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| 5
| 2020-09-25T01:51:05Z
| 2020-09-26T22:50:28Z
| 2020-09-26T22:50:28Z
|
MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/36591 added a pre-commit hook for flagging the use of single instead of double backticks for code blocks in restructured text. This is a pretty common mistake in the docs so a lot of files have to be skipped, but we should instead remove these exclusions and fix the relevant files.
Note that this might mean _removing_ rather than adding backticks if the text is not actual code. Also certain linked references require single backticks, see https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html#xref-syntax.
In addition to most release notes these files all have errors:
- [x] doc/source/user_guide/10min.rst
- [x] doc/source/user_guide/timeseries.rst
- [x] doc/source/user_guide/visualization.rst
- [x] doc/source/user_guide/missing_data.rst
- [x] doc/source/user_guide/options.rst
- [x] doc/source/user_guide/reshaping.rst
- [x] doc/source/user_guide/scale.rst
- [x] doc/source/user_guide/merging.rst
- [x] doc/source/user_guide/cookbook.rst
- [x] doc/source/user_guide/enhancingperf.rst
- [x] doc/source/user_guide/groupby.rst
- [x] doc/source/user_guide/io.rst
- [x] doc/source/getting_started/overview.rst
- [x] doc/source/reference/panel.rst
- [x] doc/source/reference/plotting.rst
- [x] doc/source/user_guide/basics.rst
- [ ] doc/source/user_guide/categorical.rst
- [ ] doc/source/development/contributing.rst
- [ ] doc/source/development/contributing_docstring.rst
- [ ] doc/source/development/extending.rst
- [ ] doc/source/ecosystem.rst
- [ ] doc/source/getting_started/comparison/comparison_with_sql.rst
- [ ] doc/source/getting_started/install.rst
- [ ] doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst
- [ ] doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst
Easiest worfklow for fixing is probably to install the pre-commit hooks, delete exclusions from https://github.com/pandas-dev/pandas/blob/master/.pre-commit-config.yaml, run `pre-commit run rst-backticks --all-files`, fix the errors and then commit. Any PRs can link back to this issue we'll cross things off when merged.
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CLN: Break up aggregate.transform
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| 2020-09-25T01:57:24Z
| 2020-10-11T13:22:10Z
| 2020-09-26T23:21:42Z
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MEMBER
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- [ ] closes #xxxx
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Precursor to #36478. Just breaks up transform without any code changes.
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CLN: share setitem/getitem validators
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| 0
| 2020-09-25T01:59:46Z
| 2020-09-26T01:21:03Z
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MEMBER
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- [ ] closes #xxxx
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BUG: Grouper should have a dropna attribute
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| 0
| 2020-09-25T02:03:25Z
| 2020-09-26T01:17:59Z
| 2020-09-26T01:17:59Z
|
MEMBER
| 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.
---
xref #35612
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
...: df = pd.DataFrame({"A": [0, 0, 1, None], "B": [10, 2, 10, None]})
...: gb = df.groupby('A', dropna=False)
In [2]: gb.dropna
Out[2]: False
In [3]: gb.grouper.dropna
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-4d8081f9c78e> in <module>
----> 1 gb.grouper.dropna
AttributeError: 'BaseGrouper' object has no attribute 'dropna'
```
#### Problem description
Now that we support a `dropna` argument to `DataFrameGroupBy` and `SeriesGroupBy` we need to be able to use it in `Grouper` (#35751)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f34a56b4ddcf51a8a03a9d20f07ab039f64ff2e6
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+484.gf34a56b4d
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 45.2.0.post20200210
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.19.0
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.4.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : 0.6.2
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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MDU6SXNzdWU3MDg1ODQ5OTA=
| 36,621
|
BUG: isin - dtype conversions (Timestamp vs ints)
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| 0
| 2020-09-25T02:07:51Z
| 2020-11-23T11:22:20Z
| 2020-11-22T02:12:25Z
|
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.
---
related: #34125 : could be closed in favor of that if it is too much of a duplicate.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series(pd.date_range("jan-01-2013", "jan-05-2013"))
s.isin([1356998400000000000])
>>>
0 True
1 False
2 False
3 False
4 False
dtype: bool
```
#### Problem description
Problem arises here because `algos._ensure_dtype(s)` returns an `np.int64` array (that matches the data) and `dtype=<M8[ns]`.
#### Expected Output
```py
0 False
1 False
2 False
3 False
4 False
dtype: bool
```
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|
Comment on stale PRs
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| 5
| 2020-09-25T02:11:21Z
| 2020-09-30T17:44:41Z
| 2020-09-30T17:17:06Z
|
MEMBER
| null |
Updating the stale PR action to comment in the PR. Should help to automate the process of pinging contributors who go quiet. Also updating to run once daily instead of every six hours.
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BUG: Categorical setitem, comparison with tuple category
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| 2
| 2020-09-25T02:39:06Z
| 2020-10-02T22:59:40Z
| 2020-10-02T22:43:24Z
|
MEMBER
| null |
- [x] closes #20439
- [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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CLN: simplify interpolate_2d and callers
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CLN: de-duplicate numeric type check in _libs/testing
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- [ ] closes #xxxx
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In `testing.pyx` we keep a list of numeric dtypes and use that for type checking. But we have functions that do this in `tslibs/util.pxd` so it's better to use those instead
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CI: add py38 slow build #35160
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MEMBER
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- [x] closes #35160
This issue was created before we dropped py36 support. So I think we should add equivalent CI build for py38.
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DOC: Replaced single backticks with double backticks in several rst files …
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CONTRIBUTOR
| null |
…(#36617)
I did not fix all of the files, but it's a start :).
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TST: check whether printing an IntervalIndex works (GH32553)
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CONTRIBUTOR
| null |
- [x] closes #32553
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (not applicable)
I just added a test that checks whether print succeeds, would we also want to check the exact output of the `str` function on an `IntervalIndex`?
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CLN: Improve setting of resample deprecation warnings' stacklevel
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| 2020-09-25T08:37:02Z
| 2020-12-18T11:11:51Z
| 2020-12-18T11:11:51Z
|
CONTRIBUTOR
| null |
- [N/A] closes #xxxx (see https://github.com/pandas-dev/pandas/pull/36369#issuecomment-698763910)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [N/A] whatsnew entry
The essence of the problem is that the tests for deprecated resample kwargs require that the warning is raised exactly where the user called the resample functionality—i.e. with the appropriate `stacklevel`— (I guess so that the warning message is more user-friendly?), but there are multiple entry points that use these deprecated parameters, so this can't be easily solved by just adjusting the stacklevel by a constant amount.
The old solution was to centralize the management of these warnings in the greatest-common-denominator call site (which was `Grouper.__new__`) and "guess" the correct value of `stacklevel` by relying on the current call graph (and hoping it never changes). This of course is poorly maintainable. (For a more detailed explanation of what is wrong with this, see https://github.com/pandas-dev/pandas/pull/36369#issuecomment-694808777.)
The essence of my proposed solution (this PR) is to add a call to the deprecated-parameter-checking code to each of these entry points—this allows instead to use a constant `stacklevel` value (2) in each of these. Now since some of these public call sites appear in the call graph of others, we have to ensure this code is only called once within any call path starting at any of these entry points. This is what the `call_once` context manager takes care of.
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CLN:replace single with double backticks
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| 2020-09-26T22:53:15Z
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CONTRIBUTOR
| null |
#36617
doc/source/development/contributing.rst
doc/source/development/contributing_docstring.rst
doc/source/development/extending.rst
doc/source/ecosystem.rst
doc/source/getting_started/install.rst
doc/source/getting_started/comparison/comparison_with_sql.rst
doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst
doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst
doc/source/user_guide/categorical.rst
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Backport PR #36610 on branch 1.1.x (REGR: DataFrame.apply() with raw option and func returning string)
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NONE
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Backport PR #36610: REGR: DataFrame.apply() with raw option and func returning string
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Replace single with double backticks in RST file #36617
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
closes https://github.com/pandas-dev/pandas/issues/36617
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Backport PR #36613 on branch 1.1.x (BUG: Fix unordered cut with Series labels)
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NONE
| null |
Backport PR #36613: BUG: Fix unordered cut with Series labels
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ENH: infer freq in timedelta_range
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| 2020-09-25T11:25:47Z
| 2021-08-13T03:34:20Z
| null |
MEMBER
| null |
xref #36582, #35897, #36595, #32377, #31195
there may be cases where a frequency should not be inferred by timedelta_range due to rounding issues
e.g. `pd.timedelta_range('0s','1s', periods=31)`
The docs https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.timedelta_range.html begin 'Return a fixed frequency TimedeltaIndex...' so having None (no inferred frequency) maybe not desirable.
cc @jbrockmendel @jreback @phofl
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TST: GH 32431 check print label indexing on nan
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| 2
| 2020-09-25T13:32:08Z
| 2020-11-18T13:53:20Z
| 2020-11-18T13:53:17Z
|
CONTRIBUTOR
| null |
- [x] closes #32431
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry --> not applicable
This is my first contribution to pandas. I added a simple test that was requested in one of the open issues.
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PERF: large perf regression in DataFrame repr
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| 1
| 2020-09-25T13:48:10Z
| 2020-09-26T07:11:26Z
| 2020-09-26T07:11:26Z
|
MEMBER
| null |
On master:
```
In [1]: df = pd.DataFrame(np.random.randn(1_000_000, 10))
In [3]: %timeit repr(df)
2.5 s ± 340 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
while on released version this takes around 20ms
cc @ivanovmg I suppose this is from https://github.com/pandas-dev/pandas/pull/36434
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CI: pd.core.reshape.pivot.crosstab doctest is failing on unrelated PRs
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| 2020-09-25T15:47:20Z
| 2020-10-05T04:25:41Z
| 2020-10-05T04:25:41Z
|
MEMBER
| null |
This doctest is failing on a few unrelated PR's:
```
=================================== FAILURES ===================================
_________________ [doctest] pandas.core.reshape.pivot.crosstab _________________
549 Examples
550 --------
551 >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
552 ... "bar", "bar", "foo", "foo", "foo"], dtype=object)
553 >>> b = np.array(["one", "one", "one", "two", "one", "one",
554 ... "one", "two", "two", "two", "one"], dtype=object)
555 >>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
556 ... "shiny", "dull", "shiny", "shiny", "shiny"],
557 ... dtype=object)
558 >>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
Differences (unified diff with -expected +actual):
@@ -1,5 +1,6 @@
-b one two
+Index(['__dummy__'], dtype='object')
+b one two
c dull shiny dull shiny
-a
+a
bar 1 2 1 0
foo 2 2 1 2
```
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PERF: fix long string representation
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| 2020-09-25T15:55:36Z
| 2020-10-04T13:21:53Z
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MEMBER
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- [x] closes #36636
- [x] tests added / passed
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- Fix long string representation for large dataframes.
- Eliminate for loop, which was filtering out the proper rows/columns to be displayed.
- Revert to the original implementation with concat-ing head+tail and left+right parts.
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MDExOlB1bGxSZXF1ZXN0NDkzMjI2Nzcx
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CFG use black profile in setup.cfg for isort
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| 2020-09-25T17:02:58Z
| 2020-09-28T16:57:33Z
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MEMBER
| null |
Since isort v5 some of the existing configs are unnecessary
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fix test test warnings
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CLN: cleanup DataFrameInfo
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| 2020-09-25T17:51:47Z
| 2020-09-30T14:25:06Z
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MEMBER
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- [ ] closes #xxxx
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Slightly clean-up DataFrameInfo.
Make construction of header and separator cleaner.
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CI: Deduplicate linting checks
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| 2020-09-25T18:27:27Z
| 2020-10-20T23:08:47Z
| 2020-10-20T23:08:47Z
|
MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/36471 added some linting checks (e.g., black, flake8) to a pre-commit workflow which are duplicated in https://github.com/pandas-dev/pandas/blob/master/ci/code_checks.sh. Once we feel good that the pre-commit checks are right we should go ahead and delete them from `code_checks.sh`.
cc @MarcoGorelli
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MDExOlB1bGxSZXF1ZXN0NDkzMzA1NDc5
| 36,643
|
TYP: selection and groups type-hinting in groupby
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| 2020-09-25T18:42:21Z
| 2020-10-11T13:22:02Z
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDkzMzQzMDI1
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CLN: Fix some spelling
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MDExOlB1bGxSZXF1ZXN0NDkzMzk1NzY0
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REF: privatize in core.missing, remove unused kwarg
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| 2020-09-25T22:09:50Z
| 2020-10-01T01:30:10Z
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|
MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Sits on top of #36624
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BUG: ndarray[td64] // TimedeltaArray
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ENH: match stdlib behavior for datetimelike comparisons
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- [x] whatsnew entry
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TST: GH23452 test reorder_categories() on categorical index
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CONTRIBUTOR
| null |
- [x] closes #23452
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Couldn't see entries for tests in whatsnew files so I assume it's not needed if only a test case is added This is my first Pandas contribution, looking forward to your feedback.
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DEPR: min_periods=None behavior for Rolling.count
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| 2020-09-26T00:16:08Z
| 2020-10-02T21:01:47Z
| 2020-10-02T20:35:12Z
|
MEMBER
| null |
- [x] closes #31302
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Additionally refactors `count` to take the `_apply` path to make this op consistent with the others.
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MDExOlB1bGxSZXF1ZXN0NDkzNDU2ODY2
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DOC: Fix remaining typos in docstrings 'handler' --> 'handle'
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CONTRIBUTOR
| null |
Follow-on to https://github.com/pandas-dev/pandas/pull/36427#issuecomment-694249512
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MDU6SXNzdWU3MDkzODg5NjE=
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|
QST: Error importing pandas
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| 2020-09-26T01:16:31Z
| 2020-10-09T09:02:52Z
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|
NONE
| null |
hello!
when I import any module,this is a misstake show"No module named'pandas._libs.join'
why?and how to resoled this?
thanks!
|
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Backport PR #36557 on branch 1.1.x ([BUG]: Fix bug with pre epoch normalization)
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| 2020-09-26T08:24:06Z
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NONE
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Backport PR #36557: [BUG]: Fix bug with pre epoch normalization
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CLN: de-duplicate IntervalArray validators
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CLN: dont special-case DatetimeArray indexing
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cc @jorisvandenbossche
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BUG: DataFrame.pivot drops column level names when both rows and columns are multiindexed
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| 2020-09-26T03:35:11Z
| 2020-10-31T17:52:00Z
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MEMBER
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Fixed by checking for multi-indexed columns in `DataFrameGroupBy._wrap_aggregated_output`
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ASV: used integer ndarray as indexer for Series.take indexing benchmark
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| 2
| 2020-09-26T04:19:56Z
| 2020-10-03T01:42:33Z
| 2020-10-02T23:08:40Z
|
CONTRIBUTOR
| null |
- [x] closes #18000
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Series.take was using a list of booleans as indexers. Now, It is replaced with a random list of integers.
Here are the benchmark results with `asv run --bench indexing.Take`
### Benchmarks Results:
#### Before:
```
[100.00%] ··· indexing.Take.time_take ok
[100.00%] ··· ========== =============
index
---------- -------------
int 4.13±0.08ms
datetime 4.13±0.01ms
========== =============
```
#### After:
```
[100.00%] ··· indexing.Take.time_take ok
[100.00%] ··· ========== =============
index
---------- -------------
int 6.36±0.08ms
datetime 6.29±0.03ms
========== =============
```
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CLN: More pytest idioms in pandas/tests/window
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| 1
| 2020-09-26T05:14:53Z
| 2020-09-30T03:51:00Z
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|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Removed some unused variables, added some fixtures for DataFrames used in `test_pairwise.py`, and utilize more `pytest.mark.parameterize`
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Backport PR #36444 on branch 1.1.x (BUG: inconsistent replace)
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| 2
| 2020-09-26T08:26:56Z
| 2020-09-26T10:21:57Z
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|
NONE
| null |
Backport PR #36444: BUG: inconsistent replace
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Backport PR #36595 on branch 1.1.x (Partial Revert "ENH: infer freq in timedelta_range (#32377)")
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| 1
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NONE
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Backport PR #36595: Partial Revert "ENH: infer freq in timedelta_range (#32377)"
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| 2020-09-26T10:30:27Z
| 2021-03-21T00:32:59Z
| 2021-03-21T00:32:59Z
|
CONTRIBUTOR
| null |
Make deprecation DateOffset args don't end in 's'.
Mentioned [here](https://github.com/pandas-dev/pandas/pull/36516#discussion_r492885786)
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Backport PR #36560 on branch 1.1.x ([BUG]: Fix regression in read_table with delim_whitespace=True)
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NONE
| null |
Backport PR #36560: [BUG]: Fix regression in read_table with delim_whitespace=True
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test_pivot_empty on windows py_3.8_32 and linux py_3.8_32 on MacPython.pandas-wheels nightly failing
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| 2
| 2020-09-26T11:09:26Z
| 2020-10-25T20:07:35Z
| 2020-10-25T20:07:17Z
|
MEMBER
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=43395&view=logs&j=bb1c2637-64c6-57bd-9ea6-93823b2df951
windows py_3.8_32
```
=========================== short test summary info ===========================
FAILED test_venv/lib/site-packages/pandas/tests/frame/test_reshape.py::TestDataFrameReshape::test_pivot_empty
= 1 failed, 66757 passed, 7472 skipped, 1130 xfailed, 14 warnings in 829.09s (0:13:49) =
```
linux py_3.8_32
```
=========================== short test summary info ============================
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/frame/test_reshape.py::TestDataFrameReshape::test_pivot_empty
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/parser/test_c_parser_only.py::test_float_precision_options[c_high]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/parser/test_c_parser_only.py::test_float_precision_options[c_low]
= 3 failed, 66801 passed, 7441 skipped, 1129 xfailed, 14 warnings in 428.19s (0:07:08) =
```
test_c_parser_only is a seperate issue xref #36429
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CLN: lint fixup on 1.1.x
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MEMBER
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xref https://github.com/pandas-dev/pandas/pull/36658#issuecomment-699475451
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DOC: minor fix for 1.1.3 release notes
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MDU6SXNzdWU3MDk1MzI1Nzc=
| 36,665
|
BUG: Join changes dtypes
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| 2020-09-26T12:53:50Z
| 2020-09-26T16:33:43Z
| 2020-09-26T16:33:43Z
|
NONE
| null |
Hello,
#### Problem description
I have two pandas dataframes that I want to join using pandas (1.1.12).
However, when I join them, "alerts", in the table B gets its type changed from int64 to float64 ("alerts_cards"):
```python
(Pdb) A.dtypes
threshold_step int64
precision float64
alerts int64
alerted_money float64
dtype: object
(Pdb) B.dtypes
threshold_step int64
precision float64
alerts int64
dtype: object
(Pdb) A.join(B, on="threshold_step", rsuffix="_cards", sort=True).dtypes
threshold_step int64
precision float64
alerts int64
alerted_money float64
threshold_step_cards float64
precision_cards float64
alerts_cards float64
dtype: object
```
I usually then remove the join key ("threshold_step_cards"), but now I'm noticing it became a float as well.
The join key column has the same entries in both tables (all integers in range 0 to 100) and there are no NaN's in my dataframes.
#### Expected Output
The join should preserve the datatypes of the original columns. I spent several hours looking for a bug in my code until with the debugger I found it came from this pandas join.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_PT.UTF-8
pandas : 1.1.2
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.2.0.post20200511
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
Thank you
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DEPR deprecate pd.to_timedelta('1 M') and pd.to_timedelta('1 Y')
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| 2020-10-31T18:52:07Z
| 2020-10-31T18:52:07Z
|
MEMBER
| null |
We have that `pd.to_timedelta(1, unit='M')` and `pd.to_timedelta(1, unit='Y')` are deprecated, and so so should these (xref https://github.com/pandas-dev/pandas/pull/34979#issuecomment-699494617 ).
Once deprecated, the note added in #34979 should be removed
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| 2020-09-26T13:33:09Z
| 2020-09-26T14:52:01Z
| 2020-09-26T14:51:33Z
|
MEMBER
| null |
- [ ] closes #36495
alternative to #34407 that only restores `self._consolidate_inplace()` to fix reported regression in #36495
This WILL fail see https://github.com/pandas-dev/pandas/pull/34407#issuecomment-699488350 and https://github.com/pandas-dev/pandas/pull/34407#issuecomment-699495512
if all green apart from those two failures can then maybe revert #35839 and #35854
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| 8
| 2020-09-26T14:15:29Z
| 2020-09-28T10:21:15Z
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|
MEMBER
| null |
- [ ] xref #36495
yet another alternative to fix directly on 1.1.x pending further discussion in #34407 on how to resolve the issues on master.
cc @jorisvandenbossche @jbrockmendel
This won't close issue until fix on master. could copy test to master with xfail (and would need to sync release notes if we merge this direct to 1.1.x)
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NONE
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Backport PR #36664: DOC: minor fix for 1.1.3 release notes
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[MRG] TST: Added test for groupby apply datetimeindex fix
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| 2
| 2020-09-26T16:31:18Z
| 2020-10-02T22:59:11Z
| 2020-10-02T22:59:02Z
|
CONTRIBUTOR
| null |
- [x] closes #26182
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
worked on by @ezebunandu and @amy12xx
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| 709,578,798
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MDExOlB1bGxSZXF1ZXN0NDkzNjA1NzA4
| 36,673
|
Update pre-commit-config.yaml
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| 2020-09-26T17:36:22Z
| 2020-09-26T22:57:02Z
| 2020-09-26T22:56:59Z
|
MEMBER
| null |
Got this error from pre-commit when attempting to manually backport a PR. I guessed it may be because the pre-commit-config.yaml files between this branch and master were out of sync? That seemed to fix things for me, and regardless I figure it shouldn't be wrong to align these files (this PR is a straight checkout from upstream/master).
```
(pandas-dev) ➜ pandas git:(1.1.x) ✗ git commit -am 'Backport PR #36670: DOC: Fix release note typo'
[INFO] Initializing environment for https://github.com/pre-commit/mirrors-mypy.
[INFO] Installing environment for https://github.com/pre-commit/mirrors-mypy.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
An unexpected error has occurred: CalledProcessError: command: ('/Users/danielsaxton/opt/miniconda3/envs/pandas-dev/bin/python', '-mvirtualenv', '/Users/danielsaxton/.cache/pre-commit/repop3iybga6/py_env-python3.8', '-p', '/Users/danielsaxton/opt/miniconda3/envs/pandas-dev/bin/python')
return code: 1
expected return code: 0
stdout:
ModuleNotFoundError: No module named 'virtualenv.seed.via_app_data'
```
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CLN: cleanups in DataFrame._reduce
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| 2020-09-26T18:18:46Z
| 2020-10-02T23:00:37Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Broken off from non-CLN work in the vicinity.
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REGR: Series.loc with a MultiIndex containing Timestamp raises InvalidIndexError
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| 5
| 2020-09-26T19:59:34Z
| 2020-10-03T09:51:25Z
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|
MEMBER
| null |
ci testing fix in https://github.com/pandas-dev/pandas/issues/35858#issuecomment-678683559
- [ ] closes #35858
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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CLN: moments/test_moments_rolling.py for apply
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| 2020-09-26T20:12:35Z
| 2020-09-26T23:39:40Z
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- [x] tests added / passed
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Overall easier to grok these tests.
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CLN/TYP: aggregation methods in core.base
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| 2020-09-26T21:00:28Z
| 2020-10-11T13:22:02Z
| 2020-10-02T23:02:02Z
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] whatsnew entry
This is in order to move _aggregate and _aggregate_multiple_funcs into core.aggregation - I think that's desirable. Type-hinting is slightly more strong there (checks untyped defs), and would involve renaming `self` to `obj`. Thus the renaming of `obj` to `selected_obj` here.
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ENH: JSON output from Styler as well as DataFrame
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| 2021-11-11T12:08:08Z
| null |
CONTRIBUTOR
| null |
Currently there are ways of performing a ``to_json()`` method with the desired output formatting but the procedure is a bit cumbersome:
```
df = pd.DataFrame(data=[[1.9999, 1.9999, 1.9999, 1.9999, 1.99]], columns=['A', 'B', 'C', 'D', 'E'])
df['A'] = df['A'].apply(lambda x: float(f'{x:,.00f}'))
df['B'] = df['B'].apply(lambda x: int(f'{x:,.00f}'))
df['C'] = df['C'].apply(lambda x: f'{x:,.01f}')
print(df.to_json(double_precision=3))
```
> {"A":{"0":2.0},
"B":{"0":2},
"C":{"0":"2.0"},
"D":{"0":2.0},
"E":{"0":1.99}}
The ``Styler`` has its own formatter which can make things a little more aesthetic, and has the advantage of not affecting the underlying data:
```
df = pd.DataFrame(data=[[1.9999, 1.9999, 1.9999, 1.9999, 1.99]], columns=['A', 'B', 'C', 'D', 'E'])
print(Styler(df, uuid="", precision=3).format({
'A': lambda x: float(f'{x:,.00f}'),
'B': lambda x: int(f'{x:,.00f}'),
'C': '{:,.01f}'.format
}).render())
```
>
<tr>
<th id="T_7e7fd_level0_row0" class="row_heading level0 row0" >0</th>
<td id="T_7e7fd_row0_col0" class="data row0 col0" >2.0</td>
<td id="T_7e7fd_row0_col1" class="data row0 col1" >2</td>
<td id="T_7e7fd_row0_col2" class="data row0 col2" >2.0</td>
<td id="T_7e7fd_row0_col3" class="data row0 col3" >2.000</td>
<td id="T_7e7fd_row0_col4" class="data row0 col4" >1.990</td>
</tr>
But Styler does not have a ``to_json()`` method.
#### Describe the solution you'd like
I would like to add a simple extension to the Styler to create JSON from the formatted Styler Object, in a similar way as it constructs HTML tables:
```
class Styler:
...
def to_json(self, **kwargs):
"""
Applies the ``:meth:DataFrame.to_json`` method to the ``Styler`` object
including existing formats.
Parameters
----------
**kwargs
Any additional keyword arguments are passed to ``DataFrame.to_json()``.
Useful to control the output structure of the JSON return.
Returns
-------
None or str
Examples
--------
>>> df = pd.DataFrame(data=[[1.9999, 1.9999, 1.9999, 1.9999, 1.99]],
columns=['A', 'B', 'C', 'D', 'E'])
>>> df.style.format({
... 'A': lambda x: float(f'{x:,.00f}'),
... 'B': lambda x: int(f'{x:,.00f}'),
... 'C': '{:,.01f}'.format
... }).to_json()
{"A":{"0":2.0},"B":{"0":2.0},"C":{"0":"2.0"},"D":{"0":"1.999900"}}
"""
df = self.data.copy()
for r in range(len(df.index)):
for c in range(len(df.columns)):
formatter = self._display_funcs[(r, c)]
df.iloc[r, c] = formatter(self.data.iloc[r, c])
return df.to_json(**kwargs)
```
#### API breaking implications
None - new feature
Might be useful to make some minor adjustments to Styler factory default formatter, but this is optional and can happen later.
#### Describe alternatives you've considered
- Building a formatter for a DataFrame without using styler and applying `to_json` to that.
- Adding a `formatter` argument to `DataFrame.to_json` which performs the necessary adjustments before
Since Styler is inherently a 'display' context component and specifically designed for the web I thought it more suitable to add `to_json` to the `Styler` object.
Opinions welcome.. I will probably submit a PR if consensus approval..
|
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MDExOlB1bGxSZXF1ZXN0NDkzNzEwNjc0
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CI: debug file leak
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MEMBER
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Debug file leaks in cov build. Unrelated CI builds are commented out to save resource.
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TST: insert 'match' to bare pytest raises in pandas/tests/tseries/off…
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CONTRIBUTOR
| null |
…sets/test_ticks.py
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
ref: https://github.com/pandas-dev/pandas/issues/30999
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bump pytables to 3.5.1 #24839
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|
MEMBER
| null |
- [x] closes #24839
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
PyTables 3.5.1 was released on March 14, 2019. And it offers better support for np16.
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CLN remove unnecessary noqas from pandas/core
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MEMBER
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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DEPR: Deprecate params levels & codes in MultiIndex.copy
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| 3
| 2020-09-27T18:18:08Z
| 2020-10-01T05:09:05Z
| 2020-10-01T01:22:05Z
|
CONTRIBUTOR
| null |
Deprecates params `levels` & `codes` in `MultiIndex.copy`.
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MDExOlB1bGxSZXF1ZXN0NDkzNzYwMTcz
| 36,686
|
TST: Add pytest-instafail to environment.yml
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| 6
| 2020-09-27T18:42:20Z
| 2020-09-29T13:34:44Z
| 2020-09-29T13:02:13Z
|
MEMBER
| null |
Pretty useful pytest plugin for getting immediate feedback on test failures instead of waiting for an entire test suite to run:
```
(pandas-dev) ➜ pandas git:(add-pytest-instafail) ✗ pytest pandas/tests/groupby --instafail
============================================================================================ test session starts ============================================================================================
platform darwin -- Python 3.8.5, pytest-6.0.2, py-1.9.0, pluggy-0.13.1
rootdir: /Users/danielsaxton/pandas, configfile: setup.cfg
plugins: hypothesis-5.28.0, forked-1.2.0, asyncio-0.12.0, xdist-2.1.0, cov-2.10.1, instafail-0.4.1
collected 4533 items
pandas/tests/groupby/test_allowlist.py .............................................................................................................................................................. [ 3%]
........................................................... [ 4%]
pandas/tests/groupby/test_apply.py ............x.x.x...................................................x..........................F
________________________________________________________________________________________________ test_fails _________________________________________________________________________________________________
def test_fails():
> assert False
E assert False
pandas/tests/groupby/test_apply.py:1101: AssertionError
pandas/tests/groupby/test_apply_mutate.py ... [ 6%]
pandas/tests/groupby/test_bin_groupby.py .......... [ 7%]
pandas/tests/groupby/test_categorical.py .....................................................................................xxx..............................sss................................x.. [ 10%]
...
```
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MDExOlB1bGxSZXF1ZXN0NDkzNzY0MjYw
| 36,687
|
Fix typo in timeseries.rst
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ENH: Change Pandas User-Agent and add possibility to set custom http_headers to pd.read_* functions
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| 14
| 2020-09-27T20:38:20Z
| 2020-12-15T13:34:23Z
| 2020-12-15T00:35:00Z
|
NONE
| null |
Currently Pandas makes HTTP requests using "Python-urllib/3.8" as a User Agent.
This prevents from downloading some resources and static files from various places.
What if, Pandas would make requests using "Pandas/1.1.0" headers instead?
There should be possibility to add custom headers too (`auth`, `csrf tokens`, `api versions` and so on).
Use Case:
I am writing a book on Pandas:
- https://python.astrotech.io/numerical-analysis/index.html#pandas
I published data in CSV and JSON to use in code listings:
- https://python.astrotech.io/_static/iris.json
- https://python.astrotech.io/_static/iris.csv
You can access those resources via browser, `curl`, or even `requests`, but not using Pandas.
The only change you'd need to do is to set User-Agent.
This is due to the `readthedocs.io` blocking "Python-urllib/3.8" User Agent for whatever reason.
The same problem affects many other places where you can get data (not only `readthedocs.io`).
Currently I get those resources with `requests` and then put `response.text` to one of:
- `pd.read_csv`
- `pd.read_json`
- `pd.read_html`
Unfortunately this makes even simplest code listings... quite complex (due to the explanation of `requests` library and why I do this like that).
Pandas uses `urllib.request.urlopen` which does not allow to set `http_headers`
https://github.com/pandas-dev/pandas/blob/master/pandas/io/common.py#L146
Although `urllib.request.urlopen` can take `urllib.request.Request` as an argument.
And `urllib.request.Request` object has possibility to set custom `http_headers`
https://docs.python.org/3/library/urllib.request.html#urllib.request.Request
Possibility to add custom `http_headers` should be in `pd.read_csv`, `pd.read_json` and `pd.read_html` functions.
From what I see, the `read_*` call stack is three to four function deep.
There are only 6 references in 4 files to `urlopen(*args, **kwargs)` function.
So the change shouldn't be quite hard to implement.
`http_headers` parameter can be `Optional[List]` which will be fully backward compatible and would not require any changes to others code.
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DOC: Start v1.1.4 release notes
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| 2020-10-05T20:16:33Z
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MEMBER
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I think this requires the v1.1.3 tag in order to pass
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BUG: Don't ignore na_rep in DataFrame.to_html
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- [x] closes #13828
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BUG: DatetimeIndex.shift(1) with empty index
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| 1
| 2020-09-27T22:13:20Z
| 2020-09-30T02:42:51Z
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|
MEMBER
| null |
- [x] closes #14811
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DOC: Different behaviour with respect to `Series.all` docs when `NA` values are present
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| 5
| 2020-09-27T22:17:30Z
| 2021-11-09T12:22:45Z
| 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
In[29]: s = pd.Series([None, None, None], dtype=pd.BooleanDtype())
In[30]: s
Out[30]:
0 <NA>
1 <NA>
2 <NA>
dtype: boolean
In[31]: s.all(skipna=True)
Out[31]: True
In[32]: s.all(skipna=False)
Out[32]: <NA>
```
#### Problem description
The [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.all.html) says:
```
If skipna is False, then NA are treated as True, because these are not equal to zero.
```
But looks like a `pd.NA` value is being returned instead.
#### Expected Output
The expected behavior is the result of `all` should be `True` if we go by the docs or if this was a conscious design decision to return `NA` as scalar we should be updating the docs to reflect the same.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : 0.29.21
pytest : 6.0.2
hypothesis : 5.28.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.2
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
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CLN: OrderedDict->dict
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| 3
| 2020-09-27T22:28:56Z
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MEMBER
| null |
- [x] closes #30469
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Grepping for OrderedDict in *.py files I see 84 more usages, but AFAICT these are all explicitly checking for the class, so I'm considering this as closing #30469.
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ENH: DatetimeArray/PeriodArray median
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| 2020-09-28T00:42:32Z
| 2020-10-07T15:00:51Z
| 2020-10-07T03:28:11Z
|
MEMBER
| null |
- [x] closes #33760
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- [ ] whatsnew entry
nanops.nanmedian needs to be re-worked, and there's a lot more we can do to share reduction tests. saving for separate branches.
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API: Deprecate regex=True default in Series.str.replace
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| 9
| 2020-09-28T00:58:54Z
| 2020-10-10T23:01:27Z
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|
MEMBER
| null |
From some old discussion it looks like there was interest in changing the default value of regex from True to False within Series.str.replace. I think this makes sense since it would align this method with others within pandas along with the standard library, and would also make fixing https://github.com/pandas-dev/pandas/issues/24804 slightly less disruptive. I'm assuming this would warrant a deprecation note in the next whatsnew?
I think this part of the docs will need to be updated: https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html#splitting-and-replacing-strings
https://github.com/pandas-dev/pandas/pull/24809
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TYP: mostly datetimelike
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Categorical in GroupBy with aggregations raise error under specific conditions
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| 3
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| 2021-08-13T13:37:55Z
| null |
NONE
| null |
```python
ticks = pd.DataFrame.from_dict({
'cid': [1, 1, 2, 2, 3],
'date': ['2019-01-01' , '2020-01-02' , '2020-01-03' , '2019-01-04' , '2020-01-05'],
'tid': [1, 2, 3, 4, 5],
'amount':[1, 1, 2, 2, 3],
})
ticks['date'] = pd.to_datetime(ticks['date'])
ticks['year'] = ticks['date'].dt.year
ticks['year'] = ticks['year'].astype('category')
ticks.groupby(['cid', 'year'], as_index=False, observed=False).agg({'amount': sum})
```
Outputs a: `ValueError: Length of values (5) does not match length of index (6)`
#### Full traceback
<details>
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-15-502fa0f135e2> in <module>
9
10
---> 11 ticks.groupby(['cid', 'year'], as_index=False, observed=False).agg({'amount': sum})
12 )
c:\users\a.jouanjean\htdocs\factbook-py\.venv\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
992
993 if not self.as_index:
--> 994 self._insert_inaxis_grouper_inplace(result)
995 result.index = np.arange(len(result))
996
c:\users\a.jouanjean\htdocs\factbook-py\.venv\lib\site-packages\pandas\core\groupby\generic.py in _insert_inaxis_grouper_inplace(self, result)
1716 # When using .apply(-), name will be in columns already
1717 if in_axis and name not in columns:
-> 1718 result.insert(0, name, lev)
1719
1720 def _wrap_aggregated_output(
c:\users\a.jouanjean\htdocs\factbook-py\.venv\lib\site-packages\pandas\core\frame.py in insert(self, loc, column, value, allow_duplicates)
3620 """
3621 self._ensure_valid_index(value)
-> 3622 value = self._sanitize_column(column, value, broadcast=False)
3623 self._mgr.insert(loc, column, value, allow_duplicates=allow_duplicates)
3624
c:\users\a.jouanjean\htdocs\factbook-py\.venv\lib\site-packages\pandas\core\frame.py in _sanitize_column(self, key, value, broadcast)
3761
3762 # turn me into an ndarray
-> 3763 value = sanitize_index(value, self.index)
3764 if not isinstance(value, (np.ndarray, Index)):
3765 if isinstance(value, list) and len(value) > 0:
c:\users\a.jouanjean\htdocs\factbook-py\.venv\lib\site-packages\pandas\core\internals\construction.py in sanitize_index(data, index)
745 """
746 if len(data) != len(index):
--> 747 raise ValueError(
748 "Length of values "
749 f"({len(data)}) "
ValueError: Length of values (5) does not match length of index (6)
```
</details>
#### Problem description
After quite some time trying to narrow down the origin of a `ValueError: Length of values (N) does not match length of index (M)`, it seems to occur, only when these conditions are met:
* `groupby()` done using a categorical variables in the `by` list
* `as_index=False`, but `as_index=True` is OK
* `observed=False`, but `observed=True` is OK
* `aggregate()` is performed, while applying directly a `sum()` on the DataFrameGroupBy is OK
see the different combinations in details below.
#### Expected Output
Would it be possible to issue an early check and error/exception raise when such conditions are met?
It would definitely help user to understand clearly where the problem comes from and how to correct it.
I am aware that some parts of the issue are being addressed (see PR #35967), but this will not help the user to understand what is actually going on when all conditions are met.
#### Conditions under which Error is not raised
<details>
```python
# with observed=True
ticks.groupby(['cid', 'year'], as_index=False, observed=True).agg({'amount': sum}),
# with as_index=True
ticks.groupby(['cid', 'year'], as_index=True, observed=False).agg({'amount': sum}),
# without using aggregate() but sum() directly [will also sum tid, but still no error]
ticks.groupby(['cid', 'year'], as_index=False, observed=False).sum(),
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
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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MDExOlB1bGxSZXF1ZXN0NDkzODQ3NDM4
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CLN: Format doc code blocks
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| 2020-09-28T03:43:03Z
| 2020-10-01T08:08:54Z
| 2020-09-29T20:29:25Z
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MEMBER
| null |
Trying to make code blocks in documentation black compliant. Also a small update to the flake8-rst config making max-line-length equal to 88.
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BUG: .replace() of an empty string for None returns value from previous row
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| 2020-09-28T13:27:39Z
| 2020-09-28T13:27:39Z
|
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
d = {'col1' : ['test']*9, 'col2': [0,'',3,4,'', '', 6, 7, '']}
test = pd.DataFrame(data=d)
In [4]:
test['col2']
Out[4]:
0 0
1
2 3
3 4
4
5
6 6
7 7
8
Name: col2, dtype: object
In [5]:
test['col2'].replace('', None)
Out[5]:
0 0
1 0
2 3
3 4
4 4
5 4
6 6
7 7
8 7
Name: col2, dtype: object
In [6]:
test['col2'].replace({'': None})
Out[6]:
0 0.0
1 NaN
2 3.0
3 4.0
4 NaN
5 NaN
6 6.0
7 7.0
8 NaN
Name: col2, dtype: float64
```
#### Problem description
.replace() of an empty string for None value returns the value of previous row (instead of None). However .replace() of the same empty strings using dict structure returns None as it was expected.
#### Expected Output
It is expected that .replace('', None) will return the same result as .replace({'': None})
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.17.2
pytz : 2018.7
dateutil : 2.7.5
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.1.1
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.0.2
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 : 0.8.3
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Undocumented behavior change (1.0.5->1.1.0) when using arithmetic operations on dataframes
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| 2020-09-28T14:32:45Z
| 2020-10-16T19:24:40Z
| 2020-10-16T19:24:40Z
|
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
pandas 1.0.5:
```python
(pd.DataFrame({'x':[1,2,],'y':[1,2]})+[pd.Series([1,1]),pd.Series([1,1]) ]).iloc[0,0]
Out[37]: 2
```
pandas 1.1.0:
```python
>>> (pd.DataFrame({'x':[1,2,],'y':[1,2]})+[pd.Series([1,1]),pd.Series([1,1]) ]).iloc[0,0]
0 2
1 2
dtype: int64
```
#### Problem description
When applying arithmetic operations between dataframe and list of Series prior to 1.0.5 and we get different results then starting with 1.1.0. Starting with 1.1.0 we seem to get a another level of indexing, altough the dataframes columns and index return the same values, iterating over it returns different objects.
Since now the return type of the chained iloc has changed from float/int to series, this breaks functions using the arithmetic operator like this.
#### Expected Output
#### Output of ``pd.show_versions()``
using the latest pandas version:
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
using 1.0.3
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-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 : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : NoneI
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BUG: String upgraded to complex128 (and cascading to other columns) in df.mean() and df.agg('mean')
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| 5
| 2020-09-28T15:02:14Z
| 2021-11-13T17:32:56Z
| 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 (1.1.1; latest from conda).
- [ ] (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 numpy as np
import pandas as pd
df = pd.DataFrame([{'db': 'J', 'numeric': 123}])
df2 = pd.DataFrame([{'db': 'J'}])
df3 = pd.DataFrame([{'db': 'j'}])
df4 = pd.DataFrame([{'db': 'J', 'numeric': 123},
{'db': 'J', 'numeric': 456}])
df5 = pd.DataFrame([{'db': 'J'}], dtype='string')
# Initial columns are _correct_ types
df.dtypes
# complex128 and across columns
df.mean()
# agg('mean') is the same.
df.agg('mean')
# Happens even when the columns at issue is alone.
df2.mean()
# Case of the 'J' doesn't matter.
df3.mean()
# Numeric only works as expected.
df.mean(numeric_only=True)
# Two rows works as expected, too.
df4.mean()
# The new StringDtype doesn't appear to matter.
np.mean(df['db'].astype('string').array)
type(np.mean(df['db'].astype('string').array))
df['db'].astype('string').dtype
df5.mean()
# Happens in PandasArray.
np.mean(df['db'].array)
type(np.mean(df['db'].array))
# Not in a numpy array.
np.mean(df['db'].to_numpy())
# Also happens in a Series.
df['db'].mean()
type(df['db'])
# The conversion happens in nanops.nanmean().
pd.core.nanops.nanmean(df['db'])
# Doesn't look like _get_values() is to blame.
pd.core.nanops._get_values(df['db'], True)[0].mean()
# The sum doesn't appear to be it, either.
values = pd.core.nanops._get_values(df['db'], True)[0]
dtype_sum = pd.core.nanops._get_values(df['db'], True)[2]
type(values.sum(None, dtype=dtype_sum))
# The conversion happens in pd.core.nanops._ensure_numeric().
pd.core.nanops._ensure_numeric(values.sum(None, dtype=dtype_sum))
# Interestingly enough, it looks like a sum somewhere is concatenating
# the two 'J's together.
pd.Series(['J', 'J']).astype('complex128').mean()
pd.Series(['J', 'J']).mean()
pd.Series(['J']).mean()
# Here's an example. Then, that sum can't be cast.
pd.core.nanops._get_values(df4['db'], True)[0].sum()
```
With output:
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> df = pd.DataFrame([{'db': 'J', 'numeric': 123}])
>>> df2 = pd.DataFrame([{'db': 'J'}])
>>> df3 = pd.DataFrame([{'db': 'j'}])
>>> df4 = pd.DataFrame([{'db': 'J', 'numeric': 123},
... {'db': 'J', 'numeric': 456}])
>>> df5 = pd.DataFrame([{'db': 'J'}], dtype='string')
>>>
>>> # Initial columns are _correct_ types
>>> df.dtypes
db object
numeric int64
dtype: object
>>>
>>> # complex128 and across columns
>>> df.mean()
db 0.000000+1.000000j
numeric 123.000000+0.000000j
dtype: complex128
>>>
>>> # agg('mean') is the same.
>>> df.agg('mean')
db 0.000000+1.000000j
numeric 123.000000+0.000000j
dtype: complex128
>>>
>>> # Happens even when the columns at issue is alone.
>>> df2.mean()
db 0.000000+1.000000j
dtype: complex128
>>>
>>> # Case of the 'J' doesn't matter.
>>> df3.mean()
db 0.000000+1.000000j
dtype: complex128
>>>
>>> # Numeric only works as expected.
>>> df.mean(numeric_only=True)
numeric 123.0
dtype: float64
>>>
>>> # Two rows works as expected, too.
>>> df4.mean()
numeric 289.5
dtype: float64
>>> # The new StringDtype doesn't appear to matter.
>>> np.mean(df['db'].astype('string').array)
1j
>>> type(np.mean(df['db'].astype('string').array))
<class 'complex'>
>>> df['db'].astype('string').dtype
StringDtype
>>> df5.mean()
Series([], dtype: float64)
>>>
>>> # Happens in PandasArray.
>>> np.mean(df['db'].array)
1j
>>> type(np.mean(df['db'].array))
<class 'complex'>
>>>
>>> # Not in a numpy array.
>>> np.mean(df['db'].to_numpy())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<__array_function__ internals>", line 5, in mean
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 3372, in mean
return _methods._mean(a, axis=axis, dtype=dtype,
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/numpy/core/_methods.py", line 172, in _mean
ret = ret / rcount
TypeError: unsupported operand type(s) for /: 'str' and 'int'
>>>
>>> # Also happens in a Series.
>>> df['db'].mean()
1j
>>> type(df['db'])
<class 'pandas.core.series.Series'>
>>>
>>> # The conversion happens in nanops.nanmean().
>>> pd.core.nanops.nanmean(df['db'])
1j
>>>
>>> # Doesn't look like _get_values() is to blame.
>>> pd.core.nanops._get_values(df['db'], True)[0].mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/numpy/core/_methods.py", line 172, in _mean
ret = ret / rcount
TypeError: unsupported operand type(s) for /: 'str' and 'int'
>>>
>>> # The sum doesn't appear to be it, either.
>>> values = pd.core.nanops._get_values(df['db'], True)[0]
>>> dtype_sum = pd.core.nanops._get_values(df['db'], True)[2]
>>> type(values.sum(None, dtype=dtype_sum))
<class 'str'>
>>>
>>> # The conversion happens in pd.core.nanops._ensure_numeric().
>>> pd.core.nanops._ensure_numeric(values.sum(None, dtype=dtype_sum))
1j
>>>
>>> # Interestingly enough, it looks like a sum somewhere is concatenating
>>> # the two 'J's together.
>>> pd.Series(['J', 'J']).astype('complex128').mean()
1j
>>> pd.Series(['J', 'J']).mean()
Traceback (most recent call last):
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/nanops.py", line 1427, in _ensure_numeric
x = float(x)
ValueError: could not convert string to float: 'JJ'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/nanops.py", line 1431, in _ensure_numeric
x = complex(x)
ValueError: complex() arg is a malformed string
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/generic.py", line 11459, in stat_func
return self._reduce(
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/series.py", line 4236, in _reduce
return op(delegate, skipna=skipna, **kwds)
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/nanops.py", line 71, in _f
return f(*args, **kwargs)
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/nanops.py", line 129, in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/nanops.py", line 563, in nanmean
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
File "/usr/local/Caskroom/miniconda/base/lib/python3.8/site-packages/pandas/core/nanops.py", line 1434, in _ensure_numeric
raise TypeError(f"Could not convert {x} to numeric") from err
TypeError: Could not convert JJ to numeric
>>> pd.Series(['J']).mean()
1j
>>>
>>> # Here's an example. Then, that sum can't be cast.
>>> pd.core.nanops._get_values(df4['db'], True)[0].sum()
'JJ'
```
#### Problem description
It seems like `df.mean()` is being too aggressive about attempting to work with string columns in a one-row dataframe. In my use case, if I get a query result with one row, I get this bug. I have lots of these queries, and even one of them being affected will flow through to the concatenated dataframe downstream.
I first noticed this by getting an error that I couldn't write a parquet file with a complex128 column, and tracked it back from there.
#### Expected Output
Expected output is what we get with `numeric_only=True` set or more than one row. See above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Github action for release
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[] | null | 4
| 2020-09-28T15:47:38Z
| 2021-01-05T19:18:59Z
| 2021-01-05T19:18:58Z
|
MEMBER
| null |
for initial feedback. maybe way off base with this.
can be seen in action at https://github.com/simonjayhawkins/pandas/actions
the alternative of retaining a separate repo for release can be seen at https://github.com/simonjayhawkins/pandas-release/actions
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CLN: remove unnecessary CategoricalIndex._convert_arr_indexer
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This changes loads of files, but most changes are quite trivial
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ENH: Type annotations for Index
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| 8
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| 2020-10-07T14:23:26Z
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NONE
| null |
#### Is your feature request related to a problem?
As described in #26766, it would be good to have type annotations for Index.
#### Describe the solution you'd like
I would like the type of the publicly-exposed sub-objects to be part of the `Index` type. For example, these two `Index` instances contain `Timestamp` from the user's perspective, regardless of the internal implementation:
```python
>>> import pandas as pd
>>> import numpy as np
>>> import datetime
>>> pd.Index([np.datetime64(100, "s")])[0]
Timestamp('1970-01-01 00:01:40')
>>> pd.Index([datetime.datetime(2020, 9, 28)])[0]
Timestamp('2020-09-28 00:00:00')
```
Because the fact `Index` returns different subclasses of itself, getting the type checker to can acknowledge that correctly is tricky. What's more, you'll notice a naive "Index[S] based on fact it's created with List[S]" won't work: `Index([np.datetime64(100, "s"])` contains `Timestamp` instances, at least as far as the user is concerned, and `Timestamp` is very much not a `np.datetime64`.
Here is the only solution I've come up with that works; see also https://github.com/python/mypy/issues/9482, there is no way at the moment to have this work without breaking up `Index` into a parent class that does `__new__` and a subclass that does all the work.
The basic idea is that you have a protocol, `IndexType`. This is a sketch, because demonstrating this with real code would be that much harder:
```python
from typing import TypeVar, Generic, List, Union, overload
from typing_extensions import Protocol
from datetime import datetime
T = TypeVar("T", covariant=True) # need to look into why covariant is required, might not be, not fundamental
S = TypeVar("S")
class datetime64(int):
"""Stand-in for np.datetime64."""
class IndexType(Protocol[T]):
def first(self) -> T: ...
class Index:
@overload
def __new__(cls, values: List[datetime64]) -> "Datetime64Index": ...
@overload
def __new__(cls, values: List[datetime]) -> "Datetime64Index": ...
@overload
def __new__(cls, values: List[S]) -> "DefaultIndex[S]": ...
def __new__(cls, values):
if type(values[0]) in (datetime, datetime64):
cls = Datetime64Index
else:
cls = DefaultIndex
return object.__new__(cls)
class DefaultIndex(Index, Generic[S]):
def __init__(self, values: List[S]):
self.values = values
def first(self) -> S:
return self.values[0]
class Datetime64Index(DefaultIndex):
def __init__(self, values: Union[List[datetime], List[datetime64]]):
self.values : List[datetime64] = [
datetime64(o.timestamp()) if isinstance(o, datetime) else o
for o in values
]
def first(self) -> datetime:
return datetime.fromtimestamp(self.values[0])
# Should work
a: IndexType[datetime] = Index([datetime64(100)])
b: IndexType[datetime] = Index([datetime(2000, 10, 20)])
c: IndexType[bool] = Index([True])
# Should complain
d: IndexType[datetime] = Index(["a"])
e: IndexType[bool] = Index(["a"])
```
#### API breaking implications
Hopefully nothing.
#### Describe alternatives you've considered
I tried lots and lots of other ways of structuring this. None of them worked, except this variant.
#### Additional context
Part of my motivation here is to help use type checking so that users can check whether switching from Pandas to Pandas-alikes like Modin/Dask/etc.. works, by having everyone use matching type annotations.
As such, just saying "this API accepts an Index" is not good enough, because some Pandas APIs have e.g. special cases for `Index[bool]`, you really do need to have some way of indicating the Index type for annotations to be sufficiently helpful.
## What I'd like
Some feedback on whether this approach is something you all would be OK with. If so, I can try to implement it for the real Index classes.
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MDExOlB1bGxSZXF1ZXN0NDk0MjkzNzcw
| 36,709
|
Fix delim_whitespace behavior in read_table, read_csv
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|
BUG: df.diff axis=1 mixed dtypes
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MDExOlB1bGxSZXF1ZXN0NDk0MzE2Mzg3
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Fix small typo in timedeltas error message
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CONTRIBUTOR
| null |
Small typo fix
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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MDU6SXNzdWU3MTA0ODA0ODA=
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ENH: add option to get nullable dtypes to pd.read_csv
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| 2
| 2020-09-28T18:20:43Z
| 2021-03-19T23:19:48Z
| 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
In[2]: import pandas as pd
In[3]: s = pd.Series([1, 2, None, 3], dtype=pd.UInt64Dtype())
In[4]: s
Out[4]:
0 1
1 2
2 <NA>
3 3
dtype: UInt64
In[5]: df = pd.DataFrame({"a":s})
In[6]: df
Out[6]:
a
0 1
1 2
2 <NA>
3 3
In[7]: df.to_csv('temp.csv')
In[8]: pd.read_csv('temp.csv')
Out[8]:
Unnamed: 0 a
0 0 1.0
1 1 2.0
2 2 NaN
3 3 3.0
```
#### Problem description
While reading csv, pandas should likely read in the above dataframe column as a nullable integer dtype instead of converting to floating data type.
#### Expected Output
```python
pd.read_csv('temp.csv')
Out[8]:
Unnamed: 0 a
0 0 1
1 1 2
2 2 <NA>
3 3 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-48-generic
Version : #52-Ubuntu SMP Thu Sep 10 10:58:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : 0.29.21
pytest : 6.0.2
hypothesis : 5.28.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.2
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
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MDExOlB1bGxSZXF1ZXN0NDk0MzI0OTk3
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CLN: pandas/core/indexing.py
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- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Slight clean-up of ``pandas/core/indexing.py``.
- Use ``suppress`` instead of ``try/except/pass`` (starting from Python 3.7)
- Slight rearrangement of statements
- Extract method ``_validate_key_length``
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MDExOlB1bGxSZXF1ZXN0NDk0MzQ5NDk0
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REF: rearrange test_to_latex.py
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MEMBER
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- [ ] closes #xxxx
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Rearranged tests in ``test_to_latex.py`` to the corresponding classes.
Sorry for the messy diffs.
All I did (except to small commits cd92db9 and 2b66e2f) was rearrangement of the tests.
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MDExOlB1bGxSZXF1ZXN0NDk0MzU5OTUz
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TST: implement test to_string_empty_col for Series (GH13653)
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CONTRIBUTOR
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MDExOlB1bGxSZXF1ZXN0NDk0MzczMzIy
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CI: remove duplicated code check #36642
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| 9
| 2020-09-28T20:07:54Z
| 2020-10-21T00:28:58Z
| 2020-10-20T23:08:47Z
|
MEMBER
| null |
- [x] closes #36642
|
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| null | 0
|
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