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} | 2 | 2020-09-12T23:02:45Z | 2020-09-13T13:25:51Z | 2020-09-13T12:39:59Z | CONTRIBUTOR | null | Avoid needless call to `lib.infer_dtype`, when string dtype.
Performance example:
```python
>>> x = np.array([str(u) for u in range(1_000_000)], dtype=object)
>>> %timeit pd.Series(x, dtype=str)
344 ms ± 59.7 ms per loop # v1.1.0
157 ms ± 7.04 ms per loop # after #35519
22.6 ms ± 191 µs per loop # after #36304
11.2 ms ± 48.6 µs per loop # after this PR
```
Similar speed-up is possible for `pd.Series(x, dtype="string")`, but requires some refactorng of `StringArray`, so I'll do that is a seperate PR.
xref #35519 & #36304. | {
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} | 0 | 2020-09-12T23:45:34Z | 2021-11-20T23:21:23Z | 2020-09-13T12:26:22Z | MEMBER | null | It will also turn out that _coerce_scalar_to_index is effectively another alias for this, though it has slightly different behavior i need to check/fix/test before replacing it | {
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} | 0 | 2020-09-13T01:28:45Z | 2020-11-26T17:26:00Z | 2020-11-26T17:26:00Z | MEMBER | null | There are a bunch of setitem-like methods and we should try to be consistent in what we allow in them. In particular, `Float64Index.putmask` will reject a bool `value` while `insert` will cast it to float. | {
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"description": "Issue that has not been ... | closed | false | null | [] | null | 2 | 2020-09-13T01:54:44Z | 2021-05-28T23:22:40Z | 2021-05-28T23:22:39Z | MEMBER | null | For get_indexer we do type inference on object dtype, but we dont do this for get_indexer_non_unique. Is this intentional?
```
dti = pd.date_range("2016-01-01", periods=3)
target = dti.astype(object)
>>> dti.get_indexer_non_unique(target)
(array([-1, -1, -1]), array([-1, -1, -1]))
>>> dti.get_indexer(target)
array([0, 1, 2])
```
We could also conceivably fall back to object-vs-object instead of returning no-matches.
There's also Cateegorical with matching categories dtype:
```
ci = pd.CategoricalIndex(dti)
>>> dti.get_indexer_non_unique(ci)
(array([-1, -1, -1]), array([-1, -1, -1]))
>>> dti.get_indexer(ci)
array([0, 1, 2])
``` | {
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"gists... | null | 6 | 2020-09-13T02:57:47Z | 2021-01-22T02:40:36Z | 2021-01-22T02:40:36Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
The `dropna=True` case runs
```python
In [7]: df = pd.DataFrame({"A": [0, 0, 7, 1], "B": [1, 2, 4, 3]}, index=list('abcd'))
...: gb = df.groupby('B', dropna=True, axis=1)
...: gb['B'].transform(len)
Out[7]: Series([], Name: B, dtype: int64)
```
but with `dropna=False` the call to `transform` throws with a somewhat unfriendly message
```
In [9]: df = pd.DataFrame({"A": [0, 0, 7, 1], "B": [1, 2, 4, 3]}, index=list('abcd'))
...: gb = df.groupby('B', dropna=False, axis=1)
...: gb['B'].transform(len)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-c7271aa13f24> in <module>
1 df = pd.DataFrame({"A": [0, 0, 7, 1], "B": [1, 2, 4, 3]}, index=list('abcd'))
2 gb = df.groupby('B', dropna=False, axis=1)
----> 3 gb['B'].transform(len)
/workspaces/pandas-arw2019/pandas/core/groupby/generic.py in transform(self, func, engine, engine_kwargs, *args, **kwargs)
506
507 if not isinstance(func, str):
--> 508 return self._transform_general(func, *args, **kwargs)
509
510 elif func not in base.transform_kernel_allowlist:
/workspaces/pandas-arw2019/pandas/core/groupby/generic.py in _transform_general(self, func, *args, **kwargs)
546
547 concatenated = concat(results)
--> 548 result = self._set_result_index_ordered(concatenated)
549 else:
550 result = self.obj._constructor(dtype=np.float64)
/workspaces/pandas-arw2019/pandas/core/groupby/groupby.py in _set_result_index_ordered(self, result)
691 result = result.sort_index(axis=self.axis)
692
--> 693 result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
694 return result
695
/workspaces/pandas-arw2019/pandas/core/series.py in set_axis(self, labels, axis, inplace)
4365 @Appender(generic.NDFrame.set_axis.__doc__)
4366 def set_axis(self, labels, axis: Axis = 0, inplace: bool = False):
-> 4367 return super().set_axis(labels, axis=axis, inplace=inplace)
4368
4369 @doc(
/workspaces/pandas-arw2019/pandas/core/generic.py in set_axis(self, labels, axis, inplace)
657 """
658 self._check_inplace_and_allows_duplicate_labels(inplace)
--> 659 return self._set_axis_nocheck(labels, axis, inplace)
660
661 def _set_axis_nocheck(self, labels, axis: Axis, inplace: bool):
/workspaces/pandas-arw2019/pandas/core/generic.py in _set_axis_nocheck(self, labels, axis, inplace)
662 # NDFrame.rename with inplace=False calls set_axis(inplace=True) on a copy.
663 if inplace:
--> 664 setattr(self, self._get_axis_name(axis), labels)
665 else:
666 obj = self.copy()
/workspaces/pandas-arw2019/pandas/core/generic.py in __setattr__(self, name, value)
5384 try:
5385 object.__getattribute__(self, name)
-> 5386 return object.__setattr__(self, name, value)
5387 except AttributeError:
5388 pass
/workspaces/pandas-arw2019/pandas/_libs/properties.pyx in pandas._libs.properties.AxisProperty.__set__()
64
65 def __set__(self, obj, value):
---> 66 obj._set_axis(self.axis, value)
/workspaces/pandas-arw2019/pandas/core/series.py in _set_axis(self, axis, labels, fastpath)
425 if not fastpath:
426 # The ensure_index call above ensures we have an Index object
--> 427 self._mgr.set_axis(axis, labels)
428
429 # ndarray compatibility
/workspaces/pandas-arw2019/pandas/core/internals/managers.py in set_axis(self, axis, new_labels)
217
218 if new_len != old_len:
--> 219 raise ValueError(
220 f"Length mismatch: Expected axis has {old_len} elements, new "
221 f"values have {new_len} elements"
ValueError: Length mismatch: Expected axis has 2 elements, new values have 4 elements
```
#### Expected Output
Since there are no missing values in the input `dropna=True` and `dropna=False` should give the same results.
#### Problem description
(Edited)
As discussed below and in #35751 `SeriesGroupBy` with `axis=1` never makes sense. We should have a more explicit error message.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 0cdea22618723e4f4e2f1a29f98eab8084db9183
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-47-generic
Version : #51-Ubuntu SMP Fri Sep 4 19:50:52 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+354.g0cdea2261
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0.post20200704
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.19.0
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : 0.6.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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} | 11 | 2020-09-13T05:30:33Z | 2020-09-13T12:22:18Z | 2020-09-13T12:22:18Z | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
data_right = [['Alex','10',100],['Bob','12',90],['Clarke','13.0',80]]
data_left =[[10.0],[12.0],[13.0]]
df_right = pd.DataFrame(data_right,columns=['Name','Age','score'])
df_left = pd.DataFrame(data_left,columns=['Age'])
df_result=df_left.merge(df_right,on='Age',how='inner')
print(df_result)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
sometimes when I read from files in some storage .I am not sure of the datatype of a column . Hence I get an Exception while merging
#### Expected Output
What I Propose is we could have a parameter having ignore_data_type=true in the merge . When its true We could mandatorily cast the column of right_dataframe to the data type of left one and Proceed. Hence we could avoid this Exception.
#### Output of ``pd.show_versions()``
<details>
Age Name score
0 13.0 Clarke 80
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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} | 7 | 2020-09-13T06:52:50Z | 2020-09-17T17:10:26Z | 2020-09-17T16:51:00Z | CONTRIBUTOR | null | Currently, when constructing through `Series(data, dtype="string")`, pandas first converts to strings/NA, then does a check that all scalars are actually strings or NA. The check is not needed in cases where we explicitly already have converted.
Performance example:
```python
>>> x = np.array([str(u) for u in range(1_000_000)], dtype=object)
>>> %timeit pd.Series(x, dtype="string")
357 ms ± 40.2 ms per loop # v1.1.0
148 ms ± 713 µs per loop # after #35519
26.3 ms ± 291 µs per loop # after #36304
12.6 ms ± 115 µs per loop # this PR
```
xref #35519, #36304 & #36317. | {
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} | 6 | 2020-09-13T09:10:35Z | 2020-09-13T22:23:02Z | 2020-09-13T22:18:22Z | MEMBER | null | - [x] closes #25610
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Was fixed in the past. Added test to avoid regression
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} | 4 | 2020-09-13T09:48:31Z | 2021-01-21T04:25:26Z | null | NONE | null | I have confirmed this bug exists on the master branch of pandas.
```python
# Your code here
ser = pd.Series([1., 1., 1., 1.])
cat = pd.Categorical(['a', 'b', 'c', np.nan])
print(ser.groupby(cat, dropna=False).sum())
```
```
# output:
a 1.0
b 1.0
c 1.0
dtype: float64
```
#### Expected Output
```
a 1.0
b 1.0
c 1.0
NaN 1.0
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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- [ ] Look into moving try-except to `_get_value`
- [ ] Handle remaining hashable cases in try-except. | {
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} | 1 | 2020-09-13T13:32:56Z | 2020-09-13T20:41:16Z | 2020-09-13T20:22:37Z | CONTRIBUTOR | null | - [x] closes #31355
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Added a test to trigger the reported error using code from the issue. An if statement was included to return a more helpful error message - borrowing the solution from the issue.
Replacement of [this](https://github.com/pandas-dev/pandas/pull/36169) pull request. | {
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} | 22 | 2020-09-13T15:06:43Z | 2020-09-16T18:32:16Z | 2020-09-15T02:04:47Z | MEMBER | null | Adding a GitHub Action for ~closing stale PRs~ labeling PRs as stale which I think could help in managing the backlog
https://github.com/actions/stale | {
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} | 2 | 2020-09-13T15:32:48Z | 2020-09-14T00:31:00Z | 2020-09-13T20:37:19Z | MEMBER | null | - [ ] 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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} | 0 | 2020-09-13T19:00:02Z | 2020-09-13T20:26:17Z | 2020-09-13T20:19:41Z | MEMBER | null | Update some constructor calls in Categorical | {
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} | 4 | 2020-09-13T19:39:21Z | 2020-09-19T20:14:06Z | 2020-09-19T20:14:06Z | NONE | null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
[test excel.xlsx](https://github.com/pandas-dev/pandas/files/5214984/test.excel.xlsx)
```python
# Your code here
import pandas as pd
df=pd.read_excel('test excel.xlsx')
df
```
#### Problem description
I have a customer that is sending me a file that has ZERO WIDTH NO-BREAK SPACE
only the first header reads unless the character is deleted
I created a repro by copying the header from the original file to the new one in excel
#### Expected Output
```
Building Park Name Building Name New / Renewal Address City State Class
```
#### 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 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
</details>
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"following_url": "https://api.github.com/users/avinashpancham/following{/oth... | null | 5 | 2020-09-13T20:01:04Z | 2020-10-08T00:59:25Z | 2020-10-08T00:59:25Z | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#running-the-performance-test-suite
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
Ran entire performance test suite for #36161 and it took close to 8 hours. Docs state: "Running the full test suite can take up to one hour and use up to 3GB of RAM". From @jreback I understood that this is because the test suite has increase a lot in size, since the last doc update.
#### Suggested fix for documentation
I would add a disclaimer regarding your hardware and state that running the tests on a laptop can take up to X hours. For X we can take 8 or another number if others have experienced an even longer duration for running the full performance test suite. So we end up with something like this:
"Projected duration for running the full test suite may vary depending on your machine, but on a modern laptop it will take up to 8 hours and use up to 3GB of RAM"
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} | 2 | 2020-09-13T20:04:13Z | 2020-09-19T16:42:45Z | 2020-09-19T15:21:56Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
`Styler` uuid is randomly generated from 16bytes, or 128bit entropy, which is poorly formatted for data transmission over the web.
`Styler` uuid1 is super-ceded by uuid4 which is more network secure.
This PR addresses the two above items by switching uuid method, and then coding the default entropy to 5 characters (20bit) which should be more than sufficient to avoid HTML table collision on a webpage, but make substantial data transfer savings for large tables.
uuid length remains configurable. | {
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} | 4 | 2020-09-13T20:18:21Z | 2020-09-17T02:51:57Z | 2020-09-17T02:41:14Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
ATM we are pretty inconsistent in how we handle strings for (DTI|TDI|PI|Series).searchsorted. The underlying EAs will cast scalar strings but not Sequene[str]. The Indexes will raise on both scalars and sequences. Series follows the EA and casts "2016-01-01" but not `["2016-01-01"]`.
This makes us consistently cast in all cases. | {
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} | 0 | 2020-09-13T20:33:46Z | 2020-09-13T21:42:34Z | 2020-09-13T21:42:34Z | NONE | null | Backport PR #36174: BUG: Ensure read_spss accepts pathlib Paths (GH33666) | {
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} | 9 | 2020-09-13T22:50:51Z | 2020-09-16T11:59:19Z | 2020-09-15T22:20:49Z | MEMBER | null | - [x] closes #36031
- [x] closes #32761
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- [x] xref #11645 (fixes the issue expect the 0, will add an additional pr about the doc)
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- [x] tests added / passed
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I implemented the Kahan summation as suggested by @jreback. I used the variable names from https://en.wikipedia.org/wiki/Kahan_summation_algorithm. If there exists a name convention am not aware of, I am happy to rename the variables. | {
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} | 5 | 2020-09-14T01:01:07Z | 2020-09-18T22:11:03Z | 2020-09-18T22:08:58Z | MEMBER | null | Not certain this will work, but nonetheless I think something like this would be helpful (analogous to "Needs Triage" for issues) | {
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} | 13 | 2020-09-14T01:21:44Z | 2020-11-14T16:50:23Z | 2020-11-14T16:50:19Z | CONTRIBUTOR | null | - [x] closes #36308
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Updated `pandas.core.groupby.generic._wrap_transformed_output` as suggested by @rhshadrach but also had to modify `pandas.core.groupby.groupby.pct_change` to get 2 tests passed where the operation passed to `transform` is `pct_change`.
All tests passed except for `pandas/tests/io/test_clipboard.py` and `pandas/tests/io/test_parquet.py` that are already broken on the latest `master` as of September 14, 2020 01:15 UTC.
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} | 4 | 2020-09-14T07:45:59Z | 2020-09-14T11:29:17Z | 2020-09-14T11:28:28Z | NONE | null | ISO 8601 specifies that the date and time portion of a timestamp is separated by a "T", but python's datetime.isoformat allows any character to separate them. To comply with ISO8601, [line 650](https://github.com/pandas-dev/pandas/blob/6b2d0260c818e62052eaf535767f3a8c4b446c69/pandas/_libs/tslibs/timestamps.pyx#L650) should be changed to include a "T" in between the date and the time. Since this also complies with datetime.isoformat, there is no reason not to change this. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/36352 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36352/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36352/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36352/events | https://github.com/pandas-dev/pandas/issues/36352 | 700,939,794 | MDU6SXNzdWU3MDA5Mzk3OTQ= | 36,352 | BUG: interpolate with limit keyword partially fills gaps larger than limit | {
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"description": "np.nan, pd.NaT, pd.NA... | open | false | null | [] | null | 5 | 2020-09-14T09:38:06Z | 2021-08-13T02:48:01Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Interpolating "inside" gaps with limit gap size of 3
import pandas as pd
import numpy as np
ts_index = pd.date_range('2016-01-01','2016-01-2',freq='H')
limit_gap_size = 3
df = pd.DataFrame(index=ts_index, data={'raw':np.random.uniform(size=ts_index.size)})
df.iloc[0:3] = np.nan
df.iloc[5:7] = np.nan
df.iloc[10:16] = np.nan
df.iloc[17:20] = np.nan
df.iloc[23:25] = np.nan
df['filled'] = df.interpolate(limit=limit_gap_size,limit_area='inside')
# solution to specific case
# credit functions below: https://stackoverflow.com/a/54512613/752092
def bfill_nan(arr):
""" Backward-fill NaNs """
mask = np.isnan(arr)
idx = np.where(~mask, np.arange(mask.shape[0]), mask.shape[0]-1)
idx = np.minimum.accumulate(idx[::-1], axis=0)[::-1]
out = arr[idx]
return out
def calc_mask(arr, maxgap):
""" Mask NaN gaps longer than `maxgap` """
isnan = np.isnan(arr)
cumsum = np.cumsum(isnan).astype('float')
diff = np.zeros_like(arr)
diff[~isnan] = np.diff(cumsum[~isnan], prepend=0)
diff[isnan] = np.nan
diff = bfill_nan(diff)
return (diff <= maxgap) | ~isnan # <= instead of < compared to SO answer
df['expected'] = df['raw'].interpolate(limit=3,limit_area='inside').where(calc_mask(df['raw'],limit_gap_size))
df
raw filled expected
2016-01-01 00:00:00 NaN NaN NaN
2016-01-01 01:00:00 NaN NaN NaN
2016-01-01 02:00:00 NaN NaN NaN
2016-01-01 03:00:00 0.781920 0.781920 0.781920
2016-01-01 04:00:00 0.732783 0.732783 0.732783
2016-01-01 05:00:00 NaN 0.545743 0.545743
2016-01-01 06:00:00 NaN 0.358704 0.358704
2016-01-01 07:00:00 0.171664 0.171664 0.171664
2016-01-01 08:00:00 0.689487 0.689487 0.689487
2016-01-01 09:00:00 0.131983 0.131983 0.131983
2016-01-01 10:00:00 NaN 0.140856 NaN
2016-01-01 11:00:00 NaN 0.149729 NaN
2016-01-01 12:00:00 NaN 0.158601 NaN
2016-01-01 13:00:00 NaN NaN NaN
2016-01-01 14:00:00 NaN NaN NaN
2016-01-01 15:00:00 NaN NaN NaN
2016-01-01 16:00:00 0.194093 0.194093 0.194093
2016-01-01 17:00:00 NaN 0.330719 0.330719
2016-01-01 18:00:00 NaN 0.467345 0.467345
2016-01-01 19:00:00 NaN 0.603971 0.603971
2016-01-01 20:00:00 0.740598 0.740598 0.740598
2016-01-01 21:00:00 0.223751 0.223751 0.223751
2016-01-01 22:00:00 0.383625 0.383625 0.383625
2016-01-01 23:00:00 NaN NaN NaN
2016-01-02 00:00:00 NaN NaN NaN
```
#### Problem description
When passing a limit this is expected to be respected and gaps larger than this should not be interpolated at all. Partially filling the beginning (or end, depending on limit_direction) of the gaps is not reasonable behavior
#### Expected Output
See output in example
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 3 | 2020-09-14T10:22:57Z | 2020-09-15T22:35:41Z | 2020-09-15T22:35:41Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
`data.csv`:
```csv
, Q1, Q1, Q1, Q2, Q2, Q2
, V1, V2, V3, V1, V2, V3
Ram , 11, 19, 10, 10, 12, 14
Syam, 11, 19, 10, 10, 12, 14
```
```python
# Your code here
df = pd.read_clipboard(',', header=[0,1], index_col=0)
df.stack(0)
ValueError: The name occurs multiple times, use a level number
```
#### Problem description
Well, I *am* using a level number, not a name, but there still is an error because the level names are duplicated: each is ` ` (4 spaces). I would expect it to work, or to have a more descriptive error, maybe upon creation of the data-frame: "MultiIndex should not have identical names".
I believe this is related to #19029
However, interestingly `df.stack(1)` works correctly, no problems with duplicate names.
#### Expected Output
```
H2 V1 V2 V3
H1
Ram Q1 11 19 10
Q2 10 12 14
Syam Q1 11 19 10
Q2 10 12 14
```
Solved it with:
```
df.columns.names = ['H1', 'H2']
df.stack(0) # works
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
Version : Darwin Kernel Version 19.4.0: Wed Mar 4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_UK.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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"d... | open | false | null | [] | null | 4 | 2020-09-14T11:24:56Z | 2021-08-13T02:49:38Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
On Windows 64 bit, latest commit from `master` branch, etc. (see output of `pd.show_versions()` below), fresh virtual environment, install pandas in editable mode from repo root and run the `validate_docstrings.py` script on `pandas.core.generic.NDFrame.resample`:
```cmd
pip install -e .
python scripts\validate_docstrings.py pandas.core.generic.NDFrame.resample
```
A bunch of doctests fail, with the only difference between actual and expected being int32/int64 respectively:
<details>
```none
################################################################################
################### Docstring (pandas.core.generic.NDFrame) ###################
################################################################################
[...]
################################################################################
################################## Validation ##################################
################################################################################
2 Errors found:
Return value has no description
Examples do not pass tests
################################################################################
################################### Doctests ###################################
################################################################################
**********************************************************************
Line 291, in pandas.core.generic.NDFrame.resample
Failed example:
ts
Expected:
2000-10-01 23:30:00 0
2000-10-01 23:37:00 3
2000-10-01 23:44:00 6
2000-10-01 23:51:00 9
2000-10-01 23:58:00 12
2000-10-02 00:05:00 15
2000-10-02 00:12:00 18
2000-10-02 00:19:00 21
2000-10-02 00:26:00 24
Freq: 7T, dtype: int64
Got:
2000-10-01 23:30:00 0
2000-10-01 23:37:00 3
2000-10-01 23:44:00 6
2000-10-01 23:51:00 9
2000-10-01 23:58:00 12
2000-10-02 00:05:00 15
2000-10-02 00:12:00 18
2000-10-02 00:19:00 21
2000-10-02 00:26:00 24
Freq: 7T, dtype: int32
**********************************************************************
Line 303, in pandas.core.generic.NDFrame.resample
Failed example:
ts.resample('17min').sum()
Expected:
2000-10-01 23:14:00 0
2000-10-01 23:31:00 9
2000-10-01 23:48:00 21
2000-10-02 00:05:00 54
2000-10-02 00:22:00 24
Freq: 17T, dtype: int64
Got:
2000-10-01 23:14:00 0
2000-10-01 23:31:00 9
2000-10-01 23:48:00 21
2000-10-02 00:05:00 54
2000-10-02 00:22:00 24
Freq: 17T, dtype: int32
**********************************************************************
Line 311, in pandas.core.generic.NDFrame.resample
Failed example:
ts.resample('17min', origin='epoch').sum()
Expected:
2000-10-01 23:18:00 0
2000-10-01 23:35:00 18
2000-10-01 23:52:00 27
2000-10-02 00:09:00 39
2000-10-02 00:26:00 24
Freq: 17T, dtype: int64
Got:
2000-10-01 23:18:00 0
2000-10-01 23:35:00 18
2000-10-01 23:52:00 27
2000-10-02 00:09:00 39
2000-10-02 00:26:00 24
Freq: 17T, dtype: int32
**********************************************************************
Line 319, in pandas.core.generic.NDFrame.resample
Failed example:
ts.resample('17min', origin='2000-01-01').sum()
Expected:
2000-10-01 23:24:00 3
2000-10-01 23:41:00 15
2000-10-01 23:58:00 45
2000-10-02 00:15:00 45
Freq: 17T, dtype: int64
Got:
2000-10-01 23:24:00 3
2000-10-01 23:41:00 15
2000-10-01 23:58:00 45
2000-10-02 00:15:00 45
Freq: 17T, dtype: int32
**********************************************************************
Line 329, in pandas.core.generic.NDFrame.resample
Failed example:
ts.resample('17min', origin='start').sum()
Expected:
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17T, dtype: int64
Got:
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17T, dtype: int32
**********************************************************************
Line 336, in pandas.core.generic.NDFrame.resample
Failed example:
ts.resample('17min', offset='23h30min').sum()
Expected:
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17T, dtype: int64
Got:
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17T, dtype: int32
**********************************************************************
Line 346, in pandas.core.generic.NDFrame.resample
Failed example:
ts.resample('17min', offset='2min').sum()
Expected:
2000-10-01 23:16:00 0
2000-10-01 23:33:00 9
2000-10-01 23:50:00 36
2000-10-02 00:07:00 39
2000-10-02 00:24:00 24
Freq: 17T, dtype: int64
Got:
2000-10-01 23:16:00 0
2000-10-01 23:33:00 9
2000-10-01 23:50:00 36
2000-10-02 00:07:00 39
2000-10-02 00:24:00 24
Freq: 17T, dtype: int32
**********************************************************************
Line 360, in pandas.core.generic.NDFrame.resample
Failed example:
ts_out
Expected:
2000-10-01 23:33:00 0
2000-10-01 23:50:00 9
2000-10-02 00:07:00 21
2000-10-02 00:24:00 54
2000-10-02 00:41:00 24
Freq: 17T, dtype: int64
Got:
2000-10-01 23:33:00 0
2000-10-01 23:50:00 9
2000-10-02 00:07:00 21
2000-10-02 00:24:00 54
2000-10-02 00:41:00 24
Freq: 17T, dtype: int32
```
But when I run these examples from the python console of the virtual environment, I get the correct results (i.e. with int64).
</details>
#### Expected Output
No doctests fail.
#### Problem description
The selection of the integer dtype (int64 vs int32) shouldn't differ between running the `validate_dosctrings.py` script and running the examples manually. Why does this happen?
#### Output of ``pd.show_versions()``
<details>
```none
INSTALLED VERSIONS
------------------
commit : 8df0218a4bfae50a7191ff0869e63c28884fb733
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.0.dev0+378.g8df0218a4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.11.0
sphinx : 3.2.1
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fsspec : 0.8.2
fastparquet : 0.3.3
gcsfs : 0.7.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.1
```
</details>
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 20 | 2020-09-14T13:17:27Z | 2020-10-15T13:44:06Z | 2020-09-30T12:22:26Z | CONTRIBUTOR | null | This is a large refactor of our string methods. The goal is to have the `Series.str` accessor dispatch the actual compute down to the ExtensionArray. The motivation is to allow a Series backed by Arrow memory to use the Arrow string kernel (e.g. `Series.str.lower()` on an Arrow-backed StringArray should eventually call `pyarrow.compute.utf8_lower()`.)
To facilitate this, I made the following changes
1. Split `core/strings.py` into a sub package:
- `core/strings/accessor.py`: Implements the `Series.str` accessor, which (mostly) just delegates to the EA and wraps
- `core/strings/object_array.py`: Implements the string methods for object-type ndarray.
- `core/strings/categorical.py`, `core/strings/string_array.py`, implements categorical & StringArray-specific methods
2. Defines a new `ExtensionArray._str` extension point. This is where EAs get to take over and use their compute
Note that there are a few methods like `cat`, `extract`, and `extractall` that don't yet dispatch. I need to look into them a bit more, there implementation is a bit confusing.
Closes https://github.com/pandas-dev/pandas/issues/36216 | {
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} | 4 | 2020-09-14T14:25:26Z | 2020-09-18T11:32:37Z | 2020-09-17T22:39:42Z | CONTRIBUTOR | null | - [x] closes https://github.com/pandas-dev/pandas/pull/36228#discussion_r487587534
- [ ] tests added / passed
N/A
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
N/A
A followup to PR #36228 to get the documentation string right for version changed. (was in wrong place, missing blank line)
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} | 1 | 2020-09-14T14:34:18Z | 2020-09-19T19:56:06Z | 2020-09-19T19:56:06Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> ax1 = pd.Index([1, 2, 3])
>>> ax2 = pd.Index([1, 1, 2])
>>> ans1 = ax1.get_indexer([1])
>>> ans2 = ax2.get_indexer_non_unique([1])
>>> print(ans1, ans1.dtype)
[0] int64
>>> print(ans2[0], ans2[0].dtype, ans2[1], ans2[1].dtype)
[0 1] int64 [] int64
```
#### Problem description
Found in #35498. When looking at the implementation of the `get_indexer` or `get_indexer_non_unique` in pandas/_libs/index.pyx, I noticed that the returned array dtype will always be int64. Since these methods return indices arrays, I believe that intp is a more appropriate type because it will choose a size depending on ssize_t, which is guaranteed to be large enough to represent all possible indices in the array.
#### Expected Output
```python
[0] intp
[0 1] intp [] intp
```
#### Output of ``pd.show_versions()``
```python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 7b14cf6b0b9dbcddce7b9bb22a81c73bdebc1be8
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0rc0+406.g7b14cf6b0
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 45.2.0.post20200210
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.20.2
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.4.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.1
gcsfs : 0.6.2
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
```
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} | 0 | 2020-09-14T15:07:52Z | 2020-10-31T17:51:55Z | 2020-10-31T17:51:55Z | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
d = pd.DataFrame([[1,2,3],[2,2,3],[3,2,3],[4,2,3],[5,2,3]],
columns=pd.MultiIndex.from_tuples([[0, 0, 0], [0, 0, 1], [0, 0, 2]], names=['c1', 'c2', 'c3']),
index=pd.MultiIndex.from_tuples([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 0, 0, 1, 1],
[1, 0, 0, 0, 1, 0, 0]
],
names=['i1', 'i2', 'i3', 'i4', 'i5', 'i6', 'i7']))
e = d.pivot_table(index=['i1',], columns=['i2', 'i3',], values=[(0, 0, 1), (0, 0, 2)], aggfunc=lambda v: v.values[0])
e.columns
```
result is:
```MultiIndex([(0, 0, 1, 0, 0),
(0, 0, 1, 0, 1),
(0, 0, 1, 1, 0),
(0, 0, 1, 1, 1),
(0, 0, 2, 0, 0),
(0, 0, 2, 0, 1),
(0, 0, 2, 1, 0),
(0, 0, 2, 1, 1)],
names=[None, None, None, 'i2', 'i3'])
```
some names is None, but must be
```names=['c1', 'c2', 'c3', 'i2', 'i3'])```
#### Problem description
Problems seems in pandas/core/groupby/generic.py in class DataFrameGroupBy in function _wrap_aggregated_output
Line
```python
name = self._obj_with_exclusions._get_axis(1 - self.axis).name
```
seems to must be
```python
name_axis = self._obj_with_exclusions._get_axis(1 - self.axis)
name = name_axis.names if isinstance(name_axis, MultiIndex) else name_axis.name
```
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https://pandas.pydata.org/docs/dev/reference/api/pandas.read_csv.html
#### Documentation problem
The new `storage_options` keyword for `read_csv` and `read_table` is not documented in the docstring.
#### Suggested fix for documentation
Add docstring
@martindurant
NOTE: I noticed this when running `validate_docstrings` locally for `read_csv` and it reported this.
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} | 0 | 2020-09-14T18:39:08Z | 2020-09-15T02:29:00Z | 2020-09-15T02:01:37Z | MEMBER | null | We have a lot of methods that do something like this; trying to consolidate them with consistent naming conventions. | {
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It would be nice to ensure that the script only runs on files that are being tracked by git, or at least have an option to exclude a directory when doing the checks. | {
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} | 18 | 2020-09-14T20:02:42Z | 2020-12-21T20:15:23Z | 2020-12-21T20:14:39Z | CONTRIBUTOR | null | - [n/a] closes #xxxxx
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] tests added
Enhanced some docstrings for time-series related methods; mainly `asfreq`. Namely:
- Converted `NDFrame.asfreq` into a template to use the specific `klass` in `Series.asfreq` and `DataFrame.asfreq`
- Fully explain the behaviour of `NDFrame.asfreq` in the extended summary.
- Add an extended summary to `asfreq` in `PeriodArray`/`PeriodIndex`
- Use `klass` kwarg in docstring template for `PeriodAray.asfreq`\`PeriodIndex.asfreq`
- Add an extended summary to `date_range`
- Other very minor fixes/enhancements
The original motivator for this PR was [this question](https://stackoverflow.com/q/63860589/6117426), in particular the confusion of the asker. | {
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} | 2 | 2020-09-14T20:53:47Z | 2020-09-15T22:40:59Z | 2020-09-15T22:40:56Z | CONTRIBUTOR | null | Adds dataframe_sql, a package for querying pandas dataframes with SQL, to the ECO system page. Also closes #30480 | {
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} | 2 | 2020-09-14T21:06:39Z | 2020-09-16T09:53:02Z | 2020-09-15T22:35:41Z | MEMBER | null | - [x] closes #36353
- [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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} | 0 | 2020-09-14T21:41:47Z | 2020-09-17T23:49:47Z | 2020-09-17T23:21:40Z | MEMBER | null | cc @jschendel can you suggest a sentence for a docstring on why is_overlapping is the relevant check for these behaviors?
Maybe something related to `index.get_loc(some_int)` can be a non-integer? | {
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} | 6 | 2020-09-15T00:45:16Z | 2020-10-30T08:59:45Z | 2020-10-30T08:59:45Z | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"A": [1, 2, 3]})
a = df["A"]
a += 999
print(df)
print(df["A"])
assert a is df["A"]
```
#### Problem description
I expect `print(df)` and `print(df["A"])` to show identical values for column `A`. This is not the case. The output of the code above:
```
A
0 1
1 2
2 3
0 1000
1 1001
2 1002
Name: A, dtype: int64
```
#### Expected Output
```
A
0 1000
1 1001
2 1002
0 1000
1 1001
2 1002
Name: A, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : fd20f7d34b8601a99b450a615241c233f034fc88
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-47-generic
Version : #51-Ubuntu SMP Fri Sep 4 19:50:52 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+379.gfd20f7d34
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
None
</details>
Possibly related to https://github.com/pandas-dev/pandas/pull/36051? | {
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} | 2 | 2020-09-15T03:10:16Z | 2020-09-17T23:50:12Z | 2020-09-17T23:20:30Z | MEMBER | null | - [ ] 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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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 3 | 2020-09-15T04:09:50Z | 2021-01-02T01:31:45Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [23]: df2 = pd.DataFrame({'a': [np.nan, 1, 2, 1], 'b': [1, 1, 2, 1], 'd': [np.nan, np.nan, np.nan, np.nan], 'c': [1, 1, 1, 1]})
In [24]: df2.dtypes
Out[24]:
a float64
b int64
d float64
c int64
dtype: object
In [29]: s2 = df2.groupby(['a', 'b', 'd'], dropna=False)['c'].sum()
In [30]: s2.index
Out[30]:
MultiIndex([(1.0, 1, nan),
(2.0, 2, nan),
(nan, 1, nan)],
names=['a', 'b', 'd'])
In [31]: s2.index.get_level_values(2).dtype
Out[31]: dtype('float64')
In [32]: pd.MultiIndex.from_tuples(s2.index)
Out[32]:
MultiIndex([(1.0, 1, nan),
(2.0, 2, nan),
(nan, 1, nan)],
)
In [34]: pd.MultiIndex.from_tuples(s2.index).get_level_values(2).dtype
Out[34]: dtype('O')
```
#### Problem description
When a `MultiIndex` is re-created from another `MultiIndex` which contains a nan-only column by using `.from_tuples()`, the dtype of the nan-only level is `object` when it should be `float64`.
As a workaround, the dtype can be inferred correctly when the original index is wrapped in a `tuple`:
```python
In [35]: pd.MultiIndex.from_tuples(tuple(s2.index)).get_level_values(2).dtype
Out[35]: dtype('float64')
```
This problem also doesn't occur when there is at least one non-nan value in the index column:
```python
In [36]: df3 = pd.DataFrame({'a': [np.nan, 1, 2, 1], 'b': [1, 1, 2, 1], 'd': [np.nan, np.nan, np.nan, 1], 'c': [1, 1, 1, 1]})
In [37]: pd.MultiIndex.from_tuples(df3.groupby(['a', 'b', 'd'], dropna=False)['c'].sum().index).get_level_values(2).dtype
Out[37]: dtype('float64')
```
#### Expected Output
The dtype is inferred correctly even without wrapping the source index into a tuple.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- Remove some leftover implementation after https://github.com/pandas-dev/pandas/pull/36240
- Delegate numba func caching to methods in `numba_.py`
- Combine some helper methods | {
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} | 5 | 2020-09-15T07:31:17Z | 2020-09-18T21:55:06Z | 2020-09-18T21:55:06Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
random_state = np.random.RandomState(seed=42)
ncols = 64
nrows = 156
test_data = {
"col{}".format(int(i)): random_state.randint(
0, 100, size=(nrows)
)
for i in range(ncols)
}
df = pd.DataFrame(test_data)
df.eq("a") # it works good
nrows = 157 # change row count
test_data = {
"col{}".format(int(i)): random_state.randint(
0, 100, size=(nrows)
)
for i in range(ncols)
}
df = pd.DataFrame(test_data)
df.eq("a") # it doesn't work
# ValueError: unknown type str32
nrows = 156 # return row count to valid value
ncols = 65 # change col count
test_data = {
"col{}".format(int(i)): random_state.randint(
0, 100, size=(nrows)
)
for i in range(ncols)
}
df = pd.DataFrame(test_data)
df.eq("a") # it doesn't work as well
# ValueError: unknown type str32
```
#### Problem description
Looks like the problem is related to size of operands for comparison operations. Could anyone explain please? Is it normal behavior?
#### Output of ``pd.show_versions()``
<details>
pandas : 1.1.2
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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... | open | false | null | [] | null | 5 | 2020-09-15T08:20:58Z | 2020-11-26T23:07:13Z | null | NONE | null | #### Is your feature request related to a problem?
I want to apply chained operations on the same groupby groups without having to actually make identical costly groupby calls before each operation. The Related problem is available on the following SO question:
https://codereview.stackexchange.com/questions/249222/get-exactly-n-unique-randomly-sampled-rows-per-category-in-a-dataframe
#### Describe the solution you'd like
Add and optional argument that changes the behavior of non-aggregating methods on groupby groups to return groups instead of DataFrames.
This is useful if you want to make multiple chained operations on the Groups without having to do a GroupBy each time (split once, apply multiple times, combine once). This would also have the benefit of actually decoupling the apply and combine parts of the split-apply-combine paradigm when possible. This does not make sense for aggregating methods, but for something like `groupby.DataFrameGroupBy.filter` or `groupby.DataFrameGroupBy.sample` (maybe anything that does not reduce the groups to a single value) returning groups was actually the expected behavior for me and for at least the person who answered the SO question above.
#### API breaking implications
This would not break the API afaik, as the current behavior can be set as the default i.e. `return_groups=False`.
#### Describe alternatives you've considered
The alternatives that I could come up with all involved multiple groupby calls (I think there is an implicit call in `value_counts`). For my specific use case (see SO question), an option to drop groups with insufficient rows would actually suffice, but I think the proposed solution is more general and decoupled.
#### Additional context
Here is the code to randomly sample exactly `n` rows per group, dropping groups with insufficient rows:
```python
n = 4
df = pd.DataFrame({'category': [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1],
'value' : range(12)})
# sample exactly x rows per category
df = df.groupby('category').filter(lambda x: len(x) >= n).groupby('category').sample(n)
# proposed alternative solution:
df = df.groupby('category').filter(lambda x: len(x) >= n, return_groups=True).sample(n)
```
I also expected `filter` to actually pass groups instead of DataFrames to the lambda. | {
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} | 8 | 2020-09-15T11:37:56Z | 2020-09-23T12:07:04Z | 2020-09-15T11:52:36Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# The error persists with non-empty files
! touch test_file
pd.read_table('./test_file', delim_whitespace=True)
```
#### Problem description
```
~/.local/lib/python3.6/site-packages/pandas/io/parsers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
623 if delim_whitespace and delimiter != default_sep:
624 raise ValueError(
--> 625 "Specified a delimiter with both sep and "
626 "delim_whitespace=True; you can only specify one."
627 )
ValueError: Specified a delimiter with both sep and delim_whitespace=True; you can only specify one.
```
#### Expected Output
Parsed dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-lp151.28.67-default
Version : #1 SMP Fri Sep 4 15:23:21 UTC 2020 (2c5a14f)
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.17.3
pytz : 2018.5
dateutil : 2.7.3
pip : 19.3.1
setuptools : 40.5.0
Cython : 0.27.3
pytest : None
hypothesis : None
sphinx : 1.7.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.0.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.4.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.0
sqlalchemy : 1.2.14
tables : None
tabulate : 0.8.5
xarray : None
xlrd : None
xlwt : None
numba : 0.48.0
</details>
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} | 2 | 2020-09-15T12:13:44Z | 2020-09-17T22:03:07Z | 2020-09-17T22:03:04Z | MEMBER | null | I think also that it could be good to skip the PR message for when debugging is turned off and only label / manually review the labeled PRs to start (until we're comfortable with the tagging logic and message) | {
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} | 0 | 2020-09-15T13:32:56Z | 2020-09-17T02:31:09Z | 2020-09-17T02:31:09Z | NONE | null | - [ X] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{
"Name": ["baz", "foo", "baz", "foo", "bar", "bar", "bar"],
"Criticality": ["High", "Critical", "Low", "Medium", "High", "Low", "Medium"],
}
)
SEVERITY = ["Critical", "High", "Medium", "Low"]
def sorter(column):
if column.name != "Criticality":
return column
cat = pd.Categorical(column, categories=SEVERITY, ordered=True)
return pd.Series(cat)
df.sort_values(by=["Name", "Criticality"], key=sorter)
```
#### Problem description
This raises AttributeError: 'Series' object has no attribute 'categories'. If I sort by just Name, or just Criticality there are no issues. Likewise if I replace the sorter with the following it works sorting on both columns:
```python
def sorter(column):
if column.name != "Criticality":
return column
mapper = {name: order for order, name in enumerate(SEVERITY)}
return column.map(mapper)
```
#### Expected Output
I expected to be able to do a multi-column sort when one or more of them are Categorical, or at least being sorted categorically.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.0.0
Version : Darwin Kernel Version 18.0.0: Wed Aug 22 20:13:40 PDT 2018; root:xnu-4903.201.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.15
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 6 | 2020-09-15T15:07:29Z | 2020-09-18T23:18:17Z | 2020-09-18T22:18:03Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Changes the behavior in one test where ATM we behave differently for Series/ndarray than we do for Index arg | {
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} | 7 | 2020-09-15T16:06:37Z | 2020-09-19T19:39:03Z | 2020-09-17T02:31:09Z | MEMBER | null | - [x] closes #36383
- [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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] | closed | false | null | [] | null | 14 | 2020-09-15T19:05:11Z | 2020-11-03T14:42:09Z | 2020-11-03T14:42:09Z | CONTRIBUTOR | null | - [x] closes #36368
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Previously, some of the checks in `code_checks.sh` ran unrestricted on all the
contents of the repository root (recursively), so that if any files extraneous
to the repo were present (e.g. a virtual environment directory, or generated source files), they were
checked too, potentially causing many false positives when a developer runs
`./ci/code_checks.sh` locally to check that the code is ready to be put in a PR.
The checker invocations that were already scoped (i.e. they were already
restricted, in one way or another, to the actual pandas code, e.g. by
restricting the search to the `pandas` subfolder) have been left as-is,
while those that weren't are now given an explicit list of files that are
tracked in the repo.
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} | [] | closed | false | null | [] | null | 0 | 2020-09-15T19:26:03Z | 2020-09-15T20:23:16Z | 2020-09-15T20:17:40Z | NONE | null | Successfully removed the unnecessary trailing commas from the below files:
1. pandas/tests/scalar/test_na_scalar.py
2. pandas/tests/scalar/timestamp/test_arithmetic.py
3. pandas/tests/scalar/timestamp/test_constructors.py | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Added a test to avoid regression | {
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As of the latest verison of master, `read_hdf()` does not support generic buffers as input, and raises a `NotImplementedError` if one is passed in. This stems from line 374 of `pandas/pandas/io/pytables.py`.
#### Describe the solution you'd like
There's already a nice stub in which to insert this code; somebody just needs to implement it. If nobody is currently working on this, I'm happy to give a try myself!
#### API breaking implications
Shouldn't break anything; just would be adding more functionality.
#### Describe alternatives you've considered
For my specific use case I've found a workaround which involves not writing to a generic buffer, but if `pandas` were to have this functionality, my own code would certainly be more elegant.
#### Additional context
As I said above, happy to try to do this myself - seems like a good first contribution and not really any potential to break anything else! But if someone's already working on this, please let me know and I will spare myself the effort. Thanks! | {
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Need #36298 first. | {
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"description": "Related to pd.NA and nul... | closed | false | null | [] | null | 5 | 2020-09-16T09:16:22Z | 2021-01-05T09:09:31Z | 2021-01-05T09:09:31Z | NONE | null | Premise: I tried to look for similar issues (closed or not), but (to my surprise) I couldn't find any.
#### Problem
I often filter my data through comparison with given values, this breaks using pd.NA.
```python
import pandas as pd
from numpy import nan
from pandas import NA
NA == 1, nan == 1
>> (<NA>, False)
NA != 1, nan != 1
>> (<NA>, True)
NA > 1, nan > 1
>> (<NA>, False)
NA < 1, nan < 1
>> (<NA>, False)
```
Which implies:
```python
import pandas as pd
df = pd.DataFrame([1,2,NA], dtype="Int8")
df == 2
>>
0
0 1
1 2
2 <NA>
```
and, even worse:
```python
df[df == 2]
>>
Traceback (most recent call last):
[ ... ]
File "../lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 2861, in _extract_bool_array
assert mask.dtype == bool, mask.dtype
AssertionError: object
```
As you surely know, this would have worked flawlessly with numpy.nan.
#### The solution I'd like
pd.NA and numpy.nan should behave the same, especially in regards of comparisons.
#### API breaking implications
As far as I know pd.NA has been declared experimental, so this should not break much, but may greatly simplify the transition to it for performance and type consistency purposes (which are, from my point of view, the main advantages).
#### Describe alternatives you've considered
I'm not entirely sure pd.NA should be the same as nan in all regards. The documentation itself does not imply it at all.
That being said, I'd still like to be able to filter my data without so much pain. :) | {
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} | 1 | 2020-09-16T09:53:39Z | 2020-09-19T10:18:19Z | 2020-09-19T10:18:19Z | NONE | null | Backport PR #36371: BUG: Fix MultiIndex column stacking with dupe names | {
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} | 0 | 2020-09-16T14:14:45Z | 2020-09-17T23:12:10Z | 2020-09-17T23:11:44Z | CONTRIBUTOR | null | https://travis-ci.org/github/pandas-dev/pandas/jobs/727691006
these are failing (and have been for a while).
I believe we *can* now use the numpy wheels so this should make this build fast enough to include in the main matrix (and avoid using the often ignored allow_failures).
- [ ] use wheels for numpy
- [ ] remove from the allow_failures matrix
this could be for 1.1.3 or 1.2 | {
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} | 1 | 2020-09-16T16:32:48Z | 2020-09-16T17:59:27Z | 2020-09-16T17:59:18Z | CONTRIBUTOR | null | Working on: CLN remove unnecessary trailing commas to get ready for new version of black #35925.
Checked the following files to make sure they are ready for black :
pandas/tests/scalar/timestamp/test_arithmetic.py
pandas/tests/scalar/timestamp/test_constructors.py
pandas/tests/series/methods/test_argsort.py
pandas/tests/series/methods/test_convert_dtypes.py
pandas/tests/series/methods/test_drop_duplicates.py
pandas/tests/series/methods/test_interpolate.py
pandas/tests/series/methods/test_unstack.py
pandas/tests/series/test_cumulative.py
pandas/tests/test_algos.py
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2020-09-16T16:34:35Z | 2020-09-17T23:20:00Z | 2020-09-17T16:40:49Z | MEMBER | null | Avoids casting to ndarray which in some cases means an object-dtype cast. | {
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} | 5 | 2020-09-16T17:12:58Z | 2020-12-03T07:52:52Z | 2020-09-17T16:39:10Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Previously in ``_typing.py`` we had the alias ``IndexLabel``, which contained ``Optional``.
The present PR eliminates ``Optional`` from the alias, but adds it in the code where necessary.
Related to the issue raised by @simonjayhawkins in https://github.com/pandas-dev/pandas/pull/36046#discussion_r485510442 | {
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closes #34149 and
closes #30041
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-09-16T20:10:05Z | 2020-09-17T07:54:29Z | null | MEMBER | null | #### Is your feature request related to a problem?
class DataFrameFormatter seems mostly to target console formatting, while it plays a role of the mediator for ``to_latex`` and ``to_html`` conversions.
It is noticeable, that some kwargs, like ``table_id``, ``render_links`` are pertaining to html only. Meanwhile dataframe truncation (present in init) is not applicable to latex formatting.
Probably needs refactoring.
#### Describe the solution you'd like
I suggest creating ``TableFormatterAbstract`` class.
Inherit from it: ``ConsoleFormatter``, ``HTMLFormatter`` and ``LatexFormatter``.
Put all the required input parameters into the corresponding init of each class (would not need to provide unnecessary kwargs).
Generic functionality like dataframe truncation can be shared via a mixin class ``TruncateMixin``.
Drop ``DataFrameFormatter`` and instead of it call proper ``XFormatter`` where required.
#### API breaking implications
Should not affect public API, as will deal only with the internal implementation.
#### Describe alternatives you've considered
Separate ``ConsoleFormatter``, similarly to ``HTMLFormatter`` and ``LatexFormatter`` and call it from ``DataFrameFormatter.to_string`` method.
This, however will leave ``DataFrameFormatter`` with only three public methods ``to_string``, ``to_html`` and ``to_latex``.
The problem here is that we still carry unnecessary kwargs, which are not applicable to a particular formatting.
#### Additional context
I would be interested to work on this. Would the team be interested in such refactoring? | {
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```python
except exc.SQLAlchemyError as err:
# GH34431
msg = "(1054, \"Unknown column 'inf' in 'field list'\")"
err_text = str(err.orig)
if re.search(msg, err_text):
raise ValueError("inf cannot be used with MySQL") from err
else:
raise err
```
This is in pandas.io.sql line 1400-1407.
`err.orig` part seems wrong, and does not exist as a method in the sqlalchemy class `SQLAlchemyError`.
Should I remove it or am I missing something? | {
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} | 8 | 2020-09-16T23:08:20Z | 2020-09-19T11:37:54Z | 2020-09-19T00:56:48Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @simonjayhawkins
Attempting to add typing to results in aggregation.transform. Here, the obj can be a Series but the list passed into concat contain frames. mypy correctly identifies this as an issue, I think concat needs to be typed as FrameOrSeriesUnion for this purpose.
When I try just typing the function definition and not the overload, mypy complains:
error: Overloaded function implementation does not accept all possible arguments of signature 2 [misc]
so I've changed the type of the overload to FrameOrSeriesUnion as well. I don't believe this is any weaker than the current state because it is an overload. | {
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 10 | 2020-09-17T02:42:49Z | 2020-10-25T18:43:14Z | 2020-10-25T18:43:14Z | CONTRIBUTOR | null | in this PR: https://github.com/pandas-dev/pandas/pull/36346
we updated `.searchsorted()` on the datetimelike indexes to accept string and a list-like, we should add some examples to the doc-string.
cc @jbrockmendel | {
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https://api.github.com/repos/pandas-dev/pandas/issues/36412 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36412/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36412/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36412/events | https://github.com/pandas-dev/pandas/pull/36412 | 703,229,648 | MDExOlB1bGxSZXF1ZXN0NDg4MzczNjU3 | 36,412 | Bump flake8 version in pre-commit-config.yaml | {
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] | closed | false | null | [] | null | 1 | 2020-09-17T03:11:47Z | 2020-09-17T12:13:21Z | 2020-09-17T08:11:01Z | MEMBER | null | The flake8 version used in pre-commit-config.yaml seems to emit spurious errors when there are mypy comments for ignoring specific kinds of type errors. Bumping to the latest version of flake8 seems to fix this.
```
flake8...................................................................Failed
- hook id: flake8
- exit code: 1
pandas/core/generic.py:701:36: F821 undefined name 'ignore'
pandas/core/generic.py:701:36: F821 undefined name 'arg'
pandas/core/generic.py:4220:39: F723 syntax error in type comment 'ignore[return-value, arg-type]'
pandas/core/generic.py:4283:39: F723 syntax error in type comment 'ignore[return-value, arg-type]'
```
For reference this is line 701 that it's complaining about:
```python
new_values, *new_axes # type: ignore[arg-type]
```
ref https://github.com/PyCQA/pyflakes/pull/455 | {
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} | 9 | 2020-09-17T03:12:58Z | 2020-10-07T18:46:01Z | 2020-10-07T11:33:29Z | CONTRIBUTOR | null | - [X] closes #35847
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This takes care of some inconsistency in how names are handled by `Index` functions `union` and `intersection`, as discussed in #35847. I believe this covers all index types, either through the base class or in the subclass when necessary.
What I've implemented here actually uses the unanimous convention, wherein all input names must match to get assigned to the output. Originally, I was thinking consensus would be better (assign if there is only one non-None input name), but looking through the existing tests, it seems that unanimous was usually expected. I also had some worries about whether index names would become too "contagious" with consensus. Anyway, it's easy to change between the two if people have strong opinions on this. | {
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} | 1 | 2020-09-17T03:32:13Z | 2020-09-17T16:52:21Z | 2020-09-17T16:10:35Z | MEMBER | null | Makes clear that these have very similar logic, can be shared after a few tweaks. | {
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