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| 742,725,927
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MDExOlB1bGxSZXF1ZXN0NTIwNzk5MDkw
| 37,819
|
Deprecate partial slicing of unordered DatetimeIndex when both keys are not present
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| 1
| 2020-11-13T19:13:00Z
| 2020-11-15T17:41:54Z
| 2020-11-15T17:39:05Z
|
MEMBER
| null |
- [x] xref #18531
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
cc @jreback @jorisvandenbossche
This would deprecate #37796 before removing it.
|
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MDU6SXNzdWU3NDI3NTAzODY=
| 37,820
|
BUG: MultiIndex.drop does not raise if labels are partially found
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| 0
| 2020-11-13T19:59:51Z
| 2020-11-26T22:38:05Z
| 2020-11-26T22:38:05Z
|
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
```python
mi = MultiIndex.from_tuples([(1, 2), (2, 2), (3, 2)])
mi.drop([1, 4], level=0) # does not raise
mi.drop(4, level=0) # raises
```
#### Problem description
The docs say
```
Raises
KeyError
If not all of the labels are found in the selected axis
```
I think both statements should raise ``KeyError``
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.drop.html
#### Expected Output
Both are raising ``KeyError: 'labels [4] not found in level'``
For regular Indexes this holds True
```
index = Index([1, 2])
index.drop([1, 3]) # Raises
```
#### Output of ``pd.show_versions()``
<details>
master
</details>
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MDU6SXNzdWU3NDI3NjAzODk=
| 37,821
|
BUG: Groupby.last accepts min_count but implementation raises
|
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| 0
| 2020-11-13T20:18:45Z
| 2020-11-26T15:36:26Z
| 2020-11-26T15:36:26Z
|
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
```python
df = DataFrame({"a": [1, 2, 3, 4, 5, 6]})
df.groupby(level=0).last(min_count=1)
```
#### Problem description
This raises
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2020.2/scratches/scratch_4.py", line 97, in <module>
print(df.groupby(level=0).last(min_count=1))
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/groupby.py", line 1710, in last
return self._agg_general(
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/groupby.py", line 1032, in _agg_general
result = self._cython_agg_general(
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/generic.py", line 1018, in _cython_agg_general
agg_mgr = self._cython_agg_blocks(
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/generic.py", line 1116, in _cython_agg_blocks
new_mgr = data.apply(blk_func, ignore_failures=True)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 425, in apply
applied = b.apply(f, **kwargs)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/blocks.py", line 368, in apply
result = func(self.values, **kwargs)
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/generic.py", line 1067, in blk_func
result, _ = self.grouper.aggregate(
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 594, in aggregate
return self._cython_operation(
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 547, in _cython_operation
result = self._aggregate(result, counts, values, codes, func, min_count)
File "/home/developer/PycharmProjects/pandas/pandas/core/groupby/ops.py", line 608, in _aggregate
agg_func(result, counts, values, comp_ids, min_count)
File "pandas/_libs/groupby.pyx", line 906, in pandas._libs.groupby.group_last
AssertionError: 'min_count' only used in add and prod
Process finished with exit code 1
```
#### Expected Output
I would expect, that this function does not accept ``min_count`` as input (similar to ``Resample.last``) and raises the same error as described in #37768
Additonally the docs need adjustments, because ``min_count`` is documented. This probably holds true for most of the functions mentioned in https://github.com/pandas-dev/pandas/issues/37768#issuecomment-727009192 too.
#### Output of ``pd.show_versions()``
<details>
master
</details>
|
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MDExOlB1bGxSZXF1ZXN0NTIwODQ1OTI4
| 37,822
|
CI: pin build to use pyarrow==1.0
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| 2
| 2020-11-13T20:51:04Z
| 2020-11-14T13:55:01Z
| 2020-11-14T02:22:23Z
|
MEMBER
| null |
closes #37306
@jorisvandenbossche picked build at random. we can discuss here what the preferences are? cc @jreback
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BUG: Allow custom error values in parse_dates argument of read_sql like functions (GH35185)
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| 4
| 2020-11-13T21:31:54Z
| 2020-12-14T01:41:53Z
| 2020-12-14T01:41:44Z
|
CONTRIBUTOR
| null |
- [x] closes #35185
- [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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MDExOlB1bGxSZXF1ZXN0NTIwODkwNjYz
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CLN: avoid incorrect usages of values_for_argsort
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MDExOlB1bGxSZXF1ZXN0NTIwOTQyNDE0
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CLN: Old shell scripts
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MEMBER
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The scripts were not documented and don't seem to be used anywhere.
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CI: update 3.9 packages as they become available
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| 2
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| 2021-07-09T13:58:43Z
| 2021-07-09T13:58:43Z
|
CONTRIBUTOR
| null |
we now have 3.9 support via conda-forge: https://github.com/pandas-dev/pandas/pull/37039
we should periodically update the tested packages as they become available.
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BUG: DataFrame reductions inconsistent with Series counterparts
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MEMBER
| null |
- [x] closes #36076
- [x] closes #28949
- [x] closes #21020
- [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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CLN: File handling for PyArrow parquet
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CONTRIBUTOR
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
De-duplicate file handling of PyArrows' parquet.
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Backport PR #37801 on branch 1.1.x: REGR: SeriesGroupBy where index has a tuple name fails
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MEMBER
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Backport PR #37801
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BUG: MultiIndex.drop does not raise if labels are partially found
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| 3
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| 2020-11-27T01:02:10Z
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CONTRIBUTOR
| null |
- [x] closes #37820
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: pandas.read_excel() - AttributeError: 'ElementTree' object has no attribute 'getiterator'
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| 2
| 2020-11-14T11:48:41Z
| 2020-11-14T13:49:54Z
| 2020-11-14T13:49:54Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pandas.read_excel("data.xlsx")
```
#### Problem description
Running the above code raises the exception below:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-524fa4fa6612> in <module>
1 path_prefix: str = "data/raw/"
----> 2 df = pandas.read_excel(f"{path_prefix}data.xlsx")
..lib/python3.9/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
294 )
295 warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
--> 296 return func(*args, **kwargs)
297
298 return wrapper
..lib/python3.9/site-packages/pandas/io/excel/_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols)
302
303 if not isinstance(io, ExcelFile):
--> 304 io = ExcelFile(io, engine=engine)
305 elif engine and engine != io.engine:
306 raise ValueError(
..lib/python3.9/site-packages/pandas/io/excel/_base.py in __init__(self, path_or_buffer, engine)
865 self._io = stringify_path(path_or_buffer)
866
--> 867 self._reader = self._engines[engine](self._io)
868
869 def __fspath__(self):
..lib/python3.9/site-packages/pandas/io/excel/_xlrd.py in __init__(self, filepath_or_buffer)
20 err_msg = "Install xlrd >= 1.0.0 for Excel support"
21 import_optional_dependency("xlrd", extra=err_msg)
---> 22 super().__init__(filepath_or_buffer)
23
24 @property
..lib/python3.9/site-packages/pandas/io/excel/_base.py in __init__(self, filepath_or_buffer)
351 self.book = self.load_workbook(filepath_or_buffer)
352 elif isinstance(filepath_or_buffer, str):
--> 353 self.book = self.load_workbook(filepath_or_buffer)
354 elif isinstance(filepath_or_buffer, bytes):
355 self.book = self.load_workbook(BytesIO(filepath_or_buffer))
..lib/python3.9/site-packages/pandas/io/excel/_xlrd.py in load_workbook(self, filepath_or_buffer)
35 return open_workbook(file_contents=data)
36 else:
---> 37 return open_workbook(filepath_or_buffer)
38
39 @property
..lib/python3.9/site-packages/xlrd/__init__.py in open_workbook(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)
128 if 'xl/workbook.xml' in component_names:
129 from . import xlsx
--> 130 bk = xlsx.open_workbook_2007_xml(
131 zf,
132 component_names,
..lib/python3.9/site-packages/xlrd/xlsx.py in open_workbook_2007_xml(zf, component_names, logfile, verbosity, use_mmap, formatting_info, on_demand, ragged_rows)
810 del zflo
811 zflo = zf.open(component_names['xl/workbook.xml'])
--> 812 x12book.process_stream(zflo, 'Workbook')
813 del zflo
814 props_name = 'docprops/core.xml'
..lib/python3.9/site-packages/xlrd/xlsx.py in process_stream(self, stream, heading)
264 self.tree = ET.parse(stream)
265 getmethod = self.tag2meth.get
--> 266 for elem in self.tree.iter() if Element_has_iter else self.tree.getiterator():
267 if self.verbosity >= 3:
268 self.dump_elem(elem)
AttributeError: 'ElementTree' object has no attribute 'getiterator'
```
#### Expected Output
`pandas.DataFrame`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/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 : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTIxMDA0NTA3
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Return view for xs when droplevel=False with regular Index
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| 2
| 2020-11-14T13:07:24Z
| 2020-11-15T17:26:38Z
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|
MEMBER
| null |
- [x] xref #37776
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Is there a better way to test, if the object is a view?
cc @jbrockmendel
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| 6
| 2020-11-14T16:17:40Z
| 2020-11-19T02:07:37Z
| 2020-11-19T02:07:37Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#replace.FillNa.time_fillna?python=3.8&Cython=0.29.21&p-inplace=False&commits=2eb353063d6bc4f2ed22e9943d26465e284b8393-68f53cd9e556698fe786aefe90466f233eb55841
https://github.com/pandas-dev/pandas/compare/2eb353063d6bc4f2ed22e9943d26465e284b8393...68f53cd9e556698fe786aefe90466f233eb55841
- [ ] 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.
---
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MDExOlB1bGxSZXF1ZXN0NTIxMDMxMTI2
| 37,834
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BUG: Parse missing values using read_json with dtype=False to NaN instead of None (GH28501)
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CONTRIBUTOR
| null |
- [x] closes #28501
- [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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MDExOlB1bGxSZXF1ZXN0NTIxMDM3MTk5
| 37,835
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COMPAT: should an empty string match a format (or just be NaT)
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| 2020-11-14T17:39:54Z
| 2020-11-19T18:58:21Z
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CONTRIBUTOR
| null |
- [x] closes #13044
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NTIxMDM3NDE1
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Backport PR #37787 on branch 1.1.x (Fix regression for loc and __setitem__ when one-dimensional tuple was given for MultiIndex)
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Backport PR #37787: Fix regression for loc and __setitem__ when one-dimensional tuple was given for MultiIndex
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CLN: remove unnecessary close calls and add a few necessary ones
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| 2
| 2020-11-14T17:50:45Z
| 2020-11-15T17:29:32Z
| 2020-11-15T17:07:59Z
|
CONTRIBUTOR
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Follow up to #37639. Close file handles as early as possible.
Remove `close` calls that are already within a try-except/finally block that closes the file handles.
Add `close` call for `read_json` when we read the entire content in one go.
Add close call in `read_csv` when reaching the end of the iterator.
Put `to/read_excel` in a try-finally block to make sure that file handles are closed even in case of an exception.
|
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MDExOlB1bGxSZXF1ZXN0NTIxMDM4NzAw
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Fix cases of inconsistent namespacing in tests
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CONTRIBUTOR
| null |
Sometimes e.g. Series and pd.Series are used
in the same test file. This fixes some of these
cases, generally by using the explicitly
imported class.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTIxMDQzMDI4
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|
DOC: section in indexing user guide to show use of np.where
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Add a section in the user guide to illustrate the use of `np.where` and `np.select` to enlarge a dataframe conditionally. The example is taken from SO, as mentioned in [this issue](https://github.com/MarcoGorelli/PyDataGlobal2020-sprint/issues/5#issuecomment-727241262).
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TST: Add test for setting item using python list (#19406)
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| null |
- [X] closes #19406
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [NA] whatsnew entry
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CI: Skip test when no xlwt or openpyxl
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CONTRIBUTOR
| null |
Ref: https://github.com/pandas-dev/pandas/pull/37818
Seeing error in CI: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=47726&view=logs&j=acc1347f-8235-55b0-95c7-0e2189e61659&t=486f34f1-eda6-516a-fc5d-d8195128dacd
Skip logic same as `test_to_excel` in this file
cc @jreback
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MDExOlB1bGxSZXF1ZXN0NTIxMDQ2NDgw
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Fix cases of inconsistent namespacing in tests
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| 2020-11-15T09:09:07Z
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CONTRIBUTOR
| null |
Additional fixes for MarcoGorelli/PyDataGlobal2020-sprint#1.
Continues the work of lpkirwin in #37838.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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namespace consistency for test_dtype.py
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- [x ] references #37838
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: add examples to docstrings of assert_ functions
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CONTRIBUTOR
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: SS01 docstrings errors fixed
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CONTRIBUTOR
| null |
xref #27977
- [ ] closes part of [MarcoGorelli/PyDataGlobal2020-sprint#4](https://github.com/MarcoGorelli/PyDataGlobal2020-sprint/issues/4)
- [ ] tests added / passed
- [X] passes `black pandas`
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MDExOlB1bGxSZXF1ZXN0NTIxMDQ4ODYw
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Added docs to fix GL08 errors in CustomBusinessHours
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| 2020-11-14T19:18:58Z
| 2020-11-17T01:32:36Z
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| null |
- [ ] xref #27977
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Currently even after adding the docstrings the command `python scripts/validate_docstrings.py --errors=GL08` does not pick up the changes.
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MDExOlB1bGxSZXF1ZXN0NTIxMDQ5ODgz
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DOC: add unqiue value counts example to groupyby guide
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| 5
| 2020-11-14T19:28:09Z
| 2020-11-25T13:30:39Z
| 2020-11-25T13:30:36Z
|
CONTRIBUTOR
| null |
Co-authored-by: Abdulelah Almesfer <28743265+abdulelahsm@users.noreply.github.com>
Co-authoerd-by: Abdulellah Alnumay <33042538+Abo7atm@users.noreply.github.com>
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Adding an example to the groupby user guide. The example showcases the usage of ```nunique``` aggregate function. This is from the PyData Global sprint, from [this issue](https://github.com/MarcoGorelli/PyDataGlobal2020-sprint/issues/5). The example is taken from [StackOverflow](https://stackoverflow.com/questions/15411158/pandas-countdistinct-equivalent).
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Updated script to check inconsistent pandas namespace
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| 2020-11-14T19:43:11Z
| 2020-11-18T19:04:43Z
| 2020-11-18T19:04:37Z
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CONTRIBUTOR
| null |
- [ ] closes #37838
- [ ] tests added / passed
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- [ ] whatsnew entry
Updates the script for inconsistent namespace usage. To be merged after all the errors are fixed with the updated script. The script is described here : https://github.com/MarcoGorelli/PyDataGlobal2020-sprint/issues/1 by @MarcoGorelli
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Backport PR #37787 on branch 1.1.x (Fix regression for loc and __setitem__ when one-dimensional tuple was given for MultiIndex)
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| 2020-11-14T19:48:25Z
| 2020-11-15T11:16:31Z
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MEMBER
| null |
Backport #37787
``frame_or_series`` not know here.
cc @simonjayhawkins
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MDU6SXNzdWU3NDMwNjg0NTY=
| 37,850
|
BUG: any and all are much slower than min and max for GroupBy.transform
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[] | null | 2
| 2020-11-14T19:53:14Z
| 2021-08-14T05:11:14Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
pd.__version__
N=10**6
z=pd.DataFrame({'a': np.random.randint(1, 10, N), 'b': np.random.randint(1, 100000, N)})
z['c'] = (z.b % 2).astype(bool)
%timeit z.groupby('a').c.transform(max)
%timeit z.groupby('a').c.transform(min)
%timeit z.groupby('a').c.transform(any)
%timeit z.groupby('a').c.transform(all)
```
#### Problem description
Logically any or all on bools should be faster or as fast as max or min which give exactly the same results.
```
In [7]: %timeit z.groupby('a').c.transform(max)
11.8 ms ± 86.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [8]: %timeit z.groupby('a').c.transform(min)
11.8 ms ± 78.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [9]: %timeit z.groupby('a').c.transform(any)
99.7 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [10]: %timeit z.groupby('a').c.transform(all)
99.8 ms ± 665 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
#### Expected Output
Times for any and all are lower or same as for max and min
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.18-200.fc32.x86_64
Version : #1 SMP Mon Nov 2 19:49:11 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.5.2
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.1.2
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTIxMDUyNjk5
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CLN: remove no-longer-used ignore_failures in frame_apply
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MDExOlB1bGxSZXF1ZXN0NTIxMDUyODgy
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making namespace usage more consistent
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| 2020-11-16T10:00:16Z
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|
CONTRIBUTOR
| null |
- [x] closes #37838
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: indexing plane_indexer -> pi
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| 0
| 2020-11-14T21:30:51Z
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MEMBER
| null |
We know plane_indexer is a 1-tuple, can simplify a bit.
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| 743,085,221
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MDExOlB1bGxSZXF1ZXN0NTIxMDY0Mzk1
| 37,854
|
CLN: Another unused script
|
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| 3
| 2020-11-14T21:46:32Z
| 2020-11-17T17:09:20Z
| 2020-11-17T17:09:16Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/37825
Another script that looks unused and undocumented
|
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MDExOlB1bGxSZXF1ZXN0NTIxMDY1MDE1
| 37,855
|
DOC: Add a reference to window.rst in computation.rst
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| 0
| 2020-11-14T21:52:54Z
| 2020-11-15T19:44:11Z
| 2020-11-15T17:25:42Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/37575#issuecomment-727235030
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| 743,090,812
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MDExOlB1bGxSZXF1ZXN0NTIxMDY4MjUy
| 37,856
|
DEP: update Versioneer
|
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| 2
| 2020-11-14T22:25:58Z
| 2020-12-05T18:50:45Z
| 2020-11-17T12:55:16Z
|
MEMBER
| null |
bump versioneer from 0.15 to 0.19
```
(pandas-dev) fangchenli@Fangchens-MacBook-Pro pandas-fangchenli % python setup.py version
running version
keywords are unexpanded, not using
got version from VCS {'version': '1.2.0.dev0+1185.g82894a4b6', 'full-revisionid': '82894a4b6329b8af74bb9c593858453a01b91bf6', 'dirty': False, 'error': None, 'date': '2020-11-14T16:24:02-0600'}
Version: 1.2.0.dev0+1185.g82894a4b6
full-revisionid: 82894a4b6329b8af74bb9c593858453a01b91bf6
dirty: False
date: 2020-11-14T16:24:02-0600
```
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MDExOlB1bGxSZXF1ZXN0NTIxMTI4NTEy
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|
CLN: remove something.xlsx
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| 2020-11-15T01:31:32Z
| 2020-11-15T04:17:10Z
| 2020-11-15T04:14:12Z
|
CONTRIBUTOR
| null |
`test_register_writer` in `pandas/tests/io/excel/test_writers.py` wasn't expecting that `something.xls` and `something.xlsx` will ever be created.
Since #37639, `ExcelWriter` opens the file which will create it.
|
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CI: xfail a windows test
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| 1
| 2020-11-15T02:41:45Z
| 2020-11-15T15:15:14Z
| 2020-11-15T10:22:41Z
|
MEMBER
| null |
At least we can fix a subset of the windows builds.
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QST: Is the behavior between "Index.astype" and "Series.astype" different ??
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| 2020-11-15T02:48:48Z
| 2020-11-16T07:48:12Z
| 2020-11-16T01:16:30Z
|
NONE
| null |
#### Question about pandas
When I cast the type with `astype` for `Index` or `Series`,
I noticed that their behavior for `bool` casting is slightly different.
```python
>>> pd.Series([1, None]).astype(bool)
0 True
1 True
dtype: bool
>>> pd.Index([1, None]).astype(bool)
Index([True, False], dtype='object')
```
As shown above, `None` is casted `True` from `Series.astype`, but casted `False` from `Index.astype`.
Is this normal for some reason??
I used pandas 1.1.4.
Thanks :)
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CLN: use putmask_simple instead of putmask
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BUG: Prevent Series.isin from unwantedly casting isin values from float to integer (GH21804)
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|
CONTRIBUTOR
| null |
- [x] closes #21804
- [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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CLN/TYP: pandas/io/formats/excel.py
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| 2020-12-03T07:54:23Z
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|
MEMBER
| null |
- [ ] xref #37715
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- Handle two type ignore items in ``pandas/io/formats/excel.py``
- De-duplicate ``has_aliases`` variable by extracting property
- Add typing for iterators yielding ``ExcelCell``
- Use named arguments for ``ExcelCell`` on some occasions (in two places de-duplicate by updating kwargs inplace)
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API/BUG: DatetimeIndex.argsort does not match DatetimeArray.argsort
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| 2
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| 2020-12-17T13:50:58Z
| 2020-12-17T13:50:58Z
|
MEMBER
| null |
The DatetimeIndex returns `self.asi8.argsort(*args, **kwargs)`, while the DatetimeArray uses the M8[ns] values. This puts NaTs at the front for DTI and the end for DTA.
Changing the DTI method (actually the DTI/TDI/PI methods) to match their array counterparts breaks 12 tests, 11 of them resample, 1 test_grouping.
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ENH: Implement cross method for Merge Operations
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| 13
| 2020-11-15T16:35:06Z
| 2020-11-26T21:12:43Z
| 2020-11-26T00:31:47Z
|
MEMBER
| null |
- [x] closes #5401
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is a first draft of a ``cross`` method for merge operations. As suggested by @jreback I added a new column and dispatched to ``inner`` as a first naive implementation. I am currently wondering, if there would be a better place to adjust the arguments than the places I selected. I wanted to avoid to modify ``self`` in some method called within the constructor, so i modified the inputs before adding the parameters to ``self``. This currently does not support the ``cross`` method for join. If we get a consensus where to put the modifications, I would add support for ``join``. Also have to add more tests probably and something to the userguide
Have to look into the performance afterwards. Copies of the Left and Right frames may be a big performance hit for bigger frames?
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TYP: NDFrame sort_values signature
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| 2020-11-15T17:08:21Z
| 2021-01-10T23:41:00Z
| 2021-01-10T23:40:49Z
|
MEMBER
| null |
#### NDFrame sort_values and inheritance
The question is related to method ``sort_values`` of ``NDFrame``, ``DataFrame`` and ``Series``.
When trying to close one issue in (#37715) below
```
pandas/core/frame.py:5361: def sort_values( # type: ignore[override]
```
I found out that the function signatures for ``NDFrame`` and ``DataFrame`` do not match because of the argument ``by``.
That was the cause of the mypy error.
For ``Series.sort_values`` it is reasonable not to have ``by``, but for ``DataFrame`` it is mandatory.
So, the function signatures for ``Series`` and ``DataFrame`` must be different and should not share the common interface.
Presumably, currently the method ``NDFrame.sort_values`` only stores the docstring for ``DataFrame.sort_values``.
It is reasonable to move the docstring to ``DataFrame.sort_values`` and completely remove abstract method ``NDFrame.sort_values``?
This will solve the problem with incorrect overriding.
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| 21
| 2020-11-15T17:22:18Z
| 2020-12-03T19:11:39Z
| 2020-11-29T19:11:30Z
|
CONTRIBUTOR
| null |
Membership checks on ExtensionArrays containing `NA` values raises ValueError in some circumstances (but not in other):
```python
>>> arr1 = pd.array(["a", pd.NA])
>>> arr2 = pd.array([pd.NA, "a"])
>>> "a" in arr1
True # ok
>>> "a" in arr2
TypeError: boolean value of NA is ambiguous # not ok
>>> pd.NA in arr1
TypeError: boolean value of NA is ambiguous # not ok
>>> pd.NA in arr2
True # ok
```
So overall quite random failures. This PR fixes this problem by adding a custom `__contains__` method on `ExtensionArray`.
I assume that we want `pd.NA in arr1` to keep returning True. Note however that `np.nan in np.array([np.nan])` return False, so pandas' behaviour is different.
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REF: prepare dataframe info for series info
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| 2
| 2020-11-15T18:53:36Z
| 2020-12-03T07:57:23Z
| 2020-11-24T14:09:34Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Precursor for #37320
Refactor ``pandas/io/formats/info.py`` to simplify further introduction of ``Series.info``.
Basically what is done here:
- Move docstring to ``BaseInfo`` from ``DataFrameInfo`` - supposedly will be shared with ``SeriesInfo``
- Move ``col_count``, ``ids`` from ``BaseInfo`` to ``DataFrameInfo`` (will not be relevant to ``SeriesInfo``)
- Move ``memory_usage_bytes`` to ``DataFrameInfo`` (will have different implementation for ``SeriesInfo``)
- Extract ``InfoPrinterAbstract``. Concrete classes will differ by the ``_create_table_builder``
- Move common methods and properties from ``DataFrameTableBuilder`` to ``TableBuilderAbstract``
- Extract ``TableBuilderVerboseMixin`` from ``DataFrameTableBuilderVerbose`` as the generator functions there will be used for verbose series info builders
I suggest moving the substitution decorator for the docstring from ``pandas/core/frame.py`` to ``pandas/io/formats/info.py`` later on in a separate PR.
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MDExOlB1bGxSZXF1ZXN0NTIxMjQ3OTg1
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ENH/POC: ExtensionIndex for arbitrary EAs
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| 2020-11-15T19:39:27Z
| 2021-10-11T17:40:30Z
| 2021-03-13T21:37:07Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Currently 4 tests failing locally bc lib.infer_dtype raises on DecimalArray and JsonArray (xref #37367)
Not _remotely_ enough testing.
cc @jorisvandenbossche
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ENH: Add support for min_count keyword for Resample and Groupby functions
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| 7
| 2020-11-15T19:50:09Z
| 2020-11-26T16:03:45Z
| 2020-11-26T15:36:26Z
|
MEMBER
| null |
- [x] closes #37768
- [x] closes #37821
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This would add support for the documented keyword ```min_count``. If we decide to move forward with this, we have to decide what to do in the following case:
```
df = DataFrame({"a": [1] * 3, "b": [np.nan] * 3})
result = df.groupby("a").func(min_count=0)
```
``first`` and ``last`` return ``0.0`` which is fine I think and consistent with ``sum``, but ``min`` returns ``inf`` while ``max`` returns ``-inf``.
Default parameter is set to ``1`` to avoid backwards incompatible changes. We should keep this at least until 2.0
If we decide to not implement ``min_count`` here, we should adjust the docs and the function signatures for the groupby functions.
cc @rhshadrach
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REF: share IntervalIndex._check_method with CategoricalIndex
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MDExOlB1bGxSZXF1ZXN0NTIxMjUzNzE4
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REF: share _simple_new
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| 2020-11-15T20:22:34Z
| 2020-11-17T03:14:50Z
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|
MEMBER
| null |
@simonjayhawkins two questions: 1) is there a nice way to annotate the `values` arg (maybe a Protocol thing?) and 2) is the use of `cls.__annotations__["_data"]` an egregious antipattern?
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MDExOlB1bGxSZXF1ZXN0NTIxMjU2OTg4
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|
BUG: __getitem__ raise blank KeyError for IntervalIndex and missing keys
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| 2020-11-15T20:46:31Z
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MEMBER
| null |
- [x] closes #27365
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Without ``tolist`` error would look like ``KeyError: array([6])``
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BUG in reindex raises IndexingError when df is empty sometimes
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| 1
| 2020-11-15T21:28:31Z
| 2020-11-17T08:35:37Z
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MEMBER
| null |
- [x] closes #27315
- [x] tests added / passed
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Build on top of #27326
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MDU6SXNzdWU3NDMzNDkyNjI=
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|
DOC: Fix EX01 issues in docstrings
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| 2020-11-15T21:29:59Z
| 2021-03-22T17:03:32Z
| null |
CONTRIBUTOR
| null |
#### Location of the documentation
In various pandas modules. See list below for the complete list of functions raising EX01 errors.
#### Documentation problem
The listed functions are missing the example section, i.e. code EX01.
#### Suggested fix for documentation
As proposed in #27977, I attach the list of raised errors below. Use this thread to comment on which function/module you will be working so we don't duplicate work. Please mention this issue in your PR so that the list below can be updated.
#### Current errors:
(checked means someone is working on them)
- [X] pandas.Timestamp.asm8
- [ ] pandas.Timestamp.dayofweek
- [x] pandas.Timestamp.day_of_week
- [ ] pandas.Timestamp.dayofyear
- [x] pandas.Timestamp.day_of_year
- [x] pandas.Timestamp.days_in_month
- [ ] pandas.Timestamp.daysinmonth
- [x] pandas.Timestamp.is_leap_year
- [x] pandas.Timestamp.is_month_end
- [x] pandas.Timestamp.is_month_start
- [x] pandas.Timestamp.is_quarter_end
- [x] pandas.Timestamp.is_quarter_start
- [x] pandas.Timestamp.is_year_end
- [x] pandas.Timestamp.is_year_start
- [x] pandas.Timestamp.quarter
- [ ] pandas.Timestamp.resolution
- [x] pandas.Timestamp.tz
- [x] pandas.Timestamp.week
- [ ] pandas.Timestamp.weekofyear
- [ ] pandas.Timestamp.astimezone
- [x] pandas.Timestamp.ceil
- [x] pandas.Timestamp.combine
- [ ] pandas.Timestamp.ctime
- [ ] pandas.Timestamp.date
- [x] pandas.Timestamp.day_name
- [ ] pandas.Timestamp.dst
- [x] pandas.Timestamp.floor
- [ ] pandas.Timestamp.freqstr
- [x] pandas.Timestamp.fromordinal
- [x] pandas.Timestamp.fromtimestamp
- [ ] pandas.Timestamp.isocalendar
- [ ] pandas.Timestamp.isoweekday
- [x] pandas.Timestamp.month_name
- [x] pandas.Timestamp.normalize
- [x] pandas.Timestamp.now
- [x] pandas.Timestamp.replace
- [x] pandas.Timestamp.round
- [x] pandas.Timestamp.strftime
- [ ] pandas.Timestamp.strptime
- [ ] pandas.Timestamp.time
- [x] pandas.Timestamp.timestamp
- [ ] pandas.Timestamp.timetuple
- [ ] pandas.Timestamp.timetz
- [ ] pandas.Timestamp.to_datetime64
- [x] pandas.Timestamp.to_numpy
- [x] pandas.Timestamp.to_julian_date
- [x] pandas.Timestamp.to_period
- [x] pandas.Timestamp.to_pydatetime
- [x] pandas.Timestamp.today
- [ ] pandas.Timestamp.toordinal
- [x] pandas.Timestamp.tz_convert
- [x] pandas.Timestamp.tz_localize
- [ ] pandas.Timestamp.tzname
- [x] pandas.Timestamp.utcfromtimestamp
- [x] pandas.Timestamp.utcnow
- [ ] pandas.Timestamp.utcoffset
- [ ] pandas.Timestamp.utctimetuple
- [x] pandas.Timestamp.weekday
- [ ] pandas.arrays.DatetimeArray
- [ ] pandas.Timedelta
- [ ] pandas.Timedelta.components
- [ ] pandas.Timedelta.days
- [ ] pandas.Timedelta.max
- [ ] pandas.Timedelta.microseconds
- [ ] pandas.Timedelta.min
- [ ] pandas.Timedelta.resolution
- [ ] pandas.Timedelta.seconds
- [ ] pandas.Timedelta.view
- [ ] pandas.Timedelta.ceil
- [ ] pandas.Timedelta.floor
- [ ] pandas.Timedelta.round
- [ ] pandas.Timedelta.to_pytimedelta
- [ ] pandas.Timedelta.to_timedelta64
- [ ] pandas.Timedelta.to_numpy
- [ ] pandas.Timedelta.total_seconds
- [ ] pandas.arrays.TimedeltaArray
- [ ] pandas.Period
- [ ] pandas.Period.asfreq
- [ ] pandas.Period.to_timestamp
- [ ] pandas.arrays.PeriodArray
- [ ] pandas.Interval.closed
- [ ] pandas.Interval.closed_left
- [ ] pandas.Interval.closed_right
- [ ] pandas.Interval.left
- [ ] pandas.Interval.length
- [ ] pandas.Interval.mid
- [ ] pandas.Interval.open_left
- [ ] pandas.Interval.open_right
- [ ] pandas.Interval.right
- [ ] pandas.arrays.IntervalArray.left
- [ ] pandas.arrays.IntervalArray.right
- [ ] pandas.arrays.IntervalArray.closed
- [ ] pandas.arrays.IntervalArray.mid
- [ ] pandas.arrays.IntervalArray.length
- [ ] pandas.arrays.IntervalArray.is_non_overlapping_monotonic
- [ ] pandas.arrays.IntervalArray.from_arrays
- [ ] pandas.arrays.IntervalArray.to_tuples
- [ ] pandas.Int8Dtype
- [ ] pandas.Int16Dtype
- [ ] pandas.Int32Dtype
- [ ] pandas.Int64Dtype
- [ ] pandas.UInt8Dtype
- [ ] pandas.UInt16Dtype
- [ ] pandas.UInt32Dtype
- [ ] pandas.UInt64Dtype
- [ ] pandas.CategoricalDtype.categories
- [ ] pandas.CategoricalDtype.ordered
- [ ] pandas.Categorical.dtype
- [ ] pandas.Categorical.categories
- [ ] pandas.Categorical.ordered
- [ ] pandas.Categorical.codes
- [ ] pandas.Categorical.__array__
- [ ] pandas.SparseDtype
- [ ] pandas.DatetimeTZDtype.unit
- [ ] pandas.DatetimeTZDtype.tz
- [ ] pandas.PeriodDtype.freq
- [ ] pandas.IntervalDtype.subtype
- [ ] pandas.io.formats.style.Styler
- [ ] pandas.io.formats.style.Styler.from_custom_template
- [ ] pandas.io.formats.style.Styler.env
- [ ] pandas.io.formats.style.Styler.template
- [ ] pandas.io.formats.style.Styler.loader
- [ ] pandas.io.formats.style.Styler.applymap
- [ ] pandas.io.formats.style.Styler.where
- [ ] pandas.io.formats.style.Styler.set_precision
- [ ] pandas.io.formats.style.Styler.set_caption
- [ ] pandas.io.formats.style.Styler.set_uuid
- [ ] pandas.io.formats.style.Styler.set_na_rep
- [ ] pandas.io.formats.style.Styler.clear
- [ ] pandas.io.formats.style.Styler.highlight_max
- [ ] pandas.io.formats.style.Styler.highlight_min
- [ ] pandas.io.formats.style.Styler.highlight_null
- [ ] pandas.io.formats.style.Styler.background_gradient
- [ ] pandas.io.formats.style.Styler.bar
- [ ] pandas.io.formats.style.Styler.render
- [ ] pandas.io.formats.style.Styler.export
- [ ] pandas.io.formats.style.Styler.use
- [ ] pandas.describe_option
- [ ] pandas.reset_option
- [ ] pandas.get_option
- [ ] pandas.set_option
- [ ] pandas.testing.assert_series_equal
- [ ] pandas.testing.assert_index_equal
- [ ] pandas.testing.assert_extension_array_equal
- [ ] pandas.errors.AccessorRegistrationWarning
- [ ] pandas.errors.EmptyDataError
- [ ] pandas.errors.InvalidIndexError
- [ ] pandas.errors.MergeError
- [ ] pandas.errors.NullFrequencyError
- [ ] pandas.errors.NumbaUtilError
- [ ] pandas.errors.OutOfBoundsTimedelta
- [ ] pandas.errors.ParserError
- [ ] pandas.errors.PerformanceWarning
- [ ] pandas.errors.UnsortedIndexError
- [ ] pandas.errors.UnsupportedFunctionCall
- [ ] pandas.api.types.- [ ] pandas.dtype
- [ ] pandas.api.types.is_bool
- [ ] pandas.api.types.is_complex
- [ ] pandas.api.types.is_float
- [ ] pandas.api.types.is_integer
- [ ] pandas.show_versions
- [ ] pandas.core.window.rolling.Rolling.max
- [ ] pandas.core.window.rolling.Rolling.cov
- [ ] pandas.core.window.rolling.Rolling.skew
- [ ] pandas.core.window.rolling.Rolling.apply
- [ ] pandas.core.window.expanding.Expanding.max
- [ ] pandas.core.window.expanding.Expanding.cov
- [ ] pandas.core.window.expanding.Expanding.skew
- [ ] pandas.core.window.expanding.Expanding.apply
- [ ] pandas.core.window.ewm.ExponentialMovingWindow.mean
- [ ] pandas.core.window.ewm.ExponentialMovingWindow.std
- [ ] pandas.core.window.ewm.ExponentialMovingWindow.var
- [ ] pandas.core.window.ewm.ExponentialMovingWindow.corr
- [ ] pandas.core.window.ewm.ExponentialMovingWindow.cov
- [ ] pandas.api.indexers.BaseIndexer
- [ ] pandas.api.indexers.VariableOffsetWindowIndexer
- [ ] pandas.DataFrame.index
- [ ] pandas.DataFrame.columns
- [ ] pandas.DataFrame.insert
- [ ] pandas.DataFrame.__iter__
- [ ] pandas.DataFrame.keys
- [ ] pandas.DataFrame.iterrows
- [ ] pandas.DataFrame.lookup
- [ ] pandas.DataFrame.get
- [ ] pandas.DataFrame.pipe
- [ ] pandas.DataFrame.corrwith
- [ ] pandas.DataFrame.kurt
- [ ] pandas.DataFrame.kurtosis
- [ ] pandas.DataFrame.mad
- [ ] pandas.DataFrame.mean
- [ ] pandas.DataFrame.median
- [ ] pandas.DataFrame.sem
- [ ] pandas.DataFrame.skew
- [ ] pandas.DataFrame.std
- [ ] pandas.DataFrame.var
- [ ] pandas.DataFrame.align
- [ ] pandas.DataFrame.backfill
- [ ] pandas.DataFrame.bfill
- [ ] pandas.DataFrame.ffill
- [ ] pandas.DataFrame.pad
- [ ] pandas.DataFrame.reorder_levels
- [ ] pandas.DataFrame.swaplevel
- [ ] pandas.DataFrame.swapaxes
- [ ] pandas.DataFrame.slice_shift
- [ ] pandas.DataFrame.tshift
- [ ] pandas.DataFrame.first_valid_index
- [ ] pandas.DataFrame.last_valid_index
- [ ] pandas.DataFrame.to_period
- [ ] pandas.DataFrame.to_timestamp
- [ ] pandas.DataFrame.tz_convert
- [ ] pandas.Flags
- [ ] pandas.DataFrame.attrs
- [ ] pandas.DataFrame.plot
- [ ] pandas.DataFrame.sparse.density
- [ ] pandas.DataFrame.sparse.to_coo
- [ ] pandas.DataFrame.to_html
- [ ] pandas.DataFrame.to_feather
- [ ] pandas.DataFrame.to_gbq
- [ ] pandas.DataFrame.style
- [ ] pandas.Index.values
- [ ] pandas.Index.is_monotonic
- [ ] pandas.Index.is_unique
- [ ] pandas.Index.hasnans
- [ ] pandas.Index.dtype
- [ ] pandas.Index.inferred_type
- [ ] pandas.Index.is_all_dates
- [ ] pandas.Index.shape
- [ ] pandas.Index.name
- [ ] pandas.Index.nbytes
- [ ] pandas.Index.ndim
- [ ] pandas.Index.size
- [ ] pandas.Index.T
- [ ] pandas.Index.memory_usage
- [ ] pandas.Index.copy
- [ ] pandas.Index.drop
- [ ] pandas.Index.identical
- [ ] pandas.Index.insert
- [ ] pandas.Index.is_
- [ ] pandas.Index.reindex
- [ ] pandas.Index.take
- [ ] pandas.Index.putmask
- [ ] pandas.Index.unique
- [ ] pandas.Index.droplevel
- [ ] pandas.Index.fillna
- [ ] pandas.Index.dropna
- [ ] pandas.Index.astype
- [ ] pandas.Index.item
- [ ] pandas.Index.map
- [ ] pandas.Index.ravel
- [ ] pandas.Index.to_list
- [ ] pandas.Index.to_native_types
- [ ] pandas.Index.append
- [ ] pandas.Index.join
- [ ] pandas.Index.asof_locs
- [ ] pandas.Index.get_indexer_for
- [ ] pandas.Index.get_indexer_non_unique
- [ ] pandas.Index.get_slice_bound
- [ ] pandas.Index.get_value
- [ ] pandas.RangeIndex
- [ ] pandas.Int64Index
- [ ] pandas.UInt64Index
- [ ] pandas.Float64Index
- [ ] pandas.RangeIndex.start
- [ ] pandas.RangeIndex.stop
- [ ] pandas.RangeIndex.step
- [ ] pandas.RangeIndex.from_range
- [ ] pandas.CategoricalIndex.codes
- [ ] pandas.CategoricalIndex.categories
- [ ] pandas.CategoricalIndex.ordered
- [ ] pandas.CategoricalIndex.reorder_categories
- [ ] pandas.CategoricalIndex.add_categories
- [ ] pandas.CategoricalIndex.remove_categories
- [ ] pandas.CategoricalIndex.remove_unused_categories
- [ ] pandas.CategoricalIndex.set_categories
- [ ] pandas.CategoricalIndex.as_ordered
- [ ] pandas.CategoricalIndex.as_unordered
- [ ] pandas.CategoricalIndex.equals
- [ ] pandas.IntervalIndex.closed
- [ ] pandas.IntervalIndex.values
- [ ] pandas.IntervalIndex.is_non_overlapping_monotonic
- [ ] pandas.IntervalIndex.to_tuples
- [ ] pandas.DatetimeIndex
- [ ] pandas.DatetimeIndex.date
- [ ] pandas.DatetimeIndex.time
- [ ] pandas.DatetimeIndex.timetz
- [ ] pandas.DatetimeIndex.dayofyear
- [ ] pandas.DatetimeIndex.day_of_year
- [ ] pandas.DatetimeIndex.weekofyear
- [ ] pandas.DatetimeIndex.week
- [ ] pandas.DatetimeIndex.quarter
- [ ] pandas.DatetimeIndex.tz
- [ ] pandas.DatetimeIndex.freq
- [ ] pandas.DatetimeIndex.freqstr
- [ ] pandas.DatetimeIndex.inferred_freq
- [ ] pandas.DatetimeIndex.indexer_at_time
- [ ] pandas.DatetimeIndex.indexer_between_time
- [ ] pandas.DatetimeIndex.snap
- [ ] pandas.DatetimeIndex.to_perioddelta
- [ ] pandas.DatetimeIndex.to_pydatetime
- [ ] pandas.DatetimeIndex.to_series
- [ ] pandas.DatetimeIndex.mean
- [ ] pandas.TimedeltaIndex
- [ ] pandas.TimedeltaIndex.days
- [ ] pandas.TimedeltaIndex.seconds
- [ ] pandas.TimedeltaIndex.microseconds
- [ ] pandas.TimedeltaIndex.nanoseconds
- [ ] pandas.TimedeltaIndex.components
- [ ] pandas.TimedeltaIndex.inferred_freq
- [ ] pandas.TimedeltaIndex.to_pytimedelta
- [ ] pandas.TimedeltaIndex.mean
- [ ] pandas.PeriodIndex.day
- [ ] pandas.PeriodIndex.dayofweek
- [ ] pandas.PeriodIndex.day_of_week
- [ ] pandas.PeriodIndex.dayofyear
- [ ] pandas.PeriodIndex.day_of_year
- [ ] pandas.PeriodIndex.days_in_month
- [ ] pandas.PeriodIndex.daysinmonth
- [ ] pandas.PeriodIndex.freq
- [ ] pandas.PeriodIndex.freqstr
- [ ] pandas.PeriodIndex.hour
- [ ] pandas.PeriodIndex.is_leap_year
- [ ] pandas.PeriodIndex.minute
- [ ] pandas.PeriodIndex.month
- [ ] pandas.PeriodIndex.quarter
- [ ] pandas.PeriodIndex.second
- [ ] pandas.PeriodIndex.week
- [ ] pandas.PeriodIndex.weekday
- [ ] pandas.PeriodIndex.weekofyear
- [ ] pandas.PeriodIndex.year
- [ ] pandas.PeriodIndex.to_timestamp
- [ ] pandas.infer_freq
- [ ] pandas.util.hash_array
- [ ] pandas.util.hash_- [ ] pandas.object
- [ ] pandas.tseries.offsets.DateOffset.__call__
- [ ] pandas.tseries.offsets.BusinessDay
- [ ] pandas.tseries.offsets.BDay
- [ ] pandas.tseries.offsets.BusinessDay.__call__
- [ ] pandas.tseries.offsets.BusinessHour
- [ ] pandas.tseries.offsets.BusinessHour.__call__
- [ ] pandas.tseries.offsets.CustomBusinessDay
- [ ] pandas.tseries.offsets.CDay
- [ ] pandas.tseries.offsets.CustomBusinessDay.__call__
- [ ] pandas.tseries.offsets.CustomBusinessHour
- [ ] pandas.tseries.offsets.CustomBusinessHour.__call__
- [ ] pandas.tseries.offsets.MonthEnd
- [ ] pandas.tseries.offsets.MonthEnd.__call__
- [ ] pandas.tseries.offsets.MonthBegin
- [ ] pandas.tseries.offsets.MonthBegin.__call__
- [ ] pandas.tseries.offsets.BusinessMonthEnd.__call__
- [ ] pandas.tseries.offsets.BusinessMonthBegin.__call__
- [ ] pandas.tseries.offsets.CustomBusinessMonthEnd.__call__
- [ ] pandas.tseries.offsets.CustomBusinessMonthBegin.__call__
- [ ] pandas.tseries.offsets.SemiMonthEnd
- [ ] pandas.tseries.offsets.SemiMonthEnd.__call__
- [ ] pandas.tseries.offsets.SemiMonthBegin
- [ ] pandas.tseries.offsets.SemiMonthBegin.__call__
- [ ] pandas.tseries.offsets.Week
- [ ] pandas.tseries.offsets.Week.__call__
- [ ] pandas.tseries.offsets.WeekOfMonth
- [ ] pandas.tseries.offsets.WeekOfMonth.__call__
- [ ] pandas.tseries.offsets.LastWeekOfMonth
- [ ] pandas.tseries.offsets.LastWeekOfMonth.__call__
- [ ] pandas.tseries.offsets.BQuarterEnd.__call__
- [ ] pandas.tseries.offsets.BQuarterBegin.__call__
- [ ] pandas.tseries.offsets.QuarterEnd
- [ ] pandas.tseries.offsets.QuarterEnd.__call__
- [ ] pandas.tseries.offsets.QuarterBegin
- [ ] pandas.tseries.offsets.QuarterBegin.__call__
- [ ] pandas.tseries.offsets.BYearEnd.__call__
- [ ] pandas.tseries.offsets.BYearBegin.__call__
- [ ] pandas.tseries.offsets.YearEnd
- [ ] pandas.tseries.offsets.YearEnd.__call__
- [ ] pandas.tseries.offsets.YearBegin
- [ ] pandas.tseries.offsets.YearBegin.__call__
- [ ] pandas.tseries.offsets.FY5253
- [ ] pandas.tseries.offsets.FY5253.__call__
- [ ] pandas.tseries.offsets.FY5253Quarter
- [ ] pandas.tseries.offsets.FY5253Quarter.__call__
- [ ] pandas.tseries.offsets.Easter
- [ ] pandas.tseries.offsets.Easter.__call__
- [ ] pandas.tseries.offsets.Tick.__call__
- [ ] pandas.tseries.offsets.Day.__call__
- [ ] pandas.tseries.offsets.Hour.__call__
- [ ] pandas.tseries.offsets.Minute.__call__
- [ ] pandas.tseries.offsets.Second.__call__
- [ ] pandas.tseries.offsets.Milli.__call__
- [ ] pandas.tseries.offsets.Micro.__call__
- [ ] pandas.tseries.offsets.Nano.__call__
- [ ] pandas.core.groupby.GroupBy.__iter__
- [ ] pandas.core.groupby.GroupBy.groups
- [ ] pandas.core.groupby.GroupBy.indices
- [ ] pandas.core.groupby.GroupBy.get_group
- [ ] pandas.core.groupby.GroupBy.apply
- [ ] pandas.core.groupby.GroupBy.all
- [ ] pandas.core.groupby.GroupBy.any
- [ ] pandas.core.groupby.GroupBy.bfill
- [ ] pandas.core.groupby.GroupBy.backfill
- [ ] pandas.core.groupby.GroupBy.count
- [ ] pandas.core.groupby.GroupBy.cummax
- [ ] pandas.core.groupby.GroupBy.cummin
- [ ] pandas.core.groupby.GroupBy.cumprod
- [ ] pandas.core.groupby.GroupBy.cumsum
- [ ] pandas.core.groupby.GroupBy.ffill
- [ ] pandas.core.groupby.GroupBy.first
- [ ] pandas.core.groupby.GroupBy.last
- [ ] pandas.core.groupby.GroupBy.max
- [ ] pandas.core.groupby.GroupBy.median
- [ ] pandas.core.groupby.GroupBy.min
- [ ] pandas.core.groupby.GroupBy.ohlc
- [ ] pandas.core.groupby.GroupBy.pad
- [ ] pandas.core.groupby.GroupBy.prod
- [ ] pandas.core.groupby.GroupBy.rank
- [ ] pandas.core.groupby.GroupBy.pct_change
- [ ] pandas.core.groupby.GroupBy.size
- [ ] pandas.core.groupby.GroupBy.sem
- [ ] pandas.core.groupby.GroupBy.std
- [ ] pandas.core.groupby.GroupBy.sum
- [ ] pandas.core.groupby.GroupBy.var
- [ ] pandas.core.groupby.DataFrameGroupBy.all
- [ ] pandas.core.groupby.DataFrameGroupBy.any
- [ ] pandas.core.groupby.DataFrameGroupBy.backfill
- [ ] pandas.core.groupby.DataFrameGroupBy.bfill
- [ ] pandas.core.groupby.DataFrameGroupBy.count
- [ ] pandas.core.groupby.DataFrameGroupBy.cummax
- [ ] pandas.core.groupby.DataFrameGroupBy.cummin
- [ ] pandas.core.groupby.DataFrameGroupBy.cumprod
- [ ] pandas.core.groupby.DataFrameGroupBy.cumsum
- [ ] pandas.core.groupby.DataFrameGroupBy.ffill
- [ ] pandas.core.groupby.DataFrameGroupBy.mad
- [ ] pandas.core.groupby.DataFrameGroupBy.pad
- [ ] pandas.core.groupby.DataFrameGroupBy.pct_change
- [ ] pandas.core.groupby.DataFrameGroupBy.plot
- [ ] pandas.core.groupby.DataFrameGroupBy.rank
- [ ] pandas.core.groupby.DataFrameGroupBy.shift
- [ ] pandas.core.groupby.DataFrameGroupBy.size
- [ ] pandas.core.groupby.DataFrameGroupBy.skew
- [ ] pandas.core.groupby.DataFrameGroupBy.tshift
- [ ] pandas.core.groupby.SeriesGroupBy.hist
- [ ] pandas.core.groupby.SeriesGroupBy.nunique
- [ ] pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing
- [ ] pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing
- [ ] pandas.core.groupby.DataFrameGroupBy.corrwith
- [ ] pandas.Series
- [ ] pandas.Series.index
- [ ] pandas.Series.dtype
- [ ] pandas.Series.shape
- [ ] pandas.Series.nbytes
- [ ] pandas.Series.ndim
- [ ] pandas.Series.size
- [ ] pandas.Series.T
- [ ] pandas.Series.hasnans
- [ ] pandas.Series.dtypes
- [ ] pandas.Series.to_period
- [ ] pandas.Series.to_timestamp
- [ ] pandas.Series.to_list
- [ ] pandas.Series.get
- [ ] pandas.Series.__iter__
- [ ] pandas.Series.keys
- [ ] pandas.Series.item
- [ ] pandas.Series.pipe
- [ ] pandas.Series.kurt
- [ ] pandas.Series.mad
- [ ] pandas.Series.mean
- [ ] pandas.Series.median
- [ ] pandas.Series.mode
- [ ] pandas.Series.sem
- [ ] pandas.Series.skew
- [ ] pandas.Series.std
- [ ] pandas.Series.var
- [ ] pandas.Series.kurtosis
- [ ] pandas.Series.is_unique
- [ ] pandas.Series.is_monotonic
- [ ] pandas.Series.is_monotonic_increasing
- [ ] pandas.Series.is_monotonic_decreasing
- [ ] pandas.Series.align
- [ ] pandas.Series.backfill
- [ ] pandas.Series.bfill
- [ ] pandas.Series.ffill
- [ ] pandas.Series.pad
- [ ] pandas.Series.argsort
- [ ] pandas.Series.reorder_levels
- [ ] pandas.Series.swaplevel
- [ ] pandas.Series.ravel
- [ ] pandas.Series.first_valid_index
- [ ] pandas.Series.last_valid_index
- [ ] pandas.Series.tz_convert
- [ ] pandas.Series.tshift
- [ ] pandas.Series.slice_shift
- [ ] pandas.Series.dt.date
- [ ] pandas.Series.dt.time
- [ ] pandas.Series.dt.timetz
- [ ] pandas.Series.dt.week
- [ ] pandas.Series.dt.weekofyear
- [ ] pandas.Series.dt.dayofyear
- [ ] pandas.Series.dt.day_of_year
- [ ] pandas.Series.dt.quarter
- [ ] pandas.Series.dt.daysinmonth
- [ ] pandas.Series.dt.days_in_month
- [ ] pandas.Series.dt.tz
- [ ] pandas.Series.dt.days
- [ ] pandas.Series.dt.seconds
- [ ] pandas.Series.dt.microseconds
- [ ] pandas.Series.dt.nanoseconds
- [ ] pandas.Series.str.center
- [ ] pandas.Series.str.decode
- [ ] pandas.Series.str.encode
- [ ] pandas.Series.str.find
- [ ] pandas.Series.str.index
- [ ] pandas.Series.str.ljust
- [ ] pandas.Series.str.match
- [ ] pandas.Series.str.normalize
- [ ] pandas.Series.str.rfind
- [ ] pandas.Series.str.rindex
- [ ] pandas.Series.str.rjust
- [ ] pandas.Series.str.translate
- [ ] pandas.Series.sparse
- [ ] pandas.DataFrame.sparse
- [ ] pandas.Series.cat.categories
- [ ] pandas.Series.cat.ordered
- [ ] pandas.Series.cat.codes
- [ ] pandas.Series.cat.reorder_categories
- [ ] pandas.Series.cat.add_categories
- [ ] pandas.Series.cat.remove_categories
- [ ] pandas.Series.cat.remove_unused_categories
- [ ] pandas.Series.cat.set_categories
- [ ] pandas.Series.cat.as_ordered
- [ ] pandas.Series.cat.as_unordered
- [ ] pandas.Series.sparse.fill_value
- [ ] pandas.Series.attrs
- [ ] pandas.Series.plot
- [ ] pandas.Series.hist
- [ ] pandas.Series.to_string
- [ ] pandas.api.extensions.ExtensionDtype
- [ ] pandas.api.extensions.ExtensionArray
- [ ] pandas.arrays.- [ ] pandas.rray
- [ ] pandas.api.extensions.ExtensionArray._concat_same_type
- [ ] pandas.api.extensions.ExtensionArray._formatter
- [ ] pandas.api.extensions.ExtensionArray._from_factorized
- [ ] pandas.api.extensions.ExtensionArray._from_sequence
- [ ] pandas.api.extensions.ExtensionArray._from_sequence_of_strings
- [ ] pandas.api.extensions.ExtensionArray._reduce
- [ ] pandas.api.extensions.ExtensionArray._values_for_argsort
- [ ] pandas.api.extensions.ExtensionArray._values_for_factorize
- [ ] pandas.api.extensions.ExtensionArray.argsort
- [ ] pandas.api.extensions.ExtensionArray.astype
- [ ] pandas.api.extensions.ExtensionArray.copy
- [ ] pandas.api.extensions.ExtensionArray.view
- [ ] pandas.api.extensions.ExtensionArray.dropna
- [ ] pandas.api.extensions.ExtensionArray.equals
- [ ] pandas.api.extensions.ExtensionArray.factorize
- [ ] pandas.api.extensions.ExtensionArray.fillna
- [ ] pandas.api.extensions.ExtensionArray.isna
- [ ] pandas.api.extensions.ExtensionArray.ravel
- [ ] pandas.api.extensions.ExtensionArray.searchsorted
- [ ] pandas.api.extensions.ExtensionArray.shift
- [ ] pandas.api.extensions.ExtensionArray.unique
- [ ] pandas.api.extensions.ExtensionArray.dtype
- [ ] pandas.api.extensions.ExtensionArray.nbytes
- [ ] pandas.api.extensions.ExtensionArray.ndim
- [ ] pandas.api.extensions.ExtensionArray.shape
- [ ] pandas.plotting.deregister_matplotlib_converters
- [ ] pandas.plotting.plot_params
- [ ] pandas.plotting.register_matplotlib_converters
- [ ] pandas.plotting.table
- [ ] pandas.read_clipboard
- [ ] pandas.ExcelFile.parse
- [ ] pandas.HDFStore.put
- [ ] pandas.HDFStore.append
- [ ] pandas.HDFStore.get
- [ ] pandas.HDFStore.select
- [ ] pandas.HDFStore.info
- [ ] pandas.HDFStore.keys
- [ ] pandas.HDFStore.groups
- [ ] pandas.HDFStore.walk
- [ ] pandas.read_feather
- [ ] pandas.read_parquet
- [ ] pandas.read_orc
- [ ] pandas.read_sas
- [ ] pandas.read_spss
- [ ] pandas.read_sql_query
- [ ] pandas.read_sql
- [ ] pandas.read_gbq
- [ ] pandas.io.stata.StataReader.data_label
- [ ] pandas.io.stata.StataReader.value_labels
- [ ] pandas.io.stata.StataReader.variable_labels
- [ ] pandas.core.resample.Resampler.__iter__
- [ ] pandas.core.resample.Resampler.groups
- [ ] pandas.core.resample.Resampler.indices
- [ ] pandas.core.resample.Resampler.get_group
- [ ] pandas.core.resample.Resampler.ffill
- [ ] pandas.core.resample.Resampler.pad
- [ ] pandas.core.resample.Resampler.asfreq
- [ ] pandas.core.resample.Resampler.count
- [ ] pandas.core.resample.Resampler.nunique
- [ ] pandas.core.resample.Resampler.first
- [ ] pandas.core.resample.Resampler.last
- [ ] pandas.core.resample.Resampler.max
- [ ] pandas.core.resample.Resampler.median
- [ ] pandas.core.resample.Resampler.min
- [ ] pandas.core.resample.Resampler.ohlc
- [ ] pandas.core.resample.Resampler.prod
- [ ] pandas.core.resample.Resampler.size
- [ ] pandas.core.resample.Resampler.sem
- [ ] pandas.core.resample.Resampler.std
- [ ] pandas.core.resample.Resampler.sum
- [ ] pandas.core.resample.Resampler.var
- [ ] pandas.core.resample.Resampler.quantile
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MDExOlB1bGxSZXF1ZXN0NTIxMjY0OTM4
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CLN: Adding comment/more info about a possible alternative approach to murmur2-hash
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| 1
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| 2020-11-15T23:12:32Z
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|
CONTRIBUTOR
| null |
Small comment as requested here: https://github.com/pandas-dev/pandas/pull/36729#discussion_r522956690
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MDExOlB1bGxSZXF1ZXN0NTIxMjY3OTA4
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DEPR: Index.asi8
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| 3
| 2020-11-15T22:10:38Z
| 2020-11-19T00:27:24Z
| 2020-11-19T00:22:36Z
|
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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MDExOlB1bGxSZXF1ZXN0NTIxMjY4MTY0
| 37,878
|
ENH: Support groupby.ewm operations
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| 6
| 2020-11-15T22:12:32Z
| 2020-11-18T01:38:45Z
| 2020-11-18T00:36:49Z
|
MEMBER
| null |
- [x] closes #16037
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Additionally enables a Numba engine for `groupby(...).ewm(...).mean(...)`
```
In [1]: df = pd.DataFrame({"A": range(10_000), "B": range(10_000)})
In [2]: gb_ewm = df.groupby("A").ewm(com=1.0)
--cache time first
In [3]: %timeit -r 1 -n 1 gb_ewm.mean(engine='numba')
1.02 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
In [4]: %timeit gb_ewm.mean(engine='numba')
578 ms ± 28.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [5]: %timeit df.groupby('A').apply(lambda x: x.ewm(com=1.0).mean())
3.43 s ± 208 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [6]: %timeit gb_ewm.mean(engine='cython')
4.19 s ± 204 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
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MDExOlB1bGxSZXF1ZXN0NTIxMjcxNDY0
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CI: DataFrame.apply(np.any)
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| 1
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| 2020-11-16T00:41:01Z
| 2020-11-16T00:40:58Z
|
MEMBER
| null |
Should fix these failures https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=47831&view=logs&j=f016abb9-7827-5fa2-935a-22bd9b1477b6&t=c86edbe1-8c1d-5e5c-5b6f-d970fa4acf6d
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|
BUG: Failed to install Pandas with pip3 and Python3.9 on macOS Big Sur
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| 3
| 2020-11-15T23:42:46Z
| 2020-11-16T00:04:05Z
| 2020-11-15T23:56:19Z
|
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
pip install pandas
pip3 install pandas
pip3 install pandas --no-build-isolation --no-cache-dir
sudo pip3 install pandas --no-cache-dir
```
#### Problem description
I've done a clean installation of macOS Big Sur and a clean install of Python3.9 via Homebrew (`brew install python3`). After that I installed pip3 with `brew install pip` command and I wanted to install Pandas but I'm always getting the following error. I've tried with all the commands listed above to install Pandas on macOS Big Sur and Python3...
Anyone can help? These are the only packages actually installed:
Package Version
---------- -------
pip 20.2.4
setuptools 50.3.2
wheel 0.35.1
#### Expected Output
#### Output of ``pip install pandas``
<details>
Getting requirements to build wheel ... error
ERROR: Command errored out with exit status 1:
command: /usr/local/opt/python@3.9/bin/python3.9 /usr/local/lib/python3.9/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/tmpzr3sthd6
cwd: /private/var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/pip-install-pvlaq6iq/pandas
Complete output (23 lines):
init_dgelsd failed init
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/pip/_vendor/pep517/_in_process.py", line 280, in <module>
main()
File "/usr/local/lib/python3.9/site-packages/pip/_vendor/pep517/_in_process.py", line 263, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "/usr/local/lib/python3.9/site-packages/pip/_vendor/pep517/_in_process.py", line 114, in get_requires_for_build_wheel
return hook(config_settings)
File "/private/var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/pip-build-env-ly4c8n5_/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 149, in get_requires_for_build_wheel
return self._get_build_requires(
File "/private/var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/pip-build-env-ly4c8n5_/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 130, in _get_build_requires
self.run_setup()
File "/private/var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/pip-build-env-ly4c8n5_/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 253, in run_setup
super(_BuildMetaLegacyBackend,
File "/private/var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/pip-build-env-ly4c8n5_/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 145, in run_setup
exec(compile(code, __file__, 'exec'), locals())
File "setup.py", line 488, in <module>
import numpy as np
File "/private/var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/pip-build-env-ly4c8n5_/overlay/lib/python3.9/site-packages/numpy/__init__.py", line 286, in <module>
raise RuntimeError(msg)
RuntimeError: Polyfit sanity test emitted a warning, most likely due to using a buggy Accelerate backend. If you compiled yourself, see site.cfg.example for information. Otherwise report this to the vendor that provided NumPy.
RankWarning: Polyfit may be poorly conditioned
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/local/opt/python@3.9/bin/python3.9 /usr/local/lib/python3.9/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /var/folders/zv/0q58rjv549bd4qjdqlrwlvl40000gn/T/tmpzr3sthd6 Check the logs for full command output.
</details>
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MDExOlB1bGxSZXF1ZXN0NTIxMjgxNzM0
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|
REF: simplify setitem_with_indexer
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| 0
| 2020-11-15T23:54:02Z
| 2020-11-17T01:28:59Z
| 2020-11-17T01:23:41Z
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MEMBER
| null |
Good news: this is the last pure-cleanup before we start actually changing the logic.
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BUG: `pandas.Index` cannot be subclassed (despite saying it can be in the docs)
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| 2021-08-14T05:12:32Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
class CustomIndex(pandas.Index):
def __new__(cls, data, **kwargs):
return super().__new__(cls, data, **kwargs).view(cls)
def __init__(self, data, **kwargs):
super().__init__(self, data, **kwargs)
if __name__ == '__main__':
index = CustomIndex([1,2,3])
print(type(index), index)
```
The result is: `<class 'pandas.core.indexes.numeric.Int64Index'> Int64Index([1, 2, 3], dtype='int64')`
#### Problem description
In the [`pandas` docs](https://pandas.pydata.org/pandas-docs/stable/development/internals.html): "The motivation for having an Index class in the first place was to enable different implementations of indexing. This means that it’s possible for you, the user, to implement a custom Index subclass that may be better suited to a particular application than the ones provided in pandas."
#### Expected Output
`<class 'CustomIndex'> CustomIndex([1, 2, 3], dtype='int64')`
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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BUG: DataFrame.from_dict gives me 3 duplicates
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| 3
| 2020-11-16T02:16:17Z
| 2020-11-17T02:22:27Z
| 2020-11-16T02:39:32Z
|
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.
get_omdb_data function is defined to get movie information in JSON format from OMDB website and convert it to dataframe with pandas ```DataFrame.from_dict``` function. However, "Titanic" will give me 3 DF duplicates. I also tried with "Godfather" which has only 1 record.
```python
def get_omdb_data(movie):
"""[get OMDB movie data in the world]
Args:
movie ([str]): [valid movie name]
"""
movie_name = str(movie)
middle_string = f'i={i_string}&apikey={apikey}&t='
url = serviceurl + middle_string + movie_name
try:
uh = urllib.request.urlopen(url)
except HTTPError as e:
print("Sorry! Could not retrieve anything on {}".format(movie_name))
return None
except URLError as e:
print("Failed to reach a server.")
print("Reason:", e.reason)
return None
else:
data = uh.read().decode()
json_data = json.loads(data)
if json_data['Response'] == 'True':
print(
"Retrieved data on {}. Total {} characters read.".format(
movie_name, len(data)
)
)
return json_data
else:
print("Sorry! {} can not be found. Your spelling might be wrong".format(movie_name))
return None
data = get_omdb_data('Titanic')
print(data)
df = pd.DataFrame.from_dict(data)
print(df)
```
#### Problem description
Retrieved data on Titanic. Total 1051 characters read.
{'Title': 'Titanic', 'Year': '1997', 'Rated': 'PG-13', 'Released': '19 Dec 1997', 'Runtime': '194 min', 'Genre': 'Drama, Romance', 'Director': 'James Cameron', 'Writer': 'James Cameron', 'Actors': 'Leonardo DiCaprio, Kate Winslet, Billy Zane, Kathy Bates', 'Plot': 'A seventeen-year-old aristocrat falls in love with a kind but poor artist aboard the luxurious, ill-fated R.M.S. Titanic.', 'Language': 'English, Swedish, Italian, French', 'Country': 'USA, Mexico, Australia, Canada', 'Awards': 'Won 11 Oscars. Another 112 wins & 83 nominations.', 'Poster': 'https://m.media-amazon.com/images/M/MV5BMDdmZGU3NDQtY2E5My00ZTliLWIzOTUtMTY4ZGI1YjdiNjk3XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_SX300.jpg', 'Ratings': [{'Source': 'Internet Movie Database', 'Value': '7.8/10'}, {'Source': 'Rotten Tomatoes', 'Value': '89%'}, {'Source': 'Metacritic', 'Value': '75/100'}], 'Metascore': '75', 'imdbRating': '7.8', 'imdbVotes': '1,029,426', 'imdbID': 'tt0120338', 'Type': 'movie', 'DVD': 'N/A', 'BoxOffice': 'N/A', 'Production': '20th Century Fox, Lightstorm Entertainment, Paramount Pictures', 'Website': 'N/A', 'Response': 'True'}
Title Year ... Website Response
0 Titanic 1997 ... N/A True
1 Titanic 1997 ... N/A True
2 Titanic 1997 ... N/A True
**[3 rows x 25 columns]**
#### Expected Output
Above has Titanic information in JSON format. DataFrame.from_dict should give me 1 Titanic record, not 3.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.utf8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 41.2.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
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 : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTIxMzIxNzcy
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REF: make casting explicit in CategoricalIndex
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| 0
| 2020-11-16T02:49:43Z
| 2020-11-17T01:32:52Z
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|
MEMBER
| null |
Motivated by trying to get stricter typing in CategoricalIndex._shallow_copy.
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REF: Index._intersection
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MEMBER
| null |
Follow the same pattern as Index._union. Motivated by trying to get stricter typing in CategoricalIndex._shallow_copy, which is blocked by pinning down return types for set ops.
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REF: Use more memory views in rolling aggregations
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TST: parametrize test_info
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| 2020-12-03T07:56:54Z
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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
Precursor for #37320
- Parametrize tests
- Rename some of test functions
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MDU6SXNzdWU3NDM1MTA4Njk=
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|
BUG: df.str.contains(XX) return False when XX contains "+" character
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| 2020-11-17T02:28:12Z
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|
NONE
| null |
- [ y] I have checked that this issue has not already been reported.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(["ABC", "ABC+534"], index=["AA","BB"]).T
df["BB"].str.contains("ABC+5")
```
returns
```
0 False
Name: BB, dtype: bool
```
#### Problem description
When I ran the `df.str.contains(XX)`, I found a new bug.
I run the below notebook, it returns False, despite it is definitely include keyword with in the cell.
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MDU6SXNzdWU3NDM2MjYzMjI=
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BUG:BadZipFile CRC-32 for file 'docProps/core.xml' when I use pandas read excel accord an error "
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| 2020-11-16T08:18:47Z
| 2020-11-18T03:02:59Z
| 2020-11-18T03:02:41Z
|
NONE
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- [ ] 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.
---
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#### 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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BUG:BadZipFile CRC-32 for file 'docProps/core.xml' when I use pandas read excel accord an error "BadZipFile CRC-32 for file 'docProps/core.xml' ". Python
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| 2020-11-16T08:19:36Z
| 2021-03-27T01:25:13Z
| 2021-03-27T01:25:13Z
|
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
```
#### 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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MDU6SXNzdWU3NDM2NTQ3MzQ=
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|
CI upgrade pyupgrade to v2.7.4
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| 2
| 2020-11-16T09:02:25Z
| 2020-11-17T09:23:09Z
| 2020-11-17T09:23:09Z
|
MEMBER
| null |
This should just be a matter of changing the version number to v2.7.4 for pyupgrade in .pre-commit-config.yaml, and checking that all still passes:
```
pre-commit run pyupgrade --all-files
```
If it does, please open a PR to upgrade - else, report on any failures
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MDU6SXNzdWU3NDM2Nzg4MzE=
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|
BUG: pandas not connecting to private S3 endpoint
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[] | null | 9
| 2020-11-16T09:36:53Z
| 2021-01-07T12:16:18Z
| 2021-01-07T12:16:18Z
|
NONE
| null |
#### Problem description
Connecting to https://bucket.s3.amazonaws.com rather then private endpoint
```python
df = pd.read_parquet('s3://bucket/file.parquet', storage_options={'client_kwargs':{'endpoint_url': S3_ENDPOINT}})
```
as workaround I do:
```python
# ecs is a S3FileSystem object with client_kwargs
with ecs.open('bucket/file.parquet', 'rb') as f:
df = pd.read_parquet(f)
```
#### Expected Output
Connecting to private S3 bucket
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.19.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0.post20200704
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
fastparquet : 0.4.0
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.50.1
</details>
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MDU6SXNzdWU3NDM4NjE1ODc=
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|
BUG: fixed length string data type specifiers, are ignored on astype calls, if they are passed in a dictionary
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| false
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[] | null | 0
| 2020-11-16T14:03:23Z
| 2021-08-14T05:13:56Z
| 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
def test_astype_string_conversion():
df = pd.DataFrame([{'n': 0, 's':'qwerty'}] * 3)
direct_conversion_df = df.copy()
direct_conversion_df['s'] = direct_conversion_df['s'].astype('|S10')
# direct column conversion : String to fixed len bytes works as expected
assert direct_conversion_df['s'].dtype != np.dtype('O')
# Where as type specified with a dictionary
dictionary_conversion_df = df.astype({'n': 'float64', 's':'|S10'})
# the float conversion works,
assert dictionary_conversion_df['n'].dtype == np.dtype('float64')
# but the strings are still objects,
assert dictionary_conversion_df['s'].dtype != np.dtype('O') # FAIL
```
#### Problem description
pandas.DataFrame.astype, 'dtype' parameter accepts a dictionary which can specify the columns to be converted. It looks like limited length string specifiers, e.g: '|S10', are ignored, if they are specified in the dictionary. Directly converting columns to the same type, works as expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : 0.16.1
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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| 37,894
|
BUG: rowLabels and colLabels args in pandas.plotting.table have no effect (#37811)
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| 2020-11-16T16:02:06Z
| 2021-07-19T16:15:59Z
| 2021-02-11T01:41:19Z
|
NONE
| null |
- [ ] closes #37811
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] Pass `rowLabels` and `colLabels` args to `plot_backend.table`. Docs improved: the descriptions of `rowLables` and `colLabels` added to their own entries in the docstring.
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BUG: pandas read_csv silently switches date parsing scheme in the middle of reading file corrupting data
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| 3
| 2020-11-16T17:55:46Z
| 2020-11-17T03:38:31Z
| 2020-11-17T03:38:30Z
|
NONE
| null |
In pandas 1.1.4
```python
# Your code here
pd.DataFrame(data={'timestamp': ['13/06/2018', '12/06/2018']}).to_csv('test.csv', index=False)
pd.read_csv('test.csv', parse_dates=['timestamp'])
Out[84]:
timestamp
0 2018-06-13
1 2018-12-06
```
#### Problem description
I provoked pandas here (initially by mistake) by letting it parse a file with date format day / month / year, with it's default parser month / day / year. To my horror, it parses the ones that give a valid date with the default parser, and automatically uses the alternative parser on the ones that fails. Giving a mix of dates and without any warning.
#### Expected Output
Pandas should have failed with an error, or a very severe warning when a date failed to parse with the same parser as the other dates. It should not be allowed to use different parsing schemes within the same data file.
<details>
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
</details>
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BUG: RangeIndex is being materialized to a column while writing to parquet file when `index=True`
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| 2
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| 2020-11-18T13:56:13Z
| 2020-11-18T13:55:53Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
In[20]: import pandas as pd
In[21]: import pyarrow as pa
In[22]: pdf = pd.DataFrame({'a':[1, 2, 3], 'b':[10, 11, 12]}, index=pd.RangeIndex(2, 5, 1))
In[23]: pdf.to_parquet('temp', index=True)
In[24]: pdf
Out[24]:
a b
2 1 10
3 2 11
4 3 12
In[25]: pd.read_parquet('temp')
Out[25]:
a b
2 1 10
3 2 11
4 3 12
In[26]: x = pa.parquet.read_metadata('temp')
In[27]: x.metadata
Out[27]:
{b'ARROW:schema': b'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',
b'pandas': b'{"index_columns": ["__index_level_0__"], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "a", "field_name": "a", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "b", "field_name": "b", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": null, "field_name": "__index_level_0__", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}], "creator": {"library": "pyarrow", "version": "1.0.1"}, "pandas_version": "1.1.4"}'}
In[28]: pd.read_parquet('temp').index
Out[28]: Int64Index([2, 3, 4], dtype='int64')
In[29]: pdf.index
Out[29]: RangeIndex(start=2, stop=5, step=1)
```
#### Problem description
The RangeIndex object should not be materialized and stored in the parquet file. Instead pandas should be storing the rangeIndex in metadata under `index_columns` like below when we pass `index=None`.
#### Expected Output
```python
In[32]: pdf.to_parquet('temp', index=None)
In[33]: pd.read_parquet('temp')
Out[33]:
a b
2 1 10
3 2 11
4 3 12
In[34]: pd.read_parquet('temp').index
Out[34]: RangeIndex(start=2, stop=5, step=1)
In[35]: x = pa.parquet.read_metadata('temp')
In[36]: x.metadata
Out[36]:
{b'ARROW:schema': b'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',
b'pandas': b'{"index_columns": [{"kind": "range", "name": null, "start": 2, "stop": 5, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "a", "field_name": "a", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}, {"name": "b", "field_name": "b", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}], "creator": {"library": "pyarrow", "version": "1.0.1"}, "pandas_version": "1.1.4"}'}
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-53-generic
Version : #59-Ubuntu SMP Wed Oct 21 09:38:44 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.2
hypothesis : 5.41.1
sphinx : 3.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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| 3
| 2020-11-16T18:18:42Z
| 2020-11-18T13:50:05Z
| 2020-11-18T13:49:57Z
|
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
In[38]: df = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
In[39]: df.to_parquet('a')
In[40]: pd.read_parquet('a')
Out[40]:
Empty DataFrame
Columns: []
Index: []
In[41]: df.to_parquet('a', index=True)
In[42]: pd.read_parquet('a')
Out[42]:
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
In[43]: pd.read_parquet('a').index
Out[43]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')```
```
#### Problem description
When `index` is `None`/`True` parquet reader must retrieve only `RangeIndex` and not empty `RangeIndex` or `Int64Index`.
#### Expected Output
We should be able to get rangeIndex when we read from parquet file `a`.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-53-generic
Version : #59-Ubuntu SMP Wed Oct 21 09:38:44 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.2
hypothesis : 5.41.1
sphinx : 3.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
This could be related to https://github.com/pandas-dev/pandas/issues/37896, but it appears the parquet reader is also not able to retrieve any Index at all when `index` is `None`. Hence filing this as a separate issue.
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TYP: __getitem__ method of EA
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| 2
| 2020-11-16T18:31:16Z
| 2020-11-17T19:56:05Z
| 2020-11-17T12:57:52Z
|
MEMBER
| null |
as a precursor to https://github.com/pandas-dev/pandas/pull/35259#discussion_r522951940
there are more changes/additions needed but this PR might get too big to review and push through in a timely manor.
overload is required to avoid casts
Any is used for any type, whereas should use object.
only the abstract classes have been typed.
could create a scalar type alias in each of the EA classes similar to DTScalarOrNaT in DatetimeLikeArrayMixin
import and expose pandas._libs.missing.NAType in pandas._typing
any of these could be done here or as follow-on
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BUG: pd.DataFrame.replace(to_replace: list, method: str) raises TypeError: No matching signature found
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| 2021-08-14T05:15:11Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported: [search query](https://github.com/pandas-dev/pandas/issues?page=2&q=is%3Aissue+is%3Aopen+replace+Categorical)
- [ ] 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: Tested with '1.2.0.dev0+1137.g50b34a4a8')
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({
'x': pd.Categorical([1, 2, 3], [1, 2, 3])
})
df.replace([1, 2], method='bfill')
# > Traceback excerpt: 635 def backfill_inplace(algos_t[:] values, const uint8_t[:] mask, limit=None):
# > TypeError: No matching signature found
```
#### Problem description
This should work fine, within bounds of considerations like #18250.
#### Expected Output
Should be equivalent to `pd.Categorical([3, 3, 3], [1,2,3])` (or `pd.Categorical([3, 3, 3], [3])` depending on the what's done with unused categories elsewhere.)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 50b34a4a8008b6da24c3dac6ae6d66536e463239
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
Version : Darwin Kernel Version 19.4.0: Wed Mar 4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.0.dev0+1137.g50b34a4a8
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.16.2
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
Noticed while working on #32542
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|
quantile function failure
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| 2020-11-16T21:33:21Z
| 2021-01-27T19:34:26Z
| 2020-11-16T23:16:31Z
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NONE
| null |
If I create a dataframe like this:
```
gravity_df = pd.DataFrame(columns=["accel_time_seconds", "gravity_x", "gravity_y", "gravity_z"])
gravity_df['accel_time_seconds'] = list(range(100))
gravity_df.loc[:70, 'gravity_x'] = -0.145438
gravity_df.loc[:70, 'gravity_y'] = -0.355129
gravity_df.loc[:70, 'gravity_z'] = -0.923434
gravity_df.loc[70:, 'gravity_x'] = -0.114892
gravity_df.loc[70:, 'gravity_y'] = -0.428598
gravity_df.loc[70:, 'gravity_z'] = -0.89616
```
quantile function would not work properly for multiple columns:
```
gravity_df[['gravity_x', 'gravity_y']].quantile(0.5)
```
Error message as follows:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-17d112cd6897> in <module>
----> 1 gravity_df[['gravity_x']].quantile(0.5)
~/.local/share/virtualenvs/crashes-ETafZQlO/lib/python3.7/site-packages/pandas/core/frame.py in quantile(self, q, axis, numeric_only, interpolation)
8279
8280 result = data._data.quantile(
-> 8281 qs=q, axis=1, interpolation=interpolation, transposed=is_transposed
8282 )
8283
~/.local/share/virtualenvs/crashes-ETafZQlO/lib/python3.7/site-packages/pandas/core/internals/managers.py in quantile(self, axis, consolidate, transposed, interpolation, qs, numeric_only)
533
534 # single block, i.e. ndim == {1}
--> 535 values = _concat._concat_compat([b.values for b in blocks])
536
537 # compute the orderings of our original data
~/.local/share/virtualenvs/crashes-ETafZQlO/lib/python3.7/site-packages/pandas/core/dtypes/concat.py in _concat_compat(to_concat, axis)
172 to_concat = [x.astype("object") for x in to_concat]
173
--> 174 return np.concatenate(to_concat, axis=axis)
175
176
ValueError: need at least one array to concatenate
```
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BUG: obj.loc[listlike] with missing keys and CategoricalIndex
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| 0
| 2020-11-16T22:02:37Z
| 2020-11-17T15:25:12Z
| 2020-11-17T12:56:22Z
|
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
This behavior was deprecated for all other Index subclasses a few years ago and finally removed in 1.0. AFAICT not removing it for CategoricalIndex was an oversight. xref https://github.com/pandas-dev/pandas/commit/8ab61851eeae0d16deaa2f6eafcf59393b06aaeb#r44233424
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MDExOlB1bGxSZXF1ZXN0NTIyMDI2Mzg3
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|
TYP: Add cast to ABC classes.
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| 7
| 2020-11-16T23:00:05Z
| 2020-11-24T16:55:18Z
| 2020-11-24T13:30:34Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Implements the idea of @simonjayhawkins from https://github.com/pandas-dev/pandas/issues/27353#issuecomment-526391462 to use cast on ABC classes. Currently only implements ABCDataFrame, ABCNDFrame, and ABCSeries. Previously, mypy would resolve e.g. ABCSeries as any, now it gets resolved as Series.
One issue remains: I don't understand why the code below generates this complaint.
> pandas/core/common.py:472: error: Incompatible return value type (got "Union[Any, List[Any], Series]", expected "Union[List[Any], ExtensionArray]") [return-value]
```
def convert_to_list_like(
values: Union[Scalar, Iterable, AnyArrayLike]
) -> Union[List, AnyArrayLike]:
if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
return values
...
```
One can add Series to the return Union to resolve this, but it doesn't seem like that should be necessary.
I'm also getting:
> pandas/core/base.py:923: note: Internal mypy error checking function redefinition
on the lines
```
def map_f(values, f):
return lib.map_infer_mask(values, f, isna(values).view(np.uint8))
```
where `map_f` is defined in various ways in if-else blocks. I'm not sure if this is a mypy bug.
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MDExOlB1bGxSZXF1ZXN0NTIyMDI5NTcw
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CLN: Unused file 'path_to_file.zip'
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| 3
| 2020-11-16T23:07:46Z
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|
CONTRIBUTOR
| null |
New file [path_to_file.zip](https://github.com/pandas-dev/pandas/blob/master/scripts/path_to_file.zip) was added in #37844.
I believe this is file is (accidentally?) generated when running [scripts/validate_docstrings.py](https://github.com/pandas-dev/pandas/blob/master/scripts/validate_docstrings.py) and probably is not needed in the repository.
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MDExOlB1bGxSZXF1ZXN0NTIyMDQ3NTQ0
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DOC: Timestamp EX01 errors
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| 19
| 2020-11-16T23:58:34Z
| 2021-04-30T00:47:25Z
| 2021-04-30T00:47:01Z
|
CONTRIBUTOR
| null |
Example section added for several methods in class [pd.Timestamp](https://github.com/pandas-dev/pandas/blob/1f89b82cc65f512e78b0038975425cc3d0772a05/pandas/_libs/tslibs/timestamps.pyx#L768)
- [X] xref #37875
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Examples added for:
- pandas.Timestamp.asm8
- pandas.Timestamp.day_of_week
- pandas.Timestamp.day_of_year
- pandas.Timestamp.days_in_month
- pandas.Timestamp.is_leap_year
- pandas.Timestamp.is_month_end
- pandas.Timestamp.is_month_start
- pandas.Timestamp.is_quarter_end
- pandas.Timestamp.is_quarter_start
- pandas.Timestamp.is_year_end
- pandas.Timestamp.is_year_start
- pandas.Timestamp.quarter
- pandas.Timestamp.tz
- pandas.Timestamp.week
- pandas.Timestamp.ceil
- pandas.Timestamp.combine
- pandas.Timestamp.day_name
- pandas.Timestamp.floor
- pandas.Timestamp.fromordinal
- pandas.Timestamp.fromtimestamp
- pandas.Timestamp.month_name
- pandas.Timestamp.normalize
- pandas.Timestamp.now
- pandas.Timestamp.replace
- pandas.Timestamp.round
- pandas.Timestamp.strftime
- pandas.Timestamp.timestamp
- pandas.Timestamp.to_numpy
- pandas.Timestamp.to_julian_date
- pandas.Timestamp.to_period
- pandas.Timestamp.to_pydatetime
- pandas.Timestamp.today
- pandas.Timestamp.tz_convert
- pandas.Timestamp.tz_localize
- pandas.Timestamp.utcfromtimestamp
- pandas.Timestamp.utcnow
- pandas.Timestamp.weekday
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BUG: groupby resample different results with .agg() vs .mean()
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CONTRIBUTOR
| null |
- [x] closes #33548
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- [x] passes `black pandas`
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CI: update pyupgrade to 2.7.4 in pre-commit
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CONTRIBUTOR
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Updating pyupgrade to 2.7.4 in pre-commit yaml. `pre-commit run pyupgrade --all-files` runs good :+1:
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REF: simplify dt64 formatter functions
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CLN: defer CategoricalIndex.astype to Categorical.astype
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| 2
| 2020-11-17T05:00:42Z
| 2020-11-19T04:12:57Z
| 2020-11-18T13:24:56Z
|
CONTRIBUTOR
| null |
`read_fwf` was using `compression=None` to infer compression (in addition to `compression="infer"`). This is undocumented but was enforced in a test.
In #37639, I thought this was the expected behavior for all functions. That as wrong, `read_csv` explicitly states that `compression=None` does not infer any compression.
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BUG: EA inplace add (and other ops) with non-EA arg broken
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| 2
| 2020-11-17T08:49:49Z
| 2020-11-24T00:24:01Z
| 2020-11-24T00:24:01Z
|
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 (`1.0.4`).
- [X] (optional) I have confirmed this bug exists on the master branch of pandas (`87247370c14629c1797fcf3540c6bd93b777a17e`).
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
ser1 = pd.Series([1], dtype="Int64")
ser2 = pd.Series([1.0], dtype=np.float64)
ser1 += ser2
```
Note that the issue is NOT specific to the integer EA, but happens with all EAs that don't use NumPy dtypes.
#### Problem description
```
Traceback (most recent call last):
File "bug.py", line 6, in <module>
ser1 += ser2
File ".../pandas/core/generic.py", line 11305, in __iadd__
return self._inplace_method(other, type(self).__add__) # type: ignore[operator]
File ".../pandas/core/generic.py", line 11289, in _inplace_method
if self.ndim == 1 and result._indexed_same(self) and result.dtype == self.dtype:
TypeError: Cannot interpret 'Int64Dtype()' as a data type
```
This is likely a fallout of #37508. See here:
https://github.com/pandas-dev/pandas/blob/87247370c14629c1797fcf3540c6bd93b777a17e/pandas/core/generic.py#L11283-L11292
There, the result dtype (which is a NumPy dtype in this case) is compared with the EA dtype. This raises the TypeError, because NumPy only allows comparing its dtype objects with "real" NumPy dtypes (and EAs don't provide NumPy-compatible dtypes).
#### Expected Output
Pass. I'm pretty sure that worked with `1.0.3` (the mentioned PR in question was backported to the `1.0.x` branch between the `1.0.3` and `1.0.4` release).
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MDExOlB1bGxSZXF1ZXN0NTIyMzAxNTg3
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CLN: Add *.zip to .gitignore
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| 1
| 2020-11-17T10:26:39Z
| 2020-11-17T13:01:31Z
| 2020-11-17T13:01:27Z
|
CONTRIBUTOR
| null |
- [X] xref #37903
- [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: add two new examples to the transform docs
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| 3
| 2020-11-17T17:17:43Z
| 2020-11-19T18:40:17Z
| 2020-11-19T18:40:13Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR adds two examples to the transform user guide. The examples apply the transform method to a groupby object. This is from the PyData Global sprint, from [this issue](https://github.com/MarcoGorelli/PyDataGlobal2020-sprint/issues/5). The examples are taken from [here](https://stackoverflow.com/a/37189976/14583166) and [here](https://stackoverflow.com/a/54417351/14583166).
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CI: fix mypy error
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MEMBER
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cc @jbrockmendel
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CI: fix mypy error (quick fix)
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see https://github.com/pandas-dev/pandas/pull/37913#issuecomment-729155748
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ENH: should we support np.allclose for ExtensionArrays?
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MEMBER
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xref https://github.com/pandas-dev/pandas/pull/33435/files#r406534850
I'm looking into picking up #33435. Currently the issue is that `np.allclose` throws when called on an EA:
``` python
In [1]: import numpy as np
...: import pandas as pd
...:
...: A = pd.array([1, 2], dtype='Int64')
...: B = pd.array([1, 2], dtype='Int64')
...: np.allclose(A, B)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-01ae8e8fc321> in <module>
4 A = pd.array([1, 2], dtype='Int64')
5 B = pd.array([1, 2], dtype='Int64')
----> 6 np.allclose(A, B)
<__array_function__ internals> in allclose(*args, **kwargs)
~/anaconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/numeric.py in allclose(a, b, rtol, atol, equal_nan)
2187
2188 """
-> 2189 res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
2190 return bool(res)
2191
<__array_function__ internals> in isclose(*args, **kwargs)
~/anaconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/numeric.py in isclose(a, b, rtol, atol, equal_nan)
2285 y = array(y, dtype=dt, copy=False, subok=True)
2286
-> 2287 xfin = isfinite(x)
2288 yfin = isfinite(y)
2289 if all(xfin) and all(yfin):
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
The error message is about the `isfinite` ufunc not being implemented. The root cause here is that `np.isclose` calls `np.asanyarray` on its inputs and the following triggers the same error:
```python
In [14]: a = np.asanyarray(A)
...: b = np.asanyarray(B)
...: np.allclose(a, b)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-9c8d10e21030> in <module>
1 a = np.asanyarray(A)
2 b = np.asanyarray(B)
----> 3 np.allclose(a, b)
<__array_function__ internals> in allclose(*args, **kwargs)
~/anaconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/numeric.py in allclose(a, b, rtol, atol, equal_nan)
2187
2188 """
-> 2189 res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
2190 return bool(res)
2191
<__array_function__ internals> in isclose(*args, **kwargs)
~/anaconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/numeric.py in isclose(a, b, rtol, atol, equal_nan)
2285 y = array(y, dtype=dt, copy=False, subok=True)
2286
-> 2287 xfin = isfinite(x)
2288 yfin = isfinite(y)
2289 if all(xfin) and all(yfin):
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according
```
The issue is that when `np.asanyarray` is called on EA input it returns an `object` dtype:
``` python
In [16]: A = pd.array([1, 2], dtype='Int64')
...: np.asanyarray(A)
Out[16]: array([1, 2], dtype=object)
```
As far as #33435 is concerned, one solution is to cast to a NumPy array before calling `np.allclose`. Would we, however, want to make `np.allclose` work directly on the integer/floating EAs?
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REF: de-duplicate IntervalIndex compatiblity checks
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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
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REF: ensure CategoricalIndex._shallow_copy only ever gets Categorical
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| 0
| 2020-11-17T21:14:57Z
| 2020-11-18T02:59:59Z
| 2020-11-18T00:38:16Z
|
MEMBER
| null |
still not as tightly restricted as id like, but a step in the right direction.
|
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https://api.github.com/repos/pandas-dev/pandas/issues/37918
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| 745,101,534
|
MDExOlB1bGxSZXF1ZXN0NTIyNzE4MjM5
| 37,918
|
Deprecate inplace in Categorical.remove_unused_categories
|
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] |
closed
| false
| null |
[] |
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| 4
| 2020-11-17T21:29:28Z
| 2020-11-18T13:32:02Z
| 2020-11-18T13:31:50Z
|
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Related to #37643
|
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