organization string | repo_name string | base_commit string | iss_html_url string | iss_label string | title string | body string | code null | pr_html_url string | commit_html_url string | file_loc string | own_code_loc list | ass_file_loc list | other_rep_loc list | analysis dict | loctype dict | iss_has_pr int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
xtekky | gpt4free | c5691c5993f8595d90052e4a81b582d63fe81919 | https://github.com/xtekky/gpt4free/issues/913 | bug
stale | TypeError: unhashable type: 'Model' | import g4f, asyncio
async def run_async():
_providers = [
g4f.Provider.ChatgptAi,
g4f.Provider.ChatgptLogin,
g4f.Provider.DeepAi,
g4f.Provider.Opchatgpts,
g4f.Provider.Vercel,
g4f.Provider.Wewordle,
g4f.Provider.You,
g4f.Provider.Yqcloud,
]
responses =... | null | https://github.com/xtekky/gpt4free/pull/924 | null | {'base_commit': 'c5691c5993f8595d90052e4a81b582d63fe81919', 'files': [{'path': 'README.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [241, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 277]}}}, {'p... | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/Provider/CodeLinkAva.py",
"g4f/Provider/H2o.py",
"g4f/Provider/ChatgptLogin.py",
"g4f/Provider/Aivvm.py",
"g4f/Provider/HuggingChat.py",
"g4f/Provider/__init__.py",
"g4f/__init__.py",
"g4f/models.py",
"g4f/Provider/Vitalentum.py",
"g4f/Provider/Bard.py",
"g... | 1 |
xtekky | gpt4free | 2dcdce5422cd01cd058490d4daef5f69300cca89 | https://github.com/xtekky/gpt4free/issues/2006 | bug
stale | CORS not enabled for API | **Bug description**
Run docker image
Try to access the Completion API via Javascript console in Browser
`fetch("http://localhost:1337/v1/chat/completions", {
"headers": {
"accept-language": "de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US;q=0.6",
"cache-control": "no-cache",
"content-type": "applicat... | null | https://github.com/xtekky/gpt4free/pull/2281 | null | {'base_commit': '2dcdce5422cd01cd058490d4daef5f69300cca89', 'files': [{'path': 'g4f/api/__init__.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [14]}, "(None, 'create_app', 24)": {'add': [26]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/api/__init__.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
xtekky | gpt4free | 5d8e603095156303a016cc16e2811a8f2bc74f15 | https://github.com/xtekky/gpt4free/issues/1338 | bug | How to use providers via HTTP Request ? | I am trying to use the api version of this project, but, the providers option in my request is not working, am i doing something wrong?
```js
const response = await axios.post(
`${API_BASE}`,
{
provider: 'g4f.Provider.ChatgptAi',
temperature:0.75,
top_p: 0.6,
model:... | null | https://github.com/xtekky/gpt4free/pull/1344 | null | {'base_commit': '5d8e603095156303a016cc16e2811a8f2bc74f15', 'files': [{'path': 'g4f/api/__init__.py', 'status': 'modified', 'Loc': {"('Api', 'chat_completions', 71)": {'add': [86, 94, 100]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/api/__init__.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
xtekky | gpt4free | 0d8e4ffa2c0706b0381f53c3985d04255b7170f5 | https://github.com/xtekky/gpt4free/issues/2334 | bug | Model "command-r+" returning 401 error: "You have to be logged in" | **Bug description**
I'm experiencing an issue with the model "command-r+" not working. When attempting to use this model through the g4f API (running "g4f api"), I receive the following error:
```
ERROR:root:Request failed with status code: 401, response: {"error":"You have to be logged in."}
Traceback (most re... | null | https://github.com/xtekky/gpt4free/pull/2313 | null | {'base_commit': '0d8e4ffa2c0706b0381f53c3985d04255b7170f5', 'files': [{'path': 'README.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [72], 'mod': [31, 169, 186, 197, 198, 199, 200, 293, 299, 305, 773, 776]}}}, {'path': 'docs/async_client.md', 'status': 'modified', 'Loc': {'(None, None, None)': {'add'... | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/Provider/Ai4Chat.py",
"g4f/Provider/ChatifyAI.py",
"g4f/Provider/ChatGptEs.py",
"g4f/Provider/DeepInfraChat.py",
"g4f/Provider/AiMathGPT.py",
"g4f/Provider/Allyfy.py",
"g4f/Provider/ChatGpt.py",
"g4f/Provider/Bing.py",
"g4f/Provider/AIUncensored.py",
"g4f/Provi... | 1 |
xtekky | gpt4free | b2bfc88218d3ffb367c6a4bcb14c0748666d348f | https://github.com/xtekky/gpt4free/issues/1206 | bug
stale | OpenaiChat:\lib\asyncio\base_events.py", line 498, in _make_subprocess_transport raise NotImplementedError | 
this problem happens today after I update to the latest version! | null | https://github.com/xtekky/gpt4free/pull/1207 | null | {'base_commit': 'b2bfc88218d3ffb367c6a4bcb14c0748666d348f', 'files': [{'path': 'g4f/Provider/needs_auth/OpenaiChat.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [4]}, "(None, 'get_arkose_token', 146)": {'mod': [147, 148, 149, 150, 151, 177, 178, 179, 180, 182, 186, 187]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"g4f/Provider/needs_auth/OpenaiChat.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
Z4nzu | hackingtool | b2cf73c8f414cd9c30d920beb2e7a000934c1f92 | https://github.com/Z4nzu/hackingtool/issues/354 | target not found yay and python-pip.19.1.1-1 | i have a problem when i try to run bash install.sh it says error target not found yay, python-pip.19.1.1-1 , i have installed the yay and i have no idea how to install python-pip so i need help.
OS: Arch linux 64x_86X
shell: bash 5.1.6
': {'mod': [74, 96, 111]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [],
"doc": [],
"test": [],
"config": [],
"asset": [
"install.sh"
]
} | 1 | |
Z4nzu | hackingtool | 1e088ad35b66dda0ee9139a5220627f86cb54365 | https://github.com/Z4nzu/hackingtool/issues/347 | enhancement | Typos found by codespell | ./tools/xss_attack.py:107: vulnerabilites ==> vulnerabilities
./tools/information_gathering_tools.py:87: Scaning ==> Scanning
./tools/information_gathering_tools.py:117: informations ==> information
./tools/information_gathering_tools.py:168: informations ==> information
./tools/forensic_tools.py:60: Aquire ==> Acq... | null | https://github.com/Z4nzu/hackingtool/pull/350 | null | {'base_commit': '1e088ad35b66dda0ee9139a5220627f86cb54365', 'files': [{'path': '.github/workflows/lint_python.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [25]}}}, {'path': 'tools/forensic_tools.py', 'status': 'modified', 'Loc': {"('Guymager', None, 59)": {'mod': [60]}}}, {'path': 'tools/informatio... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"tools/wireless_attack_tools.py",
"tools/xss_attack.py",
"tools/phising_attack.py",
"tools/forensic_tools.py",
"tools/others/socialmedia_finder.py",
"tools/payload_creator.py",
"tools/webattack.py",
"tools/information_gathering_tools.py"
],
"doc": [],
"test": [],
"c... | 1 |
Z4nzu | hackingtool | 0a4faeac9c4f93a61c937b0e57023b693beeca6f | https://github.com/Z4nzu/hackingtool/issues/174 | SyntaxError: invalid syntax | Traceback (most recent call last):
File "/home/kali/hackingtool/hackingtool.py", line 11, in <module>
from tools.ddos import DDOSTools
File "/home/kali/hackingtool/tools/ddos.py", line 29
"sudo", "python3 ddos", method, url, socks_type5.4.1, threads, proxylist, multiple, timer])
I'm getting this erro... | null | https://github.com/Z4nzu/hackingtool/pull/176 | null | {'base_commit': '0a4faeac9c4f93a61c937b0e57023b693beeca6f', 'files': [{'path': 'tools/ddos.py', 'status': 'modified', 'Loc': {"('ddos', 'run', 20)": {'mod': [29]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"tools/ddos.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 | |
scikit-learn | scikit-learn | 0e8e38e3b2f4b79f03fe8a3e655b9f506ab0f2a6 | https://github.com/scikit-learn/scikit-learn/issues/768 | Arpack wrappers fail with new scipy | I have scipy 0.11.0.dev-c1ea274. This does not seem to play well with the current arpack wrappers.
I'm a bit out of my depth there, though.
| null | https://github.com/scikit-learn/scikit-learn/pull/802 | null | {'base_commit': '0e8e38e3b2f4b79f03fe8a3e655b9f506ab0f2a6', 'files': [{'path': 'sklearn/utils/arpack.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [55]}, "(None, 'svds', 1540)": {'add': [1598], 'mod': [1540]}, "(None, 'eigs', 1048)": {'mod': [1048]}, "(None, 'eigsh', 1264)": {'mod': [1264]}}}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/utils/arpack.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 | |
scikit-learn | scikit-learn | bb7e34bc52461749e6014787a05a9507eda11011 | https://github.com/scikit-learn/scikit-learn/issues/21668 | Build / CI
cython | CI with boundscheck=False | I really dislike segmentation faults! Unfortunately, there are many issues reporting them.
Findings in #21654, #21283 were easier with setting `boundscheck = True`.
**Proposition**
Set up one CI configuration that runs with `boundscheck = True` globally which should be easier now that #21512 is merged. | null | https://github.com/scikit-learn/scikit-learn/pull/21779 | null | {'base_commit': 'c9e5067cb14de578ab48b64f399743b994e3ca94', 'files': [{'path': 'azure-pipelines.yml', 'status': 'modified', 'Loc': {'(None, None, 202)': {'add': [202]}}}, {'path': 'doc/computing/parallelism.rst', 'status': 'modified', 'Loc': {'(None, None, 216)': {'add': [216]}}}, {'path': 'sklearn/_build_utils/__init_... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/_build_utils/__init__.py"
],
"doc": [
"doc/computing/parallelism.rst"
],
"test": [],
"config": [
"azure-pipelines.yml"
],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 64ab789905077ba8990522688c11177442e5e91f | https://github.com/scikit-learn/scikit-learn/issues/29358 | Documentation | Sprints page | ### Describe the issue linked to the documentation
The following sprints are listed:
https://scikit-learn.org/stable/about.html#sprints
But, that is a small subset, given the list here:
https://blog.scikit-learn.org/sprints/
Are the sprints posted on the "About Us" page of a certain criteria, such as Dev spr... | null | https://github.com/scikit-learn/scikit-learn/pull/29418 | null | {'base_commit': '64ab789905077ba8990522688c11177442e5e91f', 'files': [{'path': 'doc/about.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [548, 549, 551, 552, 553, 554, 555, 557, 558, 559, 560, 561, 563, 564, 565]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [],
"doc": [
"doc/about.rst"
],
"test": [],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 41e129f1a6eb17a39ff0b25f682d903d0ae3c5af | https://github.com/scikit-learn/scikit-learn/issues/5991 | Easy
Enhancement | PERF : StratifiedShuffleSplit is slow when using large number of classes | When using large number of classes (e.g. > 10000, e.g for recommender systems), `StratifiedShuffleSplit` is very slow when compared to `ShuffleSplit`. Looking at the code, I believe that the following part:
``` python
for i, class_i in enumerate(classes):
permutation = rng.permutation(clas... | null | https://github.com/scikit-learn/scikit-learn/pull/9197 | null | {'base_commit': '41e129f1a6eb17a39ff0b25f682d903d0ae3c5af', 'files': [{'path': 'doc/whats_new.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [219]}}}, {'path': 'sklearn/model_selection/_split.py', 'status': 'modified', 'Loc': {"('StratifiedShuffleSplit', '_iter_indices', 1495)": {'add': [1523], 'mod'... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/model_selection/_split.py"
],
"doc": [
"doc/whats_new.rst"
],
"test": [],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 4143356c3c51831300789e4fdf795d83716dbab6 | https://github.com/scikit-learn/scikit-learn/issues/10336 | help wanted | Should mixture models have a clusterer-compatible interface | Mixture models are currently a bit different. They are basically clusterers, except they are probabilistic, and are applied to inductive problems unlike many clusterers. But they are unlike clusterers in API:
* they have an `n_components` parameter, with identical purpose to `n_clusters`
* they do not store the `labe... | null | https://github.com/scikit-learn/scikit-learn/pull/11281 | null | {'base_commit': '4143356c3c51831300789e4fdf795d83716dbab6', 'files': [{'path': 'doc/whats_new/v0.20.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [583]}}}, {'path': 'sklearn/mixture/base.py', 'status': 'modified', 'Loc': {"('BaseMixture', 'fit', 172)": {'add': [190], 'mod': [175, 243]}}}, {'path': '... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/mixture/base.py"
],
"doc": [
"doc/whats_new/v0.20.rst"
],
"test": [
"sklearn/mixture/tests/test_bayesian_mixture.py",
"sklearn/mixture/tests/test_gaussian_mixture.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | d7795a431e30d23f7e8499bdbe89dbdc6e9a068e | https://github.com/scikit-learn/scikit-learn/issues/16001 | Bug
Easy
good first issue
help wanted | Possible infinite loop iterations in synthetic data sets generation module | Hello,
I found two code snippets in https://github.com/scikit-learn/scikit-learn/blob/7e85a6d1f/sklearn/datasets/_samples_generator.py are susceptible to infinite loop iterations when using make_multilabel_classification():
1) https://github.com/scikit-learn/scikit-learn/blob/7e85a6d1f/sklearn/datasets/_samples_g... | null | https://github.com/scikit-learn/scikit-learn/pull/16006 | null | {'base_commit': 'd7795a431e30d23f7e8499bdbe89dbdc6e9a068e', 'files': [{'path': 'doc/whats_new/v0.23.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [65]}}}, {'path': 'sklearn/datasets/_samples_generator.py', 'status': 'modified', 'Loc': {"(None, 'make_multilabel_classification', 263)": {'add': [344]}}... | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/datasets/_samples_generator.py"
],
"doc": [
"doc/whats_new/v0.23.rst"
],
"test": [
"sklearn/datasets/tests/test_samples_generator.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 0e3cbbdcdfeec1c6b10aea11524add6350a8f4e0 | https://github.com/scikit-learn/scikit-learn/issues/933 | Speed up tree construction | CC: @pprett @amueller @bdholt1
Hi folks,
Everyone will agree that tree-based methods have shown to perform quite well (e.g., the recent achievement of Peter!) and are increasingly used by our users. However, the tree module still has a major drawback: it is slow as hell in comparison to other machine learning packag... | null | https://github.com/scikit-learn/scikit-learn/pull/946 | null | {'base_commit': '0e3cbbdcdfeec1c6b10aea11524add6350a8f4e0', 'files': [{'path': 'doc/whats_new.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [11]}}}, {'path': 'sklearn/ensemble/_gradient_boosting.c', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [381, 637, 673, 746, 931, 973, 4913, 5993... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/tree/_tree.pyx",
"sklearn/ensemble/_gradient_boosting.pyx",
"sklearn/ensemble/_gradient_boosting.c",
"sklearn/ensemble/gradient_boosting.py",
"sklearn/ensemble/forest.py",
"sklearn/tree/tree.py"
],
"doc": [
"doc/whats_new.rst"
],
"test": [
"sklearn/tree/tes... | 1 | |
scikit-learn | scikit-learn | 77aeb825b6494de1e3a2c1e7233b182e05d55ab0 | https://github.com/scikit-learn/scikit-learn/issues/27982 | Documentation
good first issue
help wanted | Ensure that we have an example in the docstring of each public function or class | We should make sure that we have a small example for all public functions or classes. Most of the missing examples are linked to functions.
I could list the following classes and functions for which `numpydoc` did not find any example:
- [x] sklearn.base.BaseEstimator
- [x] sklearn.base.BiclusterMixin
- [x] skl... | null | https://github.com/scikit-learn/scikit-learn/pull/28564 | null | {'base_commit': 'd967cfe8124902181892411b18b50dce9921a32d', 'files': [{'path': 'sklearn/datasets/_samples_generator.py', 'status': 'modified', 'Loc': {"(None, 'make_low_rank_matrix', 1359)": {'add': [1413]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/datasets/_samples_generator.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | e11c4d21a4579f0d49f414a4b76e386f80f0f074 | https://github.com/scikit-learn/scikit-learn/issues/19269 | New Feature
module:datasets | sklearn.datasets.load_files select file extension | <!--
If you want to propose a new algorithm, please refer first to the scikit-learn
inclusion criterion:
https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms
-->
#### Describe the workflow you want to enable
When using load_files in a directory where there are different ki... | null | https://github.com/scikit-learn/scikit-learn/pull/22498 | null | {'base_commit': 'e11c4d21a4579f0d49f414a4b76e386f80f0f074', 'files': [{'path': 'doc/whats_new/v1.1.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [175]}}}, {'path': 'sklearn/datasets/_base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [13]}, "(None, 'load_files', 99)": {'add': [108... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/datasets/_base.py"
],
"doc": [
"doc/whats_new/v1.1.rst"
],
"test": [
"sklearn/datasets/tests/test_base.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | cdd693bf955acd2a97cce48011d168c6b1ef316d | https://github.com/scikit-learn/scikit-learn/issues/8364 | Easy
Documentation
Sprint | Matplotlib update on CI makes example look different | The examples look different on the current dev website, in particular the classifier comparison that's on the landing pages looks a bit odd now:
http://scikit-learn.org/dev/auto_examples/classification/plot_classifier_comparison.html
I suspect the culprit is the CI upgrading to matplotlib v2. I think we should go t... | null | https://github.com/scikit-learn/scikit-learn/pull/8516 https://github.com/scikit-learn/scikit-learn/pull/8369 | null | {'base_commit': '676e8630243b894aa2976ef6fb6048f9880b8a23', 'files': [{'path': 'examples/svm/plot_separating_hyperplane.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [15, 20], 'mod': [18, 19, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45]}}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"examples/svm/plot_separating_hyperplane.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | 839b356f45fac7724eab739dcc129a0c8f650a23 | https://github.com/scikit-learn/scikit-learn/issues/15005 | API | Implement SLEP009: keyword-only arguments | [SLEP009](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep009/proposal.html) is all but accepted.
It proposes to make most parameters keyword-only.
We should do this by first:
* [x] Merging #13311
* [x] Perhaps getting some stats on usage of positional arguments as per https://github.co... | null | https://github.com/scikit-learn/scikit-learn/pull/17007 https://github.com/scikit-learn/scikit-learn/pull/17046 https://github.com/scikit-learn/scikit-learn/pull/17006 https://github.com/scikit-learn/scikit-learn/pull/17005 https://github.com/scikit-learn/scikit-learn/pull/13311 https://github.com/scikit-learn/scikit-l... | null | {'base_commit': '839b356f45fac7724eab739dcc129a0c8f650a23', 'files': [{'path': 'sklearn/datasets/_base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [19]}, "(None, 'load_files', 83)": {'mod': [83]}, "(None, 'load_wine', 270)": {'mod': [270]}, "(None, 'load_iris', 384)": {'mod': [384]}, "(None, 'load_... | [] | [] | [] | {
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"sklearn/datasets/_olivetti_faces.py",
"sklearn/datasets/_samples_generator.py",
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scikit-learn | scikit-learn | 62d205980446a1abc1065f4332fd74eee57fcf73 | https://github.com/scikit-learn/scikit-learn/issues/12779 | Easy
good first issue | Remove "from __future__ import XXX" | Given #12746, I think we should remove ``from __future__ import XXX``, right? @adrinjalali
```
$ git grep "from __future__ import" | wc -l
147
``` | null | https://github.com/scikit-learn/scikit-learn/pull/13079 | null | {'base_commit': '62d205980446a1abc1065f4332fd74eee57fcf73', 'files': [{'path': 'sklearn/utils/_random.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [0], 'mod': [16]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 45019594938f92f3344c80bb0d351793dd91334b | https://github.com/scikit-learn/scikit-learn/issues/12306 | module:impute | SimpleImputer to Crash on Constant Imputation with string value when dataset is encoded Numerically | #### Description
The title kind of describes it. It might be pretty logical, but just putting it out here as it took a while for me to realize and debug what exactly happened.
The SimpleImputer has the ability to impute missing values with a constant. If the data is categorical, it is possible to impute with a str... | null | https://github.com/scikit-learn/scikit-learn/pull/25081 | null | {'base_commit': '45019594938f92f3344c80bb0d351793dd91334b', 'files': [{'path': 'sklearn/impute/_base.py', 'status': 'modified', 'Loc': {"('SimpleImputer', None, 142)": {'mod': [179, 180, 181]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | 5ad3421a5b5759ecfaaab93406592d988f5d487f | https://github.com/scikit-learn/scikit-learn/issues/16556 | New Feature
module:ensemble | Add Pre-fit Model to Stacking Model | <!--
If you want to propose a new algorithm, please refer first to the scikit-learn
inclusion criterion:
https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms
-->
#### Describe the workflow you want to enable
Allow pre-fit models to stacking model such as `StackingClassif... | null | https://github.com/scikit-learn/scikit-learn/pull/22215 | null | {'base_commit': '5ad3421a5b5759ecfaaab93406592d988f5d487f', 'files': [{'path': 'doc/whats_new/v1.1.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [372]}}}, {'path': 'sklearn/ensemble/_stacking.py', 'status': 'modified', 'Loc': {"('StackingClassifier', None, 281)": {'add': [328], 'mod': [309, 317, 366... | [] | [] | [] | {
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scikit-learn | scikit-learn | 9b2aac9e5c8749243c73f2377519d2f2c407b095 | https://github.com/scikit-learn/scikit-learn/issues/7603 | When min_samples_split and min_samples_leaf are greater than or equal to 1.0 and 0.5, no error is thrown. | <!-- Instructions For Filing a Bug: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md#filing-bugs -->
#### Description
This is a silent bug in version 0.18.0, as a result of the following change: "Random forest, extra trees, decision trees and gradient boosting estimator accept the parameter min... | null | https://github.com/scikit-learn/scikit-learn/pull/7604 | null | {'base_commit': '9b2aac9e5c8749243c73f2377519d2f2c407b095', 'files': [{'path': 'sklearn/tree/tests/test_tree.py', 'status': 'modified', 'Loc': {"(None, 'test_error', 496)": {'add': [511, 523]}}}, {'path': 'sklearn/tree/tree.py', 'status': 'modified', 'Loc': {"('BaseDecisionTree', 'fit', 117)": {'add': [218, 220, 223, 2... | [] | [] | [] | {
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scikit-learn | scikit-learn | 86476582a3759b82fd163d27522bd2de6ad95b6c | https://github.com/scikit-learn/scikit-learn/issues/11568 | TST: optics function is not tested | Related to https://github.com/scikit-learn/scikit-learn/pull/1984 that was merged: it seems that the `optics` function (that @amueller added to the `cluster/__init__.py` in https://github.com/scikit-learn/scikit-learn/pull/11567) is not tested (at least not in `test_optics.py`)
(so the function `optics` that wraps t... | null | https://github.com/scikit-learn/scikit-learn/pull/13271 | null | {'base_commit': '86476582a3759b82fd163d27522bd2de6ad95b6c', 'files': [{'path': 'doc/modules/classes.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [117]}}}, {'path': 'sklearn/cluster/__init__.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [14, 35]}}}, {'path': 'sklearn/cluster/dbsca... | [] | [] | [] | {
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scikit-learn | scikit-learn | ebf2bf81075ae1f4eb47ea0f54981c512bda5ceb | https://github.com/scikit-learn/scikit-learn/issues/5022 | Deprecate n_iter in SGDClassifier and implement max_iter. | We should implement a stopping condition based on the scaled norm of the parameter update as done in the new SAG solver for LogisticRegression / Ridge. The convergence check should be done at the end of the each epoch to avoid introducing too much overhead.
Other classes sharing the same underlying implementation shou... | null | https://github.com/scikit-learn/scikit-learn/pull/5036 | null | {'base_commit': 'ebf2bf81075ae1f4eb47ea0f54981c512bda5ceb', 'files': [{'path': 'benchmarks/bench_covertype.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [105]}}}, {'path': 'benchmarks/bench_sgd_regression.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [23, 27], 'mod': [1, 2, 4, 5, 6... | [] | [] | [] | {
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"sklearn/linear_model/passive_aggressive.py",
"examples/linear_model/plot_sgd_weighted_samples.py",
"sklearn/utils/weight_vector.pyx",
"sklearn/linear... | 1 | |
scikit-learn | scikit-learn | dc1cad2b3fddb8b9069d7cfd89cb1039260baf8e | https://github.com/scikit-learn/scikit-learn/issues/28976 | Documentation
help wanted | `min_samples` in HDSCAN | ### Describe the issue linked to the documentation
I find the description of the `min_samples` argument in sklearn.cluster.HDBSCAN confusing.
It says "The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself."
But if I understand everything correctly `m... | null | https://github.com/scikit-learn/scikit-learn/pull/29263 | null | {'base_commit': 'dc1cad2b3fddb8b9069d7cfd89cb1039260baf8e', 'files': [{'path': 'sklearn/cluster/_hdbscan/_reachability.pyx', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [65, 66]}}}, {'path': 'sklearn/cluster/_hdbscan/hdbscan.py', 'status': 'modified', 'Loc': {"('HDBSCAN', None, 419)": {'mod': [444, 445]... | [] | [] | [] | {
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scikit-learn | scikit-learn | 127415b209ca1df3f8502bdf74de56c33aff2565 | https://github.com/scikit-learn/scikit-learn/issues/901 | add predict and fit_predict to more clustering algorithms | We should add `predict` and `fit_predict` to other clustering algorithms than `KMeans`: they are useful to retrieve cluster labels independently of the underlying attribute names...
| null | https://github.com/scikit-learn/scikit-learn/pull/907 | null | {'base_commit': '127415b209ca1df3f8502bdf74de56c33aff2565', 'files': [{'path': 'sklearn/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [365]}}}, {'path': 'sklearn/cluster/affinity_propagation_.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [12]}, "('AffinityPropagation', None, 1... | [] | [] | [] | {
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"sklearn/cluster/dbscan_.py",
"sklearn/cluster/k_means_.py",
"sklearn/cluster/spectral.py"
],
"doc": [],
"test": [
"sklearn/cluster/test... | 1 | |
scikit-learn | scikit-learn | 9385c45c0379ceab913daa811b1e7d4128faee35 | https://github.com/scikit-learn/scikit-learn/issues/4700 | Bug | cross_val_predict AttributeError with lists | When calling the cross_val_predict with an X parameter that is a list type, an AttributeError is raised on line 1209. This is because it is checking for the shape of the X parameter, but a list does not have the shape attribute.
The documentation says that this function supports lists so I am supposing that it isn't i... | null | https://github.com/scikit-learn/scikit-learn/pull/4705 | null | {'base_commit': '9385c45c0379ceab913daa811b1e7d4128faee35', 'files': [{'path': 'sklearn/cross_validation.py', 'status': 'modified', 'Loc': {"(None, 'cross_val_predict', 958)": {'mod': [1027]}}}, {'path': 'sklearn/tests/test_cross_validation.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1037]}, "('Mo... | [] | [] | [] | {
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scikit-learn | scikit-learn | 053d2d1af477d9dc17e69162b9f2298c0fda5905 | https://github.com/scikit-learn/scikit-learn/issues/19705 | [RFC] Minimal scipy version for 1.0 (or 0.26) release | #### Proposal
I'd like to propose to increase the minimal scipy version to 1.0.
```python
SCIPY_MIN_VERSION = '1.0.0'
```
#### Reasoning
1. In case we should release scikit-learn 1.0, it would be a good fit:smirk:
2. Linear quantile regression #9978 could make it into the next release. It uses `scipy.optimiz... | null | https://github.com/scikit-learn/scikit-learn/pull/20069 | null | {'base_commit': '053d2d1af477d9dc17e69162b9f2298c0fda5905', 'files': [{'path': '.circleci/config.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [6, 11, 50, 99, 133]}}}, {'path': '.travis.yml', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [43, 48, 49, 50, 51, 52, 53, 54]}}}, {'path': 'a... | [] | [] | [] | {
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"sklearn/_min_dependencies.py"
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"doc/whats_new/v1.0.rst",
"doc/tutorial/statistical_inference/supervised_learning.rst",
"doc/modules/sgd.rst"
],
"test": [
"sklearn/utils... | 1 | |
scikit-learn | scikit-learn | a0ba256dbe9380b5d2cf9cee133482fc87768267 | https://github.com/scikit-learn/scikit-learn/issues/19304 | New Feature
Easy
module:ensemble | Poisson criterion in RandomForestRegressor | #### Describe the workflow you want to enable
I want to officially use the Poisson splitting criterion in `RandomForestRegressor`.
#### Describe your proposed solution
#17386 implemented the poisson splitting criterion for `DecisionTreeRegressor` and `ExtraTreeRegressor`. This also enabled—somewhat silently&... | null | https://github.com/scikit-learn/scikit-learn/pull/19464 | null | {'base_commit': 'a0ba256dbe9380b5d2cf9cee133482fc87768267', 'files': [{'path': 'sklearn/ensemble/_forest.py', 'status': 'modified', 'Loc': {"('BaseForest', 'fit', 274)": {'add': [317]}, "('RandomForestRegressor', None, 1279)": {'mod': [1301, 1304, 1305, 1307, 1308]}}}]} | [] | [] | [] | {
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} | {
"code": [
"sklearn/ensemble/_forest.py"
],
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} | 1 |
scikit-learn | scikit-learn | 8453daa6b983ee2fd73d537e81e58b3f6b0e3147 | https://github.com/scikit-learn/scikit-learn/issues/4846 | Bug | RidgeClassifier triggers data copy | RidgeClassifier always triggers a data copy even when not using sample weights.
Regression introduced in #4838.
See:
https://github.com/scikit-learn/scikit-learn/pull/4838#discussion_r32090535
| null | https://github.com/scikit-learn/scikit-learn/pull/4851 | null | {'base_commit': '99d08b571e4813e8d91d809b851b46e8cd5dd88f', 'files': [{'path': 'sklearn/linear_model/ridge.py', 'status': 'modified', 'Loc': {"('RidgeClassifier', 'fit', 575)": {'mod': [593, 594, 601, 602, 603]}, "('RidgeClassifierCV', 'fit', 1053)": {'mod': [1073, 1074, 1080, 1081, 1082]}}}]} | [] | [] | [] | {
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scikit-learn | scikit-learn | c9e227b70d64f73b953d8d60629d6ac63e02a91c | https://github.com/scikit-learn/scikit-learn/issues/7467 | Bug | float numbers can't be set to RFECV's parameter "step" | #### Description
When I use RFECV with parameter 'step' as a float number will cause warnings/errors "rfe.py:203: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future". And the analysis can't be finished until integer or 1/2.
I read description of RFECV an... | null | https://github.com/scikit-learn/scikit-learn/pull/7469 | null | {'base_commit': 'c9e227b70d64f73b953d8d60629d6ac63e02a91c', 'files': [{'path': 'sklearn/feature_selection/rfe.py', 'status': 'modified', 'Loc': {"('RFECV', 'fit', 378)": {'add': [398], 'mod': [427]}}}, {'path': 'sklearn/feature_selection/tests/test_rfe.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1... | [] | [] | [] | {
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"code": [
"sklearn/feature_selection/rfe.py"
],
"doc": [],
"test": [
"sklearn/feature_selection/tests/test_rfe.py"
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"config": [],
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scikit-learn | scikit-learn | 9b42b0cc7d5cf6978805619bc2433e3888c38d0c | https://github.com/scikit-learn/scikit-learn/issues/17814 | Bug | l1_ratio in sklearn.linear_model's ElasticNet greater than 1? | I accidentally ran ElasticNet (from sklearn.linear_model) for l1_ratio >1, and no error or warning was raised. From the docsstring, it says that ``0 < l1_ratio < 1``. Should we raise a ValueError or something? Found this with @mathurinm.
If this turns out to be something to be done, I could help out if someone could... | null | https://github.com/scikit-learn/scikit-learn/pull/17846 | null | {'base_commit': '9b42b0cc7d5cf6978805619bc2433e3888c38d0c', 'files': [{'path': 'sklearn/linear_model/_coordinate_descent.py', 'status': 'modified', 'Loc': {"('ElasticNet', 'fit', 719)": {'add': [757]}}}, {'path': 'sklearn/linear_model/tests/test_coordinate_descent.py', 'status': 'modified', 'Loc': {'(None, None, None)'... | [] | [] | [] | {
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"code": [
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],
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scikit-learn | scikit-learn | 38c7e93b1edcbfb85060cf7c14cca3ab47b9267c | https://github.com/scikit-learn/scikit-learn/issues/8499 | Bug | Memory leak in LogisticRegression | Dear all,
while running many logistic regressions, I encountered a continuous memory increase on several (Debian) machines. The problem is isolated in this code:
```python
import sklearn
from sklearn.linear_model import LogisticRegression
import numpy as np
import time
import psutil
import os
if __name__... | null | https://github.com/scikit-learn/scikit-learn/pull/9024 | null | {'base_commit': '38c7e93b1edcbfb85060cf7c14cca3ab47b9267c', 'files': [{'path': 'sklearn/svm/src/liblinear/liblinear_helper.c', 'status': 'modified', 'Loc': {"(None, 'free_problem', 217)": {'add': [221]}}}, {'path': 'sklearn/svm/src/liblinear/linear.cpp', 'status': 'modified', 'Loc': {"(None, 'free_model_content', 2907)... | [] | [] | [] | {
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"loc_scope": null,
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} | {
"code": [
"sklearn/svm/src/liblinear/liblinear_helper.c",
"sklearn/svm/src/liblinear/linear.cpp"
],
"doc": [],
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"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | e25e8e2119ab6c5aa5072b05c0eb60b10aee4b05 | https://github.com/scikit-learn/scikit-learn/issues/29906 | Bug | Incorrect sample weight handling in `KBinsDiscretizer` | ### Describe the bug
Sample weights are not properly passed through when specifying subsample within KBinsDiscretizer.
### Steps/Code to Reproduce
```python
from sklearn.datasets import make_blobs
from sklearn.preprocessing import KBinsDiscretizer
import numpy as np
rng = np.random.RandomState(42)
# F... | null | https://github.com/scikit-learn/scikit-learn/pull/29907 | null | {'base_commit': 'e25e8e2119ab6c5aa5072b05c0eb60b10aee4b05', 'files': [{'path': 'sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py', 'status': 'modified', 'Loc': {"(None, 'make_missing_value_data', 564)": {'mod': [571]}}}, {'path': 'sklearn/inspection/tests/test_permutation_importance.py', 'status... | [] | [] | [] | {
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"loc_scope": null,
"info_type": null
} | {
"code": [
"sklearn/utils/_indexing.py",
"sklearn/utils/stats.py",
"sklearn/preprocessing/_discretization.py",
"sklearn/utils/_test_common/instance_generator.py"
],
"doc": [],
"test": [
"sklearn/preprocessing/tests/test_discretization.py",
"sklearn/tests/test_docstring_parameters.py",
... | 1 |
scikit-learn | scikit-learn | dcfb3df9a3df5aa2a608248316d537cd6b3643ee | https://github.com/scikit-learn/scikit-learn/issues/6656 | New Feature
module:ensemble | var.monotone option in GradientBoosting | Hi, is it possible to add the equivalent of the var.monotone option in R GBM package to the GradientBoostingClassifier/Regressor? Sometimes it is really useful when we know/want some factors to have monotonic effect to avoid overfitting and non-intuitive results.
Thanks!
| null | https://github.com/scikit-learn/scikit-learn/pull/15582 | null | {'base_commit': 'dcfb3df9a3df5aa2a608248316d537cd6b3643ee', 'files': [{'path': 'doc/modules/ensemble.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1052], 'mod': [900]}}}, {'path': 'doc/whats_new/v0.23.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [186]}}}, {'path': 'sklearn/ense... | [] | [] | [] | {
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"sklearn/ensemble/_hist_gradient_boosting/common.pxd",
"sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py"
],
"doc": [
"doc/modules/ensemble.rst",
"doc/wh... | 1 |
scikit-learn | scikit-learn | 417788c6a54c39614b82acf1a04b1f97f8a32199 | https://github.com/scikit-learn/scikit-learn/issues/6783 | "scoring must return a number" error with custom scorer | #### Description
I'm encountering the same error (`ValueError: scoring must return a number, got [...] (<class 'numpy.core.memmap.memmap'>) instead.`) as #6147, despite running v0.17.1. This is because I'm creating my own scorer, following the example in this [article](http://bigdataexaminer.com/data-science/dealing-w... | null | https://github.com/scikit-learn/scikit-learn/pull/6789 | null | {'base_commit': '417788c6a54c39614b82acf1a04b1f97f8a32199', 'files': [{'path': 'sklearn/cross_validation.py', 'status': 'modified', 'Loc': {"(None, '_score', 1645)": {'add': [1650]}}}, {'path': 'sklearn/model_selection/_validation.py', 'status': 'modified', 'Loc': {"(None, '_score', 298)": {'add': [303]}}}, {'path': 's... | [] | [] | [] | {
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} | {
"code": [
"sklearn/model_selection/_validation.py",
"sklearn/cross_validation.py"
],
"doc": [],
"test": [
"sklearn/model_selection/tests/test_validation.py"
],
"config": [],
"asset": []
} | 1 | |
scikit-learn | scikit-learn | 3f49cee020a91a0be5d0d5602d29b3eefce9d758 | https://github.com/scikit-learn/scikit-learn/issues/3722 | Bug
Easy | preprocessing.scale provides consistent results on arrays with zero variance | I'm using Python 2.7, NumPy 1.8.2 and scikit-learn 0.14.1 on x64 linux (all installed through Anaconda) and getting very inconsistent results for preprocessing.scale function:
> print preprocessing.scale(np.zeros(6) + np.log(1e-5))
> [ 0. 0. 0. 0. 0. 0.]
>
> print preprocessing.scale(np.zeros(8) + np.log(1e-5))
... | null | https://github.com/scikit-learn/scikit-learn/pull/4436 | null | {'base_commit': 'ad26ae47057885415f74893d6329a481b0ce01bd', 'files': [{'path': 'doc/whats_new.rst', 'status': 'modified', 'Loc': {'(None, None, 231)': {'add': [231]}, '(None, None, 3378)': {'add': [3378]}}}, {'path': 'sklearn/preprocessing/_weights.py', 'status': 'modified', 'Loc': {}}, {'path': 'sklearn/preprocessing/... | [] | [] | [] | {
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"code": [
"sklearn/preprocessing/_weights.py",
"sklearn/preprocessing/data.py"
],
"doc": [
"doc/whats_new.rst"
],
"test": [
"sklearn/preprocessing/tests/test_data.py"
],
"config": [],
"asset": []
} | 1 |
scikit-learn | scikit-learn | eda99f3cec70ba90303de0ef3ab7f988657fadb9 | https://github.com/scikit-learn/scikit-learn/issues/13362 | Bug
Blocker | return_intercept==True in ridge_regression raises an exception | <!--
If your issue is a usage question, submit it here instead:
- StackOverflow with the scikit-learn tag: https://stackoverflow.com/questions/tagged/scikit-learn
- Mailing List: https://mail.python.org/mailman/listinfo/scikit-learn
For more information, see User Questions: http://scikit-learn.org/stable/support.ht... | null | https://github.com/scikit-learn/scikit-learn/pull/13363 | null | {'base_commit': 'eda99f3cec70ba90303de0ef3ab7f988657fadb9', 'files': [{'path': 'doc/whats_new/v0.21.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [342]}}}, {'path': 'sklearn/linear_model/ridge.py', 'status': 'modified', 'Loc': {"(None, '_ridge_regression', 366)": {'mod': [371, 372, 373, 374, 375, 37... | [] | [] | [] | {
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"code": [
"sklearn/linear_model/ridge.py"
],
"doc": [
"doc/whats_new/v0.21.rst"
],
"test": [
"sklearn/linear_model/tests/test_ridge.py"
],
"config": [],
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} | 1 |
pandas-dev | pandas | df2fb490a58f272067b33aad372bb4fe2393bb93 | https://github.com/pandas-dev/pandas/issues/7261 | Bug
Missing-data
Dtype Conversions | API: Should Index.min and max use nanmin and nanmax? | Index and Series `min` and `max` handles `nan` and `NaT` differently. Even though `min` and `max` are defined in `IndexOpsMixin`, `Series` doesn't use them and use `NDFrame` definitions.
```
pd.Index([np.nan, 1.0]).min()
# nan
pd.Index([np.nan, 1.0]).max()
# nan
pd.DatetimeIndex([pd.NaT, '2011-01-01']).min()
# NaT
... | null | https://github.com/pandas-dev/pandas/pull/7279 | null | {'base_commit': 'df2fb490a58f272067b33aad372bb4fe2393bb93', 'files': [{'path': 'doc/source/v0.14.1.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [67]}}}, {'path': 'pandas/core/base.py', 'status': 'modified', 'Loc': {"('IndexOpsMixin', 'max', 237)": {'mod': [239]}, "('IndexOpsMixin', 'min', 241)": {'... | [] | [] | [] | {
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"code": [
"pandas/core/base.py",
"pandas/tseries/index.py"
],
"doc": [
"doc/source/v0.14.1.txt"
],
"test": [
"pandas/tests/test_base.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | abd5333e7a3332921707888de9621c52dd3408e6 | https://github.com/pandas-dev/pandas/issues/7943 | Enhancement
API Design
Timezones | tz_localize should support is_dst input array | When storing datetimes with timezone information in mysql I split out the is_dst flag into a separate column. Then when reconstructing the Timestamps I am either forced to iterate through each row and call pytz.timezone.localize on every Timestamp which is very slow or do some magic with localizing what I can and then... | null | https://github.com/pandas-dev/pandas/pull/7963 | null | {'base_commit': 'abd5333e7a3332921707888de9621c52dd3408e6', 'files': [{'path': 'doc/source/timeseries.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1359, 1490, 1509], 'mod': [1492, 1493, 1494, 1503, 1507, 1511, 1512, 1513, 1514, 1516, 1517]}}}, {'path': 'doc/source/v0.15.0.txt', 'status': 'modified... | [] | [] | [] | {
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"iss_reason": "2",
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"pandas/core/generic.py",
"pandas/tslib.pyx",
"pandas/tseries/index.py"
],
"doc": [
"doc/source/timeseries.rst",
"doc/source/v0.15.0.txt"
],
"test": [
"pandas/tseries/tests/test_timezones.py",
"pandas/tseries/tests/test_tslib.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a9421af1aac906cc38d025ed5db4a2b55cb8b9bc | https://github.com/pandas-dev/pandas/issues/16773 | Performance
Sparse | SparseDataFrame constructor has horrible performance for df with many columns | #### Code Sample
This is an example taken directly from the [docs](https://pandas.pydata.org/pandas-docs/stable/sparse.html#sparsedataframe), only that I've changed the sparsity of the arrays from 90% to 99%.
```python
import pandas as pd
from scipy.sparse import csr_matrix
import numpy as np
arr = np.rando... | null | https://github.com/pandas-dev/pandas/pull/16883 | null | {'base_commit': 'a9421af1aac906cc38d025ed5db4a2b55cb8b9bc', 'files': [{'path': 'asv_bench/benchmarks/sparse.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [0, 29]}}}, {'path': 'doc/source/whatsnew/v0.21.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [137]}}}, {'path': 'pandas/core... | [] | [] | [] | {
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"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"asv_bench/benchmarks/sparse.py",
"pandas/core/sparse/frame.py"
],
"doc": [
"doc/source/whatsnew/v0.21.0.txt"
],
"test": [
"pandas/tests/reshape/test_reshape.py",
"pandas/tests/sparse/test_frame.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | ba48fc4a033f11513fa2dd44c946e18b7bc27ad2 | https://github.com/pandas-dev/pandas/issues/26058 | Docs
CI | DOC: test new sphinx 2 release | The docs are currently being built with sphinx 1.8.5 (see eg https://travis-ci.org/pandas-dev/pandas/jobs/518832177 for a recent build on master).
Sphinx has released 2.0.0 (http://www.sphinx-doc.org/en/master/changes.html#release-2-0-0-released-mar-29-2019), and it would be good to test our docs with this new relea... | null | https://github.com/pandas-dev/pandas/pull/26519 | null | {'base_commit': 'ba48fc4a033f11513fa2dd44c946e18b7bc27ad2', 'files': [{'path': 'pandas/core/indexes/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [54]}, "('Index', None, 165)": {'add': [2790]}}}, {'path': 'pandas/core/indexes/interval.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mo... | [] | [] | [] | {
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"pandas/core/indexes/interval.py",
"pandas/core/indexes/base.py"
],
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"test": [],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 45d8d77f27cf0dbc8cefe932f8fb64f6982b9527 | https://github.com/pandas-dev/pandas/issues/10078 | good first issue
Needs Tests | Pandas attempts to convert some strings to timestamps when grouping by a timestamp and aggregating? | I am working through logs of web requests, and when I want to find the most common, say, user agent string for a (disguised) user, I run something like the following:
```
from pandas import Series, DataFrame, Timestamp
tdf = DataFrame({'day': {0: Timestamp('2015-02-24 00:00:00'), 1: Timestamp('2015-02-24 00:00:00'),... | null | https://github.com/pandas-dev/pandas/pull/30646 | null | {'base_commit': '45d8d77f27cf0dbc8cefe932f8fb64f6982b9527', 'files': [{'path': 'pandas/tests/frame/test_constructors.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2427], 'mod': [2]}}}, {'path': 'pandas/tests/frame/test_missing.py', 'status': 'modified', 'Loc': {"('TestDataFrameInterpolate', 'test_in... | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [],
"doc": [],
"test": [
"pandas/tests/reshape/test_pivot.py",
"pandas/tests/groupby/test_apply.py",
"pandas/tests/indexing/test_loc.py",
"pandas/tests/frame/test_constructors.py",
"pandas/tests/indexing/multiindex/test_loc.py",
"pandas/tests/groupby/test_groupby.py",
"pandas... | 1 |
pandas-dev | pandas | 636dd01fdacba0c8f0e7b5aaa726165983fc861d | https://github.com/pandas-dev/pandas/issues/21356 | IO JSON
good first issue | JSON nested_to_record Silently Drops Top-Level None Values | xref https://github.com/pandas-dev/pandas/pull/21164#issuecomment-394510095
`nested_to_record` is silently dropping `None` values that appear at the top of the JSON. This is IMO unexpected and undesirable.
#### Code Sample, a copy-pastable example if possible
```python
In [3]: data = {
...: "id": None... | null | https://github.com/pandas-dev/pandas/pull/21363 | null | {'base_commit': '636dd01fdacba0c8f0e7b5aaa726165983fc861d', 'files': [{'path': 'doc/source/whatsnew/v0.23.1.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [33]}}}, {'path': 'pandas/io/json/normalize.py', 'status': 'modified', 'Loc': {"(None, 'nested_to_record', 24)": {'mod': [83, 84]}}}, {'path': 'pa... | [] | [] | [] | {
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"pandas/io/json/normalize.py"
],
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"doc/source/whatsnew/v0.23.1.txt"
],
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"pandas/tests/io/json/test_normalize.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 19f715c51d16995fc6cd0c102fdba2f213a83a0f | https://github.com/pandas-dev/pandas/issues/24607 | Missing-data
Complex | DES: Should util.is_nan check for complex('nan')? | It doesn't at the moment. A handful of functions in libs.missing _do_ check for complex nan, and could be simplified/de-duplicated if we make util.is_nan also catch the complex case. | null | https://github.com/pandas-dev/pandas/pull/24628 | null | {'base_commit': 'd106e9975100cd0f2080d7b1a6111f20fb64f906', 'files': [{'path': 'pandas/_libs/missing.pyx', 'status': 'modified', 'Loc': {'(None, None, 15)': {'mod': [15]}, '(None, None, 23)': {'mod': [23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}, '(None, None, 65)': {'mod': [65, 66, 67, 68, 69, 70, ... | [] | [] | [] | {
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"code": [
"pandas/_libs/missing.pyx",
"pandas/_libs/tslibs/nattype.pyx",
"pandas/_libs/tslibs/nattype.pxd",
"pandas/_libs/tslibs/util.pxd"
],
"doc": [],
"test": [
"pandas/tests/dtypes/test_missing.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a797b28c87d90a439dfa2c12b4a11e62bf0d6db2 | https://github.com/pandas-dev/pandas/issues/7778 | Bug
Datetime
Dtype Conversions
Timedelta | BUG: df.apply handles np.timedelta64 as timestamp, should be timedelta | I think there may be a bug with the row-wise handling of `numpy.timedelta64` data types when using `DataFrame.apply`. As a check, the problem does not appear when using `DataFrame.applymap`. The problem may be related to #4532, but I'm unsure. I've included an example below.
This is only a minor problem for my use-cas... | null | https://github.com/pandas-dev/pandas/pull/7779 | null | {'base_commit': 'a797b28c87d90a439dfa2c12b4a11e62bf0d6db2', 'files': [{'path': 'doc/source/v0.15.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [189]}}}, {'path': 'pandas/core/frame.py', 'status': 'modified', 'Loc': {"('DataFrame', '_apply_standard', 3516)": {'add': [3541], 'mod': [3550]}}}, {'path... | [] | [] | [] | {
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"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
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"code": [
"pandas/core/internals.py",
"pandas/core/frame.py",
"pandas/core/series.py"
],
"doc": [
"doc/source/v0.15.0.txt"
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"test": [
"pandas/tests/test_frame.py",
"pandas/tests/test_internals.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | fcb0263762a31724ba6db39bf1564569dda068a0 | https://github.com/pandas-dev/pandas/issues/16991 | Bug
Indexing | ValueError on df.columns.isin(pd.Series()) | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(columns=list('ab'))
s1 = pd.Series(['a'])
s2 = pd.Series()
df.columns.isin(s1)
df.columns.isin(s2)
```
#### Problem description
The second call to `df.columns.isin(s2)` fails with
D:\Anaconda\env... | null | https://github.com/pandas-dev/pandas/pull/17006 | null | {'base_commit': 'fcb0263762a31724ba6db39bf1564569dda068a0', 'files': [{'path': 'doc/source/whatsnew/v0.21.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [206]}}}, {'path': 'pandas/core/algorithms.py', 'status': 'modified', 'Loc': {"(None, '_ensure_data', 41)": {'add': [67]}}}, {'path': 'pandas/test... | [] | [] | [] | {
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"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/core/algorithms.py"
],
"doc": [
"doc/source/whatsnew/v0.21.0.txt"
],
"test": [
"pandas/tests/test_algos.py",
"pandas/tests/indexes/test_base.py",
"pandas/tests/series/test_analytics.py",
"pandas/tests/frame/test_analytics.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 0e8331f85cde8db2841aad92054d8e896e88fcef | https://github.com/pandas-dev/pandas/issues/51236 | Docs
good first issue | DOC fix EX02 errors in docstrings | pandas has a script for validating docstrings
https://github.com/pandas-dev/pandas/blob/ced983358b06576af1a73c3e936171cc6dc98a6d/ci/code_checks.sh#L560-L568
which can be run with
```
./ci/code_checks.sh docstrings
```
Currently, many functions fail the EX02 check, and so are excluded from the check.
The ... | null | https://github.com/pandas-dev/pandas/pull/51724 | null | {'base_commit': 'ce3260110f8f5e17c604e7e1a67ed7f8fb07f5fc', 'files': [{'path': 'ci/code_checks.sh', 'status': 'modified', 'Loc': {'(None, None, 82)': {'mod': [82, 83]}, '(None, None, 560)': {'mod': [560, 561, 562, 563, 564, 565, 566, 567, 568]}}}, {'path': 'pandas/core/dtypes/common.py', 'status': 'modified', 'Loc': {"... | [] | [] | [] | {
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"iss_reason": "1",
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"code": [
"pandas/plotting/_core.py",
"pandas/plotting/_misc.py",
"pandas/core/dtypes/common.py"
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"test": [],
"config": [],
"asset": [
"ci/code_checks.sh"
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} | 1 |
pandas-dev | pandas | 2e087c7841aec84030fb489cec9bfeb38fe8086f | https://github.com/pandas-dev/pandas/issues/10043 | Indexing | iloc breaks on read-only dataframe | This is picking up #9928 again. I don't know if the behavior is expected, but it is a bit odd to me. Maybe I'm doing something wrong, I'm not that familiar with the pandas internals.
We call `df.iloc[indices]` and that breaks with a read-only dataframe. I feel that it shouldn't though, as it is not writing.
Minimal r... | null | https://github.com/pandas-dev/pandas/pull/10070 | null | {'base_commit': '2e087c7841aec84030fb489cec9bfeb38fe8086f', 'files': [{'path': 'pandas/src/generate_code.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [148, 170], 'mod': [96, 97, 98, 99, 100, 101, 143, 145]}}}, {'path': 'pandas/tests/test_common.py', 'status': 'modified', 'Loc': {"('TestTake', '_test... | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "1",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/src/generate_code.py"
],
"doc": [],
"test": [
"pandas/tests/test_common.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 89b3d6b201b5d429a202b5239054d5a70c8b5071 | https://github.com/pandas-dev/pandas/issues/38495 | Performance
Regression | Major Performance regression of df.groupby(..).indices | I'm experiencing major performance regressions with pandas=1.1.5 versus 1.1.3
Version 1.1.3:
```
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Ty... | null | https://github.com/pandas-dev/pandas/pull/38892 | null | {'base_commit': '89b3d6b201b5d429a202b5239054d5a70c8b5071', 'files': [{'path': 'asv_bench/benchmarks/groupby.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [128]}}}]} | [] | [] | [] | {
"iss_type": "2",
"iss_reason": "2",
"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"asv_bench/benchmarks/groupby.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 03e58585036c83ca3d4c86d7d3d7ede955c15130 | https://github.com/pandas-dev/pandas/issues/37748 | Bug
Indexing | BUG: ValueError is mistakenly raised if a numpy array is assigned to a pd.Series of dtype=object and both have the same length | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
impor... | null | https://github.com/pandas-dev/pandas/pull/38266 | null | {'base_commit': '03e58585036c83ca3d4c86d7d3d7ede955c15130', 'files': [{'path': 'doc/source/whatsnew/v1.2.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [680]}}}, {'path': 'pandas/core/indexers.py', 'status': 'modified', 'Loc': {"(None, 'is_scalar_indexer', 68)": {'add': [81]}}}, {'path': 'pandas/te... | [] | [] | [] | {
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"pandas/tests/indexing/test_loc.py"
],
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"asset": []
} | 1 |
pandas-dev | pandas | f09d514cf0b09e65baf210a836de04e69b208cef | https://github.com/pandas-dev/pandas/issues/49247 | Bug
Reshaping
Warnings | BUG: Getting FutureWarning for Groupby.mean when using .pivot_table | ### 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 of pandas.
### Reproducible Example
... | null | https://github.com/pandas-dev/pandas/pull/49615 | null | {'base_commit': 'f09d514cf0b09e65baf210a836de04e69b208cef', 'files': [{'path': 'pandas/core/reshape/pivot.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [23]}, "(None, '__internal_pivot_table', 113)": {'mod': [167]}}}, {'path': 'pandas/tests/reshape/test_pivot.py', 'status': 'modified', 'Loc': {"('Tes... | [] | [] | [] | {
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"pandas/util/_exceptions.py"
],
"doc": [],
"test": [
"pandas/tests/reshape/test_pivot.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | e226bacd9e0d69ce3a81abfa09ae850f4610f888 | https://github.com/pandas-dev/pandas/issues/8169 | Bug
Groupby
Dtype Conversions | BUG: groupby.count() on different dtypes seems buggy | from [SO](http://stackoverflow.com/questions/25648923/groupby-count-returns-different-values-for-pandas-dataframe-count-vs-describ)
something odd going on here:
```
vals = np.hstack((np.random.randint(0,5,(100,2)), np.random.randint(0,2,(100,2))))
df = pd.DataFrame(vals, columns=['a', 'b', 'c', 'd'])
df[df==2] = np.n... | null | https://github.com/pandas-dev/pandas/pull/8171 | null | {'base_commit': 'e226bacd9e0d69ce3a81abfa09ae850f4610f888', 'files': [{'path': 'doc/source/v0.15.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [671]}}}, {'path': 'pandas/core/groupby.py', 'status': 'modified', 'Loc': {"(None, '_count_compat', 149)": {'mod': [150, 151, 152, 153]}, "('BaseGrouper', ... | [] | [] | [] | {
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"code": [
"pandas/core/groupby.py"
],
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],
"test": [
"pandas/tests/test_groupby.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 9ea0d4485e77c95ff0d8766990ab55d43472b66e | https://github.com/pandas-dev/pandas/issues/4312 | Indexing
Dtype Conversions | BUG: astype assignment via iloc/loc not working | http://stackoverflow.com/questions/17778139/pandas-unable-to-change-column-data-type/17778560#17778560
This might be trying to coerce `object` dtype to a real dtype (int/float) and is failing
Should prob raise for now (or work). Not working with iloc/loc.
```
In [66]: df = DataFrame([['1','2','3','.4',5,6.,'foo']],co... | null | https://github.com/pandas-dev/pandas/pull/4624 | null | {'base_commit': '9ea0d4485e77c95ff0d8766990ab55d43472b66e', 'files': [{'path': 'doc/source/release.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [267]}}}, {'path': 'pandas/core/common.py', 'status': 'modified', 'Loc': {"(None, '_possibly_downcast_to_dtype', 960)": {'add': [989]}, "(None, '_maybe_upc... | [] | [] | [] | {
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} | {
"code": [
"pandas/core/internals.py",
"pandas/core/common.py",
"pandas/core/indexing.py",
"pandas/core/groupby.py"
],
"doc": [
"doc/source/release.rst"
],
"test": [
"pandas/tests/test_common.py",
"pandas/tests/test_indexing.py",
"pandas/tests/test_frame.py"
],
"config": [... | 1 |
pandas-dev | pandas | 70435eba769c6bcf57332306455eb70db9fa1111 | https://github.com/pandas-dev/pandas/issues/40730 | Bug
cut
NA - MaskedArrays | BUG: qcut fails with Float64Dtype | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
series = pd.Series([1.0, 2... | null | https://github.com/pandas-dev/pandas/pull/40969 | null | {'base_commit': '70435eba769c6bcf57332306455eb70db9fa1111', 'files': [{'path': 'doc/source/whatsnew/v1.3.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [698]}}}, {'path': 'pandas/core/reshape/tile.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [28], 'mod': [27]}, "(None, '_coerce_... | [] | [] | [] | {
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} | {
"code": [
"pandas/core/reshape/tile.py"
],
"doc": [
"doc/source/whatsnew/v1.3.0.rst"
],
"test": [
"pandas/tests/reshape/test_qcut.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 38afa9310040f1bd4fb122008e96fe6d719b12a2 | https://github.com/pandas-dev/pandas/issues/19787 | Missing-data
Categorical
Clean
good first issue | Clean: Categorical.fillna NaN in categories checking | We don't allow NaN in the categories anymore, so this block should be unreachable.
https://github.com/pandas-dev/pandas/blob/8bfcddc7728deaf8e840416d83c8feda86630d27/pandas/core/arrays/categorical.py#L1622-L1628
If anyone wants to remove it and test things out. | null | https://github.com/pandas-dev/pandas/pull/19880 | null | {'base_commit': '38afa9310040f1bd4fb122008e96fe6d719b12a2', 'files': [{'path': '.gitignore', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [63], 'mod': [93]}}}, {'path': 'pandas/core/arrays/categorical.py', 'status': 'modified', 'Loc': {"('Categorical', 'fillna', 1590)": {'mod': [1630, 1631, 1632, 1633, 1... | [] | [] | [] | {
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],
"doc": [],
"test": [],
"config": [
".gitignore"
],
"asset": []
} | 1 |
pandas-dev | pandas | 2dad23f766790510d09e66f1e02b57a395d479b1 | https://github.com/pandas-dev/pandas/issues/9570 | Enhancement
Timedelta | timedelta string conversion requires two-digit hour value | `Timedelta('00:00:00')` works fine whereas `Timedelta('0:00:00')` raises and error. Unsure whether to call this a bug, but under some circumstances the `datetime` module in pure python will produce time delta strings without the leading 0.
| null | https://github.com/pandas-dev/pandas/pull/9868 | null | {'base_commit': '2dad23f766790510d09e66f1e02b57a395d479b1', 'files': [{'path': 'doc/source/whatsnew/v0.16.1.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [52]}}}, {'path': 'pandas/tseries/tests/test_timedeltas.py', 'status': 'modified', 'Loc': {"('TestTimedeltas', 'test_construction', 35)": {'add': ... | [] | [] | [] | {
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],
"doc": [
"doc/source/whatsnew/v0.16.1.txt"
],
"test": [
"pandas/tseries/tests/test_timedeltas.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | b03df731095154e94d23db51d11df5dd736622f8 | https://github.com/pandas-dev/pandas/issues/3925 | Datetime | Access DateTimeIndexed dataframe by timestamp | Hello,
I am new to pandas and thanks for this great library!
I have a data frame like this:
```
Gold_2012.head()
open high low close volume
date_time
2012-01-02 18:01:00 1571.0 1571.0 1569.1 1569.8 351
2012-01-02 18:02:00 1569.8 1570.0 1569.7 1569.8 ... | null | https://github.com/pandas-dev/pandas/pull/3931 | null | {'base_commit': 'b03df731095154e94d23db51d11df5dd736622f8', 'files': [{'path': 'RELEASE.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [256, 357, 360]}}}, {'path': 'pandas/core/indexing.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2]}}}, {'path': 'pandas/tseries/index.py', 'statu... | [] | [] | [] | {
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"loc_scope": null,
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} | {
"code": [
"pandas/tseries/index.py",
"pandas/core/indexing.py"
],
"doc": [
"RELEASE.rst"
],
"test": [
"pandas/tseries/tests/test_timeseries.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | f231c9a74a544ec94cd12e813cb2543fb5a18556 | https://github.com/pandas-dev/pandas/issues/35331 | good first issue
Needs Tests | BUG: np.argwhere on pandas series | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
numpy/numpy#15555 reports an issue with `np.argwhere` on pandas Series. Reporting ... | null | https://github.com/pandas-dev/pandas/pull/53381 | null | {'base_commit': 'f231c9a74a544ec94cd12e813cb2543fb5a18556', 'files': [{'path': 'pandas/tests/series/test_npfuncs.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [5, 7]}, "(None, 'test_numpy_unique', 19)": {'add': [21]}}}]} | [] | [] | [] | {
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} | {
"code": [],
"doc": [],
"test": [
"pandas/tests/series/test_npfuncs.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 5de6b84f5117b005a8f010d4510a758b50f3d14e | https://github.com/pandas-dev/pandas/issues/12081 | Reshaping
Error Reporting | DataFrame.merge with Series should give nice error message | Right now trying this results in "IndexError: list index out of range". It should say can't merge DataFrame with a Series...
I know this for quite a while now, but still get trapped on it every once in a while. This would be very helpful for beginners.
Other people also get confused: http://stackoverflow.com/question... | null | https://github.com/pandas-dev/pandas/pull/12112 | null | {'base_commit': '5de6b84f5117b005a8f010d4510a758b50f3d14e', 'files': [{'path': 'doc/source/whatsnew/v0.18.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [205]}}}, {'path': 'pandas/tools/merge.py', 'status': 'modified', 'Loc': {"('_MergeOperation', '__init__', 157)": {'add': [186]}}}, {'path': 'pand... | [] | [] | [] | {
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],
"test": [
"pandas/tools/tests/test_merge.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a3c0e7bcfb8bbe9ca45df7e571a305d403e0f066 | https://github.com/pandas-dev/pandas/issues/44597 | API Design
Deprecate | API/DEPR: int downcasting in DataFrame.where |
`Block.where` has special downcasting logic that splits blocks differently from any other Block methods. I would like to deprecate and eventually remove this bespoke logic.
The relevant logic is only reached AFAICT when we have integer dtype (non-int64) and an integer `other` too big for this dtype, AND the pas... | null | https://github.com/pandas-dev/pandas/pull/45009 | null | {'base_commit': 'a3c0e7bcfb8bbe9ca45df7e571a305d403e0f066', 'files': [{'path': 'doc/source/whatsnew/v1.4.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [547]}}}, {'path': 'pandas/core/internals/blocks.py', 'status': 'modified', 'Loc': {"('Block', 'where', 1138)": {'add': [1229]}}}, {'path': 'pandas... | [] | [] | [] | {
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],
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],
"test": [
"pandas/tests/frame/methods/test_clip.py",
"pandas/tests/frame/indexing/test_where.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 32f789fbc5d5a72d9d1ac14935635289eeac9009 | https://github.com/pandas-dev/pandas/issues/52151 | Bug
Groupby
Categorical | BUG: Inconsistent behavior with `groupby/min` and `observed=False` on categoricals between 2.0 and 2.1 | ### 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/... | null | https://github.com/pandas-dev/pandas/pull/52236 | null | {'base_commit': '32f789fbc5d5a72d9d1ac14935635289eeac9009', 'files': [{'path': 'pandas/core/groupby/ops.py', 'status': 'modified', 'Loc': {"('WrappedCythonOp', '_ea_wrap_cython_operation', 358)": {'add': [404]}}}, {'path': 'pandas/tests/groupby/test_min_max.py', 'status': 'modified', 'Loc': {"(None, 'test_min_max_nulla... | [] | [] | [] | {
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"code": [
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],
"doc": [],
"test": [
"pandas/tests/groupby/test_min_max.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 8924277fa3dbe775f46e679ab8bd97b293e465ea | https://github.com/pandas-dev/pandas/issues/41556 | Bug
Groupby
Algos | BUG: groupby.shift return keys filled with `fill_value` when `fill_value` is specified | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28... | null | https://github.com/pandas-dev/pandas/pull/41858 | null | {'base_commit': '8924277fa3dbe775f46e679ab8bd97b293e465ea', 'files': [{'path': 'asv_bench/benchmarks/groupby.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [371]}}}, {'path': 'doc/source/whatsnew/v1.4.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [170, 264]}}}, {'path': 'pandas/c... | [] | [] | [] | {
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"code": [
"asv_bench/benchmarks/groupby.py",
"pandas/core/groupby/groupby.py"
],
"doc": [
"doc/source/whatsnew/v1.4.0.rst"
],
"test": [
"pandas/tests/groupby/test_groupby_shift_diff.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 92093457ca13ba037257d0b8d41735268535c84f | https://github.com/pandas-dev/pandas/issues/3573 | Bug
Output-Formatting | Unintuitive default behavior with wide DataFrames in the IPython notebook | In the IPython notebook, HTML output it the default and whether summary view is displayed should not be governed by hypothetical line width. I ran into this problem in a demo recently and it took me a minute to figure out what was wrong, definitely a bad change in 0.11.
| null | https://github.com/pandas-dev/pandas/pull/3663 | null | {'base_commit': '0ed4549ac857fbf2c7e975acdf1d987bacc3ea32', 'files': [{'path': 'RELEASE.rst', 'status': 'modified', 'Loc': {'(None, None, 65)': {'add': [65]}, '(None, None, 87)': {'add': [87]}, '(None, None, 141)': {'add': [141]}}}, {'path': 'doc/source/faq.rst', 'status': 'modified', 'Loc': {'(None, None, 38)': {'mod'... | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "2",
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"loc_scope": null,
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} | {
"code": [
"pandas/core/common.py",
"pandas/core/frame.py",
"pandas/core/config_init.py",
"pandas/core/format.py"
],
"doc": [
"RELEASE.rst",
"doc/source/faq.rst"
],
"test": [
"pandas/tests/test_format.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | a214915e241ea15f3d072d54930d0e0c8f42ee10 | https://github.com/pandas-dev/pandas/issues/19482 | Dtype Conversions
Error Reporting
Numeric Operations | Rank With 'method=first' Broken for Objects | Came across this working on #15779
```python
In []: df = pd.DataFrame({'key': ['a'] * 5, 'val': ['bar', 'bar', 'foo', 'bar', 'baz']})
In []: df.groupby('key').rank(method='first')
Out []:
Empty DataFrame
Columns: []
Index: []
```
#### Expected Output
```python
Out[]:
val
0 1.0
1 2.0
... | null | https://github.com/pandas-dev/pandas/pull/19481 | null | {'base_commit': 'a214915e241ea15f3d072d54930d0e0c8f42ee10', 'files': [{'path': 'doc/source/whatsnew/v0.23.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [583]}}}, {'path': 'pandas/_libs/algos.pxd', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [13]}}}, {'path': 'pandas/_libs/algos.pyx... | [] | [] | [] | {
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"loc_way": "pr",
"loc_scope": null,
"info_type": null
} | {
"code": [
"pandas/_libs/groupby.pyx",
"pandas/_libs/groupby_helper.pxi.in",
"pandas/_libs/algos.pyx",
"pandas/core/groupby.py",
"pandas/_libs/algos.pxd"
],
"doc": [
"doc/source/whatsnew/v0.23.0.txt"
],
"test": [
"pandas/tests/groupby/test_groupby.py"
],
"config": [],
"asset... | 1 |
pandas-dev | pandas | 679dbd021eccc238e422057009365e2ee1c04b25 | https://github.com/pandas-dev/pandas/issues/21687 | Docs
Usage Question
Algos
Window | "on" argument of DataFrame.rolling only works for datetime columns | the `on=` argument of `DataFrame.rolling` only works for datetime columns.
```
df = pd.DataFrame([
[18, 0],
[2, 0],
[1, 0],
[9, 1],
[8, 1],
], columns=['value', 'roll'])
```
```
df.roll = pd.to_datetime(df.roll, unit='s')
df.rolling('1s', on='roll').value.max()
```
returns:
``... | null | https://github.com/pandas-dev/pandas/pull/27265 | null | {'base_commit': '679dbd021eccc238e422057009365e2ee1c04b25', 'files': [{'path': 'pandas/core/window.py', 'status': 'modified', 'Loc': {"('Window', None, 489)": {'mod': [516, 517]}}}]} | [] | [] | [] | {
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"pandas/core/window.py"
],
"doc": [],
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} | 1 |
pandas-dev | pandas | 940104efc9e708bc93744dfaa36c9492b03b1ca4 | https://github.com/pandas-dev/pandas/issues/20452 | Reshaping
API Design | BUG: New feature allowing merging on combination of columns and index levels drops levels of index | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.23.0.dev0+657.g01882ba5b'
In [3]: df1 = pd.DataFrame({'v1' : range(12)}, index=pd.MultiIndex.from_product([list('abc'),list('xy'),[1,2]], names=['abc','xy','num']))
...: df1
... | null | https://github.com/pandas-dev/pandas/pull/20475 | null | {'base_commit': '940104efc9e708bc93744dfaa36c9492b03b1ca4', 'files': [{'path': 'doc/source/merging.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [1202], 'mod': [1136, 1137, 1141, 1142, 1143, 1146, 1164]}}}, {'path': 'doc/source/whatsnew/v0.24.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)... | [] | [] | [] | {
"iss_type": "2",
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"loc_scope": null,
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} | {
"code": [
"pandas/core/reshape/merge.py"
],
"doc": [
"doc/source/whatsnew/v0.24.0.rst",
"doc/source/merging.rst"
],
"test": [
"pandas/tests/reshape/merge/test_merge.py",
"pandas/tests/reshape/merge/test_join.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 13940c7f3c0371d6799bbd88b9c6546392b418a1 | https://github.com/pandas-dev/pandas/issues/35650 | good first issue
Needs Tests | BUG: pd.factorize with read-only datetime64 numpy array raises ValueError | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28... | null | https://github.com/pandas-dev/pandas/pull/35775 | null | {'base_commit': '13940c7f3c0371d6799bbd88b9c6546392b418a1', 'files': [{'path': 'pandas/tests/test_algos.py', 'status': 'modified', 'Loc': {"('TestFactorize', 'test_object_factorize', 245)": {'add': [253]}}}]} | [] | [] | [] | {
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"pandas/tests/test_algos.py"
],
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} | 1 |
pandas-dev | pandas | 816f94575c9ec1af2169a28536217c4d16dd6b4b | https://github.com/pandas-dev/pandas/issues/16033 | Docs | DOC: styler warnings in doc-build | https://travis-ci.org/pandas-dev/pandas/jobs/222779268
```
/tmp/doc/source/generated/pandas.io.formats.style.Styler.rst:74: WARNING: failed to import template:
/tmp/doc/source/generated/pandas.io.formats.style.Styler.rst:74: WARNING: toctree references unknown document 'generated/template:'
```
cc @TomAugspurg... | null | https://github.com/pandas-dev/pandas/pull/16094 | null | {'base_commit': 'f0bd908336a260cafa9d83c8244dd1a0a056f72d', 'files': [{'path': 'pandas/tests/io/formats/test_css.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [2]}, "(None, 'test_css_parse_strings', 46)": {'mod': [48, 49, 50, 51]}, "(None, 'test_css_parse_invalid', 79)": {'mod': [80]}, "(None, 'test_... | [] | [] | [] | {
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pandas-dev | pandas | 2067d7e306ae720d455f356e4da21f282a8a762e | https://github.com/pandas-dev/pandas/issues/35811 | Bug
Usage Question
API Design
Series | BUG/QST: Series.transform with a dictionary | What is the expected output of passing a dictionary to `Series.transform`? For example:
s = pd.Series([1, 2, 3])
result1 = s.transform({'a': lambda x: x + 1})
result2 = s.transform({'a': lambda x: x + 1, 'b': lambda x: x + 2})
The docs say that `dict of axis labels -> functions` is acceptable, but I... | null | https://github.com/pandas-dev/pandas/pull/35964 | null | {'base_commit': '2067d7e306ae720d455f356e4da21f282a8a762e', 'files': [{'path': 'doc/source/whatsnew/v1.2.0.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [344]}}}, {'path': 'pandas/core/aggregation.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [23], 'mod': [21]}, "(None, 'validate_... | [] | [] | [] | {
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"code": [
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"pandas/core/base.py",
"pandas/core/generic.py",
"pandas/core/frame.py",
"pandas/core/series.py",
"pandas/core/shared_docs.py"
],
"doc": [
"doc/source/whatsnew/v1.2.0.rst"
],
"test": [
"pandas/tests/series/apply/test_series_apply.py",
... | 1 |
pandas-dev | pandas | 889c2ff67af14213e8ed065df2957b07e34ac95b | https://github.com/pandas-dev/pandas/issues/33810 | Testing
IO Parquet | TST: add Feather V2 round-trip test | no that pyarrow 0.17 has landed, we should have a round-trip Feather V2 test to ensure we have dtype preservation (we can likely re-use some of our test frames from the parquet tests). | null | https://github.com/pandas-dev/pandas/pull/33422 | null | {'base_commit': '889c2ff67af14213e8ed065df2957b07e34ac95b', 'files': [{'path': 'doc/source/conf.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [418]}}}, {'path': 'doc/source/user_guide/io.rst', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [4586, 4588, 4589, 4595, 4596]}}}, {'path': 'doc... | [] | [] | [] | {
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"code": [
"pandas/io/feather_format.py",
"pandas/core/frame.py",
"doc/source/conf.py"
],
"doc": [
"doc/source/user_guide/io.rst",
"doc/source/whatsnew/v1.1.0.rst"
],
"test": [
"pandas/tests/io/test_feather.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | b6691127523f965003dbf877a358c81af5012989 | https://github.com/pandas-dev/pandas/issues/15630 | Numeric Operations
Algos | Pandas (0.18) Rank: unexpected behavior for method = 'dense' and pct = True | I find the behavior of rank function with method = 'dense' and pct = True unexpected as it looks like, in order to calculate percentile ranks, the function is using the total number of observations instead of the number of _distinct_ observations.
#### Code Sample, a copy-pastable example if possible
```
import ... | null | https://github.com/pandas-dev/pandas/pull/15639 | null | {'base_commit': 'b6691127523f965003dbf877a358c81af5012989', 'files': [{'path': 'doc/source/whatsnew/v0.23.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [909]}}}, {'path': 'pandas/_libs/algos_rank_helper.pxi.in', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [216, 388]}}}, {'path': 'p... | [] | [] | [] | {
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],
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"doc/source/whatsnew/v0.23.0.txt"
],
"test": [
"pandas/tests/series/test_rank.py",
"pandas/tests/frame/test_rank.py"
],
"config": [],
"asset": []
} | 1 |
pandas-dev | pandas | 95be01dbc060f405b7928cc6e4ba4d6d6181c22a | https://github.com/pandas-dev/pandas/issues/13420 | Groupby
Categorical | DataFrame.groupby(grp, axis=1) with categorical grp breaks | While attempting to use `pd.qcut` (which returned a Categorical) to bin some data in groups for plotting, I encountered the following error. The idea is to group a DataFrame by columns (`axis=1`) using a Categorical.
#### Minimal breaking example
```
>>> import pandas
>>> df = pandas.DataFrame({'a':[1,2,3,4], 'b':[-1,... | null | https://github.com/pandas-dev/pandas/pull/27788 | null | {'base_commit': '54e58039fddc79492e598e85279c42e85d06967c', 'files': [{'path': 'pandas/tests/groupby/test_categorical.py', 'status': 'modified', 'Loc': {"(None, 'test_seriesgroupby_observed_apply_dict', 1159)": {'add': [1165]}}}]} | [] | [] | [] | {
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"pandas/tests/groupby/test_categorical.py"
],
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"asset": []
} | 1 |
pandas-dev | pandas | be61825986ba565bc038beb2f5df2750fc1aca30 | https://github.com/pandas-dev/pandas/issues/13565 | Docs
Usage Question
Timezones | Call unique() on a timezone aware datetime series returns non timezone aware result | Call unique() on a timezone aware datetime series returns non timezone aware result.
#### Code Sample
import pandas as pd
import pytz
import datetime
In [242]: ts = pd.Series([datetime.datetime(2011,2,11,20,0,0,0,pytz.utc), datetime.datetime(2011,2,11,20,0,0,0,pytz.utc), datetime.datetime(2011,2,11,21,0,0,0,pytz.utc... | null | https://github.com/pandas-dev/pandas/pull/13979 | null | {'base_commit': 'be61825986ba565bc038beb2f5df2750fc1aca30', 'files': [{'path': 'doc/source/whatsnew/v0.19.0.txt', 'status': 'modified', 'Loc': {'(None, None, None)': {'add': [460, 906]}}}, {'path': 'pandas/core/base.py', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [10, 11, 24]}, "('IndexOpsMixin', None,... | [] | [] | [] | {
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],
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],
"test": [
"pandas/util/testing.py",
"pandas/tests/test_base.py",
"pandas/tests/index... | 1 |
pandas-dev | pandas | c4a996adfc91f023b46ce3cb67e33fc8b2ca3627 | https://github.com/pandas-dev/pandas/issues/9400 | Visualization
Error Reporting | Improve error message in plotting.py's _plot | This a minor enhancement proposal. At the moment I cannot submit a pull request. I will probably have time to create one during the next week.
This is a snippet from `tools/plotting.py`: https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2269-2283
``` python
def _plot(data, x=None, y=None, subplo... | null | https://github.com/pandas-dev/pandas/pull/9417 | null | {'base_commit': 'c4a996adfc91f023b46ce3cb67e33fc8b2ca3627', 'files': [{'path': 'pandas/tools/plotting.py', 'status': 'modified', 'Loc': {"(None, '_plot', 2269)": {'mod': [2269, 2277]}}}]} | [] | [] | [] | {
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],
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pandas-dev | pandas | 53243e8ec73ecf5035a63f426a9c703d6835e9a7 | https://github.com/pandas-dev/pandas/issues/54889 | Build | BUILD: Race condition between .pxi.in and .pyx compiles in parallel build of 2.1.0 | ### Installation check
- [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas).
### Platform
Linux-6.4.7-gentoo-dist-x86_64-AMD_Ryzen_5_3600_6-Core_Processor-with-glibc2.38
### Installation Method
Built from source
### pandas Version... | null | https://github.com/pandas-dev/pandas/pull/54958 | null | {'base_commit': '53243e8ec73ecf5035a63f426a9c703d6835e9a7', 'files': [{'path': 'pandas/_libs/meson.build', 'status': 'modified', 'Loc': {'(None, None, None)': {'mod': [72]}}}]} | [] | [] | [] | {
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]
} | 1 |
meta-llama | llama | 7565eb6fee2175b2d4fe2cfb45067a61b35d7f5e | https://github.com/meta-llama/llama/issues/658 | documentation | Confusion about the default max_seq_len = 2048 | When reading the class Transformer, I found that the code use max_seq_len * 2 to prepare the rotary positional encoding, which confused me for a while. Then I realized that the default max_seq_len was set to 2048, and the 'max_seq_len * 2' aims to generate 4096 positional embeddings, corresponding to the 4K context len... | null | https://github.com/meta-llama/llama/pull/754 | null | {'base_commit': '7565eb6fee2175b2d4fe2cfb45067a61b35d7f5e', 'files': [{'path': 'llama/model.py', 'status': 'modified', 'Loc': {"('Transformer', '__init__', 414)": {'add': [450]}}}]} | [] | [] | [] | {
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pallets | flask | f88765d504ce2fa9bc3926c76910b11510522892 | https://github.com/pallets/flask/issues/1224 | Starting up a public server. | I ran into this problem today with one of my applications trying to make it public to my local network.
C:\Users\Savion\Documents\GitHub\Example-Flask-Website>flask\Scripts\python run.
py
- Running on http://127.0.0.1:5000/
- Restarting with reloader
10.101.37.124 - - [26/Oct/2014 15:51:23] "GET / HTTP/1.1" 404 -
... | null | null | null | {'base_commit': 'f88765d504ce2fa9bc3926c76910b11510522892', 'files': [{'path': 'flask/views.py', 'Loc': {}}]} | [] | [] | [] | {
"iss_type": "1\n404 error",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"flask/views.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 2d8a21c7321a9ead8e27208b49a18f4b8b27e2c1 | https://github.com/pallets/flask/issues/834 | How to get the serialized version of the session cookie in 0.10? | In version 0.9 I could simply get the value of the `session` like this:
```
flask.session.serialize()
```
But after upgrading to 0.10 this is not working anymore.. what's the alternative? How can I get the session value?
(`flask.request.cookies.get('session')` is not good for me, because I would like to get the ses... | null | null | null | {'base_commit': '2d8a21c7321a9ead8e27208b49a18f4b8b27e2c1', 'files': [{'path': 'flask/sessions.py', 'Loc': {"('SecureCookieSessionInterface', 'get_signing_serializer', 308)": {'mod': []}, "('TaggedJSONSerializer', 'dumps', 60)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "3\nhow to do …",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"flask/sessions.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 22d82e70b3647ed16c7d959a939daf533377382b | https://github.com/pallets/flask/issues/4015 | 2.0.0: build requires ContextVar module | Simple I cannot find it.
```console
+ /usr/bin/python3 setup.py build '--executable=/usr/bin/python3 -s'
Traceback (most recent call last):
File "setup.py", line 4, in <module>
setup(
File "/usr/lib/python3.8/site-packages/setuptools/__init__.py", line 144, in setup
return distutils.core.setup(**attr... | null | null | null | {'base_commit': '22d82e70b3647ed16c7d959a939daf533377382b', 'files': [{'path': 'setup.py', 'Loc': {'(None, None, None)': {'mod': [7]}}, 'status': 'modified'}]} | [] | [] | [] | {
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"info_type": "Code"
} | {
"code": [
"setup.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 43e2d7518d2e89dc7ed0b4ac49b2d20211ad1bfa | https://github.com/pallets/flask/issues/2977 | Serial port access problem in DEBUG mode. | ### Expected Behavior
Sending commands through the serial port.
```python
app = Flask(__name__)
serialPort = serial.Serial(port = "COM5", baudrate=1000000,
bytesize=8, timeout=2, stopbits=serial.STOPBITS_ONE)
lamp = {
1 : {'name' : 'n1', 'state' : True},
2 : {'name' : 'n2'... | null | null | null | {} | [
{
"Loc": [
7
],
"path": null
}
] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "comment",
"loc_scope": "3",
"info_type": "Code"
} | {
"code": [
null
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
pallets | flask | 1a7fd980f8579bd7d7d53c812a77c1dc64be52ba | https://github.com/pallets/flask/issues/1749 | JSONEncoder and aware datetimes | I was surprised to see that though flask.json.JSONEncoder accepts datetime objects, it ignores the timezone. I checked werkzeug.http.http_date and it can handle timezone aware dates just fine if they are passed in, but the JSONEncoder insists on transforming the datetime to a timetuple, like this
`return http_date(o.... | null | null | null | {'base_commit': '1a7fd980f8579bd7d7d53c812a77c1dc64be52ba', 'files': [{'path': 'flask/json.py', 'Loc': {"('JSONEncoder', 'default', 60)": {'mod': [78]}}, 'status': 'modified'}]} | [] | [] | [] | {
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"info_type": "Code"
} | {
"code": [
"flask/json.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
sherlock-project | sherlock | 144d43830f663808c5fbca75b797350060acf7dd | https://github.com/sherlock-project/sherlock/issues/559 | Results files saved to specific folder | Having just installed Sherlock I was surprised to see the results files are just jumbled in with everything else instead of being in their own Results folder.
Having a separate folder would keep things cleaner especially as you use it more and the number of files increases. | null | null | null | {'base_commit': '144d43830f663808c5fbca75b797350060acf7dd', 'files': [{'path': 'README.md', 'Loc': {'(None, None, 65)': {'mod': [65]}}, 'status': 'modified'}, {'path': 'sherlock/sherlock.py', 'Loc': {"(None, 'main', 462)": {'mod': [478]}}, 'status': 'modified'}]} | [] | [] | [] | {
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"loc_scope": "0",
"info_type": "Code\n+\nDoc"
} | {
"code": [
"sherlock/sherlock.py"
],
"doc": [
"README.md"
],
"test": [],
"config": [],
"asset": []
} | null | |
sherlock-project | sherlock | 7ec56895a37ada47edd6573249c553379254d14a | https://github.com/sherlock-project/sherlock/issues/1911 | question | How do you search for usernames? New to this. | <!--
######################################################################
WARNING!
IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE.
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## Checklist
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Put x into all boxes (like this [x]) once you hav... | null | null | null | {'base_commit': '7ec56895a37ada47edd6573249c553379254d14a', 'files': [{'path': 'README.md', 'Loc': {}}]} | [] | [] | [] | {
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sherlock-project | sherlock | 65ce128b7fd8c8915c40495191d9c136f1d2322b | https://github.com/sherlock-project/sherlock/issues/1297 | bug | name 'requests' is not defined | <!--
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WARNING!
IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE
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## Checklist
<!--
Put x into all boxes (like this [x]) once you ha... | null | null | null | {'base_commit': '65ce128b7fd8c8915c40495191d9c136f1d2322b', 'files': [{'path': 'sherlock/sites.py', 'Loc': {}}]} | [] | [] | [] | {
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"info_type": "Code"
} | {
"code": [
"sherlock/sites.py"
],
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"test": [],
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} | null |
sherlock-project | sherlock | f63e17066dc4881ee5a164aed60b6e8f1e9ab129 | https://github.com/sherlock-project/sherlock/issues/462 | environment | File "sherlock.py", line 24, in <module> from requests_futures.sessions import FuturesSession ModuleNotFoundError: No module named 'requests_futures' | File "sherlock.py", line 24, in <module>
from requests_futures.sessions import FuturesSession
ModuleNotFoundError: No module named 'requests_futures' | null | null | null | {'base_commit': 'f63e17066dc4881ee5a164aed60b6e8f1e9ab129', 'files': [{'path': 'requirements.txt', 'Loc': {}}]} | [] | [] | [] | {
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"info_type": "Code"
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"code": [],
"doc": [],
"test": [],
"config": [
"requirements.txt"
],
"asset": []
} | null |
sherlock-project | sherlock | 6c6faff416896a41701aa3e24e5b5a584bd5cb44 | https://github.com/sherlock-project/sherlock/issues/236 | question | No module named 'torrequest' | Hi,
similar problem to module "requests_futures"
Traceback (most recent call last):
File "sherlock.py", line 25, in <module>
from torrequest import TorRequest
ModuleNotFoundError: No module named 'torrequest'
| null | null | null | {'base_commit': '6c6faff416896a41701aa3e24e5b5a584bd5cb44', 'files': [{'path': 'requirements.txt', 'Loc': {}}]} | [] | [] | [] | {
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"loc_way": "comment",
"loc_scope": "0",
"info_type": "Doc\n依赖声明"
} | {
"code": [],
"doc": [],
"test": [],
"config": [
"requirements.txt"
],
"asset": []
} | null |
keras-team | keras | c0d95fd6c2cd8ffc0738819825c3065e3c89977c | https://github.com/keras-team/keras/issues/4954 | TimeDistributed Wrapper not working with LSTM/GRU | Please make sure that the boxes below are checked before you submit your issue. If your issue is an implementation question, please ask your question on [StackOverflow](http://stackoverflow.com/questions/tagged/keras) or [join the Keras Slack channel](https://keras-slack-autojoin.herokuapp.com/) and ask there instead o... | null | null | null | {'base_commit': 'c0d95fd6c2cd8ffc0738819825c3065e3c89977c', 'files': [{'path': 'Version', 'Loc': {}}, {'path': 'Version', 'Loc': {}}]} | [] | [] | [] | {
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"test": [],
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"asset": [
"Version"
]
} | null | |
keras-team | keras | 980a6be629610ee58c1eae5a65a4724ce650597b | https://github.com/keras-team/keras/issues/16234 | type:support | Compiling model in callback causes TypeError | **System information**.
- Have I written custom code (as opposed to using a stock example script provided in Keras): yes
- TensorFlow version (use command below): 2.8.0 (2.4 too)
- Python version: 3.7
**Describe the problem**.
In a fine-tuning case I would like to do transfer-learning phase first (with fine-tu... | null | null | null | {'base_commit': '980a6be629610ee58c1eae5a65a4724ce650597b', 'files': [{'path': 'keras/engine/training.py', 'Loc': {"('Model', 'make_train_function', 998)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
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"info_type": "Code"
} | {
"code": [
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],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null |
keras-team | keras | 90f441a6a0ed4334cac53760289061818a68b7c1 | https://github.com/keras-team/keras/issues/2893 | Is the cifar10_cnn.py example actually performing data augmentation? | When `datagen.fit(X_train)` is called in the [`cifar10_cnn.py` example](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py#L103), shouldn't it be (when `data_augmentation=True`):
``` python
datagen.fit(X_train, augment=True)
```
as the [default value for `augment` is `False`](https://github.com/fch... | null | null | null | {'base_commit': '90f441a6a0ed4334cac53760289061818a68b7c1', 'files': [{'path': 'keras/preprocessing/image.py', 'Loc': {"('ImageDataGenerator', 'fit', 404)": {'mod': [419, 420, 421, 422, 423]}}, 'status': 'modified'}]} | [] | [] | [] | {
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"iss_reason": "5",
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"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/preprocessing/image.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | 654404c2ed8db47a5361a3bff9126a16507c9c4c | https://github.com/keras-team/keras/issues/1787 | What happened to WordContextProduct? | ``` python
In [1]: import keras
In [2]: keras.__version__
Out[2]: '0.3.2'
In [3]: from keras.layers.embeddings import WordContextProduct
Using Theano backend.
/usr/local/lib/python3.5/site-packages/theano/tensor/signal/downsample.py:5: UserWarning: downsample module has been moved to the pool module.
warnings.warn(... | null | null | null | {} | [
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"Loc": [
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],
"path": null
}
] | [] | [] | {
"iss_type": "1",
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"info_type": "Code"
} | {
"code": [
null
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | 8778add0d66aed64a8970c34576bf5800bc19170 | https://github.com/keras-team/keras/issues/3335 | Masking the output of a conv layer | Hi,
I am trying to apply a given mask in the output of a conv layer. The simplest form of my problem can be seen in the img
The mask should be considered as an input when training/predicting. I have already t... | null | null | null | {'base_commit': '8778add0d66aed64a8970c34576bf5800bc19170', 'files': [{'path': 'keras/src/models/model.py', 'Loc': {}}]} | [] | [] | [] | {
"iss_type": "3",
"iss_reason": "5",
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"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/src/models/model.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | ed07472bc5fc985982db355135d37059a1f887a9 | https://github.com/keras-team/keras/issues/13101 | type:support | model.fit : AttributeError: 'Model' object has no attribute '_compile_metrics' | **System information**
- Have I written custom code (as opposed to using example directory): Yes
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Mint 19.3
- TensorFlow backend (yes / no): yes
- TensorFlow version: 2.0.0b1
- Keras version: 2.2.4-tf
- Python version: 3.6
- CUDA/cuDNN version: /
... | null | null | null | {'base_commit': 'ed07472bc5fc985982db355135d37059a1f887a9', 'files': [{'path': 'keras/engine/training.py', 'Loc': {"('Model', 'compile', 40)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "comment",
"loc_scope": "3",
"info_type": "Code"
} | {
"code": [
"keras/engine/training.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null |
keras-team | keras | a3d160b9467c99cbb27f9aa0382c759f45c8ee66 | https://github.com/keras-team/keras/issues/9741 | Improve Keras Documentation User Experience for Long Code Snippets By Removing The Need For Horizontal Slide Bars | **Category**: documentation user-experience
**Comment**: modify highlight.js <code></code> to wrap long documentation code snippets
**Why**: eliminates the need for a user to manually click and slide a horizontal slider just to get a quick sense of what available parameters and their default values are
**Context**... | null | null | null | {'base_commit': 'a3d160b9467c99cbb27f9aa0382c759f45c8ee66', 'files': [{'path': 'docs/autogen.py', 'Loc': {}}]} | [] | [] | [] | {
"iss_type": "4",
"iss_reason": "5",
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"info_type": "Code"
} | {
"code": [
"docs/autogen.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | 7a12fd0f8597760cf8e1238a9b021e247693517b | https://github.com/keras-team/keras/issues/2372 | problem of save/load model | HI,
Thanks for making such a wonderful tool!
I'm using Keras 1.0. I want to save and load the model both the arch and the parameters. So I use the method in FAQ. Here is the code.
```
def save_model(self, model, options):
json_string = model.to_json()
open(options['file_arch'], 'w').write(json_string)
m... | null | null | null | {'base_commit': '7a12fd0f8597760cf8e1238a9b021e247693517b', 'files': [{'path': 'keras/src/trainers/trainer.py', 'Loc': {"('Trainer', 'compile', 40)": {'mod': []}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/src/trainers/trainer.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | 284ef7b495a61238dccc6149996c4cb88fef1c5a | https://github.com/keras-team/keras/issues/933 | Same model but graph gives bad performance | Hello,
I am learning to use Graph as it seems more powerful so I implemented one of my previous model which uses Sequential. Here is the model using sequential (number of dimension set in random):
```
def build_generation_embedding_model(self, dim):
print "Build model ..."
input_model = Sequential()
inpu... | null | null | null | {} | [
{
"Loc": [
36
],
"path": null
}
] | [] | [] | {
"iss_type": "1",
"iss_reason": "3",
"loc_way": "comment",
"loc_scope": "3",
"info_type": "Code"
} | {
"code": [
null
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | c2b844ba2fe8d0d597da9ef6a9af3b20d18d0bec | https://github.com/keras-team/keras/issues/7603 | Loss Increases after some epochs | I have tried different convolutional neural network codes and I am running into a similar issue. The network starts out training well and decreases the loss but after sometime the loss just starts to increase. I have shown an example below:
Epoch 15/800
1562/1562 [==============================] - 49s - loss: 0.9050... | null | null | null | {'base_commit': 'c2b844ba2fe8d0d597da9ef6a9af3b20d18d0bec', 'files': [{'path': 'examples/cifar10_cnn.py', 'Loc': {'(None, None, None)': {'mod': [65]}}, 'status': 'modified'}]} | [] | [] | [] | {
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} | {
"code": [
"examples/cifar10_cnn.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | 530eff62e5463e00d73e72c51cc830b9ac3a14ab | https://github.com/keras-team/keras/issues/3997 | Using keras for Distributed training raise RuntimeError("Graph is finalized and cannot be modified.") | I'm using keras for distributed training with following code:
``` python
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Created by Enigma on 2016/9/26
import numpy as np
import tensorflow as tf
# Define Hyperparameters
FLAGS = tf.app.flags.FLAGS
# For missions
tf.app.flags.DEFINE_string("ps_hosts", "",
... | null | null | null | {'base_commit': '530eff62e5463e00d73e72c51cc830b9ac3a14ab', 'files': [{'path': 'keras/engine/training.py', 'Loc': {"('Model', '_make_train_function', 685)": {'mod': []}, "('Model', '_make_test_function', 705)": {'mod': []}, "('Model', '_make_predict_function', 720)": {'mod': []}}, 'status': 'modified'}, {'path': 'keras... | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/engine/training.py",
"keras/backend/tensorflow_backend.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null | |
keras-team | keras | c2e36f369b411ad1d0a40ac096fe35f73b9dffd3 | https://github.com/keras-team/keras/issues/4810 | Parent module '' not loaded, cannot perform relative import with vgg16.py | just set up my ubuntu and have the python 3.5 installed, together with Keras...the following occurs:
RESTART: /usr/local/lib/python3.5/dist-packages/keras/applications/vgg16.py
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/keras/applications/vgg16.py", line 14, in <module>
... | null | null | null | {'base_commit': 'c2e36f369b411ad1d0a40ac096fe35f73b9dffd3', 'files': [{'path': 'keras/applications/vgg16.py', 'Loc': {'(None, None, None)': {'mod': [14, 15, 16, 17, 18, 19, 20, 21]}}, 'status': 'modified'}]} | [] | [] | [] | {
"iss_type": "1",
"iss_reason": "5",
"loc_way": "comment",
"loc_scope": "0",
"info_type": "Code"
} | {
"code": [
"keras/applications/vgg16.py"
],
"doc": [],
"test": [],
"config": [],
"asset": []
} | null |
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