandas\io\parsers\base_parser.py:1142](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1142), in _make_date_converter.<locals>.converter(col, *date_cols)
[1139](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1139) return date_cols[0]
[1141](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1141) if date_parser is lib.no_default:
-> [1142](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1142) strs = parsing.concat_date_cols(date_cols)
[1143](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1143) date_fmt = (
[1144](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1144) date_format.get(col) if isinstance(date_format, dict) else date_format
[1145](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1145) )
[1147](file:///C:/ProgramData/miniconda3/envs/DLL_Forecast/Lib/site-packages/pandas/io/parsers/base_parser.py:1147) with warnings.catch_warnings():
File parsing.pyx:1155, in pandas._libs.tslibs.parsing.concat_date_cols()
ValueError: not all elements from date_cols are numpy arrays
</details>
### Expected Behavior
As with dtype_backend="numpy_nullable" the index column should be parsed correctly.
It works with the following set-up, so likely an issue with the C parser (?)
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a671b5a8bf5dd13fb19f0e88edc679bc9e15c673
python : 3.11.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : AMD64 Family 23 Model 24 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Dutch_Belgium.1252
pandas : 2.1.4
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0
setuptools : 69.2.0
pip : 24.0
Cython : None
pytest : 8.1.1
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : 3.1.9
lxml.etree : 5.1.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.22.2
pandas_datareader : None
bs4 : 4.12.3
bottleneck : 1.3.8
dataframe-api-compat: None
fastparquet : None
fsspec : 2024.3.0
gcsfs : None
matplotlib : 3.8.3
numba : 0.59.0
numexpr : 2.9.0
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
pyxlsb : 1.0.10
s3fs : None
scipy : 1.12.0
sqlalchemy : 2.0.28
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.1
zstandard : 0.22.0
tzdata : 2024.1
qtpy : 2.4.1
pyqt5 : None
</details>
|
[
"Bug",
"Datetime",
"IO CSV",
"ExtensionArray",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hm, this works on main for me.\r\n\r\nCan you try a newer version of pandas (e.g. 2.2.1)?",
"@lithomas1 I could reproduce this error on pandas version 2.3.0\r\n",
"@lithomas1 Do you need any help in other triaging other defects/ bugs? ",
"My bad, forgot to run the second command.\r\n\r\nNice catch.\r\n\r\nLooks like ``parsing.concat_date_cols`` (which concatenates date_cols) is getting passed an ArrowExtensionArray.\r\n\r\nThat behavior is deprecated (to be removed in 3.0), so it should be easy to make this work for 3.0.\r\n",
"> Can you try a newer version of pandas (e.g. 2.2.1)?\r\n\r\nMy bad. I should have checked and not have assumed the kernel I was using used the latest version. Thanks for the work.",
"FWIW I was getting the same issue on pandas 2.2 when using \"numpy_nullable\" as the backend, but this appears to be working on pandas version 3.0.0.dev0+811.gcc427ec305? ",
"Still present in 2.2.2\r\n\r\n<details>\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit : d9cdd2ee5a58015ef6f4d15c7226110c9aab8140\r\npython : 3.12.5.final.0\r\npython-bits : 64\r\nOS : Windows\r\nOS-release : 10\r\nVersion : 10.0.19042\r\nmachine : AMD64\r\nprocessor : AMD64 Family 23 Model 24 Stepping 1, AuthenticAMD\r\nbyteorder : little\r\nLC_ALL : None\r\nLANG : None\r\nLOCALE : Dutch_Belgium.1252\r\n\r\npandas : 2.2.2\r\nnumpy : 2.0.1\r\npytz : 2024.1\r\ndateutil : 2.9.0\r\nsetuptools : 72.2.0\r\npip : 24.2\r\nCython : None\r\npytest : 8.3.2\r\nhypothesis : None\r\nsphinx : 7.4.7\r\nblosc : None\r\nfeather : None\r\nxlsxwriter : 3.2.0\r\nlxml.etree : 5.3.0\r\nhtml5lib : 1.1\r\npymysql : None\r\npsycopg2 : None\r\njinja2 : 3.1.4\r\nIPython : 8.21.0\r\npandas_datareader : None\r\nadbc-driver-postgresql: None\r\nadbc-driver-sqlite : None\r\nbs4 : 4.12.3\r\nbottleneck : 1.4.0\r\ndataframe-api-compat : None\r\nfastparquet : None\r\nfsspec : 2024.6.1\r\ngcsfs : None\r\nmatplotlib : 3.9.1\r\nnumba : 0.60.0\r\nnumexpr : 2.10.0\r\nodfpy : None\r\nopenpyxl : 3.1.5\r\npandas_gbq : None\r\npyarrow : 17.0.0\r\npyreadstat : None\r\npython-calamine : None\r\npyxlsb : 1.0.10\r\ns3fs : None\r\nscipy : 1.14.1\r\nsqlalchemy : None\r\ntables : None\r\ntabulate : 0.9.0\r\nxarray : None\r\nxlrd : 2.0.1\r\nzstandard : 0.23.0\r\ntzdata : 2024.1\r\nqtpy : 2.4.1\r\npyqt5 : None\r\n</details>",
"Since this works on main I think it would be good to first and foremost add a test to the repository, so that we can prevent regression in the future. Would anyone be willing to contribute that?\r\n\r\nAs far as fixing on the 2.x series...we unfortunately don't have too many releases left for that. It is possible we can fix this on a 2.3 bugfix branch, although that may be rather challenging to fix given we don't have the problem on our main development branch",
"@WillAyd : I can try and write a test for that in the next couple of days. Just have to dive into the requirements.\nThanks for confirming that ir works on `main`",
"@WillAyd : first try is here. Let me know if it's in the right spot before I make a pull request.\r\nhttps://github.com/pandas-dev/pandas/commit/6e83c4b8114539701c17b7ced0973ac32356eaf8",
"Thanks! I think that's a great start. There's a few things to tweak but if you push up as a PR first we can give the feedback directly there",
"I'll do that ! "
] |
2,197,532,723
| 57,929
|
DOC: fix minor typos and grammar missing_data.rst
|
closed
| 2024-03-20T13:26:20
| 2024-03-21T17:17:04
| 2024-03-20T16:32:14
|
https://github.com/pandas-dev/pandas/pull/57929
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57929
|
https://github.com/pandas-dev/pandas/pull/57929
|
hutch3232
| 1
|
I did not make a corresponding issue for this minor improvement.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @hutch3232 "
] |
2,197,529,892
| 57,928
|
ENH: Improve error message when specifying dtype="float32[pyarrow]" while PyArrow is not installed
|
closed
| 2024-03-20T13:24:55
| 2024-11-27T18:46:44
| 2024-11-27T18:46:43
|
https://github.com/pandas-dev/pandas/issues/57928
| true
| null | null |
wanglc02
| 2
|
### Feature Type
- [ ] Adding new functionality to pandas
- [X] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
If PyArrow is not installed properly, running the following code snippet from the [User Guide](https://pandas.pydata.org/docs/user_guide/pyarrow.html):
`ser = pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]")`
will result in:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../python3.12/site-packages/pandas/core/series.py", line 493, in __init__
dtype = self._validate_dtype(dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".../python3.12/site-packages/pandas/core/generic.py", line 515, in _validate_dtype
dtype = pandas_dtype(dtype)
^^^^^^^^^^^^^^^^^^^
File ".../python3.12/site-packages/pandas/core/dtypes/common.py", line 1624, in pandas_dtype
result = registry.find(dtype)
^^^^^^^^^^^^^^^^^^^^
File ".../python3.12/site-packages/pandas/core/dtypes/base.py", line 576, in find
return dtype_type.construct_from_string(dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".../python3.12/site-packages/pandas/core/dtypes/dtypes.py", line 2251, in construct_from_string
pa_dtype = pa.type_for_alias(base_type)
^^
NameError: name 'pa' is not defined
```
which is not very informative.
### Feature Description
We can improve the error message by letting the user know that there is something wrong regarding the installation of PyArrow, especially when the user believes that he/she has installed PyArrow, but actually installed it in a wrong location or installed an outdated version.
### Alternative Solutions
Catch the `NameError` and raise another `ImportError` from it describing what happened.
### Additional Context
This conforms to the description in [Installation Guide](https://pandas.pydata.org/docs/getting_started/install.html#optional-dependencies): If the optional dependency is not installed, pandas will raise an `ImportError` when the method requiring that dependency is called.
|
[
"Enhancement",
"Error Reporting",
"Dependencies",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"PRs for this would be greatly appreciated!",
"take"
] |
2,197,337,458
| 57,927
|
DateOffset add to ndarray
|
closed
| 2024-03-20T11:48:44
| 2024-04-29T04:02:58
| 2024-04-29T04:02:58
|
https://github.com/pandas-dev/pandas/pull/57927
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57927
|
https://github.com/pandas-dev/pandas/pull/57927
|
hottwaj
| 2
|
This is WIP to implement changes needed to allow DateOffset subclasses e.g. MonthEnd to be directly added to numpy.ndarray, e.g. `numpy.array(['2022-01-01'], dtype='datetime64') + pandas.offsets.MonthEnd(1)`
I need some guidance on:
- cython mechanism to check that passed ndarray is datetime64 dtype i.e.cython equivalent of pandas.api.types.is_datetime64_any_dtype
- how to approach adding unit tests? two ideas I have
- somehow duplicate existing offset tests but run them on ndarray instead of Series? looks on the surface to be more difficult to implement
- apply a set of offsets to a predefined Series and ndarray equivalent, and then check results match
Many thanks!
- [x] closes #47014
- [ ] [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.
|
[
"Numeric Operations",
"Frequency",
"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.",
"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."
] |
2,196,900,775
| 57,925
|
BUG: `NotImplementedError |S8` when converting categories from object containing byte arrays to bytes
|
open
| 2024-03-20T08:02:58
| 2024-03-20T08:03:42
| null |
https://github.com/pandas-dev/pandas/issues/57925
| true
| null | null |
smarie
| 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
# Imagine we receive byte array like this but much larger (e.g. 10M rows of 128 bit uuids in bytes)
# Unfortunately not already converted to bytes dtype for some reason
a = pd.Series([b"fdhijklm", b"fdhijklm", b"fdaijklm"], dtype=object)
# We want to first convert to categorical for efficiency reasons (it is faster to first detect unique values and then convert them)
b = a.astype("category")
# Then we would like to convert the categories to proper numpy byte array for memory reasons
new_cats = b.cat.categories.astype(bytes) # <-- NotImplementedError: |S8
```
### Issue Description
See comments in the example
### Expected Behavior
No error: new_cats would be created. Hopefully that would allow to convert the previous categories into the new ones, so that the categories take less space in memory.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.9.13.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : AMD64 Family 25 Model 68 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 67.1.0
pip : 23.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : 6.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 5.1.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.18.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : 2024.2.0
fsspec : 2024.2.0
gcsfs : None
matplotlib : 3.8.3
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 : 1.12.0
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
|
[] |
2,196,560,997
| 57,924
|
DEPR: Execute all csv deprecations
|
closed
| 2024-03-20T02:38:19
| 2024-04-23T17:53:57
| 2024-04-23T17:53:57
|
https://github.com/pandas-dev/pandas/pull/57924
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57924
|
https://github.com/pandas-dev/pandas/pull/57924
|
lithomas1
| 3
|
- [ ] 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.
|
[
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Looks like this PR's gotten too large, esp. with date_parser removal.\r\n\r\nI'll split it up.",
"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.",
"Since it looks like some of these have been chipped away already, going to close. Feel free to open PRs to enforce the deprecations if you have time"
] |
2,196,297,839
| 57,923
|
BUG: Inconsistent implicit conversion of 1-arrays inside data frames
|
open
| 2024-03-20T00:03:07
| 2025-08-17T02:20:36
| null |
https://github.com/pandas-dev/pandas/issues/57923
| true
| null | null |
erezinman
| 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
import pandas as pd
import numpy as np
df = pd.DataFrame(
[None, None], dtype=object
)
df.at[0, 0] = np.arange(2)
df.at[1, 0] = np.arange(1)
print(df)
# 0
# 0 [0, 1]
# 1 0
```
### Issue Description
There's an inconsistent handling of 1D arrays in pandas: arrays of length 1 are converted to scalars while arrays of other shapes aren't. To make things worse, pandas can be tricked into holding 1-arrays using:
```python
df.applymap(lambda x: x.reshape(-1))
print(df)
# 0
# 0 [0, 1]
# 1 [0]
```
The cause of this error in the code is in the file `core/internals/base.py` (which doesn't exist in the `main` branch):
```
def setitem_inplace(self, indexer, value, warn: bool = True) -> None:
# ....
if isinstance(value, np.ndarray) and value.ndim == 1 and len(value) == 1:
# NumPy 1.25 deprecation: https://github.com/numpy/numpy/pull/10615
value = value[0, ...]
```
This comment does appear in a few places in the `master` branch so it should happen there as well.
A possible fix is to check the case when the `dtype` of the block is `object`. It is logical that in such cases, implicit conversions should be avoided.
### Expected Behavior
Setting the values should be consistent.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : fd3f57170aa1af588ba877e8e28c158a20a4886d
python : 3.9.18.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-75-generic
Version : #82~20.04.1-Ubuntu SMP Wed Jun 7 19:37:37 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.0
numpy : 1.26.3
pytz : 2023.4
dateutil : 2.8.2
setuptools : 69.1.1
pip : 24.0
Cython : 3.0.8
pytest : 7.4.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.12.2
gcsfs : 2023.12.2post1
matplotlib : None
numba : None
numexpr : 2.9.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.12.0
sqlalchemy : 2.0.25
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Needs Triage",
"Nested Data"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"take"
] |
2,196,198,436
| 57,922
|
BUG: NamedTuples do no match tuples in pandas.Index
|
closed
| 2024-03-19T23:00:28
| 2024-07-22T22:47:12
| 2024-07-22T22:47:12
|
https://github.com/pandas-dev/pandas/issues/57922
| true
| null | null |
Apteryx0
| 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
$ ipython
Python 3.11.8 (main, Mar 19 2024, 17:46:15) [GCC 11.4.0]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.22.2 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas
In [2]: import collections
In [3]: MyNamedTuple = collections.namedtuple("MyNamedTuple", "id sub_id")
In [4]: first = MyNamedTuple('identity','1234')
In [5]: idx = pandas.Index([('identity','1234')])
In [6]: idx
Out[6]:
MultiIndex([('identity', '1234')],
)
In [7]: idx2 = idx.to_flat_index()
In [8]: idx2
Out[8]: Index([('identity', '1234')], dtype='object')
In [9]: first in idx
Out[9]: True
In [10]: first in idx2
Out[10]: False
In [11]: first in idx2.to_list()
Out[11]: True
In [12]: first == idx2[0]
Out[12]: True
In [13]: pandas.__version__
Out[13]: '2.2.1'
In [14]: idx.get_loc(first)
Out[14]: 0
In [15]: idx2.get_loc(first)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/.pyenv/versions/3.11.8/envs/test-venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
3804 try:
-> 3805 return self._engine.get_loc(casted_key)
3806 except KeyError as err:
File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()
File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()
File pandas/_libs/hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item()
File pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: MyNamedTuple(id='identity', sub_id='1234')
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
Cell In[15], line 1
----> 1 idx2.get_loc(first)
File ~/.pyenv/versions/3.11.8/envs/test-venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
3807 if isinstance(casted_key, slice) or (
3808 isinstance(casted_key, abc.Iterable)
3809 and any(isinstance(x, slice) for x in casted_key)
3810 ):
3811 raise InvalidIndexError(key)
-> 3812 raise KeyError(key) from err
3813 except TypeError:
3814 # If we have a listlike key, _check_indexing_error will raise
3815 # InvalidIndexError. Otherwise we fall through and re-raise
3816 # the TypeError.
3817 self._check_indexing_error(key)
KeyError: MyNamedTuple(id='identity', sub_id='1234')
In [16]:
```
### Issue Description
Upgraded from pandas 1.2.5 to pandas 1.3.5 and noticed that I was unable to reference columns in a dataframe with column labels that were tuples via a NamedTuple, i.e. KeyError. Grabbed the latest pandas and reduced the issue down to pandas.Index.get_loc - though it works in the case where I leave the Index as a MultiIndex.
Note: I have seen the code work in about 25% of cases, so if you see it succeed please try again
### Expected Behavior
NamedTuples should match regular tuples as they do elsewhere in python (as illustrated by the fact that they match when one does `idx.to_list()`)
### Installed Versions
<details>
In [16]: pandas.show_versions()
/home/russellm/.pyenv/versions/3.11.8/envs/test-venv/lib/python3.11/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.11.8.final.0
python-bits : 64
OS : Linux
OS-release : 6.5.0-21-generic
Version : #21~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 9 13:32:52 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 65.5.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.22.2
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 : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : 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
In [17]:
</details>
|
[
"Bug",
"Indexing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"FWIW\r\n\r\n```\r\nIn [20]: idx2._engine\r\nOut[20]: <pandas._libs.index.ObjectEngine at 0x7f071ddcab00>\r\n\r\nIn [21]: idx2._engine.values\r\nOut[21]: array([('identity', '1234')], dtype=object)\r\n\r\nIn [22]: first in idx2._engine.values\r\nOut[22]: False\r\n\r\nIn [23]: first == idx2._engine.values[0]\r\nOut[23]: True\r\n\r\nIn [24]: hash(first)\r\nOut[24]: 5766037510587733218\r\n\r\nIn [25]: hash(idx2._engine.values[0])\r\nOut[25]: 5766037510587733218\r\n\r\nIn [26]: \r\n\r\n```\r\n",
"Happens on main as well, including the non deterministic success in a minority of attempts.",
"Thanks for the report. In general you will find very little support for containers as elements of an index or columns. \r\n\r\nRelated: https://github.com/pandas-dev/pandas/pull/57004#issuecomment-1906984802\r\n\r\n",
"Hi, my partner (GitHub: mwanink) and I would like to work on this issue.",
"take",
"take"
] |
2,196,184,282
| 57,921
|
BUG: Offsets of offsets in holiday raise type error during usage
|
closed
| 2024-03-19T22:50:10
| 2024-03-20T11:15:16
| 2024-03-20T11:15:16
|
https://github.com/pandas-dev/pandas/pull/57921
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57921
|
https://github.com/pandas-dev/pandas/pull/57921
|
dontgoto
| 0
|
- [x] closes #29049
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/v3.0.0.rst` file if fixing a bug or adding a new feature.
Still needs some work on the typing since I am touching things that have been working implicitly since forever, but that mypi does not find acceptable. Type annotations are hard to get consistent here without changing the behaviour of `Holiday` and without ugly workarounds.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,196,157,622
| 57,920
|
CLN: Remove unused code
|
closed
| 2024-03-19T22:30:25
| 2024-06-27T07:17:39
| 2024-03-20T00:23:14
|
https://github.com/pandas-dev/pandas/pull/57920
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57920
|
https://github.com/pandas-dev/pandas/pull/57920
|
tqa236
| 1
|
Some more unused code. Most of them are not detected by `vulture` but only visible on the editor through Pylance, so I'm not quite sure how to detect them systematically.
|
[
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @tqa236 "
] |
2,196,137,202
| 57,919
|
Remove Cython warnings
|
closed
| 2024-03-19T22:19:43
| 2024-03-20T00:22:35
| 2024-03-20T00:22:29
|
https://github.com/pandas-dev/pandas/pull/57919
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57919
|
https://github.com/pandas-dev/pandas/pull/57919
|
WillAyd
| 1
|
warning: /Users/runner/work/pandas/pandas/pandas/_libs/tslibs/util.pxd:188:38: noexcept clause is ignored for function returning Python object
warning: /Users/runner/work/pandas/pandas/pandas/_libs/tslibs/util.pxd:193:40: noexcept clause is ignored for function returning Python object
|
[
"Internals"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @WillAyd "
] |
2,196,089,946
| 57,918
|
STYLE: Detect unnecessary pylint ignore
|
closed
| 2024-03-19T21:52:17
| 2024-03-19T22:27:28
| 2024-03-19T22:20:15
|
https://github.com/pandas-dev/pandas/pull/57918
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57918
|
https://github.com/pandas-dev/pandas/pull/57918
|
tqa236
| 0
|
Enable `pylint`'s `I0021`/`useless-suppression` ([doc](https://pylint.pycqa.org/en/latest/user_guide/messages/information/useless-suppression.html))
This should make it easier to recognize how many `pylint` rules that `ruff` is still missing.
|
[
"Code Style"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,195,803,216
| 57,917
|
DOC: Update docs with the use of meson instead of setup.py
|
closed
| 2024-03-19T19:22:14
| 2024-03-22T15:11:13
| 2024-03-22T14:57:06
|
https://github.com/pandas-dev/pandas/pull/57917
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57917
|
https://github.com/pandas-dev/pandas/pull/57917
|
DeaMariaLeon
| 3
|
- [ ] 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
- [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.
Not sure if this is still needed: https://github.com/pandas-dev/pandas/blob/main/gitpod/gitpod.Dockerfile#L38C5-L38C45
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I would go ahead and update the gitpod as well - no reason to still be using setuptools anywhere in our code base",
"I think that the file [`gitpod.Dockerfile`](https://github.com/pandas-dev/pandas/blob/main/gitpod/gitpod.Dockerfile#L38C5-L38C45) is not being used. Since it's not being called here: https://github.com/pandas-dev/pandas/blob/main/.gitpod.yml#L9C3-L9C19 (but of course, I could be wrong). \r\n\r\nPlus it seems that there is some pending work for gitpod: https://github.com/pandas-dev/pandas/pull/54046\r\n\r\nMaybe updating gitpod could be done on a separate PR? \r\n\r\n ",
"Thanks @DeaMariaLeon "
] |
2,195,750,063
| 57,916
|
DOC: Getting started tutorials css adjustments
|
closed
| 2024-03-19T18:54:56
| 2024-03-21T08:09:40
| 2024-03-20T16:39:36
|
https://github.com/pandas-dev/pandas/pull/57916
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57916
|
https://github.com/pandas-dev/pandas/pull/57916
|
SilvestriStefano
| 3
|
- [x] closes #57912
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
I implemented what I suggested in the issue.
|
[
"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/57916/",
"Thanks @SilvestriStefano. Agreed looks a lot better"
] |
2,195,747,702
| 57,915
|
BUG: pretty print all Mappings, not just dicts
|
closed
| 2024-03-19T18:53:53
| 2024-03-20T00:23:37
| 2024-03-20T00:23:31
|
https://github.com/pandas-dev/pandas/pull/57915
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57915
|
https://github.com/pandas-dev/pandas/pull/57915
|
NickCrews
| 2
|
This was discovered in https://github.com/ibis-project/ibis/issues/8687
Due to a strict `isinstance(x, dict)` check, custom mapping types would instead get treated as mere iterables, so only the keys would get printed as a tuple:
```python
import pandas as pd
from ibis.common.collections import frozendict
pd.Series([frozendict({"a": 5, "b": 6})])
# 0 (a, b)
# dtype: object
```
- [x] NA: closes #xxxx
- [ ] [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).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] NA Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"Output-Formatting"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"that was a pretty nice experience setting up a dev environment locally, pass along my compliments to the rest of the pandas maintainers @mroeschke !",
"Thanks @NickCrews "
] |
2,195,692,565
| 57,914
|
CI: Make pep8 validation work with multiple files simultaneously
|
closed
| 2024-03-19T18:26:07
| 2024-05-08T16:55:47
| 2024-05-08T16:55:46
|
https://github.com/pandas-dev/pandas/pull/57914
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57914
|
https://github.com/pandas-dev/pandas/pull/57914
|
dontgoto
| 6
|
The validation is bottlenecked due to the repeated sys calls, subprocess spawning, and pep8 calls that only validate a single file each. Running the validation on a list of files is approximately as fast as running it on a single file. This PR makes the first step, enabling the pep8 validation of a list of files, but for now still running it only on a single file. It will be followed by a few more PRs. The end result will be a complete refactoring of `validate_docstrings.py`, that will then take about 10 seconds to validate all docstrings.
- [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.
@datapythonista
|
[
"Docs",
"CI",
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I'm travelling and I could only revew with the phone, but looks good.\r\n\r\nQuick question, would it make sense to use ruff instead of flake8 for this?",
"Thanks for the quick review!\r\n\r\n> Quick question, would it make sense to use ruff instead of flake8 for this?\r\n\r\nI think it would be slightly faster, but someone would have to configure ruff to behave the same way as we intend flake8 to behave here. On my machine, the whole `code_check.sh docstrings` validation finishes in about 10 seconds, 7s of those are separate calls to `numpydoc.validate` (I put in a PR there that brings this down to 5.5s), and the remaining 3s are split between our `validate.py`and pep8. \r\n\r\nSo I think even with ruff we would at best get to 5.5s instead of 8.5s if we keep `numpydoc.validate`. Might still be worth an issue to discuss the (future) role of ruff here and in other places.\r\n\r\n",
"Yep, trying to improve 10s doesn't make sense. I know we are using ruff in the CI nowz bit I didn't check the exact state. The question was more about consistency, I didn't check if we already replaced flake8 with ruff.",
"I see ruff being used as part of the pre-commit hooks. And there it let a docstring error that I introduced in a non-replaced docstring through. It's also replaced flake8 for the docstring formatting, but back when I introduced my error, only the ``code_check.sh`` showed up as failed.",
"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 PR but appears there not much appetite for this change so closing"
] |
2,195,583,197
| 57,913
|
REF/PERF: Use concat(..., ignore_index=True) when index doesn't matter
|
closed
| 2024-03-19T17:38:04
| 2024-03-22T20:52:43
| 2024-03-22T20:48:52
|
https://github.com/pandas-dev/pandas/pull/57913
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57913
|
https://github.com/pandas-dev/pandas/pull/57913
|
mroeschke
| 1
|
For internal `concat` usages where the result index is set later, setting `ignore_index=True` to avoid More Work calculating the result concat index
|
[
"Refactor",
"Reshaping"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @mroeschke "
] |
2,194,789,748
| 57,912
|
DOC: Getting started tutorial headings and buttons are not readable
|
closed
| 2024-03-19T12:05:56
| 2024-03-20T16:39:37
| 2024-03-20T16:39:37
|
https://github.com/pandas-dev/pandas/issues/57912
| true
| null | null |
SilvestriStefano
| 1
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/getting_started/index.html
### Documentation problem
Hello,
I have found that the headings to the various Getting Started Tutorials are not readable in dark mode.
Also the text in the button is not readable in light mode
### Suggested fix for documentation
I believe the issue with the heading is due to the fact that the CSS variable `--bs-card-cap-color` is not initialized
I suggest to fix it as follows
```diff
/* line 250 of the file pandas/doc/source/_static/css/getting_started.css */
.tutorial-card .card-header {
+ --bs-card-cap-color: var(--pst-color-text-base);
cursor: pointer;
background-color: var(--pst-color-surface);
border: 1px solid var(--pst-color-border)
}
```
It adjusts for either choice of theme
For the button instead I suggest the following change :
```diff
/* line 271 of the file pandas/doc/source/_static/css/getting_started.css */
.tutorial-card .gs-badge-link a {
- color: var(--pst-color-text-base);
+ color: var(--pst-color-primary-text);
text-decoration: none;
}
```
It adjust for either choice of theme
|
[
"Docs",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Sounds good to me, I'm guessing you meant to link to https://pandas.pydata.org/docs/getting_started/index.html#intro-to-pandas"
] |
2,194,784,233
| 57,911
|
BUG: regression, is_unique is incorrect since pandas 2.1.0
|
closed
| 2024-03-19T12:04:05
| 2024-08-06T01:06:28
| 2024-08-06T01:06:28
|
https://github.com/pandas-dev/pandas/issues/57911
| true
| null | null |
morotti
| 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.
- [ ] 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
print("pandas version", pd.__version__)
values = [1, 1, 2, 3, 4]
index = pd.Index(values)
print("===========")
print(index)
print("is_unique=", index.is_unique)
filtered_index = index[2:].copy()
print("===========")
print(filtered_index)
print("is_unique=", filtered_index.is_unique)
index = pd.Index(values)
filtered_index = index[2:].copy()
print("===========")
print(filtered_index)
print("is_unique=", filtered_index.is_unique)
```
### Issue Description
Hello,
We found a regression, `index.is_unique` is incorrect since pandas 2.1.0.
I looked for open issues but did not find any fix or existing discussion.
Having a look at the changelog, there were lots of changes in 2.1.0 to introduce copy-on-write optimizations on the index.
I think the issue could be related to that, my best guess, maybe `index[2:]` cached something from the original `index` that is no longer correct?
Attaching a simple repro, it's very easy to reproduce. :)
Thank you.
### Expected Behavior
```
pandas version 1.5.3
===========
Int64Index([1, 1, 2, 3, 4], dtype='int64')
is_unique= False
===========
Int64Index([2, 3, 4], dtype='int64')
is_unique= True
===========
Int64Index([2, 3, 4], dtype='int64')
is_unique= True
```
```
pandas version 2.2.1
===========
Index([1, 1, 2, 3, 4], dtype='int64')
is_unique= False
===========
Index([2, 3, 4], dtype='int64')
is_unique= False # <---------------- INCORRECT
===========
Index([2, 3, 4], dtype='int64')
is_unique= True
```
### Installed Versions
tested on:
* pandas 1.5.3: PASS
* pandas 2.0.0: PASS
* pandas 2.0.3: PASS
* pandas 2.1.0: INCORRECT
* pandas 2.1.4: INCORRECT
* pandas 2.2.1 (latest): INCORRECT
|
[
"Bug",
"Regression",
"Index"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"While this is a lazy fix, I've found that in `pandas/_libs/index.pyx` if we omit the functionality of `IndexEngine`'s `_update_from_sliced` method (such that `self.need_unique_check` and `self.need_monotonic_check` = 1), it causes `is_unique` to work as expected and all related tests pass",
"Hi, I can reproduce this on main - tentatively marking as for 2.2.2.\r\n\r\nI haven't looked into this further myself - but further investigation/PRs would be welcome.",
"take",
"thank you for working on a fix,\r\n\r\nwould it be possible to roll the fix to the 2.1.x branch as well? \r\nit would be really helpful since the regression was introduced with 2.1.0\r\n\r\n",
"We don't support the 2.1.x branch anymore, we will only release this on 2.2.x",
"Quick update, in case anybody runs into this bug.\r\nI found one way to patch it in 2.1.x, only modifying python code to not require compilation.\r\n\r\nThe bug seems to be in the logic of `_update_from_sliced()` in cython code, it's only called by one python function.\r\nAs far as I understand, unique and monotonic properties can only be maintained if the original index was unique.\r\n\r\n```\r\n--- a/pandas/core/indexes/base 2024-03-28 14:14:22.086831383 +0000\r\n+++ b/pandas/core/indexes/base.py 2024-03-28 14:31:57.291193802 +0000\r\n@@ -5397,7 +5397,8 @@ class Index(IndexOpsMixin, PandasObject)\r\n result = type(self)._simple_new(res, name=self._name, refs=self._references)\r\n if \"_engine\" in self._cache:\r\n reverse = slobj.step is not None and slobj.step < 0\r\n- result._engine._update_from_sliced(self._engine, reverse=reverse) # type: ignore[union-attr] # noqa: E501\r\n+ if self._engine.is_unique:\r\n+ result._engine._update_from_sliced(self._engine, reverse=reverse) # type: ignore[union-attr] # noqa: E501\r\n\r\n return result\r\n\r\n```",
"Thanks @morotti! There's an open PR for fixing this bug at #57958, but it has not been merged yet"
] |
2,194,065,239
| 57,910
|
Backport PR #57886 on branch 2.2.x (CI: Remove ASAN job)
|
closed
| 2024-03-19T05:32:07
| 2024-03-19T16:29:17
| 2024-03-19T16:29:17
|
https://github.com/pandas-dev/pandas/pull/57910
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57910
|
https://github.com/pandas-dev/pandas/pull/57910
|
meeseeksmachine
| 0
|
Backport PR #57886: CI: Remove ASAN job
|
[
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,193,868,594
| 57,909
|
pd.read_parquet ignore my partition (after repartition ) : ArrowInvalid: No match for FieldRef.Name
|
open
| 2024-03-19T02:23:49
| 2024-03-19T02:51:30
| null |
https://github.com/pandas-dev/pandas/issues/57909
| true
| null | null |
eromoe
| 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
The case is:
1. I use joblib process 4000 items, and write to `partition_cols=['year', 'month', 'code']
```python
def etl_book_feature(code):
....
df.to_parquet(DATA_PATH, partition_cols=['year', 'month', 'code'])
Parallel(n_jobs=-1)(delayed(etl_book_feature)(i) for i in codes)
```
2. Because `read_parquet` is very slow on `DATA_PATH`, I merge data under each year&month combination folders, and use a new column '__time__' to write back : `df.to_parquet(target_dir, partition_cols=['year', 'month', '__time__'])`
```python
def repartition_by_date(base_dir, date, target_dir=None):
# date = pd.to_datetime(date)
year = date.year
month = date.month
year_month_dirpath = os.path.join(base_dir, f'year={year}', f'month={month}')
if not os.path.exists(year_month_dirpath):
print(f'Partition not found:{year_month_dirpath}')
return
df = pd.read_parquet(year_month_dirpath)
df['year'] = year
df['month'] = month
# ensure
df['date'] = pd.to_datetime(df['date'])
if not target_dir:
target_dir = base_dir
if target_dir == base_dir:
delete_path(year_month_dirpath)
os.mkdir(year_month_dirpath)
df['__time__'] = df['time'].map(convert_time_to_short_str)
df.to_parquet(target_dir, partition_cols=['year', 'month', '__time__'])
Parallel(n_jobs=5, verbose=51, batch_size=5)(
delayed(repartition_by_date)(DATA_PATH, item,) for item in pd.date_range('20180101', '20240301', freq='M')
)
```
3. test
```
pd.read_parquet(DATA_PATH, filters=[[('__time__', '=', '1000')], [('year', '=', 2021)], [('month', '=', 1)]])
```
got
```
ArrowInvalid: No match for FieldRef.Name(__time__)
```
I am not sure is it a bug or document problem.
### Issue Description
Current, `read_parquet` perform very poor when there are too much partitions .
1. As the example I mention, I have to do ETL in parallel , so the initial number of partions must be very large. I have to do repartition to improve reading speed.
2. I read each year/month partiion ,and rewrite to orginal folder with `df.to_parquet(target_dir, partition_cols=['year', 'month', '__time__'])`
3. read new data got ArrowInvalid: No match for FieldRef.Name(__time__)
### Expected Behavior
success
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a671b5a8bf5dd13fb19f0e88edc679bc9e15c673
python : 3.10.13.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936
pandas : 2.1.4
numpy : 1.24.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.1.1
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 : 3.1.3
IPython : 8.22.1
pandas_datareader : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat: None
fastparquet : 2024.2.0
fsspec : 2024.2.0
gcsfs : None
matplotlib : 3.8.3
numba : 0.57.1
numexpr : 2.9.0
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 15.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2024.2.0
scipy : 1.12.0
sqlalchemy : 2.0.27
tables : 3.9.2
tabulate : 0.9.0
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 write a reproducable code:\r\n\r\n```python\r\nimport os\r\nimport shutil\r\n\r\ndef delete_path(path):\r\n '''\r\n os.path.isfile 文件不存在也是返回False ! 不如直接用try\r\n '''\r\n try:\r\n os.remove(path)\r\n except OSError:\r\n # 只接收目录为参数，os.rmdir 不能删除非空文件夹\r\n shutil.rmtree(path)\r\n\r\ndef repartition_by_date(base_dir, date, target_dir=None):\r\n '''\r\n 要求目标路径必须有 year 和 month, 将下层数据进行合并输出\r\n '''\r\n # date = pd.to_datetime(date)\r\n year = date.year\r\n month = date.month\r\n \r\n year_month_dirpath = os.path.join(base_dir, f'year={year}', f'month={month}')\r\n \r\n if not os.path.exists(year_month_dirpath):\r\n print(f'Partition not found:{year_month_dirpath}')\r\n return\r\n \r\n ## 如果没有读到分区会报错\r\n # df = pd.read_parquet(base_dir, filters=[('year', '=', year), ('month', '=', month)])\r\n \r\n # 直接构造path提高读取效率，并防止报错\r\n df = pd.read_parquet(year_month_dirpath)\r\n df['year'] = year\r\n df['month'] = month\r\n\r\n # ensure\r\n df['date'] = pd.to_datetime(df['date'])\r\n \r\n # 用parquet写， 为了防止按月写 相互覆盖，必须得加上 code partition\r\n \r\n if not target_dir:\r\n target_dir = base_dir\r\n \r\n if target_dir == base_dir:\r\n delete_path(year_month_dirpath)\r\n os.mkdir(year_month_dirpath)\r\n \r\n \r\n df['__time__'] = df['day'].astype('str')\r\n \r\n df.to_parquet(target_dir, partition_cols=['year', 'month', '__time__'])\r\n \r\ntry:\r\n delete_path('./new_data2')\r\nexcept:\r\n pass\r\n\r\n# create data\r\nsr = pd.date_range('2020','2023', freq='H').to_series()\r\ndf = pd.DataFrame(dict(date=sr, year=sr.dt.year, month=sr.dt.month, day=sr.dt.day, hour=sr.dt.hour))\r\ndf.to_parquet('./new_data2', partition_cols=['year','month', 'hour'])\r\n\r\n# repartion (remove hour=xx folder, add new folder __time__=xx )\r\n_ = [repartition_by_date('./new_data2', d) for d in pd.date_range('2020','2023', freq='MS')]\r\n\r\n# read new data\r\npd.read_parquet('./new_data2', filters=[('__time__', '=', 1), ('year', '=', 2021), ('month', '=', 1), ])\r\n```"
] |
2,193,844,759
| 57,908
|
CI: Improve API of --ignore_errors in validate_docstrings.py
|
closed
| 2024-03-19T01:56:50
| 2024-03-29T06:47:58
| 2024-03-19T16:57:33
|
https://github.com/pandas-dev/pandas/pull/57908
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57908
|
https://github.com/pandas-dev/pandas/pull/57908
|
datapythonista
| 5
|
Follow up of #57879, to use `--ignore_errors PR02,SA01` instead of `--ignore_errors * PR02,SA01`.
I also make the script ignore the specified errors when running for a single document (i.e. `validate_docstrings.py pandas.Series -i SA01` was reporting the SA01 error before this PR, and it won't with the changes here).
CC: @mroeschke
|
[
"Docs",
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"> It's probably overly safe, but one could verify that only one CLI parameter is specifying globally ignored errors, otherwise one set would overwrite the other\r\n\r\nIt shouldn't overwrite it, I'm not sure if I explicitly tested it, but the way I implemented was to allow to do `validate_docstrings.py -i PR01 -i SA01` and ignore each of them:\r\n\r\n```python\r\nignore_errors[None].update(set(error_codes.split(\",\")))\r\n```\r\n\r\nWhat I don't check is if there are duplicates, but that's not likely going to be useful since we just have one error being fully ignored, and I don't think we'll be adding any or many more errors to the script.\r\n\r\nWhat I'm not sure is what happens if an object (e.g. `pandas.Series.head`) contains a typo, maybe it's ignored silently.",
"Thanks @datapythonista ",
"Thanks for all the work on this @datapythonista! `code_checks.sh` is looking awesome with thse new improvements. Just wanted to check and make sure this was the last change you had pending. If this is the final update, I can go ahead and open new issues to address these. ",
"Yes, no more changes to the ignore functions in `code_checks.sh`. You should also have the triage permissions now.",
"Opened [DOC: Enforce Numpy Docstring Validation (Parent Issue) #58063](https://github.com/pandas-dev/pandas/issues/58063) as a parent issue for fixing docstrings based on the refactoring in code_checks.sh \r\n\r\nFeel free to swing by and help out! 🙂"
] |
2,193,729,903
| 57,907
|
Backport PR #57905 on branch 2.2.x (Revert "Fix issue with Tempita recompilation (#57796)")
|
closed
| 2024-03-19T00:14:29
| 2024-03-19T00:55:34
| 2024-03-19T00:55:34
|
https://github.com/pandas-dev/pandas/pull/57907
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57907
|
https://github.com/pandas-dev/pandas/pull/57907
|
meeseeksmachine
| 0
|
Backport PR #57905: Revert "Fix issue with Tempita recompilation (#57796)"
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,193,658,255
| 57,906
|
BUG: groupby(group_keys=False).apply(func=transform_function) with duplicate index does not preserve original dataframe order
|
open
| 2024-03-18T23:28:05
| 2024-03-31T11:55:10
| null |
https://github.com/pandas-dev/pandas/issues/57906
| true
| null | null |
sfc-gh-mvashishtha
| 9
|
### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame( [
["k0", 13, "e"],
["k1", 14, "d"],
["k0", 15, "c"],
["k0", 16, "b"],
[None, 17, "a"],
],
index=pd.Index(["i1", None, "i0", "i2", None], name="index"),
columns=pd.Index(["string_col_1", "int_col", "string_col_2"], name="x"),)
print(df.index)
result = df.groupby("index", sort=False, dropna=False, group_keys=False)['int_col'].apply(lambda v: v)
print(result.index)
assert result.index.equals(df.index)
```
### Issue Description
groupby.apply transforms should restore the original dataframe order.
The current implementation loses the original order so when the axis has duplicates, there's no way to correctly reindex the result back to the original order [here](https://github.com/pandas-dev/pandas/blob/e14a9bd41d8cd8ac52c5c958b735623fe0eae064/pandas/core/groupby/groupby.py#L1234-L1246).
### Expected Behavior
when `func` acts as a transform, groupby.apply should produce a result that has the same index as the original. The result for the nth value with group key a in the input dataframe should be the nth value with group key a in the output dataframe.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.9.18.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:45:49 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.3
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 : None
IPython : 8.18.1
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 : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None
</details>
|
[
"Bug",
"Groupby",
"Apply"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hello I would like to work on this issue please. ",
"Thanks for the report; agreed apply should reorder the result here. For a workaround, `groupby.transform` appears to behave correctly.",
"Hi @ChaikhiBelaid, are you currently working on this?\r\n",
"Hi @rhshadrach, i was looking at this issue with intent on completing it but i must have missed something, shouldnt the behave of said function group the indexes which are the same? I seem to not be comprehending what exactly is wrong. Is the problem that when we use group_key = False the indexes should remain in the original order?\r\n",
"@AfonsoOrmonde - here is a slightly simpler reproducer. `result` below is incorrect, and should be the same as `expected`.\r\n\r\n```\r\ndf = pd.DataFrame({\"key\": [np.nan, 1, np.nan], \"value\": [1, 2, 3]}).set_index(\"key\")\r\nresult = df.groupby(\"key\", dropna=False, group_keys=False)[\"value\"].apply(lambda v: v + 1)\r\nprint(result)\r\n# key\r\n# NaN 2\r\n# NaN 4\r\n# 1.0 3\r\n# Name: value, dtype: int64\r\n\r\nexpected = df.groupby(\"key\", dropna=False)[\"value\"].transform(lambda v: v + 1)\r\nprint(expected)\r\n# key\r\n# NaN 2\r\n# 1.0 3\r\n# NaN 4\r\n# Name: value, dtype: int64\r\n```",
"OK thank you! I think i might have a solution\r\n",
"take",
"@rhshadrach i was able to get the desired output, however some tests fail due to reindexing not being possible with duplicate labels. Is this not supposed to happens with the new changes? Because if they are supposed to regain their original order, shouldnt they have their correspondent starting indexes (which are not reindexable)? I apologize upfront if i misunderstood something.",
"> some tests fail due to reindexing not being possible with duplicate labels. Is this not supposed to happens with the new changes?\r\n\r\nCorrect - such a case should not lead to failure here. If your solution is to call Series/DataFrame.reindex, I do not think that is the right approach. Rather, we'd want to use the groupby internals (likely, `_grouper.result_ilocs`) to reorder the result with a `.take`."
] |
2,193,647,699
| 57,905
|
Revert "Fix issue with Tempita recompilation (#57796)"
|
closed
| 2024-03-18T23:20:27
| 2024-03-19T00:14:28
| 2024-03-19T00:14:21
|
https://github.com/pandas-dev/pandas/pull/57905
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57905
|
https://github.com/pandas-dev/pandas/pull/57905
|
WillAyd
| 1
|
I don't think this had the intended effect - it did prevent unwanted recompilation but also ignored when the tempita files were updated and a recompilation _should_ occur
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @WillAyd "
] |
2,193,644,647
| 57,904
|
Allow Dockerfile to use local requirements.txt
|
closed
| 2024-03-18T23:18:47
| 2024-03-19T00:54:26
| 2024-03-19T00:15:20
|
https://github.com/pandas-dev/pandas/pull/57904
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57904
|
https://github.com/pandas-dev/pandas/pull/57904
|
WillAyd
| 1
| null |
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @WillAyd "
] |
2,193,594,684
| 57,903
|
DOC: update link in benchmarks.md
|
closed
| 2024-03-18T22:50:37
| 2024-03-18T23:57:50
| 2024-03-18T23:54:46
|
https://github.com/pandas-dev/pandas/pull/57903
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57903
|
https://github.com/pandas-dev/pandas/pull/57903
|
yukikitayama
| 4
|
- [x] closes #57850
- [ ] [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 [H20.ai benchmark link](https://h2oai.github.io/db-benchmark/) in the [benchmarks webpage](https://pandas.pydata.org/community/benchmarks.html) contains the outdated benchmark results (e.g. the last benchmark was performed July 2021 and it used the pandas==1.2.5). DuckDB published a new benchmark results (e.g. the last benchmark was December 2023 with pandas==2.1.1) in the [link](https://duckdblabs.github.io/db-benchmark/) with the same approach described in [their blog](https://duckdb.org/2023/11/03/db-benchmark-update). So it's a good idea to replace the benchmark link. Thank you @rootsmusic for the issue.
|
[
"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/57903/",
"Thank you @yukikitayama, very nice! ",
"Thanks for your review @datapythonista 👍 "
] |
2,193,393,568
| 57,902
|
REF: Avoid new object creation when reverse slicing when possible
|
closed
| 2024-03-18T21:39:52
| 2024-03-20T16:41:56
| 2024-03-20T16:41:53
|
https://github.com/pandas-dev/pandas/pull/57902
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57902
|
https://github.com/pandas-dev/pandas/pull/57902
|
mroeschke
| 3
| null |
[
"Refactor"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"No objection but why? Is this a perf thing or a style thing?",
"Just a minor perf thing; avoiding extra memory allocations where possible",
"Since tests are passing here going to merge."
] |
2,193,350,877
| 57,901
|
DOC: Remove doc of deprecated week and weekofyear
|
closed
| 2024-03-18T21:22:24
| 2024-03-20T03:51:23
| 2024-03-18T23:54:22
|
https://github.com/pandas-dev/pandas/pull/57901
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57901
|
https://github.com/pandas-dev/pandas/pull/57901
|
Aloqeely
| 3
|
- [x] closes #57887 (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",
"Datetime"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"/preview",
"Website preview of this PR available at: https://pandas.pydata.org/preview/pandas-dev/pandas/57901/",
"Thanks @Aloqeely "
] |
2,193,284,339
| 57,900
|
TST: Dask failure in the CI
|
closed
| 2024-03-18T20:49:45
| 2024-03-18T20:52:40
| 2024-03-18T20:50:57
|
https://github.com/pandas-dev/pandas/issues/57900
| true
| null | null |
datapythonista
| 2
|
We have a [failure](https://github.com/pandas-dev/pandas/actions/runs/8332117541/job/22800532331#step:8:86) in `pandas/core/test_downstream.py` in our CI, which I guess it's caused by a change in Dask.
The reason is that when creating a `Series` from a dask dataframe, the object is treated as a dict, since it's a dict-like, but then we check if the object is empty by calling `bool()` on it, and dask raises. This is the behavior causing the issue:
```python
>>> from pandas.core.dtypes.common import is_dict_like
>>> data = dask.dataframe.from_array(numpy.array([1, 2, 3]))
>>> is_dict_like(data)
True
>>> bool(data)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/mgarcia/.miniforge3/envs/pandas-dev/lib/python3.10/site-packages/dask_expr/_collection.py", line 435, in __bool__
raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.any() or a.all().
```
I personally don't understand this test much, creating a `Series` from a `DataFrame` seems a bit odd, what's the expected output, a Series where each element is another `Series`?
This also seems to be failing when the dataframe is a pandas dataframe instead of a dask one, but we don't seem to have a test to check that it works:
```python
>>> df = pandas.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> pandas.Series(df)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/mgarcia/src/pandas/pandas/core/series.py", line 456, in __init__
data, index = self._init_dict(data, index, dtype)
File "/home/mgarcia/src/pandas/pandas/core/series.py", line 546, in _init_dict
if data:
File "/home/mgarcia/src/pandas/pandas/core/generic.py", line 1482, in __nonzero__
raise ValueError(
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
Any objection to just get rid of the test @mroeschke @phofl ?
|
[
"Testing",
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Fixed by #57889, so closing here. The test was actually helpful",
"Ah, cool, I missed that PR, thanks!"
] |
2,193,262,257
| 57,899
|
Backport PR #57889 on branch 2.2.x (BUG: Handle Series construction with Dask, dict-like, Series)
|
closed
| 2024-03-18T20:38:30
| 2024-03-18T22:01:56
| 2024-03-18T22:01:56
|
https://github.com/pandas-dev/pandas/pull/57899
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57899
|
https://github.com/pandas-dev/pandas/pull/57899
|
meeseeksmachine
| 0
|
Backport PR #57889: BUG: Handle Series construction with Dask, dict-like, Series
|
[
"Compat",
"Series",
"Constructors"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,193,000,194
| 57,898
|
Backport PR #57892 on branch 2.2.x (CI: xfail Pyarrow slicing test)
|
closed
| 2024-03-18T18:46:36
| 2024-03-18T20:36:47
| 2024-03-18T20:36:47
|
https://github.com/pandas-dev/pandas/pull/57898
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57898
|
https://github.com/pandas-dev/pandas/pull/57898
|
meeseeksmachine
| 0
|
Backport PR #57892: CI: xfail Pyarrow slicing test
|
[
"Testing",
"Upstream issue",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,192,989,202
| 57,897
|
TST: Reduce some test data sizes
|
closed
| 2024-03-18T18:41:17
| 2024-03-18T21:00:05
| 2024-03-18T20:38:56
|
https://github.com/pandas-dev/pandas/pull/57897
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57897
|
https://github.com/pandas-dev/pandas/pull/57897
|
mroeschke
| 1
| null |
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thx @mroeschke "
] |
2,192,978,851
| 57,896
|
BLD, TST: Build and test Pyodide wheels for `pandas` in CI
|
closed
| 2024-03-18T18:36:04
| 2024-05-08T19:35:07
| 2024-05-08T16:51:58
|
https://github.com/pandas-dev/pandas/pull/57896
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57896
|
https://github.com/pandas-dev/pandas/pull/57896
|
agriyakhetarpal
| 14
|
- [x] closes #57891
- [ ] [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.
## Description
This PR adds a workflow, `emscripten.yml`, to the GitHub Actions CI activities where WebAssembly wheels for `pandas` using Pyodide/Emscripten are built and then tested. I have fixed up a few of the tests, but most of them are skipped for now and these differences _probably_ do not affect major functionality.
|
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
[
"Here is one of the latest workflow runs on my fork: https://github.com/agriyakhetarpal/pandas/actions/runs/8331354944/job/22798065116\r\n\r\nThe associated failures right now are mostly coming from a singular test that (which is associated with a hypothesis generator that brings additional test cases?)\r\n\r\nFor example, there is a floating point inaccuracy, here – which could be corrected by setting the relative and absolute tolerances differently:\r\n\r\n```\r\nE AssertionError: DataFrame.iloc[:, 2] (column name=\"dt_as_dt\") are different\r\nE \r\nE DataFrame.iloc[:, 2] (column name=\"dt_as_dt\") values are different (100.0 %)\r\nE [index]: [0, 1]\r\nE [left]: [253402127999999, 1564703999999]\r\nE [right]: [253402127999998, 1564703999998]\r\n```\r\n\r\nand some `tzlocal` tests, which seem to bring overflow errors. Note: this output is truncated, please see the entirety of them in the workflow logs:\r\n\r\n```\r\n_______________ test_apply_out_of_range[tzlocal()-BusinessHour] ________________\r\n[XPASS(strict)] OverflowError inside tzlocal past 2038\r\n_____________ test_apply_out_of_range[tzlocal()-BusinessMonthEnd] ______________\r\n[XPASS(strict)] OverflowError inside tzlocal past 2038\r\n____________ test_apply_out_of_range[tzlocal()-BusinessMonthBegin] _____________\r\n[XPASS(strict)] OverflowError inside tzlocal past 2038\r\n```\r\n\r\nI am not completely sure how to fix these tests – I don't get why these aren't skipped completely? I would appreciate another pair of eyes on this; happy to receive pointers from someone experienced with the codebase :)",
"> I am not completely sure how to fix these tests – I don't get why these aren't skipped completely? I would appreciate another pair of eyes on this; happy to receive pointers from someone experienced with the codebase :)\r\n\r\nAt least for the second test it looks like it is imperatively skipping for platforms that are not 64 bit. I take it the platform these are running on is still 64 bit but we are cross compiling to a 32 bit WASM target?\r\n\r\nhttps://github.com/pandas-dev/pandas/blob/495f80896852e450badc8866ee7ebe8c434fa228/pandas/tests/indexes/datetimes/methods/test_resolution.py#L25\r\n",
"> At least for the second test it looks like it is imperatively skipping for platforms that are not 64 bit. I take it the platform these are running on is still 64 bit but we are cross compiling to a 32 bit WASM target?\r\n\r\nHi, @WillAyd, yes – cross-compilation is indeed what is happening here and the platform is 64-bit, but the Python interpreter that runs the test suite is 32-bit (so it should, in theory, skip this, but it doesn't). I disabled the check in https://github.com/pandas-dev/pandas/pull/57896/commits/d54d198a3824a6c13150a494b7f4b2af247737aa which I hope should fix it.",
"Pyodide had a new release recently to fix an error in the `pyodide build` command. I have just merged `main` and pushed a944f5287dce1439778f32525ebf5d973e75b817 to fix that. The next step from my end would be to fix some of the failing tests (which I did not have much progress on the last time and I don't want them to xfail them yet), but also to wait here until #53007 lands – I suppose that it would make running the tests against a built wheel a bit more difficult, but without the PR getting merged, I'm not sure how that will be navigated here. The good thing is that `pandas-tests` will be a pure Python package and will have a few APIs to collect and pass data files around, and installation for pure Python wheels is supported out-of-the-box in Pyodide.\r\n\r\nIf there's something else here that I should do in the meantime, I'm happy to do that – open to suggestions about resolving the two failing tests.",
"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.",
"Putting up my response to the above comment that I am still interested in working on this.",
"@agriyakhetarpal it seems reasonable to either skip the `tzlocal` test under WASM or lower the test tolerance specifically when running under WASM. If that is all that is needed to get the test suite to pass, I suggest doing that and then marking this as ready for review. It's easier for reviewers to give feedback when something works already.",
"Just merged the `main` branch, which re-triggered CI – would be good to see if some new tests or features have been added that will fail, before I resolve everything.",
"Thanks for sticking with this. It's looking close to merge",
"With most of the tests skipped where it is not possible or relevant to fix them, we are down to zero failures, and all other tests pass or xpass (or are xfailed for other reasons). The logs from the workflow run are here: https://github.com/pandas-dev/pandas/actions/runs/8945452670/job/24574452414. The doc build failures at the time of writing look to be unrelated to the changes made in this PR.\r\n\r\nSome of the tests that have been skipped can be, perhaps, adapted to use a 32-bit version of them; I am happy to add a few of those where necessary. I'm marking this PR ready for review so that it can get the attention of more maintainers.",
"Great work here @agriyakhetarpal ",
"Thanks for the merge, @mroeschke! I wish to ask if I can write a PR directly for pushing these wheels [nightly](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) to the `scientific-python` Anaconda.org index, or is it preferred to open an issue first and then link a PR to it?\r\n\r\nFor context, this is a part of the greater goal as highlighted in https://github.com/pandas-dev/pandas/issues/57891#issue-2192853050, in order to push towards interactive Sphinx-based documentation via JupyterLite. Doing this will be a much more trivial change in comparison; I am happy to contribute further.",
"An example that highlights some interactive documentation is available for the API reference for PyWavelets: https://pywavelets.readthedocs.io/en/latest/ref/dwt-discrete-wavelet-transform.html (work on the Usage Examples section is underway)",
"Feel free just to open a PR directly to add the wheels configurations to `.github/workflows/wheels.yml` and `pyproject.toml`"
] |
2,192,978,088
| 57,895
|
Bump meson
|
closed
| 2024-03-18T18:35:39
| 2024-05-15T17:56:18
| 2024-05-15T17:56:18
|
https://github.com/pandas-dev/pandas/pull/57895
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57895
|
https://github.com/pandas-dev/pandas/pull/57895
|
WillAyd
| 3
|
Meson 1.4.0 adds first class support for NumPy > 2.0 detection https://mesonbuild.com/Release-notes-for-1-4-0.html#new-numpy-custom-dependency
Not an urgent upgrade but figured worth starting
|
[
"Build",
"Stale"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I believe the pypy failure is a Cython issue - see https://github.com/cython/cython/issues/6099\r\n\r\nGenerally I was surprised that the amount of warnings changed with this PR. But I fixed a few / ignored others (mostly for MSVC). Would be nice to get a lot of the MSVC warnings fixed up but I think that will take a dedicated initiative.\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 since there hasn't been much activity here, but feel free to reopen if/when you have time to revist"
] |
2,192,975,310
| 57,894
|
Testing a Tool
|
closed
| 2024-03-18T18:34:02
| 2024-03-18T18:43:12
| 2024-03-18T18:43:12
|
https://github.com/pandas-dev/pandas/pull/57894
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57894
|
https://github.com/pandas-dev/pandas/pull/57894
|
Noman654
| 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.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,192,923,784
| 57,893
|
TST: add pytest-localserver to dev deps
|
closed
| 2024-03-18T18:09:35
| 2024-03-18T18:56:05
| 2024-03-18T18:54:44
|
https://github.com/pandas-dev/pandas/pull/57893
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57893
|
https://github.com/pandas-dev/pandas/pull/57893
|
jbrockmendel
| 1
|
Needed for the httpserver fixture that we use.
|
[
"Testing"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @jbrockmendel "
] |
2,192,858,015
| 57,892
|
CI: xfail Pyarrow slicing test
|
closed
| 2024-03-18T17:36:30
| 2024-03-18T18:46:34
| 2024-03-18T18:46:27
|
https://github.com/pandas-dev/pandas/pull/57892
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57892
|
https://github.com/pandas-dev/pandas/pull/57892
|
mroeschke
| 1
|
https://github.com/apache/arrow/issues/40642
|
[
"Testing",
"Upstream issue",
"Arrow"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Since the pyarrow nightly build is green, going to merge"
] |
2,192,853,050
| 57,891
|
ENH: out-of-tree Pyodide builds in CI for `pandas`
|
closed
| 2024-03-18T17:34:05
| 2024-05-08T16:51:59
| 2024-05-08T16:51:59
|
https://github.com/pandas-dev/pandas/issues/57891
| true
| null | null |
agriyakhetarpal
| 11
|
### Feature Type
- [x] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
This feature proposes adding out-of-tree Pyodide builds for `pandas` on its own CI. As opposed to in-tree builds in the Pyodide packages repository (see the [recipe](https://github.com/pyodide/pyodide/blob/main/packages/pandas/meta.yaml)), this will enable building WASM wheels (which are 32-bit)[^1] via [the Emscripten toolchain](https://emscripten.org/) on GitHub Actions.
In my most recent work assignment, I am working on improving the interoperability for the Scientific Python ecosystem of packages with Pyodide and with each other (https://github.com/Quansight-Labs/czi-scientific-python-mgmt/issues/18), which shall culminate with efforts towards bringing interactive documentation for these packages where they can then be run in [JupyterLite notebooks](https://jupyter.org/try-jupyter/lab/index.html), through nightly builds and wheels for these packages pushed to PyPI-like indices on Anaconda.org (https://github.com/Quansight-Labs/czi-scientific-python-mgmt/issues/19) at and during a later phase during the project.
[^1]: It is to be noted that #15889 removed 32-bit support by removing the wheels, but if I understand correctly, `pandas` continues to be supported when building from source on 32-bit platforms.
### Feature Description
1. A CI pipeline via GitHub Actions that builds and tests `pandas` in an activated virtual environment created and handled by Pyodide.
2. Fixing up the tests wherever applicable, conforming to [limitations](https://pyodide.org/en/stable/usage/wasm-constraints.html) that come from the Emscripten toolchain (current limitations include the lack of threading support and for running subprocesses, limited file system access, and others).
### Alternative Solutions
N/A
### Additional Context
I notice that the build and the tests are going to be split into separate packages in #53007, which can be handled (I assume that the imminent `pandas-tests` package will be a pure Python one, which does not need compilation for Pyodide – they are supported by default).
cc @lithomas1 for any additional comments here, as required. Thomas and I have been in conversation about this just about a fortnight ago, and we had agreed that reducing the size of the wheels would be helpful for the WebAssembly rendition of `pandas` as well – it is particularly useful where reducing bandwidth usage is in the question. I am happy to follow along on further developments on that regard.
|
[
"Enhancement",
"Build"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I have experimented with this in a branch on my fork, where I have managed to bring the test suite down to just 31 failures by skipping certain tests and fixing a few other ones – I can open a draft PR for this for the purposes of visibility as of now, and work towards getting down to zero failures with the help and support of the maintainers and core developers.",
"xref some other discussions about Pyodide as follows (some of them coming from the PyArrow requirement that revolves around [PDEP-10](https://pandas.pydata.org/pdeps/0010-required-pyarrow-dependency.html)):\r\n\r\n1. https://github.com/pandas-dev/pandas/issues/54466\r\n2. https://github.com/pandas-dev/pandas/issues/52509\r\n3. https://github.com/pandas-dev/pandas/pull/52711\r\n4. https://github.com/pandas-dev/pandas/pull/47428\r\n\r\nThe issue at hand with PyArrow as a required dependency is the increased size of the download, which brings negative implications for using `pandas` in a JupyterLite-enabled kernel/notebook – I haven't followed up on the NumPy string DType improvements that have come up recently, but I would happy if someone could fill me in on the latest developments :)\r\n\r\n",
"I am not super optimistic about having to debug pyodide stuff in my PRs, I am really hesitant on adding official support for the platform",
"Hi, @phofl – I totally understand that. It does come with its concerns related to maintenance; if patches might be required to disable certain functionality and debugging them is indeed painful. I would be happy to take on that sort of maintenance if needed, but realistically, yes, it won't be possible to do so for me or anyone else every time there is a failure or if any critical changes are required in a jiffy. I will keep my PR marked as draft for now, but I hope that the team changes their mind :) \r\n\r\nThat being said, if it is ever enabled it will still be regarded as an \"experimental\" platform though, and it will stay that way for a long while until things in the Pyodide ecosystem become stable and we have a v1 release. But I suppose it also depends upon the popularity of the platform continues to evolve in the coming years. Here's what the scikit-learn developers think about that if you wish to have a read: https://github.com/scikit-learn/scikit-learn/issues/23727",
"How far is Numpy's progress on this front? I would also be hesitant with officially supporting this too until pandas' required dependencies have WASM support (at least CI testing)",
"numpy has a workflow testing this already\r\nhttps://github.com/numpy/numpy/blob/main/.github/workflows/emscripten.yml.",
"Thank you, @lithomas1. Yes, @mroeschke, NumPy is currently testing against Pyodide for all PRs with the link to the workflow as shared above, restarting with this PR: https://github.com/numpy/numpy/pull/25894 – thanks to @rgommers for his help. It did require a bit of back and forth with the migration to the new build system, but that's not really required with `pandas` and with my limited experience I have seen that Emscripten handles Cython code well enough. A lot of the tests for NumPy were being skipped through a WASM platform check and a pytest decorator/marker already before the PR came to fruition, which would ideally be the case here too (that's what has been done in my PR wherever applicable).",
"A bit of context:\r\n\r\n- Both NumPy and scikit-learn have had a CI job for running with Pyodide for quite a while now (maybe 1.5 years, sklearn maybe longer).\r\n - NumPy only disabled it during the transition from setuptools to Meson, to not have to think about it for a while. Other than that it's been quite unproblematic.\r\n- Adding a CI job is not a commitment, so I wouldn't call it \"officially supported\". Putting wheels up on PyPI for a new platform is a commitment, because once they're there and users rely on them, you can't easily stop producing them. However, PyPI does not allow `wasm32` wheels, so that is not what is proposed here.\r\n - For this CI job, the `numpy` (and `pyarrow` and other deps) to build/test against would come from Pyodide itself.\r\n- Why is this a useful exercise? At the moment, to ensure the Pandas test suite passes on Pyodide. Later, to also enable interactive docstring examples with little extra work, through support in the `pydata-sphinx-theme` and `jupyterlite-sphinx`.\r\n- If you don't want to add the CI job before other projects have more experience or before the interactive docs are fully ready, then just merging the test suite improvements is also a useful thing to do.\r\n\r\nSince @agriyakhetarpal is ready to do the work, including changes/improvements after initial merge, it seems quite low-risk to merge the job - and if it turns out to be a burden, then you can simply decide to disable the job again.",
"> I am not super optimistic about having to debug pyodide stuff in my PRs, I am really hesitant on adding official support for the platform\r\n\r\nThat's a fair point. \r\n\r\nIs there an official Docker image for pyodide? I personally would be comfortable debugging if this was available, not sure about others, though.",
"Since other pydata libraries have been testing it, I would be OK adding it to the CI but, if it comes to it, allow it to fail without blocking a merge",
"> Is there an official Docker image for pyodide? I personally would be comfortable debugging if this was available, not sure about others, though.\r\n\r\n@lithomas1 there is a Pyodide Docker image, yes: https://pyodide.org/en/stable/development/building-from-sources.html#using-docker. I don't use it personally and I prefer debugging on GitHub Actions because they don't have an aarch64 image yet (https://github.com/pyodide/pyodide/issues/1386), and emulation is slow up to multiple magnitudes for me."
] |
2,192,800,577
| 57,890
|
BUG: Join with a list of a single element should behave as a join with a single element (#57676)
|
closed
| 2024-03-18T17:08:32
| 2024-08-09T17:28:02
| 2024-08-09T17:28:01
|
https://github.com/pandas-dev/pandas/pull/57890
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57890
|
https://github.com/pandas-dev/pandas/pull/57890
|
Dacops
| 34
|
- [x] closes #57676
- [x] Added a test to pandas/tests/frame/methods/test_join.py
Even though it does not make much sense, joining a dataframe with a list of a single element should behave as joining the dataframe with that element.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Seems like the same test is failing over and over again, however it does not make sense to me, the test is:\r\n```\r\ndef test_suffix_on_list_join():\r\n first = DataFrame({\"key\": [1, 2, 3, 4, 5]})\r\n second = DataFrame({\"key\": [1, 8, 3, 2, 5], \"v1\": [1, 2, 3, 4, 5]})\r\n third = DataFrame({\"keys\": [5, 2, 3, 4, 1], \"v2\": [1, 2, 3, 4, 5]})\r\n \r\n # check proper errors are raised\r\n msg = \"Suffixes not supported when joining multiple DataFrames\"\r\n with pytest.raises(ValueError, match=msg):\r\n> first.join([second], lsuffix=\"y\")\r\nE Failed: DID NOT RAISE <class 'ValueError'>\r\n```\r\n\r\nIn this example multiple DataFrames are not passed, just a list with a single DataFrame",
"I've updated the test mentioned above, before any list passed with the Dataframe.join method was assumed to have 2+ elements, by adding this edge case where a list can have a single element (behaving has a Dataframe.join with a single element) that condition in the test \"test_suffix_on_list_join\" no longer makes sense.",
"LGTM. @mroeschke would you mind taking a look?",
"Thanks for the review @Aloqeely, in the meantime I've rebased my PR to address conflicts in the \"whatsnew\" file. I've noticed that the PR started failing the Numpy Dev tests because it was rebased from the main Pandas branch which is failing the same tests. No other changes (except the whatsnew file) were made since the last review. If any developer familiar with this area of the code could take a look, I'd appreciate it (cc @mroeschke, @WillAyd, @jbrockmendel ) I apologize for the broader ping, but I wasn't sure who would be best suited to review this change.",
"Hmm unfortunately I am -1 on making this change. I'm not sure I understand why we need to special case a single element list.\r\n\r\nGenerally our requirement for joins / merges is that the key be hashable",
"Sorry ignore my hashability comment - I see this is in regards to the join argument not the key elements themselves. Still, I'm not sure why we need to special case this",
"There's a problem, in some rare cases, that joining a dataframe x with a list containing a single dataframe y would lose information in relation to just joining dataframe x with dataframe y (one example of this on the original issue: https://github.com/pandas-dev/pandas/issues/57676)",
"Ah OK thanks @Dacops . This only solves that then though with a single element list right? What happens when there are multiple list elements? I think there is a fix somewhere else in the code that would work generally - ideally we avoid special-casing solutions like this as they don't add up well over time",
"With multiple list elements the behaviour remains the same. Basically, what the code did was if the joining element is an Object deal with it in the way A, if it's a List deal with it in the way B. I've added that if-statemen that just checks if the list has a single element. If it does use way A else keep using way B.",
"I think what he's saying is that a better fix would be to not treat lists with 1 item as a special case that uses different behavior, and instead fix the merge logic such that it does not fail even if the list has 1 item only",
"Hmmm, yeah I did thought of that, however the line that handles that is on [frame.py/10685](https://github.com/pandas-dev/pandas/blob/ac65c1391b3a07423c21fcb96c380bb2ae51b677/pandas/core/frame.py#L10685): `can_concat = all(df.index.is_unique for df in frames)` and it is been there for 12 years. Therefore I assumed the logic was correct since it wasn't changed for that long and it only fails in very rare cases. I thought it would be more prudent to add the if-statement for the edge cases instead of changing code that has been unaltered for so long.",
"It might very well be broken logic even if it was there for 12 years, funnily enough I just saw [another comment](https://github.com/pandas-dev/pandas/pull/57494#discussion_r1587386787) saying that some broken logic was introduced 13 years ago",
"I think I've identified the problem, that [expression](https://github.com/pandas-dev/pandas/blob/ac65c1391b3a07423c21fcb96c380bb2ae51b677/pandas/core/frame.py#L10685) evaluates if all inserted indexes from all objects in the list are unique so that they can be concatenated (stacked on top of each other). However if a MultiIndex is used the entire MultiIndex is compared not separated indexes inside it. A problem can arise here, for example on the original [issue](https://github.com/pandas-dev/pandas/issues/57676) a Indexed dataframe is getting joined to a MultiIndexed dataframe, the pairs of indexes of the MultiIndexed dataframe are compared to the indexes of the Indexed dataframe. There's no repetition, so they get concatenated, but here's the error. One of the MultiIndexed dataframes index is 'y' same as the index of the Indexed dataframe, and those no longer are unique, which means they need to be merged. Solution I'm thinking of, after the lambda function to check if all indexes are unique, if this evaluates to True add an extra check to look for repeated indexes and if any of these are not unique change the value to False and skip to the Merge instead of a Concat.",
"Hmm I'm not sure I follow - I feel like the indexes should be the same when trying to join. Assuming some kind of matching behavior between an Index and MultiIndex might have a lot of logic pitfalls",
"The problem is with the MultiIndexes, for example in the original [issue](https://github.com/pandas-dev/pandas/issues/57676), the Index is `index=pd.Index(['a', 'b', 'c'], name='y'))`, while the MultiIndex is `index=pd.MultiIndex.from_tuples([(0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')], names=('x', 'y'))`. When `all(df.index.is_unique for df in frames)` is run over both it gives out the values: `'a', 'b', 'c', (0, 'a'), (0, 'b'), (0, 'c'), (1, 'a'), (1, 'b'), (1, 'c')` which returns True (there are no repetitions, value of indexes are unique, concatenate them). However this cannot happen since the MultiIndex has a 'y' index, if we extract it and repeat `all(df.index.is_unique for df in frames)` over both 'y' we get `'a', 'b', 'c', 'a', 'b', 'c', 'a', 'b', 'c'` which returns False (there are repeated values, we can't concatenate them). Tl;dr when a MultiIndex is passed it's getting evaluated as a whole and not the individual indexs inside it where the merge may occur.",
"I believe I've got the solution for this, I'll push it so you can take a look at it",
"So is this a problem with `is_unique` on `MultiIndex`? Do you think it's current behavior makes sense? I think it should return True only if each level has unique values, and if I understood you correctly that seems to be failing right now\n\nIf you agree with my suggested behavior then I will open an issue to fix it, if not we should at least document how it operates on `MultiIndex` (don't see any examples for that)",
"@Aloqeely I believe the current behaviour of `is_unique` for MultiIndex is correct, however for this case this behaviour would not work. This because we're joining an Indexed dataframe with a MultiIndexed dataframe, the join will only occur in a single index (the one of the Indexed dataframe), so the remaining indexs of the MultiIndex are irrelevant to this operation.",
"this doc build and upload check is something I did wrong or do I just re-run the checks, I haven't touched that `/home/runner/work/pandas/pandas/doc/source/user_guide/merging.rst`",
"It does seem related to your changes, I've summarized the example in the doc that fails:\r\n```python\r\nimport pandas as pd\r\n\r\nleft = pd.DataFrame(\r\n {\r\n \"A\": [\"A0\", \"A1\", \"A2\", \"A3\"],\r\n \"B\": [\"B0\", \"B1\", \"B2\", \"B3\"],\r\n \"key2\": [\"K0\", \"K1\", \"K0\", \"K1\"],\r\n },\r\n index=pd.Index([\"K0\", \"K0\", \"K1\", \"K2\"], name=\"key1\"),\r\n )\r\n\r\nright = pd.DataFrame({\"v\": [7, 8, 9]}, index=[\"K1\", \"K1\", \"K2\"])\r\n\r\nresult = left.join([right])\r\n```\r\nRaises\r\n```\r\nUnboundLocalError: cannot access local variable 'can_concat' where it is not associated with a value\r\n```\r\nIt is surprising that only the doc build failed but not any tests, so once you fix it for this case please add tests.",
"We are fixing the wrong thing from the original issue. I think specifying `on=...` is reasonable to allow an Index and a MultiIndex to be joined together, but we should not be doing anything to automatically try and determine this. We should actually be raising when the indexes have a different number of levels in join",
"Oh thanks @Aloqeely , I figured out what it was, on this [line](https://github.com/pandas-dev/pandas/blob/04ac369326a4a3211e60cfc31df85f9c61a19933/pandas/core/frame.py#L10687), in case \"common_indexes\" was empty can_concat would not be initialized and it is called in the following if statement, so it threw an error there. I initialized it to a default value before the for loop so that doesn't occur anymore. Everything's good now, @WillAyd if you could re-review the PR I would appreciate it, thanks.",
"Oh just saw your comment sorry\r\n",
"Hmmm, so if `on=...` is not defined it should just raise an error?",
"Right if you are trying to join two index objects with non-equal levels",
"Ok, so I've changed the default value of \"common_indexes\" to a raise if \"common_indexes\" is empty",
"@Aloqeely how can I get better errors on the Doc build and upload test like you sent instead of just `RuntimeError: Unexpected exception in `/home/runner/work/pandas/pandas/doc/source/user_guide/merging.rst` line 930` ?",
"You can open the doc file which has the errors and go to the error line, run the examples near to that line manually until you see which example causes the error.",
"Oh thanks @Aloqeely, so if my error says `RuntimeError: Unexpected exception in /home/runner/work/pandas/pandas/doc/source/user_guide/merging.rst line 930`, [here](https://github.com/pandas-dev/pandas/blob/e0d30615a0450f95ba89c06a4f0fd6f017259085/doc/source/user_guide/merging.rst?plain=1#L930). Then the test that fails is the one above? The one that starts on line 906, [here](https://github.com/pandas-dev/pandas/blob/e0d30615a0450f95ba89c06a4f0fd6f017259085/doc/source/user_guide/merging.rst?plain=1#L906)?",
"I think so"
] |
2,192,788,855
| 57,889
|
BUG: Handle Series construction with Dask, dict-like, Series
|
closed
| 2024-03-18T17:03:38
| 2024-03-18T20:59:25
| 2024-03-18T20:38:20
|
https://github.com/pandas-dev/pandas/pull/57889
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57889
|
https://github.com/pandas-dev/pandas/pull/57889
|
mroeschke
| 1
|
Should hopefully fix `test_construct_dask_float_array_int_dtype_match_ndarray` which is failing because dask `Series` now raises on `__bool__`?
|
[
"Compat",
"Series",
"Constructors"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"thx @mroeschke "
] |
2,192,753,496
| 57,888
|
Backport PR #57883 on branch 2.2.x (Bump pypa/cibuildwheel from 2.16.5 to 2.17.0)
|
closed
| 2024-03-18T16:50:25
| 2024-03-18T17:43:02
| 2024-03-18T17:43:02
|
https://github.com/pandas-dev/pandas/pull/57888
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57888
|
https://github.com/pandas-dev/pandas/pull/57888
|
meeseeksmachine
| 0
|
Backport PR #57883: Bump pypa/cibuildwheel from 2.16.5 to 2.17.0
|
[
"Build",
"CI",
"Dependencies"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,192,715,427
| 57,887
|
DOC: Reference to deprecated week and weekofyear attributes
|
closed
| 2024-03-18T16:37:46
| 2024-03-18T23:54:23
| 2024-03-18T23:54:23
|
https://github.com/pandas-dev/pandas/issues/57887
| true
| null | null |
tray3j
| 1
|
### Pandas version checks
- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/dev/user_guide/timeseries.html#time-date-components
### Documentation problem
Per this discussion: [https://github.com/pandas-dev/pandas/issues/55092](url) the week and weekofyear attributes are deprecated since 1.1.0 and DatetimeIndex.isocalendar().week is now recommended.
### Suggested fix for documentation
We should update the user guide to reflect isocalendar().week funcitonality. We should delete the rows in the table relating to week and weekofyear attributes under .dt. A reference already exists to isocalendar() but it is currently not clear to the user that this is the only way to access week of year date information.
|
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"PRs would be welcome."
] |
2,192,401,913
| 57,886
|
CI: Remove ASAN job
|
closed
| 2024-03-18T14:37:27
| 2024-03-19T05:32:09
| 2024-03-19T05:31:59
|
https://github.com/pandas-dev/pandas/pull/57886
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57886
|
https://github.com/pandas-dev/pandas/pull/57886
|
WillAyd
| 3
| null |
[
"CI"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"I think for now can just remove this until we can root cause it",
"If you want to come back to this at a later time to debug and not completely remove some of the configurations, you could do:\r\n\r\n\r\n```diff\r\n--- a/.github/workflows/unit-tests.yml\r\n+++ b/.github/workflows/unit-tests.yml\r\n@@ -160,7 +160,7 @@ jobs:\r\n \r\n - name: Test (not single_cpu)\r\n uses: ./.github/actions/run-tests\r\n- if: ${{ matrix.name != 'Pypy' }}\r\n+ if: ${{ matrix.name != 'Pypy' || matrix.name != 'ASAN / UBSAN'}}\r\n with:\r\n preload: ${{ matrix.preload }}\r\n asan_options: ${{ matrix.asan_options }}\r\n```",
"Thanks @WillAyd "
] |
2,191,782,528
| 57,885
|
BUG: read_csv with multi character sep does not raise error in Jupyter Notebook/Python in terminal
|
closed
| 2024-03-18T10:13:48
| 2024-03-19T02:13:38
| 2024-03-18T20:32:09
|
https://github.com/pandas-dev/pandas/issues/57885
| true
| null | null |
auggie246
| 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
data = pd.read_csv(
"file.tsv",
sep="\t",
quotechar=False,
engine="pyarrow",
header=None,
names=["class", "written", "normalized"],
na_filter=False,
)
```
### Issue Description
I have tried running the code in a notebook and in a terminal with python, it works to my surprise.
Because while using a similar read_csv method in kedro CSVDataset (important thing here is that it uses the same pandas in the same environment), it throws the following error: `the 'pyarrow' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex)`
Which according to read_csv documentation and parser source code is correct
So I am not too sure what is causing the method to work in a notebook when it is not suppose to. I have also check that my data that is read from the above code is correct, by inspecting the contents
### Expected Behavior
The following error should be raised but not
`the 'pyarrow' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex)`
### Installed Versions
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-1048-gke
Version : #53-Ubuntu SMP Tue Nov 28 00:39:01 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_SG.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.23.5
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.2.0
pip : 23.1.2
Cython : 3.0.9
pytest : 7.4.4
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 : 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 : 3.8.3
numba : 0.59.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 15.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 2023.12.2
scipy : 1.12.0
sqlalchemy : 2.0.28
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
|
[
"Bug",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Maybe I'm misinterpreting the issue, but you're expecting it to raise if `sep='\\t'` with the `pyarrow` engine? \r\n\r\n`len('\\t') == 1` ",
"> Maybe I'm misinterpreting the issue, but you're expecting it to raise if `sep='\\t'` with the `pyarrow` engine?\r\n> \r\n> `len('\\t') == 1`\r\n\r\nYes, exactly. For some reason, it works fine in notebook which isn't suppose to be the right behavior.\r\nHowever, the dataframe looks perfectly fine.\r\nIf it works in notebook, but doesn't in kedro (which uses pandas), what should be the right behavior",
"any chance they are using ``sep=r\"\\t\",``?",
"Closing this for now, it looks like this is on their end. Please ping to reopen if you have validated that is indeed a pandas bug"
] |
2,191,671,175
| 57,884
|
ENH: Allow args to be specified for the pivot_table aggfunc
|
closed
| 2024-03-18T09:29:04
| 2024-06-06T21:16:35
| 2024-06-06T21:16:34
|
https://github.com/pandas-dev/pandas/issues/57884
| true
| null | null |
j-kapp
| 6
|
### Feature Type
- [X] Adding new functionality to pandas
- [ ] Changing existing functionality in pandas
- [ ] Removing existing functionality in pandas
### Problem Description
Currently the `pivot_table` has an `aggfunc` parameter which is used to do a groupby aggregation [here](https://github.com/pandas-dev/pandas/blob/main/pandas/core/reshape/pivot.py#L170). However, no additional arguments can be passed into that `agg` call. I'm specifically referring to the `*args` which can be specified in (df/series) [groupby.agg](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.agg.html) function. It would be very useful if `pivot_table` could accept additional arguments for the `aggfunc`.
### Feature Description
I'm not 100% sure, but I think it would be something like this:
1. Add a `**aggfunc_args` or `aggfunc_args: dict` parameter to the `pivot_table` function.
2. Do the same for the `__internal_pivot_table` function
3. Change the line [here](https://github.com/pandas-dev/pandas/blob/main/pandas/core/reshape/pivot.py#L170), from `agged = grouped.agg(aggfunc)` to `agged = grouped.agg(aggfunc, **aggfunc_args)`.
4. Update the docs
### Alternative Solutions
The same functionality can currently be achieved by specifying a custom function as `aggfunc`, but using that is much slower. My use case is pretty much the same as [this](https://stackoverflow.com/questions/56890105/how-to-keep-nan-in-pivot-table).
Instead of using `pd.pivot_table(... , aggfunc=lambda x: x.sum(min_count=1))`, I would like to be able to do `pd.pivot_table(... , aggfunc=sum, min_count=1)` or similiar.
### Additional Context
_No response_
|
[
"Enhancement",
"Reshaping"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the request - it makes sense to me to support passing through kwargs here. The only question is how:\r\n\r\n def pivot_table(self, ..., **kwargs)\r\n def pivot_table(self, ..., kwargs)\r\n def pivot_table(self, ..., args, kwargs)\r\n\r\nI do not think we should do\r\n\r\n def pivot_table(self, *args, ..., **kwargs)\r\n\r\nIf we want to agree with the current direction of apply/agg/transform, it would be the first of the above three options. Ref: https://github.com/pandas-dev/pandas/issues/40112",
"take\r\n",
"Hi, regarding that first option, would passing positional arguments to the aggfunc not be supported?\r\nI must say I agree on the signature you proposed on the referenced issue ( `func, args, kwargs, other_arg`, sort of like the third option here) being the best overall, however, it seems like this is not the current standard for pandas.",
"take",
"> Hi, regarding that first option, would passing positional arguments to the aggfunc not be supported? I must say I agree on the signature you proposed on the referenced issue ( `func, args, kwargs, other_arg`, sort of like the third option here) being the best overall, however, it seems like this is not the current standard for pandas.\r\n\r\n@rhshadrach Sorry to bother you, but could you share your thoughts on this matter?",
"@PF2100 - thanks for the ping! \r\n\r\n> I must say I agree on the signature you proposed on the referenced issue ( `func, args, kwargs, other_arg`, sort of like the third option here) being the best overall, however, it seems like this is not the current standard for pandas.\r\n\r\nSame here, but for consistency, it seems best to go with (1) for now. Changing from `**kwargs` to `kwargs` is likely to be quite noisy or users and I don't feel confident it will get enough support.\r\n\r\n> regarding that first option, would passing positional arguments to the aggfunc not be supported?\r\n\r\nCorrect."
] |
2,191,508,606
| 57,883
|
Bump pypa/cibuildwheel from 2.16.5 to 2.17.0
|
closed
| 2024-03-18T08:15:40
| 2024-03-18T16:49:57
| 2024-03-18T16:49:53
|
https://github.com/pandas-dev/pandas/pull/57883
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57883
|
https://github.com/pandas-dev/pandas/pull/57883
|
dependabot[bot]
| 0
|
Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.16.5 to 2.17.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/releases">pypa/cibuildwheel's releases</a>.</em></p>
<blockquote>
<h2>v2.17.0</h2>
<ul>
<li>🌟 Adds the ability to inherit configuration in TOML overrides. This makes certain configurations much simpler. If you're overriding an option like <code>before-build</code> or <code>environment</code>, and you just want to add an extra command or environment variable, you can just append (or prepend) to the previous config. See <a href="https://cibuildwheel.pypa.io/en/stable/options/#inherit">the docs</a> for more information. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1730">#1730</a>)</li>
<li>🌟 Adds official support for native arm64 macOS GitHub runners. To use them, just specify <code>macos-14</code> as an <code>os</code> of your job in your workflow file. You can also keep <code>macos-13</code> in your build matrix to build x86_64. Check out the new <a href="https://cibuildwheel.pypa.io/en/stable/setup/#github-actions">GitHub Actions example config</a>.</li>
<li>✨ You no longer need to specify <code>--platform</code> to run cibuildwheel locally! Instead it will detect your platform automatically. This was a safety feature, no longer necessary. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1727">#1727</a>)</li>
<li>🛠 Removed setuptools and wheel pinned versions. This only affects old-style projects without a <code>pyproject.toml</code>, projects with <code>pyproject.toml</code> are already getting fresh versions of their <code>build-system.requires</code> installed into an isolated environment. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1725">#1725</a>)</li>
<li>🛠 Improve how the GitHub Action passes arguments (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1757">#1757</a>)</li>
<li>🛠 Remove a system-wide install of pipx in the GitHub Action (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1745">#1745</a>)</li>
<li>🐛 No longer will cibuildwheel override the PIP_CONSTRAINT environment variable when using the <code>build</code> frontend. Instead it will be extended. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1675">#1675</a>)</li>
<li>🐛 Fix a bug where building and testing both x86_86 and arm64 wheels on the same runner caused the wrong architectures in the test environment (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1750">#1750</a>)</li>
<li>🐛 Fix a bug that prevented testing a CPython 3.8 wheel targeting macOS 11+ on x86_64 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1768">#1768</a>)</li>
<li>📚 Moved the docs onto the official PyPA domain - they're now available at <a href="https://cibuildwheel.pypa.io">https://cibuildwheel.pypa.io</a> . (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1775">#1775</a>)</li>
<li>📚 Docs and examples improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1762">#1762</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1734">#1734</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md">pypa/cibuildwheel's changelog</a>.</em></p>
<blockquote>
<h3>v2.17.0</h3>
<p><em>11 March 2024</em></p>
<ul>
<li>🌟 Adds the ability to inherit configuration in TOML overrides. This makes certain configurations much simpler. If you're overriding an option like <code>before-build</code> or <code>environment</code>, and you just want to add an extra command or environment variable, you can just append (or prepend) to the previous config. See <a href="https://cibuildwheel.pypa.io/en/stable/options/#inherit">the docs</a> for more information. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1730">#1730</a>)</li>
<li>🌟 Adds official support for native arm64 macOS GitHub runners. To use them, just specify <code>macos-14</code> as an <code>os</code> of your job in your workflow file. You can also keep <code>macos-13</code> in your build matrix to build x86_64. Check out the new <a href="https://cibuildwheel.pypa.io/en/stable/setup/#github-actions">GitHub Actions example config</a>.</li>
<li>✨ You no longer need to specify <code>--platform</code> to run cibuildwheel locally! Instead it will detect your platform automatically. This was a safety feature, no longer necessary. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1727">#1727</a>)</li>
<li>🛠 Removed setuptools and wheel pinned versions. This only affects old-style projects without a <code>pyproject.toml</code>, projects with <code>pyproject.toml</code> are already getting fresh versions of their <code>build-system.requires</code> installed into an isolated environment. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1725">#1725</a>)</li>
<li>🛠 Improve how the GitHub Action passes arguments (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1757">#1757</a>)</li>
<li>🛠 Remove a system-wide install of pipx in the GitHub Action (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1745">#1745</a>)</li>
<li>🐛 No longer will cibuildwheel override the PIP_CONSTRAINT environment variable when using the <code>build</code> frontend. Instead it will be extended. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1675">#1675</a>)</li>
<li>🐛 Fix a bug where building and testing both x86_86 and arm64 wheels on the same runner caused the wrong architectures in the test environment (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1750">#1750</a>)</li>
<li>🐛 Fix a bug that prevented testing a CPython 3.8 wheel targeting macOS 11+ on x86_64 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1768">#1768</a>)</li>
<li>📚 Moved the docs onto the official PyPA domain - they're now available at <a href="https://cibuildwheel.pypa.io">https://cibuildwheel.pypa.io</a> . (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1775">#1775</a>)</li>
<li>📚 Docs and examples improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1762">#1762</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1734">#1734</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pypa/cibuildwheel/commit/8d945475ac4b1aac4ae08b2fd27db9917158b6ce"><code>8d94547</code></a> Bump version: v2.17.0</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/ca06deb26f92b2b2c6019a3bc223875215fe4cf2"><code>ca06deb</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1775">#1775</a> from pypa/doc-domain</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f7e19222253830775777d4dc7e8cf56aa098d97f"><code>f7e1922</code></a> CirrusCI fixes (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1786">#1786</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/0d8e919dfc5b7631e641377671db317556dcc7ef"><code>0d8e919</code></a> [Bot] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1784">#1784</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/022de07dc13bb25455653a082449a0c038632ac0"><code>022de07</code></a> Merge pull request <a href="https://redirect.github.com/pypa/cibuildwheel/issues/1785">#1785</a> from pypa/revert-1783</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/920f574191fe30782d55398b7a0e70d62c999024"><code>920f574</code></a> Remove manylinux1 docker pin</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/5c06f3c28934b3830d065b17ab853c4465ce6623"><code>5c06f3c</code></a> docs: Add how to run tests in development (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1698">#1698</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/e2a0839555d4d2ffd366ac4cd933262f5974fd10"><code>e2a0839</code></a> fix: set SYSTEM_VERSION_COMPAT=0 during pip install on macos (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1768">#1768</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/87fff7728267ddada9c54df079e5864e5c5e5dfb"><code>87fff77</code></a> chore(deps): bump the actions group with 1 update (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1776">#1776</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/8ef9486aab2cc0aea71870a765265e294d84a679"><code>8ef9486</code></a> Add <code>pedalboard</code> to projects.yml. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/1781">#1781</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/pypa/cibuildwheel/compare/v2.16.5...v2.17.0">compare view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
- `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it
- `@dependabot merge` will merge this PR after your CI passes on it
- `@dependabot squash and merge` will squash and merge this PR after your CI passes on it
- `@dependabot cancel merge` will cancel a previously requested merge and block automerging
- `@dependabot reopen` will reopen this PR if it is closed
- `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
- `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency
- `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
- `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
- `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
</details>
|
[
"Build",
"CI",
"Dependencies"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,191,432,261
| 57,882
|
QST：pandas.groupby().diff()
|
closed
| 2024-03-18T07:30:32
| 2024-03-19T20:32:25
| 2024-03-19T20:32:25
|
https://github.com/pandas-dev/pandas/issues/57882
| true
| null | null |
zybmiximixi
| 1
|
### 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/78178582/pandas-for-groupby-diff
### Question about pandas
The groupby().diff() operation of pandas does not comply with the default as_index=True parameter and does not use the keys in groupby as indexes. Is this what is the reason.
|
[
"Usage Question",
"Needs Triage"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks for the report - the `as_index` argument [is documented](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.groupby.html) as:\r\n\r\n> This argument has no effect on filtrations (see the [filtrations in the user guide](https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration)), such as head(), tail(), nth() and in transformations (see the [transformations in the user guide](https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation)).\r\n\r\n`.diff` is a transformation. Closing."
] |
2,191,395,603
| 57,881
|
DOC: Fix a bug in the docstring of stack method
|
closed
| 2024-03-18T07:07:30
| 2024-03-18T16:29:46
| 2024-03-18T16:29:40
|
https://github.com/pandas-dev/pandas/pull/57881
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57881
|
https://github.com/pandas-dev/pandas/pull/57881
|
wanglc02
| 1
| null |
[
"Docs"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Thanks @wanglc02 "
] |
2,191,300,418
| 57,880
|
EHN: add ability to format index and col names to Styler
|
closed
| 2024-03-18T05:58:17
| 2024-04-12T00:11:48
| 2024-03-26T17:26:08
|
https://github.com/pandas-dev/pandas/pull/57880
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57880
|
https://github.com/pandas-dev/pandas/pull/57880
|
quangngd
| 15
|
- [ ] closes #48936 and closes #47489
- [ ] [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 #57362
Stage one. Part of an multi-stages effort: https://github.com/pandas-dev/pandas/pull/57880#issuecomment-2003636401
Adding a method to Styler and ensuring it works for the default HTML cases with tests in the appropriate pages.
|
[
"Enhancement",
"Styler"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"Hi thanks for this PR.\r\n\r\nA `Styler.format_names` or `Styler.format_index_names` method has been on the requested agenda for a while now.\r\nThis PR mixes three concepts, however. \r\n\r\n- One is adding a method to `Styler` and ensuring it works for the default HTML cases with tests in the appropriate pages.\r\n- Two is extending it to the `Styler.to_latex` implementation and adding specific to_latex tests.\r\n- Three is ensuring that the legacy `DataFrame.to_latex` implementation is capable of using it (in its limited form).\r\n\r\n- (Four would be about checking if `Styler.to_string` and `Styler.to_excel` will function accordingly)\r\n- (Five is ensuring documentation and examples exist in `Styler.format_names` )\r\n\r\nSince a method addition is quite a significant undertaking. I would ask that you split this PR into multiple stages.\r\nThe first stage I would look to review is part One. Adding the method just to `Styler` and adding tests in generic places, including testing the default implementation for HTML output.\r\n\r\nFrom what I can see you have followed the template of previous code and that is a great start.\r\n\r\n\r\n\r\n ",
"Hi @attack68,\r\n\r\nI've updated my code. It's now my attempt at the first point in your list.\r\nPlease help me take a look.",
"I think we should probably call this method `format_index_names`. Even though its longer I think it is more obvious what it does.\r\n\r\nI think we also need greater test coverage to cover lines.\r\nTake a look at https://github.com/pandas-dev/pandas/blob/main/pandas/tests/io/formats/style/test_format.py\r\n\r\nYou should probably look to replicate the tests for the specific case :\r\n\r\n- test_format_index_level\r\n- test_format_clear\r\n- test_format_callable\r\n- test_format_dict\r\n- test_display_format_index",
"Since your code also involves cases where things are hidden you should also include a test to show that, say if a multi-index has a level hidden, the format function still works correctly on the levels that are still shown. ",
"@attack68 added a bunch of new tests.\r\n\r\ndon't immediately see the point of `test_display_format_index` for this case so i didnt adapt it.",
"Are you familiar with `coverage` ? The idea was that that collection of tests should pretty much cover all your code branches. Some of the re-used material like `_maybe_wrap_formatter` is already well covered.",
"> Are you familiar with `coverage` ? The idea was that that collection of tests should pretty much cover all your code branches. Some of the re-used material like `_maybe_wrap_formatter` is already well covered.\r\n\r\nYeah. I ran coverage and see my new method is well covered. Mostly because i reuse a lot of things, as you have pointed out.\r\n\r\nSo i don't really understand what your suggestion is. Can you elaborate? Or maybe most of the material is reuse so you suggest we could achieve the same coverage without so much test? I hope not.",
"My point is you need to cover your code with your tests. Aim for 100%. It seems you have achieved that. Do you agree?",
"Thought so too.\n\nIf there is nothing major, I think i would fix up whats left and wrap this up. Then ill start to work on the next point in a new PR.\n\nIn case if things go stale, this PR can still be merged as an enhancement on its own. (ofc versionadded and whatsnew have to be filled before merge).\n\nDoes that sound good to you?",
"Indeed, is what I would suggest. Thanks for the good work here and following the structure. Hopefully the other reviewers will recognise this as quite a tidy addition and be fairly quick approve also. I'll look to add my approval when all the bits in and tests pass.\r\n\r\nThere is one additional file you have to add to: doc/source/reference/style.rst",
"Do you know the target version? Or should we wait for an active member?",
"> Could you add a whatsnew note in `v3.0.0.rst` under `Other Enhancements`?\r\n\r\nAdded",
"Thanks @quangngd ",
"@quangngd are you interested to work on stage 2 of this. I think it would be good to get in so that there is no disconnect for users between these functions and the LaTeX output.\r\n\r\nTo my knowledge there are currently no undocumented issues between the styler and the LaTeX output so good for me to keep an eye on.",
"@attack68 \r\nSure! Been a bit busy at work lately but I'll find some time to do it in the upcoming weekends."
] |
2,191,043,897
| 57,879
|
CI: Better error control in the validation of docstrings
|
closed
| 2024-03-18T01:30:35
| 2024-03-29T06:48:04
| 2024-03-19T01:05:39
|
https://github.com/pandas-dev/pandas/pull/57879
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57879
|
https://github.com/pandas-dev/pandas/pull/57879
|
datapythonista
| 7
|
Making the validation of docstrings more robust, main changes:
- If we ignore an error that doesn't fail, the CI will report it and break (there were 30 errors being ignored that are already fixed and removed from the list of ignores here)
- Instead of specifying all the errors to validate (that are all but one now) we can specify errors to skip (there is one only that we skip, the lack of an extended summary in a docstring). This simplifies the script a bit
- In case an unknown error code is used when ignoring errors, the message should be more descriptive
- I created an alias for `--ignore_errors` as `-i`, so the list of errors to ignore is a bit easier to read
CC: @dontgoto @jordan-d-murphy
|
[
"Docs",
"CI"
] | 2
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
[
"Nice work! thank you @datapythonista ",
"with this ticket (#57879), and now that [CI: speedup docstring check consecutive runs #57826](https://github.com/pandas-dev/pandas/pull/57826) is merged in, I'm closing the following issues And opening a new issue to address these based on the new approach we've implemented. \r\n\r\n> [DOC: fix GL08 errors in docstrings](https://github.com/pandas-dev/pandas/issues/57443)\r\n> [DOC: fix PR01 errors in docstrings](https://github.com/pandas-dev/pandas/issues/57438)\r\n> [DOC: fix PR07 errors in docstrings](https://github.com/pandas-dev/pandas/issues/57420)\r\n> [DOC: fix SA01 errors in docstrings](https://github.com/pandas-dev/pandas/issues/57417)\r\n> [DOC: fix RT03 errors in docstrings](https://github.com/pandas-dev/pandas/issues/57416)\r\n> [DOC: fix PR02 errors in docstrings](https://github.com/pandas-dev/pandas/issues/57111)\r\n\r\nThanks for all the work that's gone into this! this is a much cleaner approach, and fixing these will now be more straightforward. Big win in my opinion! ",
"> And opening a new issue to address these based on the new approach we've implemented.\r\n\r\nWhat I'd do is create a master issue if there is not one already to fix the docstrings, and then create smaller issues labelled as \"good first issue\". For example:\r\n\r\nIssue 1 to address:\r\n```\r\n -i pandas.Categorical.__array__ SA01\\\r\n -i pandas.Categorical.codes SA01\\\r\n -i pandas.Categorical.dtype SA01\\\r\n -i pandas.Categorical.from_codes SA01\\\r\n -i pandas.Categorical.ordered SA01\\\r\n -i pandas.CategoricalDtype.categories SA01\\\r\n -i pandas.CategoricalDtype.ordered SA01\\\r\n -i pandas.CategoricalIndex.codes SA01\\\r\n -i pandas.CategoricalIndex.ordered SA01\\\r\n```\r\n\r\nIssue 2 to address:\r\n```\r\n -i pandas.HDFStore.append PR01,SA01\\\r\n -i pandas.HDFStore.get SA01\\\r\n -i pandas.HDFStore.groups SA01\\\r\n -i pandas.HDFStore.info RT03,SA01\\\r\n -i pandas.HDFStore.keys SA01\\\r\n -i pandas.HDFStore.put PR01,SA01\\\r\n -i pandas.HDFStore.select SA01\\\r\n -i pandas.HDFStore.walk SA01\\\r\n```\r\n\r\nIssue 3 to address:\r\n```\r\n -i pandas.Int16Dtype SA01\\\r\n -i pandas.Int32Dtype SA01\\\r\n -i pandas.Int64Dtype SA01\\\r\n -i pandas.Int8Dtype SA01\\\r\n```\r\n\r\nIssue 4 to address:\r\n```\r\n -i pandas.Interval PR02\\\r\n -i pandas.Interval.closed SA01\\\r\n -i pandas.Interval.left SA01\\\r\n -i pandas.Interval.mid SA01\\\r\n -i pandas.Interval.right SA01\\\r\n```\r\n\r\n...\r\n\r\nI think it'll make the work of contributors easier by addressing those in groups. In particular, the see also section of many of those would be quite easy since the docstrings they'll be cross-referencing each other in many cases.\r\n\r\nIf you don't have triagge permissions in this repo, please let me know, I'll give them to you, so you can labelled the issues as \"good first issue\" and anything else needed.",
"Thanks for the guidance, @datapythonista ! \r\n\r\nI agree, that sounds like a great approach. I'll set it up once this gets merged in so I can grab the updated code snippets from main. \r\n\r\nI don't have those permissions, it would be helpful if you can grant them to me. Thank you! \r\n\r\n",
"That's some great simplifications for the error handling logic!",
"Thanks @datapythonista ",
"Opened [DOC: Enforce Numpy Docstring Validation (Parent Issue) #58063](https://github.com/pandas-dev/pandas/issues/58063) as a parent issue for fixing docstrings based on the refactoring in code_checks.sh \r\n\r\nFeel free to swing by and help out! 🙂"
] |
2,190,987,355
| 57,878
|
CI: make docstring checks "instantaneous"
|
closed
| 2024-03-18T00:23:30
| 2024-03-19T18:27:43
| 2024-03-19T18:27:43
|
https://github.com/pandas-dev/pandas/pull/57878
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57878
|
https://github.com/pandas-dev/pandas/pull/57878
|
dontgoto
| 2
|
- [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.
This PR reduces the `check_code.sh docstrings` runtime to under 10 seconds.
The key changes are in [`validate_pep8_for_examples`](https://github.com/dontgoto/pandas/blob/14e3915f21dc35fcdcc5ce198b35245fb51a68dd/scripts/validate_docstrings.py#L151), which was refactored to call the pep8 CLI once with a list of files to analyse. The functions in the call stack above were changed to handle lists instead of single objects as well. I also added a new test since the `prefix` parameter was not properly tested before.
Let me know if you'd rather see this split into multiple PRs.
I'd also wait until #57879 is merged as there will be quite a few merge conflicts.
Unrelated code checks seem to be failing here and in other PRs.
|
[] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"@MarcoGorelli @datapythonista ",
"Sorry @dontgoto, this is now conflicting. Results look very impressive, thanks for the work on this. If you can split it in different PRs that would be great, it'll make reviewing easier. "
] |
2,190,896,041
| 57,877
|
CLN: replace deprecated freqs `H`/`M` with `h`/`ME` in tests for plotting
|
closed
| 2024-03-17T22:26:25
| 2024-03-21T10:06:31
| 2024-03-21T10:06:31
|
https://github.com/pandas-dev/pandas/pull/57877
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57877
|
https://github.com/pandas-dev/pandas/pull/57877
|
natmokval
| 0
|
xref #52064, #54939
replaced deprecated frequencies `H` and `M` with correct `h` and `ME` in tests for plotting, marked as `@pytest.mark.xfail`
|
[
"Frequency",
"Clean"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[] |
2,190,752,007
| 57,876
|
BUG: pandas pivot_table count over a dataframe with 2 columns results in empty dataset when using aggfunc="count"
|
closed
| 2024-03-17T16:29:36
| 2025-04-23T21:47:19
| 2025-04-23T21:47:18
|
https://github.com/pandas-dev/pandas/issues/57876
| true
| null | null |
luxspes
| 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
Run:
```python
import pandas as pd
data = {'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'],
'Value': [10, 20, 30, 40, 50, 60, 70, 80, 90]}
df = pd.DataFrame(data)
pivot_table = df.pivot_table(index='Category', columns='Value', values='Value', aggfunc="count")
print(pivot_table)
```
and you will get:
```
Empty DataFrame
Columns: []
```
Change to
```
pivot_table = df.pivot_table(index='Category', columns='Value', values='Value', aggfunc=len)
```
and you will get:
```
Value 10 20 30 40 50 60 70 80 90
Category
A 1.0 NaN NaN 1.0 NaN NaN 1.0 NaN NaN
B NaN 1.0 NaN NaN 1.0 NaN NaN 1.0 NaN
C NaN NaN 1.0 NaN NaN 1.0 NaN NaN 1.0
```
### Issue Description
Try to use df.pivot_table over a dataframe with 2 columns, using same column name for the columns and values paramete, aggregate using "count" gets you an empty dataset. Switch to len or size and you get the right result.
Furthermore, strangely, this workaround somehow fixes "count":
```python
df["ValueCopy"] = df["Value"]
pivot_table = df.pivot_table(index='Category', columns='Value', values='ValueCopy', aggfunc="count")
```
ValueCopy and Value contain identical data, so they should be interchangeable, that is, using either should lead to the same result, but they do not.
For sanity checking I tried the same pivoting in **Polars**, and there, count works fine:
```python
import polars as pl
# Create a DataFrame
data = {
'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'],
'Value': [10, 20, 30, 40, 50, 60, 70, 80, 90]
}
df = pl.DataFrame(data)
# Pivot the DataFrame
pivot_table = df.pivot(index='Category', columns='Value', values='Value', aggregate_function="count")
# Print the pivot table
print(pivot_table)
```
results in:
```
Shape: (3, 10)
┌──────────┬──────┬──────┬──────┬───┬──────┬──────┬──────┬──────┐
│ Category ┆ 10 ┆ 20 ┆ 30 ┆ … ┆ 60 ┆ 70 ┆ 80 ┆ 90 │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ u32 ┆ u32 ┆ u32 ┆ ┆ u32 ┆ u32 ┆ u32 ┆ u32 │
╞══════════╪══════╪══════╪══════╪═══╪══════╪══════╪══════╪══════╡
│ A ┆ 1 ┆ null ┆ null ┆ … ┆ null ┆ 1 ┆ null ┆ null │
│ B ┆ null ┆ 1 ┆ null ┆ … ┆ null ┆ null ┆ 1 ┆ null │
│ C ┆ null ┆ null ┆ 1 ┆ … ┆ 1 ┆ null ┆ null ┆ 1 │
└──────────┴──────┴──────┴──────┴───┴──────┴──────┴──────┴──────┘
```
Likewise with **DuckDb**:
```python
import pandas as pd
import duckdb
# Your existing DataFrame
data = {'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'],
'Value': [10, 20, 30, 40, 50, 60, 70, 80, 90]}
df = pd.DataFrame(data)
# Create a DuckDB connection
con = duckdb.connect()
# Register the DataFrame with DuckDB
con.register('df_pivot', df)
# Perform the pivot operation in DuckDB
query = """
PIVOT df_pivot
ON Value
USING Count(Value)
GROUP BY Category
"""
pivot_table = con.execute(query).fetchdf()
print(pivot_table)
```
it works out all-right:
```
Category 10 20 30 40 50 60 70 80 90
0 A 1 0 0 1 0 0 1 0 0
1 B 0 1 0 0 1 0 0 1 0
2 C 0 0 1 0 0 1 0 0 1
```
### Expected Behavior
`pivot_table = df.pivot_table(index='Category', columns='Value', values='Value', aggfunc="count")`
and
`pivot_table = df.pivot_table(index='Category', columns='Value', values='Value', aggfunc="size")`
and
`pivot_table = df.pivot_table(index='Category', columns='Value', values='Value', aggfunc=len)`
should all produce an non empty dataset like the one below (but `count` somehow fails and produces an empty one):
```
Value 10 20 30 40 50 60 70 80 90
Category
A 1.0 NaN NaN 1.0 NaN NaN 1.0 NaN NaN
B NaN 1.0 NaN NaN 1.0 NaN NaN 1.0 NaN
C NaN NaN 1.0 NaN NaN 1.0 NaN NaN 1.0
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 6.5.0-1015-gcp
Version : #15~22.04.1-Ubuntu SMP Wed Feb 14 21:22:00 UTC 2024
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.1
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.0.2.post0
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 : None
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 : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : 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
|
[
"When I read the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html) of `pandas.pivot_table`, I think it assumes that the dataframe has a value column(s) to aggregate, but your `Value` column works like a category (`index` or `columns`) to aggregate values.\r\n\r\nI guess probably this is not what you are looking for, but here is one way to avoid empty dataframe.\r\n\r\n```python\r\nimport pandas as pd\r\n\r\ndata = {'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'],\r\n 'Value': [10, 20, 30, 40, 50, 60, 70, 80, 90]}\r\n\r\ndf = pd.DataFrame(data)\r\ndf[\"num\"] = 1\r\n\r\npivot_table = df.pivot_table(index='Category', columns='Value', values='num', aggfunc=\"count\")\r\n\r\nprint(pivot_table)\r\n```",
"Thanks for the folllow up @yukikitayama \r\nIndeed, I am aware of that \"dummy column\" workaround, however I think that should not be necessary (as is not necessary in Polars and in SQL itself)\r\n\r\nMaybe the solution is what @e-motta suggests in [this Stackoverflow comment](https://stackoverflow.com/questions/78173429/why-pandas-pivot-table-count-over-a-dataframe-with-2-columns-works-with-aggfunc#comment137831529_78176878)?\r\n"
] |
2,190,683,862
| 57,875
|
ENH: add warning when export to tar, zip in append mode
|
closed
| 2024-03-17T13:55:14
| 2024-04-01T18:24:41
| 2024-04-01T18:24:34
|
https://github.com/pandas-dev/pandas/pull/57875
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57875
|
https://github.com/pandas-dev/pandas/pull/57875
|
quangngd
| 5
|
- [ ] closes #56778 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
|
[
"IO Data",
"Warnings"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"tagging @twoertwein since you commented on the issue",
"Can you add a test case for this, e.g., calling to_csv with mode=\"a\"?",
"pre-commit.ci autofix",
"@twoertwein test added",
"Thanks @quangngd "
] |
2,190,575,911
| 57,874
|
DOC: add note to how pandas deduplicate header when read from file
|
closed
| 2024-03-17T09:48:29
| 2024-04-01T18:43:01
| 2024-04-01T18:42:54
|
https://github.com/pandas-dev/pandas/pull/57874
| true
|
https://api.github.com/repos/pandas-dev/pandas/pulls/57874
|
https://github.com/pandas-dev/pandas/pull/57874
|
quangngd
| 3
|
- [ ] closes #57792 (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.
<img width="904" alt="image" src="https://github.com/pandas-dev/pandas/assets/30631476/3d5ee26f-8b6c-4493-bcfd-5491cd68417d">
|
[
"Docs",
"IO CSV"
] | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
[
"/preview",
"Website preview of this PR available at: https://pandas.pydata.org/preview/pandas-dev/pandas/57874/",
"Thanks @quangngd "
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.