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TST closes #28980 Certain comparison operations misbehaving for period dtype
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CONTRIBUTOR
| null |
I've added test to compare `pd.Series([...], dtype="period[A-DEC]")` to dtype `str`, `bool`, `int`, `float` and `None`.
- [ x ] closes #28980
- [ 5 ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DEPR: deprecate non keyword arguments in read_excel
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CONTRIBUTOR
| null |
Follow-up to #27573.
Allows two non-keyword arguments, ``io`` and ``sheet_name``. I think ``sheet_name`` is quite often (e.g. Interactively) supplied without being a keyword argument and requiring it will just be needlessly annoying.
Also some clean-up in pandas/tests/io/excel.
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REF: make remaining offset classes cdef
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MEMBER
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After this its just down to
1) a pass to clean up pickle-based kludges that were needed when these were mixed python/cython
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3) move to_offset int liboffsets
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| 2021-11-20T23:23:10Z
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- [x] closes #34367
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 2
| 2020-05-28T04:09:08Z
| 2020-06-03T22:26:16Z
| 2020-06-03T22:26:15Z
|
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
```python3
s = pd.Series([1,1,2,2,3,3,4,5])
s.groupby(s.values).agg(one = pd.NamedAgg(column='anything',aggfunc='sum'))
one
1 2
2 4
3 6
4 4
5 5
s.groupby(s.values).agg(one=('something','sum'))
one
1 2
2 4
3 6
4 4
5 5
```
#### Problem description
For `SeriesGroupBy.agg` named aggregation, it accepts any column name as mentioned in the above examples but Series doesn't have any columns.
#### Expected Output
After discussing with @TomAugspurger and @MarcoGorelli in another issue #34380 regarding this issue in the comments, We came to a solution that
> So for SeriesGroupBy.agg, if there are any tuples or NamedAgg present in kwargs then I think we should raise.
Disallowing `NamedAgg` and tuples with `SeriesGroupBy`.
```python3
s.groupby(s.values).agg(one = pd.NamedAgg(column='anything',aggfunc='sum'))
# Error should be raised.
s.groupby(s.values).agg(one=('something','sum'))
# Error should be raised.
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 2020-05-29T19:08:49Z
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|
MEMBER
| null |
- [x] closes #34410
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
What would be the best way to validate that this fixes the MacPython/pandas-wheels @TomAugspurger
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DOC: intersphinx inventory link for statsmodels
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| 2020-05-28T08:53:27Z
| 2020-05-28T09:41:45Z
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|
MEMBER
| null |
intersphinx inventory has moved: http://www.statsmodels.org/devel/objects.inv -> https://www.statsmodels.org/devel/objects.inv
EDIT: this is already change on master. this PR directly against 1.0.x
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Categorical.(get|from)_dummies
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| 2020-05-28T10:40:18Z
| 2021-03-21T00:30:25Z
| 2021-03-21T00:30:25Z
|
CONTRIBUTOR
| null |
Simply converting categorical variables to and from dummy variables.
- [x] closes #8745
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Intentionally smaller-scoped than #31795 (and indeed `get_dummies`) as a broadly useful MVP which can be chained with other basic functionality. The tests are fairly rudimentary and I welcome any edge cases which should be picked out.
## For discussion
### from_dummies
* class method rather than free function: Keeps categorical-related functionality together, reduces surface in the global namespace, more obvious what is produced.
* silently drop column with NA header: wasn't sure about this. Maybe it should raise a warning?
* No handling of masked dataframes or dataframes with NA values
* No subsetting or renaming of columns: callers can do this themselves
### to_dummies
* Name: I went for `Categorical.to_dummies` instead of matching the free function `get_dummies`. The symmetry of to/from aids understanding, and using `get_` might imply A) that something is being got, which it isn't or B) signature/ feature parity with the existing method, which wasn't a design goal for me.
* `to_dummies` return type: `cls.to_dummies` returns bools, where `get_dummies` returns uint8s by default, which doesn't make a lot of sense to me as we are representing boolean data (and they're the same in memory anyway). Dummy variables are generally used for regression where a continuous variable is required, so ints don't get us any closer to what we may want, and being able to index into the categories using the row may be valuable.
* No `dtype` argument: I didn't see any benefit of `cls.to_dummies(dtype=float)` over `cls.to_dummies().astype(float)`. The latter is more explicit, no slower, and minimises API surface.
* No `prefix`, `prefix_sep` arguments: these unnecessarily assume string column headers. If someone wants to rename their columns, they can: IMO it's not a core requirement of this method.
* `dumma_na` replaced with `na_column`: if we're including the argument, we may as well let the user decide what they want to call their column, using the dtype they prefer (e.g. `"other"`, `-1` etc). They can always supply `np.nan` if they want `get_dummies`-like behaviour.
* No `sparse` argument: This I regret. Producing a sparse array would be valuable. However, it would drastically complicate the method, so I left it out for the MVP. The caller can always sparsify it after it's produced, so long as RAM isn't an issue for the temporary df.
* No `drop_first` argument: If someone wants to drop one of their columns, they can (`cls.to_dummies().drop(columns="my_col")`): again, not a core requirement of this method. Broadly speaking, I'm not in favour of adding arguments to save the caller <=1 line of their own code unless there are e.g. speed gains.
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CLN: is pandas/tests/data used?
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| 4
| 2020-05-28T11:07:59Z
| 2020-06-05T23:04:24Z
| 2020-06-05T23:04:24Z
|
MEMBER
| null |
In `pandas/tests/data` there are two files:
```
iris.csv
tips.csv
```
Both of these files are also in `pandas/tests/io/data/csv`.
Do they need to be in both places? If not, can they be removed from one of them?
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MDExOlB1bGxSZXF1ZXN0NDI0NDI5Njgx
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DOC: 1.0.4 whatsnew
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| 2020-05-28T11:38:59Z
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CONTRIBUTOR
| null |
Brings 1.0.4.rst to master & sets the release date (for tomorrow)
cc @simonjayhawkins.
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MDExOlB1bGxSZXF1ZXN0NDI0NDM1ODc4
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Fix read_sql empty result with chunksize bug GH34411
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| 2020-05-28T11:52:29Z
| 2021-01-14T18:52:51Z
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|
CONTRIBUTOR
| null |
- [x ] closes #34411
- [ x] tests added / passed
- [ x] passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ x] whatsnew entry
`pd.read_sql` was returning an empty generator when chunksize is set and the query returns zero results. Now it correctly returns a generator with a single empty DataFrame (:issue:`34411`).
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DOC: cleanup whatsnew post 1.0.4 release
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BUG: pandas.to_sql raises unexpected column error if data contains -numpy.inf
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| 2020-07-01T18:28:31Z
| 2020-07-01T18:28:31Z
|
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
import numpy
pandas.DataFrame({'foo': [numpy.inf]}).to_sql('foobar1', engine) #ProgrammingError: inf can not be used with MySQL
pandas.DataFrame({'foo': [-numpy.inf]}).to_sql('foobar2', engine) # OperationalError: (1054, "Unknown column 'infe0' in 'field list'")
pandas.DataFrame({'foo': [-numpy.inf], 'infe0':['bar']}).to_sql('foobar3', engine) # inserts (foo:Null, infe0: 'bar') into db
```
#### Problem description
I'd expect consistent behaviour between `numpy.inf` and `-numpy.inf`. Raising ProgrammingError in both cases is clear for the user. Furthermore, the query send should **not** interpret -inf as field `infe0`.
#### Expected Output
`ProgrammingError: inf can not be used with MySQL` in all 3 cases.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.76-linuxkit
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: None
pip: 20.0.2
setuptools: 40.6.2
Cython: None
numpy: 1.15.4
scipy: None
pyarrow: 0.11.1
xarray: None
IPython: 7.13.0
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 4.4.1
bs4: None
html5lib: None
sqlalchemy: 1.3.11
pymysql: None
psycopg2: None
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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CLN: assorted tslibs cleanups
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MDExOlB1bGxSZXF1ZXN0NDI0NjM0Njg4
| 34,433
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DOC: reduce API docs for offset aliases
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| 4
| 2020-05-28T17:38:54Z
| 2020-06-19T11:01:29Z
| 2020-06-19T11:01:22Z
|
MEMBER
| null |
Test to see if this works with sphinx
|
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MDExOlB1bGxSZXF1ZXN0NDI0NjUyOTUw
| 34,434
|
TST: additional regression cases for slicing blockwise op (GH34421)
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| 2
| 2020-05-28T18:17:28Z
| 2020-05-29T06:38:58Z
| 2020-05-28T22:20:24Z
|
MEMBER
| null |
Adding the other case from https://github.com/pandas-dev/pandas/issues/34367 to the test as well (although also fixed by the PR, it was failing with a different error originally, so might be worth it to add as test case as well).
cc @jbrockmendel
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MDExOlB1bGxSZXF1ZXN0NDI0NjY0ODUx
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BUG: Fix to GH34422 SeriesGroupBy works only with 'func' now
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| 14
| 2020-05-28T18:42:25Z
| 2020-06-05T17:30:50Z
| 2020-06-03T22:26:16Z
|
CONTRIBUTOR
| null |
- [X] closes #34422
- [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
This PR tries to fix the bug pointed in #34422 where `SeriesGroupBy.agg` works with any given column name in `NamedAgg`.
After discussing with @TomAugspurger and @MarcoGorelli in another issue #34380 regarding this issue in the comments, We came to a solution that
> So for SeriesGroupBy.agg, if there are any tuples or NamedAgg present in kwargs then I think we should raise.
> Disallowing `NamedAgg` and tuples with `SeriesGroupBy`.
#### Before fix:
```python3
s = pd.Series([1,1,2,2,3,3,4,5])
s.groupby(s.values).agg(one = pd.NamedAgg(column='anything',aggfunc='sum'))
one
1 2
2 4
3 6
4 4
5 5
s.groupby(s.values).agg(one=('something','sum'))
one
1 2
2 4
3 6
4 4
5 5
```
#### After fix:
```python3
s = pd.Series([1,1,2,2,3,3,4,5])
s.groupby(s.values).agg(one = pd.NamedAgg(column='anything',aggfunc='sum'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "d:\#gh34422\pandas\pandas\core\groupby\generic.py", line 243, in aggregate
raise TypeError(tuple_given_message.format(type(kwargs[col]).__name__))
TypeError: 'func' is expected but recieved NamedAgg in **kwargs.
s.groupby(s.values).agg(one=('something','sum'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "d:\#gh34422\pandas\pandas\core\groupby\generic.py", line 243, in aggregate
raise TypeError(tuple_given_message.format(type(kwargs[col]).__name__))
TypeError: 'func' is expected but recieved tuple in **kwargs.
```
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MDU6SXNzdWU2MjY3NDA1ODI=
| 34,436
|
ENH: Implement min_max() function that returns both
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| 2020-05-28T19:02:13Z
| 2020-08-21T17:05:43Z
| 2020-05-28T19:59:49Z
|
CONTRIBUTOR
| null |
#### Describe the solution you'd like
It might be a common enough situation for someone to want to know the min AND max of an index or series. I assume the min and max are not stored as attributes so the function has to scan all the data. It would be an improvement to scan once, and return both values as a tuple.
#### API breaking implications
None
```python
my_min, my_max = df.index.min_max()
```
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MDU6SXNzdWU2MjY3NTQ1OTM=
| 34,437
|
BUG: pivot_table calculates incorrect standard deviation when np.std passed to aggfunc
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| 2020-05-28T19:27:21Z
| 2020-05-29T12:46:05Z
| 2020-05-29T06:49:34Z
|
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.
---
`
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': ['a', 'a'], 'X': [2.8597, 2.8503]})
pivot = pd.pivot_table(df, index='A', values='X', aggfunc=(np.mean, np.std))
my_std = np.std(df.X) # 0.0047000000000001485
pivot_std = pivot.loc['a', 'std'] # 0.006646803743153757
`
#### Problem description
pivot_table calculates incorrect standard deviation when np.std passed to aggfunc.
#### Expected Output
Numpy calculates the correct std in the given example.
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|
BUG: Styler.set_table_styles does not apply to Styler.to_excel
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| 2020-05-28T19:37:38Z
| 2021-06-28T07:53:24Z
| 2021-06-28T07:53:24Z
|
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.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
# Dummy dataframe
df = pd.DataFrame(np.random.rand(15).reshape(5,3), columns='x y z'.split())
# Apply style with `set_table_styles` and `applymap`
style = df.style \
.set_table_styles([{'selector': 'th', 'props': [('background-color', 'orange')]}]) \
.applymap(lambda x: 'color: red' if x >.5 else 'color: blue')
# Test with different engines
for eng in ('xlsxwriter', 'openpyxl'):
style.to_excel(f'tbl_style_tst_{eng}.xlsx', engine=eng)
```
#### Problem description
In both `xlsx` files output above, only the element styling (`applymap`) are observed. Any `set_table_styles` styling seems to be ignored regardless of engine. The html from `style.render()` confirms the styling is correct, and is observed in html output as well:
```html
<style type="text/css" >
#T_4987f224_a119_11ea_b2cc_48452026764f th {
background-color: orange;
} #T_4987f224_a119_11ea_b2cc_48452026764frow0_col0 {
color: red;
color: red;
color: red;
color: red;
color: red;
} #T_4987f224_a119_11ea_b2cc_48452026764frow0_col1 {
color: blue;
color: blue;
color: blue;
color: blue;
color: blue;
} <!-- ...continues ... -->
```
```python
with open(r'pd_css_test.html', 'w') as f:
f.write(style.render())
```
#### Expected Output
Unable to show excel/colors here, but the columns/index should have colour styling applied as expected from `set_table_styles`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Canada.1252
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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TST #24444 converting Period to Timestamp
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| 2020-05-28T20:44:49Z
| 2020-06-07T09:03:17Z
| 2020-06-07T08:45:06Z
|
CONTRIBUTOR
| null |
I've tested intervals `ms`, `s` and `min`.
Note: the test raises an error for `ms` on v1.0.3, but is successful on master
Note: I did not choose to close this issue has the test fail for `ns` unit
[ ] #24444
[ 1 ] tests added / passed
[ x ] passes `black pandas`
[ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: exponential moving window covariance fails for multiIndexed DataFrame
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| 7
| 2020-05-28T20:51:27Z
| 2021-08-18T11:11:57Z
| 2020-06-25T23:24:26Z
|
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
import numpy as np
columns = pd.MultiIndex.from_product([['a','b','c'],['x','y','w','z'], list(range(9))])
index = range(1000)
df = pd.DataFrame(
np.random.normal(size=(len(index), len(columns))),
index=index,
columns=columns
)
df.ewm(alpha=0.1).cov() #Throws AssertionError: Length of order must be same as number of levels (4), got 3
```
#### Problem description
When calculating ewm covariance, pandas fails when the DataFrame has multiindex columns. However it works when columns are simple Index dataframes.
It works for:
```python
pd.DataFrame(df.values).ewm(alpha=0.1).cov()
```
#### Expected Output
The covariance, actually only the last matrix (last level of index)
#### 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 : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDI0NzMwMDc1
| 34,441
|
Add date dtype
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| 2020-12-29T20:41:26Z
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CONTRIBUTOR
| null |
- [ ] closes #32473
This is the beginning of the date data type, and it definitely works properly on a high level. For some of the places where strings might need to be converted and such I used the cython code that was implemented in tslib. The time complexity is still linear, but some of those methods may need to be rewritten for dates in cython, which I'm happy to do.
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TST #28981 comparison operation for interval dtypes
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| 1
| 2020-05-28T21:41:00Z
| 2020-06-07T09:03:01Z
| 2020-06-03T18:04:41Z
|
CONTRIBUTOR
| null |
As a complement of #28980, one test was modified to take into account this issue:
- [ x ] closes #28981
- [ 1 ] test modified
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: simplify to_offset
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MDU6SXNzdWU2MjY4NzExMDM=
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BUG: to_numpy on empty dataframe allocates O(len(df)) array
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[] | null | 4
| 2020-05-28T23:00:58Z
| 2020-06-01T22:22:58Z
| 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
```python
import pandas as pd
import tracemalloc as tm
import numpy as np
N = 123456789
df = pd.DataFrame(index=range(N))
tm.start()
arr = df.to_numpy(dtype=np.uint8)
memory, _ = tm.get_traced_memory()
print(memory, arr.size, arr.shape)
tm.stop()
```
Output:
```
123457803 0 (123456789, 0)
```
#### Problem description
If a DataFrame `df` has no columns, the result of `df.to_numpy(...)` is an array without any data, meaning there's nothing that can be read from/written to memory at all. This array doesn't require a large memory allocation, but one is still allocated (specifically it is allocated as if all the 0s in the shape were 1s). This could be optimised in some form.
This is almost certainly the same bug as https://github.com/numpy/numpy/issues/16410, but could have an explicit work-around in Pandas (e.g. so that the memory is still not allocated on older versions of NumPy, and because it's somewhat unclear how/if the NumPy bug will be fixed).
#### Expected Output
The reported memory allocated by the `to_numpy` call should be minimal, and not change with `N`, e.g.
```
2640 0 (123456789, 0)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CLN: liboffsets de-duplicate pickle code
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| 1
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| 2020-05-29T14:45:18Z
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MEMBER
| null |
privatize/cdef functions that are now only used in liboffsets
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REF: move offset_to_period_map from liboffsets
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MEMBER
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its only used in tseries.frequencies, so this puts it back there
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDI0ODM3NDEx
| 34,449
|
BUG: Period.to_timestamp not matching PeriodArray.to_timestamp
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| 2020-05-29T01:39:17Z
| 2020-06-01T16:34:25Z
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MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
With this change, I _think_ we can move to share some methods between Period and PeriodArray.
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MDExOlB1bGxSZXF1ZXN0NDI0ODQ3MTA1
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|
Fix MultiIndex .loc "Too Many Indexers" with None as return value
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| 2
| 2020-05-29T02:21:59Z
| 2020-06-03T18:02:27Z
| 2020-06-03T18:02:23Z
|
CONTRIBUTOR
| null |
- [x] closes #34318
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Three changes in indexing.py :
- function _handle_lowerdim_multi_index_axis0 default return value was None, now it raises an Exception.
- function _getitem_lowerdim checks for a raised exception instead of a value None.
- function _getitem_nested_tuple checks for a raised exception instead of a value None(had to be changed because it also uses _handle_lowerdim_multi_index_axis0)
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MDU6SXNzdWU2MjY5ODY1MzA=
| 34,451
|
BUG:pandas use all() or any() get wrong result
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| 2020-05-29T04:21:17Z
| 2020-05-29T06:24:30Z
| 2020-05-29T06:18:48Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
yes
- [ ] I have confirmed this bug exists on the latest version of pandas.
yes
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
yes
---
**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
print(data['shift1'].values.all() > 0,data['shift1'].values)
print(data['shift1'].values.any() > 0,data['shift1'].values[0],data['shift1'].values[1],-3680.0>0)
```
#### Problem description
True [-3680. -3680.]
True -3680.0 -3680.0 False
The value of series is clearly less than 0, but using all () and any () gives the wrong result
#### Expected Output
True [-3680. -3680.]
True -3680.0 -3680.0 False
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 39.1.0
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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BUG: groupby.tshift inconsistent behavior with other groupby transformations
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| 2020-05-29T10:10:40Z
| 2021-08-07T22:25:12Z
| 2021-08-07T22:25:12Z
|
MEMBER
| null |
I discovered this while trying to tackle issue #32344, where @ryankarlos mentioned `groupby.transform('tshift', ...)` seems to behave incorrectly.
However, before we can address #32344, we probably need to address this.
```python
# on current master
>>> import pandas as pd
>>> import numpy as np
>>> pd.__version__
'1.1.0.dev0+1708.g043b60920'
>>> df = pd.DataFrame(
... {
... "A": ["foo", "foo", "foo", "foo", "bar", "bar", "baz"],
... "B": [1, 2, np.nan, 3, 3, np.nan, 4],
... },
... index=pd.date_range('2020-01-01', '2020-01-07')
... )
>>> df
A B
2020-01-01 foo 1.0
2020-01-02 foo 2.0
2020-01-03 foo NaN
2020-01-04 foo 3.0
2020-01-05 bar 3.0
2020-01-06 bar NaN
2020-01-07 baz 4.0
>>> df.groupby("A").tshift(1, "D")
B
A
bar 2020-01-06 3.0
2020-01-07 NaN
baz 2020-01-08 4.0
foo 2020-01-02 1.0
2020-01-03 2.0
2020-01-04 NaN
2020-01-05 3.0
>>> df.groupby("A").ffill()
B
2020-01-01 1.0
2020-01-02 2.0
2020-01-03 2.0
2020-01-04 3.0
2020-01-05 3.0
2020-01-06 3.0
2020-01-07 4.0
>>> df.groupby("A").cumsum()
B
2020-01-01 1.0
2020-01-02 3.0
2020-01-03 NaN
2020-01-04 6.0
2020-01-05 3.0
2020-01-06 NaN
2020-01-07 4.0
```
We can see that `groupby.tshift` is inconsistent with other groupby transformations. It retains the groupby column, and more importantly reordered the data.
Since 0.25 we have had deliberate effort to make all groupby transformations consistent, see https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html#dataframe-groupby-ffill-bfill-no-longer-return-group-labels
Following this thinking I would expect the returned data to behave more like
```python
>>> df.groupby("A").tshift(1, "D") # this is actually the result of df.tshift(1, "D").drop(columns='A')
B
2020-01-02 1.0
2020-01-03 2.0
2020-01-04 NaN
2020-01-05 3.0
2020-01-06 3.0
2020-01-07 NaN
2020-01-08 4.0
```
However, if we are to make `groupby.tshift` consistent with other groupby transformation like the above, this makes it no different from `df.tshift(1, "D").drop(columns='A')', and `groupby` has lost its meaning here.
Perhaps we should just deprecate `groupby.tshift` entirely? I know #11631 discussed about deprecating `tshift`, but that has been stalled for a long time.
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MDExOlB1bGxSZXF1ZXN0NDI1MDIyODQy
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[ENH] Allow pad, backfill and cumcount in groupby.transform
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| 2020-05-29T10:32:25Z
| 2020-06-04T04:06:48Z
| 2020-06-03T23:28:12Z
|
MEMBER
| null |
- [x] closes #31269
- [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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| 34,454
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TST/REF: refactor the arithmetic tests for IntegerArray
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| 7
| 2020-05-29T11:53:24Z
| 2020-06-06T08:29:54Z
| 2020-06-06T08:29:20Z
|
MEMBER
| null |
This is an attempt to make the arithmetic tests for the masked arrays more understandable and maintainable (doing it here for IntegerArray as a start, but the idea would be to do the same for BooleanArray, and later FloatingArray as well, and at that point also share some of those tests).
The problem with the current `arrays/integer/test_arithmetic.py` is that is quite complex to see what is going on (which I experienced when making a version for FloatingArray in https://github.com/pandas-dev/pandas/pull/34307): for example, there is a huge `_check_op` method that has all the logic to created the expected result, but so this also has all the special cases of all ops combined, making it very difficult to see what is going on or to know what is now exactly tested for a certain op.
The reason for this structure is that those tests originally came from the base extension tests in `tests/extension/`, where the tests needed to be very generic. However, we already moved out those tests for IntgerArray, since they were all customized (nothing was still being inherited from the base class), but so we also don't need to maintain the original class structure then.
The logic how I constructed the new tests:
- I first test each `op` with an explicitly constructed expected result. Here, we test the IntegerArray-specific special cases (like things as `1 ** pd.NA`, or division resulting in a float numpy array, ..). Explicitly writing it down makes it a lot easier to read and to see what is tested (no complex `if op == ...: ..,` constructs, and no complex correcting of expected results based on ndarray)
- Those first tests are all with EAs. In a next set of tests, I added tests for array+scalar ops, array+ndarray ops, ... But here, I just rely on creating the expected result with the EA itself (since EA+EA ops were already tested before)
- Similar tests for frame+scalar, series+scalar, series+array with the EA dtypes -> rely on the actual EA op for the expected result instead of having `_check_op` handle that as well.
cc @dsaxton @jreback
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BUG: Groupby.apply raises KeyError for Float64Index depending on order of index and column
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| 2020-05-29T11:54:42Z
| 2020-10-24T17:06:08Z
| null |
MEMBER
| null |
When performing a groupby.apply on a dataframe with a float index, I receive a `KeyError`, depending on whether or not the index has the same ordering as the column I am grouping on.
---
#### Code Sample
broken
```python
import pandas as pd
df = pd.DataFrame({"col": [1, 2, 3,]}, index=[0.1, 0.3, 0.2,])
df.groupby("col").apply(lambda x: x)
```
working (mind the sorting of col vs index)
```python
import pandas as pd
df = pd.DataFrame({"col": [1, 2, 3,]}, index=[0.1, 0.2, 0.3,])
df.groupby("col").apply(lambda x: x)
```
```python
import pandas as pd
df = pd.DataFrame({"col": [1, 4, 3,]}, index=[0.1, 0.4, 0.2,])
df.groupby("col").apply(lambda x: x)
```
#### Example traceback
<details>
```
KeyError Traceback (most recent call last)
<ipython-input-14-fac5566be46e> in <module>
----> 1 df.groupby("col").apply(lambda x: x.copy())
~/workspace/pandas/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
854 with option_context("mode.chained_assignment", None):
855 try:
--> 856 result = self._python_apply_general(f, self._selected_obj)
857 except TypeError:
858 # gh-20949
~/workspace/pandas/pandas/core/groupby/groupby.py in _python_apply_general(self, f, data)
887 data after applying f
888 """
--> 889 keys, values, mutated = self.grouper.apply(f, data, self.axis)
890
891 return self._wrap_applied_output(
~/workspace/pandas/pandas/core/groupby/ops.py in apply(self, f, data, axis)
176 ):
177 try:
--> 178 result_values, mutated = splitter.fast_apply(f, sdata, group_keys)
179
180 except libreduction.InvalidApply as err:
~/workspace/pandas/pandas/core/groupby/ops.py in fast_apply(self, f, sdata, names)
962 # must return keys::list, values::list, mutated::bool
963 starts, ends = lib.generate_slices(self.slabels, self.ngroups)
--> 964 return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
965
966 def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
~/workspace/pandas/pandas/_libs/reduction.pyx in pandas._libs.reduction.apply_frame_axis0()
482 results = []
483
--> 484 slider = BlockSlider(frame)
485
486 mutated = False
~/workspace/pandas/pandas/_libs/reduction.pyx in pandas._libs.reduction.BlockSlider.__init__()
549
550 self.frame = frame
--> 551 self.dummy = frame[:0]
552 self.index = self.dummy.index
553
~/workspace/pandas/pandas/core/frame.py in __getitem__(self, key)
2810
2811 # Do we have a slicer (on rows)?
-> 2812 indexer = convert_to_index_sliceable(self, key)
2813 if indexer is not None:
2814 # either we have a slice or we have a string that can be converted
~/workspace/pandas/pandas/core/indexing.py in convert_to_index_sliceable(obj, key)
2113 idx = obj.index
2114 if isinstance(key, slice):
-> 2115 return idx._convert_slice_indexer(key, kind="getitem")
2116
2117 elif isinstance(key, str):
~/workspace/pandas/pandas/core/indexes/numeric.py in _convert_slice_indexer(self, key, kind)
382 # We always treat __getitem__ slicing as label-based
383 # translate to locations
--> 384 return self.slice_indexer(key.start, key.stop, key.step, kind=kind)
385
386 # ----------------------------------------------------------------
~/workspace/pandas/pandas/core/indexes/base.py in slice_indexer(self, start, end, step, kind)
4940 slice(1, 3, None)
4941 """
-> 4942 start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
4943
4944 # return a slice
~/workspace/pandas/pandas/core/indexes/base.py in slice_locs(self, start, end, step, kind)
5147 end_slice = None
5148 if end is not None:
-> 5149 end_slice = self.get_slice_bound(end, "right", kind)
5150 if end_slice is None:
5151 end_slice = len(self)
~/workspace/pandas/pandas/core/indexes/base.py in get_slice_bound(self, label, side, kind)
5063 except ValueError:
5064 # raise the original KeyError
-> 5065 raise err
5066
5067 if isinstance(slc, np.ndarray):
~/workspace/pandas/pandas/core/indexes/base.py in get_slice_bound(self, label, side, kind)
5057 # we need to look up the label
5058 try:
-> 5059 slc = self.get_loc(label)
5060 except KeyError as err:
5061 try:
~/workspace/pandas/pandas/core/indexes/numeric.py in get_loc(self, key, method, tolerance)
444 return nan_idxs
445
--> 446 return super().get_loc(key, method=method, tolerance=tolerance)
447
448 @cache_readonly
~/workspace/pandas/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2876 return self._engine.get_loc(casted_key)
2877 except KeyError as err:
-> 2878 raise KeyError(key) from err
2879
2880 if tolerance is not None:
KeyError: 0
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 043b60920ede5a2589ed29685ba7247fbbc4ea5f
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Feb 10 21:08:45 PST 2020; root:xnu-4903.278.28~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.0.dev0+1708.g043b60920
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0.post20200528
Cython : 0.29.19
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : 1.3.0
numba : None
</details>
- [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 (043b60920ede5a2589ed29685ba7247fbbc4ea5f)
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|
BUG: behaviour of astype_nansafe(.., copy=False) / SparseArray.astype bug
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| 1
| 2020-05-29T12:16:00Z
| 2020-12-31T01:49:34Z
| 2020-12-31T01:49:34Z
|
MEMBER
| null |
So our internal `astype_nansafe` has the following behaviour:
```
In [53]: pd.core.dtypes.cast.astype_nansafe(np.array([1, 2, 3]), np.dtype("float64"))
Out[53]: array([1., 2., 3.])
In [54]: pd.core.dtypes.cast.astype_nansafe(np.array([1, 2, 3]), np.dtype("float64"), copy=False)
Out[54]: array([4.9e-324, 9.9e-324, 1.5e-323])
```
where it seems it assumes it can take a view if `copy=False`, which of course depends on the dtype it is casting to:
https://github.com/pandas-dev/pandas/blob/043b60920ede5a2589ed29685ba7247fbbc4ea5f/pandas/core/dtypes/cast.py#L990
I am not fully sure about the intent of `astype_nansafe` or whether this was intentional (I would say it is a bug, as I would expect that function to take care of whether `copy=False` is possible or not).
This also causes a bug in SparseArray's `astype` with `copy=False`:
```
In [59]: pd.arrays.SparseArray([1, 2, 3]).astype(float, copy=False)
Out[59]:
[5e-324, 1e-323, 1.5e-323]
Fill: 0.0
IntIndex
Indices: array([0, 1, 2], dtype=int32)
```
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API: SparseArray.astype behaviour to always preserve sparseness
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[] | null | 4
| 2020-05-29T12:26:14Z
| 2021-10-20T23:29:09Z
| null |
MEMBER
| null |
Currently, the `SparseArray.astype` function will always convert the specified target dtype to a sparse dtype, if it is not one. For example, this gives:
```
In [64]: arr = pd.arrays.SparseArray([1, 0, 0, 2])
In [65]: arr
Out[65]:
[1, 0, 0, 2]
Fill: 0
IntIndex
Indices: array([0, 3], dtype=int32)
In [66]: arr.astype(float)
Out[66]:
[1.0, 0.0, 0.0, 2.0]
Fill: 0.0
IntIndex
Indices: array([0, 3], dtype=int32)
```
This ensures that a simple `astype` doesn't densify the sparse array (and you don't need to do `astype(pd.SparseDtype(float, fill_value))`).
And note this also gives this behaviour to `Series.astype(..)`
But, this also gives the inconsistency that `arr.astype(target_dtype).dtype != target_dtype`, so you can rely on the fact that you get back an array of the actual dtype that you specified.
See eg the workaround I need to add for this in https://github.com/pandas-dev/pandas/pull/34338
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CLN: Clean csv files in test data GH34427
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| 12
| 2020-05-29T12:30:26Z
| 2020-06-09T11:51:08Z
| 2020-06-05T23:04:24Z
|
CONTRIBUTOR
| null |
- [x] closes #34427
- [x] tests passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I deleted the `iris.csv` and `tips.csv` files which are unused duplicates (i.e. those in `pandas/tests/io/data/csv`).
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API: expected result of concat of SparseArray with Categorical?
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| null |
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/34338
The current behaviour when Sparse and Categorical are concatenated, is that for both the "dense" values are used (for sparse the densified values, for categorical the non-categorical version of the values).
For example, Sparse[int] with Categorical[int] results in a plain `int` series:
```
In [1]: pd.concat([
...: pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0)),
...: pd.Series([3, 4, 5], dtype="category")
...: ])
Out[1]:
0 1
1 0
2 2
0 3
1 4
2 5
dtype: int64
```
An alternative could also be to preserve the sparseness in the result, which is what happens when concatting with a plain int series (not categorical):
```
In [2]: pd.concat([
...: pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0)),
...: pd.Series([3, 4, 5], dtype="int64")
...: ])
Out[2]:
0 1
1 0
2 2
0 3
1 4
2 5
dtype: Sparse[int64, 0]
```
(alternatively, you could maybe also say that concatting with categorical should never give a numerical result, but rather object dtype or so)
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MDU6SXNzdWU2MjczNzA5NDY=
| 34,460
|
Mixed object array with astype to Extension Array throws TypeError
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[] | null | 1
| 2020-05-29T15:57:58Z
| 2020-06-02T06:20:39Z
| null |
MEMBER
| null |
This works
```python
>>> pd.Series([1, "1"]).astype(int)
0 1
1 1
dtype: int64
```
This doesn't
```python
>>> pd.Series([1, "1"]).astype("Int64")
Traceback (most recent call last):
File "/Users/willayd/clones/pandas/pandas/core/arrays/integer.py", line 165, in safe_cast
return values.astype(dtype, casting="safe", copy=copy)
TypeError: Cannot cast array from dtype('O') to dtype('int64') according to the rule 'safe'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/willayd/clones/pandas/pandas/core/generic.py", line 5635, in astype
new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors,)
File "/Users/willayd/clones/pandas/pandas/core/internals/managers.py", line 573, in astype
return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
File "/Users/willayd/clones/pandas/pandas/core/internals/managers.py", line 402, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/willayd/clones/pandas/pandas/core/internals/blocks.py", line 581, in astype
values = astype_nansafe(vals1d, dtype, copy=True)
File "/Users/willayd/clones/pandas/pandas/core/dtypes/cast.py", line 912, in astype_nansafe
return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
File "/Users/willayd/clones/pandas/pandas/core/arrays/integer.py", line 362, in _from_sequence
return integer_array(scalars, dtype=dtype, copy=copy)
File "/Users/willayd/clones/pandas/pandas/core/arrays/integer.py", line 153, in integer_array
values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
File "/Users/willayd/clones/pandas/pandas/core/arrays/integer.py", line 270, in coerce_to_array
values = safe_cast(values, dtype, copy=False)
File "/Users/willayd/clones/pandas/pandas/core/arrays/integer.py", line 172, in safe_cast
raise TypeError(
TypeError: cannot safely cast non-equivalent object to int64
```
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|
mask based multi-index assignment of column values described
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| 1
| 2020-05-29T16:04:08Z
| 2020-06-02T22:57:49Z
| 2020-06-02T22:57:44Z
|
CONTRIBUTOR
| null |
- [x] closes #34383
- [x] passes `black pandas`
not sure what this means, will look it up.
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- ~~[ ] tests added / passed~~
this is an PR on documentation only, unit tests are not needed.
- ~~[ ] whatsnew entry~~
I think I don't need this as this is a PR related to documentation only.
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REF: make Resolution an enum
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| 2
| 2020-05-29T17:41:57Z
| 2020-06-01T00:02:06Z
| 2020-05-31T23:22:37Z
|
MEMBER
| null |
I find this much clearer than just using an int. No measured performance impact.
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|
ENH: Support logical and/or (bitwise ops) for nullable IntegerArray
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| 4
| 2020-05-29T17:46:40Z
| 2020-05-30T15:24:15Z
| 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: [cc6348](https://github.com/pandas-dev/pandas/commit/cc634842a1d73e77145d62c4390dfa10f282e06b)
---
Logical and/or (``&``, ``|``) on integer Series returns an integer Series:
```python
>>> import pandas as pd
>>> df = pd.DataFrame({'A': [1, 1, 0, 0], 'B': [1, 0, 1, 0]})
>>> df.A & df.B
0 1
1 0
2 0
3 0
dtype: int64
```
Doing this for a nullable IntegerArray, I expect the result to be another IntegerArray, but it raises a TypeError:
```python
>>> df.A.astype('Int64') & df.B.astype('Int64')
```
#### Traceback
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "(snip)/pandas/core/ops/common.py", line 65, in new_method
return method(self, other)
File "(snip)/pandas/core/ops/__init__.py", line 393, in wrapper
res_values = logical_op(lvalues, rvalues, op)
File "(snip)/pandas/core/ops/array_ops.py", line 336, in logical_op
res_values = op(lvalues, rvalues)
TypeError: unsupported operand type(s) for &: 'IntegerArray' and 'IntegerArray'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : cc634842a1d73e77145d62c4390dfa10f282e06b
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
Version : Darwin Kernel Version 19.4.0: Wed Mar 4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1710.gcc634842a
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : 0.29.19
pytest : 5.4.2
hypothesis : 5.16.0
sphinx : 3.0.4
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDI1MzI0Mzk1
| 34,464
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CLN: drop **kwds from pd.read_excel
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| 2
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| 2020-06-01T16:06:50Z
| 2020-06-01T15:37:10Z
|
CONTRIBUTOR
| null |
Drop ``**kwds`` from ``pd.read_excel``.
Wrt. "new" keyword parameter ``na_filter``: This parameter already exists in the doc string and is just masked in the ``kwds`` in the signature. If you follow the code paths all the way down to parsers.py, you can see that it indeed has a default of `True``.
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BUG: unexpected behavior of json_normalize meta arg
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| 3
| 2020-05-29T21:23:38Z
| 2020-11-26T10:28:07Z
| 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.
---
**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
test_input = [{
"injection": {
"time": "injection time"
},
"results": [
{
"time": "result time",
"peaks": [{
"name": "peak name A",
"area": 1
}, {
"name": "peak name B",
"area": 2
}]
},
]
}]
df = pd.json_normalize(test_input, record_path=['results', "peaks"], meta=[["injection", "time"]])
print(df)
```
```
Output
name area injection.time
0 peak name A 1 result time
1 peak name B 2 result time
```
#### Problem description
I expected the string `"injection time"` to populate the `$.injection.time` column. Instead, the contents of `$.results.time` is populating this column. If I specify `meta=["injection"]`, then `{"time": "injection time"}` correctly populates the column.
#### Expected Output
```
name area injection.time
0 peak name A 1 injection time
1 peak name B 2 injection time
```
#### Output of ``pd.show_versions()``
<details>
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util.testing import assert_frame_equal
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 46.4.0
Cython : 0.29.18
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.8.1
bs4 : 4.6.3
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.48.0
</details>
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MDU6SXNzdWU2Mjc1NjM5ODI=
| 34,466
|
BUG: Rolling count aggregation produces unexpected results
|
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[] | null | 14
| 2020-05-29T21:49:32Z
| 2020-10-03T06:31:40Z
| 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.
---
```python
>>> pd.__version__
'1.1.0.dev0+1690.g70d7c04ff'
>>> pd.Series([1,1,1,None]).rolling(2, min_periods=2, center=True).count()
0 NaN
1 2.0
2 2.0
3 1.0
dtype: float64
```
#### Problem description
Apologies if this is expected behavior and not a bug. I noticed that prior to version 1.0, `rolling.count` ignored the `min_periods` parameter, as discussed in https://github.com/pandas-dev/pandas/pull/30923. However I'm having trouble understanding this output. Shouldn't the last element of the output be `NaN`, given that the final window includes the last and second to last elements, of which only one is valid?
#### Expected Output
```
0 NaN
1 2.0
2 2.0
3 NaN
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 70d7c04ff585de361622e4fe1788480a7a4526b5
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1690.g70d7c04ff
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
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
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2Mjc1NjY2ODA=
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|
BUG: read_parquet no longer supports file-like objects
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| 26
| 2020-05-29T21:56:30Z
| 2020-06-23T16:04:00Z
| 2020-06-15T11:22:01Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
from io import BytesIO
import pandas as pd
buffer = BytesIO()
df = pd.DataFrame([1,2,3], columns=["a"])
df.to_parquet(buffer)
df2 = pd.read_parquet(buffer)
```
#### Problem description
The current behavior of `read_parquet(buffer)` is that it raises the following exception:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./working_dir/tvenv/lib/python3.7/site-packages/pandas/io/parquet.py", line 315, in read_parquet
return impl.read(path, columns=columns, **kwargs)
File "./working_dir/tvenv/lib/python3.7/site-packages/pandas/io/parquet.py", line 131, in read
path, filesystem=get_fs_for_path(path), **kwargs
File "./working_dir/tvenv/lib/python3.7/site-packages/pyarrow/parquet.py", line 1162, in __init__
self.paths = _parse_uri(path_or_paths)
File "./working_dir/tvenv/lib/python3.7/site-packages/pyarrow/parquet.py", line 47, in _parse_uri
path = _stringify_path(path)
File "./working_dir/tvenv/lib/python3.7/site-packages/pyarrow/util.py", line 67, in _stringify_path
raise TypeError("not a path-like object")
TypeError: not a path-like object
```
#### Expected Output
Instead, `read_parquet(buffer)` should return a new DataFrame with the same contents as the serialized DataFrame stored in `buffer`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-99-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
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 : 0.17.1
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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MDExOlB1bGxSZXF1ZXN0NDI1MzU5OTUw
| 34,468
|
BUG: Index with right_index=True or left_index=True merge
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[] | null | 5
| 2020-05-29T22:27:24Z
| 2021-04-26T21:59:56Z
| 2021-02-11T01:38:13Z
|
MEMBER
| null |
- [x] xref #28243
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Hi,
I would expect the output form the merge function as specified in #28243, i.e. if you join right with right_index=True the resulting DataFrame should have the right index.
The documentation says, that the index is passed on if it is part of the merge.
This change broke a few test, which expected in this case that the index was the index which was merged onto the base DataFrame. I fixed them temporarily until I know how to proceed in this case.
Should this as implemented be the desired behavior for the right_index=True case?
The code is not final, it is just a first draft. I would have to change the tests for good after we decide how to implement this.
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MDExOlB1bGxSZXF1ZXN0NDI1MzY0NjQw
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TYP: annotations in plotting code
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| 0
| 2020-05-29T22:46:13Z
| 2020-05-31T23:07:50Z
| 2020-05-31T23:02:31Z
|
MEMBER
| null |
Trying to sort out what is getting passed to the tseries.frequencies funcs in these modules.
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ENH: Handing missing data in scatter_matrix when using hue/color
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[] | null | 0
| 2020-05-29T22:59:10Z
| 2020-05-30T10:33:46Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I would like to use scatter_matrix on a dataframe with missing entries when using the c parameter.
This works:
```python
import pandas as pd
import numpy as np
X = pd.DataFrame(np.random.normal(size=(150, 4)))
y = pd.Series(np.random.randint(0, 4, size=150))
pd.plotting.scatter_matrix(X, c=y)
```
this works:
```python
X[X > 1] = np.NaN
pd.plotting.scatter_matrix(X)
```
This breaks:
```python
X[X > 1] = np.NaN
pd.plotting.scatter_matrix(X, c=y)
```
```pythontb
ValueError: Invalid RGBA argument: 1.0
During handling of the above exception, another exception occurred:
ValueError: 'c' argument has 150 elements, which is inconsistent with 'x' and 'y' with size 103.
```
(using pandas 1.0.3)
#### Describe the solution you'd like
scatter_matrix should make sure the shapes are consistent. I haven't looked into the internals. Calling ``plt.scatter`` with missing values actually does the right thing.
#### API breaking implications
Don't think it'll break anything.
#### Describe alternatives you've considered
I could use seaborn but seaborn doesn't allow me to pass a series as 'c'. I can also use ``dabl`` ;)
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MDExOlB1bGxSZXF1ZXN0NDI1MzczODE5
| 34,471
|
BUG: Fix read_json shape error on multi-indexed DF/SR with orient='split' #4889
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|
NONE
| null |
- [ ] closes #xxxx
- [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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|
DEPS: drop 3.6
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| 6
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| 2020-08-11T01:17:48Z
| 2020-08-11T01:17:48Z
|
CONTRIBUTOR
| null |
according to schedule we should 3.6 in june 2020
https://numpy.org/neps/nep-0029-deprecation_policy.html
prob ok to keep for the 1.1 release then should drop for 1.2. we will likely be adding. 3.9 support so this makes sense.
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fix to_json for numbers larger than sys.maxsize
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| 22
| 2020-05-30T00:40:35Z
| 2020-06-26T17:51:37Z
| 2020-06-24T23:44:41Z
|
MEMBER
| null |
- [x] closes #34395
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] passes performance benchmarks
- [x] whatsnew entry
Currently this patch causes a significant reduction for a number of the JSON performance benchmarks. A printout of my results is included below.
[json_benchmarks_results.txt](https://github.com/pandas-dev/pandas/files/4704831/json_benchmarks_results.txt)
I'd love to keep working on this if anybody has ideas for making the solution more efficient!
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BUG: fix origin epoch when freq is Day and harmonize epoch between timezones
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| 2
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| 2020-06-01T22:18:07Z
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|
MEMBER
| null |
Follow-up of #31809.
The purpose of this PR is to fix the current behavior on master:
```python
import pandas as pd
import numpy as np
start, end = "2000-08-02 23:30:00+0500", "2000-12-02 00:30:00+0500"
rng = pd.date_range(start, end, freq="7min")
ts = pd.Series(np.random.randn(len(rng)), index=rng)
result_1 = ts.resample("D", origin="epoch").count()
result_2 = ts.resample("24H", origin="epoch").count()
print(f"result_1:\n\n{result_1}\n")
print(f"result_2:\n\n{result_2}\n")
```
Outputs on master:
```
result_1:
2000-08-02 00:00:00+05:00 5
2000-08-03 00:00:00+05:00 205
2000-08-04 00:00:00+05:00 206
2000-08-05 00:00:00+05:00 206
2000-08-06 00:00:00+05:00 206
...
2000-11-28 00:00:00+05:00 206
2000-11-29 00:00:00+05:00 206
2000-11-30 00:00:00+05:00 205
2000-12-01 00:00:00+05:00 206
2000-12-02 00:00:00+05:00 5
Freq: D, Length: 123, dtype: int64
result_2:
2000-08-02 05:00:00+05:00 48
2000-08-03 05:00:00+05:00 205
2000-08-04 05:00:00+05:00 206
2000-08-05 05:00:00+05:00 206
2000-08-06 05:00:00+05:00 205
...
2000-11-27 05:00:00+05:00 206
2000-11-28 05:00:00+05:00 206
2000-11-29 05:00:00+05:00 206
2000-11-30 05:00:00+05:00 205
2000-12-01 05:00:00+05:00 168
Freq: 24H, Length: 122, dtype: int64
```
Expected Outputs (with this PR):
```
result_1:
2000-08-02 00:00:00+05:00 5
2000-08-03 00:00:00+05:00 205
2000-08-04 00:00:00+05:00 206
2000-08-05 00:00:00+05:00 206
2000-08-06 00:00:00+05:00 206
...
2000-11-28 00:00:00+05:00 206
2000-11-29 00:00:00+05:00 206
2000-11-30 00:00:00+05:00 205
2000-12-01 00:00:00+05:00 206
2000-12-02 00:00:00+05:00 5
Freq: D, Length: 123, dtype: int64
result_2:
2000-08-02 00:00:00+05:00 5
2000-08-03 00:00:00+05:00 205
2000-08-04 00:00:00+05:00 206
2000-08-05 00:00:00+05:00 206
2000-08-06 00:00:00+05:00 206
...
2000-11-28 00:00:00+05:00 206
2000-11-29 00:00:00+05:00 206
2000-11-30 00:00:00+05:00 205
2000-12-01 00:00:00+05:00 206
2000-12-02 00:00:00+05:00 5
Freq: 24H, Length: 123, dtype: int64
```
------
I thought of two possible solutions while fixing that:
1. Consider 'epoch' as a UNIX epoch: `pd.Timestamp(0, tz=index_tz)`
2. Consider that 'epoch' is just an helper to fix the origin: `pd.Timestamp("1970-01-01", tz=index_tz)`
To have similar results between timezones, I thought the solution 2 was a better choice. The solution 2 could also help us to set the behavior `origin=epoch` as the default one in a future version since the results would be quite similar with `origin=start_day` (that is not quite the case with a DST timezone, I can provide further details if needed on why this is the case).
(The solution 1 is correct in theory but is giving results that could be hard to explain to the end user.)
------
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry (follow-up PR, not necessary I think)
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MDExOlB1bGxSZXF1ZXN0NDI1Mzg5NTg2
| 34,475
|
BLD/PERF: bump cython to 0.29.19 for searchsorted
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| 2020-05-30T00:56:28Z
| 2020-06-02T18:31:03Z
| 2020-06-01T23:03:36Z
|
MEMBER
| null |
cython 0.29.19 fixed a bug that now allows us to use `cnp.PyArray_SearchSorted` to make `ndarray.searchsorted` into a C call. As a demonstration, this PR makes that change in `normalize_i8_timestamps` and shaves about 12% off of it for the scalar case (the non-scalar case is not noticeably affected)
```
In [4]: ts2 = pd.Timestamp.now("US/Pacific")
In [5]: %timeit ts2.normalize()
59.3 µs ± 197 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
51.8 µs ± 362 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
```
Not a particularly big deal on its own, _but_ this makes viable passing `int64_t*` instead of `int64_t[:]`, which in turn makes it so we can skip a wrapping/unwrapping step in the scalar code _and_ ideally share code between Timestamp/DatetimeArray, Period/PeriodArray, Timedelta/TimedeltaArray.
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REF: make normalize_i8_timestamps cpdef
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| 2020-05-30T01:15:05Z
| 2020-05-31T23:06:19Z
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|
MEMBER
| null |
there's a tiny perf bump, but it all comes from doing `own_tz = self.tzinfo` up-front instead of accessing `self.tz` multiple times within `Timestamp.normalize`. With just the cimport, this is actually slightly slower, which is something of a mystery.
The main upside is that we now only cimport from libconversion, which im hopeful will make some other circular-imports easier to simplify.the perf improvement
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CLN: Removed duplicated test data
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| 2020-05-30T01:17:43Z
| 2020-06-08T01:45:31Z
| 2020-06-08T01:45:23Z
|
CONTRIBUTOR
| null |
Remove duplicated test data.
"test1.csv" & "tips.csv" are present in both "tests/io/parser/data" & "tests/io/data/csv".
Move the "*.csv.bz2" & "*.csv.gz" files since the s3_resource fixture in tests/io/conftest requires these.
Follow up from: https://github.com/pandas-dev/pandas/issues/29439
Think we should move all the data in io/parser/data/* into io/data/ so it can be re-used. Will save for follow up.
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BUG: groupby apply inconsistent output
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| 2020-07-08T14:23:19Z
| 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'group_col': [0.0, np.nan, 0.0, 0.0], 'value_col': [2,2,2,2]})
df2 = pd.DataFrame({'group_col': [np.nan, 0.0, 0.0, 0.0], 'value_col': [2,2,2,2]})
```
groupby apply on `df1` gives multi-index output
```python
df1.groupby('group_col').value_col.apply(lambda x: x.value_counts().reindex(index=[1,2,3]))
group_col
0.0 1 NaN
2 3.0
3 NaN
Name: value_col, dtype: float64
```
groupby apply on `df2` gives a single level index output:
```python
df2.groupby('group_col').value_col.apply(lambda x: x.value_counts().reindex(index=[1,2,3]))
0 NaN
1 NaN
2 3.0
3 NaN
Name: value_col, dtype: float64
```
#### Problem description
groupby apply on `df1` and `df2` produce different outputs. They should produce the same output.
#### Expected Output
The output of both the above mentioned examples should be this:
```python3
group_col
0.0 1 NaN
2 3.0
3 NaN
Name: value_col, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 34,479
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API: any/all logical operation for datetime-like dtypes
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| 2020-05-30T06:58:51Z
| 2021-08-07T22:31:52Z
| null |
MEMBER
| null |
In my attempt to enable DataFrame reductions for ExtensionArrays (and at the same time cleaning up `DataFrame._reduce` a bit) at https://github.com/pandas-dev/pandas/pull/32867, I am running into the following inconsistency.
Consider a Timedelta array, and how it behaves for the `any` and `all` logical functions (on current master, bit similar for released pandas):
```python
>>> td = pd.timedelta_range(0, periods=3)
# Index
>>> td.any()
...
TypeError: cannot perform any with this index type: TimedeltaIndex
# Series
>>> pd.Series(td).any()
...
TypeError: cannot perform any with type timedelta64[ns]
# DataFrame
>>> pd.DataFrame({'a': td}).any()
a True
dtype: bool
```
(and exactly the same pattern for datetime64[ns])
For DataFrame, it works because there we call `pandas.core.nanops.nanany()`, and our internal version of nan-aware `any`/`all` work fine with timedelta64/datetime64 dtypes.
The reason it fails for Index and Series is because we simply up front say: this operation is not possible with this dtype (we never get to call `nanany()` in those cases). But in the DataFrame reduction, we simply try it on `df.values`, and it works.
This is clearly an inconsistency that we should solve. And especially for my PR https://github.com/pandas-dev/pandas/pull/32867 this gives problems, as I want to rely on the array (column-wise) ops for DataFrame reductions, but thus those give a differen result leading to failing tests.
----
The question is thus: **do we think that the `any()` and `all()` operations make sense for datetime-like data?**
(and if yes, we can simply enable them for the array/index/series case)
- For timedelta, I think you can say there is a concept of "zero" and "non-zero" (which is what any/all are testing in the end, for non-boolean, numeric data)
- But for datetimes this might make less sense? The fact that 1970-01-01 00:00:00 would be "False" is just an implementation detail of the numbering starting at that point in time.
As reference, this operation fails in numpy for both timedelta and datetime:
```python
>>> np.array([0, 1], dtype="datetime64[ns]").any()
...
TypeError: No loop matching the specified signature and casting was found for ufunc logical_or
>>> np.array([0, 1], dtype="timedelta64[ns]").any()
...
TypeError: No loop matching the specified signature and casting was found for ufunc logical_or
```
But for timedeltas, that might rather be an issue of "never got implemented" rather than "deliberately not implemented" ? (there was a similar conversation about "mean" for timedelta) cc @shoyer
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| 34,480
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DOC: `merge` doc for `indicator` parameter can be improved
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| 2020-05-30T09:43:21Z
| 2020-06-07T20:26:14Z
| 2020-06-07T20:26:14Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html#pandas.DataFrame.merge
#### Documentation problem
The documentation is correct but hard to read, currently:
```... If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string. Information column is Categorical-type and ...```
#### Suggested fix for documentation
I believe the following paragraph will be easier to read:
```
If True, adds a column to the output DataFrame called “_merge” with information on
the source of each row. The column can be given a different name by providing a
string argument. The column will have a Categorical type with the value of “left_only”
for observations whose merge key only appears in ‘left’ DataFrame, “right_only” for
observations whose merge key only appears in ‘right’ DataFrame, and “both” if
the observation’s merge key is found in both.
```
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DOC: start 1.0.5
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/33970#issuecomment-624029963
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QST: Integrate a function with pandas and scipy.integrate.quad
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[] | null | 2
| 2020-05-30T14:19:59Z
| 2021-03-26T05:59:05Z
| 2021-03-26T05:59:05Z
|
NONE
| null |
I'm trying to integrate a function with pandas and scipy in the most elegant way possible, but I'm getting the following error when executing:
TypeError: 'float' object is not subscriptable
This is a preview of the data set:
```
0 1 Live time: 11646
0 1.0 2.0 0.0 0.0 0.0
1 2.0 3.0 0.0 0.0 0.0
2 3.0 4.0 0.0 0.0 0.0
3 4.0 5.0 1.0 0.0 0.0
4 5.0 6.0 2.0 0.0 0.0
... ... ... ... ... ...
4094 4095.0 4096.0 4092.0 1.0 0.0
4095 4096.0 4097.0 4093.0 0.0 0.0
4096 4097.0 4098.0 4094.0 1.0 0.0
4097 4098.0 4099.0 4095.0 2.0 0.0
4098 4099.0 4100.0 4096.0 0.0 0.0
```
I know there are other ways to solve the problem, but I would like to keep the lambda function, because it makes things simpler.
Code:
```
import pandas as pd
import pylab as plt
import numpy as np
from scipy.integrate import quad
dados = pd.read_csv('data.csv', sep=',| |\t|\s+', engine='python')
dados = dados.apply(pd.to_numeric,errors='coerce').fillna(0).astype('float')
def func(x):
return x[3]/x[3].max()
dados['I'] = dados.apply(lambda x: quad(func, 140.0, 4000.0), axis=1)
```
CSV file:
[data.txt](https://github.com/pandas-dev/pandas/files/4705783/data.txt)
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REF: Move sampling logic into algorithms.py
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| 0
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| 2021-07-02T00:05:13Z
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MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/34069 is implementing a sampling procedure for groupby which then uses the same from core/generic.py, however it may be preferable to implement lower-level code in core/algorithms.py which then both can share.
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BUG: merge_asof should be treated as a left join
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MEMBER
| null |
- [x] closes #33463
- [x] tests added / passed
- [x] passes `black pandas`
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The fix broke one test (`test_merge_index_column_tz` in `test_merge_asof.py`) which expected the right index instead of the left index.
The default index selection does not work in case of asof and `left_index=True`. I had to catch this case here.
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DOC: updating the `indicator` wording in `merge` doc
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CONTRIBUTOR
| null |
- [ ] closes #34480
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDI1NTA2ODIx
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ENH: mul(Tick, float); simplify to_offset
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| 2020-06-01T03:22:12Z
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MEMBER
| null |
- [ ] closes #xxxx
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This will let us remove Resolution.get_stride_from_decimal
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TST, TYP: _use_dynamic_x
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|
MEMBER
| null |
This condition (which, from the current plotting test suite, is unreachable) was introduced in #9814, when `_use_dynamic_x` was written.
Is there any reason why `get_period_alias(freq)` would return `None` if `freq` is a valid time frequency? If so, I'll add a test - else, this PR removes unreachable code.
Added an annotation for `data` and the return type while I was here - is there a way to annotate `ax`?
UPDATE
------
As it turns out (thanks jbrockmendel and jorisvandenbossche), this line can be hit, so I've included a test which does
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BUG: Raise ValueError for non numerical join columns in merge_asof
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| 2020-05-30T19:57:50Z
| 2021-07-25T21:58:03Z
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|
MEMBER
| null |
- [x] closes #29130
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I checked for dtype object. Normally, I would try to check for allowed dtypes, but that would require a lot of dtype checks instead of only one. I hope object dtype is sufficient to exclude
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Added documentation for building using pyenv.
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CONTRIBUTOR
| null |
Added documentation for building using pyenv. Discussed this PR with @WillAyd on gitter on May 20.
Also fixed up another line he mentioned (python setup.py build_ext --inplace -j 4
instead of:
python setup.py build_ext --inplace -j 0) for conda/venv/pyenv.
Have rebuilt the docs and ensured they build properly.
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Added documentation for building using pyenv. Discussed this PR with @WillAyd on gitter on May 20.
Also fixed up another line he mentioned (python setup.py build_ext --inplace -j 4
instead of:
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CLN: de-duplicate bits lib timestamps
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MEMBER
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- [ ] closes #xxxx
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CLN: remove unused JsonReader.path_or_buf
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Test for pd.to_sql column error if data contains -np.inf
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ENH: Add read_logfmt()
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- [x] closes #34501
- [x] tests added / passed
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DOC: Clarify behaviour of pd.merge
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BUG: Fix value setting in case of merging via index on one side and column on other side.
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| 2021-04-26T22:00:03Z
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MEMBER
| null |
- [x] xref #15692
- [x] xref #17257
- [x] xref #28220
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Until now the `_maybe_add_join_keys` function changed the target column in the result, if the join was done over index on one side and the column on the other side. This resulted in taking values from the index and setting them for the target column, which explained the weird behavior in the issues referenced. Together with #34468 this will fix the issues. Both combined will transfer the merged index in these cases and the merged columns with the correct name. As of now this fix will only transfer the values, so the index will be wrong here.
We can not skip the method completly, because this would cause issues in case of the column is in both `DataFrames` we have to run through this steps to ensure that the target values are right. So I check if this is only contained in one side.
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BUG: Cannot index into DataFrame with Nullable Integer as index
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[] | null | 4
| 2020-05-31T14:40:34Z
| 2020-05-31T18:29:29Z
| null |
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.array([[1,2,3,4],[5,6,7,8],[1,2,np.nan,np.nan]]).T,columns=('a','b','c'),dtype="Int64")
df2 = df.set_index('c')
df2.loc[1]
```
#### Problem description
When there is more than one missing value in the dataframe index, indexing into the dataframe results in
```
TypeError: Cannot convert bool to numpy.ndarray
```
If only one NaN index exists, this works:
```python
df = pd.DataFrame(data=np.array([[1,2,3,4],[5,6,7,8],[1,2,3,np.nan]]).T,columns=('a','b','c'),dtype="Int64")
df2 = df.set_index('c')
df2.loc[1]
```
```
a 1
b 5
Name: 1, dtype: Int64
```
#### Expected Output
I Expect `df2.loc[1]` to return
```
a 1
b 5
Name: 1, dtype: Int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-53-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.1
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.1.2
numexpr : 2.6.4
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.4.2
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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| 34,498
|
DOC: improve the .equals() docstring
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| 1
| 2020-05-31T15:06:47Z
| 2020-08-07T19:16:40Z
| 2020-08-07T19:16:40Z
|
MEMBER
| null |
The current docstring for Series/DataFrame `equals` method can use some improvement (https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.equals.html):
- " The column headers do not need to have the same type" is not very clear, and also doesn't apply to Series.equals. Something like "The row/column index do not need to have the same type (as long as the values are still considered equal)" might be better
- The "Notes" section is mostly duplicating the long description at the top.
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REF/PERF: PeriodDtype decouple from DateOffset
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| 6
| 2020-05-31T15:49:01Z
| 2020-06-04T16:50:19Z
| 2020-06-04T05:51:16Z
|
MEMBER
| null |
ATM we define PeriodDtype in terms of DateOffsets, but this is a misnomer. In fact, virtually every Period/PeriodArray method has to start off by taking its `.freq` and finding the corresponding integer code. This makes the integer code itself into a dtype. We lose a little bit of ground on the constructor, then make it back up in subsequent calls.
```
In [2]: per = pd.Period("2016Q1")
In [3]: %timeit per.year
556 ns ± 13.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
94.5 ns ± 1.36 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
In [4]: %timeit pd.Period("2016Q1")
21.2 µs ± 176 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
25.8 µs ± 457 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
```
The constructor perf I think we can improve by eventually cutting the DateOffset out of the process altogether.
We'll also be able to de-duplicate a _bunch_ of other stuff: FreqGroup can be defined in terms of PeriodDypeCode, Resolution can be defined in terms of FreqGroup (xref #34462), we can avoid redundant definitions of the dtype codes in period.pyx, and a lot of the rest of libfrequencies becomes unnecessary.
In a follow-up I plan to mix the cython-space PeriodDtype into the core.dtypes PeriodDtype and we can get the same perf improvements in the PeriodArray methods.
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REG: Fix read_parquet from file-like objects
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| 14
| 2020-05-31T16:58:23Z
| 2020-06-12T22:33:50Z
| 2020-06-12T18:17:07Z
|
CONTRIBUTOR
| null |
- [x] xref #34467
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry - waiting on https://github.com/pandas-dev/pandas/pull/34481
Use arrow parquet.read_table opposed to ParquetDataset
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MDU6SXNzdWU2MjgwMDg3OTc=
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|
ENH: add logfmt support
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| 2020-05-31T17:40:06Z
| 2020-08-21T17:05:43Z
| 2020-05-31T21:39:56Z
|
CONTRIBUTOR
| null |
[logfmt](https://www.brandur.org/logfmt) is a structured log format similar to JSON lines, but much simpler. Each line has a format `column1=value1 column2=value2 ...`. Pandas already has support for JSON lines, which can be read with `pd.read_json(path, lines=True)`, but to read logfmt logs a conversion is needed.
I have been using [agrind](https://github.com/rcoh/angle-grinder) to convert logs to JSON lines, but it is an additional step that I would like to eliminate from my workflow. You can see an example at https://gitlab.com/nsnam/ns-3-dev/-/merge_requests/303
Besides, I would like to use logfmt as an output format for simple computational experiments (think C/Fortran without dependencies), because it is easier to maintain one line of code that outputs keys and values interleaved, in contrast to maintaining the code to output headers and code to output values separately. And logfmt becomes easier to read than CSV as the number of columns grows. Compare
```
node=1 throughput=63.0 delay=52.0 x=0.0 y=5.0
node=2 throughput=100.0 delay=35.0 x=5.0 y=5.0
```
to
```
node,throughput,delay,x,y
1,63.0,52.0,0.0,5.0
2,100.0,35.0,5.0,5.0
```
To read logfmt directly with pandas, I want a function `pd.read_logfmt()` that can be used in place of `pd.read_csv()` and `pd.read_json()` in described above cases.
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MDExOlB1bGxSZXF1ZXN0NDI1NjQ2Mzk2
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CLN: GH29547 format with f-strings
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CONTRIBUTOR
| null |
replace .format() for f-strings in the following:
1. pandas/tests/series/indexing/test_numeric.py
2. pandas/tests/series/indexing/test_take.py
3. pandas/tests/series/indexing/test_where.py
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Backport PR #34481 on branch 1.0.x (DOC: start 1.0.5)
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NONE
| null |
Backport PR #34481: DOC: start 1.0.5
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CLN: stronger typing in tzconversion
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NDI1NjU2MTM0
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CLN: unreachable branch in tzconversion
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BUG: DataFrame.apply(raw=True) applies function twice on first row/column
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| 9
| 2020-05-31T19:46:31Z
| 2020-09-24T15:58:07Z
| 2020-09-24T15:58:07Z
|
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
In [1]: import pandas as pd
...:
...: df = pd.DataFrame({'a': [1,2,3]})
...:
...: def mul2(x):
...: print('Hello')
...: return x*2
...:
...: print('Apply:')
...: df.apply(mul2)
...:
...: print('\nApply Raw:')
...: df.apply(mul2, raw=True)
Apply:
Hello
Apply Raw:
Hello
Hello
```
#### Problem description
Because of an implementation detail, the applied function is applied twice on the first row/column,
This results in side effects happening twice.
Other than the `apply_raw` code path, this issues should be fixed by https://github.com/pandas-dev/pandas/pull/34183.
#### Expected Output
```python
Apply:
Hello
Apply Raw:
Hello
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 07edc871f5f220c4e4835447cdcf61d64d5a93ab
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Thu Apr 30 18:25:59 PDT 2020; root:xnu-6153.121.1~7/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1706.g07edc871f.dirty
numpy : 1.18.3
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.17
pytest : 5.4.1
hypothesis : 5.11.0
sphinx : 3.0.3
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDI1NjkwODIz
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|
REF: use standard pattern in normalize_i8_timestamps
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| 2
| 2020-06-01T00:04:20Z
| 2020-06-09T15:00:58Z
| 2020-06-01T02:31:19Z
|
MEMBER
| null |
From here the plan is to move to passing `int64_t*` which will allow us to share code between the scalar/vector versions of this function
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DOC: Docstring updated for DataFrame.equals
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| 6
| 2020-06-01T04:44:14Z
| 2020-08-07T19:16:54Z
| 2020-08-07T19:16:40Z
|
CONTRIBUTOR
| null |
- [ ] closes #34498
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BUG: fix BooleanArray.astype('string') (GH34110)
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MEMBER
| null |
This should fix CI (bug caused by https://github.com/pandas-dev/pandas/pull/34110, due to another PR that was merged a few days ago adding this capability)
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Performance regression in TimedeltaIndexing.time_get_loc
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| 2
| 2020-06-01T11:51:48Z
| 2020-06-15T14:38:54Z
| 2020-06-15T14:38:54Z
|
MEMBER
| null |
Setup:
```python
index = pd.timedelta_range(start="1985", periods=1000, freq="D")
timedelta = index[500]
%timeit index.get_loc(timedelta)
```
```
# master
7.39 µs ± 78.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
# 1.0.3
2.82 µs ± 13.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
See https://pandas.pydata.org/speed/pandas/#timedelta.TimedeltaIndexing.time_get_loc?commits=9929fca8-62c7dd3e
Happened somewhere between May 11 and May 27 (the gap in the benchmarks)
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BUG: DateOffset pickle bug when months=12
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| 2
| 2020-06-01T12:17:34Z
| 2020-08-14T16:17:38Z
| 2020-08-14T16:17:38Z
|
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
```
import pickle
import pandas as pd
offset = pd.DateOffset(months=12)
pickle.dump(offset, open("/tmp/o.p", "wb"))
loaded = pickle.load(open("/tmp/o.p", "rb"))
print(loaded)
# <DateOffset: months=12, years=1>
```
#### Problem description
If you pickle a pandas DateOffset instance with months=12, and then unpickle it, you have a DateOffset(months=12, years=1) instance (effectively a 2 year date offset object.
Arguably one shouldn't create a 12 month date offset in preference to a 1 year offset, but this is still bad behaviour imho.
#### Expected Output
print(loaded)
# <DateOffset: months=12>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
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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MDExOlB1bGxSZXF1ZXN0NDI1OTkyODYz
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Update : In DataFrame Class, Columns should not contain duplicate val…
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NONE
| null |
Also updated the Docstring.(#12991)
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|
Release notes still missing for 1.0.4
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[] | null | 4
| 2020-06-01T14:04:55Z
| 2020-08-21T17:05:44Z
| 2020-06-01T14:07:14Z
|
NONE
|
too heated
|
What's up with the release notes still missing for 1.0.4? It has been four days since the release. This is unprofessional.
https://github.com/pandas-dev/pandas/releases/tag/v1.0.4 links to https://pandas.pydata.org/docs/whatsnew/v1.0.4.html but the whatsnew page has been missing. Shouldn't it be made available no later than two hours after a release? People look for it and can't find it. A release shouldn't be published if its notes are not ready to go.
@simonjayhawkins you seem responsible.
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MDExOlB1bGxSZXF1ZXN0NDI2MDI0Nzgz
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BUG: asymmetric error bars for series (GH9536)
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| 6
| 2020-06-01T14:46:29Z
| 2020-07-17T16:46:15Z
| 2020-07-17T16:26:47Z
|
CONTRIBUTOR
| null |
closes #9536
This fix enables asymmetric errors bars for pd.Series plots. In the current implementation the bars are symmetric, even if two sets of errors are provided.
Took inspirations from #12046
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CLN: remove Resolution.get_stride_from_decimal
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| 2020-06-01T15:11:32Z
| 2020-06-01T22:57:15Z
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|
MEMBER
| null |
as it is no longer used; update its tests
rename Resolution.get_reso -> Resolution.from_attrname
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| 3
| 2020-06-01T15:14:54Z
| 2020-06-02T07:22:02Z
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/34503#issuecomment-636651219
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