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https://api.github.com/repos/pandas-dev/pandas/issues/27913 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27913/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27913/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27913/events | https://github.com/pandas-dev/pandas/issues/27913 | 480,441,842 | MDU6SXNzdWU0ODA0NDE4NDI= | 27,913 | Empty series dtype discarded by reset_index with MultiIndex | {
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... | closed | false | null | [] | null | 2 | 2019-08-14T01:41:33Z | 2019-08-14T13:33:09Z | 2019-08-14T13:33:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# this is fine
pd.DataFrame({'x': pd.Series([], dtype='datetime64[ns]'), 'y': pd.Series([]), 'z': pd.Series([])}).set_index(['x']).reset_index().dtypes
# this changes 'x' to float64
pd.DataFrame({'x': pd.Series([], dtype='datetime64[ns]'), 'y': pd.Series([]), 'z': pd.Series([])}).set_index(['x', 'y']).reset_index().dtypes
# same behaviour with all missing
pd.DataFrame({'x': pd.Series(['NaT'], dtype='datetime64[ns]'), 'y': pd.Series([2.]), 'z': pd.Series([3.])}).set_index(['x', 'y']).reset_index().dtypes
```
#### Problem description
`DataFrame.reset_index` shouldn't discard the type of the index/index levels when empty or all missing. It correctly handles this for a single level index (eg DatetimeIndex), but doesn't for MultiIndex.
In my case the symptom was a strange TypeError on a join, which I tracked down to the dtype of the series being lost after a `reset_index`.
The problem may be the branch in [`_maybe_casted_values`](https://github.com/pandas-dev/pandas/blob/v0.25.0/pandas/core/frame.py#L4621), where values is overwritten with an np.empty() if all values are missing. I modified the condition in my copy to `if len(mask) and mask.all()` which avoids the problem for empty arrays, though not for all-missing.
#### Output of ``pd.show_versions()``
I've only been able to run this with 0.24.2 so far as 0.25 fails to import on python 3.5.2 (due to [this](https://github.com/python/typing/issues/266) I think) but the relevant code in reset_index looks the same.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-142-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.24.2
pytest: None
pip: 8.1.1
setuptools: 20.7.0
Cython: None
numpy: 1.17.0
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: None
xlwt: None
xlsxwriter: 1.1.8
lxml.etree: 3.5.0
bs4: 4.4.1
html5lib: 0.999
sqlalchemy: 1.3.5
pymysql: 0.9.3
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27914 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27914/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27914/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27914/events | https://github.com/pandas-dev/pandas/issues/27914 | 480,449,185 | MDU6SXNzdWU0ODA0NDkxODU= | 27,914 | Unable to use lambda function in pandas NamedAgg function | {
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} | 1 | 2019-08-14T02:18:50Z | 2019-08-14T13:26:19Z | 2019-08-14T13:26:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
'height': [9.1, 6.0, 9.5, 34.0],
'weight': [7.9, 7.5, 9.9, 198.0]})
# using lambda with the new NamedAgg function doesn't work
(
df.groupby(by='kind')
.agg(height_sqr_min=pd.NamedAgg(column='height', aggfunc=lambda x: np.min(x**2)),
height_max=pd.NamedAgg(column='height', aggfunc='max'),
weight_max=pd.NamedAgg(column='weight', aggfunc='max')
)
)
# using lambda with implicit NamedAgg function doesn't work either
(
df.groupby(by='kind')
.agg(height_sqr_min=('height', lambda x: np.min(x**2)),
height_max=('height', 'max'),
weight_max=('weight', 'max'),
)
)
```
#### Problem description
NamedAgg function introduced in pandas 0.25 doesn't seem to work with lambda functions.
#### Expected Output
| kind|height_sqr_min | height_max |weight_max|
|----------|:-------------:|------:|------:|
| cat | 82.81 | 9.5 |9.9 |
| dog| 36.00 | 34.0 |198.0 |
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 11 | 2019-08-14T09:40:45Z | 2019-08-30T17:05:45Z | 2019-08-30T17:05:32Z | CONTRIBUTOR | null | I noticed that `pre-commit` is used and ran `pre-commit run --all-files` which results in massive diffs, mostly because of `isort`.
I have added the correct `noqa` and `isort:skip` flags to the lines which needed it to function correctly with the current `.pre-commit-config.yaml`, such that the imports are correctly sorted now.
I also added `seed-isort-config` such that `setup.cfg` is parsed for `pre-commit` and fixed the formatting of `.pre-commit-config.yaml`.
~~Finally, there is `pandas/__init__.py` that is touched by `pre-commit run --all-files`, however, this has less trivial changes and might result in something breaking, so I don't dare to touch it.~~
It was because the `skip` entry in `[isort]` in `setup.cfg` is somehow ignored when running pre-commit and I can't figure out why (I will open an issue in the `isort` repo when it's clear that this will be merged). I have also deleted the other ignored files because I fixed the problems there. `pandas/core/api.py` needs to stay in `skip` because `isort` and `black` make conflicting edits. | {
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} | 4 | 2019-08-14T15:28:09Z | 2019-08-20T13:54:28Z | 2019-08-20T07:00:45Z | MEMBER | null | - [x] closes #19643
- [x] tests added / passed
- [x] passes `black pandas`
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- [ ] whatsnew entry
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
@TomAugspurger Can you merge? | {
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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
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https://api.github.com/repos/pandas-dev/pandas/issues/27924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27924/events | https://github.com/pandas-dev/pandas/issues/27924 | 480,980,677 | MDU6SXNzdWU0ODA5ODA2Nzc= | 27,924 | why when i read data from '.xlsx',values of one colum which type is 'str' with hyHyperlink be read as 0 | {
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```python
`import pandas as pd
df=pd.read_excel(r'C:\abc.xlsx')
df`
```
#### Problem description
my pandas version is 0.25.0
I do not know why that happened, I have a excel with one column whose type is str with hyperlink,when I use the read_excel function to read it ,that column data become 0,and its type is int64, that is strange. And if I open the excel to delete the hyperlink of just one cell of that columns ,this problem is solved.
Actually I do not know is it really the hyperlink make this problem。
How can I just read it as str without open my excel to cancel the hyperlink?
And I really don't know why it is become 0 and int64.
Thanks
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binary mode, but the python csv reader errors on binary buffers.
- [x] closes #23779
- [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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} | 11 | 2019-08-15T06:54:38Z | 2019-08-20T06:47:37Z | 2019-08-19T19:26:21Z | MEMBER | null | closes #27865
cc @jbrockmendel this is another one from limiting the catched exceptions in https://github.com/pandas-dev/pandas/pull/27223/files#diff-7489e290a23303c4db4f803011ecaf8eR121. But also this time it's again rather a bug in the code that raises this error.
For some reason, `DataFrame._get_value` for a DataFrame with an IntervalIndex raises an AttributeError. That's a bug in itself which should be solved (and probably is the cause of more bugs in other places where `_get_value` is used, eg in `.at`). But will open a separate issue for that. | {
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} | 5 | 2019-08-15T07:00:18Z | 2021-06-19T03:37:55Z | 2021-06-19T03:37:54Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/27926 and https://github.com/pandas-dev/pandas/issues/27865
`DataFrame._get_value` with an IntervalIndex is not working properly:
```
In [24]: df = DataFrame(
...: np.random.randn(5, 2),
...: columns=["a", "b"],
...: index=pd.IntervalIndex.from_breaks([-np.inf, 0, 1, 2, 3, np.inf]),
...: )
In [26]: df._get_value(df.index[0], 'a')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-26-ce242a095ada> in <module>
----> 1 df._get_value(df.index[0], 'a')
~/scipy/pandas/pandas/core/frame.py in _get_value(self, index, col, takeable)
2953
2954 try:
-> 2955 return engine.get_value(series._values, index)
2956 except KeyError:
2957 # GH 20629
AttributeError: 'pandas._libs.interval.IntervalTree' object has no attribute 'get_value'
```
This `_get_value` is used in `ix` which is deprecated, but also in `at` which is not deprecated:
```
In [27]: df.at[df.index[0], 'a']
...
AttributeError: 'pandas._libs.interval.IntervalTree' object has no attribute 'get_value'
```
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} | 8 | 2019-08-15T07:16:16Z | 2019-09-13T13:19:42Z | 2019-09-13T13:19:38Z | CONTRIBUTOR | null | - [x] closes #27395
- [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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} | 19 | 2019-08-15T10:33:36Z | 2019-12-02T10:47:34Z | 2019-12-02T10:37:12Z | CONTRIBUTOR | null | - closes #25303 and follow up https://github.com/pandas-dev/pandas/pull/27801#discussion_r313075771
- 1 test added
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 3 | 2019-08-15T11:24:17Z | 2019-08-15T13:38:43Z | 2019-08-15T12:32:35Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
kmPerMinute = df['mileageInKm'].div(df['driveDurationInMinutes'])
Traceback (most recent call last):
File "/app/src/report_trip.py", line 215, in check_car
kmPerMinute = df['mileageInKm'].div(df['driveDurationInMinutes'])
File "/home/python/.local/lib/python3.6/site-packages/pandas/core/ops.py", line 1870, in flex_wrapper
return self._binop(other, op, level=level, fill_value=fill_value)
File "/home/python/.local/lib/python3.6/site-packages/pandas/core/series.py", line 2459, in _binop
result = func(this_vals, other_vals)
TypeError: unsupported operand type(s) for /: 'NoneType' and 'float'
```
#### Problem description
I can't see how I could even get this error. It would require that `this_vals is None` in `series.py:_binop()`, which I don't see how could ever happen.
#### Expected Output
A `pandas.Series` with the result of the division.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.127+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.2.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: 1.3.1
pyarrow: 0.13.0
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: 0.8.1
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-08-15T13:14:55Z | 2021-07-10T19:38:06Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df_plot_0.plot(ax=ax[0], linestyle='None', marker='o', markersize=2.5, rot=90)
```
#### Problem description
My dataframe is 2015-2019, 5 years timeseries. Use The default parameter to plot, got wrong xticks.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.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 : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.5
tables : 3.5.1
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 3 | 2019-08-15T13:27:46Z | 2019-08-22T08:38:45Z | 2019-08-16T11:22:20Z | MEMBER | null | - [x] closes #26215
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2019-08-15T14:12:40Z | 2019-10-29T12:12:31Z | 2019-08-16T12:14:43Z | CONTRIBUTOR | null | - [x] closes #13628
- [x] tests added / passed
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} | 15 | 2019-08-15T14:32:22Z | 2019-12-26T14:51:04Z | 2019-10-22T01:53:38Z | CONTRIBUTOR | null | - [x] closes #13257
- [x] tests added / passed
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I've tried to fix the set_precision format, does that fix it?
### INPUT -> df.style.set_precision(2)
| A | B |
| --- | --- |
| 1 | 2.0090 |
| 3.2121 | 4.566 |
### OUTPUT
| A | B |
| --- | --- |
| 1 | 2.01 |
| 3.21 | 4.57 |
@vinigl
@samuelgranato
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"description": "Arithmet... | open | false | null | [] | null | 10 | 2019-08-15T14:37:57Z | 2020-09-01T12:06:30Z | null | CONTRIBUTOR | null | A cool new feature would be to have a reset value as an argument in the `cumsum` method. So for example if the cumsum value reaches the _lower_ boundary of 0, it resets to 0. Here's an example stolen from [this](https://stackoverflow.com/questions/57510604/perfrom-cumulative-sum-over-a-column-but-reset-to-0-if-sum-become-negative-in-pa/57511154?noredirect=1#comment101490704_57511154) question:
```
df = pd.DataFrame({'Item':'A',
'Value':[7,2,-6,-70,8,0]})
Item Value
0 A 7
1 A 2
2 A -6
3 A -70
4 A 8
5 A 0
```
**Expected output**
```
[7, 9, 3, 0, 8, 8]
```
As you can see, the cumsum restes after it hits `0` or less.
There are some solutions on SO right:
- [Solution 1](https://stackoverflow.com/a/57511154/9081267)
- [Solution 2](https://stackoverflow.com/a/56905635/9081267) (note: different question, but same problem)
- [Solution 3](https://stackoverflow.com/a/56904650/9081267) (note: different question, but same problem)
I think this would be a nice improvement to the `cumsum` method as an argument for example:
```
df['Value'].cumsum(lower_boundary=0)
```
Somewhat the same as `np.clip` does with the `a_min` and `a_max` arguments. | {
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} | 1 | 2019-08-15T15:44:38Z | 2019-08-16T13:56:59Z | 2019-08-16T12:12:28Z | MEMBER | null | This begins the process of separating the (ndarray|EA)-specific parts of Series ops out from the Series ops themselves. Eventually these will a) become the op implementations for PandasArray, and b) be used block-wise instead of column-wise for DataFrame ops. | {
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} | 2 | 2019-08-15T20:16:50Z | 2021-10-05T00:32:37Z | 2021-10-05T00:32:37Z | NONE | null | #### Reproducing the error:
```python
import numpy as np, pandas as pd
# providing CategoricalDtype via e.g. pd.cut will also produce the error
s = pd.Series([np.nan, np.nan, np.nan, np.nan], dtype=pd.CategoricalDtype(categories=[pd.Interval(0,1)]))
s.iloc[:3] = [np.nan, pd.Interval(0,1), np.nan]
# ValueError: Reindexing only valid with uniquely valued Index objects
# these work:
s.iloc[:3] = [np.nan, np.nan, np.nan]
s.iloc[:3] = [np.nan, pd.Interval(0,1), pd.Interval(0,1)]
# as does:
s = pd.Series([np.nan, np.nan, np.nan, np.nan], dtype=pd.CategoricalDtype(categories=['not-an-interval', pd.Interval(0,1)]))
s.iloc[:3] = [np.nan, pd.Interval(0,1), np.nan]
# as does:
d = pd.DataFrame({'s': pd.Series([np.nan]*4, dtype=pd.cut(range(3), bins=2).dtype), 'a': range(4)})
d['s'] = [np.nan, pd.Interval(1.0, 2.0), np.nan, np.nan]
d.loc[slice(0,2,1), 's'] = [pd.Interval(1.0, 2.0), np.nan, np.nan]
# but NOT:
d = pd.DataFrame({'s': pd.Series([np.nan]*4, dtype=pd.cut(range(3), bins=2).dtype), 'a': range(4)})
d.loc[slice(0,2,1), 's'] = [pd.Interval(1.0, 2.0), np.nan, np.nan]
```
The full traceback:
<details>
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-ca2aabf517a0> in <module>
1 s = pd.Series([np.nan, np.nan, np.nan, np.nan], dtype=pd.CategoricalDtype(categories=[pd.Interval(0,1)]))
----> 2 s.iloc[:3] = [np.nan, pd.Interval(0,1), np.nan]
c:\python36\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value)
200 key = com.apply_if_callable(key, self.obj)
201 indexer = self._get_setitem_indexer(key)
--> 202 self._setitem_with_indexer(indexer, value)
203
204 def _validate_key(self, key, axis: int):
c:\python36\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer(self, indexer, value)
577 # actually do the set
578 self.obj._consolidate_inplace()
--> 579 self.obj._data = self.obj._data.setitem(indexer=indexer, value=value)
580 self.obj._maybe_update_cacher(clear=True)
581
c:\python36\lib\site-packages\pandas\core\internals\managers.py in setitem(self, **kwargs)
558
559 def setitem(self, **kwargs):
--> 560 return self.apply("setitem", **kwargs)
561
562 def putmask(self, **kwargs):
c:\python36\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
436 kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
437
--> 438 applied = getattr(b, f)(**kwargs)
439 result_blocks = _extend_blocks(applied, result_blocks)
440
c:\python36\lib\site-packages\pandas\core\internals\blocks.py in setitem(self, indexer, value)
1834
1835 check_setitem_lengths(indexer, value, self.values)
-> 1836 self.values[indexer] = value
1837 return self
1838
c:\python36\lib\site-packages\pandas\core\arrays\categorical.py in __setitem__(self, key, value)
2182 from pandas import Index
2183
-> 2184 to_add = Index(rvalue).difference(self.categories)
2185
2186 # no assignments of values not in categories, but it's always ok to set
c:\python36\lib\site-packages\pandas\core\indexes\interval.py in func(intvidx_self, other, sort)
163 raise TypeError(msg.format(op=self.op_name))
164
--> 165 return setop(intvidx_self, other, sort)
166
167 return func
c:\python36\lib\site-packages\pandas\core\indexes\interval.py in func(self, other, sort)
1267 @SetopCheck(op_name=op_name)
1268 def func(self, other, sort=sort):
-> 1269 result = getattr(self._multiindex, op_name)(other._multiindex, sort=sort)
1270 result_name = get_op_result_name(self, other)
1271
c:\python36\lib\site-packages\pandas\core\indexes\multi.py in difference(self, other, sort)
3299 this = self._get_unique_index()
3300
-> 3301 indexer = this.get_indexer(other)
3302 indexer = indexer.take((indexer != -1).nonzero()[0])
3303
c:\python36\lib\site-packages\pandas\core\indexes\multi.py in get_indexer(self, target, method, limit, tolerance)
2423 if not self.is_unique:
2424 raise ValueError(
-> 2425 "Reindexing only valid with uniquely valued " "Index objects"
2426 )
2427
ValueError: Reindexing only valid with uniquely valued Index objects
```
</details>
#### Problem description
The problem is a bit specific: the assignment has to be to a slice, the Categorical Series/DataFrame must have all Intervals for categories, and the rhs must have at least 2 `np.nan` (or None) but not all of its elements should be `np.nan` or else the assignment will work.
My guess is that this has something to do with the fact that when a Series or DataFrame column has a CategoricalDtype where the categories are all Intervals, inspection of the object shows that the categories are stored as an IntervalIndex:
```python
In [1]: s.dtype.categories
Out[1]:
IntervalIndex([(0, 1]],
closed='right',
dtype='interval[int64]')
```
whereas a categorical object with non-Interval categories mixed in stores them as just a vanilla Index, and in the case of setting values to the DataFrame column it changes the column's dtype to `dtype('O')`.
#### Expected Output
```python
import numpy as np, pandas as pd
s = pd.Series([np.nan, np.nan, np.nan, np.nan], dtype=pd.CategoricalDtype(categories=[pd.Interval(0,1)]))
s.iloc[:3] = [np.nan, pd.Interval(0,1), np.nan]
print(s)
#0 NaN
#1 (0, 1]
#2 NaN
#3 NaN
#Length: 4, dtype: category
#Categories (1, interval[int64]): [(0, 1]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.1.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 : 0.25.0
numpy : 1.17.0
pytz : 2018.3
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : 0.25.2
pytest : 3.7.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.9999999
pymysql : 0.7.11.None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : 2.7.0
odfpy : None
openpyxl : 2.4.8
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.0.0
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-08-16T03:34:21Z | 2019-08-16T12:08:17Z | 2019-08-16T12:08:17Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: data1 = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
In [2]: data2 = pd.DataFrame(np.arange(20).reshape((5, 4)) + 1, columns=['a', 'b', 'x', 'y'])
In [3]: import pyarrow as pa
In [4]: d1 = pa.deserialize(pa.serialize(data1).to_buffer())
In [5]: d2 = pa.deserialize(pa.serialize(data2).to_buffer())
In [6]: d1.merge(d2)
```
#### Problem description
The above code raises an exception:
```python
In [7]: d1.merge(d2)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-f852b96f603a> in <module>
----> 1 d1.merge(d2)
~/pandas/pandas/core/frame.py in merge(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
7261 copy=copy,
7262 indicator=indicator,
-> 7263 validate=validate,
7264 )
7265
~/pandas/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
82 validate=validate,
83 )
---> 84 return op.get_result()
85
86
~/pandas/pandas/core/reshape/merge.py in get_result(self)
625 self.left, self.right = self._indicator_pre_merge(self.left, self.right)
626
--> 627 join_index, left_indexer, right_indexer = self._get_join_info()
628
629 ldata, rdata = self.left._data, self.right._data
~/pandas/pandas/core/reshape/merge.py in _get_join_info(self)
842 )
843 else:
--> 844 (left_indexer, right_indexer) = self._get_join_indexers()
845
846 if self.right_index:
~/pandas/pandas/core/reshape/merge.py in _get_join_indexers(self)
821 """ return the join indexers """
822 return _get_join_indexers(
--> 823 self.left_join_keys, self.right_join_keys, sort=self.sort, how=self.how
824 )
825
~/pandas/pandas/core/reshape/merge.py in _get_join_indexers(left_keys, right_keys, sort, how, **kwargs)
1285
1286 # get left & right join labels and num. of levels at each location
-> 1287 llab, rlab, shape = map(list, zip(*map(fkeys, left_keys, right_keys)))
1288
1289 # get flat i8 keys from label lists
~/pandas/pandas/core/reshape/merge.py in _factorize_keys(lk, rk, sort)
1882 rizer = klass(max(len(lk), len(rk)))
1883
-> 1884 llab = rizer.factorize(lk)
1885 rlab = rizer.factorize(rk)
1886
~/pandas/pandas/_libs/hashtable.pyx in pandas._libs.hashtable.Int64Factorizer.factorize()
109 return self.count
110
--> 111 def factorize(self, int64_t[:] values, sort=False,
112 na_sentinel=-1, na_value=None):
113 """
~/pandas/pandas/_libs/hashtable.cpython-37m-darwin.so in View.MemoryView.memoryview_cwrapper()
~/pandas/pandas/_libs/hashtable.cpython-37m-darwin.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
```
#### Expected Output
`d1.copy(deep=True).merge(d2.copy(deep=True))` could give the correct result:
```python
In [10]: d1.copy(deep=True).merge(d2.copy(deep=True))
Out[10]:
a b c d e x y
0 1 2 3 4 5 3 4
```
#### Output of ``pd.show_versions()``
I'm working with pandas master so `show_version()` doesn't work. The git commit hash is `a818281a45f7b5bd24f050e5d6868894c5108db6` (the latest version on master branch at 2019-08-16). | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] closes #20519
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 42 | 2019-08-16T15:22:23Z | 2019-10-07T11:33:37Z | 2019-10-05T23:17:41Z | CONTRIBUTOR | null | This adds a new extension type 'string' for storing string data.
The data model is essentially unchanged from master. String are still
stored in an object-dtype ndarray. Scalar elements are still Python
strs, and `np.nan` is still used as the string dtype.
Things are pretty well contained. The major changes outside the new array are
1. docs
2. `core/strings.py` to handle things correctly (mostly, returning `string` dtype when there's a `string` input.
No rush on reviewing this. Just parking it here for now. | {
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} | 1 | 2019-08-16T16:25:21Z | 2019-09-02T22:53:49Z | 2019-09-02T21:31:29Z | MEMBER | null | Hopefully before long we can get rid of `box` fixture altogether, then rename `box_with_array` to `box` | {
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} | 13 | 2019-08-16T16:52:28Z | 2019-09-07T12:17:38Z | 2019-09-07T11:29:41Z | MEMBER | null | - [x] Closes #27904
- [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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} | 3 | 2019-08-16T17:56:52Z | 2019-11-02T20:19:35Z | 2019-11-02T20:19:35Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
datetimes = pd.Series(['2019-01-01', '2019-01-01', '2019-01-02'], dtype='datetime64[ns]')
categorical = datetimes.astype('category')
categorical.dt.tz_localize(None)
```
Produces:
```
0 2019-01-01
1 2019-01-02
dtype: datetime64[ns]
```
#### Problem description
`.dt.tz_localize()` is operating on `categorical.cat.categories`. It should be operating on `categorical.astype('datetime64[ns]').values`. This is just plain wrong.
#### Expected Output
According to [Categorical docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#string-and-datetime-accessors), "The returned Series (or DataFrame) is of the same type as if you used the .str.<method> / .dt.<method> on a Series of that type (and not of type category!).". So I think the expected value to be:
```
>>> datetimes.dt.tz_localize(None)
0 2019-01-01
1 2019-01-01
2 2019-01-02
dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.8-200.fc30.x86_64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.3.0
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 : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 0 | 2019-08-16T17:59:09Z | 2020-05-09T19:56:15Z | 2020-05-09T19:56:15Z | CONTRIBUTOR | null | I *think* this should be supported.
```python
In [8]: pd.DataFrame(columns=['a'], dtype=pd.CategoricalDtype())
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-a44ecacab92a> in <module>
----> 1 pd.DataFrame(columns=['a'], dtype=pd.CategoricalDtype())
~/sandbox/pandas/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
405 )
406 elif isinstance(data, dict):
--> 407 mgr = init_dict(data, index, columns, dtype=dtype)
408 elif isinstance(data, ma.MaskedArray):
409 import numpy.ma.mrecords as mrecords
~/sandbox/pandas/pandas/core/internals/construction.py in init_dict(data, index, columns, dtype)
224 # no obvious "empty" int column
225 if missing.any() and not is_integer_dtype(dtype):
--> 226 if dtype is None or np.issubdtype(dtype, np.flexible):
227 # GH#1783
228 nan_dtype = object
~/Envs/pandas-dev/lib/python3.7/site-packages/numpy/core/numerictypes.py in issubdtype(arg1, arg2)
391 """
392 if not issubclass_(arg1, generic):
--> 393 arg1 = dtype(arg1).type
394 if not issubclass_(arg2, generic):
395 arg2_orig = arg2
TypeError: data type not understood
```
**Expected Output**
```python
In [10]: pd.DataFrame(columns=['a']).astype('category')
Out[10]:
Empty DataFrame
Columns: [a]
Index: []
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"descriptio... | closed | false | null | [] | null | 9 | 2019-08-16T18:41:30Z | 2020-03-08T17:20:36Z | 2020-03-08T17:20:35Z | NONE | null | 1- If index in a df is datetime.date format, when you export it to csv files it returns as timestamp with date and 00 for hours, minutes seconds
2- If column type in dateframe is %b-%y when you export it to csv and back it converts the year to day and each value is current year.
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} | 1 | 2019-08-16T19:03:09Z | 2019-10-04T19:48:04Z | 2019-10-04T19:48:04Z | MEMBER | null | Wes is doing great work in Apache Arrow on parquet's categorical support, which means that roundtripping to parquet with `to_parquet`/`read_parquet` will preserve categorical dtypes (and with a much better performance as before).
See https://issues.apache.org/jira/browse/ARROW-3246 (and linked issues), https://github.com/apache/arrow/pull/5110
We will need to:
- update the tests for pyarrow to test this faithful roundtrip (depending on the pyarrow version): https://github.com/pandas-dev/pandas/blob/802f67046bbae0a815b2fe9d20d2217485bbc942/pandas/tests/io/test_parquet.py#L409, https://github.com/pandas-dev/pandas/blob/802f67046bbae0a815b2fe9d20d2217485bbc942/pandas/tests/io/test_parquet.py#L451
- update the documentation. Eg the caveats section at https://dev.pandas.io/user_guide/io.html#parquet
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} | 0 | 2019-08-16T19:16:26Z | 2019-08-16T20:06:39Z | 2019-08-16T20:06:36Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27902 | {
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} | 2 | 2019-08-17T19:08:27Z | 2019-08-18T08:23:48Z | 2019-08-18T08:23:48Z | CONTRIBUTOR | null | #### Problem description
As a part of #27804 I tried adding `num-jobs=4` while generating docs. But the doc generation part fails since `doc/source/whatsnew/v0.7.3.rst` file refers to an image in `../savefig/scatter_matrix_kde.png` which is not generated from `doc/source/user_guide/visualization.rst`
#### Proposed solutions
Few solutions from the top of my mind
1. We can move these referenced files and have a static copy in `_static`(there are only 4 files we need to copy)
2. Remove these references from the `v0.7.3` since it is outdated
Discussing this with @datapythonista open to the community's thought on this. | {
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responsibilities of user. Unified wording of legacy support for sqlite
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"d... | closed | false | null | [] | null | 6 | 2019-08-17T20:26:36Z | 2019-10-22T01:37:11Z | 2019-10-22T01:37:11Z | NONE | null | - [x] closes #27905
- [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/27975 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27975/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27975/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27975/events | https://github.com/pandas-dev/pandas/pull/27975 | 481,924,892 | MDExOlB1bGxSZXF1ZXN0MzA4MzE3Njkz | 27,975 | Series.rename now handles values by Series constructor | {
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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
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https://api.github.com/repos/pandas-dev/pandas/issues/27976 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27976/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27976/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27976/events | https://github.com/pandas-dev/pandas/issues/27976 | 481,927,234 | MDU6SXNzdWU0ODE5MjcyMzQ= | 27,976 | Fix PR02 issues in docstrings | {
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"id": 7171206... | open | false | null | [] | null | 15 | 2019-08-17T21:18:31Z | 2021-07-10T19:41:35Z | null | MEMBER | null | Fix the docstrings where an unknown parameter is documented (likely that they are typos, errors in the parameter format...).
Current errors:
```
$ ./scripts/validate_docstrings.py --errors=PR02
pandas.Series.astype: Unknown parameters {kwargs}
pandas.Series.pipe: Unknown parameters {args, kwargs}
pandas.Series.clip: Unknown parameters {*args, **kwargs}
pandas.Series.cummax: Unknown parameters {*args, **kwargs :}
pandas.Series.cummin: Unknown parameters {*args, **kwargs :}
pandas.Series.cumprod: Unknown parameters {*args, **kwargs :}
pandas.Series.cumsum: Unknown parameters {*args, **kwargs :}
pandas.Series.mad: Unknown parameters {**kwargs, numeric_only}
pandas.Series.compound: Unknown parameters {**kwargs, numeric_only}
pandas.Series.drop: Unknown parameters {index, columns}
pandas.Series.idxmax: Unknown parameters {*args, **kwargs}
pandas.Series.idxmin: Unknown parameters {*args, **kwargs}
pandas.Series.reindex: Unknown parameters {level, limit, copy, fill_value, tolerance, method}
pandas.Series.rename: Unknown parameters {copy, inplace, level}
pandas.Series.rename_axis: Unknown parameters {mapper, copy, inplace, index, columns, axis}
pandas.Series.argmin: Unknown parameters {*args, **kwargs, skipna, axis}
pandas.Series.argmax: Unknown parameters {*args, **kwargs, skipna, axis}
pandas.Series.swaplevel: Unknown parameters {i, j}
pandas.Series.dt.to_period: Unknown parameters {freq}
pandas.Series.dt.tz_localize: Unknown parameters {tz, errors, ambiguous, nonexistent}
pandas.Series.dt.tz_convert: Unknown parameters {tz}
pandas.Series.dt.strftime: Unknown parameters {date_format}
pandas.Series.dt.round: Unknown parameters {nonexistent, ambiguous, freq}
pandas.Series.dt.floor: Unknown parameters {nonexistent, ambiguous, freq}
pandas.Series.dt.ceil: Unknown parameters {nonexistent, ambiguous, freq}
pandas.Series.dt.month_name: Unknown parameters {locale}
pandas.Series.dt.day_name: Unknown parameters {locale}
pandas.Series.str.cat: Unknown parameters {na_rep, join, others, sep}
pandas.Series.str.center: Unknown parameters {fillchar, width}
pandas.Series.str.contains: Unknown parameters {flags, regex, case, pat, na}
pandas.Series.str.count: Unknown parameters {flags, pat}
pandas.Series.str.encode: Unknown parameters {encoding, errors}
pandas.Series.str.endswith: Unknown parameters {pat, na}
pandas.Series.str.extract: Unknown parameters {expand, flags, pat}
pandas.Series.str.extractall: Unknown parameters {flags, pat}
pandas.Series.str.find: Unknown parameters {sub, start, end}
pandas.Series.str.findall: Unknown parameters {flags, pat}
pandas.Series.str.index: Unknown parameters {sub, start, end}
pandas.Series.str.join: Unknown parameters {sep}
pandas.Series.str.ljust: Unknown parameters {fillchar, width}
pandas.Series.str.lstrip: Unknown parameters {to_strip}
pandas.Series.str.match: Unknown parameters {flags, case, pat, na}
pandas.Series.str.normalize: Unknown parameters {form}
pandas.Series.str.pad: Unknown parameters {side, fillchar, width}
pandas.Series.str.partition: Unknown parameters {expand, pat, sep}
pandas.Series.str.repeat: Unknown parameters {repeats}
pandas.Series.str.replace: Unknown parameters {flags, n, repl, regex, case, pat}
pandas.Series.str.rfind: Unknown parameters {sub, start, end}
pandas.Series.str.rindex: Unknown parameters {sub, start, end}
pandas.Series.str.rjust: Unknown parameters {fillchar, width}
pandas.Series.str.rpartition: Unknown parameters {expand, pat, sep}
pandas.Series.str.rstrip: Unknown parameters {to_strip}
pandas.Series.str.slice_replace: Unknown parameters {stop, repl, start}
pandas.Series.str.split: Unknown parameters {expand, pat, n}
pandas.Series.str.rsplit: Unknown parameters {expand, pat, n}
pandas.Series.str.startswith: Unknown parameters {pat, na}
pandas.Series.str.strip: Unknown parameters {to_strip}
pandas.Series.str.translate: Unknown parameters {table}
pandas.Series.str.wrap: Unknown parameters {break_on_hyphens, replace_whitespace, width, drop_whitespace, break_long_words, expand_tabs}
pandas.Series.str.zfill: Unknown parameters {width}
pandas.Series.str.get_dummies: Unknown parameters {sep}
pandas.Series.cat.rename_categories: Unknown parameters {inplace, new_categories}
pandas.Series.cat.reorder_categories: Unknown parameters {inplace, ordered, new_categories}
pandas.Series.cat.add_categories: Unknown parameters {inplace, new_categories}
pandas.Series.cat.remove_categories: Unknown parameters {inplace, removals}
pandas.Series.cat.remove_unused_categories: Unknown parameters {inplace}
pandas.Series.cat.set_categories: Unknown parameters {rename, inplace, ordered, new_categories}
pandas.Series.cat.as_ordered: Unknown parameters {inplace}
pandas.Series.cat.as_unordered: Unknown parameters {inplace}
pandas.Series.plot: Unknown parameters {xticks, xlim, mark_right, yticks, table, legend, ylim, logx, fontsize, include_bool, colormap, title, logy, `**kwds`, kind, position, colorbar, yerr, y, x, style, rot, loglog, figsize, grid, use_index, xerr}
pandas.Series.plot.area: Unknown parameters {stacked, **kwds}
pandas.Series.plot.bar: Unknown parameters {**kwds}
pandas.Series.plot.barh: Unknown parameters {**kwds}
pandas.Series.plot.box: Unknown parameters {**kwds}
pandas.Series.plot.density: Unknown parameters {**kwds}
pandas.Series.plot.hist: Unknown parameters {**kwds}
pandas.Series.plot.kde: Unknown parameters {**kwds}
pandas.Series.plot.line: Unknown parameters {**kwds}
pandas.Series.plot.pie: Unknown parameters {y, **kwds}
pandas.Series.hist: Unknown parameters {`**kwds`}
pandas.Series.to_csv: Unknown parameters {mode, columns, float_format, encoding, index_label, header, quoting, chunksize, na_rep, quotechar, date_format, sep, doublequote, compression, escapechar, line_terminator, decimal, path_or_buf, index}
pandas.Series.to_hdf: Unknown parameters {mode, append, format, complevel, complib, errors, fletcher32, data_columns, dropna}
pandas.Series.to_msgpack: Unknown parameters {path, append, compress}
pandas.core.resample.Resampler.pipe: Unknown parameters {args, kwargs}
pandas.core.resample.Resampler.sem: Unknown parameters {ddof}
pandas.DataFrame.select_dtypes: Unknown parameters {include, exclude}
pandas.DataFrame.astype: Unknown parameters {kwargs}
pandas.DataFrame.pipe: Unknown parameters {args, kwargs}
pandas.DataFrame.clip: Unknown parameters {*args, **kwargs}
pandas.DataFrame.compound: Unknown parameters {**kwargs, numeric_only}
pandas.DataFrame.cummax: Unknown parameters {*args, **kwargs :}
pandas.DataFrame.cummin: Unknown parameters {*args, **kwargs :}
pandas.DataFrame.cumprod: Unknown parameters {*args, **kwargs :}
pandas.DataFrame.cumsum: Unknown parameters {*args, **kwargs :}
pandas.DataFrame.eval: Unknown parameters {kwargs}
pandas.DataFrame.mad: Unknown parameters {**kwargs, numeric_only}
pandas.DataFrame.reindex: Unknown parameters {level, limit, copy, fill_value, tolerance, method, index, columns, labels, axis}
pandas.DataFrame.rename: Unknown parameters {columns, level, mapper, copy, inplace, errors, axis, index}
pandas.DataFrame.rename_axis: Unknown parameters {mapper, copy, inplace, index, columns, axis}
pandas.DataFrame.swaplevel: Unknown parameters {i, j}
pandas.DataFrame.melt: Unknown parameters {frame}
pandas.DataFrame.T: Unknown parameters {copy, *args, **kwargs}
pandas.DataFrame.transpose: Unknown parameters {copy, *args, **kwargs}
pandas.DataFrame.update: Unknown parameters {other, filter_func, join, overwrite, errors}
pandas.DataFrame.plot: Unknown parameters {xticks, xlim, mark_right, yticks, table, legend, ylim, logx, fontsize, include_bool, colormap, title, logy, `**kwds`, kind, position, colorbar, yerr, y, x, style, rot, loglog, figsize, grid, use_index, xerr}
pandas.DataFrame.plot.area: Unknown parameters {stacked, **kwds}
pandas.DataFrame.plot.bar: Unknown parameters {**kwds}
pandas.DataFrame.plot.barh: Unknown parameters {**kwds}
pandas.DataFrame.plot.box: Unknown parameters {**kwds}
pandas.DataFrame.plot.density: Unknown parameters {**kwds}
pandas.DataFrame.plot.hexbin: Unknown parameters {**kwds}
pandas.DataFrame.plot.hist: Unknown parameters {**kwds}
pandas.DataFrame.plot.kde: Unknown parameters {**kwds}
pandas.DataFrame.plot.line: Unknown parameters {**kwds}
pandas.DataFrame.plot.pie: Unknown parameters {y, **kwds}
pandas.DataFrame.plot.scatter: Unknown parameters {**kwds}
pandas.DataFrame.sparse.from_spmatrix: Unknown parameters {index, columns}
pandas.DataFrame.to_hdf: Unknown parameters {mode, append, format, complevel, complib, errors, fletcher32, data_columns, dropna}
pandas.DataFrame.to_html: Unknown parameters {min_rows}
pandas.DataFrame.to_stata: Unknown parameters {encoding, fname, time_stamp, version, convert_dates, data_label, write_index, convert_strl, byteorder, variable_labels}
pandas.DataFrame.to_msgpack: Unknown parameters {path, append, compress}
pandas.read_excel: Unknown parameters {io, usecols, header, names, sheet_name, engine, mangle_dupe_cols, **kwds, skiprows, true_values, parse_dates, dtype, verbose, na_values, convert_float, thousands, converters, skipfooter, date_parser, keep_default_na, false_values, nrows, skip_footer, squeeze, index_col, comment}
pandas.ExcelWriter: Unknown parameters {mode, datetime_format, date_format}
pandas.read_hdf: Unknown parameters {iterator, columns, where, stop , errors, start, chunksize}
pandas.HDFStore.put: Unknown parameters {encoding, value , append , format , dropna , data_columns, key }
pandas.HDFStore.append: Unknown parameters {chunksize , expectedrows, nan_rep , append , dropna , data_columns, min_itemsize, encoding }
pandas.HDFStore.select: Unknown parameters {stop }
pandas.read_feather: Unknown parameters {path, nthreads, columns, use_threads}
pandas.read_stata: Unknown parameters {iterator, encoding, columns, convert_dates, filepath_or_buffer, convert_missing, preserve_dtypes, order_categoricals, index_col, convert_categoricals, chunksize}
pandas.io.stata.StataReader.data: Unknown parameters {columns, convert_dates, convert_missing, preserve_dtypes, order_categoricals, index_col, convert_categoricals}
pandas.core.window.rolling.Rolling.sum: Unknown parameters {*args, **kwargs}
pandas.core.window.rolling.Rolling.var: Unknown parameters {*args, **kwargs}
pandas.core.window.rolling.Rolling.std: Unknown parameters {*args, **kwargs}
pandas.core.window.rolling.Rolling.max: Unknown parameters {*args, **kwargs}
pandas.core.window.rolling.Rolling.apply: Unknown parameters {*args, **kwargs}
pandas.core.window.rolling.Rolling.aggregate: Unknown parameters {func}
pandas.core.window.rolling.Rolling.quantile: Unknown parameters {**kwargs:}
pandas.core.window.rolling.Window.sum: Unknown parameters {*args, **kwargs}
pandas.core.window.expanding.Expanding.sum: Unknown parameters {*args, **kwargs}
pandas.core.window.expanding.Expanding.var: Unknown parameters {*args, **kwargs}
pandas.core.window.expanding.Expanding.std: Unknown parameters {*args, **kwargs}
pandas.core.window.expanding.Expanding.max: Unknown parameters {*args, **kwargs}
pandas.core.window.expanding.Expanding.apply: Unknown parameters {*args, **kwargs}
pandas.core.window.expanding.Expanding.aggregate: Unknown parameters {func}
pandas.core.window.expanding.Expanding.quantile: Unknown parameters {**kwargs:}
pandas.core.window.ewm.EWM.mean: Unknown parameters {*args, **kwargs}
pandas.core.window.ewm.EWM.std: Unknown parameters {*args, **kwargs}
pandas.core.window.ewm.EWM.var: Unknown parameters {*args, **kwargs}
pandas.core.window.ewm.EWM.corr: Unknown parameters {bias}
pandas.io.formats.style.Styler.apply: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.applymap: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.where: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.set_properties: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.pipe: Unknown parameters {*args, **kwargs :}
pandas.io.formats.style.Styler.background_gradient: Unknown parameters {low, high}
pandas.plotting.andrews_curves: Unknown parameters {color, frame, colormap, class_column, ax, samples, kwds}
pandas.plotting.bootstrap_plot: Unknown parameters {**kwds :}
pandas.plotting.lag_plot: Unknown parameters {kwds}
pandas.plotting.parallel_coordinates: Unknown parameters {xticks, color, frame, colormap, axvlines, sort_labels, class_column, axvlines_kwds, kwds, ax, use_columns, cols}
pandas.plotting.radviz: Unknown parameters {kwds}
pandas.plotting.scatter_matrix: Unknown parameters {kwds}
pandas.describe_option: Unknown parameters {_print_desc, pat}
pandas.reset_option: Unknown parameters {pat}
pandas.get_option: Unknown parameters {pat}
pandas.set_option: Unknown parameters {pat, value}
pandas.api.types.infer_dtype: Unknown parameters {value, skipna}
pandas.api.types.is_list_like: Unknown parameters {allow_sets, obj}
pandas.api.types.is_scalar: Unknown parameters {val}
pandas.Timestamp: Unknown parameters {hour, minute, second, microsecond, year, month, day}
pandas.Timestamp.max: Unknown parameters {nanosecond, hour, minute, second, microsecond, year, month, day, ts_input, tzinfo, tz, unit, freq}
pandas.Timestamp.min: Unknown parameters {nanosecond, hour, minute, second, microsecond, year, month, day, ts_input, tzinfo, tz, unit, freq}
pandas.Timedelta.max: Unknown parameters {value, unit, **kwargs}
pandas.Timedelta.min: Unknown parameters {value, unit, **kwargs}
pandas.Period.asfreq: Unknown parameters {freq, how}
pandas.Period.to_timestamp: Unknown parameters {freq, how}
pandas.Interval: Unknown parameters {closed, right, left}
pandas.Interval.overlaps: Unknown parameters {other}
pandas.factorize: Unknown parameters {values, size_hint, sort, na_sentinel, order}
pandas.to_datetime: Unknown parameters {infer_datetime_format, format, box, arg, yearfirst, exact, cache, dayfirst, origin, unit, utc, errors}
pandas.to_timedelta: Unknown parameters {arg, errors, unit, box}
pandas.Grouper: Unknown parameters {level, sort, convention, label, base, loffset, freq, closed, axis, key}
pandas.core.groupby.GroupBy.apply: Unknown parameters {args, kwargs}
pandas.core.groupby.GroupBy.pipe: Unknown parameters {args, kwargs}
pandas.core.groupby.DataFrameGroupBy.corr: Unknown parameters {method, min_periods}
pandas.core.groupby.DataFrameGroupBy.cov: Unknown parameters {min_periods}
pandas.core.groupby.DataFrameGroupBy.describe: Unknown parameters {exclude, percentiles, include}
pandas.core.groupby.DataFrameGroupBy.diff: Unknown parameters {periods, axis}
pandas.core.groupby.DataFrameGroupBy.fillna: Unknown parameters {inplace, value, limit, downcast, method, axis}
pandas.core.groupby.DataFrameGroupBy.filter: Unknown parameters {f}
pandas.core.groupby.DataFrameGroupBy.hist: Unknown parameters {data, column, ylabelsize, sharex, by, sharey, layout, **kwds, yrot, figsize, ax, xlabelsize, bins, grid, xrot}
pandas.core.groupby.DataFrameGroupBy.idxmax: Unknown parameters {skipna, axis}
pandas.core.groupby.DataFrameGroupBy.idxmin: Unknown parameters {skipna, axis}
pandas.core.groupby.DataFrameGroupBy.mad: Unknown parameters {level, skipna, numeric_only, **kwargs, axis}
pandas.core.groupby.DataFrameGroupBy.resample: Unknown parameters {*args, **kwargs}
pandas.core.groupby.DataFrameGroupBy.skew: Unknown parameters {level, skipna, numeric_only, **kwargs, axis}
pandas.core.groupby.DataFrameGroupBy.take: Unknown parameters {indices, **kwargs, axis, is_copy}
pandas.core.groupby.DataFrameGroupBy.tshift: Unknown parameters {periods, axis, freq}
pandas.core.groupby.SeriesGroupBy.nlargest: Unknown parameters {n, keep}
pandas.core.groupby.SeriesGroupBy.nsmallest: Unknown parameters {n, keep}
pandas.core.groupby.DataFrameGroupBy.corrwith: Unknown parameters {method, other, axis, drop}
pandas.core.groupby.DataFrameGroupBy.boxplot: Unknown parameters {`**kwds`}
pandas.Index.to_native_types: Unknown parameters {kwargs}
pandas.CategoricalIndex.rename_categories: Unknown parameters {inplace, new_categories}
pandas.CategoricalIndex.reorder_categories: Unknown parameters {inplace, ordered, new_categories}
pandas.CategoricalIndex.add_categories: Unknown parameters {inplace, new_categories}
pandas.CategoricalIndex.remove_categories: Unknown parameters {inplace, removals}
pandas.CategoricalIndex.remove_unused_categories: Unknown parameters {inplace}
pandas.CategoricalIndex.set_categories: Unknown parameters {rename, inplace, ordered, new_categories}
pandas.CategoricalIndex.as_ordered: Unknown parameters {inplace}
pandas.CategoricalIndex.as_unordered: Unknown parameters {inplace}
pandas.MultiIndex: Unknown parameters {copy, names, verify_integrity, sortorder, levels, labels, codes}
pandas.MultiIndex.set_codes: Unknown parameters {inplace, verify_integrity, codes, level}
pandas.DatetimeIndex: Unknown parameters {periods , data , copy }
pandas.DatetimeIndex.indexer_between_time: Unknown parameters {start_time, end_time}
pandas.DatetimeIndex.strftime: Unknown parameters {date_format}
pandas.DatetimeIndex.tz_convert: Unknown parameters {tz}
pandas.DatetimeIndex.tz_localize: Unknown parameters {tz, errors, ambiguous, nonexistent}
pandas.DatetimeIndex.round: Unknown parameters {nonexistent, ambiguous, freq}
pandas.DatetimeIndex.floor: Unknown parameters {nonexistent, ambiguous, freq}
pandas.DatetimeIndex.ceil: Unknown parameters {nonexistent, ambiguous, freq}
pandas.DatetimeIndex.month_name: Unknown parameters {locale}
pandas.DatetimeIndex.day_name: Unknown parameters {locale}
pandas.DatetimeIndex.to_period: Unknown parameters {freq}
pandas.DatetimeIndex.to_perioddelta: Unknown parameters {freq}
pandas.DatetimeIndex.mean: Unknown parameters {skipna}
pandas.TimedeltaIndex: Unknown parameters {periods , data , copy }
pandas.TimedeltaIndex.round: Unknown parameters {nonexistent, ambiguous, freq}
pandas.TimedeltaIndex.floor: Unknown parameters {nonexistent, ambiguous, freq}
pandas.TimedeltaIndex.ceil: Unknown parameters {nonexistent, ambiguous, freq}
pandas.TimedeltaIndex.mean: Unknown parameters {skipna}
pandas.PeriodIndex: Unknown parameters {quarter, month, day, hour, second, year, minute}
pandas.PeriodIndex.asfreq: Unknown parameters {freq, how}
pandas.PeriodIndex.strftime: Unknown parameters {date_format}
pandas.PeriodIndex.to_timestamp: Unknown parameters {freq, how}
pandas.api.extensions.ExtensionArray.argsort: Unknown parameters {*args, **kwargs:}
pandas.tseries.offsets.CBMonthEnd.apply_index: Unknown parameters {i}
pandas.tseries.offsets.CBMonthBegin.apply_index: Unknown parameters {i}
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27977/events | https://github.com/pandas-dev/pandas/issues/27977 | 481,927,305 | MDU6SXNzdWU0ODE5MjczMDU= | 27,977 | DOC: Fix formatting errors in docstrings | {
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"following_url": "https://api.github.com/users/ericmariasis/following{/other_user... | null | 8 | 2019-08-17T21:19:25Z | 2020-05-29T22:13:26Z | null | MEMBER | null | Historically, there was no validation on how docstrings were written. Some conventions were usually followed, but as the project grew, it was more difficult to ensure that all the API documentation pages are consistent, and don't have mistakes.
For the last two years, we've been implementing all sorts of validations to make sure every class, method, function and attribute is correctly documented.
The list of validations can be found here in the script that validates them: https://github.com/pandas-dev/pandas/blob/master/scripts/validate_docstrings.py#L77
Many of them have already been fixed in all the pages, and they could be added to the CI so they are not reintroduced again. The list of errors currently validated can be seen at the CI script: https://github.com/pandas-dev/pandas/blob/master/ci/code_checks.sh#L267
The list of pending errors making the difference is:
```python
{'ES01': 'No extended summary found',
'EX01': 'No examples section found',
'EX02': 'Examples do not pass tests:\n{doctest_log}',
'EX03': 'flake8 error: {error_code} {error_message}{times_happening}',
'GL01': 'Docstring text (summary) should start in the line immediately after '
'the opening quotes (not in the same line, or leaving a blank line in '
'between)',
'GL02': 'Closing quotes should be placed in the line after the last text in '
'the docstring (do not close the quotes in the same line as the text, '
'or leave a blank line between the last text and the quotes)',
'GL08': 'The object does not have a docstring',
'PR01': 'Parameters {missing_params} not documented',
'PR02': 'Unknown parameters {unknown_params}',
'PR06': 'Parameter "{param_name}" type should use "{right_type}" instead of '
'"{wrong_type}"',
'PR07': 'Parameter "{param_name}" has no description',
'PR08': 'Parameter "{param_name}" description should start with a capital '
'letter',
'PR09': 'Parameter "{param_name}" description should finish with "."',
'RT02': 'The first line of the Returns section should contain only the type, '
'unless multiple values are being returned',
'RT03': 'Return value has no description',
'SA01': 'See Also section not found',
'SA02': 'Missing period at end of description for See Also "{reference_name}" '
'reference',
'SA03': 'Description should be capitalized for See Also "{reference_name}" '
'reference',
'SA04': 'Missing description for See Also "{reference_name}" reference',
'SS01': 'No summary found (a short summary in a single line should be present '
'at the beginning of the docstring)',
'SS02': 'Summary does not start with a capital letter',
'SS03': 'Summary does not end with a period',
'SS06': 'Summary should fit in a single line',
'YD01': 'No Yields section found'}
```
Some of them makes more sense to work when fixing the content of an object (like adding the description, or objects that simply don't have any documentation).
But some of them are just formatting errors, those are the ones I'd start with:
- EX03: flake8 error: {error_code} {error_message}{times_happening}
- GL01/GL02: Docstring text (summary) should start/end in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
- PR02: Unknown parameters {unknown_params}
- PR06: Parameter "{param_name}" type should use "{right_type}" instead of "{wrong_type}"
- PR08: Parameter "{param_name}" description should start with a capital letter
- PR09: Parameter "{param_name}" description should finish with "."
- RT02: The first line of the Returns section should contain only the type, unless multiple values are being returned
- SA02: Missing period at end of description for See Also "{reference_name}" reference
- SA03: Description should be capitalized for See Also "{reference_name}" 'reference'
- SS02/SS03: Summary does not start/end with a capital letter
- SS03: Summary does not end with a period
- SS06: Summary should fit in a single line
To find errors for one of them you can use:
```
./scripts/validate_docstrings.py --errors=EX02
```
Or for errors that makes sense to address together:
```
./scripts/validate_docstrings.py --errors=GL01,GL02
```
This should give the list of errors to fix. We've got a list of steps to follow when fixing a docstring that it can be useful to you at: https://python-sprints.github.io/pandas/dashboard.html
**VERY IMPORTANT**
The main challenge will be not repeating the same work as other sprinters, which is very frustrating, and happened massively at every sprint. My recommendation is BEFORE doing any work, to create an issue for the error code you plan to work on (check that it hasn't already been created). In the error write the list of errors that `validate_docstrings.py` returns. Then in a comment, take 10 of them, and write that you're going to fix them. Other people can work on a different 10. When opening a PR, reference the issue.
I created an issue for reference: #27976
Good luck!
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} | [] | closed | false | null | [] | null | 2 | 2019-08-17T23:46:32Z | 2019-08-18T00:06:03Z | 2019-08-18T00:06:02Z | CONTRIBUTOR | null | …ead_csv function, for issue 27655.
- [] closes #xxxx
- [ 1 ] tests added / passed
- [ 1 ] passes `black pandas`
- *[ 0 ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ 0 ] whatsnew entry
Do not close issue, waiting for user response before further testing.
* Returns: fatal: bad revision 'upstream/master' | {
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] | open | false | null | [] | null | 3 | 2019-08-17T23:46:37Z | 2019-09-06T03:52:17Z | null | NONE | null | Fix the following docstrings and make them end with a period to follow the pandas convention.
```
pandas.DataFrame.keys: Summary does not end with a period
pandas.read_clipboard: Summary does not end with a period
pandas.ExcelFile.parse: Summary does not end with a period
pandas.HDFStore.put: Summary does not end with a period
pandas.HDFStore.get: Summary does not end with a period
pandas.HDFStore.select: Summary does not end with a period
pandas.HDFStore.keys: Summary does not end with a period
pandas.HDFStore.groups: Summary does not end with a period
pandas.HDFStore.walk: Summary does not end with a period
pandas.io.stata.StataReader.data: Summary does not end with a period
pandas.io.formats.style.Styler.loader: Summary does not end with a period
pandas.io.formats.style.Styler.set_caption: Summary does not end with a period
pandas.plotting.deregister_matplotlib_converters: Summary does not end with a period
pandas.plotting.register_matplotlib_converters: Summary does not end with a period
```
Issue noted by @datapythonista | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-18T00:04:08Z | 2019-08-20T14:04:57Z | 2019-08-20T14:04:57Z | NONE | null | - [yes] closes #27482
- [yes] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [yes] whatsnew entry
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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
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- [ 1 ] tests added / passed
- [ 1 ] passes `black pandas`
- *[ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Do not close issue, waiting for user response before continuing further testing.
*Returns "fatal: bad revision 'upstream/master'" | {
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- [x] tests added / passed
- [x] passes `black pandas`
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- [x] whatsnew entry
Not sure where to add whatsnew entry #PandasHack2019
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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input: python .\scripts\validate_docstrings.py pandas.tseries.offsets.DateOffset --errors=F821
output: Docstring for "pandas.tseries.offsets.DateOffset" correct. :) | {
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<details>
For the following files:
pandas/conftest.py
pandas/io/html.py
</details>
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Fixed docstrings text that aren’t on the next line after opening `"""` and followed immediately by the line with closing `"""`. Issue #27986 | {
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} | 3 | 2019-08-18T09:20:31Z | 2019-08-20T19:24:49Z | 2019-08-20T19:24:47Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/27095, where I forgot to apply this setting in the html repr as well.
Thoughts on including this in 0.25.1 or not? It's kind of a oversight of the 0.25 feature, but also an actual change of course in the user experience in the notebook. | {
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} | 10 | 2019-08-18T11:25:59Z | 2019-09-09T15:24:05Z | 2019-09-09T12:06:01Z | CONTRIBUTOR | null | closes #19995
Timezone info is dropped in GroupBy.shift, bfill, and ffill
since index calculated by Cythonized functions are applied to
NumPy representation of values without timezone.
Could you please find and review this fix?
Many thanks,
-noritada
```
In [1]: import pandas as pd
...: pd.__version__
Out[1]: '0.24.2'
In [2]: df = pd.DataFrame([
...: ['a', pd.Timestamp('2019-01-01 00:00:00+09:00')],
...: ['b', pd.Timestamp('2019-01-01 00:05:00+09:00')],
...: ['a', None],
...: ['b', None],
...: ['a', pd.Timestamp('2019-01-01 00:20:00+09:00')],
...: ['b', pd.Timestamp('2019-01-01 00:25:00+09:00')],
...: ], columns=['id', 'time'])
In [3]: df['time'].shift(1)
Out[3]:
0 NaT
1 2019-01-01 00:00:00+09:00
2 2019-01-01 00:05:00+09:00
3 NaT
4 NaT
5 2019-01-01 00:20:00+09:00
Name: time, dtype: datetime64[ns, pytz.FixedOffset(540)]
In [4]: df.groupby('id')['time'].shift(1)
Out[4]:
0 NaT
1 NaT
2 2018-12-31 15:00:00
3 2018-12-31 15:05:00
4 NaT
5 NaT
Name: time, dtype: datetime64[ns]
In [5]: df.groupby('id')['time'].bfill()
Out[5]:
0 2018-12-31 15:00:00
1 2018-12-31 15:05:00
2 2018-12-31 15:20:00
3 2018-12-31 15:25:00
4 2018-12-31 15:20:00
5 2018-12-31 15:25:00
Name: time, dtype: datetime64[ns]
In [6]: df.groupby('id')['time'].ffill()
Out[6]:
0 2018-12-31 15:00:00
1 2018-12-31 15:05:00
2 2018-12-31 15:00:00
3 2018-12-31 15:05:00
4 2018-12-31 15:20:00
5 2018-12-31 15:25:00
Name: time, dtype: datetime64[ns]
```
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} | 3 | 2019-08-18T11:41:09Z | 2019-08-20T14:18:59Z | 2019-08-20T14:18:56Z | MEMBER | null | Due to previous PRs, there is an issue with xlim wrongly plotted for lines. I digged into database a bit, and found it also affects `plot(kind='area')` because they share the same `LinePlot`. And this issue is produced in both DataFrame and Series. Now looks like the issue is gone:
Duplicated closed issue 27796 is solved as well:
Also looks like issue in #25160 is gone as well.
The same to #24784 looks like issue is also gone
- [x] closes #27686 , #25160, #24784
- [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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} | 9 | 2019-08-18T13:38:22Z | 2021-07-28T01:03:31Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({
{'value': [1, 2]},
index=[pd.Timestamp('2019-01-01'), pd.Timestamp('2019-01-01')]
})
df = df.drop(df[df['value'] > 10].index)
```
#### Problem description
When calling `drop` for dataframe with DatetimeIndex, the function may raise an exception. See the following traceback:
```
Traceback (most recent call last):
File "main.py", line 7, in <module>
df = df.drop(df[df['value'] > 10].index)
File "pandas/pandas/core/frame.py", line 4042, in drop
errors=errors,
File "pandas/pandas/core/generic.py", line 3891, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "pandas/pandas/core/generic.py", line 3940, in _drop_axis
labels_missing = (axis.get_indexer_for(labels) == -1).any()
File "pandas/pandas/core/indexes/base.py", line 4769, in get_indexer_for
indexer, _ = self.get_indexer_non_unique(target, **kwargs)
File "pandas/pandas/core/indexes/base.py", line 4752, in get_indexer_non_unique
indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
File "pandas/_libs/index.pyx", line 291, in pandas._libs.index.IndexEngine.get_indexer_non_unique
stargets = set(targets)
TypeError: 'NoneType' object is not iterable
```
This can be avoided by not using `DataFrame.drop` like this:
```python
df = df[df['value'] <= 10]
```
But nonetheless I'm reporting the issue as I expect `drop` to not raise the exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9f93d57025d7b906de6956f287b71cd18c17b13d
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0.dev0+1184.g9f93d5702
numpy : 1.16.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : 0.29.13
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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"description": "Unexpect... | open | false | null | [] | null | 3 | 2019-08-18T13:54:00Z | 2021-07-11T05:52:22Z | null | NONE | null | Would it be possible to infer ExtensionDtypes based on the objects inside a list/array that are used to construct a series?
Eg with an array of pint's Quantity objects:
```python3
>>> array_of_qty
array([<Quantity(67.0, 'meter')>, <Quantity(61.0, 'meter')>,
<Quantity(30.0, 'meter')>, ..., <Quantity(293.0, 'meter')>,
<Quantity(886.0, 'meter')>, <Quantity(165.0, 'meter')>],
dtype=object)
```
Could pandas infer the dtype as PintType in this command?:
```python3
df.elevation = array_of_qty
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- [x] closes #27394
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [X] closes #21910
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Simply put, added support to use dataclasses in the following way:
```python
from dataclasses import dataclass
@dataclass
class Person:
name: str
age: int
df = DataFrame([Person("me", 25), Person("you", 35)])
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28000 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28000/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28000/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28000/events | https://github.com/pandas-dev/pandas/pull/28000 | 482,017,929 | MDExOlB1bGxSZXF1ZXN0MzA4MzgwNjcw | 28,000 | When a DataFrame is merged with an empty dataframe, it was creating an incorrect index for the resulting dataframe. Fix for it | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Partially resolves issue | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28002 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28002/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28002/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28002/events | https://github.com/pandas-dev/pandas/pull/28002 | 482,019,048 | MDExOlB1bGxSZXF1ZXN0MzA4MzgxNDA1 | 28,002 | TST 27905 - testing groupby.transform('sum') | {
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... | closed | false | null | [] | null | 3 | 2019-08-18T17:52:20Z | 2019-10-03T19:11:42Z | 2019-10-03T19:11:36Z | NONE | null | - [X] closes #24784
- [X] tests added / passed - 1
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry - Test utility method "_check_plot_works_with_continuous_dates" and Test case "test_continuous_dates"
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```python
def sample_MSE_implementation(df):
from sklearn.metrics import mean_squared_error
# If the user wish,they can pass the normalized df as a parameter
# df=(df-df.min())/(df.max()-df.min())
Matrix = [[round(mean_squared_error(df.iloc[:, row],df.iloc[:, col]),6) for row in range(df.columns.size)] for col in range(df.columns.size)]
cols = df.columns
mse_df = pd.DataFrame(Matrix, columns=cols, index=cols)
return mse_df
```
#### Problem description
I think there is a need for a `df.mse()`, similar to how there is a `df.corr()`. I have attached my own code and output to illustrate output and a very elementary implementation.
_My apologies if this is not the proper way to submit a feature requests. I would have made this a pull, however, I doubt the quility of my code_
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
please note that I am using colab.research.google.com
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.79+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.6.4
pip: 19.2.1
setuptools: 41.0.1
Cython: 0.29.13
numpy: 1.16.4
scipy: 1.3.1
pyarrow: 0.14.1
xarray: 0.11.3
IPython: 5.5.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.5.3
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 3.0.3
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.6
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.3.6
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: 0.3.3
fastparquet: None
pandas_gbq: 0.4.1
pandas_datareader: 0.7.4
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28005 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28005/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28005/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28005/events | https://github.com/pandas-dev/pandas/issues/28005 | 482,049,815 | MDU6SXNzdWU0ODIwNDk4MTU= | 28,005 | Explode on identical index causes duplication | {
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] | closed | false | null | [] | null | 2 | 2019-08-18T23:15:01Z | 2019-09-17T13:38:14Z | 2019-09-17T13:38:14Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame({'col': [['a', 'b'], ['c', 'd']]}, index=[0,0])
>>> df.explode('col')
col
0 a
0 b
0 c
0 d
0 a
0 b
0 c
0 d
```
#### Problem description
I'm not sure if this is expected behaviour. But it took me slightly off guard when I had a datetime index with occasionally identical timestamps. This is the output I expected:
```python
col
0 a
0 b
0 c
0 d
```
Thanks for your work on pandas! Apologies if this is actually normal behaviour.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-139-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.14.3
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.2
setuptools : 40.8.0
Cython : None
pytest : 4.5.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
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/28006 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28006/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28006/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28006/events | https://github.com/pandas-dev/pandas/pull/28006 | 482,051,298 | MDExOlB1bGxSZXF1ZXN0MzA4NDAzODAw | 28,006 | DOC: Improve melt example (#23844) | {
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] | closed | false | null | [] | null | 13 | 2019-08-18T23:30:19Z | 2019-08-27T08:13:19Z | 2019-08-27T08:13:19Z | NONE | null | - [x] closes #23844
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https://api.github.com/repos/pandas-dev/pandas/issues/28007 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28007/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28007/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28007/events | https://github.com/pandas-dev/pandas/issues/28007 | 482,081,028 | MDU6SXNzdWU0ODIwODEwMjg= | 28,007 | OverflowError: value too large to convert to int | {
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... | open | false | null | [] | null | 5 | 2019-08-19T02:55:10Z | 2020-05-16T20:38:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df.to_hdf('../data/base.h5', 'all')
/work/.pylib/3/pandas/core/generic.py in to_hdf(self, path_or_buf, key, **kwargs)
2529 from pandas.io import pytables
2530
-> 2531 pytables.to_hdf(path_or_buf, key, self, **kwargs)
2532
2533 def to_msgpack(self, path_or_buf=None, encoding="utf-8", **kwargs):
/work/.pylib/3/pandas/io/pytables.py in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
276 path_or_buf, mode=mode, complevel=complevel, complib=complib
277 ) as store:
--> 278 f(store)
279 else:
280 f(path_or_buf)
/work/.pylib/3/pandas/io/pytables.py in <lambda>(store)
269 f = lambda store: store.append(key, value, **kwargs)
270 else:
--> 271 f = lambda store: store.put(key, value, **kwargs)
272
273 path_or_buf = _stringify_path(path_or_buf)
/work/.pylib/3/pandas/io/pytables.py in put(self, key, value, format, append, **kwargs)
957 format = get_option("io.hdf.default_format") or "fixed"
958 kwargs = self._validate_format(format, kwargs)
--> 959 self._write_to_group(key, value, append=append, **kwargs)
960
961 def remove(self, key, where=None, start=None, stop=None):
/work/.pylib/3/pandas/io/pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1523
1524 # write the object
-> 1525 s.write(obj=value, append=append, complib=complib, **kwargs)
1526
1527 if s.is_table and index:
/work/.pylib/3/pandas/io/pytables.py in write(self, obj, **kwargs)
3230 blk_items = data.items.take(blk.mgr_locs)
3231 self.write_array(
-> 3232 "block{idx}_values".format(idx=i), blk.values, items=blk_items
3233 )
3234 self.write_index("block{idx}_items".format(idx=i), blk_items)
/work/.pylib/3/pandas/io/pytables.py in write_array(self, key, value, items)
2997
2998 vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
-> 2999 vlarr.append(value)
3000 else:
3001 if empty_array:
/usr/local/anaconda3/lib/python3.6/site-packages/tables/vlarray.py in append(self, sequence)
545 nparr = None
546
--> 547 self._append(nparr, nobjects)
548 self.nrows += 1
549
tables/hdf5extension.pyx in tables.hdf5extension.VLArray._append()
OverflowError: value too large to convert to int
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.5.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : zh_CN.utf8
LANG : None
LOCALE : zh_CN.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.8
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.5
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
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https://api.github.com/repos/pandas-dev/pandas/issues/28008 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28008/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28008/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28008/events | https://github.com/pandas-dev/pandas/pull/28008 | 482,124,185 | MDExOlB1bGxSZXF1ZXN0MzA4NDU4NzE5 | 28,008 | [CI] Add pip dependence explicitly | {
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} | 6 | 2019-08-19T06:14:58Z | 2019-08-21T14:21:49Z | 2019-08-21T14:21:45Z | CONTRIBUTOR | null | Close #27374. Get rid of the conda warning.
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https://api.github.com/repos/pandas-dev/pandas/issues/28009 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28009/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28009/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28009/events | https://github.com/pandas-dev/pandas/pull/28009 | 482,277,773 | MDExOlB1bGxSZXF1ZXN0MzA4NTgyOTQz | 28,009 | DOC: Corrected file description in read_fwf() | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28010 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28010/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28010/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28010/events | https://github.com/pandas-dev/pandas/pull/28010 | 482,290,528 | MDExOlB1bGxSZXF1ZXN0MzA4NTkzMDcw | 28,010 | BUG: Avoid duplicating entire exploded column when joining back with origi… | {
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```
=================================== FAILURES ===================================
_ TestCategoricalSeriesAnalytics.test_drop_duplicates_categorical_non_bool[True-datetime64[D]] _
[gw1] linux -- Python 3.7.4 /home/vsts/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH#7996
_ TestCategoricalSeriesAnalytics.test_drop_duplicates_categorical_non_bool[False-datetime64[D]] _
[gw1] linux -- Python 3.7.4 /home/vsts/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH#7996
_ TestCategoricalSeriesAnalytics.test_drop_duplicates_categorical_non_bool[None-datetime64[D]] _
[gw1] linux -- Python 3.7.4 /home/vsts/miniconda3/envs/pandas-dev/bin/python
[XPASS(strict)] GH#7996
```
xref https://github.com/pandas-dev/pandas/pull/27715.
Can you take a look @jbrockmendel? | {
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