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} | 8 | 2019-05-08T02:07:47Z | 2019-05-08T06:32:53Z | 2019-05-08T06:32:44Z | CONTRIBUTOR | null | As suggested in https://github.com/pandas-dev/pandas/issues/24807#issuecomment-454905470
Replace `.values` with `.to_numpy()` in the benchmark demonstration code. | {
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} | 47 | 2019-05-08T09:39:49Z | 2021-07-02T05:45:00Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
predictor_purchases_p = predictor_purchases.groupby(["ag", "artikelnr"])["som"].max().unstack().fillna(0)
or
predictor_purchases_p = predictor_purchases.pivot(index="ag", columns="artikelnr", value="som")
```
#### Problem description
I'm working on rather large data (>100GB in memory) on a beefy server (3TB ram)
When refactoring my code from pandas 0.21 to latest version, the pivot / unstack now returns an exception.
Unstacked DataFrame is too big, causing int32 overflow
I was able to eliminate the problem by changing the reshape.py:
modify line 121from dtype np.int32 to dtype np.int64:
`num_cells = np.multiply(num_rows, num_columns, dtype=np.int64)`
#### Expected Output
Not being limited by int32 dims when reshaping a data frame.
This feels like a restriction which should not be there.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.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: 0.24.2
pytest: None
pip: 19.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: 0.3.0
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-05-08T11:11:20Z | 2019-05-24T03:06:14Z | 2019-05-24T03:06:14Z | CONTRIBUTOR | null | - [x] xref #25882
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 14 | 2019-05-08T11:55:19Z | 2019-09-10T04:14:18Z | 2019-09-10T02:28:48Z | CONTRIBUTOR | null | - [x] closes #25022
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} | 1 | 2019-05-08T14:02:57Z | 2019-05-08T15:25:51Z | 2019-05-08T14:58:25Z | CONTRIBUTOR | null | Trivial documentation change
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26318 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26318/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26318/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26318/events | https://github.com/pandas-dev/pandas/issues/26318 | 441,757,973 | MDU6SXNzdWU0NDE3NTc5NzM= | 26,318 | circular import when doing "import pandas" on pandas‑0.24.2‑cp38‑cp38m‑win_amd64.whl | {
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} | 8 | 2019-05-08T14:14:31Z | 2019-06-16T14:39:35Z | 2019-05-08T22:50:57Z | CONTRIBUTOR | null | Maybe it's the Python-3.8.0.a4 windows dll change ?
just a remark, as it's all alpha. using cgohlke first wheel on Python-3.8.0a4, I have a strange message
````
Python 3.8.0a4 (v3.8.0a4:c1004b8546, May 7 2019, 12:46:11) [MSC v.1916 32 bit (Intel)] on win32
Type "copyright", "credits" or "license()" for more information.
>>> import pandas
Traceback (most recent call last):
File "<pyshell#0>", line 1, in <module>
import pandas
File "C:\WinP\bd38\bu\WPy32-3800a4\python-3.8.0a4\lib\site-packages\pandas\__init__.py", line 49, in <module>
from pandas.io.api import *
File "C:\WinP\bd38\bu\WPy32-3800a4\python-3.8.0a4\lib\site-packages\pandas\io\api.py", line 8, in <module>
from pandas.io.excel import ExcelFile, ExcelWriter, read_excel
File "C:\WinP\bd38\bu\WPy32-3800a4\python-3.8.0a4\lib\site-packages\pandas\io\excel.py", line 34, in <module>
from pandas.io.parsers import TextParser
File "C:\WinP\bd38\bu\WPy32-3800a4\python-3.8.0a4\lib\site-packages\pandas\io\parsers.py", line 19, in <module>
import pandas._libs.parsers as parsers
File "pandas\_libs\parsers.pyx", line 1418, in init pandas._libs.parsers
AttributeError: partially initialized module 'pandas' has no attribute '_NA_VALUES' (most likely due to a circular import)
>>>
````
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} | 4 | 2019-05-08T15:34:45Z | 2019-05-09T13:23:23Z | 2019-05-09T12:31:58Z | CONTRIBUTOR | null | - [x] xref #25725, #26281 & #26289
Third part of the removal of ``compat.lrange`` from the code base. | {
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 3 | 2019-05-08T19:44:41Z | 2021-07-02T05:48:34Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: class Test:
...: def __ror__(self, other):
...: print('here')
...: return self
...:
In [2]: {} | Test()
here
Out[2]: <__main__.Test at 0x7f37a1601ba8>
In [3]: import pandas
In [4]: pandas.DataFrame() | Test()
Out[4]:
Empty DataFrame
Columns: []
Index: []
```
#### Problem description
I would expect DataFrame to return `NotImplemented` in this case, so we can properly delegate to `__ror__`.
#### Expected Output
```
Out[3]: <__main__.Test at 0x7f37a1601ba8>
```
#### Output of ``pd.show_versions()``
<details>
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.67-ts1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.11.1
xarray: None
IPython: 7.4.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 1.1.5
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-05-09T03:28:12Z | 2020-07-16T16:08:55Z | 2020-07-16T16:08:55Z | MEMBER | null | #### Code Sample, a copy-pastable example:
```python
import pandas as pd
import numpy as np
pd.show_versions()
dates = ['2019-05-09', '2019-05-09', '2019-05-09']
date_series = pd.Series(dates)
# it appears to happen on object dtype aggregations
# where there is another object dtype in the frame with missing values
# hence column 'a' being populated with strings
# and missing and the dt.date accessor in 'c'
date_series_parsed = pd.to_datetime(date_series, format='%Y-%m-%d').dt.date
df = pd.DataFrame({
'a': [np.nan, '1', np.nan],
'b': [0, 1, 1],
'c': date_series_parsed,
})
# as expected the series values returned are each populated with non missing data
# I'm ignoring that this is returning a Series
# and the other method returns a DataFrame
# the logic is my real concern
print(df.groupby('b')['c'].min())
# output (as is and as expected):
# b
# 0 2019-05-09
# 1 2019-05-09
# Name: c, dtype: object
# looks like it's dependent on column 'a' being fully populated for some reason
# as the group df['b'] == 1 min value is not returned appropriately here
# but the group df['b'] == 0 min value does appear to be working
print(df.groupby('b', as_index=False)['c'].min())
# output (as is):
# b c
# 0 0 2019-05-09
# 1 1 NaN
#
# output (as expected):
# b c
# 0 0 2019-05-09
# 1 1 2019-05-09
```
#### Problem description
As there is no missing value in column 'c', I would expect that every subgroup of the series would have the minimum as being well defined and non-missing. This is consistent with the behavior shown when as_index in the groupby method is left as it's default parameter of False. However it can clearly be seen that the minimum of column 'c' depends on column 'a' in some capacity. It appears as though at some point in time the indexing is looking to column 'a' instead of column 'c' though I'm not sure why or when.
I have tested this both on the version of pandas available on PyPI (0.24.2 as of the time of writing) and through the pandas-dev master branch. The issue persists regardless of versions. Furthermore the issue has been experienced on a variety of machines.
#### Expected Output
I would expect the results to be consistent with the calculation performed when as_index is left as it's default value of False, as it seems that the minimum of the values which comprise the group where column b is equal to 1 is a well defined result (explicitly 2019-05-09).
Thank you for the consideration!
#### Output of ``pd.show_versions()``
Note: I ran this on the master branch, and 0.24.2, the below is the output from the master branch. If this is insufficient please let me know and I will supplement with the PyPI version of 0.24.2 when able.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.4-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C
LOCALE: None.None
pandas: 0+unknown
pytest: None
pip: 18.1
setuptools: 40.8.0
Cython: 0.29.7
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-05-09T06:28:37Z | 2019-05-10T22:05:42Z | 2019-05-10T21:38:01Z | CONTRIBUTOR | null | - [x] xref #25725
Removal of ``compat.lmap`` from the code base. | {
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} | 6 | 2019-05-09T06:39:56Z | 2019-05-10T21:58:24Z | 2019-05-09T23:53:14Z | CONTRIBUTOR | null | Minor clean-up: Some classes are created with a ``()`` which is not needed. | {
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} | 5 | 2019-05-09T08:49:14Z | 2019-05-14T19:33:09Z | 2019-05-09T12:36:35Z | CONTRIBUTOR | null | - [x] closes #26125
- [x] 2 tests added
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
When working on the original issue, I came across something similar :
- `RangeIndex` had its own implementation of `max`/`min` with the same behavior
- `Index` & `RangeIndex` could both benefit from adding another compat call
```
In [5]: np.argmin(pd.Index([1,2,3]), out=0)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
venv\lib\site-packages\numpy\core\fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
55 try:
---> 56 return getattr(obj, method)(*args, **kwds)
57
TypeError: argmin() got an unexpected keyword argument 'out'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-5-5c9b894867ef> in <module>
----> 1 np.argmin(pd.Index([1,2,3]), out=0)
venv\lib\site-packages\numpy\core\fromnumeric.py in argmin(a, axis, out)
1170
1171 """
-> 1172 return _wrapfunc(a, 'argmin', axis=axis, out=out)
1173
1174
venv\lib\site-packages\numpy\core\fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
64 # a downstream library like 'pandas'.
65 except (AttributeError, TypeError):
---> 66 return _wrapit(obj, method, *args, **kwds)
67
68
venv\lib\site-packages\numpy\core\fromnumeric.py in _wrapit(obj, method, *args, **kwds)
44 except AttributeError:
45 wrap = None
---> 46 result = getattr(asarray(obj), method)(*args, **kwds)
47 if wrap:
48 if not isinstance(result, mu.ndarray):
TypeError: output must be an array
```
I changed it to be consistent with the rest of pandas `ValueError: the 'out' parameter is not supported in the pandas implementation of argmax()`.
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} | 3 | 2019-05-09T10:00:44Z | 2019-05-10T12:04:32Z | 2019-05-10T02:48:30Z | NONE | null | #### Empty dataframe does not keep index name when index is used
```python
import pandas as pd
df = pd.DataFrame({'a': [], 'b':[], 'c': []})
df = df.set_index('c')
print(df.index.name) # prints 'c'
# using index to generate new column
df['d'] = df.index
# looking at the name of the index
print(df.index.name) # shows None
```
#### Problem description
I tested this for pandas==0.24.2
When generating a new column based on the data in index (not necessarily equality but any transformation of the index) I lose the index name in a special case where the dataframe is empty.
It becomes a problem when I reset the index and it becomes a column with name `index` instead of `c`.
#### Expected Output
Expected output is `'c'` in both cases (before and after using the index).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-47-generic
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: 0.24.2
pytest: 3.1.3
pip: 19.0.3
setuptools: 40.5.0
Cython: 0.29
numpy: 1.15.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: None
sqlalchemy: 1.2.12
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: Non
</details>
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} | 5 | 2019-05-09T10:56:16Z | 2019-05-20T00:23:09Z | 2019-05-20T00:23:09Z | NONE | null | #### Code
```python
m_index = pd.MultiIndex.from_product([['Mona', 'Dennis'], ['good', 'great']], names=['name', 'desc'])
df = pd.DataFrame({'foo': [1, 2, 1, 2], 'bar': np.random.randn(4)}, index=m_index)
n1 = df.groupby([pd.Grouper(level='name'), 'foo']).ngroups
n2 = len(df.groupby([pd.Grouper(level='name'), 'foo']))
n3 = len(df.groupby([pd.Grouper(level='name'), 'foo']).groups)
print(n1, n2, n3)
# 4 2 2
```
#### Problem description
I grouped a DataFrame by a list of pandas.Grouper object and column label, the length of groups or group keys should be 4, but I checked the length of GroupBy object as n2, it is 2, why? Thanks for your answer in advance.
<details>
ommit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: zh_CN.UTF-8
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} | 7 | 2019-05-09T16:36:31Z | 2019-06-28T16:25:39Z | 2019-06-28T16:25:04Z | CONTRIBUTOR | null | This PR adds type hints to pandas/core/dtypes/dtypes.py down to and including the ``CategoricalDtype`` part of the module. I'm more familiar with Categoricals than DateTimeArrays etc. and because I think there are some difficulties in implementing type hints, I think it would make sense to add a general ruleset for adding type hints to pandas, before too much work is put into this.
I'm putting some thoughts up for discussion in #25601 about how we add type hints to pandas, that probably should be discussed before this goes further.
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- [x] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Improves robustness of json_normalize for inconsistently formatted json.
Fixes all observations in #26284 | {
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} | 1 | 2019-05-10T02:09:28Z | 2019-05-18T15:09:40Z | 2019-05-18T15:09:40Z | NONE | null | This is a suggestion, not a bug. The following resulted in a `FutureWarning`
df = df.assign(block=(df.index.to_series().diff() != pd.Timedelta(15,'m')).cumsum())
> FutureWarning: The default of the 'keep_tz' keyword will change to True in a future release. You can set 'keep_tz=True' to obtain the future behaviour and silence this warning.
It wasn't in any way obvious why since the warning doesn't mention the method name and there's no stack trace (and futures warnings are only printed once). I had to search the source (which was also difficult as the message was split to maintain a character limit per line).
It would be nice if future warnings were more explicit, i.e. name the class and method as well as the argument.
(In this case it's the `to_series()` method, which ideally wouldn't be required but `DateTimeIndex` doesn't yet implement diff()) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26332/events | https://github.com/pandas-dev/pandas/pull/26332 | 442,530,740 | MDExOlB1bGxSZXF1ZXN0Mjc3NjI1MzAw | 26,332 | Remove Panel References from Tests | {
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} | 4 | 2019-05-10T04:14:41Z | 2020-01-16T00:34:00Z | 2019-05-12T20:45:33Z | MEMBER | null | ref #25632 this removes a good deal of Panel references from tests though not all (excluding anything for msgpack).
I think after this and #25567 get merged we can starting finalizing the Panel removal and get out of docs as well | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 7 | 2019-05-10T09:29:21Z | 2021-11-04T01:02:23Z | null | MEMBER | null | Assigning a value to a single location in a DataFrame (using `.loc` with scalar indexers) started to fail with "values with a length".
Consider the following example:
```
In [1]: df = pd.DataFrame({'a': [1, 2, 3], 'b': [(1, 2), (1, 2, 3), (3, 4)]})
In [2]: df
Out[2]:
a b
0 1 (1, 2)
1 2 (1, 2, 3)
2 3 (3, 4)
In [3]: df.loc[0, 'b'] = (7, 8, 9)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-a2d59e11519a> in <module>
----> 1 df.loc[0, 'b'] = (7, 8, 9)
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
187 key = com._apply_if_callable(key, self.obj)
188 indexer = self._get_setitem_indexer(key)
--> 189 self._setitem_with_indexer(indexer, value)
190
191 def _validate_key(self, key, axis):
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
604
605 if len(labels) != len(value):
--> 606 raise ValueError('Must have equal len keys and value '
607 'when setting with an iterable')
608
ValueError: Must have equal len keys and value when setting with an iterable
```
This raises on 0.23.4 - master, but worked in 0.20.3 - 0.22.0 (the ones I tested):
```
In [1]: pd.__version__
Out[1]: '0.22.0'
In [2]: df = pd.DataFrame({'a': [1, 2, 3], 'b': [(1, 2), (1, 2, 3), (3, 4)]})
In [3]: df
Out[3]:
a b
0 1 (1, 2)
1 2 (1, 2, 3)
2 3 (3, 4)
In [4]: df.loc[0, 'b'] = (7, 8, 9)
In [5]: df
Out[5]:
a b
0 1 (7, 8, 9)
1 2 (1, 2, 3)
2 3 (3, 4)
```
Related to https://github.com/pandas-dev/pandas/issues/25806
We don't have very robust support in general for list-like values, but, for the specific case above of updating a single value, I don't think there is anything ambiguous about it? You are updating a single value, so the passed value should simply be put in that place?
Note, the above is with tuples. But, there are also custom objects like MultiPolygons that represent single objects, but do define a `__len__` .. | {
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} | 8 | 2019-05-10T10:02:04Z | 2019-05-12T14:37:04Z | 2019-05-12T14:36:58Z | CONTRIBUTOR | null | - [x] closes #N/A
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
For detection details see: https://github.com/pandas-dev/pandas/pull/26220#issuecomment-491143978
The slowdown was due to the use of `convert_to_tsobject` function.
Asv result:
master| patch| ratio| test_name
-|-|-|-
31.3±0.5μs| 21.9±0.2μs| 0.70| timestamp.TimestampConstruction.time_parse_iso8601_tz
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} | 1 | 2019-05-10T12:07:23Z | 2019-05-14T16:04:00Z | 2019-05-14T16:04:00Z | CONTRIBUTOR | null | I stumbled across some unexpected behavior when performing a left join on a specified column with time zone aware columns.
```python
# Your code here
df1 = pd.DataFrame({
'date': pd.date_range(start='2018-01-01', periods=5, tz='America/Chicago'),
'vals': list('abcde')}
)
df2 = pd.DataFrame({
'date': pd.date_range(start='2018-01-03', periods=5, tz='America/Chicago'),
'vals_2': list('tuvwx')}
)
df1.join(df2.set_index('date'), on='date')
```
```
date vals vals_2
0 2018-01-01 00:00:00-06:00 a NaN
1 2018-01-02 00:00:00-06:00 b NaN
2 2018-01-03 00:00:00-06:00 c NaN
3 2018-01-04 00:00:00-06:00 d NaN
4 2018-01-05 00:00:00-06:00 e NaN
```
When i was expecting
```
date vals vals_2
0 2018-01-01 00:00:00-06:00 a NaN
1 2018-01-02 00:00:00-06:00 b NaN
2 2018-01-03 00:00:00-06:00 c t
3 2018-01-04 00:00:00-06:00 d u
4 2018-01-05 00:00:00-06:00 e v
```
In PR #25260 the test case was specified with all NaN in vals_2 as expected. I don't understand why considering how merge on two columns or join on two indicies work:
```python
df1.set_index('date').join(df2.set_index('date')).reset_index()
pd.merge(df1, df2, on='date', how='left')
```
Both yield the expected behavior.
Is there something I'm missing or is this inconsistency a bug?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.1
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 13 | 2019-05-10T15:59:56Z | 2019-07-03T08:26:38Z | 2019-07-03T08:26:38Z | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/pull/26327#discussion_r282733852.
We currently allow CategoricalDtype.ordered to be None. It should just be a bool (default False).
```
In [8]: CategoricalDtype(categories=None, ordered=None).ordered
# None
```
categories can be None, which means "infer later on". But (IIRC) we don't do that for ordered. | {
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} | 9 | 2019-05-10T18:36:53Z | 2019-05-12T15:03:14Z | 2019-05-12T14:36:23Z | CONTRIBUTOR | null | - [x] xref #25725
Minor cleanup: Cleanup some regexes that guard against python2 style longs in their error messages. | {
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} | 2 | 2019-05-10T18:59:19Z | 2019-05-11T06:39:17Z | 2019-05-10T21:31:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
df = pd.DataFrame([["c", "a", 1, 2], ["c", "b", 3, 4]])
df.groupby(0).sum()
```
#### Problem description
I expect the sum aggregation after groupby to work
similarly to normal aggregation: df.sum().
However, the columns containing strings don't
concatenate, but are ignored.
But, if I give the sum method any keyword parameter,
it works as expected.
For example, the following works:
df.groupby(0).sum(whatever=1)
#### Expected Output
```
1 2 3
0
c ab 4 6
```
#### Output of ``pd.show_versions()``
<details>
I have tried the latest Pandas version 0.24.2, but the
same issue is with older Pandas releases.
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 4.0.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.5.11
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
[
</details>
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} | 2 | 2019-05-10T21:42:14Z | 2019-05-12T20:02:38Z | 2019-05-12T20:02:33Z | MEMBER | null | Follow-up to https://github.com/pandas-dev/pandas/pull/26330#issuecomment-491438048
@WillAyd | {
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} | 3 | 2019-05-11T01:06:38Z | 2019-05-12T06:51:25Z | 2019-05-11T02:46:53Z | NONE | null | According to these sources, `pyarrow.parquet` supports an `nthreads` argument for parallel reads, i.e.
- https://wesmckinney.com/blog/python-parquet-multithreading/ the pyarrow.parquet engine
- https://arrow.apache.org/docs/python/parquet.html#multithreaded-reads
```python
import pyarrow.parquet as pq
table = pq.read_table(file_path, nthreads=4)
```
The `nthreads=4` argument raises an exception in `pandas.read_parquet` rather than passing the argument through to the `pyarrow.parquet` engine. The pandas documentation indicates the `**kwargs` are passed to the parquet engine (`pyarrow` in this case), e.g.
```python
import pandas as pd
import pyarrow.parquet
pd.read_parquet(
parquet_path, engine="pyarrow", columns=query_columns, nthreads=6
)
```
```versions
$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=18.04
DISTRIB_CODENAME=bionic
DISTRIB_DESCRIPTION="Ubuntu 18.04.2 LTS"
$ pip freeze | grep pandas
geopandas==0.4.1
pandas==0.24.2
$ pip freeze | grep pyarrow
pyarrow==0.13.0
```
```text
Traceback (most recent call last):
File "./poc/hazard_to_parquet.py", line 128, in <module>
convert()
File "/home/joe/miniconda3/envs/proj/lib/python3.7/site-packages/click/core.py", line 764, in __call__
return self.main(*args, **kwargs)
File "/home/joe/miniconda3/envs/proj/lib/python3.7/site-packages/click/core.py", line 717, in main
rv = self.invoke(ctx)
File "/home/joe/miniconda3/envs/proj/lib/python3.7/site-packages/click/core.py", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/joe/miniconda3/envs/proj/lib/python3.7/site-packages/click/core.py", line 555, in invoke
return callback(*args, **kwargs)
File "./poc/hazard_to_parquet.py", line 114, in convert
parquet_path, engine="pyarrow", columns=query_columns, nthreads=6
File "/home/joe/miniconda3/envs/proj/lib/python3.7/site-packages/pandas/io/parquet.py", line 282, in read_parquet
return impl.read(path, columns=columns, **kwargs)
File "/home/joe/miniconda3/envs/proj/lib/python3.7/site-packages/pandas/io/parquet.py", line 129, in read
**kwargs).to_pandas()
TypeError: read_table() got an unexpected keyword argument 'nthreads'
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} | 7 | 2019-05-11T13:45:06Z | 2019-05-13T08:18:47Z | 2019-05-12T14:52:13Z | CONTRIBUTOR | null | - [x] closes #26148
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2019-05-11T15:20:27Z | 2019-05-12T14:35:54Z | 2019-05-12T14:35:54Z | CONTRIBUTOR | null | Working on removing ``compat.lrange``, I've discovered that 1-dim lists, iterators and generators work as expected:
```python
>>> pd.DataFrame(list(range(3))) # ok
0
0 0
1 1
2 2
>>> pd.DataFrame(iter(range(3))) # ok
0
0 0
1 1
2 2
>>> pd.DataFrame((i for i in range(3)) # ok
0
0 0
1 1
2 2
```
while constructing with a plain ``range`` does not work:
```python
>>> pd.DataFrame(range(3)) # not ok
AttributeError: 'range' object has no attribute 'ndim'
```
Fix upcoming. | {
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} | 6 | 2019-05-11T15:35:04Z | 2019-05-12T15:02:54Z | 2019-05-12T14:35:54Z | CONTRIBUTOR | null | - [x] closes #26342
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixes bug when constructing a ``DataFrame`` using a ``range``. | {
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} | 6 | 2019-05-11T16:56:52Z | 2019-12-24T14:56:56Z | 2019-12-24T14:56:56Z | MEMBER | null | Azure now supports using bash in windows scripts, so I _think_ we can de-duplicate some of the windows-specific shell scripts. cc @datapythonista | {
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```sh
CondaValueError: Malformed version string '~': invalid character(s).
```
Which I assume is the root cause, though the dependency file hasn't been updated in some time.
@TomAugspurger if you have any thoughts; maybe a conda-forge issue? | {
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} | 2 | 2019-05-11T18:37:31Z | 2021-03-11T02:16:52Z | 2019-05-11T18:44:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import resource
import pandas as pd
# some random data, some of them as array columns
path = 'data.parquet'
batches = 5000
df = pd.DataFrame({
't': [pd.np.array(range(0, 180 * 60, 5))] * batches,
})
# read the data above and convert it to json (e.g. the backend of a restful API)
for i in range(100):
# comment any of the 2 lines for the leak to vanish.
print(df['t'].iloc[0].shape, df['t'].iloc[0].dtype)
df['t'] = df['t'].apply(lambda x: pd.np.array(list(x)))
df['t'].to_json()
print(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
```
#### Problem description
The code above gives the following result (only works on linux/Mac)
```
254384
337780
422984
508720
593996
679336
762564
848036
933768
1019040
1104384
1187612
1273084
1358552
1444088
1529432
1612660
1698128
1783600
1869136
1954244
2037708
2122980
...
```
i.e. the memory peak is increasing, aka a memory leak.
#### Expected Output
The values above should not be unbounded.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1075-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.0.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: 0.13.0
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.1
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 5 | 2019-05-11T18:40:34Z | 2019-05-12T06:49:51Z | 2019-05-12T06:31:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import resource
import pandas as pd
# some random data, some of them as array columns
batches = 5000
df = pd.DataFrame({
't': [pd.np.array(range(0, 180 * 60, 5))] * batches,
})
# read the data above and convert it to json (e.g. the backend of a restful API)
for i in range(100):
# comment any of the 2 lines for the leak to vanish.
df['t'] = df['t'].apply(lambda x: pd.np.array(list(x))) # see comment below
df['t'].to_json()
print(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
```
#### Problem description
The code above gives the following result (only works on linux/Mac)
```
254384
337780
422984
508720
593996
679336
762564
848036
933768
1019040
1104384
1187612
1273084
1358552
1444088
1529432
1612660
1698128
1783600
1869136
1954244
2037708
2122980
...
```
i.e. the memory peak is increasing, aka a memory leak.
The line that does a seemmesly stupid operation is important because it is the equivalent of reading from a Parquet file using pyarrow. See [issue 5302](https://issues.apache.org/jira/projects/ARROW/issues/ARROW-5302) for a script that reproduces this within pyarrow.
Regardless of how "dumb" the code above is, it demonstrates a memory leak, which should not happen in Python. The code on the issue above demonstrates an important use-case where this leak emerges.
#### Expected Output
The values above should not be unbounded.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1075-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.0.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: 0.13.0
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.1
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-05-11T19:37:24Z | 2019-05-20T00:24:36Z | 2019-05-20T00:24:36Z | CONTRIBUTOR | null | Working on removing ``compat.lrange``, I've found that Series will accept iterators and generators as input:
```python
>>> pd.Series(reversed(range(3))) # Iterator work for Series
0 0
1 1
2 2
dtype: int64
>>> pd.Series((i for i in range(3))) # Generators work for Series
0 0
1 1
2 2
dtype: int64
```
If iterators and generators work when instantiating Series, I'd expect that they'd work as dict-values for DataFrames also, but they don't:
```python
>>> pd.DataFrame({'A': reversed(range(3))}) # iterator do not work
TypeError: object of type 'range_iterator' has no len()
>>> pd.DataFrame({'A': (i for i in range(3))}) # generator do not work
TypeError: object of type 'generator' has no len()
>>> pd.DataFrame({'A': range(3)}) # ranges work
A
0 0
1 1
2 2
```
The docs say that list-likes will work as dict values, and because iterators and generators are iterables and list-like, they should work.
This issue is different than #26342, but the fix is still relatively straight forward.
I can can put up a PR one of the coming days, | {
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"description": "resample... | closed | false | null | [] | null | 5 | 2019-05-11T21:20:49Z | 2019-05-13T00:52:53Z | 2019-05-13T00:52:38Z | MEMBER | null | This change is to allow `quantile` to accept an `int` argument to make it easier to compute a set of evenly-spaced quantiles. For example, instead of typing `s.quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])` or using `np.linspace` you can type `s.quantile(10)`.
- [ - ] closes #xxxx (no issue)
- [ x ] tests added / passed
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ x ] whatsnew entry | {
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} | 4 | 2019-05-12T01:21:34Z | 2019-05-12T22:06:14Z | 2019-05-12T22:06:10Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
Python 3.7.3 (default, May 8 2019, 05:28:42)
[GCC 6.3.0 20170516] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import datetime
>>> df = pd.DataFrame([[datetime.time(1, 1, 0), 1]], columns=['time', 'num'])
>>> df.set_index('time', inplace=True)
>>> df.index > datetime.time(1, 1, 0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 84, in cmp_method
result = op(self.values, np.asarray(other))
TypeError: '>' not supported between instances of 'int' and 'datetime.time'
>>> pd.__version__
'0.24.2'
```
The above code works on my local MacOS but doesn't inside a container `FROM python:3.7.3`. Same python version, same pandas version.
But locally, python is from the miniconda.
What could be the reason please? | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry (Not needed)
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- tests added
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
`_values_for_argsort` returns different values for missing value in different EA. For example, missing value in `PandasArray` gets `np.nan` and in `PeriodArray` gets `-9223372036854775808` (in my machine). `np.argsort` places `np.nan` at the end and the negative number in the beginning.
In this PR, `ExtensionArray.argsort` separates the array into two parts, data and missing values. Sort the data part and finally append missing part to it. The approach can work in general, no matter what the replacement of missing value in `values_for_argsort` is. | {
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} | 4 | 2019-05-12T13:15:24Z | 2019-05-16T13:51:21Z | 2019-05-16T01:47:40Z | CONTRIBUTOR | null | - [x] closes #26303
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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This fix for #26303 would avoid an indexing issue caused by using MultiIndexes which results to a segfault in the example provided in #26303. | {
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} | 7 | 2019-05-12T15:37:46Z | 2019-05-20T00:24:42Z | 2019-05-20T00:24:37Z | CONTRIBUTOR | null | - [x] closes #26349
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Allows iterators as values when instantiating DataFrames using dicts.
Also collected the iterable-to-list functionality from core/series.py into core/common.py, so it is shared.
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Also realized that the `gcsfs` install was placed in the wrong place (needs to be placed after the `activate` command). However, you will notice then that the install is the culprit for the earlier 3.7 build failures (something is not right on `conda-forge` and / with `gcsfs` and `3.7`). Note though that the install of `gcsfs` works just fine on `3.6`. | {
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} | 5 | 2019-05-12T19:57:36Z | 2019-05-18T14:45:37Z | 2019-05-18T14:45:06Z | CONTRIBUTOR | null | - [x] closes #26310
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A way to avoid incorrect coercion to float64 in the case of uint64 input is to introduce an additional check to `ensure_int64_or_float64`
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} | 6 | 2019-05-12T20:47:17Z | 2019-05-14T17:13:05Z | 2019-05-14T17:13:01Z | CONTRIBUTOR | null | - [x] closes #25848
- [x] tests added
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Gap in performance between use dayfirst or format is acceptable now.
`asv run -E existing -b ParseDateComparison -a warmup_time=2 -a sample_time=2`:
| io.csv.ParseDateComparison.time_read_csv_dayfirst | parameter| time
:-:|:-:|:-:
-|cache_dates| -
-|False| 16.3±0.4ms
-|True| 7.22±0.2ms
| **io.csv.ParseDateComparison.time_to_datetime_dayfirst** | **parameter**| **time**
-|cache_dates| -
-|False| 15.0±0.3ms
-|True| 5.67±0.2ms
| **io.csv.ParseDateComparison.time_to_datetime_format_DD_MM_YYYY** | **parameter**| **time**
-| cache_dates|-
-|False| 25.0±0.4ms
-|True| 5.47±0.4ms | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26361 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26361/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26361/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26361/events | https://github.com/pandas-dev/pandas/issues/26361 | 443,152,121 | MDU6SXNzdWU0NDMxNTIxMjE= | 26,361 | COMPAT: numpy_dev build failing | {
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} | 2 | 2019-05-12T22:04:02Z | 2019-05-13T17:17:04Z | 2019-05-13T17:17:04Z | CONTRIBUTOR | null | https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=11349
I think this was just working
```
=================================== FAILURES ===================================
______________________ TestRangeIndex.test_numpy_argsort _______________________
[gw1] linux -- Python 3.7.3 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.test_range.TestRangeIndex object at 0x7f157c45ff60>
def test_numpy_argsort(self):
for k, ind in self.indices.items():
> result = np.argsort(ind)
pandas/tests/indexes/common.py:331:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/numpy/core/overrides.py:150: in public_api
implementation, public_api, relevant_args, args, kwargs)
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/numpy/core/fromnumeric.py:1084: in argsort
return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/numpy/core/fromnumeric.py:61: in _wrapfunc
return bound(*args, **kwds)
pandas/core/indexes/range.py:355: in argsort
nv.validate_argsort(args, kwargs)
pandas/compat/numpy/function.py:56: in __call__
self.defaults)
pandas/util/_validators.py:218: in validate_args_and_kwargs
validate_kwargs(fname, kwargs, compat_args)
pandas/util/_validators.py:157: in validate_kwargs
_check_for_default_values(fname, kwds, compat_args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fname = 'argsort', arg_val_dict = {'axis': -1, 'kind': None, 'order': None}
compat_args = OrderedDict([('axis', -1), ('kind', 'quicksort'), ('order', None)])
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overridden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except ValueError:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
> format(fname=fname, arg=key)))
E ValueError: the 'kind' parameter is not supported in the pandas implementation of argsort()
pandas/util/_validators.py:69: ValueError
_________________________ TestIndex.test_numpy_argsort _________________________
[gw0] linux -- Python 3.7.3 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.test_base.TestIndex object at 0x7f2124480630>
def test_numpy_argsort(self):
for k, ind in self.indices.items():
> result = np.argsort(ind)
pandas/tests/indexes/common.py:331:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/numpy/core/overrides.py:150: in public_api
implementation, public_api, relevant_args, args, kwargs)
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/numpy/core/fromnumeric.py:1084: in argsort
return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/numpy/core/fromnumeric.py:61: in _wrapfunc
return bound(*args, **kwds)
pandas/core/indexes/range.py:355: in argsort
nv.validate_argsort(args, kwargs)
pandas/compat/numpy/function.py:56: in __call__
self.defaults)
pandas/util/_validators.py:218: in validate_args_and_kwargs
validate_kwargs(fname, kwargs, compat_args)
pandas/util/_validators.py:157: in validate_kwargs
_check_for_default_values(fname, kwds, compat_args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fname = 'argsort', arg_val_dict = {'axis': -1, 'kind': None, 'order': None}
compat_args = OrderedDict([('axis', -1), ('kind', 'quicksort'), ('order', None)])
def _check_for_default_values(fname, arg_val_dict, compat_args):
"""
Check that the keys in `arg_val_dict` are mapped to their
default values as specified in `compat_args`.
Note that this function is to be called only when it has been
checked that arg_val_dict.keys() is a subset of compat_args
"""
for key in arg_val_dict:
# try checking equality directly with '=' operator,
# as comparison may have been overridden for the left
# hand object
try:
v1 = arg_val_dict[key]
v2 = compat_args[key]
# check for None-ness otherwise we could end up
# comparing a numpy array vs None
if (v1 is not None and v2 is None) or \
(v1 is None and v2 is not None):
match = False
else:
match = (v1 == v2)
if not is_bool(match):
raise ValueError("'match' is not a boolean")
# could not compare them directly, so try comparison
# using the 'is' operator
except ValueError:
match = (arg_val_dict[key] is compat_args[key])
if not match:
raise ValueError(("the '{arg}' parameter is not "
"supported in the pandas "
"implementation of {fname}()".
> format(fname=fname, arg=key)))
E ValueError: the 'kind' parameter is not supported in the pandas implementation of argsort()
pandas/util/_validators.py:69: ValueError
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} | 3 | 2019-05-13T05:57:16Z | 2019-05-14T16:42:04Z | 2019-05-14T16:04:00Z | MEMBER | null | - [x] closes #26335
- [x] tests added / passed
- [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/26363 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26363/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26363/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26363/events | https://github.com/pandas-dev/pandas/issues/26363 | 443,270,319 | MDU6SXNzdWU0NDMyNzAzMTk= | 26,363 | Fix type annotations for pandas.core.indexes.base | {
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} | [] | closed | false | null | [] | null | 7 | 2019-05-13T08:33:01Z | 2019-05-13T20:21:54Z | 2019-05-13T20:21:54Z | CONTRIBUTOR | null | Part of #25882
Current errors are:
pandas\core\indexes\base.py:9: error: Module 'pandas._libs' has no attribute 'join' | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26364/events | https://github.com/pandas-dev/pandas/issues/26364 | 443,285,935 | MDU6SXNzdWU0NDMyODU5MzU= | 26,364 | Fix type annotations for pandas.core.indexes.datetimelike | {
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Current errors are:
pandas\core\indexes\datetimelike.py:65: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:66: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:67: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:69: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:70: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:137: error: Decorated property not supported
pandas\core\indexes\datetimelike.py:207: error: Name '_box_values' already defined on line 72
pandas\core\indexes\datetimelike.py:761: error: Need type annotation for '_raw_methods'
pandas\core\indexes\datetimelike.py:763: error: Need type annotation for '_raw_properties' | {
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- [ ] tests passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-05-13T09:58:05Z | 2019-05-24T15:32:15Z | 2019-05-24T15:32:15Z | CONTRIBUTOR | null | Part of #25882
Current errors are:
pandas\core\indexes\datetimes.py:7: error: Module 'pandas._libs' has no attribute 'join'
pandas\core\indexes\datetimes.py:89: error: Definition of "_data" in base class "DatetimeIndexOpsMixin" is incompatible with definition in base class "Index"
pandas\core\indexes\datetimes.py:89: error: Definition of "_data" in base class "DatetimeIndexOpsMixin" is incompatible with definition in base class "DatetimelikeDelegateMixin"
pandas\core\indexes\datetimes.py:89: error: Definition of "map" in base class "DatetimeIndexOpsMixin" is incompatible with definition in base class "Index"
pandas\core\indexes\datetimes.py:89: error: Definition of "_format_with_header" in base class "DatetimeIndexOpsMixin" is incompatible with definition in base class "Index"
pandas\core\indexes\datetimes.py:89: error: Definition of "isin" in base class "DatetimeIndexOpsMixin" is incompatible with definition in base class "Index"
pandas\core\indexes\datetimes.py:1115: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimes.py:1116: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimes.py:1117: error: "Callable[[Any], Any]" has no attribute "fget"
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"following_url": "https://api.github.com/users/avinashpancham/following{/oth... | null | 7 | 2019-05-13T13:52:12Z | 2020-07-12T11:45:35Z | 2020-07-12T11:45:35Z | MEMBER | null | With released pandas (tested with 0.23.4), you get the following:
```
>>> import warnings
>>> warnings.filterwarnings('error')
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/joris/miniconda3/lib/python3.7/site-packages/pandas/__init__.py", line 26, in <module>
from pandas._libs import (hashtable as _hashtable,
File "/home/joris/miniconda3/lib/python3.7/site-packages/pandas/_libs/__init__.py", line 4, in <module>
from .tslib import iNaT, NaT, Timestamp, Timedelta, OutOfBoundsDatetime
File "pandas/_libs/tslibs/conversion.pxd", line 11, in init pandas._libs.tslib
File "pandas/_libs/tslibs/conversion.pyx", line 1, in init pandas._libs.tslibs.conversion
File "pandas/_libs/tslibs/nattype.pyx", line 14, in init pandas._libs.tslibs.nattype
ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__
```
(you also get a long output with `warnings.filterwarnings('always')`).
This is not happening any more on master, so the reason seems to be fixed.
But I was wondering, do we want to guarantee that this does not happen? (to the extent possible of course, meaning that we try to test this) | {
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Hi,
I've noticed that corrwith on pandas 0.24 is much slower than in 0.23, especially when trying to correlate dataframes where the length of the axis of correlation is much smaller than the length of the other axis.
Example:
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.rand(10000, 100))
df2 = pd.DataFrame(np.random.rand(10000, 100))
df1.corrwith(df2, axis=1)
```
With pandas 0.23.4 the snippet above finishes in about 0.1 sec, whereas with pandas 0.24.1 it takes about 10 seconds (a 100 times slower...).
If we increase the length of the correlation axis, 0.23.4 still performs much better, but the results are a bit less dramatic, for example with 10000 on both axes:
```python
df1 = pd.DataFrame(np.random.rand(10000, 10000))
df2 = pd.DataFrame(np.random.rand(10000, 10000))
df1.corrwith(df2, axis=1)
```
Pandas 0.23.4 finishes in ~10 seconds whereas pandas 0.24.1 finishes in about ~30 seconds ("only" 3 times slower)
Thanks! | {
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} | 4 | 2019-05-13T15:02:38Z | 2019-05-14T17:04:15Z | 2019-05-13T16:40:21Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "quantile... | closed | false | null | [] | null | 3 | 2019-05-13T15:12:07Z | 2021-07-02T05:55:57Z | 2021-07-02T05:55:57Z | NONE | null | There seems to always be the debate when calculating the 1st and 3rd quartiles in statistics about whether the median is included or not included in calculating the q1, q3. Some calculations include the median, while other exclude it. Regardless of the "correct" way to calculate this out, it would be nice if pandas could accommodate both scenarios, with a parameter of some sort, maybe `include_median = False` to get the results.
For example:
Looking at this series of values:
```
s = pd.Series([.0022, .0032, .0043, .0043, .0059])
s.quantile([0.25,0.5,0.75])
```
yields a result of:
```
Out[41]:
0.25 0.0032
0.50 0.0043
0.75 0.0043
dtype: float64
```
Which is the median of the lower and upper halves (including the median value)
When I am looking for the results of, something along the lines of:
`s.quantile([0.25,0.5,0.75], include_median = False)`
```
0.25 0.0027
0.50 0.0043
0.75 0.0051
dtype: float64
```
which would be the medians of the lower and upper halves (excluding the median value from those sets)
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} | 6 | 2019-05-13T15:19:17Z | 2019-05-15T05:40:20Z | 2019-05-13T20:21:54Z | CONTRIBUTOR | null | - [x] closes #26363
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26372 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26372/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26372/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26372/events | https://github.com/pandas-dev/pandas/pull/26372 | 443,459,237 | MDExOlB1bGxSZXF1ZXN0Mjc4MzIyMjI5 | 26,372 | Misc Type Annotation fixes | {
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} | 8 | 2019-05-13T15:20:49Z | 2019-05-20T08:43:10Z | 2019-05-18T00:34:32Z | CONTRIBUTOR | null | Part of #25882
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Cleanup of type annotations in files that only had a handful of straightforward errors (or no longer threw errors). These include:
# pandas.core.util.hasing
`pandas/core/util/hashing.py:8: error: Module 'pandas._libs' has no attribute 'hashing'`
# pandas.core.indexing
```
pandas/core/indexing.py:1487: error: Incompatible types in assignment (expression has type "Type[Exception]", base class "_NDFrameIndexer" defined the type as "Type[KeyError]")
pandas/core/indexing.py:1783: error: Incompatible types in assignment (expression has type "str", base class "_NDFrameIndexer" defined the type as "None")
pandas/core/indexing.py:2056: error: Incompatible types in assignment (expression has type "str", base class "_NDFrameIndexer" defined the type as "None")
pandas/core/indexing.py:2058: error: Incompatible types in assignment (expression has type "Type[IndexError]", base class "_NDFrameIndexer" defined the type as "Type[KeyError]")
```
# pandas.core.internals.blocks
`pandas/core/internals/blocks.py:2200: error: Definition of "_can_hold_na" in base class "ExtensionBlock" is incompatible with definition in base class "DatetimeBlock"`
# pandas.core.series
`pandas/core/series.py:98: error: Definition of "_update_inplace" in base class "IndexOpsMixin" is incompatible with definition in base class "NDFrame"`
# pandas.core.panel
`pandas/core/panel.py:867: error: Incompatible types in assignment (expression has type "Callable[[Any, Any], Any]", base class "NDFrame" defined the type as "Callable[[Any, Any, Any, Any], Any]")`
# pandas.core.reshape.reshape
`pandas/core/reshape/reshape.py:6: error: Module 'pandas._libs' has no attribute 'reshape'`
# pandas.io.pytables
```
pandas/io/pytables.py:2462: error: Need type annotation for 'attributes'
pandas/io/pytables.py:2845: error: Incompatible types in assignment (expression has type "str", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:2882: error: Incompatible types in assignment (expression has type "str", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:2906: error: Incompatible types in assignment (expression has type "str", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:3025: error: Incompatible types in assignment (expression has type "str", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:3026: error: Incompatible types in assignment (expression has type "Type[DataFrame]", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:3054: error: Incompatible types in assignment (expression has type "str", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:3849: error: Incompatible types in assignment (expression has type "str", base class "Table" defined the type as "None")
pandas/io/pytables.py:3877: error: Incompatible types in assignment (expression has type "str", base class "Table" defined the type as "None")
pandas/io/pytables.py:3878: error: Incompatible types in assignment (expression has type "int", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:3894: error: Incompatible types in assignment (expression has type "None", base class "LegacyTable" defined the type as "List[IndexCol]")
pandas/io/pytables.py:4129: error: Incompatible types in assignment (expression has type "Type[DataFrame]", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:4195: error: Incompatible types in assignment (expression has type "Type[Series]", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:4253: error: Incompatible types in assignment (expression has type "Type[DataFrame]", base class "Fixed" defined the type as "None")
pandas/io/pytables.py:4301: error: Incompatible types in assignment (expression has type "Type[DataFrame]", base class "Fixed" defined the type as "None")
```
# pandas.core.reshape.merge
`pandas/core/reshape/merge.py:11: error: Module 'pandas._libs' has no attribute 'join'`
# pandas.util.testing
`pandas/util/testing.py:24: error: Module 'pandas._libs' has no attribute 'testing'`
# pandas.core.window
```
pandas/core/window.py:45: error: Need type annotation for 'exclusions'
pandas/core/window.py:314: error: Incompatible types in assignment (expression has type "Callable[[Arg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "SelectionMixin" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
pandas/core/window.py:1806: error: Definition of "_apply" in base class "_GroupByMixin" is incompatible with definition in base class "_Rolling"
pandas/core/window.py:2077: error: Definition of "_apply" in base class "_GroupByMixin" is incompatible with definition in base class "_Rolling"
```
# pandas.core.resample
```
pandas/core/resample.py:34: error: Need type annotation for '_shared_docs_kwargs'
pandas/core/resample.py:876: error: All conditional function variants must have identical signatures
pandas/core/resample.py:884: error: All conditional function variants must have identical signatures
pandas/core/resample.py:891: error: All conditional function variants must have identical signatures
pandas/core/resample.py:1101: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1101: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1101: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1101: error: Definition of "_groupby_and_aggregate" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1217: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "PeriodIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1217: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "PeriodIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_groupby_and_aggregate" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1247: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1247: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1247: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1247: error: Definition of "_groupby_and_aggregate" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1299: error: Incompatible types in assignment (expression has type "Tuple[str, ...]", base class "Grouper" defined the type as "Tuple[str, str, str, str, str]")
```
**pandas.util._doctools** was removed because it no longer threw mypy errors for some reason. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26373 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26373/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26373/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26373/events | https://github.com/pandas-dev/pandas/pull/26373 | 443,485,711 | MDExOlB1bGxSZXF1ZXN0Mjc4MzQzMDgw | 26,373 | Compat with numpy dev argsort (radix sort added) | {
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} | 4 | 2019-05-13T16:13:58Z | 2019-05-13T17:19:50Z | 2019-05-13T17:17:04Z | CONTRIBUTOR | null | https://github.com/numpy/numpy/commit/12fb1015511ac3804d0785bb0d1fe539385548ad
adds radix sort to np.argsort. For versions of NumPy with this change
(>=1.17), we need to adjust our validator.
Closes https://github.com/pandas-dev/pandas/issues/26361 | {
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} | 6 | 2019-05-13T16:54:35Z | 2019-05-20T00:23:13Z | 2019-05-20T00:23:09Z | CONTRIBUTOR | null | - [x] closes #26326
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-05-13T17:47:50Z | 2019-05-14T13:39:40Z | 2019-05-14T01:40:54Z | CONTRIBUTOR | null | Trivial documentation changes
Dictionary should have one set of brackets (as seen below in documentation example `{'a': np.float64, 'b': np.int32}`).
Brackets don't make sense around `'w' or 'a'`.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-05-13T18:23:18Z | 2021-09-12T17:47:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import urllib.request
import pandas as pd
url='ftp://ftp.ncdc.noaa.gov/pub/data/igra/igra2-station-list.txt'
urllib.request.urlretrieve(url,url.split('/')[-1])
igrainv=pd.read_fwf(url.split('/')[-1],widths=(2,1,3,5,9,10,7,4,30,5,5,7),
names=('CC','Network','Code','StationId','Latitude','Longitude','Elev','dummy','StationName','From','To','Nrec'))
f=urllib.request.urlopen(url)
igrainv=pd.read_fwf(f,widths=(2,1,3,5,9,10,7,4,30,5,5,7),
names=('CC','Network','Code','StationId','Latitude','Longitude','Elev','dummy','StationName','From','To','Nrec'))
```
#### Problem description
The first read_fwf from a file downloaded from the internet works, the second read_fwf of the same file using an url pointer fails.
I get the following error:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 803, in read_fwf
return _read(filepath_or_buffer, kwds)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 429, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 895, in __init__
self._make_engine(self.engine)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1132, in _make_engine
self._engine = klass(self.f, **self.options)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 3605, in __init__
PythonParser.__init__(self, f, **kwds)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2238, in __init__
self.unnamed_cols) = self._infer_columns()
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2614, in _infer_columns
line = self._buffered_line()
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2689, in _buffered_line
return self._next_line()
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2794, in _next_line
orig_line = self._next_iter_line(row_num=self.pos + 1)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2854, in _next_iter_line
return next(self.data)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 3589, in __next__
line = next(self.f)
TypeError: 'addinfourl' object is not an iterator
#### 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: 3.7.0.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-754.10.1.el6.centos.plus.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.2
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 9 | 2019-05-13T20:12:58Z | 2019-05-16T01:27:08Z | 2019-05-16T00:15:23Z | CONTRIBUTOR | null | Part of #25882
- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Corrects these errors in `pandas.core.ops`:
```
pandas/core/ops.py:632: error: Unsupported target for indexed assignment
pandas/core/ops.py:633: error: Value of type "object" is not indexable
pandas/core/ops.py:635: error: "object" has no attribute "copy"
pandas/core/ops.py:636: error: Unsupported target for indexed assignment
pandas/core/ops.py:637: error: Unsupported target for indexed assignment
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26378 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26378/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26378/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26378/events | https://github.com/pandas-dev/pandas/issues/26378 | 443,609,929 | MDU6SXNzdWU0NDM2MDk5Mjk= | 26,378 | SparseDataFrame.to_parquet fails with new error | {
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} | [] | closed | false | null | [] | null | 11 | 2019-05-13T21:27:53Z | 2019-05-18T15:21:57Z | 2019-05-13T21:54:57Z | NONE | null | #### Code Sample
```python
import pandas as pd # v0.24.2
import scipy.sparse # v1.1.0
df = pd.SparseDataFrame(scipy.sparse.random(1000, 1000),
columns=list(map(str, range(1000))),
default_fill_value=0.0)
df.to_parquet('rpd.pq', engine='pyarrow')
```
Gives the error
```
ArrowTypeError: ('Did not pass numpy.dtype object', 'Conversion failed for column 0 with type Sparse[float64, 0.0]')
```
#### Problem description
This error occurs when trying to save a Pandas sparse DataFrame using the `to_parquet` method. The error can be avoided by running `df.to_dense().to_parquet()`. However, this can require a lot of memory for very large sparse matrices.
The issue was also raised https://github.com/apache/arrow/issues/1894 and https://github.com/pandas-dev/pandas/issues/20692
#### Expected Output
The expected output is a parquet file on disk.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.9.1
pip: 19.0.3
setuptools: 40.2.0
Cython: None
numpy: 1.16.3
scipy: 1.1.0
pyarrow: 0.13.0
xarray: None
IPython: 6.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-05-14T00:06:47Z | 2019-07-22T06:13:28Z | 2019-05-19T23:55:20Z | MEMBER | null | - [x] closes #26283
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | open | false | null | [] | null | 11 | 2019-05-14T00:37:27Z | 2021-03-22T22:27:50Z | null | NONE | null | It would be useful to have `__array_function__` support as described in [NEP 18]( https://www.numpy.org/neps/nep-0018-array-function-protocol.html#alternatives ) implemented for Pandas objects. This would allow users to run NumPy functions on Pandas objects while deferring to Pandas on how those operations should run. | {
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} | 14 | 2019-05-14T02:51:55Z | 2019-05-17T17:50:13Z | 2019-05-17T17:50:06Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew: handle the case where adding of business hour to the timestamp results in an ending time landing in the next business time period. Example
Input:
```python
bh = BusinessHour(n=4, start='00:00', end='23:00')
dt = datetime(2014, 7, 3, 22)
dt + bh
```
Expected:
```python
datetime(2014, 7, 4, 3)
``` | {
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} | [] | closed | false | null | [] | null | 2 | 2019-05-14T03:58:35Z | 2019-05-14T04:07:58Z | 2019-05-14T04:00:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.DataFrame({'a': np.random.randint(0, 3, 10)}).groupby('a').apply(lambda x:print(x.name))
0
0
1
2
In [4]: pd.__version__
Out[4]: '0.24.2'
```
#### Problem description
Above code show the problem
#### Expected Output
```
In [3]: pd.DataFrame({'a': np.random.randint(0, 3, 10)}).groupby('a').apply(lambda x:print(x.name))
0
1
2
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 41.0.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.12.0
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.6
lxml.etree: 4.3.3
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html5lib: 1.0.1
sqlalchemy: 1.3.2
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: 0.2.0
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 7 | 2019-05-14T05:33:51Z | 2019-05-15T14:42:54Z | 2019-05-15T12:20:45Z | MEMBER | null | Found this while investigating #26345 may or may not be related but figured worth a separate PR in either case | {
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} | 1 | 2019-05-14T06:00:42Z | 2019-08-20T19:02:10Z | 2019-08-20T19:02:10Z | MEMBER | null | Was looking to consolidate nth, first and last in the GroupBy world but was hitting some issues with categorical tests and handling of missing data. Note that the following calls are not equivalent, though they should be:
```python
>>> import numpy as np
>>> import pandas as pd
>>> s1 = pd.Categorical([np.nan, 'a', np.nan, 'a'], categories=['a', 'b', 'c'])
>>> s2 = pd.Series([1, 2, 3, 4])
>>> df = pd.DataFrame({'s1': s1, 's2': s2})
>>> grp = df.groupby('s1', observed=False)
>>> grp.first()
s2
s1
a 2.0
b NaN
c NaN
>>> grp.nth(0)
s2
s1
a 2
```
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} | 1 | 2019-05-14T10:15:27Z | 2019-05-14T10:21:11Z | 2019-05-14T10:20:48Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> pd.to_datetime(['30/04/19', '01/05/19'])
DatetimeIndex(['2019-04-30', '2019-01-05'], dtype='datetime64[ns]', freq=None)
```
#### Problem description
The converter should infer the same format for both values - so it should infer that both values are in dd/mm/yy format, as must be so for the first value. Otherwise `to_datetime` will give dangerous and unusable results for `dd/mm` format dates, because the months / days will reverse within a column.
#### Expected Output
```
DatetimeIndex(['2019-04-30', '2019-05-01'], dtype='datetime64[ns]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.0
pytest: 4.1.1
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: Non
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 15 | 2019-05-14T10:36:57Z | 2021-06-15T12:34:00Z | 2020-04-26T21:41:36Z | NONE | null | When using spark to process data and save to s3, the files are like
```bash
s3://bucket/path/_SUCCESS
s3://bucket/path/part-00000-uuid.snappy.parquet
s3://bucket/path/part-00002-uuid.snappy.parquet
s3://bucket/path/part-00001-uuid.snappy.parquet
s3://bucket/path/part-00003-uuid.snappy.parquet
```
It would be nice to load these files in one line.
```python
df = pd.read_parquet("s3://bucket/path")
``` | {
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] | closed | false | null | [] | null | 4 | 2019-05-14T11:27:19Z | 2019-08-21T16:20:30Z | 2019-08-21T16:20:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
dirs = os.listdir(path)
frame = pandas.DataFrame()
#frame = frame.astype(str)
output_folder_time_stamp = str(datetime.datetime.now()).replace(":", " ").rsplit(".", 1)[0]
final_output_path = outputpath + str(output_folder_time_stamp)
try:
for filename in dirs :
subdirs=path + filename
subdirs = os.listdir(subdirs)
if len(subdirs)>0:
for jsonfilename in subdirs :
root, ext = os.path.splitext(jsonfilename)
if ext == '.json':
tmp_frame = pandas.read_json(path+filename+"/"+jsonfilename)
frame = frame.append(tmp_frame, ignore_index=True)
if not os.path.exists(final_output_path):
os.makedirs(final_output_path)
frame.to_csv(final_output_path +"/"+ filename +"_"+output_folder_time_stamp+'.csv',encoding='utf-8',index=False,header=True, columns=["FILE NAME","PROPERTY VALUE"])
frame = pandas.DataFrame()
tmp_frame=''
```
#### Problem description
In Json as
[{"FILE NAME": "EXP_data.xml", "PROPERTY VALUE": "3-4"}]
PROPERTY VALUE": "3-4" converting as "4-Mar" in CSV
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
PROPERTY VALUE": "3-4"
#### Output of ``pd.show_versions()``
PROPERTY VALUE": "4-Mar"
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 6 | 2019-05-14T14:21:18Z | 2019-05-19T18:11:28Z | 2019-05-19T18:11:28Z | MEMBER | null | Assigning a PandasArray (so also the result of `df['a'].array`) of the correct length to add a column fails:
```python
In [1]: df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': ['a', 'b', 'c', 'd']})
In [2]: df['c'] = pd.array([1, 2, None, 3])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/scipy/pandas/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2672 try:
-> 2673 return self._engine.get_loc(key)
2674 except KeyError:
~/scipy/pandas/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
~/scipy/pandas/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
~/scipy/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
~/scipy/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'c'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/scipy/pandas/pandas/core/internals/managers.py in set(self, item, value)
1048 try:
-> 1049 loc = self.items.get_loc(item)
1050 except KeyError:
~/scipy/pandas/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2674 except KeyError:
-> 2675 return self._engine.get_loc(self._maybe_cast_indexer(key))
2676 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
~/scipy/pandas/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
~/scipy/pandas/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
~/scipy/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
~/scipy/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'c'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-2-03925b585d9b> in <module>
----> 1 df['c'] = pd.array([1, 2, None, 3])
~/scipy/pandas/pandas/core/frame.py in __setitem__(self, key, value)
3334 else:
3335 # set column
-> 3336 self._set_item(key, value)
3337
3338 def _setitem_slice(self, key, value):
~/scipy/pandas/pandas/core/frame.py in _set_item(self, key, value)
3410 self._ensure_valid_index(value)
3411 value = self._sanitize_column(key, value)
-> 3412 NDFrame._set_item(self, key, value)
3413
3414 # check if we are modifying a copy
~/scipy/pandas/pandas/core/generic.py in _set_item(self, key, value)
3232
3233 def _set_item(self, key, value):
-> 3234 self._data.set(key, value)
3235 self._clear_item_cache()
3236
~/scipy/pandas/pandas/core/internals/managers.py in set(self, item, value)
1050 except KeyError:
1051 # This item wasn't present, just insert at end
-> 1052 self.insert(len(self.items), item, value)
1053 return
1054
~/scipy/pandas/pandas/core/internals/managers.py in insert(self, loc, item, value, allow_duplicates)
1152
1153 block = make_block(values=value, ndim=self.ndim,
-> 1154 placement=slice(loc, loc + 1))
1155
1156 for blkno, count in _fast_count_smallints(self._blknos[loc:]):
~/scipy/pandas/pandas/core/internals/blocks.py in make_block(values, placement, klass, ndim, dtype, fastpath)
3052 values = DatetimeArray._simple_new(values, dtype=dtype)
3053
-> 3054 return klass(values, ndim=ndim, placement=placement)
3055
3056
~/scipy/pandas/pandas/core/internals/blocks.py in __init__(self, values, placement, ndim)
2584 values = np.array(values, dtype=object)
2585
-> 2586 super().__init__(values, ndim=ndim, placement=placement)
2587
2588 @property
~/scipy/pandas/pandas/core/internals/blocks.py in __init__(self, values, placement, ndim)
74
75 def __init__(self, values, placement, ndim=None):
---> 76 self.ndim = self._check_ndim(values, ndim)
77 self.mgr_locs = placement
78 self.values = values
~/scipy/pandas/pandas/core/internals/blocks.py in _check_ndim(self, values, ndim)
111 msg = ("Wrong number of dimensions. values.ndim != ndim "
112 "[{} != {}]")
--> 113 raise ValueError(msg.format(values.ndim, ndim))
114
115 return ndim
ValueError: Wrong number of dimensions. values.ndim != ndim [1 != 2]
```
Note this only fails for the PandasArray types (so when creating a FloatBlock or IntBlock, .. which expect 2D data, so when not creating an ExtensionBlock as is done for an "actual" ExtensionArray). | {
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} | 6 | 2019-05-14T15:16:05Z | 2019-05-28T03:56:18Z | 2019-05-28T03:55:53Z | CONTRIBUTOR | null | - xref #24813
- benchmark added
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
This PR follows #25159 (in which I broke something when merging).
```
All benchmarks:
before after ratio
[304d8d4b] [4ec1fe97]
<master> <intv-is-unique>
228±0.2ns 230±0.2ns 1.01 index_object.IntervalIndexMethod.time_is_unique(1000)
- 450±7ns 277±2ns 0.62 index_object.IntervalIndexMethod.time_is_unique(100000)
```
The approach this time doesn't worsen the performance. | {
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} | 8 | 2019-05-14T15:32:51Z | 2019-05-26T12:59:26Z | 2019-05-25T19:24:39Z | CONTRIBUTOR | null | - follow up the comment in #26292
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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I happened to notice that pandas.tseries.offsets.CustomBusinessDay can not handle holidays based
on the lunar calendar.
I think it would be great if the class CustomBusinessDay support the lunar holidays in Korea, China, etc.
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} | 12 | 2019-05-14T15:53:35Z | 2019-05-20T11:11:56Z | 2019-05-20T11:11:36Z | CONTRIBUTOR | null | From discussion below: `pd.to_numeric` does not validate the value passed to the `errors` keyword, so any random value is interpreted as `errors='coerce'`.
---
*Original report*:
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'Strings': ['fire', 'hose'], 'Numbers': ['3838.2', '99']})
print(df.apply(pd.to_numeric, args={'errors': 'ignore'}).to_string())
```
#### Problem description
The code above should return:
Strings Numbers
0 'fire' 3838.2
1 'hose' 99.0
Instead it returns:
Strings Numbers
0 NaN 3838.2
1 NaN 99.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.1
pip: 19.1
setuptools: 41.0.0
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: 0.10.0
xarray: 0.12.1
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: 0.4.0
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.7
lxml.etree: 4.3.3
bs4: 4.6.3
html5lib: 0.9999999
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "Related to indexing on s... | open | false | null | [] | null | 1 | 2019-05-14T16:21:23Z | 2021-07-02T05:58:24Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Python 3.5.6 |Anaconda custom (64-bit)| (default, Aug 26 2018, 21:41:56)
# assignment behavior with DataFrame.loc and DataFrame.at
# df.loc[] = x will promote dataframe column type to x's type
# df.at[] = x will cast x value to column's type
# apparently undocumented.
import pandas as pd
df = pd.DataFrame(index=['A','B','C'])
df['D'] = 0
df.at['C','D'] = 2
df
# D
# A 0
# B 0
# C 2
df.dtypes
# D int64
# dtype: object
df.at['C','D'] = 44.5
df
# D
# A 0
# B 0
# C 44
df.dtypes
# D int64
# dtype: object
df.loc['C','D'] = 44.5
df
# D
# A 0.0
# B 0.0
# C 44.5
df.dtypes
# D float64
# dtype: object
df.loc['C','D'] = 'hello'
df
# D
# A 0
# B 0
# C hello
df.dtypes
# D object
# dtype: object
```
#### Problem description
the type-conversion/cast behavior is not documented for either .loc or .at.
#### Expected Output
expect same behavior. also expect a warning/error if casting from float to int
#### 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: 3.5.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.22.0
pytest: 3.7.3
pip: 18.0
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.7
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.1
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.5
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.8
lxml: 4.2.4
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.6.0
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} | 3 | 2019-05-14T18:34:49Z | 2019-05-15T13:04:10Z | 2019-05-15T12:16:08Z | CONTRIBUTOR | null | - [x] xref #25725,
- [x] xref #26281, #26289 & #26319
Fourth and final part of the removal of ``compat.lrange`` from the code base. | {
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} | 4 | 2019-05-14T20:58:13Z | 2019-06-01T14:52:36Z | 2019-06-01T14:52:36Z | CONTRIBUTOR | null | Right now, the output shape and dtype of DataFrame.describe for object columns depends on whether the DataFrame is empty.
#### Code Sample, a copy-pastable example if possible
```python
In [75]: x = pd.DataFrame({"A": ['a', np.nan, np.nan]})
In [76]: x.describe()
Out[76]:
A
count 1
unique 1
top a
freq 1
In [77]: x.iloc[:0].describe()
Out[77]:
A
count 0
unique 0
```
#### Problem description
This leads to instability in the output dtypes and shape.
Would people prefer that we use `np.NaN` or `None` for the top and freq in this case? I believe there's no ambiguity, since we drop missing values before computing.
While the output consistency would be nice, it's not clear to me what's actually best for users here. | {
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} | 3 | 2019-05-14T22:30:55Z | 2019-05-16T01:28:03Z | 2019-05-15T23:46:51Z | CONTRIBUTOR | null | Part of #25882
- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This PR addresses the following mypy errors in **pandas.core.resample**:
```
pandas/core/resample.py:34: error: Need type annotation for '_shared_docs_kwargs'
pandas/core/resample.py:876: error: All conditional function variants must have identical signatures
pandas/core/resample.py:884: error: All conditional function variants must have identical signatures
pandas/core/resample.py:891: error: All conditional function variants must have identical signatures
pandas/core/resample.py:1101: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1101: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1101: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1101: error: Definition of "_groupby_and_aggregate" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1217: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "PeriodIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1217: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "PeriodIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1217: error: Definition of "_groupby_and_aggregate" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1247: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1247: error: Definition of "_upsample" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1247: error: Definition of "_downsample" in base class "_GroupByMixin" is incompatible with definition in base class "DatetimeIndexResampler"
pandas/core/resample.py:1247: error: Definition of "_groupby_and_aggregate" in base class "_GroupByMixin" is incompatible with definition in base class "Resampler"
pandas/core/resample.py:1299: error: Incompatible types in assignment (expression has type "Tuple[str, ...]", base class "Grouper" defined the type as "Tuple[str, str, str, str, str]")
``` | {
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} | 39 | 2019-05-15T03:39:08Z | 2019-05-30T01:29:59Z | 2019-05-30T01:29:41Z | CONTRIBUTOR | null | This adds special support for controlling the output column names when performing column-specific groupby aggregations. We use kwargs, using the keywords as the output names, and expecting tuples of `(selection, aggfunc)`.
```python
In [2]: df = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
...: 'height': [9.1, 6.0, 9.5, 34.0],
...: 'weight': [7.9, 7.5, 9.9, 198.0]})
...:
In [3]: df
Out[3]:
kind height weight
0 cat 9.1 7.9
1 dog 6.0 7.5
2 cat 9.5 9.9
3 dog 34.0 198.0
In [4]: df.groupby('kind').agg(min_height=('height', 'min'), max_weight=('weight', 'max'))
Out[4]:
min_height max_weight
kind
cat 9.1 9.9
dog 6.0 198.0
```
Closes #18366
---
Putting this up as a WIP before the meeting on Thursday.
TODO:
- [ ] Update old deprecation message to point to new way
- [ ] Decide if we want to allow this elsewhere (DataFrame.agg? SeriesGroupBy.agg?)
cc @jorisvandenbossche @WillAyd | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2019-05-15T05:32:48Z | 2019-05-15T12:31:23Z | 2019-05-15T12:31:23Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Doctests in `to_datetime()`.
#### Problem description
It is being failed as below:
```
Failed example:
df = pd.DataFrame({'year': [2015, 2016],
Exception raised:
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/helpers/pycharm/docrunner.py", line 140, in __run
compileflags, 1), test.globs)
File "<doctest to_datetime[0]>", line 1
df = pd.DataFrame({'year': [2015, 2016],
^
SyntaxError: unexpected EOF while parsing
```
```
Failed example:
pd.to_datetime([1, 2, 3], unit='D',
origin=pd.Timestamp('1960-01-01'))
Expected:
0 1960-01-02
1 1960-01-03
2 1960-01-04
Got:
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)
```
```
Failed example:
%timeit pd.to_datetime(s,infer_datetime_format=False)
Exception raised:
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/helpers/pycharm/docrunner.py", line 140, in __run
compileflags, 1), test.globs)
File "<doctest to_datetime[7]>", line 1
%timeit pd.to_datetime(s,infer_datetime_format=False)
^
SyntaxError: invalid syntax
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: 0.13.0
xarray: None
IPython: None
sphinx: 2.0.1
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-05-15T05:33:36Z | 2019-05-15T12:53:19Z | 2019-05-15T12:31:23Z | CONTRIBUTOR | null | - [x] closes #26401
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 12 | 2019-05-15T06:27:48Z | 2019-11-28T04:31:01Z | 2019-07-03T08:26:38Z | MEMBER | null | - [X] closes #26336
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
All of the logic related to breaking changes looks to be consolidated in `CategoricalDtype.update_dtype`, so I'm raising the warning there when a breaking change would occur and letting it flow through from there.
Aside from `CategoricalDtype.update_dtype`, I think the only other place where a breaking change can manifest is in `astype` as described in the issue. Will need to double check the test logs to see if anything else triggered a warning though.
cc @TomAugspurger @jorisvandenbossche
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} | 13 | 2019-05-15T06:53:47Z | 2019-05-25T00:12:59Z | 2019-05-24T15:32:15Z | CONTRIBUTOR | null | - [x] closes #26366
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
`pandas/core/indexes/timedeltas.py` taken care of as well since the errors were the same. | {
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} | 8 | 2019-05-15T06:55:56Z | 2019-11-21T01:17:50Z | 2019-05-16T07:15:28Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/18262
Not sure if we need to point to an alternative in the deprecation warning. I can put the exact "formula" for what we are currently calculating, but I don't know enough from finance to know if that is actually good to point to (eg I don't directly see that formula on the first google hit https://en.wikipedia.org/wiki/Compound_interest) | {
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} | 1 | 2019-05-15T08:21:12Z | 2020-11-30T00:09:10Z | 2020-11-30T00:09:10Z | MEMBER | null | ```
In [1]: s = pd.Series(pd.Categorical(pd.date_range("2012", periods=3, tz='Europe/Brussels')))
In [2]: s.to_numpy()
Out[2]:
array(['2011-12-31T23:00:00.000000000', '2012-01-01T23:00:00.000000000',
'2012-01-02T23:00:00.000000000'], dtype='datetime64[ns]')
In [3]: s = pd.Series(pd.date_range("2012", periods=3, tz='Europe/Brussels'))
In [4]: s.to_numpy()
Out[4]:
array([Timestamp('2012-01-01 00:00:00+0100', tz='Europe/Brussels', freq='D'),
Timestamp('2012-01-02 00:00:00+0100', tz='Europe/Brussels', freq='D'),
Timestamp('2012-01-03 00:00:00+0100', tz='Europe/Brussels', freq='D')],
dtype=object)
```
We opted for the default behaviour of `to_numpy` to be as much preserving the values, so for normal datetimetz series, this is object dtype. So probably we should do the same for Categorical?
@TomAugspurger since `to_numpy` is still new, I suppose we can see this is as a bug fix and not change in behaviour (to not go through deprecation cycle)? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26407 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26407/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26407/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26407/events | https://github.com/pandas-dev/pandas/issues/26407 | 444,331,432 | MDU6SXNzdWU0NDQzMzE0MzI= | 26,407 | SparseArrays backed by other Extension array/dtype ? | {
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"description": "Sparse D... | open | false | null | [] | null | 3 | 2019-05-15T09:29:00Z | 2020-04-25T20:53:08Z | null | MEMBER | null | You can create an SparseDtype with a ExtensionDtype as subtype:
```
In [14]: spdt = pd.SparseDtype(pd.DatetimeTZDtype(tz='Europe/Brussels'))
In [15]: spdt
Out[15]: Sparse[datetime64[ns, Europe/Brussels], NaT]
```
but I don't think that you can actually create a SparseArray with that?
At least, there seems to be several places in the sparse code that assumes the subtype is a numpy dtype, and also creating one fails:
```
In [16]: pd.SparseArray([pd.Timestamp('2012-01-01', tz="Europe/Brussels"), pd.NaT], dtype=spdt)
Out[16]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/scipy/repos/ipython/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/scipy/repos/ipython/IPython/lib/pretty.py in pretty(self, obj)
400 if cls is not object \
401 and callable(cls.__dict__.get('__repr__')):
--> 402 return _repr_pprint(obj, self, cycle)
403
404 return _default_pprint(obj, self, cycle)
~/scipy/repos/ipython/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
695 """A pprint that just redirects to the normal repr function."""
696 # Find newlines and replace them with p.break_()
--> 697 output = repr(obj)
698 for idx,output_line in enumerate(output.splitlines()):
699 if idx:
~/scipy/pandas/pandas/core/base.py in __repr__(self)
65 Return a string representation for a particular object.
66 """
---> 67 return str(self)
68
69
~/scipy/pandas/pandas/core/base.py in __str__(self)
50 Return a string representation for a particular Object
51 """
---> 52 return self.__unicode__()
53
54 def __bytes__(self):
~/scipy/pandas/pandas/core/arrays/sparse.py in __unicode__(self)
1826 def __unicode__(self):
1827 return '{self}\nFill: {fill}\n{index}'.format(
-> 1828 self=printing.pprint_thing(self),
1829 fill=printing.pprint_thing(self.fill_value),
1830 index=printing.pprint_thing(self.sp_index))
~/scipy/pandas/pandas/io/formats/printing.py in pprint_thing(thing, _nest_lvl, escape_chars, default_escapes, quote_strings, max_seq_items)
215 result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars,
216 quote_strings=quote_strings,
--> 217 max_seq_items=max_seq_items)
218 elif isinstance(thing, str) and quote_strings:
219 result = "'{thing}'".format(thing=as_escaped_unicode(thing))
~/scipy/pandas/pandas/io/formats/printing.py in _pprint_seq(seq, _nest_lvl, max_seq_items, **kwds)
111 r = [pprint_thing(next(s),
112 _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
--> 113 for i in range(min(nitems, len(seq)))]
114 body = ", ".join(r)
115
~/scipy/pandas/pandas/io/formats/printing.py in <listcomp>(.0)
111 r = [pprint_thing(next(s),
112 _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
--> 113 for i in range(min(nitems, len(seq)))]
114 body = ", ".join(r)
115
~/scipy/pandas/pandas/core/arrays/base.py in __iter__(self)
282 # calls to ``__getitem__``, which may be slower than necessary.
283 for i in range(len(self)):
--> 284 yield self[i]
285
286 # ------------------------------------------------------------------------
~/scipy/pandas/pandas/core/arrays/sparse.py in __getitem__(self, key)
1075
1076 if is_integer(key):
-> 1077 return self._get_val_at(key)
1078 elif isinstance(key, tuple):
1079 data_slice = self.values[key]
~/scipy/pandas/pandas/core/arrays/sparse.py in _get_val_at(self, loc)
1118 return self.fill_value
1119 else:
-> 1120 return libindex.get_value_at(self.sp_values, sp_loc)
1121
1122 def take(self, indices, allow_fill=False, fill_value=None):
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got DatetimeArray)
```
(although it is actually the repr that fails)
Is this something we would like to support? (cc @TomAugspurger ) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26408/events | https://github.com/pandas-dev/pandas/pull/26408 | 444,344,576 | MDExOlB1bGxSZXF1ZXN0Mjc5MDIxODk0 | 26,408 | BUG: MultiIndex not dropping nan level and invalid code value | {
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} | 10 | 2019-05-15T09:56:17Z | 2019-06-02T08:40:33Z | 2019-06-01T14:56:35Z | CONTRIBUTOR | null | - [x] closes #19387
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Please take note that another bug is also fixed in this PR:
When the MultiIndex is constructed with code value < -1, a segmentation fault would be resulted in subsequent operations e.g. putting the MultiIndex in a DataFrame
Input:
```python
x = pd.MultiIndex(levels=[['a'], ['x', np.nan]], labels=[[0,-2], [0,1]])
pd.DataFrame(index=x)
```
A segmentation fault can be resulted by these commands
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https://api.github.com/repos/pandas-dev/pandas/issues/26409 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26409/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26409/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26409/events | https://github.com/pandas-dev/pandas/pull/26409 | 444,358,669 | MDExOlB1bGxSZXF1ZXN0Mjc5MDMzMTk4 | 26,409 | DEPR: deprecate get_values | {
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} | 21 | 2019-05-15T10:25:55Z | 2019-07-01T15:57:47Z | 2019-07-01T15:57:42Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/19617
Reviving an old branch I had lying around. So this was more a quick experiment to see what it involves. The main "problem" is that we ourselves use `get_values` in a bunch of places (eg `values_from_object` which is massively used).
The current situation:
- `DataFrame.get_values` simply points to `.values` so is (I think?) always a numpy ndarray.
- `Series.get_values` points to `_data.get_values()` (the SingleBlockManager), which in turn does `np.array(self._block.to_dense(), copy=False)`.
So the `Block.to_dense()` in general returns a view on the array, although for ExtensionBlock it does `np.array(self.values)`, with the exception for DatetimeTZBlock, which ensures a non-object array with `np.asarray(self.values, dtype=_NS_DTYPE)`, and for CategoricalBlock, it returns the Categorical.get_values which can be a DatetimeIndex.
- `Index.get_values` points to `.values`, so is a numpy ndarray with the exception of:
- newer Index objects which are already backed by EA: IntervalIndex returns IntervalArray here (but eg DatetimeIndex with tz aware data does return a numpy array)
- `CategoricalIndex` overrides the parent method to return the `get_values` of the underlying Categorical, which in turn mostly returns an ndarray, but an EA for interval-backed dtypes and DatetimeIndex for datetimetz aware backed categorical.
Summary: a complete mess :-)
This PR is for now just an experiment to see where it is used. Now, we should see for all those places what would be the best replacement (unless we are fine with keeping such a `_internal_get_values`, but it would be good to clean this up somewhat ..) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26410 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26410/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26410/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26410/events | https://github.com/pandas-dev/pandas/issues/26410 | 444,366,635 | MDU6SXNzdWU0NDQzNjY2MzU= | 26,410 | API: getting the "densified" values of a Categorical (preserving categories dtype) ? | {
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"description": "Categori... | open | false | null | [] | null | 3 | 2019-05-15T10:43:54Z | 2021-07-02T06:01:25Z | null | MEMBER | null | Assume we have a Categorical, and want to convert to a dense array (not encoded). We have `np.asarray(..)` and the `to_dense()` method (which uses asarray under the hood):
```
In [1]: cat = pd.Categorical(['a', 'b', 'a'])
In [2]: np.asarray(cat)
Out[2]: array(['a', 'b', 'a'], dtype=object)
In [3]: cat.to_dense()
Out[3]: array(['a', 'b', 'a'], dtype=object)
```
In addition, we also have `get_values`:
```
In [4]: cat.get_values()
Out[4]: array(['a', 'b', 'a'], dtype=object)
```
`get_values` is mostly the same, with the exception that returns an Index for datetime/period/timedelta, and an object array for integers if there are missing values instead of float array:
```
In [10]: cat = pd.Categorical(pd.date_range("2012", periods=3))
In [11]: cat.to_dense()
Out[11]:
array(['2012-01-01T00:00:00.000000000', '2012-01-02T00:00:00.000000000',
'2012-01-03T00:00:00.000000000'], dtype='datetime64[ns]')
In [12]: cat.get_values()
Out[12]: DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03'], dtype='datetime64[ns]', freq='D')
```
With the result that it preserves somewhat more the dtype (although only specifically for datetime-like, it will not do it for any EA)
While looking into the deprecation of `get_values` (https://github.com/pandas-dev/pandas/pull/26409), I was wondering: do we want some method to actually get a "dense" version of the array, but with the exact same dtype? (so returning an EA in case the categories have an extension dtype)
And should we deprecate `to_dense()` ?
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} | 3 | 2019-05-15T11:13:04Z | 2021-07-22T20:50:16Z | 2021-02-24T16:39:00Z | NONE | null | #### Code Sample
```python
import pandas as pd
empty_df = pd.DataFrame([], columns=["a", "b"], index=pd.TimedeltaIndex([]))
resampled_df = empty_df.groupby("a").resample(rule=pd.to_timedelta("00:00:01")).mean()
print resampled_df
print resampled_df["b"]
```
#### Problem description
After grouping and subsequently resampling as shown above, the returned data frame seems to lose all its meta information, such as column names and what type of index it uses. This leads to a key error when trying to access columns of the data frame that were existent, but empty, before doing the grouping and resampling.
Perhaps somewhere in the code a "generic" empty data frame is returned without the attached information about column names and indices?
#### Expected Output
```
Empty DataFrame
Columns: [a, b]
Index: []
Series([], Name: b, dtype: object)
```
or similar, and `empty_df["b"] == resampled_df["b"]` would make sense to me.
#### Actual Output
```
Empty DataFrame
Columns: []
Index: []
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 2927, in __getitem__
indexer = self.columns.get_loc(key)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.py", line 2659, in get_lo
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'b'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Linux OS-release: 4.9.93-linuxkit-aufs machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C.UTF-8 LANG: C.UTF-8 LOCALE: None.None pandas: 0.24.2 pytest: None pip: 8.1.1 setuptools: 20.7.0 Cython: None numpy: 1.16.2 scipy: 0.17.0 pyarrow: None xarray: None IPython: None sphinx: 1.3.6 patsy: None dateutil: 2.8.0 pytz: 2018.9 blosc: None bottleneck: None tables: 3.5.1 numexpr: 2.6.9 feather: None matplotlib: 1.5.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml.etree: 3.5.0 bs4: 4.4.1 html5lib: 0.999 sqlalchemy: 1.3.1 pymysql: 0.7.2.None psycopg2: None jinja2: 2.10 s3fs: None fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2019-05-15T13:51:49Z | 2020-03-08T15:45:26Z | 2020-03-08T15:45:26Z | NONE | null | #### Code Sample
```python
>>> d = pd.DataFrame({'a': range(5), 'b': range(2,7), 'c': range(3,8)})
>>> d
a b c
0 0 2 3
1 1 3 4
2 2 4 5
3 3 5 6
4 4 6 7
>>> d.loc[0:2, 0:2] = 42 # note that loc is used with an integer slice! should this raise?
>>> d # this is somehow the expected result
a b c
0 42 42 3
1 42 42 4
2 42 42 5
3 3 5 6
4 4 6 7
>>> d.iloc[0:2, 0:2] # expected result
a b
0 42 42
1 42 42
>>> d.loc[0:2, 0:2] # this is the same syntax as above, but raises
...
TypeError: cannot do slice indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>
```
#### Problem description
I'd expect a symmetry in `d.loc.__getitem__` and `d.loc.__setitem__`. At the moment only __getitem__ raises if integer slices are passed. Usage of `.loc` was probably a mistake, instead `.iloc` should have been used. Even if it works correctly, it is confusing for users.
Probably this is simply a missing exception in the special case of `d.loc.__setitem__` being called with integer slices.
See #13831 for a very similar problems with `pd.Series`.
#### Expected Output
Assuming `d.loc.__getitem__` should raise for integer slices (as it does), then also `d.loc.__setitem__` should raise:
```
>>> d.loc[0:2, 0:2]
...
SomException: int & loc don't go together
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.6.4
pip: 9.0.1
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: 0.11.3
IPython: 5.5.0
sphinx: 1.7.9
patsy: 0.4.1+dev
dateutil: 2.8.0
pytz: 2019.1
blosc: 1.5.1
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.5.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details> | {
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