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https://api.github.com/repos/pandas-dev/pandas/issues/30614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30614/events | https://github.com/pandas-dev/pandas/issues/30614 | 544,438,638 | MDU6SXNzdWU1NDQ0Mzg2Mzg= | 30,614 | ENH: Create a devcontainer.json env to work with VS Code in Containers | {
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"description": "Library ... | closed | false | null | [] | null | 3 | 2020-01-02T03:56:56Z | 2020-01-19T01:00:59Z | 2020-01-19T01:00:59Z | CONTRIBUTOR | null | VS Code has a great feature where you can work on a repo fully within a Docker container, either on your computer (called Remote - Containers) or remotely on Azure hosted by them (called VS Code Online). This could be particularly useful for setting up a quick dev environment to work on the pandas codebase, and not have to manually go through the setup steps in the [docs](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#working-with-the-code).
Per the VS Code docs (https://code.visualstudio.com/docs/remote/containers and https://docs.microsoft.com/en-us/visualstudio/online/reference/configuring) you can create a `.devcontainer.json` file in the root of the repo that will contain everything needed to setup the environment, similar to the conda `environment.yml` file. You can also create a DockerFile inside a `devcontainer` folder to use with the `devcontainer.json` file.
I will try to put something together for this, figured I'd first start an issue here so that if anyone else is working on this we can collaborate and not duplicate our work. | {
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} | 11 | 2020-01-02T04:28:17Z | 2020-01-08T09:54:00Z | 2020-01-06T13:21:33Z | CONTRIBUTOR | null | - [x] closes #27357
- [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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] | closed | false | null | [] | null | 1 | 2020-01-02T05:26:21Z | 2020-01-02T21:13:28Z | 2020-01-02T21:13:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**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
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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] | closed | false | null | [] | null | 2 | 2020-01-02T12:15:24Z | 2020-01-02T12:44:16Z | 2020-01-02T12:42:19Z | NONE | null | I am using using pandas version 0.25.3 and Python 3.7.4
```python
students = pd.DataFrame({
'name': ['Dev', 'Prem', 'Kiran', 'Manish', 'Sandeep', 'Deepika', 'Amit'],
'class': ['7A', '7B', '7D', '7A', '7B', '7B', '7C'],
'gender': ['M', 'M', 'F', 'M', 'M', 'F', 'M'],
'roll': [1, 2, 3, 4, 5, 6, 7]
}).set_index('roll')
maths = pd.DataFrame({
'sr': [1, 2, 3, 5, 6],
'marks': [45, 23, 76, 87, 45],
}).set_index('sr')
students.merge(maths, left_on='roll', right_on='sr')
# KeyError: "None of ['roll'] are in the columns"
pd.merge(students, maths, left_on='roll', right_on='sr')
# KeyError: "None of ['roll'] are in the columns"
```
#### Problem description
When I used pandas 0.25.1, `students.merge` worked but `pd.merge` did not work.
Now both aren't working.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-36-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
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
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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"description": "Non-arithmetic algos: va... | open | false | null | [] | null | 5 | 2020-01-02T15:10:35Z | 2021-07-25T04:33:37Z | null | CONTRIBUTOR | null | If I call `pd.Categorical` using the results of `pd.factorize`, sometimes the encodings produced by them will be different:
```python
import pandas as pd, numpy as np
df = pd.DataFrame({
"col1" : ["S","C","S","S","Q"]
})
df["col1"] = pd.Categorical(df["col1"])
encoded, factors = pd.factorize(df["col1"])
recoded = pd.Categorical(df["col1"], factors).codes
```
I would expect in this case that all three objects (`encoded`, `recoded`, and the original) will have the same encoding, but these are the results:
```python
df.col1.cat.codes.values
```
```
>>> array([2, 0, 2, 2, 1], dtype=int8)
```
```python
encoded
```
```
>>> array([0, 1, 0, 0, 2])
```
```python
recoded
```
```
>>> array([2, 0, 2, 2, 1], dtype=int8)
```
The issue can be solved by using `pd.Categorical` with `factors.to_numpy()` instead, but I think it'd be very reasonable to assume that the output from `factorize` is the expected input to `Categorical`. | {
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} | 2 | 2020-01-02T15:36:41Z | 2020-01-05T10:02:51Z | 2020-01-04T18:28:54Z | MEMBER | null | …g types
By default, mypy allows always-false comparisons like 42 == 'no'. Use this flag to prohibit such comparisons of non-overlapping types, and similar identity and container checks
```
pandas\core\dtypes\base.py:239: error: Non-overlapping equality check (left operand type: "str", right operand type: "Callable[[ExtensionDtype], str]")
pandas\core\arrays\period.py:657: error: Non-overlapping container check (element type: "Callable[[Any], Any]", container item type: "Callable[[Any, Any], Any]")
pandas\core\arrays\period.py:658: error: Non-overlapping identity check (left operand type: "Callable[[Any], Any]", right operand type: "Callable[[Any, Any], Any]")
pandas\io\formats\format.py:307: error: Non-overlapping equality check (left operand type: "bool", right operand type: "Literal['truncate']")
pandas\io\formats\format.py:461: error: Non-overlapping equality check (left operand type: "bool", right operand type: "Literal['truncate']")
pandas\io\excel\_odfreader.py:159: error: Non-overlapping equality check (left operand type: "str", right operand type: "float")
``` | {
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} | 12 | 2020-01-02T17:27:01Z | 2020-01-07T16:29:07Z | 2020-01-03T19:34:02Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/16232.
I tried to keep the commits somewhat clean.
https://github.com/pandas-dev/pandas/commit/e96624b8a3f903798f7977fb92da09fc7417ec98 and https://github.com/pandas-dev/pandas/commit/d52d35f9d6a5e721ae0e6aa1579e44dac820ed7a can probably be ignored. That's just moving `pandas.util.testing` to `pandas.util._testing` and updating all the relevant imports.
Main question: do we want to make the `tm.make*` methods public? I'd say we probably should, but wanted to confirm. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30621/events | https://github.com/pandas-dev/pandas/issues/30621 | 544,696,878 | MDU6SXNzdWU1NDQ2OTY4Nzg= | 30,621 | DataFrameGroupBy object cannot be passed to dict ctor | {
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```python
import pandas as pd
iris = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/master/pandas/tests/data/iris.csv")
groupby_obj = iris.groupby('Name')
d = dict(groupby_obj)
```
#### Problem description
Instance of `DataFrameGroupBy` is an iterable yielding pairs (*key*, *frame*),
hence it ought to work with buildin `dict`.
However, it raises error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: attribute of type 'str' is not callable
```
#### Expected Output
`d` should be a dict with groups:
```
{'Iris-setosa': SepalLength SepalWidth PetalLength PetalWidth Name
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
...
7 4.6 3.2 1.4 0.2 Iris-setosa
48 5.3 3.7 1.5 0.2 Iris-setosa
49 5.0 3.3 1.4 0.2 Iris-setosa, 'Iris-versicolor': SepalLength SepalWidth PetalLength PetalWidth Name
50 7.0 3.2 4.7 1.4 Iris-versicolor
51 6.4 3.2 4.5 1.5 Iris-versicolor
...
146 6.3 2.5 5.0 1.9 Iris-virginica
147 6.5 3.0 5.2 2.0 Iris-virginica
148 6.2 3.4 5.4 2.3 Iris-virginica
149 5.9 3.0 5.1 1.8 Iris-virginica}
```
Notice, it is possible to *workaround* this by use of intermediate
iterable:
```python
d = dict(list(groupby_obj))
```
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30622/events | https://github.com/pandas-dev/pandas/pull/30622 | 544,698,382 | MDExOlB1bGxSZXF1ZXN0MzU4NzU1MDcz | 30,622 | DOC: Fixing PR09 formatting errors | {
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} | 1 | 2020-01-02T18:46:30Z | 2020-01-06T16:46:55Z | 2020-01-03T00:52:21Z | CONTRIBUTOR | null | - part of #28602
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
just the good stuff from #29530 | {
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} | 1 | 2020-01-02T20:03:02Z | 2020-01-04T18:21:37Z | 2020-01-04T18:21:33Z | CONTRIBUTOR | null | The test ensures that ExtensionArrays are correctly constructed when
concat(copy=False) is used.
- [x] closes #20756
- [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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} | 2 | 2020-01-02T20:41:34Z | 2020-01-06T15:38:22Z | 2020-01-03T09:59:26Z | MEMBER | null | cc @jschendel is the usage of _simple_new instead of _shallow_copy `take` important? Should it always be the case that `idx.close == idx._data.closed`? | {
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} | 1 | 2020-01-02T21:06:42Z | 2020-01-03T00:52:19Z | 2020-01-03T00:48:24Z | MEMBER | null | cc @jschendel the existing behavior treats `name` differently than pretty much all our other copy methods. Was there a reason for that? It doesn't appear to be tested. | {
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} | 1 | 2020-01-02T21:33:29Z | 2020-01-03T21:39:21Z | 2020-01-03T13:18:35Z | CONTRIBUTOR | null | - [x] closes #30590
- [ ] tests added / passed
- N/A
- [ ] passes `black pandas`
- N/A
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- N/A
- [ ] whatsnew entry
- N/A
Decided to add all valid strings for all the types in the table about dtypes. I didn't want to decide which ones to leave out, and if we want to leave some of them out, we should decide whether they should be removed from the code as well (e.g., `'Sparse[int, 0]'`)
Had to reformat the table so it would look nice, by splitting the list of strings into multiple lines (e.g., a merged cell)
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} | 3 | 2020-01-02T21:44:35Z | 2020-01-03T02:09:43Z | 2020-01-03T02:00:15Z | MEMBER | null | In following steps, I plan to
- remove the DatetimeDelegateMixin entirely and just use inherit_names
- de-duplicate the slightly-different comparison method code from CategoricalIndex vs DatetimelikeIndex
- move the remaining wrapping utilities from datetimelike to extension.py, after double-checking if we can remove any layers
- delegate more methods, some of which depends on smoothing out small idiosyncrasies, e.g. #30627. | {
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} | 7 | 2020-01-02T23:25:10Z | 2020-01-03T02:45:10Z | 2020-01-03T02:45:05Z | CONTRIBUTOR | null | -ref https://github.com/pandas-dev/pandas/pull/30478#issuecomment-570068174
We see the below in the logs:
```
google.api_core.exceptions.Conflict: 409 POST https://bigquery.googleapis.com/bigquery/v2/projects/pandas-travis/datasets: Already Exists: Dataset pandas-travis:pydata_pandas_bq_testing_py31
```
https://travis-ci.org/pandas-dev/pandas/jobs/631599036
Despite attempting to delete the dataset in the previous line.
`self.client.delete_dataset(self.dataset, delete_contents=True)`
Since we run with `dist=loadfile` these tests are run sequentially by pytest. But they could potentially clash across builds?
We now create a unique dataset name per test function and teardown when complete
GBQ Tests will run against my fork will post results on here.
cc. @jreback, @tswast | {
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} | 2 | 2020-01-02T23:28:55Z | 2020-01-03T23:17:46Z | 2020-01-03T22:21:19Z | CONTRIBUTOR | null | - [x] contributes to #29547
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Updates:
pandas/core/ops/array_ops.py
pandas/core/ops/dispatch.py
pandas/core/ops/docstrings.py
pandas/core/ops/invalid.py
pandas/core/ops/methods.py
pandas/core/ops/roperator.py
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} | 2 | 2020-01-03T00:34:03Z | 2020-01-05T00:03:54Z | 2020-01-04T23:37:18Z | CONTRIBUTOR | null | Add Types to args for `to_gbq` and `read_gbq`. | {
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} | 3 | 2020-01-03T00:40:51Z | 2020-01-04T15:16:04Z | 2020-01-03T01:16:52Z | CONTRIBUTOR | null | xref: https://github.com/pydata/pandas-gbq/issues/271 is now resolved.
This can be removed since `pandas-gbq` depends on `google-cloud-bigquery` ref: https://github.com/pydata/pandas-gbq/blob/master/setup.py | {
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```python
# Your code here
import pandas as pd
import numpy as np
df = pd.DataFrame({'a':[1,2,3,4,5], 'b':[10,9,8,7,np.nan], 'c':[8,7,6,5,4], 'd':[4,np.nan,6,7,8]})
df
def sum_(data):
return data.sum()
res= df.rolling(3,center=False).apply(sum_,raw=True)
res
```
#### Problem description
[pd.DataFrame.rolling.apply Can't deal with np.nan]
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} | 0 | 2020-01-03T02:34:15Z | 2020-01-03T03:47:55Z | 2020-01-03T03:40:48Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #17246, #21311
After this we now use the same pattern when calling DatetimeIndex, TimedeltaIndex, PeriodIndex, and IntervalIndex, so we can make a helper function and de-duplicate this code.
The cases of categorical or range data with object-dtype are not yet handled. | {
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} | 4 | 2020-01-03T02:41:41Z | 2020-01-09T03:26:09Z | 2020-01-09T03:25:48Z | CONTRIBUTOR | null | Co-authored-by: Luca Ionescu <lucaionescu@users.noreply.github.com>
- Continuing https://github.com/pandas-dev/pandas/pull/28562
- [x] closes #28512
@lucaionescu - i've merged master and pushed to here (I don't have the permissions to push to your branch), I will aim to fix up the tests. Or feel free to take it from here if you have time?
Opening up WIP PR to see what test failures need addressing.
I've also co-authored the commit. | {
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} | 1 | 2020-01-03T03:21:47Z | 2020-01-03T15:21:12Z | 2020-01-03T12:20:31Z | MEMBER | null | It will take a couple of steps to get these three methods to all behave the same. Following that we can move to share code between them.
This came up when trying to make the indexes dispatch `searchsorted` and it turns out that the DTI/TDI/PI searchsorted methods are not consistent with their DTA/TDA/PA counterparts. The fix is to have shared validation/casting methods, which should in turn be consistent with the comparison ops. | {
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} | 5 | 2020-01-03T04:03:13Z | 2020-01-19T03:53:33Z | 2020-01-19T01:00:59Z | CONTRIBUTOR | null | - [x] closes #30614
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The instructions aren't that well written, and should probably be improved, but here's a first shot. | {
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- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
cc @TomAugspurger | {
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} | 38 | 2020-01-03T11:10:46Z | 2020-05-12T16:02:40Z | 2020-05-12T16:02:40Z | CONTRIBUTOR | null | Added arm64 test support in travis-ci .
Modified environment creation by using archiconda instead of miniconda as miniconda is not supported in arm64 currently .
- [X] closes https://github.com/pandas-dev/pandas/issues/28986
- [X] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2020-01-03T12:11:06Z | 2020-01-09T12:29:05Z | 2020-01-09T12:29:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.26.0.dev0+1563.g1feefc692'
In [3]: pd.SparseArray
Out[3]: pandas.core.arrays.sparse.array.SparseArray
In [4]: pd.arrays.SparseArray
Out[4]: pandas.core.arrays.sparse.array.SparseArray
In [5]: pd.arrays.IntegerArray
Out[5]: pandas.core.arrays.integer.IntegerArray
In [6]: pd.IntegerArray
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-12476104dd13> in <module>
----> 1 pd.IntegerArray
C:\Code\pandas_dev\pandas\pandas\__init__.py in __getattr__(name)
246 return type(name, (), {})
247
--> 248 raise AttributeError(f"module 'pandas' has no attribute '{name}'")
249
250
AttributeError: module 'pandas' has no attribute 'IntegerArray'
In [7]: pd.arrays.StringArray
Out[7]: pandas.core.arrays.string_.StringArray
In [8]: pd.StringArray
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-86553ff3c48c> in <module>
----> 1 pd.StringArray
C:\Code\pandas_dev\pandas\pandas\__init__.py in __getattr__(name)
246 return type(name, (), {})
247
--> 248 raise AttributeError(f"module 'pandas' has no attribute '{name}'")
249
250
AttributeError: module 'pandas' has no attribute 'StringArray'
```
#### Problem description
I discovered this while working on #30628 . The docs for `SparseArray` are at the top level (https://dev.pandas.io/docs/reference/api/pandas.SparseArray.html), while the docs for `IntegerArray` (https://dev.pandas.io/docs/reference/api/pandas.arrays.IntegerArray.html), `StringArray`(https://dev.pandas.io/docs/reference/api/pandas.arrays.StringArray.html), etc. are at the `pandas.arrays` level.
In the code `SparseArray` is at both levels, but `IntegerArray`, `StringArray`, etc. is only at the `arrays` level.
#### Expected Output
Unsure.
It seems that this should be consistent. Options are:
1. Put all `*Array` classes at top level, and document them that way. (i.e., use the pattern currently used for `SparseArray`). That would involve code and documentation changes for all of the arrays except `SparseArray`.
2. Put all `*Array` classes at both levels (like `SparseArray`), but document them at the `pandas.arrays` level (like `IntegerArray` and `StringArray`). That would involve code changes for all of the arrays, and doc changes for `SparseArray`.
3. Put all `*Array` classes only at the `pandas.arrays` level and document them all that way. That would involve only changing code and docs for `SparseArray` and leaving the others alone.
It's not clear to me which is preferred.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 1feefc69241a8944bbdf3e7cd042336ef9554be7
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.26.0.dev0+1563.g1feefc692
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
numba : 0.46.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30643 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30643/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30643/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30643/events | https://github.com/pandas-dev/pandas/issues/30643 | 544,974,317 | MDU6SXNzdWU1NDQ5NzQzMTc= | 30,643 | Plotting error with datetime indices and horizontal line | {
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} | 4 | 2020-01-03T12:27:04Z | 2020-01-03T13:53:26Z | 2020-01-03T13:53:19Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib.pyplot as plt
import datetime
a = pd.Series([1, 2], index=[datetime.date(2020, 1, 1), datetime.date(2020, 1, 2)])
fig, ax = plt.subplots(1, 1)
a.plot(ax=ax)
ax.axhline(1)
a.plot(ax=ax)
```
#### Problem description
Last line results in error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\plotting\_core.py", line 794, in __call__
return plot_backend.plot(data, kind=kind, **kwargs)
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\plotting\_matplotlib\__init__.py", line 62, in plot
plot_obj.generate()
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\plotting\_matplotlib\core.py", line 287, in generate
self._post_plot_logic_common(ax, self.data)
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\plotting\_matplotlib\core.py", line 441, in _post_plot_logic_common
self._apply_axis_properties(ax.xaxis, rot=self.rot, fontsize=self.fontsize)
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\pandas\plotting\_matplotlib\core.py", line 525, in _apply_axis_properties
labels = axis.get_majorticklabels() + axis.get_minorticklabels()
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\matplotlib\axis.py", line 1252, in get_majorticklabels
ticks = self.get_major_ticks()
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\matplotlib\axis.py", line 1407, in get_major_ticks
numticks = len(self.get_majorticklocs())
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\matplotlib\axis.py", line 1324, in get_majorticklocs
return self.major.locator()
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\matplotlib\dates.py", line 1428, in __call__
self.refresh()
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\matplotlib\dates.py", line 1448, in refresh
dmin, dmax = self.viewlim_to_dt()
File "C:\Users\gpepe\AppData\Local\Continuum\anaconda3\envs\latest\lib\site-packages\matplotlib\dates.py", line 1199, in viewlim_to_dt
.format(vmin))
ValueError: view limit minimum 0.0 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units
```
This does not happen if ```axhline``` is run last. Feel free to close if this is a matplotlib issue and I will open this issue in that repo.
#### Expected Output
No error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.7
tables : None
xarray : 0.12.3
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
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Change all references in docs to use `arrays.SparseArray` rather than just `SparseArray`
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} | 1 | 2020-01-03T15:59:59Z | 2020-01-04T03:48:09Z | 2020-01-03T17:35:16Z | CONTRIBUTOR | null | Previously, `pandas.api` was imported indirectly via `pandas.io.json._table_schema`. This makes things a bit more direct.
I think we'll want a code check for importing from `pandas.api` within `pandas.core` but I wasn't able to easily write one (things like docstrings may want to have `import pandas.api` for example). | {
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} | 1 | 2020-01-03T16:57:43Z | 2020-01-04T18:18:04Z | 2020-01-04T18:10:21Z | MEMBER | null | Re-use the comparison method wrapper in CategoricalIndex. | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 2 | 2020-01-03T17:02:57Z | 2021-07-25T04:36:59Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(data={'a':[0,0]})
# df['b'] = None
# df['b'] = df['b'].astype('object')
df.at[0, 'b'] = ['a']
df.at[1, 'b'] = ['a']
>>> df
a b
0 0 a
1 0 [a]
```
#### Problem description
df.at[0, 'b'] should be ['a'] instead of 'a'.
I understand that it does not work when while adding the column but it feels inconsistent anyways.
When adding line 2 and 3 it works as expected.
#### Expected Output
```python
>>> df
a b
0 0 [a]
1 0 [a]
```
#### 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 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2020-01-03T17:15:56Z | 2020-01-04T18:09:16Z | 2020-01-04T18:07:25Z | MEMBER | null | cc @jschendel @jreback any idea why IntervalIndex doesn't use _assert_take_fillable like the others? If it can/should, then we can share this method between all of our EA-backed indexes. (Actually also need to make sure the slice behavior in the DTI/TDI/PI is OK to do for all of them) | {
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} | 1 | 2020-01-03T17:53:16Z | 2020-01-04T18:05:21Z | 2020-01-04T18:05:14Z | MEMBER | null | - [ ] xref #30578 #30405 #30402
This follow-up PR is to parametrize and deduplicate `inplace` cases brought up in the above PRs. | {
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} | 11 | 2020-01-03T18:01:16Z | 2020-05-09T07:57:18Z | 2020-05-09T07:57:17Z | CONTRIBUTOR | null | - [x] closes #27081
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
--- Jan 3 10:00 PST
Hey team,
Looking for a bit of feedback on this API design and appropriate testing for this PR before I get too deep into it. Could you provide some guidance regarding how to appropriately handle updating this test? https://github.com/pandas-dev/pandas/blob/6f03e76f9d47ecfcfdd44641de6df1fc7dd57a01/pandas/tests/extension/base/ops.py#L162 It appears to contradict the issue #27081.
Appreciate the help,
Dave
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 12 | 2020-01-03T19:36:17Z | 2020-01-07T14:35:23Z | 2020-01-05T16:16:34Z | CONTRIBUTOR | null | - [x] closes #30642
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- in #30644
Change all references in code from `pd.SparseArray` to `pd.arrays.SparseArray` . Add deprecation message for `pd.SparseArray`
Per comment from @TomAugspurger here: https://github.com/pandas-dev/pandas/pull/30644#issuecomment-570671439, this may require discussion. | {
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} | 3 | 2020-01-03T19:46:08Z | 2020-01-05T17:57:51Z | 2020-01-04T03:43:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
t = s.copy()
print(s.index == t.index)
# np.array([True, True, True, True, True])
```
#### Problem description
Comparing two indices returns an `np.ndarray`, and not another index. The behavior is different from series and dataframes. Is it a bug?
#### Expected Output
The output I expect should be achievable with
```python
pd.Index(s.index == t.index)
```
Possibly, the type of the index should be 'bool' (compare it with series, e.g. `s == t`).
It seems that `pd.Index(s.index == t.index).astype(bool)` doesn't return an index of bool either.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
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pandas\core\reshape\concat.py:477: error: Missing type parameters for generic type "List"
pandas\core\reshape\concat.py:504: error: Missing type parameters for generic type "List" | {
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} | 1 | 2020-01-03T20:40:22Z | 2020-01-03T23:59:30Z | 2020-01-03T23:59:25Z | CONTRIBUTOR | null | This test makes a potentially incorrect assertion about the data provided to the test. We already require that `data[0] != data[1]`, so it can be used instead.
This failed in https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=24731&view=logs&j=a67b4c4c-cd2e-5e3c-a361-de73ac9c05f9&t=33d2fdd0-c376-5f94-e6d3-957bdd23a3b8. | {
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} | 2 | 2020-01-03T22:04:07Z | 2020-01-04T17:40:05Z | 2020-01-04T17:36:53Z | MEMBER | null | PeriodIndex._simple_new is not as simple as it should be. In order to get it to have the appropriate signature we need to implement some of the set methods correctly. This is the first of those. | {
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} | 1 | 2020-01-03T22:19:43Z | 2020-01-20T16:28:01Z | 2020-01-20T16:28:01Z | NONE | null | #### Code Sample
```python
import pandas as pd
df=pd.DataFrame([['x','p'],['x','p'],['x','q']], columns=['X','Y'], index=[1,2,2])
print(df)
df=df.groupby(['Y']).apply(lambda x: x)
df=pd.DataFrame([['x','p'],['x','p'],['x','o']], columns=['X','Y'], index=[1,2,2])
print(df)
df=df.groupby(['Y']).apply(lambda x: x)
```
#### Problem description
On dataframe:
```
X Y
1 x p
2 x p
2 x q
```
`groupby.apply` on column `Y` works.
but throws duplicate axis exception with:
```
X Y
1 x p
2 x p
2 x o
```
the exception does not happen if the dataframe was sorted on `Y`
```
Traceback (most recent call last):
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 725, in apply
result = self._python_apply_general(f)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 745, in _python_apply_general
keys, values, not_indexed_same=mutated or self.mutated
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 372, in _wrap_applied_output
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 955, in _concat_objects
result = result.reindex(ax, axis=self.axis)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/util/_decorators.py", line 221, in wrapper
return func(*args, **kwargs)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/frame.py", line 3976, in reindex
return super().reindex(**kwargs)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/generic.py", line 4514, in reindex
axes, level, limit, tolerance, method, fill_value, copy
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/frame.py", line 3864, in _reindex_axes
index, method, copy, level, fill_value, limit, tolerance
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/frame.py", line 3886, in _reindex_index
allow_dups=False,
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/generic.py", line 4577, in _reindex_with_indexers
copy=copy,
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 1251, in reindex_indexer
self.axes[axis]._can_reindex(indexer)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3362, in _can_reindex
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/hm106930/ipv/web/groupby_bug.py", line 16, in <module>
df=df.groupby(['Y']).apply(lambda x: x)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 737, in apply
return self._python_apply_general(f)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 745, in _python_apply_general
keys, values, not_indexed_same=mutated or self.mutated
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 372, in _wrap_applied_output
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 955, in _concat_objects
result = result.reindex(ax, axis=self.axis)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/util/_decorators.py", line 221, in wrapper
return func(*args, **kwargs)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/frame.py", line 3976, in reindex
return super().reindex(**kwargs)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/generic.py", line 4514, in reindex
axes, level, limit, tolerance, method, fill_value, copy
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/frame.py", line 3864, in _reindex_axes
index, method, copy, level, fill_value, limit, tolerance
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/frame.py", line 3886, in _reindex_index
allow_dups=False,
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/generic.py", line 4577, in _reindex_with_indexers
copy=copy,
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 1251, in reindex_indexer
self.axes[axis]._can_reindex(indexer)
File "/home/hm106930/.local/share/virtualenvs/ipv-work-62kMEXht/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3362, in _can_reindex
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
```
#### Expected Output
```
X Y
1 x p
2 x p
2 x o
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.27.2.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 : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30668 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30668/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30668/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30668/events | https://github.com/pandas-dev/pandas/issues/30668 | 545,209,245 | MDU6SXNzdWU1NDUyMDkyNDU= | 30,668 | Concatenating Single-element dense series with SparseArray Series Raises Error | {
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} | 6 | 2020-01-03T23:49:31Z | 2020-09-03T09:30:33Z | 2020-05-25T12:41:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
a = pd.Series(pd.SparseArray([1, None]), dtype=np.float)
b = pd.Series([1], dtype=np.float)
pd.concat([a, b])
```
With interpreter output
```python
>>>import pandas as pd
>>>import numpy as np
>>>
>>>
>>> a = pd.Series(pd.SparseArray([1, None]), dtype=np.float)
>>> b = pd.Series([1], dtype=np.float)
>>> pd.concat([a, b], axis=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/henighan/Documents/henighan-pandas/pandas/core/reshape/concat.py", line 246, in concat
return op.get_result()
File "/Users/henighan/Documents/henighan-pandas/pandas/core/reshape/concat.py", line 426, in get_result
[x._data for x in self.objs], self.new_axes
File "/Users/henighan/Documents/henighan-pandas/pandas/core/internals/managers.py", line 1629, in concat
values = concat_compat(values)
File "/Users/henighan/Documents/henighan-pandas/pandas/core/dtypes/concat.py", line 117, in concat_compat
return _concat_sparse(to_concat, axis=axis, typs=typs)
File "/Users/henighan/Documents/henighan-pandas/pandas/core/dtypes/concat.py", line 478, in _concat_sparse
for x in to_concat
File "/Users/henighan/Documents/henighan-pandas/pandas/core/dtypes/concat.py", line 478, in <listcomp>
for x in to_concat
File "/Users/henighan/Documents/henighan-pandas/pandas/core/arrays/sparse/array.py", line 361, in __init__
data, kind=kind, fill_value=fill_value, dtype=dtype
File "/Users/henighan/Documents/henighan-pandas/pandas/core/arrays/sparse/array.py", line 1504, in make_sparse
if arr.ndim > 1:
AttributeError: 'float' object has no attribute 'ndim'
```
#### Problem description
When concatenating a series of a sparse type (eg `Sparse[float64, nan]` above) with a serires of a dense type (eg `float64` above) that has only a single element, the above exception is raised.
I believe this may be the result of the `squeeze` happening here:
https://github.com/henighan/pandas/blob/45d8d77f27cf0dbc8cefe932f8fb64f6982b9527/pandas/core/dtypes/concat.py#L477
If I remove this `squeeze`, it resolves the issue for me (yielding the output below), and all tests still pass on my mac when running `./test_fast.sh`. If this seems right to others, I'd be happy to open a pull request.
#### Expected Output
```python
>>> import pandas as pd
>>> import numpy as np
>>>
>>>
>>> a = pd.Series(pd.SparseArray([1, None]), dtype=np.float)
>>> b = pd.Series([1], dtype=np.float)
>>> pd.concat([a, b])
0 1.0
1 NaN
0 1.0
dtype: Sparse[float64, nan]
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pd.show_versions()
/Users/henighan/Documents/henighan-pandas/pandas/core/index.py:29: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
FutureWarning,
INSTALLED VERSIONS
------------------
commit : 45d8d77f27cf0dbc8cefe932f8fb64f6982b9527
python : 3.7.6.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 : 0.26.0.dev0+1586.g45d8d77f2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200102
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 5.1.0
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 : 7.10.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.46.0
</details>
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} | 20 | 2020-01-04T03:15:35Z | 2020-09-21T18:21:36Z | 2020-01-18T03:24:36Z | MEMBER | null | Instead of returning a Series, return a tuple with the index and values to be passed to Series.
Where possible I've changed the methods to use `_values_for_factorized` in the hopes of converging on a base class implementation. This is proving elusive, suggestions welcome. cc @TomAugspurger @jorisvandenbossche
xref #22843, #23074. | {
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} | 1 | 2020-01-04T06:01:07Z | 2020-01-06T02:00:11Z | 2020-01-05T21:39:10Z | MEMBER | null | - [x] closes #30593
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} | 10 | 2020-01-04T07:19:38Z | 2020-01-27T06:55:10Z | 2020-01-26T01:03:37Z | MEMBER | null | - [X] closes #30543
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This implements a shortcut in the Timestamp constructor to cut down on processing if Timestamp is passed. We still need to check if the timezone was passed correctly. Then, if a Timestamp was passed, and there is no timezone, we just return that same Timestamp.
A test is added to check that the Timestamp is still the same object.
PR for timedelta to be added once I confirm that this is the approach we want to go with.
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```python
In [15]: df = pd.DataFrame({'num_legs': [None, 4], 'num_wings': [np.nan, 0]},ind
...: ex=['falcon', 'dog'])
In [16]: df.isin([np.nan, None])
Out[16]:
num_legs num_wings
falcon True True
dog False False
In [17]: df.isin([None, None])
Out[17]:
num_legs num_wings
falcon True True
dog False False
In [18]: df.isin([np.nan, np.nan])
Out[18]:
num_legs num_wings
falcon True True
dog False False
```
But Index case,
```
In [20]: idx = Index([np.nan,'a','b', None])
In [21]: idx.isin([None])
Out[21]: array([False, False, False, True])
In [22]: idx.isin([np.nan])
Out[22]: array([ True, False, False, False])
```
#### Problem description
'.isin' handle NA values differently depending on object types. 'np.nan' and 'None' both are NA value. so, 'Index.isin' do not distinguish 'np.nan' and 'None' like 'DataFrame' case.
#### Expected Output
```
In [20]: idx = Index([np.nan,'a','b', None])
In [21]: idx.isin([None])
Out[21]: array([True, False, False, True])
In [22]: idx.isin([np.nan])
Out[22]: array([ True, False, False, True])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ko_KR.UTF-8
LOCALE : ko_KR.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
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} | 6 | 2020-01-04T15:22:01Z | 2020-01-21T11:00:06Z | 2020-01-20T16:28:01Z | MEMBER | null | This is a more of a patch than a complete solution to this groupby apply paradigm.
When there are duplicates in the groupby axis, we restore the axis to its original order, but not guaranteeing that the order of the data with the same axis values is restored.
- [x] closes #30667
- [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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} | 8 | 2020-01-04T16:32:18Z | 2020-01-06T08:02:06Z | 2020-01-05T16:22:12Z | CONTRIBUTOR | null | This will otherwise raise a failure during a `mypy` update. Also increase type precision of this file to 100%.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @simonjayhawkins | {
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] | closed | false | null | [] | null | 3 | 2020-01-04T16:51:26Z | 2020-09-25T07:36:29Z | 2020-09-25T07:36:29Z | CONTRIBUTOR | null | As a followup from #28215
_parsers and _engines as used in the code for the `.query` and `.eval` methods should be de-privatized and named to something more informative.
```
_engines: Dict[str, Type[AbstractEngine]] = {
"numexpr": NumExprEngine,
"python": PythonEngine,
}
```
```
_parsers = {"python": PythonExprVisitor, "pandas": PandasExprVisitor}
```
@jreback | {
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} | 1 | 2020-01-04T16:54:25Z | 2020-01-04T20:42:06Z | 2020-01-04T17:39:10Z | CONTRIBUTOR | null | This is quite helpful in developing with typing, especially if you plan to update the mypy version.
- [ ] closes #xxxx
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"de... | open | false | null | [] | null | 1 | 2020-01-04T16:54:38Z | 2020-02-25T16:05:01Z | null | CONTRIBUTOR | null | As a followup from #28215
Moving the axis resolvers to core/computation
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} | 1 | 2020-01-04T17:02:47Z | 2020-01-04T20:41:47Z | 2020-01-04T17:58:57Z | CONTRIBUTOR | null | This seems to be quite common nowadays. | {
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} | 3 | 2020-01-04T17:09:19Z | 2020-01-09T19:19:35Z | 2020-01-09T19:19:35Z | MEMBER | null | ```
arr = pd.array([True, False, np.nan])
>>> arr
<BooleanArray>
[True, False, NA]
Length: 3, dtype: boolean
>>> arr.value_counts(dropna=False)
True 1
False 1
False 1
dtype: int64
```
Instead of two False entries, one of them should be pd.NA right? cc @TomAugspurger
```
arr = pd.array(['foo', 'bar', None])
>>> arr.value_counts(dropna=False)
foo 1
bar 1
NaN 1
dtype: int64
```
The NaN should be pd.NA right?
Side-note: the defaults for dropna are not consistent across our EAs | {
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} | 2 | 2020-01-04T17:13:30Z | 2020-01-04T18:10:10Z | 2020-01-04T18:04:05Z | MEMBER | null | I'm _pretty_ sure that for non-comparable we should be returning an array of -1s, not raising. Can you double-check me on that @jreback?
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} | 1 | 2020-01-04T17:45:46Z | 2020-01-04T21:33:47Z | 2020-01-04T21:33:41Z | MEMBER | null | closes #14563
closes #14958
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-04T18:20:33Z | 2020-01-04T23:12:40Z | 2020-01-04T23:12:36Z | CONTRIBUTOR | null | - [x] closes #29070
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- New PR since https://github.com/pandas-dev/pandas/pull/29087 went stale
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} | 2 | 2020-01-04T18:25:52Z | 2020-01-04T23:22:53Z | 2020-01-04T23:21:27Z | CONTRIBUTOR | null | Currently, the NDFrame indexers (.loc/.iloc/.at/.iat) are too complex set up to let mypy understand them. This PR makes their implementations understandable for mypy (and humans also maybe, the old imp. was a bit indirect so took som effort to understand).
One issue is that I - for the life of me - can't programatically set doc strings of properties and make mypy understand the properties' types at the same time. Python/mypy seem to demand that doc strings be set directly on the property if the type is to be understandable by mypy. E.g. Appender on a property is not supported by mypy. So I've set the doc string on the NDFrame properties, and reference them from the indexer classes instances. This makes the PR seem large, but it's mostly just moving the doc strings.
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} | 8 | 2020-01-04T18:44:34Z | 2020-01-08T03:26:09Z | 2020-01-07T21:18:36Z | CONTRIBUTOR | null | - [x] closes #13318
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] add whats new note
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} | [] | closed | false | null | [] | null | 1 | 2020-01-04T19:12:22Z | 2020-01-04T19:46:41Z | 2020-01-04T19:46:41Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```import pandas as pd; pd.__version__
tz = "dateutil/Europe/London"
ts = pd.Timestamp("2013-10-27 01:00:00")
print("Initial value is {}".format(ts.value))
ts = ts.tz_localize(tz, ambiguous=False, nonexistent="raise")
print("After localization value is {}".format(ts.value))
ts = pd.Timestamp(ts)
print("After casting Timestamp as Timestamp value is {}".format(ts.value))
Output:
0.26.0.dev0+1608.g06c5d2488
Initial value is 1382835600000000000
After localization value is 1382835600000000000
After casting Timestamp as Timestamp value is 1382832000000000000
```
#### Problem description
TLDR: Calling the Timestamp constructor on a Timestamp shouldn't change the object in any way.
Long version: Ran into this while working on solving #30543. When we localize into a Daylight Savings Time timezone, we are forced to alter the underlying value of a Timestamp to make it fit non-DST timezones. WIthout this, date arithmetic won't work properly between timezones. This is also necessary, for example, to make sure that a `pd.date_range` with one of the ends on a DST shift doesn't break (the test for this is implemented by `test_dti_construction_ambiguous_endpoint` in `pandas.tests.indexes.datetimes.test_timezones.TestDatetimeIndexTimezones`). Unfortunately, what happens currently is that `ts.tz_localize` doesn't change the underlying value on a DST shift. It instead changes when the Timestamp constructor is called on a localized Timestamp (you can take a look, for example, [here](https://github.com/pandas-dev/pandas/blob/06c5d2488e2898497fa4767dc78a7c85ac8d19d4/pandas/core/arrays/_ranges.py#L40)).
#### Expected Output
```
Initial value is 1382835600000000000
After localization value is 1382832000000000000
After casting Timestamp as Timestamp value is 1382832000000000000
```
The value should change on localization, not on calling the constructor.
I'd like to work on a PR, because without fixing this it's impossible to solve #30543 without introducing ugly hacks.
xref: #30543 | {
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} | 2 | 2020-01-04T19:40:42Z | 2020-01-14T23:43:05Z | 2020-01-04T22:57:37Z | CONTRIBUTOR | null | - [x] closes #30433
As per https://docs.pytest.org/en/latest/deprecations.html#junit-family-default-value-change-to-xunit2
This will mean we produce xml output with xsd as per: (opposed to old legacy v1)
https://github.com/jenkinsci/xunit-plugin/blob/xunit-2.3.2/src/main/resources/org/jenkinsci/plugins/xunit/types/model/xsd/junit-10.xsd
Done following check
- [x] Warning no longer present in builds -> checked [here](https://travis-ci.org/pandas-dev/pandas/jobs/632732296?utm_medium=notification&utm_source=github_status)
- [x] CodeCov unaffected since it uses the output xml -> [here](https://codecov.io/gh/pandas-dev/pandas/compare/b29d58d1a3e0ebb1ed3bf3bed1e1cf4848092c77...2a2d21a14a78e028e8e9a9c3bbf815ea76501036)
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} | 1 | 2020-01-04T20:33:45Z | 2020-01-04T23:12:01Z | 2020-01-04T23:08:34Z | MEMBER | null | Besides de-duplicating, this moves the ball down the field in getting rid of PeriodIndex._shallow_copy cases where we pass an object ndarray | {
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} | 2 | 2020-01-04T20:49:39Z | 2020-01-05T18:57:34Z | 2020-01-05T18:42:18Z | MEMBER | null | Following this we'll be able to share the method. | {
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} | 2 | 2020-01-04T20:52:23Z | 2020-01-05T18:08:39Z | 2020-01-05T16:22:42Z | MEMBER | null | Analogous to #30666. After this we'll be able to share some code between the set operations.
The edit in pd._testing is mostly unrelated. Both are motivated by tracking down the places where object-dtype ndarray is passed to PeriodIndex._shallow_copy. | {
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} | 2 | 2020-01-04T21:14:05Z | 2020-01-04T22:06:46Z | 2020-01-04T22:06:33Z | CONTRIBUTOR | null | A small embarrassing mistake is corrected here that was introduced in the recently merged #28215 | {
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} | 12 | 2020-01-04T21:43:39Z | 2021-01-08T14:10:02Z | 2021-01-08T14:10:02Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Consider the following setup on master:
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1576.gdd0d353fb'
In [2]: dti = pd.date_range("2020", periods=3)
...: dti_tz = pd.date_range("2020", periods=3, tz="UTC")
...: tdi = pd.timedelta_range("0 days", periods=3)
...: pi = pd.period_range("2020Q1", periods=3, freq="Q")
```
Equality comparisons with an equivalent `Categorical` are incorrect for `DatetimeIndex`:
```python
In [3]: dti == pd.Categorical(dti)
Out[3]: array([False, False, False])
In [4]: dti_tz == pd.Categorical(dti_tz)
Out[4]: array([False, False, False])
```
Equality comparisons raise for `PeriodIndex`:
```python
In [5]: pi == pd.Categorical(pi)
---------------------------------------------------------------------------
ValueError: Value must be Period, string, integer, or datetime
```
Looks good for `TimedeltaIndex`:
```python
In [6]: tdi == pd.Categorical(tdi)
Out[6]: array([ True, True, True])
```
The incorrect behavior above is generally consistent when replacing the index with its extension array/Series equivalent, `Categorical` with `CategoricalIndex`/`Series[Categorical]`, and `==` with `!=`.
The only exception appears to be that a couple cases work when when you have a `Categorical`/ `CategoricalIndex` on the RHS and an extension array on the LHS:
```python
In [7]: pd.Categorical(dti) == dti.array
Out[7]: array([ True, True, True])
In [8]: pd.CategoricalIndex(pi) == pi.array
Out[8]: array([ True, True, True])
```
Though note that the above does not work for `dti_tz.array`
#### Problem description
Equality comparisons for datetimelike arrays/indexes are largely incorrect when comparing to equivalent categoricals. There is some tie in to #19513 but I think this specific component is pretty clear cut.
#### Expected Output
I'd expect all variants of `==` to in the examples above to return `array([ True, True, True])`, and all variants of `!=` to return `array([False, False, False])`.
cc @jbrockmendel
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} | 1 | 2020-01-04T22:14:19Z | 2020-01-05T06:50:20Z | 2020-01-04T23:27:39Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Ref to #29547 | {
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} | 3 | 2020-01-05T11:03:40Z | 2020-01-06T23:42:16Z | 2020-01-06T17:24:23Z | MEMBER | null | - [ ] closes #xxxx
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Test case for:
```python
foo = (
"bar"
" baz"
)
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and on master:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=24897&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=fe74a338-551b-5fbb-553d-25f48b1836e8
Believe the error is:
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} | 2 | 2020-01-05T15:33:20Z | 2020-01-14T23:42:25Z | 2020-01-05T16:13:31Z | CONTRIBUTOR | null | - [x] closes #30709
hmm.. im not sure on this. But don't see any usage of this ret value.
The C extensions now build with this change
Potentially related change on the numpy side:
https://github.com/numpy/numpy/pull/15232
cc. @WillAyd @jreback | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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