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Follow-up:
https://github.com/pandas-dev/pandas/pull/30771#discussion_r363684666 | {
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} | 1 | 2020-01-11T10:52:45Z | 2020-01-12T09:37:58Z | 2020-01-11T23:12:34Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-11T12:20:47Z | 2020-01-24T19:16:01Z | 2020-01-24T19:16:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'baz'],
'B': [1, 2, np.nan, 3, 3, np.nan, 4],
'C': [np.nan]*7,
'D': [0,1,2,3,4,5,6],
'E': [np.nan] + [datetime.datetime(2020,1,1)]*3 + [datetime.datetime(2020,1,2)]*2 +[datetime.datetime(2020,1,3)],
'F': list('abcdefg'),
'G': list('abc') + [np.nan] + list('efg'),
'id': range(0,7),
}
).set_index('id')
df.groupby('A').transform('fillna', value=9999)
```
**Output**
B|C|D|E|F|G
---|---|---|---|---|---
9999.0|9999.0|2|2020-01-01 00:00:00|c|c
9999.0|9999.0|2|2020-01-01 00:00:00|c|c
9999.0|9999.0|2|2020-01-01 00:00:00|c|c
9999.0|9999.0|2|2020-01-01 00:00:00|c|c
1.0|9999.0|0|9999|a|a
1.0|9999.0|0|9999|a|a
2.0|9999.0|1|2020-01-01 00:00:00|b|b
#### Problem description
When using `GroupBy.transform` together with 'fillna' I expected it to work like `GroupBy.transform` together with `lambda x: x.fillna()`. Instead, it seems to also change values that are not NaN. Even worse, it seems to shuffle contents between groups.
Is this how it is expected to work?
#### Expected Output
```python
df.groupby('A').transform(lambda x: x.fillna(9999))
```
B|C|D|E|F|G
---|---|---|---|---|---
1.0|9999.0|0|9999|a|a
2.0|9999.0|1|2020-01-01 00:00:00|b|b
9999.0|9999.0|2|2020-01-01 00:00:00|c|c
3.0|9999.0|3|2020-01-01 00:00:00|d|9999
3.0|9999.0|4|2020-01-02 00:00:00|e|e
9999.0|9999.0|5|2020-01-02 00:00:00|f|f
4.0|9999.0|6|2020-01-03 00:00:00|g|g
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.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 : de_DE.UTF-8
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
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 : 7.11.1
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
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | 1 | 2020-01-11T13:57:37Z | 2020-01-11T23:11:44Z | 2020-01-11T23:11:35Z | CONTRIBUTOR | null | - [x] contributes to #29547
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Added a simple check not to allow nan values for calculation. Numpy seems to throw error when nan values are passed to searchsorted function.
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description:
Access "DataFrame" each column, fills up NA values using "Series.fillna"
and what i found is if dataframe is duplicated, ".fillna" not guarantee filling NA. So i change them can fill NA.
Q. Why access column base not index base?
A. problem of Index base f".illna" is not preserve column's dtype. so i use column base.
Q. Why assign in column new values not use inplace=True ?
A. To avoid chained indexing. If column and index are both duplicated, chained indexing happens, i want to avoid this situation.
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} | 21 | 2020-01-11T16:27:36Z | 2020-01-26T04:45:07Z | 2020-01-26T00:29:40Z | MEMBER | null | - [x] closes #26996
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} | 2 | 2020-01-11T16:31:15Z | 2020-01-18T16:23:32Z | 2020-01-18T16:23:32Z | MEMBER | null | From docstring, `index` and `values` can be optional in df.pivot, but `columns` is not
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html
```
index : string or object, optional
Column to use to make new frame’s index. If None, uses existing index.
columns : string or object
Column to use to make new frame’s columns.
```
However, the error message is confusing now, should raise `columns` is not optional.
Code example:
```python
>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],"bar": ['A', 'A', 'B', 'C'],"baz": [1, 2, 3, 4]})
>>> df.pivot(columns=None)
KeyError: 'None of [None] are in the columns'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/30925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30925/events | https://github.com/pandas-dev/pandas/pull/30925 | 548,450,688 | MDExOlB1bGxSZXF1ZXN0MzYxNzQ0MTk4 | 30,925 | BUG: correct wrong error message in df.pivot when columns=None | {
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} | 11 | 2020-01-11T16:33:22Z | 2020-01-18T16:23:36Z | 2020-01-18T16:23:32Z | MEMBER | null | - [x] closes #30924
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2020-01-11T19:42:32Z | 2020-01-13T17:13:38Z | 2020-01-13T08:31:17Z | MEMBER | null | Was reading over the whatsnew and noticed that the [1.0 Contributors section
](https://dev.pandas.io/docs/whatsnew/v1.0.0.html#contributors) is empty, and the [0.25.3 Contributors section](https://dev.pandas.io/docs/whatsnew/v0.25.3.html#contributors) looks way to big. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30927/events | https://github.com/pandas-dev/pandas/issues/30927 | 548,477,203 | MDU6SXNzdWU1NDg0NzcyMDM= | 30,927 | DOC: Whatsnew for 1.0 has deprecations listed in Enhancements section | {
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} | 2 | 2020-01-11T20:35:17Z | 2020-01-13T13:08:37Z | 2020-01-13T13:08:37Z | CONTRIBUTOR | null | In 1.0.0RC0, at https://dev.pandas.io/docs/whatsnew/v1.0.0.html#other-enhancements (i.e., the list of enhancements), we list the following:
- The pandas.np submodule is now deprecated. Import numpy directly instead (GH30296)
- The pandas.datetime class is now deprecated. Import from datetime instead (GH30296)
Shouldn't those two lines appear in the deprecations section https://dev.pandas.io/docs/whatsnew/v1.0.0.html#deprecations instead?
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https://api.github.com/repos/pandas-dev/pandas/issues/30928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30928/events | https://github.com/pandas-dev/pandas/pull/30928 | 548,479,771 | MDExOlB1bGxSZXF1ZXN0MzYxNzY0NTY4 | 30,928 | ENH: Allow multi values for index and columns in df.pivot | {
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} | 9 | 2020-01-11T21:01:38Z | 2020-02-09T21:57:34Z | 2020-02-09T21:57:29Z | MEMBER | null | - [x] closes #21425
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30929/events | https://github.com/pandas-dev/pandas/pull/30929 | 548,479,946 | MDExOlB1bGxSZXF1ZXN0MzYxNzY0Njk0 | 30,929 | ENH: Implement convert_dtypes | {
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} | 47 | 2020-01-11T21:03:28Z | 2020-01-28T20:56:46Z | 2020-01-24T03:24:16Z | CONTRIBUTOR | null | - [x] xref #29752
- [x] tests added / passed
- pandas/tests/series/test_dtypes.py:test_convert_dtypes
- pandas/tests/frame/test_dtypes.py:test_convert_dtypes
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This implements `DataFrame.convert_dtypes()` and `Series.convert_dtypes()`, which will make it much easier to use the new `pd.NA` functionality.
Added documentation in the section about the new `pd.NA` functionality.
I'm sure there will be comments about how I could have done this in a more/better/different way, and I'm open to resolving them so we get this into 1.0. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30930/events | https://github.com/pandas-dev/pandas/pull/30930 | 548,484,167 | MDExOlB1bGxSZXF1ZXN0MzYxNzY3NjQx | 30,930 | PERF: RangeIndex.get_loc | {
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} | 6 | 2020-01-11T21:47:01Z | 2020-01-15T16:40:21Z | 2020-01-15T04:22:18Z | MEMBER | null | Small simplification gives a small speedup
```
In [2]: rng = pd.Index(range(10**5))
In [3]: %timeit rng.get_loc(5.0)
6.38 µs ± 381 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) <-- master
1.15 µs ± 40.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) <-- PR
In [5]: %timeit rng.get_loc(5)
845 ns ± 5.71 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) <--master
In [5]: %timeit rng.get_loc(5)
860 ns ± 16.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) <-- PR
In [9]: def foo():
...: try:
...: return rng.get_loc(None)
...: except KeyError:
...: pass
...:
In [10]: %timeit foo()
6.72 µs ± 656 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) <-- master
1.11 µs ± 94 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) <-- PR
``` | {
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... | closed | false | null | [] | null | 10 | 2020-01-11T22:13:24Z | 2020-01-30T01:40:01Z | 2020-01-30T01:40:00Z | NONE | null | Installing the release candidate into a new Python 3.7 conda environment throws errors.
Do I need to be doing something differently?
#### Other notes:
- Installing jupyterlab in the same conda environment works fine.
- Creating a virtual environment with venv and using pip to install pandas 1.0.0rc0 works fine on the same machine.
#### Command
```
conda install -c conda-forge/label/rc pandas==1.0.0rc0
```
#### Error
```
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: /
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Package pip conflicts for:
defaults/osx-64::python==3.7.5=h359304d_0 -> pip
Package sqlite conflicts for:
defaults/osx-64::python==3.7.5=h359304d_0 -> sqlite[version='>=3.30.1,<4.0a0']
Package openssl conflicts for:
defaults/osx-64::python==3.7.5=h359304d_0 -> openssl[version='>=1.1.1d,<1.1.2a']
Package pCollecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: /
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Package tk conflicts for:
python=3.7 -> tk[version='>=8.6.7,<8.7.0a0|>=8.6.8,<8.7.0a0']
Package libcxx conflicts for:
pandas==1.0.0rc0 -> libcxx[version='>=9.0.1']
python=3.7 -> libcxx[version='>=4.0.1']
Package openssl conflicts for:
python=3.7 -> openssl[version='>=1.0.2o,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1d,<1.1.2a']
Package pytz conflicts for:
pandas==1.0.0rc0 -> pytz[version='>=2017.2']
Package python-dateutil conflicts for:
pandas==1.0.0rc0 -> python-dateutil[version='>=2.6.1']
Package readline conflicts for:
python=3.7 -> readline[version='>=7.0,<8.0a0']
Package libffi conflicts for:
python=3.7 -> libffi[version='>=3.2.1,<4.0a0']
Package pip conflicts for:
python=3.7 -> pip
Package ncurses conflicts for:
python=3.7 -> ncurses[version='>=6.1,<7.0a0']
Package xz conflicts for:
python=3.7 -> xz[version='>=5.2.4,<6.0a0']
Package zlib conflicts for:
python=3.7 -> zlib[version='>=1.2.11,<1.3.0a0']
Package numpy conflicts for:
pandas==1.0.0rc0 -> numpy[version='>=1.14.6,<2.0a0']
Package sqlite conflicts for:
python=3.7 -> sqlite[version='>=3.24.0,<4.0a0|>=3.25.2,<4.0a0|>=3.25.3,<4.0a0|>=3.26.0,<4.0a0|>=3.27.2,<4.0a0|>=3.29.0,<4.0a0|>=3.30.1,<4.0a0']
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Collecting package metadata (current_repodata.json): done
-bash: syntax error near unexpected token `('
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Solving environment: failed with initial frozen solve. Retrying with flexible solve.
-bash: Solving: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
-bash: Solving: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Collecting package metadata (repodata.json): done
-bash: syntax error near unexpected token `('
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Solving environment: failed with initial frozen solve. Retrying with flexible solve.
-bash: Solving: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Solving environment: /
-bash: Solving: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Found conflicts! Looking for incompatible packages.
-bash: Found: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ This can take several minutes. Press CTRL-C to abort.
-bash: This: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ failed
-bash: failed: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ UnsatisfiableError: The following specifications were found to be incompatible with each other:
-bash: UnsatisfiableError:: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package pip conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> pip
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package sqlite conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> sqlite[version='>=3.30.1,<4.0a0']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package openssl conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> openssl[version='>=1.1.1d,<1.1.2a']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package pytz conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ pandas==1.0.0rc0 -> pytz[version='>=2017.2']
-bash: -: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package numpy conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ pandas==1.0.0rc0 -> numpy[version='>=1.14.6,<2.0a0']
-bash: -: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package ncurses conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> ncurses[version='>=6.1,<7.0a0']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package readline conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> readline[version='>=7.0,<8.0a0']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package libcxx conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ pandas==1.0.0rc0 -> libcxx[version='>=9.0.1']
-bash: -: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> libcxx[version='>=4.0.1']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package libffi conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> libffi[version='>=3.2.1,<4.0a0']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package xz conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> xz[version='>=5.2.4,<6.0a0']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package python-dateutil conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ pandas==1.0.0rc0 -> python-dateutil[version='>=2.6.1']
-bash: -: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package tk conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> tk[version='>=8.6.8,<8.7.0a0']
-bash: defaults/osx-64::python==3.7.5=h359304d_0: No such file or directory
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ Package zlib conflicts for:
-bash: Package: command not found
(myenv) Jeffs-MacBook-Pro:course-info jeffhale$ defaults/osx-64::python==3.7.5=h359304d_0 -> zlib[version='>=1.2.11,<1.3.0a0']
```
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} | 1 | 2020-01-11T22:23:43Z | 2020-01-12T00:06:57Z | 2020-01-11T23:53:35Z | MEMBER | null | The check doesn't do anything, and we still a raise `KeyError` anyways | {
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"gists_ur... | null | 8 | 2020-01-11T23:13:30Z | 2020-02-12T16:01:46Z | 2020-02-12T16:01:46Z | CONTRIBUTOR | null | I found calling of `@Appender()` are using `%` to format string. I think it might be better to replace with `.format` based on [PEP 3101](https://www.python.org/dev/peps/pep-3101/) and [a discussion on Stack Overflow](https://stackoverflow.com/questions/5082452/string-formatting-vs-format).
#29547 is working on replacing `%` with f-strings. This change would also help to keep the code more consistent in string template. Be more specific, the template using `%` to formate will be something like `%(XXX)s`, but using `.format` and f-strings will be the same as `{XXX}`. | {
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} | 0 | 2020-01-12T03:45:01Z | 2020-01-16T21:15:38Z | 2020-01-16T21:15:38Z | MEMBER | null | In 1.0, we're adding a `engine` keyword argument to `rolling.apply` to run the routine using Cython or Numba.
By natural extension, we should allow `expanding.apply` to utilize Numba by adding the same `engine` keyword argument to `expanding.apply` | {
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} | 1 | 2020-01-12T04:36:31Z | 2020-01-16T21:15:52Z | 2020-01-16T21:15:39Z | MEMBER | null | - [x] closes #30936
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30938 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30938/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30938/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30938/events | https://github.com/pandas-dev/pandas/issues/30938 | 548,532,953 | MDU6SXNzdWU1NDg1MzI5NTM= | 30,938 | Getting ValueError: no results with apply(pd.to_numeric(df2.BATTING_Tests_Inns, errors='coerce')) | {
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} | [] | closed | false | null | [] | null | 1 | 2020-01-12T07:21:47Z | 2020-01-13T06:09:37Z | 2020-01-13T06:09:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
# convert just selected columns to numeric
df2[["BATTING_Tests_Mat", "BATTING_Tests_Inns","BATTING_Tests_NO","BATTING_Tests_Runs","BATTING_Tests_HS","BATTING_Tests_Ave","BATTING_Tests_SR"]]= df2[["BATTING_Tests_Mat", "BATTING_Tests_Inns","BATTING_Tests_NO","BATTING_Tests_Runs","BATTING_Tests_HS","BATTING_Tests_Ave","BATTING_Tests_SR"]].apply(pd.to_numeric(df2.BATTING_Tests_Inns, errors='coerce'))
```
#### Getting ValueError: no results with apply(pd.to_numeric(df2.BATTING_Tests_Inns, errors='coerce'))
[Getting ValueError: no results with apply(pd.to_numeric(df2.BATTING_Tests_Inns, errors='coerce'))]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
It should convert to numeric without any error
#### Output of ``pd.show_versions()``
'0.25.3' in Google colab
[paste the output of ``pd.show_versions()`` here below this line]
</details>
The dataset is taken from below website.
https://data.world/raghav333/cricket-players-espn
Full code is in my Github below
https://github.com/subhobrata/PandasIssue/blob/master/POC_Dataset.ipynb
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} | 1 | 2020-01-12T09:02:20Z | 2020-01-12T10:07:30Z | 2020-01-12T10:07:22Z | CONTRIBUTOR | null | Fixes SS03 errors.
```
None:None:SS03:pandas.DatetimeIndex.freqstr:Summary does not end with a period
None:None:SS03:pandas.PeriodIndex.freqstr:Summary does not end with a period
pandas/pandas/core/window/indexers.py:34:SS03:pandas.api.indexers.BaseIndexer:Summary does not end with a period
```
Related to #27977, #30733 cc @datapythonista
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes black pandas
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff
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When I ran the script, there are others left, but they might be false positives since I can't find them in the codebase. | {
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 9 | 2020-01-12T18:12:25Z | 2020-01-30T12:47:32Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
On `master` trying to use `pd.NA` as an input to `searchsorted` fails, and trying to use the `searchsorted` of an array containing `pd.NA` also fails:
```python-traceback
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '1.0.0rc0+15.g4e2546d89'
In [2]: s = pd.Series([-1, 1, 3, 5])
In [3]: arr_pd_na = pd.array([0, 1, 2, pd.NA])
In [4]: s.searchsorted(arr_pd_na)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
In [5]: s.searchsorted(pd.NA)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
In [6]: arr_pd_na.searchsorted(10)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
```
Note that the `np.nan` equivalent works fine:
```python-traceback
In [7]: arr_np_nan = np.array([0, 1, 2, np.nan])
In [8]: s.searchsorted(arr_np_nan)
Out[8]: array([1, 1, 2, 4])
In [9]: s.searchsorted(np.nan)
Out[9]: 4
In [10]: arr_np_nan.searchsorted(10)
Out[10]: 3
```
This has downstream effects on anything that relies on `searchsorted`, e.g. `pd.cut`, which has the same failing behavior as above for `pd.NA` but succeeds for `np.nan`:
```python-traceback
In [11]: pd.cut(arr_pd_na, bins=3)
---------------------------------------------------------------------------
TypeError: boolean value of NA is ambiguous
In [12]: pd.cut(arr_np_nan, bins=3)
Out[12]:
[(-0.002, 0.667], (0.667, 1.333], (1.333, 2.0], NaN]
Categories (3, interval[float64]): [(-0.002, 0.667] < (0.667, 1.333] < (1.333, 2.0]]
```
#### Problem description
`pd.NA` is not compatible with `searchsorted`.
#### Expected Output
I'd expect the output for the `pd.NA` operations above to match the output of the equivalent `np.nan` operations.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 4e2546d89260fda592332a3988573c26edc7152c
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.14-041914-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0+15.g4e2546d89
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.0
hypothesis : 4.36.2
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
pytest : 5.2.0
s3fs : 0.3.4
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.5.1
tabulate : None
xarray : 0.13.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
numba : 0.46.0
</details>
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} | 2 | 2020-01-12T18:13:58Z | 2020-01-24T13:15:42Z | 2020-01-24T01:07:44Z | CONTRIBUTOR | null | … types. (GH25596)
- [x] closes #25596
- [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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} | 1 | 2020-01-12T20:40:49Z | 2020-01-13T20:22:55Z | 2020-01-13T19:28:23Z | CONTRIBUTOR | null | This PR types up the method ``NDFrame.resample``.
Also renames the func ``resample`` to ``get_resampler`` and import the groupby types used for type checking in frame.py/series.py undir the ``if TYPE_CHECKING:`` clause. | {
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} | 2 | 2020-01-12T21:35:59Z | 2020-01-15T16:51:32Z | 2020-01-15T03:14:41Z | MEMBER | null | Fixes: incorrectly letting integers or mismatched types through.
Removes: maybe_datetimelike_to_i8, along with the recently-removed _to_M8 and he hopefully soon-removed pydt_to_i8 (xred #30854) i think we'll be rid of our kludgy datetime casting functions.
Performance neutral:
```
before after ratio
[28e909c6] [890204ca]
<master> <cln-maybe_datetimelike_to_i8>
+ 459±3μs 694±300μs 1.51 groupby.GroupByMethods.time_dtype_as_field('int', 'tail', 'transformation')
+ 1.84±0.2μs 2.38±2μs 1.29 index_cached_properties.IndexCache.time_shape('Float64Index')
+ 1.09±0.07μs 1.27±0.09μs 1.16 index_cached_properties.IndexCache.time_inferred_type('Float64Index')
+ 1.43±0.01μs 1.63±0.2μs 1.14 tslibs.timestamp.TimestampConstruction.time_fromordinal
+ 510±7μs 576±60μs 1.13 ctors.SeriesConstructors.time_series_constructor(<function list_of_str at 0x7fe19dbc09d8>, True, 'int')
+ 6.75±0.06μs 7.51±0.05μs 1.11 index_object.Indexing.time_get_loc_non_unique_sorted('Int')
+ 517±8μs 573±70μs 1.11 ctors.SeriesConstructors.time_series_constructor(<function list_of_str at 0x7fe19dbc09d8>, True, 'float')
+ 4.83±0.06ms 5.33±0.07ms 1.10 index_object.SetOperations.time_operation('int', 'symmetric_difference')
+ 1.08±0.09μs 1.19±0.07μs 1.10 index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
+ 1.74±0.02ms 1.92±0.04ms 1.10 inference.ToNumericDowncast.time_downcast('datetime64', 'float')
- 25.4±0.8μs 23.1±0.2μs 0.91 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
- 8.31±0.06ms 7.53±0.2ms 0.91 inference.DateInferOps.time_timedelta_plus_datetime
- 16.1±0.7ms 14.6±0.3ms 0.91 timedelta.ToTimedelta.time_convert_string_days
- 14.0±0.8μs 12.7±0.08μs 0.90 index_object.Indexing.time_get_loc('Float')
- 1.94±0.01ms 1.75±0.02ms 0.90 index_object.Indexing.time_get_loc_non_unique('Int')
- 12.8±0.04ms 11.5±0.1ms 0.90 multiindex_object.GetLoc.time_med_get_loc_warm
- 14.1±0.7μs 12.6±0.1μs 0.89 index_object.Indexing.time_get_loc_sorted('Float')
- 12.5±0.04μs 11.1±0.1μs 0.89 multiindex_object.GetLoc.time_string_get_loc
- 10.2±0.6μs 9.05±0.05μs 0.89 index_object.Float64IndexMethod.time_get_loc
- 12.3±0.1ms 10.8±0.1ms 0.88 multiindex_object.GetLoc.time_small_get_loc_warm
- 34.4±0.6μs 30.2±0.6μs 0.88 index_object.Indexing.time_get_loc_non_unique_sorted('Float')
- 104±3μs 91.0±2μs 0.88 multiindex_object.GetLoc.time_large_get_loc
- 4.90±0.1ms 4.21±0.02ms 0.86 timeseries.ResampleSeries.time_resample('period', '1D', 'ohlc')
- 3.78±0.2ms 3.23±0.01ms 0.85 timeseries.ResampleSeries.time_resample('datetime', '5min', 'mean')
- 10.4±0.6μs 8.74±0.03μs 0.84 tslibs.timedelta.TimedeltaConstructor.time_from_iso_format
- 4.38±0.1ms 3.69±0.02ms 0.84 timeseries.ResampleSeries.time_resample('period', '5min', 'mean')
- 3.23±0.5μs 2.24±0.7μs 0.69 index_cached_properties.IndexCache.time_values('TimedeltaIndex')
- 641±200μs 443±1μs 0.69 groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'direct')
- 2.73±1μs 1.45±0.2μs 0.53 index_cached_properties.IndexCache.time_values('UInt64Index')
``` | {
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} | 1 | 2020-01-12T23:03:40Z | 2020-01-14T23:25:06Z | 2020-01-14T23:25:00Z | CONTRIBUTOR | null | This hopefully clarifies the documentation for the behaviour of the convert_dates and keep_default_dates parameters on the read_json function.
I recently had to use this function and found the existing docs a bit confusing. I actually created a truth table (below), but this is probably a bit much for the docs.
| convert_dates | keep_default_dates | Result |
| ------------- | ------------------ | --------------------------- |
| True | True | Convert default date fields |
| True | False | Don’t convert anything |
| False | True | Don’t convert anything |
| False | False | Don’t convert anything |
| List | True | Convert listed and default date fields |
| List | False | Convert just listed date fields |
I hope you agree that my changes make the behaviour clearer - but perhaps people will disagree... | {
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```python
# Your code here
dtf = dtf.groupby('xGUID', sort=True).apply(x_apply)
```
#### Problem description
```
2020-01-12 21:32:07,672|root|99634|MainProcess|CRITICAL| Exception Information
2020-01-12 21:32:07,672|root|99634|MainProcess|CRITICAL| Type: <class 'FutureWarning'>
2020-01-12 21:32:07,672|root|99634|MainProcess|CRITICAL| Value: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.
To accept the future behavior, pass 'sort=False'.
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/x-1.1.1-py3.8.egg/xpack/core/formation_parser.py", line 91, in formation_parser
dtf = dtf.groupby('xGUID', sort=True).apply(x_apply)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 737, in apply
return self._python_apply_general(f)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 744, in _python_apply_general
return self._wrap_applied_output(
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 372, in _wrap_applied_output
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 937, in _concat_objects
result = concat(values, axis=self.axis)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 244, in concat
op = _Concatenator(
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 428, in __init__
self.new_axes = self._get_new_axes()
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 497, in _get_new_axes
new_axes[i] = self._get_comb_axis(i)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 528, in _get_comb_axis
return _get_objs_combined_axis(
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/indexes/api.py", line 93, in _get_objs_combined_axis
return _get_combined_index(obs_idxes, intersect=intersect, sort=sort)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/indexes/api.py", line 140, in _get_combined_index
index = _union_indexes(indexes, sort=sort)
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/indexes/api.py", line 215, in _union_indexes
warnings.warn(_sort_msg, FutureWarning, stacklevel=8)
```
#### Expected Output
No FutureWarning or better warning message. sort=True or False don't remove the warning in this case.
#### Output of ``pd.show_versions()``
<details>
NSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
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numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: 0.8.1
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
None
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} | 2 | 2020-01-13T09:30:48Z | 2020-01-13T19:03:26Z | 2020-01-13T19:03:26Z | MEMBER | null | In https://github.com/pandas-dev/pandas/pull/30097, we added special cases for 1 and -1 not propagating pd.NA:
```
In [59]: 1 ** pd.NA
Out[59]: 1
In [60]: (-1) ** pd.NA
Out[60]: -1
```
But for -1 this should probably be pd.NA as result in the end, see https://mail.python.org/pipermail/pandas-dev/2020-January/001174.html
```
In [58]: (-1) ** np.array([2, 3, 2.5])
/home/joris/miniconda3/envs/dev/bin/ipython:1: RuntimeWarning: invalid value encountered in power
#!/home/joris/miniconda3/envs/dev/bin/python
Out[58]: array([ 1., -1., nan])
```
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} | 1 | 2020-01-13T10:02:21Z | 2020-01-18T15:35:40Z | 2020-01-18T15:35:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I'm not sure this is a bug or intended behavior. If intended, I'm glad to be pointed to how API design was discussed and decided.
On `1.0.0 rc0`, `pd.Series.sum()`returns `nan` when using `skipna=False` for `'Int64'` with `pd.NA`.
```python
>>> pd.Series([1, 2, pd.NA]).sum(skipna=False)
<NA>
>>> pd.Series([1, 2, pd.NA], dtype='Int64').sum(skipna=False)
nan
>>> pd.Series([1, 2, np.nan]).sum(skipna=False)
nan
>>> pd.Series([1, 2, np.nan], dtype='Int64').sum(skipna=False)
nan
>>> pd.Series([1, 2, None]).sum(skipna=False)
nan
>>> pd.Series([1, 2, None], dtype='Int64').sum(skipna=False)
nan
```
#### Problem description
I understand the second and third example returns `nan` for backward compatibility, but the first example of `dtype='Int64'` seems natural to return `pd.NA`.
#### Expected Output
```python
>>> pd.Series([1, 2, pd.NA], dtype='Int64').sum(skipna=False)
<NA>
# When breaking backward compatibility is allowed, this is also expected (on and after pandas 2.0.0?)
>>> pd.Series([1, 2, np.nan], dtype='Int64').sum(skipna=False)
<NA>
>>> pd.Series([1, 2, None], dtype='Int64').sum(skipna=False)
<NA>
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.184-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0
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 : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```Dockerfile
# Dockerfile
FROM python:3.7.6
WORKDIR /home
RUN pip install pandas==1.0.0rc0
CMD ["/bin/bash"]
```
</details>
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} | 7 | 2020-01-13T11:13:48Z | 2020-07-28T14:41:36Z | 2020-01-14T16:02:32Z | CONTRIBUTOR | null | Add support for writing Stata 119 format files
Rename new writer to StataWriterUTF8 since no longer version-specific
Improve exception message for unsupported files
Fix small issues in to_stata missed in 118
- [X] xref #23573 (already closed)
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 8 | 2020-01-13T11:54:51Z | 2020-02-25T17:13:10Z | 2020-01-13T19:03:26Z | MEMBER | null | - [x] closes #30956
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} | 2 | 2020-01-13T12:18:14Z | 2020-01-13T13:08:52Z | 2020-01-13T13:08:37Z | CONTRIBUTOR | null | - [x] closes #30927
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} | 2 | 2020-01-13T12:41:15Z | 2020-01-22T14:16:46Z | 2020-01-22T14:16:46Z | CONTRIBUTOR | null | ```python
import pandas as pd
df = pd.DataFrame({"A": [1, 2]})
df.to_hdf("foo.h5", "foo")
with pd.HDFStore("foo.h5") as hdf:
for key in hdf.keys():
storer = hdf.get_storer(key)
print(type(storer), storer.format_type)
```
On 0.25.3, we have
```python
<class 'pandas.io.pytables.FrameFixed'> fixed
```
On 1.0.0rc0
```pytb
Traceback (most recent call last):
File "bug.py", line 10, in <module>
print(storer.format_type)
AttributeError: 'FrameFixed' object has no attribute 'format_type'
```
Didn't see anything about this in the release notes. cc @jbrockmendel (if this was part of your tables cleanup). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30963 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30963/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30963/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30963/events | https://github.com/pandas-dev/pandas/pull/30963 | 548,918,528 | MDExOlB1bGxSZXF1ZXN0MzYyMTA4MDAy | 30,963 | Backport PR #30961 on branch 1.0.x (DOC: Move couple of deprecations whatsnew to correct section) | {
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} | 14 | 2020-01-13T13:21:35Z | 2021-07-07T17:10:28Z | 2021-07-07T17:10:27Z | MEMBER | null | This page: https://github.com/pandas-dev/pandas/wiki/Tips-&-Tricks-for-pandas-dev is mostly outdated, or provides general Python information.
Personally, I'd just keep the last section on `PANDAS_TESTING_MODE`. Not sure if the best would be to move it to the CI part of the docs: https://pandas.io/docs/development/contributing.html#testing-with-continuous-integration
Or I'd probably just add a comment where that parameter is being used: https://github.com/pandas-dev/pandas/blob/master/.travis.yml#L46
Anything else that people would like to move to the docs from that page? | {
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"description": nu... | open | false | null | [] | null | 0 | 2020-01-13T13:58:13Z | 2021-07-27T04:24:38Z | null | CONTRIBUTOR | null | As part of https://github.com/pandas-dev/pandas/pull/30588, we now raise when trying to create a 2D index. This introduces a behavior change when you call DataFrame.set_index with duplicate data.
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame([[1, 2, 3]], columns=['a', 'a', 'b'])
In [3]: result = df.set_index('a')
```
On pandas 0.25.3, that gives back a DataFrame with a broken Index. Some DataFrame operations will work, but even things like printing the repr will fail
```python
# 0.25.3
In [17]: type(result)
Out[17]: pandas.core.frame.DataFrame
In [18]: result.shape
Out[18]: (1, 1)
```
With 1.0.0rc0, that raises
```pytb
~/sandbox/pandas/pandas/core/indexes/numeric.py in __new__(cls, data, dtype, copy, name)
76 if subarr.ndim > 1:
77 # GH#13601, GH#20285, GH#27125
---> 78 raise ValueError("Index data must be 1-dimensional")
79
80 name = maybe_extract_name(name, data, cls)
ValueError: Index data must be 1-dimensional
```
#### Problem description
The old output is clearly broken, so I wouldn't consider this a (major) regression. And I don't think people should be doing this in the first place. But I wanted to ask, should `DataFrame.set_index(scalar)` return a MultiIndex when `scalar` is a duplicate label? | {
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} | 5 | 2020-01-13T14:28:56Z | 2020-01-14T12:39:48Z | 2020-01-14T12:39:48Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'], dtype="string")
type(s.str.lower()[5]) # returns <class 'float'>
```
#### Problem description
the `str` accessor, when working on string-typed series, should return a string-typed series, which should be an array of [`string`, `pd.NA`] only, but it seems that some functions (see list below) can return series that contains `float('nan')`.
#### Affected functions
As of now, I found these str accessor functions to be affected:
`upper` `lower` `replace`
also, `extract(expand=False)` on a string type series returns an object type series, which seems unintended as well.
#### Expected Output
`<class 'pandas._libs.missing.NAType'>`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-38-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IL
LOCALE : en_IL.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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 : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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} | [] | closed | false | null | [] | null | 1 | 2020-01-13T14:50:24Z | 2020-01-23T19:00:10Z | 2020-01-23T19:00:10Z | MEMBER | null | Related to #30889 (I've opened a separate issue as the error is different)
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({"a": [1, 2, 3]}, dtype="Int16")
df.diff()
```
#### Problem description
This returns
```
IndexError: list assignment index out of range
```
#### Expected Output
Something like
```
a
0 pd.NA
1 1
2 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 46c2864c34eee0cd94c8842353331e293b0f2004
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0+1754.g46c2864c3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.55.4
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.4.0
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
numba : 0.46.0
</details>
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} | 1 | 2020-01-13T16:04:47Z | 2020-01-15T02:56:36Z | 2020-01-15T02:56:36Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.Series(['a1', 'b2', 'c3'], dtype="string").str.extract(r'([ab])(\d)', expand=False)
df.dtypes
```
returns
```
0 object
1 object
dtype: object
```
#### Problem description
the str accessor, when working on string-typed series, should return a string-typed dataframe. It seems that `extract(expand=False)` returns an object-typed dataframe
see @TomAugspurger in https://github.com/pandas-dev/pandas/issues/30966#issuecomment-573724531
#### Expected Output
```
0 string
1 string
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-38-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IL
LOCALE : en_IL.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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 : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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- [ ] tests added / passed
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} | 2 | 2020-01-13T16:25:27Z | 2020-01-19T20:07:37Z | 2020-01-18T15:35:40Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/30958
Just realized that I should probably check boolean dtype as well | {
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} | 2 | 2020-01-13T17:03:46Z | 2020-01-14T04:17:39Z | 2020-01-13T20:45:43Z | CONTRIBUTOR | null | Closes #30869 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30974 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30974/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30974/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30974/events | https://github.com/pandas-dev/pandas/issues/30974 | 549,067,823 | MDU6SXNzdWU1NDkwNjc4MjM= | 30,974 | DOC: Fix isort output in CI | {
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"description": n... | closed | false | null | [] | null | 6 | 2020-01-13T17:12:03Z | 2020-02-07T06:12:19Z | 2020-02-07T06:12:19Z | MEMBER | null | In the CI, we're using `isort` to check whether the imports in Python files are sorted in an standard way. See https://github.com/pandas-dev/pandas/blob/master/ci/code_checks.sh#L114
The way it is implemented, we expect isort to not print anything in the terminal if everything is ok, and to show errors for the errors encountered. This is mostly true, except that `isort` is also reporting when there are files being skipped.
The result of this, is that we highlight in the CI as an error when there are files being skipped. See https://github.com/pandas-dev/pandas/pull/30946/checks?check_run_id=385833706#step:6:36
This can be misleading, and we should try to avoid it. Without adding much complexity to the already complex CI. Not sure if there is an `isort` parameter to simply not report the skipped files. Or if there is an easy way to not highlight the skipped files as an error.
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} | 1 | 2020-01-13T17:29:36Z | 2020-01-24T03:37:33Z | 2020-01-24T03:37:33Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
The behavior of the following changed with 1.0.0rc0
```python
>>> import pandas as pd
>>> df = pd.DataFrame({"A": [1,2], "B": [3, 4]})
>>> df.A.append(df)
```
On 0.25.3, we have
```python
0 A B
0 1.0 NaN NaN
1 2.0 NaN NaN
0 NaN 1.0 3.0
1 NaN 2.0 4.0
```
On 1.0.0rc0, we have
```pytb
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-9-fde662a5b7c4> in <module>
----> 1 df.A.append(df)
~/sandbox/pandas/pandas/core/series.py in append(self, to_append, ignore_index, verify_integrity)
2541 return self._ensure_type(
2542 concat(
-> 2543 to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity
2544 )
2545 )
~/sandbox/pandas/pandas/core/base.py in _ensure_type(self, obj)
93 Used by type checkers.
94 """
---> 95 assert isinstance(obj, type(self)), type(obj)
96 return obj
97
AssertionError: <class 'pandas.core.frame.DataFrame'>
```
#### Problem description
I don't think the behavior of 0.25.3 is necessarily correct. I would expect either a TypeError from passing a DataFrame to Series.append, or something like
```python
In [17]: r
Out[17]:
A B
0 1 3.0
1 2 4.0
0 1 NaN
1 2 NaN
```
where the name of the Series is aligned with the DataFrame. We clearly document that the elements passed to append should be Series, so IMO we should raise. | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 9 | 2020-01-13T18:13:51Z | 2020-02-06T17:06:46Z | 2020-01-18T15:34:13Z | MEMBER | null | closes #30904
Came across these adding parametrized tests in #30903 which this pulls some elements from but which will also ultimately refactor to avoid duplication; just getting this to work as is
To be clear the regressions were:
1. ISO dates with datetime.date objects was broken
```python
>>> import pandas as pd
>>> import datetime
>>> data = [datetime.date(year=2020, month=1, day=1), "a"]
>>> pd.Series(data).to_json(date_format="iso")
TypeError: Expected datetime object
```
2. `pd.NaT` labels would now yield the sentinel value instead of writing "null"
```python
>>> data = [pd.Timestamp("2020-01-01"), pd.NaT]
>>> pd.Series(data, index=data).to_json()
'{"1577836800000":1577836800000,"-9223372036854":null}'
```
Both have been fixed here | {
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} | 2 | 2020-01-13T19:02:05Z | 2020-01-17T23:57:10Z | 2020-01-17T23:57:04Z | MEMBER | null | - [ ] closes #18321
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} | 1 | 2020-01-13T19:12:37Z | 2020-01-13T22:05:23Z | 2020-01-13T22:01:39Z | MEMBER | null | - [ ] closes #xxxx
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} | 2 | 2020-01-13T19:21:36Z | 2020-01-14T12:39:51Z | 2020-01-14T12:39:49Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/30966
~There were a few ways we could have done this. I opted for this way since it still only requires a single pass over the data to validate the values. Other ways like checking for `np.isnan` after checking `is_string_array` would have required a second pass.~
I changed the implementation in subsequent commits. The basic idea is the same, don't allow NaN in the array passed to StringArray, so that we only make a single pass over the data. We do this by changing `StringValidator.is_valid_null` to only allow NA. This required a small change to `PandasArray._from_sequence`, since previously we relied on creating a temporarily invalid StringArray before doing an inplace `__setitem__` to replace NaNs with NA.
cc @tsvikas.
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} | 22 | 2020-01-13T20:48:20Z | 2020-03-30T07:07:44Z | 2020-03-27T16:08:26Z | MEMBER | null | The current `nanops` has quite some complexity that is not needed for the masked arrays. This is a small proof of concept to have separate implementations for our nullable masked arrays, taking the sum case (and still ignoring the additional kwargs).
This is also quite a bit faster. On master:
```
In [1]: a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan]*1000)
In [2]: s1 = pd.Series(a, dtype="Int64")
In [3]: s2 = pd.Series(a, dtype="float64")
In [4]: %timeit s1.sum()
79.1 µs ± 2.39 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [5]: %timeit s2.sum()
79.1 µs ± 1.48 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
the nullable Int64 basically does the same as the nanops implementation for float.
With this PR:
```
In [4]: %timeit s1.sum()
21.5 µs ± 69.9 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [5]: %timeit s2.sum()
79.8 µs ± 1.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
(when using bigger arrays, the speed-up becomes less big in relative factor. Here it's almost 4x with 10k elements, but with 1M it's aound 2x).
I think it would be interesting to gradually implement some of the ops specifically for the nullable masked arrays. Personally I think it will be easier to do this in new functions than trying to fit it into the existing nanops function.
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} | 5 | 2020-01-13T21:44:44Z | 2020-02-05T19:09:23Z | 2020-02-05T19:09:05Z | MEMBER | null | The new plotting backend option is documented for package authors implementing such a backend: https://dev.pandas.io/docs/development/extending.html#plotting-backends, but we should probably mention this in the user guide (https://dev.pandas.io/docs/user_guide/visualization.html) as well. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30985 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30985/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30985/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30985/events | https://github.com/pandas-dev/pandas/pull/30985 | 549,202,540 | MDExOlB1bGxSZXF1ZXN0MzYyMzM5MTQx | 30,985 | DOC: remove Table of Contents on the contributing page | {
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] | closed | false | null | [] | null | 8 | 2020-01-13T21:50:29Z | 2020-03-07T20:03:53Z | 2020-03-07T20:03:52Z | MEMBER | null | Currently, the contributing page (https://pandas.pydata.org/pandas-docs/version/1.0.0/development/contributing.html) starts with "Table of Contents".
This PR removes that, because 1) we do that on none of the other pages in our docs and 2) with the new theme, there is a page table of contents in the right sidebar as well (although this shows, when landing on the page, only the first level) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30986 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30986/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30986/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30986/events | https://github.com/pandas-dev/pandas/issues/30986 | 549,205,157 | MDU6SXNzdWU1NDkyMDUxNTc= | 30,986 | Cannot write MultiIndex DataFrame to excel (to_excel) which contains Peroid IntervalIndex | {
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} | 1 | 2020-01-13T21:56:01Z | 2020-01-24T19:04:55Z | 2020-01-24T19:04:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
midx = pd.MultiIndex.from_arrays(
[
np.repeat([1, 2], 2),
pd.interval_range(start=pd.Timestamp("2020-01-01"), periods=4, freq="6M"),
]
)
df = pd.DataFrame(data=np.random.randint(0, 2, size=(4,)), index=midx)
print(df)
df.to_excel("tmp.xlsx")
```
#### Problem description
I receive a TypeError and the DataFrame does not write to file.
```python
...
File "pandas/_libs/tslibs/timestamps.pyx", line 412, in pandas._libs.tslibs.timestamps.Timestamp.__new__
File "pandas/_libs/tslibs/conversion.pyx", line 329, in pandas._libs.tslibs.conversion.convert_to_tsobject
TypeError: Cannot convert input [True] of type <class 'bool'> to Timestamp
```
#### Expected Output
Expect the DataFrame to write to file. A DataFrame with a Period/IntervalIndex alone can be written to an excel file. Seems to be an issue with MultiIndex.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.7-200.fc31.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : 4.54.2
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.9
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30987 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30987/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30987/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30987/events | https://github.com/pandas-dev/pandas/pull/30987 | 549,205,981 | MDExOlB1bGxSZXF1ZXN0MzYyMzQxOTc5 | 30,987 | FIX: Added small corrections to the test for interpolate limit_area | {
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} | 1 | 2020-01-13T21:57:44Z | 2020-01-13T22:54:26Z | 2020-01-13T22:30:25Z | CONTRIBUTOR | null | - [ ] closes # nothing to close here
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry # not required for this minor fix
Some `tm.assert_series_equal()` have been forgotten in https://github.com/pandas-dev/pandas/blob/2baf788819cd1073d9c15444ebcefcfab8e4b9c8/pandas/tests/series/test_missing.py#L1338
There was also a typo in https://github.com/pandas-dev/pandas/blob/2baf788819cd1073d9c15444ebcefcfab8e4b9c8/pandas/tests/series/test_missing.py#L1374 where `direction` has to be `limit_direction`.
These things have been fixed in this small PR. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30988 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30988/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30988/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30988/events | https://github.com/pandas-dev/pandas/issues/30988 | 549,209,538 | MDU6SXNzdWU1NDkyMDk1Mzg= | 30,988 | BUG: assigment of list with boolean mask scrambles values | {
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} | 4 | 2020-01-13T22:05:39Z | 2020-01-14T12:02:14Z | 2020-01-14T12:01:53Z | MEMBER | null | From [gitter](https://gitter.im/pydata/pandas?at=5e0a26d5d5a7f357e6a84f42) (didn't see it reported in an issue):
```
x = pd.Series([None] * 10)
y = [False] * 3 + [True] * 5 + [False] * 2
x[y] = list(range(5))
x
```
gives:
```
0 None
1 None
2 None
3 3
4 4
5 0
6 1
7 2
8 None
9 None
dtype: object
```
For some reason, the assigned values are not in the correct order.
With an array, it works fine (but I think we support list as well?):
```
In [4]: x[y] = np.array(list(range(5)))
In [5]: x
Out[5]:
0 None
1 None
2 None
3 0
4 1
5 2
6 3
7 4
8 None
9 None
dtype: object
```
Tested on pandas master, and also on pandas 0.25 it is buggy (eg in pandas 0.18 it worked fine). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30989 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30989/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30989/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30989/events | https://github.com/pandas-dev/pandas/pull/30989 | 549,229,154 | MDExOlB1bGxSZXF1ZXN0MzYyMzYxMTEx | 30,989 | Update _json.py | {
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`TypeError: initial_value must be str or None, not bytes`
- [x] closes #28906
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 12 | 2020-01-13T23:48:43Z | 2020-02-16T18:41:27Z | 2020-02-16T18:41:24Z | MEMBER | null | gcc on macOS I think executes clang more often than not, and it appears we didn't have a inline helper defined for that. This is ported from `CYTHON_INLINE` which essentially does the same in cythonized files, save the static declaration
This might add a bit of build time to macOS (I think partially explains why it's a few minutes faster than the rest) but makes it more similar in performance to the others
Running the full benchmark suite to see where this may help | {
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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During the test, I also realized that the Pandas kurtosis value was constantly changing according to the starting point.
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dti = pd.date_range("2016-01-01", periods=3)
dti2 = dti.tz_localize("UTC").tz_convert("US/Pacific")
>>> dti.get_loc(dti2[0])
0
>>> dti2.get_loc(dti[0])
KeyError: Timestamp('2016-01-01 00:00:00-0800', tz='US/Pacific', freq='D')
ser = pd.Series(range(3), index=dti)
ser2 = pd.Series(range(3), index=dti2)
>>> ser.loc[dti2[1]]
1
>>> ser2.loc[dti[1]]
KeyError: Timestamp('2016-01-02 00:00:00-0800', tz='US/Pacific', freq='D')
```
get_loc is effectively an equality check, so I think it should behave like all our other comparisons and require tzawareness-compat. So all of these would raise KeyError.
xref #17920, https://github.com/pandas-dev/pandas/pull/30819#issuecomment-572281006 | {
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} | 84 | 2020-01-14T12:17:56Z | 2021-01-05T18:47:07Z | 2021-01-05T18:47:07Z | MEMBER | null | End users rely on error messages for their debugging purposes. Thus, it is important that we make sure that the correct error messages are surfaced depending on the error triggered.
The core idea is to convert this:
```python
with pytest.raises(klass):
# Some code that raise an error
```
To this:
```python
with pytest.raises(klass, match=msg):
# Some code that raise an error
```
You can read more about ```pytest.raises``` [here](https://docs.pytest.org/en/latest/assert.html#assertions-about-expected-exceptions).
---
#### Side note:
In case that the raised error message is an external error message (meaning that's not pandas specific), you should use the ```external_error_raised``` instead of ```pytest.raises```.
the usage of ```external_error_raised``` is __exactly__ like ```pytest.raises``` the only difference is that you don't pass in the ```match``` argument.
For example:
```python
import pandas._testing as tm
def test_foo():
with tm.external_error_raised(ValueError):
raise ValueError("foo")
```
---
#### Keynotes:
* Don't forget to link this issue in your PR, paste this
```
https://github.com/pandas-dev/pandas/issues/30999
```
in your PR.
* Please comment what you are planning to work on, so we won't do double the work (no need to mention me, you can just declare what you are planning to work on, just remember to check if something is already taken).
* If a file/files that should be marked as "done" (as if there is no more work to do), isn't marked as "done", please comment letting me know about that (And mentioning me by putting ```@MomIsBestFriend``` at the comment's body, so I'll know where to look).
---
To generate the full list yourself, you can add:
```yaml
- id: unwanted-patterns-bare-pytest-raises
name: Check for use of bare use of pytest raises
language: python
entry: python scripts/validate_unwanted_patterns.py --validation-type="bare_pytest_raises"
types: [python]
files: ^pandas/tests/
exclude: ^pandas/tests/extension
```
to `.pre-commit-config.yaml` in the `- repo: local` section, and then run
```
pre-commit run unwanted-patterns-bare-pytest-raises --all-files.
```
---
### The current list is:
- [ ] pandas/tests/arrays/boolean/test_arithmetic.py
- [ ] pandas/tests/computation/test_compat.py
- [ ] pandas/tests/dtypes/test_inference.py
- [x] pandas/tests/indexes/multi/test_indexing.py
- [x] pandas/tests/indexes/multi/test_setops.py
- [x] pandas/tests/indexes/period/test_indexing.py
- [x] pandas/tests/indexes/test_common.py
- [x] pandas/tests/indexes/test_numpy_compat.py
- [ ] pandas/tests/indexing/multiindex/test_partial.py
- [ ] pandas/tests/indexing/test_coercion.py
- [ ] pandas/tests/io/test_sql.py
- [ ] pandas/tests/libs/test_hashtable.py
- [x] pandas/tests/reductions/test_reductions.py
- [x] pandas/tests/reductions/test_stat_reductions.py
- [ ] pandas/tests/resample/test_resampler_grouper.py
- [ ] pandas/tests/reshape/test_get_dummies.py
- [ ] pandas/tests/reshape/test_union_categoricals.py
- [ ] pandas/tests/series/apply/test_series_apply.py
- [ ] pandas/tests/series/test_ufunc.py
- [ ] pandas/tests/window/moments/test_moments_ewm.py
- [ ] pandas/tests/window/test_apply.py
---
#### NOTE:
The list may change as files are moved/renamed constantly.
---
Took pretty much everything from #23922, that was originally opened by @gfyoung. | {
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} | 0 | 2020-01-14T12:40:21Z | 2020-01-14T13:24:42Z | 2020-01-14T13:24:41Z | NONE | null | Backport PR #30980: API: Disallow NaN in StringArray constructor | {
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} | 1 | 2020-01-14T13:24:19Z | 2020-01-15T21:24:54Z | 2020-01-14T20:18:42Z | MEMBER | null | - [x] ref #30999
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | open | false | null | [] | null | 2 | 2020-01-14T13:24:37Z | 2021-07-27T04:26:05Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df_1 = pd.DataFrame(data=[1,2,3],columns=['a'])
df_2 = pd.DataFrame(data=[4,5,6],index=[0.5,1.5,2.5],columns=['b'])
df_2.reindex_like(df_1, method="bfill") # backfills as if columns matched
df_2.reindex_like(df_1, method="ffill") # all NaN
df_2.reindex_like(df_1, method="nearest") # exception
```
#### Problem description
It appears different methods of filling treat non matching columns differently. I came across this when trying to reindex two dataframes with one column each, whose names didn't match (as they were essentially irrelevant).
#### Workaround
before reindex do `df_2.columns = df_1.columns `
#### Expected Output
The documentation says:
> Its row and column indices are used to define the new indices of this object.
Exactly whether this means column name or position is not super clear, but either way the current behaviour is inconsistent. I would prefer if all of them worked like bfill.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.0
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.0.1
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.1
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31003 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31003/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31003/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31003/events | https://github.com/pandas-dev/pandas/issues/31003 | 549,570,647 | MDU6SXNzdWU1NDk1NzA2NDc= | 31,003 | incorrect docs: pd.NA ** 0 should return 1 | {
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} | 3 | 2020-01-14T13:33:21Z | 2020-01-14T21:33:15Z | 2020-01-14T21:33:15Z | CONTRIBUTOR | null | `pd.NA ** 0` returns 1. which is correct, as far as I understand <sup>1</sup>
However, the [documents, line 832](https://dev.pandas.io/docs/user_guide/missing_data.html) incorrectly claims that `pd.NA ** 0` returns 0.
----
<sup>1</sup> although one can ask if `NULL**0` should behave like `NAN**0` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31004 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31004/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31004/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31004/events | https://github.com/pandas-dev/pandas/pull/31004 | 549,590,993 | MDExOlB1bGxSZXF1ZXN0MzYyNjU0MTEz | 31,004 | docs: removed wrong value in `pd.NA ** 0` | {
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} | [] | closed | false | null | [] | null | 0 | 2020-01-14T14:07:18Z | 2020-01-14T14:14:45Z | 2020-01-14T14:14:45Z | CONTRIBUTOR | null | `pd.NA ** 0` returns 1. The docs fixed to auto-calculate these values.
- [x] closes #31003
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 9 | 2020-01-14T14:20:06Z | 2020-02-03T10:32:41Z | 2020-01-14T20:28:50Z | CONTRIBUTOR | null | - [x] closes #31003
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2020-01-14T14:56:33Z | 2020-01-14T17:16:39Z | 2020-01-14T15:37:32Z | NONE | null | #### Problem description
Currently, in .agg, one cannot use the same column more than once, so the following code results in an error:
```
df = pd.DataFrame({
'group': [1,1,2,2],
'var1': [1,2,3,4]
})
df.groupby(
'group'
).agg(
sum1 = ('var1', lambda x: x[x > 1].sum()),
sum2 = ('var1', lambda x: x[x > 2].sum())
)
```
I understand that it is possible to create a MultiIndex DataFrame using a dictionary input rather than keyword arguments, but this required complicated renaming of columns and flattening of the resulting MI DataFrame. Is there some fundamental reason that a column cannot be used twice for two different column specs of .agg? | {
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... | open | false | null | [] | null | 2 | 2020-01-14T15:51:18Z | 2021-04-06T11:40:24Z | null | NONE | null | #### Problem description
When using a rolling agg function on a groupby object, we cannot ommit the groupby columns from the resulting dataframe's index using `as_index = False`, like we can when applying a non-rolling agg function.
#### Code Sample
```python
import pandas as pd
data = {
'groupby_col': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', ],
'agg_col': [1, 1, 0, 1, 0, 0, 0, 0, 1, 0],
}
df = pd.DataFrame(data)
df.groupby(['groupby_col'], as_index=False).rolling(4).agg({'agg_col': 'mean'})
```
#### Expected Output
```python
pd.DataFrame({'agg_col': {
0: np.nan,
1: np.nan,
2: np.nan,
3: 0.75,
4: 0.5,
5: np.nan,
6: np.nan,
7: np.nan,
8: 0.25,
9: 0.25}})
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>agg_col</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>0.75</td>
</tr>
<tr>
<th>4</th>
<td>0.50</td>
</tr>
<tr>
<th>5</th>
<td>NaN</td>
</tr>
<tr>
<th>6</th>
<td>NaN</td>
</tr>
<tr>
<th>7</th>
<td>NaN</td>
</tr>
<tr>
<th>8</th>
<td>0.25</td>
</tr>
<tr>
<th>9</th>
<td>0.25</td>
</tr>
</tbody>
</table>
#### Actual Output
```python
pd.DataFrame({'agg_col': {
(0, 0): np.nan,
(0, 1): np.nan,
(0, 2): np.nan,
(0, 3): 0.75,
(0, 4): 0.5,
(1, 5): np.nan,
(1, 6): np.nan,
(1, 7): np.nan,
(1, 8): 0.25,
(1, 9): 0.25}})
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>agg_col</th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">0</th>
<th>0</th>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>NaN</td>
</tr>
<tr>
<th>2</th>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>0.75</td>
</tr>
<tr>
<th>4</th>
<td>0.50</td>
</tr>
<tr>
<th rowspan="5" valign="top">1</th>
<th>5</th>
<td>NaN</td>
</tr>
<tr>
<th>6</th>
<td>NaN</td>
</tr>
<tr>
<th>7</th>
<td>NaN</td>
</tr>
<tr>
<th>8</th>
<td>0.25</td>
</tr>
<tr>
<th>9</th>
<td>0.25</td>
</tr>
</tbody>
</table>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 40.8.0
Cython : 0.29.14
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2020-01-14T15:59:38Z | 2020-01-18T16:57:52Z | 2020-01-18T16:41:11Z | CONTRIBUTOR | null | Added comments for travis.yml for PANDAS_TESTING_MODE from tips and tricks section of wiki
- [X] xref #30964
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2020-01-14T16:08:32Z | 2020-01-14T20:22:29Z | 2020-01-14T20:22:20Z | CONTRIBUTOR | null | When reading through the contributing guidelines (https://dev.pandas.io/docs/development/contributing.html), there are links to the Sphinx documentation and an introduction to reST.
_Sphinx documentation_: http://sphinx.pocoo.org/
_Introduction to reST_: http://sphinx.pocoo.org/rest.html
However, these links are not working and are leading to 400 bad requests.
I propose we replace these links with the following links:
_Sphinx documentation_: http://www.sphinx-doc.org/en/master/
_Introduction to reST_: https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html
I think this will be an easy fix, editing the links in the contributing.rst file in the documentation folder.
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} | 1 | 2020-01-14T18:12:43Z | 2020-01-14T20:21:05Z | 2020-01-14T20:20:51Z | CONTRIBUTOR | null | GH31010: fixed this issue where inactive links for sphinx documentation were replaced with active links in the contributing documentation.
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- [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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Made to the wrong branch. | {
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