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} | 6 | 2019-11-23T08:10:29Z | 2020-07-15T06:18:18Z | 2020-04-16T21:22:53Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> from scipy.sparse import coo_matrix
>>> sparse_data = coo_matrix((
... [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
... ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1])
... ))
>>> sparse_data.todense()
matrix([[0., 0., 0.],
[0., 1., 1.],
[1., 0., 1.],
[1., 1., 0.]])
>>> pd.DataFrame.sparse.from_spmatrix(sparse_data)
0 1 2
0 0.0 0.0 0.0
1 0.0 1.0 1.0
2 0.0 0.0 1.0
3 1.0 1.0 0.0
>>> pd.DataFrame.sparse.from_spmatrix(sparse_data.tocsr())
0 1 2
0 0.0 0.0 0.0
1 0.0 1.0 1.0
2 0.0 0.0 1.0
3 1.0 1.0 0.0
```
#### Problem description
`sparse_data.todense()` and the other matrixes should be same, but not.
(i.e. the [2, 0] value in bottom 2 matrixes should be 1.)
#### Expected Output
When converting sparse matrix to Pandas DataFrame values of the sparse array should stay the same.
#### Output of ``pd.show_versions()``
<details>
python : 3.7.5.final.0
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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xref https://github.com/pandas-dev/pandas/pull/29428#issuecomment-555882765
Puts `pandas/_libs/window.pyx` and `pandas/_libs/window_indexer.pyx` into their own directory as
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https://api.github.com/repos/pandas-dev/pandas/issues/29819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29819/events | https://github.com/pandas-dev/pandas/issues/29819 | 527,612,054 | MDU6SXNzdWU1Mjc2MTIwNTQ= | 29,819 | 0.25.1 pandas.core.frame.DataFrame.eval() erroring-out on Python 3.7 (but not on Python 3.6) | {
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"id... | open | false | null | [] | null | 0 | 2019-11-23T21:31:01Z | 2020-04-26T21:11:09Z | null | NONE | null | #### Code Sample
```python
import sys
import pandas
import numpy
print("# sys.version: " + sys.version)
print("# pandas.__version__: " + pandas.__version__)
print("# numpy.__version__: " + numpy.__version__)
d = pandas.DataFrame({
"a": [True, False],
"b": [1, 2],
"c": [3, 4]})
pandas_eval_env = {
"if_else": lambda c, x, y: numpy.where(c, x, y),
}
d.eval("@if_else(a, 1, c)", local_dict=pandas_eval_env, global_dict=None)
```
#### Problem description
For Python 3.6 the above code runs. For Python 3.7 it errors-out with "`TypeError: unhashable type: 'numpy.ndarray'`". If I am returning the wrong type (an array instead of a named series or dataframe) I apologize.
#### Expected Output
For Python 3.6 we get what I expected:
`array([1, 4])`
For Python 3.7 0.25.1 pandas we get the following:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 3300, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/eval.py", line 327, in eval
ret = eng_inst.evaluate()
File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/engines.py", line 70, in evaluate
res = self._evaluate()
File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/engines.py", line 118, in _evaluate
_check_ne_builtin_clash(self.expr)
File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/engines.py", line 27, in _check_ne_builtin_clash
names = expr.names
File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 850, in names
return frozenset([self.terms.name])
TypeError: unhashable type: 'numpy.ndarray'
```
Note: both failing and successful runs are using Pandas `0.25.1` and numpy `1.17.2`.
Edit: Pandas `0.25.3` with Python `3.7.5`, numpy `1.17.3` works fine. My question then is: is there a work around for when we are using Pandas `0.25.1`?
#### Output of ``pd.show_versions()``
<details>
Working version:
```
sys.version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 13:42:17)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
pandas.__version__:0.25.1
numpy.__version__: 1.17.2
Out[4]: array([1, 4])
pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : None
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
Failing version:
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
```
</details>
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} | 1 | 2019-11-23T23:03:34Z | 2019-11-25T22:58:22Z | 2019-11-25T22:54:58Z | CONTRIBUTOR | null | np.nan is always encoded to -1 in a Categorical, so encoded smaller than all other possible values.
So, if we're checking if a Categorical is equal or larger than a scalar, it is not necessary to find locations of -1, as those will always be False already.
### Performance
```python
>>> n = 1_000_000
>>> c = pd.Categorical([np.nan] * n + ['b'] * n + ['c'] * n,
dtype=pd.CategoricalDtype(['a', 'b', 'c']), ordered=True)
>>> %timeit c == 'b'
9.79 ms ± 32.4 µs per loop # master
3.94 ms ± 38.7 µs per loop # this PR
# the improvement isn't available for less than etc. ops
>>> %timeit c <= 'b'
10.1 ms ± 44.1 µs per loop # both master and this PR
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} | 1 | 2019-11-24T01:46:56Z | 2019-11-25T15:22:53Z | 2019-11-25T07:55:17Z | MEMBER | null | xref #18264, #16971
Looks like seaborn updated their usage in https://github.com/mwaskom/seaborn/pull/1241
and statsmodels https://github.com/statsmodels/statsmodels/pull/3618 both in 2017. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29823/events | https://github.com/pandas-dev/pandas/issues/29823 | 527,681,300 | MDU6SXNzdWU1Mjc2ODEzMDA= | 29,823 | Update logo in GitHub organization | {
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} | [] | closed | false | null | [] | null | 3 | 2019-11-24T10:58:33Z | 2019-11-25T13:36:51Z | 2019-11-25T13:36:42Z | MEMBER | null | Just realized that we still have the old logo in the GitHub organization: https://github.com/pandas-dev/
We probably want to have the [new one](https://dev.pandas.io/static/img/pandas_mark.svg), but I don't have permissions to do that.
May be an admin of the organization can make the change? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29824/events | https://github.com/pandas-dev/pandas/issues/29824 | 527,725,672 | MDU6SXNzdWU1Mjc3MjU2NzI= | 29,824 | using to_dict with the 'records' orient produces different results from the default one | {
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"description": "Requires discussion from... | open | false | null | [] | null | 9 | 2019-11-24T16:55:15Z | 2021-07-23T05:01:39Z | null | NONE | null | consider the following `df`
```
df = pd.DataFrame({'d': pd.date_range('2018-01-01', freq='12h', periods=2)})
for col in df.select_dtypes(['datetime']):
df[col] = pd.Series(df[col].dt.to_pydatetime(), dtype = 'object')
```
using the normal `to_dict` returns the wanted results with the native python types
```
print(df.to_dict())
{'d': {0: datetime.datetime(2018, 1, 1, 0, 0), 1: datetime.datetime(2018, 1, 1, 12, 0)}}
```
however doing it with a different orient, produces different results
```
print(df.to_dict('records')) # also true for the 'split' orient
[{'d': Timestamp('2018-01-01 00:00:00')}, {'d': Timestamp('2018-01-01 12:00:00')}]
```
---
as a side note, it would be nice to have the ability to convert a dataframe or a series into a native types structure (no unlike `to_json`)
---
versions:
pandas : 0.25.2
numpy : 1.17.3 | {
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} | 2 | 2019-11-24T18:52:42Z | 2019-12-01T23:37:55Z | 2019-12-01T23:29:54Z | MEMBER | null | Working on arithmetic, we're going to be sending some functions through BlockManager.apply. This is just cleaning that up a bit as a preliminary. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29826/events | https://github.com/pandas-dev/pandas/pull/29826 | 527,761,152 | MDExOlB1bGxSZXF1ZXN0MzQ0OTUxOTU3 | 29,826 | Fix mypy errors for pandas\tests\series\test_operators.py | {
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} | 3 | 2019-11-24T21:39:41Z | 2019-11-27T21:15:38Z | 2019-11-27T21:15:31Z | CONTRIBUTOR | null | part of #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The errors were:
```
pandas/tests/series/test_operators.py:824: error: "Type[Series]" has no attribute "div"
pandas/tests/series/test_operators.py:824: error: "Type[Series]" has no attribute "rdiv"
```
The problem is that these methods are only added at RunTime, so mypy can't see them. I'm not sure if my solution is the best way to silence these errors though.
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} | [] | closed | false | null | [] | null | 1 | 2019-11-24T22:31:06Z | 2019-11-25T02:17:06Z | 2019-11-25T02:17:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import_date_end = pd.to_datetime("2019-01-01")
import_date_end - pd.offsets.YearOffset(n=8, normalize=True, month=1)
```
#### Problem description
It's because the YearOffset month is Jan and so is the given month
```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-50-1062b3c9831a> in <module>
1 import_date_end# - pd.offsets.YearOffset(n=8, normalize=True, month=1)
----> 2 pd.to_datetime("2019-01-01") - pd.offsets.YearOffset(n=8, normalize=True, month=1)
pandas/_libs/tslibs/offsets.pyx in pandas._libs.tslibs.offsets.BaseOffset.__rsub__()
pandas/_libs/tslibs/offsets.pyx in pandas._libs.tslibs.offsets._BaseOffset.__add__()
~\miniconda3\envs\test\lib\site-packages\pandas\tseries\offsets.py in wrapper(self, other)
110 other = other.tz_localize(None)
111
--> 112 result = func(self, other)
113
114 if self._adjust_dst:
~\miniconda3\envs\test\lib\site-packages\pandas\tseries\offsets.py in apply(self, other)
1950 @apply_wraps
1951 def apply(self, other):
-> 1952 years = roll_yearday(other, self.n, self.month, self._day_opt)
1953 months = years * 12 + (self.month - other.month)
1954 return shift_month(other, months, self._day_opt)
pandas/_libs/tslibs/offsets.pyx in pandas._libs.tslibs.offsets.roll_yearday()
pandas/_libs/tslibs/offsets.pyx in pandas._libs.tslibs.offsets.get_day_of_month()
NotImplementedError:
```
#### Expected Output
Timestamp('2012-01-01 00:00:00')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.6
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"i... | open | false | null | [] | null | 4 | 2019-11-25T06:16:51Z | 2020-04-14T05:30:19Z | null | NONE | null | #### Problem description
pd.read_hdf() has the same issue as #15086. If the file path contains special characters (like Chinese) on Windows, it fails to read the file.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.14
pytest : 5.2.4
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
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https://api.github.com/repos/pandas-dev/pandas/issues/29833 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29833/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29833/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29833/events | https://github.com/pandas-dev/pandas/issues/29833 | 527,889,585 | MDU6SXNzdWU1Mjc4ODk1ODU= | 29,833 | Aggregation fails when grouping on more than one key and the column to aggregate is a tz-aware timestamp series | {
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"de... | closed | false | null | [] | null | 3 | 2019-11-25T07:02:02Z | 2019-11-26T01:00:31Z | 2019-11-26T00:59:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
(
pd.DataFrame(
data=[[pd.Timestamp('2019-11-11 09:00:00+00:00'), 'x1', 'y1']],
columns=['timestamp', 'x', 'y']
)
.groupby(['x', 'y'])
.agg({'timestamp': lambda series: None})
)
```
#### Problem description
Potentially relates to #23683.
When upgrading from Pandas version 0.24.2 to 0.25.3 I noticed some of my old code failed of a sudden.
The above code isolates the issue: when grouping on more than one key and aggregating a Timestamp column, the `_try_cast()` method is called twice from inside `_python_agg_general()` method - the first call transforms the object type from Series to DatetimeArray and the second call fails because the new object doesn't have the `_values` attribute.
If the aggregated column is not of Timestamp dtype but eg., string, the code runs just fine:
```python
import pandas as pd
(
pd.DataFrame(
data=[['2019-11-11 09:00:00+00:00', 'x1', 'y1']],
columns=['timestamp', 'x', 'y']
)
.groupby(['x', 'y'])
.agg({'timestamp': lambda series: None})
)
```
If we keep the Timestamp dtype but remove the timezone, the code runs just fine:
```python
import pandas as pd
(
pd.DataFrame(
data=[[pd.Timestamp('2019-11-11 09:00:00'), 'x1', 'y1']],
columns=['timestamp', 'x', 'y']
)
.groupby(['x', 'y'])
.agg({'timestamp': lambda series: None})
)
```
If we keep the Timestamp dtype and the timezone but aggregate on a single key, the code runs just fine:
```python
import pandas as pd
(
pd.DataFrame(
data=[[pd.Timestamp('2019-11-11 09:00:00+00:00'), 'x1', 'y1']],
columns=['timestamp', 'x', 'y']
)
.groupby(['x'])
.agg({'timestamp': lambda series: None})
)
```
Running the initial code sample produces the following exception:
```python
AttributeError Traceback (most recent call last)
<ipython-input-130-fb69a372d092> in <module>()
8 )
9 .groupby(['x', 'y'])
---> 10 .agg({'timestamp': lambda series: None})
11 )
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, arg, *args, **kwargs)
1453 @Appender(_shared_docs["aggregate"])
1454 def aggregate(self, arg=None, *args, **kwargs):
-> 1455 return super().aggregate(arg, *args, **kwargs)
1456
1457 agg = aggregate
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
227 func = _maybe_mangle_lambdas(func)
228
--> 229 result, how = self._aggregate(func, _level=_level, *args, **kwargs)
230 if how is None:
231 return result
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
504
505 try:
--> 506 result = _agg(arg, _agg_1dim)
507 except SpecificationError:
508
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/base.py in _agg(arg, func)
454 result = OrderedDict()
455 for fname, agg_how in arg.items():
--> 456 result[fname] = func(fname, agg_how)
457 return result
458
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/base.py in _agg_1dim(name, how, subset)
438 "nested dictionary is ambiguous " "in aggregation"
439 )
--> 440 return colg.aggregate(how, _level=(_level or 0) + 1)
441
442 def _agg_2dim(name, how):
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, func_or_funcs, *args, **kwargs)
858
859 if self.grouper.nkeys > 1:
--> 860 return self._python_agg_general(func_or_funcs, *args, **kwargs)
861
862 try:
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in _python_agg_general(self, func, *args, **kwargs)
916 values = ensure_float(values)
917
--> 918 output[name] = self._try_cast(values[mask], result)
919
920 return self._wrap_aggregated_output(output)
~/python/anaconda3/envs/env1/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in _try_cast(self, result, obj, numeric_only)
805 # to the target timezone
806 try:
--> 807 result = obj._values._from_sequence(
808 result, dtype="datetime64[ns, UTC]"
809 )
AttributeError: 'DatetimeArray' object has no attribute '_values'
```
#### Expected Output
<pre>
timestamp
x y
x1 y1 NaT
</pre>
<details>
#### Output of ``pd.show_versions()``
$ python -c 'import pandas as pd; pd.show_versions()'
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2018.5
dateutil : 2.7.3
pip : 18.0
setuptools : 40.4.0
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : 0.9.2
psycopg2 : None
jinja2 : 2.10
IPython : 6.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.0
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.11
tables : 3.5.2
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29834/events | https://github.com/pandas-dev/pandas/issues/29834 | 528,129,533 | MDU6SXNzdWU1MjgxMjk1MzM= | 29,834 | pivot_table with MultiIndex and margin=True fails with ValueError | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 0 | 2019-11-25T14:38:06Z | 2021-07-23T05:02:57Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import string
import random
from datetime import datetime, timezone, date
import pandas as pd
import numpy as np
size_ = 100000
data_ = dict()
for n in range(2):
cat_ = ["".join(cc) for cc in np.random.choice(
[c for c in string.ascii_lowercase], size=(4,6))]
data_.update({("categ", f"cdata.{n:02}"):
pd.Categorical(
np.random.choice(cat_, size=size_,
p=[.1, .5, .25, .15]),
categories=cat_,
ordered=random.choice([True, False]))})
for n in range(2):
data_.update({("float", f"fdata.{n:02}"): np.random.rand(size_)})
data_.update({
("period", "created"): np.random.choice(
[date(2019,2,1), date(2019,5,1), date(2019,8,1), date(2019,11,1)],
size=size_)})
_df = pd.DataFrame(data=data_)
# When margin=False the output is as expected
_df.pivot_table(
values=[("float", "fdata.00"), ("float", "fdata.01")],
index=[("categ", "cdata.00"), ("categ", "cdata.01")],
columns=[("period", "created")],
aggfunc=np.sum,
# margins=True,
)
# When margins=True a ValueError is raised
_df.pivot_table(
values=[("float", "fdata.00"), ("float", "fdata.01")],
index=[("categ", "cdata.00"), ("categ", "cdata.01")],
columns=[("period", "created")],
aggfunc=np.sum,
margins=True,
)
# This seems to be related to MultiIndex and Categorical but I am unsure of which.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-169-e6e29487da7e> in <module>()
4 columns=[("period", "created")],
5 aggfunc=np.sum,
----> 6 margins=True,
7 )
4 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py in pivot_table(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
6087 dropna=dropna,
6088 margins_name=margins_name,
-> 6089 observed=observed,
6090 )
6091
/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
160 observed=dropna,
161 margins_name=margins_name,
--> 162 fill_value=fill_value,
163 )
164
/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/pivot.py in _add_margins(table, data, values, rows, cols, aggfunc, observed, margins_name, fill_value)
206 if hasattr(table, "columns"):
207 for level in table.columns.names[1:]:
--> 208 if margins_name in table.columns.get_level_values(level):
209 raise ValueError(msg)
210
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/multi.py in get_level_values(self, level)
1596 Index(['d', 'e', 'f'], dtype='object', name='level_2')
1597 """
-> 1598 level = self._get_level_number(level)
1599 values = self._get_level_values(level)
1600 return values
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/multi.py in _get_level_number(self, level)
1290 if (count > 1) and not is_integer(level):
1291 raise ValueError(
-> 1292 "The name %s occurs multiple times, use a " "level number" % level
1293 )
1294 try:
ValueError: The name None occurs multiple times, use a level number
```
#### Problem description
A ValueError is unexpected. There seems to be an underlying issue with a number of pivot-like functions (pivot, pivot_table, crosstable) when processing MultiIndex and/or Categorical features. I am unsure as to the source of the issue and if this is indeed expected behaviour. The error suggests using a level number but that recommendation is non-specific as to where this would be applied.
#### Expected Output
I would expect an "All" column to be calculated based on the aggfunc and an error to not have been raised.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 5.5.0
pandas_datareader: 0.7.4
bs4 : 4.6.3
bottleneck : 1.3.0
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29835 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29835/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29835/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29835/events | https://github.com/pandas-dev/pandas/issues/29835 | 528,170,077 | MDU6SXNzdWU1MjgxNzAwNzc= | 29,835 | deleting index column but index=False not working | {
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} | [] | closed | false | null | [] | null | 3 | 2019-11-25T15:43:35Z | 2020-04-24T18:39:24Z | 2020-04-24T18:39:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df_new.to_excel('Misclassification.xlsx',index=False)
df.to_excel(output_file_name,index=False)
```
#### Problem description
I tried to export the dataframe into excel, and wanted to avoid the index column. However even I use index=false, it still failed. Would you able to help with it? Thanks. I'm using python 3.6 on Jupyter notebook
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https://api.github.com/repos/pandas-dev/pandas/issues/29836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29836/events | https://github.com/pandas-dev/pandas/pull/29836 | 528,196,106 | MDExOlB1bGxSZXF1ZXN0MzQ1MzA1MDYw | 29,836 | ENH: XLSB support | {
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} | 31 | 2019-11-25T16:26:33Z | 2020-02-03T12:12:35Z | 2020-01-20T23:49:14Z | CONTRIBUTOR | null | Hey all, a moderately commonly requested feature is xlsb support. I thought I'd go ahead and make a PR for it, based on Pyxlsb. The library isn't very full-featured: datetimes are loaded in as floats without any indication they're datetimes. Would that be grounds for rejection?
Alternative would be to implement xlsb support in Openpyxl which looks like it will take a long time for someone not familiar with the file formats (as I am).
- [X] closes #8540
- [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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} | 7 | 2019-11-25T17:37:52Z | 2020-06-29T07:26:52Z | 2020-06-29T07:26:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'A': [1, 1, 1, 1],
'B': [1, 2, 1, 2],
'numerical_col': [.1, .2, np.nan, .3],
'object_col': ['foo','bar','foo','fee'],
'categorical_col': ['foo','bar','foo','fee']
})
df = df.astype({'categorical_col':'category'})
df.groupby(['A','B']).agg(lambda df: df.isna().sum())
# numerical_col object_col
# A B
# 1 1 1.0 0
# 2 0.0 0
```
#### Problem description
The categorical column "categorical_col" is expected to survive the aggregation, however, it gets dropped.
#### Expected Output
``` python
# numerical_col object_col categorical_col
# A B
# 1 1 1.0 0 0
# 2 0.0 0 0
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.11.6.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: 0.23.4
pytest: 4.3.1
pip: 19.3.1
setuptools: 40.6.3
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 7.1.1
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml: 4.3.0
bs4: None
html5lib: None
sqlalchemy: 1.2.13
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 5 | 2019-11-25T17:41:38Z | 2019-12-09T09:41:50Z | 2019-12-09T09:41:33Z | MEMBER | null | Currently, the creation of a BooleanArray from an int/float array goes through a conversion to object dtype (to do it together with the generic conversion from any list-like):
https://github.com/pandas-dev/pandas/blob/7d7f885856b0c3d51eaf15beaac9d4f30c23797d/pandas/core/arrays/boolean.py#L133-L145
For the specific case of int/float ndarray, this could be optimized with a specific path for those cases without the casting to object array (probably just skipping the `np.asarray(values, dtype=object)` if `values` is a float/int ndarray will be enough). | {
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} | 1 | 2019-11-25T17:45:38Z | 2020-04-28T05:59:38Z | null | MEMBER | null | Currently, in the IntegerArray or BooleanArray `astype`, we first convert to object dtype, and then convert to the requested dtype.
For example, in `IntegerArray.astype`:
https://github.com/pandas-dev/pandas/blob/7d7f885856b0c3d51eaf15beaac9d4f30c23797d/pandas/core/arrays/integer.py#L551-L553
where `self._coerce_to_ndarray()` creates an object ndarray.
In case of converting to integer (and if there are no NaNs), or in case of converting to float, this roundtrip through object dtype is not needed (we can directly convert the `_data` and in case of float set NaNs)
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} | 34 | 2019-11-25T21:58:26Z | 2019-12-09T08:54:21Z | 2019-12-09T08:54:06Z | CONTRIBUTOR | null | xref #29556
I have a few TODOs, and a few tests that I need to unxfail. Putting this up now so that @jorisvandenbossche can take a look. | {
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} | 0 | 2019-11-25T23:18:32Z | 2019-12-27T09:06:26Z | 2019-12-27T09:06:26Z | MEMBER | null | xref #29801 we currently have a number of places where we do something like `dti._data._freq = new_freq` (where we have already validated `new_freq`), we should have some private method to do this. | {
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} | 1 | 2019-11-25T23:38:55Z | 2019-11-27T19:09:23Z | 2019-11-27T18:59:23Z | MEMBER | null | The remaining roadblock to removing ix in #27620 involves a bunch of tests that call this check_result. This simplifies check_result in the hopes of making that roadblock easier. | {
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} | 5 | 2019-11-26T00:44:22Z | 2019-12-15T22:12:02Z | 2019-12-15T22:11:58Z | CONTRIBUTOR | null | `itertools.islice()` should be used to get slice of a deque. `itertools.islice()` also can be used (and is efficient) for other collections. So `unique(arg[:check_count])` was replaced with `set(islice(arg, check_count))` which is also much faster on test data.
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} | [] | closed | false | null | [] | null | 2 | 2019-11-26T01:44:22Z | 2019-12-01T14:42:35Z | 2019-12-01T14:42:35Z | NONE | null | 1- Short summary of the changes:
create a to_cyclical function that transforms ordinal or continous cyclical features to sine transform and cosine transform representing the reality of the feature.
example if hours is to be taken as a feature, 23 and 01 is 22 units apart but in reality they are only two units apart.
If the idea is accepted as an enhancement, I would like to work on it and implement it then create a pull request.
2- Motivation for the changes:
This could add beneficial performance to classifiers and regressors in ML/DL
3-An explanation of why the change is in scope for pandas.
Dataframe / Series related, similar to pandas functions already implemented.
4-Detailed design: Preferably with example-usage (even if not implemented yet) and API documentation:
below is a quick code to the idea
```
def to_cyclical(a_series, max=None):
if max:
sin_ = np.sin(2*np.pi*a_series/max)
cos_ = np.cos(2*np.pi*a_series/max)
else:
sin_ = np.sin(2*np.pi*df.col_name/a_series.max())
cos_ = np.cos(2*np.pi*df.col_name/a_series.max())
return pd.concat([sin_ , cos_ ], axis=1)
```
```
pandas.to_cyclical(series, max=None)
```
5-API Change: Any API changes that may result from the proposal:
Nothing i guess
Further readings for the solution:
https://ianlondon.github.io/blog/encoding-cyclical-features-24hour-time/
https://stats.stackexchange.com/questions/126230/optimal-construction-of-day-feature-in-neural-networks
https://datascience.stackexchange.com/questions/5990/what-is-a-good-way-to-transform-cyclic-ordinal-attributes | {
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Looks like in GitHub actions this test is receiving an empty string for the environment variable `LOCALE_OVERRIDE` instead of `None`. Not sure why, but I guess they should be equivalent, and we don't want to fail when an empty string is provided.
The change also makes the test return a descriptive error message if an unexpected value is set in that environment variable. | {
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} | 0 | 2019-11-26T08:16:08Z | 2019-11-26T22:55:15Z | 2019-11-26T22:55:15Z | MEMBER | null | CI is failing in the doc build (eg https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=21689&view=logs), with the following error related to excel:
```
>>>-------------------------------------------------------------------------
Exception in /home/vsts/work/1/s/doc/source/whatsnew/v0.20.0.rst at block ending on line None
Specify :okexcept: as an option in the ipython:: block to suppress this message
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-51-dde24f57e06d> in <module>
----> 1 styled.to_excel('styled.xlsx', engine='openpyxl')
~/work/1/s/pandas/io/formats/style.py in to_excel(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes)
232 startcol=startcol,
233 freeze_panes=freeze_panes,
--> 234 engine=engine,
235 )
236
~/work/1/s/pandas/io/formats/excel.py in write(self, writer, sheet_name, startrow, startcol, freeze_panes, engine)
742 )
743 if need_save:
--> 744 writer.save()
~/work/1/s/pandas/io/excel/_openpyxl.py in save(self)
42 Save workbook to disk.
43 """
---> 44 return self.book.save(self.path)
45
46 @classmethod
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/workbook/workbook.py in save(self, filename)
406 if self.write_only and not self.worksheets:
407 self.create_sheet()
--> 408 save_workbook(self, filename)
409
410
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/writer/excel.py in save_workbook(workbook, filename)
291 archive = ZipFile(filename, 'w', ZIP_DEFLATED, allowZip64=True)
292 writer = ExcelWriter(workbook, archive)
--> 293 writer.save()
294 return True
295
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/writer/excel.py in save(self)
273 def save(self):
274 """Write data into the archive."""
--> 275 self.write_data()
276 self._archive.close()
277
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/writer/excel.py in write_data(self)
73 archive.writestr(ARC_THEME, theme_xml)
74
---> 75 self._write_worksheets()
76 self._write_chartsheets()
77 self._write_images()
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/writer/excel.py in _write_worksheets(self)
213
214 ws._id = idx
--> 215 self.write_worksheet(ws)
216
217 if ws._drawing:
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/writer/excel.py in write_worksheet(self, ws)
198 else:
199 writer = WorksheetWriter(ws)
--> 200 writer.write()
201
202 ws._rels = writer._rels
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/worksheet/_writer.py in write(self)
352 High level
353 """
--> 354 self.write_top()
355 self.write_rows()
356 self.write_tail()
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/worksheet/_writer.py in write_top(self)
96 cols
97 """
---> 98 self.write_properties()
99 self.write_dimensions()
100 self.write_views()
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/worksheet/_writer.py in write_properties(self)
58 def write_properties(self):
59 props = self.ws.sheet_properties
---> 60 self.xf.send(props.to_tree())
61
62
~/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/worksheet/_writer.py in get_stream(self)
292 continue
293 else:
--> 294 xf.write(el)
295 except GeneratorExit:
296 pass
src/lxml/serializer.pxi in lxml.etree._IncrementalFileWriter.write()
TypeError: got invalid input value of type <class 'xml.etree.ElementTree.Element'>, expected string or Element
<<<-------------------------------------------------------------------------
```
Comparing the last working build (https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=21675) with the first failing (https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=21689) on master, there is a change in openpyxl version (30.01 -> 3.0.2), which might be the cause.
(also pandoc 2.7.3 -> 2.8, but don't think that can be related) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29855 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29855/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29855/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29855/events | https://github.com/pandas-dev/pandas/issues/29855 | 528,621,819 | MDU6SXNzdWU1Mjg2MjE4MTk= | 29,855 | ValueError when slicing dataframe with duplicates datetime index | {
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} | 3 | 2019-11-26T10:14:28Z | 2020-11-23T22:32:57Z | 2020-11-23T22:32:48Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
a = pd.Series([1, 2], index=[pd.Timestamp('2019-11-26'), pd.Timestamp('2019-11-26')])
a.loc[pd.date_range(pd.Timestamp('2019-11-26'), pd.Timestamp('2019-11-27'))]
```
With error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\arrays\datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1978 try:
-> 1979 values, tz_parsed = conversion.datetime_to_datetime64(data)
1980 # If tzaware, these values represent unix timestamps, so we
pandas\_libs\tslibs\conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64()
TypeError: Unrecognized value type: <class 'int'>
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-64-649353f35dc8> in <module>
----> 1 a.loc[pd.date_range(pd.Timestamp('2019-11-26'), pd.Timestamp('2019-11-27'))]
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1422
1423 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1424 return self._getitem_axis(maybe_callable, axis=axis)
1425
1426 def _is_scalar_access(self, key: Tuple):
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1837 raise ValueError("Cannot index with multidimensional key")
1838
-> 1839 return self._getitem_iterable(key, axis=axis)
1840
1841 # nested tuple slicing
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexing.py in _getitem_iterable(self, key, axis)
1131 else:
1132 # A collection of keys
-> 1133 keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
1134 return self.obj._reindex_with_indexers(
1135 {axis: [keyarr, indexer]}, copy=True, allow_dups=True
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1087 keyarr = ax.reindex(keyarr)[0]
1088 else:
-> 1089 keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
1090
1091 self._validate_read_indexer(
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexes\base.py in _reindex_non_unique(self, target)
3478 new_indexer[~check] = -1
3479
-> 3480 new_index = self._shallow_copy_with_infer(new_labels, freq=None)
3481 return new_index, indexer, new_indexer
3482
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexes\base.py in _shallow_copy_with_infer(self, values, **kwargs)
673 except (TypeError, ValueError):
674 pass
--> 675 return Index(values, **attributes)
676
677 def _update_inplace(self, result, **kwargs):
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
344 else:
345 result = DatetimeIndex(
--> 346 data, copy=copy, name=name, dtype=dtype, **kwargs
347 )
348 return result
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\indexes\datetimes.py in __new__(cls, data, freq, start, end, periods, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name, verify_integrity)
332 yearfirst=yearfirst,
333 ambiguous=ambiguous,
--> 334 int_as_wall_time=True,
335 )
336
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\arrays\datetimes.py in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous, int_as_wall_time)
444 yearfirst=yearfirst,
445 ambiguous=ambiguous,
--> 446 int_as_wall_time=int_as_wall_time,
447 )
448
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\arrays\datetimes.py in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous, int_as_wall_time)
1864 # or M8[ns] to denote wall times
1865 data, inferred_tz = objects_to_datetime64ns(
-> 1866 data, dayfirst=dayfirst, yearfirst=yearfirst
1867 )
1868 tz = maybe_infer_tz(tz, inferred_tz)
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\arrays\datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1982 return values.view("i8"), tz_parsed
1983 except (ValueError, TypeError):
-> 1984 raise e
1985
1986 if tz_parsed is not None:
~\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\core\arrays\datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1973 dayfirst=dayfirst,
1974 yearfirst=yearfirst,
-> 1975 require_iso8601=require_iso8601,
1976 )
1977 except ValueError as e:
pandas\_libs\tslib.pyx in pandas._libs.tslib.array_to_datetime()
pandas\_libs\tslib.pyx in pandas._libs.tslib.array_to_datetime()
ValueError: mixed datetimes and integers in passed array
```
#### Problem description
The error message is not helpful, as the index and slicing is done on datetime only. The error disappear if the duplicates are removed.
#### Expected Output
```
2019-11-26 1.0
2019-11-26 2.0
2019-11-27 NaN
Freq: D, dtype: float64
```
Or better error message if this is an unwanted behaviour on duplicates.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : 0.12.3
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29856/events | https://github.com/pandas-dev/pandas/issues/29856 | 528,629,989 | MDU6SXNzdWU1Mjg2Mjk5ODk= | 29,856 | df.Truncate now fails when arguments have different tzinfo | {
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... | open | false | null | [] | null | 4 | 2019-11-26T10:28:25Z | 2021-06-22T11:08:04Z | null | NONE | null | #### Code Sample
```python
df = pd.DataFrame()
date1 = pd.Timestamp('20190101', tz='Europe/Brussels')
date2 = pd.Timestamp('20190201T00:00:00+01:00')
df.truncate(before=date1, after=date2)
```
```python
File "/python3.7/site-packages/pandas/core/indexes/base.py", line 5244, in slice_locs
raise ValueError("Both dates must have the " "same UTC offset")
ValueError: Both dates must have the same UTC offset
```
#### Problem description
In #25263, these lines were added (line 4881):
```python
if not tz_compare(ts_start.tzinfo, ts_end.tzinfo):
raise ValueError("Both dates must have the same UTC offset"
```
1. The timestamps in the example do have the same UTC offset. The difference is that one has an actual timezone, while the other has a fixed offset. So a simple comparison of `tzinfo` is insufficient here.
2. It is unclear to my *why* you shouldn't be able to truncate with timestamps with different offsets!?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 28.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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Right now using `pd.read_gbq` will raise a warning.
I think `pandas.read_gbq` should align with it. | {
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- [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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- [x] tests added / passed
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Bug when indexing with ``.loc`` and the index is a CateggoricalIndex with integer categories. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29862 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29862/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29862/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29862/events | https://github.com/pandas-dev/pandas/pull/29862 | 528,848,001 | MDExOlB1bGxSZXF1ZXN0MzQ1ODM0MDky | 29,862 | CI: Fix version openpyxl | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-11-26T16:49:28Z | 2019-11-27T21:19:55Z | 2019-11-27T20:54:20Z | MEMBER | null | AFAICT this is a legacy of SparseDataFrame | {
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} | 1 | 2019-11-26T16:50:11Z | 2019-12-01T21:32:57Z | 2019-12-01T21:32:57Z | NONE | null | Hi!
I don't know how to solve following issue, can you please take a look? What am I doing wrong?
#### Problem description
```python
import io
import pandas as pd
data_1 = io.StringIO("""
xyz
from_date
2019-10-01 00:30:00+00:00 0.9
2019-10-01 01:00:00+00:00 0.8
2019-10-01 01:30:00+00:00 0.7
2019-10-01 02:00:00+00:00 0.6""")
df = pd.read_csv(data_1, sep='\s{2,}', engine='python')
df.index = pd.to_datetime(df.index, utc=True)
data_2 = io.StringIO("""
from_date abc
2019-10-01 00:00:00+00:00 2.46
2019-10-01 00:30:00+00:00 2.46
2019-10-01 01:00:00+00:00 2.46
2019-10-01 01:30:00+00:00 2.46
2019-10-01 02:00:00+00:00 2.19
""")
df2 = pd.read_csv(data_2, sep='\s{2,}', engine='python')
df2['from_date'] = pd.to_datetime(df2['from_date'], utc=True)
print(f"pandas version: {pd.__version__}")
print(f"df index dtype: {df.index.dtype}")
print(f"df2 dt column dtype: {df2['from_date'].dtype}")
print("check", df.index.dtype == df2.from_date.dtype )
pd.merge_asof(left=df, right=df2, left_index=True, right_on=['from_date'])
```
#### Output
```python
pandas version: 0.25.3
df index dtype: datetime64[ns, UTC]
df2 dt column dtype: datetime64[ns, UTC]
check True
---------------------------------------------------------------------------
MergeError Traceback (most recent call last)
<ipython-input-82-bdb9feab8f76> in <module>
28 print(f"df2 dt column dtype: {df2['from_date'].dtype}")
29 print("check", df.index.dtype == df2.from_date.dtype )
---> 30 pd.merge_asof(left=df, right=df2, left_index=True, right_on=['from_date'], direction='nearest')
c:\users\asd\lib\site-packages\pandas\core\reshape\merge.py in merge_asof(left, right, on, left_on, right_on, left_index, right_index, by, left_by, right_by, suffixes, tolerance, allow_exact_matches, direction)
537 tolerance=tolerance,
538 allow_exact_matches=allow_exact_matches,
--> 539 direction=direction,
540 )
541 return op.get_result()
c:\users\asd\lib\site-packages\pandas\core\reshape\merge.py in __init__(self, left, right, on, left_on, right_on, left_index, right_index, by, left_by, right_by, axis, suffixes, copy, fill_method, how, tolerance, allow_exact_matches, direction)
1552 how=how,
1553 suffixes=suffixes,
-> 1554 fill_method=fill_method,
1555 )
1556
c:\users\asd\lib\site-packages\pandas\core\reshape\merge.py in __init__(self, left, right, on, left_on, right_on, left_index, right_index, axis, suffixes, copy, fill_method, how)
1442 how=how,
1443 suffixes=suffixes,
-> 1444 sort=True, # factorize sorts
1445 )
1446
c:\users\asd\lib\site-packages\pandas\core\reshape\merge.py in __init__(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)
624 self.right_join_keys,
625 self.join_names,
--> 626 ) = self._get_merge_keys()
627
628 # validate the merge keys dtypes. We may need to coerce
c:\users\asd\lib\site-packages\pandas\core\reshape\merge.py in _get_merge_keys(self)
1636 )
1637 )
-> 1638 raise MergeError(msg)
1639
1640 # validate tolerance; must be a Timedelta if we have a DTI
MergeError: incompatible merge keys [0] dtype('<M8[ns]') and datetime64[ns, UTC], must be the same type
```
#### Expected Output
merged dataframes
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https://api.github.com/repos/pandas-dev/pandas/issues/29865 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29865/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29865/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29865/events | https://github.com/pandas-dev/pandas/pull/29865 | 528,851,949 | MDExOlB1bGxSZXF1ZXN0MzQ1ODM3MzU1 | 29,865 | Changed string formatting to fstring | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
I am new to contributing to open source projects bare with me. I normally only push to my own
master branch. I have never used git diff before(I know it is supposed to compare changed) and I just keep getting the error: fatal: bad revision 'upstream/master' if someone could tell me how to properly use it that would be great! It passed the black pandas test though. Once I figure out how to properly submit a PR abiding by pandas standards I will do trying a lot more PR. This is only a sample
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} | 0 | 2019-11-26T17:06:02Z | 2019-11-27T18:39:49Z | 2019-11-27T18:39:49Z | CONTRIBUTOR | null | - [x] closes #29772
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-11-26T17:25:44Z | 2019-11-27T21:08:53Z | 2019-11-27T21:08:51Z | MEMBER | null | Updating PATH only once for the whole job, so it doesn't need to be set at every step.
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"de... | open | false | null | [] | null | 2 | 2019-11-26T17:26:11Z | 2021-07-23T05:19:06Z | null | CONTRIBUTOR | null | While working in https://github.com/pandas-dev/pandas/pull/29799, it was quite hard to add a new return value from `lib.infer_dtype`. There are many places where we do something like
```python
inferred_dtype = lib.infer_dtype(values)
if inferred_dtype == "string":
...
if inferred_dtype in {"datetime", "integer"}
...
```
It was hard to grep for these. Could we instead define an enum
```
class InferredType(enum.Enum):
STRING: "string"
INTEGER: "intger"
MIXED_INTEGER: "mixed-integer"
...
```
And then return `InferredType.INTEGER`, and update all our checks for
```python
if inferred_dtype == InferredType.INTEGER
```
I think this should be entirely backwards compatible, since we're still just returning the string. | {
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- [ ] tests added / passed
- [x] passes `black pandas`
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} | 3 | 2019-11-26T21:53:43Z | 2019-12-03T16:08:38Z | 2019-12-03T13:49:46Z | MEMBER | null | Wouldn't be surprised if there is a perf hit here, will run asvs. | {
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} | 7 | 2019-11-26T23:22:57Z | 2019-12-30T16:43:46Z | 2019-12-30T13:30:19Z | MEMBER | null | - [x] closes #19296
- [ ] tests added / passed
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cc @TomAugspurger | {
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In Azure-pipelines we publish the built docs to GitHub pages, served at dev.pandas.io.
After this is merged, I'll work on publishing the Actions docs to the new OVH server. Possibly, publishing the docs of every PR.
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https://api.github.com/repos/pandas-dev/pandas/issues/29876 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29876/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29876/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29876/events | https://github.com/pandas-dev/pandas/pull/29876 | 529,078,263 | MDExOlB1bGxSZXF1ZXN0MzQ2MDIxNDEx | 29,876 | DEPR: infer_dtype default for skipna is now True | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29877/events | https://github.com/pandas-dev/pandas/pull/29877 | 529,081,304 | MDExOlB1bGxSZXF1ZXN0MzQ2MDIzNzY1 | 29,877 | CI: Fix tests broken by np 1.18 sorting change | {
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} | 3 | 2019-11-27T02:46:51Z | 2019-11-27T16:23:02Z | 2019-11-27T09:51:15Z | MEMBER | null | Longer-term, do we want to use numpy's new behavior?
cc @TomAugspurger
<b>Update</b> A little more background: consider two ndarrays in np<1.18
```
arr1 = np.array([1, 2, np.nan, 3, 4])
arr2 = np.array([1, 2, np.datetime64("NaT"), 3, 4], dtype="datetime64[ns]")
>>> np.sort(arr1)
array([ 1., 2., 3., 4., nan])
>>> np.sort(arr2)
array([ 'NaT', '1970-01-01T00:00:00.000000001',
'1970-01-01T00:00:00.000000002', '1970-01-01T00:00:00.000000003',
'1970-01-01T00:00:00.000000004'], dtype='datetime64[ns]')
```
In numpy 1.18, the behavior of `np.sort(arr2)` is changing to put the NaT at the end instead of at the beginning, to behave more like NaN. This breaks a few tests, which this PR fixes.
Side-note: as of now, 1.18 changes the behavior for datetime64, but not timedelta64. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29878/events | https://github.com/pandas-dev/pandas/pull/29878 | 529,098,860 | MDExOlB1bGxSZXF1ZXN0MzQ2MDM3NTYy | 29,878 | ENH: Allow users to definite their own window bound calculations in rolling | {
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} | 7 | 2019-11-27T03:53:34Z | 2019-12-05T16:17:53Z | 2019-12-05T12:56:12Z | MEMBER | null | Currently `rolling` allows integer and offset as `window` arguments. This PR allows users to define a custom method for calculating window boundaries by passing a subclass of a new `BaseIndexer`
```
from pandas.api.indexers import BaseIndexer
class MyWindowIndexer(BaseIndexer):
def get_window_bounds(self, ...):
....
return (start, end)
indexer = MyWindowIndexer(arg1, arg2, ...)
result = df.rolling(indexer).mean()
```
Could help address more custom rolling window operations as described in #26959, #13969, #25510, #24295
- [x] documented new feature
- [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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} | 4 | 2019-11-27T04:39:54Z | 2020-01-21T10:50:47Z | 2020-01-21T10:50:47Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
print(pd.__version__) # '0.25.3'
# simple frame with a name attribute for columns
df = pd.DataFrame({'a': [1.1], 'b': [2.2]})
df.columns.name = 'x'
print(df)
# x a b
# 0 1.1 2.2
# tile rows to a new frame, apparently using the copy of the first frame
df2 = pd.concat([df] * 2, copy=True)
print(df2)
# x a b
# 0 1.1 2.2
# 0 1.1 2.2
# clear the column name for the first frame
df.columns.name = None
# and observe the name for the second
assert df2.columns.name is None
print(df2)
# a b
# 0 1.1 2.2
# 0 1.1 2.2
# aha, it's the same object
assert df.columns is df2.columns
```
#### Problem description
It is expected that `copy=True` should make a copy of the `columns` index. However, the demonstration shows it's the same object. Modifications, such as the `name` property, are made to the source frame and the output frame created by `pd.concat`.
#### Expected Output
It is expected that `df2.columns` is a copy of the original `df.columns`. This would prevent (e.g.) changing the `name` property for both frames.
A workaround is to manually copy this property after `pd.concat`:
```python
df2 = pd.concat([df] * 2, copy=True)
df2.columns = df2.columns.copy()
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1.post20191125
Cython : 0.29.14
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
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} | 5 | 2019-11-27T12:55:58Z | 2020-01-01T16:25:42Z | 2020-01-01T16:25:37Z | MEMBER | null | - [X] xref #23923, https://github.com/pandas-dev/pandas/pull/29852#discussion_r351228283
There are couple of things I don't understand from the locale:
- For what I see, we test with different locales (e.g. `it_IT.UTF-8`) but pandas never uses the language (`it_IT`) only the encoding (`UTF-8`). So, not sure if testing with different locales is being useful.
- The variable `LOCALE_OVERRIDE` seems to add more complexity than value. Unless `LC_ALL`/`LANG` are being set afterwards, but I don't think so.
- There is a test that is using `LOCALE_OVERRIDE` but doesn't seem to depend on the actual locale of the system. It probably makes more sense to parametrize and test all the locales we want, than use the variable value (when testing locally this test will be more useful).
- There is a "test" being skipped in `run_tests.sh` that is probably worth converting to an actual test.
Addressing these things here, but I may be missing something here. Please let me know if that's the case. | {
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} | 17 | 2019-11-27T14:28:59Z | 2020-09-08T00:15:51Z | 2020-09-08T00:15:51Z | CONTRIBUTOR | null | Followup to https://github.com/pandas-dev/pandas/pull/29877. cc @jbrockmendel if you could fill out the details.
* Adopt NaT at the end for Index? (we already do for Series)
* Report timedelta NaT-sorting issue to NumPy? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29885 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29885/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29885/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29885/events | https://github.com/pandas-dev/pandas/issues/29885 | 529,425,047 | MDU6SXNzdWU1Mjk0MjUwNDc= | 29,885 | Cannot install pandas on alpine | {
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Dockerfile
```bash
FROM alpine:3.9
RUN apk add --no-cache python3 && \
python3 -m ensurepip && \
pip3 install --upgrade pip setuptools
RUN pip3 install --upgrade pip
RUN pip3 install numpy==1.16.1 pandas==0.23.4
```
#### Problem description
I get the following error when I try build the image
```
~/work/algorithms(master*) » docker build . -t algo-old [py3ds]kamil@beast
Sending build context to Docker daemon 83.6MB
Step 1/4 : FROM alpine:3.9
---> 055936d39205
Step 2/4 : RUN apk add --no-cache python3 && python3 -m ensurepip && pip3 install --upgrade pip setuptools
---> Running in 7e9b12fa6c77
fetch http://dl-cdn.alpinelinux.org/alpine/v3.9/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.9/community/x86_64/APKINDEX.tar.gz
(1/11) Installing libbz2 (1.0.6-r7)
(2/11) Installing expat (2.2.8-r0)
(3/11) Installing libffi (3.2.1-r6)
(4/11) Installing gdbm (1.13-r1)
(5/11) Installing xz-libs (5.2.4-r0)
(6/11) Installing ncurses-terminfo-base (6.1_p20190105-r0)
(7/11) Installing ncurses-terminfo (6.1_p20190105-r0)
(8/11) Installing ncurses-libs (6.1_p20190105-r0)
(9/11) Installing readline (7.0.003-r1)
(10/11) Installing sqlite-libs (3.28.0-r1)
(11/11) Installing python3 (3.6.9-r2)
Executing busybox-1.29.3-r10.trigger
OK: 68 MiB in 25 packages
Looking in links: /tmp/tmpu5zsnjnn
Requirement already satisfied: setuptools in /usr/lib/python3.6/site-packages (40.6.2)
Requirement already satisfied: pip in /usr/lib/python3.6/site-packages (18.1)
Collecting pip
Downloading https://files.pythonhosted.org/packages/00/b6/9cfa56b4081ad13874b0c6f96af8ce16cfbc1cb06bedf8e9164ce5551ec1/pip-19.3.1-py2.py3-none-any.whl (1.4MB)
Collecting setuptools
Downloading https://files.pythonhosted.org/packages/9e/d5/444a443d890f09fc1ca1a2c3c9fc7e84cb148177b05ac94fe5084e3d9abb/setuptools-42.0.1-py2.py3-none-any.whl (582kB)
Installing collected packages: pip, setuptools
Found existing installation: pip 18.1
Uninstalling pip-18.1:
Successfully uninstalled pip-18.1
Found existing installation: setuptools 40.6.2
Uninstalling setuptools-40.6.2:
Successfully uninstalled setuptools-40.6.2
Successfully installed pip-19.3.1 setuptools-42.0.1
Removing intermediate container 7e9b12fa6c77
---> e6282c1ebea1
Step 3/4 : RUN pip3 install --upgrade pip
---> Running in 311cd9140e02
Requirement already up-to-date: pip in /usr/lib/python3.6/site-packages (19.3.1)
Removing intermediate container 311cd9140e02
---> 7af0fdc6e218
Step 4/4 : RUN pip3 install numpy==1.16.1 pandas==0.23.4
---> Running in 6fe4f2f0f09f
Collecting numpy==1.16.1
Downloading https://files.pythonhosted.org/packages/2b/26/07472b0de91851b6656cbc86e2f0d5d3a3128e7580f23295ef58b6862d6c/numpy-1.16.1.zip (5.1MB)
Collecting pandas==0.23.4
Downloading https://files.pythonhosted.org/packages/e9/ad/5e92ba493eff96055a23b0a1323a9a803af71ec859ae3243ced86fcbd0a4/pandas-0.23.4.tar.gz (10.5MB)
ERROR: Command errored out with exit status 1:
command: /usr/bin/python3.6 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-6_f3wxk2/pandas/setup.py'"'"'; __file__='"'"'/tmp/pip-install-6_f3wxk2/pandas/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-install-6_f3wxk2/pandas/pip-egg-info
cwd: /tmp/pip-install-6_f3wxk2/pandas/
Complete output (61 lines):
WARNING: The wheel package is not available.
ERROR: Command errored out with exit status 1:
command: /usr/bin/python3.6 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-wheel-1tx04x9_/numpy/setup.py'"'"'; __file__='"'"'/tmp/pip-wheel-1tx04x9_/numpy/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-ooxld4dn
cwd: /tmp/pip-wheel-1tx04x9_/numpy/
Complete output (7 lines):
Running from numpy source directory.
usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
or: setup.py --help [cmd1 cmd2 ...]
or: setup.py --help-commands
or: setup.py cmd --help
error: invalid command 'bdist_wheel'
----------------------------------------
ERROR: Failed building wheel for numpy
ERROR: Command errored out with exit status 1:
command: /usr/bin/python3.6 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-wheel-1tx04x9_/numpy/setup.py'"'"'; __file__='"'"'/tmp/pip-wheel-1tx04x9_/numpy/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' clean --all
cwd: /tmp/pip-wheel-1tx04x9_/numpy
Complete output (10 lines):
Running from numpy source directory.
`setup.py clean` is not supported, use one of the following instead:
- `git clean -xdf` (cleans all files)
- `git clean -Xdf` (cleans all versioned files, doesn't touch
files that aren't checked into the git repo)
Add `--force` to your command to use it anyway if you must (unsupported).
----------------------------------------
ERROR: Failed cleaning build dir for numpy
ERROR: Failed to build one or more wheels
Traceback (most recent call last):
File "/usr/lib/python3.6/site-packages/setuptools/installer.py", line 119, in fetch_build_egg
subprocess.check_call(cmd)
File "/usr/lib/python3.6/subprocess.py", line 311, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3.6', '-m', 'pip', '--disable-pip-version-check', 'wheel', '--no-deps', '-w', '/tmp/tmpm2wxn48a', '--quiet', 'numpy>=1.9.0']' returned non-zero exit status 1.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-install-6_f3wxk2/pandas/setup.py", line 749, in <module>
**setuptools_kwargs)
File "/usr/lib/python3.6/site-packages/setuptools/__init__.py", line 144, in setup
_install_setup_requires(attrs)
File "/usr/lib/python3.6/site-packages/setuptools/__init__.py", line 139, in _install_setup_requires
dist.fetch_build_eggs(dist.setup_requires)
File "/usr/lib/python3.6/site-packages/setuptools/dist.py", line 721, in fetch_build_eggs
replace_conflicting=True,
File "/usr/lib/python3.6/site-packages/pkg_resources/__init__.py", line 782, in resolve
replace_conflicting=replace_conflicting
File "/usr/lib/python3.6/site-packages/pkg_resources/__init__.py", line 1065, in best_match
return self.obtain(req, installer)
File "/usr/lib/python3.6/site-packages/pkg_resources/__init__.py", line 1077, in obtain
return installer(requirement)
File "/usr/lib/python3.6/site-packages/setuptools/dist.py", line 777, in fetch_build_egg
return fetch_build_egg(self, req)
File "/usr/lib/python3.6/site-packages/setuptools/installer.py", line 121, in fetch_build_egg
raise DistutilsError(str(e))
distutils.errors.DistutilsError: Command '['/usr/bin/python3.6', '-m', 'pip', '--disable-pip-version-check', 'wheel', '--no-deps', '-w', '/tmp/tmpm2wxn48a', '--quiet', 'numpy>=1.9.0']' returned non-zero exit status 1.
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
The command '/bin/sh -c pip3 install numpy==1.16.1 pandas==0.23.4' returned a non-zero code: 1
```
#### Expected Output
no errors :)
#### Output of ``pd.show_versions()``
not applicable
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https://api.github.com/repos/pandas-dev/pandas/issues/29886 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29886/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29886/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29886/events | https://github.com/pandas-dev/pandas/issues/29886 | 529,432,776 | MDU6SXNzdWU1Mjk0MzI3NzY= | 29,886 | Replace "foo!r" to "repr(foo)" syntax | {
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} | 30 | 2019-11-27T15:52:28Z | 2019-12-30T19:59:49Z | 2019-12-30T19:59:49Z | MEMBER | null | Since we are moving to f-strings and dropping old strings formats (```%``` and ```.format()```), we no longer need to have the redundant ```%r``` and ```!r``` specifiers, instead we use the ```repr()``` format.
#### Notes:
* Don't forget to link this issue in your PR, paste this ```https://github.com/pandas-dev/pandas/issues/29886``` in your PR.
* If any of your changed files are related to #29547 , please make sure to link your pull request to that issue as well, simply paste this ```https://github.com/pandas-dev/pandas/issues/29547``` in your pull request's body message as well.
* Please comment what you are planning to work on, so we won't do double work.
* If a file/files that should be marked as done, is'nt marked, please comment letting me know.
---
To check what files still needs to be fixed in the "pandas" directory:
```
grep -R -e '%r' -e '!r}' -e "'{}'" --include=*.{py,pyx} pandas/
```
##### Tip:
If you want to see the line number of the occurrence, replace the ```-l``` with ```-n```
for example:
```
grep -n -R '%r' --include=*.{py,pyx} pandas/
```
---
### The current list is:
- [x] ci/print_skipped.py
- [x] pandas/_config/config.py
- [x] pandas/conftest.py
- [x] pandas/core/accessor.py
- [x] pandas/core/algorithms.py
- [x] pandas/core/arrays/categorical.py
- [ ] pandas/core/arrays/datetimelike.py
- [ ] pandas/core/arrays/period.py
- [x] pandas/core/arrays/numpy_.py
- [x] pandas/core/computation/align.py
- [x] pandas/core/computation/eval.py
- [x] pandas/core/computation/expressions.py
- [x] pandas/core/computation/expr.py
- [x] pandas/core/computation/ops.py
- [x] pandas/core/computation/pytables.py
- [x] pandas/core/dtypes/common.py
- [x] pandas/core/dtypes/dtypes.py
- [x] pandas/core/frame.py
- [x] pandas/core/generic.py
- [x] pandas/core/groupby/grouper.py
- [x] pandas/core/indexes/base.py
- [x] pandas/core/indexes/datetimelike.py
- [x] pandas/core/indexes/interval.py
- [x] pandas/core/indexes/numeric.py
- [x] pandas/core/internals/concat.py
- [x] pandas/core/internals/managers.py
- [x] pandas/core/missing.py
- [x] pandas/core/nanops.py
- [x] pandas/core/reshape/concat.py
- [x] pandas/core/reshape/merge.py
- [x] pandas/core/reshape/tile.py
- [x] pandas/core/series.py
- [x] pandas/core/sorting.py
- [x] pandas/core/strings.py
- [x] pandas/core/window/rolling.py
- [x] pandas/\_\_init\_\_.py
- [x] pandas/io/formats/css.py
- [x] pandas/io/formats/excel.py
- [ ] pandas/io/formats/style.py
- [x] pandas/io/html.py
- [x] pandas/io/parsers.py
- [x] pandas/_libs/interval.pyx
- [x] pandas/plotting/_core.py
- [x] pandas/tests/computation/test_eval.py
- [x] pandas/tests/frame/test_alter_axes.py
- [x] pandas/tests/frame/test_analytics.py
- [x] pandas/tests/frame/test_query_eval.py
- [x] pandas/tests/groupby/test_whitelist.py
- [x] pandas/tests/indexes/datetimes/test_datetime.py
- [x] pandas/tests/indexes/multi/test_integrity.py
- [x] pandas/tests/indexes/period/test_period.py
- [x] pandas/tests/indexes/test_common.py
- [ ] pandas/tests/indexes/timedeltas/test_timedelta.py
- [x] pandas/tests/io/msgpack/test_case.py
- [x] pandas/tests/io/msgpack/test_extension.py
- [x] pandas/tests/io/parser/test_unsupported.py
- [x] pandas/tests/io/test_html.py
- [x] pandas/tests/test_expressions.py
- [x] pandas/tests/test_strings.py
- [ ] pandas/tests/io/test_sql.py
- [ ] pandas/tests/reshape/test_concat.py
- [ ] pandas/tests/scalar/timedelta/test_timedelta.py
- [x] pandas/tests/series/test_missing.py
- [x] pandas/tests/tseries/offsets/test_fiscal.py
- [x] pandas/tests/tseries/offsets/test_offsets.py
- [x] pandas/tseries/offsets.py
- [x] pandas/util/_decorators.py
- [x] pandas/util/_test_decorators.py
- [x] pandas/util/testing.py
- [x] pandas/_version.py
- [ ] /scripts/tests/test_validate_docstrings.py
---
#### Lastly:
- [ ] Add a test case (so we won't regress) | {
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Here adds a warning to warn the user about this.
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- [x] tests added / passed
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} | 6 | 2019-11-27T16:50:05Z | 2019-11-27T19:07:58Z | 2019-11-27T18:14:19Z | MEMBER | null | @jreback the pertinent `delete` methods are never hit in tests. any chance we can rip them out? | {
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} | 1 | 2019-11-27T19:25:28Z | 2020-06-29T17:38:56Z | 2020-06-29T17:38:55Z | CONTRIBUTOR | null | ```
Assignment to multiple columns of a :class:`DataFrame` when some of the columns do not exist would previously assign the values to the last column. Now, new columns would be constructed with the right values.
.. ipython:: python
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]})
df
*Previous behavior*:
.. code-block:: ipython
In [3]: df[['a', 'c']] = 1
In [4]: df
Out[4]:
a b
0 1 1
1 1 1
2 1 1
*New behavior*:
.. ipython:: python
df[['a', 'c']] = 1
df
```
#### Code Sample, a copy-pastable example if possible
```python
>>>
>>> df = pd.DataFrame({
... 'a': [0, 0, 0, 0],
... 'b': [0, 2, 3, 4],
... 'c': ['A', 'B', 'C', 'D']
... }).set_index(['a', 'b'])
>>>
>>> df
c
a b
0 0 A
2 B
3 C
4 D
>>> df.index
MultiIndex([(0, 0),
(0, 2),
(0, 3),
(0, 4)],
names=['a', 'b'])
>>> mi_2 = pd.MultiIndex.from_product([[0], [-1, 0, 1, 3, 4, 5]])
>>>
>>> df.reindex(mi_2, method='backfill')
c
0 -1 A
0 A
1 D
3 A
4 A
5 C
>>>
>>>
>>> df.reindex(mi_2, method='pad')
c
0 -1 NaN
0 NaN
1 D
3 NaN
4 A
5 C
>>>
```
#### Problem description
As far as I understand, the ordering logic for reindexing here should be tuple ordering with the constituent tuples of the MultiIndex -- i.e. `df`'s index has tuples:
```
(0, 0),
(0, 2),
(0, 3),
(0, 4)
```
and `mi_2`'s tuples are:
```
(0, -1),
(0, 0),
(0, 1),
(0, 3),
(0, 4),
(0, 5)
```
where tuple ordering properties are, for tuples `x = (x_1, ..., x_n)`, `y = (y_1, ..., y_n)` that `x > y` iff there exists some i in 1, ..., n such that `x_i > y_i` and `x_j >= y_j` for all j in 1, ..., i-1. Moreover, `x = y` <=> `x_i = y_i` for all i.
As such, the reindexing of the DataFrame with backfilling should:
- "match" (0, -1) and (0, 0) from `mi_2` both to (0, 0) in `df.index`
- match (0, 1) from `mi_2` to (0, 2) in `df.index`
- match (0, 3) from `mi_2` to (0, 3) in `df.index`
- match (0, 4) from `mi_2` to (0, 4) in `df.index`
- not match (0, 5) from `mi_2`
Similarly, the reindexing of the DataFrame with forward-filling, aka padding, should:
- not match (0, -1) from `mi_2`
- match (0, 0) and (0, 1) from `mi_2` to (0, 0) in `df.index`
- match (0, 3) from `mi_2` to (0, 3) in `df.index`
- match (0, 4) and (0, 5) from `mi_2` to (0, 5) in `df.index`
In summary, as far as I can tell, this is simply a bug which was introduced with the new implementation of the multi-indexing backend in 0.23, since I couldn't find anything in the docs about changing the semantics of reindexing. I have a diff locally (will prepare a PR shortly if contributors here are in agreement that this warrants fixing) which addresses these and does not break any existing tests (and which also adds tests which pass on 0.22 but fail on versions >= 0.23), which also suggests to me that this is a bug.
#### Expected Output
This is with python2.7, numpy 1.16.5, and pandas 0.22.0 -- as far as I can tell, the issue was introduced in 0.23.0.
```python
>>> df.reindex(mi_2, method='backfill')
c
0 -1 A
0 A
1 B
3 C
4 D
5 NaN
>>>
>>> df.reindex(mi_2, method='pad')
c
0 -1 NaN
0 A
1 A
3 C
4 D
5 D
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.1.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 : 0.25.3
numpy : 1.17.0
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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```python
import datetime
import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar
bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar())
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),start='07:00')
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),holidays=['2014-01-21'])
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),holidays=['2014-01-21','2014-01-22'])
#bhour_us = pd.offsets.CustomBusinessHour(calendar=USFederalHolidayCalendar(),holidays=['2014-01-21','2014-02-06'])
dt = datetime.datetime(2014, 1, 17, 13)
for i in range(1,30):
y = dt + bhour_us * i
print(y)
```
>>>
2014-01-17 14:00:00
2014-01-17 15:00:00
2014-01-17 16:00:00
2014-01-21 09:00:00
2014-01-21 10:00:00
2014-01-21 11:00:00
2014-01-21 12:00:00
**_2014-01-20 13:00:00
2014-01-31 14:00:00
2014-01-31 15:00:00
2014-01-31 16:00:00
2014-02-03 09:00:00
2014-02-03 10:00:00
2014-02-03 11:00:00
2014-02-03 12:00:00_**
2014-01-21 13:00:00
2014-01-21 14:00:00
2014-01-21 15:00:00
2014-01-21 16:00:00
2014-01-22 09:00:00
2014-01-22 10:00:00
2014-01-22 11:00:00
2014-01-22 12:00:00
2014-01-22 13:00:00
2014-01-22 14:00:00
2014-01-22 15:00:00
2014-01-22 16:00:00
2014-01-23 09:00:00
2014-01-23 10:00:00
#### Problem description
CustomBusinessHour behaves strangely when I try to use a custom calendar (same behaviour when using a list of holidays directly).
In the example above, I modified the example from the official doc.
https://pandas.pydata.org/pandas-docs/version/0.18.1/timeseries.html#timeseries-custombusinesshour
Something strange happens (here at i in range(8, 16)).
Then, the rest is fine, but shifted away.
Playing with the calendar and length of the business day (start and end) affects this.
This seems related in some sense to a mod function somewhere... But I can't get my finger on it.
#### Expected Output
2014-01-17 14:00:00
2014-01-17 15:00:00
2014-01-17 16:00:00
2014-01-21 09:00:00
2014-01-21 10:00:00
2014-01-21 11:00:00
2014-01-21 12:00:00
2014-01-21 13:00:00
2014-01-21 14:00:00
2014-01-21 15:00:00
2014-01-21 16:00:00
2014-01-22 09:00:00
2014-01-22 10:00:00
2014-01-22 11:00:00
2014-01-22 12:00:00
2014-01-22 13:00:00
2014-01-22 14:00:00
2014-01-22 15:00:00
2014-01-22 16:00:00
2014-01-23 09:00:00
2014-01-23 10:00:00
2014-01-23 11:00:00
2014-01-23 12:00:00
2014-01-23 13:00:00
2014-01-23 14:00:00
2014-01-23 15:00:00
2014-01-23 16:00:00
2014-01-24 09:00:00
2014-01-24 10:00:00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1.post20191125
Cython : 0.29.14
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
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- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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It takes a while to find the failing line of the benchmarks logs at the moment. This should highlight the failing ones, and also publish the log as an artifact so it can be analyzed easier.
CC: @jbrockmendel | {
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} | 2 | 2019-11-27T23:44:17Z | 2019-12-01T13:18:16Z | 2019-11-30T17:48:11Z | MEMBER | null | - [ ] closes #xxxx
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} | 8 | 2019-11-27T23:57:39Z | 2019-12-08T17:56:17Z | 2019-12-08T17:55:03Z | CONTRIBUTOR | null | - [x] #28277
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} | 2 | 2019-11-28T01:12:37Z | 2019-11-29T21:11:59Z | 2019-11-29T21:07:56Z | MEMBER | null | Timedelta.resolution used to return a string, but that was deprecated in 0.25.0. Enforce that deprecation to return Timedelta(nanoseconds=1), mirroring the stdlib timedelta.resolution behavior.
Timestamp.resolution correctly mirrors datetime.resolution by returning Timedelta(nanoseconds=1), but it is a property whereas for the stdlib it is a class attribute. This PR makes it a class attribute for Timestamp. | {
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} | 3 | 2019-11-28T03:43:07Z | 2019-11-29T19:50:48Z | 2019-11-29T17:39:40Z | MEMBER | null | - [x] xref #22644
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} | 0 | 2019-11-28T03:52:45Z | 2019-11-29T23:19:14Z | 2019-11-29T22:39:28Z | MEMBER | null | - [x] closes #27745
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Also deprecates take_nd as an alias for take. | {
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} | 1 | 2019-11-28T04:02:47Z | 2019-11-29T23:23:07Z | 2019-11-29T22:36:50Z | MEMBER | null | the whatsnew note here seems awkward, suggestions welcome | {
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