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CLN: remove extant uses of built-in filter function
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MEMBER
| null |
No internet for a good part of yesterday so some time to experiment with code cleanups.
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CI: doctest failure for read_hdf on 1.1.x
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MEMBER
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NOTE: PR against 1.1.x branch
(fixed in #35214 on master)
xref https://github.com/pandas-dev/pandas/pull/35699#issuecomment-673392019
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REGR: pd.to_hdf(dropna=True) not dropping all nan rows
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| 1
| 2020-08-14T13:52:25Z
| 2020-11-04T01:59:22Z
| 2020-11-04T01:59:22Z
|
NONE
| null |
#### Location of the documentation
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"]
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html
#### Documentation problem
[this should provide a description of what documentation you believe needs to be fixed/improved]
On the section:
"HDFStore will by default not drop rows that are all missing. This behavior can be changed by setting dropna=True."
The last example:
In [370]: pd.read_hdf('file.h5', 'df_with_missing')
Out[370]:
col1 col2
0 0.0 1.0
1 NaN NaN
2 2.0 NaN
should output:
col1 col2
0 0.0 1.0
1 2.0 NaN
which does not have rows that are all missing.
#### Suggested fix for documentation
[this should explain the suggested fix and **why** it's better than the existing documentation]
This has been provided above.
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MDExOlB1bGxSZXF1ZXN0NDY4MDE4MjYz
| 35,720
|
update io documentation
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| 2020-08-14T14:34:07Z
| 2020-08-15T11:13:23Z
| 2020-08-15T02:31:54Z
|
NONE
| null |
The HDFS dropna=True parameter does not drop the NaN values. Perhaps this is a bug to the core Pandas, or really a typo in the documentation
- [X] closes #35719
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Backport PR #35707 on branch 1.1.x (REGR: fix DataFrame.diff with read-only data)
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NONE
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Backport PR #35707: REGR: fix DataFrame.diff with read-only data
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MDExOlB1bGxSZXF1ZXN0NDY4MDIwMjQy
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Backport PR #35664 on branch 1.1.x (BUG: Styler cell_ids fails on multiple renders)
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NONE
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Backport PR #35664: BUG: Styler cell_ids fails on multiple renders
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MDExOlB1bGxSZXF1ZXN0NDY4MDYxODc1
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agg with list of non-aggregating functions
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| 3
| 2020-08-14T15:59:40Z
| 2021-04-23T01:26:59Z
| 2020-08-14T20:59:10Z
|
MEMBER
| null |
- [x] closes #35490
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Reverts to 1.0.5 behavior while maintaining the bugfix that introduced the "regression". Ideally `agg` would instead raise in these scenarios ([ref](https://github.com/pandas-dev/pandas/issues/35490#issuecomment-672981745)), but that would be an API change. Much of this PR should be reverted once this is done, I've marked such places with TODOs.
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QST: "Dummy" is rooted in ableist language
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| 2020-08-14T16:01:46Z
| 2021-08-10T04:23:56Z
| null |
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Although extremely common in the industry, "dummy" has some unfortunate history. One current use is for substitutes - mannequins, stand-ins, etc. This use grew from its original definition, "mute person". Mute people are not substitutes or stand-ins and I would prefer Pandas to not contribute to this view. There are other words, like "indicator", for statistics.
[Pandas currently uses "get_dummies" as a function name, with the documentation referencing "indicator" as a synonym.](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html)
Citations:
1. https://www.etymonline.com/word/dummy
2. https://www.etymonline.com/word/dumb
3. https://www.etymonline.com/word/indicator
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MDU6SXNzdWU2NzkyNjAzOTg=
| 35,725
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ENH: groupby(...).agg should only accept reducers
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| 2020-08-14T16:13:58Z
| 2021-02-28T17:17:28Z
| null |
MEMBER
| null |
Issues where `agg` is used with non-reducing functions:
* #18103
* #14741
* #35490
I think `agg` should raise if the function(s) provided are not reducers. This can be tested by if the resulting index is equal to `self.grouper.result_index`.
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MDExOlB1bGxSZXF1ZXN0NDY4MDczNjc3
| 35,726
|
CLN: remove unused variable
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| 1
| 2020-08-14T16:26:34Z
| 2020-08-14T21:38:46Z
| 2020-08-14T19:50:21Z
|
MEMBER
| null |
Tiny step towards #34714.
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|
QST: Any support for df["x"] = y where y.columns are MultiIndexed?
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| 9
| 2020-08-14T16:47:21Z
| 2020-08-23T13:57:47Z
| 2020-08-23T13:57:47Z
|
NONE
| null |
We're working with DataFrames where the columns are MultiIndexed, so e.g. ones that look like this:
```
pca
pca1 pca2
0 0.754675 1.868685
1 -1.861651 -0.048236
2 -0.797750 0.388400
```
which one can get through `pd.DataFrame(np.random.normal(size=(6,)).reshape((3,2)), columns=pd.MultiIndex.from_product([['pca'], ["pca1", "pca2"]]))`.
We now want to combine several of those to e.g. get this:
```
pca nmf
pca1 pca2 nmf1 nmf2
0 1.671707 0.452155 1.671707 0.452155
1 0.861315 -0.100849 0.861315 -0.100849
2 1.056616 -0.852532 1.056616 -0.852532
```
We know that we can do this through e.g. `pd.concat([df_pca, df_nmf], axis=1)`. Is there any support for doing the same like this: `df["pca"] = df_pca` for some df? We get `ValueError: Wrong number of items passed 4, placement implies 1`.
It's really important for us to allow usage like this: `df["pca"] = df_pca` and not just through concat.
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| 679,281,025
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MDExOlB1bGxSZXF1ZXN0NDY4MDg0MzMx
| 35,728
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BLD: bump xlrd min version to 1.2.0
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| 2
| 2020-08-14T16:53:02Z
| 2020-08-15T16:02:08Z
| 2020-08-14T21:01:03Z
|
MEMBER
| null |
Warnings about time.clock are filling up the py37 min_version build. xlrd 1.2.0 was released Dec 15, 2018, so I think we're safe to bump it.
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DOC: Fix broken link in cookbook.rst
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| 1
| 2020-08-14T18:29:27Z
| 2020-08-14T22:06:03Z
| 2020-08-14T21:01:49Z
|
CONTRIBUTOR
| null |
The original link [Aggregation and plotting time series](http://nipunbatra.github.io/2015/06/timeseries/) found in the [Pandas Cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html?highlight=get_group#timeseries) is broken.
This appears to have been moved to the authors [Blog](https://nipunbatra.github.io/blog/visualisation/2013/05/01/aggregation-timeseries.html)
While the date does not match ( 2013/05 vs 2015/06 ) the contents appear identical. I was able to determine this after viewing the archive at [WayBack Machine](https://web.archive.org/web/20161202094122/http://nipunbatra.github.io/2015/06/timeseries/)
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDY4MTk1Mjk2
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|
REF: move towards making _apply_blockwise actually block-wise
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| 5
| 2020-08-14T21:42:51Z
| 2020-08-23T01:26:18Z
| 2020-08-23T00:06:17Z
|
MEMBER
| null |
A step towards #34714, orthogonal to the other outstanding PR in this vein #35696.
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MDU6SXNzdWU2Nzk0MjE0MjM=
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BUG: Series has no attribute "reshape" after adding a new category in df
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| 8
| 2020-08-14T21:55:57Z
| 2020-08-31T12:36:12Z
| 2020-08-31T12:36:12Z
|
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
# Your code here
import pandas as pd
from pandas.api.types import CategoricalDtype
# create dataframe (note: every single column is a category)
df = pd.DataFrame(
{
"a": pd.Series([np.nan, 2.0, 3.0, 1.0]).astype("category"),
"b": pd.Series(["A", "A", "B", "C"]).astype("category"),
"c": pd.Series(["D", "E", "E", np.nan]).astype("category"),
}
)
# get all categories from column "a"
cats =df["a"].cat.categories.tolist()
# append new category
cats.append(0.0)
df["a"] = df["a"].astype(CategoricalDtype(categories=cats, ordered=False))
# fillna with that new category
df["a"].fillna(0, inplace=True)
# run df.isnull()
df.isnull().sum()
```
#### Problem description
We are trying to add the new fillna as a new category in the dataframe, but it fails when we are trying to use `df.isnull()` In this case we are pretty much blocked from using df.isull().sum() functionality. Running the above snippet will get us the attribute error
`AttributeError: 'Series' object has no attribute 'reshape'`
Full stack trace is shown below.
```
AttributeError Traceback (most recent call last)
<ipython-input-29-cc81fd27034f> in <module>
----> 1 df.isnull()
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/frame.py in isnull(self)
4865 @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
4866 def isnull(self) -> "DataFrame":
-> 4867 return self.isna()
4868
4869 @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/frame.py in isna(self)
4860 @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
4861 def isna(self) -> "DataFrame":
-> 4862 result = self._constructor(self._data.isna(func=isna))
4863 return result.__finalize__(self, method="isna")
4864
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/internals/managers.py in isna(self, func)
500
501 def isna(self, func) -> "BlockManager":
--> 502 return self.apply("apply", func=func)
503
504 def where(
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, **kwargs)
394 applied = b.apply(f, **kwargs)
395 else:
--> 396 applied = getattr(b, f)(**kwargs)
397 result_blocks = _extend_blocks(applied, result_blocks)
398
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/internals/blocks.py in apply(self, func, **kwargs)
346 result = func(self.values, **kwargs)
347
--> 348 return self._split_op_result(result)
349
350 def _split_op_result(self, result) -> List["Block"]:
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/internals/blocks.py in _split_op_result(self, result)
361
362 if not isinstance(result, Block):
--> 363 result = self.make_block(result)
364
365 return [result]
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/internals/blocks.py in make_block(self, values, placement)
250 placement = self.mgr_locs
251 if self.is_extension:
--> 252 values = _block_shape(values, ndim=self.ndim)
253
254 return make_block(values, placement=placement, ndim=self.ndim)
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/internals/blocks.py in _block_shape(values, ndim)
2745 # block.shape is incorrect for "2D" ExtensionArrays
2746 # We can't, and don't need to, reshape.
-> 2747 values = values.reshape(tuple((1,) + shape)) # type: ignore
2748 return values
2749
~/.pyenv/versions/3.7.0/envs/fair_ml/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5128 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5129 return self[name]
-> 5130 return object.__getattribute__(self, name)
5131
5132 def __setattr__(self, name: str, value) -> None:
AttributeError: 'Series' object has no attribute 'reshape'
```
#### Expected Output
a 0
b 0
c 1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 18.1
setuptools : 47.3.1
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2Nzk0MzQ3NTg=
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BLD: minimum versions need update in `pandas/compat/_optional.py`
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MEMBER
| null |
The minimum versions in `pandas/compat/_optional.py` were not updated in #35214.
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BLD: update min versions #35732
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MEMBER
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- [x] closes #35732
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MEMBER
| null |
Reverts part of #35693
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BUG: DataFrame.merge() fails with program termination, consumed entire available memory(Memory Leak)
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| 2020-08-15T09:02:11Z
| 2020-09-04T18:01:47Z
| 2020-09-04T18:01:47Z
|
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
df1 = pd.read_csv(path_of_csv1)
df2 = pd.read_csv(path_of_csv2)
resultant_df = df1.merge(df2, left_on='left_csv_column_name', right_on='right_csv_column_name', how='inner') <= Exception thrown here.
```
```
[1] 31330 killed python
```
#### Problem description
Memory Leak occurs while trying to merge two Dataframes read from two different CSV's of bigger size (>=10Mb in my case)
For smaller CSV files (let's say of <=10Mb) each merging process is working fine.
My system is having 8Gb of RAM and before starting the Merging process, Enough Memory is available(>3Gb) and after starting the merging process, the system consumes the entire available memory and killed the merging process in between with the below exception:-
```
[1] 31330 killed python
```
#### Expected Output
`resultant_df` contains the two DataFrame `df1` and `df2` based on the join type we have given as a parameter `how='inner'` in pandas merge function
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
Version : Darwin Kernel Version 19.4.0: Wed Mar 4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None]
</details>
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BUG/ENH: to_pickle/read_pickle support compression for file ojects
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| 2
| 2020-08-15T15:24:31Z
| 2020-09-05T17:47:58Z
| 2020-09-05T14:50:04Z
|
CONTRIBUTOR
| null |
- [x] closes #26237, closes #29054, and closes #29570
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This was basically already supported but `to/read_pickle` set `compression` to `None` for file objects.
Some functions called `get_filepath_or_buffer` (might convert a string to a file object) before calling `infer_compression` (doesn't work with file objects). I moved `infer_compression` and `get_compression_method` inside `get_filepath_or_buffer`.
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MDU6SXNzdWU2Nzk1OTk0MDA=
| 35,737
|
CI: Travis failing with INTERNALERROR
|
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| 2020-08-15T15:54:42Z
| 2020-08-17T08:50:01Z
| 2020-08-17T08:50:01Z
|
MEMBER
| null |
I'm seeing this on every recent Travis 37-coverage build:
```
============================= test session starts ==============================
platform linux -- Python 3.7.7, pytest-6.0.1, py-1.9.0, pluggy-0.13.1
rootdir: /home/travis/build/pandas-dev/pandas, configfile: setup.cfg, testpaths: pandas
plugins: xdist-2.0.0, cov-2.10.0, forked-1.2.0, hypothesis-5.24.0
gw0 I
INTERNALERROR> Traceback (most recent call last):
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/_pytest/main.py", line 238, in wrap_session
INTERNALERROR> config.hook.pytest_sessionstart(session=session)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/hooks.py", line 286, in __call__
INTERNALERROR> return self._hookexec(self, self.get_hookimpls(), kwargs)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/manager.py", line 93, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/manager.py", line 87, in <lambda>
INTERNALERROR> firstresult=hook.spec.opts.get("firstresult") if hook.spec else False,
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/callers.py", line 208, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/callers.py", line 80, in get_result
INTERNALERROR> raise ex[1].with_traceback(ex[2])
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/callers.py", line 187, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/xdist/dsession.py", line 78, in pytest_sessionstart
INTERNALERROR> nodes = self.nodemanager.setup_nodes(putevent=self.queue.put)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/xdist/workermanage.py", line 65, in setup_nodes
INTERNALERROR> return [self.setup_node(spec, putevent) for spec in self.specs]
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/xdist/workermanage.py", line 65, in <listcomp>
INTERNALERROR> return [self.setup_node(spec, putevent) for spec in self.specs]
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/xdist/workermanage.py", line 73, in setup_node
INTERNALERROR> node.setup()
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/xdist/workermanage.py", line 260, in setup
INTERNALERROR> self.config.hook.pytest_configure_node(node=self)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/hooks.py", line 286, in __call__
INTERNALERROR> return self._hookexec(self, self.get_hookimpls(), kwargs)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/manager.py", line 93, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/manager.py", line 87, in <lambda>
INTERNALERROR> firstresult=hook.spec.opts.get("firstresult") if hook.spec else False,
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/callers.py", line 208, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/callers.py", line 80, in get_result
INTERNALERROR> raise ex[1].with_traceback(ex[2])
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pluggy/callers.py", line 187, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pytest_cov/plugin.py", line 239, in pytest_configure_node
INTERNALERROR> self.cov_controller.configure_node(node)
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pytest_cov/engine.py", line 274, in configure_node
INTERNALERROR> workerinput(node).update({
INTERNALERROR> File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pytest_cov/compat.py", line 42, in fn
INTERNALERROR> return getattr(obj, attr, *args)
INTERNALERROR> AttributeError: 'WorkerController' object has no attribute 'slaveinput'
The command "ci/run_tests.sh" exited with 3.
```
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MDExOlB1bGxSZXF1ZXN0NDY4MzM1MzYz
| 35,738
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Backport PR #35723 on branch 1.1.x (agg with list of non-aggregating functions)
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| 2020-08-15T16:59:37Z
| 2020-08-15T16:59:36Z
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NONE
| null |
Backport PR #35723: agg with list of non-aggregating functions
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MDU6SXNzdWU2Nzk2MDM4Mzc=
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BUG: Invalid cache invalidation leading to assertion errors later
|
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| 2020-09-05T07:27:53Z
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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
In [44]: df = pd.DataFrame({"a": [3, 4]})
In [45]: df
Out[45]:
a
0 3
1 4
In [46]: mask = df["a"] > 0
In [47]: mask
Out[47]:
0 True
1 True
Name: a, dtype: bool
In [48]: df_a = df.loc[:, "a"]
In [49]: df_a["a"] = 1
In [50]: df_a
Out[50]:
0 3
1 4
a 1
Name: a, dtype: int64
In [51]: df.__dict__
Out[51]:
{'_is_copy': None,
'_mgr': BlockManager
Items: Index(['a'], dtype='object')
Axis 1: RangeIndex(start=0, stop=2, step=1)
IntBlock: slice(0, 1, 1), 1 x 2, dtype: int64,
'_item_cache': {'a': 0 3
1 4
a 1
Name: a, dtype: int64},
'_attrs': {}}
In [52]: mask
Out[52]:
0 True
1 True
Name: a, dtype: bool
In [53]: df
Out[53]:
a
0 3
1 4
In [54]: df.loc[mask, "a"]
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-54-e9cf788ed9f4> in <module>
----> 1 df.loc[mask, "a"]
~/miniconda3/envs/conduit-poetry/lib/python3.8/site-packages/pandas/core/indexing.py in __getitem__(self, key)
871 # AttributeError for IntervalTree get_value
872 pass
--> 873 return self._getitem_tuple(key)
874 else:
875 # we by definition only have the 0th axis
~/miniconda3/envs/conduit-poetry/lib/python3.8/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
1053 return self._multi_take(tup)
1054
-> 1055 return self._getitem_tuple_same_dim(tup)
1056
1057 def _get_label(self, label, axis: int):
~/miniconda3/envs/conduit-poetry/lib/python3.8/site-packages/pandas/core/indexing.py in _getitem_tuple_same_dim(self, tup)
751 # We should never have retval.ndim < self.ndim, as that should
752 # be handled by the _getitem_lowerdim call above.
--> 753 assert retval.ndim == self.ndim
754
755 return retval
AssertionError:
```
#### Problem description
By manipulating one column that was created by copying a part of the DataFrame, the cache for this one column is updated in the DataFrame. However, the cached data are not compatible with the DataFrame index and causes problems later, e.g. the AssertionError from the example.
#### Expected Output
Manipulating separate object would not influence the original object in a way that leads to AssertionError.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.14-200.fc32.x86_64
Version : #1 SMP Fri Aug 7 23:16:37 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : 1.9.1
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : 0.6.2
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0NDY4MzU1NDE2
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|
REF: _apply_blockwise define exclude in terms of skipped
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| 2020-08-21T22:06:35Z
| 2020-08-21T21:56:51Z
|
MEMBER
| null |
orthogonal to #35730
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DEPR: Deprecate pandas/io/date_converters.py
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CONTRIBUTOR
| null |
- [x] closes #24518
- [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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TST: encoding for URLs in read_csv
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| 1
| 2020-08-15T20:08:03Z
| 2020-08-17T19:53:42Z
| 2020-08-17T18:59:43Z
|
CONTRIBUTOR
| null |
- [x] closes #10424
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Reading CSVs from URLs with a non UTF-8 encoding should already work.
|
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BUG: Assign with "df.loc[index_value][column_name] = value" fails to assign properly
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| 4
| 2020-08-15T20:27:35Z
| 2020-08-16T04:18:13Z
| 2020-08-15T20:51:25Z
|
NONE
| null |
- [Y] I have checked that this issue has not already been reported.
- [Y] I have confirmed this bug exists on the latest version of pandas.
- [N] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
This line of code:
```python
self.strategy.loc[bar]['FundingRate'] = np.log(F1/F0)
```
Fails to assign the proper value . I think it either does nothing (leaves the original NaN from when the df "strategy" was created) or assigns a NaN. In any case, I added a set of flags to get the values input with this code:
```python
print('Inputs for np.log F0={} F1={}'.format(F0,F1))
print('Index label bar={}, type={}'.format(bar, type(bar)))
value = np.log(F1/F0)
print('This should be the assigned value={}'.format(value))
self.strategy.loc[bar]['FundingRate'] = value
assert self.strategy.loc[bar]['FundingRate'] == value, 'Error PANDAS fails to assign {}, instead we find {}'.format(value, self.strategy.loc[bar]['FundingRate'])
```
#### Problem description
Assignment operations have started to fail to properly assign values after upgrading from v 1.0.4 to v1.0.5. While I need to run my code in Python 3.7/Pandas 1.0.5 due to other package dependencies, I recreated the problem with Python 3.8 and Pandas v1.1.0
The assignments work if I change to:
```python
df.loc[bar, 'FundingRate'] = value
```
Or with:
```python
df['FundingRate'].loc[bar] = value
```
I can't reproduce this problem in a simple setting. It only occurs in runtime on a system of +6k lines of code which has been working seamlessly with pervasive use of these types of assignments (df.loc[indexvalue][column_name] = value). Also, I can't do a ```dill.dump``` because there are ```tensorflow``` objects that are not serializable.
#### Runtime Output:
The assert fails. This is the printout of the code output:
```
Inputs for np.log F0=180.4753051802489 F1=180.4753051802489
Index label bar=2000-01-03 00:00:00, type=<class 'pandas._libs.tslibs.timestamps.Timestamp'>
This should be the assigned value=0.0
Traceback (most recent call last):
File "/media/WORK/Boris/LEM_Strategy/Software/LEM_Classes/main_nn_seq.py", line 150, in <module>
run_stats = AA.run_strategy(consensus_type=consensus_type)
File "/media/WORK/Boris/LEM_Strategy/Software/LEM_Classes/lib/nn_seqstrat.py", line 642, in run_strategy
date, _ = self.get_date_price(bar, when='_CLOSE')
File "/media/WORK/Boris/LEM_Strategy/Software/LEM_Classes/lib/backtester_sequential.py", line 152, in get_date_price
assert self.strategy.loc[bar]['FundingRate'] == value, 'Error PANDAS fails to assign {}, instead we find {}'.format(value, self.strategy.loc[bar]['FundingRate'])
AssertionError: Error PANDAS fails to assign 0.0, instead we find nan
```
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.1.15-surface-linux-surface
Version : #8 SMP Thu Jun 27 12:03:55 EDT 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
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 : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
#### This is the file used to create the environment where the error occurs.
<details>
```
########################################
#
# LEM_Strategy Conda Environment
#
# run: conda env create -f quant_conda_env.yml
#
########################################
name: quant # COMMENT OUT TO CREATE TEST ENVIRONMENTS (conda env create -n test -f quant_conda_env.yml)
########################################
channels:
- plotly
- defaults
- conda-forge
#- bjrn # Channel for google, V20 package (RAY)
########################################
dependencies:
- python=3.8 #
- numpy
- scipy
- pandas #==1.0.4
- numba
- cython
- numexpr
- statsmodels
- scikit-learn
- xlrd
- xlsxwriter
- ipywidgets
- pathos
- tensorflow-gpu #==2.1.*
- keras-gpu
- tsfresh
- pytables
- pyzmq # ZeroMQ: sockets
# Graphics and Plotting
- plotly
- plotly-orca
- requests
- matplotlib
- seaborn
- cufflinks-py
# Utilities
- nb_conda_kernels # For Jupyter
- spyder-kernels # Spyder
- git # Github support: custom packages and bug forks
# PIP Dependencies (otherwise installed via PIP: Use Conda to install to improve environment integrity over time)
- yaml # For TPQOA (OANDA Wrapper)
- ujson # For TPQOA (OANDA Wrapper)
# - v20 # OANDA API V2.0 (from brjn conda channel) (TPQOA installs from PIP))
# - modin # Ray Tutorial INSTALL MANUALLY
# - opencv # Ray Tutorial INSTALL MANUALLY
# - gym # Ray Tutorial INSTALL MANUALLY
- aiohttp # Ray
- colorama # Ray
- filelock # Ray
- redis # Ray
- multidict # Ray
- yarl # Ray
- async_timeout # Ray
- beautifulsoup4 # Ray
- soupsieve # Ray
# - redis # Ray (get from "$pip_deps ray" shell script. Output '<3.5.0,>=3.3.2') (Ray installs from PIP))
# - py-spy>=0.2.0 # Ray (get from "$pip_deps ray" shell script) (Ray installs from PIP))
# - google # Ray (from brjn conda channel) (Ray installs from PIP))
# Non-Conda Packages via PIP
- pip # First install PIP PACKAGE MANAGER
- pip:
- "git+git://github.com/yhilpisch/tpqoa.git" # TPQ OANDA Wrapper
- "git+git://github.com/yhilpisch/tstables.git" # Time Series Tables with pandas=1.0 bug fix
#- "git+git://github.com/tensorflow/model-optimization.git" # TensorFlow Model Optimization "import tensorflow_model_optimization as tfmot"
- cardinality
- ray # Ray: fast and simple framework for building and running distributed applications.
```
</details>
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MDU6SXNzdWU2Nzk2NDY5NTk=
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BUG: pd.Series() constructor with 2d NumPy array raises bare Exception
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| 3
| 2020-08-15T21:51:29Z
| 2020-10-10T16:46:24Z
| 2020-10-10T16:46:24Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
import numpy as np
arr = np.random.rand(10, 4)
pd.Series(arr)
```
I get this output
```
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-4-974eca309da1> in <module>
2 import numpy as np
3 arr = np.random.rand(10, 4)
----> 4 pd.Series(arr)
~/.pyenv/versions/3.8.3/envs/census/lib/python3.8/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
325 data = data.copy()
326 else:
--> 327 data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
328
329 data = SingleBlockManager.from_array(data, index)
~/.pyenv/versions/3.8.3/envs/census/lib/python3.8/site-packages/pandas/core/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
490 elif subarr.ndim > 1:
491 if isinstance(data, np.ndarray):
--> 492 raise Exception("Data must be 1-dimensional")
493 else:
494 subarr = com.asarray_tuplesafe(data, dtype=dtype)
Exception: Data must be 1-dimensional
```
#### Problem description
Yes, creating a series from an array is invalid. But the problem is that a bare `Exception` is raised for this. A different exception type like `ValueError` is expected. User code should [mostly] never be catching bare Exceptions, but may reasonable catch `ValueError`s or `TypeError`s from pandas routines.
I'm not entirely sure of the rationale for raising a bare Exception in the `sanitize_array` method, I started looking through the git blame but didn't go back that far.
I think a PR that just raises a `ValueError` in `sanitize_array` would be an appropriate fix and I can create that if given the thumbs up.
#### Expected Output
A `ValueError` is raised, that can be caught be user code.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2Nzk2NTAyMTk=
| 35,745
|
BUG: slicing DataFrameGroupBy to SeriesGroupBy doesn't propagate dropna
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[] | null | 4
| 2020-08-15T22:16:52Z
| 2020-08-17T14:52:57Z
| 2020-08-16T04:12:02Z
|
MEMBER
| 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.
---
xref #9959, #35444
Slicing a `DataFrameGroupBy` object to `SeriesGroupBy` doesn't correctly propagate `dropna`.
The `DataFrameGroupBy` → `DataFrameGroupBy` case is being handled in #35444. Opening this because as far as I can tell the `DataFrameGroupBy` → `SeriesGroupBy` variant is an independent bug.
#### Code Sample, a copy-pastable example
```
In [11]: df = pd.DataFrame({"a": [1], "b": [2], "c": [3]})
...: gb = df.groupby('a', dropna=False)
In [12]: gb.dropna
Out[12]: False
In [13]: gb['b'].dropna
Out[13]: True
```
#### Expected Output
I'd like to see:
```
In [22]: gb.dropna == gb['b'].dropna
Out[22]: True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : a3f5c6a5a3f05edc1c56ab8051f28c4cf322c45c
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+99.ga3f5c6a5a.dirty
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0.post20200704
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.19.0
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : 0.6.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDY4MzcwNDYy
| 35,746
|
REF: insert self.on column _after_ concat
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| 1
| 2020-08-15T22:17:21Z
| 2020-08-20T17:53:01Z
| 2020-08-20T16:59:24Z
|
MEMBER
| null |
The idea here is to push towards #34714 by making _wrap_results do things in the same order as other other similar methods do. cc @mroeschke LMK if there is a simpler way to accomplish this.
Orthogonal to #35470, #35730, #35696.
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BUG: Cannot index `frozenset` elements from a pd.Series or pd.DataFrame
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| 6
| 2020-08-15T22:18:57Z
| 2020-09-12T21:39:40Z
| 2020-09-12T21:39:40Z
|
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.
- [ ] (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
print(pd.__version__)
# 1.1.0
# Create DataFrame
data = {0: {frozenset({'Otu000010', 'Otu000505'}): 'white',
frozenset({'Otu000067', 'Otu000073'}): 'white',
frozenset({'Otu000151', 'molar'}): 'white',
frozenset({'Otu000380', 'etec_stp'}): 'white',
frozenset({'Otu000281', 'ghrp'}): 'white'},
14: {frozenset({'Otu000010', 'Otu000505'}): 'white',
frozenset({'Otu000067', 'Otu000073'}): 'white',
frozenset({'Otu000151', 'molar'}): 'white',
frozenset({'Otu000380', 'etec_stp'}): 'white',
frozenset({'Otu000281', 'ghrp'}): 'white'},
28: {frozenset({'Otu000010', 'Otu000505'}): 'red',
frozenset({'Otu000067', 'Otu000073'}): 'white',
frozenset({'Otu000151', 'molar'}): 'blue',
frozenset({'Otu000380', 'etec_stp'}): 'white',
frozenset({'Otu000281', 'ghrp'}): 'white'}}
df = pd.DataFrame(data)
# Get first item in index
id_query = df.index[0]
# Grab a column
vector = df[14]
# Index the vector using query
vector[id_query]
# ---------------------------------------------------------------------------
# KeyError Traceback (most recent call last)
# <ipython-input-145-493fc06aeb40> in <module>
# 24
# 25 # Index the vector using query
# ---> 26 vector[id_query]
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/series.py in __getitem__(self, key)
# 906 return self._get_values(key)
# 907
# --> 908 return self._get_with(key)
# 909
# 910 def _get_with(self, key):
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/series.py in _get_with(self, key)
# 946
# 947 # handle the dup indexing case GH#4246
# --> 948 return self.loc[key]
# 949
# 950 def _get_values_tuple(self, key):
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/indexing.py in __getitem__(self, key)
# 877
# 878 maybe_callable = com.apply_if_callable(key, self.obj)
# --> 879 return self._getitem_axis(maybe_callable, axis=axis)
# 880
# 881 def _is_scalar_access(self, key: Tuple):
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
# 1097 raise ValueError("Cannot index with multidimensional key")
# 1098
# -> 1099 return self._getitem_iterable(key, axis=axis)
# 1100
# 1101 # nested tuple slicing
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
# 1035
# 1036 # A collection of keys
# -> 1037 keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
# 1038 return self.obj._reindex_with_indexers(
# 1039 {axis: [keyarr, indexer]}, copy=True, allow_dups=True
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
# 1252 keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
# 1253
# -> 1254 self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
# 1255 return keyarr, indexer
# 1256
# ~/anaconda3/envs/soothsayer5_env/lib/python3.8/site-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
# 1296 if missing == len(indexer):
# 1297 axis_name = self.obj._get_axis_name(axis)
# -> 1298 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
# 1299
# 1300 # We (temporarily) allow for some missing keys with .loc, except in
# KeyError: "None of [Index(['Otu000010', 'Otu000505'], dtype='object')] are in the [index]"
```
#### Problem description
In previous versions, I was able to use `frozenset` objects as the elements of the index. These are great objects to have for network analysis where I use as edges in my pd.Series and pd.DataFrame
#### Expected Output
I should be able to index using these objects.
#### Output of ``pd.show_versions()``
<details>
pandas v1.1.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDY4MzczOTk1
| 35,748
|
BUG: close file handles in mmap
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| 1
| 2020-08-15T23:04:48Z
| 2020-08-17T19:44:57Z
| 2020-08-17T18:58:32Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Broken off from #35711
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BUG: AttributeError: 'Cell' object has no attribute 'font' when using to_excel() with openpyxl engine
|
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| 2020-08-15T23:35:42Z
| 2020-08-16T00:01:00Z
| 2020-08-16T00:01:00Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] I have confirmed this bug exists on the latest version of openpyxl.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
I am using to_excel() with openpyxl engine to export a pandas dataframe to a sheet of an .xlsx file. My code works just fine two days ago, but it does not work now with Error:
```
with pd.ExcelWriter(PATH+ '/' + FILENAME, engine='openpyxl', mode='a') as writer:
df.head().to_excel(writer, sheet_name='test')
writer.save()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-69-1e1ecfd20f72> in <module>
1 with pd.ExcelWriter(PATH+ '/' + FILENAME, engine='openpyxl', mode='a') as writer:
----> 2 df.head().to_excel(writer, sheet_name='test')
3 writer.save()
4
5 writer.close()
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/generic.py in to_excel(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes)
2027 startcol=startcol,
2028 freeze_panes=freeze_panes,
-> 2029 engine=engine,
2030 )
2031
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/formats/excel.py in write(self, writer, sheet_name, startrow, startcol, freeze_panes, engine)
737 startrow=startrow,
738 startcol=startcol,
--> 739 freeze_panes=freeze_panes,
740 )
741 if need_save:
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/excel/_openpyxl.py in write_cells(self, cells, sheet_name, startrow, startcol, freeze_panes)
437 if style_kwargs:
438 for k, v in style_kwargs.items():
--> 439 setattr(xcell, k, v)
440
441 if cell.mergestart is not None and cell.mergeend is not None:
AttributeError: 'Cell' object has no attribute 'font'
```
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MDExOlB1bGxSZXF1ZXN0NDY4Mzk0MzMw
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Pass check_dtype to assert_extension_array_equal
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: DataFrame.groupby(., dropna=True, axis=0) incorrectly throws ShapeError
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| 2020-12-19T02:34:50Z
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MEMBER
| null |
- [x] closes #35612
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR makes a few more changes to propagate `dropna` correctly for sliced groupby objects.
It depends on #35444 for the changes to `pandas/core/groupby/generic.py`. I put them in by hand for now so that the tests pass but there should be no diff once #35444 is merged.
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BUG: rolling count on string Series and TimeIndex raises an error
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| 2020-08-16T15:40:03Z
|
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
d = {'vv': {pd.Timestamp('2020-01-13 08:22:00', freq='T'): 'aa',
pd.Timestamp('2020-01-13 08:23:00', freq='T'): 'bb',
pd.Timestamp('2020-01-13 08:24:00', freq='T'): 'cc',
pd.Timestamp('2020-01-13 08:25:00', freq='T'): np.nan,
pd.Timestamp('2020-01-13 08:26:00', freq='T'): 'dd'}}
df = pd.DataFrame(d)
print(df['vv'].rolling('72s').count())
```
#### Problem description
this raises DataError: No numeric types to aggregate insted if the expected output:
2020-01-13 08:22:00 1.0
2020-01-13 08:23:00 2.0
2020-01-13 08:24:00 2.0
2020-01-13 08:25:00 1.0
2020-01-13 08:26:00 1.0
Name: vv, dtype: float64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.16299
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200807
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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|
BUG: read_excel not accepting encoding on 1.1.0
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| 42
| 2020-08-16T17:22:10Z
| 2021-07-11T04:45:11Z
| 2020-10-29T15:49:36Z
|
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
# Your code here
def date_parse(value: str) -> datetime.datetime:
return datetime.datetime.strptime(value.strip(), "%d/%m/%y")
df = pd.read_excel(
filepath,
encoding="iso-8859-1",
usecols="B",
date_parser=date_parse,
skiprows=4,
)
```
#### Problem description
TypeError: read_excel() got an unexpected keyword argument 'encoding'.
#### Expected Output
No error, as in previous versions.
#### Output of ``pd.show_versions()``
```INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.1.0
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
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 : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None```
</details>
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MDExOlB1bGxSZXF1ZXN0NDY4NDg0OTAy
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CI: Min Pytest Cov Version/Restrict xdist version
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| 2020-08-17T08:51:47Z
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|
CONTRIBUTOR
| null |
- [x] closes #35737
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|
BUG: custom window not working in groupby
|
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| 2020-08-16T22:03:08Z
| 2020-08-17T14:51:27Z
| 2020-08-17T06:48:32Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
readings = pd.DataFrame(
[
('A', 'Saturday', 101),
('A', 'Sunday', 88),
('A', 'Saturday', 103),
('A', 'Sunday', 82),
('A', 'Saturday', 100),
('B', 'Saturday', 27),
('B', 'Sunday', 13),
('B', 'Saturday', 21),
('B', 'Sunday', 17),
('B', 'Saturday', 25)
],
columns=['building', 'day', 'reading']
)
class ShiftedWindow(pd.api.indexers.BaseIndexer):
def __init__(self, window_size):
self.window_size = window_size
def get_window_bounds(self, num_values=0, min_periods=None, center=None, closed=None):
starts = np.arange(-self.window_size, num_values - self.window_size)
ends = starts + self.window_size
starts[:self.window_size] = 0
return starts, ends
readings.groupby('building')['reading'].rolling(window=ShiftedWindow(2), min_periods=1).mean()
```
#### Problem description
I've defined a custom window that uses the previous values, and therefore ignores the current value. It's very useful for, say, target encoding on time series.
#### Expected Output
I would be expecting the following output:
```py
>>> readings.groupby('building')['reading'].apply(lambda x: x.shift(1).rolling(2, min_periods=1).mean())
0 NaN
1 101.0
2 94.5
3 95.5
4 92.5
5 NaN
6 27.0
7 20.0
8 17.0
9 19.0
Name: reading, dtype: float64
```
Instead, I'm getting:
```py
>>> readings.groupby('building')['reading'].rolling(window=ShiftedWindow(2), min_periods=1).mean()
building
A 0 101.0
1 88.0
2 103.0
3 82.0
4 100.0
B 5 27.0
6 13.0
7 21.0
8 17.0
9 25.0
Name: reading, dtype: float64
```
I've checked and my custom window works as expected without using `groupby`. I've checked to see if `get_window_bounds` gets called when a `groupby` is used, and the answer is no. Basically, it seems that my custom window is being ignored entirely.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fsspec : 0.5.2
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.9
tables : 3.5.2
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.45.1
</details>
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CI: Unpin pytest-xdist
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| 0
| 2020-08-16T23:26:26Z
| 2020-08-27T16:06:48Z
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|
CONTRIBUTOR
| null |
Ref: https://github.com/pandas-dev/pandas/issues/35737
Ref: https://github.com/scipy/scipy/pull/12730
Restricting version for now.
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CI: Unpin Pytest + Pytest Asyncio Min Version
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| 6
| 2020-08-17T00:09:59Z
| 2020-12-22T19:03:19Z
| 2020-08-19T17:42:48Z
|
CONTRIBUTOR
| null |
- [x] closes #35620
Pytest 6.0.0+ will require pytest-asyncio>=0.12.0 for these async tests to run.
Also worth noting `pytest-asyncio 0.12.0 requires pytest>=5.4.0`. (So maybe we should think about bumping min pytest version)
```
____________________________________________________________________________________ ERROR collecting pandas/tests/indexes/test_base.py _____________________________________________________________________________________
../../.conda/envs/pandas-dev/lib/python3.8/site-packages/pluggy/hooks.py:286: in __call__
return self._hookexec(self, self.get_hookimpls(), kwargs)
../../.conda/envs/pandas-dev/lib/python3.8/site-packages/pluggy/manager.py:93: in _hookexec
return self._inner_hookexec(hook, methods, kwargs)
../../.conda/envs/pandas-dev/lib/python3.8/site-packages/pluggy/manager.py:84: in <lambda>
self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
../../.conda/envs/pandas-dev/lib/python3.8/site-packages/pytest_asyncio/plugin.py:39: in pytest_pycollect_makeitem
item = pytest.Function(name, parent=collector)
../../.conda/envs/pandas-dev/lib/python3.8/site-packages/_pytest/nodes.py:95: in __call__
warnings.warn(NODE_USE_FROM_PARENT.format(name=self.__name__), stacklevel=2)
E pytest.PytestDeprecationWarning: Direct construction of Function has been deprecated, please use Function.from_parent.
E See https://docs.pytest.org/en/stable/deprecations.html#node-construction-changed-to-node-from-parent for more details.
```
cc @simonjayhawkins
|
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REGR: re-add encoding for read_excel
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| 3
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| 2020-10-29T18:09:49Z
| 2020-08-18T18:02:42Z
|
CONTRIBUTOR
| null |
- [ ] closes #35753
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Needs tests!
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|
PERF: Allow jitting of groupby agg loop
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| 3
| 2020-08-17T06:01:31Z
| 2020-08-22T03:31:34Z
| 2020-08-22T03:30:05Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
New performance comparison for 10,000 groups
```
In [1]: In [1]: df_g = pd.DataFrame({'a': range(10**4), 'b': range(10**4), 'c': range(10**4)})
In [2]: In [2]: def f(x):
...: ...: return np.sum(x) + 1
...:
In [3]: df_g.groupby('a').agg(f)
Out[3]:
b c
a
0 1 1
1 2 2
2 3 3
3 4 4
4 5 5
... ... ...
9995 9996 9996
9996 9997 9997
9997 9998 9998
9998 9999 9999
9999 10000 10000
[10000 rows x 2 columns]
In [4]: %timeit df_g.groupby('a').agg(f)
1.2 s ± 70.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [5]: def f(values, index):
...: return np.sum(values) + 1
...:
In [6]: df_g.groupby('a').agg(f, engine='numba', engine_kwargs={'parallel': True})
Out[6]:
b c
a
0 1.0 1.0
1 2.0 2.0
2 3.0 3.0
3 4.0 4.0
4 5.0 5.0
... ... ...
9995 9996.0 9996.0
9996 9997.0 9997.0
9997 9998.0 9998.0
9998 9999.0 9999.0
9999 10000.0 10000.0
In [8]: %timeit df_g.groupby('a').agg(f, engine='numba', engine_kwargs={'parallel': True})
2.07 ms ± 64.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
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DOC: downcast argument for pd.to_numeric should be integer, not int
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| 3
| 2020-08-17T07:21:08Z
| 2020-08-19T13:16:42Z
| 2020-08-19T13:16:42Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.to_numeric.html?highlight=to_numeric#pandas.to_numeric
#### Documentation problem
The documentation declares that the `downcast` argument of `pd.to_numeric` can use the value `int`. However, only `integer` is working in the real code:
```
In [8]: pd.to_numeric(["42"], downcast="int", errors="coerce")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-3b178e77dcd5> in <module>
----> 1 pd.to_numeric(["42"], downcast="int", errors="coerce")
/usr/lib/python3.8/site-packages/pandas/core/tools/numeric.py in to_numeric(arg, errors, downcast)
110 """
111 if downcast not in (None, "integer", "signed", "unsigned", "float"):
--> 112 raise ValueError("invalid downcasting method provided")
113
114 if errors not in ("ignore", "raise", "coerce"):
ValueError: invalid downcasting method provided
In [9]: pd.to_numeric(["42"], downcast="integer", errors="coerce")
Out[9]: array([42], dtype=int8)
```
#### Suggested fix for documentation
Replace `int` with `integer`
|
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Backport PR #35754 on branch 1.1.x (CI: Min Pytest Cov Version/Restrict xdist version)
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| 2020-08-17T09:39:03Z
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| null |
Backport PR #35754: CI: Min Pytest Cov Version/Restrict xdist version
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BUG: Sparse[datetime64[ns]] TypeError: data type not understood
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| 5
| 2020-08-17T09:09:57Z
| 2020-08-27T02:22:02Z
| 2020-08-27T02:22:02Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported ([related, but different](https://github.com/pandas-dev/pandas/issues/26407)).
- [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 numpy as np
import pandas as pd
# Taken from the example here: https://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#sparsedtype
dtype = pd.SparseDtype(np.dtype('datetime64[ns]'))
# Some value
values = [np.datetime64('2012-05-01T01:00:00.000000'), np.datetime64('2016-05-01T01:00:00.000000')]
# Create the series
series = pd.Series(values, dtype=dtype)
```
Alternatively:
```
series = pd.Series(values, dtype="Sparse[datetime64[ns]]")
```
#### Problem description
As a user I would expect that `datetime64[ns]` is supported as SparseDtype for the SparseArray based on the [_Sparse data structures_ page](https://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#sparsedtype) in the documentation. This is desireable for the same rationale as supporting other sparse types, since date(time)s can also contain mostly NaT values that the users wants to store efficiently.
Running the code above yields the following TypeError:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-52-9fc6a02721e3> in <module>
----> 1 series = pd.Series(values, dtype=dtype)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
325 data = data.copy()
326 else:
--> 327 data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
328
329 data = SingleBlockManager.from_array(data, index)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
439 elif isinstance(data, (list, tuple)) and len(data) > 0:
440 if dtype is not None:
--> 441 subarr = _try_cast(data, dtype, copy, raise_cast_failure)
442 else:
443 subarr = maybe_convert_platform(data)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\construction.py in _try_cast(arr, dtype, copy, raise_cast_failure)
551 subarr = arr
552 else:
--> 553 subarr = maybe_cast_to_datetime(arr, dtype)
554
555 # Take care in creating object arrays (but iterators are not
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in maybe_cast_to_datetime(value, dtype, errors)
1327 # pandas supports dtype whose granularity is less than [ns]
1328 # e.g., [ps], [fs], [as]
-> 1329 if dtype <= np.dtype("M8[ns]"):
1330 if dtype.name == "datetime64":
1331 raise ValueError(msg)
TypeError: data type not understood
```
#### Expected Output
The expected output is for `Sparse[datetime[ns]]` similar to what one would expect from other dtypes:
```
>>> series.values
[2012-05-01 01:00:00, 2018-05-01 01:00:00]
Fill: NaT
IntIndex
Indices: array([0, 1])
>>> series.dtype
Sparse[datetime64[ns], NaT]
```
#### Workaround
Note that the following workaround partially gives the expected results:
```python
series = pd.Series(pd.arrays.SparseArray(values))
```
For which these operations work as expected:
```
>>> series.values
[2012-05-01 01:00:00, 2018-05-01 01:00:00]
Fill: NaT
IntIndex
Indices: array([0, 1])
>>> series.dtype
Sparse[datetime64[ns], NaT]
```
However `series.head()` will throw an error:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
C:\ProgramData\Anaconda3\lib\site-packages\IPython\lib\pretty.py in pretty(self, obj)
400 if cls is not object \
401 and callable(cls.__dict__.get('__repr__')):
--> 402 return _repr_pprint(obj, self, cycle)
403
404 return _default_pprint(obj, self, cycle)
C:\ProgramData\Anaconda3\lib\site-packages\IPython\lib\pretty.py in _repr_pprint(obj, p, cycle)
695 """A pprint that just redirects to the normal repr function."""
696 # Find newlines and replace them with p.break_()
--> 697 output = repr(obj)
698 for idx,output_line in enumerate(output.splitlines()):
699 if idx:
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\series.py in __repr__(self)
1319 min_rows=min_rows,
1320 max_rows=max_rows,
-> 1321 length=show_dimensions,
1322 )
1323 result = buf.getvalue()
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\series.py in to_string(self, buf, na_rep, float_format, header, index, length, dtype, name, max_rows, min_rows)
1384 max_rows=max_rows,
1385 )
-> 1386 result = formatter.to_string()
1387
1388 # catch contract violations
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\formats\format.py in to_string(self)
356
357 fmt_index, have_header = self._get_formatted_index()
--> 358 fmt_values = self._get_formatted_values()
359
360 if self.truncate_v:
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\formats\format.py in _get_formatted_values(self)
345 None,
346 float_format=self.float_format,
--> 347 na_rep=self.na_rep,
348 )
349
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\formats\format.py in format_array(values, formatter, float_format, na_rep, digits, space, justify, decimal, leading_space, quoting)
1177 )
1178
-> 1179 return fmt_obj.get_result()
1180
1181
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\formats\format.py in get_result(self)
1208
1209 def get_result(self) -> List[str]:
-> 1210 fmt_values = self._format_strings()
1211 return _make_fixed_width(fmt_values, self.justify)
1212
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\formats\format.py in _format_strings(self)
1465
1466 if not isinstance(values, DatetimeIndex):
-> 1467 values = DatetimeIndex(values)
1468
1469 if self.formatter is not None and callable(self.formatter):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in __new__(cls, data, freq, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name)
277 dayfirst=dayfirst,
278 yearfirst=yearfirst,
--> 279 ambiguous=ambiguous,
280 )
281
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\arrays\datetimes.py in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous)
321 dayfirst=dayfirst,
322 yearfirst=yearfirst,
--> 323 ambiguous=ambiguous,
324 )
325
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\arrays\datetimes.py in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous)
1957 if is_datetime64tz_dtype(data_dtype):
1958 # DatetimeArray -> ndarray
-> 1959 tz = _maybe_infer_tz(tz, data.tz)
1960 result = data._data
1961
AttributeError: 'SparseArray' object has no attribute 'tz'
```
Expected:
```
0 2012-05-01 01:00:00
1 2018-05-01 01:00:00
dtype: Sparse[datetime64[ns]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
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 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.4.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fsspec : 0.5.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.6.0
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.45.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDY4NzIwNDg4
| 35,763
|
MAINT: Initialize year to silence warning
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| 2
| 2020-08-17T09:47:13Z
| 2020-08-17T18:27:49Z
| 2020-08-17T18:27:45Z
|
CONTRIBUTOR
| null |
Initialize year to silence warning due to subtracting from
value that compiler cannot reason must be either initialized
or never reached
closes #35622
- [X] closes #35622
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Backport PR #35543 on branch 1.1.x (REGR: Fix interpolation on empty dataframe)
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NONE
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Backport PR #35543: REGR: Fix interpolation on empty dataframe
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Backport PR #35697 on branch 1.1.x (REGR: Don't ignore compiled patterns in replace)
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NONE
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Backport PR #35697: REGR: Don't ignore compiled patterns in replace
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Revert "REGR: Fix interpolation on empty dataframe"
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MEMBER
| null |
Reverts pandas-dev/pandas#35543
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BUG: datetime ExtensionDtype do not work with DataFrame
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| 6
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| 2020-08-19T11:11:30Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported. (at least I couldn't find one)
- [X] I have confirmed this bug exists on the latest version of pandas. (1.1.0)
- [X] (optional) I have confirmed this bug exists on the master branch of pandas. (`934e9f840ebd2e8b5a5181b19a23e033bd3985a5`)
---
#### Code Sample, a copy-pastable example
This is some high-level example that lead to the investion. It relies on [`rle-array`](https://github.com/JDASoftwareGroup/rle-array/) (commit `dfa79295a580d533ee9d2ea901e8808496dbcdc9` was used), because the pandas-provided [`DatetimeArray`](https://github.com/pandas-dev/pandas/blob/934e9f840ebd2e8b5a5181b19a23e033bd3985a5/pandas/core/arrays/datetimes.py#L116) uses a NumPy dtype or `DatetimeTZDtype`. Both cases somewhat work (see "Problem description").
```python
import pandas as pd
from rle_array import RLEArray
array = RLEArray._from_sequence([], dtype="datetime64[ns]")
df = pd.DataFrame({"x": array})
```
```text
Traceback (most recent call last):
File "bug.py", line 5, in <module>
pd.DataFrame({"x": array})
File ".../lib/python3.8/site-packages/pandas/core/frame.py", line 467, in __init__
mgr = init_dict(data, index, columns, dtype=dtype)
File ".../lib/python3.8/site-packages/pandas/core/internals/construction.py", line 283, in init_dict
return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
File ".../lib/python3.8/site-packages/pandas/core/internals/construction.py", line 93, in arrays_to_mgr
return create_block_manager_from_arrays(arrays, arr_names, axes)
File ".../lib/python3.8/site-packages/pandas/core/internals/managers.py", line 1650, in create_block_manager_from_arrays
blocks = form_blocks(arrays, names, axes)
File ".../lib/python3.8/site-packages/pandas/core/internals/managers.py", line 1703, in form_blocks
block_type = get_block_type(v)
File ".../lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 2672, in get_block_type
assert not is_datetime64tz_dtype(values.dtype)
AssertionError
```
#### Problem description
See here:
https://github.com/pandas-dev/pandas/blob/934e9f840ebd2e8b5a5181b19a23e033bd3985a5/pandas/core/internals/blocks.py#L2647-L2690
datetime (and also interval) types are checked BEFORE extension types which means that extension datetime types never end up in `ExtensionBlock`s. The latter one would be useful if:
- the datetime objects is not compatible with NumPy
- the data should not be converted to to NumPy (e.g. due to compression, like in the `rle-array` case)
Furthermore the invariant `issubclass(vtype, np.datetime64) => not is_datetime64tz_dtype(values.dtype)` does NOT hold for all extension dtypes, at least not under the current implementation of `is_datetime64tz_dtype`:
https://github.com/pandas-dev/pandas/blob/934e9f840ebd2e8b5a5181b19a23e033bd3985a5/pandas/core/dtypes/common.py#L415-L421
#### Expected Output
The code example works and `df._data` shows that the data ends up in an `ExtensionBlock`.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
```
</details>
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MDU6SXNzdWU2ODAxNzg2OTY=
| 35,768
|
ExtensionBlock.take_nd crashes in 1.1.0
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| 2020-08-17T11:57:26Z
| 2021-01-11T13:24:25Z
| 2020-08-17T17:09:53Z
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NONE
| null |
https://github.com/pandas-dev/pandas/blob/934e9f840ebd2e8b5a5181b19a23e033bd3985a5/pandas/core/internals/blocks.py#L1718
I've got `self.values` of type `pd.Series` with `pd.Timestamp`-s, and that does not have `fill_value` and `allow_fill`, so the kwargs check fails.
```
athenian/api/controllers/miners/github/branches.py:34: in extract_branches
for repo, repo_branches in branches.groupby(Branch.repository_full_name.key, sort=False):
/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/ops.py:133: in get_iterator
for key, (i, group) in zip(keys, splitter):
/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/ops.py:935: in __iter__
sdata = self._get_sorted_data()
/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/ops.py:948: in _get_sorted_data
return self.data.take(self.sort_idx, axis=self.axis)
/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:3341: in take
new_data = self._mgr.take(
/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py:1414: in take
return self.reindex_indexer(
/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py:1251: in reindex_indexer
new_blocks = [
/usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py:1252: in <listcomp>
blk.take_nd(
/usr/local/lib/python3.8/dist-packages/pandas/core/internals/blocks.py:1720: in take_nd
new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)
/usr/local/lib/python3.8/dist-packages/pandas/core/series.py:829: in take
nv.validate_take(tuple(), kwargs)
/usr/local/lib/python3.8/dist-packages/pandas/compat/numpy/function.py:68: in __call__
validate_kwargs(fname, kwargs, self.defaults)
/usr/local/lib/python3.8/dist-packages/pandas/util/_validators.py:148: in validate_kwargs
_check_for_invalid_keys(fname, kwargs, compat_args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fname = 'take', kwargs = {'allow_fill': True, 'fill_value': numpy.datetime64('NaT')}, compat_args = OrderedDict([('out', None), ('mode', 'raise')])
def _check_for_invalid_keys(fname, kwargs, compat_args):
"""
Checks whether 'kwargs' contains any keys that are not
in 'compat_args' and raises a TypeError if there is one.
"""
# set(dict) --> set of the dictionary's keys
diff = set(kwargs) - set(compat_args)
if diff:
bad_arg = list(diff)[0]
> raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'")
E TypeError: take() got an unexpected keyword argument 'allow_fill'
/usr/local/lib/python3.8/dist-packages/pandas/util/_validators.py:122: TypeError
```
```
(Pdb++) self.values
0 2019-11-01 09:08:16+00:00
1 2017-01-30 18:04:00+00:00
2 2016-12-05 10:59:00+00:00
3 2019-05-16 11:16:00+00:00
Name: commit_date, dtype: datetime64[ns, UTC]
```
It appeared due to
```
-> fc.replace(0, pd.NaT, inplace=True)
[50] /usr/local/lib/python3.8/dist-packages/pandas/core/series.py(4563)replace()
-> return super().replace(
[51] /usr/local/lib/python3.8/dist-packages/pandas/core/generic.py(6583)replace()
-> return self._update_inplace(result)
[52] /usr/local/lib/python3.8/dist-packages/pandas/core/generic.py(3955)_update_inplace()
-> self._maybe_update_cacher(verify_is_copy=verify_is_copy)
[53] /usr/local/lib/python3.8/dist-packages/pandas/core/generic.py(3235)_maybe_update_cacher()
-> ref._maybe_cache_changed(cacher[0], self)
[54] /usr/local/lib/python3.8/dist-packages/pandas/core/generic.py(3196)_maybe_cache_changed()
-> self._mgr.iset(loc, value)
[55] /usr/local/lib/python3.8/dist-packages/pandas/core/internals/managers.py(1066)iset()
-> blk.set(blk_locs, value_getitem(val_locs))
[56] > /usr/local/lib/python3.8/dist-packages/pandas/core/internals/blocks.py(1593)set()
-> self.values = values
```
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MDExOlB1bGxSZXF1ZXN0NDY4ODI1NDEx
| 35,769
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BUG: Raise ValueError instead of bare Exception in sanitize_array
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| 8
| 2020-08-17T13:16:03Z
| 2021-01-25T21:15:38Z
| 2020-10-10T16:46:25Z
|
CONTRIBUTOR
| null |
- [x] closes #35744
- [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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Backport PR #35519 on branch 1.1.x (REF: StringArray._from_sequence, use less memory)
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NONE
| null |
Backport PR #35519: REF: StringArray._from_sequence, use less memory
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MDU6SXNzdWU2ODAzMjUxNTg=
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|
BUG: df.replace()
|
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| 2020-08-17T15:31:36Z
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| 2020-08-17T18:48:03Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame.from_dict(
{
'a_original': ['w', 'x', 'y', 'z', 'w', 'x', 'y', 'z'],
'a_expected': ['w', 'x', 'y', 'z', 'w', 'x', 'y', 'z'],
'a_result': ['w', 'x', 'y', 'z', 'w', 'x', 'y', 'z'],
'b_original': ['w', 'x', 'y', 'z', 'z', 'y', 'x', 'w'],
'b_expected': ['w', 'x', 'y', 'z', 'z', 'y', 'x', 'w'],
'b_result': ['w', 'x', 'y', 'z', 'z', 'y', 'x', 'w']
}
)
# Produces expectation.
df.replace(
{column: {replace: None for replace in ['x', 'z']} for column in ['a_expected', 'b_expected']},
inplace=True
)
# Does not produce expectation.
df.replace(
{column: ['x', 'z'] for column in ['a_result', 'b_result']},
{column: None for column in ['a_result', 'b_result']},
inplace=True
)
>>
a_original a_expected a_result b_original b_expected b_result
0 w w w w w w
1 x None w x None w
2 y y y y y y
3 z None y z None y
4 w w w z None y
5 x None w y y y
6 y y y x None y
7 z None y w w w
```
#### Problem description
`df.replace()` has multiple ways to define which values in which columns ought to be replaced. I expected the two uses of `df.replace()` above to be equivalent, but the second does not replace the values "x" and "z" in the declared columns with `None`. Instead, some form of filling/padding is used.
#### Expected Output
The second instance of `df.replace()` should be the same as the first; all instances of "x" and "z" should be replaced with `None`, but there seems to be a default usage of `method` underneath; this result remains the case, no matter what is entered into `method`.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.112+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.18.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 49.2.0
Cython : 0.29.21
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : 1.3.3
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.8.1
bs4 : 4.6.3
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 1.0.0
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.3.3
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDY4OTkyOTI1
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|
CI: close sockets in SQL tests
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| 2020-08-18T15:46:19Z
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MEMBER
| null |
closes #35660
closes #29514
Broken off from #35711
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Backport PR #35750 on branch 1.1.x (Pass check_dtype to assert_extension_array_equal)
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NONE
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Backport PR #35750: Pass check_dtype to assert_extension_array_equal
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REGR: follow-up to return copy with df.interpolate on empty DataFrame
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| 5
| 2020-08-17T19:27:18Z
| 2020-08-18T13:06:35Z
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|
MEMBER
| null |
follow-up to #35543
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TST: Verify whether read-only datetime64 array can be factorized (35650)
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Changed 'int' type to 'integer' in to_numeric docstring
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BUG: DataFrame.apply with result_type=reduce incorrect index
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Added numba as an argument
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CI: unpin numpy and matplotlib
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| null |
revert #35312 and #35358
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CI: unpin numpy and matplotlib #35779
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| 2020-08-19T21:38:01Z
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- [x] closes #35779
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BUG: Series.replace doesn't distinguish single and double quotes
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| 2
| 2020-08-18T01:10:35Z
| 2020-08-18T11:18:53Z
| 2020-08-18T11:05:34Z
|
MEMBER
| 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.
If we try to replace the single quotes in a string with double quotes or vice versa using Series.replace it doesn't work (Series.str.replace does):
```python
import pandas as pd
print(pd.__version__)
# 1.2.0.dev0+112.g0d0f53f3c
print(pd.Series([" ' ' "]).replace("'", '"'))
# 0 ' '
# dtype: object
print(pd.Series([" ' ' "]).str.replace("'", '"'))
# 0 " "
# dtype: object
pd.Series([' " " ']).replace('"', "'")
# 0 " "
# dtype: object
pd.Series([' " " ']).str.replace('"', "'")
# 0 ' '
# dtype: object
```
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|
BUG: groupby.apply() unexpectedly returns DataFrame when Series is explicitly specified
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[] | null | 2
| 2020-08-18T01:55:34Z
| 2020-09-03T01:15:35Z
| 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 as pd
def calcscore(df):
return pd.Series([[','.join(df.item), df.score.sum()]], index=pd.Index([df.index[0]]))
df = pd.DataFrame(columns=['time', 'item', 'param', 'score'],
data=[[1, 'N1', 10, 100],
[1, 'N2', 20, 150],
[2, 'N3', 70, 300]])
for p in [5, 50]:
s = df[df.param > p].groupby('time', group_keys=False).apply(calcscore)
print(type(s))
print(s.to_list())
```
#### Problem description
I'm looping through a dataframe, selectively filtering by varying values for a `param` column and returning aggregated data based on that filtering (the actual code is more complex). I'm intentionally returning a Series object from the apply function since I've benchmarked it to be faster than returning a DataFrame. The problem is that sometimes, a DataFrame is unexpectedly returned by the apply function instead of the Series object that I explicitly returned. This seems to happen when the Series object has only one row. Here is the unexpected output that I'm seeing:
```
<class 'pandas.core.series.Series'>
[['N1,N2', 250], ['N3', 300]]
<class 'pandas.core.frame.DataFrame'>
Traceback (most recent call last):
File "/tmp/applytest.py", line 22, in <module>
print(s.to_list())
File "/usr/lib64/python3.8/site-packages/pandas/core/generic.py", line 5130, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'to_list'
```
#### Expected Output
```
<class 'pandas.core.series.Series'>
[['N1,N2', 250], ['N3', 300]]
<class 'pandas.core.series.Series'>
[['N3', 300]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.11-200.fc32.x86_64
Version : #1 SMP Wed Jul 29 17:15:52 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDY5MTg3MjE3
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CLN: remove unused variable
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| 2020-08-18T13:05:30Z
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DOC: Updated docstring for Series.str.cat (pandas-dev#35556)
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|
CONTRIBUTOR
| null |
- [X] closes #35556
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I have updated the docstring for `Series.str.cat` to make the issue in #35556 more explicit to the user.
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BUG: pivot broken on the latest version 1.1.0
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| 8
| 2020-08-18T07:28:16Z
| 2021-08-17T07:58:26Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
from datetime import datetime
df = pd.DataFrame.from_records([{"date": datetime(2016, 3, 2), "col1": 1, "col2": 2}], index=["date"])
df.pivot(index=df.index, columns="col1")
```
#### Problem description
The exmple code raise a `KeyError`:
```python
KeyError: "None of [Timestamp('2016-03-02 00:00:00')] are in the columns"
```
It works on version `1.0.5`
#### Expected Output
The output is something like this in version `1.0.5`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
Version : Darwin Kernel Version 19.4.0: Wed Mar 4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.0.post20200616
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.10.0
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.18
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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ENH: round(inplace=True)
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| 2
| 2020-08-18T11:55:05Z
| 2020-08-18T13:26:51Z
| 2020-08-18T13:26:42Z
|
NONE
| null |
#### Is your feature request related to a problem?
I frequently round numerical columns in my dataframe to two decimals using `pandas.DataFrame.round`. I would like to do this inplace, since it will make code simpler and presumable less prone to errors.
#### Describe the solution you'd like
`DataFrame.round` should get a new argument `inplace` such that if `True` the operation is done inplace. I think `DataFrame.apply` could also improve from this enhancement.
#### Example code
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"foo": [1.11, 2.22, 3.33, 4.44, 5.55],
"bar": [6.66, 7.77, 8.88, 9.99, np.nan]})
# e.g. dropna() works with inplace
df.dropna(inplace=True)
# but round() does not
df["foo"] = df["foo"].round(1)
# I would like a feature like:
df["foo"].round(1, inplace=True) # does not run
```
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DOC: clean v1.1.1 release notes
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BUG: Series.groupby returns an error if index is float and size is >= 1000000
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| 1
| 2020-08-18T12:53:17Z
| 2020-09-01T01:20:41Z
| 2020-09-01T01:20:41Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```
# Imports
import numpy as np
import pandas as pd
# Values
nsteps = 5*10**6
sv = np.random.normal(loc=100.0, scale=1.0, size=nsteps+1)
# Series with int index
se = pd.Series(sv)
# Series with float index
flind = np.arange(nsteps+1)/nsteps
sef = pd.Series(sv, index=flind)
# Group with int index works
seg = se.groupby(level=0).last()
# Group with float index and size<1000000 works
sefg = sef.iloc[:999999].groupby(level=0).last()
# Group with float index and size>=1000000 doesn't work
sefg2 = sef.iloc[:1000000].groupby(level=0).last()
```
#### Problem description
Series.groupby returns an error if index is float and size is >= 1000000 (no problem with this size if index is int):
File "pandas/_libs/index.pyx", line 345, in pandas._libs.index._bin_search
TypeError: '<' not supported between instances of 'float' and 'NoneType'
#### Expected Output
sefg2 should be similar to sefg (with one additional row)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0.post20200704
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
</details>
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Backport PR #35774 on branch 1.1.x (REGR: follow-up to return copy with df.interpolate on empty DataFrame)
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| 2020-08-18T13:06:29Z
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NONE
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Backport PR #35774: REGR: follow-up to return copy with df.interpolate on empty DataFrame
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QST: need to refactor pandas/io/formats/latex.py?
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| 2
| 2020-08-18T15:25:44Z
| 2020-09-08T07:01:19Z
| 2020-09-08T07:01:19Z
|
MEMBER
| null |
#### Refactoring
Recently ``pandas/io/formats/latex.py`` undertook some updates and extension of it functionality.
Adding more into it led to some code smell.
Recently there was a refactoring done by @SylvainLan, PR https://github.com/pandas-dev/pandas/pull/35649.
Even after that I see some improvements that can be done.
1. Split the process of writing the table into the beginning of the environment, the body and end of the environment.
2. Implement polymorphism to smoothly handle transition from regular table to longtable (currently there are multiple ifs here and there).
I made some changes in the local branch (not in sync with master because of the recent refactoring).
See https://github.com/ivanovmg/pandas/tree/refactor/latex-formatting for the preliminary solution.
Would you be interested in following this direction?
If so, then I would make it in sync with master and continue improvements.
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BUG: failing test for pyarrow/s3fs on windows
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| 2
| 2020-08-18T18:56:39Z
| 2020-08-25T09:47:48Z
| 2020-08-25T09:47:47Z
|
CONTRIBUTOR
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] 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
```
FAILED pandas/tests/io/test_parquet.py::TestParquetPyArrow::test_s3_roundtrip_for_dir[partition_col0]
```
#### Problem description
See build https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=40576&view=logs&jobId=404760ec-14d3-5d48-e580-13034792878f&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a
env:
```
pyarrow 1.0.0
botocore 1.17.44
fsspec 0.8.0
moto 1.3.14
s3fs 0.4.2
```
I do not know the cause of this problem, but it appears in https://github.com/pandas-dev/pandas/pull/35655 and also for the original code, so skipping it on win for now.
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| 13
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MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
First part of #35412. I could not find a relevant issue for the test change/whatsnew update.
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| 3
| 2020-08-18T22:39:30Z
| 2021-08-10T04:44:46Z
| 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 as pd
from pandas.api.types import is_categorical_dtype
s = pd.Series(
["a", "b", "c", "a", "b", "c"],
dtype=pd.SparseDtype(pd.CategoricalDtype(['a', 'b', 'c']))
)
print(s.dtype)
# out: Sparse[category, nan]
print(is_categorical_dtype(s))
# out: False
```
#### Problem description
The `is_categorical_dtype` function returns `False` when sparse. This is inconsistent with other types (example for unsigned integer below).
```python
from pandas.api.types import is_unsigned_integer_dtype
s = pd.Series(
[0, 1, 2, 3],
name="pd_unsigned_integer",
dtype=pd.SparseDtype(pd.UInt8Dtype(), pd.NA)
)
print(s.dtype)
# out: Sparse[UInt8, <NA>]
print(is_unsigned_integer_dtype(s))
# out: True
```
```python
s = pd.Series(
["a", "b", "c", "a", "b", "c"],
dtype=pd.CategoricalDtype(['a', 'b', 'c'])
)
print(s.dtype)
# out: category
print(is_categorical_dtype(s))
# out: True
```
#### Expected Output
```python
print(is_categorical_dtype(s))
# out: True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0.post20200113
Cython : None
pytest : 5.3.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.47.0
</details>
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BUG: issubclass check with dtype instead of type, closes GH#24883
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| 2020-08-27T09:18:49Z
| 2020-08-21T21:53:51Z
|
MEMBER
| null |
- [x] closes #24883
- [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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| 35,795
|
BUG: pd.DataFrame.astype for SparseDtype with different fill_value results in data loss
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| 2020-08-18T23:24:47Z
| 2020-08-25T12:11:28Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([[1,1,1,1,0,1]]).T
sdf1 = df.astype(pd.SparseDtype(int, fill_value=1))
sdf2 = sdf1.astype(pd.SparseDtype(int, fill_value=0))
sdf3 = sdf1.astype('Sparse[int]')
print(sdf1)
print(sdf2)
print(sdf3)
```
Output:
```python
>>> print(sdf1)
0
0 1
1 1
2 1
3 1
4 0
5 1
>>> print(sdf2)
0
0 0
1 0
2 0
3 0
4 0
5 0
>>> print(sdf3)
0
0 0
1 0
2 0
3 0
4 0
5 0
```
#### Expected Output
```python
>>> print(sdf1)
0
0 1
1 1
2 1
3 1
4 0
5 1
>>> print(sdf2)
0
0 1
1 1
2 1
3 1
4 0
5 1
>>> print(sdf3)
0
0 1
1 1
2 1
3 1
4 0
5 1
```
#### Problem description
Performing `astype` operation should not change the data in the dataframe. Currently doing `astype` on a dataframe containing sparse columns to a `SparseDtype` with a different `fill_value` results in data loss, e.g. everything becomes 0 for sparse ints. I believe similar phenomenon might exist for other sparse dtypes but have not tested that.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-179-generic
Version : #209-Ubuntu SMP Fri Apr 24 17:48:44 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.2
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.49.1
</details>
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|
ENH:dataframe columns dropping with column names case-insensitive
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[] | null | 7
| 2020-08-19T00:25:03Z
| 2020-08-23T13:50:19Z
| 2020-08-23T13:50:13Z
|
NONE
| null |
#### Is your feature request related to a problem?
NO
#### Describe the solution you'd like
Currently the data frame columns are case-sensitive, when we try to drop the column with different case, the error is thrown.
My suggestion is to have one more option like ignorecase as boolean, if the flag is true, then drop the column if the name satisifies
#### API breaking implications
[this should provide a description of how this feature will affect the API]
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
Currently, changing the both the dataframe column and the drop column to lower case and delete it.
#### Additional context
Its good to have feature..
```python
# Your code here, if applicable
import io
import pandas as pd
source = io.StringIO("""col1, col2,col3
1,2,3
""")
df = pd.read_csv(source)
print (df)
df = df.drop("COL3", axis=1)
print(df)
col1 col2 col3
0 1 2 3
Error
File "test/test.py", line 16, in <module>
df = df.drop("COL3", axis=1)
KeyError: "['COL3'] not found in axis"
```
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BUG: Fix is_categorical_dtype for Sparse[category]
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| 2020-09-19T16:39:42Z
| 2020-09-19T16:39:38Z
|
MEMBER
| null |
- [x] closes #35793
- [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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Bump asv Python version
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| 2020-08-26T17:06:12Z
| 2020-08-26T13:57:41Z
|
MEMBER
| null |
Got this error trying to run the asvs locally and bumping the Python version in asv.conf.json fixed it:
```
STDOUT -------->
Processing /Users/danielsaxton/pandas/asv_bench/env/a36d556a9d1611aba4092dc48036d261/project
Preparing wheel metadata: started
Preparing wheel metadata: finished with status 'done'
STDERR -------->
ERROR: Package 'pandas' requires a different Python: 3.6.10 not in '>=3.7.1'
```
Let me know if this is needed / correct in general or if it was just a thing with my machine.
cc @TomAugspurger
Closes https://github.com/pandas-dev/pandas/issues/34677
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MDExOlB1bGxSZXF1ZXN0NDcwMDAwOTAy
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TST: resample does not yield empty groups (#10603)
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| 2
| 2020-08-19T08:19:44Z
| 2020-08-21T22:42:56Z
| 2020-08-21T22:42:51Z
|
CONTRIBUTOR
| null |
- [x] closes #10603
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This adds a test to verify that resample does not yield empty groups.
I'm new to contributing to pandas, and I'm not sure where to add this test.
Let me know if this place is inappropriate.
|
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Backport PR #35787 on branch 1.1.x (DOC: clean v1.1.1 release notes)
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NONE
| null |
Backport PR #35787: DOC: clean v1.1.1 release notes
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MDExOlB1bGxSZXF1ZXN0NDcwMTMxMjY5
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DOC: another pass of v1.1.1 release notes
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| 2
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| 2020-08-20T15:07:07Z
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MEMBER
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https://pandas.pydata.org/docs/dev/whatsnew/v1.1.1.html
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MDU6SXNzdWU2ODE4MTczMjQ=
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|
BUG: Was trying to read an ods file and ran into UnboundLocalError in odfreader.py
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| 8
| 2020-08-19T12:54:55Z
| 2020-10-07T07:09:27Z
| 2020-09-13T22:57:45Z
|
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
for file in os.listdir('data'): pandas.read_excel(pathlib.Path('data', file), engine='odf')
```
Sorry I don't have a minimal data example at this time.
#### Problem description
Was trying to test pandas reading a collection of ods files and ran into this error.
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/util/_decorators.py", line 296, in wrapper
return func(*args, **kwargs)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 311, in read_excel
return io.parse(
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 906, in parse
return self._reader.parse(
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_base.py", line 443, in parse
data = self.get_sheet_data(sheet, convert_float)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py", line 91, in get_sheet_data
value = self._get_cell_value(sheet_cell, convert_float)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py", line 175, in _get_cell_value
return self._get_cell_string_value(cell)
File "/home/michael/.local/share/virtualenvs/merge-csv-NFbvYFrS/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py", line 211, in _get_cell_string_value
value.append(" " * spaces)
UnboundLocalError: local variable 'spaces' referenced before assignment
```
I took a look at the [code in question](https://github.com/pandas-dev/pandas/blob/ddf4f2430dd0a6fd51d7409ad3f524aeb5cbace2/pandas/io/excel/_odfreader.py#L211) and it seems like the line may be on the wrong indent level?
#### Expected Output
The usual dataframes :+1:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
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 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2ODE4MjY3NzQ=
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Performance regression in tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc
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| 8
| 2020-08-19T13:09:01Z
| 2020-11-26T15:35:30Z
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CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc?p-size=1000000&p-tz=datetime.timezone.utc&commits=3701a9b1bfc3ad3890c2fb1fe1974a4768f6d5f8-522f855a38d52763c4e2b07480786163b91dad38
Somewhere in https://github.com/pandas-dev/pandas/compare/3701a9b1bfc3ad3890c2fb1fe1974a4768f6d5f8...522f855a38d52763c4e2b07480786163b91dad38
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Performance regression in reindex.LevelAlign.time_reindex_level
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| 4
| 2020-08-19T13:09:49Z
| 2020-12-18T16:44:50Z
| 2020-12-18T16:44:50Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#reindex.LevelAlign.time_reindex_level?commits=a0c8425a5f2b74e8a716defd799c4a3716f66eff-2a319238e47d3d7362188f7da4eb911199c423b8
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Performance regression in index_object.IndexEquals.time_non_object_equals_multiindex
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| 0
| 2020-08-19T13:10:33Z
| 2020-12-18T16:44:51Z
| 2020-12-18T16:44:51Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#index_object.IndexEquals.time_non_object_equals_multiindex?commits=a0c8425a5f2b74e8a716defd799c4a3716f66eff-2a319238e47d3d7362188f7da4eb911199c423b8
Somewhere in https://github.com/pandas-dev/pandas/compare/a0c8425a5f2b74e8a716defd799c4a3716f66eff...2a319238e47d3d7362188f7da4eb911199c423b8
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Backport PR #35776 on branch 1.1.x (Changed 'int' type to 'integer' in to_numeric docstring)
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NONE
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Backport PR #35776: Changed 'int' type to 'integer' in to_numeric docstring
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BUG: Issue with Pandas df.str.split with expand
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| 15
| 2020-08-19T14:02:05Z
| 2021-08-10T04:47:04Z
| 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
>>> df=pd.DataFrame({'a':['Kickoff_2'],'b':['bla'],'c':[1],'x':[None],'y':[None],'z':[None]})
>>> df[['x','y','z']] = df['a'].str.split('_',expand=True,n=2)
...
raise ValueError("Columns must be same length as key")
ValueError: Columns must be same length as key
>>> df_long=pd.DataFrame({'a':['Kickoff_2','Kickoff','Kickoff_pass_jump'],'b':['bla','bla','bla'],'c':[1,2,3],'x':[None,None,None],'y':[None,None,None],'z':[None,None,None]})
>>> df_long[['x','y','z']] = df_long['a'].str.split('_',expand=True,n=2)
>>> df_long
a b c x y z
0 Kickoff_2 bla 1 Kickoff 2 None
1 Kickoff bla 2 Kickoff None None
2 Kickoff_pass_jump bla 3 Kickoff pass jump
```
#### Problem description
String + split() with expand only works if there is a row in the df that will later contain the desired number of columns. It would be nice if the first example here would get filled up with NaNs.
#### Expected Output
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Backport PR #35672 on branch 1.1.x CI: test_chunks_have_consistent_numerical_type periodically fails on 1.1.x
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| 1
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MEMBER
| null |
PR directly against 1.1.x
effectively backport of #35672
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CI: more xfail failing 32-bit tests
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| 5
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MEMBER
| null |
xref #35294 and https://github.com/pandas-dev/pandas/issues/35489#issuecomment-676550757
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MDU6SXNzdWU2ODIxNTc5MDg=
| 35,810
|
ENH: dataframe to markdown doesn't have option to hide index
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| 2020-08-19T20:27:31Z
| 2020-08-21T17:04:46Z
| 2020-08-19T20:29:27Z
|
NONE
| null |
Suppose I have the following `pd.DataFrame`:
```
df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
```
If I want to print it without the index, I could do:
To string:
```
df.to_string(index=False)
```
To html:
```
df.to_html(index=False)
```
To json:
```
df.to_json(index=False, orient='table')
```
But it fails in `to_markdown`:
```
df.to_markdown(index=False)
TypeError: tabulate() got an unexpected keyword argument 'index'
```
Shouldn't `to_markdown` method have the option to hide the index too?
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BUG/QST: Series.transform with a dictionary
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| 3
| 2020-08-19T21:55:53Z
| 2020-09-12T21:36:52Z
| 2020-09-12T21:36:52Z
|
MEMBER
| null |
What is the expected output of passing a dictionary to `Series.transform`? For example:
s = pd.Series([1, 2, 3])
result1 = s.transform({'a': lambda x: x + 1})
result2 = s.transform({'a': lambda x: x + 1, 'b': lambda x: x + 2})
The docs say that `dict of axis labels -> functions` is acceptable, but I can't find any example in the docs where the output is described/shown. Under the hood, `Series.transform` is just calling `Series.aggregate` which produces the following outputs for `result1` and `result2`.
````
# result1
a 0 2
1 3
2 4
dtype: int64
# result2
a 0 2
1 3
2 4
b 0 3
1 4
2 5
dtype: int64
````
`result1` is deemed acceptable (the length of the result equals the length of the input) and is returned, but `result2` raises; it is not a transformation.
I am wondering if a better return would be a DataFrame where the keys are the column names ('a' and 'b' in this example).
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MDExOlB1bGxSZXF1ZXN0NDcwNDk2MzM3
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WEB: Add new Maintainers to Team Page
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| 1
| 2020-08-19T22:37:48Z
| 2020-08-20T16:26:36Z
| 2020-08-20T16:26:31Z
|
CONTRIBUTOR
| null |
Add new maintainers.
cc:
@bashtage
@charlesdong1991
@Dr-Irv
@dsaxton
@MarcoGorelli
@rhshadrach
In case anyone doesn't want to appear here: https://pandas.pydata.org/about/team.html
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MDU6SXNzdWU2ODIyNDc0NTQ=
| 35,813
|
CI: Failures due to pyarrow 0.11 in Travis 37 Locale Build
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| 2
| 2020-08-19T23:30:50Z
| 2020-08-20T21:26:05Z
| 2020-08-20T21:26:05Z
|
CONTRIBUTOR
| null |
Looks like recently the Travis 37 Locale Build has started picking up pyarrow 0.11.1
https://travis-ci.org/github/pandas-dev/pandas/jobs/719430999
pyarrow 0.11.1 py37he6710b0_0
This causes the pyarrow tests to run which seem to be failing.
Previously no pyarrow in env: https://travis-ci.org/github/pandas-dev/pandas/jobs/719138849 (passing build)
Feels like one of our deps has started to depend to pyarrow.
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|
TST: Fix test_parquet failures for pyarrow 1.0
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| 2020-08-25T07:08:14Z
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|
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| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/35791
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
cc @martindurant -> looks like this is a pyarrow 1.0.0 compat issue (read_table uses the new API) - https://arrow.apache.org/docs/python/generated/pyarrow.parquet.read_table.html
I noticed the partition cols are casted from int64 -> int32 is that expected pyarrow behaviour? From the write_table docs looking at version 1.0/2.0 it suggests it is https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow.parquet.write_table
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| 2020-08-20T04:22:42Z
| 2020-08-25T12:42:52Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Make sure you have Pandas and `xlrd` installed in a virtualenv, and launch the Python interpreter. You'll need `openpyxl` only if you decide to create the Excel file from Python (see below).
```bash
python3 -m venv /tmp/pandas-issue-35816
. /tmp/pandas-issue-35816/bin/activate
pip install pandas xlrd openpyxl
python
```
To get the Excel files for reproducing the bug, you can either
1. download [index_dtype.xls](https://github.com/pandas-dev/pandas/files/5100657/index_dtype.xls.gz) (note: attachment gzipped) or [index_dtype.xlsx](https://github.com/pandas-dev/pandas/files/5100653/index_dtype.xlsx), or
1. copy-paste the text inside `Details` below into a `index_dtype.csv` file, import that into Excel, and save it in Excel format as `index_dtype.xlsx`, or
<details>
<pre>string,value for string index
42,value for int index</pre>
</details>
1. create an Excel file from Python by copy-pasting from `Details` below:
<details>
<pre>df = pd.DataFrame(
{1: ["value for string index", "value for int index"]}, ["string", 42]
)
df.to_excel("index_dtype.xlsx", header=None)</pre>
</details>
Finally, copy-paste the following code into Python:
```python
import pandas as pd
pd.read_excel("index_dtype.xlsx", header=None, index_col=0, dtype=str).iloc[:, 0].to_dict()
```
Output:
```python
{'string': 'value for string index', 42: 'value for int index'}
```
#### Problem description
The integer value in the index column appears as an `int` instead of a `str` in the index of the `DataFrame` read from an Excel file, even though `dtype=str` was specified.
I verified this behavior with spreadsheets
- exported from LibreOffice 6.2.8.2 as `Excel 2007-2019 (.xlsx)`,
- exported from LibreOffice 6.2.8.2 as `Excel 97-2003 (.xls)`, and
- generated like shown in `3. create an Excel file from Python` above.
Unfortunately I don't have Excel to check if "genuine" Excel spreadsheets cause this as well.
The index dtype is correct if:
- `read_csv()` is used, or
- `index_col=0` is omitted (values in the first column all become `str`)
The index dtype is still wrong even if `dtype={0: str, 1: str}` is used to specify the dtype for each column separately.
#### Expected Output
```python
{'string': 'value for string index', '42': 'value for int index'}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.11-100.fc29.x86_64
Version : #1 SMP Tue Nov 12 20:41:25 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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