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MDExOlB1bGxSZXF1ZXN0Mzg1MTQzODM3
| 32,517
|
CLN: Suppres compile warnings of pandas/io/sas/sas.pyx
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| 2020-03-07T13:15:03Z
| 2020-03-13T16:59:08Z
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|
MEMBER
| null |
- [x] ref #32163
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This is the warning that get removed:
```
pandas/io/sas/sas.c: In function ‘__pyx_f_6pandas_2io_3sas_4_sas_rdc_decompress’:
pandas/io/sas/sas.c:3732:65: warning: ‘__pyx_v_ctrl_bits’ may be used uninitialized in this function [-Wmaybe-uninitialized]
3732 | __pyx_t_8 = (((__pyx_v_ctrl_bits & __pyx_v_ctrl_mask) == 0) != 0);
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~
```
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MDExOlB1bGxSZXF1ZXN0Mzg1MTQ2MTA2
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CLN: removed unused import
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| 5
| 2020-03-07T13:43:01Z
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|
MEMBER
| null |
- [ ] closes #xxxx
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---
Not 100% sure about this, can close anytime if not a good PR
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MDExOlB1bGxSZXF1ZXN0Mzg1MTYwMzg5
| 32,519
|
TST: fix test creating invalid CategoricalBlock
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| 1
| 2020-03-07T16:24:17Z
| 2020-03-14T15:50:59Z
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|
MEMBER
| null |
The incorrect tests here turned up when trying to replace Block.get_values calls with simpler alternatives.
|
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MDExOlB1bGxSZXF1ZXN0Mzg1MTcxMDg0
| 32,520
|
BUG: Multiindexed series .at fix
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| 2020-03-07T17:00:57Z
| 2020-07-11T16:02:07Z
| 2020-05-25T22:07:51Z
|
MEMBER
| null |
- [x] closes #26989
- [x] tests added / passed
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| 577,372,379
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MDExOlB1bGxSZXF1ZXN0Mzg1MTczMTEw
| 32,521
|
CLN: rename get_block_values, simplify
|
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| 0
| 2020-03-07T17:23:11Z
| 2020-03-11T02:27:23Z
| 2020-03-11T02:22:42Z
|
MEMBER
| null |
cc @WillAyd name more explicit, implementation more transparent.
|
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| 577,378,948
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MDExOlB1bGxSZXF1ZXN0Mzg1MTc3NzY2
| 32,522
|
CLN: remove SingleBlockManager.get_values
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| 6
| 2020-03-07T18:14:02Z
| 2020-03-11T14:42:41Z
| 2020-03-11T02:20:33Z
|
MEMBER
| null |
It is only used in one place, and its behavior doesn't match `self.blocks[0].get_values()`, which is counter-intuitive.
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| 577,379,482
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MDExOlB1bGxSZXF1ZXN0Mzg1MTc4MTY1
| 32,523
|
CLN: simplify get_values usage in groupby
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| 2
| 2020-03-07T18:17:49Z
| 2020-03-11T03:10:49Z
| 2020-03-11T02:52:01Z
|
MEMBER
| null |
This will avoid an object-casting for e.g. IntervalArray-backed ExtensionBlock.
|
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| 577,380,703
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MDExOlB1bGxSZXF1ZXN0Mzg1MTc4OTgx
| 32,524
|
CLN: avoid Block.get_values in io.sql
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| 2020-03-07T18:27:24Z
| 2020-03-11T03:07:37Z
| 2020-03-11T02:52:55Z
|
MEMBER
| null |
This particular usage is especially non-transparent, since it is effectively `blk.get_values().astype(object)` which is apparently _not_ equivalent to `blk.get_values(object)`
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MDExOlB1bGxSZXF1ZXN0Mzg1MTgyMDYy
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TST: remove unused kwargs in assert_sp_array_equal
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MDU6SXNzdWU1NzczODY5MTc=
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API: Timestamp positional arguments not aligned with datetime.datetime
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| 2020-03-07T19:16:30Z
| 2020-09-21T22:15:58Z
| null |
MEMBER
| null |
Our `Timestamp` constructor is not positionally aligned with `datetime.datetime` since we support a `nanosecond` argument which comes in between `microsecond` and `tzinfo`.
This is the main cause for https://github.com/pandas-dev/pandas/issues/32174 as dispatching to `datetime.datetime` methods sometimes reconstructs `Timestamp` with positional arguments.
`nanosecond` should be moved to the last positional argument in the `Timestamp` constructor.
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MDExOlB1bGxSZXF1ZXN0Mzg1MTg2NDY0
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TST: make tests stricter
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MEMBER
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I expect to see some platform-specific failures, will be nice to document those where possible.
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MDU6SXNzdWU1NzczOTUzMjI=
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|
DataFrame.describe() output is not deterministic
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| 2020-03-07T20:23:45Z
| 2021-07-30T03:03:19Z
| null |
MEMBER
| null |
xref #31472
Running this:
```python
pd.DataFrame({
'categorical': pd.Categorical(['d', 'e', 'f']),
'numeric': [1, 2, 3],
'object': ['a', 'b', 'c']
})
df.describe(include="all")
```
Will sometimes have the output of:
```
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN a
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
```
And sometimes the output of:
```
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
```
(Changes at the row of ```top```)
This is making the doctests fail. We should remove the `SKIP` in the tests in the `describe` docstring when we identify the problem: https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L9649
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MDU6SXNzdWU1NzczOTk0OTM=
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|
DOC: Remove absolute urls from the docs
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| 6
| 2020-03-07T20:58:40Z
| 2020-03-12T07:15:30Z
| 2020-03-12T07:15:30Z
|
MEMBER
| null |
Looks like we've got some absolute urls (including the domain) in the docs, like https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases (see here: https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L178 ).
Sphinx (the tool we use to build the docs) provides a way to create those automatically, with the `:ref:` directive.
See for example how to automatically generate a link [here](https://github.com/pandas-dev/pandas/blame/master/doc/source/user_guide/computation.rst#L113):
```
:ref:`caveats <computation.covariance.caveats>`
```
Which will point to the label defined [here](https://github.com/pandas-dev/pandas/blame/master/doc/source/user_guide/computation.rst#L51):
```
.. _computation.covariance.caveats:
```
To avoid having new cases in the future after fixing the existing ones, we should add a check in `ci/code_checks.sh` to validate that the pattern `pandas.pydata.org` doesn't exist in the repository (see other examples of patterns we don't want to find in `ci/code_checks.sh` that use the `invgrep` function). If there is a better patter than `pandas.pydata.org` feel free to use it.
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MDExOlB1bGxSZXF1ZXN0Mzg1MTk3Mjc3
| 32,530
|
CI: Update web and docs to OVH with the right structure
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| 2
| 2020-03-07T21:53:36Z
| 2020-03-20T16:36:14Z
| 2020-03-11T12:58:03Z
|
MEMBER
| null |
- [X] closes #32303
I'm uploading all the docs from the NumFOCUS server to the OVH (manually). And I'm changing the CI in this PR to upload the website and the dev docs to the server automatically.
I'll have a look and see if we can upload a new version of the docs automatically to OVH when a new GitHub release is generated. So, we never need to upload the website manually.
After merging this PR, and when we're confident this is working well, we'll have to redirect pandas.pydata.org to the OVH server. The dev docs will be available at pandas.pydata.org/pandas-docs/dev/ (and we can stop using pandas.io and dev.pandas.io).
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| 577,408,070
|
MDExOlB1bGxSZXF1ZXN0Mzg1MTk4NjA3
| 32,531
|
DOC: Update pandas.core.groupby.GroupBy.pipe docstring
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| 2020-03-31T17:41:32Z
| 2020-03-31T17:41:31Z
|
NONE
| null |
- [x] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/2
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TYP: Add type hint for DataFrame.T and certain array types
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| 1
| 2020-03-07T23:31:02Z
| 2020-03-11T03:19:15Z
| 2020-03-11T03:19:11Z
|
CONTRIBUTOR
| null |
While updating a large pandas codebase with type coverage to 1.0, I noticed that `DataFrame.transpose()` is annotated to return `DataFrame` while `DataFrame.T` has no such hint. This PR also adds hints in a few places other places where they're trivial.
The definition of `pandas.core.base.IndexOpsMixIn.transpose` (and associated `.T`) currently do not have any type hints and fixing this seems more complicated, meaning `Index.T` and `Series.T` are not fixed by this PR.
Happy to amend to cover that case if anyone has suggestions, but I believe `DataFrame.T` to be the vast majority of usage.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: remove unnecessary to_dense call
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| 2
| 2020-03-07T23:40:04Z
| 2020-03-10T14:54:35Z
| 2020-03-10T13:31:26Z
|
MEMBER
| null |
This call is not reached in the tests, and if it were the following line would make it superfluous anyway.
I'm pretty sure the comment there is wrong too.
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MDExOlB1bGxSZXF1ZXN0Mzg1MjA2NTE4
| 32,534
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CLN: remove unused in pd._testing
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| 577,431,890
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MDExOlB1bGxSZXF1ZXN0Mzg1MjE2NDA3
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|
BUG: retain tz in to_records
|
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| 1
| 2020-03-08T02:13:42Z
| 2020-03-14T21:29:43Z
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|
MEMBER
| null |
plus the initial motivation: get rid of two of our `_internal_get_values` calls (of which i count 16 left in master)
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| 577,435,716
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MDExOlB1bGxSZXF1ZXN0Mzg1MjE5MTYy
| 32,536
|
TST: separate out pd.crosstab tests from test_pivot
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| 577,436,268
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MDExOlB1bGxSZXF1ZXN0Mzg1MjE5NTU4
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CLN: avoid values_from_object in reshape.merge
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| 2020-03-12T15:23:44Z
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MDExOlB1bGxSZXF1ZXN0Mzg1MjE5OTY1
| 32,538
|
ENH: IntegerArray.astype(dt64)
|
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| 2020-03-08T03:09:38Z
| 2020-04-05T17:46:02Z
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|
MEMBER
| null |
- [x] closes #32435
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Let's us de-kludge to_datetime code, getting rid of another _ndarray_values usage.
|
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MDExOlB1bGxSZXF1ZXN0Mzg1MjI5MTYy
| 32,539
|
DOC: Remove absolute urls from the docs
|
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| 10
| 2020-03-08T05:37:10Z
| 2020-03-12T08:03:38Z
| 2020-03-12T07:15:30Z
|
CONTRIBUTOR
| null |
- [x] closes #32529
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
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| 577,498,656
|
MDU6SXNzdWU1Nzc0OTg2NTY=
| 32,540
|
pdf documentation is not bookmarked
|
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| 2020-03-08T13:11:24Z
| 2020-03-10T13:39:51Z
| 2020-03-10T13:39:40Z
|
CONTRIBUTOR
| null |
The last PDF documentation 1.0.1. (Feb 05, 2020) has no table of contents, no bookmarks.
I made Contents in the form of bookmarks, meanwhile up to level 2.
The edited (bookmarked) pdf file is here: [pandas.pdf](https://github.com/pandas-dev/pandas/files/4303229/pandas.pdf).
|
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| 577,503,065
|
MDExOlB1bGxSZXF1ZXN0Mzg1MjY4NDE2
| 32,541
|
Fix failure to convert string "uint64" to NaN
|
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| 4
| 2020-03-08T13:44:42Z
| 2020-03-11T15:48:23Z
| 2020-03-09T03:29:30Z
|
CONTRIBUTOR
| null |
Including regression test
- [x] closes #32394
- [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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MDExOlB1bGxSZXF1ZXN0Mzg1MjcwMTk2
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|
ENH: Add `replace` method to `Index`
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| 2021-04-11T00:29:52Z
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CONTRIBUTOR
| null |
- [X] closes #19495
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [x] docstring for the new Index.replace() method
- [X] won't do: type hints
- [ ] merge with `ENH:` commit message
Added a replace method to Index classes, as well as tests.
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obj.fillna(fill_value) doesn't shallow copy if obj is an empty Series
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| 1
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| 2020-03-16T22:34:14Z
| 2020-03-16T22:34:14Z
|
CONTRIBUTOR
| null |
While working on #32483 I bumped into the following issue:
#### Code Sample, a copy-pastable example if possible
```python
obj = pd.Series()
result = obj.fillna(0)
assert obj is not result # raises AssertionError
```
#### Problem description
See `test_fillna` in the mentioned PR (or master when it is merged): With any value of `obj`, even if it is an empty `Index` the shallow copy is executed. This can potentially yield bugs related to unintended mutations of obj further down the road.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 343dd6751347b8a7ff67970c762d4b50e8038dfd
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
Version : Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : de_DE.utf-8
LANG : de_DE.utf-8export
LOCALE : de_DE.UTF-8
pandas : 1.1.0.dev0+698.g343dd6751.dirty
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.1
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.3.0
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.47.0
</details>
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BUG: pd.ExcelFile closes stream on destruction
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| 2020-03-08T19:14:23Z
| 2020-03-12T21:35:56Z
| 2020-03-12T12:18:21Z
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CONTRIBUTOR
| null |
- [x] closes #31467
- [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: to_dense->np.asarray
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MEMBER
| null |
This is the only non-test use of Categorical.to_dense, which is slightly different from _internal_get_values (for SparseArray the two methods are identical), and so liable to cause confusion.
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MDExOlB1bGxSZXF1ZXN0Mzg1MzA1ODIx
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BUG: Dataframe.groupby aggregations with categorical columns lead to incorrect results.
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- [x] closes #32494
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0Mzg1MzE1MzA4
| 32,547
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CLN: remove unnecessary values_from_objects in groupby.ops
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MEMBER
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2 values_from_object calls left to go...
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MDExOlB1bGxSZXF1ZXN0Mzg1MzE2OTcw
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BUG: Add extra check for failing UTF-8 conversion
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CONTRIBUTOR
| null |
- [x] closes #23809
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0Mzg1MzE4NzU4
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DOC: Added docstring for Series.name and corrected docstring guide
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| 2
| 2020-03-08T21:54:37Z
| 2020-03-17T00:07:43Z
| 2020-03-17T00:07:36Z
|
CONTRIBUTOR
| null |
The main contribution is to add a docstring with examples for the "name" property of the Series object. Also corrected some typos and grammatical points in the "pandas docstring guide".
The type hint gives the type as "Label" and I could not find any other reference to a custom type defined in pandas._typing which was explicitly mentioned in the docs (instead they specify "str" or "int" or somesuch), so I chose "Label (int, str or other hashable object)".
Further I chose "whenever displaying the Series in the interpreter" as clearer alternative to "when invoking the __repr__ method" or a similar precise statement.
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MDU6SXNzdWU1Nzc2MjcwODA=
| 32,550
|
Fix capitalization among headings in documentation files
|
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| 2020-03-09T02:53:20Z
| 2021-05-20T20:43:14Z
| null |
CONTRIBUTOR
| null |
In #26933, we made the capitalization of titles consistent. For example, a title used to be capitalized like, "This is the Section Title", and many of the titles in the _pandas_ documentation was changed to a correct format, like "This is the section title".
In #31114, we made a script called `scripts/validate_rst_title_capitalization.py` that extracts all titles in the documentation, making sure that only the first letter of the sentence is uppercase, or words defined in a short list, like Series, DataFrame, etc. The script also outputs how to fix the title as well.
We validated capitalization is correct by integrating this script into CI (continuous integration). The idea is that we should run this script through `ci/code_checks.sh`, and when title capitalization errors show up on CI, the user should fix those errors on the specified files.
To verify the code is working on your side, the command below instructs the program to validate the `doc/source/development/contributing.rst` file. There should be no output from this command as this file as no capitalization errors:
`./scripts/validate_rst_title_capitalization.py doc/source/development/contributing.rst`
This command below instructs the program to validate both `doc/source/index.rst` and `doc/source/development/policies.rst` files.
```
./scripts/validate_rst_title_capitalization.py doc/source/index.rst doc/source/development/policies.rst
```
This command produces the output below:
```
doc/source/development/policies.rst:9:Heading capitalization formatted incorrectly. Please correctly capitalize "Version Policy" to "Version policy"
doc/source/development/policies.rst:51:Heading capitalization formatted incorrectly. Please correctly capitalize "Python Support" to "Python support"
```
The goal of this issue is to correct the title capitalization of all files in the _pandas_ documentation.
In order to see all titles that need to be validated in the documentation folder, one should run the following command below on the command line.
`./scripts/validate_rst_title_capitalization.py doc/source`
This program validates all RST files in the doc/source folder. Once all titles are all correctly validated, we would like to add the above command into the `ci/code_checks.sh` file.
Here's a checklist of all the files that had at least one incorrectly capitalized heading:
```
- [ ] doc/source/user_guide/timedeltas.rst
- [ ] doc/source/whatsnew/v0.7.0.rst
- [ ] doc/source/whatsnew/v0.23.4.rst
- [ ] doc/source/whatsnew/v0.6.0.rst
- [ ] doc/source/whatsnew/v1.0.2.rst
- [ ] doc/source/whatsnew/v0.18.0.rst
- [ ] doc/source/whatsnew/v0.16.2.rst
- [ ] doc/source/whatsnew/v0.7.1.rst
- [ ] doc/source/whatsnew/v0.8.0.rst
- [ ] doc/source/user_guide/integer_na.rst
- [ ] doc/source/reference/io.rst
- [ ] doc/source/user_guide/computation.rst
- [ ] doc/source/whatsnew/v0.16.0.rst
- [ ] doc/source/whatsnew/v0.23.2.rst
- [ ] doc/source/whatsnew/v0.12.0.rst
- [ ] doc/source/getting_started/10min.rst
- [ ] doc/source/user_guide/advanced.rst
- [ ] doc/source/reference/arrays.rst
- [ ] doc/source/development/maintaining.rst
- [ ] doc/source/user_guide/groupby.rst
- [ ] doc/source/user_guide/cookbook.rst
- [ ] doc/source/development/developer.rst
- [ ] doc/source/development/meeting.rst
- [ ] doc/source/getting_started/intro_tutorials/03_subset_data.rst
- [ ] doc/source/whatsnew/v0.4.x.rst
- [ ] doc/source/whatsnew/v0.16.1.rst
- [ ] doc/source/whatsnew/v1.0.0.rst
- [ ] doc/source/whatsnew/v0.23.1.rst
- [ ] doc/source/getting_started/tutorials.rst
- [ ] doc/source/reference/series.rst
- [ ] doc/source/getting_started/intro_tutorials/02_read_write.rst
- [ ] doc/source/whatsnew/v0.6.1.rst
- [ ] doc/source/whatsnew/v0.13.1.rst
- [ ] doc/source/whatsnew/v0.21.0.rst
- [ ] doc/source/reference/frame.rst
- [ ] doc/source/whatsnew/v0.20.0.rst
- [ ] doc/source/getting_started/intro_tutorials/09_timeseries.rst
- [ ] doc/source/whatsnew/index.rst
- [ ] doc/source/user_guide/merging.rst
- [ ] doc/source/whatsnew/v0.18.1.rst
- [ ] doc/source/user_guide/enhancingperf.rst
- [ ] doc/source/development/contributing_docstring.rst
- [ ] doc/source/whatsnew/v0.9.0.rst
- [ ] doc/source/whatsnew/v0.25.2.rst
- [ ] doc/source/development/extending.rst
- [ ] doc/source/reference/window.rst
- [ ] doc/source/whatsnew/v0.7.3.rst
- [ ] doc/source/user_guide/options.rst
- [ ] doc/source/ecosystem.rst
- [ ] doc/source/getting_started/intro_tutorials/01_table_oriented.rst
- [ ] doc/source/user_guide/categorical.rst
- [ ] doc/source/whatsnew/v0.14.1.rst
- [ ] doc/source/whatsnew/v0.19.0.rst
- [ ] doc/source/whatsnew/v0.20.2.rst
- [ ] doc/source/whatsnew/v0.24.0.rst
- [ ] doc/source/development/roadmap.rst
- [ ] doc/source/whatsnew/v0.17.0.rst
- [ ] doc/source/user_guide/boolean.rst
- [ ] doc/source/getting_started/comparison/comparison_with_r.rst
- [ ] doc/source/whatsnew/v0.17.1.rst
- [ ] doc/source/whatsnew/v0.22.0.rst
- [ ] doc/source/reference/indexing.rst
- [ ] doc/source/user_guide/missing_data.rst
- [ ] doc/source/getting_started/install.rst
- [ ] doc/source/user_guide/index.rst
- [ ] doc/source/user_guide/visualization.rst
- [ ] doc/source/getting_started/comparison/comparison_with_stata.rst
- [ ] doc/source/whatsnew/v0.19.1.rst
- [ ] doc/source/whatsnew/v0.15.1.rst
- [ ] doc/source/whatsnew/v0.10.0.rst
- [ ] doc/source/whatsnew/v0.19.2.rst
- [ ] doc/source/whatsnew/v0.25.3.rst
- [ ] doc/source/user_guide/gotchas.rst
- [ ] doc/source/whatsnew/v0.14.0.rst
- [ ] doc/source/user_guide/reshaping.rst
- [ ] doc/source/reference/groupby.rst
- [ ] doc/source/whatsnew/v0.23.3.rst
- [ ] doc/source/user_guide/timeseries.rst
- [ ] doc/source/whatsnew/v0.9.1.rst
- [ ] doc/source/getting_started/comparison/comparison_with_sql.rst
- [ ] doc/source/whatsnew/v0.24.1.rst
- [ ] doc/source/reference/index.rst
- [ ] doc/source/development/policies.rst
- [ ] doc/source/whatsnew/v0.21.1.rst
- [ ] doc/source/whatsnew/v0.20.3.rst
- [ ] doc/source/development/code_style.rst
- [ ] doc/source/user_guide/sparse.rst
- [ ] doc/source/whatsnew/v0.24.2.rst
- [ ] doc/source/whatsnew/v0.15.2.rst
- [ ] doc/source/whatsnew/v1.1.0.rst
- [ ] doc/source/reference/offset_frequency.rst
- [ ] doc/source/whatsnew/v1.0.1.rst
- [ ] doc/source/getting_started/basics.rst
- [ ] doc/source/whatsnew/v0.5.0.rst
- [ ] doc/source/user_guide/text.rst
- [ ] doc/source/user_guide/indexing.rst
- [ ] doc/source/whatsnew/v0.11.0.rst
- [ ] doc/source/whatsnew/v0.8.1.rst
- [ ] doc/source/getting_started/comparison/comparison_with_sas.rst
- [ ] doc/source/whatsnew/v0.23.0.rst
- [ ] doc/source/user_guide/io.rst
- [ ] doc/source/whatsnew/v0.25.1.rst
- [ ] doc/source/whatsnew/v0.13.0.rst
- [ ] doc/source/whatsnew/v0.25.0.rst
- [ ] doc/source/whatsnew/v0.15.0.rst
- [ ] doc/source/whatsnew/v0.10.1.rst
```
|
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MDExOlB1bGxSZXF1ZXN0Mzg1Mzk5NjY0
| 32,551
|
DOC: Fix EX01 in pandas.DataFrame.idxmax
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| 2
| 2020-03-09T05:53:51Z
| 2020-03-11T01:35:56Z
| 2020-03-11T01:35:50Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
```
################################################################################
################################## Validation ##################################
################################################################################
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Backport PR #32242 on branch 1.0.x (BUG: Fixed bug, where pandas._libs.lib.maybe_convert_objects function improperly handled arrays with bools and NaNs)
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| 6
| 2020-03-09T15:42:45Z
| 2020-03-10T16:22:32Z
| 2020-03-10T16:22:32Z
|
NONE
| null |
Backport PR #32242: BUG: Fixed bug, where pandas._libs.lib.maybe_convert_objects function improperly handled arrays with bools and NaNs
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IntervalIndex raises TypeError on PrettyPrint
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| 2020-12-03T14:23:34Z
| 2020-12-03T14:23:34Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Consider the following series
```python
left = Float64Index([329.973, 345.137, 360.191, 376.313, 393.235, 411.969, 434.968, 464.453], dtype='float64')
right = pd.Float64Index([345.137, 360.191, 376.313, 393.235, 411.969, 434.968, 464.453, 507.679], dtype='float64')
idx = pd.IntervalIndex([pd.Interval(l, r) for l, r in zip(left, right)])
markers = pd.Series(['^', 'p', 'd', 'P', 'X', 'o', 's', 'v'], index=idx)
```
#### Problem description
the `IntervalIndex` raises a `TypeError` when printing
```python
>>> markers
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/.conda/envs/test-env/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/.conda/envs/test-env/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
403 if cls is not object \
404 and callable(cls.__dict__.get('__repr__')):
--> 405 return _repr_pprint(obj, self, cycle)
406
407 return _default_pprint(obj, self, cycle)
~/.conda/envs/test-env/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
693 """A pprint that just redirects to the normal repr function."""
694 # Find newlines and replace them with p.break_()
--> 695 output = repr(obj)
696 lines = output.splitlines()
697 with p.group():
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/series.py in __repr__(self)
1369 min_rows=min_rows,
1370 max_rows=max_rows,
-> 1371 length=show_dimensions,
1372 )
1373 result = buf.getvalue()
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/series.py in to_string(self, buf, na_rep, float_format, header, index, length, dtype, name, max_rows, min_rows)
1435 max_rows=max_rows,
1436 )
-> 1437 result = formatter.to_string()
1438
1439 # catch contract violations
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/io/formats/format.py in to_string(self)
358 )
359
--> 360 fmt_index, have_header = self._get_formatted_index()
361 fmt_values = self._get_formatted_values()
362
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/io/formats/format.py in _get_formatted_index(self)
338 else:
339 have_header = index.name is not None
--> 340 fmt_index = index.format(name=True)
341 return fmt_index, have_header
342
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/indexes/base.py in format(self, name, formatter, **kwargs)
964 return header + list(self.map(formatter))
965
--> 966 return self._format_with_header(header, **kwargs)
967
968 def _format_with_header(self, header, na_rep="NaN", **kwargs):
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/indexes/interval.py in _format_with_header(self, header, **kwargs)
1000
1001 def _format_with_header(self, header, **kwargs):
-> 1002 return header + list(self._format_native_types(**kwargs))
1003
1004 def _format_native_types(self, na_rep="NaN", quoting=None, **kwargs):
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/indexes/interval.py in _format_native_types(self, na_rep, quoting, **kwargs)
1004 def _format_native_types(self, na_rep="NaN", quoting=None, **kwargs):
1005 # GH 28210: use base method but with different default na_rep
-> 1006 return super()._format_native_types(na_rep=na_rep, quoting=quoting, **kwargs)
1007
1008 def _format_data(self, name=None):
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/indexes/base.py in _format_native_types(self, na_rep, quoting, **kwargs)
1026 Actually format specific types of the index.
1027 """
-> 1028 mask = isna(self)
1029 if not self.is_object() and not quoting:
1030 values = np.asarray(self).astype(str)
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in isna(obj)
124 Name: 1, dtype: bool
125 """
--> 126 return _isna(obj)
127
128
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_old(obj)
181 return False
182 elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
--> 183 return _isna_ndarraylike_old(obj)
184 elif isinstance(obj, ABCGeneric):
185 return obj._constructor(obj._data.isna(func=_isna_old))
~/.conda/envs/test-env/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_ndarraylike_old(obj)
288 result = values.view("i8") == iNaT
289 else:
--> 290 result = ~np.isfinite(values)
291
292 # box
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
When we change the index to a `categorical`, the visual representation prints
```python
>>> markers.index = pd.CategoricalIndex(markers.index)
>>> markers
(329.973, 345.137] ^
(345.137, 360.191] p
(360.191, 376.313] d
(376.313, 393.235] P
(393.235, 411.969] X
(411.969, 434.968] o
(434.968, 464.453] s
(464.453, 507.679] v
dtype: object
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.3.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0Mzg1NzEzMzk3
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CLN: remove Categorical.put
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| 1
| 2020-03-09T17:16:17Z
| 2020-03-09T21:28:30Z
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MDExOlB1bGxSZXF1ZXN0Mzg1NzE3MDIy
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CLN: avoid _internal_get_values in Categorical.__iter__
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Dataframe.query uses numexpr even when 'compute.use_numexpr' is False
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| 2020-03-09T18:07:52Z
| 2021-07-28T01:23:32Z
| 2021-07-28T01:23:32Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import io
content = io.StringIO('''
time,val
212.23, 32
''')
pd.set_option('compute.use_numexpr', False)
print("use numexpr? %d" % pd.get_option('compute.use_numexpr'))
df = pd.read_csv(
content,
sep=',',
usecols=['time', 'val'],
dtype= {'val': 'Int64'},
# parse_dates=date_cols,
)
q = "val == 32"
# passing `engine="python"` fixes it
df_dest = df.query(q, )
```
throws with pandas 1.0
```
Traceback (most recent call last):
File "test.py", line 21, in <module>
df_dest = df.query(q, )
File "/nix/store/0yyjswv58jk0fv1miyklmppvpd0p45sy-python3.7-pandas-1.0.1/lib/python3.7/sit
e-packages/pandas/core/frame.py", line 3231, in query
res = self.eval(expr, **kwargs)
File "/nix/store/0yyjswv58jk0fv1miyklmppvpd0p45sy-python3.7-pandas-1.0.1/lib/python3.7/sit
e-packages/pandas/core/frame.py", line 3346, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "/nix/store/0yyjswv58jk0fv1miyklmppvpd0p45sy-python3.7-pandas-1.0.1/lib/python3.7/sit
e-packages/pandas/core/computation/eval.py", line 337, in eval
ret = eng_inst.evaluate()
File "/nix/store/0yyjswv58jk0fv1miyklmppvpd0p45sy-python3.7-pandas-1.0.1/lib/python3.7/sit
e-packages/pandas/core/computation/engines.py", line 73, in evaluate
res = self._evaluate()
File "/nix/store/0yyjswv58jk0fv1miyklmppvpd0p45sy-python3.7-pandas-1.0.1/lib/python3.7/sit
e-packages/pandas/core/computation/engines.py", line 114, in _evaluate
return ne.evaluate(s, local_dict=scope)
File "/nix/store/k6wr7w16q0aq9mfkf0w15wxlhx76sxqc-python3.7-numexpr-2.7.1/lib/python3.7/si
te-packages/numexpr/necompiler.py", line 822, in evaluate
zip(names, arguments)]
File "/nix/store/k6wr7w16q0aq9mfkf0w15wxlhx76sxqc-python3.7-numexpr-2.7.1/lib/python3.7/si
te-packages/numexpr/necompiler.py", line 821, in <listcomp>
signature = [(name, getType(arg)) for (name, arg) in
File "/nix/store/k6wr7w16q0aq9mfkf0w15wxlhx76sxqc-python3.7-numexpr-2.7.1/lib/python3.7/si
te-packages/numexpr/necompiler.py", line 703, in getType
raise ValueError("unknown type %s" % a.dtype.name)
ValueError: unknown type object
```
#### Problem description
Dataframe.query fails when using UInt* dtypes see https://github.com/pandas-dev/pandas/issues/25369#issuecomment-594905994.
My first idea to solve this was to disable numexpr but this isn't enough, one has to pass `engine="python"` to forcefully disable numexpr when calling `query`
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MDExOlB1bGxSZXF1ZXN0Mzg1NzUwNjUw
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CLN: values_from_object in computation.pytables
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By my count there is just one more `values_from_object` call to go.
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MDExOlB1bGxSZXF1ZXN0Mzg1NzU4MDE2
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BUG: pivot_table losing tz
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| 14
| 2020-03-09T18:46:52Z
| 2020-03-14T23:31:12Z
| 2020-03-14T23:28:54Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This gets rid of the last values_from_object usage (pending other PRs)
`_tile_compat` likely makes more sense as an Index method. I kept it here as a proof of concept because I think we actually need it in the EA interface too. Being able to broadcast a length=1 EA to a length=N EA will be necessary for some of the arithmetic perf going on.
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MDU6SXNzdWU1NzgxMTk0NTY=
| 32,559
|
date_range() : allowing argument closed to have value 'open'
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| 2020-03-09T18:50:05Z
| 2020-03-18T19:50:45Z
| 2020-03-18T19:50:45Z
|
NONE
| null |
#### Code Sample
```
# My current code to get rid of both extremities of the DateTimeIndex returned by date_range()
def my_date_range(start: pd.Timestamp, end: pd.Timestamp, freq: str) -> pd.DatetimeIndex:
"""
Remove automatically first and last timestamps from the generated DateTimeIndex.
"""
DTI = pd.date_range(start = start, end = end, freq = freq, closed = 'left')
# Last timestamp has not been kept with use of 'closed' keyword, 'left' value.
# With delete, the 1st timestamp is also removed.
return DTI.delete(0)
# Now, I can use date_range() the way I want with apply() function :)
time_range = df.apply(lambda x: pd.Series(my_date_range(x.name, x.End, x.Freq)), axis=1).stack()
```
#### Problem description
Please, could the keyword 'closed' accept a 4th value (for instance 'open') that allows suppressing both extremities of the DateTimeIndex?
Thanks for your help and this marvelous library! Loving it!
;)
|
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TST: Add extra test for pandas.to_numeric() for issue #32394
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CONTRIBUTOR
| null |
- [x] closes #32394
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (covered by PR #32541)
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Ensure valid Block mutation in SeriesBinGrouper.
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| 8
| 2020-03-09T19:16:11Z
| 2020-03-11T18:34:49Z
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|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31802
~This "fixes" #31802 by expanding the number of cases where we swallow an
exception in libreduction. Currently, we're creating an invalid Series
in SeriesBinGrouper where the `.mgr_locs` doesn't match the values. See
https://github.com/pandas-dev/pandas/issues/31802#issuecomment-595954511
for more.~
~For now, we simply catch more cases that fall back to Python. I've gone
with a minimal change which addresses only issues hitting this exact
exception. We might want to go broader, but that's not clear.~
cc @jbrockmendel & @WillAyd
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CI: TravisCI Failing
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| 2020-03-09T19:30:42Z
| 2020-03-09T20:03:06Z
| 2020-03-09T20:03:06Z
|
CONTRIBUTOR
| null |
Good: https://travis-ci.org/pandas-dev/pandas/jobs/660156644#L7
Bad: https://travis-ci.org/pandas-dev/pandas/jobs/660210493#L1
https://travis-ci.org/pandas-dev/pandas/jobs/660210493#L175-L177 looks strange.
```
$ export PATTERN=""(not slow and not network and not clipboard)""
/home/travis/.travis/functions: eval: line 109: syntax error near unexpected token `('
/home/travis/.travis/functions: eval: line 109: `export PATTERN=""(not slow and not network and not clipboard)"" '
```
Most likely something changed on the TravisCI end. Opening this to track it.
|
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| 578,171,061
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MDExOlB1bGxSZXF1ZXN0Mzg1ODAxODIy
| 32,563
|
Fix warning in io/excel/test_openpyxl
|
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| 2020-03-09T20:28:59Z
| 2020-03-11T15:47:52Z
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CONTRIBUTOR
| null |
as requested in PR #32544
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MDExOlB1bGxSZXF1ZXN0Mzg1ODAyMjIz
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|
DOC: Add missing question mark icon
|
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| 2
| 2020-03-09T20:29:51Z
| 2020-03-10T07:18:29Z
| 2020-03-10T07:18:29Z
|
CONTRIBUTOR
| null |
- [x] closes #32469
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I added the question mark to the `_static` folder. When rebuilding the documentation pages locally using the latest version of the `pydata-bootstrap-sphinx-theme`, the `div.highlight` seems to be styled correctly (`background: white`), removing the _strange green around the code block_.
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MDExOlB1bGxSZXF1ZXN0Mzg1ODA0ODg0
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|
TST: Fix bare pytest raises in generic/test_frame.py
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| 2
| 2020-03-09T20:34:14Z
| 2020-03-10T10:37:41Z
| 2020-03-10T10:37:29Z
|
CONTRIBUTOR
| null |
- [x] refers https://github.com/pandas-dev/pandas/issues/30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Adds match arguments to pytest.raises calls in pandas/tests/generic/test_frame.py
|
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MDExOlB1bGxSZXF1ZXN0Mzg1ODQxMjI1
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BUG: Fix segfault in csv tokenizer
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| 4
| 2020-03-09T22:07:07Z
| 2020-03-16T20:08:32Z
| 2020-03-16T02:21:30Z
|
CONTRIBUTOR
| null |
- [x] closes #28071
- [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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MDExOlB1bGxSZXF1ZXN0Mzg1ODQ3ODA2
| 32,567
|
TST: add test.indexes.common.Base.create_index and annotate .create_index
|
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| 2
| 2020-03-09T22:26:59Z
| 2020-03-10T11:09:31Z
| 2020-03-10T11:09:21Z
|
CONTRIBUTOR
| null |
Makes ``test.indexes.common.Base`` and ``create_index`` a bit easier to work with.
|
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MDExOlB1bGxSZXF1ZXN0Mzg1ODQ5ODI5
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|
PERF: copy cached attributes on index shallow_copy
|
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| 5
| 2020-03-09T22:33:40Z
| 2020-03-11T09:02:49Z
| 2020-03-11T03:06:03Z
|
CONTRIBUTOR
| null |
- [x] closes #28584
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The performance of the example in #28584 is:
```python
>>> idx = pd.Index(np.arange(100_000))
>>> %timeit idx.get_loc(99_999)
1.17 µs ± 80.6 ns per loop # master and this PR
>>> %timeit idx._shallow_copy().get_loc(99_999)
3.57 ms ± 286 ns per loop # master
3.67 µs ± 286 ns per loop # this PR
```
The issue is still on the extension indexes, e.g. ``CategoricalIndex._shallow_copy``. I'd like to take them afterwards.
|
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Arithmetic doesn't broadcast when MultiIndex has the same data, different names
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| 2020-03-09T23:37:24Z
| 2021-07-30T03:06:25Z
| null |
NONE
| null |
#### Code Sample
```python
import pandas as pd
index_st = pd.MultiIndex.from_tuples([(1,1),(1,2),(2,1),(2,2)], names=['second','third'])
index_ft1 = pd.MultiIndex.from_tuples([(1,1),(1,2),(2,1),(2,2)], names=['first','third'])
index_ft2 = pd.MultiIndex.from_tuples([(1,1),(2,1),(1,2),(2,2)], names=['first','third'])
st = pd.DataFrame([1,2,3,4], index=index_st, columns=['val'])
ft1 = pd.DataFrame([10,20,30,40], index=index_ft1, columns=['val'])
ft2 = pd.DataFrame([10,30,20,40], index=index_ft2, columns=['val'])
print(st.add(ft1))
print(st.add(ft2))
```
#### Problem description
The calculation of `ft1` doesn't broadcast as expected. Instead, it treats the differently-named indexed columns as identical and performs a component-wise addition.
The calculation of `ft2` is as expected.
`ft1` and `ft2` having different index columns is *definitely* not expected.
#### Output That I See
```
val
second third
1 1 11
2 22
2 1 33
2 44
val
third second first
1 1 1 11
2 31
2 1 13
2 33
2 1 1 22
2 42
2 1 24
2 44
```
#### Expected Output
The expected output of the two calculations should be the same, possibly with the rows returned in a different order.
```
val
third second first
1 1 1 11
2 31
2 1 13
2 33
2 1 1 22
2 42
2 1 24
2 44
val
third second first
1 1 1 11
2 31
2 1 13
2 33
2 1 1 22
2 42
2 1 24
2 44
```
#### Where the problem lies
The problem appears to be in [MultiIndex.equals](https://github.com/pandas-dev/pandas/blob/ae79bb23c7d10bd5b307f542c1a1c78d1600aecb/pandas/core/indexes/multi.py#L3126)
This is called by [NDFrame. _indexed_same](https://github.com/pandas-dev/pandas/blob/ae79bb23c7d10bd5b307f542c1a1c78d1600aecb/pandas/core/generic.py#L1233), which is called by [_align_method_FRAME](https://github.com/pandas-dev/pandas/blob/ae79bb23c7d10bd5b307f542c1a1c78d1600aecb/pandas/core/ops/__init__.py#L624)
The problem is that MultiIndex.equals doesn't check the names, and so reports indices as equal when they're not. This in turn means the non-broadcasting case is taken, when it should broadcast.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ae79bb23c7d10bd5b307f542c1a1c78d1600aecb
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Thu Oct 17 19:31:58 UTC 2019
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+725.gae79bb23c
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.15
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>
#### See also (I haven't checked if these are related, but they "sound similar")
https://github.com/pandas-dev/pandas/issues/20565
https://github.com/pandas-dev/pandas/issues/5645
https://github.com/pandas-dev/pandas/issues/19606
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https://github.com/pandas-dev/pandas/pull/32570
| 578,295,549
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MDExOlB1bGxSZXF1ZXN0Mzg1OTAzOTM2
| 32,570
|
CLN: avoid _internal_get_values in pandas._testing
|
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| 1
| 2020-03-10T02:26:43Z
| 2020-03-13T17:28:11Z
| 2020-03-11T02:19:30Z
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MEMBER
| null |
7 more usages left after this
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MDExOlB1bGxSZXF1ZXN0Mzg1OTA0NjUx
| 32,571
|
TST: stricter tests, avoid check_categorical=False, check_less_precise
|
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| 2020-03-10T02:30:06Z
| 2020-03-11T02:30:00Z
| 2020-03-11T02:27:15Z
|
MEMBER
| null |
Won't be too surprised if 32bit builds need some troubleshooting.
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MDU6SXNzdWU1NzgzNjg2NjU=
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|
BUG: get_loc / get_indexer with NaT and tz-aware DatetimeIndex
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| 4
| 2020-03-10T06:39:51Z
| 2021-07-30T03:11:24Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> pd.date_range('2020', 'now').get_loc(pd.NaT, method='nearest')
0
# Ok? NaT would be better to propagate.
>>> pd.date_range('2020', 'now', tz='US/Central').get_loc(pd.NaT, method='nearest')
-----------------------------------------------------------------------------------
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "pandas/core/indexes/datetimes.py", line 582, in get_loc
return Index.get_loc(self, key, method, tolerance)
File "pandas/core/indexes/base.py", line 2869, in get_loc
indexer = self.get_indexer([key], method=method, tolerance=tolerance)
File "pandas/core/indexes/base.py", line 2951, in get_indexer
target, method=method, limit=limit, tolerance=tolerance
File "pandas/core/indexes/base.py", line 2962, in get_indexer
indexer = self._get_nearest_indexer(target, limit, tolerance)
File "pandas/core/indexes/base.py", line 3046, in _get_nearest_indexer
left_distances = np.abs(self[left_indexer] - target)
File "pandas/core/indexes/base.py", line 2361, in __sub__
return Index(np.array(self) - other)
File "pandas/core/indexes/base.py", line 2367, in __rsub__
return Index(other - Series(self))
File "pandas/core/series.py", line 646, in __array_ufunc__
self, ufunc, method, *inputs, **kwargs
File "pandas/_libs/ops_dispatch.pyx", line 91, in pandas._libs.ops_dispatch.maybe_dispatch_ufunc_to_dunder_op
File "pandas/core/ops/common.py", line 63, in new_method
return method(self, other)
File "pandas/core/ops/__init__.py", line 500, in wrapper
result = arithmetic_op(lvalues, rvalues, op, str_rep)
File "pandas/core/ops/array_ops.py", line 218, in arithmetic_op
res_values = dispatch_to_extension_op(op, lvalues, rvalues)
File "pandas/core/ops/dispatch.py", line 125, in dispatch_to_extension_op
res_values = op(left, right)
File "pandas/core/ops/roperator.py", line 13, in rsub
return right - left
File "pandas/core/arrays/datetimelike.py", line 1428, in __rsub__
f"cannot subtract {type(self).__name__} from {type(other).__name__}"
TypeError: cannot subtract DatetimeArray from ndarray
```
#### Problem description
`pd.NaT` is NaT regardless of timezone.
#### Expected Output
```py
>>> pd.date_range('2020', 'now').get_loc(pd.NaT, method='nearest')
NaT
>>> pd.date_range('2020', 'now', tz='US/Central').get_loc(pd.NaT, method='nearest')
NaT
```
#### Output of ``pd.show_versions()``
<details>
pandas 1.1.0.dev0+725.gae79bb23c
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg1OTczNTYx
| 32,573
|
Backport PR #32564 on branch 1.0.x (DOC: Add missing question mark icon)
|
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| 2020-03-10T07:54:42Z
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NONE
| null |
Backport PR #32564: DOC: Add missing question mark icon
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Backport PR #31875 on branch 1.0.x (DOC: add redirects from Rolling to rolling.Rolling)
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NONE
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Backport PR #31875: DOC: add redirects from Rolling to rolling.Rolling
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TST: Add tests for duplicated and drop_duplicates
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| 2020-04-16T17:15:04Z
| 2020-04-06T23:23:00Z
|
CONTRIBUTOR
| null |
- [x] refers to #15752
- [x] tests added / passed
- [x] tests duplicated and drop_duplicates for period, categorical, datetimes, timedelta
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MDU6SXNzdWU1Nzg0NjY0OTE=
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|
Index.insert(-1, value) should add value in the last position
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| 2020-03-10T10:07:25Z
| 2020-03-10T12:22:42Z
| 2020-03-10T12:22:42Z
|
NONE
| null |
#### Problem description and Expected Output
``Index.insert(-1, value)`` should add value in the last position instead of one before the last position, shouldn't it?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.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 : en
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg2MDQ4MTcz
| 32,577
|
REG: Restore read_csv function for some file-likes
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| 1
| 2020-03-10T10:22:21Z
| 2020-03-11T02:22:06Z
| 2020-03-11T02:22:02Z
|
MEMBER
| null |
Restore `read_csv` function for some file-likes
Restores behavior down to the fact that the Python engine cannot handle NamedTemporaryFile.
Closes https://github.com/pandas-dev/pandas/issues/31819
Credit to @sasanquaneuf for [originating idea](https://github.com/pandas-dev/pandas/issues/31819#issuecomment-584529415).
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Backport PR #31684 on branch 1.0.x (BUG: string methods with NA)
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NONE
| null |
Backport PR #31684: BUG: string methods with NA
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[QST] Clarify when `agg()` will return groups as index
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| 2020-05-27T12:43:34Z
| 2020-05-27T12:43:34Z
|
CONTRIBUTOR
| null |
```
In [44]: a = pd.DataFrame({'a': [1, 1, 2], 'b': [1, 2, 3]})
In [45]: a.groupby('a').agg('sum') # 'a' is returned as index
Out[45]:
b
a
1 3
2 3
In [46]: a.groupby('a').agg('nunique') # 'a' is returned both as index and column
Out[46]:
a b
a
1 1 2
2 1 1
```
The groupby [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#aggregation) notes:
> Aggregation functions will not return the groups that you are aggregating over if they are named columns, when as_index=True, the default. The grouped columns will be the indices of the returned object.
However, in the snippet above, it looks like the `nunique` aggregation behaves differently in this respect from the `sum` aggregation.
Is it possible to predict when an aggregation will return groups as index columns v/s a "SQL-style output" v/s a combination of the two (as above)?
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MDU6SXNzdWU1Nzg2ODY5Nzg=
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[BUG] Grouped-by column loses name when empty list of aggregations is specified.
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CONTRIBUTOR
| null |
```
In [53]: a = pd.DataFrame({'a': [1, 1, 2], 'b': [1, 2, 3], 'c': [1, 2, 4]})
In [54]: a.groupby('a').agg({'c': ['min']}) # name preserved in index
Out[54]:
c
min
a
1 1
2 4
In [55]: a.groupby('a').agg({'b': [], 'c': ['min']}) # name lost in index
Out[55]:
c
min
1 1
2 4
```
In the above snippet, the name of the grouped by column (`'a'`) is preserved in the former case, and lost in the latter case.
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DataFrame.astype does not handle multiple timezone aware Timestamps
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CONTRIBUTOR
| null |
```python
import pandas
df = pandas.DataFrame({'a': [pandas.Timestamp('20200309T120000.000000-0400'), pandas.Timestamp('20200309T130000.000000-0400')]})
df.astype({'a': pandas.api.types.DatetimeTZDtype(unit='ns', tz='UTC')})
```
```
a
0 2020-03-09 16:00:00+00:00
1 2020-03-09 17:00:00+00:00
```
```python
df = pandas.DataFrame({'a': [pandas.Timestamp('20200309T120000.000000-0400'), pandas.Timestamp('20200309T130000.000000-0500')]})
df.astype({'a': pandas.api.types.DatetimeTZDtype(unit='ns', tz='UTC')})
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
Similarly, something like `df['a'].dt.tz_convert(tz='utc')` also returns the same error
#### Problem description
Calling `pandas.DataFrame.astype` with a column of mixed timezone aware Timestamps fails to convert to `UTC`
#### Expected Output
I would expect this to return the same result as `df.applymap(lambda x: x.tz_convert(tz='utc'))`
```
0 2020-03-09 16:00:00+00:00
1 2020-03-09 18:00:00+00:00
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-28-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
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 : 7.12.0
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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MDU6SXNzdWU1Nzg2OTMzNTQ=
| 32,582
|
Creating a column with a set replicates the set n times
|
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| 2020-03-10T16:06:41Z
| 2020-03-15T00:41:34Z
| 2020-03-15T00:41:34Z
|
NONE
| null |
#### Code Sample
If we try to define a dataframe using a dictionary containing a set, we get:
```python
pd.DataFrame({'a':{1,2,3}})
a
0 {1, 2, 3}
1 {1, 2, 3}
2 {1, 2, 3}
```
#### Problem description
The set is being replicated `n` times, `n` being the length of the actual set.
While defining a column with a set directly might not make a lot of sense given that they are by definition unordered collections, the behaviour in any case seems clearly unexpected.
#### Expected Output
In the case of a list, in order to obtain a single row containing a list, we would have to define a nested list, such as `pd.DataFrame({'a':[[1,2,3]]})`.
So similarly, with sets I would expect the same behaviour by defining the row with `pd.DataFrame({'a':[{1,2,3}]})`.
In the case of a single set, even if the order is not guaranteed to be preserved, I'd see more reasonable the same output that we would obtain with:
```python
pd.DataFrame({'a':[1,2,3]})
a
0 1
1 2
2 3
```
So:
```python
pd.DataFrame({'a':{1,2,3}})
a
0 1
1 2
2 3
```
Where:
```python
pd.__version__
# '1.0.0'
```
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MDExOlB1bGxSZXF1ZXN0Mzg2MjI3MTIy
| 32,583
|
CLN: remove unreachable code in pandas/core/groupby/generic.py::DataFrameGroupBy::_transform_general
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| 2020-03-10T16:09:32Z
| 2020-10-10T14:14:54Z
| 2020-04-11T13:04:18Z
|
MEMBER
| null |
Is this part of the code
```
else:
# Handle cases like BinGrouper
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
```
necessary? The condition before it is
```
elif self.grouper.groupings is not None
```
. However, even in the case of `BinGrouper`, I don't believe `BinGrouper.groupings` can be `None`:
```
>>> from pandas.core.groupby.ops import BinGrouper
>>> BinGrouper([], []).groupings
[Grouping(None)]
```
and so this code is unreachable. It's not covered by the tests, at least - https://codecov.io/gh/pandas-dev/pandas/src/master/pandas/core/groupby/generic.py#L1354
If it can be removed, I've taken it out and then shifted the code above it back by one tab (because we no longer need the guard)
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MDU6SXNzdWU1Nzg2OTUzMjM=
| 32,584
|
Document `DataError` in the API
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| 1
| 2020-03-10T16:09:40Z
| 2021-07-30T03:13:32Z
| null |
CONTRIBUTOR
| null |
Currently, some groupby operations can raise a `DataError` that does not seem to be documented:
```
In [7]: a = pd.DataFrame({'a': [1, 2, 3, 4], 'b': ['a', 'b', 'c', 'd']})
In [8]: a.groupby('a').mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-8-52f32e3da133> in <module>
----> 1 a.groupby('a').mean()
~/miniconda3/envs/pandas-py37/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in mean(self, *args, **kwargs)
1230 nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
1231 return self._cython_agg_general(
-> 1232 "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
1233 )
1234
~/miniconda3/envs/pandas-py37/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
1002 ) -> DataFrame:
1003 agg_blocks, agg_items = self._cython_agg_blocks(
-> 1004 how, alt=alt, numeric_only=numeric_only, min_count=min_count
1005 )
1006 return self._wrap_agged_blocks(agg_blocks, items=agg_items)
~/miniconda3/envs/pandas-py37/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _cython_agg_blocks(self, how, alt, numeric_only, min_count)
1103
1104 if not (agg_blocks or split_frames):
-> 1105 raise DataError("No numeric types to aggregate")
1106
1107 if split_items:
DataError: No numeric types to aggregate
```
Could this error be documented as part of the API, or alternatively, could a different exception be raised so that users can catch and handle it appropriately?
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| 32,585
|
March 2020 Dev Meeting
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| 2020-03-10T16:18:40Z
| 2020-03-11T19:05:11Z
| 2020-03-11T19:05:11Z
|
CONTRIBUTOR
| null |
The monthly dev meeting is tomorrow at 18:00 UTC. Note that your local time may be different than last month. The US had a DST transition last weekend, while Europe didn't. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar
Video Call: https://meet.google.com/hav-rmax-zjx
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda.
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| 578,707,495
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| 32,586
|
EA: revisit interface
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[] | null | 52
| 2020-03-10T16:28:14Z
| 2020-11-11T20:38:25Z
| null |
MEMBER
| null |
This is as good a time as any to revisit the "experimental" EA interface.
My read of the Issues and recollection of threads suggests there are three main groups of topics:
Clarification of the Interface
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
1) _values_for_argsort and values_for_factorize
- Do we need both? The docs both say they should be order-preserving.
- Is it safe to return a view? (Categorical.values_for_argsort makes a copy for no obvious reason)
- What else can they be used for internally? e.g. in #32467 _values_for_argsort is used for ExtensionIndex join_non_unique and join_monotonic
2) <s>What characteristics should _ndarray_values have? Is it needed? (#32412)</s> _ndarray_values has been removed
3) What should _from_sequence accept?
- Should it only be sequences that are unambiguously this dtype?
- In particular, should DTA/TDA/PA _not_ accept i8 values?
4) What should fillna accept? (#22954, #32414)
4.5) Require that `__iter__` return native types? #29738
Ndarray Compat
^^^^^^^^^^^^^^^^^
5) Headaches have been caused by trivial ndarray methods not being on EA
- #31199 size
- #32342 "T" (just the most recent; this has come up a lot)
- #24583 ravel
6) For arithmetic we're going to need something like either `tile` or `broadcast_to`
Methods Needed/Wanted For Index/Series/DataFrame/Block
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
7) Suggested Methods (partial list)
- #27264 duplicated
- #23437 empty
- #28955 apply
- #23179 map
- #22680 hasnas
- #27081 equals
- #24144 where
- putmask would be helpful for ExtensionIndex
I suggest we discuss these in order. Before jumping in, is there anything vital missing from this list? (this is only a small subset of the issues on the tracker)
cc @pandas-dev/pandas-core @xhochy
|
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| 578,740,610
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MDU6SXNzdWU1Nzg3NDA2MTA=
| 32,587
|
Arrow discards timezones
|
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| 2020-03-10T17:21:14Z
| 2020-03-10T19:27:27Z
| 2020-03-10T17:51:33Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from datetime import datetime, timezone
df = pd.DataFrame.from_records([
(1, datetime.now().replace(tzinfo=timezone.utc)),
(2, datetime.now().replace(tzinfo=timezone.min))],
columns=["1", "2"])
print(df["2"])
print()
df.to_feather("/tmp/1")
df2 = pd.read_feather("/tmp/1")
print(df2["2"])
```
This code will output:
```
0 2020-03-10 18:13:49.405598+00:00
1 2020-03-10 18:13:49.405626-23:59
Name: 2, dtype: object
0 2020-03-10 18:13:49.405598
1 2020-03-10 18:13:49.405626
Name: 2, dtype: datetime64[ns]
```
#### Problem description
The round-trip dtype changed from the correct `object` to incorrect `datetime64`. Thus the timezones were discarded in Arrow and the timestamps became invalid.
#### Expected Output
(identical)
```
0 2020-03-10 18:13:49.405598+00:00
1 2020-03-10 18:13:49.405626-23:59
Name: 2, dtype: object
0 2020-03-10 18:13:49.405598+00:00
1 2020-03-10 18:13:49.405626-23:59
Name: 2, dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-40-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.4
pytz : 2019.2
dateutil : 2.7.3
pip : 19.3.1
setuptools : 42.0.1
Cython : 0.29.14
pytest : 5.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.1
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.12
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDU6SXNzdWU1Nzg3NjMzMTM=
| 32,588
|
parsing ragged csv doesn't return nan
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[] | null | 0
| 2020-03-10T18:00:23Z
| 2021-07-30T03:14:40Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df2 = pd.read_csv(io.StringIO('col1,col2,col3\n2,1'), keep_default_na=False)
```
#### Problem description
This returns an array of [[col1,col2,col3], [0,2,'']]. This doesn't make sense to me since the final row is malformed and doesn't have enough entries. I don't see any way to check if there are rows with too few columns. I can get rid of keep_default_na, but then Pandas won't distinguish between the above and the empty string:
```python
df2 = pd.read_csv(io.StringIO('col1,col2,col3\n2,1,'), keep_default_na=False)
```
#### Expected Output
An array of [[col1,col2,col3], [0,2,NaN]] or an Error.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: None
setuptools: None
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.10dev0
feather: None
matplotlib: None
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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MDU6SXNzdWU1Nzg3NjYyODM=
| 32,589
|
regex with word boundary in pandas query expression doesn't work correctly
|
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[] | null | 0
| 2020-03-10T18:05:34Z
| 2020-06-07T00:34:21Z
| null |
NONE
| null |
Minimal example to highlight the issue I'm seeing when using pandas query to filter rows:
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(['A test case', 'Another Testing Case'], columns=list('A'))
df.query("A.str.contains(r'\btest\b', regex=True, case=False)", engine='python')
```
(incorrectly) returns
```python
Empty DataFrame
Columns: [A]
Index: []
```
whereas
#### Expected Output
```python
df[df.A.str.contains(r'\btest\b', regex=True, case=False)]
A
0 A test case
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : 0.4.0
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
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 : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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|
Histogram or kde from datetime column
|
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| 11
| 2020-03-10T18:15:03Z
| 2020-10-10T23:07:42Z
| 2020-10-10T23:07:42Z
|
NONE
| null |
It always annoyed me that it was not possible to generate a histogram or kernel density estimation plot from a datetime column.
Is there interest in a solution following this approach?
[Link to SO](https://stackoverflow.com/a/60617297)
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({"datetime": pd.to_datetime(np.random.randint(1582800000000000000, 1583500000000000000, 100, dtype=np.int64))})
fig, ax = plt.subplots()
df["datetime"].astype(np.int64).plot.hist(ax=ax)
labels = ax.get_xticks().tolist()
labels = pd.to_datetime(labels)
ax.set_xticklabels(labels, rotation=90)
plt.show()
```
I didn't look into the the pandas source code yet, but if there is interest in something like this I will look into it.
I think the main issue will be finding good x tick labels, if thats a requirement.
Greetings!
|
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| 578,795,470
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MDExOlB1bGxSZXF1ZXN0Mzg2MzA5OTgy
| 32,591
|
REG: dt64 shift with integer fill_value
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| 2020-03-12T17:02:55Z
| 2020-03-12T02:26:13Z
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MEMBER
| null |
- [x] closes #31971
- [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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MDExOlB1bGxSZXF1ZXN0Mzg2MzE3NzI3
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DOC: cleanup 1.0.2 whatsnew
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CONTRIBUTOR
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[BUG] Dataframe.rank() produces wrong results for float columns
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| 3
| 2020-03-10T19:23:15Z
| 2020-12-30T21:11:28Z
| 2020-12-30T21:11:28Z
|
NONE
| null |
#### Code to reproduce the issue
```python
import numpy as np
import pandas as pd
indx = np.array([5, 4, 3, 2, 1, 6, 7, 8, 9, 10])
col1 = np.array([5, 4, 3, 5, 8, 5, 2, 1, 6, 6])
col2 = np.array([5, 4, np.nan, 5, 8, 5, np.inf, np.nan, 6, -np.inf])
pdf = pd.DataFrame(index=indx)
pdf["col1"] = col1.astype('f8')
pdf["col2"] = col2.astype('f8')
ranked_df = pdf.rank()
ranked_series = pd.DataFrame(index=indx)
ranked_series["col1"] = pdf["col1"].rank()
ranked_series["col2"] = pdf["col2"].rank()
print(ranked_df.equals(ranked_series))
#False
```
#### Problem description
Dataframe rank produces individual column ranks. But when float column is present, individual ranks are not equal to Dataframe rank.
#### Expected Output
`True`
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
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 : 7.12.0
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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BUG: Don't multiply sets during construction
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| 4
| 2020-03-10T19:25:56Z
| 2020-03-15T00:42:47Z
| 2020-03-15T00:41:34Z
|
MEMBER
| null |
- [x] closes #32582
- [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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MDU6SXNzdWU1Nzg4MTI5MjY=
| 32,595
|
DOCS SUGGESTION for pd.MultiIndex (Output of df.index.levels)
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[] | null | 2
| 2020-03-10T19:33:18Z
| 2021-07-30T03:18:03Z
| 2021-07-30T03:18:03Z
|
NONE
| null |
I've been wondering if it is possible to change the output of Example 2 to indicate that the index is a `FrozenList`. Currently it only indicates that it is an `Index` and I initially had trouble realizing that the index is also a `FrozenList` (and subsequently wondering why the frozen list index values weren't updating after modifying the original dataframe).
If possible, I believe the inclusion of `FrozenList` would be helpful for users utilizing MultiIndex functionality of pandas.
Input Dataframe
```
INPUT: df
Box '1' '2' '3'
Latency code latency loc code latency loc code latency loc
0 9170. 948. L. 8170. 328. R. 9160. 238. L.
1 7540 1501. R 9170. 9028. L. 7170. 94. L.
```
Example 1
```
In:df.columns.levels
Out: FrozenList([['1', '2', '3'], ['event_code', 'latency', 'location']])
```
Example 2
```
In:df.columns.levels[0]
Out: Index(['1', '2', '3'], dtype='object', name='Box Number')
```
versions:
python=3.6.8
pandas==1.0
Note: I originally asked this question on SO [Link](https://stackoverflow.com/questions/60623606/unexpected-behavior-for-pd-dataframe-drop-when-using-with-multilevel-index-dataf#60623606):
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.15.4
pytz : 2018.7
dateutil : 2.7.5
pip : 20.0.2
setuptools : 40.6.2
Cython : 0.29
pytest : 4.0.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.1.1
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.5.9
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
pytest : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.14
tables : 3.4.4
tabulate : None
xarray : 0.12.3
xlrd : 1.1.0
xlwt : 1.2.0
xlsxwriter : 1.1.2
numba : 0.40.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzg2MzU2NDA4
| 32,596
|
Backport PR #32592 on branch 1.0.x (DOC: cleanup 1.0.2 whatsnew)
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| 2020-03-10T20:46:56Z
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NONE
| null |
Backport PR #32592: DOC: cleanup 1.0.2 whatsnew
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REF: implement nanops.na_accum_func
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| 2020-03-14T17:03:00Z
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this is a follow-up that was requested a few months ago
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BUG: Fix file descriptor leak
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| 1
| 2020-03-10T21:47:38Z
| 2020-03-15T07:14:03Z
| 2020-03-15T00:37:42Z
|
CONTRIBUTOR
| null |
- [x] closes #31488
- [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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MDU6SXNzdWU1Nzg4OTgxMzY=
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[BUG] Groupby with as_index=False raises error when type is Category.
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| 1
| 2020-03-10T22:28:14Z
| 2020-06-15T12:29:19Z
| 2020-06-15T12:29:19Z
|
NONE
| null |
#### Code to reproduce the issue
```python
import pandas as pd
test = pd.DataFrame([[1, 1], [2, 2], [3, 3]], columns=['col1', 'col2'])
test['col1'] = test['col1'].astype('category')
test.groupby(['col1', 'col2'], as_index=False).size()
```
#### Problem description
With pandas 1.0.1, the code throws an error `ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>`.
With pandas 0.25.3, the code works, but `as_index` argument do not function as already mentioned in #25011.
This happened with categorical type, the output of the new version is similar to 0.25.3 with other types.
#### Expected Output
| | col1 | col2 | 0 |
|---:|-------:|-------:|----:|
| 0 | 1 | 1 | 1 |
| 1 | 1 | 2 | 0 |
| 2 | 1 | 3 | 0 |
| 3 | 2 | 1 | 0 |
| 4 | 2 | 2 | 1 |
| 5 | 2 | 3 | 0 |
| 6 | 3 | 1 | 0 |
| 7 | 3 | 2 | 0 |
| 8 | 3 | 3 | 1 |
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
Pyarrow and custom dtypes
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| 2020-03-10T22:43:21Z
| 2020-03-11T00:19:29Z
| 2020-03-11T00:19:29Z
|
NONE
| null |
Hello,
Are there anymore robust examples showing how to convert custom dtypes to and from Apache arrow?
Essentially my dtype is a dictionary like data structure but I want to use the arrow storage format.
|
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MDExOlB1bGxSZXF1ZXN0Mzg2NDAxODA3
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|
TST: assert monotonic is True
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| 2020-03-10T22:52:20Z
| 2020-08-08T08:31:25Z
| 2020-03-10T22:59:20Z
|
CONTRIBUTOR
| null |
Makes tests for monoticity stricter. Follow-up to #23256.
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| 0
| 2020-03-10T23:20:13Z
| 2020-03-11T03:10:38Z
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|
MEMBER
| null |
The link we currently have for Google calendar is to embed, and let you see the calendar, but not subscribe to it. Fixing it here.
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Avoid bare pytest.raises in dtypes/cast/test_upcast.py
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CONTRIBUTOR
| null |
* [x] ref #30999
* [x] tests added / passed
* [x] passes `black pandas`
* [x] passes `git diff origin/master -u -- "*.py" | flake8 --diff`
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Avoid bare pytest.raises in dtypes/test_dtypes.py
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CONTRIBUTOR
| null |
* [x] ref #30999
* [x] tests added / passed
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* [x] passes `git diff origin/master -u -- "*.py" | flake8 --diff`
Please be aware that I changed dtypes.py as there was a typo in the exception message.
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Backport PR #32499: Better error message for OOB result
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NONE
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Backport PR #32386: BUG: Fix rolling functions with variable windows on decreasing index
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TST: fixturize skipna in test_nanops
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Deprecate fill_value argument for df.add
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| 9
| 2020-03-11T02:20:30Z
| 2020-09-25T22:37:01Z
| null |
MEMBER
| null |
This came up in the submission for #13488
This is a documented argument but strangely not universally implemented for all argument types. Both @jbrockmendel and I agreed it is probably better to deprecate and push users towards using something like what @jreback suggested in https://github.com/pandas-dev/pandas/issues/13488#issuecomment-227016181
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Backport PR #32577 on branch 1.0.x (REG: Restore read_csv function for some file-likes)
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NONE
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Backport PR #32577: REG: Restore read_csv function for some file-likes
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CLN: Clean frame/test_constructors.py
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| 6
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| 2020-03-14T19:44:07Z
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MEMBER
| null |
I think these classes are all imported directly so don't need to reference the pandas namespace
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REF: implement _get_engine_target
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| 2
| 2020-03-11T03:01:59Z
| 2020-03-12T15:30:46Z
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MEMBER
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Follow-up to #32467.
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CLN: remove Block.array_dtype, SingleBlockManager.array_dtype
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| 1
| 2020-03-11T04:35:40Z
| 2020-03-14T16:27:26Z
| 2020-03-14T03:30:13Z
|
MEMBER
| null |
We can get a more accurately-dtyped empty array instead.
|
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MDExOlB1bGxSZXF1ZXN0Mzg2NDgxOTM1
| 32,613
|
DOC: Validating that the word pandas is correctly capitalized
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| 2020-03-11T04:48:02Z
| 2020-04-01T15:36:21Z
| 2020-04-01T15:36:21Z
|
CONTRIBUTOR
| null |
- [x] closes #32316
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As suggested by @datapythonista , I've added checks in `ci/code_checks.sh` to look if the `pandas` is being referenced in a standardized way i.e `pandas`, not ` *pandas* ` or `Pandas`
@datapythonista I've only added checks to see if pandas is being referenced correctly, but not showing where it's changed or how many references are wrong.
Should I add checks for that also ? What do you think ?
|
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MDU6SXNzdWU1NzkwNDY4OTE=
| 32,614
|
Negated str.isnumeric() fails on column with None when called together with notnull() in the same boolean expression
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[] | null | 2
| 2020-03-11T06:52:27Z
| 2020-09-21T12:03:53Z
| 2020-09-21T12:03:53Z
|
NONE
| null |
#### Code Sample
Create a dataframe:
```
import pandas as pd
df = pd.DataFrame({'A': ['0', '1', '2', 'X', None]})
```
Obviously this fails:
```
print(df[df.A.str.isnumeric()])
```
So I can do this and it works:
```
print(df[df.A.notnull() & df.A.str.isnumeric()])
```
However when negating the `str.isnumeric()` with unary `~` it fails:
```
print(df[df.A.notnull() & ~df.A.str.isnumeric()])
```
Error message:
>TypeError: bad operand type for unary ~: 'NoneType'
Full error:
<details>
> print(df[df.A.notnull() & ~df.A.str.isnumeric()])
> Traceback (most recent call last):
>
> File "<ipython-input-32-9d35109941d7>", line 1, in <module>
> df[df.A.notnull() & ~df.A.str.isnumeric()]
>
> File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\generic.py", line 1473, in __invert__
> new_data = self._data.apply(operator.invert)
>
> File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\internals\managers.py", line 440, in apply
> applied = b.apply(f, **kwargs)
>
> File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\internals\blocks.py", line 390, in apply
> result = func(self.values, **kwargs)
>
> TypeError: bad operand type for unary ~: 'NoneType'
</details>
When doing it in two step it works:
```
dft = df[df.A.notnull()].copy()
print(dft[~dft.A.str.isnumeric()])
```
#### Problem description
It's inconsistent for the combined expression to fail because of the unary `~`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200308
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDU6SXNzdWU1NzkwODEyOTk=
| 32,615
|
PARSE ERROR
|
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[] | null | 3
| 2020-03-11T08:12:49Z
| 2020-03-11T21:46:21Z
| 2020-03-11T21:46:21Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
PLEASE HELP!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
CODE::
import pandas as pd
file1 = r'D:\C_pH\Book1 Lat_LongVspH.csv';
df = pd.read_csv(file1)
**#### Output**
ParserError Traceback (most recent call last)
<ipython-input-2-21d97f424a6d> in <module>
----> 1 df = pd.read_csv(file1)
C:\Users\KIIT\AppData\Roaming\Python\Python37\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
683 )
684
--> 685 return _read(filepath_or_buffer, kwds)
686
687 parser_f._name_ = name
C:\Users\KIIT\AppData\Roaming\Python\Python37\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
461
462 try:
--> 463 data = parser.read(nrows)
464 finally:
465 parser.close()
C:\Users\KIIT\AppData\Roaming\Python\Python37\site-packages\pandas\io\parsers.py in read(self, nrows)
1152 def read(self, nrows=None):
1153 nrows = _validate_integer("nrows", nrows)
-> 1154 ret = self._engine.read(nrows)
1155
1156 # May alter columns / col_dict
C:\Users\KIIT\AppData\Roaming\Python\Python37\site-packages\pandas\io\parsers.py in read(self, nrows)
2057 def read(self, nrows=None):
2058 try:
-> 2059 data = self._reader.read(nrows)
2060 except StopIteration:
2061 if self._first_chunk:
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas\_libs\parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()
pandas\_libs\parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 1 fields in line 4, saw 3
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MDU6SXNzdWU1NzkwOTQ4NzI=
| 32,616
|
Incorrect result with DateOffset.rollback for normalize=True
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[] | null | 1
| 2020-03-11T08:40:46Z
| 2020-06-28T20:26:55Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas.tseries.offsets import MonthBegin
MonthBegin(normalize=True).rollback('2020-4-1 12:34')
Out[2]: Timestamp('2020-03-01 00:00:00')
```
#### Problem description
It rollbacks to the beginning of the previous month instead of the current month. This happens only when normalize=True and the timestamp is within the first day of the month but strangely, it's correct for midnight:
```python
from pandas.tseries.offsets import MonthBegin
MonthBegin(normalize=True).rollback('2020-4-1 00:00')
Out[3]: Timestamp('2020-04-01 00:00:00')
```
#### Expected Output
```
Out[2]: Timestamp('2020-04-01 00:00:00')
```
In general, I would expect the following to hold for any timestamp `dt`:
```python
assert MonthBegin(normalize=True).rollback(dt) == MonthBegin().rollback(dt.normalize())
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.6.2
Cython : 0.29.4
pytest : 5.3.5
hypothesis : None
sphinx : None
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 : 4.6.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.2.1
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : None
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