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} | 2 | 2020-01-08T15:10:33Z | 2020-01-15T09:49:50Z | 2020-01-15T09:49:50Z | CONTRIBUTOR | null | Fixes #30811
This now will only detect typing problems in the files you have edited. This may still lead to typing problems detected in CI but is hopefully a compromise between speed and early detection of issues before they hit public CI. One line changes in a single file still stay for me in the <1s mark.
cc @gfyoung @jorisvandenbossche | {
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} | 2 | 2020-01-08T16:55:32Z | 2020-06-29T17:40:03Z | 2020-06-02T21:04:15Z | NONE | null | ```
- :meth:`DataFrame.apply` and :meth:`DataFrame.applymap` now evaluates first row/column only once (:issue:`31620`, :issue:`30815`, :issue:`33879`).
.. ipython:: python
df = pd.DataFrame({'a': [1, 2], 'b': [3, 6]})
def func(row):
print(row)
return row
*Previous behavior*:
.. code-block:: ipython
In [4]: df.apply(func, axis=1)
a 1
b 3
Name: 0, dtype: int64
a 1
b 3
Name: 0, dtype: int64
a 2
b 6
Name: 1, dtype: int64
Out[4]:
a b
0 1 3
1 2 6
*New behavior*:
.. ipython:: python
df.apply(func, axis=1)
```
#### some example....
```python
def foo(row):
status = row.get("status")
print(status)
status = True if status==1 else False
row["status"] = status
return row
df = pd.DataFrame([{"name":"bob", "status":1}])
df.apply(foo, axis=1)
>>>>>>>> output:
1
True
```
#### Problem description
today, I have a problem that df.apply(foo) will run twice at the first row like old issue #2936.
I read a lot of explanations, but still can't find some help. As I wrote above, when df just have 1 row and then apply function will run twice at this row lead to print diffierent values.
(It just modified my original value "1" to "True", but it confused me a lot...)
I am wandering how to handle this issue, and is this a pandas bug?
thanks a lot~~
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} | 1 | 2020-01-08T18:49:58Z | 2020-01-08T22:22:52Z | 2020-01-08T22:22:46Z | CONTRIBUTOR | null | - [X] contributes to #29547
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
One format that I left untouched in `pandas/tests/util/test_assert_frame_equal.py`:
```
def test_frame_equal_unicode(df1, df2, msg, by_blocks_fixture, obj_fixture):
# see gh-20503
#
# Test ensures that `tm.assert_frame_equals` raises the right exception
# when comparing DataFrames containing differing unicode objects.
msg = msg.format(obj=obj_fixture)
with pytest.raises(AssertionError, match=msg):
tm.assert_frame_equal(df1, df2, by_blocks=by_blocks_fixture, obj=obj_fixture)
```
However it looks the the test function isnt being used anywhere:
```
$ grep -rnw . -e "test_frame_equal_unicode"
./pandas/tests/util/test_assert_frame_equal.py:217:def test_frame_equal_unicode(df1, df2, msg, by_blocks_fixture, obj_fixture):
```
Wasn't sure if I should leave as is? or assume person calling the function will format `msg` properly before passing to the test function. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30818 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30818/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30818/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30818/events | https://github.com/pandas-dev/pandas/issues/30818 | 547,046,042 | MDU6SXNzdWU1NDcwNDYwNDI= | 30,818 | date_range calculates ranges off by one bin when passing period | {
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"following_url": "https://api.github.com/users/parkerdgabel/following{/other_use... | null | 4 | 2020-01-08T19:03:02Z | 2020-01-09T16:06:07Z | 2020-01-09T16:06:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
start_t = pd.Timestamp('2008-11-24')
end_t = pd.Timestamp('2008-12-08')
print(end_t)
# 2008-12-08 00:00:00
diff = end_t-start_t
dates = pd.date_range(start_t, end_t, periods=bins, tz=None)
diff = dates[1] - dates[0]
print(diff)
# 0 days 03:23:38.181818
wrong = start_t + 100*diff
# 2008-12-08 03:23:38.181818100
correct = start_t + 99*diff
#2008-12-07 23:59:59.999999919
```
#### Problem description
the date range is created with the frequency of 101 bins when passing period = 100.
#### Expected Output
multiplying the time length of a bin by the number of bins should return the end date.
instead it returns the end date +1
#### Output of ``pd.show_versions()``
<details>
NSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.13-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2020-01-08T19:31:40Z | 2020-01-09T05:08:52Z | 2020-01-09T02:48:28Z | MEMBER | null | Also add a dedicated test or it to hit currently un-covered case of np.datetime64 key. That case works in master, but doesn't go through the expected code path | {
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} | 2 | 2020-01-08T19:34:24Z | 2020-01-09T09:03:10Z | 2020-01-08T22:33:24Z | CONTRIBUTOR | null | As discussed on today's call. Just adding `assert_extension_array_equal` relative to 0.25. | {
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} | 7 | 2020-01-08T19:56:20Z | 2020-01-09T19:26:10Z | 2020-01-09T16:18:59Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/30415
```python
In [2]: df = pd.DataFrame({"A": pd.array([1, 2, None])})
In [3]: df
Out[3]:
A
0 1
1 2
2 <NA>
In [4]: df.A
Out[4]:
0 1
1 2
2 <NA>
Name: A, dtype: Int64
In [5]: df.A.array
Out[5]:
<IntegerArray>
[1, 2, <NA>]
Length: 3, dtype: Int64
```
I think adding color with ANSI codes / custom HTML formatting is still worth doing as in #30778, but this is an improvement for now. | {
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... | open | false | null | [] | null | 8 | 2020-01-08T21:21:25Z | 2021-07-25T04:50:06Z | null | NONE | null | This is a place to discuss whether supporting datetime logic with sub-nanosecond precision is desirable and feasible.
### Motivation
I'm using pandas to interact with neuroscientific data—specifically, electrophysiology data. These data are sampled at very precise frequencies that cannot be expressed as an integer number of nanoseconds. Often the datasets span a large enough duration that rounding the sampling frequency to the nearest nanosecond would result an unacceptable accumulation of rounding errors over the duration, so it's important to represent the sampling frequency as accurately as possible. This precludes using pandas' datetime logic to represent timestamps. It would be useful to be able to leverage that logic, though, because it makes things like down/up-sampling very easy.
The [`attotime`](https://pypi.org/project/attotime/) library appears to offer support for arbitrary-precision timestamps with a `datetime` interface, so it might be a good starting point for this. I just learned of it now though, so I'm not sure whether it's mature enough, etc.
If this is out of scope for the project, or if there's an obvious workaround or best practice that I'm missing, sorry for the noise and please let me know! | {
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We should fix it, and indent the function properly: https://github.com/pandas-dev/pandas/blame/master/doc/source/development/contributing_docstring.rst#L25 | {
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} | 1 | 2020-01-09T01:13:40Z | 2020-01-10T20:43:01Z | 2020-01-09T02:39:51Z | MEMBER | null | Implemented ```__all__``` for each changed file
- [x] ref https://github.com/pandas-dev/pandas/pull/30828#discussion_r364500688
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-09T01:19:40Z | 2020-01-10T20:42:33Z | 2020-01-09T15:52:31Z | MEMBER | null | - [x] closes #30830
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"description": "np.nan, ... | open | false | null | [] | null | 3 | 2020-01-09T01:32:17Z | 2021-07-25T04:51:25Z | null | MEMBER | null | I'm not really sure whether this is the correct place to request for this (maybe it should be numpy?), but here it is.
Often times when we deal with data we may want to compare two `Series` or `DataFrames`. In pandas/numpy standard and many other, if not all, programming standards, `NaN == NaN` yields ``False``.
However, sometimes we really mean to make them equal. Say we are comparing two raw datasets. In this case it'd be nice if we have a context manager within which `NaN == NaN` yields True.
```python
>>> import numpy as np
>>> import pandas as pd
>>> pd.__version__
'0.25.3'
>>> s1 = pd.Series([1, 2, np.nan])
>>> s2 = pd.Series([1, 3, np.nan])
# Current behavior
>>> s1 == s2
0 True
1 False
2 False
dtype: bool
# Desired feature
>>> with pd.make_nans_equal():
>>> print(s1 == s2)
0 True
1 False
2 True
dtype: bool
```
In addition, it'd be nice if we can configure the proposed `pd.make_nans_equal()` with parameters to set if we want `NaN == None` to be ``True`` too.
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} | 0 | 2020-01-09T01:43:34Z | 2020-01-09T05:03:51Z | 2020-01-09T02:33:00Z | MEMBER | null | Separating out mostly-cosmetic bits from other get_value work | {
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | [] | closed | false | null | [] | null | 0 | 2020-01-09T06:51:25Z | 2020-01-09T15:51:26Z | 2020-01-09T15:51:26Z | CONTRIBUTOR | null | The test case fails when data is e.g. `np.array([np.array([np.nan, None], dtype="object"), None], dtype="object")`.
```python
def test_to_numpy(self, data):
expected = np.asarray(data)
result = data.to_numpy()
> self.assert_equal(result, expected)
```
This happens in `fletcher` for the `list<item: string>` type. | {
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} | 1 | 2020-01-09T06:53:25Z | 2020-01-09T18:11:49Z | 2020-01-09T15:51:26Z | CONTRIBUTOR | null | - [x] closes #30841
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2020-01-09T07:28:35Z | 2020-02-12T16:00:42Z | 2020-02-12T16:00:42Z | CONTRIBUTOR | null | Currently the check only matches `.py` files. Cython files aren't checked. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30844 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30844/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30844/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30844/events | https://github.com/pandas-dev/pandas/issues/30844 | 547,348,684 | MDU6SXNzdWU1NDczNDg2ODQ= | 30,844 | Value Counts with categorical dtypes has unexpected behavior | {
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} | [] | closed | false | null | [] | null | 2 | 2020-01-09T09:18:13Z | 2020-01-09T13:59:17Z | 2020-01-09T13:59:17Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({"A": [1, 2, 3], "B":[2, 3, 4],
"C": pd.Categorical(["A", "B", "C"]),
"D": pd.Categorical(["B", "C", "D"])})
df.dtypes.value_counts()
```
gives
```
int64 2
category 1
category 1
dtype: int64
```
#### Problem description
I would have expected to have an aggregated count for categories here. This also seems to be related to groupby on dtypes
#### Expected Output
```
int64 2
category 2
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1054-aws
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.36.2
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: 0.8.1
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : 0.13.0
pyarrow : 0.11.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.10
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.6
</details>
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"id... | open | false | null | [] | null | 4 | 2020-01-09T11:09:49Z | 2020-04-26T21:12:46Z | null | NONE | null | ```python
df = pd.DataFrame([5, 10, 15, 5, 10, 15, 5, 10, 15])
op = '<'
level = 10
t = df[0]
df['t'] = t[numexpr.evaluate(f't {op} {level}')]
trueConds = (df['t'] == np.nan)
```
#### Problem description
Column evaluated with numexpr and successfully creating a new col (t), however the trueConds finds no NaNs, which are present in df.
#### Expected Output
Expecting trueConds as:
0 False
1 True
2 True
3 False
4 True
5 True
6 False
7 True
8 True
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
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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"id": 71268330,... | open | false | null | [] | null | 6 | 2020-01-09T11:26:55Z | 2020-06-28T23:34:39Z | null | NONE | null | I'm working to add a `CategoricalIndex` support in xarray (pydata/xarray#3647, pydata/xarray#3670), but have a trouble.
#### Code Sample, a copy-pastable example if possible
```python
In [39]: cat = pd.CategoricalDtype(categories=['foo', 'bar', 'baz'])
...: i1 = pd.Series(['foo', 'bar'], dtype=cat)
...: i2 = pd.Series(['bar', 'bar'], dtype=cat)
...: df = pd.DataFrame({'i1': i1, 'i2': i2, 'values': [1, 2]})
In [40]: df1 = df.set_index('i1')
In [42]: df1.index
Out[42]: CategoricalIndex(['foo', 'bar'],
categories=['foo', 'bar', 'baz'], ordered=False, name='i1', dtype='category')
# remove_unused_categories() is working
In [43]: df1.index.remove_unused_categories()
Out[43]: CategoricalIndex(['foo', 'bar'],
categories=['foo', 'bar'], ordered=False, name='i1', dtype='category')
In [44]: df2 = df.set_index(['i1', 'i2'])
In [45]: df2.index
Out[45]:
MultiIndex([('foo', 'bar'),
('bar', 'bar')],
names=['i1', 'i2'])
# remove_unused_categories() is NOT working
In [47]: df2.index.levels[0].remove_unused_categories()
Out[47]: CategoricalIndex(['foo', 'bar', 'baz'],
categories=['foo', 'bar', 'baz'], ordered=False, name='i1', dtype='category') # <-- `baz` is not removed.
```
#### Problem description
When `CategoricalIndex` is a level variable of MultiIndex, `remove_unused_categories` does not work.
#### Expected Output
```python
Out[43]: CategoricalIndex(['foo', 'bar'], categories=['foo', 'bar'], ordered=False, name='i1', dtype='category')
```
#### Output of ``pd.show_versions()``
<details>
In [48]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1066-oem
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 : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
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 : 0.14.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 10 | 2020-01-09T13:27:14Z | 2020-02-12T16:00:49Z | 2020-02-12T16:00:42Z | CONTRIBUTOR | null | - [x] closes #30843
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"id": 35818298,... | open | false | null | [] | null | 6 | 2020-01-09T14:55:58Z | 2021-10-29T02:09:45Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
>>> import pandas as pd
>>> pd.__version__
'0.26.0.dev0+1717.g6437f5eb3'
>>> s = (
... pd.DataFrame({0: [1, 2, 3], 1: [4, 5, 6]})
... .unstack()
... .rename_axis([(1,), None])
... )
>>> s
(1,)
0 0 1
1 2
2 3
1 0 4
1 5
2 6
dtype: int64
>>>
>>> s.rename({0: "foo"}, level=None)
(1,)
foo foo 1
1 2
2 3
1 foo 4
1 5
2 6
dtype: int64
```
#### Problem description
Is this an edge case or not supported.
#### Expected Output
```python
>>> s.rename({0: "foo"}, level=1)
(1,)
0 foo 1
1 2
2 3
1 foo 4
1 5
2 6
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30849/events | https://github.com/pandas-dev/pandas/issues/30849 | 547,539,535 | MDU6SXNzdWU1NDc1Mzk1MzU= | 30,849 | read_fwf reading large numbers as object dtype | {
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"descript... | closed | false | null | [] | null | 3 | 2020-01-09T15:13:53Z | 2020-06-17T21:42:13Z | 2020-06-17T21:42:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
>>> pd.read_fwf(r'D:\Other\ggg.txt', header=None, widths =[26,3,3,26])
0 1 2 3
0 +0000000000000000000000000 NaN CCY 30000000000000
1 +0077800000000000000000000 NaN ABF 18244030000000000000
>>> pd.read_fwf(r'D:\Other\ggg.txt', header=None, widths =[26,3,3,26])
0 1 2 3
0 +0000000000000000000000000 NaN CCY +0000000000030000000000000
1 +0077800000000000000000000 NaN ABF +0000018544030000000000000
>>> pd.read_fwf(r'D:\Other\ggg.txt', header=None, widths =[26,3,3,26])[3]
0 +0000000000030000000000000
1 +0000018544030000000000000
Name: 3, dtype: object
```
#### Problem description
I have a fixed width file with numbers specifications up to 25 characters with a 1 character sign in front. Normally for small numbers these numbers read properly. As the numbers get bigger as shown above, read_fwf starts to read them as objects even though they are numbers. Attempting to convert said column series with bigger number results in overflow error.
This may cause incompatibility of dtypes when the field is expected to be numerical. read_fwf could process the column as np.float64 to maintain the numerical dtype if np.int64 is not compaitable.
#### Expected Output
```python
>>> pd.read_fwf(r'D:\Other\ggg.txt', header=None, widths =[26,3,3,26])[3].astype(np.float64)
0 3.000000e+13
1 1.854403e+19
Name: 3, dtype: float64
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30850/events | https://github.com/pandas-dev/pandas/issues/30850 | 547,558,615 | MDU6SXNzdWU1NDc1NTg2MTU= | 30,850 | to_timedelta str int discrepancy | {
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```python
assert pd.to_timedelta(1,unit='s') == pd.to_timedelta('1',unit='s')
```
fails
```python
>>> pd.to_timedelta(20000,unit='s')
Timedelta('0 days 05:33:20')
>>> pd.to_timedelta('20000',unit='s')
Timedelta('0 days 00:00:00.000020')
```
#### Problem description
it looks like the `unit` parameter has no effect in `pd.to_timedelta('1',unit='s')`
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.27.2.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 : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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"color": "bfe5bf",
"default": false,
"description": "read_excel, to_excel",
"id": 49254273,
"name": "IO Excel",
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] | closed | false | null | [] | null | 5 | 2020-01-09T16:05:04Z | 2020-01-10T16:53:06Z | 2020-01-09T22:48:43Z | NONE | null | Hello,
Under some circumstances that I'm unable to systematically reproduce, pd.read_excel still uses xlrd.
With python 3.8, I get a DeprecationWarning, which I can't fix.
Since there is no maintainer anymore for xlrd, I must come back to you to get some advises. Would you be so kind to help me?
I'm using pd.read_excel within joblib parallel subprocesses, using the devault backend. There is no specific option given to read_excel, and I have a mix of xls and xlsx files. The "interesting part" of the exception is show below.
Unfortunately, when I try it manually using ipython interactive shell, I have no issue, even with the parallel joblib processing.
Thanks a lot.
```text
File "########/src/context/erpcrm_new/etl/_chunks/_base.py", line 229, in readExcel
ret = pd.read_excel(
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/pandas/util/_decorators.py", line 208, in wrapper
return func(*args, **kwargs)
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 310, in read_excel
io = ExcelFile(io, engine=engine)
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 819, in __init__
self._reader = self._engines[engine](self._io)
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py", line 21, in __init__
super().__init__(filepath_or_buffer)
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 359, in __init__
self.book = self.load_workbook(filepath_or_buffer)
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py", line 36, in load_workbook
return open_workbook(filepath_or_buffer)
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/xlrd/__init__.py", line 130, in open_workbook
bk = xlsx.open_workbook_2007_xml(
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/xlrd/xlsx.py", line 812, in open_workbook_2007_xml
x12book.process_stream(zflo, 'Workbook')
File "########/.local/share/virtualenvs-_GHTURhP/lib/python3.8/site-packages/xlrd/xlsx.py", line 266, in process_stream
for elem in self.tree.iter() if Element_has_iter else self.tree.getiterator():
File "/opt/python/3.8.1/lib/python3.8/xml/etree/ElementTree.py", line 622, in getiterator
warnings.warn(
DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
```
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} | 27 | 2020-01-09T16:14:08Z | 2020-05-28T17:08:53Z | 2020-05-28T17:08:46Z | MEMBER | null | Added `DataFrame.differences` and `Series.differences` methods.
Have not yet added whatsnew entries. Will do so after review of API and behavior design.
A few design considerations open for discussion (among other things):
1. Index/column names: (self, other) vs (left, right) vs (old, new)
I used the (self, other) pair as I think they naturally match with the parameter names and the concept of comparing `self` with `other`. Although (left, right) or (old, new) may be slight more intuitive for users in some use cases, these new functions not necessarily compare two objects that are left and right (may be stacked on `axis=0`, or old and new.
1. Parameter name: axis
I feel axis may not be a very clear name, since we are stacking the output along this axis, rather than comparing objects along this axis. I considered using the name `stack_axis` or `orient` like `DataFrame.to_json` does.
1. I made the methods such that two NaNs are considered equal, hence not different. I think this should be the desired behavior since we are trying to compare two objects. However, under current implementation, a `None` will be considered equal to `np.nan` too. This is probably something we need to be careful for.
Let me know what you guys think. I'm ready to be scrutinized and criticized!
- [x] closes #30429
- [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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} | 3 | 2020-01-09T17:05:39Z | 2020-01-24T04:32:18Z | 2020-01-24T03:56:07Z | MEMBER | null | It's only used in one non-test place, and all supported python versions now support datetime.timestamp, so we can use that directly. Its a little bit less performant, but not used often. | {
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} | 4 | 2020-01-09T18:04:47Z | 2020-01-20T23:31:25Z | 2020-01-20T23:31:20Z | MEMBER | null | xref #29116
https://github.com/pandas-dev/pandas/pull/29116#pullrequestreview-340272465
based on @jreback comment, this PR is a precursor PR for #29116 to make PR smaller and more readable | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30857 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30857/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30857/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30857/events | https://github.com/pandas-dev/pandas/issues/30857 | 547,636,411 | MDU6SXNzdWU1NDc2MzY0MTE= | 30,857 | DataFrame arithmetic functions with multiindex, NaN index values, and fill_value set returns an invalid result with non-unique index. | {
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 1 | 2020-01-09T18:07:21Z | 2020-03-12T19:35:28Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from unittest import TestCase
import numpy as np
import pandas as pd
class TestSubtract(TestCase):
def test_subtract_partially_aligned_multi_index_dataframes_with_nans(self):
df0 = pd.DataFrame(
data=[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
[9, 10],
[11, 12],
[13, 14],
[15, 16],
[17, 18],
],
columns=["happy", "sad"],
index=pd.MultiIndex.from_product(
[["a", "b", None], [0, 1, np.nan]], names=["l0", "l1"]
),
)
df1 = pd.DataFrame(
data=[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
[9, 10],
[11, 12],
[13, 14],
[15, 16],
[17, 18],
],
columns=["happy", "sad"],
index=pd.MultiIndex.from_product(
[["b", "c", None], [1, 2, np.nan]], names=["l0", "l1"]
),
)
result = df0.subtract(df1, fill_value=0)
expected = pd.DataFrame.from_records(
[
["a", 0, 1 - 0, 2 - 0],
["a", 1, 3 - 0, 4 - 0],
["a", np.nan, 5 - 0, 6 - 0],
["b", 0, 7 - 0, 8 - 0],
["b", 1, 9 - 1, 10 - 2],
["b", 2, 0 - 3, 0 - 4],
["b", np.nan, 11 - 5, 12 - 6],
["c", 1, 0 - 7, 0 - 8],
["c", 2, 0 - 9, 0 - 10],
["c", np.nan, 0 - 11, 0 - 12],
[np.nan, 0, 13 - 0, 14 - 12],
[np.nan, 1, 15 - 13, 16 - 14],
[np.nan, 2, 0 - 15, 0 - 16],
[np.nan, np.nan, 17 - 17, 18 - 18],
],
columns=["l0", "l1", "happy", "sad"],
).set_index(["l0", "l1"])
print(expected)
print(result)
pd.testing.assert_frame_equal(expected, result)
self.assertTrue(result.index.is_unique)
```
#### Problem description
I have two data frames with multi-level indices that represent the same sets of values, although both data frames may have index values that are not represented in the other (In my case, I am comparing two SQL queries results with a shifted date time index for week-over-week comparison). I'd expect the `DataFrame.subtract` function using fill values to replace a missing value on either side of the data equation with the fill value. Instead, I am seeing a value returned with a mismatched index with non-unique values.
In my expected dataframe in the test case, I populate each value with the expression I would expect the `subtract` function to perform.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.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 : 1.16.2
pytz : 2019.1
dateutil : 2.8.0
pip : 18.1
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.8.0
psycopg2 : 2.7.3.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 26 | 2020-01-09T18:22:21Z | 2020-07-15T07:15:33Z | 2020-07-14T20:34:56Z | MEMBER | null | - [x] closes #30880
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xref #30092
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} | 5 | 2020-01-09T19:13:45Z | 2020-09-07T20:47:40Z | 2020-09-07T20:47:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.DataFrame({'first': [1], 'second': [1], 'value': [16148277970000000000]}).groupby(['first', 'second'])['value'].max()
```
#### Problem description
When that code snippet runs, the result is not `16148277970000000000` as you would expect, but `16148277969999998976`. Note that `int(float(16148277970000000000)) == 16148277969999998976`.
Additional notes:
1. The problem only appears to happen if there are multiple groupby keys. For example just doing `groupby(['first'])` returns the expected result. So does removing the `groupby` statement entirely.
2. The problem is not specific to `max`. I get the same problem for `min`, `first`, `last`, `median`, `mean`, but not `head`, `tail`, or `apply`.
3. The problem also happens if you do `.transform(max)`.
4. The smallest number I've found so far that has a problem is 2**63 + 1
#### Expected Output
16148277970000000000
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.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 : 1.17.5
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 42.0.2
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 : 1.3.1
fastparquet : None
gcsfs : 0.6.0
lxml.etree : 4.2.6
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.4.4
xarray : 0.14.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30861/events | https://github.com/pandas-dev/pandas/pull/30861 | 547,676,039 | MDExOlB1bGxSZXF1ZXN0MzYxMTIxMzQx | 30,861 | BUG: ExtensionArray._formatter does not need to handle nulls | {
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} | 1 | 2020-01-09T19:31:40Z | 2020-01-09T20:19:43Z | 2020-01-09T20:19:39Z | MEMBER | null | See https://github.com/pandas-dev/pandas/pull/30821/files#r364917242
Currently, I don't think this is really specified whether the `_formatter` of ExtensionArray should be able to handle NAs/nulls or not, but in practice, it didn't need to do that. And eg the geopandas one is also implemented right now that it would fail if passed a NA (https://github.com/geopandas/geopandas/blob/add5fe9139883fa54d14fa28426478619948349c/geopandas/array.py#L1026-L1047)
This is certainly something that can be discussed how to specify this, but for now I think it is best to preserve the existing behaviour. | {
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} | 19 | 2020-01-09T19:33:18Z | 2020-01-24T04:01:20Z | 2020-01-24T04:01:16Z | MEMBER | null | Originally thought this only affected Windows, but parallel Cythonization is also broken on Python 3.8 on macOS as they shifted from using fork to start sub-processes to using spawn
https://bugs.python.org/issue33725
This fix should make things work regardless of platform / python version
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This PR gets that number down to 106, which is nice. | {
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xref https://github.com/pandas-dev/pandas/pull/30773, https://github.com/pandas-dev/pandas/pull/30814
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} | 3 | 2020-01-09T20:45:50Z | 2020-01-09T21:14:10Z | 2020-01-09T21:14:05Z | MEMBER | null | cc @TomAugspurger
Adding a note about the recommended way to upgrade to pandas 1.0, if that sounds OK | {
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} | 2 | 2020-01-09T21:10:32Z | 2020-07-08T00:34:43Z | null | CONTRIBUTOR | null | ```python
In [2]: df = pd.DataFrame({'a': [1], 'b': [None]})
In [3]: df
Out[3]:
a b
0 1 None
In [4]: df.set_index(['a', 'b'])
Out[4]:
Empty DataFrame
Columns: []
Index: [(1, nan)]
```
Values should not be silently converted from `None` to NaN when a column is used in a MultiIndex.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.7
</details> | {
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} | 11 | 2020-01-09T21:18:52Z | 2020-07-10T13:58:36Z | null | CONTRIBUTOR | null | * Avoid DeprecationWarning followed by exception: https://github.com/pandas-dev/pandas/pull/30588#discussion_r364930269
* Update tests to filter warnings in plotting https://github.com/pandas-dev/pandas/pull/30588/files/74dffbedacb34bde7e5a8fb2396e0fef9efac499#diff-4b6d08e53050950f83ea75e432302f89
* Change CategoricalIndex behavior to raise, matching IntervalIndex https://github.com/pandas-dev/pandas/pull/30588#discussion_r364924210
* Deprecate it in `Series.__getitem__` as well, similar to `Index.__getitem__` | {
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pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other0] FAILED [ 4%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other1] FAILED [ 8%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other2] FAILED [ 12%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other3] FAILED [ 16%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other4] FAILED [ 20%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other5] FAILED [ 25%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other6] FAILED [ 29%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other7] FAILED [ 33%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other8] FAILED [ 37%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_arraylike_invalid[tzlocal()-other9] FAILED [ 41%]
pandas/tests/arithmetic/test_datetime64.py::TestDatetime64ArrayLikeComparisons::test_dt64arr_cmp_mixed_invalid[tzlocal()] FAILED [ 45%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz['UTC'-tzlocal()] FAILED [ 50%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz['US/Eastern'-tzlocal()] FAILED [ 54%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz['Asia/Tokyo'-tzlocal()] FAILED [ 58%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz['dateutil/US/Pacific'-tzlocal()] FAILED [ 62%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz['dateutil/Asia/Singapore'-tzlocal()] FAILED [ 66%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[tzutc()-tzlocal()] FAILED [ 70%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[tzlocal()-tzlocal()] FAILED [ 75%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[pytz.FixedOffset(300)-tzlocal()] FAILED [ 79%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[<UTC>-tzlocal()] FAILED [ 83%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[pytz.FixedOffset(-300)-tzlocal()] FAILED [ 87%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[datetime.timezone.utc-tzlocal()] FAILED [ 91%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[datetime.timezone(datetime.timedelta(seconds=3600))-tzlocal()] FAILED [ 95%]
pandas/tests/dtypes/cast/test_promote.py::test_maybe_promote_datetimetz_with_datetimetz[datetime.timezone(datetime.timedelta(days=-1, seconds=82800), 'foo')-tzlocal()] FAILED [100%]
```
#### traceback of one case
<details>
```
_ test_maybe_promote_datetimetz_with_datetimetz[datetime.timezone(datetime.timedelta(days=-1, seconds=82800), 'foo')-tzlocal()] _
[gw2] win32 -- Python 3.7.5 C:\Users\simon\Anaconda3\envs\pandas-dev\python.exe
data = <[OSError("[Errno 22] Invalid argument") raised in repr()] ndarray object at 0x25accfedf80>
dayfirst = False, yearfirst = False, utc = False, errors = 'raise'
require_iso8601 = False, allow_object = True
def objects_to_datetime64ns(
data,
dayfirst,
yearfirst,
utc=False,
errors="raise",
require_iso8601=False,
allow_object=False,
):
"""
Convert data to array of timestamps.
Parameters
----------
data : np.ndarray[object]
dayfirst : bool
yearfirst : bool
utc : bool, default False
Whether to convert timezone-aware timestamps to UTC.
errors : {'raise', 'ignore', 'coerce'}
allow_object : bool
Whether to return an object-dtype ndarray instead of raising if the
data contains more than one timezone.
Returns
-------
result : ndarray
np.int64 dtype if returned values represent UTC timestamps
np.datetime64[ns] if returned values represent wall times
object if mixed timezones
inferred_tz : tzinfo or None
Raises
------
ValueError : if data cannot be converted to datetimes
"""
assert errors in ["raise", "ignore", "coerce"]
# if str-dtype, convert
data = np.array(data, copy=False, dtype=np.object_)
try:
result, tz_parsed = tslib.array_to_datetime(
data,
errors=errors,
utc=utc,
dayfirst=dayfirst,
yearfirst=yearfirst,
> require_iso8601=require_iso8601,
)
pandas\core\arrays\datetimes.py:1852:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> cpdef array_to_datetime(ndarray[object] values, str errors='raise',
pandas\_libs\tslib.pyx:481:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise ValueError('Tz-aware datetime.datetime '
E ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
pandas\_libs\tslib.pyx:559: ValueError
During handling of the above exception, another exception occurred:
> return tzinfo._utcoffset
E AttributeError: 'tzlocal' object has no attribute '_utcoffset'
pandas\_libs\tslibs\timezones.pyx:153: AttributeError
During handling of the above exception, another exception occurred:
tz_aware_fixture = datetime.timezone(datetime.timedelta(days=-1, seconds=82800), 'foo')
tz_aware_fixture2 = tzlocal()
def test_maybe_promote_datetimetz_with_datetimetz(tz_aware_fixture, tz_aware_fixture2):
dtype = DatetimeTZDtype(tz=tz_aware_fixture)
fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture2)
# create array of given dtype; casts "1" to correct dtype
> fill_value = pd.Series([10 ** 9], dtype=fill_dtype)[0]
pandas\tests\dtypes\cast\test_promote.py:429:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas\core\series.py:304: in __init__
data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
pandas\core\construction.py:442: in sanitize_array
subarr = maybe_cast_to_datetime(subarr, dtype)
pandas\core\dtypes\cast.py:1220: in maybe_cast_to_datetime
value = to_datetime(value, errors=errors).array
pandas\core\tools\datetimes.py:741: in to_datetime
result = convert_listlike(arg, format)
pandas\core\tools\datetimes.py:447: in _convert_listlike_datetimes
allow_object=True,
pandas\core\arrays\datetimes.py:1856: in objects_to_datetime64ns
values, tz_parsed = conversion.datetime_to_datetime64(data)
pandas\_libs\tslibs\conversion.pyx:189: in pandas._libs.tslibs.conversion.datetime_to_datetime64
_ts = convert_datetime_to_tsobject(val, None)
pandas\_libs\tslibs\conversion.pyx:388: in pandas._libs.tslibs.conversion.convert_datetime_to_tsobject
offset = get_utcoffset(obj.tzinfo, ts)
pandas\_libs\tslibs\timezones.pyx:155: in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
..\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py:216: in utcoffset
if self._isdst(dt):
..\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py:288: in _isdst
if self.is_ambiguous(dt):
..\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py:250: in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = tzlocal()
dt = <[OSError("[Errno 22] Invalid argument") raised in repr()] Timestamp object at 0x25acc12a148>
def _naive_is_dst(self, dt):
timestamp = _datetime_to_timestamp(dt)
> return time.localtime(timestamp + time.timezone).tm_isdst
E OSError: [Errno 22] Invalid argument
..\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py:254: OSError
---------------------------- Captured stderr call -----------------------------
Traceback (most recent call last):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 144, in fromutc
return f(self, dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 258, in fromutc
dt_wall = self._fromutc(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 222, in _fromutc
dtoff = dt.utcoffset()
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 288, in _isdst
if self.is_ambiguous(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 250, in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Exception ignored in: 'pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc'
Traceback (most recent call last):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 144, in fromutc
return f(self, dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 258, in fromutc
dt_wall = self._fromutc(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 222, in _fromutc
dtoff = dt.utcoffset()
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 288, in _isdst
if self.is_ambiguous(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 250, in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Traceback (most recent call last):
File "C:\Users\simon\pandas\pandas\core\arrays\datetimes.py", line 1852, in objects_to_datetime64ns
require_iso8601=require_iso8601,
File "pandas\_libs\tslib.pyx", line 481, in pandas._libs.tslib.array_to_datetime
cpdef array_to_datetime(ndarray[object] values, str errors='raise',
File "pandas\_libs\tslib.pyx", line 559, in pandas._libs.tslib.array_to_datetime
raise ValueError('Tz-aware datetime.datetime '
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 155, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 288, in _isdst
if self.is_ambiguous(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 250, in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Exception ignored in: 'pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc'
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 155, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 288, in _isdst
if self.is_ambiguous(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 250, in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Traceback (most recent call last):
File "C:\Users\simon\pandas\pandas\core\arrays\datetimes.py", line 1852, in objects_to_datetime64ns
require_iso8601=require_iso8601,
File "pandas\_libs\tslib.pyx", line 481, in pandas._libs.tslib.array_to_datetime
cpdef array_to_datetime(ndarray[object] values, str errors='raise',
File "pandas\_libs\tslib.pyx", line 559, in pandas._libs.tslib.array_to_datetime
raise ValueError('Tz-aware datetime.datetime '
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 155, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 288, in _isdst
if self.is_ambiguous(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 250, in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Exception ignored in: 'pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc'
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 155, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 288, in _isdst
if self.is_ambiguous(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 250, in is_ambiguous
(naive_dst != self._naive_is_dst(dt - self._dst_saved)))
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Traceback (most recent call last):
File "C:\Users\simon\pandas\pandas\core\arrays\datetimes.py", line 1852, in objects_to_datetime64ns
require_iso8601=require_iso8601,
File "pandas\_libs\tslib.pyx", line 481, in pandas._libs.tslib.array_to_datetime
cpdef array_to_datetime(ndarray[object] values, str errors='raise',
File "pandas\_libs\tslib.pyx", line 559, in pandas._libs.tslib.array_to_datetime
raise ValueError('Tz-aware datetime.datetime '
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 144, in fromutc
return f(self, dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 258, in fromutc
dt_wall = self._fromutc(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 222, in _fromutc
dtoff = dt.utcoffset()
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 285, in _isdst
dstval = self._naive_is_dst(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Exception ignored in: 'pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc'
Traceback (most recent call last):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 144, in fromutc
return f(self, dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 258, in fromutc
dt_wall = self._fromutc(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\_common.py", line 222, in _fromutc
dtoff = dt.utcoffset()
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 285, in _isdst
dstval = self._naive_is_dst(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Traceback (most recent call last):
File "C:\Users\simon\pandas\pandas\core\arrays\datetimes.py", line 1852, in objects_to_datetime64ns
require_iso8601=require_iso8601,
File "pandas\_libs\tslib.pyx", line 481, in pandas._libs.tslib.array_to_datetime
cpdef array_to_datetime(ndarray[object] values, str errors='raise',
File "pandas\_libs\tslib.pyx", line 559, in pandas._libs.tslib.array_to_datetime
raise ValueError('Tz-aware datetime.datetime '
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 153, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo._utcoffset
AttributeError: 'tzlocal' object has no attribute '_utcoffset'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 155, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 285, in _isdst
dstval = self._naive_is_dst(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
Exception ignored in: 'pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc'
Traceback (most recent call last):
File "pandas\_libs\tslibs\timezones.pyx", line 155, in pandas._libs.tslibs.timezones.get_utcoffset
return tzinfo.utcoffset(obj)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 216, in utcoffset
if self._isdst(dt):
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 285, in _isdst
dstval = self._naive_is_dst(dt)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\dateutil\tz\tz.py", line 254, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3ddd495e43ab878b6d139e2458cb3c387f7237c4
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+1731.g3ddd495e4
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : 4.36.2
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: 0.8.1
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.2.2
s3fs : 0.3.4
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.5.1
tabulate : None
xarray : 0.13.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.46.0
</details>
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} | 8 | 2020-01-09T22:49:44Z | 2020-01-13T20:45:43Z | 2020-01-13T20:45:43Z | CONTRIBUTOR | null | 0.25.3
```python
In [1]: import pandas
In [2]: pandas.util.testing
Out[2]: <module 'pandas.util.testing' from '/private/tmp/pandas/util/testing.py'>
```
1.0
```python
In [1]: import pandas
In [2]: pandas.util.testing
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-968e027d039f> in <module>
----> 1 pandas.util.testing
AttributeError: module 'pandas.util' has no attribute 'testing'
``` | {
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] | closed | false | null | [] | null | 16 | 2020-01-09T22:51:42Z | 2020-04-07T16:22:43Z | 2020-04-07T16:22:42Z | NONE | null | * changes to `set_index()` and `ensure_index_from_sequences()` to pass current dtype of key through call stack onto Index constructor, rather than letting Index constructor infer the dtype. For now I just aimed to patch a single key param from `set_index()`, as that is the lowest hanging fruit for this issue I think.
* removed `maybe_convert_objects()` call from `_maybe_casted_values()`. This will preserve an `object` dtype on a `set_index().reset_index()` round-trip. I came to the exact same conclusion as the user in [this PR comment](https://github.com/pandas-dev/pandas/pull/27370#issuecomment-511051996), for context. This makes sense conceptually, if your index was an object dtype, it should be an object dtype as a column. Of course, this is somewhat subjective so let me know your opinions, but this is what I interpreted from the discussion on the git issue.
* both of these changes break quite a few existing tests, most of which are testing specifically for the exact opposite conditions that this PR is intended to patch **e.g.** tests that look for object dtypes being coerced to more-specific types. What's the process for handling failed tests for a change like this? Would you attempt to refactor all, or delete the ones where the logic is no longer something to test for? I also ran `pytest pandas` on a fresh pull of master and had 13 fails, with 108 failures on my branch specifically, so I am also curious what to do about those previously-failing tests, or what is acceptable for a merge.
- [x] closes #30517
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 0 | 2020-01-09T23:15:59Z | 2020-01-20T18:11:02Z | 2020-01-20T18:11:02Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>>
>>> pd.__version__
'0.26.0.dev0+1731.g3ddd495e4'
>>>
>>> df = pd.DataFrame({"Name": ["Alice"], "Gender": ["F"]})
>>> df
Name Gender
0 Alice F
>>>
>>> df.append({"Name": "Bob", "Gender": "M"})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\frame.py", line 7013, in append
"Can only append a Series if ignore_index=True "
TypeError: Can only append a Series if ignore_index=True or if the Series has a name
>>>
```
#### Problem description
dict-like is valid type for `other` parameter of DataFrame.append
#### Expected Output
ValueError: Can only append a dict-like if ignore_index=True
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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] | closed | false | null | [] | null | 12 | 2020-01-09T23:21:49Z | 2021-07-25T04:59:39Z | 2021-07-25T04:59:39Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.show_versions()
```
#### Problem description
Gets this output:
```
C:\Code\pandas_dev\pandas36\pandas\core\index.py:29: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
FutureWarning,
INSTALLED VERSIONS
------------------
```
followed by all the version stuff
#### Expected Output
No `FutureWarning`
#### Output of ``pd.show_versions()``
<details>
C:\Code\pandas_dev\pandas36\pandas\core\index.py:29: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
FutureWarning,
INSTALLED VERSIONS
------------------
commit : 7cce2c7f43380a6224cde0b85650b88271e93997
python : 3.6.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.26.0.dev0+1739.g7cce2c7f4
numpy : 1.15.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200102
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 5.1.1
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
pytest : 5.3.2
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.46.0
</details>
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... | open | false | null | [] | null | 8 | 2020-01-09T23:22:47Z | 2021-05-27T04:27:00Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```bash
python setup.py build_ext --inplace -j 8
```
#### Problem description
See some discussion in #30862 . I have 8 cores on Windows. When I use `-j 8`, in the middle of the build, I will get this error message:
```
c:\Code\pandas_dev\pandas\pandas\_libs\tslibs\src\datetime\np_datetime.c : fatal error C1083: Cannot open compiler generated file: 'c:\Code\pandas_dev\pandas\build\temp.win-amd64-3.7\Release\pandas\_libs\tslibs\src\datetime\np_datetime.obj': Permission denied
```
What I think is happening is that in `setup.py`, `_libs.tslibs.conversion`, `libs.tslibs.np_datetime` and `_libs.tslibs.period` all have to compile `pandas/_libs/tslibs/src/datetime/np_datetime.c` . If the parallel build is timed in such a way that two of those extensions are being built at the same time, then they can conflict in writing `np_datetime.obj`. A similar thing can happen with `_libs.lib`, `_libs.parsers`, and `_libs.tslibs.parsing` all having to compile `pandas/_libs/src/parser/tokenizer.c` .
If I do two `python setup.py build_ext --inplace -j 8` steps in a row, then everything is fine. Also things are fine with `-j 4`. But I can imagine that even with `-j 4`, you could end up with the same kind of issue, even in CI.
I don't know enough about how extensions are built, distutils and setuptools in order to avoid this potential collision during the build process.
#### Output of ``pd.show_versions()``
<details>
c:\Code\pandas_dev\pandas\pandas\core\index.py:29: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
FutureWarning,
INSTALLED VERSIONS
------------------
commit : 13858f6e11790bb0f52092d4f8ac65c4057d9e74
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.26.0.dev0+1734.g13858f6e1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.57.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.46.0
</details>
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- [x] ~~closes #xxxx~~
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2020-01-10T07:50:41Z | 2020-01-10T14:04:27Z | 2020-01-10T14:01:10Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/30872 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30879 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30879/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30879/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30879/events | https://github.com/pandas-dev/pandas/issues/30879 | 547,972,533 | MDU6SXNzdWU1NDc5NzI1MzM= | 30,879 | `read_sql_table` fails on Geopackge SQLite file due to DATETIME field with string value | {
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"description": "to_sql, read_sql, read_s... | open | false | null | [] | null | 0 | 2020-01-10T09:47:46Z | 2020-05-16T04:09:01Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import sqlite3
import pandas as pd
con = sqlite3.connect("tmp.db")
c = con.cursor()
c.execute("CREATE TABLE gpkg_contents (last_change DATETIME);")
c.execute("INSERT INTO gpkg_contents VALUES ('2019-01-01T00:00:00.000Z');")
con.commit()
con.close()
df = pd.read_sql_table(
"gpkg_contents",
"sqlite:///tmp.db",
parse_dates=["last_change"]
)
df["last_change"][0]
```
#### Problem description
The above minimal example raises the following error:
```pytb
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-172-a1e2291991c6> in <module>
6 con.commit()
7 con.close()
----> 8 df = pd.read_sql_table("gpkg_contents", "sqlite:///tmp.db", parse_dates=["last_change"])
9 df["last_change"][0]
~/.venv/lib/python3.7/site-packages/pandas/io/sql.py in read_sql_table(table_name, con, schema, index_col, coerce_float, parse_dates, columns, chunksize)
252 parse_dates=parse_dates,
253 columns=columns,
--> 254 chunksize=chunksize,
255 )
256
~/.venv/lib/python3.7/site-packages/pandas/io/sql.py in read_table(self, table_name, index_col, coerce_float, parse_dates, columns, schema, chunksize)
1143 parse_dates=parse_dates,
1144 columns=columns,
-> 1145 chunksize=chunksize,
1146 )
1147
~/.venv/lib/python3.7/site-packages/pandas/io/sql.py in read(self, coerce_float, parse_dates, columns, chunksize)
803 data = result.fetchall()
804 self.frame = DataFrame.from_records(
--> 805 data, columns=column_names, coerce_float=coerce_float
806 )
807
~/.venv/lib/python3.7/site-packages/pandas/core/frame.py in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
1611 arr_columns = columns
1612 else:
-> 1613 arrays, arr_columns = to_arrays(data, columns, coerce_float=coerce_float)
1614
1615 arr_columns = ensure_index(arr_columns)
~/.venv/lib/python3.7/site-packages/pandas/core/internals/construction.py in to_arrays(data, columns, coerce_float, dtype)
485 else:
486 # last ditch effort
--> 487 data = [tuple(x) for x in data]
488 return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
489
~/.venv/lib/python3.7/site-packages/pandas/core/internals/construction.py in <listcomp>(.0)
485 else:
486 # last ditch effort
--> 487 data = [tuple(x) for x in data]
488 return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
489
ValueError: Couldn't parse datetime string: '2019-01-01T00:00:00.000Z
```
Surprisingly, using
```python
pd.read_sql_query("SELECT * FROM gpkg_contents", ...)
```
instead of `read_sql_table` does give exactly the expected result.
As the table name suggests, I encountered this bug when working on a Geopackage file, that uses a `CREATE` statement with `DATETIME` type and string values as per the [format specification](http://www.geopackage.org/spec/) and this is thus implemented as such in many GIS tools.
#### Expected Output
```python
Timestamp('2019-01-01 00:00:00+0000', tz='UTC')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-24-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 0 | 2020-01-10T10:00:41Z | 2020-07-14T20:34:56Z | 2020-07-14T20:34:56Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
from functools import partial
df = pd.DataFrame(
np.random.randn(1000, 3),
index=pd.date_range('1/1/2012', freq='S', periods=1000),
columns=['A', 'B', 'C']
)
dfg = df.resample('3T').agg(
{'A': [
partial(np.quantile, q=.9999),
partial(np.quantile, q=.1111),
]}
)
```
returns
```
A
quantile quantile
2012-01-01 00:00:00 -1.035997 -1.035997
2012-01-01 00:03:00 -1.326052 -1.326052
2012-01-01 00:06:00 -1.267661 -1.267661
2012-01-01 00:09:00 -1.321961 -1.321961
2012-01-01 00:12:00 -1.103027 -1.103027
2012-01-01 00:15:00 -0.987614 -0.987614
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : 5e9fe4e0843266cb1c64bff4af70b3665a4d6a8d
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0+1719.g5e9fe4e08.dirty
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.55.4
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
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 : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.4.0
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
numba : 0.46.0
</details>
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} | 4 | 2020-01-10T13:06:54Z | 2020-02-03T13:08:39Z | 2020-01-20T18:04:01Z | MEMBER | null | https://dev.pandas.io/docs/reference/api/pandas.Series.set_axis.html
https://dev.pandas.io/docs/reference/api/pandas.DataFrame.set_axis.html
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- [x] closes #30871
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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(Prompted by [this Stack Overflow question](https://stackoverflow.com/q/59529424/2144390).)
When using `to_sql` with `mssql+pyodbc` and `fast_executemany=True`, uploading a DataFrame with a single row containing a tz-aware datetime into a `datetimeoffset` column causes the timezone offset to be lost. Doing the same thing with a DataFrame containing more than one row produces the correct result.
```python
from pprint import pprint
import sys
import urllib
import pandas as pd
import sqlalchemy as sa
print(sys.version)
# 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 22:39:24) [MSC v.1916 32 bit (Intel)]
print(f'SQLAlchemy {sa.__version__}, pandas {pd.__version__}, pyodbc {pyodbc.version}')
# SQLAlchemy 1.3.12, pandas 0.25.3, pyodbc 4.0.28
connection_string = (
r'DRIVER=ODBC Driver 17 for SQL Server;'
r'SERVER=localhost,49242;'
r'DATABASE=myDb;'
r'Trusted_Connection=Yes;'
r'UseFMTONLY=Yes;'
)
connection_uri = 'mssql+pyodbc:///?odbc_connect=' + urllib.parse.quote_plus(connection_string)
engine = sa.create_engine(connection_uri, fast_executemany=True)
# test environment
table_name = 'DateTimeOffset_Test'
engine.execute(sa.text(f"DROP TABLE IF EXISTS [{table_name}]"))
engine.execute(sa.text(f"CREATE TABLE [{table_name}] (id int primary key, dto datetimeoffset)"))
# test data
my_tz = datetime.timezone(datetime.timedelta(hours=-7))
dto_value = datetime.datetime(2020, 1, 1, 0, 0, 0, tzinfo=my_tz)
print(dto_value) # 2020-01-01 00:00:00-07:00
# ^
# test code
num_rows = 1
row_data = [(x, dto_value) for x in range(num_rows)]
df = pd.DataFrame(row_data, columns=['id', 'dto'])
print(df)
# id dto
# 0 0 2020-01-01 00:00:00-07:00
# ^ - good
df.to_sql(table_name, engine, if_exists='append', index=False)
result = engine.execute(sa.text(f"SELECT id, CAST(dto as varchar(50)) AS foo FROM [{table_name}]")).fetchall()
pprint(result)
# [(1, '2020-01-01 00:00:00.0000000 +00:00')]
# ^ - bad
```
Simply changing `num_rows = 1` to `num_rows = 2` produces correct results.
```python
# test code
num_rows = 2
row_data = [(x, dto_value) for x in range(num_rows)]
df = pd.DataFrame(row_data, columns=['id', 'dto'])
print(df)
# id dto
# 0 0 2020-01-01 00:00:00-07:00
# 1 1 2020-01-01 00:00:00-07:00
# ^ - good
df.to_sql(table_name, engine, if_exists='append', index=False)
result = engine.execute(sa.text(f"SELECT id, CAST(dto as varchar(50)) AS foo FROM [{table_name}]")).fetchall()
pprint(result)
# [(0, '2020-01-01 00:00:00.0000000 -07:00'),
# (1, '2020-01-01 00:00:00.0000000 -07:00')]
# ^ - good
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 32
OS : Windows
OS-release : 8.1
machine : AMD64
processor : AMD64 Family 18 Model 1 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext)
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 : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 11 | 2020-01-10T14:36:13Z | 2020-01-30T05:39:19Z | 2020-01-15T14:19:17Z | CONTRIBUTOR | null | On 0.25.3
```python
In [3]: df = pd.DataFrame({"A": pd.date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex([2, 3, 4])
In [4]: df.sum()
```
```pytb
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
TypeError: unsupported operand type(s) for +: 'Timestamp' and 'Timestamp'
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
<ipython-input-4-7e5fdb616c56> in <module>
----> 1 df.sum()
~/sandbox/pandas/pandas/core/generic.py in stat_func(self, axis, skipna, level, numeric_only, min_count, **kwargs)
11035 skipna=skipna,
11036 numeric_only=numeric_only,
> 11037 min_count=min_count,
11038 )
11039
~/sandbox/pandas/pandas/core/frame.py in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
7885 values = self.values
7886 try:
-> 7887 result = f(values)
7888
7889 if filter_type == "bool" and is_object_dtype(values) and axis is None:
~/sandbox/pandas/pandas/core/frame.py in f(x)
7843
7844 def f(x):
-> 7845 return op(x, axis=axis, skipna=skipna, **kwds)
7846
7847 def _get_data(axis_matters):
~/sandbox/pandas/pandas/core/nanops.py in _f(*args, **kwargs)
67 try:
68 with np.errstate(invalid="ignore"):
---> 69 return f(*args, **kwargs)
70 except ValueError as e:
71 # we want to transform an object array
~/sandbox/pandas/pandas/core/nanops.py in nansum(values, axis, skipna, min_count, mask)
491 elif is_timedelta64_dtype(dtype):
492 dtype_sum = np.float64
--> 493 the_sum = values.sum(axis, dtype=dtype_sum)
494 the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
495
~/Envs/pandas-dev/lib/python3.7/site-packages/numpy/core/_methods.py in _sum(a, axis, dtype, out, keepdims, initial, where)
36 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
37 initial=_NoValue, where=True):
---> 38 return umr_sum(a, axis, dtype, out, keepdims, initial, where)
39
40 def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
~/sandbox/pandas/pandas/_libs/tslibs/c_timestamp.pyx in pandas._libs.tslibs.c_timestamp._Timestamp.__add__()
~/sandbox/pandas/pandas/_libs/tslibs/c_timestamp.pyx in pandas._libs.tslibs.c_timestamp.integer_op_not_supported()
SystemError: <class 'TypeError'> returned a result with an error set
```
On 0.25.x
```python
In [1]: import pandas as pd
df
In [2]: df = pd.DataFrame({"A": pd.date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex([2, 3, 4])
In [3]: df.sum()
Out[3]:
B 7.0
dtype: float64
```
cc @jbrockmendel | {
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} | 0 | 2020-01-10T15:26:58Z | 2020-01-13T15:22:04Z | 2020-01-13T15:22:04Z | CONTRIBUTOR | null | On 1.0.0rc0, this raises
```pytb
In [7]: pd.util.hash_pandas_object(pd.Series(['a', 'b']), hash_key=None)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-00d1f153287e> in <module>
----> 1 pd.util.hash_pandas_object(pd.Series(['a', 'b']), hash_key=None)
~/sandbox/pandas/pandas/core/util/hashing.py in hash_pandas_object(obj, index, encoding, hash_key, categorize)
93
94 elif isinstance(obj, ABCSeries):
---> 95 h = hash_array(obj.values, encoding, hash_key, categorize).astype(
96 "uint64", copy=False
97 )
~/sandbox/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize)
302 codes, categories = factorize(vals, sort=False)
303 cat = Categorical(codes, Index(categories), ordered=False, fastpath=True)
--> 304 return _hash_categorical(cat, encoding, hash_key)
305
306 try:
~/sandbox/pandas/pandas/core/util/hashing.py in _hash_categorical(c, encoding, hash_key)
221 # Convert ExtensionArrays to ndarrays
222 values = np.asarray(c.categories.values)
--> 223 hashed = hash_array(values, encoding, hash_key, categorize=False)
224
225 # we have uint64, as we don't directly support missing values
~/sandbox/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize)
305
306 try:
--> 307 vals = hashing.hash_object_array(vals, hash_key, encoding)
308 except TypeError:
309 # we have mixed types
~/sandbox/pandas/pandas/_libs/hashing.pyx in pandas._libs.hashing.hash_object_array()
AttributeError: 'NoneType' object has no attribute 'encode'
```
On 0.25.3
```python
In [7]: pd.util.hash_pandas_object(pd.Series(['a', 'b']), hash_key=None)
Out[7]:
0 4578374827886788867
1 17338122309987883691
dtype: uint64
```
It's only for object dtype. | {
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} | 5 | 2020-01-10T15:48:25Z | 2020-01-13T18:10:26Z | 2020-01-13T13:05:17Z | CONTRIBUTOR | null | - [x] closes #30808
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2020-01-10T16:01:40Z | 2020-01-23T19:00:09Z | 2020-01-23T19:00:09Z | CONTRIBUTOR | null | c.f. #28285
#### Code Sample, a copy-pastable example if possible
```python
pd.Series([1, 2, 3], dtype="Int16").diff()
```
```
0 NaN
1 1
2 1
dtype: object
```
#### Problem description
Output dtype becomes `object`.
#### Expected Output
Output dtype remains `Int16Dtype`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-42-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.2
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200102
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2020-01-10T16:26:59Z | 2020-01-10T19:26:27Z | 2020-01-10T19:26:21Z | MEMBER | null | Just realized that the discourse links are still in the web. They should be removed, since we're not planning to use discourse for now. | {
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} | 8 | 2020-01-10T16:33:39Z | 2020-02-05T15:50:58Z | 2020-02-05T15:50:45Z | MEMBER | null | The navigation of the web was implemented assuming the docs will render with the same layout. Since this is not finally the case, and the website and the docs are finally independent, I guess it makes more sense to simply link to the home of the docs.
Otherwise, we'd need to rethink those links, since the last one is broken, and the rest are inconsistent with the navigation of the docs.
Closes https://github.com/pandas-dev/pandas/issues/31657
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} | 4 | 2020-01-10T16:52:11Z | 2020-01-28T01:59:32Z | 2020-01-28T01:59:32Z | NONE | null | #### Problem Description
in new pre-release 1.0.0.rc0, sliced value assignment for multi-indexed dataframe with duplicated datetime indices would fail
```python
import pandas as pd
x = pd.DataFrame([[1,2,3],['a','a','c']],index=['c1','index2'],
columns=pd.to_datetime(['20190101','20190101','20190102'])).T
x['c2'] = np.nan
x.index.name = 'index1'
x.set_index(['index2'],append=True,inplace=True)
In [0]: x
Out[0]:
c1 c2
index1 index2
20190101 a 1 NaN
a 2 NaN
20190102 c 3 NaN
x.loc[(pd.to_datetime('20190101'),'a'),'c2'] = 1.0 # ok
x.loc[[(pd.to_datetime('20190101'),'a')],'c2'] = 1.0 # ValueError
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} | 0 | 2020-01-10T17:06:52Z | 2020-01-10T23:21:21Z | 2020-01-10T23:21:21Z | MEMBER | null | The docstring validation script was moved to numpydoc, so it's probably worth removing this task from the roadmap.
The integration of the new numpydoc script in pandas is ready in #30746 | {
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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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But I think we should at least have a link from the documentation to the website, so users can navigate both sites without having to edit the url. | {
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As a bonus, added a Description column for the existing libraries. | {
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similar to @cbertinato great work in #28595 but done deeper in the C extension | {
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} | 0 | 2020-01-10T23:18:21Z | 2020-01-18T15:34:13Z | 2020-01-18T15:34:13Z | MEMBER | null | This is a regression between 0.25.3 and 1.0
```python
>>> import pandas as pd
>>> import datetime
>>> data = [datetime.date(year=2020, month=1, day=1), "a"]
>>> pd.Series(data).to_json(date_format="iso")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/core/generic.py", line 2363, in to_json
indent=indent,
File "/Users/williamayd/clones/pandas/pandas/io/json/_json.py", line 85, in to_json
indent=indent,
File "/Users/williamayd/clones/pandas/pandas/io/json/_json.py", line 145, in write
self.indent,
File "/Users/williamayd/clones/pandas/pandas/io/json/_json.py", line 199, in _write
indent,
File "/Users/williamayd/clones/pandas/pandas/io/json/_json.py", line 167, in _write
indent=indent,
TypeError: Expected datetime object
```
Noticed while working on #30903 | {
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} | 6 | 2020-01-10T23:21:16Z | 2020-01-15T16:12:40Z | 2020-01-15T14:19:18Z | MEMBER | null | - [x] closes #30886
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2020-01-11T10:09:56Z | 2020-04-20T22:34:36Z | 2020-04-20T22:34:36Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/30894#discussion_r365510231
Example: writing
```
from pandas.conftest import ALL_EA_INT_DTYPES
```
in a test file should be disallowed because there exists the fixture `any_nullable_int_dtype` | {
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