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} | 5 | 2020-12-01T22:29:33Z | 2021-01-24T05:32:54Z | 2020-12-02T12:09:14Z | CONTRIBUTOR | null | Additionally I switched to the miniforge3 image as this bases its installation on conda-forge
and I also have used `mamba` to install the environment as this massively saves time.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2020-12-01T22:49:48Z | 2020-12-15T18:46:47Z | 2020-12-14T23:55:16Z | MEMBER | null | - [x] closes #36293
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As far as I know, isin needs a level input when only a string is given for a MultiIndex. If this should not change, we have to set level=0 in this case. | {
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} | 3 | 2020-12-01T23:24:22Z | 2021-05-25T08:09:06Z | 2021-05-25T08:09:06Z | MEMBER | null | The first pipeline to fail was 2a115d5d750c65765913c1940ac4c85f4ab51df9 which also was the first pipeline using pip 20.3
This seems to be a problem with scipy version.
@jreback @jbrockmendel @jorisvandenbossche
Last pipeline to work:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=49130&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=71ac627c-7b38-59b1-014b-81753001ec5e
First fo fail:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=49159&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=71ac627c-7b38-59b1-014b-81753001ec5e&l=333 | {
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} | 9 | 2020-12-02T02:06:07Z | 2021-06-11T12:40:53Z | null | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/pull/38102#discussion_r532446692
for `np.nan` we define `bool(np.nan)`
```
In [189]: 1 or np.nan
Out[189]: 1
In [190]: np.nan or 1
Out[190]: nan
```
however we don't do this for `pd.NA`
```
>>> pd.NA or 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/missing.pyx", line 360, in pandas._libs.missing.NAType.__bool__
raise TypeError("boolean value of NA is ambiguous")
TypeError: boolean value of NA is ambiguous
```
I think this is pretty odd and not very useful. | {
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} | 4 | 2020-12-02T02:54:26Z | 2020-12-04T20:40:23Z | 2020-12-04T18:58:48Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Allows (and encourages) the following use:
```py
import pandas as pd
filename = "pandas/tests/io/data/csv/iris.csv"
chunksize = 2
with pd.read_csv(filename, chunksize=chunksize) as reader:
for chunk in reader:
# risky code that might raise
```
Same can be done for `read_json/sas` (I think these are all methods that support `chunksize`). If this PR should make it into 1.2, I can quickly add the changes for json/sas as well.
Are there more places to promote this new context manager? | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
identify unnecessary dict call - rewrite as a literal
Rewrites as dictionary literals for the following files in two spots:
- pandas/tests/frame/test_indexing
- pandas/tests/frame/test_where
based on @jreback comments:
https://github.com/pandas-dev/pandas/pull/38207#issuecomment-736960208 | {
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} | 3 | 2020-12-02T04:02:55Z | 2020-12-04T23:45:03Z | 2020-12-04T23:45:03Z | NONE | null | Hello there,
Consider this simple example
```
pd.__version__
Out[158]: '1.1.3'
df = pd.DataFrame({'col': [1,2,3],
'group' : ['a',np.NaN,'b']})
df
Out[160]:
col group
0 1 a
1 2 NaN
2 3 b
df.groupby('group').apply(lambda x: x)
Out[161]:
col group
0 1.0 a
1 NaN NaN
2 3.0 b
```
There are a few things that are puzzling.
1. I thought `groupby` would drop the NA groups by default
2. Why is the variable `col` set to missing?
This looks like a bug to me, unless I am missing something (apologies if this is the case)
What do you think?
Thanks! | {
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```python
import pandas as pd
import datetime as dt
df = pd.DataFrame([["05SEP2014", "a"]], columns=["date", "other_col"])
def fix_date_a(df):
df['date'] = df["date"].apply(lambda x: dt.datetime.strptime(x, "%d%b%Y"))
return df
# of course this example could have been vectorized much better like so
def fix_date_b():
df['date'] = pd.to_datetime(df['date'], format='%d%b%Y')
return df
```
This is ok, but I would much rather write this in a fluent style. As far as I am aware this is only possible with `.assign` and `.pipe`, and would give something like so:
```python
from functools import partial
def element_wise_date_parser(x):
return dt.datetime.strptime(x, "%d%b%Y")
vectorized_date_parser = partial(pd.to_datetime, format='%d%b%Y')
def fix_date_c():
return df.assign(date=lambda x: x["date"].apply(element_wise_date_parser))
def fix_date_d():
return df.assign(date=lambda x: vectorized_date_parser(x["date"]))
```
I find that syntax not so easy to read and write. And as I find myself needing to perform an operation on a single column of a dataframe quite often, I would like to have a better way to do that. I propose add an apply_to method for the element wise which I made an example monkeypatch implementation:
```python
def apply_to(self, column_name, function):
return self.assign(**{column_name: lambda x: x[column_name].apply(function)})
pd.DataFrame.apply_to = apply_to
def fix_date_d(df):
return df.apply_to("date", element_wise_date_parser)
# not sure what a column wise function could be named, lets say operate_on
def operate_on(self, column_name, function):
return self.assign(**{column_name: lambda x: function(x[column_name])})
pd.DataFrame.operate_on= operate_on
def fix_date_e(df):
return df.operate_on("date", vectorized_date_parser)
```
With these examples I find it much is easier to in-place modify a column without breaking the fluent interface. Of course adding even more methods to the already broad dataframe API is not free so am not 100% sure this is a good idea. But I wanted to put it up here anyway as often see myself and others cluttering code with unnecessary intermediate dataframes and or repeatedly reassigning `df` due to not knowing how to keep the fluent interface. And sprinkling around "assign with lambdas" everywhere is also not that appealing | {
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} | 4 | 2020-12-02T11:45:09Z | 2020-12-12T23:00:28Z | 2020-12-12T23:00:24Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR adds messages to the bare pytest raises in reduce.py and getitem.py in pandas/test/extension/base. This PR references #30999. | {
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} | 1 | 2020-12-02T14:25:27Z | 2020-12-02T19:20:55Z | 2020-12-02T19:20:55Z | NONE | null | As from title.
Currently calling df.xs(key) throws KeyError if key is not in the index. I would advocate for it returning an empty dataframe. | {
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} | 2 | 2020-12-02T14:41:16Z | 2020-12-23T14:38:03Z | 2020-12-23T14:38:02Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]])
s = Styler(df, uuid="_", cell_ids=False)
assert '<th id="T__level0_row0" class="row_heading level0 row0" rowspan="2">l0</th>' in s.render()
```
#### Problem description
This issue is very similar to #35706. The ``colspan`` attribute has been fixed in #36223 but the ``rowspan`` attribute hasn't been fixed yet. Fixing this would improve HTML correctness and make the ``Styler.render()`` behavior more consistent.
Compare [line 330](https://github.com/attack68/pandas/blob/42f600e2acc0cd905c078ab77b473f0537699cc5/pandas/io/formats/style.py#L330) (colspan attribute) and [line 376](https://github.com/attack68/pandas/blob/42f600e2acc0cd905c078ab77b473f0537699cc5/pandas/io/formats/style.py#L376) (rowspan attribute).
#### Expected Output
The ``rowspan`` attribute uses quotes and the assert statement doesn't fail.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2020-12-02T14:59:53Z | 2020-12-02T23:59:59Z | 2020-12-02T23:33:28Z | MEMBER | null | Consolidate two for-loops into one.
Instead of using maybe_cast_result in two places, use it only once, immediately after the function call being wrapped. Replace those two usages with the less-invasive `maybe_downcast_to_dtype` | {
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} | 1 | 2020-12-02T15:12:33Z | 2020-12-19T02:22:44Z | 2020-12-19T02:22:44Z | MEMBER | null | - [ ] xref #37715
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Handle mypy ignore comments in ``pandas/_testing.py``, function ``write_to_compressed``. | {
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} | 2 | 2020-12-02T15:17:11Z | 2020-12-02T23:48:48Z | 2020-12-02T23:35:19Z | MEMBER | null | Orthogonal to #38235. After this and 38235, we'll be down to only two usages of maybe_cast_result, only one of which is sketchy. | {
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} | 13 | 2020-12-02T17:17:27Z | 2021-03-28T16:23:33Z | 2021-03-28T16:23:32Z | MEMBER | null | - [ ] xref #37715
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Handle mypy ignore comments in ``pandas/core/base.py``, class ``SelectionMixin``.
There were issues with attributes ``obj`` and ``exclusions`` not defined in the mixin, but defined in the child classes.
I wish I made ``obj`` of type ``FrameOrSeries`` rather than ``Any``.
This is the type used in the child classes, but this results in multiple mypy errors of this kind.
```
pandas\core\window\rolling.py:197: error: FrameOrSeries? has no attribute "ndim" [attr-defined]
```
If I go with ``FrameOrSeriesUnion``, then it is necessary to go and do casting where required to DataFrame or Series.
This is necessary since, for example, some kwargs for either of the types are irrelevant. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38240 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38240/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38240/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38240/events | https://github.com/pandas-dev/pandas/issues/38240 | 755,480,736 | MDU6SXNzdWU3NTU0ODA3MzY= | 38,240 | BUG: find_common_type incorrect for Categorical with NaNs | {
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... | open | false | null | [] | null | 1 | 2020-12-02T17:44:13Z | 2021-08-14T17:09:13Z | null | MEMBER | null | ```
import pandas as pd
from pandas.core.dtypes.cast import find_common_type
left = pd.Index([1, 2, 3, 4])
right = pd.CategoricalIndex([1, None], categories=left)
dtype = find_common_type([left.dtype, right.dtype])
>>> right.astype(dtype)
ValueError: Cannot convert float NaN to integer
```
xref #37930; I expect this would also be relevant for #19371
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} | 1 | 2020-12-02T17:50:01Z | 2020-12-03T07:55:24Z | 2020-12-02T23:37:07Z | MEMBER | null | - [ ] closes #23239
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Add tests required to close the issue.
The tests are based on the example in the issue. | {
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} | 1 | 2020-12-02T17:52:12Z | 2020-12-03T14:23:38Z | 2020-12-03T14:23:34Z | MEMBER | null | - [x] closes #32553
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Picking up #36628 | {
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... | closed | false | null | [] | null | 2 | 2020-12-02T17:58:13Z | 2021-08-14T17:10:21Z | 2021-08-14T17:10:21Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# reference, works: 2 normal days.
i0 = pd.date_range('2020-09-24', '2020-09-26', freq='H', closed='left', tz='Europe/Berlin')
pd.infer_freq(i0) # 'H' (i0.freq == '<Hour>')
# problem, does not work: summertime to wintertime changeover.
i1 = pd.date_range('2020-10-24', '2020-10-26', freq='H', closed='left', tz='Europe/Berlin')
pd.infer_freq(i1) # '' <-- !!!!! (even though i1.freq == '<Hour>')
# for completeness, works: wintertime to summertime changeover
i2 = pd.date_range('2020-03-28', '2020-03-30', freq='H', closed='left', tz='Europe/Berlin')
pd.infer_freq(i2) # 'H' (i2.freq == '<Hour>')
```
#### Problem description
Even though `i1` has the frequency attribute correctly set, `pd.infer_freq(i1)` returns an empty string.
#### Expected Output
`'H'`, just like when using `i0` or `i2`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : de_DE.cp1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3
setuptools : 50.3.2.post20201202
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 3 | 2020-12-02T18:14:43Z | 2020-12-03T11:11:12Z | 2020-12-02T23:30:04Z | MEMBER | null | - [ ] closes #37115
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2020-12-02T18:47:11Z | 2020-12-03T16:01:44Z | 2020-12-03T15:55:03Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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The problem was introduced a couple weeks ago, so shouldn't need a whatsnew. | {
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- [x] tests added / passed
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Picking up #35341 (original PR addressed all comments - here I merged master + added whatsnew note)
cc @simonjayhawkins @jreback @jbrockmendel | {
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Locally this gets me to within 3-4% of 1.1.4 on the asv mentioned in #37462 | {
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} | 1 | 2020-12-02T22:27:22Z | 2020-12-04T15:20:27Z | 2020-12-04T14:20:41Z | MEMBER | null | Fixes some problems in get_indexer_non_unique that AFAICT are not actually causing any problems.
Categorical kludge is needed because of #38240. | {
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} | 1 | 2020-12-03T00:15:45Z | 2020-12-03T02:35:19Z | 2020-12-03T02:24:55Z | MEMBER | null | Fixes a `flake8` bug on current master:
```
(pandas-dev) andrewwieteska@Andrews-MacBook-Pro pandas % flake8 pandas/core/groupby/groupby.py
pandas/core/groupby/groupby.py:53:1: F401 'pandas.core.dtypes.cast.maybe_cast_result' imported but unused
``` | {
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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
The existing code is implicitly assuming that _from_sequence is strict, so that maybe_cast_to_extension_array will only return an EA when correct. Since that assumption is untrue, the current code will return incorrect results, as in the test this adds.
If we just removed L729-L741 in groupby.ops, we would have 12ish test failures. A few of those would be for float64 result failing to cast back to Float64, and the rest would be for `ndarray[Decimal objects]` failing to cast back to DecimalArray. Until `_from_sequence` is reliable, I would rather remove these few lines and return correct-but-suboptimally-casted results than have these kludges.
cc @jorisvandenbossche | {
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"description": "read_json, to_json, json... | open | false | null | [] | null | 6 | 2020-12-03T08:06:11Z | 2020-12-10T10:16:14Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
test = pd.DataFrame([[1,2,3],[4,5,6]], columns=[1, 2, 3])
s = test.to_json(orient="table")
print(s)
# wrong string :
# '{"schema":{"fields":[{"name":"index","type":"integer"},{"name":1,"type":"integer"},{"name":2,"type":"integer"},{"name":3,"type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"1":1,"2":2,"3":3},{"index":1,"1":4,"2":5,"3":6}]}'
pd.read_json(s, orient="table")
#ValueError: Cannot convert non-finite values (NA or inf) to integer
```
#### Problem description
When the initial columns are integers, the schema dict returns correct names (that unquoted integers), but the data dict identifies columns as string (quoted integers). Therefore, any loaded dataframe from this json format will return a dataframe full of empty (NaN) values or fail with an exception (I don't know which triggers which ; this minimal example here will trigger an exception ; my original dataset with multiindexes [in stackoverflow](https://stackoverflow.com/questions/65115871/pandas-read-json-with-orient-table) returned an empty dataframe...
#### Expected Output
This output for pandas.to_json(orient="table") could be read (though it is losing the "int" label key and transforming it to strings) :
'{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"1","type":"integer"},{"name":"2","type":"integer"},{"name":"3","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.0.20"},"data":[{"index":0,"1":1,"2":2,"3":3},{"index":1,"1":4,"2":5,"3":6}]}'
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.6.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : fr
LOCALE : None.None
pandas : 1.1.4
numpy : 1.18.4
pytz : 2017.2
dateutil : 2.8.1
pip : 20.2.4
setuptools : 36.6.0
Cython : 0.27.2
pytest : 3.2.3
hypothesis : None
sphinx : 1.6.5
blosc : 1.5.1
feather : 0.4.0
xlsxwriter : 1.0.2
lxml.etree : 4.1.0
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.9.6
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.7.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.1.14
tables : None
tabulate : 0.8.5
xarray : 0.9.6
xlrd : 1.1.0
xlwt : None
numba : 0.35.0
</details>
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} | 2 | 2020-12-03T08:22:13Z | 2020-12-08T00:39:56Z | 2020-12-04T23:45:03Z | CONTRIBUTOR | null | - [x] closes #38227
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Marked all excel-related tests as slow.
There are a lot of time for setup and teardown for every test.
Overall, moving these tests to "slow" category may probably save about 5 minutes of test time for "not slow" pipelines. | {
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} | 0 | 2020-12-03T11:10:30Z | 2020-12-03T12:02:33Z | 2020-12-03T12:02:28Z | MEMBER | null | Backport PR #38244 | {
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} | 1 | 2020-12-03T11:45:08Z | 2020-12-04T14:23:41Z | 2020-12-04T14:23:37Z | CONTRIBUTOR | null | - [x] xref #38138
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Rewrote dict calls to dict literals in the following files
./pandas/tests/io/json/test_ujson.py
./pandas/tests/io/json/test_normalize.py
Also changed list calls to literals in line 730 of the following file
pandas/tests/io/json/test_ujson.py | {
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mask.html#pandas-dataframe-mask
#### Documentation problem
The first line of docstring states:
>> Replace values where the condition is True.
However, `where` replaces the values for which the `cond` is False.
e.g.
```
zeros = pd.DataFrame(np.zeros(5))
zeros.where(zeros == 0.0, "REPLACED")
>>>
0.0
0.0
0.0
0.0
0.0
zeros.where(zeros != 0.0, "REPLACED")
>>>
REPLACED
REPLACED
REPLACED
REPLACED
REPLACED
#### Suggested fix for documentation
I think documentation should say "Replace values where the condition is False."
The rest of the `where` documentation should also be changed to reflect the actual behaviour of `where`.
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} | 1 | 2020-12-03T14:34:39Z | 2020-12-19T02:19:07Z | 2020-12-19T02:19:00Z | MEMBER | null | - [ ] closes #33748
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- [ ] passes `black pandas`
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Fix the bug that the subclasses of a dataframe and a series cannot be saved ``to_hdf``.
The tests were added.
When testing I found that ``assert_frame_equal`` does not work on subclasses as well - probably need to fix it in a separate PR.
For now in the tests I compare values only. | {
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} | 2 | 2020-12-03T15:40:40Z | 2020-12-03T22:58:47Z | 2020-12-03T22:58:33Z | CONTRIBUTOR | null | - [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Identify unnecessary dict call - rewrite as a literal in `./pandas/tests/io/pytables/test_store.py`, `./pandas/core/arrays/interval.py`
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} | 0 | 2020-12-03T16:53:39Z | 2020-12-03T21:39:58Z | 2020-12-03T21:39:58Z | CONTRIBUTOR | null | - [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Identify unnecessary dict call - rewrite as a literal in `pandas\core\indexes\numeric.py`
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} | 6 | 2020-12-03T17:33:05Z | 2020-12-06T00:28:19Z | 2020-12-05T17:14:06Z | CONTRIBUTOR | null | closes #37748
closes #37486
closes #38271
- [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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} | 5 | 2020-12-03T17:35:06Z | 2020-12-28T14:55:46Z | 2020-12-28T14:55:46Z | NONE | null | ### Code Sample
This code does work as expected (0 value is replaced by `<NA>` in an Int64 series).
```python
>>> import pandas as pd
>>> s = pd.Series([0, 1]).astype("Int64")
>>> s.replace(0, pd.NA)
0 <NA>
1 1
dtype: Int64
```
In contrast, when having `<NA>` values in the series `.replace()` does not work as expected (0 is **not** replaced by `<NA>`).
```python
>>> import pandas as pd
>>> s = pd.Series([0, None]).astype("Int64")
>>> s.replace(0, pd.NA)
0 0
1 <NA>
dtype: object
```
However, the code does work if we add `.fillna()` before replacing:
```python
>>> import pandas as pd
>>> s = pd.Series([0, None]).astype("Int64")
>>> s.fillna(0).replace(0, pd.NA)
0 <NA>
1 <NA>
dtype: Int64
```
### Problem description
When applying the replace on a Int64 series with or without `<NA>` I expect consistent behavior.
Also the result type of `.replace()` seems to be inconsistent based on the usage of `.fillna()` on the series (without `fillna()` the type changes to `object`).
### Expected behavior
I would expect that `s.replace(0, pd.NA)` and `s.fillna(0).replace(0, pd.NA)` on an Int64 series will **always** lead to the same result.
### Output of pd.show_versions()
<details>
<summary>Details</summary>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-53-generic
Version : #59~18.04.1-Ubuntu SMP Wed Oct 21 12:14:56 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3
setuptools : 50.3.2.post20201201
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
fsspec : 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
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} | 2 | 2020-12-03T17:58:15Z | 2020-12-04T02:08:54Z | 2020-12-04T02:01:12Z | 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
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
class MyDataFrame(DataFrame):
_metadata = ['my_metadata']
data = MyDataFrame({'test': [1,1,1,2,2,2]})
data.my_metadata = 2
for group_id, group_data in data.groupby('test'):
assert group_data.my_metadata == 2
```
#### Problem description
As per documentation [here](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#define-original-properties), it seems that any derivatives of a DataFrame should inherit the metadata properties assigned. This is true with slicing operations, but is not true with groupby(). It would be more consistent if the property persisted for slicing and groupby().
#### Expected Output
Should not raise an assertion error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-56-generic
Version : #62-Ubuntu SMP Mon Nov 23 19:20:19 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : 0.14.1
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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"description": "Warnings that appear or ... | open | false | null | [] | null | 1 | 2020-12-03T18:39:18Z | 2021-08-14T17:12:59Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
#### Code Sample, a copy-pastable example
```python
"""Missing SettingwithCopyWarning."""
import pandas as pd
df = pd.DataFrame(
[["Alice", 10]],
columns=pd.MultiIndex.from_product([["person"], ["first_name", "age"]]),
)
df.person.age = 11
print(df)
```
#### Problem description
Value `11` is not assigned. Output is
```
person
first_name age
0 Alice 10
```
No SettingwithCopyWarning is shown.
#### Expected Output
Value `11` is assigned. Output is
```
person
first_name age
0 Alice 11
```
Or: SettingwithCopyWarning is shown.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.18-lp152.44-default
Version : #1 SMP Wed Sep 30 18:51:43 UTC 2020 (914f31e)
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
```
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} | 0 | 2020-12-03T18:41:21Z | 2020-12-05T17:14:05Z | 2020-12-05T17:14:05Z | MEMBER | null | On 1.2 master:
``` python
In [11]: import numpy as np
...: import pandas as pd
...:
...: ser = pd.Series(1, index=list("abcde"), dtype="object")
...:
...: expected = pd.array([0,0,3,0,0])
...: ser.loc["a"] = expected
...: result = ser[0]
In [12]: ser
Out[12]:
a 0
b 0
c 3
d 0
e 0
dtype: Int64
```
I expect:
``` python
In [14]: ser
Out[14]:
a [0, 0, 3, 0, 0]
b 1
c 1
d 1
e 1
dtype: object
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} | 8 | 2020-12-03T18:51:39Z | 2020-12-05T21:01:42Z | 2020-12-04T16:21:24Z | MEMBER | null | - [x] closes #36785
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"d... | closed | false | null | [] | null | 6 | 2020-12-03T19:52:25Z | 2021-02-11T01:30:51Z | 2021-02-11T01:30:51Z | NONE | null | Creates tests for GH26859
- [X] closes #26859
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2020-12-03T20:53:31Z | 2020-12-04T14:24:58Z | 2020-12-04T14:24:58Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import unittest
import pandas as pd
class Test(unittest.TestCase):
def testError(self):
cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'Price': [22000,25000,27000,35000]
}
df = pd.DataFrame(cars, columns = ['Brand', 'Price'])
dataError = df.drop_duplicates(ignore_index='error')
print("Should give ValueError: \n"+
" ValueError: For argument ignore_index expected type bool, received type str.")
if __name__ == '__main__' :
unittest.main()
```
#### Problem description
Hi! So I am doing white testing for school and found a tiny bug in drop_duplicates. When using the boolean parameter "ignore_index" it does not check if it is boolean or not. The "inplace" parameter is checked using the function validate_bool_kwarg(--). The "ignore_index" should be checked the same way. I have never reported a bug and I could fix it myself but I am a bit afraid to mess things up. Thank you!
#### Expected Output
ValueError: For argument "ignore_index" expected type bool, received type str.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 4 | 2020-12-03T22:00:40Z | 2020-12-04T14:22:42Z | 2020-12-04T14:22:38Z | CONTRIBUTOR | null | - [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Removed the dict calls for the files:
`./pandas/core/arrays/floating.py`
`./pandas/core/arrays/numpy_.py`
`./pandas/core/arrays/interval.py` | {
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} | 1 | 2020-12-03T22:36:46Z | 2020-12-04T23:46:21Z | 2020-12-04T14:24:58Z | MEMBER | null | - [x] closes #38274
- [x] tests added / passed
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- [x] whatsnew entry
Saw this on the tracker. I think we should validate this, some inputs could lead to weird results.
The last drop duplicates entry in whatsnew was also under reshaping, so put it there | {
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} | 2 | 2020-12-03T22:55:27Z | 2021-05-17T12:52:27Z | 2021-05-17T12:52:27Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'id': ['1', '1', '2', '3'], 'title': ['title1', 'title2', 'title3', 'title4']})
# df['id'] = df['id'].astype('int')
# df.dtypes
df.groupby('id')['title'].rank(method = "first")
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
For a simple dataframe, I cannot rank a grouped dataframe on non-numeric data type. For example, I want to group by ID and rank a column. My aim here is to identify duplicates in one column by ranking it.
This is the problem I get:
```
df.groupby('id')['title'].rank(method = "first")
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
~/projects/dentaway-marketing/linkedin-jobs-scraper/process_linkedin_jobs_data.py in
----> 276 df.groupby('id')['title'].rank(method = "first")
~/anaconda3/envs/dev/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in rank(self, method, ascending, na_option, pct, axis)
2299 msg = "na_option must be one of 'keep', 'top', or 'bottom'"
2300 raise ValueError(msg)
-> 2301 return self._cython_transform(
2302 "rank",
2303 numeric_only=False,
~/anaconda3/envs/dev/lib/python3.8/site-packages/pandas/core/groupby/groupby.py in _cython_transform(self, how, numeric_only, **kwargs)
970
971 if len(output) == 0:
--> 972 raise DataError("No numeric types to aggregate")
973
974 return self._wrap_transformed_output(output)
DataError: No numeric types to aggregate
```
#### Expected Output
The expected output should be a ranked series.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
pandas is installed with pip.
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.18.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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} | 4 | 2020-12-03T23:16:56Z | 2020-12-07T17:28:41Z | 2020-12-04T14:25:42Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Using the new hashtables!
```
In [3]: arr = np.arange(10**7).astype(np.int32)
In [4]: %timeit isin(arr, arr[:10])
88.8 ms ± 497 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
42.7 ms ± 385 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- PR
``` | {
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} | 1 | 2020-12-04T00:52:36Z | 2020-12-04T17:57:30Z | 2020-12-04T14:21:39Z | CONTRIBUTOR | null | xref #38138
File modified: pandas/core/strings/accesory.py
Changed unnecessary dict calls for flake8 (C408) standard:
- Rewrite dict() as {}
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"following_url": "https://api.github.com/users/erfannariman/following{/other_use... | null | 2 | 2020-12-04T01:33:03Z | 2020-12-04T02:23:44Z | null | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.endswith.html
https://pandas.pydata.org/docs/reference/api/pandas.Series.str.startswith.html
#### Documentation problem
Right now there is no indication that these methods accept a tuple for testing on multiple character sequences.
#### Suggested fix for documentation
Add in the documentation that tuples are accepted as values.
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} | 4 | 2020-12-04T07:18:02Z | 2020-12-07T17:56:54Z | 2020-12-06T18:46:41Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Create a DataFrame with numbers and missing entries, first() on groupby works as expected,
# skipping the missing leading entry in column b
>>> df2=pd.DataFrame({"a": [4,4,4,4,4,4], "b": [None,3,None,3,3,3]})
>>> df2.groupby("a").first()
b
a
4 3.0
# Create a similar DataFrame, but with strings and missing entries, first() on groupby
# does not work as expected, it doesn't skip the missing leading entry
df2=pd.DataFrame({"a": [4,4,4,4,4,4], "b": [None,"foo",None,"foo","foo","foo"]})
>>> df2.groupby("a").first()
b
a
4 None
```
#### Problem description
One utility of the GroupBy.first() function is its coalescing behavior--it skips missing values within groups. This behavior does not seem to happen when a column has string values, which creates two problems: 1) type-dependent behavior of an otherwise generic method, and 2) I'm not able to find a workaround to get the behavior I'd like (returning the first non-missing string in a group.
#### Expected Output
I would expect the output of the second example above to be "foo", not None. If the behavior above is the expected behavior of Pandas, then please provide the correct code to use in order to extract the first non-missing string in a groupby column.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.2.0
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2020-12-04T10:58:24Z | 2020-12-30T13:49:06Z | 2020-12-30T13:49:06Z | CONTRIBUTOR | null | #### Location of the documentation
e.g. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.sort_values.html, https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_index.html
#### Documentation problem
List of values for `kind` should be updated to reflect changes in NumPy: https://numpy.org/doc/stable/reference/generated/numpy.sort.html
Specifically, since NumPy 1.15.0, the list has been `{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}`
#### Suggested fix for documentation
Full list of options is provided. Also, the statement that "‘mergesort’ is the only stable algorithm" is now incorrect.
I can do this, when I get time. But I'm more than happy for someone else to do it if they want.
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} | 1 | 2020-12-04T14:00:49Z | 2020-12-04T15:58:50Z | 2020-12-04T15:58:37Z | NONE | null | #### Is your feature request related to a problem?
[this should provide a description of what the problem is, e.g. "I wish I could use pandas to do [...]"]
#### Describe the solution you'd like
[this should provide a description of the feature request, e.g. "`DataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature]
#### API breaking implications
[this should provide a description of how this feature will affect the API]
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
# Your code here, if applicable
```
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} | 10 | 2020-12-04T14:08:21Z | 2020-12-12T08:50:42Z | 2020-12-11T22:49:23Z | MEMBER | null | The `integer_array()` function stems from a time that `pd.array(..)` didn't exist yet. But since now `pd.array` can do this, IMO we shouldn't be using that outside of the integer module (eg I didn't add a `floating_array(..)`).
Only in `tests/arrays/integer/tst_construction.py` I still left the usage of `integer_array()` as is (but probably could also change it there). Could in principle remove the function altogether, since it is just a small wrapper around the private `arrays.integer.coerce_to_array` | {
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} | 1 | 2020-12-04T14:32:41Z | 2020-12-05T09:23:06Z | 2020-12-04T23:30:52Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/38110 | {
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} | 7 | 2020-12-04T17:09:05Z | 2020-12-08T16:07:35Z | 2020-12-08T10:22:11Z | MEMBER | null | Addresses part of #37494. And xref https://github.com/pandas-dev/pandas/pull/38162/files#r536191377
cc @jorisvandenbossche | {
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} | 0 | 2020-12-04T18:23:59Z | 2020-12-13T20:01:38Z | 2020-12-13T20:01:38Z | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import io
import pandas as pd
pd.read_csv(io.StringIO('a0, a1, a2\n b0, b1, b2\n'), header=[0, 1], index_col=0)
```
#### Problem description
This is a legal, if empty, csv. Note that variations such as
```pd.read_csv(io.StringIO('a0, a1, a2\n'), header=[0], index_col=0)```,
```pd.read_csv(io.StringIO('a0, a1, a2\n b0, b1, b2\n'), header=[0, 1])```, and
```pd.read_csv(io.StringIO('a0, a1, a2\n b0, b1, b2\ndata,data'), header=[0, 1], index_col=0)``` work fine.
#### Expected Output
```
Empty DataFrame
Columns: [( a1, b1), ( a2, b2)]
Index: []
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
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 : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 15 | 2020-12-04T18:37:19Z | 2020-12-23T16:32:40Z | 2020-12-23T16:32:23Z | CONTRIBUTOR | null | - [X] closes #36791
- [X] closes #34548
- [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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} | 12 | 2020-12-04T18:37:23Z | 2020-12-06T02:10:52Z | 2020-12-05T11:07:17Z | NONE | null | #### Is your feature request related to a problem?
```python
import pandas as pd
from pandas.api.types import CategoricalDtype
df = pd.DataFrame(data={'col': ['small', 'medium', 'large']})
ordered = CategoricalDtype(categories=['small', 'medium', 'large'], ordered=True)
df.col = df.col.astype(ordered)
df.col.cat.codes
# 0 0
# 1 1
# 2 2
# dtype: int8
```
I often wish I can set the beginning underline code value in a Categorical. Right, no matter ordered or unordered, pandas will always begin the first code value as 0. Sometimes, I want it to begin with 1. like
```
ordered = CategoricalDtype(categories=['small', 'medium', 'large'], start=1, ordered=True)
```
Currently, there is no alternative I can think of other than manually storing the modified codes in a new column but this will not affect the underline coding. I really wish this feature could be added.
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} | 0 | 2020-12-04T19:04:18Z | 2020-12-04T20:03:20Z | 2020-12-04T20:03:20Z | NONE | null | Backport PR #38272: BUG: DataFrame.apply with axis=1 and EA dtype | {
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} | 6 | 2020-12-04T19:54:38Z | 2020-12-05T19:54:06Z | 2020-12-05T18:54:59Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
I may have a dataframe with 100 columns, of which I want to transform 3.
`DataFrame.transform()` accepts a dict with column -> function mappings, therefore my intuition is to provide 3 mappings for the 3 columns I want transformed. However, upon providing such a mapping dict, the function drops the non mentioned 97 columns.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# create a dataframe with 100 columns
d = {'col%d' % i: [0] for i in range(100)}
df = pandas.DataFrame(d)
# transform 3 arbitrary columns with a function
func42 = lambda x: 42
df.transform({
'col10': func42,
'col20': func42,
'col42': func42
})
```
#### Problem description
The transformation lost the 97 columns for which there was no transformation mapping provided.
The natural intuition is to preserve all columns that were not mentioned in the transformation.
#### Expected Output
A dataframe containing all 100 columns: the 3 transformed columns with the rest of the 97 columns untouched.
The current implementation returns 3 columns.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-123-generic
Version : #126~16.04.1-Ubuntu SMP Wed Oct 21 13:48:05 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3
setuptools : 49.6.0.post20201009
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 : 5.8.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Page 47
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"]
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
merge() link is broken and links back to the first page
[this should provide a description of what documentation you believe needs to be fixed/improved]
#### Suggested fix for documentation
[this should explain the suggested fix and **why** it's better than the existing documentation]
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} | 1 | 2020-12-04T21:20:04Z | 2020-12-04T23:27:19Z | 2020-12-04T23:27:16Z | CONTRIBUTOR | null | xref #38138
File modified: pandas/core/strings/accesory.py
Changed unnecessary dict calls for flake8 (C408) standard:
Rewrite dict() as {}
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/38299 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38299/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38299/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38299/events | https://github.com/pandas-dev/pandas/issues/38299 | 757,447,677 | MDU6SXNzdWU3NTc0NDc2Nzc= | 38,299 | BUG: find_common_type([interval[f8], interval[i8]]) returns object | {
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"description": "I... | closed | false | null | [] | null | 1 | 2020-12-04T22:23:58Z | 2020-12-12T23:21:45Z | 2020-12-12T23:21:45Z | MEMBER | null | I would expect it to return interval[f8], analogous to what we'd get with find_common_type([f8, i8]) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38300 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38300/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38300/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38300/events | https://github.com/pandas-dev/pandas/issues/38300 | 757,458,543 | MDU6SXNzdWU3NTc0NTg1NDM= | 38,300 | ENH: Make pandas API more consistent | {
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"color": "... | closed | false | null | [] | null | 1 | 2020-12-04T22:51:30Z | 2020-12-09T16:20:30Z | 2020-12-09T16:20:30Z | NONE | null | #### Is your feature request related to a problem?
The pandas DataFrame uses both arguments of `inplace` and `copy` (`DataFrame.astype` and `Series.astype`) in different methods to indicate change in place. It would be great if it can be unified to use `inplace` in all methods.
#### Describe the solution you'd like
Always use the argument `inplace` (instead of `copy`) to indicate change in place.
#### API breaking implications
Some methods such as `DataFrame.astype` and `Series.astype` will be affected as they argument `copy` is changed to `inplace`.
#### Describe alternatives you've considered
#### Additional context
| {
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} | [] | closed | false | null | [] | null | 1 | 2020-12-04T22:57:54Z | 2020-12-05T01:57:12Z | 2020-12-05T01:57:04Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Just removing the duplicated 'i' in "resultiing" of the futurewarning in melt about using `value_name` with the same name as a column the in the original dataframe. Filing an issue or a whatsnew entry for such a minor change seemed excessive. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38302/events | https://github.com/pandas-dev/pandas/pull/38302 | 757,487,915 | MDExOlB1bGxSZXF1ZXN0NTMyODU2NzU4 | 38,302 | BUG: Clean intersection and fix resulting names when MultiIndex are equal | {
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} | 6 | 2020-12-05T00:19:31Z | 2020-12-07T15:08:43Z | 2020-12-07T15:08:39Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Names were wrong when MultiIndexes where equal and unique with different names. Additional cleanups
cc @jbrockmendel | {
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} | 0 | 2020-12-05T01:33:26Z | 2021-01-28T02:37:06Z | 2021-01-28T02:37:06Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
ser = pd.Series([0, 0, 0], dtype=object)
ser.loc[0] = pd.Series([42]) # ValueError: No axis named 1 for object type Series
```
#### Problem description
<details>
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/sources/official.clone/pandas/pandas/core/generic.py in _get_axis_number(cls, axis)
456 try:
--> 457 return cls._AXIS_TO_AXIS_NUMBER[axis]
458 except KeyError:
KeyError: 1
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-24-7c4b5d72ca25> in <module>
----> 1 ser.loc[0] = pd.Series([0])
~/sources/official.clone/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
689
690 iloc = self if self.name == "iloc" else self.obj.iloc
--> 691 iloc._setitem_with_indexer(indexer, value, self.name)
692
693 def _validate_key(self, key, axis: int):
~/sources/official.clone/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value, name)
1634 self._setitem_with_indexer_split_path(indexer, value, name)
1635 else:
-> 1636 self._setitem_single_block(indexer, value, name)
1637
1638 def _setitem_with_indexer_split_path(self, indexer, value, name: str):
~/sources/official.clone/pandas/pandas/core/indexing.py in _setitem_single_block(self, indexer, value, name)
1848 # setting for extensionarrays that store dicts. Need to decide
1849 # if it's worth supporting that.
-> 1850 value = self._align_series(indexer, Series(value))
1851
1852 elif isinstance(value, ABCDataFrame) and name != "iloc":
~/sources/official.clone/pandas/pandas/core/indexing.py in _align_series(self, indexer, ser, multiindex_indexer)
2018
2019 elif is_scalar(indexer):
-> 2020 ax = self.obj._get_axis(1)
2021
2022 if ser.index.equals(ax):
~/sources/official.clone/pandas/pandas/core/generic.py in _get_axis(self, axis)
467 @final
468 def _get_axis(self, axis: Axis) -> Index:
--> 469 axis_number = self._get_axis_number(axis)
470 assert axis_number in {0, 1}
471 return self.index if axis_number == 0 else self.columns
~/sources/official.clone/pandas/pandas/core/generic.py in _get_axis_number(cls, axis)
457 return cls._AXIS_TO_AXIS_NUMBER[axis]
458 except KeyError:
--> 459 raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
460
461 @final
ValueError: No axis named 1 for object type Series
```
</details>
#### Expected Output
The same as with
```python
ser.iloc[0] = pd.Series([42])
```
```
0 0 42
dtype: int64
1 0
2 0
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 03e58585036c83ca3d4c86d7d3d7ede955c15130
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-0.bpo.2-amd64
Version : #1 SMP Debian 5.8.10-1~bpo10+1 (2020-09-26)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+1465.g03e585850
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 40.8.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None```
</details>
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} | 1 | 2020-12-05T02:36:23Z | 2020-12-14T13:26:47Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = pd.DataFrame({"a": [1]})
data.index = pd.IntervalIndex.from_arrays(
[0.5],
[0.9],
)
data.to_hdf('test.hdf', 'df') # fails (as index)
data.reset_index().to_hdf('test.hdf', 'df') # fails (as column)
```
#### Problem description
Pytables doesn't seems to know IntervalIndex. If pytables doesn't support IntervalIndex, pandas should either convert the IntervalIndex into two columns (and then merge them while reading) or throw an error early in `to_hdf`.
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} | 3 | 2020-12-05T03:50:01Z | 2020-12-12T17:26:25Z | 2020-12-07T19:30:58Z | MEMBER | null | use np.putmask in a few places, as it is appreciably more performant than `__setitem__` | {
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} | 0 | 2020-12-05T04:11:33Z | 2020-12-07T15:34:02Z | 2020-12-07T13:35:33Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 13 | 2020-12-05T04:46:56Z | 2020-12-19T11:06:37Z | 2020-12-19T10:23:41Z | CONTRIBUTOR | null | #### Location of the documentation
[`pandas.Timedelta.isoformat`](https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.isoformat.html#pandas.Timedelta.isoformat)
#### Documentation problem
The output on an example in the docs has the wrong output(`Timedelta.isoformat` works fine)
While it should've been `'P0DT1H0M10S'`
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} | 1 | 2020-12-05T05:22:16Z | 2021-08-14T21:57:35Z | null | MEMBER | null | #38175 added correct handling of nanoseconds in the Period constructor but the code path as is parses the input twice. We should integrate the nanoseconds code into `parse_time_string` so this happens only once.
xref https://github.com/pandas-dev/pandas/pull/38175/files#r536100941
It might be worth adding a nanosecond Period constructor benchmark too. | {
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} | 2 | 2020-12-05T05:49:53Z | 2020-12-07T20:53:24Z | 2020-12-07T19:23:24Z | MEMBER | null | cast_scalar_to_array is only used in two places, and in each of them there is a better option available. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38314/events | https://github.com/pandas-dev/pandas/issues/38314 | 757,650,648 | MDU6SXNzdWU3NTc2NTA2NDg= | 38,314 | BUG: Sliced subclassed Series returns full data result when custom method is applied | {
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"description": "Subclassing pandas objec... | closed | false | null | [] | null | 5 | 2020-12-05T10:31:34Z | 2020-12-06T08:55:20Z | 2020-12-06T08:55:20Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
class _Subseries(pd.Series):
_metadata = ['custom_method']
@property
def _constructor(self):
return _Subseries
@property
def _constructor_expanddim(self):
return _Subseries
def custom_method(self, value):
return self + value
s = _Subseries(range(5))
s = s.iloc[-3:]
s.custom_method(1)
```
#### Problem description
The above code returns:
0 1
1 2
2 3
3 4
4 5
A similar method, such as .add(), on a sliced Series will return the result of that method on the sliced data and not on the original full data. I would expect this to be the same in a subclassed version of a Series.
#### Expected Output
2 3
3 4
4 5
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Belgium.1252
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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"description": "E... | open | false | null | [] | null | 19 | 2020-12-05T13:09:44Z | 2021-05-24T17:59:00Z | null | MEMBER | null | Closes #33254
Closes #31108
This adds a ``ExtensionArray._from_scalars`` function which is more strict than the existing `_from_sequence` constructor: in theory, it should only accept a sequence of scalars of its own type (i.e. the type you get with `arr[0]`, plus NAs), or arrays with the appropriate dtype.
This can then be used in a context where we try to create an EA of the original dtype (in an attempt to preserve the original dtype), but we don't want to *coerce* any value to that dtype.
**Note**: this is a WIP PR, which mostly adds *dummy* implementations of this method, just to mimic the desired behaviour for now, to be able to explore how this would work.
If this turns out to be working, we will need to make better implementations for `_from_scalars` for our own EAs.
Apart from that, this needs tests, docs, etc. But let's first discuss semantics.
* [ ] Add tests for example case from https://github.com/pandas-dev/pandas/issues/31108, period case from https://github.com/pandas-dev/pandas/pull/38254, test case for categorical with non-categories
* [ ] Reopen https://github.com/pandas-dev/pandas/pull/30282 once this is merged | {
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} | 1 | 2020-12-05T15:10:20Z | 2021-08-14T21:58:29Z | null | MEMBER | null | Somewhere in the conversion, before factorizing, we convert the interval array/index to a object-dtype numpy array of Interval objects, and so afterwards *infer* the IntervalDtype again when creating the categories.
Example consequence is that if you have uint64 intervals, they get inferred as int64 afterwards:
```python
In [29]: index = pd.IntervalIndex.from_breaks(np.arange(5, dtype="uint64"))
In [30]: index
Out[30]:
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4]],
closed='right',
dtype='interval[uint64]') # <---- unsigned ints
In [31]: pd.CategoricalIndex(index)
Out[31]: CategoricalIndex([(0, 1], (1, 2], (2, 3], (3, 4]], categories=[(0, 1], (1, 2], (2, 3], (3, 4]], ordered=False, dtype='category')
In [33]: pd.CategoricalIndex(index).dtype.categories
Out[33]:
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4]],
closed='right',
dtype='interval[int64]') # <---- no longer uint64
``` | {
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} | 2 | 2020-12-05T17:04:46Z | 2021-01-02T14:15:03Z | 2020-12-08T10:11:55Z | MEMBER | null | - [x] closes #26552
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Deprecates both specifying `engine="xlwt"` and the option `io.excel.xls.writer`. Note that a warning is emitted when a user sets the `io.excel.xls.writer` option, but not via the context manager. Whatsnew now reads:
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