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MDExOlB1bGxSZXF1ZXN0NDk4NjkyMDQ4
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fixed capitalization on ecosystem.rst
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NONE
| null |
Fixed capitalization on doc/source/ecosystem.rst
|
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TYP: check_untyped_defs core.computation.pytables
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| 2020-10-06T17:23:38Z
| 2020-10-06T19:23:53Z
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|
MEMBER
| null |
pandas\core\computation\pytables.py:46: error: Argument 1 to "__new__" of "object" has incompatible type "object"; expected "Type[object]" [arg-type]
pandas\core\computation\pytables.py:189: error: Incompatible types in assignment (expression has type "Callable[[Any, int, Union[Mapping[str, str], Iterable[str], None], bool, bool, Optional[int]], str]", variable has type "partial[bytes]") [assignment]
pandas\core\computation\pytables.py:262: error: Incompatible types in assignment (expression has type "Tuple[Any, ...]", variable has type "Optional[Tuple[Any, Any, Index]]") [assignment]
pandas\core\computation\pytables.py:351: error: Incompatible types in assignment (expression has type "str", variable has type "None") [assignment]
pandas\core\computation\pytables.py:357: error: Incompatible types in assignment (expression has type "str", variable has type "None") [assignment]
pandas\core\computation\pytables.py:364: error: Incompatible types in assignment (expression has type "str", variable has type "None") [assignment]
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TYP: check_untyped_defs core.groupby.ops
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| 1
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| 2020-10-07T15:45:08Z
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MEMBER
| null |
pandas\core\groupby\ops.py:889: error: Need type annotation for 'sdata' [var-annotated]
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MDU6SXNzdWU3MTU5MDA0MTY=
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DOC: code example isn't formatted in `pandas.DataFrame.to_sql`
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| 0
| 2020-10-06T18:01:16Z
| 2020-10-06T23:04:34Z
| 2020-10-06T23:04:34Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
The code example in [pandas.DataFrame.to_sql](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html?highlight=to_sql#pandas.DataFrame.to_sql) isn't formatted properly.
(it's the example that inserts User 4 and User 5 into the db)
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
oh no, the code isn't formatted correctly!
#### Suggested fix for documentation
The reason is there's no blank line before the code snippet, see:
https://github.com/pandas-dev/pandas/blob/b58ee5779583b31de513b3d5c12f5c69c035e920/pandas/core/generic.py#L2692-L2695
Add a blank line above the code snippet :), i have a PR in progress
|
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| 1
| 2020-10-06T18:02:41Z
| 2020-10-07T02:48:19Z
| 2020-10-06T23:04:35Z
|
CONTRIBUTOR
| null |
- [x] closes #36922
- [x] tests added / passed n/a (didn't add any new tests/change code)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
not sure if i need to add a whatsnew entry to 1.1.4 or 1.2.0...
would love to get a hacktoberfest tag if your org supports this. #36837
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TYP: check_untyped_defs core.indexes.base
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| 2020-10-06T19:05:51Z
| 2020-10-10T16:05:47Z
| 2020-10-10T15:43:15Z
|
MEMBER
| null |
pandas\core\indexes\base.py:975: error: Argument 1 to "format_object_attrs" has incompatible type "Index"; expected "Sequence[Any]" [arg-type]
pandas\core\indexes\base.py:1612: error: "Index" has no attribute "levels"; maybe "nlevels"? [attr-defined]
pandas\core\indexes\base.py:1613: error: "Index" has no attribute "codes" [attr-defined]
pandas\core\indexes\base.py:3763: error: "Index" has no attribute "levels"; maybe "nlevels"? [attr-defined]
pandas\core\indexes\base.py:3779: error: "Index" has no attribute "codes" [attr-defined]
pandas\core\indexes\base.py:3787: error: "Index" has no attribute "codes" [attr-defined]
pandas\core\indexes\base.py:3790: error: "Index" has no attribute "codes" [attr-defined]
pandas\core\indexes\base.py:3793: error: "Index" has no attribute "levels"; maybe "nlevels"? [attr-defined]
pandas\core\indexes\base.py:3837: error: "Index" has no attribute "codes" [attr-defined]
pandas\core\indexes\base.py:3840: error: "Index" has no attribute "codes" [attr-defined]
pandas\core\indexes\base.py:4807: error: Too many arguments for "get_indexer_non_unique" of "Index" [call-arg]
pandas\core\indexes\base.py:5403: error: Cannot assign to a method [assignment]
pandas\core\indexes\base.py:5404: error: Cannot assign to a method [assignment]
pandas\core\indexes\base.py:5405: error: Unsupported left operand type for < ("Type[Index]") [operator]
pandas\core\indexes\base.py:5406: error: Unsupported left operand type for > ("Type[Index]") [operator]
pandas\core\indexes\base.py:5407: error: Unsupported left operand type for <= ("Type[Index]") [operator]
pandas\core\indexes\base.py:5408: error: Unsupported left operand type for >= ("Type[Index]") [operator]
pandas\core\indexes\base.py:5415: error: Unsupported left operand type for + ("Type[Index]") [operator]
pandas\core\indexes\base.py:5416: error: "Type[Index]" has no attribute "__radd__" [attr-defined]
pandas\core\indexes\base.py:5417: error: Unsupported left operand type for - ("Type[Index]") [operator]
pandas\core\indexes\base.py:5418: error: "Type[Index]" has no attribute "__rsub__" [attr-defined]
pandas\core\indexes\base.py:5419: error: "Type[Index]" has no attribute "__rpow__" [attr-defined]
pandas\core\indexes\base.py:5420: error: Unsupported left operand type for ** ("Type[Index]") [operator]
pandas\core\indexes\base.py:5422: error: Unsupported left operand type for / ("Type[Index]") [operator]
pandas\core\indexes\base.py:5423: error: "Type[Index]" has no attribute "__rtruediv__" [attr-defined]
pandas\core\indexes\base.py:5425: error: Unsupported left operand type for % ("Type[Index]") [operator]
pandas\core\indexes\base.py:5426: error: "Type[Index]" has no attribute "__rmod__" [attr-defined]
pandas\core\indexes\base.py:5427: error: Unsupported left operand type for // ("Type[Index]") [operator]
pandas\core\indexes\base.py:5428: error: "Type[Index]" has no attribute "__rfloordiv__" [attr-defined]
pandas\core\indexes\base.py:5429: error: Unsupported left operand type for divmod ("Type[Index]") [operator]
pandas\core\indexes\base.py:5430: error: "Type[Index]" has no attribute "__rdivmod__" [attr-defined]
pandas\core\indexes\base.py:5431: error: Unsupported left operand type for * ("Type[Index]") [operator]
pandas\core\indexes\base.py:5432: error: "Type[Index]" has no attribute "__rmul__" [attr-defined]
pandas\core\indexes\base.py:5449: error: Unsupported operand type for unary - ("Type[Index]") [operator]
pandas\core\indexes\base.py:5450: error: Unsupported operand type for unary + ("Type[Index]") [operator]
pandas\core\indexes\base.py:5451: error: "Type[Index]" has no attribute "__abs__" [attr-defined]
pandas\core\indexes\base.py:5452: error: "Type[Index]" has no attribute "__inv__" [attr-defined]
pandas\core\indexes\base.py:5564: error: "Type[Index]" has no attribute "all" [attr-defined]
pandas\core\indexes\base.py:5567: error: "Type[Index]" has no attribute "any" [attr-defined]
pandas\core\indexes\base.py:5576: error: "Type[Index]" has no attribute "all" [attr-defined]
pandas\core\indexes\base.py:5577: error: "Type[Index]" has no attribute "any" [attr-defined]
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MDExOlB1bGxSZXF1ZXN0NDk4Nzc1ODg2
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CLN: Clean groupby tests
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| 1
| 2020-10-06T19:09:07Z
| 2020-10-10T23:31:40Z
| 2020-10-10T22:53:36Z
|
MEMBER
| null |
Found a few unused parts while moving tests for another PR
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BUG:dataframe to_sql using cx_Oracle
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| 2020-10-06T19:29:36Z
| 2021-06-06T17:52:21Z
| 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
df.to_sql(name=table_name,
con=engine,
if_exists='append',
index=False,
chunksize=1000,
dtype=dtype
)
```
#### Problem description
If I run this code and the column names are greater than 30 I get:
(cx_Oracle.DatabaseError) ORA-01008: not all variables bound
It creates the table correctly, but the insert fails. In sqlalchemy, my engine is defined as:
engine = create_engine(url, echo=False, max_identifier_length=128)
If I shorten the long column names, it works perfectly.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
'1.0.5'
</details>
|
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MDExOlB1bGxSZXF1ZXN0NDk4ODA2NDI5
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BUG: Fix duplicates in intersection of multiindexes
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| 2020-10-06T20:08:06Z
| 2020-12-02T12:21:57Z
| 2020-11-29T17:21:53Z
|
MEMBER
| null |
- [x] closes #36915
- [x] xref #31326 (closes the intersection part)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Seems like this was not introduced on purpose. Probably introduced in #31312
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BUG: `dict_keys` cannot be used as `pd.read_csv`'s `names` parameter
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| 2
| 2020-10-06T21:59:28Z
| 2020-10-10T17:45:01Z
| 2020-10-10T17:45:01Z
|
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.
---
#### Code Sample, a copy-pastable example
`test.csv`:
```csv
a,b
1,2
```
```python
import pandas as pd
data = {'a': 10, 'b': 20}
pd.read_csv('test.csv', names=data.keys())
```
Error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers.py", line 691, in read_csv
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers.py", line 451, in _read
_validate_names(kwds.get("names", None))
File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers.py", line 424, in _validate_names
raise ValueError("Names should be an ordered collection.")
ValueError: Names should be an ordered collection.
```
#### Problem description
When a `dict_keys` object is passed, the keys aren't used as names. This is because `dict_keys` isn't list_like (https://github.com/pandas-dev/pandas/blob/4e553464f97a83dd2d827d559b74e17603695ca7/pandas/io/parsers.py#L423) or indexable, so the issue is understandable, but it would be nice to be able to pass `dict_keys` without issue.
#### Expected Output
Passing the dict keys to names shouldn't fail.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 4e553464f97a83dd2d827d559b74e17603695ca7
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+632.g4e553464f
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : 1.6.7
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDk4ODk0NDI5
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TYP: define Index.any, Index.all non-dynamically
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| 3
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| 2020-10-08T15:26:28Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #31160, cc @simonjayhawkins
This unearths 2.5 issues:
1) Index.any/all says that we pass *args/**kwargs to np.any/all, but we don't
1b) the RangeIndex methods don't have args/kwargs at all
2) the subclasses that disable any/all don't totally make sense. In particular it seems like Float64Index should support it and CategoricalIndex should support it iff `self.categories` supports it
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| 2
| 2020-10-06T23:52:37Z
| 2020-10-07T15:16:20Z
| 2020-10-07T15:10:11Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #31160, cc @simonjayhawkins @WillAyd
This implements OpsMixin which is more mypy-friendly than the status quo, and uses it in Series, Index, and DatetimeLikeArrayMixin. There are a handful more places where it can be used in an upcoming pass.
Will do the same for arithmetic/boolean ops if there is consensus that this is worth pursuing.
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TYP: define RangeIndex methods non-dynamically
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REF/TYP: arrays.datetimelike
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| 4
| 2020-10-07T04:04:38Z
| 2020-10-10T22:45:23Z
| 2020-10-10T22:25:10Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
@simonjayhawkins this is still giving me two mypy complaints locally:
```
pandas/core/arrays/datetimelike.py:549: error: Too many arguments for "object" [call-arg]
pandas/core/arrays/datetimelike.py:556: error: Too many arguments for "object" [call-arg]
```
These lines are both calls to `self._scalar_type(foo)`
_scalar_type is annotated as `Type[DatetimeLikeScalar]`, which is a TypeVar for Period/Timestamp/Timedelta. Is there a non-ignoring solution here?
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|
TST/CLN: roll_sum/mean/var/skew/kurt: simplification for non-monotonic indices
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| 19
| 2020-10-07T04:49:54Z
| 2020-10-25T22:09:55Z
| 2020-10-25T22:09:38Z
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CONTRIBUTOR
| null |
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The removed for-loop doesn't seem to be necessary (I hope this code is tested by an existing test).
I feel like I'm missing an obvious reason why these for-loops are needed: looking at the code I don't think we need them and the tests also pass.
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| 0
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| 2020-10-09T20:05:17Z
| 2020-10-09T20:02:09Z
|
MEMBER
| null |
- [x] closes #35869
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Closed by #31302
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TYP: clean unreachable code and duplicate test #27396
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| 1
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MEMBER
| null |
xref #27396
```
pandas/tests/frame/test_to_csv.py:363: error: Right operand of 'or' is never evaluated [unreachable]
pandas/tests/frame/test_to_csv.py:389: error: Right operand of 'or' is never evaluated [unreachable]
pandas/tests/frame/test_to_csv.py:417: error: Right operand of 'or' is never evaluated [unreachable]
pandas/tests/frame/test_to_csv.py:443: error: Right operand of 'or' is never evaluated [unreachable]
```
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| 4
| 2020-10-07T06:32:33Z
| 2020-12-28T16:41:47Z
| 2020-12-28T16:41:47Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Hi, last line of snippet throws an exception ``ValueError: operands could not be broadcast together with shapes (0,) (2,) (0,)``
```python
import pandas as pd
arrays = [['val1', 'val1', 'val2'], ['val1', 'val1', 'val2']]
index = pd.MultiIndex.from_arrays(arrays, names=('idx1', 'idx2'))
df = pd.DataFrame([1, 2, 3], index=index, columns=['value'])
df.loc[df[df['value'] == 0].index, :] = 4
```
#### Problem description
``.loc`` raises error in case dataframe has duplicates in index. ``df[df['value'] == 0].index`` produces ``MultiIndex([], names=['idx1', 'idx2'])`` and it seems to be absolutely valid to put it into ``.loc`` method.
#### Expected Output
I expect ``.loc`` operation to make no changes to original ``df`` cause multiindex in ``.loc`` is empty.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 46.0.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.0
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : 1.0.6
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
</details>
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BUG: GH36928 Allow dict_keys to be used as column names by read_csv
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CONTRIBUTOR
| null |
- [x] closes #36928
- [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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DOC: Update dependency for to_markdown documentation
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| 2020-10-17T10:21:31Z
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|
CONTRIBUTOR
| null |
Adding to the documentation that tabulate is required for to_markdown to be executed
- [x] closes #xxxx
- [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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BUG: Get_period_alias(offset_str) function behaves inconsistent
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| 2020-10-07T12:16:52Z
| 2021-08-13T04:08:23Z
| null |
NONE
| null |
`Get_period_alias(offset_str)` function behaves inconsistent
---
```python
import pandas as pd
print(pd.tseries.frequencies.get_period_alias('AS-JAN')) # works fine
print(pd.tseries.frequencies.get_period_alias('AS-FEB')) # works fine
# ...
print(pd.tseries.frequencies.get_period_alias('W-MON')) # offset not removed
print(pd.tseries.frequencies.get_period_alias('W-TUE')) # offset not removed
# ...
print(pd.tseries.frequencies.get_period_alias('A-JAN')) # offset not removed
print(pd.tseries.frequencies.get_period_alias('A-FEB')) # offset not removed
# ...
```
#### Problem description
The `Get_period_alias(offset_str)` behaves inconsistent for different frequencies. See code sample for examples. The offset information (JAN, FEB, MON, TUE, ... ) should be removed. The issue should be easy to resolve by modifying the following lines:
https://github.com/pandas-dev/pandas/blob/1f6710035a5295b2d3171ab811ae7ffca2344389/pandas/tseries/frequencies.py#L81-L87
#### Output:
```
A
A
W-MON
W-TUE
A-JAN
A-FEB
```
#### Expected Output
```
A
A
W
W
A
A
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.16.3
pytz : 2018.5
dateutil : 2.7.3
pip : 19.1.1
setuptools : 50.3.0
Cython : 0.28.5
pytest : 4.0.1
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.1.0
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 6.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.2.3
numexpr : 2.6.8
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.39.0
</details>
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MDU6SXNzdWU3MTY0OTE1ODU=
| 36,940
|
REGR: DateOffset attributes update fails with no errors.
|
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| 4
| 2020-10-07T12:38:06Z
| 2020-10-08T21:10:10Z
| 2020-10-08T21:10:10Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample
```python
import pandas
base = pandas.Timestamp("2020-01-01")
offset = pandas.DateOffset(years=1)
print(base + offset)
>>> 2021-01-01 00:00:00
print(offset)
>>> <DateOffset: years=1>
offset.years = 5
print(base + offset)
>>> 2021-01-01 00:00:00
print(offset)
>>> <DateOffset: years=5>
```
#### Problem description
I need to dynamically change some of the DateOffset attributes and just setting them seems like the most intuitive thing to do.
And it seems to work: no error is raised and the attribute value is updated.
When actually using it, though, the original value is used.
#### Expected Output
This is obviously due to the way the DateOffset is implemented, as a wrapper for cpython timedelta and dateutils relativedelta.
The ideal outcome for this would be having the object update itself, but I can understand why the class is "static".
The way it is, it may be better to inhibit attribute assignation instead of failing silently.
Event better would be having some method to generate a new DateOffset with just a couple of fields changed.
Depending on the change I may even be able to make the PR myself, once the desired behavior is clarified. :D
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-47-generic
Version : #51~18.04.1-Ubuntu SMP Sat Sep 5 14:35:50 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
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
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|
BUG: series.eq(other) does not equal series == other when the series contain pd.NA
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[] | null | 6
| 2020-10-07T15:00:35Z
| 2020-10-16T18:43:13Z
| null |
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
# Using pd.NA:
>>> import pandas as pd
>>> df = pd.DataFrame(data=[[1, 1], [2, 2], [3, 3], [pd.NA, pd.NA]], columns=['a','b'])
>>> df.a == df.b
0 True
1 True
2 True
3 False
dtype: bool
>>> df.a.eq(df.b)
0 False
1 False
2 False
3 False
dtype: bool
# Using np.nan instead of pd.NA:
>>> import numpy as np
>>> df_np = pd.DataFrame(data=[[1, 1], [2, 2], [3, 3], [np.nan, np.nan]], columns=['a','b'])
>>> df_np.a.eq(df_np.b)
0 True
1 True
2 True
3 False
dtype: bool
```
#### Problem description
From the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.eq.html#pandas.Series.eq) of `Series.eq`: "Equivalent to `series == other`", I would expect `df.a == df.b` to yield the same result as `df.a.eq(df.b)`, but it doesn't when the series contain `pd.NA`.
#### Expected Output
`df.a.eq(df.b)` gives the same result as `df.a == df.b` even when the series contain `pd.NA`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200802
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.1
lxml.etree : 4.5.1
html5lib : None
pymysql : 0.10.0
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.18
tables : None
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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TST: astyp via loc to int64
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CONTRIBUTOR
| null |
- [x] closes #31861
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Adds a test for a special case of `astype` via `loc` to extension type Int64 when data has np.nan or pd.NA.
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MEMBER
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- [ ] closes #xxxx
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- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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ENH: add string method remove prefix and suffix, python 3.9
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| 2020-10-07T16:24:25Z
| 2021-09-06T19:45:57Z
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|
CONTRIBUTOR
| null |
Opening this issue for discussion:
Since python 3.9 is out, and we have the new [string methods](https://www.python.org/dev/peps/pep-0616/) `removeprefix` and `removesuffix`, it would be nice to add them to the pandas string methods as well
```python
In [2]: import pandas as pd
In [3]: df = pd.DataFrame({'A': ['str_string1', 'str_string2', 'str_string3']})
In [4]: print(df)
A
0 str_string1
1 str_string2
2 str_string3
In [5]: df['A'].str.removeprefix('str_')
Out[5]:
0 string1
1 string2
2 string3
Name: A, dtype: object
```
An argument not to add this is that it's pretty easily to achieve with `str.split`:
```python
df['A'].str.split('_').str[-1]
```
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MEMBER
| null |
xref #36531
This is to avoid problems where a dev's own system's Python points to Python2
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| 2020-10-07T17:11:21Z
| 2020-10-08T22:22:49Z
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|
MEMBER
| null |
- [x] closes #36940
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I'm assuming this is a regression and not behavior we want?
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QST: Use object property as key?
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| 2020-10-08T14:16:47Z
| 2020-10-08T14:16:47Z
|
NONE
| null |
Is there a way to use a property of an object as the key when providing the object to a DataFrame?
I think the code makes clearer what I am looking for.
```python
class Key:
def __init__(self, name: str):
self.name = name
# Something like that
def __pandas_key__(self):
return self.name
df = DataFrame({"col": [1,2,3,4]})
key = Key("col")
df[key] # is [1,2,3,4]
```
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REGR: Dataframe.agg no longer accepts positional arguments as of v1.1.0
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| 0
| 2020-10-07T17:49:13Z
| 2020-10-09T13:32:56Z
| 2020-10-09T13:32:56Z
|
CONTRIBUTOR
| null |
Currently, passing any positional arguments to the `*args` parameter of `DataFrame.agg` fails with a `TypeError`, but the documented behavior is that positional and keyword arguments are passed on to the function that you are aggregating with. A minimal reproducer:
```python
import pandas as pd
def f(x, a):
return x.sum() + a
df = pd.DataFrame([1, 2])
print(df.agg(f, 0, 3))
```
This is a regression introduced in v1.1.0, which was introduced in 433c9007781080658553fbef1a4d0c2813b404c0 (GH-34377), and it was mainly because the previous code only incidentally worked. Prior to the "offending" commit, [all `TypeErrors` were caught](https://github.com/pandas-dev/pandas/commit/433c9007781080658553fbef1a4d0c2813b404c0#diff-1e79abbbdd150d4771b91ea60a4e1cc7R7336-R7341) and `.agg` would fall back to `apply`. This was incidentally (as far as I can tell) catching the `TypeError` raised from the fact that the `self._aggregate` call passed `axis` as a keyword argument, so:
```python
self._aggregate(func, axis=axis, *args, **kwargs)
...
def aggregate(self, func, axis, *args, **kwargs):
...
```
This means that `aggregate` takes `func` and the first positional argument from `*args` as `func` and `axis`, then takes `axis=1` to be `axis`, raising `TypeError` because `axis` is specified twice.
I believe the solution here is to pass `axis` to `self._aggregate` by position.
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DEPR: ObjectBlock.is_bool
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|
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
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REGR: Allow positional arguments in DataFrame.agg
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| 5
| 2020-10-07T17:53:27Z
| 2020-10-09T13:33:42Z
| 2020-10-09T13:32:56Z
|
CONTRIBUTOR
| null |
Although `DataFrame.agg` is documented as accepting `func, axis, *args, **kwargs` with `*args` and `**kwargs` passed to `func`, passing positional arguments raises a TypeError.
The reason for this is that the internal call to `self._aggregate` uses a keyword argument (axis) before passing *args and `**kwargs`, and as such the first positional argument is always interpreted as a second specification of `axis`, which raises TypeError.
Prior to commit 433c9007781080658553fbef1a4d0c2813b404c0, TypeErrors were being suppressed, falling back to `self.apply`, which in v1.1.0 turned into an error.
This fixes issue GH-36948.
- [x] closes #36948
- [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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REGR: resample apply fails with KeyError/AttributeError for function that works on DataFrame but fails on Series
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| 4
| 2020-10-07T17:54:35Z
| 2020-10-21T15:12:56Z
| 2020-10-20T01:30:23Z
|
MEMBER
| null |
A resample `apply` accessing a column of the subsetted dataframe fails (bubbling up the error from first trying the function on a single column, i.e. a Series):
```
In [25]: df = pd.DataFrame({"col": range(10)}, index=pd.date_range("2012-01-01", periods=10, freq="20min"))
In [26]: df.resample("H").apply(lambda group: len(group['col'].unique()))
...
KeyError: 'col'
In [27]: df.resample("H").apply(lambda group: len(group.col.unique()))
...
AttributeError: 'Series' object has no attribute 'col'
```
This seems a regression, or was there a deliberate change in `apply` to only apply it on the columns and no longer on the full DataFrame ?
cc @jbrockmendel
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CLN: dont special-case should_store, CategoricalBlock
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| 0
| 2020-10-07T17:55:18Z
| 2020-10-08T21:22:21Z
| 2020-10-08T21:13:42Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Moving towards avoiding special-casing pandas-internal EA subclasses.
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| 9
| 2020-10-07T18:04:29Z
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|
MEMBER
| null |
This seems to be triggered in a very specific case, i.e. an irregular time series and a missing value as last element in the index:
```
increment = pd.Series(pd.to_timedelta([30.0, 30.1, 29.9, 30.0], unit='s')).cumsum()
increment[-1] = None
index = pd.Timestamp("2012-01-01 09:00") + increment
df = pd.Series(range(len(index)), index=index).to_frame()
In [11]: df["2012-01-01":"2012-01-05"]
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-11-ded270432cfc> in <module>
----> 1 df["2012-01-01":"2012-01-05"]
~/miniconda3/envs/pandas11/lib/python3.8/site-packages/pandas/core/frame.py in __getitem__(self, key)
2883 # either we have a slice or we have a string that can be converted
2884 # to a slice for partial-string date indexing
-> 2885 return self._slice(indexer, axis=0)
2886
2887 # Do we have a (boolean) DataFrame?
~/miniconda3/envs/pandas11/lib/python3.8/site-packages/pandas/core/generic.py in _slice(self, slobj, axis)
3552 Slicing with this method is *always* positional.
3553 """
-> 3554 assert isinstance(slobj, slice), type(slobj)
3555 axis = self._get_block_manager_axis(axis)
3556 result = self._constructor(self._mgr.get_slice(slobj, axis=axis))
AssertionError: <class 'numpy.ndarray'>
```
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TYP/REF: use _cmp_method in EAs
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| 0
| 2020-10-07T18:14:48Z
| 2020-10-07T22:37:11Z
| 2020-10-07T22:23:29Z
|
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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Implement DataFrame.__array_ufunc__
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| 16
| 2020-10-07T18:36:37Z
| 2020-11-30T14:38:18Z
| 2020-11-25T20:52:40Z
|
CONTRIBUTOR
| null |
For some cases, this will preserve extension types of arrays by calling
the ufunc blockwise.
```python
In [1]: import pandas as pd; import numpy as np
In [2]: df = pd.DataFrame({"A": pd.array([0, 1], dtype="Sparse")})
In [3]: np.sin(df).dtypes
Out[3]:
A Sparse[float64, nan]
dtype: object
```
Implementation-wise, this was done by moving `Series.__array_ufunc__` to `NDFrame` and making it generic for Series / DataFrame. The DataFrame implementation goes through `BlockManager.apply(ufunc)`.
We don't currently handle the multi-input case well for dataframes (aside from ufuncs that are implemented as dunder ops like `np.add`). For these, we fall back to the old implementation of converting to an ndarray and wrapping the result. This loses extension types.
We also don't currently handle multi-output ufuncs (like `np.modf`). This would require a `BlockManager.apply` that returns a `Tuple[BlockManager]`, `nout` per input block. Maybe someday, but that's low priority.
closes https://github.com/pandas-dev/pandas/issues/23743
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Add optional ./ci/code_checks.sh pre-commit
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| 1
| 2020-10-07T18:41:22Z
| 2021-07-16T11:22:56Z
| 2021-07-16T11:22:55Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/36722#issuecomment-704655928
> Is there a way to have an option in code_checks.sh that actually runs the appropriate pre-commit hook? that way we have a single source of truth, and you can actually still run them (of course have to avoid running these on the CI from the script as well)
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API: series.values EA or ndarray[object]?
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| 2020-10-07T19:43:52Z
| 2020-10-16T19:04:24Z
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MEMBER
| null |
ATM `Series[categorical].values` gives a Categorical and `Series[sparse].values` gives a SparseArray. Interval, Period, and DatetimeTZ get cast to ndarray[object]. 3rd party EAs return the EA.
We should choose one or the other. I have no real preference which.
|
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DOC: Add GCS as supported filesystem for read_parquet
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CONTRIBUTOR
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Small typo in `citing.md`
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MDExOlB1bGxSZXF1ZXN0NDk5NTY4MTM3
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BUG/API: tighter checks on DTI/TDI.equals
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MEMBER
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- [x] closes #33531
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Also fixes `TimedeltaIndex(Categorical(tdi))` (which DatetimeIndex already gets right)
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MDU6SXNzdWU3MTY5MTUxMzU=
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BUG: Still getting blank when using `pandas.Series.to_string(index=False)`
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[] | null | 3
| 2020-10-07T23:27:51Z
| 2020-10-08T01:14:27Z
| 2020-10-08T00:08:06Z
|
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
print("pandas.__version__ == {:s}".format(pandas.__version__))
columns = ['name', 'age']
values = [
['name_one', '31'],
['name_two', '32']]
data_frame = pandas.DataFrame(values, columns=columns)
# filter
filtered = data_frame[(data_frame['name'] == 'name_one') & (data_frame['age'] == '31')]
# single value comes with a blank at the beginning when running 0.24.0
print("==={:s}===".format(filtered.name.to_string(index=False)))
```
#### Problem description
This was already reported by me on #24980 and supposedly fixed by PR #36094 but I keep getting the same result with pandas `1.1.3`.
Using the sample code above the output is `=== name_one===`
I might be missing something but I can't see what.
#### Expected Output
Using the sampe code provided I expect it to return `===name_one===`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.12.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.0-1.el7.elrepo.x86_64
Version : #1 SMP Sun Dec 11 15:43:54 EST 2016
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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REF/TYP: use OpsMixin for logical methods
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The fastpath it implements is equivalent to the can_hold_element fastpath in Block.replace
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MDExOlB1bGxSZXF1ZXN0NDk5NjM0NjE5
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TYP: consistent return types blocks
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- [ ] closes #xxxx
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REF: de-duplicate skipna logic
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De-duplicate logic in handling skipna
inside methods any() and all().
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DOC: make rename docs consistent
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CONTRIBUTOR
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Other doc text describing code end in a colon.
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- [ ] tests added / passed
- [ ] passes `black pandas`
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DOC: improve description of the examplewhich dataframe has two rows
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CONTRIBUTOR
| null |
#### Location of the documentation
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mode.html"]
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
[this should provide a description of what documentation you believe needs to be fixed/improved]
... but the DataFrame has two rows. it is not clear which dataframe it refers to.
#### Suggested fix for documentation
[this should explain the suggested fix and **why** it's better than the existing documentation]
... but the resulting DataFrame has two rows.
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CONTRIBUTOR
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improve description of the example which dataframe has two rows
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|
MDU6SXNzdWU3MTcwOTcwMzg=
| 36,972
|
BUG: warning when using colors 'CN'
|
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| 0
| 2020-10-08T07:28:24Z
| 2020-10-10T22:33:15Z
| 2020-10-10T22:33:15Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] 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 matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 3), columns=["a", "b", "c"])
df.plot(color='C0')
plt.show()
```
#### Problem description
On master branch when executing the code above the following warning is raised.
```
workspaces/pandas/pandas/plotting/_matplotlib/style.py:64: MatplotlibDeprecationWarning: Support for uppercase single-letter colors is deprecated since Matplotlib 3.1 and will be removed in 3.3; please use lowercase instead.
[conv.to_rgba(c) for c in colors]
```
#### Expected Output
Expected no warnings.
Related issue: https://github.com/pandas-dev/pandas/issues/15516. The feature with "CN"-like colors was introduced in https://github.com/pandas-dev/pandas/pull/15873.
I figured out that the problem lies in the line
```
maybe_color_cycle = _maybe_valid_colors(list(colors))
```
So, if colors is "C0", then it is split into ["C", "0"].
I will fix it.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1251
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU3MTcxMTkzNzc=
| 36,973
|
BUG: merge with MultiIndex including a CategoricalIndex returns wrong result when values are ordered specifically
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| 2
| 2020-10-08T08:03:50Z
| 2021-09-07T14:11:56Z
| 2021-09-07T14:11:56Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
c1 = ['A', 'C', 'C', 'D' ]
c2 = ['P2', 'P2', 'P1', 'P2']
df1 = pd.DataFrame({
'id': c1[1:],
'p': c2[1:],
'a': np.arange(len(c1[1:])),
})
df2 = pd.DataFrame({
'id': c1[:-1],
'p': c2[:-1],
'd1': np.arange(10, 10+len(c1[:-1])),
})
# ordered=True doesn't change the result
pcat = pd.api.types.CategoricalDtype(categories=['P2', 'P1'], ordered=False)
df1['p'] = df1['p'].astype(pcat)
df2['p'] = df2['p'].astype(pcat)
df1 = df1.set_index(['id', 'p'])
df2 = df2.set_index(['id', 'p'])
result = pd.merge(df1, df2, how='left', left_index=True, right_index=True)
print(df1)
print(df2)
# ('C', 'P1') is in both dataframes and should therefore be merged accordingly
print(result)
```
output:
```
a
id p
C P2 0
P1 1
D P2 2
d1
id p
A P2 10
C P2 11
P1 12
a d1
id p
C P2 0 11.0
P1 1 NaN ## <- Should be 12.0
D P2 2 NaN
```
#### Problem description
The merge should not return NaN for the ('C', 'P1') value available in df2 for the left merge.
This happens only:
- when `p` is Categorical: leaving the column as object/string gives the right result
- when category values are ordered as such (decreasing lexicographic order), **but** the `ordered` argument does not change the result
- when the `id` values are as such: changing the 'C' in 'E' or anything "above" gives the right result (!)
#### Expected Output
```
a d1
id p
C P2 0 11.0
P1 1 12.0
D P2 2 NaN
```
#### First level of investigations
Using a debugger and following the code execution:
- when the error occurs: in Index.join(), it goes here to: https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3537
```python
elif not self.is_unique or not other.is_unique:
if self.is_monotonic and other.is_monotonic:
return self._join_monotonic( ### <- HERE
other, how=how, return_indexers=return_indexers
)
```
It seems it uses the wrong branch/case.
- when the error does not occurs (e.g. swapping the values in the CategoricalDType declaration): https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3578
```python
if return_indexers:
if join_index is self:
lindexer = None
else:
lindexer = self.get_indexer(join_index)
if join_index is other:
rindexer = None
else:
rindexer = other.get_indexer(join_index) ### <- HERE
return join_index, lindexer, rindexer
else:
return join_index
```
This is how far I can get.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3MTcxMjU2NzI=
| 36,974
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BUG: InvalidIndexError when doing fillna with index that is repeated in df but not in fillna argument
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| 2020-10-08T08:13:58Z
| 2020-10-09T13:55:24Z
| 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.
---
I get an InvalidIndexError on pandas 1.0.3 when doing fillna to a DataFrame with repeated indexes and using a DataFrame with the same index but without repetitions. I've also tested this use case in pandas 1.1.3 (latest as today)
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({"a": 100*(list(range(10))+[None,None,None,None])}).sample(1000, random_state=12).set_index(pd.Series(list(range(100)) * 10, name = "my_index"))
df_fillna = pd.DataFrame({"a": list(range(100))}, index=list(range(100)))
df.fillna(df_fillna)
```
I expect to get a DF with each NaN with index i (there could be more than one) replaced with the unique value in df_fillna with index i but I get an InvalidIndexError.
#### Problem description
The output is an InvalidIndexError.
#### Expected Output
I would expect to replace each NaN with index i (there could be more than one) with the unique value in df_fillna with index i. So, the same as I get when doing this:
```python
df = pd.DataFrame({"a": 100*(list(range(10))+[None,None,None,None])}).sample(1000, random_state=12).set_index(pd.Series(list(range(100)) * 10, name = "my_index"))
df_fillna = pd.DataFrame({"a": list(range(100))*10}, index=list(range(100))*10)
df.fillna(df_fillna)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.3
setuptools : 39.1.0
Cython : 0.29.21
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 6.0.2
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.2.16
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.51.2
</details>
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MDU6SXNzdWU3MTcxOTkxODk=
| 36,975
|
BUG: mask() and where() do not work pandas.core.arrays.boolean.BooleanDtype
|
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[] | null | 2
| 2020-10-08T09:58:38Z
| 2020-10-09T09:39:54Z
| 2020-10-08T13:31:22Z
|
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.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
This does not work:
```python
>>> import pandas as pd
>>> index = pd.MultiIndex.from_arrays([['a', 'a', 'b'], ['c', 'd', 'e']], names=['level1', 'level2'])
>>> test = pd.DataFrame([[1, 2],[3, 4],[5, 6]], index=index, dtype=pd.UInt32Dtype())
>>> column_sums = test.sum(level='level1')
>>> to_remove = column_sums < 100
>>> test.mask(to_remove, inplace=True, level='level1', axis=0, other=pd.NA)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./site-packages/pandas/core/generic.py", line 9034, in mask
return self.where(
File "./site-packages/pandas/core/generic.py", line 9004, in where
return self._where(
File "./site-packages/pandas/core/generic.py", line 8854, in _where
new_data = self._mgr.putmask(
File "./site-packages/pandas/core/internals/managers.py", line 547, in putmask
return self.apply(
File "./site-packages/pandas/core/internals/managers.py", line 406, in apply
applied = getattr(b, f)(**kwargs)
File "./site-packages/pandas/core/internals/blocks.py", line 1606, in putmask
mask = _extract_bool_array(mask)
File "./site-packages/pandas/core/internals/blocks.py", line 2869, in _extract_bool_array
assert mask.dtype == bool, mask.dtype
AssertionError: object
```
This does work:
```python
>>> import pandas as pd
>>> index = pd.MultiIndex.from_arrays([['a', 'a', 'b'], ['c', 'd', 'e']], names=['level1', 'level2'])
>>> test = pd.DataFrame([[1, 2],[3, 4],[5, 6]], index=index, dtype=pd.UInt32Dtype())
>>> column_sums = test.sum(level='level1')
>>> to_remove = (column_sums < 100).astype(bool)
>>> test.mask(to_remove, inplace=True, level='level1', axis=0, other=pd.NA)
>>> test.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 3 entries, ('a', 'c') to ('b', 'e')
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 0 0 non-null UInt32
1 1 0 non-null UInt32
dtypes: UInt32(2)
memory usage: 346.0+ bytes
```
#### Problem description
Using mask() or where() with the BooleanDtype as condition raises an AssertionError.
#### Expected Output
mask() and where() should work with both the bool and the pandas.core.arrays.boolean.BooleanDtype dtype.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
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
</details>
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MDU6SXNzdWU3MTcyMzE3MDI=
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BUG:300
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| 2020-10-08T10:45:44Z
| 2020-10-08T13:34:09Z
| 2020-10-08T13:34:08Z
|
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
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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| 717,241,738
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MDU6SXNzdWU3MTcyNDE3Mzg=
| 36,977
|
rename function with agg
|
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| 2020-10-08T11:01:20Z
| 2020-10-08T18:10:47Z
| 2020-10-08T15:30:38Z
|
NONE
| null |
I just update to 1.1.3 and it seems that there is a problem (bug?) to rename function after an aggregate call. All the functions call are named <lambda> so I can't use rename as previously.
How can I do to rename my function ?
I cannot do anymore
```
.rename(columns={'<lambda_0>': 'function 0',
'<lambda_1>': 'function 1',....
```
(this is of course an example of functions names)
```python
import pandas as pd
pdf = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
pdf.agg(["sum", lambda df: df.sum(), lambda df: df.sum()])
```
#### Output
A B
sum 12 27
<lambda> 12 27
<lambda> 12 27
#### Expected Output
A B
sum 12 27
<lambda_0> 12 27
<lambda_1> 12 27
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : None
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
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MDExOlB1bGxSZXF1ZXN0NDk5ODM4NjYx
| 36,978
|
CI Upgrade isort in pre-commit
|
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| 6
| 2020-10-08T11:03:24Z
| 2020-10-08T17:43:10Z
| 2020-10-08T17:42:46Z
|
CONTRIBUTOR
| null |
- xref #36879
```console
(base) root@ada0229371a4:/home/pandas-tazminia# pre-commit autoupdate && pre-commit run --all
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... [INFO] Initializing environment for https://gitlab.com/pycqa/flake8.
already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
updating 5.2.2 -> 5.6.0.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... [INFO] Initializing environment for https://github.com/asottile/yesqa.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
[INFO] Initializing environment for https://gitlab.com/pycqa/flake8:flake8-comprehensions>=3.1.0.
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black....................................................................Passed
flake8...................................................................Passed
flake8-pyx...............................................................Passed
flake8-pxd...............................................................Passed
isort....................................................................Passed
pyupgrade................................................................Passed
rst ``code`` is two backticks............................................Passed
Generate pip dependency from conda.......................................Passed
Strip unnecessary `# noqa`s..............................................Passed
Fix End of Files.........................................................Passed
```
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MDExOlB1bGxSZXF1ZXN0NDk5ODU0MjY0
| 36,979
|
CLN: re-wrap docstrings in pandas\compat\numpy\function.py
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BUG: Pandas closes user-provided file handles that it doesn't own.
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| 3
| 2020-10-08T12:33:43Z
| 2020-11-04T04:09:17Z
| 2020-11-04T04:09:17Z
|
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.
---
`read_csv` closes user-provided file handles in specific cases. In particular, if we pass a `BytesIO` or a file opened in binary mode, **and** pass an `encoding` kwarg. If the `encoding` kwarg is not passed, pandas does not close the file handle (as expected).
Much earlier (in 2016) a related [issue](https://github.com/pandas-dev/pandas/issues/14418) has been reported and fixed. The test cases that were mentioned there still pass today, but only in case the file handle is opened in binary mode & the `encoding` kwarg is passed. (version `1.1.2` & python `3.8.5`)
#### Code Sample, a copy-pastable example
```python
import sys
import pandas
from io import BytesIO, StringIO
print(f'Python version: {sys.version}')
print(f'pandas version: {pandas.__version__}')
string_io = StringIO('a,b\n1,2')
bytes_io_1 = BytesIO(b'a,b\n1,2')
bytes_io_2 = BytesIO(b'a,b\n1,2')
pandas.read_csv(string_io)
print(f'Was StringIO closed? - {string_io.closed}')
pandas.read_csv(bytes_io_1)
print(f'Was BytesIO closed when encoding is NOT passed? - {bytes_io_1.closed}')
pandas.read_csv(bytes_io_2, encoding='utf-8')
print(f'Was BytesIO closed when encoding is passed? - {bytes_io_2.closed}')
```
prints:
```
Python version: 3.8.5 (v3.8.5:580fbb018f, Jul 20 2020, 12:11:27)
[Clang 6.0 (clang-600.0.57)]
pandas version: 1.1.2
Was StringIO closed? - False
Was BytesIO closed when encoding is NOT passed? - False
Was BytesIO closed when encoding is passed? - True
^
```
#### Problem description
Expectation is that pandas should never close user-provided handles. If pandas didn't open the file, then it doesn't own it & therefore shouldn't close it.
#### Expected Output
I would expect the script above to output:
```
Python version: 3.8.5 (v3.8.5:580fbb018f, Jul 20 2020, 12:11:27)
[Clang 6.0 (clang-600.0.57)]
pandas version: 1.1.2
Was StringIO closed? - False
Was BytesIO closed when encoding is NOT passed? - False
Was BytesIO closed when encoding is passed? - False
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
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 : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.17.2
pytz : 2020.1
dateutil : 2.7.3
pip : 20.2.3
setuptools : 40.7.1
Cython : 0.29.7
pytest : 4.6.5
hypothesis : 4.55.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.2
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.3.5
scipy : 1.5.2
sqlalchemy : 1.3.10
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDk5ODkzNzU0
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BUG: fix matplotlib warning on CN color
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- [ ] closes #36972
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDk5OTgzOTIx
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FIX: fix cleanup warnings for errorbar timeseries
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| 2020-10-08T14:52:03Z
| 2020-10-10T18:11:39Z
| 2020-10-10T17:33:48Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Currently in ``pandas/tests/plotting/tests_frame.py`` there are several warnings emitted.
```
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_mpl2_color_cycle_str
/workspaces/pandas/pandas/plotting/_matplotlib/style.py:64: MatplotlibDeprecationWarning: Support for uppercase single-letter colors is deprecated since Matplotlib 3.1 and will be removed in 3.3; please use lowercase instead.
[conv.to_rgba(c) for c in colors]
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_errorbar_plot
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_errorbar_plot
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_errorbar_plot
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_errorbar_timeseries
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_errorbar_timeseries
pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_errorbar_timeseries
/workspaces/pandas/pandas/plotting/_matplotlib/__init__.py:61: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
plot_obj.generate()
-- Docs: https://docs.pytest.org/en/stable/warnings.html
```
This PR handles warnings in ``test_errorbar_timeseries``.
If this approach is considered reasonable by the reviewers, then I will do the same for ``test_errorbar_plot`` as well as other similar warnings among the plotting-related tests.
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MDU6SXNzdWU3MTc0NDc3ODM=
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|
BUG: Index of an empty Series cannot be compared to a Timestamp anymore
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| 2020-10-10T21:26:12Z
| 2020-10-10T21:15:42Z
|
CONTRIBUTOR
| null |
- [Y] I have checked that this issue has not already been reported.
- [Y] 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
def test_series_timestamp_comparison():
my_series = pd.Series([], dtype=np.float64)
date = pd.Timestamp('2015-07-15 00:00:00')
res = my_series.index < date
assert res.dtype == bool
assert len(res) == 0
```
#### Problem description
In pandas 1.1.2 the above test passes, while in 1.1.3 it throws an error on line 5:
Error in 1.1.3:
TypeError: '<' not supported between instances of 'numpy.ndarray' and 'Timestamp'
As far as I can tell, this started to occur because of a change in pandas.core.indexes.base._make_comparison_op,
which change was added by this PR: https://github.com/pandas-dev/pandas/pull/36440
My question is if the new behaviour is legit for the above comparison, is it expected?
In 1.1.2 this comparison didn't fail, because it would end up in
```
else:
with np.errstate(all="ignore"):
result = op(self._values, np.asarray(other))
```
which was changed to:
```
elif is_interval_dtype(self.dtype):
with np.errstate(all="ignore"):
result = op(self._values, np.asarray(other))
```
and the else part started to use ops.comparison_op which now fails in the above example (since is_interval_dtype(self.dtype) is false for the above case).
A bit more background: the above comparison is used when subselecting data in the series without knowing whether the input series has data in it or not. So e.g.:
```
my_series[my_series.index < date] = 0.4
```
Apologies if this question has been raised, I couldn't find anything similar in Open Issues.
#### Output of ``pd.show_versions()``
<details>
python : 3.7.4.final.0
pandas : 1.1.3
numpy : 1.19.2
</details>
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MDU6SXNzdWU3MTc1MzkyOTI=
| 36,984
|
BUG: replacing a string with None causes an unexpected forward fill
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| 2020-10-08T17:41:41Z
| 2021-08-13T04:23:35Z
| 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
# Your code here
df = pd.DataFrame(np.zeros((3, 3)))
df.iloc[2, 2] = ''
df = df.replace('', None)
print(df.iloc[2, 2] == '') # prints False
print(df.iloc[2, 2] == df.iloc[1, 2]) # prints True
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
Replacing a string with a None should introduce a None, not forward fill the previous result.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.5.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_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2018.9
dateutil : 2.8.1
pip : 20.2
setuptools : 49.2.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 1.8.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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CLN: move maybe_casted_values from pandas/core/frame.py to pandas/core/dtype/cast.py
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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`
- [ ] whatsnew entry
xref #36876, #27370 (stale PR)
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MDU6SXNzdWU3MTc1ODI2NTU=
| 36,986
|
BUG: ExtensionArray `test_unstack` assumes missing values are nan
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| 0
| 2020-10-08T18:53:08Z
| 2020-10-08T22:01:44Z
| 2020-10-08T22:01:44Z
|
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
Running pytest with this fixture:
```python
import random
import pytest
from pandas_genomics import GenotypeDtype, GenotypeArray, Variant
random.seed(1855)
@pytest.fixture
def data():
"""Length-100 array for this type.
* data[0] and data[1] should both be non missing
* data[0] and data[1] should not be equal
"""
alleles = ['A', 'T', 'G']
variant = Variant(variant_id='rs12345', chromosome='chr1', coordinate=123456, alleles=alleles)
genotypes = [variant.make_genotype('A', 'T'), variant.make_genotype('T', 'T')]
for i in range(98):
genotypes.append(variant.make_genotype(random.choice(alleles), random.choice(alleles)))
return GenotypeArray(values=genotypes)
class TestReshaping(base.BaseReshapingTests):
pass
```
#### Problem description
I'm working on an ExtensionArray as part of a package called [pandas-genomics](https://github.com/HallLab/pandas-genomics).
I've almost got all of the ExtensionArray tests passing. It isn't possible to pass `test_unstack` without altering the test itself. For example, the `series-index1` test case fails this assertion:
```python
expected = ser.astype(object).unstack(level=level)
result = result.astype(object)
self.assert_frame_equal(result, expected)
```
In this case, `result` is
```
A B
a A/T <Missing>
b T/T T/T
```
and `expected` is
```
A B
a A/T NaN
b T/T T/T
```
`<Missing>` is the na_value for my ExtensionDtype.
I've confirmed that this is resolved by using fill_value to supply the na_value
```python
expected = ser.astype(object).unstack(level=level, fill_value=data.dtype.na_value)
```
#### Expected Output
Test passes
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
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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MDExOlB1bGxSZXF1ZXN0NTAwMTIzMzky
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Fix test_unstack
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| 5
| 2020-10-08T18:58:09Z
| 2020-10-09T15:46:31Z
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|
CONTRIBUTOR
| null |
The test was failing if the ExtensionDtype had an na_value that wasn't equivalent to nan
- [x] closes #36986
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: Added indexing views to roadmap
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| 4
| 2020-10-08T19:08:16Z
| 2020-10-19T21:25:20Z
| 2020-10-19T21:24:26Z
|
CONTRIBUTOR
| null |
This adds the indexing copy vs. view proposal to the roadmap (#36195).
Given the length of the proposal, I've included a summary in the main `roadmap.rst`, with a link to the full proposal in a second document. Here's the summary:
The major change from the Google doc shared earlier is the recommendation for "Error on Write" over "Copy on Write". I think the proposal should recommend one over the other, and I think "error on write" is probably the way to go, but I'm fairly uncertain on this point.
cc @pandas-dev/pandas-core
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TYP: generic, series, frame
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| 2020-10-10T16:24:50Z
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- [ ] closes #xxxx
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cc @simonjayhawkins
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TYP: sas, stata, style
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MEMBER
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BUG: MultiIndex loses category after .stack()
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| 3
| 2020-10-08T20:31:37Z
| 2021-03-10T13:55:50Z
| 2021-03-10T13:55:50Z
|
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.
---
#### Code Sample, a copy-pastable example
```
import pandas as pd
columns = pd.MultiIndex.from_tuples(
[('x', 1, 3), ('x', 2, 3), ('y', 1, 3), ('y', 2, 3)], names=['a', 'b', 'c'])
columns = columns.set_levels(
[columns.levels[i].astype('category') for i in range(0, 2)], level=[0, 1])
df = pd.DataFrame([[1, 2, 3, 4]], columns=columns)
dtypes = df.columns.to_frame().dtypes
assert isinstance(dtypes.a, pd.CategoricalDtype)
assert isinstance(dtypes.b, pd.CategoricalDtype)
df2 = df.stack(['a', 'b'])
dtypes2 = df2.index.to_frame().dtypes
assert isinstance(dtypes2.a, pd.CategoricalDtype)
assert isinstance(dtypes2.b, pd.CategoricalDtype) # broken
```
#### Problem description
Categorical types within a MultiIndex should be preserved after calling .stack().
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Backport PR #36946 on branch 1.1.x (REGR: Make DateOffset immutable)
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NONE
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Backport PR #36946: REGR: Make DateOffset immutable
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- [ ] closes #xxxx
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Usually I avoid `strict=False` like the plague, but these are a PITA recently.
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TYP/REF: use OpsMixin for arithmetic methods
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We'll be able to use this in DataFrame too following #36843
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TYP: IntervalIndex.SetopCheck
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MEMBER
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- [ ] closes #xxxx
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Moves us closer to #33455
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TYP: untyped decorators
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xref #33455, when i use the stub @simonjayhawkins suggested there and put `disallow_untyped_decorators = True` into setup.cfg, this PR gets us down to 27 errors, including
6 from pytest.mark.parametrize
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3 from AxisProperty
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| 11
| 2020-10-09T01:04:08Z
| 2020-11-04T04:13:30Z
| 2020-11-04T04:09:17Z
|
CONTRIBUTOR
| null |
- [x] closes #36980
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
REF/BUG: de-duplicate all file handling in `TextReader` by calling `get_handle` in `CParserWrapper`. ~~When `TextReader` gets a string it uses memory mapping (it is given a file object in all other cases).~~
REF/TYP: The second commit adds a new return value to `get_handle` (whether the buffer is wrapped inside a TextIOWrapper: in that case we cannot close it, we need to detach it (and flush it if we wrote to it)). I made `get_handle` return a typed dataclass `HandleArgs` and made sure that all created handles are in `HandleArgs.created_handles` there is no need to close `HandleArgs.handle` (unless it is created by `get_filename_or_buffer`).
I used asserts for mypy when I'm 100% certain about the type, otherwise I added mypy ignore statements.
In the future it might be good to merge `get_handle` and `get_filename_or_buffer`.
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BUG: DataFrame.diff with dt64 and NaTs
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| 2020-10-09T02:20:07Z
| 2020-10-10T18:09:04Z
| 2020-10-10T17:40:57Z
|
MEMBER
| null |
- [x] closes #32441
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
There's a related bug in algos.diff, but the elegant solution goes through the cython code, so im doing that in a separate branch.
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CLN: Move _aggregate and _aggregate_multiple_funcs to core.aggregation
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| 1
| 2020-10-09T02:52:36Z
| 2020-10-11T13:22:02Z
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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`
- [ ] whatsnew entry
Renamed functions by removing underscores and renamed `self` argument to `obj`.
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BUG: Pandas Dataframe is converting float variable to integer
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| 2020-10-09T11:39:23Z
|
NONE
| null |
- [ Done ] I have checked that this issue has not already been reported.
- [ Done ] 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
df_mle.at[words_of_bigram[0], words_of_bigram[1]] = (df_bigram.at[i,'BigramCount']/frequencies.at['Frequency',words_of_bigram[0]])
```
#### Problem description
When I assigned the answer of the division which is a decimal value ( like 0.5) then value stored in the df_mle variable is an integer (like 0).
#### Expected Output
print(df_mle.at[words_of_bigram[0], words_of_bigram[1]])
>>> 0.5
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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VIS: Allow xlabel and ylabel in DataFrame.plot.scatter plot and hexbin plot
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| 4
| 2020-10-09T11:14:05Z
| 2020-10-14T17:54:11Z
| 2020-10-14T17:54:11Z
|
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.
---
#### Code Sample
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
ax = (pd.DataFrame(np.random.randn(100, 2), columns=['A','B'])
.plot.scatter(
x='A',
y='B',
xlabel='my x label',
ylabel='my y label',
)
)
```
#### Problem description
In #34223 (released in 1.1.0), the ability to set `xlabel` and `ylabel` in `DataFrame.plot()` was added. This works fine when calling `plot()` directly or `df.plot.line()`. When calling `df.plot.scatter()` with these args, you still get the index labels as the axis labels, instead of the strings you pass in.
These args are not documented to be incompatible or unavailable on this function, and no error is printed.
#### Expected Output
The plot's axis labels should be the strings provided, "my x label" and "my y label".
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0NTAwNTI3MzU5
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Backport PR #36911: BUG: RollingGroupby not respecting sort=False
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MEMBER
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Backport PR #36911
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NONE
| null |
Backport PR #36950: REGR: Allow positional arguments in DataFrame.agg
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BLD: remove blockslider #34014
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|
MEMBER
| null |
Part of #34014
xref:
https://github.com/ivirshup/pandas/commit/2c17e72a9deffd8e0d8999b88a2e31c0a58c746c
#34997
Impact on groupby
```
before after ratio
[97877442] [a82d3773]
<master^2> <libreduction-cython>
+ 5.07±0.06ms 43.8±1ms 8.65 groupby.Apply.time_scalar_function_single_col
+ 15.5±0.2ms 108±0.6ms 6.97 groupby.Apply.time_scalar_function_multi_col
+ 453±3ms 508±1ms 1.12 groupby.Apply.time_copy_overhead_single_col
+ 1.44±0.03s 1.61±0.01s 1.12 groupby.Apply.time_copy_function_multi_col
- 26.0±0.7ms 23.4±0.2ms 0.90 groupby.Nth.time_frame_nth_any('float64')
```
Impact on index_cached_properties
```
before after ratio
[97877442] [a82d3773]
<master^2> <libreduction-cython>
+ 401±10ns 862±400ns 2.15 index_cached_properties.IndexCache.time_inferred_type('Int64Index')
+ 397±10ns 649±200ns 1.64 index_cached_properties.IndexCache.time_is_unique('Int64Index')
+ 1.01±0.03μs 1.31±0.2μs 1.30 index_cached_properties.IndexCache.time_values('DatetimeIndex')
+ 3.51±0.2μs 4.55±0.8μs 1.29 index_cached_properties.IndexCache.time_shape('CategoricalIndex')
+ 1.97±0.2μs 2.30±0.2μs 1.17 index_cached_properties.IndexCache.time_inferred_type('MultiIndex')
+ 1.75±0.04μs 2.02±0.2μs 1.15 index_cached_properties.IndexCache.time_shape('DatetimeIndex')
- 1.99±0.3μs 1.58±0.2μs 0.79 index_cached_properties.IndexCache.time_values('UInt64Index')
- 2.33±0.6μs 1.77±0.5μs 0.76 index_cached_properties.IndexCache.time_inferred_type('Float64Index')
```
No significant changes on frame_methods
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DEPR: treating bool-containing ObjectBlock as bool
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Add pandas-genomics to the ecosystem list
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Added [as suggested here](https://github.com/pandas-dev/pandas/pull/36987#pullrequestreview-505157583).
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CLN: standardize values coercion in Blocks
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| null |
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MDExOlB1bGxSZXF1ZXN0NTAwNzczNjU3
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REF/TYP: consistent return type for Block.replace
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| 1
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| 2020-10-10T18:08:15Z
| 2020-10-10T17:42:31Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
move some convert logic out of _replace_coerce to avoid an otherwise-duplicated method
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BUG: Incorrect data when modifying a dataframe column with +=
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| 2020-10-09T21:33:01Z
| 2020-10-09T21:32:52Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Get the column as a series and modify inplace with `+=`:
```python
In [1]: import pandas as pd
...: df = pd.DataFrame({"col": [1 ,2 ,3, 4]})
...: s = df["col"]
...: s += 1
...: print(s)
...: print(df["col"])
...: print(df[["col"]])
0 2
1 3
2 4
3 5
Name: col, dtype: int64
0 2
1 3
2 4
3 5
Name: col, dtype: int64
col
0 1
1 2
2 3
3 4
```
Modify the column directly with `+=`:
```python
In [2]: import pandas as pd
...: df = pd.DataFrame({"col": [1 ,2 ,3, 4]})
...: df["col"] += 1
...: print(df["col"])
...: print(df[["col"]])
0 2
1 3
2 4
3 5
Name: col, dtype: int64
col
0 2
1 3
2 4
3 5
```
#### Problem description
The data in the column should be consistent whether accessed as `df["col"]` or `df[["col"]]`.
#### Expected Output
Either `+=` modifies the values in the dataframe column or it doesn't but `df["col"]` and `df[["col"]]` should show the same values.
#### Output of ``pd.show_versions()``
```python
In [13]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
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_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : 0.10.1
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
```
For master branch:
```python
In [1]: import pandas as pd; pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 9787744272c13a1dcbbcdfc7daaae8cc73ac78a3
python : 3.8.5.final.0
...
pandas : 1.2.0.dev0+684.g978774427
...
```
</details>
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DOC: pandas.Index.astype says it raises ValueError instead of TypeError
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CONTRIBUTOR
| null |
#### Location of the documentation
The documentation is located at https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.astype.html
#### Documentation problem
The documentation says " When conversion is impossible, a ValueError exception is raised." However, checking the code it can be seen that a TypeError is raised instead of ValueError.
#### Suggested fix for documentation
Just change ValueError to TypeError in the docstring.
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DOC: pandas.Index.astype says it raises ValueError instead of TypeError
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CONTRIBUTOR
| null |
- [X] closes #37012
- [X] passes pytest
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DOC: Clean merging.rst
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| 2
| 2020-10-09T20:45:44Z
| 2020-10-10T22:46:54Z
| 2020-10-10T22:24:29Z
|
MEMBER
| null |
Closes #36777
|
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Call finalize in Series.str
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| 1
| 2020-10-09T20:56:04Z
| 2020-10-11T20:09:39Z
| 2020-10-10T22:25:59Z
|
CONTRIBUTOR
| null |
xref #28283
|
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ERR: Better error message for MultiIndex.astype
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| 1
| 2020-10-09T22:48:18Z
| 2020-10-10T02:36:19Z
| 2020-10-10T02:30:05Z
|
MEMBER
| null |
Error message could be nicer I think
```python
TypeError: Setting <class 'pandas.core.indexes.multi.MultiIndex'> dtype to anything other than object is not supported
```
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|
REF/TYP: define NDFrame numeric methods non-dynamically
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| 2020-10-10T01:42:41Z
| 2020-10-10T16:31:15Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @simonjayhawkins along with #36989 this gets most of the generic.py mypy complaints (notwithstanding complaints about `cls.foo = foo` that can be addressed by changing to `setattr(cls, "foo", foo)`.
it'd be nice to have a less-verbose way of pinning the docstrings. i haven't had any luck so far at making that work.
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MDExOlB1bGxSZXF1ZXN0NTAwODk0NjA0
| 37,018
|
CLN: require td64 in TimeDeltaBlock
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| 2020-10-10T02:36:55Z
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
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only one place where the check currently fails, fixed in core.missing
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