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MDU6SXNzdWU1NzkxMDc2MTM=
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improve read_excel performance
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[] | null | 3
| 2020-03-11T09:04:07Z
| 2020-05-08T00:47:33Z
| 2020-05-08T00:47:33Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
I have a spreadsheet of 600,000 rows and the read_excel function takes around 10mins to process.
Is there any way to improve the performance?
[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
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg2NTk2NjYw
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DOC: Clarify pivot_table fill_value description
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|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Clarify that `fill_value` gets used after performing the aggregation (i.e. it fills values in the resulting pivot table).
(Depending on the aggfunc and df, the result will be different if performing the filling before/after aggregation).
---
### Actually, why not remove it, requiring the user to more explicitly do `df.pivot_table(...).fillna(...)`?
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Allow choosing the utc timezone class in pd.to_datetime
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| 2021-07-30T03:23:32Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from datetime import datetime, timezone
import pandas as pd
dt1 = pd.to_datetime(datetime(2020, 3, 11), utc=True)
print(repr(dt1))
print(type(dt1.tz))
dt2 = pd.to_datetime(datetime(2020, 3, 11, tzinfo=timezone.utc))
print(repr(dt2))
print(type(dt2.tz))
# dragons here
print(dt1 - dt2)
```
outputs
```
Timestamp('2020-03-11 00:00:00+0000', tz='UTC')
<class 'pytz.UTC'>
Timestamp('2020-03-11 00:00:00+0000', tz='UTC')
<class 'datetime.timezone'>
TypeError: Timestamp subtraction must have the same timezones or no timezones
```
#### Problem description
There is no ability to specify which "UTC" the Timestamp should be. I suggest extending the interface of `pd.to_datetime()` to specify `utc_cls=pytz.UTC`.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-40-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.4
pytz : 2019.2
dateutil : 2.7.3
pip : 19.3.1
setuptools : 42.0.1
Cython : 0.29.14
pytest : 5.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.1
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.12
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg2NjY0MTQ0
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DOC: fix formatting / links of API refs in 1.0.2 whatsnew
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| 2020-03-11T14:00:18Z
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|
MEMBER
| null |
cc @TomAugspurger (did a few other clean-ups directly as well)
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MDU6SXNzdWU1NzkyNDMyOTM=
| 32,621
|
BUG: Replace in `string` series with NA
|
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| 6
| 2020-03-11T12:54:56Z
| 2020-04-10T17:40:51Z
| 2020-04-10T17:40:51Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
rep = {'one': '1', 'two': '2'}
a = pd.Series(['one', 'two'], dtype='string')
b = pd.Series(['one', 'two', np.nan])
c = pd.Series(['one', 'two', np.nan], dtype='string')
# A: this works
a.replace(to_replace=rep)
# B: this also works
b.replace(to_replace=rep)
# C: this throws exception
c.replace(to_replace=rep)
# TypeError: Cannot compare types 'ndarray(dtype=object)' and 'str'
```
#### Problem description
`pandas.Series.replace` cannot be used in series of type `string` that contain `<NA>`
#### Expected Output
I would expect `C` to output the same as `B` but with `string` dtype
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-112-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NzkyNTI4NTA=
| 32,622
|
df.index.is_unique inflates index memory
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[] | null | 5
| 2020-03-11T13:11:04Z
| 2020-03-11T14:55:34Z
| 2020-03-11T14:34:40Z
|
NONE
| null |
calling is_unique on an Int64Index seems to inflate the index memory.
```python
l = np.arange(9)
df = pd.DataFrame({'a':l, 'b':l, 'c':l})
print('shape', df.shape)
display(df.memory_usage(deep=True, index=True))
print()
print('drop one row')
df.drop(index=[1], inplace=True)
display(df.memory_usage(deep=True, index=True))
print()
print('is_unique: ', df.index.is_unique)
display(df.memory_usage(deep=True, index=True))
print()
print('drop another row')
df.drop(index=[3], inplace=True)
display(df.memory_usage(deep=True, index=True))
print()
print('is_unique: ', df.index.is_unique)
display(df.memory_usage(deep=True, index=True))
```
output:
```
shape (9, 3)
Index 80
a 36
b 36
c 36
dtype: int64
drop one row
Index 64
a 32
b 32
c 32
dtype: int64
is_unique: True
Index 384
a 32
b 32
c 32
dtype: int64
drop another row
Index 56
a 28
b 28
c 28
dtype: int64
is_unique: True
Index 376
a 28
b 28
c 28
dtype: int64
```
#### Problem description
any explanation why the index size grows after the call to index.is_unique?
#### Expected Output
no change in index size is expected
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 39.1.0
Cython: None
numpy: 1.17.0
scipy: None
pyarrow: None
xarray: None
IPython: 7.7.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.4
bs4: None
html5lib: None
sqlalchemy: 1.3.12
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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| 579,258,847
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MDU6SXNzdWU1NzkyNTg4NDc=
| 32,623
|
Pandas 1.0.1 unable to read excels it created itself
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[] | null | 2
| 2020-03-11T13:20:17Z
| 2020-03-23T14:01:26Z
| 2020-03-23T14:00:58Z
|
NONE
| null |
After creating lots of XLSX files with pandas.Excelwriter(), the same pandas is unable to read some of them:
`writer=pd.ExcelWriter(outfile)`
Then in a different script, when processing the same files with `pd.read_excel(outfile)`
on some (but not all) files I get error:
`xlrd.biffh.XLRDError: ZIP file contents not a known type of workbook
`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 20.0.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 5.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.10
tables : None
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 579,261,065
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MDU6SXNzdWU1NzkyNjEwNjU=
| 32,624
|
BUG in _unstack_multiple
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| 0
| 2020-03-11T13:23:40Z
| 2020-03-26T20:08:09Z
| 2020-03-26T20:08:09Z
|
NONE
| null |
#### Code Sample
Next code fail to do unstack.
```python
d = pd.DataFrame([[1,2,3],[2,2,3],[3,2,3],[4,2,3],[5,2,3]],
columns=pd.MultiIndex.from_tuples([[0, 0, 0], [0, 0, 1], [0, 0, 2]], names=['c1', 'c2', 'c3']),
index=pd.MultiIndex.from_tuples([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 0, 0, 1, 1],
[1, 0, 0, 0, 1, 0, 0]
],
names=['i1', 'i2', 'i3', 'i4', 'i5', 'i6', 'i7']))
e = d.unstack(['i2', 'i3', 'i4', 'i5', 'i6', 'i7'])
```
It rise exception IndexError with message "Too many levels: Index has only 2 levels, not 3"
#### Problem description
The reason semms to mistyping in https://github.com/pandas-dev/pandas/blob/master/pandas/core/reshape/reshape.py#L366 :
```python
clocs = [v if i > v else v - 1 for v in clocs]
```
I think it must be:
```python
clocs = [v if val > v else v - 1 for v in clocs]
```
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MDU6SXNzdWU1NzkyNjk2MDQ=
| 32,625
|
All rows | html_read
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| 2020-03-11T13:46:56Z
| 2020-03-11T13:46:47Z
|
NONE
| null |
I want to get a list of a html table, but it doesn't show all the rows. Example:
4 France 1784 NaN 33.0 NaN 12.0 1739 86.0 27.3
.. ... ... ... ... ... ... ... ... ...
115 Mongolia 1 NaN NaN NaN NaN 1 NaN 0.3
How do I get the all rows?
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MDExOlB1bGxSZXF1ZXN0Mzg2NzA0NTc0
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DOC: fix formatting / links of API refs in 1.0.2 whatsnew (#32620)
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| 2020-03-11T14:49:06Z
| 2020-03-11T14:48:58Z
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CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/pull/32620
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MDExOlB1bGxSZXF1ZXN0Mzg2NzEyNjE4
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|
Run travis tests on x86 platforms also
|
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| 2020-03-11T14:41:15Z
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NONE
| null |
Enable x86 tests for pandas under travis.
|
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MDU6SXNzdWU1NzkzMDc4NzE=
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|
Test warnings on 32bit build
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CONTRIBUTOR
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See https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=30468&view=logs&j=fd89438c-1fd3-5981-222e-115d06c89c2d&t=cbfaf76b-5a5a-5260-4a42-53e975ec839e&l=21
A few different classes
```
2020-03-11T13:55:49.8259424Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:49.8287841Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:50.2195055Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:50.2246416Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:50.6245936Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:50.6338017Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:51.0966286Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:51.1046280Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:51.5466495Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:51.5547513Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:52.0741981Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:52.0810908Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:52.5849681Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:52.5938509Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:53.1030877Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:53.1063054Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:53.4549733Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:53.4676881Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:54.0187525Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:54.0224731Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:54.5353725Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:54.5478480Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.1933213Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.2042430Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.8084672Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.8087073Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.8088705Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.8440796Z ##[warning]elementwise == comparison failed; this will raise an error in the future.
2020-03-11T13:55:55.8525038Z ##[warning]elementwise != comparison failed; this will raise an error in the future.
2020-03-11T13:56:56.1299225Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:56:56.1607719Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:56:56.1679813Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:56:56.1885542Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:56:56.2774684Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:56:56.2889289Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:24.3084605Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:24.3133681Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:24.3189697Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:24.3244160Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:24.3297429Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:24.3350495Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
2020-03-11T13:57:27.0324356Z ##[warning]In future, it will be an error for 'np.bool_' scalars to be interpreted as an index
2020-03-11T13:57:33.7975119Z ##[warning]In the future, 'NAT == x' and 'x == NAT' will always be False.
```
And a bunch from dateutil
```
2020-03-11T14:00:30.9974901Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:30.9975809Z return f(self, dt)
2020-03-11T14:00:30.9979425Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:30.9980081Z . _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:30.9992703Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:30.9993468Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:30.9994354Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:30.9994956Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:30.9995796Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:30.9996426Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:30.9996924Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:30.9997713Z Exception ignored in: 'pandas._libs.tslibs.tzconversion._tzlocal_get_offset_components'
2020-03-11T14:00:30.9998234Z Traceback (most recent call last):
2020-03-11T14:00:30.9999059Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:30.9999771Z return f(self, dt)
2020-03-11T14:00:31.0000596Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0001180Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0002026Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0002606Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0003437Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0004004Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0004844Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0005863Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0006370Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0006813Z Traceback (most recent call last):
2020-03-11T14:00:31.0007647Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0008198Z return f(self, dt)
2020-03-11T14:00:31.0009009Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0009595Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0010447Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0011031Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0011845Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0012419Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0013266Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0013884Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0014361Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0015156Z Exception ignored in: 'pandas._libs.tslibs.tzconversion._tzlocal_get_offset_components'
2020-03-11T14:00:31.0015672Z Traceback (most recent call last):
2020-03-11T14:00:31.0016485Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0017145Z return f(self, dt)
2020-03-11T14:00:31.0017977Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0018559Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0019417Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0020000Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0020809Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0021385Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0022702Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0023334Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0023832Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0024278Z Traceback (most recent call last):
2020-03-11T14:00:31.0025091Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0025636Z return f(self, dt)
2020-03-11T14:00:31.0026427Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0027100Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0028310Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0029204Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0030039Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0030619Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0031467Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0032079Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0032572Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0033369Z Exception ignored in: 'pandas._libs.tslibs.tzconversion._tzlocal_get_offset_components'
2020-03-11T14:00:31.0033878Z Traceback (most recent call last):
2020-03-11T14:00:31.0034711Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0035260Z return f(self, dt)
2020-03-11T14:00:31.0036051Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0036637Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0037499Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0038062Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0038896Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0039472Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0040297Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0040924Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0041415Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0041837Z Traceback (most recent call last):
2020-03-11T14:00:31.0042661Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0043208Z return f(self, dt)
2020-03-11T14:00:31.0043996Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0044580Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0045552Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0046114Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0046942Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0047523Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0048615Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0049269Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0049765Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0050559Z Exception ignored in: 'pandas._libs.tslibs.tzconversion._tzlocal_get_offset_components'
2020-03-11T14:00:31.0051082Z Traceback (most recent call last):
2020-03-11T14:00:31.0051916Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0052461Z return f(self, dt)
2020-03-11T14:00:31.0053275Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0053860Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0054792Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0055371Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0056201Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0056762Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0057604Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0058230Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0058713Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0059151Z Traceback (most recent call last):
2020-03-11T14:00:31.0059980Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0060517Z return f(self, dt)
2020-03-11T14:00:31.0061318Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0061898Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0062814Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0063351Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0064120Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0064637Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0065905Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0066581Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0067058Z OverflowError: timestamp out of range for platform time_t
2020-03-11T14:00:31.0067874Z Exception ignored in: 'pandas._libs.tslibs.tzconversion._tzlocal_get_offset_components'
2020-03-11T14:00:31.0068392Z Traceback (most recent call last):
2020-03-11T14:00:31.0168382Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 144, in fromutc
2020-03-11T14:00:31.0169102Z return f(self, dt)
2020-03-11T14:00:31.0169986Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 261, in fromutc
2020-03-11T14:00:31.0170570Z _fold = self._fold_status(dt, dt_wall)
2020-03-11T14:00:31.0171438Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/_common.py", line 196, in _fold_status
2020-03-11T14:00:31.0172179Z if self.is_ambiguous(dt_wall):
2020-03-11T14:00:31.0173007Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 254, in is_ambiguous
2020-03-11T14:00:31.0173591Z naive_dst = self._naive_is_dst(dt)
2020-03-11T14:00:31.0174451Z File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/dateutil/tz/tz.py", line 260, in _naive_is_dst
2020-03-11T14:00:31.0175063Z return time.localtime(timestamp + time.timezone).tm_isdst
2020-03-11T14:00:31.0175559Z OverflowError: timestamp out of range for platform time_t
```
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MDExOlB1bGxSZXF1ZXN0Mzg2NzQ0NDkz
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Backport PR #31524 on branch 1.0.x (BUG: non-iterable value in meta raise error in json_normalize)
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NONE
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Backport PR #31524: BUG: non-iterable value in meta raise error in json_normalize
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TST: revert parts of #32571
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| 2
| 2020-03-11T15:21:48Z
| 2020-03-11T16:40:59Z
| 2020-03-11T16:40:50Z
|
MEMBER
| null |
Should fix the broken CI builds.
|
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| 579,356,854
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MDExOlB1bGxSZXF1ZXN0Mzg2NzY1NjIw
| 32,631
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[WIP] Numpy dev 32bit (do not accept)
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[] | null | 1
| 2020-03-11T15:38:26Z
| 2020-03-31T19:08:07Z
| 2020-03-31T19:08:07Z
|
NONE
| null |
This will hopefull catch 32bit issues with newer numpy versions
Do NOT accept this at this point. Right now I am trying to figure out where a bug is originating and I need the tests to run.
|
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| 32,632
|
groupby nunique makes inplace replacement of NaN values to -9.223372036854776e18
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[] | null | 8
| 2020-03-11T16:59:21Z
| 2020-03-12T13:37:12Z
| 2020-03-11T22:31:58Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'x': [1, 1, 1, 2, 2, 2], 'y': [1, 2, np.nan, np.nan, np.nan, 6]})
print(df)
# Doesn't replace NaN's
df.groupby('x').sum()
print(df)
# Replaces NaN's
df.groupby('x').nunique()
print(df)
```
#### Problem description
Using DataFrameGroupBy.nunique on a dataframe makes an inplace replacement of NaN values. Simply applying the function (and not assigning it to anything) will replace existing NaN's with -9223372036854775808.
There are two distinct issues:
1. There is some connection to issue #16674 in comparing NaN with -9223372036854775808 (thanks @mroeschke)
2. The inplace replacement, which seems to be more problematic.
#### Expected Output
x y
0 1 1.0
1 1 2.0
2 1 NaN
3 2 NaN
4 2 NaN
5 2 6.0
x y
0 1 1.0
1 1 2.0
2 1 NaN
3 2 NaN
4 2 NaN
5 2 6.0
x y
0 1 1.0
1 1 2.0
2 1 NaN
3 2 NaN
4 2 NaN
5 2 6.0
#### My Output
x y
0 1 1.0
1 1 2.0
2 1 NaN
3 2 NaN
4 2 NaN
5 2 6.0
x y
0 1 1.0
1 1 2.0
2 1 NaN
3 2 NaN
4 2 NaN
5 2 6.0
x y
0 1 1.000000e+00
1 1 2.000000e+00
2 1 -9.223372e+18
3 2 -9.223372e+18
4 2 -9.223372e+18
5 2 6.000000e+00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 2.6.32-754.27.1.el6.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg2ODIyMDQy
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|
TYP: Remove _ensure_type
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| 7
| 2020-03-11T17:22:50Z
| 2020-03-12T11:21:36Z
| 2020-03-11T21:51:41Z
|
MEMBER
| null |
- [ ] closes #31925
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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pandas plotting - matplotlib 3.2.0 deprecation warning for rowNum/colNum attributes
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| 1
| 2020-03-11T17:48:09Z
| 2020-03-11T19:01:14Z
| 2020-03-11T19:00:23Z
|
NONE
| null |
With pandas 1.0.1 and matplotlib 3.2.0 in a conda Python 3.7.6 environment, I'm getting warnings about deprecation of rowNum and colNum attributes when using some pandas plotting routines.
Environment:
```
c> conda list "python|pandas|matplotlib|numpy|conda"
# packages in environment at C:\Users\me\Miniconda3\envs\forge:
#
# Name Version Build Channel
conda 4.8.2 py37_0
conda-package-handling 1.6.0 py37h62dcd97_0
ipython 7.13.0 py37h5ca1d4c_0 conda-forge
ipython_genutils 0.2.0 py37_0
matplotlib 3.2.0 1 conda-forge
matplotlib-base 3.2.0 py37h2981e6d_1 conda-forge
msys2-conda-epoch 20160418 1
numpy 1.18.1 py37h93ca92e_0
numpy-base 1.18.1 py37hc3f5095_1
numpydoc 0.9.2 py_0
pandas 1.0.1 py37h47e9c7a_0
python 3.7.6 h60c2a47_2
python-dateutil 2.8.1 py_0
python-jsonrpc-server 0.3.4 py_0
python-language-server 0.31.7 py37_0
python_abi 3.7 1_cp37m conda-forge
```
Warnings:
```
C:\Users\me\Miniconda3\envs\forge\lib\site-packages\pandas\plotting\_matplotlib\tools.py:298: MatplotlibDeprecationWarning:
The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.
layout[ax.rowNum, ax.colNum] = ax.get_visible()
C:\Users\me\Miniconda3\envs\forge\lib\site-packages\pandas\plotting\_matplotlib\tools.py:298: MatplotlibDeprecationWarning:
The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.
layout[ax.rowNum, ax.colNum] = ax.get_visible()
```
The above warnings were generated when running this code:
```
iris.plot(subplots=True)
plt.suptitle('Iris data by index', x=0.5, y=0.925)
# Display the plot
plt.show()
```
and this code:
```
df = pd.DataFrame(np.random.randn(250, 5), columns=['A','B','C','D', 'class'])
df['class'] = pd.Series(np.random.randint(0,2, 250))
cmap_tab10 = plt.get_cmap("tab10")
pd.plotting.scatter_matrix(df.iloc[:,0:4], color=cmap_tab10(df['class']), alpha=0.2)
for ax in plt.gcf().axes:
plt.sca(ax)
plt.ylabel(ax.get_ylabel(), rotation=0, labelpad=8, ha="right")
plt.grid(False)
plt.show()
```
In both of the above cases, the plots did draw as expected.
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MDExOlB1bGxSZXF1ZXN0Mzg2ODU0OTYx
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Backport PR #32561 on branch 1.0.x (Ensure valid Block mutation in SeriesBinGrouper.)
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NONE
| null |
Backport PR #32561: Ensure valid Block mutation in SeriesBinGrouper.
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MDExOlB1bGxSZXF1ZXN0Mzg2OTAxODU3
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TST: tighten check_categorical=False tests
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| 3
| 2020-03-11T20:20:36Z
| 2020-03-12T07:11:47Z
| 2020-03-12T02:42:47Z
|
MEMBER
| null |
This removes all check_categorical=False usages except for a) those in tests/util and b) a skipped json test test_latin_encoding (cc @WillAyd is that likely to be enabled in the foreseeable future?)
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Backport PR #32633: TYP: Remove _ensure_type
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CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/pull/32633
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MDU6SXNzdWU1Nzk1NzQ2ODk=
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API: _constructor and subclasses
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| 2020-03-11T22:11:30Z
| 2021-07-30T03:28:57Z
| null |
CONTRIBUTOR
| null |
Followup to https://github.com/pandas-dev/pandas/issues/31925.
Over there, we removed `_ensure_type` because it broke things like
```python
import pandas as pd
import pandas.testing as pdt
class MyDataFrame(pd.DataFrame):
pass
mdf = MyDataFrame()
df = pd.DataFrame()
# In DataFrame.reindex_like, the derived class is compared to
# the base class. The following line will throw
# 'AssertionError: <class 'pandas.core.frame.DataFrame'>'
mdf.reindex_like(df)
```
However, I don't think that example is very compelling. It's strange for MyDataFrame.reindex to return anything other than MyDataFrame. The root cause is MyDataFrame._constructor returning `DataFrame`, rather than `MyDataFrame`.
I think we should somehow get subclasses to have their _constructor return something that returns their subclass. We can
1. Require this, by doing a runtime check on the `DataFrame._constructor`
2. Change `DataFrame._constructor` to return `type(self)`
2 is nicer, but it requires that the subclasses `__init__` method be compatible enough with DataFrame's. It's *probably* safe to assume that, but it might be an API breaking change.
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DEPR: Categorical.to_dense
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MEMBER
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Ideally I'd want to keep the method and change the behavior to (an improved variant of) _internal_get_values, but so it goes.
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PERF: copy cached attributes on extension index shallow_copy
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| 0
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|
CONTRIBUTOR
| null |
Follow-up to #32568.
Copies ``._cache`` also when copying using ``.shallow_copy`` for:
* CategoricalIndex
* DatetimeIndex
* PeriodIndex
* DateTimeIndex
* IntervalIndex
After this PR, only ``MultiIndex._shallow_copy`` is missing this optimization. ``MultiIndex._shallow_copy`` is a bit special and might require a refactor so I'd like to take that in a seperate PR.
Example performance improvement:
```pythn
>>> idx = pd.CategoricalIndex(np.arange(100_000))
>>> %timeit idx.get_loc(99_999)
4.46 µs ± 62.6 ns per loop # master and this PR
>>> %timeit idx._shallow_copy().get_loc(99_999)
4.19 ms ± 117 µs per loop # master
8.58 µs ± 254 ns per loop # this PR
```
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DEPR: Index.ravel behavior
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| 2021-11-20T23:23:14Z
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|
MEMBER
| null |
The future behavior should match Series.ravel.
- [x] closes #19956
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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fix infer_freq raises section
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CONTRIBUTOR
| null |
Fixes the raises section of the `infer_freq` doc string. Seems small enough to skip a whats new entry.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: trim unnecessary checks
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ENH: implement EA.size
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MEMBER
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I was surprised this didnt already exist.
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Simplified get_blkno_indexers
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MEMBER
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This function is the last that appears to be throwing warnings in #32163
This PR doesn't solve that, but simplifies the logic to make it easier to grok
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MDExOlB1bGxSZXF1ZXN0Mzg2OTk2Mzky
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MultiIndex union/intersection with non-object other
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| 2
| 2020-03-12T01:38:42Z
| 2020-03-17T01:39:37Z
| 2020-03-17T01:37:11Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
ATM if we call MultiIndex.union with e.g. a Float64Index, we get `ValueError: Buffer dtype mismatch, expected 'Python object' but got 'double'` from `lib.fast_unique_multiple`. This PR changes that to raise NotImplementedError with a reasonable message, but we could alternatively return `self.to_flat_index().union(other)`
I prefer making the user do to_flat_index on their own because this seems like something that would often be reached by accident.
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Backport PR #32591 on branch 1.0.x (REG: dt64 shift with integer fill_value)
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| 0
| 2020-03-12T02:26:57Z
| 2020-03-12T04:26:18Z
| 2020-03-12T04:26:18Z
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NONE
| null |
Backport PR #32591: REG: dt64 shift with integer fill_value
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Backport PR #32040 on branch 1.0.x (BUG: GroupBy aggregation of DataFrame with MultiIndex columns breaks with custom function)
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| 2020-03-12T02:33:39Z
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NONE
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Backport PR #32040: BUG: GroupBy aggregation of DataFrame with MultiIndex columns breaks with custom function
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Backport PR #32223: BUG: Fix DateFrameGroupBy.mean error for Int64 dtype
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| 1
| 2020-03-12T02:46:56Z
| 2020-03-12T15:04:33Z
| 2020-03-12T04:28:27Z
|
MEMBER
| null |
cc @jreback
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MDExOlB1bGxSZXF1ZXN0Mzg3MDE3NDc0
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|
BUG: DTI/TDI/PI get_indexer_non_unique with incompatible dtype
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| 6
| 2020-03-12T03:17:52Z
| 2020-03-23T16:12:20Z
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MEMBER
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I'm pretty sure we can share more code here, but PeriodEngine needs some work first.
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MDU6SXNzdWU1Nzk2NjY2MTc=
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DataFrame with Int64 columns casts to float64 with .max()/.min()
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| 3
| 2020-03-12T03:23:23Z
| 2020-07-16T09:32:38Z
| 2020-07-16T09:32:37Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
int64_info = np.iinfo("int64")
s = pd.Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype())
df = pd.DataFrame({"Int64": s})
df.max()
Int64 9.223372e+18
dtype: float64
```
#### Problem description
`pd.Int64` data is converted to `np.float64` in certain reduction operations on `pd.DataFrame`. This causes data corruption, as `pd.Int64` is intended to avoid this exact issue.
#### Expected Output
`df.max()` should probably return a `pd.Series` of `dtype='object'` wrapping a `pd.Int64` value.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 27ad779713f1d54b83547db5465bec4245d279cd
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-29-generic
Version : #31-Ubuntu SMP Fri Jan 17 17:27:26 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+779.g27ad77971
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.4.0.dev0+62.g8ac3a4c8
fastparquet : 0.3.2
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.14.1
xlrd : None
xlwt : None
numba : 0.48.0
```
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg3MTA4MDgy
| 32,652
|
BUG: process Int64 as ints for preservable ops, not as float64
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| 2020-03-12T08:35:13Z
| 2020-04-18T02:46:09Z
| 2020-04-18T02:46:09Z
|
CONTRIBUTOR
| null |
Calling `.min()` or `.max()` on `pd.Int64` data returns integers, but uses `float64` as an intermediate representation, leading to data corruption.
This PR attempts to whitelist them to be processed entirely as `Int64`s. Also add a test that verifies this behavior, as existing tests failed to detect the bug.
- [x] closes #32651
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzg3MTIzMTc5
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DOC: Fix EX02 in pandas.Series.memory_usage
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| 2020-03-16T23:59:44Z
| 2020-03-16T23:59:43Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
```
################################################################################
################################## Validation ##################################
################################################################################
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Feature request: Allow the subset argument of dropna and drop_duplicates to take a single label
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[] | null | 2
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| 2021-07-30T03:29:41Z
| null |
NONE
| null |
This works:
```
df.drop(columns='My Col')
```
But neither of these do:
```
df.dropna(subset='My Col')
df.drop_duplicates(subset='My Col')
df.duplicated(subset='My Col')
```
all of which instead should be called with `subset=['My Col']`.
This is a feature request for the three latter functions to allow the subset argument to take a single label.
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Error in py_convert_pandas_df(x)
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NONE
| null |
#### Problem description
Hello!
I have a fresh Rstudio where i want to work with some data which come from python scripts. I try to use reticulate to source the following python script:
```python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import functools as ft
pd.set_option('display.float_format', lambda x: '%.3f' % x)
pd.set_option('display.max_columns', 10)
def get_table(df, table_ref_no = all, col_ref_no = all, row_ref_no = all, \
col_name = "", cols = ["Year", "Client", "Amount_thuf"], add_cols = []):
sub_table = df
sub_table = sub_table.reset_index().drop(columns = ["index"])
# Selection
if (cols != all):
sub_table = sub_table[cols + add_cols]
# Aggregation
if (len(row_ref_no) > 1):
sub_table = pd.DataFrame(sub_table.groupby(["Year", "Client"]).sum()).reset_index()
# Renaming
if (col_name != ""):
sub_table = sub_table.rename(columns = {"Amount_thuf" : col_name})
return(sub_table)
excel_file_path = "C:/Users/nori/Projects/data/"
excel_file_name = "Data2016-2018_final.xlsx"
excel_sheet_name = "Total"
orig_df = pd.read_excel(excel_file_path + excel_file_name, sheet_name = excel_sheet_name)
dfo = orig_df
dfo["Amount_thuf"] = pd.to_numeric(dfo["Amount_thuf"], errors = "coerce")
df = dfo
tmp5001 = get_table(df, "Stat", ["C0001"], ["R0001"], "Eredmenytartalek")
tmp5002 = get_table(df, "Stat", ["C0001"], ["R0002"], "Karingadozasi_tartalek")
tmp5003 = get_table(df, "Stat", ["C0001"], ["R0003"], "Nagykarok_tartaleka")
tmp5004 = get_table(df, "Stat", ["C0001"], ["R0004"], "Adozas_utani_eredmeny")
tmp3002 = get_table(dfo, "S05", ["C0200", "C0300"], ["R0210", "R1510"], "Brutto_MSZD_direkt")
tmp3021 = get_table(dfo, "S05", ["C0200"], ["R0220"], "Nem-elet_MSZD_aranyos_VB")
dfs500 = [tmp5001, tmp5002, tmp5003, tmp5004, tmp3002, tmp3021]
print(dfs500)
```
But when I call the following R code in Rstudio, I always got an error:
Browse[1]> reticulate::source_python(paste0(project_folder,"data.py"))
Error in py_convert_pandas_df(x) :
SystemError: <built-in method item of numpy.ndarray object at 0x0000012709211440> returned a result with an error set
Detailed traceback:
File "C:\Users\nori\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py", line 791, in iteritems
for k in self.columns:
Browse[1]>
Everything (pandas, numpy, python, r, rstudio) is up to date
Thanks in advance,
Nora
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DOC: fix announce formtting
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| 2020-03-12T12:04:27Z
| 2020-03-12T13:16:04Z
| 2020-03-12T13:16:00Z
|
CONTRIBUTOR
| null |
Master:
```
Contributors
============
A total of 143 people contributed patches to this release. People with a
"+" by their names contributed a patch for the first time.
* 3vts +
* A Brooks +
* Abbie Popa +
* Achmad Syarif Hidayatullah +
```
PR
```
Contributors
============
A total of 143 people contributed patches to this release. People with a
"+" by their names contributed a patch for the first time.
* 3vts +
* A Brooks +
* Abbie Popa +
```
I think the offset computed by textwrap.dedent changed. With fstrings it's calculated after the values are interpolated. We want it to be computed on the templated version.
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Backport PR #32544 on branch 1.0.x (BUG: pd.ExcelFile closes stream on destruction)
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NONE
| null |
Backport PR #32544: BUG: pd.ExcelFile closes stream on destruction
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DOC: Organize regressions
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| 2020-03-12T12:23:09Z
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CONTRIBUTOR
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Backport PR #32656 on branch 1.0.x (DOC: fix announce formtting)
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Backport PR #32656: DOC: fix announce formtting
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Backport PR #32490: BUG: Fix bug, where BooleanDtype columns are conv…
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CONTRIBUTOR
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…erted to Int64
https://github.com/pandas-dev/pandas/pull/32490
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Backport PR #32658 on branch 1.0.x (DOC: Organize regressions)
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| 2020-03-12T14:39:01Z
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NONE
| null |
Backport PR #32658: DOC: Organize regressions
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MDExOlB1bGxSZXF1ZXN0Mzg3MjkxOTYz
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REF: values-> _values
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| 2020-03-14T03:14:32Z
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|
MEMBER
| null |
For the affected Index subclasses .values and ._values are the same
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MDU6SXNzdWU1ODAwNzczOTE=
| 32,663
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read_sql_query convert TEXT looking like floats to float
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[] | null | 1
| 2020-03-12T16:56:46Z
| 2020-03-17T09:55:51Z
| 2020-03-17T09:55:50Z
|
NONE
| null |
#### Code Sample, you need to create a database with "float looking" strings.
```python
import pandas as pd
query = "SELECT MYSTR FROM TABLE"
df = pd.read_sql_query(query, engine)
df.info()
```
returns
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 524 entries, 0 to 523
Data columns (total 1 columns):
MYSTR 524 non-null float64
dtypes: float64(1)
```
#### Problem description
I have a MySQL table with a column saved as datatype `TEXT`. These strings may be interpreted as floats because they contain only digits and dots. If I have the right filter happening, the query may return ONLY float-looking strings, meaning either looking like normal integers or integers separated by a decimal dot.
read_sql_query converts without asking anything looking like a float to a float. This is unfortunate and breaks the application.
It is not a solution to reconvert back to string with df.astype(str). Indeed, a four-digit string will be returned as a 6-character string, e.g. `4906 `will return as `4906.0`. Also, a string having left trailing zeros will have these zeros disappear.
On another machine, the column stays a string column. It may be a version difference, either python (good: 3.6 vs bad: 3.7), or sqlalchemy (good: 1.3.12 bad: 1.3.5).
I could not update sqlalchemy to a the higher version using `conda update sqlalchemy`, it stays at 1.3.5.
The python and pandas have however a higher version (6.7 & 0.25). I tried reverting to 0.23.4 as on the functional machine, however the bug (or "feature"?) was still there.
Is python 3.7 the root cause?
#### Expected Output
TEXT data type should stay strings in pandas.
#### Output of ``pd.show_versions()``
Machine converting to float:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.5
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
Machine keeping string format:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.10.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 5.0.1
pip: 19.1.1
setuptools: 44.0.0.post20200106
Cython: 0.28.2
numpy: 1.17.4
scipy: 1.3.2
pyarrow: None
xarray: 0.11.3
IPython: 7.11.1
sphinx: 1.7.9
patsy: 0.5.1
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: 1.3.1
tables: 3.6.1
numexpr: 2.7.0
feather: None
matplotlib: 3.1.1
openpyxl: 2.5.4
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.2.7
lxml: 4.4.2
bs4: 4.8.2
html5lib: 1.0.1
sqlalchemy: 1.3.12
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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MDU6SXNzdWU1ODAxMTAyODM=
| 32,664
|
Please document expected behavior when default arguments are used.
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| 2020-03-12T17:45:19Z
| 2020-04-07T00:23:21Z
| 2020-04-07T00:23:21Z
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NONE
| null |
This is a documentation request.
The docs for read_csv currently include the following:
index_col:
int, str, sequence of int / str, or False, default None
Column(s) to use as the row labels of the DataFrame, either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used.
Note: index_col=False can be used to force pandas to not use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line.
#### Problem description
The docs explain how read_csv works in every case EXCEPT the default, which is the most common. Please document what happens when index_col=None is passed.
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MDExOlB1bGxSZXF1ZXN0Mzg3Mzk2NTU3
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|
BUG: Fix to_json when converting Period column
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[] | null | 11
| 2020-03-12T18:10:32Z
| 2020-08-06T11:19:32Z
| 2020-08-06T11:19:32Z
|
NONE
| null |
AttributeError when trying to access frequency string ``freqstr`` directly on Series of ``Period`` type.
Modified to get it through ``dtype`` of Series, which is known to be ``Period``.
- [X] closes #31917
- [X] fix #31917
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
```python
import pandas as pd
test = pd.Series(pd.period_range('1/1/2011', freq='B', periods=3))
test.to_json(None, orient='table')
>>> '{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"values","type":"datetime","freq":"B"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"values":{"day":3,"dayofyear":3,"daysinmonth":31,"freqstr":"B","is_leap_year":false,"month":1,"ordinal":10697,"qyear":2011,"start_time":"2011-01-03T00:00:00.000Z","week":1,"weekofyear":1}},{"index":1,"values":{"day":4,"dayofyear":4,"daysinmonth":31,"freqstr":"B","is_leap_year":false,"month":1,"ordinal":10698,"qyear":2011,"start_time":"2011-01-04T00:00:00.000Z","week":1,"weekofyear":1}},{"index":2,"values":{"day":5,"dayofyear":5,"daysinmonth":31,"freqstr":"B","is_leap_year":false,"month":1,"ordinal":10699,"qyear":2011,"start_time":"2011-01-05T00:00:00.000Z","week":1,"weekofyear":1}}]}'
```
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implemented additionally functionality of formatters_col in to_latex
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[] | null | 11
| 2020-03-12T18:11:02Z
| 2020-11-01T04:37:40Z
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NONE
| null |
Not sure what I should do for the whatsnew entry, bit confused about versioning.
- [ x ] closes #26278
- [ x] tests added / passed
- [ x] passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Add index=False option to_markdown()
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| 2020-07-15T12:29:54Z
| 2020-07-15T12:29:54Z
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NONE
| null |
#### Problem description
This is a feature request to add an optional `index=False` argument to the new `to_markdown()` dataframe method. This would produce a markdown table without the initial unnamed index column.
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BUG: DatetimeArray._from_sequence accepting bool dtype
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MEMBER
| null |
- [ ] closes #xxxx
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Identified when making the change this makes in Series.
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PERF: MultiIndex._shallow_copy
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| 7
| 2020-03-12T22:48:47Z
| 2020-03-29T17:48:22Z
| 2020-03-14T16:29:24Z
|
CONTRIBUTOR
| null |
- [x] xref #28584, #32568, #32640
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Improves performance of ``MultiIndex._shallow_copy``. Example:
```python
>>> n = 100_000
>>> df = pd.DataFrame({'a': range(n), 'b': range(1, n+1)})
>>> mi = pd.MultiIndex.from_frame(df)
>>> mi.is_lexsorted()
True
>>> mi.get_loc(mi[0]) # also sets up the cache
>>> %timeit mi._shallow_copy().get_loc(mi[0])
8.56 ms ± 127 µs per loop # master
75.1 µs ± 2.3 µs per loop # this PR, first commit
46.9 µs ± 792 ns per loop # this PR, second commit
```
Also adds tests for ``_shallow_copy`` for all index types. This ensures that this issue has been resolved for all index types.
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TST: reintroduce check_series_type in assert_series_equal
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| 2020-03-16T22:43:08Z
| 2020-03-16T22:43:07Z
|
CONTRIBUTOR
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Re-introduced `check_series_type` in `assert_series_equal` which was recently removed in #32513. It was a part of public API which is not used internally, but it is used elsewhere (like geopandas). Discussion in #32513 suggests that it should be put back where it was.
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REF: put all post-processing at end of DataFrame._reduce
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| 1
| 2020-03-13T02:00:15Z
| 2020-03-14T03:04:51Z
| 2020-03-14T03:03:17Z
|
MEMBER
| null |
there's some nasty try/except logic in DataFrame._reduce. This is simplifying the code _around_ that.
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Avoid bare pytest.raises in dtypes/test_dtypes.py
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| 2
| 2020-03-13T02:10:55Z
| 2020-03-21T00:41:31Z
| 2020-03-18T09:12:01Z
|
CONTRIBUTOR
| null |
* [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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API: should values_for_factorize and _from_factorized round-trip missing values?
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| 15
| 2020-03-13T02:47:59Z
| 2020-09-05T19:02:57Z
| 2020-09-05T19:02:57Z
|
MEMBER
| null |
(fixtures make this so much harder to give a copy/pasteable example)
```
from pandas.tests.extension.json.test_json import *
dtype = JSONDtype()
data = make_data()
while len(data[0]) == len(data[1]):
data = make_data()
data = JSONArray(data)
values = data._values_for_factorize()[0]
result = type(data)._from_factorized(values, data)
assert len(values) == len(result) # <-- nope!
```
Am I wrong in thinking this assertion should hold? If we had a .equals method, i'd strengthen this assertion to `assert result.equals(data)`
cc @TomAugspurger @WillAyd
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MDU6SXNzdWU1ODAzNTE4MDI=
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|
why would you call it pandas?
|
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| 2020-03-13T05:43:26Z
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NONE
| null |
why would you guys call it pandas, just because you guys love panda?
|
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|
FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead. import pandas.util.testing as tm
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| 2020-03-13T06:36:39Z
| 2020-03-22T02:19:28Z
| 2020-03-17T07:01:40Z
|
NONE
| null |
#### Problem description
Why am i getting this warning? i am just creating a dataframe using DataFrame function
#### Expected Output
#### Output of ``pd.show_versions()``
version is 1.0.1
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|
Feature request: Allow `how` and `subset` arguments for `DataFrame.isna()` and `notna()`
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| 2020-03-13T08:29:45Z
| 2021-07-30T03:31:04Z
| 2021-07-30T03:31:04Z
|
NONE
| null |
Something like this would be very useful, and would be consistent with the arguments of `DataFrame.dropna()`
```
def isna(df, how='any', subset: Collection[str] = ()) -> pd.Series:
"""
Like DataFrame.isna(), but allows `how` and `subset` arguments
"""
if not subset:
subset = df.columns
elif not (isinstance(subset, Collection) and not isinstance(subset, str)):
subset = (subset, )
if how == 'any':
op = np.logical_or
elif how == 'all':
op = np.logical_and
else:
raise ValueError(f"`how={how}` not one of 'any', 'all'")
return op.reduce([df[col].isna() for col in subset])
def notna(df, how='any', subset: Collection[str] = ()) -> pd.Series:
"""
Like DataFrame.isna(), but allows `how` and `subset` arguments
"""
if not subset:
subset = df.columns
elif not (isinstance(subset, Collection) and not isinstance(subset, str)):
subset = (subset, )
if how == 'any':
op = np.logical_or
elif how == 'all':
op = np.logical_and
else:
raise ValueError(f"`how={how}` not one of 'any', 'all'")
return op.reduce([df[col].notna() for col in subset])
```
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CLN: Remove PY2 compat code
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
Benchmarks:
#### Master:
```
In [1]: import pandas as pd
In [2]: s = pd.Series(list("ABCA"))
In [3]: %timeit pd.get_dummies(s)
482 µs ± 752 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
---
#### PR:
```
In [1]: import pandas as pd
In [2]: s = pd.Series(list("ABCA"))
In [3]: %timeit pd.get_dummies(s)
479 µs ± 2.43 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
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to_pickle does not save accessor's properties
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| 2020-03-13T11:19:00Z
| 2021-07-30T03:37:50Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
class ServiceMetadataClass(object):
"""Stores Metadata from Service endpoint"""
url = None
info = None
renderer = None
def __init__(self, url=None, renderer=None, original_fields=None):
self.url = url
self.renderer = renderer
self.original_fields = original_fields
###########################################################################
@pd.api.extensions.register_dataframe_accessor("service")
class ServiceAccessor(object):
_metadata = None
def __init__(self, data):
self._data = data
@property
def meta(self):
if self._metadata is None:
self._metadata = ServiceMetadataClass()
return self._metadata
```
#### Problem description
The to_pickle/from_pickle does not pickle the state of the accessor object.
Even if the ```__getstate__``` and ```___setstate__``` are defined, they never get called.
#### Expected Output
The assert statement should be True, not False. df1's url is None, where it should be a string.
```python
df = pd.DataFrame(data=[[1,2,3]], columns=['a', 'b', 'c'])
df.service.meta.url = "https://www.google.com"
fp = r"c:/temp/atestdata.pkl"
df.to_pickle(fp)
df1 = pd.read_pickle(fp)
assert df1.service.meta.url == df.service.meta.url #FAIL
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.0
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0Mzg3NzgxNjk0
| 32,679
|
Fix wrong docstring in qcut
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CONTRIBUTOR
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1ODA2MjAxMzc=
| 32,680
|
`pandas.Index.slice_locs` when label not found
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[] | null | 0
| 2020-03-13T14:01:24Z
| 2020-03-13T17:08:51Z
| null |
NONE
| null |
#### Problem description
The documentation for `pandas.Index.slice_locs` does not specify what happens if the label(s) are not found. Internally it seems to be using `pandas.Index.get_slice_bound`, so maybe a reference to this method should be added.
Another thing that would be nice is if `pandas.Index.slice_locs` worked like `pandas.Index.get_loc`, where the method to find the index can be specified: exact, nearest, etc.
#### Expected Output
I would expect that the documentation for `pandas.Index.slice_locs` gives details about what happens if the label is not found. Details about the `kind` argument would also be nice.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-112-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
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feather : None
xlsxwriter : None
lxml.etree : None
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jinja2 : 2.11.1
IPython : 7.8.0
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bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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PERF: Using Numpy C-API arange
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| 9
| 2020-03-13T14:39:29Z
| 2020-03-18T12:47:08Z
| 2020-03-14T15:54:56Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This PR was opened as @jbrockmendel suggested (ref https://github.com/pandas-dev/pandas/pull/32177#discussion_r382923663)
---
### Benchmarks:
#### Master:
```
In [1]: import pandas._libs.internals as internals
In [2]: %timeit internals.BlockPlacement(slice(1_000_000)).as_array
1.55 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
---
#### PR:
```
In [1]: import pandas._libs.internals as internals
In [2]: %timeit internals.BlockPlacement(slice(1_000_000)).as_array
1.46 ms ± 3.55 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
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|
Add track_times flag for HDFStore put method
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| 3
| 2020-03-13T15:10:45Z
| 2020-05-14T12:38:24Z
| 2020-05-14T12:38:24Z
|
NONE
| null |
When adding table to HDF using put method of HDFStore, it would be good to have option to set track_times flag used in pytables in method create_table. Thanks to this flag it is possible to stop tracking of times the file was changed. This is causing problems when versioning the HDF files and using checksums.
For this, it is necessary to propagate this flag from: https://github.com/pandas-dev/pandas/blob/master/pandas/io/pytables.py#L973 to this line: https://github.com/pandas-dev/pandas/blob/master/pandas/io/pytables.py#L4144
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MDExOlB1bGxSZXF1ZXN0Mzg3ODQxODQy
| 32,683
|
pd.Timestamp constructor ignores missing arguments
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| 15
| 2020-03-13T15:33:15Z
| 2020-07-17T11:21:59Z
| 2020-07-17T11:21:59Z
|
NONE
| null |
Hello. The PR fixes https://github.com/pandas-dev/pandas/issues/31930
Please review and merge if you think it is good enough :)
Regards,
Artyom.
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MDExOlB1bGxSZXF1ZXN0Mzg3ODg0NjA4
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|
BUG: Series.__getitem__ with downstream scalars
|
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| 3
| 2020-03-13T17:02:56Z
| 2020-03-19T01:38:15Z
| 2020-03-19T00:02:07Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @spencerclark I think this fixes a subset of the issues reported in https://github.com/pydata/xarray/issues/3751, can you confirm?
@jorisvandenbossche IIRC geopandas scalars not being recognized by lib.is_scalar has caused some issues there; does this address any of those?
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MDU6SXNzdWU1ODA3MzU1OTM=
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|
Regression in artihmetic ops with alignment and special cases
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| 3
| 2020-03-13T17:12:42Z
| 2020-03-17T15:41:09Z
| 2020-03-17T15:41:09Z
|
CONTRIBUTOR
| null |
I think 1.0.2 has a regression in arithmetic when we align but the result of `op(a, NA)` isn't NA. For example 1**np.nan should be 1.
Previous
```python
In [3]: pd.__version__
Out[3]: '1.0.1'
In [4]: pd.DataFrame({"A": [0, 1, 2]}) ** pd.DataFrame(index=[0, 1, 2])
Out[4]:
A
0 NaN
1 1.0
2 NaN
```
1.0.2 / master
```python
In [1]: import pandas as pd
In [2]: pd.DataFrame({"A": [0, 1, 2]}) ** pd.DataFrame(index=[0, 1, 2])
Out[2]:
A
0 NaN
1 NaN
2 NaN
```
Most likely from https://github.com/pandas-dev/pandas/pull/31679. cc @jbrockmendel if you have a chance to look (I won't today).
Are there any other special cases?
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MDU6SXNzdWU1ODA3ODExNTM=
| 32,686
|
Unable to store NaT in parquet format.
|
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| 2020-03-13T23:13:47Z
| 2020-03-13T23:13:47Z
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NONE
| null |
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame({"date": ["", "2019-05-01"]})
df.date = pd.to_datetime(df.date).dt.date
df.to_parquet("issue_NaT_parquet")
```
#### Problem description
The above gives me the following error:
<details>
> ---------------------------------------------------------------------------
> TypeError Traceback (most recent call last)
> <ipython-input-11-432405bef6ac> in <module>
> ----> 1 df.to_parquet("test.parquet")
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
> 212 else:
> 213 kwargs[new_arg_name] = new_arg_value
> --> 214 return func(*args, **kwargs)
> 215
> 216 return cast(F, wrapper)
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\core\frame.py in to_parquet(self, path, engine, compression, index, partition_cols, **kwargs)
>
> 2114 index=index,
> 2115 partition_cols=partition_cols,
> -> 2116 **kwargs,
> 2117 )
> 2118
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\io\parquet.py in to_parquet(df, path, engine, compression, index, partition_cols, **kwargs)
> 262 index=index,
> 263 partition_cols=partition_cols,
> --> 264 **kwargs,
> 265 )
> 266
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pandas\io\parquet.py in write(self, df, path, compression, coerce_timestamps, index, partition_cols,
> **kwargs)
> 99 from_pandas_kwargs["preserve_index"] = index
> 100
> --> 101 table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
> 102 if partition_cols is not None:
> 103 self.api.parquet.write_to_dataset(
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pyarrow\table.pxi in pyarrow.lib.Table.from_pandas()
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pyarrow\pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)
>
> 573 if nthreads == 1:
> 574 arrays = [convert_column(c, f)
> --> 575 for c, f in zip(columns_to_convert, convert_fields)]
> 576 else:
> 577 from concurrent import futures
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pyarrow\pandas_compat.py in <listcomp>(.0)
> 573 if nthreads == 1:
> 574 arrays = [convert_column(c, f)
> --> 575 for c, f in zip(columns_to_convert, convert_fields)]
> 576 else:
> 577 from concurrent import futures
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pyarrow\pandas_compat.py in convert_column(col, field)
> 558
> 559 try:
> --> 560 result = pa.array(col, type=type_, from_pandas=True, safe=safe)
> 561 except (pa.ArrowInvalid,
> 562 pa.ArrowNotImplementedError,
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pyarrow\array.pxi in pyarrow.lib.array()
>
> ~\AppData\Local\Continuum\miniconda3\lib\site-packages\pyarrow\array.pxi in pyarrow.lib._ndarray_to_array()
>
> TypeError: an integer is required (got type datetime.date)
</details>
I think this is an issue with pyarrow, I post here just for reference.
#### Expected Output
Parquet with null values mixed with date values.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg3OTM5OTY3
| 32,687
|
TST: Parametrize in pandas/tests/internals/test_internals.py
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| 6
| 2020-03-13T19:27:07Z
| 2020-03-19T17:54:09Z
| 2020-03-19T17:40:37Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzg3OTQwODY2
| 32,688
|
Accept dateutil frequency constants in date_range
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NONE
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Tests fail on 32bit archs with Python 3.8 and pandas 1.0.2
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[] | null | 1
| 2020-03-13T19:39:52Z
| 2020-03-13T19:45:08Z
| 2020-03-13T19:45:07Z
|
NONE
| null |
Apart from Test warnings noted [here](https://github.com/pandas-dev/pandas/issues/32628), we face plain test failures, that prevent packaging pandas for ix86 systems with Python 3.8.2 at least:
```
[ 1167s] =================================== FAILURES ===================================
[ 1167s] ______________ test_maybe_promote_int_with_int[int8-32768-int32] _______________
[ 1167s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1167s]
[ 1167s] dtype = dtype('int8'), fill_value = 32768, expected_dtype = dtype('int32')
[ 1167s]
[ 1167s] @pytest.mark.parametrize(
[ 1167s] "dtype, fill_value, expected_dtype",
[ 1167s] [
[ 1167s] # size 8
[ 1167s] ("int8", 1, "int8"),
[ 1167s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1167s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int8", -1, "int8"),
[ 1167s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1167s] # keep signed-ness as long as possible
[ 1167s] ("uint8", 1, "uint8"),
[ 1167s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1167s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1167s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1167s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1167s] # max of uint8 cannot be contained in int8
[ 1167s] ("uint8", -1, "int16"),
[ 1167s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 16
[ 1167s] ("int16", 1, "int16"),
[ 1167s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1167s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int16", -1, "int16"),
[ 1167s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint16", 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint16", -1, "int32"),
[ 1167s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1167s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 32
[ 1167s] ("int32", 1, "int32"),
[ 1167s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1167s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int32", -1, "int32"),
[ 1167s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1167s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint32", 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint32", -1, "int64"),
[ 1167s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 64
[ 1167s] ("int64", 1, "int64"),
[ 1167s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int64", -1, "int64"),
[ 1167s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint64", 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint64", -1, "object"),
[ 1167s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1167s] ],
[ 1167s] )
[ 1167s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1167s] dtype = np.dtype(dtype)
[ 1167s] expected_dtype = np.dtype(expected_dtype)
[ 1167s]
[ 1167s] # output is not a generic int, but corresponds to expected_dtype
[ 1167s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1167s]
[ 1167s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1167s]
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1167s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1167s] _assert_match(result_fill_value, expected_fill_value)
[ 1167s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1167s]
[ 1167s] result_fill_value = 32768, expected_fill_value = 32768
[ 1167s]
[ 1167s] def _assert_match(result_fill_value, expected_fill_value):
[ 1167s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1167s] res_type = type(result_fill_value)
[ 1167s] ex_type = type(expected_fill_value)
[ 1167s] if res_type.__name__ == "uint64":
[ 1167s] # No idea why, but these (sometimes) do not compare as equal
[ 1167s] assert ex_type.__name__ == "uint64"
[ 1167s] elif res_type.__name__ == "ulonglong":
[ 1167s] # On some builds we get this instead of np.uint64
[ 1167s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1167s] assert res_type(0).itemsize == 8
[ 1167s] assert ex_type == res_type or ex_type == np.uint64
[ 1167s] else:
[ 1167s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1167s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1167s] E AssertionError: assert (<class 'numpy.intc'> == <class 'numpy.int32'>
[ 1167s] E -<class 'numpy.intc'>
[ 1167s] E +<class 'numpy.int32'> or 'intc' == 'int32'
[ 1167s] E - intc
[ 1167s] E + int32)
[ 1167s]
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1167s] _____________ test_maybe_promote_int_with_int[uint8-65536-uint32] ______________
[ 1167s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1167s]
[ 1167s] dtype = dtype('uint8'), fill_value = 65536, expected_dtype = dtype('uint32')
[ 1167s]
[ 1167s] @pytest.mark.parametrize(
[ 1167s] "dtype, fill_value, expected_dtype",
[ 1167s] [
[ 1167s] # size 8
[ 1167s] ("int8", 1, "int8"),
[ 1167s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1167s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int8", -1, "int8"),
[ 1167s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1167s] # keep signed-ness as long as possible
[ 1167s] ("uint8", 1, "uint8"),
[ 1167s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1167s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1167s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1167s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1167s] # max of uint8 cannot be contained in int8
[ 1167s] ("uint8", -1, "int16"),
[ 1167s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 16
[ 1167s] ("int16", 1, "int16"),
[ 1167s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1167s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int16", -1, "int16"),
[ 1167s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint16", 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint16", -1, "int32"),
[ 1167s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1167s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 32
[ 1167s] ("int32", 1, "int32"),
[ 1167s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1167s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int32", -1, "int32"),
[ 1167s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1167s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint32", 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint32", -1, "int64"),
[ 1167s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 64
[ 1167s] ("int64", 1, "int64"),
[ 1167s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int64", -1, "int64"),
[ 1167s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint64", 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint64", -1, "object"),
[ 1167s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1167s] ],
[ 1167s] )
[ 1167s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1167s] dtype = np.dtype(dtype)
[ 1167s] expected_dtype = np.dtype(expected_dtype)
[ 1167s]
[ 1167s] # output is not a generic int, but corresponds to expected_dtype
[ 1167s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1167s]
[ 1167s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1167s]
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1167s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1167s] _assert_match(result_fill_value, expected_fill_value)
[ 1167s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1167s]
[ 1167s] result_fill_value = 65536, expected_fill_value = 65536
[ 1167s]
[ 1167s] def _assert_match(result_fill_value, expected_fill_value):
[ 1167s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1167s] res_type = type(result_fill_value)
[ 1167s] ex_type = type(expected_fill_value)
[ 1167s] if res_type.__name__ == "uint64":
[ 1167s] # No idea why, but these (sometimes) do not compare as equal
[ 1167s] assert ex_type.__name__ == "uint64"
[ 1167s] elif res_type.__name__ == "ulonglong":
[ 1167s] # On some builds we get this instead of np.uint64
[ 1167s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1167s] assert res_type(0).itemsize == 8
[ 1167s] assert ex_type == res_type or ex_type == np.uint64
[ 1167s] else:
[ 1167s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1167s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1167s] E AssertionError: assert (<class 'numpy.uintc'> == <class 'numpy.uint32'>
[ 1167s] E -<class 'numpy.uintc'>
[ 1167s] E +<class 'numpy.uint32'> or 'uintc' == 'uint32'
[ 1167s] E - uintc
[ 1167s] E + uint32)
[ 1167s]
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1167s] ___________ test_maybe_promote_int_with_int[uint8-2147483648-uint32] ___________
[ 1167s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1167s]
[ 1167s] dtype = dtype('uint8'), fill_value = 2147483648
[ 1167s] expected_dtype = dtype('uint32')
[ 1167s]
[ 1167s] @pytest.mark.parametrize(
[ 1167s] "dtype, fill_value, expected_dtype",
[ 1167s] [
[ 1167s] # size 8
[ 1167s] ("int8", 1, "int8"),
[ 1167s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1167s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int8", -1, "int8"),
[ 1167s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1167s] # keep signed-ness as long as possible
[ 1167s] ("uint8", 1, "uint8"),
[ 1167s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1167s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1167s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1167s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1167s] # max of uint8 cannot be contained in int8
[ 1167s] ("uint8", -1, "int16"),
[ 1167s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 16
[ 1167s] ("int16", 1, "int16"),
[ 1167s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1167s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int16", -1, "int16"),
[ 1167s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1167s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint16", 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1167s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint16", -1, "int32"),
[ 1167s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1167s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 32
[ 1167s] ("int32", 1, "int32"),
[ 1167s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1167s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1167s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int32", -1, "int32"),
[ 1167s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1167s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1167s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint32", 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1167s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint32", -1, "int64"),
[ 1167s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1167s] # size 64
[ 1167s] ("int64", 1, "int64"),
[ 1167s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1167s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1167s] ("int64", -1, "int64"),
[ 1167s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1167s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1167s] ("uint64", 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1167s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1167s] ("uint64", -1, "object"),
[ 1167s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1167s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1167s] ],
[ 1167s] )
[ 1167s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1167s] dtype = np.dtype(dtype)
[ 1167s] expected_dtype = np.dtype(expected_dtype)
[ 1167s]
[ 1167s] # output is not a generic int, but corresponds to expected_dtype
[ 1167s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1167s]
[ 1167s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1167s]
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1167s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1167s] _assert_match(result_fill_value, expected_fill_value)
[ 1167s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1167s]
[ 1167s] result_fill_value = 2147483648, expected_fill_value = 2147483648
[ 1167s]
[ 1167s] def _assert_match(result_fill_value, expected_fill_value):
[ 1167s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1167s] res_type = type(result_fill_value)
[ 1167s] ex_type = type(expected_fill_value)
[ 1167s] if res_type.__name__ == "uint64":
[ 1167s] # No idea why, but these (sometimes) do not compare as equal
[ 1167s] assert ex_type.__name__ == "uint64"
[ 1167s] elif res_type.__name__ == "ulonglong":
[ 1167s] # On some builds we get this instead of np.uint64
[ 1167s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1167s] assert res_type(0).itemsize == 8
[ 1167s] assert ex_type == res_type or ex_type == np.uint64
[ 1167s] else:
[ 1167s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1167s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1167s] E AssertionError: assert (<class 'numpy.uintc'> == <class 'numpy.uint32'>
[ 1167s] E -<class 'numpy.uintc'>
[ 1167s] E +<class 'numpy.uint32'> or 'uintc' == 'uint32'
[ 1167s] E - uintc
[ 1167s] E + uint32)
[ 1167s]
[ 1167s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1168s] ______________ test_maybe_promote_int_with_int[int16-32768-int32] ______________
[ 1168s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1168s]
[ 1168s] dtype = dtype('int16'), fill_value = 32768, expected_dtype = dtype('int32')
[ 1168s]
[ 1168s] @pytest.mark.parametrize(
[ 1168s] "dtype, fill_value, expected_dtype",
[ 1168s] [
[ 1168s] # size 8
[ 1168s] ("int8", 1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int8", -1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # keep signed-ness as long as possible
[ 1168s] ("uint8", 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1168s] # max of uint8 cannot be contained in int8
[ 1168s] ("uint8", -1, "int16"),
[ 1168s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 16
[ 1168s] ("int16", 1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int16", -1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint16", 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint16", -1, "int32"),
[ 1168s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 32
[ 1168s] ("int32", 1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int32", -1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint32", 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint32", -1, "int64"),
[ 1168s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 64
[ 1168s] ("int64", 1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int64", -1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint64", 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint64", -1, "object"),
[ 1168s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ],
[ 1168s] )
[ 1168s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1168s] dtype = np.dtype(dtype)
[ 1168s] expected_dtype = np.dtype(expected_dtype)
[ 1168s]
[ 1168s] # output is not a generic int, but corresponds to expected_dtype
[ 1168s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1168s]
[ 1168s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1168s] _assert_match(result_fill_value, expected_fill_value)
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s]
[ 1168s] result_fill_value = 32768, expected_fill_value = 32768
[ 1168s]
[ 1168s] def _assert_match(result_fill_value, expected_fill_value):
[ 1168s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1168s] res_type = type(result_fill_value)
[ 1168s] ex_type = type(expected_fill_value)
[ 1168s] if res_type.__name__ == "uint64":
[ 1168s] # No idea why, but these (sometimes) do not compare as equal
[ 1168s] assert ex_type.__name__ == "uint64"
[ 1168s] elif res_type.__name__ == "ulonglong":
[ 1168s] # On some builds we get this instead of np.uint64
[ 1168s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1168s] assert res_type(0).itemsize == 8
[ 1168s] assert ex_type == res_type or ex_type == np.uint64
[ 1168s] else:
[ 1168s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1168s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1168s] E AssertionError: assert (<class 'numpy.intc'> == <class 'numpy.int32'>
[ 1168s] E -<class 'numpy.intc'>
[ 1168s] E +<class 'numpy.int32'> or 'intc' == 'int32'
[ 1168s] E - intc
[ 1168s] E + int32)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1168s] _____________ test_maybe_promote_int_with_int[uint16-65536-uint32] _____________
[ 1168s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1168s]
[ 1168s] dtype = dtype('uint16'), fill_value = 65536, expected_dtype = dtype('uint32')
[ 1168s]
[ 1168s] @pytest.mark.parametrize(
[ 1168s] "dtype, fill_value, expected_dtype",
[ 1168s] [
[ 1168s] # size 8
[ 1168s] ("int8", 1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int8", -1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # keep signed-ness as long as possible
[ 1168s] ("uint8", 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1168s] # max of uint8 cannot be contained in int8
[ 1168s] ("uint8", -1, "int16"),
[ 1168s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 16
[ 1168s] ("int16", 1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int16", -1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint16", 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint16", -1, "int32"),
[ 1168s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 32
[ 1168s] ("int32", 1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int32", -1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint32", 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint32", -1, "int64"),
[ 1168s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 64
[ 1168s] ("int64", 1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int64", -1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint64", 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint64", -1, "object"),
[ 1168s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ],
[ 1168s] )
[ 1168s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1168s] dtype = np.dtype(dtype)
[ 1168s] expected_dtype = np.dtype(expected_dtype)
[ 1168s]
[ 1168s] # output is not a generic int, but corresponds to expected_dtype
[ 1168s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1168s]
[ 1168s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1168s] _assert_match(result_fill_value, expected_fill_value)
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s]
[ 1168s] result_fill_value = 65536, expected_fill_value = 65536
[ 1168s]
[ 1168s] def _assert_match(result_fill_value, expected_fill_value):
[ 1168s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1168s] res_type = type(result_fill_value)
[ 1168s] ex_type = type(expected_fill_value)
[ 1168s] if res_type.__name__ == "uint64":
[ 1168s] # No idea why, but these (sometimes) do not compare as equal
[ 1168s] assert ex_type.__name__ == "uint64"
[ 1168s] elif res_type.__name__ == "ulonglong":
[ 1168s] # On some builds we get this instead of np.uint64
[ 1168s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1168s] assert res_type(0).itemsize == 8
[ 1168s] assert ex_type == res_type or ex_type == np.uint64
[ 1168s] else:
[ 1168s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1168s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1168s] E AssertionError: assert (<class 'numpy.uintc'> == <class 'numpy.uint32'>
[ 1168s] E -<class 'numpy.uintc'>
[ 1168s] E +<class 'numpy.uint32'> or 'uintc' == 'uint32'
[ 1168s] E - uintc
[ 1168s] E + uint32)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1168s] __________ test_maybe_promote_int_with_int[uint16-2147483648-uint32] ___________
[ 1168s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1168s]
[ 1168s] dtype = dtype('uint16'), fill_value = 2147483648
[ 1168s] expected_dtype = dtype('uint32')
[ 1168s]
[ 1168s] @pytest.mark.parametrize(
[ 1168s] "dtype, fill_value, expected_dtype",
[ 1168s] [
[ 1168s] # size 8
[ 1168s] ("int8", 1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int8", -1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # keep signed-ness as long as possible
[ 1168s] ("uint8", 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1168s] # max of uint8 cannot be contained in int8
[ 1168s] ("uint8", -1, "int16"),
[ 1168s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 16
[ 1168s] ("int16", 1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int16", -1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint16", 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint16", -1, "int32"),
[ 1168s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 32
[ 1168s] ("int32", 1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int32", -1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint32", 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint32", -1, "int64"),
[ 1168s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 64
[ 1168s] ("int64", 1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int64", -1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint64", 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint64", -1, "object"),
[ 1168s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ],
[ 1168s] )
[ 1168s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1168s] dtype = np.dtype(dtype)
[ 1168s] expected_dtype = np.dtype(expected_dtype)
[ 1168s]
[ 1168s] # output is not a generic int, but corresponds to expected_dtype
[ 1168s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1168s]
[ 1168s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1168s] _assert_match(result_fill_value, expected_fill_value)
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s]
[ 1168s] result_fill_value = 2147483648, expected_fill_value = 2147483648
[ 1168s]
[ 1168s] def _assert_match(result_fill_value, expected_fill_value):
[ 1168s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1168s] res_type = type(result_fill_value)
[ 1168s] ex_type = type(expected_fill_value)
[ 1168s] if res_type.__name__ == "uint64":
[ 1168s] # No idea why, but these (sometimes) do not compare as equal
[ 1168s] assert ex_type.__name__ == "uint64"
[ 1168s] elif res_type.__name__ == "ulonglong":
[ 1168s] # On some builds we get this instead of np.uint64
[ 1168s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1168s] assert res_type(0).itemsize == 8
[ 1168s] assert ex_type == res_type or ex_type == np.uint64
[ 1168s] else:
[ 1168s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1168s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1168s] E AssertionError: assert (<class 'numpy.uintc'> == <class 'numpy.uint32'>
[ 1168s] E -<class 'numpy.uintc'>
[ 1168s] E +<class 'numpy.uint32'> or 'uintc' == 'uint32'
[ 1168s] E - uintc
[ 1168s] E + uint32)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1168s] _______________ test_maybe_promote_int_with_int[uint16--1-int32] _______________
[ 1168s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1168s]
[ 1168s] dtype = dtype('uint16'), fill_value = -1, expected_dtype = dtype('int32')
[ 1168s]
[ 1168s] @pytest.mark.parametrize(
[ 1168s] "dtype, fill_value, expected_dtype",
[ 1168s] [
[ 1168s] # size 8
[ 1168s] ("int8", 1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int8", -1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # keep signed-ness as long as possible
[ 1168s] ("uint8", 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1168s] # max of uint8 cannot be contained in int8
[ 1168s] ("uint8", -1, "int16"),
[ 1168s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 16
[ 1168s] ("int16", 1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int16", -1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint16", 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint16", -1, "int32"),
[ 1168s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 32
[ 1168s] ("int32", 1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int32", -1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint32", 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint32", -1, "int64"),
[ 1168s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 64
[ 1168s] ("int64", 1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int64", -1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint64", 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint64", -1, "object"),
[ 1168s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ],
[ 1168s] )
[ 1168s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1168s] dtype = np.dtype(dtype)
[ 1168s] expected_dtype = np.dtype(expected_dtype)
[ 1168s]
[ 1168s] # output is not a generic int, but corresponds to expected_dtype
[ 1168s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1168s]
[ 1168s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1168s] _assert_match(result_fill_value, expected_fill_value)
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s]
[ 1168s] result_fill_value = -1, expected_fill_value = -1
[ 1168s]
[ 1168s] def _assert_match(result_fill_value, expected_fill_value):
[ 1168s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1168s] res_type = type(result_fill_value)
[ 1168s] ex_type = type(expected_fill_value)
[ 1168s] if res_type.__name__ == "uint64":
[ 1168s] # No idea why, but these (sometimes) do not compare as equal
[ 1168s] assert ex_type.__name__ == "uint64"
[ 1168s] elif res_type.__name__ == "ulonglong":
[ 1168s] # On some builds we get this instead of np.uint64
[ 1168s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1168s] assert res_type(0).itemsize == 8
[ 1168s] assert ex_type == res_type or ex_type == np.uint64
[ 1168s] else:
[ 1168s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1168s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1168s] E AssertionError: assert (<class 'numpy.intc'> == <class 'numpy.int32'>
[ 1168s] E -<class 'numpy.intc'>
[ 1168s] E +<class 'numpy.int32'> or 'intc' == 'int32'
[ 1168s] E - intc
[ 1168s] E + int32)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:111: AssertionError
[ 1168s] ______________ test_maybe_promote_int_with_int[uint16--129-int32] ______________
[ 1168s] [gw7] linux -- Python 3.8.2 /usr/bin/python3
[ 1168s]
[ 1168s] dtype = dtype('uint16'), fill_value = -129, expected_dtype = dtype('int32')
[ 1168s]
[ 1168s] @pytest.mark.parametrize(
[ 1168s] "dtype, fill_value, expected_dtype",
[ 1168s] [
[ 1168s] # size 8
[ 1168s] ("int8", 1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int8", -1, "int8"),
[ 1168s] ("int8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # keep signed-ness as long as possible
[ 1168s] ("uint8", 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("int8").max + 1, "uint8"),
[ 1168s] ("uint8", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint8", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint8", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint8", np.iinfo("uint64").max + 1, "object"),
[ 1168s] # max of uint8 cannot be contained in int8
[ 1168s] ("uint8", -1, "int16"),
[ 1168s] ("uint8", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("uint8", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint8", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint8", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 16
[ 1168s] ("int16", 1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").max + 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int16", -1, "int16"),
[ 1168s] ("int16", np.iinfo("int8").min - 1, "int16"),
[ 1168s] ("int16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int16", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint16", 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint8").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("int16").max + 1, "uint16"),
[ 1168s] ("uint16", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint16", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint16", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint16", -1, "int32"),
[ 1168s] ("uint16", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("uint16", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint16", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 32
[ 1168s] ("int32", 1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").max + 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int32", -1, "int32"),
[ 1168s] ("int32", np.iinfo("int8").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int16").min - 1, "int32"),
[ 1168s] ("int32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int32", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint32", 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint8").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint16").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("int32").max + 1, "uint32"),
[ 1168s] ("uint32", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint32", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint32", -1, "int64"),
[ 1168s] ("uint32", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("uint32", np.iinfo("int64").min - 1, "object"),
[ 1168s] # size 64
[ 1168s] ("int64", 1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").max + 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").max + 1, "object"),
[ 1168s] ("int64", -1, "int64"),
[ 1168s] ("int64", np.iinfo("int8").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int16").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int32").min - 1, "int64"),
[ 1168s] ("int64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ("uint64", 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint8").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint16").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint32").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("int64").max + 1, "uint64"),
[ 1168s] ("uint64", np.iinfo("uint64").max + 1, "object"),
[ 1168s] ("uint64", -1, "object"),
[ 1168s] ("uint64", np.iinfo("int8").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int16").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int32").min - 1, "object"),
[ 1168s] ("uint64", np.iinfo("int64").min - 1, "object"),
[ 1168s] ],
[ 1168s] )
[ 1168s] def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
[ 1168s] dtype = np.dtype(dtype)
[ 1168s] expected_dtype = np.dtype(expected_dtype)
[ 1168s]
[ 1168s] # output is not a generic int, but corresponds to expected_dtype
[ 1168s] exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
[ 1168s]
[ 1168s] > _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
[ 1168s]
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s] ../../BUILDROOT/python-pandas-1.0.2-37.1.i386/usr/lib/python3.8/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
[ 1168s] _assert_match(result_fill_value, expected_fill_value)
[ 1168s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 1168s]
[ 1168s] result_fill_value = -129, expected_fill_value = -129
[ 1168s]
[ 1168s] def _assert_match(result_fill_value, expected_fill_value):
[ 1168s] # GH#23982/25425 require the same type in addition to equality/NA-ness
[ 1168s] res_type = type(result_fill_value)
[ 1168s] ex_type = type(expected_fill_value)
[ 1168s] if res_type.__name__ == "uint64":
[ 1168s] # No idea why, but these (sometimes) do not compare as equal
[ 1168s] assert ex_type.__name__ == "uint64"
[ 1168s] elif res_type.__name__ == "ulonglong":
[ 1168s] # On some builds we get this instead of np.uint64
[ 1168s] # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
[ 1168s] assert res_type(0).itemsize == 8
[ 1168s] assert ex_type == res_type or ex_type == np.uint64
[ 1168s] else:
[ 1168s] # On some builds, type comparison fails, e.g. np.int32 != np.int32
[ 1168s] > assert res_type == ex_type or res_type.__name__ == ex_type.__name__
[ 1168s] E AssertionError: assert (<class 'numpy.intc'> == <class 'numpy.int32'>
[ 1168s] E -<class 'numpy.intc'>
[ 1168s] E +<class 'numpy.int32'> or 'intc' == 'int32'
[ 1168s] E - intc
[ 1168s] E + int32)
```
You can find the full build log [here](https://build.opensuse.org/build/home:frispete:branches:openSUSE:Factory/openSUSE_Tumbleweed/i586/python-pandas/_log)
|
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MDExOlB1bGxSZXF1ZXN0Mzg3OTUzODQw
| 32,690
|
DOC: filter method example is more clear
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| 1
| 2020-03-13T19:59:42Z
| 2020-03-14T14:28:05Z
| 2020-03-13T22:03:30Z
|
CONTRIBUTOR
| null |
Added the print of the original example DataFrame. I was struggling to understand the example without looking at the starting point of it. I took the chance to do my first contribution to the project. Hopefully it is only the beginning!
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CLN: remove unused kwargs from BlockManager.downcast
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| 0
| 2020-03-13T21:28:42Z
| 2020-03-14T03:12:53Z
| 2020-03-14T03:06:14Z
|
MEMBER
| null |
add some annotations
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PandasArray does not support slicing consistent with other array types
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| 2020-03-13T22:33:17Z
| 2021-07-30T03:40:35Z
| null |
NONE
| null |
#### Code Sample
```python
>>> import pandas as pd
>>> x = pd.Series([1, 2, 3], index=pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-03']))
>>> x[:,None].ndim
>>> x.to_numpy()[:,None].ndim
>>> x.index.array[:,None].ndim
>>> x.array[:,None].ndim
```
#### Problem description
Currently PandasArray behaves differently then either the Series or or np.ndarray.
This causes a problem when passing the PandasArray to matplotlib for example.
PandasArray should implement this consistently like the other cases.
The output from above is:
<details>
```python
>>> import pandas as pd
>>> x = pd.Series([1, 2, 3], index=pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-03']))
>>> x[:,None].ndim
2
>>> x.to_numpy()[:,None].ndim
2
>>> x.index.array[:,None].ndim
2
>>> x.array[:,None].ndim
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/maartenb/anaconda3/envs/Corona/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py", line 240, in __getitem__
result = type(self)(result)
File "/Users/maartenb/anaconda3/envs/Corona/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py", line 147, in __init__
raise ValueError("PandasArray must be 1-dimensional.")
ValueError: PandasArray must be 1-dimensional.
```
</details>
#### Expected Output
<details>
```python
2
2
2
2
```
</details>
#### Output of ``pd.show_versions()``
<details>
```python
Python 3.8.1 (default, Jan 8 2020, 16:15:59)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CLN: avoid _ndarray_values in reshape.merge
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MDExOlB1bGxSZXF1ZXN0Mzg4MDYxMDk5
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REF: de-duplicate calls in arithmetic functions
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MEMBER
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comp_method_FRAME in particular cleans up nicely
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CLN: remove unused int_block
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mean with skipna either True or False on groupby gives error
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| 2020-11-20T13:07:47Z
| 2020-11-20T13:07:47Z
|
NONE
| null |
#### Problem description
Using ``.mean(skipna = True)`` or ``.mean(skipna = False)`` on a ``groupby`` object gives error:
```python
UnsupportedFunctionCall: numpy operations are not valid with groupby. Use .groupby(...).mean() instead
```
skipna is a crucial parameter while doing analysis. By default ``skipna = True``, so if not given explicitly results in expected output but if given explicitly either as True or False, returns error.
#### Code Sample:
```python
import pandas as pd
df = pd.DataFrame({'elements':[144,214,166,166,145,144,214],
'points':[1,2,3,None,1,1,1]})
df.groupby('elements').mean() ###this works
df.groupby('elements').mean(skipna = True) ###this doesn't work
df.groupby('elements').mean(skipna = False) ###this doesn't work
```
#### Expected Output
with ``mean(skipna = True)``:
```
elements points
144 1.0
145 1.0
166 3.0
214 1.5
```
with ``mean(skipna = False)``:
```
elements points
144 1.0
145 1.0
166 NaN
214 1.5
```
#### Workaround I tried which gives expected output:
```python
def custom_mean(df):
return df.mean(skipna=False)
df.groupby('elements').agg({'points':custom_mean})
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
Quite similar to issue [#19806](https://github.com/pandas-dev/pandas/issues/19806)
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MDExOlB1bGxSZXF1ZXN0Mzg4MTY3MjIy
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DOC: Fix EX01 in pandas.DataFrame.idxmin
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| 2
| 2020-03-14T05:30:19Z
| 2020-03-14T17:36:07Z
| 2020-03-14T16:50:22Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
```
################################################################################
################################## Validation ##################################
################################################################################
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|
to_sql erases timezone information
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| 2020-03-14T08:27:22Z
| 2020-03-14T15:36:09Z
| 2020-03-14T15:36:09Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
In [3]: import sqlalchemy
...: import pandas as pd
In [4]: conn = sqlalchemy.create_engine('postgres://xxx:xxx@xxx/xxx')
In [5]: dates = pd.date_range('2018-01-01', periods=5, freq='6H', tz='Europe/Brussels')
...: expected = pd.DataFrame({'nums': range(5)}, index=dates)
In [6]: expected
Out[6]:
nums
2018-01-01 00:00:00+01:00 0
2018-01-01 06:00:00+01:00 1
2018-01-01 12:00:00+01:00 2
2018-01-01 18:00:00+01:00 3
2018-01-02 00:00:00+01:00 4
In [7]: expected.to_sql('test', conn)
In [8]: pd.read_sql_table('test', conn)
Out[8]:
index nums
0 2017-12-31 23:00:00+00:00 0
1 2018-01-01 05:00:00+00:00 1
2 2018-01-01 11:00:00+00:00 2
3 2018-01-01 17:00:00+00:00 3
4 2018-01-01 23:00:00+00:00 4
```
#### Problem description
This fails the round-trip expectation. In particular, `to_sql` erases the timezone information. The timezone information is useful in some applications. Otherwise the timestamp would probably not have timezone in the first place and UTC would be assumed and used everywhere. Note that this behavior is unexpected because postgres supports `timestamptz` and in the underlying SQL table the column for `index` is the correct type `timestamptz`.
#### Expected Output
```
index nums
2018-01-01 00:00:00+01:00 0
2018-01-01 06:00:00+01:00 1
2018-01-01 12:00:00+01:00 2
2018-01-01 18:00:00+01:00 3
2018-01-02 00:00:00+01:00 4
```
#### Output of ``pd.show_versions()``
```
In [9]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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MDExOlB1bGxSZXF1ZXN0Mzg4Mjk1NTcz
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DOC: Clarify output of diff (returned type and possible overflow)
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| 13
| 2020-03-14T12:56:25Z
| 2020-06-01T10:33:00Z
| 2020-06-01T10:32:23Z
|
CONTRIBUTOR
| null |
- [x] closes #28909
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] note about dtype in `diff` in Dataframa and Series
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Track times
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|
CONTRIBUTOR
| null |
I implement feature request https://github.com/pandas-dev/pandas/issues/32682 but if someone has more experience with pytables consider these changes.
The h5 files generated with track_times=False has the same md5 hashes, that is covered in the unit test as well as the fact that the hashes are not the same with track_times=True
- [x] closes #32682
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzg4MzM1Mzkx
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BUG: Fix segfault on dir of a DataFrame with a unicode surrogate character in the column name
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CONTRIBUTOR
| null |
Return a `repr()` version if the column name string is not printable. This also means the the column name is not present in the output of `dir()`
- [x] closes #25509
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzg4MzQ4OTMx
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BUG: Add errors argument to to_csv() call to enable error handling for encoders
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CONTRIBUTOR
| null |
- [x] closes #22610
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 32,703
|
DataFrame.merge behavior unclear when both indices and data columns are keys
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| 2021-07-30T03:42:38Z
| 2021-07-30T03:42:38Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = [4,6,9,5,2,4]
pleft = pd.DataFrame.from_dict({'a':a})
pright = pleft.copy(deep=True)
# Make two indices to also be used as join columns
pleft.index = [6,5,4,7,5,5]
pright.index = [5,4,1,9,4,3]
pd_result = pleft.merge(pright, on='a', left_index=True, right_index=True, how='inner')
print('lhs:')
print(pleft)
print('rhs:')
print(pright)
print('result:')
print(pd_result)
lhs:
a
6 4
5 6
4 9
7 5
5 2
5 4
rhs:
a
5 4
4 6
1 9
9 5
4 2
3 4
result:
a
4 9
4 9
5 6
5 2
5 4
```
#### Problem description
Hello, I'm hoping someone here can clarify the behavior I'm seeing when merging dataframes using certain combinations of `left_index/right_index`, and `on`.
We can use `on` to specify a key that exists in both operands, or `left_on`/`right_on` if the keys are potentially named something different on each side. We can use `left_index` and `right_index` if we want to merge on the index column, or even `left_index` and `right_on` or (or vise versa) if we want one key to be an index, and the other to be a data column. As such I'd expect that using `left_index` and `right_index` as well as `on` would perform a merge that matches records on both the index as well as whatever data columns are specified. But it appears this is not the case.
#### Expected Output
```python
a
5 4
```
The pandas result appears to be more like what I'd get merging on just the indices and not the column `a`. I then wondered if the docs said anything about the index argument having some sort of precedence over `on`, but I didn't see anything - forgive me if I missed something here. What's more is if I manually omit `on`, I actually get something a little different:
```
result = pleft.merge(pright, left_index=True, right_index=True, how='inner')
print(result)
a_x a_y
4 9 6
4 9 2
5 6 4
5 2 4
5 4 4
```
One might argue that if the index arguments take precedence, I should get the same result that I would as if I didn't pass `on` at all.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-134-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg4MzU4NzA0
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DOC: Add examples to Series operators (#24589)
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| 2020-03-24T21:31:21Z
|
CONTRIBUTOR
| null |
Further work on #24589. PR #25524 added examples for many operations but not `pandas.Series.eq`, `pandas.Series.ne`, `pandas.Series.gt`, `pandas.Series.ge`, `pandas.series.le`, and `pandas.series.lt`. This adds examples for those.
`pandas.Series.divmod` is still missing an example as discussed in #25524.
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MDU6SXNzdWU1ODEyOTQ4MDc=
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CI: azure pipelines are not in PRS
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| 2020-03-14T19:30:02Z
| 2020-03-14T19:30:02Z
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CONTRIBUTOR
| null |
cc @pandas-dev/pandas-core
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MDExOlB1bGxSZXF1ZXN0Mzg4MzkyNDIy
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CI: Update pipelines config to trigger on PRs
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MEMBER
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- [X] closes #32705
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MDExOlB1bGxSZXF1ZXN0Mzg4MzkzNDM5
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CLN: unnecessary usages of Block.get_values
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MDExOlB1bGxSZXF1ZXN0Mzg4NDExNzc4
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skip 32 bit linux
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MEMBER
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follow up to #32706
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MDU6SXNzdWU1ODEzNDIzNTY=
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Restore 32 Bit Linux CI
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CLN: Prelims for stronger typing in Block methods
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MDExOlB1bGxSZXF1ZXN0Mzg4NTExNjcz
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REF: misplaced tests
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MDExOlB1bGxSZXF1ZXN0Mzg4NTI5ODUy
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REF: merge NonConsolidateableMixin into ExtensionArray
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without this mypy complains when we try to annotate some NonConsolidateableMixin methods
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MDExOlB1bGxSZXF1ZXN0Mzg4NTM1MzY4
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CLN: remove values_from_object
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MDExOlB1bGxSZXF1ZXN0Mzg4NTc3NjA5
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CLN: remove SingleBlockManager._values
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