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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
import random
# Dataset
ts = pd.DatetimeIndex([pd.Timestamp('2021/01/01 00:30'),
pd.Timestamp('2021/01/01 00:45'),
pd.Timestamp('2021/01/01 02:00'),
pd.Timestamp('2021/01/01 03:50'),
pd.Timestamp('2021/01/01 05:00')])
length = len(ts)
random.seed(1)
value = random.sample(range(1, length+1), length)
df = pd.DataFrame({'value': value, 'ts': ts})
df['td'] = df['ts'] - df['ts'].shift(1, fill_value=ts[0]-pd.Timedelta('1h'))
df['amount'] = df['value']*10
df.loc[:2,'grps'] = 'a'
df.loc[2:,'grps'] = 'b'
```
```python
In [16]: df
Out[16]:
value ts td amount grps
0 2 2021-01-01 00:30:00 0 days 01:00:00 20 a
1 1 2021-01-01 00:45:00 0 days 00:15:00 10 a
2 5 2021-01-01 02:00:00 0 days 01:15:00 50 b
3 4 2021-01-01 03:50:00 0 days 01:50:00 40 b
4 3 2021-01-01 05:00:00 0 days 01:10:00 30 b
```
```python
# cumsum and Timedelta work when used through pd.Series
df['td'].cumsum()
# cumsum and Timedelta do not work when used in groupby with named aggregation
agg_rules = {'td':('td', 'cumsum')}
res = df.groupby('grps').agg(**agg_rules)
```
Error message that is returned
```python
res = df.groupby('grps').agg(**agg_rules)
Traceback (most recent call last):
File "<ipython-input-14-c0f4606d675a>", line 2, in <module>
res = df.groupby('grps').agg(**agg_rules)
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 945, in aggregate
result, how = aggregate(self, func, *args, **kwargs)
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/aggregation.py", line 582, in aggregate
return agg_dict_like(obj, arg, _axis), True
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/aggregation.py", line 768, in agg_dict_like
results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/aggregation.py", line 768, in <dictcomp>
results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 247, in aggregate
ret = self._aggregate_multiple_funcs(func)
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 315, in _aggregate_multiple_funcs
results[base.OutputKey(label=name, position=idx)] = obj.aggregate(func)
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 241, in aggregate
return getattr(self, func)(*args, **kwargs)
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 2497, in cumsum
return self._cython_transform("cumsum", **kwargs)
File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 996, in _cython_transform
raise DataError("No numeric types to aggregate")
DataError: No numeric types to aggregate
```
#### Problem description
Cumsum works with Timedelta in some workflows, but for some other workflows (groupby, named aggregation) it does not.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-53-generic
Version : #60~20.04.1-Ubuntu SMP Thu May 6 09:52:46 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : 0.6.3
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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* Splits the previous `Dockerfile` in two: a simplified `Dockerfile` + a new `container_create.sh` that only runs the first time the container starts, making it possible to install the dependencies inside the workspace.
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- [ ❌] tests added / passed
- [ ❌] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] closes #40823
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/41724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41724/events | https://github.com/pandas-dev/pandas/issues/41724 | 906,528,921 | MDU6SXNzdWU5MDY1Mjg5MjE= | 41,724 | BUG: pd.array(floatdata, dtype=np.inty) should use astype_nansafe | {
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] | open | false | null | [] | null | 4 | "2021-05-29T18:09:51Z" | "2021-06-06T17:14:43Z" | null | MEMBER | null | ```
In [8]: arr = np.random.randn(2)
In [9]: arr[0] = np.nan
In [10]: pd.array(arr, dtype="i8")
Out[10]:
<PandasArray>
[-9223372036854775808, 1]
Length: 2, dtype: int64
```
Our other constructors all refuse to cast nan to a nonsense int | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41725/events | https://github.com/pandas-dev/pandas/issues/41725 | 906,543,309 | MDU6SXNzdWU5MDY1NDMzMDk= | 41,725 | CI: Conda connection errors | {
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] | closed | false | null | [] | null | 3 | "2021-05-29T19:27:27Z" | "2021-08-21T01:54:47Z" | "2021-08-21T01:54:47Z" | MEMBER | null | Looks like we've got some connection errors in Conda that we haven't seen before, see:
```
conda env create -q --file=environment.yml
Collecting package metadata (repodata.json): ...working... failed
# >>>>>>>>>>>>>>>>>>>>>> ERROR REPORT <<<<<<<<<<<<<<<<<<<<<<
Traceback (most recent call last):
File "/home/runner/miniconda3/lib/python3.8/site-packages/urllib3/response.py", line 438, in _error_catcher
yield
File "/home/runner/miniconda3/lib/python3.8/site-packages/urllib3/response.py", line 764, in read_chunked
self._update_chunk_length()
File "/home/runner/miniconda3/lib/python3.8/site-packages/urllib3/response.py", line 694, in _update_chunk_length
line = self._fp.fp.readline()
File "/home/runner/miniconda3/lib/python3.8/socket.py", line 669, in readinto
return self._sock.recv_into(b)
File "/home/runner/miniconda3/lib/python3.8/ssl.py", line 1241, in recv_into
return self.read(nbytes, buffer)
File "/home/runner/miniconda3/lib/python3.8/ssl.py", line 1099, in read
return self._sslobj.read(len, buffer)
ConnectionResetError: [Errno 104] Connection reset by peer
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/runner/miniconda3/lib/python3.8/site-packages/requests/models.py", line 753, in generate
for chunk in self.raw.stream(chunk_size, decode_content=True):
File "/home/runner/miniconda3/lib/python3.8/site-packages/urllib3/response.py", line 572, in stream
for line in self.read_chunked(amt, decode_content=decode_content):
File "/home/runner/miniconda3/lib/python3.8/site-packages/urllib3/response.py", line 793, in read_chunked
self._original_response.close()
File "/home/runner/miniconda3/lib/python3.8/contextlib.py", line 131, in __exit__
self.gen.throw(type, value, traceback)
File "/home/runner/miniconda3/lib/python3.8/site-packages/urllib3/response.py", line 455, in _error_catcher
raise ProtocolError("Connection broken: %r" % e, e)
urllib3.exceptions.ProtocolError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/exceptions.py", line 1079, in __call__
return func(*args, **kwargs)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda_env/cli/main.py", line 80, in do_call
exit_code = getattr(module, func_name)(args, parser)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda_env/cli/main_create.py", line 119, in execute
result[installer_type] = installer.install(prefix, pkg_specs, args, env)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda_env/installers/conda.py", line 31, in install
unlink_link_transaction = solver.solve_for_transaction(
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/solve.py", line 114, in solve_for_transaction
unlink_precs, link_precs = self.solve_for_diff(update_modifier, deps_modifier,
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/solve.py", line 157, in solve_for_diff
final_precs = self.solve_final_state(update_modifier, deps_modifier, prune, ignore_pinned,
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/solve.py", line 262, in solve_final_state
ssc = self._collect_all_metadata(ssc)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/common/io.py", line 88, in decorated
return f(*args, **kwds)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/solve.py", line 425, in _collect_all_metadata
index, r = self._prepare(prepared_specs)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/solve.py", line 1020, in _prepare
reduced_index = get_reduced_index(self.prefix, self.channels,
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/index.py", line 288, in get_reduced_index
new_records = SubdirData.query_all(spec, channels=channels, subdirs=subdirs,
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/subdir_data.py", line 140, in query_all
result = tuple(concat(executor.map(subdir_query, channel_urls)))
File "/home/runner/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 611, in result_iterator
yield fs.pop().result()
File "/home/runner/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 432, in result
return self.__get_result()
File "/home/runner/miniconda3/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/home/runner/miniconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/subdir_data.py", line 132, in <lambda>
subdir_query = lambda url: tuple(SubdirData(Channel(url), repodata_fn=repodata_fn).query(
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/subdir_data.py", line 145, in query
self.load()
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/subdir_data.py", line 209, in load
_internal_state = self._load()
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/subdir_data.py", line 371, in _load
raw_repodata_str = fetch_repodata_remote_request(
File "/home/runner/miniconda3/lib/python3.8/site-packages/conda/core/subdir_data.py", line 694, in fetch_repodata_remote_request
resp = session.get(join_url(url, filename), headers=headers, proxies=session.proxies,
File "/home/runner/miniconda3/lib/python3.8/site-packages/requests/sessions.py", line 555, in get
return self.request('GET', url, **kwargs)
File "/home/runner/miniconda3/lib/python3.8/site-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/home/runner/miniconda3/lib/python3.8/site-packages/requests/sessions.py", line 697, in send
r.content
File "/home/runner/miniconda3/lib/python3.8/site-packages/requests/models.py", line 831, in content
self._content = b''.join(self.iter_content(CONTENT_CHUNK_SIZE)) or b''
File "/home/runner/miniconda3/lib/python3.8/site-packages/requests/models.py", line 756, in generate
raise ChunkedEncodingError(e)
requests.exceptions.ChunkedEncodingError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer'))
`$ /home/runner/miniconda3/bin/conda-env create -q --file=environment.yml`
environment variables:
CIO_TEST=<not set>
CONDA=/usr/share/miniconda
CONDA_AUTO_UPDATE_CONDA=false
CONDA_EXE=/home/runner/miniconda3/bin/conda
CONDA_PYTHON_EXE=/home/runner/miniconda3/bin/python
CONDA_ROOT=/home/runner/miniconda3
CONDA_SHLVL=0
CURL_CA_BUNDLE=<not set>
DEPLOYMENT_BASEPATH=/opt/runner
GITHUB_ACTION_PATH=/home/runner/work/pandas/pandas/./.github/actions/setup
GITHUB_EVENT_PATH=/home/runner/work/_temp/_github_workflow/event.json
GITHUB_PATH=/home/runner/work/_temp/_runner_file_commands/add_path_a635de78-1490-4
ff5-a562-86a9e642c5a2
PATH=/home/runner/miniconda3/bin:/home/runner/miniconda3/condabin:/home/run
ner/miniconda3/bin:/home/runner/miniconda3/bin:/home/linuxbrew/.linuxb
rew/bin:/home/linuxbrew/.linuxbrew/sbin:/home/runner/.local/bin:/opt/p
ipx_bin:/usr/share/rust/.cargo/bin:/home/runner/.config/composer/vendo
r/bin:/usr/local/.ghcup/bin:/home/runner/.dotnet/tools:/snap/bin:/usr/
local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/us
r/local/games:/snap/bin:/home/runner/.dotnet/tools
REQUESTS_CA_BUNDLE=<not set>
SELENIUM_JAR_PATH=/usr/share/java/selenium-server-standalone.jar
SSL_CERT_FILE=<not set>
SWIFT_PATH=/usr/share/swift/usr/bin
active environment : None
shell level : 0
user config file : /home/runner/.condarc
populated config files : /home/runner/.condarc
conda version : 4.10.1
conda-build version : not installed
python version : 3.8.5.final.0
virtual packages : __linux=5.4.0=0
__glibc=2.31=0
__unix=0=0
__archspec=1=x86_64
base environment : /home/runner/miniconda3 (writable)
conda av data dir : /home/runner/miniconda3/etc/conda
conda av metadata url : https://repo.anaconda.com/pkgs/main
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/runner/miniconda3/pkgs
/home/runner/.conda/pkgs
envs directories : /home/runner/miniconda3/envs
/home/runner/.conda/envs
platform : linux-64
user-agent : conda/4.10.1 requests/2.25.1 CPython/3.8.5 Linux/5.4.0-1047-azure ubuntu/20.04.2 glibc/2.31
UID:GID : 1001:121
netrc file : None
offline mode : False
An unexpected error has occurred. Conda has prepared the above report.
Upload successful.
```
Some examples:
- https://github.com/pandas-dev/pandas/runs/2701636721?check_suite_focus=true#step:3:433
- https://github.com/pandas-dev/pandas/runs/2699485760?check_suite_focus=true#step:3:431
@simonjayhawkins points in [this comment](https://github.com/pandas-dev/pandas/pull/41336#issuecomment-848696140) that it may be caused by #41336. Difficult to tell, maybe it's just some problems on the conda servers, and it stops failing by itself. Opening this issue to keep track of any further information. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41726/events | https://github.com/pandas-dev/pandas/pull/41726 | 906,545,025 | MDExOlB1bGxSZXF1ZXN0NjU3NTI0NDg3 | 41,726 | DOC: Updates Index.any docstring from #40362 | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/41727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41727/events | https://github.com/pandas-dev/pandas/pull/41727 | 906,556,342 | MDExOlB1bGxSZXF1ZXN0NjU3NTMzMzM4 | 41,727 | REF: more explicit dtypes in strings.accessor | {
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} | 8 | "2021-05-30T03:02:28Z" | "2021-07-05T03:14:37Z" | null | CONTRIBUTOR | null | Follow up to #36893. Per @xhochy's suggestion we should use `entrypoints` as a mechanism for loading alternate SQL engine backends, similar to what we do with plotting backends. Creating a new issue for it per the comment [here](https://github.com/pandas-dev/pandas/pull/40556#discussion_r625243915). | {
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} | 3 | "2021-05-30T03:07:06Z" | "2021-05-31T21:37:32Z" | "2021-05-31T21:30:51Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] tests added / passed
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this difference in behavior is one of the biggest things left keeping us from sharing more between Index/Series constructors. | {
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} | 2 | "2021-05-30T15:40:44Z" | "2021-06-01T15:58:07Z" | "2021-06-01T13:52:10Z" | MEMBER | null | - [x] closes #24435
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} | 0 | "2021-05-30T16:15:57Z" | "2021-06-01T16:00:09Z" | "2021-06-01T14:54:02Z" | MEMBER | null | - [ ] closes #xxxx
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xref #40114 which tried to change the behavior without a deprecation cycle.
Handling DataFrame separately, as it is an entirely separate barrel of worms. | {
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} | 1 | "2021-05-30T21:09:50Z" | "2021-05-31T15:46:27Z" | "2021-05-31T15:46:23Z" | MEMBER | null | Not sure why a README was created under `docs` in the first place. But having a docs/README that instead of having docs related information, just points to the contributing page, doesn't seem very useful, since the main README is already providing better information on how to contribute.
And in any case, the link in that README file is very old. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41736/events | https://github.com/pandas-dev/pandas/issues/41736 | 906,884,742 | MDU6SXNzdWU5MDY4ODQ3NDI= | 41,736 | CLN: Delete pandas-docs GitHub organization | {
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Feels like https://github.com/pandas-docs/pandas-docs-travis was created to publish the development pandas docs, but that was eventually moved elsewhere, and now there is just a two year old copy published there (in their github pages).
I think we should delete the `pandas-docs` organization, to avoid confusion, or people accessing old docs from outdated links to that copy. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41737/events | https://github.com/pandas-dev/pandas/issues/41737 | 906,887,326 | MDU6SXNzdWU5MDY4ODczMjY= | 41,737 | CI: Set up CI for ARM | {
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} | 5 | "2021-05-30T21:29:08Z" | "2021-06-01T22:30:06Z" | "2021-06-01T22:29:33Z" | MEMBER | null | xref #33971
This used to run in Travis, but seems like Travis now requires credits, and we can't get them reliably (see #38943).
[This page](https://developer.arm.com/solutions/infrastructure/developer-resources/ci-cd) contains CI systems that support ARM. Most of them require to set up our own runner, which I guess we don't want to do or maintain. So, the main options seem to be:
- drone.io
- CircleCI
CC: @bashtage | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41738 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41738/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41738/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41738/events | https://github.com/pandas-dev/pandas/pull/41738 | 906,908,407 | MDExOlB1bGxSZXF1ZXN0NjU3ODI4MzI0 | 41,738 | CLN: Remove travis build | {
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} | 0 | "2021-05-30T23:06:30Z" | "2021-06-01T13:51:18Z" | "2021-06-01T13:51:18Z" | MEMBER | null | - [X] closes #38943
Whenever we can assume that Travis is not an option for ARM builds (or any other builds due to the lack of free credits), this is the PR that removes all travis specific stuff. | {
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} | 22 | "2021-05-31T01:04:02Z" | "2021-06-04T01:22:15Z" | "2021-06-01T22:29:33Z" | MEMBER | null | - [X] closes #41737
This seems to be working, see:
https://app.circleci.com/pipelines/github/datapythonista/pandas/11/workflows/2ca0487f-5a63-4f01-936c-e83827db714f/jobs/11
But tests are failing. I opened #41740 the the failures. | {
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"merged_at": "2021-06-01T22:29:33Z",
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"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/41739"
} |
https://api.github.com/repos/pandas-dev/pandas/issues/41740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41740/events | https://github.com/pandas-dev/pandas/issues/41740 | 906,957,047 | MDU6SXNzdWU5MDY5NTcwNDc= | 41,740 | TST: Tests broken in ARM | {
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{
"color": "C4A000",
"default": false,
"description": "pandas testing functions or related to the test suite",
"id": 127685,
"name": "Testing",
"node_id": "MDU6TGFiZWwxMjc2ODU=",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Testing"
},
{
"color": "c2e0c6",
"default": false,
"description": "aarch64 architecture",
"id": 2628846204,
"name": "ARM",
"node_id": "MDU6TGFiZWwyNjI4ODQ2MjA0",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/ARM"
}
] | open | false | null | [] | null | 2 | "2021-05-31T01:27:18Z" | "2021-06-01T09:07:33Z" | null | MEMBER | null | Working on adding ARM back to the CI in #41739 I found that pandas tests are curently failing for this architecture:
```
=================================== FAILURES ===================================
_________________ TestFloatSubtype.test_subtype_integer_errors _________________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.interval.test_astype.TestFloatSubtype object at 0xffff2da3fdd0>
def test_subtype_integer_errors(self):
# float64 -> uint64 fails with negative values
index = interval_range(-10.0, 10.0)
dtype = IntervalDtype("uint64", "right")
msg = re.escape(
"Cannot convert interval[float64, right] to interval[uint64, right]; "
"subtypes are incompatible"
)
with pytest.raises(TypeError, match=msg):
> index.astype(dtype)
E Failed: DID NOT RAISE <class 'TypeError'>
pandas/tests/indexes/interval/test_astype.py:180: Failed
_________ test_downcast_nullable_numeric[data12-UInt64-signed-UInt64] __________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH38798
_______________ test_rolling_var_numerical_issues[var-1-values0] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 1, values = [5e+33, 0, 0.5, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.0, 0.5, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-1-values1] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 1
values = [7.071068e+16, 0, 0.7071068, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.0, 0.7071068, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[var-2-values2] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 2, values = [5e+33, 0.5, 0, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.5, 0.0, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-2-values3] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 2
values = [7.071068e+16, 0.7071068, 0, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.7071068, 0.0, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
=================================== FAILURES ===================================
_________________ TestFloatSubtype.test_subtype_integer_errors _________________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.interval.test_astype.TestFloatSubtype object at 0xffff2da3fdd0>
def test_subtype_integer_errors(self):
# float64 -> uint64 fails with negative values
index = interval_range(-10.0, 10.0)
dtype = IntervalDtype("uint64", "right")
msg = re.escape(
"Cannot convert interval[float64, right] to interval[uint64, right]; "
"subtypes are incompatible"
)
with pytest.raises(TypeError, match=msg):
> index.astype(dtype)
E Failed: DID NOT RAISE <class 'TypeError'>
pandas/tests/indexes/interval/test_astype.py:180: Failed
_________ test_downcast_nullable_numeric[data12-UInt64-signed-UInt64] __________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH38798
_______________ test_rolling_var_numerical_issues[var-1-values0] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 1, values = [5e+33, 0, 0.5, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.0, 0.5, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-1-values1] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 1
values = [7.071068e+16, 0, 0.7071068, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.0, 0.7071068, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[var-2-values2] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 2, values = [5e+33, 0.5, 0, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.5, 0.0, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-2-values3] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 2
values = [7.071068e+16, 0.7071068, 0, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.7071068, 0.0, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError=================================== FAILURES ===================================
_________________ TestFloatSubtype.test_subtype_integer_errors _________________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.interval.test_astype.TestFloatSubtype object at 0xffff2da3fdd0>
def test_subtype_integer_errors(self):
# float64 -> uint64 fails with negative values
index = interval_range(-10.0, 10.0)
dtype = IntervalDtype("uint64", "right")
msg = re.escape(
"Cannot convert interval[float64, right] to interval[uint64, right]; "
"subtypes are incompatible"
)
with pytest.raises(TypeError, match=msg):
> index.astype(dtype)
E Failed: DID NOT RAISE <class 'TypeError'>
pandas/tests/indexes/interval/test_astype.py:180: Failed
_________ test_downcast_nullable_numeric[data12-UInt64-signed-UInt64] __________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH38798
_______________ test_rolling_var_numerical_issues[var-1-values0] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 1, values = [5e+33, 0, 0.5, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.0, 0.5, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-1-values1] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 1
values = [7.071068e+16, 0, 0.7071068, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.0, 0.7071068, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[var-2-values2] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 2, values = [5e+33, 0.5, 0, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.5, 0.0, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-2-values3] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 2
values = [7.071068e+16, 0.7071068, 0, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.7071068, 0.0, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError=================================== FAILURES ===================================
_________________ TestFloatSubtype.test_subtype_integer_errors _________________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.interval.test_astype.TestFloatSubtype object at 0xffff2da3fdd0>
def test_subtype_integer_errors(self):
# float64 -> uint64 fails with negative values
index = interval_range(-10.0, 10.0)
dtype = IntervalDtype("uint64", "right")
msg = re.escape(
"Cannot convert interval[float64, right] to interval[uint64, right]; "
"subtypes are incompatible"
)
with pytest.raises(TypeError, match=msg):
> index.astype(dtype)
E Failed: DID NOT RAISE <class 'TypeError'>
pandas/tests/indexes/interval/test_astype.py:180: Failed
_________ test_downcast_nullable_numeric[data12-UInt64-signed-UInt64] __________
[gw0] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH38798
_______________ test_rolling_var_numerical_issues[var-1-values0] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 1, values = [5e+33, 0, 0.5, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.0, 0.5, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-1-values1] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 1
values = [7.071068e+16, 0, 0.7071068, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.0, 0.7071068, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[var-2-values2] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'var', third_value = 2, values = [5e+33, 0.5, 0, 0.5, 2, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (71.42857 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 5e+33, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17, -2.7212384506478592e+17]
E [right]: [nan, 5e+33, 0.5, 0.0, 0.5, 2.0, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
_______________ test_rolling_var_numerical_issues[std-2-values3] _______________
[gw1] linux -- Python 3.7.10 /home/circleci/miniconda3/envs/pandas-dev/bin/python
func = 'std', third_value = 2
values = [7.071068e+16, 0.7071068, 0, 0.7071068, 1.414214, 0]
@pytest.mark.parametrize(
("func", "third_value", "values"),
[
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
],
)
def test_rolling_var_numerical_issues(func, third_value, values):
# GH: 37051
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
result = getattr(ds.rolling(2), func)()
expected = Series([np.nan] + values)
> tm.assert_series_equal(result, expected)
pandas/tests/window/test_rolling.py:1089:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (42.85714 %)
E [index]: [0, 1, 2, 3, 4, 5, 6]
E [left]: [nan, 7.071067811865475e+16, 0.0, 0.0, 0.0, 0.0, 0.0]
E [right]: [nan, 7.071068e+16, 0.7071068, 0.0, 0.7071068, 1.414214, 0.0]
pandas/_libs/testing.pyx:168: AssertionError
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41741 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41741/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41741/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41741/events | https://github.com/pandas-dev/pandas/pull/41741 | 907,017,690 | MDExOlB1bGxSZXF1ZXN0NjU3OTIxMDIw | 41,741 | ENH: Improve error message in corr/cov for Rolling/Expanding/EWM when other isn't a DataFrame/Series | {
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} | 1 | "2021-05-31T03:56:59Z" | "2021-05-31T16:30:49Z" | "2021-05-31T14:48:46Z" | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Also some internal method cleanups | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41742 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41742/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41742/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41742/events | https://github.com/pandas-dev/pandas/pull/41742 | 907,187,335 | MDExOlB1bGxSZXF1ZXN0NjU4MDcxNTA5 | 41,742 | ⬆️ UPGRADE: Autoupdate pre-commit config | {
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<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.7-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.7-py2.py3-none-any.whl (7.2 MB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.7 nodeenv-1.6.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.4.7
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... updating 20.8b1 -> 21.5b1.
Updating https://github.com/codespell-project/codespell ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.18.3 -> v2.19.0.
Updating https://github.com/pre-commit/pygrep-hooks ... already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Failed
- hook id: black
- files were modified by this hook
reformatted pandas/_config/config.py
reformatted pandas/_libs/arrays.pyi
reformatted pandas/_libs/algos.pyi
reformatted pandas/_libs/groupby.pyi
reformatted pandas/_libs/hashtable.pyi
reformatted pandas/_libs/index.pyi
reformatted pandas/_libs/internals.pyi
reformatted pandas/_libs/join.pyi
reformatted pandas/_libs/ops.pyi
reformatted pandas/_libs/parsers.pyi
reformatted pandas/_libs/reshape.pyi
reformatted pandas/_libs/testing.pyi
reformatted pandas/_libs/lib.pyi
reformatted pandas/_libs/tslib.pyi
reformatted pandas/_libs/tslibs/conversion.pyi
reformatted pandas/_libs/tslibs/ccalendar.pyi
reformatted pandas/_libs/tslibs/fields.pyi
reformatted pandas/_libs/tslibs/dtypes.pyi
reformatted pandas/_libs/tslibs/parsing.pyi
reformatted pandas/_libs/tslibs/nattype.pyi
reformatted pandas/_libs/tslibs/strptime.pyi
reformatted pandas/_libs/tslibs/period.pyi
reformatted pandas/_libs/tslibs/timedeltas.pyi
reformatted pandas/_libs/tslibs/timezones.pyi
reformatted pandas/_libs/tslibs/tzconversion.pyi
reformatted pandas/_libs/tslibs/vectorized.pyi
reformatted pandas/_libs/tslibs/timestamps.pyi
reformatted pandas/_libs/window/indexers.pyi
reformatted pandas/_libs/writers.pyi
reformatted pandas/_libs/window/aggregations.pyi
reformatted pandas/_testing/__init__.py
reformatted pandas/conftest.py
reformatted pandas/core/algorithms.py
reformatted pandas/core/apply.py
reformatted pandas/core/arrays/categorical.py
reformatted pandas/core/arrays/masked.py
reformatted pandas/core/arrays/sparse/scipy_sparse.py
reformatted pandas/core/computation/expressions.py
reformatted pandas/core/computation/expr.py
reformatted pandas/core/computation/pytables.py
reformatted pandas/core/dtypes/common.py
reformatted pandas/core/dtypes/cast.py
reformatted pandas/core/dtypes/dtypes.py
reformatted pandas/core/generic.py
reformatted pandas/core/groupby/ops.py
reformatted pandas/core/indexes/category.py
reformatted pandas/core/indexes/range.py
reformatted pandas/core/indexes/multi.py
reformatted pandas/core/internals/array_manager.py
reformatted pandas/core/internals/blocks.py
reformatted pandas/core/internals/managers.py
reformatted pandas/core/ops/__init__.py
reformatted pandas/core/nanops.py
reformatted pandas/core/reshape/tile.py
reformatted pandas/core/reshape/merge.py
reformatted pandas/io/formats/format.py
reformatted pandas/io/parquet.py
reformatted pandas/io/parsers/readers.py
reformatted pandas/io/sas/sas_xport.py
reformatted pandas/io/sql.py
reformatted pandas/io/pytables.py
reformatted pandas/io/stata.py
reformatted pandas/tests/arithmetic/test_period.py
reformatted pandas/tests/base/test_constructors.py
reformatted pandas/tests/dtypes/test_common.py
reformatted pandas/tests/extension/base/reduce.py
reformatted pandas/tests/frame/methods/test_clip.py
reformatted pandas/tests/generic/test_label_or_level_utils.py
reformatted pandas/tests/generic/test_generic.py
reformatted pandas/tests/indexing/common.py
reformatted pandas/tests/indexing/test_coercion.py
reformatted pandas/tests/indexing/test_indexing.py
reformatted pandas/tests/internals/test_internals.py
reformatted pandas/tests/io/generate_legacy_storage_files.py
reformatted pandas/tests/io/pytables/common.py
reformatted pandas/tests/io/pytables/conftest.py
reformatted pandas/tests/plotting/frame/test_frame.py
reformatted pandas/tests/plotting/test_misc.py
reformatted pandas/tests/resample/test_base.py
reformatted pandas/tests/resample/test_deprecated.py
reformatted pandas/tests/reshape/merge/test_merge_asof.py
reformatted pandas/tests/series/methods/test_clip.py
reformatted pandas/tests/test_multilevel.py
All done! ✨ 🍰 ✨
83 files reformatted, 1292 files left unchanged.
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Failed
- hook id: pyupgrade
- exit code: 1
- files were modified by this hook
Rewriting pandas/tests/frame/test_arithmetic.py
Rewriting pandas/core/arrays/sparse/scipy_sparse.py
Rewriting pandas/core/reshape/melt.py
Rewriting pandas/core/arrays/sparse/array.py
Rewriting pandas/tests/frame/methods/test_describe.py
Rewriting pandas/tests/indexing/test_categorical.py
Rewriting pandas/io/formats/format.py
Rewriting pandas/io/sql.py
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed 89 files: </summary>
- .pre-commit-config.yaml
- pandas/_config/config.py
- pandas/_libs/algos.pyi
- pandas/_libs/arrays.pyi
- pandas/_libs/groupby.pyi
- pandas/_libs/hashtable.pyi
- pandas/_libs/index.pyi
- pandas/_libs/internals.pyi
- pandas/_libs/join.pyi
- pandas/_libs/lib.pyi
- pandas/_libs/ops.pyi
- pandas/_libs/parsers.pyi
- pandas/_libs/reshape.pyi
- pandas/_libs/testing.pyi
- pandas/_libs/tslib.pyi
- pandas/_libs/tslibs/ccalendar.pyi
- pandas/_libs/tslibs/conversion.pyi
- pandas/_libs/tslibs/dtypes.pyi
- pandas/_libs/tslibs/fields.pyi
- pandas/_libs/tslibs/nattype.pyi
- pandas/_libs/tslibs/parsing.pyi
- pandas/_libs/tslibs/period.pyi
- pandas/_libs/tslibs/strptime.pyi
- pandas/_libs/tslibs/timedeltas.pyi
- pandas/_libs/tslibs/timestamps.pyi
- pandas/_libs/tslibs/timezones.pyi
- pandas/_libs/tslibs/tzconversion.pyi
- pandas/_libs/tslibs/vectorized.pyi
- pandas/_libs/window/aggregations.pyi
- pandas/_libs/window/indexers.pyi
- pandas/_libs/writers.pyi
- pandas/_testing/__init__.py
- pandas/conftest.py
- pandas/core/algorithms.py
- pandas/core/apply.py
- pandas/core/arrays/categorical.py
- pandas/core/arrays/masked.py
- pandas/core/arrays/sparse/array.py
- pandas/core/arrays/sparse/scipy_sparse.py
- pandas/core/computation/expr.py
- pandas/core/computation/expressions.py
- pandas/core/computation/pytables.py
- pandas/core/dtypes/cast.py
- pandas/core/dtypes/common.py
- pandas/core/dtypes/dtypes.py
- pandas/core/generic.py
- pandas/core/groupby/ops.py
- pandas/core/indexes/category.py
- pandas/core/indexes/multi.py
- pandas/core/indexes/range.py
- pandas/core/internals/array_manager.py
- pandas/core/internals/blocks.py
- pandas/core/internals/managers.py
- pandas/core/nanops.py
- pandas/core/ops/__init__.py
- pandas/core/reshape/melt.py
- pandas/core/reshape/merge.py
- pandas/core/reshape/tile.py
- pandas/io/formats/format.py
- pandas/io/parquet.py
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- pandas/io/sas/sas_xport.py
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- pandas/tests/arithmetic/test_period.py
- pandas/tests/base/test_constructors.py
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- pandas/tests/extension/base/reduce.py
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- pandas/tests/frame/methods/test_describe.py
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- pandas/tests/resample/test_base.py
- pandas/tests/resample/test_deprecated.py
- pandas/tests/reshape/merge/test_merge_asof.py
- pandas/tests/series/methods/test_clip.py
- pandas/tests/test_multilevel.py
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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} | 3 | "2021-05-31T13:23:49Z" | "2021-09-05T01:29:53Z" | "2021-09-05T01:29:53Z" | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
ts = 1618556707013635762
pdf = pd.DataFrame([{"id": 2, "ts": ts}, {"id": 2, "ts": ts+1}])
pdf["ts"] = pd.array(pdf["ts"], dtype="Int64")
pdf.groupby("id").agg({"ts": "min"})
```
#### Problem description
The above code will output `1618556707013635840` as the minimum which is incorrect.
#### Expected Output
Minimum should be `1618556707013635762`. I suspect there is a cast happening somewhere during the aggregation. If we keep the `ts` column at `int64` (i.e. use the non-nullable type) the result is as expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.7-200.fc33.x86_64
Version : #1 SMP Wed Mar 17 18:55:20 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL :
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.24.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
#### Potentially related issues
The issues below seem related, but from what I can see are slightly different:
* https://github.com/pandas-dev/pandas/issues/37494 seems like a more general problem where it is a priori unclear what the resulting `dtype` of the output column should be. For the `min` and `max` I would expect to get the same `dtype` as in the input column.
* https://github.com/pandas-dev/pandas/issues/41137 For the present issue we actually want to keep the original precision and not cast to float. | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 15 | "2021-05-31T18:21:33Z" | "2021-06-09T15:50:27Z" | "2021-06-09T11:56:59Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Minor fixups. After I get feedback here, I plan to make a PR with detailed instructions about adding to the whatsnew.
cc @attack68 - the Styler sections had a major revisions, wanted to get any feedback here.
cc @jorisvandenbossche @simonjayhawkins | {
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] | open | false | null | [] | null | 2 | "2021-05-31T20:33:56Z" | "2021-06-09T03:53:20Z" | null | MEMBER | null | Admittedly this is extremely minor, but it's something that has always irked me. Currently for minor (single-line, not a section) enhancements and bug-fixes, we have the format:
> This is sentence one (GH28527)
which on its own is perfectly fine. However when a line has multiple sentences:
> This is sentence one. This is sentence two (GH28527)
the lack of a period at the end looks off to me. I'd prefer if we did full sentences for each line followed by the GH link at the end, this way a multi-sentence doesn't look so odd.
> This is sentence one. (GH28527)
> This is sentence one. This is sentence two. (GH28527)
Another option is to limit one-line notes to being a single sentence (which I'm opposed to), and of course we could just stay with it as is. If we are to make any changes, I'd do it for the version minor/major version after 1.3.0.
cc @simonjayhawkins | {
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- [ ] tests added / passed
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} | 0 | "2021-06-01T00:36:21Z" | "2021-06-01T01:55:44Z" | "2021-06-01T01:55:44Z" | MEMBER | null | We'll start running ARM tests in #41739. In order to see the result of the execution in that PR, we need `.circleci/config.yml` to exist in master, so CircleCI starts showing in GitHub. Adding the config file, but just doing an echo (and not the checkout, set up or the environment or the running of the tests). So we can add CircleCI safely, and see results before merging #41739.
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```diff
- s = "\n".join([_build_option_description(k) for k in keys])
+ s = "\n".join(_build_option_description(k) for k in keys)
```
However, they then reverted this feature as it was a potential performance regression (https://github.com/asottile/pyupgrade/issues/456).
I think we should revert those changes (though arguably not the entire PR) and add a rule to pandas-dev-flaker about using list comprehensions rather than generators when joining strings
If nobody takes this on earlier, I'll do this on Friday | {
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] | closed | true | null | [] | null | 7 | "2021-06-01T09:18:51Z" | "2021-06-03T07:38:54Z" | "2021-06-02T22:17:51Z" | NONE | too heated | I post this as a "Question" because I am quite new to `pandas`. So maybe I miss some understandings and the "problem" described by me is by design and you have good reasons for that.
I use pandas 1.2.4, with Python 3.9.4 on Windows 10 64 bit.
As a user I would expect that pandas check the number of fields per row when importing via csv file. But IMHO it does not in all cases.
# Example 1
Here is a csv file __without header__ and but a set `names=` attribute with three fields. So pandas should be able to know how many fields/columns should be in the CSV file. The second row contains 4 instead of 3 fields.
```
import pandas
import io
csv_without_header = io.StringIO(
'A;B;C\n'
'D;E;X;Y\n'
'F;G;H'
)
df = pandas.read_csv(csv_without_header, encoding='utf-8', sep=';',
header=None,
names=['First', 'Second', 'Third'])
```
Pandas import this without warnrings or errors. The 4th field in the 2nd row is simply ignored.
# Example 2
I added a header line into the csv file with again three fields.
So pandas should be able to know how many fields/columns should be in the CSV file.
And again the second row contains 4 instead of 3 fields.
```
csv_with_header = io.StringIO(
'First;Second;Third\n'
'A;B;C\n'
'D;E;X;Y\n'
'F;G;H'
)
df = pandas.read_csv(csv_with_header, encoding='utf-8', sep=';')
```
Here an error occurs as I expect.
`pandas.errors.ParserError: Error tokenizing data. C error: Expected 3 fields in line 3, saw 4`
# Example 3
There are less then 3 fields in the 2nd row. Again here is no warning or error. The missing field is set with `NaN`. And here it does not matter if you give the number of (expected) fields via header line in the CSV or via `names=` attribute.
```
csv_with_header = io.StringIO(
'First;Second;Third\n'
'A;B;C\n'
'D;Y\n'
'F;G;H'
)
csv_without_header = io.StringIO(
'A;B;C\n'
'D;Y\n'
'F;G;H'
)
df_a = pandas.read_csv(csv_with_header, encoding='utf-8', sep=';')
df_b = pandas.read_csv(csv_without_header, encoding='utf-8', sep=';', names=['First', 'Second', 'Third'])
```
Want I want is to import CSV files and be informed if there are to many or less then the expected number of fields in any row. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41755 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41755/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41755/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41755/events | https://github.com/pandas-dev/pandas/issues/41755 | 908,138,038 | MDU6SXNzdWU5MDgxMzgwMzg= | 41,755 | BUG: `warn=True` parameter in `pd.infer_freq()` does not prevent raising a `ValueError` as expected | {
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] | open | false | null | [] | null | 0 | "2021-06-01T10:17:35Z" | "2021-08-21T01:57:43Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# DateIndex containing only two values -- too few to infer a frequency from
too_short = pd.date_range(datetime(2021, 6, 1, 0), datetime(2021, 6, 1, 1), freq="1H")
# raises ValueError, as expected
pd.infer_freq(not_too_short, warn=False)
# also raises ValueError, not as expected
pd.infer_freq(not_too_short, warn=True) # (this is the default arg for warn)
```
#### Problem description
The [documentation currently suggests](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.infer_freq.html) that, by default, `pd.infer_freq()` will warn when it can't infer a frequency (in this case because the range does not have at least three values). Instead it always raises, regardless of whether the `warn` parameter is set to `True` or `False`.
Looking at [the implementation of `pd.infer_freq()`](https://github.com/pandas-dev/pandas/blob/v1.2.4/pandas/tseries/frequencies.py#L120-L183), the class that "does the work" is [`_FrequencyInferer`](https://github.com/pandas-dev/pandas/blob/v1.2.4/pandas/tseries/frequencies.py#L186). This class [sets `self.warn` in its `__init__()`](https://github.com/pandas-dev/pandas/blob/v1.2.4/pandas/tseries/frequencies.py#L203) but then never references it again. So it looks like the `warn` parameter is a dud.
#### Expected Output
I'd expect this line to warn instead of raising a `ValueError`:
```
pd.infer_freq(not_too_short, warn=True) # (this is the default arg for warn)
```
Since lots of code probably implicitly relies on the current implementation of always raising, it may be best to just remove the `warn` argument altogether, or document that it doesn't do anything.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : 0.29.22
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 1 | "2021-06-01T10:20:38Z" | "2021-08-21T01:58:13Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
>>> import pandas as pd
>>> import numpy as np
>>> np.mean(pd.DataFrame([[1,2,3],[4,5,6]]), axis=1)
0 2.0
1 5.0
dtype: float64
>>> np.median(pd.DataFrame([[1,2,3],[4,5,6]]), axis=1)
array([2., 5.])
```
#### Problem description
The return type when applying `numpy` ops to data frames should be consistent. In this example, `np.mean` returns a `pd.Series` while `np.median` returns an `np.ndarray`.
Code that applies dynamically selected numpy operations to pandas dataframes is likely to run into errors if the type of object returned is inconsistent, e.g. expecting to find an index and not finding one in the case of `np.median`
#### Expected Output
Both should be `pd.Series`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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] | open | false | null | [] | null | 2 | "2021-06-01T10:35:43Z" | "2021-08-21T01:58:58Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"name": ["A", "A", "A", "B"], "value": [10, 20, 30, 10]})
df.set_index("name", drop=True).to_dict("dict")
```
#### Problem description
This creates output:
> `{'value': {'A': 30, 'B': 10}}`
Which does seem odd. My expectation from pandas is instead of giving bare `30` and `10` as value to give a **list** of values. This only looks like a limitation to what pandas is able to do.
#### Expected Output
> `{'value': {'A': [10, 20, 30], 'B': 10}}`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.109+
Version : #1 SMP Tue Apr 20 19:55:43 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 56.1.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.15
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41759/events | https://github.com/pandas-dev/pandas/issues/41759 | 908,215,312 | MDU6SXNzdWU5MDgyMTUzMTI= | 41,759 | ENH: allow groupby (and drop_duplicates) on columns containing unhashable types | {
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] | closed | false | null | [] | null | 6 | "2021-06-01T11:57:47Z" | "2021-08-21T02:01:37Z" | "2021-08-21T02:01:37Z" | NONE | null | #### Is your feature request related to a problem?
well sort of, currently one can not groupby on a column containing unhashable types (e.g dicts)
#### Describe the solution you'd like
an easy workaround is to groupby on that column as type str and then remap the strings back to their orig type,
wondering if we can provide this process built in so one can groupby on unhashable types if she desires to
#### Describe alternatives you've considered
add a try except to allow hash(str(x)) in case hash(x) is impossible, or convert the column to str and add it back later
```python
# Your code here, if applicable
```
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} | 3 | "2021-06-01T12:08:36Z" | "2021-06-04T00:20:05Z" | "2021-06-04T00:20:05Z" | NONE | null | I believe a bug was introduced in the update of `xs` in pandas 1.2.0. The problem occurs when requesting multiple keys in combination with `drop_level=False`. I use the following code to highlight the problem I face:
```python
import pandas as pd
d = [[4, 4, 2, 2, 8], [0, 0, 2, 2, 0]]
tuples = [('mammal', 'cat'), ('mammal', 'dog'), ('bird', 'penguin'), ('bird', 'pidgeon'), ('insect', 'spider')]
columns = pd.MultiIndex.from_tuples(tuples)
df = pd.DataFrame(data=d, columns=columns, index=['num_legs', 'num_wings'])
print(df.xs(['mammal', 'bird'], drop_level=False, axis=1))
```
In pandas 1.1.5, the following output is generated (as expected):
```
mammal bird
cat dog penguin pidgeon
num_legs 4 4 2 2
num_wings 0 0 2 2
```
While in pandas 1.2.4, an exception is raised:
```
Traceback (most recent call last):
File "C:\python\miniconda3\envs\pandas_12\lib\site-packages\pandas\core\indexes\base.py", line 3080, in get_loc
return self._engine.get_loc(casted_key)
File "pandas\_libs\index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\hashtable_class_helper.pxi", line 4554, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas\_libs\hashtable_class_helper.pxi", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'bird'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "pandas\_libs\index.pyx", line 705, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
File "C:\python\miniconda3\envs\pandas_12\lib\site-packages\pandas\core\indexes\base.py", line 3082, in get_loc
raise KeyError(key) from err
KeyError: 'bird'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\python\miniconda3\envs\pandas_12\lib\site-packages\pandas\core\indexes\multi.py", line 3036, in _get_loc_level
return (self._engine.get_loc(key), None)
File "pandas\_libs\index.pyx", line 708, in pandas._libs.index.BaseMultiIndexCodesEngine.get_loc
KeyError: ('mammal', 'bird')
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\jimzw\AppData\Roaming\JetBrains\PyCharmCE2021.1\scratches\scratch_4.py", line 9, in <module>
print(df.xs(['mammal', 'bird'], drop_level=False, axis=1))
File "C:\python\miniconda3\envs\pandas_12\lib\site-packages\pandas\core\generic.py", line 3733, in xs
loc, new_index = index._get_loc_level(
File "C:\python\miniconda3\envs\pandas_12\lib\site-packages\pandas\core\indexes\multi.py", line 3038, in _get_loc_level
raise KeyError(key) from e
KeyError: ('mammal', 'bird')
```
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} | 1 | "2021-06-01T14:28:42Z" | "2021-08-21T02:02:13Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({'c1':[[1,2,3]], 'c2':[np.nan]})
>>> df
c1 c2
0 [1, 2, 3] NaN
>>> df.c1 + df.c2
0 NaN
dtype: object
>>> [1, 2, 3] + np.nan
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: can only concatenate list (not "float") to list
```
#### Problem description
In Pandas when using the "+" operator on a Series entry containing a list and a Series entry containing a NaN value, a NaN value is returned. This is inconsistent with standard Python where attempting to concatenate a list and a NaN raises a TypeError. In general, list concatenation between a NaN and a list is an ambiguous operation and therefore should raise an error.
#### Expected Output
A TypeError should be raised.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [X] I have confirmed this bug exists on the latest version of pandas.
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[np.nan, np.nan, np.nan]],
columns=['A', 'B', 'C'])
print(df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))) # Works fine
print(df.agg(x=('A', max), y=('B', 'min'), z=('C', lambda x: np.mean(x)))) # Does not work
```
#### Problem description
Passing lambda or user defined function causes `pd.DataFrame.agg` to crash.
```
Traceback (most recent call last):
File "/home/tobii.intra/jzn/.PyCharm2018.3/config/scratches/scratch_200.py", line 22, in <module>
print(df.agg(x=('A', max), y=('B', 'min'), z=('C', lambda x: np.mean(x))))
File "/home/tobii.intra/jzn/.virtualenvs/venv/lib/python3.7/site-packages/pandas/core/frame.py", line 7379, in aggregate
result_in_dict = relabel_result(result, func, columns, order)
File "/home/tobii.intra/jzn/.virtualenvs/venv/lib/python3.7/site-packages/pandas/core/aggregation.py", line 342, in relabel_result
s = s[col_idx_order]
File "/home/tobii.intra/jzn/.virtualenvs/venv/lib/python3.7/site-packages/pandas/core/frame.py", line 2912, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "/home/tobii.intra/jzn/.virtualenvs/venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/home/tobii.intra/jzn/.virtualenvs/venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 1298, in _validate_read_indexer
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Int64Index([0], dtype='int64')] are in the [columns]"
```
When setting a break point in the user defined function I noticed that the input is not a `Series` containing all values of the column, but a single scalar value from that column. This seems to be isolated to `DataFrame.agg`, since my workaround was to add a trivial group by and make use of `GroupBy.agg`, which works fine.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-143-generic
Version : #147-Ubuntu SMP Wed Apr 14 16:10:11 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.18.2
pytz : 2020.4
dateutil : 2.8.1
pip : 21.1.2
setuptools : 54.2.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 5 | "2021-06-02T00:07:30Z" | "2021-06-02T22:01:17Z" | "2021-06-02T21:40:04Z" | CONTRIBUTOR | null | This is the `Index.union` part of #41153. This helps simplify that PR.
The special casing in `Index.union` is currently active if both are numeric. After #41153 it should only be special cased if one dtype is uint64 and other a signed int. So after this and #41153:
* int8 & uint32 -> int64
* [u]int64 & float64 -> float64
* int64 & uint64 -> object
* int8 & uint64 -> object
The first and second case is handled correctly by `find_common_type`, but the others aren't currently.
This PR changes no functionality itself, but prepares for the changes in #41153, where we want e.g. `NumericIndex[uint8] .union(NumericIndex[int32])` to give `NumericIndex[int64]` and not `Index[object]`. | {
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} | 1 | "2021-06-02T03:54:49Z" | "2021-06-03T17:15:56Z" | "2021-06-03T17:15:56Z" | MEMBER | null | ```
umax = np.iinfo(np.uint64).max
ser = pd.Series([0, 1], index=[umax-1, umax])
>>> ser.loc[-1] # <- should raise KeyError
OverflowError: can't convert negative value to npy_uint64
>>> ser.loc[[-1]] # <- should raise KeyError
18446744073709551615 1
dtype: int64
``` | {
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} | 0 | "2021-06-02T04:40:03Z" | "2021-06-02T13:51:52Z" | "2021-06-02T13:05:19Z" | MEMBER | null | Inching towards getting rid of special-casing interval+period in sanitize_array | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 2 | "2021-06-02T06:53:30Z" | "2021-06-04T23:03:31Z" | "2021-06-04T23:03:31Z" | NONE | null | Kindly refer the following code snippet.
`import pandas as pd`
`import os`
`# Creating a corrupted file`
`with open('a.xlsx', 'w') as f:`
` pass`
`# Reading using pd.ExcelFile`
`data = pd.ExcelFile('a.xlsx', engine='openpyxl')`
`# Deleting the file`
`os.remove('a.xlsx')`
Here, I create a .xlsx file which is not the correct way to make xlsx file but this will express my concern. Then I use pd.ExcelFile to read the xlsx file but it gives me `zipfile.BadZipFile: File is not a zip file` which is fine but later when I try to delete that file I get `PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'a.xlsx'`. This doesn't happen in `pandas==1.2.1` and lower version. The current version i.e. `pandas==1.2.4` has this bug.
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} | 3 | "2021-06-02T07:23:25Z" | "2021-08-05T12:01:47Z" | "2021-08-05T12:01:47Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
# Generate a 4x6 DataFrame
df = pd.DataFrame(np.arange(24).reshape(4, 6), columns=list("abcdef"))
# Make each column a different data type
df = df.astype({"a":"float16", "b":"float32", "c":"float64", "d":"int8", "e":"int16", "f":"int32"})
print(df)
# Output:
# a b c d e f
# 0 0.0 1.0 2.0 3 4 5
# 1 6.0 7.0 8.0 9 10 11
# 2 12.0 13.0 14.0 15 16 17
# 3 18.0 19.0 20.0 21 22 23
print(df.dtypes)
# Output:
# a float16
# b float32
# c float64
# d int8
# e int16
# f int32
# dtype: object
# Rolling minimum across rows
print(df.rolling(window=2, min_periods=1, axis=1).min())
# Output. Notice how the float16 and float32 columns were removed:
# c d e f
# 0 2.0 2.0 3.0 4.0
# 1 8.0 8.0 9.0 10.0
# 2 14.0 14.0 15.0 16.0
# 3 20.0 20.0 21.0 22.0
```
#### Problem description
It seems that rolling operations along rows (`axis=1`) incorrectly omit columns containing `float16`s and `float32`s. The same operations work as expected along columns (`axis=0`), however.
#### Expected Output
```python
# Convert float16 and float32 columns to float64s as a workaround
df = df.astype({"a":"float64", "b":"float64"})
# Rolling minimum across rows again
print(df.rolling(window=2, min_periods=1, axis=1).min())
# Output:
# a b c d e f
# 0 0.0 0.0 1.0 2.0 3.0 4.0
# 1 6.0 6.0 7.0 8.0 9.0 10.0
# 2 12.0 12.0 13.0 14.0 15.0 16.0
# 3 18.0 18.0 19.0 20.0 21.0 22.0
```
#### Possible Cause
A change made in #36458, [specifically this line](https://github.com/phofl/pandas/blob/fb85045056fc9cd773e714287a7a88ac55e19023/pandas/core/window/rolling.py#L250).
It seems that `"float"` is an alias specifically for `np.float64`, not `np.float32` or `np.float16`. Changing that line to
`obj = obj.select_dtypes(include="number", exclude=["timedelta"])`
to include all numeric values seemed to fix the issue in this case. I can open a PR if there don't seem to be any issues with this.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 8 Stepping 2, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_Canada.1252
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.24.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.2
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest
---
###Problem
I am unable to import pandas into python. Whenever I try to import, it gives me a traceback
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\__init__.py", line 186, in <module>
__version__ = v.get("closest-tag", v["version"])
AttributeError: 'NoneType' object has no attribute 'get'
```
I tried reinstalling pandas but the error still exists. I am using Pycharm but even in cmd when I import pandas it gives me the same error.
#### Output of ``pd.show_versions()``
<details>
I cant show it because I cant import it. But from files, it is 1.2.4
</details>
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} | 1 | "2021-06-02T12:12:11Z" | "2021-06-18T02:24:25Z" | "2021-06-02T15:14:32Z" | MEMBER | null | followup #41769 #41774
xref #41771
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 8 | "2021-06-02T14:33:22Z" | "2021-06-26T07:54:47Z" | "2021-06-02T18:40:04Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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This was the motivation for #41707. This will introduce some overhead, but will also prevent the behaviors from getting out of sync again. | {
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But I want to discuss some ideas and thinks like that. I am not sure if am allowed to that in this "Question" section in the Issues?
btw: GitHub now offers a discussion forum, too. | {
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- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
What is the current state of amd64 compilation? Docker pip installation takes ages and I'm having trouble installing it on my macos with apple m1 chip.
Is there anything I can do to help provide precompiled binaries for release on this platform?
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} | 6 | "2021-06-02T20:29:46Z" | "2021-09-23T18:52:57Z" | "2021-06-21T13:07:35Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 15 | "2021-06-02T20:36:06Z" | "2021-06-04T10:59:48Z" | "2021-06-04T00:20:06Z" | MEMBER | null | - [x] closes #41760
- [x] tests added / passed
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cc @simonjayhawkins Should we include in 1.2.5 or push to 1.3? | {
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] | open | false | null | [] | null | 0 | "2021-06-03T00:36:07Z" | "2021-08-21T02:04:55Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.DataFrame({"i": [0, 1, 2], "b": [False, False, True], "s": ["x", "y", "z"]}).quantile(q=0.5, numeric_only=True)
```
#### Problem description
https://github.com/numpy/numpy/pull/16273#discussion_r641264085 broke the case of NumPy's `percentile` with bools which causes to break pandas pandas' `quantile` too. If this is considered as not a bug, I would expect pandas to handle it with `numeric_only`. Cross filed issue in NumPy: https://github.com/numpy/numpy/issues/19154
Currently it throws an exception as below:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../python3.8/site-packages/pandas/core/frame.py", line 9266, in quantile
result = data._mgr.quantile(
File "/.../python3.8/site-packages/pandas/core/internals/managers.py", line 491, in quantile
block = b.quantile(axis=axis, qs=qs, interpolation=interpolation)
File "/.../python3.8/site-packages/pandas/core/internals/blocks.py", line 1592, in quantile
result = nanpercentile(
File "/.../python3.8/site-packages/pandas/core/nanops.py", line 1675, in nanpercentile
return np.percentile(values, q, axis=axis, interpolation=interpolation)
File "<__array_function__ internals>", line 5, in percentile
File "/.../python3.8/site-packages/numpy/lib/function_base.py", line 3818, in percentile
return _quantile_unchecked(
File "/.../python3.8/site-packages/numpy/lib/function_base.py", line 3937, in _quantile_unchecked
r, k = _ureduce(a, func=_quantile_ureduce_func, q=q, axis=axis, out=out,
File "/.../python3.8/site-packages/numpy/lib/function_base.py", line 3515, in _ureduce
r = func(a, **kwargs)
File "/.../python3.8/site-packages/numpy/lib/function_base.py", line 4064, in _quantile_ureduce_func
r = _lerp(x_below, x_above, weights_above, out=out)
File "/.../python3.8/site-packages/numpy/lib/function_base.py", line 3961, in _lerp
diff_b_a = subtract(b, a)
TypeError: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.
```
#### Expected Output
```
i 1.0
b 0.0
Name: 0.5, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.14
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [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
test_time = pd.to_datetime('2020-06-09 00:01:14.500000')
print(pd.Series(test_time).dt.round("S"))
```
#### Problem description
If the ms is 5e5, the method of round is floor, but it should be ceil.
#### Expected Output
```
2020-06-09 00:01:15
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.el7.x86_64
Version : #1 SMP Thu Nov 8 23:39:32 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.18.0
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
print(pd.__version__)
df = pd.DataFrame(["a", "b", np.nan])
print("astype(str): \n", df.astype(str))
print("astype('category').astype(str): \n", df.astype("category").astype(str))
```
Output 1.1.3:
```
1.1.3
astype(str):
0
0 a
1 b
2 nan
astype('category').astype(str):
0
0 a
1 b
2 nan
```
Output 1.2.4:
```
1.2.4
astype(str):
0
0 a
1 b
2 nan
astype('category').astype(str):
0
0 a
1 b
2 NaN
```
#### Problem description
The above snippet run on version 1.1 (tested on 1.1.3) returns `'nan'` strings of the converted categories, whereas on 1.2 (tested on 1.2.4) keeps `np.nan` objects. Tangentially, when using the pandas StringDtype conversion (`.astype("string")`, pandas 1.1 and 1.2 both keep a NAType after conversion from categorical to string.
It is not clear whether it is expected behaviour that this conversion keeps `np.nan`, or whether it should return a `'nan'` string. The former would mean that this is now correct, the latter would mean there is a regression.
This issue is probably related to https://github.com/pandas-dev/pandas/pull/37355 and https://github.com/pandas-dev/pandas/issues/25353
#### Expected Output
Document what behaviour is desired.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-14-amd64
Version : #1 SMP Debian 4.19.171-2 (2021-01-30)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 54.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
Versions are identical except for one run at pandas 1.1.3, the other at 1.2.4. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41798/events | https://github.com/pandas-dev/pandas/issues/41798 | 910,456,366 | MDU6SXNzdWU5MTA0NTYzNjY= | 41,798 | ImportError: cannot import name 'Series' from 'pandas' (unknown location) | {
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Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'Series' from 'pandas' (unknown location) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41799/events | https://github.com/pandas-dev/pandas/issues/41799 | 910,506,004 | MDU6SXNzdWU5MTA1MDYwMDQ= | 41,799 | ISSUE: ImportError: cannot import name 'Series' from 'pandas' (unknown location) | {
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Traceback (most recent call last):
File "", line 1, in
ImportError: cannot import name 'Series' from 'pandas' (unknown location) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41800/events | https://github.com/pandas-dev/pandas/issues/41800 | 910,605,255 | MDU6SXNzdWU5MTA2MDUyNTU= | 41,800 | BUG: combine_first fails when one series is timezone-aware and the other is emtpy. | {
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] | open | false | null | [] | null | 0 | "2021-06-03T15:27:56Z" | "2021-08-21T02:06:01Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] \(optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
from datetime import datetime
>>> time_index = pd.date_range(datetime(2021, 1, 1, 1), datetime(2021, 1, 1, 10), freq='H', tz='Europe/Oslo')
>>> s1 = pd.Series(np.random.random(10), index=time_index)
>>> s2 = pd.Series()
>>> s1.combine_first(s2)
```
**Traceback**
<details>
```python
AttributeError Traceback (most recent call last)
<ipython-input-14-9af79f353bf5> in <module>
----> 1 s1.combine_first(pd.Series())
~/anaconda3/envs/powerml_dev/lib/python3.7/site-packages/pandas/core/series.py in combine_first(self, other)
2983 other = to_datetime(other)
2984
-> 2985 return this.where(notna(this), other)
2986
2987 def update(self, other) -> None:
~/anaconda3/envs/powerml_dev/lib/python3.7/site-packages/pandas/core/generic.py in where(self, cond, other, inplace, axis, level, errors, try_cast)
9285 other = com.apply_if_callable(other, self)
9286 return self._where(
-> 9287 cond, other, inplace, axis, level, errors=errors, try_cast=try_cast
9288 )
9289
~/anaconda3/envs/powerml_dev/lib/python3.7/site-packages/pandas/core/generic.py in _where(self, cond, other, inplace, axis, level, errors, try_cast)
9019 cond = com.apply_if_callable(cond, self)
9020 if isinstance(cond, NDFrame):
-> 9021 cond, _ = cond.align(self, join="right", broadcast_axis=1)
9022 else:
9023 if not hasattr(cond, "shape"):
~/anaconda3/envs/powerml_dev/lib/python3.7/site-packages/pandas/core/series.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
4228 limit=limit,
4229 fill_axis=fill_axis,
-> 4230 broadcast_axis=broadcast_axis,
4231 )
4232
~/anaconda3/envs/powerml_dev/lib/python3.7/site-packages/pandas/core/generic.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
8832 method=method,
8833 limit=limit,
-> 8834 fill_axis=fill_axis,
8835 )
8836 else: # pragma: no cover
~/anaconda3/envs/powerml_dev/lib/python3.7/site-packages/pandas/core/generic.py in _align_series(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
8985 if is_series or (not is_series and axis == 0):
8986 if is_datetime64tz_dtype(left.index.dtype):
-> 8987 if left.index.tz != right.index.tz:
8988 if join_index is not None:
8989 # GH#33671 ensure we don't change the index on
AttributeError: 'Index' object has no attribute 'tz'
```
</details>
#### Problem description
Combine fails when one series has a timezone-aware datetime index and the other is empty. This does not happen if s1 has naive datetimes.
#### Expected output
The output should be equal to s1.
#### Output of ```python pd.show_versions()```
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-142-generic
Version : #146-Ubuntu SMP Tue Apr 13 01:11:19 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.0
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41801/events | https://github.com/pandas-dev/pandas/pull/41801 | 910,616,730 | MDExOlB1bGxSZXF1ZXN0NjYxMDEyMDA2 | 41,801 | Fix typo in comment | {
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} | 0 | "2021-06-03T15:36:30Z" | "2021-06-03T23:01:58Z" | "2021-06-03T15:38:52Z" | CONTRIBUTOR | null | Fixes simple dunder method typo in a comment, no functional changes
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] | closed | false | null | [] | null | 1 | "2021-06-03T17:56:36Z" | "2021-06-03T18:34:51Z" | "2021-06-03T18:34:51Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
I need to export some data to exccel sheets throush a loop
For each sheet I need to merge some cells in column wise. I searched in the Stack also but not able to find
PFA
```python
# Your code here, if applicable
```
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https://api.github.com/repos/pandas-dev/pandas/issues/41803 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41803/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41803/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41803/events | https://github.com/pandas-dev/pandas/pull/41803 | 910,768,540 | MDExOlB1bGxSZXF1ZXN0NjYxMTQyMDEw | 41,803 | REF: do remove DTI._convert_arr_indexer | {
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} | 4 | "2021-06-03T18:54:05Z" | "2021-06-04T16:15:19Z" | "2021-06-04T16:13:15Z" | MEMBER | null | Motivation: get rid of `_convert_listlike_indexer`, `_convert_arr_indexer`, `_convert_list_indexer`. These are just convoluted bits of casting before a (inefficient-because-repeated) call to `get_indexer`.
This moves the relevant casting for the DTI/TDI/PI case directly into get_indexer, mirroring how get_loc works for these subclasses.
Upcoming PR does analogous thing for IntervalIndex and CategoricalIndex. MultiIndex is the last one, haven't landed on a solution there.
cc @phofl | {
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} | 0 | "2021-06-03T18:57:36Z" | "2021-06-03T22:51:50Z" | "2021-06-03T21:53:10Z" | MEMBER | null | xref #41803
<s>This doesn't yet address the special-cased exception messages we issue for IntervalIndex/CategoricalIndex.</s>
update: this half-addresses the special-cased exception messages | {
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] | open | false | null | [] | null | 2 | "2021-06-03T19:37:07Z" | "2021-08-21T02:06:34Z" | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print(pd.Series(data=[None], dtype='interval[datetime64[ns]]'))
```
#### Problem description
Instantiating a `Series` with NA values in `data` fails if `dtype` is specified as `interval[datetime64[ns]]`, raising an error:
<details>
> Traceback (most recent call last):
> File "main.py", line 3, in <module>
> print(pd.Series(
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/series.py", line 364, in __init__
> data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/construction.py", line 480, in sanitize_array
> subarr = _try_cast(data, dtype, copy, raise_cast_failure)
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/construction.py", line 579, in _try_cast
> subarr = array_type(arr, dtype=dtype, copy=copy)
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/arrays/interval.py", line 280, in _from_sequence
> return cls(scalars, dtype=dtype, copy=copy)
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/arrays/interval.py", line 201, in __new__
> return cls._simple_new(
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/arrays/interval.py", line 227, in _simple_new
> left = left.astype(dtype.subtype)
> File "/opt/virtualenvs/python3/lib/python3.8/site-packages/pandas/core/indexes/numeric.py", line 355, in astype
> raise TypeError(
> TypeError: Cannot convert Float64Index to dtype datetime64[ns]; integer values are required for conversion
</details>
#### Expected Output
```
0 NaN
dtype: interval
```
#### Output of ``pd.show_versions()``
<details>
> INSTALLED VERSIONS
> ------------------
> commit : 2cb96529396d93b46abab7bbc73a208e708c642e
> python : 3.8.10.final.0
> python-bits : 64
> OS : Linux
> OS-release : 5.4.0-1043-gcp
> Version : #46-Ubuntu SMP Mon Apr 19 19:17:04 UTC 2021
> machine : x86_64
> processor : x86_64
> byteorder : little
> LC_ALL : en_US.UTF-8
> LANG : en_US.UTF-8
> LOCALE : en_US.UTF-8
>
> pandas : 1.2.4
> numpy : 1.20.3
> pytz : 2021.1
> dateutil : 2.8.1
> pip : 19.3.1
> setuptools : 56.0.0
> Cython : None
> pytest : None
> hypothesis : None
> sphinx : None
> blosc : None
> feather : None
> xlsxwriter : None
> lxml.etree : None
> html5lib : 1.1
> pymysql : None
> psycopg2 : None
> jinja2 : 3.0.1
> IPython : None
> pandas_datareader: None
> bs4 : None
> bottleneck : None
> fsspec : None
> fastparquet : None
> gcsfs : None
> matplotlib : 3.4.2
> numexpr : None
> odfpy : None
> openpyxl : None
> pandas_gbq : None
> pyarrow : None
> pyxlsb : None
> s3fs : None
> scipy : 1.4.1
> sqlalchemy : 1.4.15
> tables : None
> tabulate : None
> xarray : None
> xlrd : None
> xlwt : None
> numba : None
</details>
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} | 9 | "2021-06-03T19:46:34Z" | "2021-06-06T08:15:40Z" | "2021-06-04T23:03:31Z" | CONTRIBUTOR | null | - [x] closes #41778
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
@simonjayhawkins maybe for 1.2.5? | {
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] | open | false | null | [] | null | 12 | "2021-06-03T21:52:58Z" | "2021-08-21T02:06:49Z" | null | CONTRIBUTOR | null | In `pandas/_typing.py`, we define `ArrayLike` as:
```python
ArrayLike = Union["ExtensionArray", np.ndarray]
```
In the numpy glossary https://numpy.org/doc/stable/glossary.html?highlight=array_like, numpy defines `array_like` as:
> Any scalar or sequence that can be interpreted as an ndarray. In addition to ndarrays and scalars this category includes lists (possibly nested and with different element types) and tuples. Any argument accepted by numpy.array is array_like.
Are we creating confusion by using the term `ArrayLike` to only mean arrays, whereas numpy defines it to include scalars?
This came up in terms of reconciling the arguments of `np.searchsorted()` and `ExtensionArray.searchsorted()`.
Comments from @jbrockmendel and @simonjayhawkins welcome.
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} | 0 | "2021-06-04T01:56:36Z" | "2021-06-04T23:14:18Z" | "2021-06-04T23:02:19Z" | MEMBER | null | Just about as much as we can simplify _try_cast until deprecations are enforced.
Removes the last usage of construct_1d_ndarray_preserving_na, so removes that altogether | {
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} | 2 | "2021-06-04T03:49:49Z" | "2021-06-04T21:09:18Z" | "2021-06-04T20:23:57Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Get the special-casing out of sanitize_array up the chain into maybe_infer_to_datetimelike. im hopeful we can get it all crammed into maybe_infer_objects | {
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} | 4 | "2021-06-04T10:19:09Z" | "2021-07-14T18:06:38Z" | null | NONE | null | The axis along which interpolation is performed is different for different methods in interpolate.
For example
```
df = pd.DataFrame(np.array([
[1, 2, 3],
[5, np.nan, np.nan],
[3, np.nan, 4],
]))
print(df.interpolate(method='pad', axis='index'))
print(df.interpolate(method='pad', axis='index', limit_area='inside'))
```
returns
```
0 1 2
0 1.0 2.0 3.0
1 5.0 2.0 6.0
2 3.0 2.0 4.0
0 1 2
0 1.0 2.0 3.0
1 5.0 5.0 6.0
2 3.0 3.0 4.0
```
Clearly with `limit_area` set the interpolation happens on the wrong axis.
Not contributed before but happy to have a go at this one? | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 10 | "2021-06-04T16:41:48Z" | "2021-07-07T07:34:36Z" | "2021-07-06T18:37:21Z" | MEMBER | null | In `install.rst`, we list `setuptools` as a runtime dependency. If that is the case, we don't need to vendor `packaging` #41199 since it's vendored by `pkg_resources`, which is part of `setuptools`. And we need to update `setup.cfg` and `README.md` to reflect this.
If `setuptools` is not a runtime dependency, vendoring `packaging` is necessary. And, we need to list it as a test and optional dependency because we are using `pkg_resources` in `pandas/plotting/_core.py` and `pandas/tests/plotting/test_backend.py` #41503. An alternative of `pkg_resources` for accessing entry points is `importlib.metadata`, which is only available after Python 3.8. And `importlib.metadata` is the suggested way to do this according to https://setuptools.readthedocs.io/en/latest/pkg_resources.html. | {
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} | 0 | "2021-06-04T16:41:49Z" | "2021-06-04T21:05:42Z" | "2021-06-04T20:25:38Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00'], tz='Europe/Berlin'))
```
Output:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-389898c5af02> in <module>
----> 1 pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00'], tz='Europe/Berlin'))
~/.pyenv/versions/3.8.6/envs/beam/lib/python3.8/site-packages/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize)
255 return _hash_categorical(vals, encoding, hash_key)
256 elif is_extension_array_dtype(dtype):
--> 257 vals, _ = vals._values_for_factorize()
258 dtype = vals.dtype
259
AttributeError: 'DatetimeIndex' object has no attribute '_values_for_factorize'
```
Apparently `datetime64[ns, Europe/Berlin]` is an extension array dtype but has no `_values_for_factorize` method. I've reproduced on pandas 1.1.4, 1.2.4, and on master (503ce5045d8344b69207ae045943644bd0a14881)
#### Problem description
`pd.util.hash_array` works with other Indexes, including a timezone-naive `DatetimeIndex`, it seems reasonable to expect it to work with a timezone-aware `DatetimeIndex` (or yield a better error).
#### Expected Output
Output should be similar to timezone-naive `DatetimeIndex`:
```python
In [3]: pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00']))
Out[3]: array([3152239034440746192], dtype=uint64)
```
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} | 2 | "2021-06-04T21:54:02Z" | "2021-06-18T02:24:16Z" | "2021-06-08T12:59:31Z" | MEMBER | null | xref #41815
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
This is a line in a file named as `data.txt` and I am trying to read this in pandas using `read_csv`.
`"xxx"|"xxx"|"-xxxxxxx"|"xxxxx"|"x"|"xx"|""xxxxxx""|"x"|"xx"|"xxxxxxx"|""|"x"|"xxxxxx"|"X"|"xxxx"|"xxxxx"|""`
```
df = pd.read_csv('data.txt', names=columns, dtype=column_dict, na_values=[''], keep_default_na=False, sep='|', encoding='cp1252', skiprows=1)
```
#### Problem description
The problem I am facing here is that even though all the other data are read in the data frame correctly, pandas has an issue when it comes to reading ""xxxxxx"" two double quotes and it reads it as xxxxxx"" inside the dataframe
As you can notice there is in the 7th index in the above line, there is an item with double-double quotes, that is the issue
#### Expected Output
The expected output should be that it should be read as "xxxxxx" inside the data frame
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1043-gcp
Version : #46-Ubuntu SMP Mon Apr 19 19:17:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
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