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DOC: Updated resample.py and groupby.py to fix SA04 Errors
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry: Fixed Partial SA04 Errors
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BUG: MultiIndex.is_montonic erronoes when nan in at least one level
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| 0
| 2020-10-18T13:52:48Z
| 2020-10-27T12:51:13Z
| 2020-10-27T12:51:13Z
|
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.
---
**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.MultiIndex.from_tuples([(np.nan, ), (1,)], names=["test"]).is_monotonic_increasing)
```
#### Problem description
This returns ``True``, which is obviously not correct. This was introduced by #27495. The error persists with more than one level in the ``MultiIndex``. The example is just for simplicity.
#### Expected Output
``False``
#### Output of ``pd.show_versions()``
<details>
master
</details>
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Fix regression for is_monotonic_increasing with nan in MultiIndex
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| 6
| 2020-10-18T14:03:54Z
| 2020-10-27T13:12:34Z
| 2020-10-27T12:51:14Z
|
MEMBER
| null |
- [x] closes #37220
- [x] closes #27498 (closes that one too probably, because we need this case)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Found this while debugging a different issue.
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| false
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[] | null | 0
| 2020-10-18T16:56:56Z
| 2021-08-13T04:59:24Z
| null |
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.
---
**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
idx1 = pd.MultiIndex.from_product([['A'], [1.0, 2.0]], names=['id1', 'id2'])
idx2 = pd.MultiIndex.from_product([['A'], [np.nan, 2.0]], names=['id1', 'id2'])
print(idx2.get_indexer(idx1))
print(idx1.get_indexer(idx2))
```
#### Problem description
This snippet returns
```
[0 1]
[-1 1]
```
#### Expected Output
It should return ``[-1, 1]`` for both statement.
Part of the problem is in
https://github.com/pandas-dev/pandas/blob/54fa3da2e0b65b733d12de6cbecbf2d8afef4e9f/pandas/core/indexes/multi.py#L1045-L1064
The ``self.levels`` statement does not hand the ``nans`` over. But even fixing this returns
```
[ 0 -1]
[-1 1]
```
The error must be somewhere deeper, but I could not figure out where something goes wrong. Tested on 1.0.5 and 0.25.3. Does not seem to be a regression.
#### Output of ``pd.show_versions()``
<details>
master
</details>
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Backport PR #37149 on branch 1.1.x (BUG: GroupBy().fillna() performance regression)
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NONE
| null |
Backport PR #37149: BUG: GroupBy().fillna() performance regression
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| 1
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DOC: Fix typos and broken formatting
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| 1
| 2020-10-18T19:45:07Z
| 2020-10-22T21:02:51Z
| 2020-10-22T19:37:39Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR fixes minor typos in the getting started tutorial [How to manipulate textual data?](https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/10_text_data.html). I built the documentation to confirm that I fixed the formatting of the Wikipedia link. I also fixed the inconsistent use of "element-wise" versus "element wise".
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CLN: Simplify gathering of results in aggregate
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| 2
| 2020-10-18T21:14:48Z
| 2020-10-22T23:54:28Z
| 2020-10-22T23:49:42Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Reduces the number of paths when collecting results in `aggregation.aggregate`; one where we are gathering NDFrames using `concat` and the other where we are gathering scalars using `Series`.
The reason for the one test change is as follows. In one case, we previously gathered results using `DataFrame`. When this occurs and the indexes are not all equal, `DataFrame` will sort the index whereas `concat` will have the index in order of appearance. For example with
```
df = DataFrame(
{
'A': pd.Series([1, 2], index=['b', 'a']),
'B': pd.Series([3, 4], index=['c', 'a'])
}
)
```
gives
```
A B
a 2.0 4.0
b 1.0 NaN
c NaN 3.0
```
whereas using `concat` instead of `DataFrame` on the first line with `axis=1` gives:
```
A B
b 1.0 NaN
a 2.0 4.0
c NaN 3.0
```
If in this example you replace the 2nd index with ['b', 'a'] (so that they are equal), then both `Dataframe` and `concat` will produce the same result with index `['b', 'a']`. If on the other hand you replace the 2nd index with `['a', 'b']`, then `DataFrame` will result in index `['a', 'b']` whereas concat will result in index `['b', 'a']`.
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TST: indexing tests for #21168, #27420, #15928, #30053
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| 1
| 2020-10-18T22:14:44Z
| 2020-10-20T02:08:43Z
| 2020-10-20T02:08:30Z
|
MEMBER
| null |
- [x] closes #21168
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- [x] closes #30053
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: consolidate exception messages in datetimelike validate_listlike
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| 2
| 2020-10-18T22:19:27Z
| 2020-10-20T17:31:51Z
| 2020-10-20T16:57:10Z
|
MEMBER
| null |
The next step is to consolidate the messages in _validate_scalar, then we should be able to get rid of some duplicate _validate_foo methods
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TST: collect unary tests
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MDExOlB1bGxSZXF1ZXN0NTA1NTc3Mzk1
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REF: move get_op_result_name out of ops.__init__
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Slowly chipping away at the long-time goal of not having a bunch of code in `ops.__init__`
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REF: move misplaced pd.concat tests
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REF: collect tests by method
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TST/CLN: Split out some to_string tests
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| 2
| 2020-10-19T02:06:46Z
| 2020-10-20T23:05:39Z
| 2020-10-20T23:02:57Z
|
MEMBER
| null |
test_format.py is over 3000 lines long so breaking some (but not all) to_string-specific tests into a separate test_to_string.py file (we already have test_to_html.py, test_to_latex.py, and test_to_markdown.py)
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MDU6SXNzdWU3MjQyMDkzMDI=
| 37,235
|
BUG: HDFStore.get_storer.shape returns (1, #) when the table has 0 row
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open
| false
| null |
[] | null | 0
| 2020-10-19T02:25:21Z
| 2021-08-13T05:01:06Z
| null |
NONE
| null |
```python
import pandas as pd
h5file = r'D:\test.h5'
df = pd.DataFrame(range(10)).iloc[0:0] # 0 row, 1 column
df.to_hdf(h5file, key='abc', format='f')
h5 = pd.HDFStore(h5file)
print(h5.get_storer('abc').shape) # get [1, 1] instead of [0, 1]
```
#### Problem description
I expect HDFStore.get_storer.shape to give [0, #] where there's 0 row in an HDF file, but it gives [1, #].
This happens in fixed format tables.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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MDExOlB1bGxSZXF1ZXN0NTA1NjUxMzIx
| 37,236
|
CLN: core/dtypes/cast.py::maybe_infer_to_datetimelike
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| 2021-01-05T01:17:46Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
DOC: update PeriodArray docstring
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| 1
| 2020-10-19T04:08:42Z
| 2020-10-21T18:28:34Z
| 2020-10-21T18:28:26Z
|
MEMBER
| null |
AFAICT we currently don't have dedicated pages for these methods in the API reference.
When we add them we also want add links on the `pd.PeriodArray` [page](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.arrays.PeriodArray.html) (and possibly in other places)
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CLN: clean-up test on addition of series/frames
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| 1
| 2020-10-19T08:09:30Z
| 2020-10-20T05:47:31Z
| 2020-10-20T01:22:23Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Cleanup in ``pandas/tests/arithmetic/test_numeric.py``.
- Split ``test_arith_ops_df_compat`` into ``test_add_series`` and ``test_add_frames``.
- Parametrize tests.
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DOC: documentation rendering issue for functions using typing overloads
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| 2020-10-19T09:04:18Z
| 2020-12-23T11:58:26Z
| 2020-12-23T11:58:25Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.concat.html
#### Documentation problem
This is how the method header looks for concat:
```python
pandas.concat(objs: Union[Iterable[‘DataFrame’], Mapping[Label, ‘DataFrame’]], axis='0', join: str = "'outer'",
ignore_index: bool = 'False', keys='None', levels='None', names='None', verify_integrity: bool = 'False',
sort: bool = 'False', copy: bool = 'True') → ’DataFrame’
pandas.concat(objs: Union[Iterable[FrameOrSeries], Mapping[Label, FrameOrSeries]], axis='0', join: str = "'outer'",
ignore_index: bool = 'False', keys='None', levels='None', names='None', verify_integrity: bool = 'False',
sort: bool = 'False', copy: bool = 'True') → FrameOrSeriesUnion
```
For comparison, this is merge:
```python
pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False,
right_index=False, sort=False, suffixes='_x', '_y', copy=True, indicator=False, validate=None)
```
As can be seen above, concat keeps the type information and there are two entries (likely due to the different output types).
I'm not sure why this occurs, but there are several overloaded methods in the code which could be related: https://github.com/pandas-dev/pandas/blob/54fa3da2e0b65b733d12de6cbecbf2d8afef4e9f/pandas/core/reshape/concat.py#L40-L83
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CI move non-standard-import checks over to pre-commit
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| 2020-10-23T07:17:22Z
| 2020-10-22T23:51:37Z
|
MEMBER
| null |
Like this they're cross-platform, provide faster feedback to devs, and can use pre-commit `pygrep` instead of the custom `invgrep`
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CI move code directives to pre-commit, remove some outdated checks
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| 2
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| 2020-10-20T07:39:53Z
| 2020-10-20T01:08:38Z
|
MEMBER
| null |
Moving checks for incorrect code block or IPython directives to pre-commit so they're cross-platform and give faster feedback to devs.
Also removing some no-longer-necessary code checks:
- "Check that no file in the repo contains trailing whitespaces" is taken care of by the `trailing-whitespace` hook
- "Check for extra blank lines after the class definition" is enforced by `black`
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|
CI: Split .github\workflows\ci.yml into separate workflows
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| 2021-08-13T04:59:46Z
| null |
MEMBER
| null |
CI / Checks (push) — Checks and CI / Web and docs (push) — Web and docs are part of the same workflow.
If we want to restart one of the jobs (quiet infrequent - a few instances of connection issues on web and docs IIRC), both are re-run.
It may make sense to split these.
An alternative could be to combine with pre-commit into a single workflow and only run 'Checks' and 'Web and docs' on success.
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TST: split up test_concat.py
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| 2
| 2020-10-19T13:00:21Z
| 2020-10-28T12:04:16Z
| 2020-10-28T12:04:16Z
|
CONTRIBUTOR
| null |
we should split this to a sub-module (concat) and then split logically (similar to merge)
https://github.com/pandas-dev/pandas/blob/master/pandas/tests/reshape/test_concat.py
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MDU6SXNzdWU3MjQ2MDMxMjM=
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|
QST: Rolling mean triggers exception : Cannot cast array data from dtype('float64') to dtype('int32') [Works on macOS (64 bits?) not on Linux 32 bits])
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[] | null | 4
| 2020-10-19T13:19:31Z
| 2020-12-27T23:54:12Z
| 2020-12-27T23:54:12Z
|
NONE
| null |
- [ Check] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ Check] I have asked my usage related question on [StackOverflow](https://stackoverflow.com/questions/64373351/pandas-rolling-window-cannot-cast-array-data-from-dtypeint64-to-dtypeint).
---
#### Question about pandas
I am trying to calculate the average of a rolling window [of x number of seconds, in this case 300] using Panda's rolling window function. This works perfectly fine on my mac
however the same code fails to work on a armv71 [32 bit system Linux 4.9.82-ti-r102 armv7l GNU/Linux]. here is a minimal reproducible example :
```python
import pandas as pd
import times
df = pd.read_csv('example.csv', index_col=2)
df.index = pd.to_datetime(df.index)
df.index
try:
# Interval is the step size
interval = 300
# input to rolling window
window_val = str(interval) + 's'
# Variable
start = time.time()
df['Variable_Average_' + str(interval)] = df \
.groupby('Group_By_Number') \
.rolling(window=window_val)['Variable'] \
.mean() \
.reset_index(drop=True) \
.values
except:
print(traceback.format_exc())
```
This produces the following error for me :
```
Traceback (most recent call last):
File "Test2.py", line 19, in <module>
.rolling(window=window_val)['Variable'] \
File "/home/fiq/venv/lib/python3.7/site-packages/pandas/core/window/rolling.py", line 2091, in mean
return super().mean(*args, **kwargs)
File "/home/fiq/venv/lib/python3.7/site-packages/pandas/core/window/rolling.py", line 1488, in mean
return self._apply(window_func, center=self.center, name="mean", **kwargs)
File "/home/fiq/venv/lib/python3.7/site-packages/pandas/core/window/rolling.py", line 2194, in _apply
**kwargs,
File "/home/fiq/venv/lib/python3.7/site-packages/pandas/core/window/rolling.py", line 588, in _apply
result = calc(values)
File "/home/fiq/venv/lib/python3.7/site-packages/pandas/core/window/rolling.py", line 574, in calc
closed=self.closed,
File "/home/fiq/venv/lib/python3.7/site-packages/pandas/core/window/indexers.py", line 301, in get_window_bounds
index_array = self.index_array.take(indicies)
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
```
On the line where I call the rolling mean function. I have tried typecasting all the data to int32 before reaching the rolling stage and i still get the same error.
The data used for this example is as follows :
with tmstamp being its index and all other columns of type str and int32 respectively.
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MDU6SXNzdWU3MjQ2MTA1ODI=
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|
BUG: with integer column labels, .info() throws KeyError after column subsetting with .loc[]
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| 3
| 2020-10-19T13:28:55Z
| 2020-10-20T00:51:57Z
| 2020-10-20T00:51:57Z
|
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 numpy as np
import pandas as pd
df = pd.DataFrame(np.random.random(size=(2, 3)),
index=['A', 'B'],
columns=[0, 1, 2])
print(df.info())
<class 'pandas.core.frame.DataFrame'>
Index: 2 entries, A to B
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 0 2 non-null float64
1 1 2 non-null float64
2 2 2 non-null float64
dtypes: float64(3)
memory usage: 64.0+ bytes
None
print(df.loc[:, [1, 2]])
1 2
A 0.950714 0.731994
B 0.156019 0.155995
print(df.loc[:, [1, 2]].info())
Traceback (most recent call last):
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2895, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1032, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1039, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 0
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "some_file.py", line 32, in <module>
print(df.loc[:, [1, 2]].info())
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/core/frame.py", line 2589, in info
return DataFrameInfo(
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/io/formats/info.py", line 250, in info
self._verbose_repr(lines, ids, dtypes, show_counts)
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/io/formats/info.py", line 335, in _verbose_repr
dtype = dtypes[i]
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/core/series.py", line 882, in __getitem__
return self._get_value(key)
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/core/series.py", line 989, in _get_value
loc = self.index.get_loc(label)
File ".../.pyenv/versions/dm/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2897, in get_loc
raise KeyError(key) from err
KeyError: 0
Process finished with exit code 1
```
#### Problem description
When column names are integers, calling `.info()` after selecting a subset of the columns using `.loc[:, [...]]` causes the above error.
Converting to `str` avoids the error:
```
df.columns = df.columns.astype(str)
print(df.loc[:, ['1', '2']].info())
<class 'pandas.core.frame.DataFrame'>
Index: 2 entries, A to B
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 1 2 non-null float64
1 2 2 non-null float64
dtypes: float64(2)
memory usage: 48.0+ bytes
None
```
#### Expected Output
Standard `.info()` DataFrame summary.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-51-generic
Version : #56-Ubuntu SMP Mon Oct 5 14:28:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
None]
</details>
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BUG: ValueError: The name None occurs multiple times, use a level number
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| 3
| 2020-10-19T13:55:05Z
| 2021-09-20T14:28:42Z
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame([
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2013,'MONTH':12,'DAYS': 3,'SALARY': 17},
{'JOB':'Employ','NAME':'Mary','YEAR':2013,'MONTH':12,'DAYS': 5,'SALARY': 23},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':10,'SALARY':100},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 1,'DAYS':11,'SALARY':110},
{'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 1,'DAYS':15,'SALARY':200},
{'JOB':'Worker','NAME':'Bob' ,'YEAR':2014,'MONTH': 2,'DAYS': 8,'SALARY': 80},
{'JOB':'Employ','NAME':'Mary','YEAR':2014,'MONTH': 2,'DAYS': 5,'SALARY':190},
{'JOB':'Cloudbeds','NAME':'Rob','YEAR':2020,'MONTH': 4,'DAYS': 20,'SALARY':690},
{'JOB':'Worker','NAME':'Jose','YEAR':2010,'MONTH': 3,'DAYS': 20,'SALARY':90},
{'JOB':'Cloudbeds','NAME':'Jose','YEAR':2030,'MONTH': 2,'DAYS': 20,'SALARY':60}
])
pivot = df.pivot_table(
index=['JOB','NAME'],
columns=['YEAR','MONTH'],
values=['DAYS','SALARY'],
aggfunc={
'DAYS': ['mean'],
'SALARY': [np.mean, np.sum, np.max, np.min],
},
dropna=False,
margins=True
)
```
#### Problem description
When executing the `pivot_table` function with `aggfunc` argument as a **dict** with multiple metrics as on a single value, e.g.: `'SALARY': [np.mean, np.sum, np.max, np.min]` and `margins=True` to get the totals the following error is raised:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-68d98b312428> in <module>
8 },
9 dropna=False,
---> 10 margins=True
11 )
/srv/conda/envs/notebook/lib/python3.7/site-packages/pandas/core/frame.py in pivot_table(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)
5757 aggfunc=aggfunc, fill_value=fill_value,
5758 margins=margins, dropna=dropna,
-> 5759 margins_name=margins_name)
5760
5761 def stack(self, level=-1, dropna=True):
/srv/conda/envs/notebook/lib/python3.7/site-packages/pandas/core/reshape/pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)
133 cols=columns, aggfunc=aggfunc,
134 observed=dropna,
--> 135 margins_name=margins_name, fill_value=fill_value)
136
137 # discard the top level
/srv/conda/envs/notebook/lib/python3.7/site-packages/pandas/core/reshape/pivot.py in _add_margins(table, data, values, rows, cols, aggfunc, observed, margins_name, fill_value)
165 if hasattr(table, 'columns'):
166 for level in table.columns.names[1:]:
--> 167 if margins_name in table.columns.get_level_values(level):
168 raise ValueError(msg)
169
/srv/conda/envs/notebook/lib/python3.7/site-packages/pandas/core/indexes/multi.py in get_level_values(self, level)
1410 Index(['d', 'e', 'f'], dtype='object', name='level_2')
1411 """
-> 1412 level = self._get_level_number(level)
1413 values = self._get_level_values(level)
1414 return values
/srv/conda/envs/notebook/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _get_level_number(self, level)
1116 if (count > 1) and not is_integer(level):
1117 raise ValueError('The name %s occurs multiple times, use a '
-> 1118 'level number' % level)
1119 try:
1120 level = self.names.index(level)
ValueError: The name None occurs multiple times, use a level number
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.112+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 20.1.1
setuptools: 49.2.0.post20200712
Cython: None
numpy: 1.19.2
scipy: 1.5.2
pyarrow: None
xarray: None
IPython: 7.16.1
sphinx: None
patsy: 0.5.1
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.18
pymysql: None
psycopg2: None
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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MDU6SXNzdWU3MjQ2NTY4ODI=
| 37,247
|
DOC: Replace pandas on Ray in ecosystem.rst with Modin
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| 0
| 2020-10-19T14:09:52Z
| 2020-10-20T00:35:16Z
| 2020-10-20T00:35:16Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/ecosystem.html#ray
#### Documentation problem
Pandas on Ray does not exist anymore.
#### Suggested fix for documentation
Update references to Modin.
I will do this.
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MDU6SXNzdWU3MjQ2NjEwNDE=
| 37,248
|
DOC: update development documentation for link to built docs
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| 2
| 2020-10-19T14:13:03Z
| 2020-10-24T07:27:52Z
| 2020-10-24T07:27:52Z
|
CONTRIBUTOR
| null |
this has the built docs: https://pandas.pydata.org/pandas-docs/dev/ rather than https://dev.pandas.io/, so need to update the contributing docs.
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MDExOlB1bGxSZXF1ZXN0NTA2MDE1NDE2
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DOC: Replace pandas on Ray in ecosystem.rst with Modin
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| 10
| 2020-10-19T14:16:46Z
| 2020-11-17T17:17:49Z
| 2020-10-20T00:35:17Z
|
CONTRIBUTOR
| null |
* Resolves #37247
Signed-off-by: Devin Petersohn <devin.petersohn@gmail.com>
- [x] closes #37247
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU3MjQ2NzE5NzE=
| 37,250
|
BUG: Rolling by day
|
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[] | null | 0
| 2020-10-19T14:23:38Z
| 2020-10-19T15:09:33Z
| 2020-10-19T15:09:33Z
|
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
dsf['sigma'] = dsf.groupby('PERMNO').rolling('365D', on='DATE')['RET'].std()
```
#### Problem description
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\frame.py in reindexer(value)
3726 try:
-> 3727 value = value.reindex(self.index)._values
3728 except ValueError as err:
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\series.py in reindex(self, index, **kwargs)
4398 def reindex(self, index=None, **kwargs):
-> 4399 return super().reindex(index=index, **kwargs)
4400
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\generic.py in reindex(self, *args, **kwargs)
4461 return self._reindex_axes(
-> 4462 axes, level, limit, tolerance, method, fill_value, copy
4463 ).__finalize__(self, method="reindex")
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4476 new_index, indexer = ax.reindex(
-> 4477 labels, level=level, limit=limit, tolerance=tolerance, method=method
4478 )
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\indexes\multi.py in reindex(self, target, method, level, limit, tolerance)
2324 # hopefully?
-> 2325 target = MultiIndex.from_tuples(target)
2326
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\indexes\multi.py in from_tuples(cls, tuples, sortorder, names)
500
--> 501 arrays = list(lib.tuples_to_object_array(tuples).T)
502 elif isinstance(tuples, list):
pandas\_libs\lib.pyx in pandas._libs.lib.tuples_to_object_array()
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long long'
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
<ipython-input-12-60461c76cedf> in <module>()
----> 1 dsf['sigma'] = dsf.groupby('PERMNO').rolling('365D', on='DATE')['RET'].std()
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
3038 else:
3039 # set column
-> 3040 self._set_item(key, value)
3041
3042 def _setitem_slice(self, key: slice, value):
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\frame.py in _set_item(self, key, value)
3114 """
3115 self._ensure_valid_index(value)
-> 3116 value = self._sanitize_column(key, value)
3117 NDFrame._set_item(self, key, value)
3118
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\frame.py in _sanitize_column(self, key, value, broadcast)
3739
3740 if isinstance(value, Series):
-> 3741 value = reindexer(value)
3742
3743 elif isinstance(value, DataFrame):
C:\Users\paris\.conda\envs\ml\lib\site-packages\pandas\core\frame.py in reindexer(value)
3735 raise TypeError(
3736 "incompatible index of inserted column with frame index"
-> 3737 ) from err
3738 return value
3739
TypeError: incompatible index of inserted column with frame index
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
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 : 5.8.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.0
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 : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTA2MDI5MzAx
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|
DOC: update PeriodArray docstring
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| 3
| 2020-10-19T14:27:37Z
| 2020-10-21T18:30:32Z
| 2020-10-21T18:28:26Z
|
MEMBER
| null |
- [x] closes #37237
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
DOC Fix some typos in the docs
|
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| 2020-10-19T15:31:03Z
| 2020-10-19T18:36:39Z
| 2020-10-19T18:36:39Z
|
MEMBER
| null |
Here's a few typos I found via `codespell`, first-timers are welcome to open a pull request to fix them
```
doc/source/ecosystem.rst:233: copyed ==> copied
doc/source/whatsnew/v0.24.1.rst:3: Whats ==> What's
doc/source/whatsnew/v0.16.2.rst:150: arithmetics ==> arithmetic
doc/source/whatsnew/v0.24.2.rst:3: Whats ==> What's
doc/source/whatsnew/v1.2.0.rst:183: Expecially ==> Especially
```
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MDExOlB1bGxSZXF1ZXN0NTA2MDkwNDUx
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|
fixed spelling errors in whatsnew, userguide, and ecosystem
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| 1
| 2020-10-19T15:54:27Z
| 2020-10-19T18:07:35Z
| 2020-10-19T18:07:27Z
|
CONTRIBUTOR
| null |
Hi there,
I fixed the spelling errors mentioned here. https://github.com/pandas-dev/pandas/issues/37252
The 2 "wit" spelling errors were confusing, and I could not understand the sentences.
This is my first pull request, so please excuse my ignorance if I did something incorrectly.
Thanks.
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[Doc] fix spelling errors
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| 2020-10-19T18:06:50Z
| 2020-10-19T18:06:50Z
|
CONTRIBUTOR
| null |
I fixed the spelling errors. #37252
|
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MDExOlB1bGxSZXF1ZXN0NTA2MTIzMDc5
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TST: use equivalent fixtures in test_multilevel
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BUG: with integer column labels, .info() throws KeyError
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|
MEMBER
| null |
- [ ] closes #37245
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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|
BUG: to_parquet corrupting temporary files
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| 2021-06-06T20:57:30Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example
I get an OSError from the following code:
```python
from tempfile import NamedTemporaryFile
import pandas as pd
with NamedTemporaryFile(suffix='.gz') as file:
df = pd.DataFrame({'A': [1, 2, 3]})
df.to_parquet(file)
df['A'] = list('abc')
df.to_parquet(file)
df = pd.read_parquet(file)
```
```python-traceback
File "C:\Users\deschman\AppData\Local\Continuum\anaconda3\Lib\site-packages\mymodules\reporting\untitled0.py", line 11, in <module>
df = pd.read_parquet(file)
File "C:\Users\deschman\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\parquet.py", line 317, in read_parquet
return impl.read(path, columns=columns, **kwargs)
File "C:\Users\deschman\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\parquet.py", line 142, in read
path, columns=columns, filesystem=fs, **kwargs
File "C:\Users\deschman\AppData\Local\Continuum\anaconda3\lib\site-packages\pyarrow\parquet.py", line 1595, in read_table
use_pandas_metadata=use_pandas_metadata)
File "C:\Users\deschman\AppData\Local\Continuum\anaconda3\lib\site-packages\pyarrow\parquet.py", line 1475, in read
use_threads=use_threads
File "pyarrow\_dataset.pyx", line 399, in pyarrow._dataset.Dataset.to_table
File "pyarrow\_dataset.pyx", line 1994, in pyarrow._dataset.Scanner.to_table
File "pyarrow\error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow\error.pxi", line 99, in pyarrow.lib.check_status
OSError: Unexpected end of stream
```
#### Problem description
The error message provided by pyarrow does not indicate where the issue is in the code. I did not test with fastparquet engine as they do not currently seem to support the _TemporaryFileWrapper. If this should go to pyarrow instead, please let me know.
#### Expected Output
If a temporary file should not be wrote over by to_parquet multiple times, I would expect to_parquet to throw an error before corrupting the file.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.1.0
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fsspec : 0.7.1
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDU6SXNzdWU3MjQ4MjAyNTk=
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REF: NumericIndex.union determine casting dtype using concat logic
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[] | null | 8
| 2020-10-19T17:29:06Z
| 2021-09-03T15:56:07Z
| null |
MEMBER
| null |
ATM NumericIndex._union has special casting rules for what dtype it casts to when unioning with another NumericIndex of a different dtype.
```
# Right now, we treat union(int, float) a bit special.
# See https://github.com/pandas-dev/pandas/issues/26778 for discussion
# We may change union(int, float) to go to object.
# float | [u]int -> float (the special case)
# <T> | <T> -> T
# <T> | <U> -> object
```
Instead, we should re-use code (not sure where this lives, cc @jorisvandenbossche ?) from concat (maybe even Block.putmask?) to determing the appropriate upcast dtype.
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TST: split up test_concat.py #37243
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[] | null | 7
| 2020-10-19T18:00:26Z
| 2020-10-28T19:36:48Z
| 2020-10-28T19:36:48Z
|
CONTRIBUTOR
| null |
- [ ] closes #37243
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: astype(float) raises TypeError for StringDtype series (float() does not accept NAType)
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[] | null | 5
| 2020-10-19T18:11:22Z
| 2021-08-13T07:23:06Z
| 2021-08-13T07:23:06Z
|
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
pd.Series(['1.0', None, '2.0'], dtype='string').astype(float)
```
#### Problem description
Running this code results in `TypeError: float() argument must be a string or a number, not 'NAType'`.
The same code works for old-style str dtype. Running
```python
import pandas as pd
pd.Series(['1.0', None, '2.0'], dtype='str').astype(float)
```
outputs
```
0 1.0
1 NaN
2 2.0
dtype: float64
```
This is a common way to convert strings to floats, and switching from the old-style 'str' to the new-style 'string' dtype will break code using it.
#### Expected Output
```
0 1.0
1 NaN
2 2.0
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.0
</details>
#### Output of ``pd.show_versions()`` (master)
<details>
INSTALLED VERSIONS
------------------
commit : c15ded3ed653447415d34b72b62127fa612fd741
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+829.gc15ded3ed
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.0
</details
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ENH: match warning messages
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| 2020-10-19T18:14:58Z
| 2020-10-20T16:51:01Z
| 2020-10-20T16:51:01Z
|
MEMBER
| null |
#### Is your feature request related to a problem?
I wish we could match warning messages using ``pandas._testing.assert_produces_warning``.
Currently it catches the warning category, but does not match the message.
Recently (https://github.com/pandas-dev/pandas/pull/36982) I stumbled upon the need for this feature.
#### Describe the solution you'd like
``pandas._testing.assert_produces_warning`` should take optional keyword argument ``match``, to make it look similar to ``pytest.warns``.
#### API breaking implications
Should not break existing API, as ``match`` would be an optional argument.
#### Describe alternatives you've considered
I used custom way of catching warnings (via ``warnings.catch_warnings``) and matching their messages, but was suggested to give preference to ``assert_produces_warning``.
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BUG: to_numeric() raises TypeError for StringDtype series
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| 4
| 2020-10-19T18:20:34Z
| 2020-10-21T07:11:18Z
| 2020-10-20T23:10:11Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.to_numeric(pd.Series(['1', None, '2'], dtype='string'))
```
#### Problem description
Running this code results in `TypeError: Invalid object type at position 1`
The same code works for old-style str dtype. Running
```python
import pandas as pd
pd.to_numeric(pd.Series(['1', None, '2'], dtype='str'))
```
outputs
```
0 1.0
1 NaN
2 2.0
dtype: float64
```
This is a common way to convert strings to floats, and switching from the old-style 'str' to the new-style 'string' dtype will break code using it.
#### Expected Output
```
0 1.0
1 NaN
2 2.0
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.0
</details>
#### Output of ``pd.show_versions()`` (master)
<details>
INSTALLED VERSIONS
------------------
commit : c15ded3ed653447415d34b72b62127fa612fd741
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+829.gc15ded3ed
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.0
</details>
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ENH: implement matching warning message
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| 1
| 2020-10-19T18:54:36Z
| 2020-11-06T15:38:58Z
| 2020-10-20T16:51:01Z
|
MEMBER
| null |
- [ ] closes #37261
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Implement matching warning message.
New kwarg ``match`` in ``assert_produces_warning``.
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BUG: another ValueError: buffer source array is read-only
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| 8
| 2020-10-19T20:24:12Z
| 2020-11-04T13:43:47Z
| 2020-11-04T13:43:47Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
This seems related to: https://github.com/pandas-dev/pandas/issues/37174
however it shows up under different conditions - appears to be triggered by comparison involving NaN.
#### Code Sample, a copy-pastable example
```python
print(temp)
temp[temp>180]=temp[temp>180]-360
```
Output:
```
2016-12-27 02:02:09.000 182.220
2016-12-27 02:02:09.017 182.226
2016-12-27 02:02:09.033 182.227
2016-12-27 02:02:09.050 182.222
2016-12-27 02:02:09.066 NaN
dtype: float64
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in __setitem__(self, key, value)
999 try:
-> 1000 self._set_with_engine(key, value)
1001 except (KeyError, ValueError):
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)
1032 # fails with AttributeError for IntervalIndex
-> 1033 loc = self.index._engine.get_loc(key)
1034 validate_numeric_casting(self.dtype, value)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
TypeError: '2016-12-27 02:02:09.000 True
2016-12-27 02:02:09.017 True
2016-12-27 02:02:09.033 True
2016-12-27 02:02:09.050 True
2016-12-27 02:02:09.066 False
dtype: bool' is an invalid key
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-33-85e39ae5f3f1> in <module>
3 temp=temp.head(5)
4 print(temp.head())
----> 5 temp[temp>180]=temp[temp>180]-360
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in __setitem__(self, key, value)
1018 key = np.asarray(key, dtype=bool)
1019 try:
-> 1020 self._where(~key, value, inplace=True)
1021 except InvalidIndexError:
1022 self.iloc[key] = value
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/generic.py in _where(self, cond, other, inplace, axis, level, errors, try_cast)
8778 if other.ndim <= self.ndim:
8779
-> 8780 _, other = self.align(
8781 other, join="left", axis=axis, level=level, fill_value=np.nan
8782 )
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
4272 broadcast_axis=None,
4273 ):
-> 4274 return super().align(
4275 other,
4276 join=join,
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/generic.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
8557 )
8558 elif isinstance(other, ABCSeries):
-> 8559 return self._align_series(
8560 other,
8561 join=join,
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/generic.py in _align_series(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
8660 join_index, lidx, ridx = None, None, None
8661 else:
-> 8662 join_index, lidx, ridx = self.index.join(
8663 other.index, how=join, level=level, return_indexers=True
8664 )
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py in join(self, other, how, level, return_indexers, sort)
884
885 this, other = self._maybe_utc_convert(other)
--> 886 return Index.join(
887 this,
888 other,
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/base.py in join(self, other, how, level, return_indexers, sort)
3500 elif self.is_monotonic and other.is_monotonic:
3501 try:
-> 3502 return self._join_monotonic(
3503 other, how=how, return_indexers=return_indexers
3504 )
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/base.py in _join_monotonic(self, other, how, return_indexers)
3800 join_index = self
3801 lidx = None
-> 3802 ridx = self._left_indexer_unique(sv, ov)
3803 elif how == "right":
3804 join_index = other
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py in wrapper(left, right)
62 right = right.view("i8")
63
---> 64 results = joinf(left, right)
65 if with_indexers:
66 # dtype should be timedelta64[ns] for TimedeltaIndex
pandas/_libs/join.pyx in pandas._libs.join.left_join_indexer_unique()
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/_libs/join.cpython-38-x86_64-linux-gnu.so in View.MemoryView.memoryview_cwrapper()
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/_libs/join.cpython-38-x86_64-linux-gnu.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
```
#### Problem description
See error above
#### Expected Output
I would have expected:
- the 'NA' value to be ignored in comparison without causing an error
- If an error is due to NA, I expect to see appropriate message, instead of 'read-only' being thrown
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit : db08276
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
lxml.etree : 4.5.2
jinja2 : 2.11.2
IPython : 7.18.1
fsspec : 0.8.4
fastparquet : 0.4.1
matplotlib : 3.3.2
pyarrow : 0.17.1
scipy : 1.5.2
xarray : 0.16.1
numba : 0.51.2
</details>
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|
BUG: PeriodIndex.dtype comparison with string exhibits contradiction
|
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| 1
| 2020-10-19T20:51:13Z
| 2020-10-20T16:50:01Z
| 2020-10-20T16:50:01Z
|
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.
---
#### Code Sample
```python
index = pd.period_range(start='01Jan2019', end='01Dec2019', freq='M')
string = "period[M]"
index.dtype == string # True
index.dtype != string # also True
```
#### Problem description
`PeriodIndex.dtype` is both `__eq__` and `__ne__` to the string representation of its frequency. I suspect that comparing the dtype to a string is not recommended, but the contradiction seemed worth reporting. I don't know when this was introduced, but it did not occur in 0.25.3.
#### Expected Output
One of the two comparisons to evaluate to `False`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : a34a408e854b6300dbbdd0b29026d19bc5477d73
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.198-152.320.amzn2.x86_64
Version : #1 SMP Wed Sep 23 23:57:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+830.ga34a408e8
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.3
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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REF: nargsort incorrectly calling _values_for_argsort
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| 2
| 2020-10-19T22:01:31Z
| 2020-10-20T00:38:13Z
| 2020-10-20T00:25:52Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @jorisvandenbossche
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BUG: offsets are now unhashable
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| 0
| 2020-10-19T22:39:36Z
| 2020-10-22T00:08:33Z
| 2020-10-22T00:08:33Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
As discussed on gitter, this was apparently an unindended consequence of https://github.com/pandas-dev/pandas/pull/34227
https://gitter.im/pydata/pandas?at=5f8d8a106c8d484be294872f
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> hash(pd.offsets.Day())
Traceback (most recent call last):
File "C:\Users\dhirschf\envs\dev\lib\site-packages\IPython\core\interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-46-d78c0d3ee4ad>", line 1, in <module>
hash(pd.offsets.Day())
TypeError: unhashable type: 'pandas._libs.tslibs.offsets.Day'
```
#### Problem description
My code relied on the hashability of offsets and so this broke my code and has so far prevented me upgrading to the latest `pandas`
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 0, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.3
sphinx : 3.2.1
blosc : 1.9.2
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.0
html5lib : 1.1
pymysql : 0.10.1
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
```
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BUG: Don't raise TypeError when converting NA from string to numeric
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DOC: troubleshoot docbuild
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REGR: Make comparisons consistent for PeriodDtype
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REF: collect tests by method
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DOC: list all options for encoding and quoting in read_csv
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NONE
| null |
#### Location of the documentation
[pandas.to_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html)
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
"encoding" and "quoting" described briefly.
#### Suggested fix for documentation
Should provide all parameters which support these options.
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BUG: to_offset has inconsistent behaviour for "W"
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[] | null | 2
| 2020-10-20T10:11:33Z
| 2021-08-13T05:09:01Z
| 2021-08-13T05:09:01Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
It might be related to https://github.com/pandas-dev/pandas/issues/34101 - I'm not sure, though
- [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.tseries.frequencies import to_offset
offsets = [
pd.offsets.YearBegin(),
pd.offsets.YearEnd(),
pd.offsets.YearEnd(month=5),
pd.offsets.BYearBegin(month=3),
pd.offsets.MonthBegin(),
pd.offsets.MonthEnd(),
pd.offsets.CustomBusinessMonthBegin(),
pd.offsets.SemiMonthBegin(),
pd.offsets.Week(), # this is the one with the inconsistency
pd.offsets.Week(weekday=4),
pd.offsets.Hour(),
pd.offsets.Hour(2),
pd.offsets.Minute(),
pd.offsets.Minute(15),
pd.offsets.Hour(2) + pd.offsets.Minute(15),
]
for offset in offsets:
freqstr = offset.freqstr
offset_from_freqstr = to_offset(freqstr)
if offset != offset_from_freqstr:
print(f"Round trip not working for offset={offset}, offset.freqstr: {freqstr}, to_offset(offset.freqstr): {offset_from_freqstr}")
```
This yields:
```
Round trip not working for offset=<Week: weekday=None>, offset.freqstr: W, to_offset(offset.freqstr): <Week: weekday=6>
```
#### Problem description
I'd expect `to_offset("W")` to produce `pd.offsets.Week()`. However it yields `pd.offsets.Week(weekday=6)`. This is inconsistent with the behaviour of basically all other offsets, where passing the `freqstr` property of an offset to `to_offset` yields the offset itsself. Also for the ones with an anchor (see code example above)
For a context: I'm trying to store a time series to a database and only want to store the values, the first time and the freqeuncy. While doing that I'd like to use the `freqstr` property to serialize the frequency, a consistent round trip would be a requirement for that, though.
#### Expected Output
No output from code above
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 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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TST: fix warnings on multiple subplots
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| 15
| 2020-10-20T12:16:02Z
| 2020-11-10T04:45:40Z
| 2020-11-09T18:55:08Z
|
MEMBER
| null |
- [ ] xref #37178 partially
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR suggests a (partial) fix for such warnings, which take place when running test suite on ``pandas/tests/plotting``.
```
UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
```
The problem is the following.
The function ``_check_plot_works`` creates subplot(211) if axes are not provided.
This is fine, when the plot to be created is a single-axes plot.
However, if the plot itself is to be plotted on multiple axes (for instance, df.hist would plot each column on a dedicated subplot, by default), then the warnings mentioned are emitted.
Two ways to handle it:
- Find cases where plot is a multiaxes-plot and catch warnings there. This is fine, but in some parametrized tests the plots are either single-axes or multiple-axes. It makes it difficult to figure out without splitting the tests.
- Change function ``_check_plot_works``: do not create subplot(211).
I created ``_check_single_plot_works`` which does not create subplots and used it in the problematic cases.
I guess, that the name is not the best (I would prefer ``_check_plot_works``), but I encourage those concerned to have a discussion here.
Please see comments in ``_check_plot_works`` (I will remove them once we come to the conclusion on the topic).
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BUG: Groupby aggregate functions (e.g. min/max) fail to preserve categorical dtype on single categorical column
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| 4
| 2020-10-20T12:53:40Z
| 2021-10-24T22:09:13Z
| 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.
---
#### Set-up Code
```python
import pandas as pd
dtype = pd.CategoricalDtype(categories=['small', 'big'], ordered=True)
df = pd.DataFrame([
[1, 'small'],
[1, 'big'],
[2, 'small']
], columns=['grp', 'description']).astype({'description': dtype})
biggest = df.groupby('grp')['description'].max()
```
#### Outputs
Here, I'm expecting a categorical dtype - the same as defined in my first line. Instead, I get an object dtype.
```python
>>> biggest.dtype
dtype('O')
```
... and that causes problems down the line, because now, the overall max of "biggest" turns out to be "small".
```python
>>> biggest.max()
'small'
```
#### Problem description
After performing a groupby (using a column or columns of any type as the "by"), I'd expect taking the min or max (or first or last, and probably other aggregations that make sense) of a column of categorical dtype to return a series of that same dtype. Instead, the dtype appears to be converted to `object` during the aggregation.
For what it's worth, the expected behavior seems to happen if I do this as a _frame_ operation as opposed to a _series_ operation. That is, if I replace this line from above
```python
biggest = df.groupby('grp')['description'].max()
```
with
```python
df_biggest = df.groupby('grp')[['description']].max()
```
the dtype _does_ seem to be preserved.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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QST: Does SciPy dependency license still apply / if so, for what version?
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[] | null | 2
| 2020-10-20T12:54:52Z
| 2021-03-17T19:13:26Z
| 2021-03-17T19:13:26Z
|
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
I noticed that a SciPy license was added to the licenses folder https://github.com/pandas-dev/pandas/blob/master/LICENSES/SCIPY_LICENSE in a commit from 2012 (doesn't mention a specific version). SciPy doesn't currently seem to get installed as a dependency via pip install. Does that dependency still apply? Asking for the purpose of attributing licenses and tracking relevance of CVEs filed on some past versions of SciPy.
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BUG/API: index.array for RangeIndex, MultiIndex
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[] | null | 3
| 2020-10-20T15:58:00Z
| 2021-08-13T05:11:32Z
| null |
MEMBER
| null |
ATM MultiIndex.array raises ValueError, since there is no single array actually backing MI. RangeIndex creates and returns a PandasArray. I think RangeIndex should also raise (at least until/unless we implement a RangeArray).
This also has implications for extract_array, which currently raises on MultiIndex, which has led to some ugly checks in e.g. Index._cmp_method.
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BUG: Undocumented, Inconsistent matplotlib figure usage (Series.hist vs Series.plot.hist)
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| 2020-10-20T16:23:31Z
| 2020-10-28T15:44:00Z
| 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
from pandas import DataFrame
import matplotlib.pyplot as plt
d = DataFrame({'y1':[0,1,2,3], 'y2':[3,4,5,6]}, index=[0,1,2,3])
# A new matplotlib figure is created
print(plt.gcf().number)
d.hist()
print(plt.gcf().number) # new figure is created, current matplotlib figure is the new one
# Can NOT assign a figure to a dataframe plot
figure = plt.figure()
print(figure.number)
print(plt.gcf().number)
d.hist(figure=figure) # Throws an exception
print(plt.gcf().number) # new figure is still created! current matplotlib figure is the new one
# Can assign a figure to a Series histogram
figure1 = plt.figure()
figure2 = plt.figure() # Current figure is always the last one created
print(figure1.number)
print(figure2.number)
print(plt.gcf().number)
d['y1'].hist(figure=figure1)
print(plt.gcf().number) # Current figure stays the same
figure1.show() # This works
# Can NOT assign a figure to a Series histogram when using the .plot CachedAccessor
# However, it does not create a new figure
figure1 = plt.figure()
figure2 = plt.figure() # Current figure is always the last one created
print(figure1.number)
print(figure2.number)
print(plt.gcf().number)
d['y1'].plot.hist(figure=figure1) # Throws an exception!
print(plt.gcf().number) # Current figure stays the same
```
#### Problem description
It is currently impossible to assign a plot of a DataFrame to an existing figure. The functionality is just broken.
The API to set a Series to an existing figure is almost completely broken (with the exception of the direct hist function).
There is a workaround in setting the global current figure `plt.figure(old_figure.number)`, but that does not work if `plt.Figure()` is used.
#### Expected Output
All three examples accept the `figure=` argument, and use it.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-51-generic
Version : #56-Ubuntu SMP Mon Oct 5 14:28:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
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 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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ENH: Add is_sparse method to check for sparse columns in a DataFrame (GH26706)
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|
CONTRIBUTOR
| null |
- [x] closes #26706
- [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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BUG: regression: pandas.read_stata(filename, iterator=True) raises ValueError
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| 5
| 2020-10-20T18:00:15Z
| 2020-10-23T12:15:12Z
| 2020-10-23T12:15:12Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas
>>> # for example https://gitlab.com/ViDA-NYU/datamart/datamart/-/blob/master/tests/data/stata118.dta
>>> iterator = pandas.read_stata(stata_file_name, iterator=True)
>>> list(iterator)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "site-packages/pandas/io/stata.py", line 1523, in __next__
raise ValueError(
ValueError: chunksize must be set to a positive integer to use as an iterator.
```
#### Problem description
`read_stata(filename, iterator=True)` no longer works in pandas 1.1.3. **It worked in pandas 1.0.5.**
#### Expected Output
DataFrame is loaded correctly
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-51-generic
Version : #56-Ubuntu SMP Mon Oct 5 14:28:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : 0.7.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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| 1
| 2020-10-20T19:17:26Z
| 2020-10-20T23:02:19Z
| 2020-10-20T23:02:15Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NTA3MDU0OTMy
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Backport PR #37256 on branch 1.1.x (BUG: with integer column labels, .info() throws KeyError )
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| 3
| 2020-10-20T19:48:53Z
| 2020-10-21T10:51:54Z
| 2020-10-21T10:51:54Z
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NONE
| null |
Backport PR #37256: BUG: with integer column labels, .info() throws KeyError
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BUG: Pyarrow 2.0.0 broke test_timezone_aware_index 6/7 tests
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| 7
| 2020-10-20T20:46:03Z
| 2020-11-28T17:54:10Z
| null |
MEMBER
| null |
Pyarrow 2.0.0 was released yesterday and broke the test `` test_timezone_aware_index`` in 6 of 7 cases. Can be seen through our CI. Location of test is ``pandas/tests/io/test_parquet.py``
Link to ci:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=45611&view=logs&j=b1c7b65e-b3ce-541a-7fd5-29b4ba56ce18&t=46ecc253-e38f-5abc-2ea7-addca6b44d0a&l=22
cc @jreback
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BUG:TypeError: Cannot interpret 'CategoricalDtype(categories=['no', 'yes'], ordered=False)' as a data type
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| 1
| 2020-10-20T21:03:42Z
| 2020-10-21T10:25:06Z
| 2020-10-21T10:25:06Z
|
NONE
| null |
I just upgraded all my python libraries, and now my previous code is started to fail.
I'm using blaze with pandas. Here is my method code
```
blaze.data(res)
```
res contains below data
```
col1 age ... col31 year
0 yes 55-64 ... NaN 2011
1 no 25-34 ... NaN 2011
2 no 55-64 ... NaN 2011
```
I'm using below dependencies
```
- python=3.6.11=h4d41432_2_cpython
- blaze=0.11.3=py36h4e06776_0
- pandas=1.0.5=py36h0573a6f_0
- numpy=1.19.1=py36h3849536_2
```
I'm getting the following error
```
File "cytoolz/functoolz.pyx", line 667, in cytoolz.functoolz.pipe
File "cytoolz/functoolz.pyx", line 642, in cytoolz.functoolz.c_pipe
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/blaze/interactive.py", line 153, in data
dshape = discover(data_source)
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/multipledispatch/dispatcher.py", line 278, in __call__
return func(*args, **kwargs)
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/odo/backends/pandas.py", line 31, in discover_dataframe
for k in df.columns])
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/odo/backends/pandas.py", line 31, in <listcomp>
for k in df.columns])
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/odo/backends/pandas.py", line 23, in dshape_from_pandas
dshape = datashape.CType.from_numpy_dtype(col.dtype)
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/datashape/coretypes.py", line 779, in from_numpy_dtype
if np.issubdtype(dt, np.datetime64):
File "/home/ubuntu/miniconda/envs/my_env/lib/python3.6/site-packages/numpy/core/numerictypes.py", line 388, in issubdtype
arg1 = dtype(arg1).type
TypeError: Cannot interpret 'CategoricalDtype(categories=['no', 'yes'], ordered=False)' as a data type
```
It's failing to parse second column values.
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MDExOlB1bGxSZXF1ZXN0NTA3MTA1NzY5
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Regression in offsets caused offsets to be no longer hashable
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| 2020-10-22T21:42:02Z
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MEMBER
| null |
- [x] closes #37267
- [x] tests added / passed Not quite sure, if these test is sufficient, if yes, I would tests for other functions.
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTA3MTE1Mzkx
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CLN: make PeriodArray signature match DTA/TDA
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: rank raises error with read-only data
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| 2
| 2020-10-20T21:51:36Z
| 2020-10-27T22:09:06Z
| 2020-10-27T22:09:06Z
|
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
import numpy as np
arr = np.arange(10)
arr.setflags(write=False)
pd.Series(arr).rank()
```
Output:
```
ValueError Traceback (most recent call last)
<ipython-input-5-afa6b4ecf509> in <module>
3 arr = np.arange(10)
4 arr.setflags(write=False)
----> 5 pd.Series(arr).rank()
~/anaconda/envs/xfactor/lib/python3.8/site-packages/pandas/core/generic.py in rank(self, axis, method, numeric_only, na_option, ascending, pct)
8334 if numeric_only is None:
8335 try:
-> 8336 return ranker(self)
8337 except TypeError:
8338 numeric_only = True
~/anaconda/envs/xfactor/lib/python3.8/site-packages/pandas/core/generic.py in ranker(data)
8319
8320 def ranker(data):
-> 8321 ranks = algos.rank(
8322 data.values,
8323 axis=axis,
~/anaconda/envs/xfactor/lib/python3.8/site-packages/pandas/core/algorithms.py in rank(values, axis, method, na_option, ascending, pct)
934 if values.ndim == 1:
935 values = _get_values_for_rank(values)
--> 936 ranks = algos.rank_1d(
937 values,
938 ties_method=method,
pandas/_libs/algos.pyx in pandas._libs.algos.rank_1d()
~/anaconda/envs/xfactor/lib/python3.8/site-packages/pandas/_libs/algos.cpython-38-darwin.so in View.MemoryView.memoryview_cwrapper()
~/anaconda/envs/xfactor/lib/python3.8/site-packages/pandas/_libs/algos.cpython-38-darwin.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
```
#### Problem description
`rank` should work with read-only data.
I noticed the problem when using `check_estimator` from `sklearn.utils.estimator_checks` on an estimator that uses pandas `rank`. I haven't explored fully but I assume `check_estimator` uses read-only data for running its tests, which causes this error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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DOC: fix a small typo
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| 1
| 2020-10-20T22:26:42Z
| 2020-10-20T22:42:33Z
| 2020-10-20T22:42:30Z
|
CONTRIBUTOR
| null |
an alternative fix would be "pandas provides a wide range of functions for cleaning textual data and extracting useful information from it."
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: Problem using to_csv with BytesIO
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| 7
| 2020-10-20T22:35:26Z
| 2020-10-21T15:52:12Z
| 2020-10-20T23:48:50Z
|
NONE
| null |
Dear people of Pandas,
first things first: Thanks for all of your excellent work conceiving and maintaining Pandas. You know who you are.
With kind regards,
Andreas.
---
- [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 io import BytesIO
df = pd.DataFrame()
buffer = BytesIO()
df.to_csv(buffer)
```
#### Problem description
The snippet above croaks with
```
TypeError: a bytes-like object is required, not 'str'
```
We expected this to work. However, we will be happy to learn otherwise. We also had a look at #22555 and #35129 which seem to be related but not exactly on the spot.
The background on this is that we are currently in the process of upgrading [Kotori](https://github.com/daq-tools/kotori) to Python 3 (yeah, we are late to the game). However, coming from this, we can confirm it worked when using Pandas 0.18.1 on Python 2 the other day.
The relevant code is
- https://github.com/daq-tools/kotori/blob/0.24.5/kotori/io/protocol/http.py#L8
- https://github.com/daq-tools/kotori/blob/0.24.5/kotori/io/protocol/http.py#L588-L602
Thanks already for looking into this!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Thu Jun 18 21:21:34 PDT 2020; root:xnu-4570.71.82.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2018.9
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : 4.6.9
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.8
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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REF: de-duplicate DTA/TDA validators by standardizing exception messages
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| 0
| 2020-10-20T23:21:40Z
| 2020-10-22T01:19:21Z
| 2020-10-22T00:07:52Z
|
MEMBER
| null |
After this, _validate_setitem_value is the same as _validate_where_value, and we are close to having _validate_insert_value and _validate_searchsorted_value math too.
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ENH: Cast string dtype to nullable numeric types in to_numeric
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| null |
MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/37268 fixed a bug so that nullable string dtype can now be cast to numeric from "number-like" strings and NA, however the output dtype is currently float64. Better would be for this to cast to either nullable Int64 or Float64 depending on the values. Below is master:
```python
[ins] In [2]: import pandas as pd
...:
...: s = pd.Series(["1", "2", None], dtype="string")
...: print(pd.to_numeric(s))
...:
...: s = pd.Series(["1", "2.5", None], dtype="string")
...: print(pd.to_numeric(s))
...:
0 1.0
1 2.0
2 NaN
dtype: float64
0 1.0
1 2.5
2 NaN
dtype: float64
```
The first case would ideally cast to Int64, and the second to Float64.
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BUG: Regression adding Timedelta to DatetimeIndex, get ValueError: Inferred frequency None from passed values does not conform to passed frequency
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| 7
| 2020-10-21T00:01:32Z
| 2020-11-13T04:40:04Z
| 2020-11-13T04:40:04Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.date_range(start=pd.Timestamp('2019-03-26 00:00:00-0400', tz='Canada/Eastern'),
end=pd.Timestamp('2020-10-17 00:00:00-0400', tz='Canada/Eastern'),
freq='D') + pd.Timedelta('1D')
```
#### Traceback
<details>
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in _validate_frequency(cls, index, freq, **kwargs)
1156 if not np.array_equal(index.asi8, on_freq.asi8):
-> 1157 raise ValueError
1158 except ValueError as e:
ValueError:
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-2-d8f454fa3ee3> in <module>
----> 1 pd.date_range(start=pd.Timestamp('2019-03-26 00:00:00-0400', tz='Canada/Eastern'), end=pd.Timestamp('2020-10-17 00:00:00-0400', tz='Canada/Eastern'), freq='D') + pd.Timedelta('1D')
~/.miniconda/lib/python3.7/site-packages/pandas/core/indexes/extension.py in method(self, other)
145 return NotImplemented
146 meth = getattr(self._data, opname)
--> 147 result = meth(_maybe_unwrap_index(other))
148 return _wrap_arithmetic_op(self, other, result)
149
~/.miniconda/lib/python3.7/site-packages/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in __add__(self, other)
1399 result = self._add_nat()
1400 elif isinstance(other, (Tick, timedelta, np.timedelta64)):
-> 1401 result = self._add_timedeltalike_scalar(other)
1402 elif isinstance(other, BaseOffset):
1403 # specifically _not_ a Tick
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in _add_timedeltalike_scalar(self, other)
1254 new_freq = self.freq
1255
-> 1256 return type(self)(new_values, dtype=self.dtype, freq=new_freq)
1257
1258 def _add_timedelta_arraylike(self, other):
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in __init__(self, values, dtype, freq, copy)
282
283 if inferred_freq is None and freq is not None:
--> 284 type(self)._validate_frequency(self, freq)
285
286 @classmethod
~/.miniconda/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py in _validate_frequency(cls, index, freq, **kwargs)
1169 f"Inferred frequency {inferred} from passed values "
1170 f"does not conform to passed frequency {freq.freqstr}"
-> 1171 ) from e
1172
1173 # monotonicity/uniqueness properties are called via frequencies.infer_freq,
ValueError: Inferred frequency None from passed values does not conform to passed frequency D
```
</details>
#### Problem description
Unexpected ValueError when adding a Timedelta to a timezone-aware DatetimeIndex. It appears using timezone-aware Timestamp objects is necessary to reproduce - simple date strings won't.
#### Expected Output
The output from the same code sample in `v0.25.3` is:
```python
DatetimeIndex(['2019-03-27 00:00:00-04:00', '2019-03-28 00:00:00-04:00',
'2019-03-29 00:00:00-04:00', '2019-03-30 00:00:00-04:00',
'2019-03-31 00:00:00-04:00', '2019-04-01 00:00:00-04:00',
'2019-04-02 00:00:00-04:00', '2019-04-03 00:00:00-04:00',
'2019-04-04 00:00:00-04:00', '2019-04-05 00:00:00-04:00',
...
'2020-10-09 00:00:00-04:00', '2020-10-10 00:00:00-04:00',
'2020-10-11 00:00:00-04:00', '2020-10-12 00:00:00-04:00',
'2020-10-13 00:00:00-04:00', '2020-10-14 00:00:00-04:00',
'2020-10-15 00:00:00-04:00', '2020-10-16 00:00:00-04:00',
'2020-10-17 00:00:00-04:00', '2020-10-18 00:00:00-04:00'],
dtype='datetime64[ns, Canada/Eastern]', length=572, freq=None)
```
don't know about later versions, before `v1.1.3`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
Version : Darwin Kernel Version 16.7.0: Thu Dec 20 21:53:35 PST 2018; root:xnu-3789.73.31~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : 0.29.20
pytest : 6.1.1
hypothesis : None
sphinx : 1.8.5
blosc : 1.9.2
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.5.2
html5lib : 0.9999999
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : None
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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MDExOlB1bGxSZXF1ZXN0NTA3MTc3NTE5
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CI: tz dtype mismatch in test_parquet following pyarrow upgrade
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| 4
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: _almost_ always rebox_native following _unbox
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|
MEMBER
| null |
A couple more special cases to handle, after which we will always _rebox_native and it can be rolled into _unbox so we can get rid of the method altogether
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CI: Add unwanted pattern check
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| 7
| 2020-10-21T01:23:52Z
| 2020-10-22T08:19:42Z
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|
MEMBER
| null |
Follow up to https://github.com/pandas-dev/pandas/pull/37188
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NONE
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Currently pandas.Timedelta only support units upto weeks. Can it be extended to months, quarters, years etc.?
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| 10
| 2020-10-21T08:55:05Z
| 2020-10-21T12:07:28Z
| 2020-10-21T10:49:56Z
|
MEMBER
| null |
See https://github.com/pandas-dev/pandas/issues/37286, this PR is not the actual fix, but at least ensures we don't have failing CI in other PRs (I prefer to skip the test instead of pinning pyarrow to <= 1.0, since all other tests are passing on 2.0, which is good to still run)
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MDExOlB1bGxSZXF1ZXN0NTA3NDE2NjU4
| 37,304
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TST: correct parquet test expected partition column dtype for pyarrow 2.0
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| 5
| 2020-10-21T10:10:07Z
| 2020-10-21T12:25:05Z
| 2020-10-21T12:06:58Z
|
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/35814, but now incorporating the changes in pyarrow 2.0
cc @alimcmaster1
Also related to https://github.com/pandas-dev/pandas/issues/37286 and https://github.com/pandas-dev/pandas/pull/37296/ (timezone related failures), but doing this fix separate as it's not something that needs to be reverted.
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Backport PR #37198 on branch 1.1.x (BUG: Regression in Resample.apply raised error when apply affected only a Series)
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| 2020-10-26T18:21:27Z
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NONE
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Backport PR #37198: BUG: Regression in Resample.apply raised error when apply affected only a Series
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CI: pin some builds to use pyarrow==1.0
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| 2020-10-21T10:54:21Z
| 2020-11-14T02:22:23Z
| 2020-11-14T02:22:23Z
|
MEMBER
| null |
Now that pyarrow 2.0 is released, it will get picked by the CI builds that don't pin the pyarrow version. It would be good to pin some builds to use pyarrow 1.0.
See this overview table of @simonjayhawkins at https://github.com/pandas-dev/pandas/pull/35259#issuecomment-712966527
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Backport PR #37270 on branch 1.1.x: REGR: Make comparisons consistent for PeriodDtype
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Backport PR #37270 on branch 1.1.x
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Backport PR #37304 on branch 1.1.x (TST: correct parquet test expected partition column dtype for pyarrow 2.0)
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NONE
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Backport PR #37304: TST: correct parquet test expected partition column dtype for pyarrow 2.0
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CI: travis-36-cov builds regularly fails to build with The build has been terminated on 1.1.x
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| 3
| 2020-10-21T12:29:53Z
| 2020-11-26T14:00:37Z
| 2020-11-26T14:00:37Z
|
MEMBER
| null |
The last good build can be seen at https://travis-ci.org/github/pandas-dev/pandas/builds/736681105
There are some failures that have started to appear since this started timing out that also need to be resolved.
```
=========================== short test summary info ============================
FAILED pandas/tests/test_downstream.py::test_pandas_gbq - AttributeError: mod...
FAILED pandas/tests/io/test_sql.py::TestMySQLAlchemy::test_to_sql_with_negative_npinf[load_iris_data0-input0]
FAILED pandas/tests/io/test_sql.py::TestMySQLAlchemy::test_to_sql_with_negative_npinf[load_iris_data0-input1]
FAILED pandas/tests/io/test_sql.py::TestMySQLAlchemy::test_to_sql_with_negative_npinf[load_iris_data0-input2]
FAILED pandas/tests/io/test_sql.py::TestMySQLAlchemyConn::test_to_sql_with_negative_npinf[load_iris_data0-input0]
FAILED pandas/tests/io/test_sql.py::TestMySQLAlchemyConn::test_to_sql_with_negative_npinf[load_iris_data0-input1]
FAILED pandas/tests/io/test_sql.py::TestMySQLAlchemyConn::test_to_sql_with_negative_npinf[load_iris_data0-input2]
FAILED pandas/tests/util/test_show_versions.py::test_show_versions - Attribut...
ERROR pandas/tests/io/test_gbq.py - AttributeError: module 'google.protobuf.d...
= 8 failed, 73783 passed, 1033 skipped, 1053 xfailed, 6 xpassed, 77 warnings, 1 error in 1533.46s (0:25:33) =
```
xref #36779, https://github.com/pandas-dev/pandas/pull/37303#issuecomment-713513751, https://github.com/pandas-dev/pandas/pull/37304#issuecomment-713515608
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| 37,310
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BUG: stabilize sort_values algorithms for Series and time-like Indices
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| 12
| 2020-10-21T12:52:49Z
| 2020-12-23T17:48:20Z
| 2020-10-31T14:49:14Z
|
MEMBER
| null |
- [X] closes #35922
- [X] 18 tests changed / 18 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## Problem
Our `sort_values` functions currently behave differently for different objects: for most `Index` subclasses they are stable when sorting in descending order (this was introduced by #35604), but for DateTime-like `Index` subclasses and `Series` they are unstable. This isn't good as sorting should be stable across the board.
## Details
Came across this one while introducing missing-value support to `Index.sort_values` in #35604, so I had to limit that PR to non-DateTime-like `Index` subclasses. The problem was that we had different expectations for sorting stability baked into our test suite, so unifying sorting algorithms and missing-value support needed a bunch of careful test changes and altering both `sort_values` and algorithms in `sorting.py`.
Since this PR necessarily includes changes in several places, I have commented on all the changes made in the code and the unusual changes in the tests to make reviewing the code easier (see "On test changes" below).
## On test changes
Most changes I made in the tests are for cases where we were expecting an unstable sort or expected NaNs to be sorted to the beginning of a list of duplicates for ascending sort and to the end for descending (we forced this by inserting NaN-likes at 0 position and reversing when sorting in descending order in `Series.sort_values`).
## Default behavior changes
Since DateTime-like `Index` subclasses now support `na_position` using the same implementation as the other `Index` subclasses, they now sort missing values to the end of the `Index` by default.
## Performance
Ran the full benchmark suite, and there are no performance regressions.
## Out-of-scope
The only type I didn't touch so far is MultiIndex. It can't be sorted the same way through nargsort, and I don't think we should be doing it in this PR, if it all (stabilizing descending order MultiIndex.sort_values will definitely be a PITA, and it's a very narrow use case, in my opinion).
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CI: troubleshoot travis-36-cov on 1.1.x
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MEMBER
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closes #37309
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BUG: comparison/indexing causes ValueError: buffer source array is read-only
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| 2
| 2020-10-21T14:41:31Z
| 2020-11-04T13:43:47Z
| 2020-11-04T13:43:47Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
di=np.array(['2016-12-27 02:02:09', '2016-12-27 02:02:09.017000',
'2016-12-27 02:02:09.033000', '2016-12-27 02:02:09.050000',
'2016-12-27 02:02:09.066000'],dtype='datetime64[ns]')
di.setflags(write=False)
v1=[95.746, 95.735, 95.722, 95.709, 10]
v2=[-86.474, -86.491, -86.505, -86.513, 11]
temp=pd.DataFrame({'v1':v1,'v2':v2}, index=di)
temp=temp.astype('float64')
temp2=temp.v1-temp.v2
temp2[temp2>180]=temp2[temp2>180]-360
```
This code throws an error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in __setitem__(self, key, value)
999 try:
-> 1000 self._set_with_engine(key, value)
1001 except (KeyError, ValueError):
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)
1032 # fails with AttributeError for IntervalIndex
-> 1033 loc = self.index._engine.get_loc(key)
1034 validate_numeric_casting(self.dtype, value)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
TypeError: '2016-12-27 02:02:09.000 True
2016-12-27 02:02:09.017 True
2016-12-27 02:02:09.033 True
2016-12-27 02:02:09.050 True
2016-12-27 02:02:09.066 False
dtype: bool' is an invalid key
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-3-05bd4183267d> in <module>
8 temp=temp.astype('float64')
9 temp2=temp.v1-temp.v2
---> 10 temp2[temp2>180]=temp2[temp2>180]-360
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in __setitem__(self, key, value)
1018 key = np.asarray(key, dtype=bool)
1019 try:
-> 1020 self._where(~key, value, inplace=True)
1021 except InvalidIndexError:
1022 self.iloc[key] = value
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/generic.py in _where(self, cond, other, inplace, axis, level, errors, try_cast)
8778 if other.ndim <= self.ndim:
8779
-> 8780 _, other = self.align(
8781 other, join="left", axis=axis, level=level, fill_value=np.nan
8782 )
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
4272 broadcast_axis=None,
4273 ):
-> 4274 return super().align(
4275 other,
4276 join=join,
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/generic.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
8557 )
8558 elif isinstance(other, ABCSeries):
-> 8559 return self._align_series(
8560 other,
8561 join=join,
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/generic.py in _align_series(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
8660 join_index, lidx, ridx = None, None, None
8661 else:
-> 8662 join_index, lidx, ridx = self.index.join(
8663 other.index, how=join, level=level, return_indexers=True
8664 )
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py in join(self, other, how, level, return_indexers, sort)
884
885 this, other = self._maybe_utc_convert(other)
--> 886 return Index.join(
887 this,
888 other,
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/base.py in join(self, other, how, level, return_indexers, sort)
3500 elif self.is_monotonic and other.is_monotonic:
3501 try:
-> 3502 return self._join_monotonic(
3503 other, how=how, return_indexers=return_indexers
3504 )
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/base.py in _join_monotonic(self, other, how, return_indexers)
3800 join_index = self
3801 lidx = None
-> 3802 ridx = self._left_indexer_unique(sv, ov)
3803 elif how == "right":
3804 join_index = other
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py in wrapper(left, right)
62 right = right.view("i8")
63
---> 64 results = joinf(left, right)
65 if with_indexers:
66 # dtype should be timedelta64[ns] for TimedeltaIndex
pandas/_libs/join.pyx in pandas._libs.join.left_join_indexer_unique()
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/_libs/join.cpython-38-x86_64-linux-gnu.so in View.MemoryView.memoryview_cwrapper()
~/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/_libs/join.cpython-38-x86_64-linux-gnu.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
```
#### Problem description
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit : db08276
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
lxml.etree : 4.5.2
jinja2 : 2.11.2
IPython : 7.18.1
fsspec : 0.8.4
fastparquet : 0.4.1
matplotlib : 3.3.2
pyarrow : 0.17.1
scipy : 1.5.2
xarray : 0.16.1
numba : 0.51.2
</details>
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CLN: always rebox_native in DatetimeLikeArray
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MDExOlB1bGxSZXF1ZXN0NTA3NjgwMjI1
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TST/REF: collect tests by method
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: `to_pickle` function takes pickle v5 but doc says only 1-4 supported
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| 2020-10-21T17:16:06Z
| 2020-10-22T00:11:03Z
| 2020-10-22T00:11:03Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_pickle.html
* https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.to_pickle.html (same as stable)
#### Documentation problem
The docstring states ` DataFrame.to_pickle(path, compression='infer', protocol=5, storage_options=None)` noting _protocol 5_ by default, the Parameters following text notes "The possible values are 0, 1, 2, 3, 4" which does not include protocol 5.
#### Suggested fix for documentation
Add "5" to the docs: "The possible values are 0, 1, 2, 3, 4, 5" which was included from Python 3.8 https://docs.python.org/3/library/pickle.html
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TST/REF: rename test files
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MDU6SXNzdWU3MjY3MTg4NTY=
| 37,318
|
Adding the filename as a Dataframe property
|
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| 3
| 2020-10-21T17:42:21Z
| 2020-10-21T19:58:55Z
| 2020-10-21T19:58:55Z
|
NONE
| null |
I some complicated programming environments involving Pandas dataframes it would be very useful to be able to recover the dataframe filename from the dataframe object. I am particularly thinking about production environment where the data source would be evolving with time. In such environments being able to recover the underlying filename would be essential to check if the right file is being used.
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