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https://api.github.com/repos/pandas-dev/pandas/issues/31015
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MDExOlB1bGxSZXF1ZXN0MzYyODI4MDkz
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DOC: Fixed documented value of `pd.NA ** 0` (#31005)
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CONTRIBUTOR
| null |
(cherry picked from commit da04c9a8fe6ddbc7028e8721c57d3e62d1da11e3)
Closes https://github.com/pandas-dev/pandas/issues/31003
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MDU6SXNzdWU1NDk4MDYwMjc=
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BUG: pivot_table with multi-index columns only and margins=True gives wrong output or fails
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| 1
| 2020-01-14T20:40:53Z
| 2020-01-20T20:08:12Z
| 2020-01-20T20:08:12Z
|
MEMBER
| null |
Discovered in #31013 , when `pivot_table` only defines `columns` with single column and with `margins` set to `True`, it will fail.
```python
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo","bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two","one", "one", "two", "two"],
"C": ["small", "large", "large", "small","small", "large", "small", "small","large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df.pivot_table(columns="A", margins=True, aggfunc=np.mean)
KeyError: 'bar'
```
However, when defining two columns, it magically works, but return a wrong shape:
```python
>>> df.pivot_table(columns=["A", "B"], margins=True, aggfunc=np.mean)
A B
D bar one 4.500000
two 6.500000
foo one 1.666667
two 3.000000
All 3.666667
E bar one 7.000000
two 9.000000
foo one 3.666667
two 5.500000
All 6.000000
```
This returns a Series, but A, B should be column index, so should get a DataFrame.
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MDExOlB1bGxSZXF1ZXN0MzYyODMwMDg4
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REG: restore format_type attr
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| 2020-01-14T20:43:43Z
| 2020-01-22T15:47:37Z
| 2020-01-22T14:16:47Z
|
MEMBER
| null |
- [x] closes #30962
cc @TomAugspurger
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BUG: Preserve string dtype in extract
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| 2
| 2020-01-14T21:08:01Z
| 2020-01-15T02:56:40Z
| 2020-01-15T02:56:37Z
|
CONTRIBUTOR
| null |
specifically with multiple capture groups and expand=False
Closes https://github.com/pandas-dev/pandas/issues/30969
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Backport PR #31015 on branch 1.0.x (DOC: Fixed documented value of `pd.NA ** 0` (#31005))
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| 2020-01-14T21:33:28Z
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NONE
| null |
Backport PR #31015: DOC: Fixed documented value of `pd.NA ** 0` (#31005)
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MDExOlB1bGxSZXF1ZXN0MzYyODY0MjU0
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CLN: remove checks for inferred_dtype==unicode
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MDExOlB1bGxSZXF1ZXN0MzYyODcwMjgz
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REF: PeriodIndex.get_loc
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| 1
| 2020-01-14T22:32:30Z
| 2020-01-15T18:40:32Z
| 2020-01-15T03:08:51Z
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MEMBER
| null |
xref #30874 which does similar cleanup for TimedeltaIndex.
Do all parsing/casting/conversion before call to self._engine or super() methods (xref #30948)
Fix+test integers incorrectly getting through.
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doc: update copyright year
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REF: DatetimeIndex.get_loc
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| 2
| 2020-01-14T22:33:59Z
| 2020-01-15T17:06:33Z
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|
MEMBER
| null |
xref #30874 which does similar cleanup for TimedeltaIndex, #31021 for PeriodIndex
Do all parsing/casting/conversion before call to self._engine or super() methods (xref #30948)
Needs test for preventing integers and Timedeltas through incorrectly.
Some of the casting may be simplified pending #30994.
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| 2
| 2020-01-14T23:57:25Z
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CONTRIBUTOR
| null |
Follow up from #30540 this exclusion is no longer required.
I checked the build config is the same using
https://config.travis-ci.com/explore
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ENH: Handle extension arrays in algorithms.diff
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| 20
| 2020-01-15T00:26:03Z
| 2020-01-23T19:29:21Z
| 2020-01-23T19:00:10Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/30889
Closes https://github.com/pandas-dev/pandas/issues/30967
This still needs a few things
1. Additional tests. It's hard to write thorough tests in `extensions/base`, since we don't know what the dtype will be after the `.diff()` (subtracting doesn't necessarily return the same dtype).
2. Decide what we want for BooleanArray.diff. Should that return an IntegerArray probably?
3. (edit) Verify that there's no perf regression for datetime, datetimetz, timedelta.
Are we comfortable doing this for 1.0.0?
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Backport PR #31024 on branch 1.0.x (CI: Travis Remove Unrequired Exclude)
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NONE
| null |
Backport PR #31024: CI: Travis Remove Unrequired Exclude
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| 549,904,292
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MDExOlB1bGxSZXF1ZXN0MzYyOTA4MTc2
| 31,027
|
REF: implement Block._split_op_result
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| 1
| 2020-01-15T00:59:10Z
| 2020-01-15T03:38:03Z
| 2020-01-15T03:23:13Z
|
MEMBER
| null |
Broken off from a branch working on arithmetic performance for `op(df1, df2)`, two changes here:
- implement _split_op_result, discussed before [citation needed]
- change an unnecessarily-deep copy to non-deep
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Series.to_json produces invalid JSON with `orient="index"` if index is multilevel
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| 9
| 2020-01-15T01:15:11Z
| 2020-01-25T17:49:55Z
| 2020-01-25T17:49:55Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
dataframe = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'b', 'c']})
series = dataframe.stack()
print(series.to_json(orient="index"))
```
The keys in the output have unescaped quotes in them, resulting in invalid JSON. Output here is indented for clarity:
```json
{
"[0,"x"]": 1,
"[0,"y"]": "a",
"[1,"x"]": 2,
"[1,"y"]": "b",
"[2,"x"]": 3,
"[2,"y"]": "c"
}
```
#### Problem description
`to_json()` should always either 1) produce valid JSON, or 2) throw an exception if it cannot.
#### Expected Output
The multilevel index in the output should be properly escaped to produce valid JSON, like so:
```json
{
"[0,\"x\"]": 1,
"[0,\"y\"]": "a",
"[1,\"x\"]": 2,
"[1,\"y\"]": "b",
"[2,\"x\"]": 3,
"[2,\"y\"]": "c"
}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 45.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : 0.4.0
scipy : None
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzYyOTE5MjMz
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Disallow bare pytest.raises
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| 2020-02-07T17:08:02Z
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|
NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
https://github.com/pandas-dev/pandas/issues/30999
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MDExOlB1bGxSZXF1ZXN0MzYyOTMyMDEz
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Backport PR #31018 on branch 1.0.x (BUG: Preserve string dtype in extract)
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NONE
| null |
Backport PR #31018: BUG: Preserve string dtype in extract
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MDExOlB1bGxSZXF1ZXN0MzYzMDAxODQx
| 31,031
|
TST: Split test_offsets.py - Test_offsets moved to test_date_offsets
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| 2020-01-15T07:50:07Z
| 2020-02-12T00:49:01Z
| 2020-02-12T00:49:00Z
|
NONE
| null |
In context of [https://github.com/pandas-dev/pandas/pull/30194](30194)
First part with move test_offsets to test_date_offsets
- [ ] closes #27085
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzYzMDAzODky
| 31,032
|
Revert "DEPR: is_copy arg of take (#30615)"
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| 3
| 2020-01-15T07:56:55Z
| 2020-01-23T14:41:39Z
| 2020-01-23T14:41:30Z
|
MEMBER
| null |
This reverts commit 7796be6e2c548de0be16e6adf08f5fca2ab9b4bc.
See https://github.com/pandas-dev/pandas/issues/27357#issuecomment-571639242 and https://github.com/pandas-dev/pandas/pull/30615#issuecomment-571637550
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MDExOlB1bGxSZXF1ZXN0MzYzMDIzMTEx
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|
DOC: typo a -> an
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CONTRIBUTOR
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TST: Split test_offsets.py - Added fixtures for date offsets and business date offsets
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| 2020-01-15T09:03:21Z
| 2020-03-24T16:12:17Z
| 2020-03-24T16:12:17Z
|
NONE
| null |
In context of #30194 and #31031
Second part with added (bussiness-)date offsets fixtures
- [ ] closes #27085
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Incorrect boolean bitwise conversion when dtype=object
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| 2020-01-15T09:49:51Z
| 2021-07-27T04:38:40Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series([False, True, None])
s[s.isnull()] = False
print('incorrect:')
print((~s).astype(bool))
print('\ncorrect:')
print(s.apply(lambda x: not x))
```
#### Problem description
The above code will produce the following unexpected output:
```bash
incorrect:
0 True
1 True
2 True
dtype: bool
correct:
0 True
1 False
2 True
dtype: bool
```
The same issues persist with using `np.nan` or other NULL-like. NULL-like are commonly encountered in data. It is very convenient and common for data scientists to use bitwise operators like |, &, and ~ to slice and dice pandas Series and DataFrames. Bitwise operator ~ returns unexpected behavior when some data has been NULL in the past (implicitly causing the dtype to become object), even after coercion of the offending elements to bool. This can cause subtle problems in data filtering that will go undetected. If it is not fixed, use of bitwise operators on `dtype=object` should at least raise a Warning.
#### Expected Output
```bash
0 True
1 False
2 True
dtype: bool
```
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.7
dateutil : 2.7.5
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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MDExOlB1bGxSZXF1ZXN0MzYzMDQ3NTYx
| 31,036
|
BUG: AssertionError on Series.append(DataFrame) fix #30975
|
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| 3
| 2020-01-15T09:51:29Z
| 2020-01-24T20:46:20Z
| 2020-01-24T03:37:33Z
|
CONTRIBUTOR
| null |
- [x] closes #30975
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Series.append() does not throw AssertionError anymore. This fix performs a precheck before proceeding with concatenation. Tests have passed. Whatsnew entry added.
Thanks!
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MDExOlB1bGxSZXF1ZXN0MzYzMDg2MDA3
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PERF: improve access of .array
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| 42
| 2020-01-15T11:17:29Z
| 2020-01-24T08:33:02Z
| 2020-01-24T00:00:16Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/30790
This improves the performance of `.array` access considerably (and thus of `extract_array`, which eg caused the slowdown in the indexing benchmark). The speed-up depends on the dtype (the numpy dtypes are still quite slow ..).
This touches quite some code, but I *think* this is OK for 1.0.0 (although I mixed a bit of clean-up into it, I can also remove that again).
@jbrockmendel I went the way of adding "yet another array/values like thing", but only on the Block (`Block.array_values()`). I think it might be possible to let `_values` return a DatetimeArray instead of datetime64 ndarray for datetime-like data, but that's something that needs more investigation, and also, even if possible, not something I would like to do on 1.0.0, but only for 1.1.
On master:
```
In [1]: s = pd.Series(pd.date_range("2012-01-01", periods=3, tz='UTC'))
In [3]: %timeit s.array
17.2 µs ± 219 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [4]: s = pd.Series(pd.date_range("2012-01-01", periods=3))
In [6]: %timeit s.array
4.3 µs ± 205 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [7]: s = pd.Series([1, 2, 3])
In [9]: %timeit s.array
6.76 µs ± 208 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
With this PR:
```
In [1]: s = pd.Series(pd.date_range("2012-01-01", periods=3, tz='UTC'))
In [3]: %timeit s.array
290 ns ± 6.26 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [4]: s = pd.Series(pd.date_range("2012-01-01", periods=3))
In [6]: %timeit s.array
809 ns ± 7.05 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [7]: s = pd.Series([1, 2, 3])
In [9]: %timeit s.array
2.34 µs ± 93.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
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MDU6SXNzdWU1NTAxMjc2NTM=
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CLN: Merge scripts/ and ci/ directories
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[] | null | 2
| 2020-01-15T11:24:32Z
| 2020-09-24T09:52:40Z
| 2020-09-24T09:52:40Z
|
MEMBER
| null |
While conceptually it can make sense to separate the scripts that are mostly called from the CI, than the ones that are not, I think in practice the division between `scripts/` and `ci/` directories is rather arbitrary.
An example is `scripts/validate_docstrings.py`. We use it from the CI, but we use it locally too to validate a single function. We have also scripts in `ci/` that are currently not called from the CI (not sure if they are still useful, will create a separate issue to discuss): `ci/check_git_tags.sh`, `ci/check_cache.sh` and `ci/travis_process_gbq_encryption.sh`
Personally, I think a different structure would make things clearer. An idea would be the next:
```
dev/release/ <- scripts used for the release
dev/validation/ <- scripts used for validation (docstrings, code checks...)
dev/deps/ <- `ci/deps`
dev/config/ <- `ci/azure`
dev/misc/ <- everything else (may be a specific directory for testing stuff?)
```
@jbrockmendel I think we discussed about this some time ago, and you didn't like the proposal then. Does this make sense as proposed here? Any idea if you don't like this proposal, but you agree the current structure is confusing?
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MDU6SXNzdWU1NTAxMzExNDU=
| 31,039
|
CLN: Possibly obsolete scripts
|
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| 2020-01-15T11:32:30Z
| 2020-09-24T08:43:49Z
| 2020-09-24T08:43:49Z
|
MEMBER
| null |
xref #31038
I'm unsure if the following scripts are still used:
```
scripts/find_commits_touching_func.py
ci/check_cache.sh
ci/check_git_tags.sh
ci/travis_process_gbq_encryption.sh
```
The next I assume are for the release, but may be there is something obsolete here:
```
scripts/build_dist_for_release.sh
scripts/build_dist.sh
scripts/download_wheels.py
```
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| 31,040
|
DOC: Add CZI to the sponsors page
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| 2020-01-15T11:59:14Z
| 2020-01-17T00:25:00Z
| 2020-01-17T00:25:00Z
|
MEMBER
| null |
I guess we want to add CZI to the sponsors page: https://pandas.io/about/sponsors.html
I think a section similar to Tidelift is the best option. Any other idea?
|
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| 550,167,966
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MDU6SXNzdWU1NTAxNjc5NjY=
| 31,041
|
BUG: something wrong at pd.Dataframe.from_dict()
|
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[] | null | 5
| 2020-01-15T12:52:35Z
| 2020-06-28T04:05:35Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
A = {"a":[1, 2, 3 ,4], "b":"b", "c":"c"}
pd.DataFrame.from_dict(A,orient='index').T
B = { "b":"b", "c":"c", "a":[1, 2, 3 ,4],}
pd.DataFrame.from_dict(B,orient='index').T
```
#### Problem description
the output A ，B are diffrent
|
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MDExOlB1bGxSZXF1ZXN0MzYzMTQzMDY5
| 31,042
|
Backport PR #29712 on branch 1.0.x (CI: Fix clipboard problems)
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| 2020-01-15T14:14:38Z
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NONE
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Backport PR #29712: CI: Fix clipboard problems
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BUG: nonexistent Timestamp pre-summer/winter DST change with dateutil timezone
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| 4
| 2020-01-15T13:48:10Z
| 2020-01-24T03:33:00Z
| 2020-01-24T03:33:00Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
This is fine:
```
>>> pd.__version__
'0.26.0.dev0+1790.gdd94e0db9'
>>> epoch = 1552211999999999871
>>> t = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
>>> t
Timestamp('2019-03-10 01:59:59.999999871-0800', tz='dateutil/US/Pacific')
>>> t.value
1552211999999999871
>>> pd.Timestamp(t)
Timestamp('2019-03-10 01:59:59.999999871-0800', tz='dateutil/US/Pacific')
>>> pd.Timestamp(t).value
1552211999999999871
```
This is also fine:
```
>>> epoch = 1552212000000000000
>>> t = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
>>> t
Timestamp('2019-03-10 03:00:00-0700', tz='dateutil/US/Pacific')
>>>
>>> t.value
1552212000000000000
>>> pd.Timestamp(t)
Timestamp('2019-03-10 03:00:00-0700', tz='dateutil/US/Pacific')
>>> pd.Timestamp(t).value
1552212000000000000
```
Meanwhile, this breaks representation and gets us nonexistent times:
```
>>> epoch = 1552211999999999872
>>> t = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
>>> t
Timestamp('2019-03-10 01:59:59.999999872-0700', tz='dateutil/US/Pacific')
>>> t.value
1552211999999999872
>>> pd.Timestamp(t)
Timestamp('2019-03-10 01:59:59.999999872-0800', tz='dateutil/US/Pacific')
>>> pd.Timestamp(t).value
1552208399999999872
```
And right on the cusp, the value breaks too:
```
>>> epoch = 1552211999999999999
>>> t = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
>>> t
Timestamp('2019-03-10 01:59:59.999999999-0700', tz='dateutil/US/Pacific')
>>> t.value
1552211999999999999
>>> pd.Timestamp(t)
Timestamp('2019-03-10 01:59:59.999999999-0800', tz='dateutil/US/Pacific')
>>> pd.Timestamp(t).value
1552208399999999999
```
#### Problem description
When we use `dateutil` timezones and try to create a Timestamp that is right on the cusp of the change from winter to summer time, we can get nonexistent times (the clock is supposed to jump from 2 A.M. to 3 A.M. and yet we get 2:59:59).
I've investigated this, and it appears that at 128 nanoseconds before the clock jump, DST offset and utcoffset in `dateutil` change, so we end up in a situation when the offsets are what they are supposed to be after the jump, but the time hasn't jumped yet, so the constructor returns a nonexistent time. Calling the constructor again moves the clock 1 hour back.
This can be checked out with:
```
>>> epoch = 1552211999999999872
>>> t = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
>>> t.tz.dst(t)
datetime.timedelta(seconds=3600)
```
My assumption is that when we need to determine UTC offset, rounding happens at some point, and we round to `epoch=1552212000000000000`, get offset, and then use it on time pre-clock jump.
I'd like to try to fix this one.
#### Expected Output
```
>>> epoch = 1552211999999999872
>>> t = pd.Timestamp(epoch, tz='dateutil/US/Pacific')
>>> t
Timestamp('2019-03-10 01:59:59.999999872-0800', tz='dateutil/US/Pacific')
>>> t.value
1552211999999999872
>>> pd.Timestamp(t)
Timestamp('2019-03-10 01:59:59.999999872-0800', tz='dateutil/US/Pacific')
>>> pd.Timestamp(t).value
1552208399999999872
```
#### Notes
This was thought to be part of #24329 but turned to be a separate bug as I worked on closing that issue in PR #30995.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : dd94e0db9556f35a3ea91ce85714c0b1e151a770
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : ru_RU.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+1790.gdd94e0db9
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 5.1.5
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzYzMTYwNTU2
| 31,044
|
Backport PR #30905 on branch 1.0.x (BUG: SystemError in df.sum)
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| 2020-01-15T15:03:48Z
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NONE
| null |
Backport PR #30905: BUG: SystemError in df.sum
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MDExOlB1bGxSZXF1ZXN0MzYzMjAxMzk5
| 31,045
|
TST: Disallow bare pytest.raises
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| 4
| 2020-01-15T15:38:54Z
| 2020-01-16T09:12:20Z
| 2020-01-16T09:12:08Z
|
CONTRIBUTOR
| null |
- [ ] ref #30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This pull request is to add appropriate match arguments to bare pytest.raises listed in the comment https://github.com/pandas-dev/pandas/issues/30999#issuecomment-574174389 as described by the referenced issue.
|
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MDExOlB1bGxSZXF1ZXN0MzYzMjI3MDE4
| 31,046
|
WEB: Improving the sponsors in the web, and adding CZI
|
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| 2020-01-15T16:30:03Z
| 2020-01-17T00:25:01Z
| 2020-01-17T00:25:01Z
|
MEMBER
| null |
- [X] closes #31040
Things addressed here:
- Added CZI as sponsor
- Adding an arbitrary number of sponsor logos in the home (previously the style only worked for 6)
- Adding a `Become a sponsor` section to the sponsors page
- Moving all sponsors to the web yaml, and standardizing how we present them in the sponsors page
|
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MDExOlB1bGxSZXF1ZXN0MzYzMjMzNTE0
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|
DOC: Add missing docstrings
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| 2
| 2020-01-15T16:43:31Z
| 2020-01-17T13:27:49Z
| 2020-01-17T13:27:38Z
|
CONTRIBUTOR
| null |
Added docstrings:
```
pandas.Index.has_duplicates
pandas.Index.is_all_dates
pandas.Index.name
pandas.Index.is_boolean
pandas.Index.is_floating
pandas.Index.is_integer
pandas.Index.is_interval
pandas.Index.is_mixed
pandas.Index.is_numeric
pandas.Index.is_object
```
There are some other docstrings left - I couldn't find the docstrings below in the code:
```
None:None:GL08:pandas.Index.names:The object does not have a docstring
None:None:GL08:pandas.Index.empty:The object does not have a docstring
```
I don't really understand what the function does so I'm leaving the docstring empty for now:
```
pandas/pandas/core/indexes/base.py:639:GL08:pandas.Index.view:The object does not have a docstring
```
cc @datapythonista
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzYzMjQ0NTky
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FIX: fix interpolate with kwarg limit area and limit direction using pad or bfill
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CONTRIBUTOR
| null |
- [x] closes #26796
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This separates a large part of the code changes from #25141 to keep things comprehendible.
|
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MDExOlB1bGxSZXF1ZXN0MzYzMjQ0OTE4
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CLN: Remove unused release scripts
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| 2
| 2020-01-15T17:07:35Z
| 2020-01-20T17:38:33Z
| 2020-01-20T17:38:28Z
|
MEMBER
| null |
- [X] ref #31039
Old scripts that are not used anymore.
CC: @TomAugspurger
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MDU6SXNzdWU1NTAzNzkwNTQ=
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HDF5 API docs missing methods
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| 2020-01-15T19:18:50Z
| 2020-02-13T02:02:53Z
| null |
CONTRIBUTOR
| null |
The [user guide IO Tools HDF5 section](https://pandas.io/docs/user_guide/io.html#hdf5-pytables) references a number of methods that are not documented in the [API docs for HDF5](https://pandas.io/docs/reference/io.html#hdfstore-pytables-hdf5).
The following `HDFStore` methods/attributes are mentioned only in the user guide:
Methods:
- `__init__`
- `remove`
- `create_table_index`
- `get_storer`
- `select_as_coordinates`
- `select_as_multiple`
- `append_to_multiple`
- `select_column`
- `flush`
Attributes:
- `is_open`
- `root`
The following are "public" (ie no leading underscore) methods/attributes in 0.25.3 not listed in either location:
Methods:
- `open`
- `close`
- `copy`
- `get_node`
- `items`
- `iteritems`
Attributes:
- `filename`
The "storer object" that is the result of `get_storer` doesn't seem to be documented. This can be a `pandas.io.pytables.FrameFixed` or `pandas.io.pytables.AppendableFrameTable` (and maybe others?). The `nrows` and `table` attributes are referenced in the user guide. There are a number of other attributes that appear public but are not documented. Seems this is starting to get into the implementation weeds, but since a method mentioned in the user guide can return this object, it would be nice to have this explicitly documented.
It would be useful to document the public methods/attributes of `HDFStore` in the API docs. is there any reason these were omitted originally? I think all the would need to be done would be to add to [io.rst](https://github.com/pandas-dev/pandas/blob/master/doc/source/reference/io.rst).
There should probably be a discussion of what should be documented for the storer objects. Perhaps just a reference from the `get_storer` API doc page and a listing of methods/attributes of the abstract base class that are implemented by all subclasses?
|
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|
Feature: Add "random" rank in the group for DataFrame.rank and similar functions.
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| 2020-01-15T19:55:07Z
| 2020-03-06T13:50:28Z
| 2020-01-16T04:24:41Z
|
NONE
| null |
Feature: Add "random" rank in the group for DataFrame.rank and similar functions.
> DataFrame.rank(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)[source]
>
> method : {‘average’, ‘min’, ‘max’, ‘first’, ‘dense’, **‘random’**}, default ‘average’
>
> How to rank the group of records that have the same value (i.e. ties):
>
> - average: average rank of the group
> - min: lowest rank in the group
> - max: highest rank in the group
> - first: ranks assigned in order they appear in the array
> - dense: like ‘min’, but rank always increases by 1 between groups
> - **random: ranks assigned randomly (between min and max), but each value is picked only once**
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame([1, 2, 1, 3, 3, 2, 1, 3], index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
In [10]: df
Out[10]:
0
a 1
b 2
c 1
d 3
e 3
f 2
g 1
h 3
# Example run 1
In [11]: df.rank(method='random')
Out[11]:
0
a 3
b 4
c 1
d 8
e 6
f 5
g 2
h 7
# Example run 2
In [12]: df.rank(method='random')
Out[12]:
0
a 2
b 5
c 1
d 7
e 8
f 4
g 3
h 6
```
It would be nice if it could be implemented in pandas as I have huge dataframes (100G) for which I need this feature.
In the worst case, is there a way to do something like this with multiple pandas commands?
A slightly related issue: https://github.com/pandas-dev/pandas/issues/9481
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MDU6SXNzdWU1NTA0MDU4ODQ=
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User guide HDF5 complevel and complib formatting strange
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| 0
| 2020-01-15T20:14:34Z
| 2020-01-20T13:34:12Z
| 2020-01-20T13:34:12Z
|
CONTRIBUTOR
| null |
Formatting for HDF5 `complevel` and `complib` is a bit weird on both the old docs and new docs.
https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#compression
https://pandas.io/docs/user_guide/io.html#compression
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DOC: Fix HDF5 complevel and complib formatting
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| 3
| 2020-01-15T20:18:05Z
| 2020-01-20T13:34:23Z
| 2020-01-20T13:34:12Z
|
CONTRIBUTOR
| null |
Fixes formatting of these params.
- [x] closes #31052
- [ ] tests added / passed (N/A)
- [ ] passes `black pandas` (N/A)
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (N/A)
- [ ] whatsnew entry (N/A)
N/A as this PR only updates an rst file for the docs.
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DOC: intended series.loc behavior undocumented for boolean series
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| 1
| 2020-01-15T21:05:39Z
| 2020-08-02T10:18:02Z
| 2020-08-02T10:18:02Z
|
NONE
| null |
#### series.loc when passed a boolean series
How will series.loc behave when passed a boolean series whose index contains the same values, but in a different order, than the focal series' index?
```python
s1 = pd.Series(list('ab'))
mask = [True, False]
s2 = pd.Series(mask, pd.Index([1,0]))
print(s1.loc[mask])
print(s1.loc[s2])
```
0 a
dtype: object
1 b
dtype: object
#### Problem description
I expected the index of `s2` to be ignored, in effect treating `s2` as a boolean array input, but I was wrong.
The [documentation on master](https://pandas-docs.github.io/pandas-docs-travis/) doesn't list a (boolean) series as an allowed input, hence doesn't provide guidance on which behavior should be expected/is intended.
What is the intended behavior? If given guidance, I'll update the documentation.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.152-127.182.amzn2.x86_64
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 : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.7.4
pip : 19.3.1
setuptools : 40.4.3
Cython : 0.29.14
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.0.dev0+4dcf606
sqlalchemy : 1.3.11
tables : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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REF: be stricter about what we pass to _simple_new
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| 2
| 2020-01-15T21:58:35Z
| 2020-01-18T16:25:23Z
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|
MEMBER
| null |
This will take a few passes to get rid of all the checks done in _simple_new that should be done elsewhere
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CLN: assorted cleanups
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| 1
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Split out JSON Date Converters
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MEMBER
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This file is pretty large so trying to make more modular. This separates out conversion routines required for getting from the Python space to JSON, but which don't actually serialize the data
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MDExOlB1bGxSZXF1ZXN0MzYzNDExNDI3
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REF: use _get_string_slice in PeriodIndex.get_value
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| 2
| 2020-01-16T01:19:55Z
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|
MEMBER
| null |
Most of _get_string_slice is duplicated inside PeriodIndex.get_value, which likely explains why get_string_slice has almost no test [coverage](https://codecov.io/gh/pandas-dev/pandas/src/master/pandas/core/indexes/period.py#L715). This also brings PeriodIndex.get_value into closer alignment with DatetimeIndex.get_value, so we may be able to share code a few steps from here.
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| 550,525,496
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MDExOlB1bGxSZXF1ZXN0MzYzNDE2OTc5
| 31,059
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CLN: Removed unused varibles from for loops
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| 3
| 2020-01-16T01:46:28Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0MzYzNDIyMjc2
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CLN: Replace Appender and Substitution with simpler doc decorator
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| 23
| 2020-01-16T02:13:33Z
| 2020-02-13T01:39:28Z
| 2020-02-12T16:01:46Z
|
CONTRIBUTOR
| null |
- [X] closes #30933
- [x] tests added / passed
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- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A new decorator to handle docstring formatting. There is also an update for an existing case to show how it works.
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MDU6SXNzdWU1NTA1Mzc3MDg=
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WEB: Broken older versions link in the website
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[] | null | 2
| 2020-01-16T02:33:33Z
| 2020-09-24T09:49:26Z
| 2020-09-24T09:49:26Z
|
MEMBER
| null |
In the top navigation of the website (see pandas.io), in the Docs section, the `Older versions` link is broken.
In #30891 I proposed to just have a single link to the docs for now, so there is no broken link or inconsistencies. But there is no agreement on getting that merged, and some discussion is needed.
Opening this issue, so ideas on what to do can be discussed.
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MDExOlB1bGxSZXF1ZXN0MzYzNDQxMTcz
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TYP/CLN: Replaced "Optional[Hashable]" with "Label" from pandas._typing
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DataFrame.GroupBy.apply has unexpected returns in some cases
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| 2020-01-16T04:04:48Z
| 2021-04-07T06:53:34Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
def agg(a, b):
return a + b
x = pd.DataFrame({'A': np.arange(10), 'B': [1] * 10, 'C': np.random.rand(10), 'D': np.random.rand(10)}).set_index(['A', 'B'])
x.groupby('B').apply(lambda g: g.C + g.D)
```
#### Problem description
This returns a DataFrame of shape (1, 10)
This seems to occur when i) The groupby key happens to have a unique value ii) The apply function takes a DataFrame and returns a Series.
#### Expected Output
I expect it returns a Series of shape (10, 1)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: 0.8.1
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0MzYzNDUwNDU4
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BUG: partial-timestamp slicing near the end of year/quarter/month
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| 0
| 2020-01-16T04:33:04Z
| 2020-01-18T16:20:56Z
| 2020-01-18T15:56:37Z
|
MEMBER
| null |
I'm fairly confident we can refactor this to be a lot less verbose, but will do that separately from the bugfix.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: remove redundant return value
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DOC: add plotting backends in visualization.rst
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| 7
| 2020-01-16T05:40:06Z
| 2020-02-05T19:19:30Z
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CONTRIBUTOR
| null |
- [x] closes #30984
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MDU6SXNzdWU1NTA2NDMyNDg=
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pd.to_datetime() time conversion error
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| 2020-01-16T08:11:40Z
| 2020-01-16T11:52:16Z
| 2020-01-16T10:46:45Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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REGR: MultiIndex level names RuntimeError in groupby.apply
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| 0
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| 2020-01-20T22:25:33Z
| 2020-01-20T22:25:33Z
|
MEMBER
| null |
```
df = pd.DataFrame({
'A': np.arange(10), 'B': [1, 2] * 5,
'C': np.random.rand(10), 'D': np.random.rand(10)}
).set_index(['A', 'B'])
df.groupby('B').apply(lambda x: x.sum())
```
On master this gives an error:
```
In [40]: df.groupby('B').apply(lambda x: x.sum())
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-40-75bc1ff12251> in <module>
----> 1 df.groupby('B').apply(lambda x: x.sum())
~/scipy/pandas/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
733 with option_context("mode.chained_assignment", None):
734 try:
--> 735 result = self._python_apply_general(f)
736 except TypeError:
737 # gh-20949
~/scipy/pandas/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
752
753 return self._wrap_applied_output(
--> 754 keys, values, not_indexed_same=mutated or self.mutated
755 )
756
~/scipy/pandas/pandas/core/groupby/generic.py in _wrap_applied_output(self, keys, values, not_indexed_same)
1200 if len(keys) == ping.ngroups:
1201 key_index = ping.group_index
-> 1202 key_index.name = key_names[0]
1203
1204 key_lookup = Index(keys)
~/scipy/pandas/pandas/core/indexes/base.py in name(self, value)
1171 # Used in MultiIndex.levels to avoid silently ignoring name updates.
1172 raise RuntimeError(
-> 1173 "Cannot set name on a level of a MultiIndex. Use "
1174 "'MultiIndex.set_names' instead."
1175 )
RuntimeError: Cannot set name on a level of a MultiIndex. Use 'MultiIndex.set_names' instead.
```
On 0.25.3 this works:
```
In [10]: df.groupby('B').apply(lambda x: x.sum())
Out[10]:
C D
B
1 2.761792 3.963817
2 1.040950 3.578762
```
It seems the additional MultiIndex level that is not used to group (['A', 'B'] are index levels, but only grouping by 'B').
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MDExOlB1bGxSZXF1ZXN0MzYzNTE1MTMz
| 31,069
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Fix DataFrame.info() range summary for DatetimeIndex
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| 2020-01-16T08:20:16Z
| 2020-02-05T08:02:10Z
| 2020-02-05T08:02:10Z
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NONE
| null |
**Fix `DataFrame.info()` range summary for DatetimeIndex**
This PR aims to provide better description of [DatetimeIndex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html) within [DataFrame.info](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) in relation to the display of the range of unsorted indexes, by replacing the original first to last element values with "min date" to "max date" values.
Fixes https://github.com/pandas-dev/pandas/issues/31116
<del>
This PR fixes range display errors that might occur when [DataFrame.info](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) describes a [DatetimeIndex](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html) of big dataframes: depending on the dataframe structure, related *"min date" to "max date"* values can be wrong.
Example of output produced by `DataFrame.info()`:
```python
import pandas as pd
idx = pd.date_range('2020-01-15 01:03:05',
periods=1000004, freq='ms')
df = pd.DataFrame({'count': range(len(idx))}, index=idx)
df
df.info()
```
`df` output:
```
count
2020-01-15 01:03:05.000 0
2020-01-15 01:03:05.001 1
2020-01-15 01:03:05.002 2
2020-01-15 01:03:05.003 3
2020-01-15 01:03:05.004 4
... ...
2020-01-15 01:19:44.999 999999
2020-01-15 01:19:45.000 1000000
2020-01-15 01:19:45.001 1000001
2020-01-15 01:19:45.002 1000002
2020-01-15 01:19:45.003 1000003
```
`df.info()` output:
```
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1000004 entries, 2020-01-15 01:03:05 to 2020-01-15 01:19:45.003000
Freq: L
Data columns (total 1 columns):
count 1000004 non-null int64
dtypes: int64(1)
memory usage: 15.3 MB
```
This PR fixes possible errors within the description: `"DatetimeIndex: ... entries, `*date_a*` to `*date_b* output, where *date_a* and *date_b* should respectively show minimum and maximum date range values.
</del>
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PERF: add shortcut to Timedelta constructor
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| 4
| 2020-01-16T09:57:08Z
| 2020-01-27T06:54:59Z
| 2020-01-24T00:36:48Z
|
MEMBER
| null |
WIP until #30676 gets merged.
- [ ] second half of #30543
- [X] tests added 1 / passed 1
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
This implements a shortcut in the Timedelta constructor to cut down on processing if Timedelta is passed. We still need to check if any other args are passed. Then, if a Timedelta with no other kwargs was passed, we just return that same Timedelta.
A test is added to check that the Timedelta is still the same object.
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MDExOlB1bGxSZXF1ZXN0MzYzNTk1MTY4
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ENH: accept a dictionary in plot colors
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| 4
| 2020-01-16T11:25:12Z
| 2020-02-01T18:19:00Z
| 2020-01-26T01:04:28Z
|
MEMBER
| null |
This is a resurrection of #28659. Have kept the original commit history, just rebased and added docstrings.
- [x] closes #8193
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzYzNjEyMzg3
| 31,072
|
CLN/MAINT: Clean and annotate stata reader and writers
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| 10
| 2020-01-16T12:09:02Z
| 2020-07-28T14:41:36Z
| 2020-01-26T00:42:01Z
|
CONTRIBUTOR
| null |
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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| 550,796,561
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MDU6SXNzdWU1NTA3OTY1NjE=
| 31,073
|
The IntervalIndex documentation makes no mention of IntervalIndex.get_loc
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| 2020-01-16T13:08:16Z
| 2020-02-25T20:52:57Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# No code, documentation issue
```
#### Problem description
Why doesn't the documentation about IntervalIndex ([here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.IntervalIndex.html)) contain any mention of the function [IntervalIndex.get_loc](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.IntervalIndex.get_loc.html)?
#### Expected Output
I would expect the list of class methods to include all the methods.
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MDU6SXNzdWU1NTA4Nzg1MDI=
| 31,074
|
Pandas .plot() on regularly spaced timeseries result in slow plotting/interaction
|
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| 2020-01-16T15:24:25Z
| 2021-07-27T04:42:39Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
import pandas as pd
N = 100000
y = np.linspace(0, 10, N)
base_dt = dt.datetime(2000,1,1)
dt_range = [base_dt + dt.timedelta(minutes=x) for x in range(N)]
plt.figure('pyplot timeseries')
plt.plot(dt_range,y)
s = pd.Series(index=dt_range, data=y)
plt.figure('Pandas plot timeseries')
s.plot()
s2 = pd.Series(data=y)
plt.figure('Pandas plot series')
s2.plot()
```
#### Problem description
I noticed that plotting time series using .plot() sometimes resulted in very slow and unresponsive plots, where it is difficult to interact with the figure (e.g. pan). I think this happens with regularly spaced time series where there either is a frequency defined or pandas is able to infer the frequency. Perhaps it has something to do with the plot tick labels on the x (time) axis? It does not happen when plotting time series that are irregular, and thus when pandas does not style the plot ticks and tick labels.
In the example code the first and the third plot are smooth to interact with, while the second plot is lagging terribly. Also see screenshot of the second plot with the pandas styled tick labels:
If changing the dt_range from minute frequency to hour frequency (replace `minutes` with `hours`) the pandas.plot() becomes much smooth to interact with despite having the same series size, and I think because it has much fewer ticks and labels:
So it might be a combination of the size of series plotted and how the ticks are drawn/updated?
Are there ways to disable the styled pandas ticks somehow? I also notice that pyplot.plot() and pandas.plot() result in very different conversions of timestamps to numeric x values - is there a way to also disable that behavior in pandas so it is compatible with pyplot?
#### Expected Output
Equally responsive plotting as with pyplot.plot()
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.12
tables : 3.6.1
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
</details>
Edit: This performance issue might be related to #15071
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| 31,075
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PERF: DataFrame.mean() is slow with datetime columns
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[] | null | 10
| 2020-01-16T15:25:28Z
| 2021-07-27T04:54:35Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
# A DataFrame containing a datetime64 column
n = 1000000
df = pd.DataFrame({
"x": np.random.normal(0.0, 1.0, n),
"d": pd.date_range(pd.datetime.today(), periods=n, freq="1H").tolist()
})
# Calculating the mean of individual columns is pretty much instantaneous
df["x"].mean()
## 1000 loops, best of 3: 1.35 ms per loop
df["d"].mean()
## 100 loops, best of 3: 2.91 ms per loop
# Using the DataFrame's .mean() method, it takes a really long time
%timeit df.mean()
## 1 loop, best of 3: 9.23 s per loop
```
#### Problem description
When DataFrame contains a `datetime64` column, the time taken to run the `.mean()` method for the whole DataFrame is thousands of times longer than than time taken to run the `.mean()` method on each column individually.
#### Expected Output
Answer is correct; just too slow.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.13.3
pytz : 2017.2
dateutil : 2.7.3
pip : 19.2.3
setuptools : 44.0.0.post20200106
Cython : 0.25.2
pytest : 3.0.7
hypothesis : None
sphinx : 1.5.6
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 3.7.3
html5lib : 0.999
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 5.3.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 3.7.3
matplotlib : 2.0.2
numexpr : 2.6.2
odfpy : None
openpyxl : 2.4.7
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 0.19.1
sqlalchemy : 1.3.8
tables : 3.3.0
xarray : None
xlrd : 1.0.0
xlwt : 1.2.0
xlsxwriter : 0.9.6
</details>
I asked about this on Stack Overflow (https://stackoverflow.com/questions/59759107/how-to-avoid-poor-performance-of-pandas-mean-with-datetime-columns); one respondent hazarded a guess the the issue may lie with these lines of code (but no rigorous debugging was done).
https://github.com/pandas-dev/pandas/blob/v0.25.3/pandas/core/arrays/datetimes.py#L601-L603
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STY: Some stuff that triggers my OCD ;)
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MEMBER
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Some stuff that got the attention of my eye
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MDU6SXNzdWU1NTA5NDIwNjg=
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|
dict to series turns tuple keys into single strings
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CONTRIBUTOR
| null |
```
In [69]: pd.Series({('a',): 1, ('a', 'b'): 2})
Out[69]:
a 1
a 2
dtype: int64
In [70]: pd.__version__
Out[70]: '0.25.3'
```
I would think this should result in tuple objects. Probably something in core/series.py ... looks like _init_dict and maybe create_series_with_explicit_dtype from poking around.
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CLN: remove unused legacy pickle compat code
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzYzNzc1MjIx
| 31,079
|
TST: Remove bare pytest.raises
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| 6
| 2020-01-16T18:02:21Z
| 2020-01-27T04:45:27Z
| 2020-01-24T03:57:24Z
|
CONTRIBUTOR
| null |
References #30999
Adds match argument to pytest.raises found in the following files. https://github.com/pandas-dev/pandas/issues/30999#issuecomment-574174389
**ToDo:**
- [x] pandas/tests/arithmetic/test_numeric
- [X] pandas/tests/arithmetic/test_object
- [x] pandas/tests/arithmetic/test_timedelta64
- [x] pandas/tests/arrays/interval/test_interval
- [ ] add whatsnew entry
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MDU6SXNzdWU1NTA5NzQ2NzU=
| 31,080
|
Pandas 0.25.0 breaks np.isin
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[] | null | 4
| 2020-01-16T18:18:57Z
| 2020-07-18T13:35:45Z
| null |
NONE
| null |
Observed with numpy 1.17.4 as well as (latest) 1.18.1:
```python
# pandas 0.24.2:
l = [pd.Timestamp]
pd.Timestamp == pd.Timestamp
>>> True
np.isin(l, l)
>>> array([ True])
```
```python
# pandas 0.25.0
l = [pd.Timestamp]
pd.Timestamp == pd.Timestamp
>>> True
np.isin(l, l)
>>> array([ False])
```
#### Problem description
Since 0.25.0, the `==` of `pd.Timestamp` is preserved but it doesn't go through `np.isin`.
This is observed as well under pandas 0.25.3.
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MDExOlB1bGxSZXF1ZXN0MzYzNzkzMjcw
| 31,081
|
CLN: Moved the same code in 'if' and the 'else' outside
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| 1
| 2020-01-16T18:50:48Z
| 2020-01-16T19:52:14Z
| 2020-01-16T19:49:50Z
|
MEMBER
| null |
AFICT this should be ok, right?
cc (@WillAyd)
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MDExOlB1bGxSZXF1ZXN0MzYzNzk4MDM3
| 31,082
|
CLN: __all__ for core/index.py
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| 2020-01-16T19:03:00Z
| 2020-01-17T17:59:01Z
| 2020-01-17T17:40:04Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzYzODAyNzcw
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CLN: Remove download_wheels.py, moved to pandas-release
|
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| 1
| 2020-01-16T19:15:37Z
| 2020-01-16T21:46:34Z
| 2020-01-16T21:46:29Z
|
MEMBER
| null |
- [X] xref #31039
Script being moved to the pandas-release repository in https://github.com/pandas-dev/pandas-release/pull/23
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MDExOlB1bGxSZXF1ZXN0MzYzODA1OTE1
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|
REF: stricter types for RangeIndex._simple_new
|
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| 1
| 2020-01-16T19:24:11Z
| 2020-01-17T01:08:23Z
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|
MEMBER
| null |
xref #31055.
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MDExOlB1bGxSZXF1ZXN0MzYzODEyMjMx
| 31,085
|
DOC: automatic 'end' year of copyright
|
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| 3
| 2020-01-16T19:41:07Z
| 2020-01-16T23:39:35Z
| 2020-01-16T21:36:54Z
|
MEMBER
| null |
#31022 Updated it after nearly 6 years, I think this will update with each new merge, so that should not be a problem from now on, (I think)
cc (@datapythonista)
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| 551,020,793
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MDU6SXNzdWU1NTEwMjA3OTM=
| 31,086
|
Issue generating a line plot from a Series when previous plots are already defined
|
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| 2020-01-16T19:55:39Z
| 2020-01-18T00:16:25Z
| 2020-01-17T21:16:04Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = self.getStats() # returns a dataFrame with a row called Rounds
totalMatches = len(df)
if (totalMatches == 0):
print("Need to play at least one match for a graph", flush = True)
return
vc = df["Rounds"].value_counts()
x = vc.plot(legend=True, figsize=[10, 8])
x.set_xlabel("Number of Rounds to Win")
x.set_ylabel("Occurrences")
x.set_title("Value-Count Graph (Matches: " + str(totalMatches) + ")")
Occurences = max(vc) + 1
# for small values set y tic mark text
if Occurences <= 20:
yText = [] # Stores our Y-Axis text
for i in range(1, Occurences):
yText.append(f'${i}$')
plt.yticks(np.arange(1, Occurences), yText, ha="center")
plt.show()
```
#### The code above works fine unless the following has been executed to make "popout" graphs
%matplotlib qt5
If a previous graph exists from a previous run, and is still up. Then this graph will have additions added directly to it. Please see the images for what is happening.
It should be noted, I am using version 0.25.3 of pandas, and I searched and could not find this issue already logged.
1) The reason this is an issue is that it leads to potentially different results each time the program is executed.
2) It only occurs with pandas Series objects. (I tested and it is not an issue for dataFrames.)
You can find the full python program at:
https://github.com/RickOldenburger/War-Card-Game-Analysis
There is a work around and I have applied it in my program.
change the statement:
x = vc.plot(legend=True, figsize=[10, 8])
to these two statments:
plt.figure(figsize=(10, 8))
x = vc.plot.line(legend=True) # no point in having the figsize defined twice
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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MDU6SXNzdWU1NTEwMjkwNTQ=
| 31,087
|
DOC: replace ggpy with plotnine on the ecosystem page
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| 0
| 2020-01-16T20:12:08Z
| 2020-01-18T17:11:21Z
| 2020-01-18T17:11:21Z
|
MEMBER
| null |
We have an entry about ggpy in the visualization section of the ecosystem page (https://dev.pandas.io/docs/ecosystem.html#visualization). However, ggpy is no longer maintained (last commit is more than 3 years ago: https://github.com/yhat/ggpy). While plotnine is an actively developed alternative ggplot-python-interface (https://github.com/has2k1/plotnine/).
So I think we can just replace the entry about ggpy with one about plotnine.
|
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MDExOlB1bGxSZXF1ZXN0MzYzODU2NzA2
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|
BUG: df.pivot_table fails when margin is True and only columns is defined
|
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| 2021-07-03T06:49:58Z
| 2020-01-20T20:08:12Z
|
MEMBER
| null |
- [x] closes #31016
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzYzOTAwMjcx
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CLN: update _simple_new usages
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| 2
| 2020-01-16T23:49:23Z
| 2020-01-18T16:14:58Z
| 2020-01-18T15:59:28Z
|
MEMBER
| null |
In preparation for making the _simple_new constructors stricter, xref #31084, #31055.
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MDU6SXNzdWU1NTExMjIxNzI=
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|
Clean Up Sphinx Warnings
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| 2021-04-10T16:13:13Z
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MEMBER
| null |
A fresh build of the docs locally yields all of the below warnings:
<details>
```sh
as-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
WARNING: [autosummary] failed to import 'pandas.SparseArray.get_values': no module named pandas.SparseArray.get_values
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/sphinx/ext/autosummary/__init__.py:635: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
obj = getattr(obj, obj_name)
WARNING: [autosummary] failed to import 'pandas.SparseArray.values': no module named pandas.SparseArray.values
WARNING: [autosummary] failed to import 'pandas.Timestamp.weekday_name': no module named pandas.Timestamp.weekday_name
WARNING: [autosummary] failed to import 'pandas.Timestamp.weekday_name': no module named pandas.Timestamp.weekday_name
WARNING: [autosummary] failed to import 'pandas.arrays.DatetimeArray.timetuple': no module named pandas.arrays.DatetimeArray.timetuple
WARNING: [autosummary] failed to import 'pandas.arrays.DatetimeArray.weekday_name': no module named pandas.arrays.DatetimeArray.weekday_name
WARNING: [autosummary] failed to import 'pandas.core.series.Series.as_blocks': no module named pandas.core.series.Series.as_blocks
WARNING: [autosummary] failed to import 'pandas.core.series.Series.as_matrix': no module named pandas.core.series.Series.as_matrix
WARNING: [autosummary] failed to import 'pandas.core.series.Series.asobject': no module named pandas.core.series.Series.asobject
WARNING: [autosummary] failed to import 'pandas.core.series.Series.base': no module named pandas.core.series.Series.base
WARNING: [autosummary] failed to import 'pandas.core.series.Series.blocks': no module named pandas.core.series.Series.blocks
WARNING: [autosummary] failed to import 'pandas.core.series.Series.clip_lower': no module named pandas.core.series.Series.clip_lower
WARNING: [autosummary] failed to import 'pandas.core.series.Series.clip_upper': no module named pandas.core.series.Series.clip_upper
WARNING: [autosummary] failed to import 'pandas.core.series.Series.compound': no module named pandas.core.series.Series.compound
WARNING: [autosummary] failed to import 'pandas.core.series.Series.compress': no module named pandas.core.series.Series.compress
WARNING: [autosummary] failed to import 'pandas.core.series.Series.convert_objects': no module named pandas.core.series.Series.convert_objects
WARNING: [autosummary] failed to import 'pandas.core.series.Series.data': no module named pandas.core.series.Series.data
WARNING: [autosummary] failed to import 'pandas.core.series.Series.flags': no module named pandas.core.series.Series.flags
WARNING: [autosummary] failed to import 'pandas.core.series.Series.from_array': no module named pandas.core.series.Series.from_array
WARNING: [autosummary] failed to import 'pandas.core.series.Series.from_csv': no module named pandas.core.series.Series.from_csv
WARNING: [autosummary] failed to import 'pandas.core.series.Series.ftype': no module named pandas.core.series.Series.ftype
WARNING: [autosummary] failed to import 'pandas.core.series.Series.ftypes': no module named pandas.core.series.Series.ftypes
WARNING: [autosummary] failed to import 'pandas.core.series.Series.get_dtype_counts': no module named pandas.core.series.Series.get_dtype_counts
WARNING: [autosummary] failed to import 'pandas.core.series.Series.get_ftype_counts': no module named pandas.core.series.Series.get_ftype_counts
WARNING: [autosummary] failed to import 'pandas.core.series.Series.get_value': no module named pandas.core.series.Series.get_value
WARNING: [autosummary] failed to import 'pandas.core.series.Series.get_values': no module named pandas.core.series.Series.get_values
WARNING: [autosummary] failed to import 'pandas.core.series.Series.imag': no module named pandas.core.series.Series.imag
WARNING: [autosummary] failed to import 'pandas.core.series.Series.is_copy': no module named pandas.core.series.Series.is_copy
WARNING: [autosummary] failed to import 'pandas.core.series.Series.itemsize': no module named pandas.core.series.Series.itemsize
WARNING: [autosummary] failed to import 'pandas.core.series.Series.ix': no module named pandas.core.series.Series.ix
WARNING: [autosummary] failed to import 'pandas.core.series.Series.nonzero': no module named pandas.core.series.Series.nonzero
WARNING: [autosummary] failed to import 'pandas.core.series.Series.ptp': no module named pandas.core.series.Series.ptp
WARNING: [autosummary] failed to import 'pandas.core.series.Series.put': no module named pandas.core.series.Series.put
WARNING: [autosummary] failed to import 'pandas.core.series.Series.real': no module named pandas.core.series.Series.real
WARNING: [autosummary] failed to import 'pandas.core.series.Series.reindex_axis': no module named pandas.core.series.Series.reindex_axis
WARNING: [autosummary] failed to import 'pandas.core.series.Series.select': no module named pandas.core.series.Series.select
WARNING: [autosummary] failed to import 'pandas.core.series.Series.set_value': no module named pandas.core.series.Series.set_value
WARNING: [autosummary] failed to import 'pandas.core.series.Series.strides': no module named pandas.core.series.Series.strides
WARNING: [autosummary] failed to import 'pandas.core.series.Series.timetuple': no module named pandas.core.series.Series.timetuple
WARNING: [autosummary] failed to import 'pandas.core.series.Series.to_dense': no module named pandas.core.series.Series.to_dense
WARNING: [autosummary] failed to import 'pandas.core.series.Series.to_msgpack': no module named pandas.core.series.Series.to_msgpack
WARNING: [autosummary] failed to import 'pandas.core.series.Series.to_sparse': no module named pandas.core.series.Series.to_sparse
WARNING: [autosummary] failed to import 'pandas.core.series.Series.valid': no module named pandas.core.series.Series.valid
```
</details>
I believe these are just removed features and we should probably just remove from the docs as well
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MDExOlB1bGxSZXF1ZXN0MzYzOTA1NzAw
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CI/TST: fix failing tests in py37_np_dev
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MDExOlB1bGxSZXF1ZXN0MzYzOTA1ODUy
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|
Fix Segfault (maybe)
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| 2020-01-23T17:37:36Z
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MEMBER
| null |
- [X] closes #21824
I have only been able to reproduce this issue once and can't verify that this works, but I think @jorisvandenbossche has had more luck reproducing so maybe can try
I noticed `_as_array` showing up in a core dump I was able to get, and this line seems a little suspect so perhaps removing the writeable requirement fixes
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Pull Request Tips
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|
MEMBER
| null |
I think these issues pop up on a good deal of PRs but aren't explicitly stated anywhere, so adding a few bullet points to the contributing guide might help
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WEB: Styling blog
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| 2020-01-17T00:22:06Z
| 2020-01-17T16:51:21Z
| 2020-01-17T16:51:20Z
|
MEMBER
| null |
Improving how the blog section looks. Changing a bit the font size, color, and spacing. And also removing html tags from the content, since it's tricky to make titles, images, links... in the posts look good in the preview.
Not convinced that the blog looks great, but that's the best I could do, and I think it looks much better than what we've got now.
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DOC: Restore ExtensionIndex.dropna.__doc__
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| 1
| 2020-01-17T01:46:15Z
| 2020-01-20T16:27:26Z
| 2020-01-20T13:35:21Z
|
MEMBER
| null |
Per request from @jorisvandenbossche on #30717.
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ENH: partial string indexing on non-monotonic PeriodIndex
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| 6
| 2020-01-17T02:40:30Z
| 2020-01-21T17:15:54Z
| 2020-01-21T16:27:25Z
|
MEMBER
| null |
Looks like the DatetimeIndex analogue of this was done in #2437 (https://github.com/pandas-dev/pandas/commit/3a173f19b308d2fb7d8e96dfc57f2e5ecd046138)
This does _not_ yet support two-sides slicing e.g. `ser["2015":"2016"]`, but im hoping to get that handled before long.
Partial overlap with #31058, nothing should actually conflict.
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MDExOlB1bGxSZXF1ZXN0MzYzOTU0OTY0
| 31,097
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DOC: Replace ggpy with plotnine in ecosystem
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| 1
| 2020-01-17T04:14:18Z
| 2020-02-19T03:52:49Z
| 2020-01-18T17:11:21Z
|
CONTRIBUTOR
| null |
- [ ] closes #31087
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Changed ggpy reference to plotnine.
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MDExOlB1bGxSZXF1ZXN0MzY0MDE1NzU1
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|
CLN: Regular expression clean
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| 2020-01-17T07:56:44Z
| 2020-01-17T13:21:58Z
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MEMBER
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- [x] ref https://github.com/pandas-dev/pandas/pull/31091#discussion_r367732954
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #31091 on branch 1.0.x (CI/TST: fix failing tests in py37_np_dev)
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MEMBER
| null |
(Manual) Backport PR #31091 on branch 1.0.x (CI/TST: fix failing tests in py37_np_dev)
cc @WillAyd
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MDExOlB1bGxSZXF1ZXN0MzY0MDM1MDc4
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Updated years in LICENSE
|
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| 5
| 2020-01-17T08:55:32Z
| 2020-01-19T06:24:48Z
| 2020-01-18T16:33:48Z
|
MEMBER
| null |
Really not sure about this change.
(Feel free to close this PR at any time, if this file should not be changed).
|
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MDExOlB1bGxSZXF1ZXN0MzY0MTEwNTg4
| 31,101
|
BUG: groupby transform fillna produces wrong result
|
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| 3
| 2020-01-17T12:05:33Z
| 2020-05-29T10:48:03Z
| 2020-01-24T19:16:01Z
|
MEMBER
| null |
- [x] closes #30918
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDU6SXNzdWU1NTE0MDU4MzQ=
| 31,102
|
BUG: Cannot astype from IntegerArray to BooleanArray with missing values
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| 2
| 2020-01-17T13:08:47Z
| 2020-01-23T15:26:39Z
| 2020-01-23T15:26:39Z
|
MEMBER
| null |
For the boolean -> integer array conversion, I added a special path for that. But `astype`ing integer to boolean is currently not working:
```
In [23]: a = pd.array([1, 0, pd.NA])
In [24]: a
Out[24]:
<IntegerArray>
[1, 0, <NA>]
Length: 3, dtype: Int64
In [25]: a.astype("boolean")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-41973ed53ee3> in <module>
----> 1 a.astype("boolean")
~/scipy/pandas/pandas/core/arrays/integer.py in astype(self, dtype, copy)
454 kwargs = {}
455
--> 456 data = self.to_numpy(dtype=dtype, **kwargs)
457 return astype_nansafe(data, dtype, copy=False)
458
~/scipy/pandas/pandas/core/arrays/masked.py in to_numpy(self, dtype, copy, na_value)
124 ):
125 raise ValueError(
--> 126 f"cannot convert to '{dtype}'-dtype NumPy array "
127 "with missing values. Specify an appropriate 'na_value' "
128 "for this dtype."
ValueError: cannot convert to 'boolean'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.
In [26]: a.astype(pd.BooleanDtype())
...
ValueError: cannot convert to 'boolean'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.
```
while for conversions to other nullable dtypes, this should be possible.
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MDExOlB1bGxSZXF1ZXN0MzY0MTM3NzI0
| 31,103
|
CLN: Index._values docstring + Block.internal/external_values
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| 4
| 2020-01-17T13:18:53Z
| 2020-01-17T17:55:46Z
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|
MEMBER
| null |
@jbrockmendel this is the "clean" part of https://github.com/pandas-dev/pandas/pull/31037, so if you merge this, I will update the other PR so that only the required changes are in the PR for 1.0.0.
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MDU6SXNzdWU1NTE0NjY2NDc=
| 31,104
|
BUG: inconsistent creation of IntegerArray from boolean values
|
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[] | null | 0
| 2020-01-17T15:06:10Z
| 2020-01-17T15:06:10Z
| null |
MEMBER
| null |
When creating a nullable integer array from boolean values, we are being inconsistent between a boolean numpy array and a object array with bools:
```python
>>> pd.array(np.array([True, False]), dtype="Int64")
<IntegerArray>
[1, 0]
Length: 2, dtype: Int64
>>> pd.array(np.array([True, False], dtype=object), dtype="Int64")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-33-bc23b09ea959> in <module>
----> 1 pd.array(np.array([True, False], dtype=object), dtype="Int64")
~/scipy/pandas/pandas/core/construction.py in array(data, dtype, copy)
282 if is_extension_array_dtype(dtype):
283 cls = cast(ExtensionDtype, dtype).construct_array_type()
--> 284 return cls._from_sequence(data, dtype=dtype, copy=copy)
285
286 if dtype is None:
~/scipy/pandas/pandas/core/arrays/integer.py in _from_sequence(cls, scalars, dtype, copy)
346 @classmethod
347 def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 348 return integer_array(scalars, dtype=dtype, copy=copy)
349
350 @classmethod
~/scipy/pandas/pandas/core/arrays/integer.py in integer_array(values, dtype, copy)
127 TypeError if incompatible types
128 """
--> 129 values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
130 return IntegerArray(values, mask)
131
~/scipy/pandas/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy)
210 "mixed-integer-float",
211 ]:
--> 212 raise TypeError(f"{values.dtype} cannot be converted to an IntegerDtype")
213
214 elif is_bool_dtype(values) and is_integer_dtype(dtype):
TypeError: object cannot be converted to an IntegerDtype
```
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MDExOlB1bGxSZXF1ZXN0MzY0MTg0MjQ2
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TST: Fix some bare pytest raises
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| null |
- [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Follow up for #30998
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CLN: prune unreachable code
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MDU6SXNzdWU1NTE0OTMyNDk=
| 31,107
|
Non-fixed/variable size moving windows in .rolling and .ewm
|
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[] | null | 3
| 2020-01-17T15:54:15Z
| 2020-05-08T20:13:53Z
| 2020-05-08T20:13:43Z
|
NONE
| null |
Hi everyone.
I'm trying to code adaptive lookback indicators in Python/pandas.
```python
import pandas as pd
import numpy as np
def ALB(src, period, TrendParam):
change = src.diff(period).abs()
volatility = src.diff().abs().rolling(window=period).sum()
ER = change / volatility
Alpha = 2.0 / (period + 1)
VER = np.power((ER - (2 * ER - 1) / 2.0 * (1 - TrendParam) + 0.5), 2)
VAlpha = Alpha * VER
VLength = round((2.0 / VAlpha) - 1)
return VLength
def percentrank(src, period):
count = src.rolling(window=period, min_periods=1).apply(lambda x: np.count_nonzero(np.where(x[-1] > x[:-1])), raw=True)
return 100 * (count / period)
time = pd.date_range(start='2020-01-17', periods=1000, freq='min', name='time')
price = pd.Index(['price'])
df = pd.DataFrame(np.random.rand(1000, 1), time, price)
df['ALB'] = ALB(src=df['price'], period=60, TrendParam=1).fillna(0).astype(int)
df['PCTRNK'] = percentrank(df['price'], period=df['ALB'])
print(df)
```
The code above results in a "ValueError: window must be an integer"
```python
Traceback (most recent call last):
File "<input>", line 22, in <module>
File "<input>", line 15, in percentrank
File "C:\Users\dd\PycharmProjects\rolling\venv\lib\site-packages\pandas\core\generic.py", line 10769, in rolling
closed=closed,
File "C:\Users\dd\PycharmProjects\rolling\venv\lib\site-packages\pandas\core\window.py", line 2849, in rolling
return Rolling(obj, **kwds)
File "C:\Users\dd\PycharmProjects\rolling\venv\lib\site-packages\pandas\core\window.py", line 93, in __init__
self.validate()
File "C:\Users\dd\PycharmProjects\rolling\venv\lib\site-packages\pandas\core\window.py", line 1738, in validate
raise ValueError("window must be an integer")
ValueError: window must be an integer
```
When using a fixed size window of 60
```python
df['PCTRNK'] = percentrank(df['price'], period=60)
```
I get the following output:
```python
price ALB PCTRNK
time
2020-01-17 00:00:00 0.728757 0 0.000000
2020-01-17 00:01:00 0.532295 0 0.000000
2020-01-17 00:02:00 0.899849 0 1.666667
2020-01-17 00:03:00 0.742664 0 1.666667
2020-01-17 00:04:00 0.334157 0 0.000000
... ... ...
2020-01-17 16:35:00 0.781244 226 75.000000
2020-01-17 16:36:00 0.190402 240 16.666667
2020-01-17 16:37:00 0.505205 220 50.000000
2020-01-17 16:38:00 0.791318 233 80.000000
2020-01-17 16:39:00 0.241293 215 18.333333
```
I would like to use ALB as _window_ (and _min_periods_) input for the .rolling part of the percentrank function, row by row.
I think it would make sense if we were able to use a non-fixed size moving window in .rolling and also in .ewm.
So ideally the parameters _window_ and _min_periods_ (in .rolling) could also accept Series.
This has already been mentioned in issue #26920. but the issue has been closed by @TomAugspurger, stating it looks like a duplicate of #23002.
I guess there is a (inefficient, naive) way of doing it, but I'm puzzled how to make it work.
A 'workaround' is mentioned [here](https://stackoverflow.com/questions/57064501/pandas-calculate-rolling-cumulative-product-with-variable-window-size) I think, but I'm still struggling.
Maybe someone can help?
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API: inconsistency in casting back to its EA dtype (try_cast_to_ea)
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[] | null | 1
| 2020-01-17T15:59:47Z
| 2021-07-27T04:59:51Z
| null |
MEMBER
| null |
With the current logic we have in `try_cast_to_ea`:
https://github.com/pandas-dev/pandas/blob/a72eef5a86979232337149961d2122f0b555c876/pandas/core/arrays/base.py#L49-L54
it's easy to get inconsistencies, and it depends critically on what `_from_sequence` accepts as valid scalar.
For example, we now have this
```
>>> s = pd.Series([0, 1, 2], dtype="Int64")
>>> s.combine(0, lambda x, y: x == y)
0 True
1 False
2 False
dtype: bool
```
However, if the IntegerArray constructor gets changed slightly to be more willing to accept all kinds of boolean like values (see https://github.com/pandas-dev/pandas/issues/31104 for a current inconsistency on this aspect, and this is what happens in https://github.com/pandas-dev/pandas/pull/30282), you can get:
```
>>> s = pd.Series([0, 1, 2], dtype="Int64")
>>> s.combine(0, lambda x, y: x == y)
0 1
1 0
2 0
dtype: Int64
```
So how "forgiving" the EA constructor is, will determine the output type.
Here the example is with `combine`, but the `try_cast_to_ea` function is also used in a part of `groupby`.
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MDU6SXNzdWU1NTE0OTk4NTQ=
| 31,109
|
Return NotImpelmented for arith ops with object-dtype Index subclasses
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| 2
| 2020-01-17T16:05:52Z
| 2020-01-28T20:57:19Z
| 2020-01-28T20:57:19Z
|
CONTRIBUTOR
| null |
This change in 1.0 impacted xarray
```python
In [22]: import xarray as xr
In [23]: import pandas as pd
In [24]: idx = pd.timedelta_range("1D", periods=5, freq="D")
In [25]: a = xr.cftime_range("2000", periods=5)
In [26]: idx + a
/Users/taugspurger/sandbox/pandas/pandas/core/arrays/datetimelike.py:1204: PerformanceWarning: Adding/subtracting array of DateOffsets to TimedeltaArray not vectorized
PerformanceWarning,
Out[26]:
Index([2000-01-02 00:00:00, 2000-01-04 00:00:00, 2000-01-06 00:00:00,
2000-01-08 00:00:00, 2000-01-10 00:00:00],
dtype='object')
In [27]: a + idx
Out[27]:
CFTimeIndex([2000-01-02 00:00:00, 2000-01-04 00:00:00, 2000-01-06 00:00:00,
2000-01-08 00:00:00, 2000-01-10 00:00:00],
dtype='object')
```
cc @jbrockmendel @dcherian. Brock suggested checking `type(other)`, and returning NotImplemented for index subclasses we don't know about, to give the subclass a chance to take over the op.
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| 31,110
|
POC: docstring inheritance
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| 2020-01-17T17:54:44Z
| 2020-02-28T00:19:13Z
| 2020-01-23T02:02:57Z
|
MEMBER
| null |
Proof of concept for automatically inheriting docstrings, so we dont have to use `@Appender(parent_class.method.__doc__)` quite so much.
This doesnt handle `@Substitution`. If we can make that work for the common case where we're just putting in the class name, we could get a _lot_ less verbose.
xref #31095, #31060.
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Strange groupby apply behaviour
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| 9
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| 2020-06-20T22:24:25Z
| 2020-06-20T22:24:25Z
|
CONTRIBUTOR
| null |
#### Minimal reproducible example
```python
import pandas as pd
import numpy as np
tdf = pd.DataFrame({"tree": [0, 0, 0, 0, 1, 1, 1, 1],
"into": ["0-2", "0-4", "0-10", np.nan, "1-2", "1-3", "1-7", np.nan],
"leaf_value": [0,0,0,3,0,0,0,4]},
index=["0-0", "0-2", "0-4", "0-10", "1-0", "1-1", "1-2", "1-7"])
def deduce_tree(df):
print("DEDUCING TREE WITH INDEX:\n",df.index)
next_id = df.index[0]
while isinstance(next_id, str):
print(next_id)
next_node = df.loc[next_id, :]
next_id = df.loc[next_id, "into"]
print("RETURNING:\n", next_node)
return next_node
tdf.groupby("tree").apply(deduce_tree)
```
<details>
DEDUCING GREE WITH INDEX:
Index(['0-0', '0-2', '0-4', '0-10'], dtype='object')
0-0
0-2
0-4
0-10
RETURNING:
tree 0
into NaN
leaf_value 3
Name: 0-10, dtype: object
DEDUCING GREE WITH INDEX:
Index(['1-0', '1-1', '1-2', '1-7'], dtype='object')
1-0
DEDUCING GREE WITH INDEX:
Index(['0-0', '0-2', '0-4', '0-10'], dtype='object')
0-0
0-2
0-4
0-10
RETURNING:
tree 0
into NaN
leaf_value 3
Name: 0-10, dtype: object
DEDUCING GREE WITH INDEX:
Index(['1-0', '1-1', '1-2', '1-7'], dtype='object')
1-0
1-2
1-7
RETURNING:
tree 1
into NaN
leaf_value 4
Name: 1-7, dtype: object
</details>
#### Problem description
This came up when trying to analyze a boosted tree internals. As the apply function gets called with a print statement, when it gets to the line `next_node = df.loc[next_id, :]` it just calls the deduce_tree function again, with the group 0.
it prints out `DEDUCING TREE WITH INDEX` 3 times as opposed to 2, and for some reason interrupts the function.
the result of `tdf.groupby("tree").apply(deduce_tree)` is correct, but it seems to do some unnecessary work and if I want to implement some side effects into `deduce_tree` it gets messed up.
Can anyone explain why it works like this? Is this some bug? How can a .loc interrupt a function?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.1-1.el7.elrepo.x86_64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2018.4
dateutil : 2.7.3
pip : 19.2.3
setuptools : 41.0.1
Cython : None
pytest : 4.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.2.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzY0MjcwNDcy
| 31,112
|
BUG: Index.get_value being called incorrectly
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| 1
| 2020-01-17T18:42:42Z
| 2020-01-18T16:09:12Z
| 2020-01-18T15:49:04Z
|
MEMBER
| null |
AFAICT there aren't user-facing consequences, just built-up kludges in `Series.__getitem__` and `Index.get_value` that we'll be able to get rid of once we fix these inconsistencies.
cc @jreback
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MDExOlB1bGxSZXF1ZXN0MzY0MzMyODU1
| 31,113
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BUG: pd.concat fails when handling MultiIndex if repeated index exists
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| 2020-01-17T21:49:53Z
| 2020-05-21T19:35:00Z
| 2020-05-21T19:35:00Z
|
MEMBER
| null |
xref #20565
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CI: Adding script to validate consistent and correct capitalization among headings in documentation (#26941)
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| 34
| 2020-01-17T22:12:44Z
| 2020-03-09T16:49:12Z
| 2020-03-07T20:06:26Z
|
CONTRIBUTOR
| null |
Closes #26941
This script should be running correctly to validate capitalization for various rst files. Many keywords will need to be added into the script as more and more files are tested on this script. Currently testing on the following rst files through code_checks.sh:
doc/source/development/contributing.rst
doc/source/index.rst doc/source/ecosystem.rst
contributing.rst and ecosystem.rst should produce an exit code of 1, indicating there are incorrect capitalization in their headings.
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