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] | closed | false | null | [] | null | 5 | 2021-02-06T05:59:15Z | 2021-02-21T12:09:27Z | 2021-02-21T09:12:54Z | 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
import pandas as pd
def f(s):
s.pop("A")
df = pd.DataFrame({"A": [1, 2, 3]})
df.apply(f, axis="columns")
```
```python-traceback
Traceback (most recent call last):
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 4554, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'A'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 7765, in apply
return op.get_result()
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 185, in get_result
return self.apply_standard()
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 276, in apply_standard
results, res_index = self.apply_series_generator()
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 290, in apply_series_generator
results[i] = self.f(v)
File "<stdin>", line 1, in f
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/series.py", line 4467, in pop
return super().pop(item=item)
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/generic.py", line 768, in pop
result = self[item]
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/series.py", line 824, in __getitem__
return self._get_value(key)
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/series.py", line 932, in _get_value
loc = self.index.get_loc(label)
File "/usr/local/google/home/rchristy/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3082, in get_loc
raise KeyError(key) from err
KeyError: 'A'
```
#### Problem description
When the function being applied by `apply` pops a column from the row, the popped column is missing on subsequent calls of the function. This seems to stem from `apply` reusing the same `Series` instance across multiple invocations but failing to fully reset its state (i.e., the popped column stays popped).
This is an apparent regression from the behavior in v1.0.5 which returned a `DataFrame` with three rows. The timing of the regression suggests that it is possibly an unintended consequence of https://github.com/pandas-dev/pandas/pull/34909.
The problem is relatively straightforward to work around. Performing a shallow copy on the `Series` fixes it:
```
>>> def f(s):
... s.copy(deep=False).pop("A")
...
>>> df = pd.DataFrame({"A": [1, 2, 3]})
>>> df.apply(f, axis="columns")
0 None
1 None
2 None
dtype: object
```
#### Expected Output
The expected output is what is shown above with the workaround which is what v1.0.5 output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.17-1rodete4-amd64
Version : #1 SMP Debian 5.7.17-1rodete4 (2020-10-01)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1.1
setuptools : 51.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 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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} | 8 | 2021-02-06T09:05:47Z | 2021-10-16T19:06:30Z | null | MEMBER | null | See https://pandas.pydata.org/speed/pandas/#timeseries.ResampleSeries.time_resample?python=3.8&Cython=0.29.21&p-index='datetime'&p-freq='1D'&p-method='mean'&commits=812c3012-71a4cb69
It's from the period there were no benchmarks runs, so no clear indication which commit (range) would be responsible.
Reproducer:
```python
idx = pd.date_range(start="1/1/2000", end="1/1/2001", freq="T")
s = pd.Series(np.random.randn(len(idx)), index=idx)
%timeit s.resample("1D").mean()
```
Last release:
```
In [2]: %timeit s.resample("1D").mean()
4.45 ms ± 507 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [3]: pd.__version__
Out[3]: '1.2.1'
```
on master:
```
In [2]: %timeit s.resample("1D").mean()
6.33 ms ± 430 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
So around 50% slowdown.
And it seems somewhat specific to `mean` (eg I don't see a similar slowdown for eg `max`) | {
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} | 16 | 2021-02-06T10:55:16Z | 2021-04-16T08:13:47Z | 2021-04-16T00:46:28Z | MEMBER | null | - [x] closes #39614
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 3 | 2021-02-06T14:55:01Z | 2021-02-08T22:05:35Z | 2021-02-08T21:47:50Z | MEMBER | null | - [x] closes #38902
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | 2021-02-06T17:05:01Z | 2021-02-07T16:43:05Z | 2021-02-07T16:41:20Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
I've applied the change here to the API function `apply` but I don't believe this is an API change as you cannot pass to `**kwds` (or `**kwargs`) by name. | {
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} | 0 | 2021-02-06T21:19:43Z | 2021-02-07T17:01:12Z | 2021-02-07T17:01:12Z | 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.
---
Currently the jinja2 template for `Styler` rendering HTML leaves unstructured spaces and newlines.
For example:
```
df = pd.DataFrame('', index=['a', 'b'], columns=['A', 'B'])
print(df.style.applymap(lambda x: 'color:red;', subset=pd.IndexSlice['a', ['B', 'A']])\
.set_table_styles([{'selector': 'th', 'props': 'color:green;att:bad;'}]).render())
```
```
<style type="text/css" >
#T_6b11f_ th {
color: green;
att: bad;
}#T_6b11f_row0_col0,#T_6b11f_row0_col1{
color: red;
}</style><table id="T_6b11f_" ><thead> <tr> <th class="blank level0" ></th> <th class="col_heading level0 col0" >A</th> <th class="col_heading level0 col1" >B</th> </tr></thead><tbody>
<tr>
<th id="T_6b11f_level0_row0" class="row_heading level0 row0" >a</th>
<td id="T_6b11f_row0_col0" class="data row0 col0" ></td>
<td id="T_6b11f_row0_col1" class="data row0 col1" ></td>
</tr>
<tr>
<th id="T_6b11f_level0_row1" class="row_heading level0 row1" >b</th>
<td id="T_6b11f_row1_col0" class="data row1 col0" ></td>
<td id="T_6b11f_row1_col1" class="data row1 col1" ></td>
</tr>
</tbody></table>
```
It would be more suitable for adopt a standard 2 space tabbed indent for new HTML elements ([w3 good code standard](https://www.w3schools.com/html/html5_syntax.asp)), to yield for example:
```
<style type="text/css">
#T_53778_ th {
color: green;
att: bad;
}
#T_53778_row0_col0, #T_53778_row0_col1 {
color: red;
}
</style>
<table id="T_53778_" >
<thead>
<tr>
<th class="blank level0" ></th>
<th class="col_heading level0 col0" >A</th>
<th class="col_heading level0 col1" >B</th>
</tr>
</thead>
<tbody>
<tr>
<th id="T_53778_level0_row0" class="row_heading level0 row0" >a</th>
<td id="T_53778_row0_col0" class="data row0 col0" ></td>
<td id="T_53778_row0_col1" class="data row0 col1" ></td>
</tr>
<tr>
<th id="T_53778_level0_row1" class="row_heading level0 row1" >b</th>
<td id="T_53778_row1_col0" class="data row1 col0" ></td>
<td id="T_53778_row1_col1" class="data row1 col1" ></td>
</tr>
</tbody>
</table>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/39627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39627/events | https://github.com/pandas-dev/pandas/pull/39627 | 802,795,209 | MDExOlB1bGxSZXF1ZXN0NTY4ODU1Njkz | 39,627 | CLN: Styler HTML output adopt structured code standard | {
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} | 2 | 2021-02-06T21:26:34Z | 2021-02-07T18:38:09Z | 2021-02-07T17:01:12Z | CONTRIBUTOR | null | - [x] closes #39626
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39628/events | https://github.com/pandas-dev/pandas/issues/39628 | 802,803,858 | MDU6SXNzdWU4MDI4MDM4NTg= | 39,628 | ENH: vector methods for conversion from Period to its ordinal and back | {
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] | closed | false | null | [] | null | 6 | 2021-02-06T22:18:56Z | 2021-02-07T15:34:09Z | 2021-02-07T15:34:09Z | NONE | null | #### Is your feature request related to a problem?
a) Currently to get Series of ordinal of Period Series I do
`s.apply(lambda x: x.ordinal)
b) Also to get back I do
`s.apply(lambda x: Period(ordinal=x, 'W-SAT'))
c) And last but not least:
s.astype(int).astype('period[W-SAT]') is different than s.
#### Describe the solution you'd like
a)
s.ordinal returns series with ordinals
b)
There is some easy method to create series of Periods from ordinals... like Period(s, 'W-SAT')
or s.astype('period[W-SAT]', from_ordinal=True)
c) last option in b) is kind of solution to c)
[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
Mostly it won't but if .astype('period[W-SAT]') would use ordinals then yes as currently 2650 means year nor ordinal for this method
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https://api.github.com/repos/pandas-dev/pandas/issues/39629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39629/events | https://github.com/pandas-dev/pandas/issues/39629 | 802,857,222 | MDU6SXNzdWU4MDI4NTcyMjI= | 39,629 | BUG: MemoryError: Unable to allocate | {
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} | 4 | 2021-02-07T04:27:56Z | 2021-02-07T16:34:47Z | 2021-02-07T16:34:39Z | NONE | null | - [ ] 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
Unfortunately this is not available since the errors occur exclusively over proprietary datasets which are very complex and large. It is not feasible to distill an example.
#### Problem description
I am seeing `MemoryError` exceptions all over the place on somewhat random lines. I am measuring the memory usage using `psutil` to ascertain that there is however vast amounts of free memory on the node. These exceptions are making Pandas completely unusable for me. It's struggling with allocating 22 MiB when there is over 2 TiB of free memory available.
```
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/frame.py"", line 7950, in merge"
return merge(
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/reshape/merge.py"", line 74, in merge"
op = _MergeOperation(
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/reshape/merge.py"", line 652, in __init__"
) = self._get_merge_keys()
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/reshape/merge.py"", line 1063, in _get_merge_keys"
self.right = self.right._drop_labels_or_levels(right_drop)
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/generic.py"", line 1637, in _drop_labels_or_levels"
dropped = self.copy()
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/generic.py"", line 5665, in copy"
data = self._mgr.copy(deep=deep)
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/internals/managers.py"", line 811, in copy"
" res = self.apply(""copy"", deep=deep)"
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/internals/managers.py"", line 409, in apply"
" applied = getattr(b, f)(**kwargs)"
" File ""/opt/conda/envs/condaenv/lib/python3.8/site-packages/pandas/core/internals/blocks.py"", line 679, in copy"
values = values.copy()
"MemoryError: Unable to allocate 22.1 MiB for an array with shape (1, 2896551) and data type object"
```
#### Expected Output
These exceptions are not supposed to happen given there is sufficient free memory. There are many prior issues noting this exception, which is suggestive of severe continued bugs in Pandas.
#### Output of ``pd.show_versions()``
This is not entirely available since it's running exclusively in the cloud, but the salient versions are:
```
python : 3.8.6.final.0
python-bits : 64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 50.3.1.post20201107
Cython : None
pandas_datareader: None
numexpr : None
pyarrow : 3.0.0
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39630/events | https://github.com/pandas-dev/pandas/issues/39630 | 802,911,079 | MDU6SXNzdWU4MDI5MTEwNzk= | 39,630 | ENH: Series.explode_range | {
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] | open | false | null | [] | null | 7 | 2021-02-07T09:44:19Z | 2021-08-15T17:30:48Z | null | NONE | null | #### Is your feature request related to a problem?
Yes. Often I have 2 columns: start and stop and what I want is instead single column with all values from start to stop. currently I can do it with explode but I need mostly int64 and for this is it is a bit slow.
#### Describe the solution you'd like
Series.explode_range method
```
def explode_range(
self, stop, left_shift=0, right_shift=0, ignore_index: bool = False
) -> "Series":
"""
Creates series from exploded ranges from this series to the ones given in stop
Currently works only in int64
left_shift and right_shift parameters are used to delay or hasten start - stop
.. versionadded:: 1.2.x
Parameters
----------
stop: Series providing stop point
left_shift: shift for start point - 0 includes it, 1 not
right_shift: shift for end point - 0 excludes it, 1 includes
ignore_index : bool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1.
Returns
-------
Series
Exploded ranges to rows; index will be duplicated for these rows.
```
#### API breaking implications
This is new feature but for int64 only for now
#### Describe alternatives you've considered
Using explode
#### Additional context
I will provide a PR with sample code | {
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- [ ] closes #39630
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | 2021-02-07T11:05:48Z | 2021-02-16T18:24:09Z | 2021-02-07T17:12:20Z | CONTRIBUTOR | null | simplifies code by removing unnecessary function def. | {
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} | 4 | 2021-02-07T12:36:14Z | 2021-02-07T16:22:38Z | 2021-02-07T16:22:38Z | 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
Output on version 1.1.5:
```python
In [5]: import pandas as pd
...: df = pd.DataFrame([], columns=["id", "field"])
...: df["id"].transform(lambda x: x + 10)
Out[5]: Series([], Name: id, dtype: object)
```
Output on version 1.2.x:
```python
In [4]: import pandas as pd
...: df = pd.DataFrame([], columns=["id", "field"])
...: df["id"].transform(lambda x: x + 10)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-d1e6cad57091> in <module>
----> 1 df["id"].transform(lambda x: x + 10)
~/.pyenv/versions/3.9.1/envs/odds-data-3.9.1/lib/python3.9/site-packages/pandas/core/series.py in transform(self, func, axis, *args, **kwargs)
3975 self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
3976 ) -> FrameOrSeriesUnion:
-> 3977 return transform(self, func, axis, *args, **kwargs)
3978
3979 def apply(self, func, convert_dtype=True, args=(), **kwds):
~/.pyenv/versions/3.9.1/envs/odds-data-3.9.1/lib/python3.9/site-packages/pandas/core/aggregation.py in transform(obj, func, axis, *args, **kwargs)
458 # when the dtype is not appropriate
459 if isinstance(result, (ABCSeries, ABCDataFrame)) and result.empty:
--> 460 raise ValueError("Transform function failed")
461 if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals(
462 obj.index
ValueError: Transform function failed
```
#### Problem description
Applying `.transform` on an empty DataFrame raises a `ValueError` on version 1.2.x. This is a change on the behavior of 1.1.5 version that returns the same empty DataFrame (as `.apply` is still doing).
The change that added this error apparently is related to this commit https://github.com/pandas-dev/pandas/pull/35964/commits/7b6ab94720024d6696b19867f5f8f59f79587ff0
#### Expected Output
```python
In [5]: import pandas as pd
...: df = pd.DataFrame([], columns=["id", "field"])
...: df["id"].transform(lambda x: x + 10)
Out[5]: Series([], Name: id, dtype: object)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.20.0
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
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 4 | 2021-02-07T12:41:00Z | 2021-02-07T16:28:05Z | 2021-02-07T16:25:04Z | MEMBER | null | Reverts pandas-dev/pandas#39557 | {
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} | 2 | 2021-02-07T12:46:04Z | 2021-02-15T22:34:51Z | 2021-02-15T22:34:48Z | CONTRIBUTOR | null | - [ ] closes #24211
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 5 | 2021-02-07T12:55:47Z | 2021-02-07T19:08:27Z | 2021-02-07T16:22:38Z | MEMBER | null | Closes #39636 | {
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} | 9 | 2021-02-07T12:59:57Z | 2021-02-24T13:36:09Z | 2021-02-24T13:36:09Z | MEMBER | null | When looking at the docstring of `pad`:
```
In [21]: pd.Series.pad?
Signature:
pd.Series.pad(
self: 'FrameOrSeries',
axis=None,
inplace: 'bool_t' = False,
limit=None,
downcast=None,
) -> 'Optional[FrameOrSeries]'
Docstring:
Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
```
Or on the online version: https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.pad.html
There is a ``{klass}`` that has not been properly substituted. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39641/events | https://github.com/pandas-dev/pandas/pull/39641 | 802,954,846 | MDExOlB1bGxSZXF1ZXN0NTY4OTc1Nzgw | 39,641 | Revert "Backport PR #39526 on branch 1.2.x (CI: pin numpy for CI / Checks github action)" | {
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} | 1 | 2021-02-07T13:39:01Z | 2021-02-08T18:07:45Z | 2021-02-08T18:07:41Z | MEMBER | null | Reverts pandas-dev/pandas#39529
will be merging this just prior to tagging 1.2.2 see https://github.com/pandas-dev/pandas/issues/39513#issuecomment-771006563 and then re-appling after release so that ci on 12.x is green | {
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- add test with freq="H" that raises the bug
- [x] closes #39556
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry => not sure where to enter this ...
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copy some code from gzip ,now df.to_csv('myfile.csv.zip') works like df.to_csv('myfile.csv.gz')
- [ ] closes #39465
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | 2021-02-07T17:06:30Z | 2021-02-08T22:52:16Z | 2021-02-08T21:47:22Z | CONTRIBUTOR | null | "We will address array-based indexing like s[[4, 3, 1]] in section." -> "We will address array-based indexing like s[[4, 3, 1]] in section on indexing."
- [ ] closes #xxxx
- [ ] tests added / passed
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] | open | false | null | [] | null | 2 | 2021-02-07T17:23:52Z | 2021-08-15T17:31:56Z | null | MEMBER | null | For `int64` the construction yields:
``` python
In [18]: import numpy as np
...: import pandas as pd
...: import pandas._testing as tm
...:
...: integer_dtype="int64"
...:
...: cats = pd.array(range(5), dtype=integer_dtype)
...: codes = np.random.randint(5, size=3)
...: categorical_dtype = pd.CategoricalDtype(cats)
...: arr = pd.Categorical.from_codes(codes, dtype=categorical_dtype)
...: arr
Out[18]:
[0, 2, 0]
Categories (5, int64): [0, 1, 2, 3, 4]
```
whereas for nullable int categories:
```
In [19]: import numpy as np
...: import pandas as pd
...: import pandas._testing as tm
...:
...: integer_dtype="Int64"
...:
...: cats = pd.array(range(5), dtype=integer_dtype)
...: codes = np.random.randint(5, size=3)
...: categorical_dtype = pd.CategoricalDtype(cats)
...: arr = pd.Categorical.from_codes(codes, dtype=categorical_dtype)
...: arr
Out[19]:
[3, 3, 4]
Categories (5, object): [0, 1, 2, 3, 4]
```
I expect instead in the nullable int case:
```
Out[19]:
[3, 3, 4]
Categories (5, Int64): [0, 1, 2, 3, 4]
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} | 2 | 2021-02-07T17:48:47Z | 2021-02-10T20:36:30Z | 2021-02-10T20:35:05Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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This basically reverts #35841, although the code itself has changed quite a bit since then, so it's simpler here as the original PR² (not dealing with blocks anymore)
The original code comment that was removed in #35841 *"We've split an object block!"* was correct, as it can actually happen in those example cases. | {
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} | 2 | 2021-02-07T20:05:08Z | 2021-02-10T23:16:06Z | 2021-02-10T23:13:01Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
sits on top of #39650
after this we'll be ready to cleanup/de-duplicate some of the putmask/where methods | {
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} | 3 | 2021-02-07T21:07:15Z | 2021-02-08T13:46:59Z | 2021-02-08T13:44:03Z | MEMBER | null | In the latest master version of the pydata-sphinx-theme, there is a bug in the menu dropdown on mobile mode (https://github.com/pydata/pydata-sphinx-theme/issues/304), and since this will probably not be fixed by tomorrow when releasing 1.2.2, I am pinning it here to a few commits earlier to avoid this specific bug (but still using the latest version to have the same looks as the current docs, since we are using a few features that are not yet in the latest stable release). | {
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} | 7 | 2021-02-07T23:12:16Z | 2021-07-07T10:01:22Z | 2021-02-19T01:10:21Z | CONTRIBUTOR | null | - [x] tests changed / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
`Styler` currently has three formats for keeping track of CSS:
A) `CSSList type: [('a1', 'v1'), ('a2', 'v2')]`
B) `List[str] type: ['a1:v1', 'a2:v2']`
C) `Str type: 'a1:v1; a2:v2;'`
`C` is only ever used as user input format, and now, as of #39564 `C` can be used in *all* cases for user input (albeit `A` remains available for backwards compatibility in `.set_table_styles()`)
`A` remains the most algorithimcally accessible, and is what `C` is already converted to in some cases for internal variables.
`B` is only ever used by one internal variable. The `ctx` object is updated with `B` type converted from `C`. For final render this `B` type is converted to `A` type for looping. In the process, white space is poorly handled.
This PR eliminates `B` type and converts `C` directly to `A`.
Tests using the `ctx` object needed updating for new type.
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} | [] | closed | false | null | [] | null | 1 | 2021-02-08T01:52:36Z | 2021-03-12T00:46:31Z | 2021-03-11T09:00:07Z | NONE | null | Enhancement dataframe to csv use zip method,now df.to_csv('myfile.csv.zip') works like df.to_csv('myfile.csv.gz'), generate a zip file with a myfile.csv as content
- [ ] closes #39465
- [ ] tests added / passed,simply test
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
## test code
>> import pandas as pd
a=pd.DataFrame({'a':[1,2,3,4,5],'b':[4,5,6,7,8]})
a.to_csv(r'z:/myfile.csv.zip')
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} | 3 | 2021-02-08T06:37:32Z | 2021-02-08T21:06:24Z | 2021-02-08T21:00:52Z | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
```
(pandas-dev) asv_bench % asv continuous -f 1.1 upstream/master HEAD -b rolling.GroupbyEWM
before after ratio
[508b9f98] [c4340436]
<master> <ref/ewm_groupby>
- 22.1±4ms 2.57±0.3ms 0.12 rolling.GroupbyEWM.time_groupby_mean('cython')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
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https://api.github.com/repos/pandas-dev/pandas/issues/39666 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39666/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39666/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39666/events | https://github.com/pandas-dev/pandas/pull/39666 | 803,261,283 | MDExOlB1bGxSZXF1ZXN0NTY5MjE4MTYw | 39,666 | CI: run database tests only #39550 | {
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} | 5 | 2021-02-08T07:00:31Z | 2021-03-21T03:45:52Z | 2021-03-19T14:22:49Z | MEMBER | null | - [x] closes #39550
- [x] closes #39758 | {
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} | 2 | 2021-02-08T07:21:03Z | 2021-02-11T18:07:21Z | 2021-02-11T13:47:33Z | CONTRIBUTOR | null | - [x] tests passed
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} | 0 | 2021-02-08T07:24:47Z | 2021-02-08T10:02:51Z | 2021-02-08T10:02:45Z | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.10.1-py2.py3-none-any.whl (185 kB)
Collecting identify>=1.0.0
Downloading identify-1.5.13-py2.py3-none-any.whl (97 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.2-py2.py3-none-any.whl (7.2 MB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.5.13 nodeenv-1.5.0 pre-commit-2.10.1 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/python/black ... already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.9.0 -> v2.10.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
updating v1.7.0 -> v1.7.1.
Updating https://github.com/asottile/yesqa ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
already up to date.
Updating https://github.com/MarcoGorelli/abs-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/abs-imports.
already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/abs-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black..................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
pyupgrade..............................................................................................Passed
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Generate pip dependency from conda.....................................................................Passed
flake8-rst.............................................................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of private functions across modules......................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
codespell..............................................................................................Passed
no-string-hints........................................................................................Passed
abs-imports............................................................................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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] | open | false | null | [] | null | 2 | 2021-02-08T13:04:14Z | 2021-09-24T16:59:33Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame([], columns=[2, 1, 0])
df2 = df[{2, 1, 0}]
# expect column order to be preserved
assert((df.columns == df2.columns).all())
```
#### Problem description
An unordered iterable such as a set iterates in a non-deterministic way. Thus a reordering of the columns when indexing with an unordered iterable would be undesirable. It should be desirable that in those cases a preservation of the order is guaranteed. This should not be considered a breaking change, since the results were never guaranteed to be in a specific ordering in those cases.
#### Expected Output
See Code Sample.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.5
numpy : 1.19.5
pytz : 2020.1
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.1
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.8.2
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 : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | null | 14 | 2021-02-08T13:05:41Z | 2021-03-06T20:40:22Z | 2021-03-06T20:40:22Z | NONE | null | Typically financial data is ordered by columns in reverse chronology ie columns '2020', '2019', '2018', '2017'.. But we need the pct_change function in reverse order. Looking at the Pandas reference (last example): https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pct_change.html?highlight=pct_change#pandas.DataFrame.pct_change
df.pct_change(axis='columns')
2016 2015 2014
GOOG NaN -0.151997 -0.086016
APPL NaN 0.337604 0.012002
Such a calculation makes no sense or at least is not useful. We need to look at change from today over past period, not past period over future period. Other than iterating in reverse order or transposing, can the pct_change function be augmented with a new argument to reverse the calculation to the form: (A0-A1)/A1 instead of (A1-A0)/A0 ?? | {
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- [ ] 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
import pandas as pd
a = 0.00026
print(pd.to_timedelta(a, unit='s'))
```
#### Problem description
As can be seen with the provided code, thanks to floating-point imprecision, the result is 0 days 00:00:00.000259999. The expected result is of course 260us. Because of this, synchronously sampled data can appear as asynchronous, which can cause issues. If this cannot be fixed differently, workaround would be to provide some rounding option to user, so it can be assured that output has the expected value
#### Expected Output
260us
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Serbian (Latin)_Serbia.1250
pandas : 1.2.1
numpy : 1.19.0
pytz : 2019.1
dateutil : 2.8.0
pip : 21.0
setuptools : 47.1.0
Cython : 0.29.16
pytest : 6.0.1
hypothesis : None
sphinx : 1.7.0
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.2
odfpy : None
openpyxl : 1.7.0
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : None
</details>
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} | 4 | 2021-02-08T14:59:53Z | 2021-02-10T14:32:10Z | 2021-02-10T14:29:17Z | MEMBER | null | Master:
```
dtype = np.dtype(float)
dtype2 = pd.Int32Dtype()
In [2]: %timeit pd.api.types.is_extension_array_dtype(dtype)
645 ns ± 59.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [3]: %timeit pd.api.types.is_extension_array_dtype(dtype2)
251 ns ± 9.68 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
with this branch, it keeps the speed for ExtensionDtype the same, and improves for numpy.dtype (avoiding a extension dtype registry lookup):
```
In [2]: %timeit pd.api.types.is_extension_array_dtype(dtype)
328 ns ± 24.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [2]: %timeit pd.api.types.is_extension_array_dtype(dtype2)
240 ns ± 19.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
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while running this command i am facing above mention Error. while i fellow is not having any error. can you please help me to rectify this issue | {
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] | open | false | null | [] | null | 1 | 2021-02-08T19:34:25Z | 2021-06-06T20:54:58Z | null | NONE | null | #### Location of the documentation
[Basic indexing on axis with MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#basic-indexing-on-axis-with-multiindex)
#### Documentation problem
The following example seems not to work:
```python
arrays = [
np.array(["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"]),
np.array(["one", "two", "one", "two", "one", "two", "one", "two"]),
]
df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
df["bar"]
```
The code throws an error:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-13-6ea2f6413a49> in <module>
----> 1 df["bar"]
~/anaconda3/envs/jupyter/lib/python3.8/site-packages/pandas/core/frame.py in __getitem__(self, key)
3022 if self.columns.nlevels > 1:
3023 return self._getitem_multilevel(key)
-> 3024 indexer = self.columns.get_loc(key)
3025 if is_integer(indexer):
3026 indexer = [indexer]
~/anaconda3/envs/jupyter/lib/python3.8/site-packages/pandas/core/indexes/range.py in get_loc(self, key, method, tolerance)
352 except ValueError as err:
353 raise KeyError(key) from err
--> 354 raise KeyError(key)
355 return super().get_loc(key, method=method, tolerance=tolerance)
356
KeyError: 'bar'
```
When asking for help, it is suggested to use `df.loc["bar"]` instead with as shown in another part of the documentation ([advanced indexing with hierarchical index](https://pandas.pydata.org/docs/dev/user_guide/advanced.html#advanced-indexing-with-hierarchical-index)).
#### Suggested fix for documentation
An explanation for when partial indexing without the usage of `.loc[]` is applicable would be helpful or choosing an example that works in the current version. Trying to reproduce documentation code that does not work leads to confusion.
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} | 1 | 2021-02-08T19:40:09Z | 2021-02-08T22:53:31Z | 2021-02-08T22:44:54Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | 2021-02-08T20:52:16Z | 2021-02-11T13:46:32Z | 2021-02-11T13:46:32Z | NONE | null | - [.] I have checked that this issue has not already been reported.
- [.] I have confirmed this bug exists on the latest version 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
```
dates = pd.date_range(start='2016-01-01 09:30:00', periods=20, freq='s')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.concatenate((dates, dates)),
'C': np.arange(40),})
df.set_index(['A', 'B'], inplace=True)
df.groupby('A').rolling('4s', on='B').C.mean()
```
#### Problem description
Multi-index dataframe gives the following error message:
> ValueError: invalid on specified as B, must be a column (of DataFrame), an Index or None
when one layer of the multi index is used for groupby, while the other is used as a rolling datetime level.
Similar to https://github.com/pandas-dev/pandas/issues/13966
#### Expected Output
```
A B
1 2016-01-01 09:30:00 0.0
2016-01-01 09:30:01 0.5
2016-01-01 09:30:02 1.0
2016-01-01 09:30:03 1.5
2016-01-01 09:30:04 2.5
2016-01-01 09:30:05 3.5
2016-01-01 09:30:06 4.5
2016-01-01 09:30:07 5.5
2016-01-01 09:30:08 6.5
2016-01-01 09:30:09 7.5
2016-01-01 09:30:10 8.5
2016-01-01 09:30:11 9.5
2016-01-01 09:30:12 10.5
2016-01-01 09:30:13 11.5
2016-01-01 09:30:14 12.5
2016-01-01 09:30:15 13.5
2016-01-01 09:30:16 14.5
2016-01-01 09:30:17 15.5
2016-01-01 09:30:18 16.5
2016-01-01 09:30:19 17.5
2 2016-01-01 09:30:00 20.0
2016-01-01 09:30:01 20.5
2016-01-01 09:30:02 21.0
2016-01-01 09:30:03 21.5
2016-01-01 09:30:04 22.5
2016-01-01 09:30:05 23.5
2016-01-01 09:30:06 24.5
2016-01-01 09:30:07 25.5
2016-01-01 09:30:08 26.5
2016-01-01 09:30:09 27.5
2016-01-01 09:30:10 28.5
2016-01-01 09:30:11 29.5
3 2016-01-01 09:30:12 32.0
2016-01-01 09:30:13 32.5
2016-01-01 09:30:14 33.0
2016-01-01 09:30:15 33.5
2016-01-01 09:30:16 34.5
2016-01-01 09:30:17 35.5
2016-01-01 09:30:18 36.5
2016-01-01 09:30:19 37.5
Name: C, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.1
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
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
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 0 | 2021-02-08T21:39:44Z | 2021-02-08T22:54:13Z | 2021-02-08T22:45:13Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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```
==================================== ERRORS ====================================
__________ ERROR collecting scripts/tests/test_validate_docstrings.py __________
ImportError while importing test module '/home/runner/work/pandas/pandas/scripts/tests/test_validate_docstrings.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/share/miniconda/envs/pandas-dev/lib/python3.8/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
scripts/tests/test_validate_docstrings.py:5: in <module>
import validate_docstrings
scripts/validate_docstrings.py:50: in <module>
from numpydoc.validate import validate, Docstring # isort:skip
E ImportError: cannot import name 'Docstring' from 'numpydoc.validate' (/usr/share/miniconda/envs/pandas-dev/lib/python3.8/site-packages/numpydoc/validate.py)
=========================== short test summary info ============================
ERROR scripts/tests/test_validate_docstrings.py
!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!
=============================== 1 error in 0.54s ===============================
Error: Process completed with exit code 2.
````
cc @pandas-dev/pandas-core
can we pin numpydoc? | {
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} | 0 | 2021-02-09T04:00:55Z | 2021-02-10T16:19:54Z | 2021-02-10T16:17:51Z | MEMBER | null | we'll be able to get rid of get_unique_index altogether following #38140 | {
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} | 11 | 2021-02-09T08:52:15Z | 2021-02-19T07:16:47Z | 2021-02-19T01:11:36Z | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
The regular expression wasn't working too well, this solution should be better. Also, like this, you can pass `--replace` and have the inconsistencies fixed up for you (though I haven't passed this by default as it may be confusing)
Just running on `pandas/tests/frame/` here, can expand to the rest in separate PRs if this is alright | {
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] | open | false | null | [] | null | 1 | 2021-02-09T08:59:44Z | 2021-08-15T17:35:50Z | null | NONE | null | This
`pd.DataFrame([[1,2,1],[3,4,1]], columns=['a', 'b', 'dummygroup']).groupby('dummygroup').agg({'a': [('spread', lambda x: x.max()-x.min())]})`
works.
This
`pd.DataFrame([[1,2,1],[3,4,1]], columns=['a', 'b', 'dummygroup']).agg({'a': [('spread', lambda x: x.max()-x.min())]})`
raises an error:
> AttributeError: 'spread' is not a valid function for 'Series' object
Only difference is leaving out the dummy grouping.
Using newest pandas version '1.2.1'
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} | 18 | 2021-02-09T09:58:43Z | 2021-03-05T07:48:06Z | 2021-03-05T01:36:00Z | MEMBER | null | This PR optimizes our internal `take` algorithm, with the following changes:
- Cache the expensive parts that are independent of the actual array values but only dependent on the dtypes / dimension / axis / fill_value (which is often the same for repeated calls), i.e. `maybe_promote` and `_get_take_nd_function`
- The above is done in the generic `take_nd` function as well, but in addition I also added a `take_1d_array` specialized version that assumes the input is already an array and only deals with 1D. This gives a small further speed-up.
An example use case where the effect of this optimization can be noticed nicely is `unstack` in case of the ArrayManager. This is typically a case where we do multiple `take` calls for 1 column (to split it into multiple columns) and thus call it many times for the same dtype/fill_value.
Using the example from the `reshape.py::SimpleReshape` ASV benchmark (this is an example of homogeneous dtype with a single block, so which is fast with the BlockManager using the `_can_fast_transpose` fastpath in `_unstack_frame`):
```python
arrays = [np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]
index = pd.MultiIndex.from_arrays(arrays)
df = pd.DataFrame(np.random.randn(10000, 4), index=index)
df_am = df._as_manager("array")
```
Master:
```
In [2]: %timeit df.unstack()
1.79 ms ± 108 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [3]: %timeit df_am.unstack()
15.1 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
PR:
```
In [2]: %timeit df.unstack()
1.81 ms ± 160 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [3]: %timeit df_am.unstack()
3.44 ms ± 69.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
So for the BlockManager this doesn't matter (it does only a single call to `take_nd` for the column values), but for the ArrayManager version this gives a nice speedup and brings it within 2x of the BlockManager version (for this benchmark). | {
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} | 0 | 2021-02-09T12:07:48Z | 2021-02-09T13:04:27Z | 2021-02-09T13:04:24Z | MEMBER | null | re-apply Backport PR #39526
xref #39641 | {
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} | 0 | 2021-02-09T12:42:17Z | 2021-02-09T13:38:40Z | 2021-02-09T13:38:40Z | NONE | null | Backport PR #39669: DOC: Start v1.2.3 release notes | {
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} | 3 | 2021-02-09T12:48:21Z | 2021-02-15T22:42:17Z | 2021-02-15T22:42:17Z | 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
dft = pd.DataFrame({"A": [0, 1], "B": [10, 11]})
dft.to_excel(r"c:\test\test3.xlsx", columns=["A", "B", "A"])
```
#### Problem description
The example works with pandas 1.1.0, but not with pandas 1.2.1 any more. With 1.2.1 it raises:
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\test\venv4\lib\site-packages\pandas\core\generic.py", line 2177, in to_excel
formatter = ExcelFormatter(
File "c:\test\venv4\lib\site-packages\pandas\io\formats\excel.py", line 470, in __init__
raise KeyError("Not all names specified in 'columns' are found")
KeyError: "Not all names specified in 'columns' are found"
```
If the column argument doesn't contain duplicates (e.g. `columns=["A", "B"]`) it works also in 1.2.1
In the documentation of .to_excel() I found no information about an intended change in behaviour.
#### Expected Output
I expected it to work like with pandas 1.1.0, producing an excel file with the following content:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.2.1
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 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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} | 0 | 2021-02-09T12:49:27Z | 2021-07-16T10:38:14Z | 2021-07-16T10:38:14Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39295#issuecomment-775329397
> for the Github actions issue, I should open a new issue and xref #38360. we may want to activate Github actions for pushes to 1.2.x and update `.github\workflows\ci.yml` which will currently upload docs on a push event...
```
- name: Upload web
run: rsync -az --delete --exclude='pandas-docs' --exclude='docs' --exclude='Pandas_Cheat_Sheet*' web/build/ docs@${{ secrets.server_ip }}:/usr/share/nginx/pandas
if: github.event_name == 'push'
```
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} | 2 | 2021-02-09T13:14:51Z | 2021-02-16T08:46:59Z | 2021-02-16T08:46:56Z | MEMBER | null | Instead of https://github.com/pandas-dev/pandas/pull/39539, for now keeping the downcasting behaviour of `fillna`. | {
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https://github.com/pandas-dev/pandas/milestone/83
Currently scheduled for March 01, 2021
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression | {
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] | open | false | null | [] | null | 0 | 2021-02-09T15:45:08Z | 2021-08-15T17:36:47Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
multi_index = pandas.MultiIndex.from_arrays([["a", "b"], ["c", "d"]])
try:
result = multi_index.intersection(["some_col"])
except Exception as e:
result = e
print(f"Intersection with a list: {repr(result)}")
result = multi_index.intersection(pandas.Index(["some_col"]))
print(f"Intersection with an Index: {result}")
```
<details><summary>Output for pandas 1.1.5</summary>
```
Intersection with a list: MultiIndex([], )
Intersection with an Index: MultiIndex([], )
```
</details>
<details><summary>Output for pandas 1.2.0+</summary>
```
Intersection with a list: TypeError('other must be a MultiIndex or a list of tuples')
Intersection with an Index: MultiIndex([], )
```
</details>
#### Problem description
It's expected to get an empty index as the result of the intersection, but getting a TypeError.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-50-generic
Version : #54-Ubuntu SMP Mon May 6 18:46:08 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : 0.13.2
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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This is on top of https://github.com/pandas-dev/pandas/pull/39612, so only the last commit is relevant for this PR: https://github.com/pandas-dev/pandas/pull/39700/commits/ec83091284ac9a388ef147e006e2d4d43d946ea7 | {
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} | 8 | 2021-02-09T16:46:08Z | 2021-02-22T18:18:09Z | 2021-02-22T18:18:09Z | 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
import contextlib, io, json
import pandas as pd
redirect = io.StringIO()
with contextlib.redirect_stdout(redirect):
pd.show_versions(as_json=True) # produces invalid json
pd_versions = redirect.getvalue()
pd_versions = json.loads(pd_versions) # raises JSONDecodeError
```
#### Problem description
`pd.show_versions(as_json=True)` is supposed to print valid JSON, but it really doesn't. It is supposed to work as advertised but it doesn't. It prints invalid JSON that is not parsable. The following error is printed when attempting to parse it:
> json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
This functionality is important because when running a job in the cloud, it is not feasible to print a multiline output. This is because every printed line has significant attached metadata. It is better to have valid JSON output instead in this case. The implementation of the `show_versions` function looks to be very hacky and poorly reviewed, given that it fails in this very basic way.
#### Expected Output
In the code example, `json.loads(pd_versions)` should have been able to parse the captured text.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 21.0.1
setuptools : 50.3.2
Cython : None
pytest : 6.2.2
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 : 3.0.0
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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} | 2 | 2021-02-09T17:23:18Z | 2021-03-18T16:36:39Z | 2021-02-15T22:14:02Z | MEMBER | null | - [x] closes #39603
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] | open | false | null | [] | null | 0 | 2021-02-09T17:27:10Z | 2021-08-15T17:37:45Z | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
from sqlalchemy import create_engine, Date
from pandas import DataFrame
from datetime import datetime
df = DataFrame([{'test': datetime.now()}])
df.to_sql(
'test_table',
create_engine('sqlite:///foo.db'),
dtype={'test': Date()}
)
```
#### Problem description
The above code works, as far as successfully storing the data in the database.
However, **it can not be read back out again.** The entire datetime is stored, instead of just the date!
Python complains and refuses to read the table.
#### Expected Output
Dates are properly stored if passed as an SQLAlchemy dtype.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : 0.16.1
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 2 | 2021-02-09T18:31:24Z | 2021-05-12T12:14:52Z | 2021-02-11T11:35:56Z | MEMBER | null | The monthly dev meeting is Wednesday February 10th, at 18:00 UTC (12 am Central). Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248?pwd=T2l2Qi9vaC82Z294ZEtFczYxMVM2dz09
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda. All are welcome to attend.
@pandas-dev/pandas-core @pandas-dev/pandas-triage | {
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] | open | false | null | [] | null | 0 | 2021-02-09T19:20:55Z | 2021-06-06T20:54:41Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# Case 1 - no error raised:
df = pd.DataFrame({"col1":[1,2,3]})
ax = df.plot()
df[df["col1"]==9].plot(ax=ax)
# Case 2 - error raised:
df = pd.DataFrame({"col1":[1,2,3]}, index=pd.date_range("2020-01-01 00:00:00", periods=3, freq="10s"))
ax = df.plot()
df[df["col1"]==9].plot(ax=ax)
```
#### Problem description
IndexError is raised when trying to plot an empty subset of a dataframe that has a DatetimeIndex to an already existing axis (case 2). The better option would be that no error is raised and simply nothing is added to the existing axis as is the case if the index is a normal index instead of a DatetimeIndex (case 1). This is better becaue it allows to plot subsets of a dataframe within a loop or similar without having to care about whether the resulting subset is gonna be empty or not.
#### Expected Output
When adding an empty subset of a dataframe with DatetimeIndex to an existing axis, nothing is added to the plot and no error is raised.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.2
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : 3.4.3
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 : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 2 | 2021-02-09T19:59:32Z | 2021-02-10T23:43:07Z | 2021-02-10T23:10:01Z | CONTRIBUTOR | null | #### Location of the documentation
Cheat Sheets
#### Documentation problem
It would be great to have web-links to the official pandas docs included in the cheat sheets.
#### Suggested fix for documentation
I already implemented this [here](https://github.com/OliEfr/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf). It works well! However, I am not sure how to create a pull request now.
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} | 2 | 2021-02-09T20:15:35Z | 2021-02-10T23:42:38Z | 2021-02-10T23:10:01Z | CONTRIBUTOR | null | - [x] closes #39706
- [x] tests added / passed (not necessary, only edited ppt and pdf)
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them (not necessary, only edited ppt and pdf)
- [x] whatsnew entry (not necesarry)
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} | 1 | 2021-02-09T21:18:41Z | 2021-03-05T00:52:34Z | 2021-03-05T00:52:34Z | 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.
---
Recent additions (1.2.0) (1.3.0) to `Styler` have introduced additional class attributes.
The existing `copy` mechanisms catered for older attributes (although not all), but now needs to be reviewed.
Tests should probably be added.
```
def _copy(self, deepcopy: bool = False) -> Styler:
styler = Styler(
self.data,
precision=self.precision,
caption=self.caption,
uuid=self.uuid,
table_styles=self.table_styles,
na_rep=self.na_rep,
)
if deepcopy:
styler.ctx = copy.deepcopy(self.ctx)
styler._todo = copy.deepcopy(self._todo)
else:
styler.ctx = self.ctx
styler._todo = self._todo
return styler
```
And it might also be worth reviewing the `export` and `use` styles methods in case something has been missed. | {
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} | 3 | 2021-02-09T22:12:35Z | 2021-02-10T02:30:19Z | 2021-02-10T00:32:07Z | MEMBER | null | xref #39688
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | 2021-02-09T23:10:42Z | 2021-02-10T23:47:33Z | 2021-02-10T23:47:33Z | 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.
---
`pd.Timedelta` with non-digit string input, such as `pd.Timedelta('-')`, successfully processes and returns `Timedelta('0 days 00:00:00')`. From discussion with @jreback in #39497 it was concluded that this is unwanted and that it should raise an error instead.
I found that this appears for string input with `'+'`, `'-'`, `','` and `' '` in various combinations, see below
```python
In [1]: pd.Timedelta('+')
Out[1]: Timedelta('0 days 00:00:00')
In [2]: pd.Timedelta('-')
Out[2]: Timedelta('0 days 00:00:00')
In [3]: pd.Timedelta(' ')
Out[3]: Timedelta('0 days 00:00:00')
In [4]: pd.Timedelta(',')
Out[4]: Timedelta('0 days 00:00:00')
In [5]: pd.Timedelta('++')
Out[5]: Timedelta('0 days 00:00:00')
In [6]: pd.Timedelta('+-')
Out[6]: Timedelta('0 days 00:00:00')
```
#### Problem description
Invalid input should raise an error instead of successfully resolving to 0
#### Expected Output
Raise an error. I would propose to generalize the `ValueError("unit abbreviation w/o a number")` on [L497](https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/tslibs/timedeltas.pyx#L497) of `timedeltas.pyx` to `ValueError("characters w/o a number")` . Since this error also shows up if there are no unit abbrevations, for example:
```python
In [2]: pd.Timedelta('foo')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-06e16138b2f6> in <module>
----> 1 pd.Timedelta('foo')
~/Documents/developer/pandas/pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.Timedelta.__new__()
1192 value = parse_iso_format_string(value)
1193 else:
-> 1194 value = parse_timedelta_string(value)
1195 value = np.timedelta64(value)
1196 elif PyDelta_Check(value):
~/Documents/developer/pandas/pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.parse_timedelta_string()
429 result += timedelta_as_neg(r, neg)
430 else:
--> 431 raise ValueError("unit abbreviation w/o a number")
432
433 # treat as nanoseconds
ValueError: unit abbreviation w/o a number
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 492c5e00d112f0977a24d219f3758c6757fa2f7c
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.0.dev0+1259.g492c5e00d1
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.3
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.0
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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] | closed | false | null | [] | null | 1 | 2021-02-10T04:58:15Z | 2021-02-10T05:12:38Z | 2021-02-10T05:12:22Z | NONE | null | #### Is your feature request related to a problem?
I wish we could manage units of measurements in pandas.
#### Describe the solution you'd like
For example a temperature column may have the unit of Fahrenheit, if we need to convert the units to Centigrade it will be nice to convert it without creating the convertor functions. Here are some ideas:
Series.unit returns the current unit of measurement (e.g., Fahrenheit).
Series.to_unit() returns a series with the unit in parameter (e.g., Series.to_unit('Fahrenheit')).
More complex units can be electricity consumption and electricity demand.
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} | 1 | 2021-02-10T05:33:06Z | 2021-02-10T18:51:57Z | 2021-02-10T18:41:25Z | MEMBER | null | Much simpler than maybe_promote, which for the most part id like to replace with something like
```
def maybe_promote(dtype, fill_value):
inferred, fv = infer_dtype_from(fill_value)
new_dtype = find_common_type([dtype, inferred])
return inferred, fv
``` | {
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} | 9 | 2021-02-10T06:39:20Z | 2021-02-22T23:50:10Z | 2021-02-22T23:20:50Z | CONTRIBUTOR | null | - [x] closes #39594
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This PR splits the Optional Dependencies table into multiple tables one for each category. It should help people compare different options when looking for a specific capability, e.g. "I need to read an Excel file. What are my options?".
The specific grouping felt pretty subjective. If you think of a better way to group them, we can do that instead. | {
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} | 2 | 2021-02-10T07:15:33Z | 2021-02-17T00:16:18Z | 2021-02-17T00:16:18Z | 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.
---
The `Styler` ignores column class on the `index_name` row and just inputs `blank`, which is a pain for controlling `table_styles` using column selectors.
```
df3 = pd.DataFrame(0, index=['a'], columns=['A','B'])
df3.index.name = 'foo'
print(df3.style.render())
```
```
<style type="text/css">
</style>
<table id="T_a6821_">
<thead>
<tr>
<th class="blank level0" ></th>
<th class="col_heading level0 col0" >A</th>
<th class="col_heading level0 col1" >B</th>
</tr>
<tr>
<th class="index_name level0" >foo</th>
<th class="blank" ></th>
<th class="blank" ></th>
</tr>
</thead>
<tbody>
<tr>
<th id="T_a6821_level0_row0" class="row_heading level0 row0" >a</th>
<td id="T_a6821_row0_col0" class="data row0 col0" >0</td>
<td id="T_a6821_row0_col1" class="data row0 col1" >0</td>
</tr>
</tbody>
</table>
```
Internal mechanics should be fixed to add the col index class.
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] | closed | false | null | [] | null | 3 | 2021-02-10T08:39:58Z | 2021-02-10T13:34:26Z | 2021-02-10T13:34:26Z | 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
diamond_data.isna().sum()
carat 0
cut 0
color 0
clarity 0
depth 691
table 0
x 0
y 0
z 0
price 0
dtype: int64
# Generate random numbers for filling nans
ideal_data = pd.Series(np.random.randint(low=float(diamond_data[diamond_data.cut == "Ideal"]["depth"].min()),
high=float(diamond_data[diamond_data.cut == "Ideal"]["depth"].max()),
size=270)).astype("float")
prem_data = pd.Series(np.random.randint(low=float(diamond_data[diamond_data.cut == "Premium"]["depth"].min()),
high=float(diamond_data[diamond_data.cut == "Premium"]["depth"].max()),
size=192)).astype("float")
vgood_data = pd.Series(np.random.randint(low=float(diamond_data[diamond_data.cut == "Very Good"]["depth"].min()),
high=float(diamond_data[diamond_data.cut == "Very Good"]["depth"].max()),
size=152)).astype("float")
good_data = pd.Series(np.random.randint(low=float(diamond_data[diamond_data.cut == "Good"]["depth"].min()),
high=float(diamond_data[diamond_data.cut == "Good"]["depth"].max()),
size=59)).astype("float")
fair_data = pd.Series(np.random.randint(low=float(diamond_data[diamond_data.cut == "Fair"]["depth"].min()),
high=float(diamond_data[diamond_data.cut == "Fair"]["depth"].max()),
size=24)).astype("float")
# Fill it up
diamond_data.loc[(diamond_data.cut == "Ideal") & (diamond_data.depth.isna()), "depth"] = ideal_data
diamond_data.loc[(diamond_data.cut == "Premium") & (diamond_data.depth.isna()), "depth"] = prem_data
diamond_data.loc[(diamond_data.cut == "Very Good") & (diamond_data.depth.isna()), "depth"] = vgood_data
diamond_data.loc[(diamond_data.cut == "Good") & (diamond_data.depth.isna()), "depth"] = good_data
diamond_data.loc[(diamond_data.cut == "Fair") & (diamond_data.depth.isna()), "depth"] = fair_data
```
#### Problem description
When trying to replace some of the nan values that are spread out randomly on the dataset I'm working on, as shown above through `.loc` indexing, I should get all the values in the`depth` column filled up. Instead, nothing is filled up and running `isna().sum()` shows presence of same missing values.
#### Expected Output
Expected output should be 0 na values for the column `depth`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.1
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.51.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39718 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39718/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39718/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39718/events | https://github.com/pandas-dev/pandas/pull/39718 | 805,334,528 | MDExOlB1bGxSZXF1ZXN0NTcwOTU1NDYx | 39,718 | CLN: strip whitespaces on `set_td_classes` | {
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"default": false,
"description": null,
"id": 211029535,
"name": "Clean",
"node_id": "MDU6TGFiZWwyMTEwMjk1MzU=",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Clean"
},
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"color": "006b75",
"default": false,
"description": "conditional formatting using DataFrame.style",
"id": 1728592794,
"name": "Styler",
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"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Styler"
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] | closed | false | null | [] | null | 2 | 2021-02-10T09:27:22Z | 2021-02-11T07:02:53Z | 2021-02-10T21:23:45Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
A use case with string DataFrames is to construct multiple and add them, needing space separators:
```
cls1 = pd.DataFrame([['', 'cls1 ']])
cls2 = pd.DataFrame([['cls2 ', '']])
cls3 = pd.DataFrame([['cls3 ', 'cls3 ']])
Styler.set_td_classes(cls1 + cls2 + cls3)
```
Stripping cleans up the output for HTML, and might be helpful in other ways..
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} |
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