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BUG: Series[dim3array] failing to raise ValueError for some Index subclasses
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ENH: add arrow engine to read_csv
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| 2020-02-09T04:27:30Z
| 2020-12-24T19:55:35Z
| 2020-12-08T18:21:40Z
|
MEMBER
| null |
- [x] closes #23697
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
ASV's, 100,000 rows, 5 columns, for reading from BytesIO & StringIO buffers (running on 2 core machine).
```
[ 75.00%] ··· io.csv.ReadCSVEngine.time_read_bytescsv ok
[ 75.00%] ··· ========= ============
engine
--------- ------------
c 51.7±6ms
python 598±40ms
pyarrow 27.8±0.8ms
========= ============
[100.00%] ··· io.csv.ReadCSVEngine.time_read_stringcsv ok
[100.00%] ··· ========= ===========
engine
--------- -----------
c 53.8±20ms
python 584±30ms
pyarrow 29.9±1ms
========= ===========
```
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DOC: '10 minutes to pandas' - <TAB> completion section now show a con…
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| 3
| 2020-02-09T04:30:31Z
| 2020-02-09T17:23:57Z
| 2020-02-09T17:23:54Z
|
CONTRIBUTOR
| null |
…sistent block of suggestions. Solve issue #31526
- [x] closes #31526
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I didn't run those listed tests above, however I doubt it will break something.
This PR only correct a *Typo* I found on documentation. I hope I'll be useful to improve this project.
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pandas 1.0.1 read_csv() is broken for some file-like objects
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| 2021-01-15T20:42:10Z
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NONE
| null |
#### Code Sample
```python
import os
import pandas
import tempfile
import traceback
# pandas.show_versions()
fname = ''
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write('てすと\nこむ'.encode('shift-jis'))
f.seek(0)
fname = f.name
try:
result = pandas.read_csv(f, encoding='shift-jis')
print('read shift-jis')
print(result)
except Exception as e:
print(e)
print(traceback.format_exc())
os.unlink(fname)
```
#### Problem description
Pandas 1.0.1, this sample does not work. But pandas 0.25.3, this sample works fine.
As stated in [issue #31575](https://github.com/pandas-dev/pandas/issues/31575), the encode of file-like object is ignored when its class is not io.BufferedIOBase neither RawIOBase.
However, some file-like objects are NOT inherited one of them, although the "actual" inner object is one of them.
In this code sample case, [according to the cpython implementation](https://github.com/python/cpython/blob/master/Lib/tempfile.py#L450), they has file as their attribute `self.file = file`, and `__getattr__()` returns the file's attribute as their attribute.
So the code is not work. The identic problems are in other file-like objects, for example, tempfile.*File class, werkzeug's FileStorage class, and so on.
**Note**: I first recognized this problem with using pandas via flask's posted file. The file-like object is an instance of werkzeug's FileStorage. I avoided this problem with following code:
```python
pandas.read_csv(request.files['file'].stream._file, encoding='shift-jis')
```
#### Expected Output
```
read shift-jis
てすと
0 こむ
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.138-89.102.amzn1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ja_JP.UTF-8
LOCALE : ja_JP.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.3
setuptools : 36.2.7
Cython : None
pytest : 3.6.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.0.5
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.6.2
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.4
tables : None
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.0.5
numba : None
</details>
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| 562,163,406
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MDExOlB1bGxSZXF1ZXN0MzcyODE5MTcz
| 31,820
|
correct redirections in doc/redirect.csv for rolling
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| 7
| 2020-02-09T11:40:01Z
| 2020-02-11T12:59:56Z
| 2020-02-09T17:17:25Z
|
CONTRIBUTOR
| null |
- [x] closes #31762
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MDU6SXNzdWU1NjIxNjYzNTA=
| 31,821
|
pd.read_csv automatically casts strings into int/float
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| 2020-02-09T12:06:08Z
| 2021-07-28T05:12:06Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import csv
d = pd.DataFrame([
['1', '2', 3],
['3', 4, 5]])
d.to_csv('test_d', quoting=csv.QUOTE_NONNUMERIC, index=False)
dr = pd.read_csv('test_d')
dr.dtypes
```
> 0 int64
1 int64
2 int64
dtype: object
#### Problem description
I have string columns that I write as strings, but when I read them I find out that they have been converted to numeric even though the csv has all the necessary quoting characters. I do not understand why they are converted to numeric if they are strings, the fact that the characters reperesent numbers should make no difference.
The option of using the parameter `dtype=...` is not an option for me because I do not necessarily know the names of all the columns.
#### Expected Output
The expected output is finding string where there are strings and numbers where there are numbers.
#### 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 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.2.2
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0MzcyODM2NTM5
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|
DOC: Fix style guide typos
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| 1
| 2020-02-09T14:49:47Z
| 2020-02-09T17:12:14Z
| 2020-02-09T17:11:05Z
|
MEMBER
| null |
A few minor spelling / grammar edits
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MDU6SXNzdWU1NjIxODkyMjU=
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|
concat ignore_index option doesnt work
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| 2020-04-22T17:39:36Z
| 2020-04-22T17:39:21Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
pd.concat([df_general.iloc[:,2:],df],ignore_index=True).fillna(method='ffill')
tried vs.
pd.concat([df_general.iloc[:,2:],df],ignore_index=False).fillna(method='ffill')
```
#### Problem description
concatenating the tables leaves 1 partially empty row that shouldnt be there, on pandas version version (0.23.4) it didn't happen.
this happened after upgrading pandas to below version.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 30 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.16.3
pytz : 2018.5
dateutil : 2.7.3
pip : 20.0.2
setuptools : 41.0.1
Cython : 0.28.5
pytest : 3.8.0
hypothesis : None
sphinx : 1.7.9
blosc : None
feather : None
xlsxwriter : 1.1.0
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 2.2.3
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.6
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.8.0
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.2.11
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.1.0
numba : 0.39.0
</details>
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Test messages test period
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| 1
| 2020-02-09T16:53:58Z
| 2020-02-09T18:53:08Z
| 2020-02-09T18:04:11Z
|
CONTRIBUTOR
| null |
- [ ] xref #30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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Backport PR #31788 on branch 1.0.x (BUG: Too aggressive typing in NDFrame.align)
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NONE
| null |
Backport PR #31788: BUG: Too aggressive typing in NDFrame.align
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Backport PR #31820 on branch 1.0.x (correct redirections in doc/redirect.csv for rolling)
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NONE
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Backport PR #31820: correct redirections in doc/redirect.csv for rolling
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Should pd.core.algorithms.match have been removed?
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| 3
| 2020-02-09T17:21:49Z
| 2020-05-06T15:43:37Z
| 2020-03-24T17:21:28Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas
pandas.core.algorithms.match
```
#### Problem description
I have just started using the 1.0 release and just realised that the `pandas.core.algorithms.match` function has been removed. I did some investigating and found it was first discussed here
https://github.com/pandas-dev/pandas/pull/15538
and then finally removed here
https://github.com/pandas-dev/pandas/pull/29249
I was often using this function and found it very useful. I'm not sure why it was removed (if there is a better way to do what it is doing I would love to know), otherwise in the first link @jorisvandenbossche said
"If someone uses it, sees the removal message, they can always complain that it was useful and we can think about how to publicly expose it (I don't think core.algorithms should be that place)."
so this is me complaining and saying it was useful.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 562,210,087
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MDU6SXNzdWU1NjIyMTAwODc=
| 31,828
|
DOC: Mention black and PEP8 in pandas style guide
|
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| 2020-02-09T17:25:09Z
| 2020-02-19T01:46:26Z
| 2020-02-19T01:46:26Z
|
MEMBER
| null |
`black` and PEP8 are pretty important elements of code style in `pandas` but they aren't mentioned in the style guide, so it may make sense to include them.
https://pandas.pydata.org/pandas-docs/stable/development/code_style.html
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| 562,212,205
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MDExOlB1bGxSZXF1ZXN0MzcyODUzOTU4
| 31,829
|
DOC: Use consistent casing in headers
|
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| 1
| 2020-02-09T17:41:36Z
| 2020-02-09T22:28:15Z
| 2020-02-09T22:26:19Z
|
MEMBER
| null |
This is super nitty, but in the left side bar of the user guide the nullable Boolean data type section is the only one that uses all caps (other than Frequently Asked Questions, which possibly makes sense), so this might look a bit better.
https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html#user-guide
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| 562,221,469
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MDExOlB1bGxSZXF1ZXN0MzcyODYwNzI3
| 31,830
|
CLN: Use self.loc for Series __getitem__ with IntervalIndex
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| 1
| 2020-02-09T18:52:18Z
| 2020-02-09T23:58:56Z
| 2020-02-09T22:17:19Z
|
MEMBER
| null |
Clarify that by the time we get to the affected code, the relevant task is determing whether to use loc or iloc.
|
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MDExOlB1bGxSZXF1ZXN0MzcyODYxMDAz
| 31,831
|
Test messages test integer
|
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| 1
| 2020-02-09T18:55:32Z
| 2020-02-09T22:28:31Z
| 2020-02-09T22:28:28Z
|
CONTRIBUTOR
| null |
- [ ] xref #30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzcyODYxODgw
| 31,832
|
CLN: disallow kind=None in _convert_slice_indexer
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| 1
| 2020-02-09T19:05:30Z
| 2020-02-09T23:55:35Z
| 2020-02-09T22:27:13Z
|
MEMBER
| null |
The one place where _convert_slice_indexer is currently called without kind is within _setitem_with_indexer, which I've determined should be iloc-only so it is a no-op. With that usage removed, we can be stricter about what gets passed.
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| 562,225,259
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MDExOlB1bGxSZXF1ZXN0MzcyODYzNDky
| 31,833
|
DOC Update documentation DataFrame.nsmallest
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| 2
| 2020-02-09T19:21:26Z
| 2020-02-11T04:48:43Z
| 2020-02-11T04:48:28Z
|
CONTRIBUTOR
| null |
Modified the example, to better demonstrate the use of the nsmallest function.
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MDExOlB1bGxSZXF1ZXN0MzcyODY0Nzc4
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REF: move loc-only validate_read_indexer to Loc
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| 1
| 2020-02-09T19:36:02Z
| 2020-02-09T23:51:52Z
| 2020-02-09T22:29:34Z
|
MEMBER
| null |
straight cut/paste
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| 562,233,844
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MDU6SXNzdWU1NjIyMzM4NDQ=
| 31,835
|
1.0.1 on conda - doc change or pushing to main conda channel?
|
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| 2020-02-09T20:26:53Z
| 2020-09-01T23:49:55Z
| 2020-09-01T23:49:55Z
|
NONE
| null |
To install 1.0.1 with conda I needed to use conda forge channel.
The install instructions in the docs say to just use `conda install pandas`, but that installs 1.0.0.
I'm not sure if the solution is to push 1.0.1 to the main conda channel (if you control that) or if it's worth updating the install instructions in the docs so they are in sync with how to install the latest pandas version.
|
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| 562,239,904
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MDExOlB1bGxSZXF1ZXN0MzcyODczODg5
| 31,836
|
Fixed mypy errors in pandas/tests/extension/json/test_json.py
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| 1
| 2020-02-09T21:11:26Z
| 2020-02-09T21:54:01Z
| 2020-02-09T21:53:46Z
|
CONTRIBUTOR
| null |
Part of #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REF: make _setitem_with_indexer iloc-only
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| 4
| 2020-02-09T21:16:26Z
| 2020-02-10T19:45:35Z
| 2020-02-10T19:45:15Z
|
MEMBER
| null |
some overlap with #31797.
_setitem_with_indexer is used for positional indexers, so it is misleading for it to be in loc anyway.
_setitem_with_indexer is an absolute beast, will be easier to sort out when we dont have to worry about what class we're in.
The diff looks misleading. All this does is
- move _setitem_with_indexer, _setitem_with_indexer_missing, _align_series, _align_frame from LocationIndexer to _iLocIndexer
- make `LocationIndexer.__setitem__` call `_has_valid_setitem_indexer` before calling _setitem_with_indexer instead of inside of it, so we get the right class's _has_valid_setitem_indexer
- dispatch to iloc if we are not already iloc
- update usages in core.frame
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MDExOlB1bGxSZXF1ZXN0MzcyODgxNzgw
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Docstring fixes for PR06 errors
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| 1
| 2020-02-09T22:30:02Z
| 2020-02-11T00:24:47Z
| 2020-02-10T16:20:40Z
|
CONTRIBUTOR
| null |
This contains some small changes that resolve some of the PR06 errors from running `./scripts/validate_docstrings.py --errors=PR06`
- [x] xref #28724
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Convert type nullable int <-> nullable string
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| 2
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| 2020-02-10T14:08:37Z
| 2020-02-10T13:54:22Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
This code raises an error:
```python
>>> s = pd.Series([0, pd.NA], dtype='Int8')
>>> s.astype('string')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-12-e5922a9c01ed> in <module>
----> 1 s.astype('string')
/opt/tljh/user/envs/py37-sf/lib/python3.7/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors)
5696 else:
5697 # else, only a single dtype is given
-> 5698 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
5699 return self._constructor(new_data).__finalize__(self)
5700
/opt/tljh/user/envs/py37-sf/lib/python3.7/site-packages/pandas/core/internals/managers.py in astype(self, dtype, copy, errors)
580
581 def astype(self, dtype, copy: bool = False, errors: str = "raise"):
--> 582 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
583
584 def convert(self, **kwargs):
/opt/tljh/user/envs/py37-sf/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
/opt/tljh/user/envs/py37-sf/lib/python3.7/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors)
605 if self.is_extension:
606 # TODO: Should we try/except this astype?
--> 607 values = self.values.astype(dtype)
608 else:
609 if issubclass(dtype.type, str):
/opt/tljh/user/envs/py37-sf/lib/python3.7/site-packages/pandas/core/arrays/integer.py in astype(self, dtype, copy)
463 kwargs = {}
464
--> 465 data = self.to_numpy(dtype=dtype, **kwargs)
466 return astype_nansafe(data, dtype, copy=False)
467
/opt/tljh/user/envs/py37-sf/lib/python3.7/site-packages/pandas/core/arrays/masked.py in to_numpy(self, dtype, copy, na_value)
130 )
131 # don't pass copy to astype -> always need a copy since we are mutating
--> 132 data = self._data.astype(dtype)
133 data[self._mask] = na_value
134 else:
TypeError: data type not understood
```
#### Problem description
Converting a nullable int to nullable string requires a double conversion:
```python
s = pd.Series([0, 1], dtype='Int8')
s.astype(str).astype('string')
```
Likewise, converting a nullable string to nullable int requires two steps:
```python
s = pd.Series(['0', '1'], dtype='string')
s.astype('int8').astype('Int8')
```
Moreover, if the nullable string series has NAs, converting to a nullable int becomes much harder (I don't know if there is a simpler way):
```python
s = pd.Series(['0', pd.NA], dtype='string')
s.astype('object').replace(pd.NA, np.nan).astype('float64').astype('Int8')
```
#### Expected Output
I would be nice to directly convert from nullable int to nullable string and vice versa in one step.
I'd like for all the following conversions to work:
```python
s = pd.Series([0, 1], dtype='Int8')
s.astype('string') # currently raises
```
```python
s = pd.Series(['0', '1'], dtype='string')
s.astype('Int8') # currently raises
```
```python
s = pd.Series(['0', pd.NA], dtype='string')
s.astype('Int8') # currently raises
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1058-aws
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.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 : 45.1.0.post20200119
Cython : 0.29.13
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 : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.45.1
</details>
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BUG/DEPR: loc.__setitem__ incorrectly accepting positional slices
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| 10
| 2020-02-10T02:42:55Z
| 2020-03-08T16:08:01Z
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|
MEMBER
| null |
- [x] closes #26412
- [x] closes #16121
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I plan to flesh out the tests before this goes in.
cc @toobaz
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MDExOlB1bGxSZXF1ZXN0MzcyOTMyNDI0
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BUG: Fix rolling.corr with time frequency
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BUG: Fix raw parameter not being respected in groupby.rolling.apply
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|
Doesn't seem possible to assign to .loc when used with IndexSlice
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| 2020-02-10T06:29:10Z
| 2021-07-28T05:12:54Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Given a dataframe,
```
Year 2018 2019
Dimension Before After Before After
A 0 1 1.5 2
B 0 1 1.5 2
C 0 1 1.0 3
D 1 1 1.5 2
```
I would like to add another column to each of my Years, e.g. I would like
```
Year 2018 2019
Dimension After Before Mean After Before Mean
A 1 0 0.5 2 1.5 0.25
B 1 0 0.5 2 1.5 0.25
C 1 0 0.5 3 1.0 1.00
D 1 1 0.0 2 1.5 0.25
```
I am trying to achieve this using `loc` as this seems the recommended way of doing this (please advise if there is a better way).
```python
df = (
pd.DataFrame({
('2018', 'Before'): [0, 0, 0, 1],
('2018', 'After'): [1, 1, 1, 1],
('2019', 'Before'): [1.5, 1.5, 1, 1.5],
('2019', 'After'): [2, 2, 3, 2]
}, index=['A', 'B', 'C', 'D'])
.rename_axis(['Year', 'Dimension'], axis='columns')
)
from pandas import IndexSlice as I
df.loc[:, I[:, 'Mean']] = (
(
df.loc[:, I[:, 'After']].droplevel(-1, axis='columns')
- df.loc[:, I[:, 'Before']].droplevel(-1, axis='columns')
) / 2
)
```
This unfortunately explodes.
```
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<ipython-input-42-15b26bf1e10b>", line 16, in <module>
) / 2
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexing.py", line 669, in __setitem__
indexer = self._get_setitem_indexer(key)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexing.py", line 647, in _get_setitem_indexer
return self._convert_tuple(key)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexing.py", line 732, in _convert_tuple
idx = self._convert_to_indexer(k, axis=i)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexing.py", line 1721, in _convert_to_indexer
return labels.get_locs(obj)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 3062, in get_locs
self.get_loc_level(k, level=i, drop_level=False)[0]
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 2846, in get_loc_level
indexer = self._get_level_indexer(key, level=level)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 2929, in _get_level_indexer
code = self._get_loc_single_level_index(level_index, key)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 2598, in _get_loc_single_level_index
return level_index.get_loc(key)
File "/home/jarrad/src/ow/fa/analyses/2020-01/postcode/.venv/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2648, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1618, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1626, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'Mean'
```
This is confusing and unexpected, because the parallel of assigning to a single new column,
```python
df.loc[:, ('2018', 'Mean')] = ( df.loc[:, ('2018', 'After') - df.loc[:, ('2018', 'Before')] ) / 2
```
works as expected.
Additionally, the need to use .droplevel to cleanly do what I want looks clunky and makes me think I might be going about this the wrong.. however maybe there isn't a right way. :)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
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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CLN-29547 replace old string formatting
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MDU6SXNzdWU1NjIzNzU3MjQ=
| 31,845
|
ENH: Add engine keyword argument to groupby.apply to leverage Numba
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| 2020-02-10T07:35:19Z
| 2020-06-15T18:55:59Z
| null |
MEMBER
| null |
Similar to https://github.com/pandas-dev/pandas/issues/30936 and https://github.com/pandas-dev/pandas/issues/28987, we should next allow `groupby.apply` to leverage the optional Numba dependency by introducing an engine keyword that operates similarly to `rolling.apply`
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MDU6SXNzdWU1NjI0MjA3NDQ=
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|
Don't force conversion to list in to_sql with method callable
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[] | null | 0
| 2020-02-10T09:11:11Z
| 2021-07-28T05:13:51Z
| null |
NONE
| null |
#### Problem description
`pandas.to_sql()` takes a `method` argument that allows one to perform the insert into the database table using a method other than the normal series of `INSERT` statements.
The callable that can be passed using this argument must take a `data_iter` argument, an iterable containing the data. Alas, I can easily imagine ways of writing to the database in which it would be a good idea not to force conversion from pandas DataFrame to iterable. Analogously to the [example in the docs of using Postgres' `COPY` function](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-sql-method), it might make sense, for example, when loading data into Snowflake to dump the pandas DataFrame to Parquet in its entirety, transfer it to Snowflake and then to [COPY](https://docs.snowflake.net/manuals/sql-reference/sql/copy-into-table.html) it into the table. Stable data types, stable encodings -- all the things one would otherwise have to worry about.
Would you consider changing the call signature of the callable to allow this?
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BUG: pd.NA doesn't pickle/unpickle faithfully
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| 2020-03-02T23:13:33Z
| 2020-03-02T23:13:33Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [5]: df['Gold Categories'].count()
Out[5]: 135218
In [6]: df['Gold Categories'].isna().sum()
Out[6]: 0
In [7]: df['Gold Categories'].iloc[256]
Out[7]: <NA>
In [8]: pd.isna(df['Gold Categories'].iloc[256])
Out[8]: False
In [9]: type(df['Gold Categories'].iloc[256])
Out[9]: pandas._libs.missing.NAType
In [10]: pd.__version__
Out[10]: '1.0.1'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.16-200.fc30.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : nb_NO.UTF-8
LOCALE : nb_NO.UTF-8
pandas : 1.0.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.2.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.5.2
tabulate : 0.8.5
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.46.0
</details>
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Query abs(column) == [1, 2] fails
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| 2020-02-10T11:32:30Z
| 2020-02-25T20:17:31Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({"col": [1, -1, 2, -2]})
# Works:
df.query("abs(col) == 1")
# Works:
df.query("col == [1, -1, 2, -2]")
# Fails:
df.query("abs(col) == [1, 2]")
```
traceback:
<details>
```
ValueError Traceback (most recent call last)
<ipython-input-42-1b78845d9a47> in <module>()
----> 1 df.query("abs(col) == [1, 2]")
~/.local/lib/python3.6/site-packages/pandas/core/frame.py in query(self, expr, inplace, **kwargs)
3229 kwargs["level"] = kwargs.pop("level", 0) + 1
3230 kwargs["target"] = None
-> 3231 res = self.eval(expr, **kwargs)
3232
3233 try:
~/.local/lib/python3.6/site-packages/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
3344 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
3345
-> 3346 return _eval(expr, inplace=inplace, **kwargs)
3347
3348 def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
~/.local/lib/python3.6/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
335 eng = _engines[engine]
336 eng_inst = eng(parsed_expr)
--> 337 ret = eng_inst.evaluate()
338
339 if parsed_expr.assigner is None:
~/.local/lib/python3.6/site-packages/pandas/core/computation/engines.py in evaluate(self)
71
72 # make sure no names in resolvers and locals/globals clash
---> 73 res = self._evaluate()
74 return reconstruct_object(
75 self.result_type, res, self.aligned_axes, self.expr.terms.return_type
~/.local/lib/python3.6/site-packages/pandas/core/computation/engines.py in _evaluate(self)
112 scope = env.full_scope
113 _check_ne_builtin_clash(self.expr)
--> 114 return ne.evaluate(s, local_dict=scope)
115
116
~/.local/lib/python3.6/site-packages/numexpr/necompiler.py in evaluate(ex, local_dict, global_dict, out, order, casting, **kwargs)
832 _numexpr_last = dict(ex=compiled_ex, argnames=names, kwargs=kwargs)
833 with evaluate_lock:
--> 834 return compiled_ex(*arguments, **kwargs)
835
836
ValueError: operands could not be broadcast together with shapes (4,) (2,)
```
</details>
#### Problem description
A value error is raised for the query
#### Expected Output
Same as ``df.query("col == [1, -1, 2, -2]")``
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.2.final.0
python-bits : 64
OS : Linux
OS-release : 2.6.32-754.23.1.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 : 1.17.1
pytz : 2018.3
dateutil : 2.6.1
pip : 19.1.1
setuptools : 38.5.1
Cython : None
pytest : None
hypothesis : None
sphinx : 1.7.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.1.1
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.1.1
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.5.1
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.45.1
```
</details>
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| 562,518,091
|
MDU6SXNzdWU1NjI1MTgwOTE=
| 31,849
|
SeriesGroupby transform unique not working in Pandas 1.0.1
|
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| 7
| 2020-02-10T12:06:59Z
| 2020-03-03T03:14:39Z
| 2020-03-03T03:14:39Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
df = pd.DataFrame({"title":['a','b','c','c','a'], "asset_id":['a1','b1','c1','c2','c1']})
df.groupby("title").asset_id.transform("unique")
```
#### Problem description
Since pandas 1.0.1, unique does not seem to be a whitelisted transform function for SeriesGroupby objects anymore. The above code works with pandas 0.25.3 but not with 1.0.1 It raises a ValueError exception:
```pytb
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-ccb0ce21c35f> in <module>
1 import pandas as pd
2 df = pd.DataFrame({"title":['a','b','c','c','a'], "asset_id":['a1','b1','c1','c2','c1']})
----> 3 df.groupby("title").asset_id.transform("unique")
~/sandbox/pandas/pandas/core/groupby/generic.py in transform(self, func, *args, **kwargs)
470 elif func not in base.transform_kernel_whitelist:
471 msg = f"'{func}' is not a valid function name for transform(name)"
--> 472 raise ValueError(msg)
473 elif func in base.cythonized_kernels:
474 # cythonized transform or canned "agg+broadcast"
ValueError: 'unique' is not a valid function name for transform(name)
```
I tried to find a documentation on that changed but was not succesful. If that is the intended behaviour, I think it would be helpful to document it.
#### Expected Output
0 [a1, c1]
1 [b1]
2 [c1, c2]
3 [c1, c2]
4 [a1, c1]
Name: asset_id, dtype: object
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
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 : None
LOCALE : None.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NjI2MjY0MjI=
| 31,850
|
ENH: str.replace on multiple columns
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[] | null | 3
| 2020-02-10T15:11:40Z
| 2021-07-28T05:18:17Z
| 2021-07-28T05:18:17Z
|
NONE
| null |
#IT WORKS
df['Total'] = df['Total'].str.replace(r'\.', '')
df['Homes'] = df['Homes'].str.replace(r'\.', '')
df['Dones'] = df['Dones'].str.replace(r'\.', '')
#IT DOESNT WORK
cols = ["Total", "Homes", "Dones"]
df[cols] = df[cols].str.replace(r'\.', '')
On Pandas 1.0.1
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MDU6SXNzdWU1NjI2NDA0MDM=
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|
ValueError on df.agg if a list of functions is given
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| 3
| 2020-02-10T15:32:54Z
| 2020-02-12T20:30:53Z
| null |
CONTRIBUTOR
| null |
#### Code Sample
```python
import numpy as np
import pandas as pd
_arr = np.array([1,2,3,4,5,np.nan])
_arr2 = np.array([1,2,3,4,5,7])
df = pd.DataFrame({"A": _arr, "B": _arr2})
def perc_fun(q):
def f(arr):
return np.nanpercentile(arr, q)
f.__name__ = f"p-{q}"
return f
```
all is as expected here:
```python
perc_fun(10)(_arr)
>>> 1.4
df.agg(perc_fun(10))
>>> A 1.4
>>> B 1.5
>>> dtype: float64
```
but this dies:
```python
df.agg(["mean", perc_fun(10)])
```
<details>
TypeError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
553 try:
--> 554 return concat(results, keys=keys, axis=1, sort=False)
555 except TypeError:
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
280 copy=copy,
--> 281 sort=sort,
282 )
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
356 )
--> 357 raise TypeError(msg)
358
TypeError: cannot concatenate object of type '<class 'numpy.float64'>'; only Series and DataFrame objs are valid
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-14-ef771c688be7> in <module>
----> 1 df.agg(["mean", perc_fun(10)])
~/.local/lib/python3.7/site-packages/pandas/core/frame.py in aggregate(self, func, axis, *args, **kwargs)
6704 result = None
6705 try:
-> 6706 result, how = self._aggregate(func, axis=axis, *args, **kwargs)
6707 except TypeError:
6708 pass
~/.local/lib/python3.7/site-packages/pandas/core/frame.py in _aggregate(self, arg, axis, *args, **kwargs)
6718 result = result.T if result is not None else result
6719 return result, how
-> 6720 return super()._aggregate(arg, *args, **kwargs)
6721
6722 agg = aggregate
~/.local/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
484 elif is_list_like(arg):
485 # we require a list, but not an 'str'
--> 486 return self._aggregate_multiple_funcs(arg, _axis=_axis), None
487 else:
488 result = None
~/.local/lib/python3.7/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
530 colg = self._gotitem(col, ndim=1, subset=obj.iloc[:, index])
531 try:
--> 532 new_res = colg.aggregate(arg)
533 except (TypeError, DataError):
534 pass
~/.local/lib/python3.7/site-packages/pandas/core/series.py in aggregate(self, func, axis, *args, **kwargs)
3686 # Validate the axis parameter
3687 self._get_axis_number(axis)
-> 3688 result, how = self._aggregate(func, *args, **kwargs)
3689 if result is None:
3690
~/.local/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
484 elif is_list_like(arg):
485 # we require a list, but not an 'str'
--> 486 return self._aggregate_multiple_funcs(arg, _axis=_axis), None
487 else:
488 result = None
~/.local/lib/python3.7/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
562 result = Series(results, index=keys, name=self.name)
563 if is_nested_object(result):
--> 564 raise ValueError("cannot combine transform and aggregation operations")
565 return result
566
ValueError: cannot combine transform and aggregation operations
</details>
#### Problem description
The same code produced a different error in 0.24 so I assume some work has been done, but the error message is not very informative and I really think this should just work by inserting the appropriate 1.4 and 1.5 values
I couldn't find any related issues, if no one has experience with this, I will look into .agg code
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.1-1.el7.elrepo.x86_64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2018.4
dateutil : 2.7.3
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : 4.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.1.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.2.8
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzczMjI0NDYz
| 31,852
|
add messages to tests
|
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| 2020-02-10T16:17:21Z
| 2020-02-19T23:19:56Z
| 2020-02-19T23:19:49Z
|
CONTRIBUTOR
| null |
- [ ] xref #30999
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzczMjI2MDM3
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|
REF: Use nonzero in place of argwhere
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| 2020-02-10T16:20:16Z
| 2020-07-28T14:41:36Z
| 2020-02-11T04:10:18Z
|
CONTRIBUTOR
| null |
Use nonzero to preserve 1d of Series
xref #31813
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MDExOlB1bGxSZXF1ZXN0MzczMjc2NjE5
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REF: make Series/DataFrame _slice always positional
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| 2020-02-10T18:08:21Z
| 2020-02-11T15:44:34Z
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|
MEMBER
| null |
ATM we have a loc/iloc kwarg that NDFrame._slice (which is in effect DataFrame._slice) ignores and always slices positionally. This makes Series._slice always slice positionally too.
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TST: parametrize tests.indexing.test_float
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| 1
| 2020-02-10T20:45:41Z
| 2020-02-15T01:53:31Z
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MEMBER
| null |
Working on making our exception-raising more consistent, getting these tests cleaned up will make that easier.
|
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`test_maybe_promote_int_with_int` error on 32bit platform
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| 2020-02-10T20:59:13Z
| 2021-07-28T05:20:47Z
| null |
NONE
| null |
The test `test_maybe_promote_int_with_int` in `tests/dtypes/cast/test_promote.py` is failing for a subset of combinations where `int8`, `uint8`, `int16`, or `uint16` values are promotoed to `int32` or `uint32` targets on i586 the platform (but not on x86_64). The error message is pretty consistent.
This failure is new in pandas 1.0.1, it was not present on 1.0.0.
The specific combination of values that fail are:
```
int16-32768-int32
int8-32768-int32
uint16--1-int32
uint16--129-int32
uint16-2147483648-uint32
uint16-65536-uint32
uint8-2147483648-uint32
uint8-65536-uint32
```
These do *not* fail:
```
int16--32769-int32
int8--32769-int32
uint16--32769-int32
uint8--32769-int32
```
A specific example of the error message is:
```
______________ test_maybe_promote_int_with_int[uint16--129-int32] ______________
linux -- Python 3.7.3 /usr/bin/python3
dtype = dtype('uint16'), fill_value = -129, expected_dtype = dtype('int32')
@pytest.mark.parametrize(
"dtype, fill_value, expected_dtype",
[
# size 8
("int8", 1, "int8"),
("int8", np.iinfo("int8").max + 1, "int16"),
("int8", np.iinfo("int16").max + 1, "int32"),
("int8", np.iinfo("int32").max + 1, "int64"),
("int8", np.iinfo("int64").max + 1, "object"),
("int8", -1, "int8"),
("int8", np.iinfo("int8").min - 1, "int16"),
("int8", np.iinfo("int16").min - 1, "int32"),
("int8", np.iinfo("int32").min - 1, "int64"),
("int8", np.iinfo("int64").min - 1, "object"),
# keep signed-ness as long as possible
("uint8", 1, "uint8"),
("uint8", np.iinfo("int8").max + 1, "uint8"),
("uint8", np.iinfo("uint8").max + 1, "uint16"),
("uint8", np.iinfo("int16").max + 1, "uint16"),
("uint8", np.iinfo("uint16").max + 1, "uint32"),
("uint8", np.iinfo("int32").max + 1, "uint32"),
("uint8", np.iinfo("uint32").max + 1, "uint64"),
("uint8", np.iinfo("int64").max + 1, "uint64"),
("uint8", np.iinfo("uint64").max + 1, "object"),
# max of uint8 cannot be contained in int8
("uint8", -1, "int16"),
("uint8", np.iinfo("int8").min - 1, "int16"),
("uint8", np.iinfo("int16").min - 1, "int32"),
("uint8", np.iinfo("int32").min - 1, "int64"),
("uint8", np.iinfo("int64").min - 1, "object"),
# size 16
("int16", 1, "int16"),
("int16", np.iinfo("int8").max + 1, "int16"),
("int16", np.iinfo("int16").max + 1, "int32"),
("int16", np.iinfo("int32").max + 1, "int64"),
("int16", np.iinfo("int64").max + 1, "object"),
("int16", -1, "int16"),
("int16", np.iinfo("int8").min - 1, "int16"),
("int16", np.iinfo("int16").min - 1, "int32"),
("int16", np.iinfo("int32").min - 1, "int64"),
("int16", np.iinfo("int64").min - 1, "object"),
("uint16", 1, "uint16"),
("uint16", np.iinfo("int8").max + 1, "uint16"),
("uint16", np.iinfo("uint8").max + 1, "uint16"),
("uint16", np.iinfo("int16").max + 1, "uint16"),
("uint16", np.iinfo("uint16").max + 1, "uint32"),
("uint16", np.iinfo("int32").max + 1, "uint32"),
("uint16", np.iinfo("uint32").max + 1, "uint64"),
("uint16", np.iinfo("int64").max + 1, "uint64"),
("uint16", np.iinfo("uint64").max + 1, "object"),
("uint16", -1, "int32"),
("uint16", np.iinfo("int8").min - 1, "int32"),
("uint16", np.iinfo("int16").min - 1, "int32"),
("uint16", np.iinfo("int32").min - 1, "int64"),
("uint16", np.iinfo("int64").min - 1, "object"),
# size 32
("int32", 1, "int32"),
("int32", np.iinfo("int8").max + 1, "int32"),
("int32", np.iinfo("int16").max + 1, "int32"),
("int32", np.iinfo("int32").max + 1, "int64"),
("int32", np.iinfo("int64").max + 1, "object"),
("int32", -1, "int32"),
("int32", np.iinfo("int8").min - 1, "int32"),
("int32", np.iinfo("int16").min - 1, "int32"),
("int32", np.iinfo("int32").min - 1, "int64"),
("int32", np.iinfo("int64").min - 1, "object"),
("uint32", 1, "uint32"),
("uint32", np.iinfo("int8").max + 1, "uint32"),
("uint32", np.iinfo("uint8").max + 1, "uint32"),
("uint32", np.iinfo("int16").max + 1, "uint32"),
("uint32", np.iinfo("uint16").max + 1, "uint32"),
("uint32", np.iinfo("int32").max + 1, "uint32"),
("uint32", np.iinfo("uint32").max + 1, "uint64"),
("uint32", np.iinfo("int64").max + 1, "uint64"),
("uint32", np.iinfo("uint64").max + 1, "object"),
("uint32", -1, "int64"),
("uint32", np.iinfo("int8").min - 1, "int64"),
("uint32", np.iinfo("int16").min - 1, "int64"),
("uint32", np.iinfo("int32").min - 1, "int64"),
("uint32", np.iinfo("int64").min - 1, "object"),
# size 64
("int64", 1, "int64"),
("int64", np.iinfo("int8").max + 1, "int64"),
("int64", np.iinfo("int16").max + 1, "int64"),
("int64", np.iinfo("int32").max + 1, "int64"),
("int64", np.iinfo("int64").max + 1, "object"),
("int64", -1, "int64"),
("int64", np.iinfo("int8").min - 1, "int64"),
("int64", np.iinfo("int16").min - 1, "int64"),
("int64", np.iinfo("int32").min - 1, "int64"),
("int64", np.iinfo("int64").min - 1, "object"),
("uint64", 1, "uint64"),
("uint64", np.iinfo("int8").max + 1, "uint64"),
("uint64", np.iinfo("uint8").max + 1, "uint64"),
("uint64", np.iinfo("int16").max + 1, "uint64"),
("uint64", np.iinfo("uint16").max + 1, "uint64"),
("uint64", np.iinfo("int32").max + 1, "uint64"),
("uint64", np.iinfo("uint32").max + 1, "uint64"),
("uint64", np.iinfo("int64").max + 1, "uint64"),
("uint64", np.iinfo("uint64").max + 1, "object"),
("uint64", -1, "object"),
("uint64", np.iinfo("int8").min - 1, "object"),
("uint64", np.iinfo("int16").min - 1, "object"),
("uint64", np.iinfo("int32").min - 1, "object"),
("uint64", np.iinfo("int64").min - 1, "object"),
],
)
def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
dtype = np.dtype(dtype)
expected_dtype = np.dtype(expected_dtype)
# output is not a generic int, but corresponds to expected_dtype
exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
> _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
../../BUILDROOT/python-pandas-1.0.1-58.1.i386/usr/lib/python3.7/site-packages/pandas/tests/dtypes/cast/test_promote.py:237:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../BUILDROOT/python-pandas-1.0.1-58.1.i386/usr/lib/python3.7/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
_assert_match(result_fill_value, expected_fill_value)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
result_fill_value = -129, expected_fill_value = -129
def _assert_match(result_fill_value, expected_fill_value):
# GH#23982/25425 require the same type in addition to equality/NA-ness
res_type = type(result_fill_value)
ex_type = type(expected_fill_value)
if res_type.__name__ == "uint64":
# No idea why, but these (sometimes) do not compare as equal
assert ex_type.__name__ == "uint64"
elif res_type.__name__ == "ulonglong":
# On some builds we get this instead of np.uint64
# Note: cant check res_type.dtype.itemsize directly on numpy 1.18
assert res_type(0).itemsize == 8
assert ex_type == res_type or ex_type == np.uint64
else:
# On some builds, type comparison fails, e.g. np.int32 != np.int32
> assert res_type == ex_type or res_type.__name__ == ex_type.__name__
E AssertionError: assert (<class 'numpy.intc'> == <class 'numpy.int32'>
E -<class 'numpy.intc'>
E +<class 'numpy.int32'> or 'intc' == 'int32'
E - intc
E + int32)
```
The error message is consistent, except that `intc` and `int32` are replaced with `uintc` and `uint32` in some cases. I can provide all error messages if desired.
The tests are being run with numpy 1.18.1.
|
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| 562,829,603
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MDExOlB1bGxSZXF1ZXN0MzczMzU0NzI0
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|
REF: move Loc method that belongs on Index
|
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| 8
| 2020-02-10T21:10:49Z
| 2020-02-22T19:38:00Z
| 2020-02-22T16:04:39Z
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MEMBER
| null |
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MDU6SXNzdWU1NjI4ODE4Njg=
| 31,858
|
API: df.loc(axis=0)[key] works differently from df.loc[key]
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| 2020-02-10T23:04:31Z
| 2020-02-11T15:45:15Z
| 2020-02-11T15:45:15Z
|
MEMBER
| null |
I would expect these to behave identically, but, from tests.indexing.multiindex.test_slice.TestMultiIndexSlicers.test_loc_axis_arguments:
```
def _mklbl(prefix, n):
return ["{prefix}{i}".format(prefix=prefix, i=i) for i in range(n)]
index = MultiIndex.from_product(
[_mklbl("A", 4), _mklbl("B", 2), _mklbl("C", 4), _mklbl("D", 2)]
)
columns = MultiIndex.from_tuples(
[("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")],
names=["lvl0", "lvl1"],
)
df = (
DataFrame(
np.arange(len(index) * len(columns), dtype="int64").reshape(
(len(index), len(columns))
),
index=index,
columns=columns,
)
.sort_index()
.sort_index(axis=1)
)
>>> result = df.loc(axis=0)["A1":"A3", :, ["C1", "C3"]] # <-- currently in the test, works
>>> result2 = df.loc["A1":"A3", :, ["C1", "C3"]]
pandas.core.indexing.IndexingError: Too many indexers
```
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MDExOlB1bGxSZXF1ZXN0MzczMzk4OTA4
| 31,859
|
REF: move loc-only methods to loc
|
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MEMBER
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| 562,886,986
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MDExOlB1bGxSZXF1ZXN0MzczNDAyMzc2
| 31,860
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DOC: Specify use of google cloud storage for CSVs
|
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| 1
| 2020-02-10T23:18:12Z
| 2020-02-11T09:59:01Z
| 2020-02-11T02:01:49Z
|
CONTRIBUTOR
| null |
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NjI4OTU2NTY=
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|
Assigned conversion via loc to Int64 fails under peculiar conditions
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| 7
| 2020-02-10T23:43:35Z
| 2021-05-31T14:27:04Z
| 2021-05-31T14:27:04Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
test = pd.DataFrame({'a': ['test'], 'b': [np.nan]})
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
# *** AttributeError: 'IntegerArray' object has no attribute 'size'
```
#### Problem description
Assignment of `.astype('Int64')` sometimes fails with `AttributeError: 'IntegerArray' object has no attribute 'size'`.
Initially, I thought this was a regression from #25584, but it only manifests under very specific conditions:
1. There is exactly one null value in the converted column
```python
# works
test = pd.DataFrame({'a': ['test', 'test'], 'b': [np.nan, 1.0]})
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
# works
test = pd.DataFrame({'a': ['test', 'test'], 'b': [np.nan, np.nan]})
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
# fails
test = pd.DataFrame({'a': ['test'], 'b': [np.nan]})
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
# *** AttributeError: 'IntegerArray' object has no attribute 'size'
```
2. There is another column in the DataFrame
```python
# works
test = pd.DataFrame({'b': [np.nan]})
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
```
3. Assignment happens via `df.loc[:, colname]` - without assignment, or with assignment to `df[colname]`, things work.
```python
test = pd.DataFrame({'a': ['test'], 'b': [np.nan]})
# fails
test.loc[:, 'b'] = test['b'].astype('Int64')
# *** AttributeError: 'IntegerArray' object has no attribute 'size'
# fails
test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')
# *** AttributeError: 'IntegerArray' object has no attribute 'size'
# works
test.loc[:, 'b'].astype('Int64')
# works
test['b'] = test['b'].astype('Int64')
```
#### Expected Output
`test.loc[:, 'b'] = test.loc[:, 'b'].astype('Int64')` shouldn't raise an `AttributeError`.
`print(test)` after assignment should read along the lines of
```
0 <NA>
dtype: Int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-46-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.16.1
pytz : 2018.9
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : 3.10.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.0
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.10.1
pyxlsb : None
s3fs : None
scipy : 1.2.0
sqlalchemy : 1.2.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 562,917,798
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MDU6SXNzdWU1NjI5MTc3OTg=
| 31,862
|
Pandas qcut - Allow right argument to make left bins inclusive/exclusive
|
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[] | null | 1
| 2020-02-11T00:55:57Z
| 2020-02-11T08:36:26Z
| 2020-02-11T08:36:26Z
|
NONE
| null |
```python
def qcut(
x,
q,
right=True,
labels=None,
retbins: bool = False,
precision: int = 3,
duplicates: str = "raise",
):
fac, bins = _bins_to_cuts(
x,
bins,
right=True,
labels=labels,
precision=precision,
include_lowest=True,
dtype=dtype,
duplicates=duplicates,
)
```
#### Similar to R HMISC cut2 Equiwidth Binning
[Having right=False makes left bins inclusive and right bins exclusive whereas right=True makes left bins exclusive and right bins inclusive. When right=False, it replicates what cut2 function from HMISC R package is doing.]
#### Expected Output
<details>
When right=False, index of the dataframe looks like below:
```
[1.0, 3.0)
[4.0, 7.0)
```
When right=True, index of the dataframe looks like below:
```
(1.0, 3.0]
(1.0, 3.0]
```
</details>
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MDExOlB1bGxSZXF1ZXN0MzczNDMxNDc5
| 31,863
|
CLN: organize MultiIndex indexing tests
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REF: organize base class Index tests
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MEMBER
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implement test_index_new for testing `Index.__new__` inference
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CLN: tests.generic
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| 1
| 2020-02-11T01:22:55Z
| 2020-02-11T15:43:47Z
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|
MEMBER
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Many of the tests here really only test either Series or DataFrame, not both.
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MDExOlB1bGxSZXF1ZXN0MzczNDQ2MDY1
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BUG: catch almost-null-slice in _convert_slice_indexer
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| 2020-02-11T02:26:20Z
| 2020-02-23T17:11:04Z
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MEMBER
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its a contrived corner case, but i wanted to use com.is_null_slice rather than duplicating it in _convert_slice_indexer
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MDExOlB1bGxSZXF1ZXN0MzczNDU1ODk4
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API/BUG: raise only KeyError failed on geitem/loc lookups
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| 6
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| 2020-02-28T00:11:25Z
| 2020-02-27T23:08:52Z
|
MEMBER
| null |
- [x] closes #21567
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
ATM we raise a mix of KeyError and TypeError.
I'm in the process of going through the Indexing issues will see what else this closes.
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MDExOlB1bGxSZXF1ZXN0MzczNDY2MDA4
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CLN: f-string formatting
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| 2
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CONTRIBUTOR
| null |
Ref to [#29547](https://github.com/pandas-dev/pandas/issues/29547)
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Replacing strings interpolated with .format() with strings interpolated with f-strings
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MDU6SXNzdWU1NjMwMjk0ODQ=
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Dividing None Series with Timedelta fails with pandas 1.0.1
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| 10
| 2020-02-11T07:48:23Z
| 2020-03-06T12:37:07Z
| 2020-02-29T06:16:12Z
|
NONE
| null |
#### Code Sample
```python
import datetime
import pandas as pd
s1 = pd.Series([datetime.date(2020, 1, 1)])
s2 = pd.Series([None])
print((s1 - s2) / pd.Timedelta(days=1))
```
#### Problem description
With pandas 1.0.0, this was working fine and putting `NaN` in the `Series`.
With pandas 1.0.1, I get the following traceback:
```
Traceback (most recent call last):
File "t.py", line 6, in <module>
print((s1 - s2) / pd.Timedelta(days=1))
File ".../lib/python3.6/site-packages/pandas/core/ops/common.py", line 64, in new_method
return method(self, other)
File ".../lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 500, in wrapper
result = arithmetic_op(lvalues, rvalues, op, str_rep)
File ".../lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 193, in arithmetic_op
res_values = dispatch_to_extension_op(op, lvalues, rvalues)
File ".../lib/python3.6/site-packages/pandas/core/ops/dispatch.py", line 125, in dispatch_to_extension_op
res_values = op(left, right)
File "pandas/_libs/tslibs/timedeltas.pyx", line 1397, in pandas._libs.tslibs.timedeltas.Timedelta.__rtruediv__
numpy.core._exceptions.UFuncTypeError: ufunc 'true_divide' cannot use operands with types dtype('O') and dtype('<m8[ns]')
```
I run `git bisect` and the new behavior has been introduced in https://github.com/pandas-dev/pandas/commit/a8aff6cdd12b3e3ed2c767b1752fd17a19be6590.
What is the correct behavior?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
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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MDU6SXNzdWU1NjMwNzgxNTQ=
| 31,870
|
REGR: changed return type for multi-dimensional indexing
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| 2020-03-08T11:56:39Z
| 2020-02-11T10:36:14Z
|
CONTRIBUTOR
| null |
I kept my code free of deprecation warnings for 0.25.3 but upgrading still broke it.
I can now see on the tracker that multi-dimensional indexing has been deprecated #27837, #30588 #30867, but it seems the introduction of the deprecation warning has changed the behaviour itself.
```python
>>> pd.__version__
'0.25.3'
>>> idx = pd.Index([f'ID_{x}' for x in range(10)])
>>> selector = np.array(np.random.randint(0, 10, (3, 10)))
>>> selector
array([[2, 3, 4, 9, 1, 8, 5, 6, 3, 3],
[3, 5, 0, 7, 1, 3, 2, 0, 2, 8],
[8, 4, 6, 8, 0, 4, 3, 4, 5, 7]])
>>> idx[selector]
Index(['ID_2', 'ID_3', 'ID_4', 'ID_9', 'ID_1', 'ID_8', 'ID_5', 'ID_6', 'ID_3',
'ID_3', 'ID_3', 'ID_5', 'ID_0', 'ID_7', 'ID_1', 'ID_3', 'ID_2', 'ID_0',
'ID_2', 'ID_8', 'ID_8', 'ID_4', 'ID_6', 'ID_8', 'ID_0', 'ID_4', 'ID_3',
'ID_4', 'ID_5', 'ID_7'],
dtype='object')
>>> idx[selector].values
array([['ID_2', 'ID_3', 'ID_4', 'ID_9', 'ID_1', 'ID_8', 'ID_5', 'ID_6',
'ID_3', 'ID_3'],
['ID_3', 'ID_5', 'ID_0', 'ID_7', 'ID_1', 'ID_3', 'ID_2', 'ID_0',
'ID_2', 'ID_8'],
['ID_8', 'ID_4', 'ID_6', 'ID_8', 'ID_0', 'ID_4', 'ID_3', 'ID_4',
'ID_5', 'ID_7']], dtype=object)
```
On `v1.0.1`, this yields an `np.array` instead of a `pd.Index`, and so the `.values` call fails.
```python
>>> idx[selector]
__main__:1: DeprecationWarning: Support for multi-dimensional indexing (e.g. `index[:, None]`) on an Index is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
array([['ID_2', 'ID_3', 'ID_4', 'ID_9', 'ID_1', 'ID_8', 'ID_5', 'ID_6',
'ID_3', 'ID_3'],
['ID_3', 'ID_5', 'ID_0', 'ID_7', 'ID_1', 'ID_3', 'ID_2', 'ID_0',
'ID_2', 'ID_8'],
['ID_8', 'ID_4', 'ID_6', 'ID_8', 'ID_0', 'ID_4', 'ID_3', 'ID_4',
'ID_5', 'ID_7']], dtype=object)
```
Note also that the warning is (at least for me) not raised on python 3.6, only on 3.7.
If desired, I can flesh out the reasons for needing this multi-dimensional indexing. I wonder how I'll be able to replace it.
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| 563,118,695
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MDU6SXNzdWU1NjMxMTg2OTU=
| 31,871
|
Hexbin plot ignores matplotlib default colormap
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open
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[] | null | 8
| 2020-02-11T10:46:31Z
| 2021-07-28T05:21:33Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib as mpl
mpl.rcParams['image.cmap'] = 'Greys'
df = pd.DataFrame(np.random.randn(1000, 2), columns=["a", "b"])
df["b"] = df["b"] + np.arange(1000)
df.plot.hexbin(x="a", y="b", gridsize=25) # this is not gray...
```
#### Problem description
I don't know if this is intentional behaviour, but plotting a data frame as hexbin with matplotlib, the matplotlib default colormap (e.g. set by the style sheet, via `rcParams`, or whatever) is ignored due to this line:
https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_matplotlib/core.py#L1010
```python
cmap = self.colormap or "BuGn"
```
What I would expect as a user is that pandas uses the default colormap I have set rather than `"BuGn"`.
Simply changing this line to
```python
cmap = self.colormap
```
is enough (since `plt.cm.get_cmap(None)` will return the default color map). If you agree, I can submit a PR.
#### Expected Output
A gray hexbin plot.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
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 : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 563,129,595
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MDU6SXNzdWU1NjMxMjk1OTU=
| 31,872
|
Type hint of DataFrame(...dtype...) excludes numpy scalar types such as np.int8
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[] | null | 0
| 2020-02-11T11:06:11Z
| 2020-05-01T15:21:41Z
| null |
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d, dtype=np.int8)
```
#### Problem description
The code sample above was taken straight from the API reference:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html
It results in a 'Type checker' inspection warning from PyCharm because `np.int8` is not an instance of the expected type `Union[str, np.dtype, "ExtensionDtype"]`. Indeed, `isinstance(np.int8, np.dtype)` is false and the numpy documentation requires a conversion to get a data type object, such as `np.dtype(np.int8)`.
#### Suggested solution
Change the type hint in DataFrame.__init__ to
```python
Union[str, np.dtype, np.generic, "ExtensionDtype"]
```
#### Less desirable solution
Change the API reference to use `np.dtype(np.int8)` instead. This would be a shame, because the current version is more elegant and works perfectly at runtime.
#### Expected Output
The code should not generate type checker warnings.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-28-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NjMxODY4MzE=
| 31,873
|
Left merging a 6628 row DF with a 127286 row DF yields a 48939191 row DF
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| 2020-02-11T12:52:41Z
| 2020-02-11T12:55:33Z
| 2020-02-11T12:55:33Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
[ins] In [10]: df_e2.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 127286 entries, 0 to 6327
Data columns (total 24 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 revenue 18258 non-null string
1 phone 49570 non-null string
2 status 127286 non-null string
3 notes 66101 non-null string
4 facebook_url 43003 non-null string
5 linkedin_url 52043 non-null string
6 email 50212 non-null string
7 description 119431 non-null string
8 crunchbase_url 76787 non-null string
9 name 127286 non-null string
10 url 113731 non-null string
11 twitter_handle 56307 non-null string
12 hq_location 72719 non-null string
13 latitude 72719 non-null float64
14 longitude 72719 non-null float64
15 country_code 72697 non-null string
16 url_redir 47957 non-null string
17 founding_year 127286 non-null string
18 created_at 127286 non-null datetime64[ns]
19 updated_at 127286 non-null datetime64[ns]
20 type 127286 non-null string
21 raw_tags 127286 non-null object
22 database_id 127286 non-null Int64
23 dupe_group_ix 127286 non-null Int64
dtypes: Int64(2), datetime64[ns](2), float64(2), object(1), string(17)
memory usage: 24.5+ MB
[ins] In [11]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6629 entries, 0 to 6628
Data columns (total 37 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 6629 non-null Int64
1 name 6629 non-null string
2 url 6370 non-null string
3 description 6593 non-null string
4 created_at 6629 non-null datetime64[ns]
5 updated_at 6629 non-null datetime64[ns]
6 email 6481 non-null string
7 slug 6021 non-null string
8 raw_tags 6629 non-null object
9 short_description 157 non-null string
10 twitter_handle 5069 non-null string
11 hq_location 6428 non-null string
12 latitude 6416 non-null float64
13 longitude 6416 non-null float64
14 company_registration_number 61 non-null string
15 company_registrar_url 163 non-null string
16 tax_registration_number 52 non-null string
17 tax_registrar_url 51 non-null string
18 financial_registration_number 56 non-null string
19 financial_registrar_url 56 non-null string
20 instagram_handle 80 non-null string
21 crunchbase_url 5660 non-null string
22 facebook_url 4319 non-null string
23 linkedin_url 5097 non-null string
24 bloomberg_url 81 non-null string
25 founding_year 5842 non-null Int64
26 revenue 2020 non-null string
27 type 6472 non-null string
28 blog_url 113 non-null string
29 rss_url 51 non-null string
30 country_code 6397 non-null string
31 domain_name 203 non-null string
32 phone 4009 non-null string
33 database_id 6629 non-null Int64
34 status 6629 non-null string
35 notes 6458 non-null string
36 complete 214 non-null boolean
dtypes: Int64(3), boolean(1), datetime64[ns](2), float64(2), object(1), string(28)
memory usage: 1.9+ MB
[nav] In [13]: df2 = df.merge(df_e2, on='crunchbase_url', how='left', copy=False)
[ins] In [14]: df2.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 48939191 entries, 0 to 48939190
Data columns (total 60 columns):
# Column Dtype
--- ------ -----
0 id Int64
1 name_x string
2 url_x string
3 description_x string
4 created_at_x datetime64[ns]
5 updated_at_x datetime64[ns]
6 email_x string
7 slug string
8 raw_tags_x object
9 short_description string
10 twitter_handle_x string
11 hq_location_x string
12 latitude_x float64
13 longitude_x float64
14 company_registration_number string
15 company_registrar_url string
16 tax_registration_number string
17 tax_registrar_url string
18 financial_registration_number string
19 financial_registrar_url string
20 instagram_handle string
21 crunchbase_url string
22 facebook_url_x string
23 linkedin_url_x string
24 bloomberg_url string
25 founding_year_x Int64
26 revenue_x string
27 type_x string
28 blog_url string
29 rss_url string
30 country_code_x string
31 domain_name string
32 phone_x string
33 database_id_x Int64
34 status_x string
35 notes_x string
36 complete boolean
37 revenue_y string
38 phone_y string
39 status_y string
40 notes_y string
41 facebook_url_y string
42 linkedin_url_y string
43 email_y string
44 description_y string
45 name_y string
46 url_y string
47 twitter_handle_y string
48 hq_location_y string
49 latitude_y float64
50 longitude_y float64
51 country_code_y string
52 url_redir string
53 founding_year_y string
54 created_at_y datetime64[ns]
55 updated_at_y datetime64[ns]
56 type_y string
57 raw_tags_y object
58 database_id_y Int64
59 dupe_group_ix Int64
dtypes: Int64(5), boolean(1), datetime64[ns](4), float64(4), object(2), string(44)
memory usage: 22.2+ GB
[ins] In [15]: pd.__version__
Out[15]: '1.0.1'
[ins] In [16]: len(df)
Out[16]: 6629
[ins] In [17]: len(df_e2)
Out[17]: 127286
[ins] In [18]: len(df2)
Out[18]: 48939191
```
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-1101-aws
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 : 1.16.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.12.1
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
tabulate : 0.8.5
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.44.1
</details>
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MDU6SXNzdWU1NjMyMTI5ODg=
| 31,874
|
Align should consider the dtype when creating new columns
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| 2020-02-11T13:39:11Z
| 2021-07-28T05:23:14Z
| null |
CONTRIBUTOR
| null |
Currently when you align columns and create a new column, align will create a new float64 column filled with NaNs.
```python
In [1]: import pandas as pd
In [2]: a = pd.DataFrame({"A": [1, 2], "B": [pd.Timestamp('2000'), pd.NaT]})
In [3]: b = pd.DataFrame({"A": [1, 2]})
In [4]: a.align(b)[1].dtypes
Out[4]:
A int64
B float64
dtype: object
```
I think it'd be more useful for the dtypes of new columns to be the same as the dtype from the other.
```python
# proposed behavior
In [4]: a.align(b)[1].dtypes
Out[4]:
A int64
B datetime64[ns]
dtype: object
```
The newly created `B` column has dtype `datetime64[ns]`, the same as `a.B`.
This proposal would make the `fill_value` keyword a bit more complex.
1. The default of `np.nan` would change to `None`, which means "the right NA value for the dtype".
2. We would maybe need to accept a Mapping so users could specify specific fill values per column.
I think this would make the workaround in https://github.com/pandas-dev/pandas/pull/31679 unnecessary, as we'd have the correct dtype going into the operation.
---
If we think this is a good idea, it's probably an API breaking change. We *might* be able to deprecate this cleanly by (ab)using `fill_value`. We would warn when creating new columns.
```python
if new_columns and fill_value is no_default:
warnings.warn("Creating new float64 columns filled with NaN. In the future... "
"Specify fill_value=None to accept the future behavior now.")
fill_value = np.nan #
```
Unfortunately, that'll happen in the background during binops. Not sure how to get around that, aside from instructing users to explicitly align first.
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MDExOlB1bGxSZXF1ZXN0MzczNjY4NTcw
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DOC: add redirects from Rolling to rolling.Rolling
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| 2020-03-10T08:35:57Z
| 2020-03-10T08:35:48Z
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CONTRIBUTOR
| null |
- [x] closes #31762
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CLN: Move info
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MEMBER
| null |
precursor to xref #31796. Have moved `DataFrame.info` code into `pandas/io/formats/info.py`, along with tests
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BUG: fix infer_dtype for StringDtype
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| 2020-02-12T15:14:15Z
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MEMBER
| null |
Closes #31731
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CLN-29547 replace old string formatting
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| 2020-02-11T14:43:22Z
| 2020-02-11T15:05:19Z
| 2020-02-11T15:05:13Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the first one
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 31,879
|
Monthly Dev Meeting
|
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| 2020-02-11T15:04:22Z
| 2020-02-12T22:09:02Z
| 2020-02-12T22:09:02Z
|
CONTRIBUTOR
| null |
The next monthly dev call is tomorrow (Wednesday, February 12th) at 18:00 UTC. We invite all to come. https://dev.pandas.io/docs/development/meeting.html
We'll use this Zoom link: https://zoom.us/j/942410248
The agenda is at https://docs.google.com/document/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?usp=sharing if you have topics to discuss.
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| 563,285,712
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MDU6SXNzdWU1NjMyODU3MTI=
| 31,880
|
MultiLevelIndex sort_index already does support ascending as a list of booleans
|
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| 2020-02-11T15:30:22Z
| 2020-02-19T01:30:09Z
| 2020-02-19T01:30:09Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df.sort_index(level=[1, 0], ascending=[True, False])
```
#### Problem description
The [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_index.html) states that `pandas.DataFrame.sort_index` parameter `accepts` is a boolean. However, in the case of multi level index, it can (already) be a list of booleans indicating which level should ascending and which descending.
Both [lexsort_indexer](https://github.com/pandas-dev/pandas/blob/master/pandas/core/sorting.py#L192) and [sort_levels](https://github.com/pandas-dev/pandas/blob/4181042aad88224c9c8be8e7c7a8525090c8aa07/pandas/core/frame.py#L5031) support this behavior.
I suggest updating both tests and documentation.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.14-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
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 : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NjMzMDAxMTU=
| 31,881
|
assert_numpy_array_equal raises TypeError for pd.NA
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| 2020-02-11T15:51:26Z
| 2020-02-13T08:16:34Z
| 2020-02-13T08:16:34Z
|
MEMBER
| null |
Discovered in #31799
```python
>>> arr1 = np.array([True, False])
>>> arr2 = np.array([True, pd.NA])
>>> tm.assert_numpy_array_equal(arr1, arr2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/_testing.py", line 1001, in assert_numpy_array_equal
if not array_equivalent(left, right, strict_nan=strict_nan):
File "/Users/williamayd/clones/pandas/pandas/core/dtypes/missing.py", line 447, in array_equivalent
ensure_object(left.ravel()), ensure_object(right.ravel())
File "pandas/_libs/lib.pyx", line 583, in pandas._libs.lib.array_equivalent_object
raise
File "pandas/_libs/lib.pyx", line 574, in pandas._libs.lib.array_equivalent_object
elif not (PyObject_RichCompareBool(x, y, Py_EQ) or
File "pandas/_libs/missing.pyx", line 360, in pandas._libs.missing.NAType.__bool__
raise TypeError("boolean value of NA is ambiguous")
TypeError: boolean value of NA is ambiguous
```
Should yield an AssertionError instead of a TypeError
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MDU6SXNzdWU1NjMzMTIyMjk=
| 31,882
|
np.nan comparison to pd.NA Equality Semantics differ elementwise in IntervalArray
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| 2020-02-11T16:09:41Z
| 2020-04-28T06:04:47Z
| null |
MEMBER
| null |
Discovered in #31799
I think this is probably a deeper rooted problem than just IntervalArray but not sure how else to describe. @jorisvandenbossche maybe has thoughts
```python
>>> from pandas.core.arrays import IntervalArray
>>> arr = IntervalArray.from_arrays([0., 1., 2., np.nan], [1., 2., 3., np.nan])
>>> arr == pd.NA
array([False, False, False, False])
>>> arr[-1] == pd.NA
<NA>
```
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Add test for MultiIndex Construction with pd.NA
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| 5
| 2020-02-11T16:17:13Z
| 2020-04-14T15:00:52Z
| 2020-04-14T15:00:52Z
|
MEMBER
| null |
Discovered in #31799
```python
>>> pd.MultiIndex.from_product([np.array([0., np.nan], dtype="object"), ["a", "B"]])
MultiIndex([(0.0, 'a'),
(0.0, 'B'),
(nan, 'a'),
(nan, 'B')],
)
>>> pd.MultiIndex.from_product([np.array([0., pd.NA], dtype="object"), ["a", "B"]])
*** TypeError: boolean value of NA is ambiguous
```
May be the same root cause as #31881
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MDU6SXNzdWU1NjMzMjUyNjg=
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|
tm.assert_index_equal broken with pd.NA and np.NaT sentinel
|
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[] | null | 2
| 2020-02-11T16:29:35Z
| 2021-07-28T05:24:03Z
| null |
MEMBER
| null |
Discovered in #31799
I couldn't reproduce this without using a np.* NaT sentinel...
```python
>>> idx1 = pd.Index([pd.NA, np.datetime64("nat")])
>>> idx2 = pd.Index([pd.NA, pd.NaT])
>>> tm.assert_index_equal(idx1, idx2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/_testing.py", line 689, in assert_index_equal
diff = np.sum((left.values != right.values).astype(int)) * 100.0 / len(left)
AttributeError: 'bool' object has no attribute 'astype'
```
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MDU6SXNzdWU1NjMzNzI0MDU=
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|
columns keyword has no effect in DataFrame.to_excel() in 1.0.1
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[] | null | 1
| 2020-02-11T17:45:34Z
| 2020-02-11T21:09:20Z
| 2020-02-11T21:09:13Z
|
NONE
| null |
The `columns` keyword argument to the `DataFrame.to_excel()` method in version 1.0.1 of pandas does not restrict the columns in the Excel sheet to the specified column list. This is different from the behavior in 0.25.2 and previous versions, and also differs from the `to_csv()` method.
This can be replicated using:
```
import pandas as pd
df = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]],columns=['a','b','c'])
df.to_excel('columns_output.xlsx',columns=['a','c'])
w.save()
```
(I'm using the `xlsxwriter` engine)
In previous versions of pandas the output from that code would be an Excel file that only contains the index (no header), column a, and column c:
<table>
<tr>
<td></td>
<td>a</td>
<td>c</td>
</tr>
<tr>
<td>0</td>
<td>1</td>
<td>3</td>
</tr>
<tr>
<td>1</td>
<td>4</td>
<td>6</td>
</tr>
<tr>
<td>2</td>
<td>7</td>
<td>9</td>
</tr>
</table>
In the new version, all three columns are outputted, yielding an Excel file with all three columns:
<table>
<tr>
<td></td>
<td>a</td>
<td><em>b</em></td>
<td>c</td>
</tr>
<tr>
<td>0</td>
<td>1</td>
<td><em>2</em></td>
<td>3</td>
</tr>
<tr>
<td>1</td>
<td>4</td>
<td><em>5</em></td>
<td>6</td>
</tr>
<tr>
<td>2</td>
<td>7</td>
<td><em>8</em></td>
<td>9</td>
</tr>
</table>
(emphasis mine)
Not all columns in the dataframe need to be shared with the end user (internal values, processing/filtering/sorting columns, etc.) and some end users prefer columns in a specific order. Restoring the previous functionality would make that possible without having to change the dataframe itself.
Also, if the `headers` keyword is specified, matching the number of columns listed, like so:
```
df.to_excel('columns_output_headers.xlsx',columns=['a','c'],header=['column A','column C'])
```
A `ValueError` is returned as the column count does not match the header list length:
```
ValueError: Writing 3 cols but got 2 aliases
```
#### Output of ``pd.show_versions()``
1.0.1
<details>
Replicated in a local version, an aws lambda python 3.7 docker image, and in aws.
Local version:
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.7
numba : None
Docker image
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.184-linuxkit
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 : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.7
numba : None
If I force the version on pandas (and change nothing else) when building and running on the local Docker image, the proper column set appears.
</details>
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MDU6SXNzdWU1NjMzNzQ2MDU=
| 31,886
|
np.ndarray.nonzero() doesn't know how to deal with pd.NA and throws type error
|
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[] | null | 7
| 2020-02-11T17:49:33Z
| 2020-04-16T00:00:52Z
| 2020-04-15T23:58:57Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
np.where(pd.Series([True, False,pd.NA], dtype='boolean'))
```
#### Problem description
np.ndarray.nonzero() doesn't know how to deal with pd.NA and throws type error.
#### Expected Output
(array([0], dtype=int64),)
Since pd.NA is a value that just doesn't exist, it should be ignored and skipped over by nonzero() when returning indices of nonzero elements.
#### Actual Output
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-55-d3635393ceeb> in <module>
----> 1 np.where(pd.Series([True, False,pd.NA], dtype='boolean'))
<__array_function__ internals> in where(*args, **kwargs)
pandas\_libs\missing.pyx in pandas._libs.missing.NAType.__bool__()
TypeError: boolean value of NA is ambiguous
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzczODI3NDkw
| 31,887
|
CLN: simplify _setitem_with_indexer
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| 8
| 2020-02-11T19:02:55Z
| 2020-02-20T23:27:36Z
| 2020-02-20T22:37:38Z
|
MEMBER
| null |
The MultiIndex check it is doing is unnecessary for positional indexing.
Other things being cleaned up here appear to be remnants from Panel
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MDExOlB1bGxSZXF1ZXN0MzczODMyMjY0
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CLN: D300 Use """triple double quotes"""
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MDExOlB1bGxSZXF1ZXN0MzczODMyOTQy
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CLN: D201 No blank lines allowed before function docstring
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MDExOlB1bGxSZXF1ZXN0MzczODMzNzIw
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CLN: D208 Docstring is over-indented
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MDExOlB1bGxSZXF1ZXN0MzczODM0NjMy
| 31,891
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CLN: D209 Multi-line docstring closing quotes should be on a separate line
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MDExOlB1bGxSZXF1ZXN0MzczODM1NDQ0
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CLN: D204 1 blank line required after class docstring
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MDExOlB1bGxSZXF1ZXN0MzczODM2NDE4
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CLN: D213: Multi-line docstring summary should start at the second line
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MDExOlB1bGxSZXF1ZXN0MzczODM3MTA4
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DOC: Fix divmod return values
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| 2020-02-11T19:25:09Z
| 2020-02-12T01:39:14Z
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|
CONTRIBUTOR
| null |
- [x] closes #31663
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0MzczODM3NzYx
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CLN: D202 No blank lines allowed after function docstring
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MEMBER
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This one could conflict which the unchecked rule blank line before comment
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MDU6SXNzdWU1NjM0MjkzMTU=
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BUG: read_parquet unexpected output with pyarrow 0.16.0 and nullable usigned int dtype
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| 1
| 2020-02-11T19:35:36Z
| 2020-02-12T15:04:32Z
| 2020-02-12T15:04:32Z
|
NONE
| null |
```python
data = [np.nan, 1, 2, 3, 4]
df = pd.DataFrame()
df['col'] = pd.Series(data, dtype = "UInt32")
df.to_parquet('example.parquet')
res = pd.read_parquet('example.parquet')
print(res)
```
#### Problem description
The above cope prints:
```
col
0 <NA>
1 0
2 1
3 0
4 2
```
This happened after I upgraded to `pyarrow 0.16.0`, and `UInt32` is the only dtype that I had trouble with.
The problem should be with `read_parquet`, I tried reading parquet with another package, and got the expected output.
#### Expected Output
```
col
0 <NA>
1 1
2 2
3 3
4 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.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 : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzczODc4NjA0
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BUG: fix length_of_indexer with boolean mask
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| 2
| 2020-02-11T21:01:49Z
| 2020-02-18T00:46:10Z
| 2020-02-18T00:15:46Z
|
MEMBER
| null |
- [x] closes #25774
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Allows for a nice cleanup in _setitem_with_indexer. Note this will conflict with #31887; the order doesn't matter.
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MDExOlB1bGxSZXF1ZXN0MzczODk4Mzk5
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|
DOC: Update sort_index docs
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| 3
| 2020-02-11T21:44:43Z
| 2020-02-19T01:36:21Z
| 2020-02-19T01:30:09Z
|
MEMBER
| null |
- [x] closes #31880
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|
Combining two Series of dtype `Int8` converts them to `Int64`
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| 2020-02-11T21:48:50Z
| 2021-07-28T05:24:31Z
| null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> s1 = pd.Series([91, 92, pd.NA, 94], dtype='Int8')
>>> s2 = s1.copy()
>>> s1 + s2 # result looks correct!
0 -74
1 -72
2 <NA>
3 -68
dtype: Int8
>>> s1.combine(s2, lambda x, y: x+y) # dtype has changed!
0 -74
1 -72
2 <NA>
3 -68
dtype: Int64
```
#### Problem description
The dtype has changed.
#### Expected Output
```
0 -74
1 -72
2 <NA>
3 -68
dtype: Int8
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 87b149f41d305fefa2a22b379b915c33b24a6d60
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+2024.g87b149f41
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.5.1
sphinx : 2.3.1
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 : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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TST: parametrize generic/internals tests
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MDExOlB1bGxSZXF1ZXN0MzczOTM3NDMz
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TST: parametrize eval tests
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MDU6SXNzdWU1NjM1NjI2MzM=
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|
Add generic buffer (s3) support to read_hdf
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| 6
| 2020-02-11T22:52:14Z
| 2020-05-14T18:27:20Z
| null |
CONTRIBUTOR
| null |
Hey folks, this is not a bug but a question/feature request.
Today `read_hdf` does not support reading hdf files directly from `s3`.
If you try to pass an _s3_ url directly, as you can do with `read_csv` you get a _file does not exist error message:
```python
>>> import pandas as pd
>>> df = pd.read_hdf('s3://mybucket/myfile.h5', 'df)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/pandas/io/pytables.py", line 395, in read_hdf
raise FileNotFoundError(f"File {path_or_buf} does not exist")
FileNotFoundError: File s3://mybucket/myfile.h5 does not exist
```
And if we try to pass a _file-like_ as the path, we get a error saying that support for generic buffers is not implemented
```python
>>> import pandas as pd
>>> from s3fs import S3FileSystem
>>> s3 = S3FileSystem(anon=False)
>>> df = pd.read_hdf(s3.open('mybucket/myfile.h5', mode='rb'), 'df')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/pandas/io/pytables.py", line 385, in read_hdf
"Support for generic buffers has not been implemented."
NotImplementedError: Support for generic buffers has not been implemented.
```
Are there any plans to implement generic buffers for `read_hdf`, so that we could read from s3 directly?
I took a quick look and this seems to be a restriction with _tables_ not supporting s3 or file-like but I'm not sure.
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MDU6SXNzdWU1NjM1NzY0MjY=
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|
PERF: Index.__getitem__ should not need to do dtype inference
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| 2020-02-11T23:04:31Z
| 2020-06-10T06:57:39Z
| 2020-06-09T20:46:42Z
|
MEMBER
| null |
Currently, the `__getitem__` call in a snippet like
```
idx = pd.Index([1, 2, 3, 4])
idx[np.array([0, 3])]
```
does type inference when creating the new Index object (using `_shallow_copy_with_infer`), while I think we can always know for sure the result will be another Int64Index (for the above example) ?
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|
pandas read_excel error:TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.
|
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| 2020-02-12T01:18:54Z
| 2020-02-12T01:32:11Z
| 2020-02-12T01:32:10Z
|
NONE
| null |
#### My code
```python
import pandas as pd
f = 'F:/example.xlsx'
ex_file = pd.ExcelFile(f)
sheet_names = ex_file.sheet_names
for name in sheet_names:
print(name)
dc = pd.read_excel(ex_file, sheet_name=name)
```
#### Problem details
```
Traceback (most recent call last):
File "D:\pandas\core\categorical.py", line 330, in __init__
codes, categories = factorize(values, sort=True)
File "D:\pandas\core\algorithms.py", line 479, in factorize
assume_unique=True)
File "D:\pandas\core\sorting.py", line 443, in safe_sort
ordered = sort_mixed(values)
File "D:\pandas\core\sorting.py", line 436, in sort_mixed
nums = np.sort(values[~str_pos])
File "D:\numpy\core\fromnumeric.py", line 847, in sort
a.sort(axis=axis, kind=kind, order=order)
TypeError: '<' not supported between instances of 'datetime.datetime' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "F:\testReadexcel.py", line 8, in <module>
dc = pd.read_excel(ex_file, sheet_name=name)
File "D:\pandas\util\_decorators.py", line 118, in wrapper
return func(*args, **kwargs)
File "D:\pandas\io\excel.py", line 238, in read_excel
false_values=false_values, squeeze=squeeze, **kwds)
File "D:\pandas\io\excel.py", line 543, in _parse_excel
output[asheetname] = parser.read()
File "D:\pandas\io\parsers.py", line 1069, in read
ret = self._engine.read(nrows)
File "D:\pandas\io\parsers.py", line 2273, in read
index, columns = self._make_index(data, alldata, columns, indexnamerow)
File "D:\pandas\io\parsers.py", line 1412, in _make_index
index = self._agg_index(index)
File "D:\pandas\io\parsers.py", line 1512, in _agg_index
index = _ensure_index_from_sequences(arrays, names)
File "D:\pandas\core\indexes\base.py", line 4152, in _ensure_index_from_sequences
return MultiIndex.from_arrays(sequences, names=names)
File "D:\pandas\core\indexes\multi.py", line 1150, in from_arrays
labels, levels = _factorize_from_iterables(arrays)
File "D:\pandas\core\categorical.py", line 2352, in _factorize_from_iterables
return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
File "D:\pandas\core\categorical.py", line 2352, in <listcomp>
return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
File "D:\pandas\core\categorical.py", line 2324, in _factorize_from_iterable
cat = Categorical(values, ordered=True)
File "D:\pandas\core\categorical.py", line 336, in __init__
raise TypeError("'values' is not ordered, please "
TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.
```
#### Expected Output
It can print the sheet name, but pandas can't read the first sheet correctly.
#### Output of ``pd.show_versions()``
0.22.0
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MDExOlB1bGxSZXF1ZXN0Mzc0MDQ0MjA0
| 31,905
|
BUG: using loc[int] with object index
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| 1
| 2020-02-12T01:32:32Z
| 2020-02-22T17:53:21Z
| 2020-02-22T17:53:09Z
|
MEMBER
| null |
- [x] closes #19456
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This causes us to raise KeyError instead of TypeError in a couple of places, consistent with #31867.
Also note this leaves us with only one non-plotting usage of `holds_integer`, and it wouldnt surprise me if that one is subtly causing problems too.
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MDExOlB1bGxSZXF1ZXN0Mzc0MDQ5Nzkw
| 31,906
|
REF: implement unpack_1tuple to clean up Series.__getitem__
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| 0
| 2020-02-12T01:55:21Z
| 2020-02-17T22:16:06Z
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|
MEMBER
| null |
Doing this up-front instead of in two places makes for a nice cleanup. There will be a small perf penalty for the cases that wouldn't otherwise need to do these checks.
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MDExOlB1bGxSZXF1ZXN0Mzc0MDUwMjk1
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|
CLN: remove odious kludge
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| 1
| 2020-02-12T01:57:37Z
| 2020-02-12T15:57:58Z
| 2020-02-12T04:17:27Z
|
MEMBER
| null |
This is a legacy of Panel/ix AFAICT, was introduced in 2011.
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|
pd.read_excel() ignores converter functions over na_filter argument
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| 2020-02-12T03:02:22Z
| 2020-05-08T00:46:10Z
| null |
NONE
| null |
Please consider the code below.
```python
import pandas as pd
__to_str = lambda x: '' if pd.isnull(x) == True else str(x)
# Let's say that the excel to be read has a column with some empty strings or nulls in them
# by default na_filter is True
df = pd.read_excel('./file1.xlsx', converters = {'Name': __to_str}) # -> Leads to empty strings being read as nan
# when na_filter is False
df = pd.read_excel('./file1.xlsx', converters = {'Name': __to_str}, na_filter=False) # -> Leads to empty strings be read as empty strings (according to __to_str function)
```
#### Why this behaviour?
I believe that if there's an explicitly provided argument(`converters`), its behaviour should override any other argument that has not been set(has a default value -> `na_filter`), **in case of such conflict or overlap of effect arises**.
#### Expected output
When one argument is specified which operates at a certain specificity level, the other arguments having default values should give in to the effect caused by the explicitly set one.
For instance, if `converters` is provided with a function to handle cell values of a certain column, then `na_filter` or `keep_default_na` values which are applied by default and have not been passed explicitly.
Please correct me if my understanding is incorrect.
#### Output of `pd.show_versions()`
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.7
numba : None
</details>
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MDU6SXNzdWU1NjM3MTkxMTk=
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|
df.to_parquet crashes on pyarrow with timedelta64[ns]
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| 4
| 2020-02-12T03:18:26Z
| 2020-02-12T20:03:47Z
| 2020-02-12T03:24:40Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# any dataframe with a timedelta64[ns] type in it
df.to_parquet('/tmp/tmp.parquet.gzip', compression='gzip')
```
versions:
```
Python 3.7.6
pandas==0.25.3
pyarrow==0.15.1
```
#### Problem description
```
Traceback (most recent call last):
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/site-packages/pandas/core/frame.py", line 2237, in to_parquet
**kwargs
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/site-packages/pandas/io/parquet.py", line 254, in to_parquet
**kwargs
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/site-packages/pandas/io/parquet.py", line 101, in write
table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
File "pyarrow/table.pxi", line 1057, in pyarrow.lib.Table.from_pandas
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 560, in dataframe_to_arrays
convert_fields))
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/concurrent/futures/_base.py", line 598, in result_iterator
yield fs.pop().result()
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/concurrent/futures/_base.py", line 428, in result
return self.__get_result()
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 546, in convert_column
raise e
File "/opt/conda/envs/gis-dataprocessing/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 540, in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
File "pyarrow/array.pxi", line 207, in pyarrow.lib.array
File "pyarrow/array.pxi", line 74, in pyarrow.lib._ndarray_to_array
File "pyarrow/array.pxi", line 62, in pyarrow.lib._ndarray_to_type
File "pyarrow/error.pxi", line 86, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: ('Unsupported numpy type 22', 'Conversion failed for column elapsed_time with type timedelta64[ns]')
```
PS, Submitting an issue on pyarrow in JIRA is a complete 🤕 😞
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MDExOlB1bGxSZXF1ZXN0Mzc0MDg1NDMz
| 31,910
|
BUG: Handle NA in assert_numpy_array_equal
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| 3
| 2020-02-12T03:29:38Z
| 2020-02-13T14:14:33Z
| 2020-02-13T08:16:34Z
|
MEMBER
| null |
- [x] closes #31881
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
I think in the the case where we have NA we want to use identity rather than equality for checking when two arrays are equal.
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CLN: implement _getitem_tuple_same_dim
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| 2
| 2020-02-12T03:42:23Z
| 2020-02-12T17:49:41Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
Obtain the category codes from `CategoricalDtype`
|
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| 2020-02-14T01:49:48Z
| 2020-02-13T00:24:00Z
|
CONTRIBUTOR
| null |
```python
cat = pd.Categorical.from_codes([-1, 1, 2, 3, 4], ['-', 'a', 'b', 'c', 'd'])
print(cat.codes)
>>> array([-1, 1, 2, 3, 4], dtype=int8)
dtype = cat.dtype
dtype.codes
>>> Traceback (most recent call last):
>>> File "<stdin>", line 1, in <module>
>>> AttributeError: 'CategoricalDtype' object has no attribute 'codes'
```
#### Problem description
Once a `pd.CategoricalDtype` is created, the codes (which are attached to `pd.Categorical`) are essentially gone. I am using another dataframe as data holder for categorical data and need to switch back and forth between code and label representation of categorical. Once a series is converted to categorical the `dtype` does not hold the code information any more.
This is where the codes go missing (`categories` was series with indices representing the codes, but only values are used to construct the `categories`).
```python
@staticmethod
def validate_categories(categories, fastpath: bool = False):
...
elif not isinstance(categories, ABCIndexClass):
categories = Index(categories, tupleize_cols=False)
```
#### Expected Output
`dtype.codes` returns something, or even better don't differentiate between pd.Categorical and `pd.CategoricalDtype`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.17-200.fc31.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.0.1
Cython : 0.29.11
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.9.6
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 2.2.4
numexpr : 2.6.9
odfpy : None
openpyxl : 1.8.6
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.12
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 0.9.6
</details>
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DataFrame.query raises ValueError when comparing columns with nullable dtypes
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| 10
| 2020-02-12T04:33:05Z
| 2021-10-25T12:24:31Z
| null |
CONTRIBUTOR
| null |
#### Code Sample
```python
In [2]: df1 = pd.DataFrame({'A': [1, 1, 2], 'B': [1, 2, 2]})
In [3]: df1.dtypes
Out[3]:
A int64
B int64
dtype: object
In [4]: df2 = pd.DataFrame({'A': [1, 1, 2], 'B': [1, 2, 2]}, dtype='Int64')
In [5]: df2.dtypes
Out[5]:
A Int64
B Int64
dtype: object
In [6]: df1.query('A == B')
Out[6]:
A B
0 1 1
2 2 2
In [7]: df2.query('A == B')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-8efe41b297d7> in <module>
----> 1 df2.query('A == B')
~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in query(self, expr, inplace, **kwargs)
3229 kwargs["level"] = kwargs.pop("level", 0) + 1
3230 kwargs["target"] = None
-> 3231 res = self.eval(expr, **kwargs)
3232
3233 try:
~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
3344 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
3345
-> 3346 return _eval(expr, inplace=inplace, **kwargs)
3347
3348 def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
~/anaconda3/lib/python3.6/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
335 eng = _engines[engine]
336 eng_inst = eng(parsed_expr)
--> 337 ret = eng_inst.evaluate()
338
339 if parsed_expr.assigner is None:
~/anaconda3/lib/python3.6/site-packages/pandas/core/computation/engines.py in evaluate(self)
71
72 # make sure no names in resolvers and locals/globals clash
---> 73 res = self._evaluate()
74 return reconstruct_object(
75 self.result_type, res, self.aligned_axes, self.expr.terms.return_type
~/anaconda3/lib/python3.6/site-packages/pandas/core/computation/engines.py in _evaluate(self)
112 scope = env.full_scope
113 _check_ne_builtin_clash(self.expr)
--> 114 return ne.evaluate(s, local_dict=scope)
115
116
~/anaconda3/lib/python3.6/site-packages/numexpr/necompiler.py in evaluate(ex, local_dict, global_dict, out, order, casting, **kwargs)
820 # Create a signature
821 signature = [(name, getType(arg)) for (name, arg) in
--> 822 zip(names, arguments)]
823
824 # Look up numexpr if possible.
~/anaconda3/lib/python3.6/site-packages/numexpr/necompiler.py in <listcomp>(.0)
819
820 # Create a signature
--> 821 signature = [(name, getType(arg)) for (name, arg) in
822 zip(names, arguments)]
823
~/anaconda3/lib/python3.6/site-packages/numexpr/necompiler.py in getType(a)
701 if kind == 'S':
702 return bytes
--> 703 raise ValueError("unknown type %s" % a.dtype.name)
704
705
ValueError: unknown type object
```
#### Problem description
`DataFrame.query` raises `ValueError: unknown type object` for boolean comparisons when the dtype is one of the new nullable types. (I have tested this for both `Int64` and `string` dtypes.)
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.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 : 0.29.15
pytest : 5.3.5
hypothesis : 5.4.1
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 : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
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 : 1.2.0
xlsxwriter : 1.2.7
numba : 0.48.0
```
</details>
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MDExOlB1bGxSZXF1ZXN0Mzc0MTA3MzUx
| 31,914
|
CLN: 29547 replace old string formatting 1
|
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| 2
| 2020-02-12T04:56:23Z
| 2020-02-21T01:30:03Z
| 2020-02-12T17:18:50Z
|
CONTRIBUTOR
| null |
I splitted PR #31844 in batches, this is the first one
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: Some groupby internals
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| 2020-02-12T07:03:11Z
| 2020-02-17T00:23:58Z
| 2020-02-17T00:23:55Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
1. Avoids calling `_get_sorted_data()` twice
2. Eliminates `*args, **kwargs` from an internal function
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