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https://api.github.com/repos/pandas-dev/pandas/issues/32717 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32717/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32717/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32717/events | https://github.com/pandas-dev/pandas/issues/32717 | 581,554,998 | MDU6SXNzdWU1ODE1NTQ5OTg= | 32,717 | 1.0.2 release is not on pypi | {
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} | [] | closed | false | null | [] | null | 1 | 2020-03-15T07:01:27Z | 2020-03-15T14:41:22Z | 2020-03-15T14:41:22Z | CONTRIBUTOR | null | As per $SUBJ the pypi contains just 1.0.1 release.
In openSUSE we validate all added archives against upstream ones and because of that the https://build.opensuse.org/request/show/784545 is now not acceptable :)
Could you please push the wheels/etc on pypi? | {
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} | 1 | 2020-03-15T07:06:11Z | 2020-03-16T22:26:39Z | 2020-03-16T22:26:32Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
Output for pandas.Index.min:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No extended summary found
```
and output for pandas.Index.max:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
No extended summary found
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32719/events | https://github.com/pandas-dev/pandas/issues/32719 | 581,578,165 | MDU6SXNzdWU1ODE1NzgxNjU= | 32,719 | unexpected behavior in pd.to_datetime | {
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} | [] | closed | false | null | [] | null | 3 | 2020-03-15T08:17:12Z | 2020-03-17T08:23:47Z | 2020-03-16T21:28:53Z | NONE | null | #### Code Sample
```python
>>> pd.to_datetime('2016-01-01 00:00 UTC-5', utc=True)
Timestamp('2015-12-31 19:00:00+0000', tz='UTC')
```
#### Problem description
The time at UTC-x timezone region should correspond to a +x shift at UTC+00 zones. The behavior of `pd.to_datetime` in this case is wrong, since it correspond to -x shift at UTC.
As opposed to the above, the following syntax *is* working as expected:
```python
>>> pd.to_datetime('2016-01-01 00:00 -05:00', utc=True)
Timestamp('2016-01-01 05:00:00+0000', tz='UTC')
```
#### Expected Output
```python
Timestamp('2016-01-01 05:00:00+0000', tz='UTC')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-smp-900.331.0.0
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: None
setuptools: unknown
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: 0.15.1
xarray: None
IPython: 2.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.10dev0
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: 0+unknown
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2020-03-15T09:48:00Z | 2020-03-17T15:48:13Z | 2020-03-15T14:07:59Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
While going through the code I observed at couple of places where `bool(o)` is being used to check whether the object `o` is `None`.
Example 1: https://github.com/pandas-dev/pandas/blob/6620dc67ded40c76e466c70027abd012898bbbb1/pandas/core/strings.py#L449
Example 2: https://github.com/pandas-dev/pandas/blob/6620dc67ded40c76e466c70027abd012898bbbb1/pandas/core/strings.py#L821
#### Problem description
`bool(o)` is several times slower than `o is not None` (see below). In the above cases however, majority of the time indeed is being taken by the `re.search` and therefore it might not matter for the overall time. But, why not switch to the possibly correct version?
Moreover, the functionality remains the same as `re.search` and `re.match` return `None` when they cannot find a match; which is fundamentally what is being checked in this code.
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} | 1 | 2020-03-15T09:57:10Z | 2020-03-15T16:59:57Z | 2020-03-15T14:07:59Z | CONTRIBUTOR | null | - [x] closes #32720
- [ ] tests added / passed - This change seemed trivial to *add* tests and should be covered by existing ones.
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [] whatsnew entry
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 8 | 2020-03-15T10:44:05Z | 2020-06-16T18:47:24Z | 2020-06-14T22:20:20Z | CONTRIBUTOR | null | - [x] closes #29916
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/32724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32724/events | https://github.com/pandas-dev/pandas/issues/32724 | 581,620,683 | MDU6SXNzdWU1ODE2MjA2ODM= | 32,724 | Inconsistent behavior of rolling with missing value | {
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... | closed | false | null | [] | null | 0 | 2020-03-15T10:48:39Z | 2020-10-02T18:31:09Z | 2020-10-02T18:31:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
'1.0.2'
>>> df = pd.DataFrame({"A": [1, None], "B":[4, 5], "C": [7, 8]})
>>> df.rolling(min_periods=1, window=2, axis=1).sum()
A B C
0 1.0 4.0 11.0
1 NaN 5.0 13.0
>>> df.T.rolling(min_periods=1, window=2).sum().T
A B C
0 1.0 5.0 11.0
1 NaN 5.0 13.0
```
#### Problem description
In the above example, I would expect that rolling from one axis or transposing and rolling from the other axis give the same result. This is true when there is no missing value in the dataframe. However, here there is a missing value the value at position .loc[0, "B"] ends up being different.
In the first case the value is not summed with the value at .loc[0, "A"], but in the second case it is. I think this may be an issue because I would expect to get the same output in both cases.
I observed this both in pandas 1.0.1 and 1.0.2.
#### Expected Output
```python
>>> df.rolling(min_periods=1, window=2, axis=1).sum()
A B C
0 1.0 5.0 11.0
1 NaN 5.0 13.0
>>> df.T.rolling(min_periods=1, window=2).sum().T
A B C
0 1.0 5.0 11.0
1 NaN 5.0 13.0
```
or
```python
>>> df.rolling(min_periods=1, window=2, axis=1).sum()
A B C
0 1.0 4.0 11.0
1 NaN 5.0 13.0
>>> df.T.rolling(min_periods=1, window=2).sum().T
A B C
0 1.0 4.0 11.0
1 NaN 5.0 13.0
```
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : 0.29.15
pytest : 3.2.1
hypothesis : None
sphinx : 1.5.6
blosc : None
feather : None
xlsxwriter : 0.9.8
lxml.etree : 3.8.0
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 3.8.0
matplotlib : 3.0.0
numexpr : 2.6.2
odfpy : None
openpyxl : 2.4.8
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.2.1
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.1.13
tables : 3.4.2
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 0.9.8
numba : 0.42.1
```
</details>
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} | 1 | 2020-03-15T11:03:39Z | 2020-03-16T01:38:06Z | 2020-03-16T01:37:45Z | CONTRIBUTOR | null | 11 PRs later, the moment has finally come: closes #23877 🎉
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2020-03-15T11:08:15Z | 2020-03-16T01:36:59Z | 2020-03-16T01:36:54Z | CONTRIBUTOR | null | Follow up of #32487 - for [this comment of jreback](https://github.com/pandas-dev/pandas/pull/32487#issuecomment-599153289) in particular
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2020-03-15T11:46:14Z | 2021-07-30T03:43:06Z | null | NONE | null | #### Code Sample
```python
pd.read_excel(fname, nrows=10)
```
#### Problem description
Pandas has option toread only several rows of excel files.
But now it always read all rows and after pandas cut some part.
For example, file have 100 columns and 50k rows, but for test need only first 10 rows.
Now pandas will read to list all 50k rows which use memory and take too many time to read.
les this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
Better solution should be read only rows which need for operation.
as I understand there shoul be some changes
pandas/io/excel/_base.py
```python
@abc.abstractmethod
def get_sheet_data(self, sheet, convert_float, header, skiprows, nrows):
pass
```
pandas/io/excel/_base.py
```python
data = self.get_sheet_data(sheet, convert_float, header, skiprows, nrows)
```
and in files _openpyxl.py, _odfreader.py, _xlrd.py
there should be something like
```python
def get_sheet_data(self, sheet, convert_float: bool, header: int, skiprows: int, nrows: int) -> List[List[Scalar]]:
data = [] # type: List[List[Scalar]]
skiprows = 0 if skiprows is None else skiprows
header = 0 if header is None else header
for row in sheet.rows:
if nrows is not None:
if header > 0:
#print("skip lines before header")
header -= 1
data.append(["", ""])
continue
if skiprows > 0:
#print("skip skiprows after header")
skiprows -= 1
data.append(["", ""])
continue
if nrows >= 0:
#print("read nrows")
nrows -= 1
else:
break
data.append([self._convert_cell(cell, convert_float) for cell in row])
return data
```
With this changes read_excel with engine='openpyxl' takes only 5 seconds instead of 50 seconds of current version. And if file will contain 1kk rows, it will take always around 5 seconds, but current version will take tens of minutes. | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
A lambda wrapper is unnecessary in these cases as the underlying methods are themselves callable. Moreover, the code does not add any logic in the wrapper to warrant the usage of lambda. | {
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} | 1 | 2020-03-15T17:15:04Z | 2020-03-16T15:40:50Z | 2020-03-16T01:27:33Z | MEMBER | null | Small cosmetic changes to rendering of release notes | {
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} | 4 | 2020-03-15T18:06:30Z | 2020-03-16T16:09:33Z | 2020-03-16T01:26:32Z | MEMBER | null | cc @WillAyd trying to get away from _internal_get_values in the json code; this is zero-logic-changing to narrow the scope of what gets passed to _internal_get_values.
AFAICT replacing the _internal_get_values call with calling `__array__` doesnt break any tests, but in general the two are not always equivalent and it isnt obvious to me whether there are untested cases where it matters. | {
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I am not sure if this is the good place to post this issue, or maybe should I post it on `pydata-bootstrap-sphinx-theme`?
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} | 8 | 2020-03-15T19:09:12Z | 2020-03-16T22:41:33Z | 2020-03-16T22:34:14Z | CONTRIBUTOR | null | - [x] closes #32543
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- [x] upgrades old string formatting to f-strings in core/generic.py
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} | 2 | 2020-03-15T22:22:11Z | 2020-03-19T19:07:07Z | 2020-03-19T01:40:41Z | CONTRIBUTOR | null | Get rid of all error printf's and produce proper Python exceptions
- [x] closes #23784
- [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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... | null | 7 | 2020-03-15T22:22:33Z | 2020-05-14T10:10:08Z | 2020-05-14T10:10:08Z | CONTRIBUTOR | null | Looking at the [plot accessor docstring][1], it's missing
* `ax`
* `subplots`
* `sharex`
* `sharey`
* `secondary_y`
* `sort_columns`
Maybe others.
Also would be nice to have documentation of all parameters for all the accessor methods i.e. `df.plot.bar`.
[1]: https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py#L579 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32739/events | https://github.com/pandas-dev/pandas/pull/32739 | 581,849,711 | MDExOlB1bGxSZXF1ZXN0Mzg4ODUyNzU4 | 32,739 | BUG: Fix join on MultiIndex for mixed Datetimelike and string levels | {
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From #26558, a minimal example is:
```python
import pandas as pd
import datetime
i1 = pd.Index(['2019-05-31'])
i2 = pd.Index([datetime.datetime(2019, 5, 31)])
mi1 = pd.MultiIndex.from_tuples([('2019-05-31',)])
mi2 = pd.MultiIndex.from_tuples([(datetime.datetime(2019, 5, 31),)])
print(i1.join(i2, return_indexers=True))
print(mi1.join(mi2, return_indexers=True))
# (DatetimeIndex(['2019-05-31'], dtype='datetime64[ns]', freq=None), None, None)
# (MultiIndex([('2019-05-31',)], ), None, array([-1]))
```
Here we can see that one of the indexers for multiindex is wrong.
# Proposed fix 🩹
The difference of handling for single index is explain by the join method for DatetimeTimedeltaMixin.
I did some refactoring in pandas/core/indexes/datetimelike.py to be able to reuse the same datetime handling method.
Then in the Index.join method, where MultiIndex are handled, I apply this method to each level of the MultiIndex that is a DatetimeTimedeltaMixin.
# PR checkboxes :ballot_box_with_check:
- [x] closes #26558
- [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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"de... | closed | false | null | [] | null | 11 | 2020-03-15T23:56:49Z | 2020-05-27T14:33:46Z | 2020-04-05T18:28:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime as dt
print(pd.__version__)
a = pd.date_range('2010-01-01', '2010-01-02', periods=24, tz='utc')
b = pd.date_range('2010-01-01', '2010-01-02', periods=23, tz='utc')
a.reindex(b, method='nearest', tolerance=dt.timedelta(seconds=20))
```
#### Problem description
Output is:
```
Python 3.7.4 (default, Jul 27 2019, 21:25:02)
[GCC 7.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import datetime as dt
>>>
>>> print(pd.__version__)
1.0.2
>>> a = pd.date_range('2010-01-01', '2010-01-02', periods=24, tz='utc')
>>> b = pd.date_range('2010-01-01', '2010-01-02', periods=23, tz='utc')
>>> a.reindex(b, method='nearest', tolerance=dt.timedelta(seconds=20))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3144, in reindex
target, method=method, limit=limit, tolerance=tolerance
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2740, in get_indexer
indexer = self._get_nearest_indexer(target, limit, tolerance)
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2830, in _get_nearest_indexer
indexer = self._filter_indexer_tolerance(target, indexer, tolerance)
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2834, in _filter_indexer_tolerance
distance = abs(self.values[indexer] - target)
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/indexes/extension.py", line 147, in method
result = meth(_maybe_unwrap_index(other))
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py", line 1458, in __rsub__
return -(self - other)
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/ops/common.py", line 64, in new_method
return method(self, other)
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py", line 1406, in __sub__
result = self._sub_datetime_arraylike(other)
File "/home/andrew/project/ch2/choochoo-1/py/env/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 667, in _sub_datetime_arraylike
f"{type(self).__name__} subtraction must have the same "
TypeError: DatetimeArray subtraction must have the same timezones or no timezones
```
The problem is that I don't see how to reindex and / or the error message appears to be nonsensical since both have UTC timezones.
#### Expected Output
With 0.25.3 I see:
```
(DatetimeIndex([ '2010-01-01 00:00:00+00:00',
'2010-01-01 01:05:27.272727272+00:00',
'2010-01-01 02:10:54.545454545+00:00',
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'2010-01-01 06:32:43.636363636+00:00',
'2010-01-01 07:38:10.909090909+00:00',
'2010-01-01 08:43:38.181818181+00:00',
'2010-01-01 09:49:05.454545454+00:00',
'2010-01-01 10:54:32.727272727+00:00',
'2010-01-01 12:00:00+00:00',
'2010-01-01 13:05:27.272727272+00:00',
'2010-01-01 14:10:54.545454545+00:00',
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'2010-01-01 16:21:49.090909090+00:00',
'2010-01-01 17:27:16.363636363+00:00',
'2010-01-01 18:32:43.636363636+00:00',
'2010-01-01 19:38:10.909090909+00:00',
'2010-01-01 20:43:38.181818181+00:00',
'2010-01-01 21:49:05.454545454+00:00',
'2010-01-01 22:54:32.727272727+00:00',
'2010-01-02 00:00:00+00:00'],
dtype='datetime64[ns, UTC]', freq=None), array([ 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, 23]))
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-lp151.28.36-default
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/32741 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32741/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32741/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32741/events | https://github.com/pandas-dev/pandas/issues/32741 | 581,893,898 | MDU6SXNzdWU1ODE4OTM4OTg= | 32,741 | Strange behavior on saving a dataframe containing = at the start of a cell value | {
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{
"color": "009800",
"default": false,
"description": "Duplicate issue or pull request",
"id": 40153326,
"name": "Duplicate Report",
"node_id": "MDU6TGFiZWw0MDE1MzMyNg==",
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... | closed | false | null | [] | null | 1 | 2020-03-16T00:33:01Z | 2020-03-19T19:16:11Z | 2020-03-19T19:16:02Z | NONE | null | ```python
import pandas as pd
index = [0, 1, 2]
text = ['====== 2', '======= abd', 'abc']
df = pd.DataFrame({'index':index, 'text': text})
df.to_excel('temp.xlsx', index=False)
print(df.head())
loaded_df = pd.read_excel('temp.xlsx')
print(loaded_df.head())
```
#### Problem description
Excel sheets interpret an `=` sign at the start of the cell as a signal for writing equations. If a column contains text data where one of the values start with `=` then the values are corrupted after saving the dataframe to an excel sheet.
#### Expected Output
```
index text
0 0 ====== 2
1 1 ======= abd
2 2 abc
```
#### Output after loading the exported xlsx sheet
```
index text
0 0 0
1 1 0
2 2 abc
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 059f9bff62d3bfe53eeece409b48b879abd1a3b1
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
Version : #88-Ubuntu SMP Tue Feb 11 20:11:34 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+793.g059f9bff6
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.6.0
sphinx : 2.4.4
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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battle = pd.read_csv('battledeath_1.csv', low_memory = False)
battle_without_duplicate = battle.drop_duplicates()
I have dropped the duplicates in the original csv file but now i am asked to delete the entries that have zero in its values. They are just 2 columns, one being the name of the country and the other being the value in it. I need help in taking out the rows with zero in them... | {
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} | 3 | 2020-03-16T06:40:20Z | 2020-03-16T18:13:50Z | 2020-03-16T17:28:23Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"descri... | closed | false | null | [] | null | 10 | 2020-03-16T09:50:59Z | 2021-03-26T05:36:08Z | 2021-03-26T05:36:07Z | NONE | null | I am trying to save a data frame to csv using the method df.to_csv(writer) by passing hdfs_client's writer but it is throwing an error
"ValueError: Invalid file path or buffer object type: <class 'hdfs.util.AsyncWriter'>"
Code:
```
with hdfs_client.write('/some/existing/path/in/datalake/dummy.csv', encoding='utf-8') as writer:
df.to_csv(writer,encoding='utf-8') (edited)
```
The issue was also mentioned in https://github.com/pandas-dev/pandas/issues/21560
Error:
```
ValueError Traceback (most recent call last)
<ipython-input-10-a363c2611af8> in <module>
1 with hdfs_client.write('/shared/ml/data/sfd.csv', encoding='utf-8') as writer:
----> 2 df.to_csv(writer,encoding='utf-8')
/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal)
3200 doublequote=doublequote,
3201 escapechar=escapechar,
-> 3202 decimal=decimal,
3203 )
3204 formatter.save()
/opt/conda/lib/python3.7/site-packages/pandas/io/formats/csvs.py in __init__(self, obj, path_or_buf, sep, na_rep, float_format, cols, header, index, index_label, mode, encoding, compression, quoting, line_terminator, chunksize, quotechar, date_format, doublequote, escapechar, decimal)
64
65 self.path_or_buf, _, _, _ = get_filepath_or_buffer(
---> 66 path_or_buf, encoding=encoding, compression=compression, mode=mode
67 )
68 self.sep = sep
/opt/conda/lib/python3.7/site-packages/pandas/io/common.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
198 if not is_file_like(filepath_or_buffer):
199 msg = f"Invalid file path or buffer object type: {type(filepath_or_buffer)}"
--> 200 raise ValueError(msg)
201
202 return filepath_or_buffer, None, compression, False
ValueError: Invalid file path or buffer object type: <class 'hdfs.util.AsyncWriter'>
```
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} | 1 | 2020-03-16T11:38:25Z | 2020-03-16T14:37:42Z | 2020-03-16T13:14:59Z | CONTRIBUTOR | null | {
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} | 2 | 2020-03-16T12:57:50Z | 2020-03-17T19:33:40Z | 2020-03-17T19:33:40Z | MEMBER | null | Consider this small example of two DataFrames, one with an Int64 extension dtype, the other with the same values but object dtype:
```
df1 = pd.DataFrame({'a': pd.array([1, 2, 3], dtype="Int64")})
df2 = df1.astype(object)
```
With pandas 1.0.1, this passes `assert_frame_equal` with the `check_dtype=False`:
```
In [5]: pd.testing.assert_frame_equal(df1, df2)
...
Attribute "dtype" are different
[left]: Int64
[right]: object
In [6]: pd.testing.assert_frame_equal(df1, df2, check_dtype=False)
```
but with master (since https://github.com/pandas-dev/pandas/pull/32570, see my comment there, cc @jbrockmendel), this fails:
```
In [2]: pd.testing.assert_frame_equal(df1, df2, check_dtype=False)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-2-d2d792202db1> in <module>
----> 1 pd.testing.assert_frame_equal(df1, df2, check_dtype=False)
~/scipy/pandas/pandas/_testing.py in assert_frame_equal(left, right, check_dtype, check_index_type, check_column_type, check_frame_type, check_less_precise, check_names, by_blocks, check_exact, check_datetimelike_compat, check_categorical, check_like, obj)
1378 check_datetimelike_compat=check_datetimelike_compat,
1379 check_categorical=check_categorical,
-> 1380 obj=f'{obj}.iloc[:, {i}] (column name="{col}")',
1381 )
1382
~/scipy/pandas/pandas/_testing.py in assert_series_equal(left, right, check_dtype, check_index_type, check_series_type, check_less_precise, check_names, check_exact, check_datetimelike_compat, check_categorical, check_category_order, obj)
1177 )
1178 elif is_extension_array_dtype(left.dtype) or is_extension_array_dtype(right.dtype):
-> 1179 assert_extension_array_equal(left._values, right._values)
1180 elif needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype):
1181 # DatetimeArray or TimedeltaArray
~/scipy/pandas/pandas/_testing.py in assert_extension_array_equal(left, right, check_dtype, check_less_precise, check_exact)
1017 """
1018 assert isinstance(left, ExtensionArray), "left is not an ExtensionArray"
-> 1019 assert isinstance(right, ExtensionArray), "right is not an ExtensionArray"
1020 if check_dtype:
1021 assert_attr_equal("dtype", left, right, obj="ExtensionArray")
AssertionError: right is not an ExtensionArray
``` | {
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} | 7 | 2020-03-16T13:20:24Z | 2020-05-26T09:34:56Z | 2020-03-20T22:22:05Z | CONTRIBUTOR | null | I think this has come up before, but which whatsnew document, if any, do we want to link from the header? The "What's New in 1.0.1" in the screenshot below.
<img width="454" alt="Screen Shot 2020-03-16 at 8 16 42 AM" src="https://user-images.githubusercontent.com/1312546/76762092-a15c7800-675e-11ea-864e-11978f436b86.png">
Choices
1. Latest release (e.g. 1.0.2)
2. Last major release (e.g. 1.0.0)
3. None
cc @jorisvandenbossche @datapythonista.
(I think my preference is for option 3, since it's not clear whether 1 or 2 is better, so put it behind an extra click via the "Release Notes" page). | {
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} | 11 | 2020-03-16T13:21:04Z | 2021-01-01T21:38:39Z | 2021-01-01T21:38:39Z | MEMBER | null |
With pandas 1.0.1:
```
In [7]: from pandas.tests.extension.decimal import DecimalArray, make_data
In [8]: s = pd.Series(DecimalArray(make_data()[:5]))
In [9]: s
Out[9]:
0 Decimal: 0.25866656130631138221787068687262944...
1 Decimal: 0.11511905452780368808163302674074657...
2 Decimal: 0.15301679241167220890673661415348760...
3 Decimal: 0.41125672759464626526693109553889371...
4 Decimal: 0.46391316685725048074573351186700165...
dtype: decimal
In [10]: s.idxmin()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-10-5060aab05d12> in <module>
----> 1 s.idxmin()
~/miniconda3/envs/pandas10/lib/python3.8/site-packages/pandas/core/series.py in idxmin(self, axis, skipna, *args, **kwargs)
2037 """
2038 skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
-> 2039 i = nanops.nanargmin(com.values_from_object(self), skipna=skipna)
2040 if i == -1:
2041 return np.nan
~/miniconda3/envs/pandas10/lib/python3.8/site-packages/pandas/core/nanops.py in _f(*args, **kwargs)
62 if any(self.check(obj) for obj in obj_iter):
63 f_name = f.__name__.replace("nan", "")
---> 64 raise TypeError(
65 f"reduction operation '{f_name}' not allowed for this dtype"
66 )
TypeError: reduction operation 'argmin' not allowed for this dtype
```
Now with master you get:
```
In [4]: s.idxmin()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-7e62303a9d43> in <module>
----> 1 s.idxmin()
~/scipy/pandas/pandas/core/series.py in idxmin(self, axis, skipna, *args, **kwargs)
1988 """
1989 skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
-> 1990 i = nanops.nanargmin(self._values, skipna=skipna)
1991 if i == -1:
1992 return np.nan
~/scipy/pandas/pandas/core/nanops.py in _f(*args, **kwargs)
69 try:
70 with np.errstate(invalid="ignore"):
---> 71 return f(*args, **kwargs)
72 except ValueError as e:
73 # we want to transform an object array
~/scipy/pandas/pandas/core/nanops.py in nanargmin(values, axis, skipna, mask)
943 """
944 values, mask, dtype, _, _ = _get_values(
--> 945 values, True, fill_value_typ="+inf", mask=mask
946 )
947 result = values.argmin(axis)
~/scipy/pandas/pandas/core/nanops.py in _get_values(values, skipna, fill_value, fill_value_typ, mask)
311 # promote if needed
312 else:
--> 313 values, _ = maybe_upcast_putmask(values, mask, fill_value)
314
315 # return a platform independent precision dtype
~/scipy/pandas/pandas/core/dtypes/cast.py in maybe_upcast_putmask(result, mask, other)
278 """
279 if not isinstance(result, np.ndarray):
--> 280 raise ValueError("The result input must be a ndarray.")
281 if not is_scalar(other):
282 # We _could_ support non-scalar other, but until we have a compelling
ValueError: The result input must be a ndarray.
```
@jbrockmendel this is from no longer using `values_from_object`, but passing `_values` to the nanops function, while this expects to always receive an ndarray | {
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} | 0 | 2020-03-16T13:23:59Z | 2020-03-16T14:34:53Z | 2020-03-16T14:34:52Z | CONTRIBUTOR | null | https://github.com/pandas-dev/pandas/pull/32746 | {
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} | 6 | 2020-03-16T13:28:14Z | 2020-03-18T18:40:22Z | 2020-03-18T18:23:19Z | CONTRIBUTOR | null | I'd like to release 1.0.3 somewhat soon, primarily motivated by https://github.com/pandas-dev/pandas/issues/32685 (which has an open PR with a fix).
Can we target tomorrow, Tuesday the 17th? If there are other regression fixes to include, ping me on them or post their issue numbers here.
---
I have a bit of free time today and tomorrow, so I might merge https://github.com/MacPython/pandas-wheels/pull/74 to use the new wheel building infrastructure.
cc @pandas-dev/pandas-core
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} | 1 | 2020-03-16T13:56:56Z | 2020-03-16T16:55:59Z | 2020-03-16T16:55:37Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/32144 | {
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} | 1 | 2020-03-16T15:29:53Z | 2020-03-17T08:12:40Z | 2020-03-16T22:42:15Z | CONTRIBUTOR | null | - [x] xref #28792
My first PR. Tell me if I'm doing this incorrectly.
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} | 3 | 2020-03-16T16:16:39Z | 2020-03-17T02:21:49Z | 2020-03-17T02:06:28Z | MEMBER | null | cc @WillAyd
As mentioned in #32731, this changes the behavior for CategoricalIndex[dt64tz] and Series with that for its index. Suggestions for where tests for that should go?
Note: the Categorical[dt64tz] cases still do a trip through object-dtype; I don't want to futz with that in C-space. | {
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} | 2 | 2020-03-16T16:56:38Z | 2020-03-22T20:34:44Z | 2020-03-22T20:34:44Z | NONE | null | When attempting to aggregate a non-existing column, this error is raised, which is not very descriptive: `SpecificationError: nested renamer is not supported`
```
In [39]: pd.DataFrame([[1, 2]], columns=['A', 'B']).groupby('A').agg({'B': 'mean'})
Out[39]:
B
A
1 2
In [40]: pd.DataFrame([[1, 2]], columns=['A', 'B']).groupby('A').agg({'B': 'mean', 'non-existing': 'mean'})
Out[40]:
...
SpecificationError: nested renamer is not supported
``` | {
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} | 7 | 2020-03-16T17:13:07Z | 2020-03-18T00:01:06Z | 2020-03-18T00:00:58Z | CONTRIBUTOR | null | The existing HAVEN_LICENSE file was lacking any form of actual license.
After checking the pandas source, it appears that the reference to HAVEN
is in relation to tidyverse/haven, copying the license from
tidyverse/haven for clarity.
Signed-off-by: Jamin Collins <jamin@amazon.com>
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} | 8 | 2020-03-16T18:40:33Z | 2020-03-17T20:56:21Z | 2020-03-17T19:33:41Z | MEMBER | null | - [x] closes #32747
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
I'm not sure if https://github.com/pandas-dev/pandas/blob/master/pandas/_testing.py#L1174 needs to change too?
cc @jorisvandenbossche | {
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} | 3 | 2020-03-16T18:58:31Z | 2020-03-17T16:01:08Z | 2020-03-17T01:34:16Z | MEMBER | null | - [x] closes #31710
- [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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"id": 241332... | closed | false | null | [] | null | 2 | 2020-03-16T21:22:55Z | 2020-05-09T09:13:46Z | 2020-05-06T21:30:51Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df.plot.bar(stacked=True)
df.plot(kind='bar', stacked=True)
```
#### Problem description
There is a keyword argument to create a stacked bar graph. This kwarg is not documented anywhere in the Pandas documentation.
#### Expected Output
The documentations for the methods `df.plot.bar` and `df.plot` should include a description of the kwd `stacked`
#### Version
Pandas 1.0.1
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} | 4 | 2020-03-16T21:46:43Z | 2020-10-05T11:23:13Z | 2020-10-05T11:23:13Z | CONTRIBUTOR | null | For example
```python
>>> import pandas
>>> pandas.test(['-k', 'TestPDApi'])
```
Has many warnings like
```
Envs/dask-dev/lib/python3.7/site-packages/pandas/tests/series/indexing/test_datetime.py:121
/Users/taugspurger/Envs/dask-dev/lib/python3.7/site-packages/pandas/tests/series/indexing/test_datetime.py:121: PytestUnknownMarkWarning: Unknown pytest.mark.slow - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html
@pytest.mark.slow
Envs/dask-dev/lib/python3.7/site-packages/pandas/tests/series/methods/test_rank.py:559
/Users/taugspurger/Envs/dask-dev/lib/python3.7/site-packages/pandas/tests/series/methods/test_rank.py:559: PytestUnknownMarkWarning: Unknown pytest.mark.single - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html
@pytest.mark.single
Envs/dask-dev/lib/python3.7/site-packages/pandas/tests/series/methods/test_rank.py:560
/Users/taugspurger/Envs/dask-dev/lib/python3.7/site-packages/pandas/tests/series/methods/test_rank.py:560: PytestUnknownMarkWarning: Unknown pytest.mark.high_memory - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html
@pytest.mark.high_memory
```
We register these: https://github.com/pandas-dev/pandas/blob/5da500af76d0031021a8e906d8f34dc3e6ded234/setup.cfg#L56-L62. But the setup.cfg is perhaps not included with the distribution, and so the registered marks aren't available perhaps? Is there anyone more familiar with pytest who knows what we should be doing here? | {
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} | 1 | 2020-03-16T22:28:45Z | 2020-09-15T22:20:48Z | 2020-09-15T22:20:48Z | NONE | null | First, thank you all for creating and maintaining this package, and for the obvious care that has gone into the numerical analysis of the `rolling` methods.
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
print('Pandas Version', pd.__version__)
pd.options.display.float_format = '{:,.15f}'.format
np.random.seed(42)
index = pd.date_range(start='2020-01-01', end= '2020-01-02', freq='1s').append(pd.DatetimeIndex(['2020-01-03']))
data = np.random.rand(len(index))
df = pd.DataFrame({'data':data}, index=index)
df['mean'] = df.rolling('60s').mean()
df
```
Result
```
>>> print('Pandas Version', pd.__version__)
Pandas Version 1.0.2
...
>>> df
data mean
2020-01-01 00:00:00 0.374540118847362 0.374540118847362
2020-01-01 00:00:01 0.950714306409916 0.662627212628639
2020-01-01 00:00:02 0.731993941811405 0.685749455689561
2020-01-01 00:00:03 0.598658484197037 0.663976712816430
2020-01-01 00:00:04 0.156018640442437 0.562385098341631
... ... ...
2020-01-01 23:59:57 0.888246435024149 0.461337636515083
2020-01-01 23:59:58 0.825973496654809 0.468473569401559
2020-01-01 23:59:59 0.487629482790733 0.473417509587717
2020-01-02 00:00:00 0.705341871943242 0.480022530163534
2020-01-03 00:00:00 0.105076984427931 0.105076984428225
[86402 rows x 2 columns]
```
#### Problem description
In the example above I show a rolling mean over a 1-minute window, but the final point jumps ahead by one day. Since this is the only point in the final window, I expect the mean here to be *exactly* the single value in the window. You can see however that there is some error in the low-order 4 dights displayed.
I believe this is due to an omission in the function `roll_mean_variable` which I am reading in the file `pandas/_libs/window/aggregations.pyx`. I believe that the function should zero the running sum whenever the number of observations goes to 0 after a "remove" operation. Otherwise, some noise from previous periods will be left in the running sum.
If I apply this patch to my local copy then I get the exact mean.
```
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index 495b436..76ba1dd 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -344,20 +344,24 @@ def roll_mean_variable(ndarray[float64_t] values, ndarray[int64_t] start,
val = values[j]
add_mean(val, &nobs, &sum_x, &neg_ct)
else:
# calculate deletes
for j in range(start[i - 1], s):
val = values[j]
remove_mean(val, &nobs, &sum_x, &neg_ct)
+ # Reset sum if NOBS goes to 0
+ if nobs == 0:
+ sum_x = 0
+
# calculate adds
for j in range(end[i - 1], e):
val = values[j]
add_mean(val, &nobs, &sum_x, &neg_ct)
output[i] = calc_mean(minp, nobs, neg_ct, sum_x)
if not is_monotonic_bounds:
for j in range(s, e):
val = values[j]
```
I understand that there is a philosophical question: Given that we know that most rolling means are likely not bit-accurate anyway, is it worth doing a check for this special case? One argument would be that for time series like mine with gaps here and there, the gaps would serve to clear the accumulated errors every time a gap occurred, and in general the accuracy of the procedure would improve.
I notice that the rolling variance calculations *do* reset the accumulators to 0 when they completely depopulate a window. I am curious though, is there a numerical-analysis reason for doing the adds before the removes there? This order nullifies the resetting-effect of clearing the accumulators I believe, because my windows will never go to empty with this ordering. This is the code I'm referring to:
https://github.com/pandas-dev/pandas/blob/5da500af76d0031021a8e906d8f34dc3e6ded234/pandas/_libs/window/aggregations.pyx#L520
Thank you,
Bishop Brock
#### Expected Output
I expect the rolling mean of any window specified with a time period with a single data point to be equal to that data point.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.2+0.g7485dbe6f.dirty
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
pytest : 5.2.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
numba : 0.45.1
</details>
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} | 1 | 2020-03-16T22:32:45Z | 2020-05-20T19:06:04Z | 2020-03-26T01:22:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
x = ['1998-01-07 13:17:00', '1998-01-07 13:18:00', '1998-01-07 13:19:00', '1998-01-07 13:20:00',
'1998-01-07 13:21:00', '1998-01-07 13:22:00', '1998-01-07 13:23:00', '1998-01-07 13:24:00',
'1998-01-07 13:25:00', '1998-01-07 13:26:00']
df = pd.DataFrame(x)
df.set_index(df.iloc[:, 0], inplace=True)
df.index = pd.DatetimeIndex(df.index.values)
```
#### Problem description
I want to apply `searchsorted` function to df index:
`df.index.searchsorted(pd.Series(np.array([df.index[0], df.index.max()])))`
but it returns an error:
> TypeError: searchsorted requires compatible dtype or scalar, not Series
Why it doesn't find the nearest datetimeindex in time? I think this works with older versions of pandas.
I use the lastest version of pandas (1.0.3.)
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} | 4 | 2020-03-16T23:34:20Z | 2020-03-26T01:25:32Z | 2020-03-26T01:22:58Z | MEMBER | null | - [x] closes #32762
- [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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} | 5 | 2020-03-17T01:43:52Z | 2020-09-15T02:06:07Z | 2020-09-15T02:06:07Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas
```
#### Problem description
```
jupiter@21:38 ➤ ipython
Python 3.9.0a4+ (heads/master:87ec86c425, Mar 16 2020, 11:05:13)
Type 'copyright', 'credits' or 'license' for more information
IPython 8.0.0.dev -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-38d4b0363d82> in <module>
----> 1 import pandas
~/.virtualenvs/bleeding/lib/python3.9/site-packages/pandas/__init__.py in <module>
120 from pandas.tseries.api import infer_freq
121 from pandas.tseries import offsets
--> 123 from pandas.core.computation.api import eval
125 from pandas.core.reshape.api import (
126 concat,
127 lreshape,
(...)
138 qcut,
139 )
141 import pandas.api
~/.virtualenvs/bleeding/lib/python3.9/site-packages/pandas/core/computation/api.py in <module>
1 # flake8: noqa
----> 3 from pandas.core.computation.eval import eval
~/.virtualenvs/bleeding/lib/python3.9/site-packages/pandas/core/computation/eval.py in <module>
11 from pandas.util._validators import validate_bool_kwarg
13 from pandas.core.computation.engines import _engines
---> 14 from pandas.core.computation.expr import Expr, _parsers
15 from pandas.core.computation.parsing import tokenize_string
16 from pandas.core.computation.scope import ensure_scope
~/.virtualenvs/bleeding/lib/python3.9/site-packages/pandas/core/computation/expr.py in <module>
188 _expr_nodes = _filter_nodes(ast.expr)
189 _expr_context_nodes = _filter_nodes(ast.expr_context)
--> 190 _slice_nodes = _filter_nodes(ast.slice)
191 _boolop_nodes = _filter_nodes(ast.boolop)
192 _operator_nodes = _filter_nodes(ast.operator)
AttributeError: module 'ast' has no attribute 'slice'
In [2]:
```
```
jupiter@21:41 ➤ git describe
v1.1.0.dev0-846-g7aa3dd274
```
It looks like this is due to https://github.com/python/cpython/pull/9605 / https://bugs.python.org/issue34822 and needs to be sorted for py39
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} | 6 | 2020-03-17T02:06:48Z | 2020-04-08T17:54:07Z | 2020-03-19T00:19:52Z | MEMBER | null | ATM core.algorithms and core.nanops are a mish-mash in terms of what inputs they expect. This implements core._algos directory intended for guaranteed-ndarray/EA-only implementations.
For the first function to move I de-duplicated a `shift` method. Need suggestions for what to call this module. | {
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} | 1 | 2020-03-17T02:47:51Z | 2020-03-19T01:30:09Z | 2020-03-19T00:24:24Z | MEMBER | null | - [x] closes #23565
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Precursor to https://github.com/pandas-dev/pandas/issues/31845 and other numba engine additions, creates `pandas/core/numba_.py` (open to move elsewhere) as a shared place for common numba operations like default parameters and jitting functions. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32773 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32773/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32773/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32773/events | https://github.com/pandas-dev/pandas/issues/32773 | 582,966,157 | MDU6SXNzdWU1ODI5NjYxNTc= | 32,773 | DISC: Validating internal docstring presence, completeness and style | {
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"description": ... | open | false | null | [] | null | 27 | 2020-03-17T12:08:36Z | 2021-05-13T01:29:49Z | null | MEMBER | null | We currently have guidelines/checks for docstrings https://pandas.pydata.org/docs/development/contributing_docstring.html for the api facing methods/functions following the numpydoc docstring guide https://numpydoc.readthedocs.io/en/latest/format.html. since we also use sphinx for building the on-line documentation.
Many of our internal functions don't have docstrings and this could be a barrier to new contributions. For the more complex/undocumented internals, I am concerned that this may also affect the sustainability of the project if only a handful of contributors can grok the code.
The pandas docstring guidelines could be considered too heavyweight for internal functions and therefore may be factor in the incomplete coverage of docstrings for internal functions. | {
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} | [] | closed | false | null | [] | null | 4 | 2020-03-17T13:14:05Z | 2020-03-17T16:19:52Z | 2020-03-17T15:52:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
data = pd.read_csv("Data_export.csv")
data.drop_duplicates(keep='first',inplace=True) # dropping duplicate rows
df_out = data.pivot(index='timeutc', columns='attributename', values='value') # produce correct columns with values
df_out = df_out.dropna()
print("BEFORE")
print(df_out.index)
df_out.index = pd.to_datetime(df_out.index)
print("AFTER")
print(df_out.index)
```
#### Problem description
I am importing a .csv file taken from the local meteorological data service.
The file contains data such as "humidity", "temperature", etc. together with a "dateutc" containing the timestamp of the data sample.
Pandas does not recognise the format immediately, so I explicitly call ```to_datetime```, but it does not seem to work.
Output of my file:
```
BEFORE
Index(['2018-06-06 15:10:00+02', '2018-06-06 15:20:00+02',
'2018-06-06 15:30:00+02', '2018-06-06 16:20:00+02',
'2018-06-06 16:30:00+02', '2018-06-06 17:10:00+02',
'2018-06-06 17:20:00+02', '2018-06-06 17:30:00+02',
'2018-06-06 17:40:00+02', '2018-06-06 17:50:00+02',
...
'2019-07-21 05:50:00+02', '2019-07-21 06:00:00+02',
'2019-07-21 06:10:00+02', '2019-07-21 06:20:00+02',
'2019-07-21 06:30:00+02', '2019-07-21 06:40:00+02',
'2019-07-21 06:50:00+02', '2019-07-21 07:00:00+02',
'2019-07-21 07:10:00+02', '2019-07-21 07:20:00+02'],
dtype='object', name='timeutc', length=52029)
```
This is type Object, as expected. But even after calling ```to_datetime``` it still remains Object:
```
AFTER
Index([2018-06-06 15:10:00+02:00, 2018-06-06 15:20:00+02:00,
2018-06-06 15:30:00+02:00, 2018-06-06 16:20:00+02:00,
2018-06-06 16:30:00+02:00, 2018-06-06 17:10:00+02:00,
2018-06-06 17:20:00+02:00, 2018-06-06 17:30:00+02:00,
2018-06-06 17:40:00+02:00, 2018-06-06 17:50:00+02:00,
...
2019-07-21 05:50:00+02:00, 2019-07-21 06:00:00+02:00,
2019-07-21 06:10:00+02:00, 2019-07-21 06:20:00+02:00,
2019-07-21 06:30:00+02:00, 2019-07-21 06:40:00+02:00,
2019-07-21 06:50:00+02:00, 2019-07-21 07:00:00+02:00,
2019-07-21 07:10:00+02:00, 2019-07-21 07:20:00+02:00],
dtype='object', name='timeutc', length=52029)
```
Calling ```print(type(data.index))``` gives
```
<class 'pandas.core.indexes.base.Index'>
```
#### Expected Output
I expected to have a DateTime index.
My code was working as intended before upgrading the Anaconda Interface. Somehow, without me realising it, upgrading the Anaconda Interface also upgraded Pandas to 1.0.1, and my code broke down in several places. I tried downgrading but I do not remember exactly which version of Pandas I was using before, when the code was working, but the above behaviour happens both with 1.0.1 and with 0.25.3.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
</details>
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} | 3 | 2020-03-17T14:19:12Z | 2020-03-21T11:45:31Z | 2020-03-19T00:52:10Z | CONTRIBUTOR | null | Move ``_cache: Dict[str, Any]`` fragment to PandasObject, where it belongs + related changes. | {
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# Your code here
import pandas as pd
pd.__version__
Out[3]: '0.24.2'
t1 = {'a': 1, 'b': 1}
t2 = {'a': 2, 'b': 2}
test = [t1, t2], # note the errernous comma at the end of the line
type(test)
Out[7]: tuple
test
Out[8]: ([{'a': 1, 'b': 1}, {'a': 2, 'b': 2}],)
pd.DataFrame(test)
Process finished with exit code -1073741819 (0xC0000005)
```
Short:
```python
pd.DataFrame(tuple([[1],[2]]))
Process finished with exit code -1073741819 (0xC0000005)
```
#### Problem description
Problem was caused in my case by having an additional comma at the end of a line that caused
a list of dicts being a tuple of list of dicts which caused that behaviour.
Althought the argument should not be accepted by Pandas, Pandas should raise an appropriate Exception instead of tearing Python down. | {
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- [ ] 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 | 2020-03-17T17:57:07Z | 2020-03-21T21:14:27Z | 2020-03-21T21:09:27Z | MEMBER | null | Apologies for the size of this PR, but it looks like the ExtensionArray tests sometimes have their own directories by type, and other times all tests for the type are in a single file (e.g., https://github.com/pandas-dev/pandas/tree/master/pandas/tests/arrays/categorical vs. https://github.com/pandas-dev/pandas/blob/master/pandas/tests/arrays/test_boolean.py). I think the "whole directory for type" structure makes it easier to find and add new tests, so I'm doing that here for the Boolean tests. There shouldn't be any changes other than moving things around. | {
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} | 6 | 2020-03-17T19:21:30Z | 2020-04-10T17:25:46Z | 2020-04-10T16:17:34Z | MEMBER | null | - [ ] <s>closes #27147</s> Not quite
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Still needs a dedicated test, but this is also a non-trivial API change, so I want to get the ball rolling on discussion. cc @jschendel | {
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- [ ] whatsnew entry
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How often do people really call the mapping function or dictionary with a bunch of NaNs, instead of just using `fillna`?
I propose changing the default value of `na_action` to `'ignore'`. | {
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} | 4 | 2020-03-17T21:56:43Z | 2020-03-22T00:14:03Z | 2020-03-22T00:13:12Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
`Series.map` treats any `na_action` other than `"ignore"` like `None`, but it should probably raise if someone provides an invalid option:
```python
In [1]: import pandas as pd
In [2]: pd.Series([1, 2, 3]).map(lambda x: x, na_action="xxxxx")
Out[2]:
0 1
1 2
2 3
dtype: int64
``` | {
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} | 8 | 2020-03-17T22:13:16Z | 2020-03-29T15:54:50Z | 2020-03-29T15:54:50Z | NONE | null | I think pandas should support passing `%z` in the format but also `utc=True`. In my opinion, one thing is the format, which tells pandas how to parse the datetime string. The other argument is just telling to return the dates in UTC, no matter which timezone they were in the beginning.
Here's a repl that shows the issue: https://repl.it/@eparizzi/Pandas-todatetime-in-UTC-with-format
If you replace that simple CSV with some big 50K row time-series CSV, the call to `to_datetime` without the format takes more than 20 seconds. On the contrary, passing the format and without `utc=True` takes less than 2 seconds. Unfortunately, this doesn't seem to work properly when there are multiple timezones in the column. It simply can't set a proper dtype in this case.
So, why can't we have a way to specify the format including timezone but also specify that we want everything in datetime64(UTC)?
I've already gone over this issue: https://github.com/pandas-dev/pandas/issues/25571 but I still think this deserves a discussion.
```python
import pandas as pd
# I know the format, I want to use it so that Pandas to_datetime() runs faster.
DATETIME_FORMAT = '%m/%d/%Y %H:%M:%S.%f%z'
try:
data = ['10/11/2018 00:00:00.045-07:00',
'10/11/2018 01:00:00.045-07:00',
'10/11/2018 01:00:00.045-08:00',
'10/11/2018 02:00:00.045-08:00',
'10/11/2018 04:00:00.045-07:00',
'10/11/2018 05:00:00.045-07:00']
df = pd.DataFrame(data, columns=["Timestamp"])
# This raises "ValueError: Cannot pass a tz argument when parsing strings with timezone information."
df.Timestamp = pd.to_datetime(df.Timestamp, format=DATETIME_FORMAT, utc=True)
except ValueError as valueError:
# I don't know why a %z in the format is not compatible with utc=True. The %z is telling pandas that it needs to deal with timezones. Then, utc=True should just convert all to UTC. It shouldn't be more complicated than that I think.
print(f"ERROR: {str(valueError)}")
print("...why not?")
# This works, but it's A LOT slower when parsing a lot of rows.
df.Timestamp = pd.to_datetime(df.Timestamp, infer_datetime_format=True, utc=True)
finally:
print(df)
print(df.Timestamp.dtype)
# Expected output:
#
# Timestamp
# 0 2018-10-11 07:00:00.045000+00:00
# 1 2018-10-11 08:00:00.045000+00:00
# 2 2018-10-11 09:00:00.045000+00:00
# 3 2018-10-11 10:00:00.045000+00:00
# 4 2018-10-11 11:00:00.045000+00:00
# 5 2018-10-11 12:00:00.045000+00:00
# datetime64[ns, UTC]
``` | {
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} | 1 | 2020-03-17T23:20:18Z | 2021-10-29T21:58:56Z | 2021-10-29T21:58:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [92]: df_ = pd.DataFrame([[0, 1], [0, np.nan]]).convert_dtypes()
In [93]: df_
Out[93]:
0 1
0 0 1
1 0 <NA>
In [108]: df_.groupby(0).agg('last')
Out[111]:
1
0
0 1
In [112]: df_.groupby(0).agg({1:'last'})
Out[115]:
1
0
0 1.0
In [116]: df_.groupby(0).agg('last').dtypes
Out[116]:
1 Int64
dtype: object
In [117]: df_.groupby(0).agg({1:'last'}).dtypes
Out[117]:
1 float64
dtype: object
```
#### Problem description
Aggregation does not preserve Int64 dtype when function passed in dictionary for the column.
#### Expected Output
Int64 dtype
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.8-100.fc30.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : nb_NO.UTF-8
LOCALE : nb_NO.UTF-8
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.2.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.5.2
tabulate : 0.8.5
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.46.0
</details>
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} | 4 | 2020-03-17T23:52:40Z | 2020-03-20T00:05:35Z | 2020-03-19T00:25:33Z | MEMBER | null | - [x] ref #32163
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This are the errors that this PR is getting rid of:
```
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_116group_last’:
pandas/_libs/groupby.c:37458:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
37458 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_118group_last’:
pandas/_libs/groupby.c:38233:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
38233 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_120group_last’:
pandas/_libs/groupby.c:39008:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
39008 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_122group_last’:
pandas/_libs/groupby.c:39781:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
39781 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_124group_last’:
pandas/_libs/groupby.c:40563:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
40563 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_128group_nth’:
pandas/_libs/groupby.c:41999:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
41999 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_130group_nth’:
pandas/_libs/groupby.c:42820:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
42820 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_132group_nth’:
pandas/_libs/groupby.c:43641:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
43641 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_134group_nth’:
pandas/_libs/groupby.c:44460:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
44460 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_136group_nth’:
pandas/_libs/groupby.c:45288:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
45288 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_152group_max’:
pandas/_libs/groupby.c:55168:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
55168 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_154group_max’:
pandas/_libs/groupby.c:55970:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
55970 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_156group_max’:
pandas/_libs/groupby.c:56772:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
56772 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_158group_max’:
pandas/_libs/groupby.c:57568:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
57568 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_162group_min’:
pandas/_libs/groupby.c:58985:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
58985 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_164group_min’:
pandas/_libs/groupby.c:59784:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
59784 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_166group_min’:
pandas/_libs/groupby.c:60583:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
60583 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
| ^~
pandas/_libs/groupby.c: In function ‘__pyx_pf_6pandas_5_libs_7groupby_168group_min’:
pandas/_libs/groupby.c:61376:30: warning: comparison of integer expressions of different signedness: ‘Py_ssize_t’ {aka ‘long int’} and ‘size_t’ {aka ‘long unsigned int’} [-Wsign-compare]
61376 | __pyx_t_3 = ((!((__pyx_t_2 == __pyx_t_1) != 0)) != 0);
```
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https://api.github.com/repos/pandas-dev/pandas/issues/32795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32795/events | https://github.com/pandas-dev/pandas/pull/32795 | 583,383,648 | MDExOlB1bGxSZXF1ZXN0MzkwMTQ5NzIz | 32,795 | BLD: Suppressing warnings when compiling pandas/_libs/writers | {
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} | 2 | 2020-03-18T00:36:35Z | 2020-03-18T09:44:28Z | 2020-03-18T02:35:10Z | MEMBER | null | - [x] ref #32163
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This is the error that this PR is getting rid of:
```
pandas/_libs/writers.c: In function ‘__pyx_pw_6pandas_5_libs_7writers_1write_csv_rows’:
pandas/_libs/writers.c:3435:15: warning: ‘__pyx_v_j’ may be used uninitialized in this function [-Wmaybe-uninitialized]
3435 | __pyx_t_1 = (__pyx_v_j + 1);
| ~~~~~~~~~~^~~~~~~~~~~~~~~~~
pandas/_libs/writers.c:2911:14: note: ‘__pyx_v_j’ was declared here
2911 | Py_ssize_t __pyx_v_j;
| ^~~~~~~~~
``` | {
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"description": "Concat, Merge/Join, Stac... | closed | false | null | [] | null | 2 | 2020-03-18T00:41:29Z | 2020-05-22T10:52:20Z | 2020-05-22T10:51:43Z | NONE | null | …x levels (#13094)
- [ ] closes #13094
- [ ] tests added / passed
- [ ] warn users when merging between different index levels
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} | 0 | 2020-03-18T00:49:39Z | 2020-03-19T13:41:03Z | 2020-03-19T10:57:50Z | CONTRIBUTOR | null | * [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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} | 3 | 2020-03-18T01:26:05Z | 2021-11-20T23:21:48Z | 2020-04-01T23:14:48Z | MEMBER | null | - [x] closes #32673
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
having this round-trip-ability is necessary for implementing an efficient general-case broadcast_to/tile compat method. | {
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} | 1 | 2020-03-18T01:47:10Z | 2020-03-19T21:51:33Z | 2020-03-19T21:15:54Z | MEMBER | null | Ultimately we want to 1) avoid using _from_sequence (so we can make that stricter in what it accepts) and 2) be more careful about when we use `freq="infer"`. Splitting up the cases handled by _add_delta will make this more feasible. | {
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} | 5 | 2020-03-18T07:33:14Z | 2020-05-26T09:36:47Z | 2020-04-17T02:53:39Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame.from_dict({
'id': ['a'],
'value': [None]
})
df_grouped = df.groupby('id', as_index=False).first()
print(df)
print(df_grouped)
```
Output:
```python
id value
0 a None
id value
0 a NaN
```
#### Problem description
Since at least version 1.0.2, the type of `df_grouped` is `NaN`. In Version 0.25.3, the type was `None`.
This breaks my code since I later check for this value using `if var is None`, which is False when `var` is `NaN` instead of `None`.
#### Expected Output
```python
id value
0 a None
id value
0 a None
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 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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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 1 | 2020-03-18T12:16:14Z | 2021-07-30T03:46:19Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series([], dtype='datetime64[ns, UTC]')
df = pd.concat([s, s], axis=1) # empty dataframe of two columns of datetime dtype
# want the minimum of the two datetime columns element wise
min_dates = df.min(axis=1) # output series is of dtype float64
# it's fine if dataframe is non-empty
import pandas as pd
s = pd.Series(pd.to_datetime([f'2020-01-0{i}' for i in range(1, 10)], utc=True))
df = pd.concat([s, s], axis=1) # non-empty dataframe of two columns of datetime dtype
min_dates = df.min(axis=1) # output series is of dtype datetime64[ns, UTC]
```
#### Problem description
When dataframe is empty, doing a `.min(axis=1)` outputs a series of dtype `float64` even though all columns were dtype `datetime64`.
Should this be consitent on dtypes and output `min_dates` with dtype `datetime64[ns, UTC]`.
This is only an issue when the dataframe is empty.
If the dataframe is not empty, the dtype is conserved and min_dates outputs with dtype `datetime64[ns, UTC]`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-862.3.3.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.2
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : 0.3.1
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.5.2
tabulate : None
xarray : 0.14.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
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https://api.github.com/repos/pandas-dev/pandas/issues/32803 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32803/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32803/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32803/events | https://github.com/pandas-dev/pandas/issues/32803 | 583,723,838 | MDU6SXNzdWU1ODM3MjM4Mzg= | 32,803 | NamedAgg error: aggregate() missing 1 required positional argument: 'func' | {
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} | 5 | 2020-03-18T13:21:30Z | 2021-08-17T16:15:27Z | 2021-08-17T16:15:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
resampled_bookings = bookings.resample('BMS').agg(
unique_identity_ids=pd.NamedAgg(column="identity_id", aggfunc=pd.Series.nunique),
booking_count=pd.NamedAgg(column="booking_id", aggfunc=pd.Series.count),
booking_distance=pd.NamedAgg(column="distance_m", aggfunc=pd.Series.sum)
)
```
#### Problem description
This error prevents me from aggregating data and (re)naming columns at the same time. Instead, it is necessary to use the following approach, which isn't altogether bad:
```py
resampled_bookings = bookings.resample('BMS').agg({
"booking_id": "count",
"distance_m": sum,
"identity_id": pd.Series.nunique
})
resampled_bookings.rename(columns={
"booking_id": "booking_count",
"distance_m": "booking_distance_m",
"identity_id": "unique_identity_ids"
}, inplace=True)
```
#### Expected Output
The aggregate function would produce a new DataFrame with the following columns:
- booking_count
- booking_distance_m
- unique_identity_ids
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-19-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 6 | 2020-03-18T13:22:04Z | 2020-03-18T23:51:12Z | 2020-03-18T19:24:43Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Somewhat of a follow up to #32681.
---
I could not benchmark this change as this function is ```cdef``` and not ```def``` or ```cpdef``` (I would also love if someone could give me a tip on how to benchmark ```cdef``` functions from an ```Ipython``` shell for example).
What I did what I ran ```cython -a pandas/_libs/internals.pyx``` and took a screenshot of the before and after.
---
#### Master:
---
#### PR:
---
Also, is there a reason not to replace every call of ```np.arange``` with ```cnp.PyArray_Arange```? (cython files only) | {
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} | 3 | 2020-03-18T13:45:59Z | 2020-07-17T10:37:14Z | 2020-07-17T10:37:14Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index = pd.CategoricalIndex([pd.Interval(-20, -10), pd.Interval(-10, 0), pd.Interval(0, 10)])
series_of_dicts = pd.Series([{'a': 1}, {'a': 2}, {'b': 3}], index=index)
frame = pd.DataFrame.from_records(series_of_dicts, index=index)
```
raises:
```
RecursionError: maximum recursion depth exceeded in comparison
```
#### Problem description
Though using `pd.DataFrame.from_records` with `pd.Series` objects is not explicitly supported, it does work in most of the cases. Only in case the index of the Series is a CategoricalIndex of Intervals, it suddenly doesn't work anymore.
E.g. the following works fine:
```python
series_of_dicts2 = pd.Series([{'a': 1}, {'a': 2}, {'b': 3}])
frame = pd.DataFrame.from_records(series_of_dicts2, index=index)
```
And it even works when `series_of_dicts2.index` is a CategoricalIndex _or_ a list of Intervals, but not when it is CategoricalIndex _of_ Intervals. It is especially strange because the index of `series_of_dicts2` is not even used for the output.
#### Expected Output
The same as when the following workaround is used:
```python
frame = pd.DataFrame.from_records(series_of_dicts.values, index=index)
Out[2]:
a b
(-20, -10] 1.0 NaN
(-10, 0] 2.0 NaN
(0, 10] NaN 3.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.2
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.6.2
Cython : 0.29.4
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.6.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.2.1
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : None
</details>
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} | 0 | 2020-03-18T15:05:54Z | 2020-03-24T21:33:02Z | 2020-03-24T21:33:02Z | CONTRIBUTOR | null | #### Problem description
`Series.str` contains methods for all the regular expression matching modes in the `re` package except for `re.fullmatch()`. `fullmatch` only returns matches that cover the entire input string, unlike `match`, which also returns matches that start at the beginning of the string but do not cover the complete string.
One can work around the lack of `fullmatch` by round-tripping to/from numpy arrays and using `np.vectorize`, i.e.
```python
>>> s = pd.Series(["foo", "bar", "foobar"])
>>> my_regex = "foo"
>>> import re
>>> import numpy as np
>>> compiled_regex = re.compile(my_regex)
>>> regex_f = np.vectorize(lambda s: compiled_regex.fullmatch(s) is not None)
>>> matches_array = regex_f(s.values)
>>> matches_series = pd.Series(matches_array)
>>> matches_series
0 True
1 False
2 False
dtype: bool
```
but it would be more convenient for users if `fullmatch` was built in.
The `fullmatch` method was added to the `re` package in Python 3.4. I think that the reason this method wasn't in previous versions of Pandas was that older versions of Python don't have `re.fullmatch`. As of Pandas 1.0, all the supported versions of Python now have `fullmatch`.
I have a pull request ready that adds this functionality. After my changes, the `Series.str` namespace gets a new method `fullmatch` that evaluates `re.fullmatch` over the series. For example:
```python
>>> s = pd.Series(["foo", "bar", "foobar"])
>>> s.str.fullmatch("foo")
0 True
1 False
2 False
dtype: bool
```
[Edit: Simplified the workaround] | {
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} | 5 | 2020-03-18T15:54:11Z | 2020-03-24T21:33:11Z | 2020-03-24T21:33:02Z | CONTRIBUTOR | null | - [X] closes #32806
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
This is my first PR against this project, so apologies if I've missed any steps in the process. I'm assuming that I'm supposed to fill in the checklist above myself.
This pull request adds the `fullmatch` regular expression matching mode to the other modes already present under the `Series.str` namespace. For example:
```python
>>> s = pd.Series(["foo", "bar", "foobar"])
>>> s.str.fullmatch("foo")
0 True
1 False
2 False
dtype: bool
```
The `fullmatch` matching mode restricts matches to those that only match the *entire* string. Note the differences from `match`:
```python
>>> s = pd.Series(["foo", "bar", "foobar"])
>>> s.str.fullmatch("foo")
0 True
1 False
2 False
dtype: bool
>>> s.str.match("foo")
0 True
1 False
2 True
dtype: bool
```
I've also added regression tests and a "what's new" entry.
I have also opened issue #32806 to cover this new feature. | {
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- [x] closes #10720
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Resurrection of #31118
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} | 1 | 2020-03-18T20:51:53Z | 2020-03-18T22:09:51Z | 2020-03-18T22:09:45Z | CONTRIBUTOR | null | I'm completely new to this. I hope i didn't do anything wrong here
https://github.com/pandas-dev/pandas/issues/29547
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---
#### Benchmarks:
This is the setup:
```python
import pandas as pd
df1 = pd.DataFrame(
{
"Customer_id": pd.Series([1, 2, 3, 4, 5, 6]),
"Product": pd.Series(
["Oven", "Oven", "Oven", "Television", "Television", "Television"]
),
}
)
df2 = pd.DataFrame(
{
"Customer_id": pd.Series([2, 4, 6]),
"State": pd.Series(["California", "California", "Texas"]),
}
)
```
And the ```%timeit```:
```python
pd.merge(df1, df2, on="Customer_id", how="left")
```
---
```
In [4]: %timeit pd.merge(df1, df2, on="Customer_id", how="left")
1.54 ms ± 102 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # Master
1.46 ms ± 21.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # PR
In [5]: %timeit pd.merge(df1, df2, on="Customer_id", how="left")
1.47 ms ± 790 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) # Master
1.45 ms ± 2.59 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # PR
In [6]: %timeit pd.merge(df1, df2, on="Customer_id", how="left")
1.47 ms ± 1.99 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # Master
1.48 ms ± 65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # PR
```
Since the results were so small I ran the same test several times: | {
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"description": "Extending pandas with cu... | open | false | null | [] | null | 0 | 2020-03-19T00:14:37Z | 2021-07-30T03:47:48Z | null | MEMBER | null | (From `master`)
```python
import pandas as pd
idx = pd.to_datetime(["2020-01-01", None])
idx.map(lambda x: pd.Timestamp("1999-01-01"), na_action="ignore")
# DatetimeIndex(['1999-01-01', '1999-01-01'], dtype='datetime64[ns]', freq=None)
idx = pd.CategoricalIndex(["a", None], categories=["a"])
idx.map(lambda x: x, na_action="ignore")
# TypeError: map() got an unexpected keyword argument 'na_action'
```
In the `DatetimeIndex` case `idx.map` should propagate the null value instead of returning `Timestamp("1999-01-01")` since `na_action == "ignore"`, and although it isn't documented I think `CategoricalIndex.map` should allow `na_action` since the other `map` methods do. | {
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} | 1 | 2020-03-19T01:44:27Z | 2020-03-21T17:23:32Z | 2020-03-21T09:44:44Z | CONTRIBUTOR | null | * [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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