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https://api.github.com/repos/pandas-dev/pandas/issues/35217
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MDU6SXNzdWU2NTQ5NDY0MDU=
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BUG: Inconsistent behavior in Index.difference
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| 2020-07-16T09:55:14Z
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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.
---
`Index.difference` seems to be treating float vs object / int vs object differently.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
float_index = pd.Index([1.0, 2, 3])
string_index = pd.Index(['1','2','3'])
integer_index = pd.Index([1, 2, 3])
print(float_index.difference(string_index))
print(string_index.difference(float_index))
print(integer_index.difference(string_index))
print(string_index.difference(integer_index))
Float64Index([], dtype='float64')
Index([], dtype='object')
Int64Index([1, 2, 3], dtype='int64')
Index(['1', '2', '3'], dtype='object')
```
#### Problem description
Index.difference should probably not type-cast and compare the elements to honor consistent behavior with other types where they are not being type-casted.
#### Expected Output
```python
Float64Index([1.0, 2.0, 3.0], dtype='float64')
Index(['1', '2', '3'], dtype='object')
Int64Index([1, 2, 3], dtype='int64')
Index(['1', '2', '3'], dtype='object')
```
or
All empty Index objects like in case of int vs object.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0
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
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
None
</details>
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BUG: test_inference.py raises stack overflowerror
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| null |
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.
---
**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
<details>
In Anaconda Prompt:
```
(pandas-dev) C:\Users\Joey\pandas-joooeey> pytest pandas
.
.
.
pandas\tests\dtypes\test_generic.py .. [ 21%]
pandas\tests\dtypes\test_inference.py .................................. [ 22%]
..............................................Windows fatal exception: stack overflow
Thread 0x00003d8c (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 302 in wait
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 558 in wait
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\site-packages\IPython\core\history.py", line 829 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\site-packages\IPython\core\history.py", line 58 in needs_sqlite
File "<decorator-gen-24>", line 2 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x00002ef8 (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 576 in _handle_results
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x00006314 (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 528 in _handle_tasks
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x000067cc (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\connection.py", line 810 in _exhaustive_wait
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\connection.py", line 878 in wait
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 499 in _wait_for_updates
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 519 in _handle_workers
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x00000ef4 (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 114 in worker
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x00006718 (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 114 in worker
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x000031c0 (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 114 in worker
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Thread 0x00002644 (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\multiprocessing\pool.py", line 114 in worker
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 870 in run
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 932 in _bootstrap_inner
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\threading.py", line 890 in _bootstrap
Current thread 0x000029fc (most recent call first):
File "C:\ProgramData\Anaconda3\envs\pandas-dev\lib\abc.py", line 98 in __instancecheck__
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 130 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
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File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
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File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
File "C:\Users\Joey\pandas-joooeey\pandas\tests\dtypes\test_inference.py", line 131 in foo
...
(pandas-dev) C:\Users\Joey\pandas-joooeey>pytest pandas/tests/dtypes/test_inference.py
============================= test session starts =============================
platform win32 -- Python 3.8.2, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
rootdir: C:\Users\Joey\pandas-joooeey, inifile: setup.cfg
plugins: hypothesis-5.15.0, asyncio-0.12.0, cov-2.8.1, forked-1.1.2, xdist-1.32.0
collected 447 items
pandas\tests\dtypes\test_inference.py .................................. [ 7%]
........................................................................ [ 23%]
........................................................................ [ 39%]
........................................................................ [ 55%]
........................................................................ [ 72%]
........................................................................ [ 88%]
..................................................... [100%]
============================= 447 passed in 1.81s =============================
(pandas-dev) C:\Users\Joey\pandas-joooeey>
```
</details>
#### Problem description
`pytest pandas` fails at 22% with `Windows fatal exception: stack overflow` in `test_inference.py` line 131. According to the test file, the offending code is expected to raise a `RecursionError`.
Curiously, the following test runs green: `pytest pandas/tests/dtypes/test_inference.py`
#### Expected Output
When I run `pytest pandas` on the master branch (like I did), I expect the entire test suite to run green.
#### Output of ``pd.show_versions()``
```
pd.show_versions()
Traceback (most recent call last):
File "<ipython-input-3-3d232a07e144>", line 1, in <module>
pd.show_versions()
File "C:\Users\Joey\pandas-joooeey\pandas\util\_print_versions.py", line 106, in show_versions
deps = _get_dependency_info()
File "C:\Users\Joey\pandas-joooeey\pandas\util\_print_versions.py", line 86, in _get_dependency_info
result[modname] = _get_version(mod) if mod else None
File "C:\Users\Joey\pandas-joooeey\pandas\compat\_optional.py", line 43, in _get_version
raise ImportError(f"Can't determine version for {module.__name__}")
ImportError: Can't determine version for hypothesis
```
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MDU6SXNzdWU2NTQ5NzM5MTM=
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|
BUG: resampling error when date_range includes a single DST
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| 5
| 2020-07-10T18:53:08Z
| 2020-09-12T21:07:53Z
| 2020-09-12T21:07:53Z
|
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
```python
>>> import pandas as pd
>>> # works as expected (note the daylight savings transitions in the head and tail)
>>> pd.Series(1., pd.date_range('2020-03-28','2020-10-27', freq='D', tz="Europe/Amsterdam")).resample('24H').pad()
2020-03-28 00:00:00+01:00 1.0
2020-03-29 00:00:00+01:00 1.0
2020-03-30 01:00:00+02:00 1.0
2020-03-31 01:00:00+02:00 1.0
2020-04-01 01:00:00+02:00 1.0
...
2020-10-23 01:00:00+02:00 1.0
2020-10-24 01:00:00+02:00 1.0
2020-10-25 01:00:00+02:00 1.0
2020-10-26 00:00:00+01:00 1.0
2020-10-27 00:00:00+01:00 1.0
Freq: 24H, Length: 214, dtype: float64
>>> # fails unexpectedly
>>> pd.Series(1., pd.date_range('2020-03-28','2020-03-31', freq='D', tz="Europe/Amsterdam")).resample('24H').pad()
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/home/felix/anaconda3/envs/bvp-venv/lib/python3.6/site-packages/pandas/core/resample.py", line 453, in pad
return self._upsample("pad", limit=limit)
File "/home/felix/anaconda3/envs/bvp-venv/lib/python3.6/site-packages/pandas/core/resample.py", line 1092, in _upsample
result.index = res_index
File "/home/felix/anaconda3/envs/bvp-venv/lib/python3.6/site-packages/pandas/core/generic.py", line 5287, in __setattr__
return object.__setattr__(self, name, value)
File "pandas/_libs/properties.pyx", line 67, in pandas._libs.properties.AxisProperty.__set__
File "/home/felix/anaconda3/envs/bvp-venv/lib/python3.6/site-packages/pandas/core/series.py", line 401, in _set_axis
self._data.set_axis(axis, labels)
File "/home/felix/anaconda3/envs/bvp-venv/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 178, in set_axis
f"Length mismatch: Expected axis has {old_len} elements, new "
ValueError: Length mismatch: Expected axis has 4 elements, new values have 3 elements
```
#### Problem description
In my first example, resampling from an offset of 1 day to an offset of 24 hours works as expected, but only when the start and end of the DatetimeIndex share the same timezone. In my second example the date range start and ends in a different timezone due to a daylight savings transition on 29 March 2020, for which resampling to 24 hours fails.
Possibly related issue:
- #35248
#### Expected Output
```python
>>> pd.Series(1., pd.date_range('2020-03-28','2020-03-31', freq='D', tz="Europe/Amsterdam")).resample('24H').pad()
2020-03-28 00:00:00+01:00 1.0
2020-03-29 00:00:00+01:00 1.0
2020-03-30 01:00:00+02:00 1.0
Freq: 24H, Length: 3, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.0-11-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
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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TYPING/DOC: Move custom type to _typing and add whatsnew
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| 1
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|
MEMBER
| null |
followup of #29116
details see : https://github.com/pandas-dev/pandas/pull/29116#discussion_r453033204
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MDExOlB1bGxSZXF1ZXN0NDQ3NjUzNjcx
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ASV: asvs for normalize functions
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| 2020-07-10T21:34:33Z
| 2020-07-10T23:38:37Z
| 2020-07-10T22:20:27Z
|
MEMBER
| null |
With this, we have pretty good coverage for everything in tslibs.vectorized
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MDExOlB1bGxSZXF1ZXN0NDQ3NjYxNzkw
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Fixing a confused method name in 02_read_write.rst
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| 2020-07-10T22:01:17Z
| 2020-07-10T22:22:55Z
| 2020-07-10T22:22:51Z
|
CONTRIBUTOR
| null |
Fixed the confused method name. It should be `read_excel` based on the context, but `to_excel` was provided instead.
Very simple PR for a simple fix to the documentation.
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PERF: Allow str.split callers to skip expensive post-processing
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[] | null | 7
| 2020-07-10T23:42:52Z
| 2021-08-30T22:00:01Z
| 2020-09-30T21:13:15Z
|
NONE
| null |
Hey team-
I've got some string processing in my pipeline and have noticed that `Series.str.split` is a pretty significant bottleneck:
<img width="806" alt="Screen Shot 2020-07-09 at 18 03 09" src="https://user-images.githubusercontent.com/8717474/87210555-cd31d980-c2ca-11ea-8c79-e247b236cc0d.png">
(For reference, the rest of the function calls from this `pyinstrument` run take 5-10 seconds.)
It seemed odd to me that it was spending more time post-processing the result (`_wrap_result`) than actually doing the work of splitting strings, so I looked into it. It turns out that when you use `expand=True`, `_wrap_result` makes three full passes over the data (in Python iteration land!). From what I gather, the procedure is to make sure that each row of the resulting data frame ends up with the same number of columns. However, for my use case, I happen to know ahead of time that this will be true; my Series is full of well-formed IPv4 addresses (and from what I gather online, this is a pretty common use of `Series.str.split`), so I know each split string list will be 4 elements long (one for each octet).
This pull request offers an escape hatch for users like me who know that this expensive post-processing step is essentially a no-op, in the form of the `pad_sequences` argument. If `True`, the default, then the original procedure will be run. If `False`, it will be skipped in favor of simply casting the result from a numpy array of lists to a list of lists, to be crammed into a DataFrame a few lines down. It's up to the caller to determine ahead of time if the split will produce uniform-length sequences. (If `expand=False`, then `pad_sequences` has no effect.)
Looking forward to hearing your thoughts!
Many thanks
---
- Related to #10090
- ~[ ] closes #xxxx~
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
EDIT: forgot to mention, I have some performance testing results. Here's the data:
```sh
$ wc -l test.csv
10663901 test.csv
$ du -h test.csv
928M test.csv
```
Here's the original performance:
```python
In [1]: pd.__version__
Out[1]: '1.1.0.dev0+2067.g2c3edaaaa'
In [2]: %%timeit
...: df.ip.str.split(".", expand=True)
...:
...:
26.7 s ± 2.42 s per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
The first optimization I tried (before adding `pad_sequences`) was simply to inline `cons_row` to save the stack frame allocation (since the function wasn't being used anywhere but the comprehension body:
```python
In [1]: pd.__version__
Out[1]: '1.1.0.dev0+2068.g15623dbe7'
In [2]: %%timeit
...: df.ip.str.split(".", expand=True)
...:
...:
24 s ± 202 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Works out to a few hundred nanoseconds of time savings per row, or as you can see here, about 2.5 seconds for my 10 million row test set.
Finally, here are the results without those three passes over the result:
```python
In [2]: pd.__version__
Out[2]: '1.1.0.dev0+2073.g23f88eedb'
In [5]: %%timeit
...: df.ip.str.split(".", expand=True, pad_sequences=False)
...:
...:
15.5 s ± 78.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
(Commit hash is a little different since I rebased onto upstream after running the test.)
So this is quite a bit faster now. If I have time, I'll keep digging to see if we can eliminate that `result.tolist()` call and go straight from the numpy-ish representation to a DataFrame (for now, I found that I needed the `tolist()` in order for `expand=True` to retain its behavior).
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DEPR: DataFrame.lookup
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| 8
| 2020-07-11T00:05:41Z
| 2021-03-01T10:04:25Z
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|
CONTRIBUTOR
| null |
- [x] xref #18262
- [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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TST: added test for groupby/apply timezone-aware with copy
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| 2020-07-11T04:51:37Z
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CONTRIBUTOR
| null |
- [x] closes #27212
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
added test for groupby/apply timezone-aware with copy
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MDU6SXNzdWU2NTUxMTM3MTQ=
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REF: Dataframe.__init__
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| 2021-08-08T16:58:02Z
| null |
CONTRIBUTOR
| null |
in #34875 several checks were added to the main Dataframe construction to handle extension arrays, in particular extension scalars
this introduced multiple checks like
if is_extensions_drype(,,):
...
else:
...
these should be refactored and pushed down to more understandable functions
this construction is now so complicated that bugs are certain to happen
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BUG: ValueError: Cannot convert non-finite values (NA or inf) to integer only when DF exceed certain size
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| 8
| 2020-07-11T02:26:24Z
| 2021-09-01T21:45:18Z
| null |
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.
---
**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.
####
Here is the code in question:
```
print(df.shape)
print(df.memory_usage().sum())
df.dropna(inplace=True) # drop rows with not enough lookback (they will be nan)
```
The line `dropna` line is what throws the error
So when i run my code with adding 500 features/columns the output is following
```
(2177432, 503)
4398412768
```
However when i run the same exact code for 1000 features
```
(2177432, 1003)
8753276768
```
I get an error
```
File "C:\Users\Ben\PycharmProjects\tradingbot\tradingbot\trainer\sample_maker.py", line 17, in get_signal_features
df.dropna(inplace=True) # drop rows with not enough lookback (they will be nan)
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\frame.py", line 4751, in dropna
count = agg_obj.count(axis=agg_axis)
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\frame.py", line 7807, in count
return result.astype("int64")
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\generic.py", line 5698, in astype
new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\internals\managers.py", line 582, in astype
return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\internals\managers.py", line 442, in apply
applied = getattr(b, f)(**kwargs)
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\internals\blocks.py", line 625, in astype
values = astype_nansafe(vals1d, dtype, copy=True)
File "C:\Users\Ben\PycharmProjects\tradingbot\venv\lib\site-packages\pandas\core\dtypes\cast.py", line 868, in astype_nansafe
raise ValueError("Cannot convert non-finite values (NA or inf) to integer")
ValueError: Cannot convert non-finite values (NA or inf) to integer
```
The version i am running is 1.0.5
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COMPAT: Ensure rolling indexers return intp
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| 5
| 2020-07-11T04:50:25Z
| 2020-07-15T15:24:15Z
| 2020-07-14T04:43:39Z
|
MEMBER
| null |
- [x] closes #35148
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Unable to test this locally for the MacPython build, but I am pretty sure this is the main culprit of that failing job.
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- [X] closes #33920
- [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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TST verify return none inplace in tests/indexing
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CONTRIBUTOR
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verify we return none for all inplace calls in tests/indexing
related: https://github.com/pandas-dev/pandas/pull/35210
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BUG: Inconsistent behavior in Index.difference
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MEMBER
| null |
- [ ] closes #35217
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Tst verify return none in tests/frame
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CONTRIBUTOR
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verify we return None for all inplace calls in tests/frame
related: https://github.com/pandas-dev/pandas/pull/35230
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MDExOlB1bGxSZXF1ZXN0NDQ3Nzg3NDU1
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|
TST: Verify filtering operations on DataFrames with categorical Series
|
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| 1
| 2020-07-11T16:20:29Z
| 2020-07-15T22:24:10Z
| 2020-07-15T22:24:07Z
|
CONTRIBUTOR
| null |
- [ x ] closes #22609
- [ 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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|
TST add test for dtype consistency with pd replace #23305
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| 2020-07-11T16:32:19Z
| 2020-07-16T22:46:02Z
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|
CONTRIBUTOR
| null |
- [x] closes #23305
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDQ3NzkwNzI1
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|
CLN/DOC: DataFrame.to_parquet supports file-like objects
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| 2
| 2020-07-11T16:50:07Z
| 2020-07-17T13:08:25Z
| 2020-07-17T12:33:05Z
|
MEMBER
| null |
Adds documentation and type-hints for supporting file-like objects when `engine == 'pyarrow'`; relevant to #30081. Tests for this behavior currently exist in `io.test_parquet.py`:
````
@td.skip_if_no("pyarrow")
def test_read_file_like_obj_support(self, df_compat):
buffer = BytesIO()
df_compat.to_parquet(buffer)
df_from_buf = pd.read_parquet(buffer)
tm.assert_frame_equal(df_compat, df_from_buf)
````
Perhaps the restrictions on the arguments when path is not a string:
* partition_cols must be None; and
* engine must end up being pyarrow
should be checked directly in `DataFrame.to_parquet`, but I'm leaving this out as that is an API change that could be made in a subsequent PR. The latter gives the clear error message `TypeError: expected str, bytes or os.PathLike object, not _io.BytesIO` but the former raises `AttributeError: 'NoneType' object has no attribute '_isfilestore'` which is slightly confusing.
Another API change that could be made subsequently is changing the `path` argument to `path_or_buf`, consistent with `DataFrame.to_csv`.
|
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TST: added tests for sparse and date range quantiles
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| 2020-07-16T15:29:24Z
| 2020-07-16T01:31:46Z
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CONTRIBUTOR
| null |
- [x] closes #24600
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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TST: GH20676 Verify equals operator for list of Numpy arrays
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- [x] closes #20676
- [ 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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REGR: concat on index with duplicate labels fails
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|
MEMBER
| null |
Noticed from failing geopandas tests:
```python
df1 = pd.DataFrame({'a': [0, 1]})
df2 = pd.DataFrame({'b': [0, 1, 2]}, index=[0, 0, 1])
pd.concat([df1, df2], axis=1)
```
this started failing recently on master:
```
In [3]: pd.concat([df1, df2], axis=1)
...
~/scipy/pandas/pandas/core/internals/managers.py in _verify_integrity(self)
314 for block in self.blocks:
315 if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
--> 316 raise construction_error(tot_items, block.shape[1:], self.axes)
317 if len(self.items) != tot_items:
318 raise AssertionError(
ValueError: Shape of passed values is (3, 2), indices imply (2, 2)
```
While the expected result is (with automatic reindex of `df1` because concat aligns on the index):
```
In [2]: pd.concat([df1, df2], axis=1)
Out[2]:
a b
0 0 0
0 0 1
1 1 2
```
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TST: GH#26367 Verify whether Pandas import succeeds when setting filt…
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|
CONTRIBUTOR
| null |
…erwarnings
- [x] closes #26367
- [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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BUG: concat along the index (axis=0) of two dataframes with duplicate column name fails
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| 13
| 2020-07-11T22:30:50Z
| 2020-09-06T10:03:50Z
| null |
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Hi,
I have a persistent problem with concatenating multiple DataFrames with shapes:
1. (48, 5674)
2. (48, 9022)
3. (48, 7340),
4. (47, 6539)
5. (47, 10369)
6. (47, 17242)
7. (47, 19248)
8. (47, 14282)
If I want to concatenate this, or even any part of it with
```python
pd.concat(df_list)
```
I get the following error:
```python
Traceback (most recent call last):
File "E:/OneDrive/Informatik Studium/KIT Master/SS20/AGD Praktikum/phase-2/1_code/MyTest.py", line 46, in <module>
df_result = __parallelize_dataframe(func=apply_functions, df_data=df_train.copy(), config_tupels=config_tupels)
File "E:/OneDrive/Informatik Studium/KIT Master/SS20/AGD Praktikum/phase-2/1_code/MyTest.py", line 22, in __parallelize_dataframe
df_pool_result = pd.concat(pool_result[0:2])
File "E:\venv\lib\site-packages\pandas\core\reshape\concat.py", line 284, in concat
return op.get_result()
File "E:\venv\lib\site-packages\pandas\core\reshape\concat.py", line 497, in get_result
mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy
File "E:\venv\lib\site-packages\pandas\core\internals\managers.py", line 2016, in concatenate_block_managers
elif is_uniform_join_units(join_units):
File "E:\venv\lib\site-packages\pandas\core\internals\concat.py", line 388, in is_uniform_join_units
all(not ju.is_na or ju.block.is_extension for ju in join_units)
File "E:\venv\lib\site-packages\pandas\core\internals\concat.py", line 388, in <genexpr>
all(not ju.is_na or ju.block.is_extension for ju in join_units)
AttributeError: 'NoneType' object has no attribute 'is_extension'
```
I found out in my research, that blocks in the join_units are sometimes None. But I don't understand why this is so...
All table entries in my DataFrames are not None/NaN.
Unfortunately I can't post the data here, because they are very extensive.
Maybe it helps to know that I split the rows in my original dataframe for multiprocessing. Afterwards I will concatenate them again, see above.
Thanks a lot!
|
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MDExOlB1bGxSZXF1ZXN0NDQ3ODI1NTUy
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BUG: GroupBy.count() and GroupBy.sum() incorreclty return NaN instead of 0 for missing categories (Version 2)
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| 2020-07-11T22:42:16Z
| 2020-07-14T08:20:53Z
| 2020-07-14T08:20:53Z
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| null |
- [x] closes #31422
- [x] closes #35028
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
_Behavioural Changes_
Fixing two related bugs: when grouping on multiple categoricals, .sum() and .count() would return NaN for the missing categories, but they are expected to return 0 for the missing categories. Both these bugs are fixed.
_Tests_
Tests were added in PR #35022 when these bugs were discovered and the tests were marked with an xfail. For this PR the xfails are removed and the tests are passing normally. As well, a few other existing tests were expecting `sum()` to return `NaN`; these have been updated so that the tests now expect to get `0` (which is the desired behaviour).
One new test is added to ensure that the exception handling of the new `try-except-finally` block behaves as expected.
_df.pivot_table_
The changes to `.sum()` & `.count()` also impacts the `df.pivot_table()` if it is called with `aggfunc=sum/count` and is pivoted on a Categorical column with observed=False. This is not explicitly mentioned in either of the bugs, but it does make the behaviour consistent (i.e. the sum of a missing category is zero, not NaN). Two tests on test_pivot.py was updated to reflect this change.
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CLN: annotate
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MDExOlB1bGxSZXF1ZXN0NDQ3ODM0NzY2
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Update asv default to 3.8
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ASV: dt64arr_to_periodarr
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REF: de-duplicate get_resolution
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| 2
| 2020-07-12T04:33:31Z
| 2020-08-04T02:01:27Z
| 2020-08-03T23:22:04Z
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MEMBER
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This changes get_resolution to use the same less-verbose pattern as ints_to_pydatetime; hopefully we'll be able to refactor out a couple of helper functions.
asv run says this is perf-neutral
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BUG: AttributeError when doing groupby with as_index=False on Empty DataFrame
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| 2020-07-17T18:49:54Z
| 2020-07-17T18:49:54Z
|
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.
---
**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
import pandas as pd
pd.DataFrame(columns=["a", "b"]).groupby(by="a", as_index=False)["b"].sum()
```
#### Problem description
in version 0.25.3, this code return an empty DataFrame with columns=["a", "b"]
#### Expected Output
an empty DataFrame with columns=["a", "b"]
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 19.0.3
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
pyxlsb : 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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| 35,247
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PERF: DataFrame.query slow when it contains PeriodIndex/Array
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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
In [1]: import numpy as np
In [2]: import pandas as pd
In [23]: country = list(range(200))
In [24]: year = list(range(1600, 2100))
In [25]: idx = pd.MultiIndex.from_product([country, year], names=['country', 'year'])
In [27]: values = np.random.rand(idx.shape[0], 1)
In [30]: df = pd.DataFrame(values, index=idx, columns=['values']).reset_index()
In [41]: df2 = df.copy()
In [42]: df2['year'] = df2['year'].astype(str).apply(lambda x: pd.Period(x, 'Y'))
# performance when year column is int dtype
In [47]: %timeit df.query("year > 2020")
3.48 ms ± 80.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [48]: %timeit df[df.year > 2020]
1.07 ms ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
# performance when year column is Peroid dtype
In [49]: %timeit df2.query("year > '2020'")
1.03 s ± 18.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [50]: %timeit df2[df2.year > '2020']
1.46 ms ± 21.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
#### Problem description
In above example, the `year` column of `df2` is a Series of Period objects. Running query() on the year column is a lot slower than running indexing (1.03s vs 1.46ms). Changing `engine` and `parser` for query() doesn't help.
#### Expected Output
I was expecting performance difference between querying and boolean indexing is similar to that when the year column's dtype is int (~3x)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : zh_CN.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.2
hypothesis : 5.16.0
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 35,248
|
BUG: Resampling a Series with a DST to 24 hours gives a different result depending on whether timedelta(hours=24) or "24H" was used.
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| null |
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.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> from datetime import timedelta
>>> # works as expected given a DST (second index is 24 hours after the first index)
>>> pd.Series(1, index=pd.date_range("2020-03-29", "2020-03-30 01:00", freq="H", tz="Europe/Amsterdam")).resample("24H").asfreq()
2020-03-29 00:00:00+01:00 1
2020-03-30 01:00:00+02:00 1
Freq: 24H, dtype: int64
>>> # works, but not as expected (second index is 23 hours after the first index)
>>> pd.Series(1, index=pd.date_range("2020-03-29", "2020-03-30 01:00", freq="H", tz="Europe/Amsterdam")).resample(timedelta(hours=24)).asfreq()
2020-03-29 00:00:00+01:00 1
2020-03-30 00:00:00+02:00 1
Freq: D, dtype: int64
```
#### Problem description
Resampling to a timedelta of 24 hours gives a result as if it's resampling to a timedelta of 1 calendar day. Using a `freq_str` of 24H does give the expected behaviour.
Possibly related issues:
- #22864
- #35219
#### Expected Output
```python
>>> pd.Series(1, index=pd.date_range("2020-03-29", "2020-03-30 01:00", freq="H", tz="Europe/Amsterdam")).resample(timedelta(hours=24)).asfreq()
2020-03-29 00:00:00+01:00 1
2020-03-30 01:00:00+02:00 1
Freq: 24H, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.0-11-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
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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Performance Regression in frame_methods.Equals.time_frame_float_equal
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| 5
| 2020-07-12T15:54:15Z
| 2020-07-17T18:49:28Z
| 2020-07-17T18:49:28Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#frame_methods.Equals.time_frame_float_equal?commits=77f4a4d673e2d2b3f7396a94818771d332bd913c
Some discussion starting at https://github.com/pandas-dev/pandas/pull/34962#issuecomment-655456601
@jbrockmendel what needs to be done for 1.1, just optimize `array_equivalent` or something larger? Is this a blocker (if so can you add the blocker tag)?
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|
REF: de-duplicate vectorized normalize functions
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| 2020-07-12T20:18:10Z
| 2021-11-20T23:22:39Z
| 2020-07-12T22:42:44Z
|
MEMBER
| null |
xref #35245.
I think we lose some ground on UTC/naive because a `with nogil` is removed here. Im OK with this tradeoff since it moves us towards being able to de-duplicate a bunch of code in this file; im hopeful we can eventually get the nogil back (in fact, i think we may be able to get it back for everything but the tzlocal case, and maybe even that pending https://github.com/cython/cython/issues/3733)
```
before after ratio
[37662ea3] [bbdbaae2]
<ref-fields-offsets> <ref-normalize>
+ 25.2±0.4μs 29.6±2μs 1.17 tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, None)
+ 25.9±0.9μs 29.8±0.9μs 1.15 tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, datetime.timezone.utc)
- 7.51±0.2μs 6.59±0.3μs 0.88 tslibs.normalize.Normalize.time_is_date_array_normalized(1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 5.43±0.08ms 4.69±0.1ms 0.86 tslibs.normalize.Normalize.time_normalize_i8_timestamps(100, tzlocal())
- 6.73±0.2μs 5.68±0.2μs 0.84 tslibs.normalize.Normalize.time_is_date_array_normalized(0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 1.13±0.03μs 939±20ns 0.83 tslibs.normalize.Normalize.time_is_date_array_normalized(1, datetime.timezone.utc)
- 1.29±0.07μs 1.01±0.01μs 0.79 tslibs.normalize.Normalize.time_is_date_array_normalized(0, datetime.timezone.utc)
```
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Inconsistent NA handling for BinGrouper and Grouper
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| 3
| 2020-07-12T20:43:58Z
| 2020-07-20T01:29:45Z
| null |
CONTRIBUTOR
| null |
BinGrouper and Grouper handle NA values differently when looking at the codes.
```python
In [79]: df = pd.DataFrame({"A": [1, 2, 3]}, index=pd.PeriodIndex([pd.NaT, pd.NaT, pd.NaT], freq="D"))
In [80]: a = df.groupby(df.index).grouper
In [81]: b = df.resample("1D")._get_binner()[1]
In [82]: a.codes
Out[82]: [array([-1, -1, -1])]
In [83]: b.codes
Out[83]: [array([], dtype=int64)]
```
With https://github.com/pandas-dev/pandas/pull/34998, this can cause a false positive on the apply `group_index` warning if you have an all NA group.
Ideally they have the same representation (I think all `-1` is correct).
xref https://github.com/pandas-dev/pandas/pull/34998#issuecomment-652497050.
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CI: warning from setuptools (Numpy Dev Build Failures)
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| null |
CONTRIBUTOR
| null |
```
def warn_distutils_present():
if 'distutils' not in sys.modules:
return
if is_pypy and sys.version_info < (3, 7):
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
return
warnings.warn(
> "Distutils was imported before Setuptools. This usage is discouraged "
"and may exhibit undesirable behaviors or errors. Please use "
"Setuptools' objects directly or at least import Setuptools first.")
E UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.
```
e.g : https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=39110&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=4d05ed0e-1ed3-5bff-dd63-1e957f2766a9&l=195
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| 3
| 2020-07-12T22:31:33Z
| 2020-07-13T22:22:54Z
| 2020-07-13T22:22:46Z
|
MEMBER
| null |
- [x] closes #34951
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Within `DataFrameGroupBy._cython_agg_blocks`, if it is aggregating a one-column DataFrame, it creates a `SeriresGroupBy`, calls the function on that and takes the returned values. But the `SeriesGroupBy` also does the missing-categories reindexing. The `DataFrameGroupBy` ends up with values that contain the missing categories, and an index that does not. When they are passed into a BlockManager it raises a `ValueError` stating that their lengths don't match.
Solutions is to have `_cython_agg_blocks` create a `SeriesGroupBy` with `observed=True` so it doesn't do any reindexing. The reindexing is left to the calling `DataFrameGroupBy`
This also explains why error only occurred in `DataFrameGroupBy` but not `SeriesGroupBy`.
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BUG: Use correct ExtensionArray reductions in DataFrame reductions
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- [x] closes #34520
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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REF: make tz_convert match pattern elsewhere
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| 1
| 2020-07-12T23:00:59Z
| 2020-07-13T20:13:24Z
| 2020-07-13T16:42:47Z
|
MEMBER
| null |
asvs for tslibs.tz_convert show this as perf-neutral
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|
PERF: array_equivalent
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|
MEMBER
| null |
xref #35249, this gives about a 3x speedup in the asv reported there
cc @jorisvandenbossche EA.equals doesn't support a strict_nan arg, is there anything we need to do to account for that here?
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Add keep parameter for duplicates values in idxmax, idxmin, argmax, and argmin
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NONE
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- [x] closes #34205
- [ ] tests added / passed
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[BUG] fixed DateOffset pickle bug when months >= 12
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| 2021-03-20T02:35:50Z
| 2020-08-14T16:17:38Z
|
MEMBER
| null |
- [x] closes #34511
- [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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ENH: Basis for a StringDtype using Arrow
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| 2020-11-20T20:50:34Z
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CONTRIBUTOR
| null |
- [x] xref #35169
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CI: Ignore setuptools distutils warning
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| 2
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| 2020-07-14T13:15:47Z
| 2020-07-13T15:27:00Z
|
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/35252. Just ignoring in the test. Can discuss a proper solution in the issue.
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Failures with pytest 6.0.0rc1
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|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=39140&view=logs&j=a3a13ea8-7cf0-5bdb-71bb-6ac8830ae35c&t=add65f64-6c25-5783-8fd6-d9aa1b63d9d4
```
=================================== FAILURES ===================================
____ TestStringMethods.test_api_per_method[index-empty0-get_dummies-object] ____
[gw0] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
copy=False,
name=None,
verify_integrity: bool = True,
_set_identity: bool = True,
):
# compat with Index
if name is not None:
names = name
if levels is None or codes is None:
raise TypeError("Must pass both levels and codes")
if len(levels) != len(codes):
raise ValueError("Length of levels and codes must be the same.")
if len(levels) == 0:
> raise ValueError("Must pass non-zero number of levels/codes")
E ValueError: Must pass non-zero number of levels/codes
pandas/core/indexes/multi.py:271: ValueError
```
In the failing cases we dynamically add a an xfail marker: https://github.com/pandas-dev/pandas/blob/f477a0e8146e3f249d2a5c08031020c15c1ecd95/pandas/tests/test_strings.py#L266-L268. Reported at https://github.com/pytest-dev/pytest/issues/7486.
For now we can pin pytest.
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Failures with pytest 6.0.0rc1
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| 2020-07-13T13:33:12Z
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|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=39140&view=logs&j=a3a13ea8-7cf0-5bdb-71bb-6ac8830ae35c&t=add65f64-6c25-5783-8fd6-d9aa1b63d9d4
```
=================================== FAILURES ===================================
____ TestStringMethods.test_api_per_method[index-empty0-get_dummies-object] ____
[gw0] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
copy=False,
name=None,
verify_integrity: bool = True,
_set_identity: bool = True,
):
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if name is not None:
names = name
if levels is None or codes is None:
raise TypeError("Must pass both levels and codes")
if len(levels) != len(codes):
raise ValueError("Length of levels and codes must be the same.")
if len(levels) == 0:
> raise ValueError("Must pass non-zero number of levels/codes")
E ValueError: Must pass non-zero number of levels/codes
pandas/core/indexes/multi.py:271: ValueError
```
In the failing cases we dynamically add a an xfail marker: https://github.com/pandas-dev/pandas/blob/f477a0e8146e3f249d2a5c08031020c15c1ecd95/pandas/tests/test_strings.py#L266-L268. Reported at https://github.com/pytest-dev/pytest/issues/7486.
For now we can pin pytest.
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MDU6SXNzdWU2NTU5NTI4OTQ=
| 35,263
|
nan + nan = 0 when skipna=True or when groupby().sum()
|
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[] | null | 1
| 2020-07-13T15:43:19Z
| 2020-07-13T19:33:54Z
| 2020-07-13T19:33:54Z
|
NONE
| null |
**Disclaimer**: Not sure whether this is expected behavior. I would have not expected it though and it is always bad in my use cases (survey data).
Example:
```python
data = pd.DataFrame(
data = {
"var1": [np.nan, 1000, 2000],
"var2": [np.nan, 100, np.nan],
"hh_id" : [1, 2, 2]
}
)
```
**Normal sum**
```
data[["var1", "var2"]].sum(axis=1)
```
Expected output:
```
0 np.nan
1 1100.0
2 2000.0
dtype: float64
```
Output:
```
0 0.0
1 1100.0
2 2000.0
dtype: float64
```
**Groupby sum**
```python
data.groupby("hh_id")["var2"].sum()
```
Expected output:
```
hh_id
1 np.nan
2 100.0
Name: var2, dtype: float64
```
Output
```
hh_id
1 0.0
2 100.0
Name: var2, dtype: float64
```
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MDU6SXNzdWU2NTU5OTc1NTk=
| 35,264
|
DOC: Incomplete method description for str.match
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[] | null | 2
| 2020-07-13T16:52:59Z
| 2020-09-04T15:05:13Z
| 2020-09-04T15:05:13Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.match.html
#### Documentation problem
The current method description is:
"Determine if each string matches a regular expression."
This description does not specify that str.match only matches the beginning of a string. This leads to confusion when regex does not function as expected.
#### Suggested fix for documentation
Change method description to:
"Determine if the beginning of each string matches a regular expression."
This makes the documentation more accurate, and also makes the method description align with the match function from the re module, from which it is taken.
|
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| 35,265
|
ENH: Add `into` argument for `to_xarray` method?
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[] | null | 8
| 2020-07-13T17:38:28Z
| 2020-08-21T17:05:24Z
| 2020-07-14T00:57:17Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to able to specify the object used for creating `xarray` objects.
#### Describe the solution you'd like
Right now, the only option is `xr.Dataset` but I tend to prefer `xr.DataArray` objects.
#### API breaking implications
It shouldn't affect the API in any complicated ways.
#### Describe alternatives you've considered
Create a `xr.Dataset` and then convert that to `xr.DataArray`. However, this step could be obviated by usage similar to [`to_dict`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_dict.html) method.
#### Additional context
```python
import pandas as pd
import xarray as xr
df = pd.read_csv("https://pastebin.com/raw/dR59vTD4", sep="\t", index_col=0)
da = df.to_xarray(into=xr.DataArray)
```
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MDExOlB1bGxSZXF1ZXN0NDQ4NDc3NjY1
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|
REGR: setting column with setitem should not modify existing array inplace
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| 5
| 2020-07-13T20:39:20Z
| 2020-08-18T13:37:46Z
| 2020-07-28T13:12:17Z
|
MEMBER
| null |
Closes #33457
This changes `ExtensionBlock.setitem` to how it was before #32831: updating the Block itself inplace, but not the array.
@jbrockmendel I know you won't like that this update the Block inplace (it's also not exactly what you suggested at https://github.com/pandas-dev/pandas/issues/33457#issuecomment-654367183). But this was the smallest change I could find to fix the issue.
The alternative is probably changing `BlockManager.iset` to not use Block.set in case of an existing ExtensionBlock, but `BlockManager.iset` is quite complex ..
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BUG: Series.equals fails when comparing numpy arrays to scalars
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| 6
| 2020-07-13T21:29:19Z
| 2020-09-19T20:29:43Z
| 2020-09-19T20:29:43Z
|
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.
---
**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
import pandas as pd
import numpy as np
arr = np.array([1, 2])
s1 = pd.Series([arr, arr])
s2 = s1.copy()
s1[1] = 9
s1.equals(s2) # This throws a ValueError
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
#### Problem description
Series.equals operation fails when comparing two Series that at the same position respectively have a single value and a list. Pandas then throws a ValueError since it cannot compare a single value to a list.
Related issues #20676 and #35237
#### Expected Output
False, since the two series are not equal
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9827ce00f95927ddf6a0565450bc175879454d4e
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.0.dev0+2088.g9827ce00f
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200712
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.19.1
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : 0.6.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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MDU6SXNzdWU2NTYyNjExOTk=
| 35,268
|
BUG: pd.replace changes category dtype to object when using a dict to replace values
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| 2020-07-14T01:45:05Z
| 2020-10-21T23:08:48Z
| null |
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.
---
**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
>>> import pandas as pd
>>> pd.__version__
'1.0.5'
>>> input_dict = {"col1": ["a"], "col2": ["obj1"], "col3": ["cat1"]}
>>> input_df = pd.DataFrame(data=input_dict).astype({"col1": "category", "col2": "category", "col3": "category"})
>>> input_df
col1 col2 col3
0 a obj1 cat1
>>> input_df.dtypes
col1 category
col2 category
col3 category
dtype: object
>>> input_df = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"})
>>> input_df
col1 col2 col3
0 z obj9 catX
>>> input_df.dtypes
col1 object
col2 object
col3 object
dtype: object
```
#### Problem description
Currently, the category dtypes are lost when we use pandas replace. This loss of category dtype is observed only when a dictionary is used to replace multiple values.
I was able to recreate the issue for `pd.__version__ == '1.1.0.dev0+2073.g280efbfcc'` as well. Also, please see related issue: https://github.com/pandas-dev/pandas/issues/23305
#### Expected Output
I would expect the categorical columns to keep their category dtype after replace is called on the dataframe. Thanks!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
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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MDU6SXNzdWU2NTYzMzMyNjE=
| 35,269
|
BUG: merge_ordered fails when left_by is set to more than one column
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| 1
| 2020-07-14T05:19:32Z
| 2020-11-29T17:45:40Z
| 2020-11-29T17:45:40Z
|
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.
- [ ] (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
import io, pandas
l = pandas.read_csv(io.StringIO('''
G H T
g h 1
g h 3
'''), delim_whitespace=True)
r = pandas.read_csv(io.StringIO('''
T
2
'''), delim_whitespace=True)
pandas.merge_ordered(l, r, on=['T'], left_by=['G', 'H'])
```
#### Problem description
This fails:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 290, in merge_ordered
result, _ = _groupby_and_merge(
File "/usr/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 162, in _groupby_and_merge
merged[k] = key
File "/usr/lib/python3.8/site-packages/pandas/core/frame.py", line 2938, in __setitem__
self._set_item(key, value)
File "/usr/lib/python3.8/site-packages/pandas/core/frame.py", line 3000, in _set_item
value = self._sanitize_column(key, value)
File "/usr/lib/python3.8/site-packages/pandas/core/frame.py", line 3636, in _sanitize_column
value = sanitize_index(value, self.index, copy=False)
File "/usr/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 611, in sanitize_index
raise ValueError("Length of values does not match length of index")
ValueError: Length of values does not match length of index
```
#### Expected Output
Not failing. Should return:
G H T
0 g h 1
1 g h 3
For comparison, the above works fine if we use `left_by=['G']` and omit the `H` column entirely.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.7-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_FYL.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NDQ4NzA4MDEw
| 35,270
|
TST: Add test to ensure DF describe does not throw an error (#32409)
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|
CONTRIBUTOR
| null |
- [x] closes #32409
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Notes:
- Tested using `pytest pandas/tests/frame/methods/test_describe.py::TestDataFrameDescribe::test_describe_does_not_raise_error` and tested error format by calling a raise error line in the try block `raise TypeError("Test message")`
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MDExOlB1bGxSZXF1ZXN0NDQ4NzE2MDA3
| 35,271
|
REGR: revert ExtensionBlock.set to be in-place
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Alternative for https://github.com/pandas-dev/pandas/pull/35266 and closes #35369
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CI: Unpin pytest
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MEMBER
| null |
- [ ] closes #35261
have made a start working through the failures. we should be able to get mypy to green here and then when pytest 6.0.0 is released the upstream fixes should cause ci to go red since we have `warn_unused_ignores = True` in setup.cfg.
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Move API changes to appropriate sections
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| 2020-07-14T17:10:00Z
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CONTRIBUTOR
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xref https://github.com/pandas-dev/pandas/issues/34801
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CI: pin pytest in minimum versions
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xref https://github.com/pandas-dev/pandas/pull/35260#discussion_r454293041
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BUG: agg on groups with different sizes fails with out of bounds IndexError
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[] | null | 14
| 2020-07-14T14:12:20Z
| 2021-08-08T17:03:30Z
| null |
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
See here as well: https://repl.it/@valkum/WrithingNotablePascal
```python
import numpy as np
import pandas as pd
data = {
'date': ['2000-01-01', '2000-01-02', '2000-01-01', '2000-01-02'],
'team': ['client1', 'client1', 'client2', 'client2'],
'temp': [0.780302, 0.035013, 0.355633, 0.243835],
}
df = pd.DataFrame( data )
df['date'] = pd.to_datetime(df['date'])
df = df.drop(df.index[1])
sampled=df.groupby('team').resample("1D", on='date')
#Returns IndexError
sampled.agg({'temp': np.mean})
#Returns IndexError as well
sampled['temp'].mean()
```
#### Problem description
agg fails with `IndexError: index 3 is out of bounds for axis 0 with size 3`
Note that this does work as expected when I do not drop a row after createing the DataFrame, so I assume it is caused by the index.
#### Expected Output
No fail.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1009-gcp
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2NTY3NjU2OTg=
| 35,276
|
DOC: remove Bug Fix / Other section in whatsnew in favor of correct placement of issues
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CONTRIBUTOR
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*very* few of the current whatsnew notes in Bug Fixes / Other actually belong. Should move everything out.
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MDExOlB1bGxSZXF1ZXN0NDQ5MDk1MDM3
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|
Revert "BUG: fix union_indexes not supporting sort=False for Index subclasses"
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CONTRIBUTOR
| null |
Reverts pandas-dev/pandas#35098
Closes https://github.com/pandas-dev/pandas/issues/35238 I'll also push a test from https://github.com/pandas-dev/pandas/issues/35238 here, and add the xfailing tests from #35098.
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BUG: values-dependent reindex behavior in Groupby.apply
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CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/34998#issuecomment-658318898.
Currently (on master and in my PR at https://github.com/pandas-dev/pandas/pull/34998), `.groupby(...).apply` has some value-dependent behavior. Specifically
```python
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame({"A": [2, 1, 2], "B": [1, 2, 3]})
...: df2 = pd.DataFrame({"A": [2, 1, 2], "B": [1, 2, 1]}) # duplicates in group "2"
In [3]: df1.groupby("A", group_keys=False).apply(lambda x: x.drop_duplicates())
Out[3]:
A B
0 2 1
1 1 2
2 2 3
In [4]: df2.groupby("A", group_keys=False).apply(lambda x: x.drop_duplicates())
Out[4]:
A B
1 1 2
0 2 1
```
Internally, groupby constructs a list of DataFrames, one per group, that are the results of the UDF applied to each group. Those are concatenated together, and are at this point in "group" order. If we detect that the `.apply` was actually a transform, we reindex the concatenated result back to the original index.
https://github.com/pandas-dev/pandas/blob/b6222ec976a71b4ba0c643411606e2376e744c4d/pandas/core/groupby/groupby.py#L1114-L1128
Out[3] has been viewed as a transform and so was reindexed. Whether or not the UDF was a transform depends on the values, and we generally discourage this type of values-dependent behavior.
To solve this, we have a few options
1. Implement a "table-wise" transform. This solves the usecase where people are using `.apply` rather than transform just because it operates on dataframes rather than columns. We could do this through `.groupby(..., axis=None).transform()` or through `.groupby(...).transform_table()` / `transform_frame()`. This doesn't help with the `drop_duplicates` example, which is more of a filter (that sometimes doesn't filter anything).
2. Implement a "table-wise" filter. Currently `.groupby().filter()` expects the UDF to return a scalar, and filters *groups* based on that. It could be expanded to also allow the UDF to return an array. In this case it would filter *rows* where the returned value evaluates to True. This would solve the `drop_duplicates` use case, but not all use cases.
3. Regardless of whether 1 or 2 are implemented, add a `reindex_output` keyword to groupby to control this very narrow case. This would only be relevant when `group_keys=False` and we've detected an apply. It gives users control over whether or not the result is reindexed.
```python
>>> df1.groupby("A", group_keys=False, reindex_output=True).apply(lambda x: x.drop_duplicates())
A B
0 2 1
1 1 2
2 2 3
>>> df1.groupby("A", group_keys=False, reindex_output=False).apply(lambda x: x.drop_duplicates())
A B
1 1 2
0 2 1
2 2 3
```
It has no effect in any other case, including `group_keys=False`. By default, it can be `None` to preserve the values-dependent behavior on master. Though we can explore deprecating it if there's any desire.
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MDU6SXNzdWU2NTY5ODAyNTE=
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pandas/tests/io/json/test_pandas.py::TestPandasContainer::test_read_json_large_numbers failing for 32-bit system
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| 2020-07-15T00:43:22Z
| null |
CONTRIBUTOR
| null |
```
@pytest.mark.parametrize("bigNum", [sys.maxsize + 1, -(sys.maxsize + 2)])
# @pytest.mark.xfail(sys.maxsize == 2**32, reason="")
def test_read_json_large_numbers(self, bigNum):
# GH20599
series = Series(bigNum, dtype=object, index=["articleId"])
json = '{"articleId":' + str(bigNum) + "}"
with pytest.raises(ValueError):
json = StringIO(json)
result = read_json(json)
tm.assert_series_equal(series, result)
df = DataFrame(bigNum, dtype=object, index=["articleId"], columns=[0])
json = '{"0":{"articleId":' + str(bigNum) + "}}"
with pytest.raises(ValueError):
json = StringIO(json)
result = read_json(json)
> tm.assert_frame_equal(df, result)
E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="0") are different
E
E Attribute "dtype" are different
E [left]: object
E [right]: int64
```
We have
```
-> tm.assert_frame_equal(df, result)
(Pdb) result
0
articleId 1
(Pdb) df
0
articleId 18446744073709551617
```
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MDExOlB1bGxSZXF1ZXN0NDQ5MTk0NDUw
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BUG: GroupBy.count() and GroupBy.sum() incorreclty return NaN instead of 0 for missing categories
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| 8
| 2020-07-15T01:40:16Z
| 2020-08-07T15:21:19Z
| 2020-08-07T15:21:13Z
|
MEMBER
| null |
- [x] closes #31422
- [x] closes #35028
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
*Behavioural Changes*
Fixing two related bugs: when grouping on multiple categoricals, `.sum()` and `.count()` would return `NaN` for the missing categories, but they are expected to return `0` for the missing categories. Both these bugs are fixed.
*Tests*
Tests were added in PR #35022 when these bugs were discovered and the tests were marked with an `xfail`. For this PR the `xfails` are removed and the tests are passing normally. As well, a few other existing tests were expecting `sum()` to return `NaN`; these have been updated so that the tests now expect to get `0` (which is the desired behaviour).
*Pivot*
The change to `.sum()` also impacts the `df.pivot_table()` if it is called with `aggfunc=sum` and is pivoted on a Categorical column with `observed=False`. This is not explicitly mentioned in either of the bugs, but it does make the behaviour consistent (i.e. the sum of a missing category is zero, not `NaN`). One test on test_pivot.py was updated to reflect this change.
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ENH: to_latex positional argument
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| 0
| 2020-07-15T07:26:40Z
| 2020-08-21T17:04:36Z
| 2020-08-07T15:32:27Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
I would like to have the possibility to specify the location of the table I'm saving, or instead of having
```
\begin{table}
...
\end{table}
```
be able to have
```
\begin{table}[h]
...
\end{table}
```
#### Describe the solution you'd like
I guess the solution would be to add another argument in the `to_latex` API which could take values in `(None, 'h', 'H', 'h!'...)`
#### Describe alternatives you've considered
I'm saving my tables to `.tex` files and then use the `\input{path/to/tex}` command, I did not find the possibility to give the [h] argument with input...
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| 35,282
|
DOC: DataFrame.equals: (row) indices are also compared
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[] | null | 1
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| 2020-09-04T15:17:34Z
| 2020-09-04T15:17:33Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.equals.html
#### Documentation problem
```
DataFrame.equals(self, other)
Test whether two objects contain the same elements.
This function allows two Series or DataFrames to be compared against each other to see if they have
the same shape and elements. NaNs in the same location are considered equal.
The column headers do not need to have the same type, but the elements within the columns
must be the same dtype.
```
#### Suggested fix for documentation
But in fact, (row) indices are also compared:
```python
df = pd.DataFrame([ [1] ], columns=['a'], index=[0])
not_exactly_equal = pd.DataFrame([ [1] ], columns=['a'], index=[1])
df.equals(not_exactly_equal) # => False
```
P.S. And what is `column header type`?
One more: does `the elements within the columns must be the same dtype` mean `columns should have the same dtype`? Or: `type(df1.iloc[i, j]) == type(df2.iloc[i, j])`?
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ENH: Implement PandasArray.equals
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NONE
| null |
Feature request: implement `PandasArray.equals` for consistency (in addition to `Series.equals` and `DataFrame.equals`).
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To latex position
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| 9
| 2020-07-15T11:57:25Z
| 2020-08-10T06:20:08Z
| 2020-08-07T15:32:27Z
|
CONTRIBUTOR
| null |
- [ ] closes #35281
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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Incorrect behavior in `pandas.merge` function
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| 2020-08-21T17:04:36Z
| 2020-07-23T10:30:33Z
|
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.
---
**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
import pandas
import numpy as np
test_data = np.random.uniform(0, 100, size=(2 ** 7, 2 ** 6))
test_data2 = np.random.uniform(0, 100, size=(2 ** 6, 2 ** 6))
pandas_df = pandas.DataFrame(
test_data,
columns=["col{}".format(i) for i in range(test_data.shape[1])],
index=pandas.Index(
[i for i in range(1, test_data.shape[0] + 1)], name="key"
),
)
pandas_df2 = pandas.DataFrame(
test_data2,
columns=["col{}".format(i) for i in range(test_data2.shape[1])],
index=pandas.Index(
[i for i in range(1, test_data2.shape[0] + 1)], name="key"
),
)
pandas_result = pandas_df.merge(pandas_df2,how="left",right_on="key",left_index=True,sort=False)
pandas_result2 = pandas_df.merge(pandas_df2,how="inner",right_on="key",left_index=True,sort=False)
pandas_result
key col0_x col1_x col2_x col3_x col4_x col5_x col6_x col7_x ... col55_y col56_y col57_y col58_y col59_y col60_y col61_y col62_y col63_y
1.0 1 22.268792 91.710456 35.425585 5.998272 7.616223 50.855316 70.778027 39.345351 ... 79.470662 1.139462 82.394800 49.279405 71.492669 19.118798 8.338031 15.240684 96.935519
2.0 2 24.359239 24.789241 83.329200 24.369174 92.494368 48.520427 73.401821 47.736986 ... 77.504507 83.228024 16.743863 28.598973 89.282114 24.783087 94.681264 76.430020 49.957027
3.0 3 66.973326 85.911902 85.839220 39.444853 43.164051 0.711455 1.728169 67.356380 ... 82.361473 69.188814 19.254839 80.721210 97.038337 50.344042 47.969856 33.863619 22.711405
4.0 4 64.578630 64.185421 97.307670 10.697612 38.090190 63.198376 91.842608 58.068671 ... 10.917623 98.375547 69.298407 69.486654 26.956081 14.512972 38.663965 44.573867 67.612123
5.0 5 73.212801 41.605993 79.149803 0.545511 61.767295 80.562112 78.970619 89.752911 ... 36.965341 99.078379 72.797682 28.273150 50.379084 14.131667 33.817043 64.267731 15.687369
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
NaN 124 13.955523 91.797658 44.563825 60.746861 28.904216 71.772330 28.841887 89.951210 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN 125 49.694130 56.604940 92.628512 14.191864 31.197524 82.710209 95.428222 35.449014 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN 126 58.950112 76.638796 89.459637 86.865600 93.866105 3.612650 88.072591 20.871411 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN 127 76.472780 72.939919 24.263068 71.021804 89.470422 29.811838 14.484325 51.755485 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
NaN 128 97.720457 31.118757 85.992609 85.823341 69.280047 71.261342 62.747467 23.256187 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
[128 rows x 129 columns]
pandas_result2
col0_x col1_x col2_x col3_x col4_x col5_x col6_x col7_x col8_x ... col55_y col56_y col57_y col58_y col59_y col60_y col61_y col62_y col63_y
key ...
1 22.268792 91.710456 35.425585 5.998272 7.616223 50.855316 70.778027 39.345351 25.069931 ... 79.470662 1.139462 82.394800 49.279405 71.492669 19.118798 8.338031 15.240684 96.935519
2 24.359239 24.789241 83.329200 24.369174 92.494368 48.520427 73.401821 47.736986 32.137170 ... 77.504507 83.228024 16.743863 28.598973 89.282114 24.783087 94.681264 76.430020 49.957027
3 66.973326 85.911902 85.839220 39.444853 43.164051 0.711455 1.728169 67.356380 83.307993 ... 82.361473 69.188814 19.254839 80.721210 97.038337 50.344042 47.969856 33.863619 22.711405
4 64.578630 64.185421 97.307670 10.697612 38.090190 63.198376 91.842608 58.068671 17.910149 ... 10.917623 98.375547 69.298407 69.486654 26.956081 14.512972 38.663965 44.573867 67.612123
5 73.212801 41.605993 79.149803 0.545511 61.767295 80.562112 78.970619 89.752911 44.926515 ... 36.965341 99.078379 72.797682 28.273150 50.379084 14.131667 33.817043 64.267731 15.687369
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
60 40.822611 50.875277 84.380647 26.325878 46.883415 0.048349 94.366177 10.860537 95.910254 ... 16.139270 16.442644 38.910856 52.139794 75.130737 79.407901 9.574825 43.741220 71.341946
61 79.372509 92.123416 60.440763 24.429257 58.681905 27.281294 24.816561 53.338771 37.340585 ... 17.580590 49.181221 23.445983 46.408847 1.805793 72.093458 46.068460 86.171202 67.535472
62 73.059258 86.432887 52.869162 26.113074 21.653439 18.971489 86.998840 36.259379 55.659895 ... 34.785796 2.524984 63.754044 21.315063 26.431136 9.647946 9.740197 55.826159 81.770851
63 73.177085 48.520687 6.165317 75.703392 32.593617 15.452280 0.227452 1.036644 90.657810 ... 30.676622 2.000542 6.701021 78.782964 50.210517 71.748162 43.102752 89.775608 51.033208
64 30.236389 10.706612 31.498764 0.872496 88.008391 65.306584 92.053531 98.306667 28.063389 ... 90.833733 35.615374 12.593278 4.371464 66.767380 11.140186 79.031274 23.489189 56.079695
[64 rows x 128 columns]
```
#### Problem description
My expectation was that both `pandas_result` and `pandas_result2` will preserve index with name `key` after performing the operations mentioned above. It turned out that `index` will be preserved if `how="inner"`, whereas when `how="left"` index will be moved into column.
#### Expected Output
```python
col0_x col1_x col2_x col3_x col4_x col5_x col6_x col7_x col8_x ... col55_y col56_y col57_y col58_y col59_y col60_y col61_y col62_y col63_y
key ...
1 22.268792 91.710456 35.425585 5.998272 7.616223 50.855316 70.778027 39.345351 25.069931 ... 79.470662 1.139462 82.394800 49.279405 71.492669 19.118798 8.338031 15.240684 96.935519
2 24.359239 24.789241 83.329200 24.369174 92.494368 48.520427 73.401821 47.736986 32.137170 ... 77.504507 83.228024 16.743863 28.598973 89.282114 24.783087 94.681264 76.430020 49.957027
3 66.973326 85.911902 85.839220 39.444853 43.164051 0.711455 1.728169 67.356380 83.307993 ... 82.361473 69.188814 19.254839 80.721210 97.038337 50.344042 47.969856 33.863619 22.711405
4 64.578630 64.185421 97.307670 10.697612 38.090190 63.198376 91.842608 58.068671 17.910149 ... 10.917623 98.375547 69.298407 69.486654 26.956081 14.512972 38.663965 44.573867 67.612123
5 73.212801 41.605993 79.149803 0.545511 61.767295 80.562112 78.970619 89.752911 44.926515 ... 36.965341 99.078379 72.797682 28.273150 50.379084 14.131667 33.817043 64.267731 15.687369
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
124 13.955523 91.797658 44.563825 60.746861 28.904216 71.772330 28.841887 89.951210 69.801427 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
125 49.694130 56.604940 92.628512 14.191864 31.197524 82.710209 95.428222 35.449014 84.495449 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
126 58.950112 76.638796 89.459637 86.865600 93.866105 3.612650 88.072591 20.871411 9.433378 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
127 76.472780 72.939919 24.263068 71.021804 89.470422 29.811838 14.484325 51.755485 41.570147 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
128 97.720457 31.118757 85.992609 85.823341 69.280047 71.261342 62.747467 23.256187 87.411083 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN
[128 rows x 128 columns]
```
#### 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 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
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 : 0.16.0
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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REGR: reindex with sparse data
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| 3
| 2020-07-15T13:11:34Z
| 2020-07-16T11:17:06Z
| 2020-07-16T11:17:06Z
|
MEMBER
| null |
See discussion at https://github.com/pandas-dev/pandas/pull/34158#issuecomment-636641583 and below. There was some discussion on the PR, but I think we didn't yet create an issue to track this regression.
The PR introduced a regression in `reindex` when having sparse data. Based on the linked discussion, there doesn't seem to direct / easy solution (but I didn't look into it again). So we might want to revert the PR for 1.1 until we figure out a correct solution.
cc @TomAugspurger
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Fix indexing, reindex on all-sparse SparseArray.
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| 2
| 2020-07-15T14:11:23Z
| 2020-07-16T11:17:10Z
| 2020-07-16T11:17:07Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/35286.
Also added a regression tests for the issue reported there.
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MDU6SXNzdWU2NTc0NDM3MDU=
| 35,288
|
TestUltraJSONTests.test_dumps_ints_larger_than_maxsize fails for 32-bit
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| 2020-07-15T15:28:48Z
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| null |
CONTRIBUTOR
| null |
test_dumps_ints_larger_than_maxsize is failing on the MacPython 32-bit tests: https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=39266&view=logs&j=bb1c2637-64c6-57bd-9ea6-93823b2df951&t=9c62e7ff-391e-517d-ede1-af63bc6a25da&l=1307
```
=================================== FAILURES ===================================
______ TestUltraJSONTests.test_dumps_ints_larger_than_maxsize[2147483648] ______
[gw1] linux -- Python 3.8.0 /venv/bin/python
self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0xd19d2e38>
bigNum = 2147483648
@pytest.mark.parametrize("bigNum", [sys.maxsize + 1, -(sys.maxsize + 2)])
def test_dumps_ints_larger_than_maxsize(self, bigNum):
# GH34395
bigNum = sys.maxsize + 1
encoding = ujson.encode(bigNum)
assert str(bigNum) == encoding
# GH20599
with pytest.raises(ValueError):
> assert ujson.loads(encoding) == bigNum
E Failed: DID NOT RAISE <class 'ValueError'>
```
Adding a skip for now. Will get proper 32-bit testing set up sometime (or we just drop support officially)
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xfail failing 32-bit tests
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CONTRIBUTOR
| null |
We need MacPython to be passing for the wheels to be built.
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|
BUG: Inversion of True is -2
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| 2020-08-21T17:04:37Z
| 2020-07-15T16:08:25Z
|
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 pandas as pd
df = pd.DataFrame()
df.equals(df)
Out: True
~df.equals(df)
Out: -2
```
#### Problem description
The inversion of a boolean result from df1.equals(df2) is an integer which will be recognized as True.
#### Expected Output
False
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-61-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 41.6.0
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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[QST] What should `ExtensionDtype.type` return?
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| 2020-07-15T17:07:16Z
| 2020-07-16T00:16:13Z
| 2020-07-16T00:16:12Z
|
CONTRIBUTOR
| null |
Greetings, Pandas devs! cuDF is building out additional dtypes such as `cudf.CategoricalDtype` and `cudf.ListDtype` based on `pd.ExtensionDtype`, and this is one question that came up.
The [documentation](https://pandas.pydata.org/docs/reference/api/pandas.api.extensions.ExtensionDtype.type.html#pandas.api.extensions.ExtensionDtype.type) states:
> It’s expected ExtensionArray[item] returns an instance of ExtensionDtype.type for scalar item, assuming that value is valid (not NA). NA values do not need to be instances of type.
However, I note that `pd.CategoricalDtype` for instance does not adhere to this:
```python
In [47]: import pandas as pd
In [48]: a = pd.Series(['a', 'b'], dtype='category')
In [49]: type(a[0])
Out[49]: str
In [50]: type(a.array[0])
Out[50]: str
In [51]: isinstance(a.array, pd.api.extensions.ExtensionArray)
Out[51]: True
In [52]: isinstance(a.dtype, pd.api.extensions.ExtensionDtype)
Out[52]: True
```
On the other hand, [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.dtype.type.html#numpy.dtype.type) defines `dtype.type` somewhat differently:
> The type object used to instantiate a scalar of this data-type.
Would love any insights as to the appropriate return value of `.type`.
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ENH: Add orient=tight format for dictionaries
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[] | null | 8
| 2020-07-15T17:36:26Z
| 2021-10-16T15:51:03Z
| 2021-10-16T15:50:53Z
|
CONTRIBUTOR
| null |
- [x] xref #4889 (see below)
- [x] tests added / passed
- tests.frame.methods.test_to_dict.test_to_dict_orient_tight()
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The issue in #4889 contains examples of how the JSON format doesn't support `MultiIndex` for the index or the columns, independent of the `orient` chosen. Also, none of the orientations support index names (or `MultiIndex` names). Finally, if you want to have a JSON format that is "tight" (or "compact"), the `split` is the closest thing, but it is incomplete due to the indexing issues.
As a first step to addressing this, I have created a `orient='tight'` option to `DataFrame.to_dict()` and `DataFrame.from_dict()` . If we agree that this is a reasonable representation, the next step (which I'd prefer to do in a second PR) would be to also support it with JSON. The challenge there is that `to_json()` is currently implemented in C. Right now, one can just use `json.dumps(df.to_dict(orient='tight'))` to get the needed JSON as a workaround.
I'm open to changing the word `'tight'` to something else, but I wanted it to start with a different letter than the other orientations.
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| 35,293
|
BUG: upsampling returns less rows than expected
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| 2020-08-21T17:04:37Z
| 2020-07-26T15:34:21Z
|
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
I have a pandas Series with a (tz-localized) DateTimeIndex with one value per day, which I want to upsample to hourly values, so that each value is repeated 24 times (or 23 or 25, depending on summer/wintertime changeover).
```python
s = pd.Series([1,2,3,4], index=pd.date_range('2020-03-28 00:00:00', periods=4, freq='D', tz='Europe/Berlin'))
r = s.resample('H').ffill() #misses the last 23 hours of March
```
```
s:
2020-03-28 00:00:00+01:00 1
2020-03-29 00:00:00+01:00 2
2020-03-30 00:00:00+02:00 3
2020-03-31 00:00:00+02:00 4
Freq: D, dtype: int64
r:
2020-03-28 00:00:00+01:00 1
2020-03-28 01:00:00+01:00 1
2020-03-28 02:00:00+01:00 1
..
2020-03-30 22:00:00+02:00 3
2020-03-30 23:00:00+02:00 3
2020-03-31 00:00:00+02:00 4 #<-- !
Freq: H, Length: 72, dtype: int64
```
#### Problem description
Each row in series `s` has the correct number of corresponding rows in series `r` - expect for the final row. The final row in `s` has only 1 corresponding row in `r`, where 24 would be expected.
#### Additional information / observations
a)
I've tried various combinations of having the original and/or the resampled timeseries be open or closed on the right side, but no setting gave me the wanted result.
b)
I assume the reason for the observed behaviour in `r`, above, is the following: I suppose that `freq == D` on a `DatetimeIndex` means, that the values are spaced one calendar day apart, and not, that they are valid for the entire calender day. Using a `PeriodIndex` instead does indeed solve this, see below.
However, using a `PeriodIndex` instead of a `DateTimeIndex` is not an option. It solves the problem with the missing hours, but loses the timezoneawareness that I need:
```python
s2 = pd.Series(s.values, pd.PeriodIndex(s.index, freq='D')) #misses timezoneawareness.
r2 = s2.resample('H').ffill() #does not miss last 23 hours of March, but is still incorrect.
```
```
s2:
2020-03-28 1
2020-03-29 2
2020-03-30 3
2020-03-31 4
Freq: D, dtype: int64
r2:
2020-03-28 00:00 1
2020-03-28 01:00 1
2020-03-28 02:00 1
..
2020-03-31 21:00 4
2020-03-31 22:00 4
2020-03-31 23:00 4
Freq: H, Length: 96, dtype: int64
```
c)
Full disclosure: I've asked this questions before, on [stackoverflow](https://stackoverflow.com/questions/61761237/keep-24h-for-each-day-when-resampling-pandas-series-from-daily-to-hourly), but did not receive a satisfactory answer - just a workaround.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
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 : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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Various 32-bit failures at MacPython/pandas-wheels
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| 2020-08-24T23:42:02Z
| 2020-08-24T23:42:02Z
|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=39280&view=logs&j=a846d25a-e32c-5640-1b53-e815fab94407&t=8fc25549-8157-5c9c-f998-0884bd2e8ccb&l=3099
```
FAILED test_venv/lib/site-packages/pandas/tests/window/test_api.py::test_multiple_agg_funcs[rolling-2-expected_vals0]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_apply.py::test_rolling_apply_args_kwargs[args_kwargs0]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_apply.py::test_rolling_apply_args_kwargs[args_kwargs1]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_groupby_rolling[1.0-True]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_groupby_rolling[0.0-False]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_getitem
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_quantile[linear]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_quantile[lower]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_quantile[higher]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_quantile[midpoint]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_quantile[nearest]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_apply[True]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_apply[False]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_grouper.py::TestGrouperGrouping::test_rolling_apply_mutability
FAILED test_venv/lib/site-packages/pandas/tests/window/test_rolling.py::test_closed_one_entry_groupby[min]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_rolling.py::test_closed_one_entry_groupby[max]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_rolling.py::test_rolling_positional_argument[True-grouping0-_index0]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_rolling.py::test_rolling_positional_argument[True-grouping1-_index1]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_rolling.py::test_rolling_positional_argument[False-grouping0-_index0]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_rolling.py::test_rolling_positional_argument[False-grouping1-_index1]
FAILED test_venv/lib/site-packages/pandas/tests/window/test_timeseries_window.py::TestRollingTS::test_groupby_monotonic
FAILED test_venv/lib/site-packages/pandas/tests/window/test_timeseries_window.py::TestRollingTS::test_non_monotonic
```
Just going to skip these for now. Maybe (probably?) some overlap with #35148 @mroeschke. Not sure.
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https://github.com/pandas-dev/pandas/issues/35294
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DOC: Reccomended use of read_csv's date_parser parameter is very slow
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[] | null | 1
| 2020-07-15T19:51:10Z
| 2020-09-04T15:20:30Z
| null |
NONE
| null |
#### Location of the documentation
The [Date parsing functions](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#date-parsing-functions) section of the CSV file parsing section, specifically the reccomended use of `date_parser` in cases where the user knows what format the date will be in in advance and/or that format is non-standard ans not supported by Pandas.
> If you know the format, use `pd.to_datetime()`: `date_parser=lambda x: pd.to_datetime(x, format=...)`.
#### Demonstration of the problem
<details>
I'm trying to parse a CSV file that contains signal power data keyed by timestamp-frequency pairs in a vertical format (376 frequencies by 14300 samples, so about 5.3 million rows, with only one timestamp per row) with the intent of pivoting it into a columnar format. The time format is rather unpleasant to deal with and is stored as 2 separate columns, but pandas' CSV parser has all the tools I need to reconstruct the timestamps correctly, so I'm not worried there.
Based on what the documentation, I tried something like this:
```py
import pandas as pd
def read_csv_slow(in_path: str):
df = pd.read_csv(
in_path,
nrows=376 * 500,
usecols=['Date','Time', 'Frequency', 'Power'], index_col=['Date_Time', 'Frequency']
dtype={'Frequency': 'int32', 'Power': 'float32'},
parse_dates=[['Date','Time']],
# Extremely slow
date_parser=lambda x: pd.to_datetime(x, format='%m/%d/%Y %H:%M:%S:%f'), # 06/17/2020 + 11:47:22:746
)
```
For testing, I limited the amount of rows parsed to 500 time samples (188000 rows/timestamps), about 3.5% of the total file, which takes a surprising 43 seconds to process, mostly due to datetime parsing according to `cProfile`:
```
> python -m cProfile -s tottime .\read_csv_slow.py
61508066 function calls (61501857 primitive calls) in 43.756 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
188000 4.507 0.000 5.728 0.000 {pandas._libs.tslibs.strptime.array_strptime}
15247920 3.286 0.000 5.884 0.000 {built-in method builtins.isinstance}
188000 2.358 0.000 2.358 0.000 {pandas._libs.tslibs.parsing._format_is_iso}
5264920 1.851 0.000 2.432 0.000 generic.py:10(_check)
1316199 1.828 0.000 3.879 0.000 common.py:1708(_is_dtype_type)
188001 1.650 0.000 34.982 0.000 datetimes.py:246(_convert_listlike_datetimes)
376150 1.400 0.000 1.400 0.000 {built-in method numpy.array}
188006 1.348 0.000 8.870 0.000 datetimes.py:1678(sequence_to_dt64ns)
188001 1.292 0.000 42.474 0.000 datetimes.py:530(to_datetime)
```
Using `date_parser` like this simply does not scale and blocks the entire CSV decoding process.
----
Meanwhile, here's an alternative version that bypasses `date_parser` and converts the datetime column in a single batch after parsing finishes:
```py
import pandas as pd
def read_mxflex_csv_fast(in_path: str):
df = pd.read_csv(
in_path,
nrows=376 * 10000,
usecols=['Date','Time', 'Frequency', 'Power'], index_col=['Date_Time', 'Frequency']
dtype={'Frequency': 'int32', 'Power': 'float32'},
parse_dates=[['Date','Time']],
)
date_data = df.index.get_level_values(0)
date_idx = pd.to_datetime(date_data, exact=True, cache=True, format='%m/%d/%Y %H:%M:%S:%f') # 06/17/2020 + 11:47:22:746
freq_idx = df.index.get_level_values(1)
df.index = pd.MultiIndex.from_arrays([date_idx, freq_idx])
```
This one completes in 6 seconds despite running on *20 times as much data* (10000 samples instead of 500, notice the change in `nrows`) than the first example.
```
> python -m cProfile -s tottime .\read_csv_fast.py
236167 function calls (229787 primitive calls) in 5.957 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1 2.353 2.353 2.412 2.412 {method 'read' of 'pandas._libs.parsers.TextReader' objects}
28 0.477 0.017 0.477 0.017 {pandas._libs.lib.infer_dtype}
2 0.442 0.221 0.442 0.221 {method 'unique' of 'pandas._libs.hashtable.StringHashTable' objects}
1 0.344 0.344 0.344 0.344 {pandas._libs.tslibs.parsing._concat_date_cols}
2 0.342 0.171 0.342 0.171 {method 'get_indexer' of 'pandas._libs.index.IndexEngine' objects}
1 0.235 0.235 0.235 0.235 {method 'factorize' of 'pandas._libs.hashtable.StringHashTable' objects}
60 0.179 0.003 0.182 0.003 {built-in method _imp.create_dynamic}
1667 0.177 0.000 0.177 0.000 {built-in method nt.stat}
3 0.136 0.045 0.136 0.045 {method 'factorize' of 'pandas._libs.hashtable.Int64HashTable' objects}
```
</details>
#### Documentation problem
Usage of the `date_parser` parameter tends to be a *huge* performance cliff given how it appears to run in an row-wise fashion (if the profiler's `ncalls` metric is to be believed), something the surrounding documentation heavily stresses as well:
> If you have `parse_dates` enabled for some or all of your columns, and your datetime strings are all formatted the same way, you may get a large speed up by setting `infer_datetime_format=True`. If set, pandas will attempt to guess the format of your datetime strings, and then use a faster means of parsing the strings. 5-10x parsing speeds have been observed.
The speedup described there isn't from Pandas having some sort of inferred date fast-path, but simply because the `date_parser` callback is being called in an extremely inefficient way for most workloads. There *is* a note above that section that could be considered as hinting at this:
> If a column or index contains an unparsable date, the entire column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use `to_datetime()` after `pd.read_csv`.
All of those conflicting advices makes the current documentation fairly misleading on that topic, and people who don't profile their code might be led to believe that this is just a problem with pandas being too slow to handle their CSVs or something along those lines.
#### Suggested fix for documentation
One subsection of the Date Handling section (either appended to "Date parsing functions" or under a new subtitle) should give concrete examples on how to deal with files that contain non-standard or strange timestamp formats.
> If your CSV file contains columns with timestamps in an unconventional format, it is usually significantly faster to
> completely parse the file while leaving your dates as text (rather than specifying a `date_parser` callback when opening the file) and later converting the entire column as a single manipulation.
>
> ```
> print(open('buzz.csv').read())
> Date,Time,Value
> 11261999,13h57m58s.345,0.0100
> 11261999,13h57m59s.999,-0.5900
> ```
>
> ```
> ts_format = "%m%d%Y %Hh%Mm%Ss.%f" # 11261999 13h57m59s.999
>
> df = pd.read_csv('buzz.csv' parse_dates=[['Date','Time']], index_col=['Date_Time'], infer_datetime_format=False)
> df.index = pd.to_datetime(df.index, exact=True, cache=True, format=ts_format)
> ```
This gives clear instructions for users dealing with a use case that's probably not all that uncommon, mentions the alternative and a reason why this is preferable and gives a code example. The code example itself also shows the interaction between `parse_dates` when combining columns and manually-specified date formats (the doc does not otherwise mention that this results in a column of space-separated values). I'm not certain how to change the above code example to make it work correctly for data columns, index columns and multi-indexes alike, if such a thing is possible.
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BUG: AmbiguousTimeError when using date_range even when argument ambiguous is set
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| 2
| 2020-07-15T20:11:40Z
| 2020-08-01T17:10:10Z
| 2020-08-01T17:10:10Z
|
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.
---
**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
>>> import pandas as pd
>>> timezone = 'America/New_York'
>>> start = pd.Timestamp(year=2020, month=11, day=1, hour=1).tz_localize(timezone, ambiguous=False)
>>> pd.date_range(start, periods=2, ambiguous=False)
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-5-d142fd70e406> in <module>
2 timezone = 'America/New_York'
3 start = pd.Timestamp(year=2020, month=11, day=1, hour=1).tz_localize(timezone, ambiguous=False)
----> 4 pd.date_range(start, periods=2, ambiguous=False)
~/anaconda3/envs/oids_tst/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py in date_range(start, end, periods, freq, tz, normalize, name, closed, **kwargs)
1178 normalize=normalize,
1179 closed=closed,
-> 1180 **kwargs,
1181 )
1182 return DatetimeIndex._simple_new(dtarr, tz=dtarr.tz, freq=dtarr.freq, name=name)
~/anaconda3/envs/oids_tst/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py in _generate_range(cls, start, end, periods, freq, tz, normalize, ambiguous, nonexistent, closed)
419 # start/end as well to compare
420 if start is not None:
--> 421 start = start.tz_localize(tz).asm8
422 if end is not None:
423 end = end.tz_localize(tz).asm8
pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.tz_localize()
pandas/_libs/tslibs/tzconversion.pyx in pandas._libs.tslibs.tzconversion.tz_localize_to_utc()
AmbiguousTimeError: Cannot infer dst time from 2020-11-01 01:00:00, try using the 'ambiguous' argument
```
#### Problem description
When the start time is at the day light saving ambiguous time, date_range raises errors even when the argument 'ambiguous' is set. It should not be raising error when the argument is set.
Issue could be due to the line
https://github.com/pandas-dev/pandas/blob/32ed15d39312193a8c36171f0e120d0f46a26c2c/pandas/core/arrays/datetimes.py#L421
does not correctly pass the argument ambiguous to the function `tz_localize()`.
#### Expected Output
No error
#### Output of ``pd.show_versions()``
AmbiguousTimeError: Cannot infer dst time from 2020-11-01 01:00:00, try using the 'ambiguous' argument
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TST: Remove deprecated use of apply_index
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| 1
| 2020-07-15T20:15:40Z
| 2020-07-16T19:39:05Z
| 2020-07-16T19:39:00Z
|
CONTRIBUTOR
| null |
Noticed this warning. apply_index is deprecated, so not likely to get attention anyway. Removing.
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ENH: add percentage threshold to DataFrame.dropna
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| 2020-07-15T21:01:28Z
| 2021-03-30T12:24:57Z
| 2020-09-29T23:45:37Z
|
CONTRIBUTOR
| null |
**Is your feature request related to a problem?**
When doing feature selection for models, or with handling missing data, in most cases you want to dynamically set a threshold to drop column which exceed this. A percentage is a more general metric to use to see how many data is missing data per row or per column. Right now we have the `thresh` argument, which accepts an int, but in most cases this is not desirable. For example when running the code in production, the shape of the data changes over time and thus a percentage threshold would make more sense to me. Or when you don't know the data well enough, what does "a number" mean in this case. Percentage is standarized.
Besides that, the thresh right now is not flexible to go over column or index axis. For data cleaning you might want to drop rows which exceed the threshold, but for feature selection, you want to treat each column separately.
**Describe the solution you'd like**
Have a percentage threshold for both column and index axis.
**API breaking implications**
Not that I can think of, but not 100% sure.
**Describe alternatives you've considered**
Writing own custom functions.
**Additional context**
Locally I created a version where the `perc` argument is created (see linked draft PR):
**example 1**:
```python
>>> df = pd.DataFrame({'col': ["A", "A", "B", "B"],
... 'A': [80, np.nan, np.nan, np.nan],
... 'B': [80, np.nan, 76, 67]})
>>> df
col A B
0 A 80.0 80.0
1 A NaN NaN
2 B NaN 76.0
3 B NaN 67.0
>>> df.dropna(perc=0.5)
col A B
0 A 80.0 80.0
2 B NaN 76.0
3 B NaN 67.0
>>> df.dropna(perc=0.5, axis=1)
col B
0 A 80.0
1 A NaN
2 B 76.0
3 B 67.0
```
---
**example 2**:
```python
>>> df = pd.DataFrame(np.random.randint(1, 10, (10,4)), columns=list('ABCD'))
>>> df.loc[3, 'B':] = np.nan
>>> df.loc[8, :'B'] = np.nan
>>> df.loc[1:6, 'C'] = np.nan
>>> df
A B C D
0 7.0 2.0 3.0 7.0
1 3.0 2.0 NaN 9.0
2 2.0 2.0 NaN 2.0
3 7.0 NaN NaN NaN
4 1.0 9.0 NaN 4.0
5 9.0 9.0 NaN 1.0
6 2.0 2.0 NaN 6.0
7 9.0 3.0 5.0 6.0
8 NaN NaN 9.0 5.0
9 7.0 7.0 3.0 1.0
```
As we can see, index 3 (`axis=0`) has 75% missing values and column C (`axis=1`) has 60% missing values. With the `percentage` argument we can specify what the threshold is, but also consider row wise or column wise:
Example consider **per row**:
```python
>>> df.dropna(perc=.4, axis=0)
A B C D
0 7.0 2.0 3.0 7.0
1 3.0 2.0 NaN 9.0
2 2.0 2.0 NaN 2.0
4 1.0 9.0 NaN 4.0
5 9.0 9.0 NaN 1.0
6 2.0 2.0 NaN 6.0
7 9.0 3.0 5.0 6.0
9 7.0 7.0 3.0 1.0
```
Above we can see row 3 and 8 got dropped because these had ` > 40%` missing values.
```python
>>> df.dropna(perc=.4, axis=1)
A B D
0 7.0 2.0 7.0
1 3.0 2.0 9.0
2 2.0 2.0 2.0
3 7.0 NaN NaN
4 1.0 9.0 4.0
5 9.0 9.0 1.0
6 2.0 2.0 6.0
7 9.0 3.0 6.0
8 NaN NaN 5.0
9 7.0 7.0 1.0
```
Same command but with `axis=1`, so we can consider percentage threshold **per column**, and we see that column C got dropped because it had 60% missing values
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ENH: add percentage threshold to dropna
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| 2020-09-29T23:45:56Z
| 2020-09-29T23:45:50Z
|
CONTRIBUTOR
| null |
- [x] closes #35299
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: xs not working with slice
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| 2020-08-07T16:58:25Z
| 2020-08-07T16:58:25Z
|
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
data = """
C1 C2 V
A1 0 10
A1 1 20
A2 0 2
A2 1 3
B1 0 2
B2 1 3
"""
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(data), sep=' +').set_index(['C1', 'C2'])
df.xs(pd.IndexSlice['A1', :])
```
#### Problem description
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/***/lib/python3.7/site-packages/pandas/core/generic.py", line 3535, in xs
loc, new_index = self.index.get_loc_level(key, drop_level=drop_level)
File "/home/***/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 2835, in get_loc_level
raise TypeError(key)
TypeError: ('A1', slice(None, None, None))
```
also similar code produce the same problem (`df.xs(('A1', slice(None)))`). Strangely this works:
```python
df = pd.DataFrame({'a': [1, 2, 3, 1], 'b': ['a', 'b', 'c', 'd'], 'v': [2, 3, 4, 5]}).set_index(['a', 'b'])
df.xs(pd.IndexSlice[1, :])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.7-100.fc31.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2019.2
dateutil : 2.7.5
pip : 20.1.1
setuptools : 41.6.0
Cython : 0.29.15
pytest : 4.0.0
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.16.1
pandas_datareader: 0.8.0
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : None
tables : 3.5.2
tabulate : 0.8.5
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.1.2
xlsxwriter : None
numba : 0.48.0
</details>
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| 3
| 2020-07-16T08:20:58Z
| 2020-08-01T17:18:24Z
| 2020-08-01T17:10:10Z
|
MEMBER
| null |
- [x] closes #35297
- [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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MDExOlB1bGxSZXF1ZXN0NDUwMTE1MDM2
| 35,303
|
Fixed reindexing arith with duplicates
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| 4
| 2020-07-16T11:20:33Z
| 2020-07-16T22:49:17Z
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|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/35194
Still need to run ASV on this. This was a regression from 0.25.x to 1.0. It doesn't have to go in 1.1, but probably better for 1.1.0.rc0 than a 1.1.1 release.
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TST: xfail more 32-bits
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| 0
| 2020-07-16T11:29:48Z
| 2020-07-16T13:29:02Z
| 2020-07-16T13:04:54Z
|
CONTRIBUTOR
| null |
xref #35294
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QST:shape mismatch: objects cannot be broadcast to a single shape
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| 2020-07-16T12:33:11Z
| 2020-08-21T17:04:38Z
| 2020-07-16T14:11:03Z
|
NONE
| null |
Hi,
Im having a problem using bar chart in python is "shape mismatch: objects cannot be broadcast to a single shape".
and the code is:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df=pd.read_csv('2017Jaipur_month accidents.csv')
data1=pd.DataFrame(df,columns=['acc_2017','acc_2016','Months'])
data1
index=np.arange(2,13)
bar_width=0.25
acc_2017=plt.bar(index,data1['acc_2017'],color='blue',width=0.25,label='Death in 2017')
acc_2016=plt.bar(index+0.25,data1['2016'],color='red',width=0.25, label='Death in 2016')
plt.title('2016-17 Death in Accident')
plt.xticks(index, data1.Months, rotation=90)
plt.xlabel('Time accidents')
plt.ylabel('no of accidents')
plt.show()
and the error is:
ValueError Traceback (most recent call last)
<ipython-input-107-3c2ff406e777> in <module>
13
14
---> 15 acc_2017=plt.bar(index,data1['acc_2017'],color='blue',width=0.25,label='Death in 2017')
16 acc_2016=plt.bar(index+0.25,data1['2016'],color='red',width=0.25, label='Death in 2016')
17
C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\pyplot.py in bar(x, height, width, bottom, align, data, **kwargs)
2439 return gca().bar(
2440 x, height, width=width, bottom=bottom, align=align,
-> 2441 **({"data": data} if data is not None else {}), **kwargs)
2442
2443
C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\__init__.py in inner(ax, data, *args, **kwargs)
1597 def inner(ax, *args, data=None, **kwargs):
1598 if data is None:
-> 1599 return func(ax, *map(sanitize_sequence, args), **kwargs)
1600
1601 bound = new_sig.bind(ax, *args, **kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
2372 x, height, width, y, linewidth = np.broadcast_arrays(
2373 # Make args iterable too.
-> 2374 np.atleast_1d(x), height, width, y, linewidth)
2375
2376 # Now that units have been converted, set the tick locations.
<__array_function__ internals> in broadcast_arrays(*args, **kwargs)
C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\stride_tricks.py in broadcast_arrays(*args, **kwargs)
262 args = [np.array(_m, copy=False, subok=subok) for _m in args]
263
--> 264 shape = _broadcast_shape(*args)
265
266 if all(array.shape == shape for array in args):
C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\stride_tricks.py in _broadcast_shape(*args)
189 # use the old-iterator because np.nditer does not handle size 0 arrays
190 # consistently
--> 191 b = np.broadcast(*args[:32])
192 # unfortunately, it cannot handle 32 or more arguments directly
193 for pos in range(32, len(args), 31):
ValueError: shape mismatch: objects cannot be broadcast to a single shape
plz help to find out the issue.
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MDExOlB1bGxSZXF1ZXN0NDUwMjA4NzQ4
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Revert BUG: Ensure same index is returned for slow and fast path in …
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| 2
| 2020-07-16T13:44:49Z
| 2020-07-16T15:06:59Z
| 2020-07-16T14:56:09Z
|
CONTRIBUTOR
| null |
…groupby.apply #31613
xref https://github.com/pandas-dev/pandas/pull/34998.
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TST: xfail 32-bit test
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|
CONTRIBUTOR
| null |
ref #35294
|
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CLN: consistent EA._reduce signatures
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| 1
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| 2020-07-17T07:58:50Z
| 2020-07-16T22:44:19Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Crashed Worker tracking issue
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| 2021-08-08T17:07:44Z
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|
CONTRIBUTOR
| null |
I've been seeing a lot of CI builds failing recently, but I have no idea what's causing them. Can maintainers add them to the table here when they occur?
| CI Job | Test | Count |
| ------------------------- | ---- | ----- |
| MacPython linux py_3.7_64 | `TestTableOrientReader::test_read_json_table_orient[vals0-index]` | 1 |
| Windows py37_np18 | `testst_unique.py::test_unique_bad_unicode[idx_or_series_w_bad_unicode0]` | 1 |
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CI: Skip test for 3.7.0 exactly
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| 2020-07-16T20:20:03Z
| 2020-07-17T15:13:58Z
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CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/35309
This test segfaults for python 3.7.0 exactly, but seems to pass for other versions. Just skipping in, to get MacPython all passing again.
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TYP: Add MyPy Error Codes
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MEMBER
| null |
- [ ] closes #29197
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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pin numpy<1.19 in doc build
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| 1
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CONTRIBUTOR
| null |
Doc build is failing on master with a warning from NumPy via matplotlib. Waiting on a release with https://github.com/matplotlib/matplotlib/pull/17289 (3.3.0), so pinning numpy<1.19 for now.
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|
ENH: pandas.DataFrame.pivot option to output hierarchically indexed rows
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| 2021-03-26T06:14:31Z
| 2021-03-26T06:14:30Z
|
NONE
| null |
#### Is your feature request related to a problem?
When I use `pivot` or `pivot_table` and pass a list of `values`, I get a multiindex on the columns, as the docs say. However, in my case I want the output to be hierarchically indexed by rows.
#### Describe the solution you'd like
It would be nice to have a `indexing_axis` kwarg in `pivot` and `pivot_table` so I could pass `indexing_axis='rows'` and get a multiindex on the rows instead of on the columns.
#### Describe alternatives you've considered
Can be fixed by running `df_pivoted.stack()`, but it would be nice to not have to do this as a separate step.
```python
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
df_pivoted = df.pivot_table(index=['A','C'], columns='B', values=['D','E'])
# df_pivoted = df.pivot_table(index=['A','C'], columns='B', values=['D','E'], indexing_axis='rows') # this is what I want to be able to do
df_pivoted.stack() # this is what I want the result to look like
```
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BUG: GroupBy.apply() returns different results if a different GroupBy method is called first
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| 10
| 2020-07-16T23:28:41Z
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|
MEMBER
| null |
- [x] closes #34656
- [x] closes #34271
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
_Behavioural Changes_
_.apply()_
Calls to `self._set_group_selection` have been replaced with `with _group_selection_context(self):` for `_agg_general`, `_make_wrapper`, and `nth`.
Previously these calls to `self._set_group_selection` created a bug in `GroupBy.apply` where calling another method before `.apply` would change the output of `.apply`. This bug is now fixed.
_Tests_
One new test is added to check that the output of `.apply` is constant whether another method is called on the same grouper first.
Two existing tests were actually dependent on the old buggy-behaviour (i.e. they called GroupBy.sum first and then expected that GroupBy.apply(sum) would exclude the index columns from the results). All of these tests have been amended in a manner that enforces the new consistent output format while preserving the existing test.
Both of the copy-pastable examples in the linked bug-reports are fixed.
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DOC: whatsnew for 1.2
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MEMBER
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@mroeschke re: #35302
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https://api.github.com/repos/pandas-dev/pandas/issues/35316/labels{/name}
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https://api.github.com/repos/pandas-dev/pandas/issues/35316/comments
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https://github.com/pandas-dev/pandas/pull/35316
| 658,732,134
|
MDExOlB1bGxSZXF1ZXN0NDUwNjU5OTQy
| 35,316
|
DOC: extra closing parens make example invalid.
|
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closed
| false
| null |
[] |
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| 1
| 2020-07-17T01:29:39Z
| 2020-07-17T10:25:52Z
| 2020-07-17T10:25:51Z
|
CONTRIBUTOR
| null |
No attached issues or need for what's new / test... unless you want to actually get a linter the parse the docstrings and make the the examples are syntactically correct ?
- [ ] 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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| null | 0
|
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