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https://api.github.com/repos/pandas-dev/pandas/issues/33217
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BUG: Block.setitem GH#32395
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MEMBER
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@h-vishal this is based on what you have in #32479. Can you adapt parts of this to address comments over there? (that should be merged, not this, as you did the real work)
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32380 deprecate squeeze in groupby
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| 2020-04-01T20:02:44Z
| 2020-05-23T19:59:10Z
| 2020-05-22T23:49:12Z
|
MEMBER
| null |
- [x] closes #32380
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I deprecated the squeeze keyword. I think I got every points this keyword was used.
I hope that I considered every point necessary to deprecate a keyword. I tried to use other points were a keyword was deprecated as example.
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REF: DataFrame.mask tests
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MEMBER
| null |
we already did this for Series.mask
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ENH: Add isocalendar accessor to DatetimeIndex and Series.dt
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| 22
| 2020-04-01T20:35:15Z
| 2020-04-13T20:01:43Z
| 2020-04-12T21:34:32Z
|
CONTRIBUTOR
| null |
This PR adds the the isocalendar property to `DatetimeIndex` and the corresponding `Series.dt` accessor. The property returns a DataFrame, e.g.:
```python
>>> idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
>>> idx.isocalendar
year week day
0 2019 52 7
1 2020 1 1
2 2020 1 2
3 2020 1 3
```
and
```python
>>> pandas.to_datetime(pandas.Series(["2020-01-01"])).dt.isocalendar
year week day
0 2020 1
>>> pandas.to_datetime(pandas.Series(["2019-12-31"])).dt.isocalendar.week
0 1
Name: week, dtype: int32
```
The behavior is consistent with `Timestamp.isocalendar` and `datetime.date.isocalendar`.
For more information about ISO 8601 calendar, see e.g. https://en.wikipedia.org/wiki/ISO_week_date.
Address GH33206
- [x] closes #33206
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Note that I am very happy to rename the field, but I wanted to go ahead and open up the PR.
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TST: misplaced validate_indices tests
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REF: misplaced Index.__contains__ tests
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compatibility issues with master branch cython
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CONTRIBUTOR
| null |
On the cython master branch the generated c code has unused functions which fails the build with:
```
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -fno-semantic-interposition -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -march=x86-64 -mtune=generic -O3 -pipe -fno-plt -fPIC -DNPY_NO_DEPRECATED_API=0 -I./pandas/_libs -Ipandas/_libs/src/klib -I/home/tcaswell/.virtualenvs/sys38/lib/python3.8/site-packages/numpy/core/include -I/home/tcaswell/.virtualenvs/sys38/include -I/usr/include/python3.8 -c pandas/_libs/algos.c -o build/temp.linux-x86_64-3.8/pandas/_libs/algos.o -Werror
pandas/_libs/algos.c:87270:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_762__defaults__’ defined but not used [-Werror=unused-function]
87270 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_762__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:78766:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_744__defaults__’ defined but not used [-Werror=unused-function]
78766 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_744__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:71391:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_726__defaults__’ defined but not used [-Werror=unused-function]
71391 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_726__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:62479:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_724__defaults__’ defined but not used [-Werror=unused-function]
62479 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_724__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:56193:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_678__defaults__’ defined but not used [-Werror=unused-function]
56193 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_678__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:50908:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_632__defaults__’ defined but not used [-Werror=unused-function]
50908 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_632__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:40806:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_586__defaults__’ defined but not used [-Werror=unused-function]
40806 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_586__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:34476:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_540__defaults__’ defined but not used [-Werror=unused-function]
34476 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_540__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:29147:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_494__defaults__’ defined but not used [-Werror=unused-function]
29147 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_494__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:19248:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_448__defaults__’ defined but not used [-Werror=unused-function]
19248 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_448__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:12162:18: error: ‘__pyx_pf_6pandas_5_libs_5algos_446__defaults__’ defined but not used [-Werror=unused-function]
12162 | static PyObject *__pyx_pf_6pandas_5_libs_5algos_446__defaults__(CYTHON_UNUSED PyObject *__pyx_self) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cc1: all warnings being treated as errors
error: command 'gcc' failed with exit status 1
```
and similar warnings from other modules (this just happens to be the first one).
If I remove the `"-Werror"` it seems to build ok.
Not sure if this should be reported here at cython. I will likely try to bisect this to the cython change later today.
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MDExOlB1bGxSZXF1ZXN0Mzk3MjQ1Mzc2
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CI: Checking all the examples in `pandas/core/series.py`
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| 1
| 2020-04-01T21:38:23Z
| 2020-04-06T08:43:47Z
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MEMBER
| null |
Since all the examples are passing, there is no reason not to check for all the examples (IMO).
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MDExOlB1bGxSZXF1ZXN0Mzk3MjY5NjQ0
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Fix typos in 09_timeseries.rst
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| 2
| 2020-04-01T22:48:58Z
| 2020-04-02T16:18:08Z
| 2020-04-02T16:18:02Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzk3MjcyMzg3
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CLN: De-privatize names
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| 2020-04-01T22:58:54Z
| 2020-04-02T15:23:16Z
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|
MEMBER
| null |
xref #32942, these are some of the more common offenders.
I'd actually prefer to not have the _NS_DTYPE and _TD_DTYPE objects and just use the pertinent strings, but if we are going to use the objects, might as well de-code-smell them.
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MDExOlB1bGxSZXF1ZXN0Mzk3MzAxNDMw
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Clean Up Categorical Test for JSON
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| 3
| 2020-04-02T00:55:27Z
| 2020-04-04T15:25:12Z
| 2020-04-03T03:20:19Z
|
MEMBER
| null |
This is a little wonky in its current state, bolting categoricals onto an existing object / fixture
Instead just localized this to the one test that covers it for now
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REF: sql insert_data operate column-wise to avoid internals
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MDExOlB1bGxSZXF1ZXN0Mzk3MzE3MDc0
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DOC: Fixed examples in `pandas/core/groupby/`
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- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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REF: put concatenate_block_managers in internals.concat
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|
merge() outer with left_on column and right_index=True produces unexpected results
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| 2020-04-02T03:54:22Z
| 2020-05-28T20:36:30Z
| null |
CONTRIBUTOR
| null |
Apologies if this has been reported before, I searched through the issues but couldn't find a duplicate, IMHO.
#### Code Sample:
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'lkey': [0, np.nan, np.nan, 3], 'value': [1, 2, 3, 5]})
>>> df1
lkey value
0 0.0 1
1 NaN 2
2 NaN 3
3 3.0 5
df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'rock', 'baz'], 'value': [0, 1, 2, 3]})
>>> df2
rkey value
0 foo 0
1 bar 1
2 rock 2
3 baz 3
res_1 = pd.merge(df1, df2, how='outer', suffixes=('_left', '_right'), left_on='lkey', right_index=True)
>>> res_1
lkey value_left rkey value_right
0.0 0.0 1.0 foo 0.0
1.0 NaN 2.0 NaN NaN
2.0 NaN 3.0 NaN NaN
3.0 3.0 5.0 baz 3.0
NaN 1.0 NaN bar 1.0
NaN 2.0 NaN rock 2.0
res_2 = pd.merge(df1, df2, how='outer', suffixes=('_left', '_right'), left_on='lkey', right_on='value')
>>> res_2
lkey value_left rkey value_right
0 0.0 1.0 foo 0.0
1 NaN 2.0 NaN NaN
2 NaN 3.0 NaN NaN
3 3.0 5.0 baz 3.0
4 NaN NaN bar 1.0
5 NaN NaN rock 2.0
```
#### Problem description
- When merging with `how=outer` and `right_index=True`, result `res_1` produces the last 2 rows with some unexpected values for the `index` and especially the `lkey`.
- Last 2 rows have `lkey` values that don't seem to exist in the original left side `df1`.
- Last 2 rows have `np.nan` for `index`. Was expecting perhaps `[4.0, 5.0]`
- Compare this to `res_2`. Which is almost identical `merge` except now instead of `right_index=True` we use a column `right_on='value'`
- the `df2` `index` and `value` column have the same type and values.
- I would expect seeing `res_2` instead of `res_1` when merging with `right_index=True` above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
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 : 20.0.2
setuptools : 45.1.0
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 : None
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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MDExOlB1bGxSZXF1ZXN0Mzk3Mzc2MjA2
| 33,233
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Ods loses spaces 32207
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CONTRIBUTOR
| null |
- [X] closes #32207
- [X] tests added / passed tests.io.excel.test_reader_spaces
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (I had to use git diff master though)
- [ ] whatsnew entry. It's only a bug fix does it need a whatsnew?
I'm not sure how you make xlsxb files so that test wasn't implement when checking for the different types of space.
There's quite possibly many other ways this parser doesn't handle the odf specification.
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MDU6SXNzdWU1OTIzOTEyNDM=
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Groupby multiple columns causes agg to have precision loss on int64
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| 3
| 2020-04-02T06:31:38Z
| 2021-09-11T00:19:07Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df=pd.DataFrame({
"KEY1": ["KEY1"],
"KEY2": ["KEY2"],
"INT64": [1583715738627261039]
})
# Grouping by one column produces correct output
print(df.groupby(["KEY1"]).agg(lambda x: x))
# Grouping by more than one column produces incorrect output
print(df.groupby(["KEY1", "KEY2"]).agg(lambda x: x))
```
#### Problem description
Precision loss on the int64 column when grouping by multiple columns.
Similar behaviour can be seen when doing
```
>>> df.groupby(["KEY1"]).sum()
INT64
KEY1
KEY1 1583715738627260928
```
but that seems like a different issue with cythonized group sum not supporting int64? According to: https://github.com/pandas-dev/pandas/issues/15027#issuecomment-269902341
#### Expected Output
```
KEY2 INT64
KEY1
KEY1 KEY2 1583715738627261039
INT64
KEY1 KEY2
KEY1 KEY2 1583715738627261039
```
#### Actual Output
```
KEY2 INT64
KEY1
KEY1 KEY2 1583715738627261039
INT64
KEY1 KEY2
KEY1 KEY2 1583715738627260928
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.16.0-77-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
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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MDExOlB1bGxSZXF1ZXN0Mzk3NTMyODMz
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TST: Use try/except block to properly catch and handle the exception
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| 10
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| 2020-05-28T22:10:39Z
| 2020-04-28T19:40:32Z
|
CONTRIBUTOR
| null |
Now no more warnings
- [x] closes #32951
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 15
| 2020-04-02T12:36:58Z
| 2020-04-09T06:34:57Z
| 2020-04-08T17:20:51Z
|
MEMBER
| null |
- [X] closes #33201
- [X] 1 test modified / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
This PR adds a forward-looking `pandas.api.indexers.BaseIndexer` subclass implementation and exposes it to users as discussed in #33180 .
### Exposing other indexers
It is also possible to expose a backward-looking indexer simply by exposing `pandas.core.window.indexers.FixedWindowIndexer` through the API. Seems redundant to me as this is basically the same as supplying an integer as the window argument when creating a rolling object but it can be done for consistency. I'd be grateful for a discussion.
### Note on tests
The class is incorporated into `test_rolling_forward_window` in `test_base_indexer.py` and this serves as a test that the new indexer is working correctly.
### Notes on performance
I've tested the implementation and it shows comparable performance to `pandas.core.window.indexers.FixedWindowIndexer`. In my tests the new indexer was slightly faster, either by luck, or because I streamlined the backward-looking indexer a bit when making the forward-looking one.
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MDU6SXNzdWU1OTI2MDg0ODc=
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Warning "Try using .loc[row_indexer,col_indexer] = value instead" is given when using ".loc"
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| 2020-04-02T12:53:19Z
| 2020-04-03T16:23:49Z
| 2020-04-03T16:23:49Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
TLDR; Pandas warning is confusing since it recommends what seems to cause the warning.
```python
# run this in one cell of a freshly started Jupyterlab Notebook
# the warning is shown once, to see it again, restart the kernel
import pandas as pd
df = pd.DataFrame([list(range(1000)), list(range(1000)), list(range(1000)), list(range(1000))]).T
df2 = df[[1, 3]]
df2.loc[df2[1] > 500, 'marker'] = True
```
#### Problem description
This gives out the following warning:
```
C:\Python37\lib\site-packages\pandas\core\indexing.py:844: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[key] = _infer_fill_value(value)
C:\Python37\lib\site-packages\pandas\core\indexing.py:965: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[item] = s
```
This warning has confused me a many, many times, mainly, because it suggests
"try using .loc" when that's exactly what I'm doing.
(I followed the link and read the doc and now know that I should have made a copy the
line before) - still the suggestion to use .loc is not helful here.
#### Expected Output
Remove the line "Try using .loc[row_indexer,col_indexer] = value instead"
when the code is using ".loc". The rest of the warning is fine.
#### 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 : 7
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : DE
LOCALE : None.None
pandas : 1.0.1
numpy : 1.16.2
pytz : 2018.7
dateutil : 2.7.5
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : 5.2.0
hypothesis : None
sphinx : 1.7.5
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.3.2
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.0
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.1.2
numba : None
</details>
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MDU6SXNzdWU1OTI2Njg1NDA=
| 33,238
|
CI/COMPAT: Linux py37_np_dev pipeline timeouts
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| 1
| 2020-04-02T14:17:55Z
| 2020-05-26T09:39:13Z
| 2020-04-02T21:13:16Z
|
MEMBER
| null |
#### Problem description
Linux py37_np_dev pipeline appears to timeout for everyone after 60 minutes.
There are a couple hundred thousand errors like this:
```
Exception ignored in: 'pandas.io.sas._sas.Parser.process_byte_array_with_data'
DeprecationWarning: tostring() is deprecated. Use tobytes() instead.
DeprecationWarning: tostring() is deprecated. Use tobytes() instead.
```
Here is a [link](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=32212&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=4d05ed0e-1ed3-5bff-dd63-1e957f2766a9&l=792078) to it failing for me.
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MDU6SXNzdWU1OTI3NzcwODQ=
| 33,239
|
Unable to Build pandas with Py38, macOS (clang?) and Cython>0.29.13 (tp_print warning)
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| 2020-04-02T16:52:24Z
| 2020-09-25T22:36:13Z
| 2020-09-25T22:36:13Z
|
MEMBER
| null |
Dedicated issue for @jbrockmendel callout in https://github.com/pandas-dev/pandas/issues/30609#issuecomment-605373212
I opened an issue on the Cython side as well https://github.com/cython/cython/issues/3474
There's a rabbit hole of conversation with this going back to CPython . This may just be a temporary issue for Py38, so one "workaround" could be to just add `-Wno-deprecated-declaration` when these particular combination of things is detected, though of course an actual fix would be ideal
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MDExOlB1bGxSZXF1ZXN0Mzk3NzEyODM5
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CLN: remove Block.merge
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| 0
| 2020-04-02T17:22:50Z
| 2020-04-03T17:39:09Z
| 2020-04-03T17:37:51Z
|
MEMBER
| null |
Then move _merge_blocks to the one module where it is used, and add annotations
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| 2020-05-05T09:10:25Z
| 2020-04-02T21:13:16Z
|
MEMBER
| null |
- [x] closes #33238
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1OTI4MDcyNTI=
| 33,242
|
agg behaviour depends on the number of arguments of the function
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[] | null | 1
| 2020-04-02T17:31:59Z
| 2020-10-11T16:59:47Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': [1,2,3,4,5,6],
'b': [1,1,0,1,1,0],
'c': ['x','x','x','z','z','z'],
'd': ['s','s','s','d','d','d']})
def myprint2(x, y):
print(x)
df.groupby(['c'])[['a', 'b']].agg(myprint2, 0)
```
#### Problem description
In the particular case of a groupby over 1 column ('c'), asking a subset of columns ('a' and 'b') and passing agg a function of 2 arguments ('mypring2'), a DataFrame of the full groups (including all columns) is passed to myprint2:
```python
>>>> df.groupby(['c'])[['a', 'b']].agg(myprint2, 0)
a b c d
0 1 1 x s
1 2 1 x s
2 3 0 x s
a b c d
3 4 1 z d
4 5 1 z d
5 6 0 z d
```
For every group, I would have expected 2 calls of myprint2, one with Series 'a' and one with Series 'b'. This is particularly confusing, because agg would have the expected behaviour if groupby is called over a list or if a function of 1 argument is passed to agg:
#### Expected Output
I would have expected the same as if groupby is called over a list or if a function of 1 argument is passed to agg:
```python
>>>> df.groupby(['c', 'd'])[['a', 'b']].agg(myprint2, 0)
0 1
1 2
2 3
Name: a, dtype: int64
3 4
4 5
5 6
Name: a, dtype: int64
0 1
1 1
2 0
Name: b, dtype: int64
3 1
4 1
5 0
Name: b, dtype: int64
>>>> df.groupby(['c'])[['a', 'b']].agg(print)
0 1
1 2
2 3
Name: a, dtype: int64
3 4
4 5
5 6
Name: a, dtype: int64
0 1
1 1
2 0
Name: b, dtype: int64
3 1
4 1
5 0
Name: b, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
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 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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 : 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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| 592,826,480
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MDExOlB1bGxSZXF1ZXN0Mzk3NzM0OTIw
| 33,243
|
DOC: Contributing - escaping backslash.
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| 1
| 2020-04-02T18:05:19Z
| 2020-04-02T23:25:34Z
| 2020-04-02T23:25:28Z
|
CONTRIBUTOR
| null |
Non-escaped backslash disappeared in resulting doc.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzk3NzM1Njkw
| 33,244
|
CLN: Clean nanops.get_corr_func
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| 2
| 2020-04-02T18:06:58Z
| 2020-04-07T01:25:31Z
| 2020-04-07T00:18:58Z
|
MEMBER
| null |
Small cleaning (instead of creating a dictionary of functions and then returning only one, just return the one)
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MEMBER
| null |
The docstrings for these two methods say that values_for_foo should preserve order, but for non-ordered Categoricals, the current implementation using self.codes is not order-preserving. Is this a problem cc @jorisvandenbossche ?
`Categorical.values_for_rank` exists pretty much to solve this problem for rank and value_counts. We have a special case in core.algorithms using values_for_rank that we should ideally avoid.
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BLD: recursive inclusion of DLLs in package data
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CONTRIBUTOR
| null |
- [x] step 1 towards #32857
Will also make a PR in https://github.com/MacPython/pandas-wheels to add the DLLs to the wheel packaging script
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BUG: Fix droped result column in groupby with as_index False
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|
MEMBER
| null |
- [x] closes #32240
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The aggregate function *lost* the results of the aggregate operation, if the ```as_index=False``` flag was set and one of the result columns is relabeled. The index was reset at first, adding columns to the DataFrame. The relabeling operation only took the columns before resetting the index as input. So the weird error occurred. I changed the order of the index reset and the relabeling. This fixes the issue.
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REF: do slicing before calling Block.to_native_types
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MEMBER
| null |
Related upcoming steps:
- Dispatch DatetimeBlock.to_native_types to DatetimeArray._format_native_types (identical behavior
- Dispatch TimeDeltaBlock.to_native_types to TimedeltaArray._format_native_types (behavior changing, but there is a FIXME in the Block version)
- Dispatch FloatBlock.to_native_types to Float64Index._format_native_types
- Possibly rename to_native_types to something more informative? (i think there's an issue about deprecating/renaming the Index method)
- in io.csvs use `df._data.apply` instead of iterating over blocks. Still touches internals, but its a lighter touch.
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| 2
| 2020-04-03T00:44:48Z
| 2020-04-03T19:42:46Z
| 2020-04-03T19:33:31Z
|
MEMBER
| null |
xref #33248
|
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MDExOlB1bGxSZXF1ZXN0Mzk3ODk1OTYz
| 33,250
|
Display Timedelta nanoseconds
|
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| 3
| 2020-04-03T02:07:48Z
| 2020-04-03T19:13:12Z
| 2020-04-03T18:53:38Z
|
MEMBER
| null |
- [x] closes #9309
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This allows us to make TimedeltaBlock.to_native_types dispatch to TimedeltaArray._format_native_types. ATM TDBlock.to_native_types has a comment
```
# FIXME:
# should use the formats.format.Timedelta64Formatter here
# to figure what format to pass to the Timedelta
# e.g. to not show the decimals say
```
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| 593,032,795
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MDU6SXNzdWU1OTMwMzI3OTU=
| 33,251
|
Pandas using matplot displays an incorrect year on the x-axis date
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[] | null | 4
| 2020-04-03T02:28:30Z
| 2020-04-05T02:59:18Z
| 2020-04-03T21:34:58Z
|
NONE
| null |
#### Code
```python
# -*- coding: UTF-8 -*-
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
hoje = datetime.datetime.now()
data = hoje.strftime("%Y%m%d")
dados_url = 'https://covid.saude.gov.br/assets/files/COVID19_'+data+'.csv'
try:
print(dados_url)
dados = pd.read_csv(dados_url, sep=';', parse_dates=['data'], dayfirst=True)
except Exception as e:
print("Os dados para a data corrente ainda não foram atualizados, buscando dados do dia anterior...")
print(e)
DD = datetime.timedelta(days=1)
hoje = hoje - DD
data = hoje.strftime("%Y%m%d")
print(str(hoje))
dados_url = 'https://covid.saude.gov.br/assets/files/COVID19_'+data+'.csv'
print(str(dados_url))
dados = pd.read_csv(dados_url, sep=';', parse_dates=['data'], dayfirst=True)
#pd.set_option('display.max_rows', None)
dados_alagoas = dados[dados['estado'] == 'AL']
dados_alagoas.set_index('data',inplace=True)
#print(dados_alagoas)
plt.style.use('ggplot')
fig, ax = plt.subplots(figsize=(30,15))
dados_alagoas.plot(ax=ax, rot=75)
#grafico em barras
#dados_alagoas.plot(ax=ax, rot=75, kind='bar', width=2.0)
ax.xaxis.set_major_locator(mdates.DayLocator(interval=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%y'))
ax.set_title('Incidência do COVID-19 em Alagoas em '+hoje.strftime('%d/%m/%Y'))
ax.set_ylabel('Ocorrências')
ax.set_xlabel('Data')
plt.show()
```
#### Problem description
In the graph, the date axis is not being displayed correctly with date formatting, the year that should be "2020" and is displayed as "51"
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.28-1-MANJARO
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : pt_BR.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.16
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: 0.8.1
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : None
None
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MDExOlB1bGxSZXF1ZXN0Mzk4MDE4MDgy
| 33,252
|
INT: provide helpers for accessing the values of DataFrame columns
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| 16
| 2020-04-03T07:53:42Z
| 2020-04-10T17:57:22Z
| 2020-04-10T17:37:22Z
|
MEMBER
| null |
Broken off from https://github.com/pandas-dev/pandas/pull/32867, also mentioned this in https://github.com/pandas-dev/pandas/pull/33229
In general, when doing certain things column-wise, we often just need the arrays (eg to calculate a reduction, to check the dtype, ..), and creating Series gives unnecessary overhead in that case.
In this PR, I added therefore two helper functions for making it easier to do this (`_ixs_values` as variant on `_ixs` but returning the array instead of Series, and `_iter_arrays` that calls `_ixs_values` for each column iteratively as an additional helper function).
I also used it in one place as illustration (in `_reduce`, what I was working on in https://github.com/pandas-dev/pandas/pull/32867, but it can of course be used more generally).
In that example case, it is to replace an apply:
```
In [1]: df = pd.DataFrame(np.random.randint(1000, size=(10000,10))).astype("Int64").copy()
In [2]: from pandas.core.dtypes.common import is_datetime64_any_dtype, is_period_dtype
In [3]: %timeit df.dtypes.apply(lambda x: is_datetime64_any_dtype(x) or is_period_dtype(x))
256 µs ± 2.66 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [5]: %timeit np.array([is_datetime64_any_dtype(values) or is_period_dtype(values) for values in df._iter_arrays()], dtype=bool)
56.2 µs ± 282 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
(and this is only fo 10 columns)
@jbrockmendel
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BUG: BooleanArray.any with all False values and skipna=False is buggy
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| 9
| 2020-04-03T08:31:58Z
| 2020-04-10T08:37:16Z
| 2020-04-10T08:37:16Z
|
MEMBER
| null |
From a report on my blog, this seems buggy:
```
In [10]: a = pd.array([False, False], dtype="boolean")
In [11]: a
Out[11]:
<BooleanArray>
[False, False]
Length: 2, dtype: boolean
In [12]: a.any()
Out[12]: False
In [13]: a.any(skipna=False)
Out[13]: <NA>
```
I don't think there is a reason to return NA if there are no NAs in the array? (even with `skipna=False`)
We probably need to do a check for the presence of any NA here in the `else` clause:
https://github.com/pandas-dev/pandas/blob/37dc5dc391333fede424964a3c8ab034318d6ed4/pandas/core/arrays/boolean.py#L520-L526
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MDU6SXNzdWU1OTMyNTQzNzY=
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|
API: EA interface - strictness of _from_sequence
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[] | null | 15
| 2020-04-03T09:41:00Z
| 2020-12-04T21:08:49Z
| null |
MEMBER
| null |
Copying part of the discussion out of https://github.com/pandas-dev/pandas/issues/32586 into a more specific issue.
Problem statement: currently, `_from_sequence` is not very explicit in what it should accept as scalars. In practice, this means that it is mostly very liberal, as it is also used under the hood when creating an array of any list-like of objects in `pd.array(.., dtype)`.
However, in some cases we need a more strict version that *only* accepts actual scalars of the array (meaning, the type of values you get from `array[0]` or `array.max()` in case it supports that kind of reductions). This causes some issues like https://github.com/pandas-dev/pandas/issues/31108.
So, what should `_from_sequence` accept? Should it only be sequences that are unambiguously this dtype?
I think it will be useful to have a "strict" version that basically only accepts instances of ExtensionDtype.type or NA values. But we also still need a "liberal" method for the other use cases like `pd.array(.., dtype)`.
The strict version would be used when, for some reason, we go through object dtype (or a list of scalars, or something equivalent). For example in groupby, where we assemble a list of scalars from the reductions into a new column.
From a testing point of view, that would mean we can test that `EA._the_strict_method(np.asarray(EA, dtype=object), dtype=EA.dtype)` and `EA._the_strict_method(list(EA), dtype=EA.dtype)` can roundtrip faithfully.
---
Assuming we agree that we need a strict version for certain use cases, I think there are two main options:
1) Keep `_from_sequence` as is, and add a new `_from_scalars` method that is more strict (that in the base class can call `_from_sequence` initially for backwards compatibility). We can use `_from_scalars` in those cases where we need the strict version, and keep using `_from_sequence` elsewhere (eg in `pd.array(.., dtype=)`)
2) Update the expectation in our spec that `_from_sequence` should only accept a sequence of scalars of the array's type (so make `_from_sequence` the strict method), and use the `astype` machinery for construction. Basically, the current flexible `_from_sequence` would then be equivalent to casting an object ndarray (or generally any type) to your EA dtype.
Are there preferences? (or other options?)
From a backwards compatibility point of view, I think both are similar (in both cases you need to update a method (`_from_scalars` or `_from_sequence`), and in both cases initially the flexible version will still be used as fallback until the update is done).
The second option of course requires an update to the astype machinery (#22384), which doesn't exist today, but on the other hand is also something we need to do at some point eventually (but a much bigger topic to solve).
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|
BUG: Cant access some `TimedeltaProperties` components directly
|
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| 2020-04-03T09:47:06Z
| 2020-04-04T18:32:25Z
| 2020-04-04T18:32:24Z
|
MEMBER
| null |
XREF: https://github.com/pandas-dev/pandas/pull/33208#discussion_r402181910
---
#### Note:
I'm using "hours" here for the examples, but this applies to
- hours
- minutes
- milliseconds
---
#### Short explanation:
Not all components are exposed directly:
Example:
```
In [1]: import pandas as pd
In [2]: s = pd.Series(pd.timedelta_range(start="1 day", periods=5, freq="H"))
In [3]: s.dt.components
Out[3]:
days hours minutes seconds milliseconds microseconds nanoseconds
0 1 0 0 0 0 0 0
1 1 1 0 0 0 0 0
2 1 2 0 0 0 0 0
3 1 3 0 0 0 0 0
4 1 4 0 0 0 0 0
In [4]: s.dt.days
Out[4]:
0 1
1 1
2 1
3 1
4 1
dtype: int64
In [5]: s.dt.hours
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-86f203890f2a> in <module>
----> 1 s.dt.hours
AttributeError: 'TimedeltaProperties' object has no attribute 'hours'
```
---
#### Long explanation:
If we create a series that contains a timedelta, we can access only some of the components directly.
Example setup:
```
In [1]: import pandas as pd
In [2]: s = pd.Series(pd.timedelta_range(start="1 day", periods=5, freq="H"))
In [3]: s
Out[3]:
0 1 days 00:00:00
1 1 days 01:00:00
2 1 days 02:00:00
3 1 days 03:00:00
4 1 days 04:00:00
dtype: timedelta64[ns]
In [4]: s.dt.components
Out[4]:
days hours minutes seconds milliseconds microseconds nanoseconds
0 1 0 0 0 0 0 0
1 1 1 0 0 0 0 0
2 1 2 0 0 0 0 0
3 1 3 0 0 0 0 0
4 1 4 0 0 0 0 0
```
We can access "days" and "seconds" directly, for example:
```
In [5]: s.dt.days
Out[5]:
0 1
1 1
2 1
3 1
4 1
dtype: int64
In [6]: s.dt.seconds
Out[6]:
0 0
1 3600
2 7200
3 10800
4 14400
dtype: int64
```
But if we try to access "hours" for example directly:
```
In [7]: s.dt.hours
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-86f203890f2a> in <module>
----> 1 s.dt.hours
AttributeError: 'TimedeltaProperties' object has no attribute 'hours'
```
---
Output of ``` pd.show_versions() ```:
<details>
INSTALLED VERSIONS
------------------
commit : 37dc5dc391333fede424964a3c8ab034318d6ed4
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13.a-1-hardened
Version : #1 SMP PREEMPT Wed, 25 Mar 2020 21:46:24 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1089.g37dc5dc39
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200311
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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REGR: DataFrame.mean(numeric_only=True) raises AttributeError on v1.0.3
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| 2020-04-03T10:59:48Z
| 2020-05-26T09:36:15Z
| 2020-05-01T18:19:11Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import numpy as np
>>>
>>> import pandas as pd
>>>
>>> pd.__version__
'1.0.3'
>>>
>>> df_wide = pd.DataFrame(np.random.randint(1000, size=(1000, 100))).astype("Int64").copy()
>>>
>>> df_wide.mean(numeric_only=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\generic.py", line 11215, in stat_func
f, name, axis=axis, skipna=skipna, numeric_only=numeric_only
File "C:\Users\simon\pandas\pandas\core\frame.py", line 7896, in _reduce
res = df._data.reduce(op, axis=1, skipna=skipna, **kwds)
File "C:\Users\simon\pandas\pandas\core\internals\managers.py", line 351, in reduce
bres = func(blk.values, *args, **kwargs)
File "C:\Users\simon\pandas\pandas\core\nanops.py", line 69, in _f
return f(*args, **kwargs)
File "C:\Users\simon\pandas\pandas\core\nanops.py", line 102, in f
if values.size == 0 and kwds.get("min_count") is None:
AttributeError: 'IntegerArray' object has no attribute 'size'
>>>
```
#### Problem description
This is a regression from 0.25.3
0aa48f7d9269206dea492ed14d5dfc6f46468de3 is the first bad commit
commit 0aa48f7d9269206dea492ed14d5dfc6f46468de3
Author: jbrockmendel <jbrockmendel@gmail.com>
Date: Wed Jan 1 09:18:20 2020 -0800
PERF: perform reductions block-wise (#29847)
on master raises `AttributeError: 'int' object has no attribute 'dtype'`
```
>>> df_wide.mean(numeric_only=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\generic.py", line 11114, in stat_func
func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
File "C:\Users\simon\pandas\pandas\core\frame.py", line 7990, in _reduce
res = df._data.reduce(blk_func)
File "C:\Users\simon\pandas\pandas\core\internals\managers.py", line 362, in reduce
bres = func(blk.values, *args, **kwargs)
File "C:\Users\simon\pandas\pandas\core\frame.py", line 7985, in blk_func
return op(values, axis=0, skipna=skipna, **kwds)
File "C:\Users\simon\pandas\pandas\core\nanops.py", line 120, in f
result = bn_func(values, axis=axis, **kwds)
File "<__array_function__ internals>", line 6, in nanmean
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\numpy\lib\nanfunctions.py", line 952, in nanmean
avg = _divide_by_count(tot, cnt, out=out)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\numpy\lib\nanfunctions.py", line 219, in _divide_by_count
return a.dtype.type(a / b)
AttributeError: 'int' object has no attribute 'dtype'
```
#### Expected Output
```
>>> import numpy as np
>>>
>>> import pandas as pd
>>>
>>> pd.__version__
'0.25.3'
>>>
>>> df_wide = pd.DataFrame(np.random.randint(1000, size=(1000, 100))).astype("Int64").copy()
>>>
>>> df_wide.mean(numeric_only=True)
0 520.057
1 507.735
2 501.618
3 506.590
4 501.500
...
95 507.594
96 483.273
97 506.330
98 497.068
99 508.118
Length: 100, dtype: float64
>>>
```
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MDExOlB1bGxSZXF1ZXN0Mzk4MTEwNTcy
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|
CLN: Reorgenized doctests order
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| 1
| 2020-04-03T11:10:20Z
| 2020-04-03T14:01:48Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This does not changes anything, just moving things around, to make it more manageable (IMO)
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MDU6SXNzdWU1OTMzMDU1MDg=
| 33,258
|
import pandas error.
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[] | null | 3
| 2020-04-03T11:12:28Z
| 2020-04-03T12:03:44Z
| 2020-04-03T11:17:54Z
|
NONE
| null |
dear pandas-dev,
I am writing this to yo because I have facing problem with pandas lib to use it with python 3.8,
when I try to import pandas its shows error, please check attachment for more details.
Thanks.
=========================================================
error message
==========================================================
>>> import pandas
Traceback (most recent call last):
File "<pyshell#1>", line 1, in <module>
import pandas
File "C:\Python\Python38-32\lib\site-packages\pandas\__init__.py", line 55, in <module>
from pandas.core.api import (
File "C:\Python\Python38-32\lib\site-packages\pandas\core\api.py", line 29, in <module>
from pandas.core.groupby import Grouper, NamedAgg
File "C:\Python\Python38-32\lib\site-packages\pandas\core\groupby\__init__.py", line 1, in <module>
from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy
File "C:\Python\Python38-32\lib\site-packages\pandas\core\groupby\generic.py", line 60, in <module>
from pandas.core.frame import DataFrame
File "C:\Python\Python38-32\lib\site-packages\pandas\core\frame.py", line 124, in <module>
from pandas.core.series import Series
File "C:\Python\Python38-32\lib\site-packages\pandas\core\series.py", line 4572, in <module>
Series._add_series_or_dataframe_operations()
File "C:\Python\Python38-32\lib\site-packages\pandas\core\generic.py", line 10349, in _add_series_or_dataframe_operations
from pandas.core.window import EWM, Expanding, Rolling, Window
File "C:\Python\Python38-32\lib\site-packages\pandas\core\window\__init__.py", line 1, in <module>
from pandas.core.window.ewm import EWM # noqa:F401
File "C:\Python\Python38-32\lib\site-packages\pandas\core\window\ewm.py", line 5, in <module>
import pandas._libs.window.aggregations as window_aggregations
ImportError: DLL load failed while importing aggregations: The specified module could not be found.
==============================================================
[import pandas error report.pdf](https://github.com/pandas-dev/pandas/files/4426773/import.pandas.error.report.pdf)
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MDExOlB1bGxSZXF1ZXN0Mzk4MTQ4MTkz
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DOC: Added an example for each series.dt field accessor
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| 2
| 2020-04-03T12:35:53Z
| 2020-04-07T10:48:22Z
| 2020-04-06T22:10:19Z
|
MEMBER
| null |
- [x] xref https://github.com/pandas-dev/pandas/pull/33208#discussion_r402189839
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
I can also add examples that would cover, ````TimedeltaProperties``` and ```PeriodProperties```, but I think that would be over verbose, wdyt?
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DOC: Fixed examples in `pandas/core/accessor.py`
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| 1
| 2020-04-03T14:01:04Z
| 2020-04-06T08:35:11Z
| 2020-04-04T15:29:08Z
|
MEMBER
| null |
Since this changes require an interactive Ipython shell (I think), we can't run check them from pytest.
This is what the docs looks like:
Before:
After:
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|
PERF: masked ops for reductions (min/max)
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| 1
| 2020-04-03T15:04:06Z
| 2020-04-07T06:57:46Z
| 2020-04-06T23:28:01Z
|
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/30982, adding similar mask reduction but now for min/max.
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MDExOlB1bGxSZXF1ZXN0Mzk4MjYwOTMx
| 33,262
|
TST: don't assert that matplotlib rejects shorthand hex colors
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| 3
| 2020-04-03T16:09:29Z
| 2020-04-03T19:14:34Z
| 2020-04-03T19:14:24Z
|
CONTRIBUTOR
| null |
From 3.2 [it accepts them](https://matplotlib.org/users/prev_whats_new/whats_new_3.2.0.html#digit-and-4-digit-hex-colors).
[Test failure log](https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=954647) (from Debian's pandas 0.25, so includes other issues we don't now have - this one is DID NOT RAISE).
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MDExOlB1bGxSZXF1ZXN0Mzk4MjcwMDcw
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|
DOC: Fixed examples in `pandas/core/aggregation.py`
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| 4
| 2020-04-03T16:28:45Z
| 2020-04-07T18:49:39Z
| 2020-04-07T17:53:06Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1OTM1MjgzNTk=
| 33,264
|
Append Mode for ExcelWriter with "xlsxwriter"
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| 2
| 2020-04-03T17:20:29Z
| 2020-04-03T18:36:09Z
| 2020-04-03T17:34:43Z
|
NONE
| null |
**Problem description**
ExcelWriter's append mode doesn't work when setting default xlsxwriter. To use append mode, I have to set the engine openpyxl. I think it is inconvenient and slow. So I suggest ExcelWriter can be set as append mode when default (xlsxwriter).
**Sample Code**
**AS-IS**
```python
writer = pd.ExcelWriter('APT_TRADE.xlsx', engine='openpyxl', mode='a')
apt_dataframe.to_excel(writer, sheet_name='city-' + date.strftime("%Y%m"), index=False)
```
**TO-BE**
```python
writer = pd.ExcelWriter('APT_TRADE.xlsx', mode='a')
apt_dataframe.to_excel(writer, sheet_name='city-' + date.strftime("%Y%m"), index=False)
```
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MDExOlB1bGxSZXF1ZXN0Mzk4MzA1ODMw
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TST: add date range test for reindex case from GH-32740
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DOC: Fixed examples in `pandas/core/window`
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TST: add DataFrame test for construct from tuple case from GH-32776
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MDExOlB1bGxSZXF1ZXN0Mzk4MzM0MjI5
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TYP: Fixed type annotaions in `scripts/validate_rst_title_capitalization`
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MDU6SXNzdWU1OTM1ODM3NDE=
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Consider adding isnative property to StringDtype?
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[] | null | 1
| 2020-04-03T19:09:19Z
| 2021-07-31T02:09:03Z
| null |
NONE
| null |
#### Code Sample
Assuming Jupyter notebook environment
```bash
!pip install rpy2
```
```python
%load_ext rpy2.ipython
from pandas import *
characters= = Series(['a', 'b'], dtype=StringDtype())
%R -i characters
```
Leads to an error in rpy2:
```
AttributeError: 'StringDtype' object has no attribute 'isnative'`
```
which I described in https://github.com/rpy2/rpy2/issues/669. However, as this concerns a new experimental feature in pandas I wanted to bring a difference in public interface between different Dtypes to your attention.
#### Problem description
The new, experimental [`StringDtype`](https://github.com/pandas-dev/pandas/blob/845c50c9e2ce611c773422ae9db7097fc3e5fba5/pandas/core/arrays/string_.py#L27) inherits from [`ExtensionDtype`](https://github.com/pandas-dev/pandas/blob/dd03c196d684d6ed594ee58451b0a36d7935a1ea/pandas/core/dtypes/base.py#L17) rather than from [`PandasExtensionDtype`](https://github.com/pandas-dev/pandas/blob/e84c3f9abb32a10b6561489589a06a1a3c7623e0/pandas/core/dtypes/dtypes.py#L111). This means that it does not have `isnative` property.
#### Questions
- is it intended to have some Dtypes with `isnative` attribute and others without?
- in your opinion, should the rpy2 issue be worked around downstream or should the API be amended in pandas (I expect that other packages may also expect dtypes to have certain attributes from `PandasExtensionDtype`)?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.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 : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk4MzQyODM4
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REF: dispatch TDBlock.to_native_types to TDA._format_native_types
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MDExOlB1bGxSZXF1ZXN0Mzk4MzQ4MDQz
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CLN: Added static types _libs/algos
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| 1
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| 2020-04-06T08:28:14Z
| 2020-04-05T20:00:29Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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HUH?: DataFrame[categorical].replace
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| 0
| 2020-04-03T19:42:10Z
| 2020-09-21T02:01:49Z
| 2020-09-21T02:01:49Z
|
MEMBER
| null |
When we have a DataFrame with 2 Categorical columns with matching dtypes and do a `.replace` on that DataFrame, the results do not match what we would get if we operate column-wise. Instead the results we get look like what we'd get if we stacked the columns, replaced, then unstacked.
i.e. AFAICT we are doing gymnastics to behave as if CategoricalBlock supported 2D Categorical.
From tests.frame.methods.test_replace:
```
replace_dict = {'a': 1, 'b': 2}
value = 3
df = pd.DataFrame([[1, 1], [2, 2]], columns=["a", "b"], dtype="category")
final_data = [[3, 1], [2, 3]]
expected = pd.DataFrame(final_data, columns=["a", "b"], dtype="category")
expected["a"] = expected["a"].cat.set_categories([1, 2, 3])
expected["b"] = expected["b"].cat.set_categories([1, 2, 3])
result = df.replace(replace_dict, value)
tm.assert_frame_equal(result, expected)
# this part is no longer from the test
col_wise = {col: df[col].replace(replace_dict[col], value) for col in df.columns}
result2 = pd.DataFrame(col_wise)
result2["a"].dtype
CategoricalDtype(categories=[3, 2], ordered=False)
```
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Pass method in __finalize__
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| 16
| 2020-04-03T19:58:08Z
| 2020-04-06T22:04:50Z
| 2020-04-06T22:04:41Z
|
CONTRIBUTOR
| null |
This passes `method` everywhere we use `__finalize__`. I'm trying to get a better sense for
1. When we call finalize (and when we fail too)
2. When we finalize multiple times
3. The overhead of finalize
I haven't called it anywhere new yet (followup PR with that coming though).
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Unpin mypy
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| 2020-04-03T20:02:39Z
| 2020-04-22T12:56:49Z
| 2020-04-22T04:11:29Z
|
MEMBER
| null |
Right now we have mypy 0.73 pinned in CI. However, newer versions will yield a lot of warnings like this:
```sh
pandas/core/groupby/base.py:32: error: "GroupByMixin" has no attribute "obj"
pandas/core/groupby/base.py:36: error: "GroupByMixin" has no attribute "_attributes"
pandas/core/groupby/base.py:40: error: "GroupByMixin" has no attribute "_groupby"
pandas/core/groupby/base.py:42: error: "GroupByMixin" has no attribute "_groupby"
pandas/core/groupby/base.py:44: error: Too many arguments for "GroupByMixin"
pandas/core/groupby/base.py:44: error: Unexpected keyword argument "groupby" for "GroupByMixin"
pandas/core/groupby/base.py:44: error: Unexpected keyword argument "parent" for "GroupByMixin"
pandas/core/groupby/base.py:45: error: "GroupByMixin" has no attribute "_reset_cache"
```
Most mixins we have will be problematic, if not all. Fortunately newer versions of mypy offer support for Protocols as a "self type", which were officially introduced in Python 3.8
This draft is an attempt at clarifying how that would work and seeing if it makes sense for the group. Depending on feedback, we may want to consider some kind of compat for this model to get it to work across Py36 and Py37 so our mypy doesn't lag too far behind
More info on the mypy release notes:
https://mypy-lang.blogspot.com/2019/11/mypy-0.html
@simonjayhawkins
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MDExOlB1bGxSZXF1ZXN0Mzk4Mzg1MzQ1
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Added finalize benchmark
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| 2020-04-06T18:36:01Z
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CONTRIBUTOR
| null |
This adds a benchmark for finalize. It scales with the number of `attrs`.
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EA interface - requirements for "hashable, value+order-preserving ndarray"
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| 2020-09-17T22:35:15Z
| null |
MEMBER
| null |
Across a set of issues/PRs (eg https://github.com/pandas-dev/pandas/issues/32586, https://github.com/pandas-dev/pandas/issues/32673, https://github.com/pandas-dev/pandas/pull/33064), there has lately been quite some discussion regarding `_values_for_factorzie` / `_values_for_argsort`, the need for roundtrippability, the need for order-preserving, the need for `_ndarray_values`, ...
Those different questions and topics that came up (as far as I kept track, probably not complete! but already too long .. ;)):
* In https://github.com/pandas-dev/pandas/issues/32586, the question was raised what the difference is between `_values_for_argsort` and `_values_for_factorize`, and whether we need both.
Some difference that came up:
* The main difference might be that the values in `_values_for_factorize` need to be hashable, while the ones in `_values_for_argsort` don't need to be (although this difference is not properly documented). Looking back at https://github.com/pandas-dev/pandas/pull/19957 from @TomAugspurger, it was mentioned the that "sortable" is an easier requirement than what other algos like factorize might need.
Related to this difference is that `_values_for_factorize` needs to return a dtype supported by the hashtables (int64, uint64, float64, object), while `_values_for_argsort` can return any sortable dtype (so also int8, int32, etc).
* The return type is different: `_values_for_factorize` also returns a `na_value` sentinel, which means you can encode missing values in a different way than a "missing value" (eg nan in float dtype). While for `_values_for_argsort`, it simply returns one array (I would need to look into the details how missing values are handled here, it seems they are filtered out in `nargsort`, so it might not matter how they are encoded in the returned array).
Is this sufficiently different to warrant two methods? Probably, with a bit work, they could be combined in a single method. However, their original purpose was *only* to help implement `EA.factorize()` and `EA.argsort()`. So for that purpose only, it might not necessarily be worth trying to combine them. And see the last bullet point for a more general "hashable, orderable array"
* In addition, we actually also have `_values_for_rank` for Categorical, which we probably should try to get rid off as well -> https://github.com/pandas-dev/pandas/issues/33245
* I have argued that in general, we should also look at a "masked" version of eg `_values_for_factorize`. Having the option to return a (values, mask) tuple in addition to (values, na_sentinel) in case this is easier/cheaper to provide (which is especially the case for the masked arrays; this will need to support of the factorize algos for masks though -> eg https://github.com/pandas-dev/pandas/pull/33064)
* We also had a vaguely defined `_ndarray_values` (https://github.com/pandas-dev/pandas/issues/23565), that was recently removed (https://github.com/pandas-dev/pandas/pull/32768). It was eg used in indexing code (index engine, joining indexes), where it was replaced with `_values_for_argsort` (https://github.com/pandas-dev/pandas/pull/32467, https://github.com/pandas-dev/pandas/pull/32611).
* *What else can they be used for internally?* (`_values_for_factorize` / `_values_for_argsort`)
As mentioned above, `_values_for_argsort` is since recently used for ExtensionIndex joining and engine values. Further, `_values_for_factorize` is used in the general merging code.
However, the initial purpose of `_values_for_factorize` / `_values_for_argsort` was *not* to be used internally in pandas, but *only* has a helper to `EA.factorize()` and `EA.argsort()`. So following our current EA interface spec, we should not use them internally (which means we should fix the few cases where we started using them).
The spec about `factorize` is clear that there are two ways to override its behaviour: implement `_values_for_factorize`/`_from_factorized`, or implement `factorize` itself:
https://github.com/pandas-dev/pandas/blob/c47e9ca8b042881d44c9e679a9bf42bacabbb732/pandas/core/arrays/base.py#L740-L747
So external EAs are not guaranteed to have an efficient implementation of `_values_for_factorize`/`_values_for_argsort` (they still have the default `astype(object)` implementation).
Fletcher is an example of external EAs that implement `factorize` and not `_values_for_factorize`.
So ideally, for anything factorize/argsort-related, we should actually always call the `EA.factorize()` or `EA.argsort()` methods.
* In https://github.com/pandas-dev/pandas/issues/32673, @jbrockmendel questioned whether the `_values_for_factorize` and `_from_factorized` combo should faithfully roundtrip? Currently, they do, but not necessarily when missing values are included.
However, when only considering them as "internal" to the `EA.factorize()` implementation, this question doesn't actually matter. But it *does* matter when we want to use those values more generally.
* I mentioned above that ideally we should use `factorize()` or `argsort()` directly as much as possible and avoid `_values_for_factorize/argsort` (since this is the official EA interface). However, there are still cases where such direct usage is not sufficient, and where we actually need some "values".
For example, in the merging/joining code, you can't "just" `factorize()` the left and right array, because then the integer codes of left and right both don't necessarily can be matched (it depends on the uniques being present what those integers mean).
I think it is clear we have *some* use case for "ndarray values", but so we should think about for which use cases we need that and what requirements we have for those.
@jbrockmendel started to list some requirements here: https://github.com/pandas-dev/pandas/issues/32586#issuecomment-605082900
---
Having read again through all the recent issues and having written up the above, my *current* take-away point are:
- To start, we should maybe put the questions around `_values_for_factorize` / `_values_for_argsort` aside for a moment. In principle they are internal to `EA.factorize()` / `EA.argsort()`, and so we *could* also remove those methods (if we wanted that) if we "just" require EA authors to implement factorize/argsort directly instead of through the helper _values_for.. .
And if we figure out the general "ndarray values" requirements (see below), we can still come back to this to see if we can actually replace both `_values_for_factorize` / `_values_for_argsort` with this single "ndarray values" interface.
- I now think that replacing `_ndarray_values` with `_values_for_argsort` to be able to remove `_ndarray_values` actually didn't solve much. We replaced one vaguely specified property (`_ndarray_values`) with another (`_values_for_argsort` for *other* purposes than just sorting, as there are currnetly also no guarantees / requirements outside of sorting specified for `_values_for_argsort`).
- Instead, I would focus on figuring out what the requirements are for the "hashable / value preserving ndarray values":
1) What are the exact use cases we need this for?
2) What are the exact semantics needed for those use cases? (hashable, orderable, deterministic across arrays, ..)
3) Do those use cases need roundtripping of those values?
4) How would we implement those values for the internal EAs?*
It might be that this ends up to be something close to what `_values_for_argsort` or `_values_for_factorize` now are. But I also think that my comment above about the possibility to include a mask in this interface is important for the nullable dtypes.
- An alternative that we didn't really mention yet, is adding more to the EA interface instead of requiring this "ndarray values" concept. For example, if we want that external EAs can have control over joining, we could have a `EA.__join__(other_EA, how) -> Tuple[ndarray[int], ndarray[int]]` that returns indices into left and right EAs that determine how to join them.
For joining that might be a relatively straightforward interface, for the indexing engine that looks more complex though (but so let's first define the use cases).
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CLN 31942/replace appender with doc 3
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ENH: Added schema kwarg to get_schema method
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Makes DataFrame.replace on Categorical behave like Series.replace.
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Troubleshoot Travis
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MEMBER
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Reverts #33228, which appears to be the first build that started failing on Travis.
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ENH: Optimize nrows in read_excel
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CONTRIBUTOR
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| 1
| 2020-04-04T10:13:53Z
| 2020-04-04T21:23:10Z
| 2020-04-04T21:23:03Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.Series.clip and pandas.DataFrame.clip`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
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MDExOlB1bGxSZXF1ZXN0Mzk4NTI2NjU2
| 33,283
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DOC: Fix EX01 in DataFrame.drop_duplicates
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
```
################################################################################
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|
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| 593,896,897
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MDExOlB1bGxSZXF1ZXN0Mzk4NTk0NjIz
| 33,284
|
BUG: fix boolean array skipna=False for .any() and .all()
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| 5
| 2020-04-04T14:36:37Z
| 2020-04-10T08:37:27Z
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|
CONTRIBUTOR
| null |
- [x] closes #33253
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I added "not self._mask.any()" to check if there is no missing value for both .any() and .all() when skipna is False. It looks fine for the example you provided.
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MDU6SXNzdWU1OTM5MjExMzA=
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DOC: Mention Index.equals takes into account order of arguments
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| 3
| 2020-04-04T15:21:05Z
| 2020-04-06T08:29:11Z
| 2020-04-06T08:29:11Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.Index(['10607', '6574', '6621', '99917']).equals(pd.Index(['99917', '10607', '6574', '6621'])) # False
set(['10607', '6574', '6621', '99917']) == set(['99917', '10607', '6574', '6621']) # True
```
#### Problem description
I have seen #13708, but according to documentation:
> Determine if two Index objects contain the same elements.
Should it return `True` instead?
Reading how the function is implemented:
https://github.com/pandas-dev/pandas/blob/3adf3340453d6704d4a2cb47058214cc697a7d29/pandas/core/indexes/base.py#L4072-L4074
It relies on:
https://github.com/pandas-dev/pandas/blob/3adf3340453d6704d4a2cb47058214cc697a7d29/pandas/core/dtypes/missing.py#L421-L431
Which is indeed strict and do take order into account.
#### Expected Output
As I understand the equality of index is about having the same elements. It is not required the elements having the same order. I have the feeling that once pandas worked this way (maybe before `v0.23`).
What is the intended way ? Maybe the documentation should be a bit more explicit about this comparison. If you indicate me where I can update the doc, I will be glad to edit it.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 46.1.3
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pytest : 5.3.2
hypothesis : None
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xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 0.999999999
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.3.2
pyxlsb : None
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scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : None
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MDExOlB1bGxSZXF1ZXN0Mzk4NjM0MjM0
| 33,286
|
CLN: Add/refine type hints to some functions in core.dtypes.cast
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| 2
| 2020-04-04T16:18:53Z
| 2020-07-11T16:02:14Z
| 2020-04-05T19:39:56Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Some cleanup resulting from PR #32894. In order to get SeriesGroupBy._transform_fast to realize self.obj is a Series, it seemed I needed to add a generic type to GroupBy and _GroupBy. I don't know if there is a better way to do this. Here, one cannot use FrameOrSeries as a Series may return a DataFrame and vice-versa, so I added _GroupByT.
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MDU6SXNzdWU1OTM5NjEyMDM=
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|
PERF: hashtable for 16, 32, 128 bit dtypes
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| 9
| 2020-04-04T17:33:08Z
| 2021-01-02T19:31:47Z
| 2021-01-02T19:31:46Z
|
MEMBER
| null |
@jorisvandenbossche has pointed out in a couple of threads recently that the hashtables we use only support 64 bit float/uint/int dtypes. As a result, whenever we need to factorize a smaller dtype, we have to copy+upcast.
khash.pxd looks like it has int32 functions; might there be uint32 or float32 variants available somewhere in the C code? If not, what would it take to implement them? cc @WillAyd @chris-b1
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MDU6SXNzdWU1OTM5ODgzNjY=
| 33,288
|
REGR: Replacing a category with itself replaces it with np.nan
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| 2
| 2020-04-04T18:28:55Z
| 2020-05-26T09:35:26Z
| 2020-04-06T22:01:57Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.Series(["a", "b"]).astype("category").replace("a", "a")
# 0 NaN
# 1 b
# dtype: category
# Categories (1, object): [b]
```
Operating on the categorical array directly, i.e. `pd.Categorical(["a", "b"]).replace("a", "a")` yields the same result.
#### Problem description
Replacing a category with itself replaces it with `np.nan`. This problem was introduced with 1.0.0.
#### Expected Output
I would have expected the behavior from 0.25.3:
```python
pd.Series(["a", "b"]).astype("category").replace("a", "a")
# 0 a
# 1 b
# dtype: category
# Categories (2, object): [a, b]
```
Note that if we work with lists, we get
```python
pd.Series(["a", "b"]).astype("category").replace(["a"], ["a"])
# dtype: category
# 0 a
# 1 b
# type: object
```
which is also not what I would expect, because we're now losing the dtype. This behavior has been described elsewhere (e.g. https://github.com/pandas-dev/pandas/pull/31734#issuecomment-583130043) and it's consistent with 0.25.3.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-176-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
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 : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
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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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MDExOlB1bGxSZXF1ZXN0Mzk4NjkyMDAy
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DOC: Improved doc for `Index.equals`
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BUG: 2D indexing on DTA/TDA/PA
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| 2020-04-04T19:23:36Z
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MEMBER
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Broken off from #32997.
The fixture this implements in the datetimelike tests can be used in a follow-up to clean up those tests a bit
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REGR: Fix bug when replacing categorical value with self
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MEMBER
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CLN: remove BlockManager.__contains__
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| 2020-04-07T23:30:08Z
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Mostly annotations
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MDU6SXNzdWU1OTQyNDE5NTA=
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BUG: Replace methods fills value from previous row when replacing with None
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[] | null | 5
| 2020-04-05T04:04:17Z
| 2020-04-05T12:44:34Z
| 2020-04-05T12:44:13Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({'x': [10, 20, np.nan], 'y': [30, 40, 50]})
print(df.replace(np.NaN, None))
# x y
# 0 10.0 30
# 1 20.0 40
# 2 20.0 50
```
#### Problem description
When replacing NaN with None using replace method, value, NaN value is replaced with value from previous row instead of None.
#### Expected Output
```
# x y
# 0 10.0 30
# 1 20.0 40
# 2 None 50
```
#### Output of ``pd.show_versions()``
<details>
```
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 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.0rc0
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk4OTEzNjk2
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BUG: DataFrame._item_cache not cleared on on .copy()
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| 2
| 2020-04-05T05:26:53Z
| 2020-04-07T19:52:35Z
| 2020-04-06T14:33:38Z
|
CONTRIBUTOR
| null |
- [x] closes #31784
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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RLS: 1.0.4
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| 53
| 2020-04-05T10:44:42Z
| 2020-06-04T20:56:03Z
| 2020-06-04T20:55:46Z
|
MEMBER
| null |
we have several regressions reported since 1.0.3 (not due to 1.0.3, since 0.25.3) and a couple that had not been fixed.
https://github.com/pandas-dev/pandas/issues?page=1&q=is%3Aopen+is%3Aissue+label%3ARegression
Is there any that warrant a 1.0.4 release?
@pandas-dev/pandas-core
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DOC/CLN: remove versionadded/changed:: 0.21
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| 2
| 2020-04-05T13:26:44Z
| 2020-04-13T14:41:51Z
| 2020-04-05T19:21:30Z
|
MEMBER
| null |
xref #29126
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API/DEPR: DataFrame/Series.replace is too complex.
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| 5
| 2020-04-05T13:52:46Z
| 2021-07-31T02:11:20Z
| null |
MEMBER
| null |
in https://github.com/pandas-dev/pandas/issues/17673#issue-260396559 @jorisvandenbossche wrote
>Underlying reason is that this function of course can do way too many things at the same time (or the same things in too many different ways) ... (orthogonal to this, we could maybe also think if certain functionality could be moved into its own function).
opened this issue to discuss.
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BUG: wrong df.groupby().groups when grouping with [Grouper(freq=), ...] (GH33132)
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| 2020-09-13T00:14:06Z
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|
NONE
| null |
- [ ] closes #33132
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzk5MTYzMzM5
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REGR: Fix construction of PeriodIndex from strings
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| 3
| 2020-04-05T16:45:53Z
| 2020-04-10T17:36:30Z
| 2020-04-10T17:29:56Z
|
MEMBER
| null |
- [ ] closes #26109
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU1OTQ1NTczNTg=
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BUG:I am using Latest python 3.8 ,the pip install pandas is working and is successful but showing an when importing it is showing an error, need help
|
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| 2020-04-05T17:02:24Z
| 2020-04-05T17:39:47Z
| 2020-04-05T17:38:41Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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ENH: Named aggregations with multiple columns
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|
NONE
| null |
- [ ] closes #29268
- [x] tests added
- [ ] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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TST: misplaced reduction/indexing tests
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REF: call _block_shape from EABlock.make_block
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DOC: include Offset.__call__ to autosummary to fix sphinx warning
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MDExOlB1bGxSZXF1ZXN0Mzk5MzM1OTk4
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CLN: remove fill_tuple kludge
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Update citation webpage
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| 18
| 2020-04-06T01:45:50Z
| 2020-05-07T10:10:11Z
| 2020-04-10T17:43:37Z
|
CONTRIBUTOR
| null |
Follow-up of #32388, addressing #24036
I will leave it to the pandas team to decide whether to put in there a BibTeX entry with the concept DOI or a specific version, some options of dealing with this are described [in this comment](https://github.com/sherpa/sherpa/pull/634#issuecomment-553668211).
Note how [this comment thread](https://github.com/pandas-dev/pandas/pull/32388#discussion_r386392799) on the previous PR asked to replace the author list by "The pandas development team". However, if users go to Zenodo to get the correct BibTeX entry of the version they're actually using, their citation will contain the full author list provided in Zenodo.
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MDExOlB1bGxSZXF1ZXN0Mzk5NDYwMDM1
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|
DOC: do not include type hints in signature in html docs
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| 7
| 2020-04-06T08:11:52Z
| 2020-04-06T20:55:44Z
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|
MEMBER
| null |
See https://github.com/pandas-dev/pandas/issues/33025. Removing the type hints in the online docs until we have a better solution to improve readability.
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BUG: Bad error message on read_parquet() when wrong version of pyarrow is installed
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| 4
| 2020-04-06T09:53:19Z
| 2020-04-08T16:45:46Z
| 2020-04-08T16:45:46Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
With `pyarrow<0.13` installed,
```python
pd.read_parquet('some_file.parquet')
```
#### Problem description
The error message says that `pyarrow` should be installed, even though `pyarrow` is already installed – which makes the error difficult to diagnose and fix, when in fact, the problem is an incompatible _version_ of pyarrow.
```
File "[...]\lib\site-packages\pandas\io\parquet.py", line 33, in get_engine
"Unable to find a usable engine; "
ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
pyarrow or fastparquet is required for parquet support
```
#### Expected Output
The message should state the real cause of the error, namely an incompatible version of pyarrow. Like:
```
File "[...]\lib\site-packages\pandas\io\parquet.py", line 40, in get_engine
return PyArrowImpl()
File "[...]\lib\site-packages\pandas\io\parquet.py", line 75, in __init__
"pyarrow", extra="pyarrow is required for parquet support."
File "[...]\lib\site-packages\pandas\compat\_optional.py", line 109, in import_optional_dependency
raise ImportError(msg)
ImportError: Pandas requires version '0.13.0' or newer of 'pyarrow' (version '0.11.1' currently installed).
```
This helpful error message does in fact get generated at a lower level in `pandas.compat._optional.import_optional_dependency()`, but then gets swallowed when its client `pandas.io.parquet.get_engine(engine="auto")` raises a new `ImportError` from scratch.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
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 : 3.0.3
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1OTQ5ODMxNjY=
| 33,314
|
BUG: clang-9: error: -Wl,-export_dynamic: 'linker' input unused (OSX)
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| 3
| 2020-04-06T10:11:20Z
| 2020-11-27T20:03:58Z
| 2020-11-27T20:03:58Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
### conda environment
```
conda create -n pandas-compile-test compilers numpy cython
```
<details>
```
binutils-meta conda-forge/osx-64::binutils-meta-1.0.4-0
c-compiler conda-forge/osx-64::c-compiler-1.0.4-h1239861_0
ca-certificates conda-forge/osx-64::ca-certificates-2020.4.5.1-hecc5488_0
cctools conda-forge/osx-64::cctools-927.0.2-h5ba7a2e_4
certifi conda-forge/osx-64::certifi-2020.4.5.1-py38h32f6830_0
clang conda-forge/osx-64::clang-9.0.1-default_hf57f61e_0
clang_osx-64 conda-forge/osx-64::clang_osx-64-9.0.1-h05bbb7f_0
clangxx conda-forge/osx-64::clangxx-9.0.1-default_hf57f61e_0
clangxx_osx-64 conda-forge/osx-64::clangxx_osx-64-9.0.1-h05bbb7f_0
compiler-rt conda-forge/osx-64::compiler-rt-9.0.1-h6a512c6_3
compiler-rt_osx-64 conda-forge/noarch::compiler-rt_osx-64-9.0.1-h99342c6_3
compilers conda-forge/osx-64::compilers-1.0.4-0
cxx-compiler conda-forge/osx-64::cxx-compiler-1.0.4-h707564b_0
cython conda-forge/osx-64::cython-0.29.16-py38hc84c608_0
fortran-compiler conda-forge/osx-64::fortran-compiler-1.0.4-he991be0_0
gfortran_impl_osx~ conda-forge/osx-64::gfortran_impl_osx-64-7.3.0-hf4212f2_2
gfortran_osx-64 conda-forge/osx-64::gfortran_osx-64-7.3.0-h22b1bf0_9
gmp conda-forge/osx-64::gmp-6.2.0-h4a8c4bd_2
isl conda-forge/osx-64::isl-0.19-0
ld64 conda-forge/osx-64::ld64-450.3-h3c32e8a_4
libblas conda-forge/osx-64::libblas-3.8.0-16_openblas
libcblas conda-forge/osx-64::libcblas-3.8.0-16_openblas
libcxx conda-forge/osx-64::libcxx-9.0.1-1
libffi conda-forge/osx-64::libffi-3.2.1-h4a8c4bd_1007
libgfortran conda-forge/osx-64::libgfortran-4.0.0-2
libiconv conda-forge/osx-64::libiconv-1.15-h0b31af3_1006
liblapack conda-forge/osx-64::liblapack-3.8.0-16_openblas
libllvm9 conda-forge/osx-64::libllvm9-9.0.1-ha1b3eb9_0
libopenblas conda-forge/osx-64::libopenblas-0.3.9-h3d69b6c_0
llvm-openmp conda-forge/osx-64::llvm-openmp-9.0.1-h28b9765_2
mpc conda-forge/osx-64::mpc-1.1.0-h4160ff4_1006
mpfr conda-forge/osx-64::mpfr-4.0.2-h44b798e_0
ncurses conda-forge/osx-64::ncurses-6.1-h0a44026_1002
numpy conda-forge/osx-64::numpy-1.18.1-py38h1f821a2_1
openssl conda-forge/osx-64::openssl-1.1.1f-h0b31af3_0
pip conda-forge/noarch::pip-20.0.2-py_2
python conda-forge/osx-64::python-3.8.2-hd5f0129_5_cpython
python_abi conda-forge/osx-64::python_abi-3.8-1_cp38
readline conda-forge/osx-64::readline-8.0-hcfe32e1_0
setuptools conda-forge/osx-64::setuptools-46.1.3-py38h32f6830_0
sqlite conda-forge/osx-64::sqlite-3.30.1-h93121df_0
tapi conda-forge/osx-64::tapi-1000.10.8-ha1b3eb9_4
tk conda-forge/osx-64::tk-8.6.10-hbbe82c9_0
wheel conda-forge/noarch::wheel-0.34.2-py_1
xz conda-forge/osx-64::xz-5.2.4-h0b31af3_1002
zlib conda-forge/osx-64::zlib-1.2.11-h0b31af3_1006
```
</details>
Error message:
```
clang-9: error: -Wl,-export_dynamic: 'linker' input unused [-Werror,-Wunused-command-line-argument]
```
Output of `pip install -e ~/Development/pandas --no-deps --no-use-pep517 -vvv`:
<details>
```
Non-user install because site-packages writeableCreated temporary directory: /private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-ephem-wheel-cache-ehxkyhsg
Created temporary directory: /private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv
Initialized build tracking at /private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv
Created build tracker: /private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv
Entered build tracker: /private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv
Created temporary directory: /private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-install-5lvoa7_h
Obtaining file:///Users/uwe/Development/pandas
Added file:///Users/uwe/Development/pandas to build tracker '/private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv'
Running setup.py (path:/Users/uwe/Development/pandas/setup.py) egg_info for package from file:///Users/uwe/Development/pandas
Running command python setup.py egg_info
Compiling pandas/_libs/hashing.pyx because it depends on /Users/uwe/miniconda3/envs/pandas-compile-test/lib/python3.8/site-packages/Cython/Includes/numpy/__init__.pxd.
Compiling pandas/_libs/reshape.pyx because it depends on /Users/uwe/miniconda3/envs/pandas-compile-test/lib/python3.8/site-packages/Cython/Includes/numpy/__init__.pxd.
Compiling pandas/_libs/window/indexers.pyx because it depends on /Users/uwe/miniconda3/envs/pandas-compile-test/lib/python3.8/site-packages/Cython/Includes/numpy/__init__.pxd.
[1/3] Cythonizing pandas/_libs/hashing.pyx
[2/3] Cythonizing pandas/_libs/reshape.pyx
[3/3] Cythonizing pandas/_libs/window/indexers.pyx
running egg_info
writing pandas.egg-info/PKG-INFO
writing dependency_links to pandas.egg-info/dependency_links.txt
writing entry points to pandas.egg-info/entry_points.txt
writing requirements to pandas.egg-info/requires.txt
writing top-level names to pandas.egg-info/top_level.txt
reading manifest file 'pandas.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '#*' found anywhere in distribution
writing manifest file 'pandas.egg-info/SOURCES.txt'
Source in /Users/uwe/Development/pandas has version 1.1.0.dev0+1122.g01f73100d.dirty, which satisfies requirement pandas==1.1.0.dev0+1122.g01f73100d.dirty from file:///Users/uwe/Development/pandas
Removed pandas==1.1.0.dev0+1122.g01f73100d.dirty from file:///Users/uwe/Development/pandas from build tracker '/private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv'
Installing collected packages: pandas
Running setup.py develop for pandas
Running command /Users/uwe/miniconda3/envs/pandas-compile-test/bin/python3.8 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/Users/uwe/Development/pandas/setup.py'"'"'; __file__='"'"'/Users/uwe/Development/pandas/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps
running develop
running egg_info
writing pandas.egg-info/PKG-INFO
writing dependency_links to pandas.egg-info/dependency_links.txt
writing entry points to pandas.egg-info/entry_points.txt
writing requirements to pandas.egg-info/requires.txt
writing top-level names to pandas.egg-info/top_level.txt
reading manifest file 'pandas.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '#*' found anywhere in distribution
writing manifest file 'pandas.egg-info/SOURCES.txt'
running build_ext
building 'pandas._libs.algos' extension
x86_64-apple-darwin13.4.0-clang -fno-strict-aliasing -Wsign-compare -Wunreachable-code -DNDEBUG -fwrapv -O3 -Wall -Wstrict-prototypes -march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O3 -pipe -fdebug-prefix-map=${SRC_DIR}=/usr/local/src/conda/${PKG_NAME}-${PKG_VERSION} -fdebug-prefix-map=/Users/uwe/miniconda3/envs/pandas-compile-test=/usr/local/src/conda-prefix -flto -Wl,-export_dynamic -march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O3 -march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include -D_FORTIFY_SOURCE=2 -mmacosx-version-min=10.9 -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include -DNPY_NO_DEPRECATED_API=0 -I./pandas/_libs -Ipandas/_libs/src/klib -I/Users/uwe/miniconda3/envs/pandas-compile-test/lib/python3.8/site-packages/numpy/core/include -I/Users/uwe/miniconda3/envs/pandas-compile-test/include/python3.8 -c pandas/_libs/algos.c -o build/temp.macosx-10.9-x86_64-3.8/pandas/_libs/algos.o -Werror
clang-9: error: -Wl,-export_dynamic: 'linker' input unused [-Werror,-Wunused-command-line-argument]
error: command 'x86_64-apple-darwin13.4.0-clang' failed with exit status 1
Cleaning up...
Removed build tracker: '/private/var/folders/3r/k0d_q3hs2mb2_jxfh_9hjyqr0000gn/T/pip-req-tracker-waeimcjv'
ERROR: Command errored out with exit status 1: /Users/uwe/miniconda3/envs/pandas-compile-test/bin/python3.8 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/Users/uwe/Development/pandas/setup.py'"'"'; __file__='"'"'/Users/uwe/Development/pandas/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
```
</details>
`export | grep FLAGS`:
```
CFLAGS='-march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
CONDA_BACKUP_LDFLAGS=' -Wl,-rpath,/Users/uwe/miniconda3/envs/pandas-compile-test/lib -L/Users/uwe/miniconda3/envs/pandas-compile-test/lib'
CPPFLAGS='-D_FORTIFY_SOURCE=2 -mmacosx-version-min=10.9 -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
CXXFLAGS='-march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe -stdlib=libc++ -fvisibility-inlines-hidden -std=c++14 -fmessage-length=0 -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
DEBUG_CFLAGS='-march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe -Og -g -Wall -Wextra -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
DEBUG_CXXFLAGS='-march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe -stdlib=libc++ -fvisibility-inlines-hidden -std=c++14 -fmessage-length=0 -Og -g -Wall -Wextra -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
DEBUG_FFLAGS='-march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include -march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe -Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments'
DEBUG_FORTRANFLAGS='-march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include -march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe -Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments'
FFLAGS='-march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
FORTRANFLAGS='-march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe -isystem /Users/uwe/miniconda3/envs/pandas-compile-test/include'
LDFLAGS=' -Wl,-rpath,/Users/uwe/miniconda3/envs/pandas-compile-test/lib -L/Users/uwe/miniconda3/envs/pandas-compile-test/lib'
LDFLAGS_LD='-pie -headerpad_max_install_names -dead_strip_dylibs -rpath /Users/uwe/miniconda3/envs/pandas-compile-test/lib -L/Users/uwe/miniconda3/envs/pandas-compile-test/lib'
XPC_FLAGS=0x0
```
Seems like this is not the first issue in response to https://github.com/pandas-dev/pandas/pull/32163, there is also e.g. https://github.com/pandas-dev/pandas/issues/33224
|
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MDU6SXNzdWU1OTQ5ODU3MjA=
| 33,315
|
Remove `-Werror` from setup.py
|
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| 26
| 2020-04-06T10:15:10Z
| 2020-11-27T20:03:58Z
| 2020-11-27T20:03:58Z
|
CONTRIBUTOR
| null |
It seems nice to have `-Werror` in CI but as a conda-forge maintainer I quite often have to write patches to remove `-Werror` from releases as the system distributions build packages on are not identical to the ones CI of upstream runs on. There is a large variety of compiler warnings and some may only show up when other C-defines are set. Also many new warning appear when you build with a newer compiler or different versions of the dependencies used.
My suggestion would be:
* Remove `-Werror` from `setup.py`.
* Add `-Werror` to `CFLAGS` on CI to have the same result for pandas-dev as it is currently.
Original PR: https://github.com/pandas-dev/pandas/pull/32163
Related issues: https://github.com/pandas-dev/pandas/issues/33314, https://github.com/pandas-dev/pandas/issues/33224
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CLN: Added static types for `pandas/_libs/reduction.pyx`
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| 0
| 2020-04-06T10:26:24Z
| 2020-04-06T15:07:07Z
| 2020-04-06T14:48:12Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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