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MDExOlB1bGxSZXF1ZXN0NDg4NDM2MTYy
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REF: use BlockManager.to_native_types in formatting code
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MEMBER
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Small follow-up on https://github.com/pandas-dev/pandas/pull/36150
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DEPR: Index.to_native_types
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MEMBER
| null |
Closes #28867
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MDU6SXNzdWU3MDMzMjk0OTk=
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ENH: I wish add a parameter to adjust bar function's height in Styler
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| 11
| 2020-09-17T07:08:43Z
| 2021-09-11T04:09:56Z
| 2021-07-13T12:02:03Z
|
NONE
| null |
#### Is your feature request related to a problem?
I have a 'Styler' object, I wish add a parameter that can adjust bar's spacing to the [bar function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.formats.style.Styler.bar.html).
eg:
now the bar example like bottom:
but I wish like bottom:
Thx!!!
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Occuring error when trying to install with setup.py (from source code) and tar file from pypi is not working
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| 2020-09-17T11:10:56Z
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NONE
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[16/40] Cythonizing pandas\_libs/reduction.pyx
Error compiling Cython file:
------------------------------------------------------------
...
self.orig_data = self.buf.data
self.orig_len = self.buf.shape[0]
self.orig_stride = self.buf.strides[0]
self.buf.data = self.values.data
^
------------------------------------------------------------
pandas\_libs\reduction.pyx:318:16: Assignment to a read-only property
Error compiling Cython file:
------------------------------------------------------------
...
cdef move(self, int start, int end):
"""
For slicing
"""
self.buf.data = self.values.data + self.stride * start
^
------------------------------------------------------------
pandas\_libs\reduction.pyx:325:16: Assignment to a read-only property
Error compiling Cython file:
------------------------------------------------------------
...
self.buf.shape[0] = end - start
cdef reset(self):
self.buf.shape[0] = self.orig_len
self.buf.data = self.orig_data
^
------------------------------------------------------------
pandas\_libs\reduction.pyx:331:16: Assignment to a read-only property
Error compiling Cython file:
------------------------------------------------------------
...
# move blocks
for i in range(self.nblocks):
arr = self.blocks[i]
# axis=1 is the frame's axis=0
arr.data = self.base_ptrs[i] + arr.strides[1] * start
^
------------------------------------------------------------
pandas\_libs\reduction.pyx:452:15: Assignment to a read-only property
Error compiling Cython file:
------------------------------------------------------------
...
# reset blocks
for i in range(self.nblocks):
arr = self.blocks[i]
# axis=1 is the frame's axis=0
arr.data = self.base_ptrs[i]
^
------------------------------------------------------------
pandas\_libs\reduction.pyx:472:15: Assignment to a read-only property
Traceback (most recent call last):
File "setup.py", line 791, in <module>
setup_package()
File "setup.py", line 761, in setup_package
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
File "setup.py", line 540, in maybe_cythonize
return cythonize(extensions, *args, **kwargs)
File "D:\Pickle Examples\examples_aix\example_aix\lib\site-packages\Cython\Build\Dependencies.py", line 1110, in cythonize
cythonize_one(*args)
File "D:\Pickle Examples\examples_aix\example_aix\lib\site-packages\Cython\Build\Dependencies.py", line 1277, in cythonize_one
raise CompileError(None, pyx_file)
Cython.Compiler.Errors.CompileError: pandas\_libs/reduction.pyx
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BUG: Pandas doesn't build with Cython3.0 pre-release
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| 1
| 2020-09-17T09:22:47Z
| 2020-09-17T11:07:04Z
| 2020-09-17T11:07:04Z
|
CONTRIBUTOR
| null |
This [line ](https://github.com/pandas-dev/pandas/blob/20569007ea256d3126214ab845190d08f88c73a9/pandas/_libs/reduction.pyx#L318):
```python
self.buf.data = self.values.data
```
will no longer build once Cython3.0 is released, due to this change: https://github.com/cython/cython/commit/22a49f403b6f00f1674664e01ee0f8212c06ef13#diff-24bcfc3298e43281fed97318a554550dL247.
`data` is no longer
```
ctypedef class numpy.ndarray [object PyArrayObject, check_size ignore]:
....
char * data
```
but
```
ctypedef class numpy.ndarray [object PyArrayObject, check_size ignore]:
...
@property
cdef inline char* data(self) nogil:
"""The pointer to the data buffer as a char*.
This is provided for legacy reasons to avoid direct struct field access.
For new code that needs this access, you probably want to cast the result
of `PyArray_DATA()` instead.
"""
return PyArray_BYTES(self)
```
#### Error:
```
Error compiling Cython file:
------------------------------------------------------------
...
self.buf.data = self.values.data
pandas\_libs\reduction.pyx:472:15: Assignment to a read-only property
```
#### Expected:
Should build.
|
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|
CLN: infer_dtype has no skipna argument
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| 2020-09-17T09:42:55Z
| 2020-09-21T18:26:55Z
| 2020-09-21T18:26:55Z
|
CONTRIBUTOR
| null |
infer_dtype function in `lib.py` has no `skipna` argument, although it is documented in the docstring:
```python
def infer_dtype(foo=None, bar=None): # real signature unknown; restored from __doc__
"""
Efficiently infer the type of a passed val, or list-like
array of values. Return a string describing the type.
Parameters
----------
value : scalar, list, ndarray, or pandas type
skipna : bool, default True
Ignore NaN values when inferring the type.
Returns
-------
str
Describing the common type of the input data.
Results can include:
```
The method is called a couple times with the `skipna=True` argument. For example in:
- `pandas/core/algorithms.py` line 220
- `pandas/core/dtypes/cast.py` line 131
Is this expected behaviour? Should I rename `foo -> value` and `bar -> skipna`?
|
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ENH: Multiple dataframe groupby/apply
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| 1
| 2020-09-17T09:58:37Z
| 2020-09-17T12:21:07Z
| 2020-09-17T12:20:54Z
|
NONE
| null |
#### Is your feature request related to a problem?
When working with larger separate data frames, it may be computationally more efficient to not join them. However, in scenarios I have personally encountered, I might still want to perform a group by/apply to both data frames while accessing both subframes based
#### Describe the solution you'd like
An example solution would be:
```python
def func(df_sub1, df_sub2):
# perform computations.
return df_sub1, df_sub2
df1, df2 = pd.grouped_apply([df1, df2], on='key', func)
```
Or if results from a single data frame are only of importance:
```python
def func(df_sub1, df_sub2):
# perform computations.
return df_sub1
df1 = df.groupby(by='key', dfs=[df2]).apply(func)
```
#### API breaking implications
Some constraints might have to be in place such as treating this as a left join, where df2 may potentially not have any values for a given key.
#### Describe alternatives you've considered
Standard merge/join/etc, which consumes a large number of resources. Can also do the following:
```python
dict1 = dict(tuple(df1.groupby('Date_A')))
dict2 = dict(tuple(df2.groupby('Date_A')))
for k, df_sub1 in dict1.items():
df_sub2 = dict2[k]
dict1[k], dict2[k] = func(df_sub1, df_sub2)
```
#### Additional context
Please forgive me if this makes no sense whatsoever :)!
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QST: Replace subset of DataFrame by another subset of DataFrame
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| 7
| 2020-09-17T12:29:07Z
| 2020-09-22T23:24:01Z
| 2020-09-22T23:24:01Z
|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
import numpy as np
import pandas as pd
dic = {'A': [1, 4, 1, 4], 'B': [9, 2, 5, 3], 'C': [0, 0, 5, 3]}
df = pd.DataFrame(dic)
df.index = [0, 0, 1, 1]
df1 = df.copy()
df1[:] = np.nan
df1.loc[0]['A'] = df.loc[0]['A']
```
original `df`:
```
A B C
0 1 9 0
0 4 2 0
1 1 5 5
1 4 3 3
```
I want to replace the subset of the copy with the subset of original `df`:
```
A B C
0 1 NaN NaN
0 4 NaN NaN
1 NaN NaN NaN
1 NaN NaN NaN
```
However, it shows no change at all:
```
A B C
0 NaN NaN NaN
0 NaN NaN NaN
1 NaN NaN NaN
1 NaN NaN NaN
```
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| 703,558,621
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MDU6SXNzdWU3MDM1NTg2MjE=
| 36,425
|
BUG: If a dataframe element is a list, using `.extend()` method affects the whole column
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[] | null | 1
| 2020-09-17T12:51:38Z
| 2020-09-17T13:56:42Z
| 2020-09-17T13:56:41Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
in pandas 1.1.1:
```python
import pandas as pd
df = pd.DataFrame({'a': [[]]*10}, index = range(10))
df.loc[0,'a'].extend([1])
assert (df.a.str.len()).sum() == 1 # fails
```
#### Problem description
The entire `a` column should not be filled when using `.loc` to reference a single element. Instead, we get this
```python
>>> df
a
0 [1]
1 [1]
2 [1]
3 [1]
4 [1]
5 [1]
6 [1]
7 [1]
8 [1]
9 [1]
```
I am not aware of another way of doing this, now that `.ix` has been removed. It is inelegant to store iterables inside of df elements, but often necessary (e.g. temporary records of inconsistent length) or when `result_type='expand'` is not appropriate.
#### Expected Output
The script at the top should not trigger the assert; `df` should be:
```python
>>> df
a
0 [1]
1 []
2 []
3 []
4 []
5 []
6 []
7 []
8 []
9 []
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.1.0
Cython : 0.29.20
pytest : 6.0.1
hypothesis : 5.33.1
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : 0.15.1
xlrd : None
xlwt : 1.3.0
numba : 0.51.2
</details>
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MDU6SXNzdWU3MDM1NjA4OTg=
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BUG: Inconsistent lint environments
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| 2021-02-14T11:38:42Z
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|
CONTRIBUTOR
| null |
As of https://github.com/pandas-dev/pandas/blob/3a15e47374762cbc89a6742c4239a069414dbbfc/.pre-commit-config.yaml#L8 , pre-commit uses flake8 3.8, where the other checks use 3.7.
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This makes it impossible to create a commit which passes pre-commit and CI.
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DOC: Fix typo in docstring 'handler' --> 'handle'
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Update isort version in pre-commit config
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| 2020-09-17T16:34:11Z
|
CONTRIBUTOR
| null |
Previously, a mismatch between the isort versions specified by
pre-commit and by the files used on CI made it impossible to make a
commit which passed both pre-commit and CI lints.
See #36426 . This is not a complete solution as the flake8 version differences are intentional and hopefully temporary (see https://github.com/pandas-dev/pandas/pull/36412#issuecomment-694072443 ), but it should resolve the current breakage.
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MDU6SXNzdWU3MDM2Nzk5NTI=
| 36,429
|
test_c_parser_only on linux py_3.8_32 failing on MacPython.pandas-wheels
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[] | null | 11
| 2020-09-17T15:14:10Z
| 2020-11-27T15:05:49Z
| null |
MEMBER
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=42365&view=logs&j=bb1c2637-64c6-57bd-9ea6-93823b2df951&t=9c62e7ff-391e-517d-ede1-af63bc6a25da
```
=========================== short test summary info ============================
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/parser/test_c_parser_only.py::test_float_precision_options[c_high]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/parser/test_c_parser_only.py::test_float_precision_options[c_low]
= 2 failed, 66511 passed, 7439 skipped, 1144 xfailed, 6 xpassed, 15 warnings in 434.87s (0:07:14) =
```
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| 36,430
|
REGR: period_range giving incorrect values for large datetimes
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| 2
| 2020-09-17T15:29:36Z
| 2020-09-22T22:08:21Z
| 2020-09-22T22:08:21Z
|
MEMBER
| null |
From the pandas-dev mailing list:
> I encountered a bug in pandas 1.1.0 after updating from 1.0.5 that I wanted to pass along. If a period range is created that extends past the maximum timestamp date of 2262-04-11, then the period range is shifted back by an hour after this date. The screenshots below show an example with the shifted times highlighted in yellow.
```python
import pandas as pd
from datetime import datetime
start = datetime(2000, 1, 1)
end = datetime(2500, 1, 1)
idx = pd.period_range(start, end, freq="6H")
assert idx[-1].hour % 2 == 0 # raises
idx
# PeriodIndex(['2000-01-01 00:00', '2000-01-01 06:00', '2000-01-01 12:00',
# '2000-01-01 18:00', '2000-01-02 00:00', '2000-01-02 06:00',
# '2000-01-02 12:00', '2000-01-02 18:00', '2000-01-03 00:00',
# '2000-01-03 06:00',
# ...
# '2499-12-29 17:00', '2499-12-30 23:00', '2499-12-30 05:00',
# '2499-12-30 11:00', '2499-12-30 17:00', '2499-12-31 23:00',
# '2499-12-31 05:00', '2499-12-31 11:00', '2499-12-31 17:00',
# '2500-01-01 23:00'],
# dtype='period[6H]', length=730489, freq='6H')
pd.__version__
# '1.1.0'
```
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MDExOlB1bGxSZXF1ZXN0NDg4Nzk1MTY0
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BUG: get_indexer returned dtype
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| 3
| 2020-09-17T16:35:37Z
| 2020-09-19T20:24:21Z
| 2020-09-19T19:56:06Z
|
MEMBER
| null |
- [x] closes #36359
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Changed the return dtype of IndexEngine’s `get_indexer()` and `get_indexer_non_unique()`. I decided to not include tests because dtypes are already implicitly tested in the following:
- pandas/tests/indexes/test_base.py (TestIndex.test_get_indexer)
- pandas/tests/base/test_misc.py (test_get_indexer_non_unique_dtype_mismatch)
Also, I ran the asv benchmarks:
```
asv continuous -f 1.1 upstream/master HEAD -b ^indexing_engines
BENCHMARKS NOT SIGNIFICANTLY CHANGED.
```
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PERF: construct DataFrame with string array and dtype=str
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| 5
| 2020-09-17T18:06:58Z
| 2020-09-19T20:06:51Z
| 2020-09-19T19:54:17Z
|
CONTRIBUTOR
| null |
Avoid inefficient call to `arr.astype()` when dtype is `str`, and use ensure_string_array instead.
Performance example:
```python
>>> x = np.array([str(u) for u in range(1_000_000)], dtype=object).reshape(500_000, 2)
>>> %timeit pd.DataFrame(x, dtype=str)
391 ms ± 17.7 ms per loop # master
11.9 ms ± 131 µs per loop # after this PR
```
xref #35519, #36304 & #36317.
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| 703,827,903
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MDExOlB1bGxSZXF1ZXN0NDg4ODY0NTg0
| 36,433
|
[DOC]: Add warning about rolling sums with large values
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| 1
| 2020-09-17T18:53:37Z
| 2020-09-18T22:11:36Z
| 2020-09-18T21:59:25Z
|
MEMBER
| null |
- [x] closes #11645
- [x] closes #28244 (probably closes that one too)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Added a warning within the user guide.
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REF: pandas/io/formats/format.py
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| 8
| 2020-09-17T19:34:09Z
| 2020-09-25T19:10:18Z
| 2020-09-19T22:05:38Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Partially addresses https://github.com/pandas-dev/pandas/issues/36407
Before splitting ``DataFrameFormatter`` into more dedicated classes, I decided to refactor the class itself, to make the outstanding refactoring more manageable.
As suggested by @jreback, I made this refactor small, trying to split big functions into smaller ones and find some better naming.
Yet, rather small changes are done so far, just to keep diffs readable.
Once approved, I will move on to further refactor.
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DOC: read_excel supports skiprows argument like read_csv, but tests and docs needed
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| 2020-09-17T21:05:54Z
| 2020-09-18T12:50:06Z
| 2020-09-18T12:50:06Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html
https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
#### Documentation problem
In `read_csv`, we say that we support:
**skiprows list-like, int or callable, optional**
_Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file._
_If callable, the callable function will be evaluated against the row indices, returning True
if the row should be skipped and False otherwise. An example of a valid callable argument
would be lambda x: x in [0, 2]._
In `read_excel`, we say that we support:
**skiprows list-like**
_Rows to skip at the beginning (0-indexed)._
It turns out that the `int` and `callable` arguments work fine with `read_excel()`, so we should indicate that in the documentation.
We also need to add tests for those 2 cases.
#### Suggested fix for documentation and additional tests
- copy the `read_csv` doc for `skiprows` over to `read_excel`
- add tests to `tests/io/excel/test_readers.py` for the `int` and `callable` options
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CI: Failed Travis jobs due to GBQ
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| 3
| 2020-09-17T22:37:52Z
| 2020-09-18T21:30:42Z
| 2020-09-18T21:30:42Z
|
MEMBER
| null |
Lots of failed jobs coming from Travis lately with tracebacks like this:
```python
@pytest.fixture()
def gbq_dataset(self):
# Setup Dataset
_skip_if_no_project_id()
_skip_if_no_private_key_path()
dataset_id = "pydata_pandas_bq_testing_" + generate_rand_str()
> self.client = _get_client()
pandas/tests/io/test_gbq.py:160:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/io/test_gbq.py:68: in _get_client
return bigquery.Client(project=project_id, credentials=credentials)
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/google/cloud/bigquery/client.py:179: in __init__
project=project, credentials=credentials, _http=_http
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/google/cloud/client.py:250: in __init__
Client.__init__(self, credentials=credentials, client_options=client_options, _http=_http)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <google.cloud.bigquery.client.Client object at 0x7f5c46e157d0>
credentials = <google.oauth2.service_account.Credentials object at 0x7f5c46e15bd0>
_http = None
client_options = ClientOptions: {'api_endpoint': None, 'client_cert_source': None}
def __init__(self, credentials=None, _http=None, client_options=None):
if isinstance(client_options, dict):
client_options = google.api_core.client_options.from_dict(client_options)
if client_options is None:
client_options = google.api_core.client_options.ClientOptions()
> if credentials and client_options.credentials_file:
E AttributeError: 'ClientOptions' object has no attribute 'credentials_file'
```
Line that's raising:
https://github.com/pandas-dev/pandas/blob/c8b44dda08ab3a2feb2c4572be960469c3f065f7/pandas/tests/io/test_gbq.py#L160
Sample Travis log:
https://api.travis-ci.org/v3/job/728073325/log.txt
cc @tswast if any ideas
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MDExOlB1bGxSZXF1ZXN0NDg4OTkzMTQ3
| 36,437
|
DOC: read_excel skiprows documentation matches read_csv (#36435)
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[] | null | 1
| 2020-09-18T00:08:52Z
| 2020-09-18T14:02:19Z
| 2020-09-18T12:50:06Z
|
CONTRIBUTOR
| null |
- [x] closes #36435
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Updated `read_excel` documentation to match `read_csv` for the `skiprows` parameter.
Changed `test_read_excel_skiprows_list` to `test_read_excel_skiprows`: now also tests `read_excel` with an `int` and a `callable`.
|
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MDExOlB1bGxSZXF1ZXN0NDg5MDAzODU0
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REF: collect IntervalArray methods by topic
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| 1
| 2020-09-18T00:50:43Z
| 2020-09-18T14:27:30Z
| 2020-09-18T13:01:35Z
|
MEMBER
| null |
cosmetic edits I find myself tempted to do on a bunch of branches
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REF: share insert between DTI/TDI/PI
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| 2020-09-18T02:25:08Z
| 2020-09-18T14:24:39Z
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MEMBER
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| 704,063,181
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MDExOlB1bGxSZXF1ZXN0NDg5MDUzNTMx
| 36,440
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REGR: Series[numeric] comparison with str raising on numexpr path
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| 2
| 2020-09-18T03:57:06Z
| 2020-09-19T08:12:40Z
| 2020-09-18T21:55:07Z
|
MEMBER
| null |
- [x] closes #36377, closes #35700
- [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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MDExOlB1bGxSZXF1ZXN0NDg5MDU3NDU5
| 36,441
|
CLN: remove trailing commas
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| 1
| 2020-09-18T04:10:34Z
| 2020-09-18T08:06:31Z
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|
CONTRIBUTOR
| null |
#35925
Checked the following file to make sure they are ready for black :
- pandas/tests/groupby/transform/test_numba.py
CC @MarcoGorelli
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MDExOlB1bGxSZXF1ZXN0NDg5MDc1MzEw
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Update 03_subset_data.rst
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- [ ] closes #xxxx
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MDExOlB1bGxSZXF1ZXN0NDg5MDk4NDQ5
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CI: fix gbq test #36436
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| 2020-09-18T06:06:52Z
| 2020-09-23T19:39:08Z
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MEMBER
| null |
- [x] closes #36436
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BUG: inconsistent replace
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| 2020-09-18T06:50:27Z
| 2020-09-26T08:26:17Z
| 2020-09-26T01:25:20Z
|
CONTRIBUTOR
| null |
- [x] tests added
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] release note in `doc/source/whatsnew/v1.1.3.rst`
Must fix some replace in IntBlock if called with float integer (such as 1.0).
closes #35376
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import pandas doesn't work using VS Code through Anaconda.
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[] | null | 0
| 2020-09-18T07:56:58Z
| 2020-09-18T07:58:34Z
| 2020-09-18T07:58:34Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
```
output:
```
PS C:\Users\19712\OneDrive\Desktop\sentiment\Scripts> & C:/Users/19712/anaconda3/python.exe c:/Users/19712/OneDrive/Desktop/sentiment/Scripts/clean.py
Traceback (most recent call last):
File "c:/Users/19712/OneDrive/Desktop/sentiment/Scripts/clean.py", line 1, in <module>
import pandas as pd
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\__init__.py", line 32, in <module>
from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\_libs\__init__.py", line 3, in <module>
from .tslibs import (
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\_libs\tslibs\__init__.py", line 3, in <module>
from .conversion import localize_pydatetime, normalize_date
File "pandas\_libs\tslibs\c_timestamp.pyx", line 1, in init pandas._libs.tslibs.c_timestamp
File "pandas\_libs\tslibs\tzconversion.pyx", line 1, in init pandas._libs.tslibs.tzconversion
File "pandas\_libs\tslibs\timedeltas.pyx", line 1, in init pandas._libs.tslibs.timedeltas
File "pandas\_libs\tslibs\offsets.pyx", line 1, in init pandas._libs.tslibs.offsets
File "pandas\_libs\tslibs\ccalendar.pyx", line 12, in init pandas._libs.tslibs.ccalendar
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\_config\__init__.py", line 19, in <module>
from pandas._config import dates # noqa:F401
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\_config\dates.py", line 18, in <module>
cf.register_option(
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\_config\config.py", line 732, in inner
return func(pkey, *args, **kwds)
File "C:\Users\19712\anaconda3\lib\site-packages\pandas\_config\config.py", line 456, in register_option
if not re.match("^" + tokenize.Name + "$", k): # type: ignore
AttributeError: module 'tokenize' has no attribute 'Name'
```
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MDExOlB1bGxSZXF1ZXN0NDg5MjA5NjUw
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|
CLN: 35925 rm trailing commas
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| 1
| 2020-09-18T09:58:06Z
| 2020-09-18T13:00:20Z
| 2020-09-18T13:00:16Z
|
CONTRIBUTOR
| null |
#35925
Checked the following files to make sure they are ready for black :
- /pandas/tests/indexes/base_class/test_indexing.py
- /pandas/tests/indexes/test_common.py
- /pandas/tests/indexes/test_numeric.py
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CLN: remove trailing commas for black update
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| 2020-09-18T11:17:13Z
| 2020-09-19T18:40:45Z
| 2020-09-19T18:40:45Z
|
NONE
| null |
File edited:
pandas/tests/io/test_sql.py
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MDU6SXNzdWU3MDQzMTQ1NDc=
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DOC: broken link in warning message
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| 5
| 2020-09-18T11:44:10Z
| 2020-09-21T17:08:56Z
| 2020-09-21T17:08:56Z
|
NONE
| null |
I couldn't figure out the spot in the code, sorry about that, but the link in this message is broken:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
It should be: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
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|
CLN upgrade to Python3.7+ syntax
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| 2020-09-18T13:37:11Z
| 2020-09-18T13:38:26Z
| 2020-09-18T13:38:19Z
|
MEMBER
| null |
Given that the next version will be Python3.7+, there's some nice updates we can make
|
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CLN upgrade to Python3.7+ syntax
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| 2
| 2020-09-18T13:43:02Z
| 2020-09-18T22:20:50Z
| 2020-09-18T22:20:32Z
|
MEMBER
| null |
Given that the next release will we Python3.7+, we might as well upgrade some of the syntax, e.g.
```diff
- for make_index_func in make_index_funcs:
- yield make_index_func
+ yield from make_index_funcs
```
or
```diff
- return "%s[%s, %s]" % (cls, tp_repr, metadata_reprs)
+ return f"{cls}[{tp_repr}, {metadata_reprs}]"
```
We can do this using [pyupgrade](https://github.com/asottile/pyupgrade).
Here are the files that can be upgraded:
- [ ] pandas/_config/display.py
- [ ] pandas/_testing.py
- [ ] pandas/_vendored/typing_extensions.py
- [ ] pandas/_version.py
- [x] pandas/core/arrays/datetimes.py
- [x] pandas/core/arrays/sparse/array.py
- [x] pandas/core/common.py
- [x] pandas/core/computation/expr.py
- [x] pandas/core/computation/pytables.py
- [x] pandas/core/generic.py
- [x] pandas/core/groupby/base.py
- [x] pandas/core/groupby/generic.py
- [x] pandas/core/indexes/base.py
- [x] pandas/core/indexes/datetimelike.py
- [x] pandas/core/reshape/merge.py
- [ ] pandas/io/clipboard/__init__.py
- [ ] pandas/io/formats/excel.py
- [ ] pandas/io/html.py
- [ ] pandas/io/json/_json.py
- [ ] pandas/io/pytables.py
- [ ] pandas/io/stata.py
- [ ] pandas/plotting/_matplotlib/core.py
- [ ] pandas/tests/arrays/categorical/test_constructors.py
- [ ] pandas/tests/arrays/integer/test_construction.py
- [ ] pandas/tests/extension/decimal/array.py
- [ ] pandas/tests/extension/test_categorical.py
- [ ] pandas/tests/frame/test_constructors.py
- [ ] pandas/tests/groupby/test_groupby.py
- [ ] pandas/tests/groupby/test_grouping.py
- [ ] pandas/tests/indexes/test_base.py
- [ ] pandas/tests/indexing/test_indexing.py
- [ ] pandas/tests/io/formats/test_format.py
- [ ] pandas/tests/io/formats/test_to_csv.py
- [ ] pandas/tests/io/formats/test_to_html.py
- [ ] pandas/tests/io/formats/test_to_latex.py
- [ ] pandas/tests/io/json/test_ujson.py
- [ ] pandas/tests/io/parser/test_c_parser_only.py
- [ ] pandas/tests/io/parser/test_common.py
- [ ] pandas/tests/io/parser/test_encoding.py
- [ ] pandas/tests/io/parser/test_read_fwf.py
- [ ] pandas/tests/io/pytables/common.py
- [ ] pandas/tests/io/test_common.py
- [ ] pandas/tests/io/test_html.py
- [ ] pandas/tests/io/test_sql.py
- [ ] pandas/tests/reshape/test_get_dummies.py
- [ ] pandas/tests/scalar/test_na_scalar.py
- [ ] pandas/tests/series/test_analytics.py
- [ ] pandas/tests/series/test_constructors.py
- [ ] pandas/tests/series/test_dtypes.py
- [ ] pandas/tests/series/test_io.py
- [ ] scripts/validate_rst_title_capitalization.py
- [ ] scripts/validate_unwanted_patterns.py
- [ ] setup.py
- [ ] versioneer.py
If you're intereseted in helping out, please choose a batch of 10-15 to run pyupgrade on.
Install pyupgrade with:
```
pip install pyupgrade==2.7.2
```
Then, run
```
pyupgrade <files you want to upgrade> --py37-plus
```
e.g.
```
pyupgrade pandas/tests/io/test_html.py pandas/tests/io/json/test_ujson.py --py37-plus
```
See #36453 for an example PR.
Once this is done, we can add it to pre-commit / CI.
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MDU6SXNzdWU3MDQ0NDU1MDU=
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|
BUG: conversion of float32 to string shows too much precision
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| 4
| 2020-09-18T14:55:41Z
| 2020-09-21T06:47:19Z
| 2020-09-21T06:47:19Z
|
MEMBER
| null |
On master (but also on 1.1):
```
In [4]: pd.Series([0.1], dtype="float64").astype("string")
Out[4]:
0 0.1
dtype: string
In [5]: pd.Series([0.1], dtype="float32").astype("string")
Out[5]:
0 0.10000000149011612
dtype: string
```
When converting to the object-dtype string, it works as expected:
```
In [6]: pd.Series([0.1], dtype="float64").astype("str")
Out[6]:
0 0.1
dtype: object
In [7]: pd.Series([0.1], dtype="float32").astype("str")
Out[7]:
0 0.1
dtype: object
```
cc @topper-123
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ASV: added benchamark tests for DataFrame.to_numpy() and .values
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| 2020-09-18T15:08:47Z
| 2020-09-20T03:48:42Z
| 2020-09-19T19:55:02Z
|
CONTRIBUTOR
| null |
- [x] closes #35023
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Adds benchmarks with single dtype and mixed frames of varying sizes.
xref #34999
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| 1
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| 2020-10-10T14:14:44Z
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xref #36450
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| 704,513,564
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MDExOlB1bGxSZXF1ZXN0NDg5NDI1NjY5
| 36,454
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[TST]: Wrong Corr with Timedelta index
|
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| 11
| 2020-09-18T16:40:00Z
| 2020-10-26T16:50:56Z
| 2020-10-26T14:49:46Z
|
MEMBER
| null |
- [x] xref #31286 (closes the issue, if there is no way around the numerical issues)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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| 704,539,244
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MDExOlB1bGxSZXF1ZXN0NDg5NDQ3MDA0
| 36,455
|
CLN: Clean some formatting
|
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| 2020-09-18T17:26:21Z
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MEMBER
| null |
ref https://github.com/pandas-dev/pandas/issues/36450
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MDExOlB1bGxSZXF1ZXN0NDg5NDUzMjIz
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|
DOC: replaced link to the documentation about setting a value on a co…
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| 2020-09-18T17:41:11Z
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|
NONE
| null |
…py of a slice from a DataFrame
- [x] closes #36448
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
CLN: Update files (as per #36450) to Python 3.7+ syntax
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| 1
| 2020-09-18T18:52:33Z
| 2020-09-19T16:48:19Z
| 2020-09-18T22:20:32Z
|
CONTRIBUTOR
| null |
Ran `pyupgrade` on all files unchecked in #36450. formatter pre-commit
hook had no complaints; removed unnecessary imports from the following
to satisfy `flake8` hook:
```
pandas/tests/io/test_sql.py
pandas/test/series/test_io.py
```
- [x] closes #36450
- [x] tests added / passed
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- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDg5NTA0OTE2
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|
[BUG]: Rolling.sum() calculated wrong values when axis is one and dtypes are mixed
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| 8
| 2020-09-18T19:39:19Z
| 2020-09-19T22:17:54Z
| 2020-09-19T22:04:01Z
|
MEMBER
| null |
- [x] closes #20649
- [x] closes #35596
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In case of ``axis=1`` and mixed dtypes, the ``_apply_blockwise`` did not calculate the sum for a complete row. Through converting the obj and consolidating the blocks we can avoid this.
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MDExOlB1bGxSZXF1ZXN0NDg5NTM3NjU1
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Align cython and python reduction code paths
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| 2020-09-18T20:57:43Z
| 2020-09-19T16:46:13Z
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|
MEMBER
| null |
Get rid of a few of the small behavior differences between these
(entirely disabling the cython path leads to 4 currently-xfailed tests passing, so there is still some subtle difference after this)
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CI: Revert PR template
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| 2020-09-18T22:50:25Z
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MEMBER
| null |
---
labels: "Needs Review"
---
I don't know if this change had the intended effect, so opening this (partially as a test if it actually works)
Edit: Yeah, it didn't. I guess this doesn't work like the issue templates.
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MDExOlB1bGxSZXF1ZXN0NDg5NTgxODEw
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REF: MultiIndex._validate_insert_value, IntervaArray._validate_setitem_value
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MDExOlB1bGxSZXF1ZXN0NDg5NTg3Mjc0
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removed unnecessary trailing commas
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|
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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MDExOlB1bGxSZXF1ZXN0NDg5NTkxNjc2
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Remove unnecessary trailing commas
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| 3
| 2020-09-19T00:43:45Z
| 2020-09-21T12:18:46Z
| 2020-09-21T06:47:19Z
|
MEMBER
| null |
- [x] closes #36451
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
It seems both bugs were caused by the same behavior from numpy:
```python
[ins] In [1]: import numpy as np
[ins] In [2]: arr = np.array([0.1], dtype=np.float32)
[ins] In [3]: arr
Out[3]: array([0.1], dtype=float32)
[ins] In [4]: np.asarray(arr, dtype="object")
Out[4]: array([0.10000000149011612], dtype=object)
```
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[] | null | 1
| 2020-09-19T01:07:54Z
| 2021-06-11T12:30:46Z
| null |
MEMBER
| null |
While working on #36443, I found several sql tests failures related to the latest pymysql and sqlalchemy (could be one of them or both, not sure). See https://travis-ci.org/github/pandas-dev/pandas/jobs/728346186
```
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]
= 6 failed, 76075 passed, 1025 skipped, 1209 xfailed, 6 xpassed, 264 warnings in 1626.88s
```
```
pymysql 0.10.1 pyh9f0ad1d_0 conda-forge
sqlalchemy 1.3.19 py37h8f50634_0 conda-forge
```
Now those two packages have been pinned to their minimal support versions.
```
pymysql 0.7.11 py_0 conda-forge
sqlalchemy 1.3.0 py37h14c3975_0 conda-forge
```
### Update
The problem is from pymysql. #34431 handled inf dtype for to_sql. But it failed to capture that error while using the latest pymysql as sql driver. The test failed for pymysql>=0.10
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API Consistency: validations on no-ops
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| 2020-09-19T01:38:42Z
| 2021-08-13T02:57:44Z
| null |
MEMBER
| null |
We are not consistent about whether we do validation checks on no-ops
Examples of no-ops:
- `datetimelike_array[empty_indexer] = empty_values_wrong_dtype` # <-- we do not raise
- `series_dt64[empty_indexer] = empty_object_dtype` # <-- we cast to object, meaning we did raise internally
- `arr_with_no_nas.fillna(invalid_fill_value)` # <-- we mostly dont raise, see below
- `interval_index.take(indexer_with_no_negative_ones, fill_value=something_invalid)` # <-- we do not raise
```
pd.array([1, 4, 6]).fillna("foo")
pd.array([1.0, 4.9, 6.0]).fillna("foo")
pd.IntervalIndex.from_breaks(range(3)).fillna("foo")
dti = pd.date_range("2016-01-01", periods=3)
dti.fillna("foo")
(dti - dti).fillna("foo")
dti.to_period("D").fillna("foo")
pd.Categorical(dti).fillna("foo") <-- raises
```
I think we should (with deprecation cycles where necessary) aim to be consistently strict in this genre of check.
Update: listing related issues as I find them
- [ ] #18561 BUG: drop(level=.) doesn't raise if label is absent
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| 2020-09-19T04:08:12Z
| 2020-11-13T15:26:41Z
| 2020-11-13T15:26:35Z
|
MEMBER
| null |
It occurs occasionally on the travis-37-cov build.
```
_____________ TestReadHtml.test_banklist_url_positional_match[bs4] _____________
[gw0] linux -- Python 3.7.8 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.io.test_html.TestReadHtml object at 0x7f40f8fef450>
@tm.network
def test_banklist_url_positional_match(self):
url = "http://www.fdic.gov/bank/individual/failed/banklist.html"
# Passing match argument as positional should cause a FutureWarning.
with tm.assert_produces_warning(FutureWarning):
df1 = self.read_html(
> url, "First Federal Bank of Florida", attrs={"id": "table"}
)
pandas/tests/io/test_html.py:130:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f40f9070190>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=18, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 54722), raddr=('172.217.204.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=17, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 54720), raddr=('172.217.204.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=21, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 52284), raddr=('142.250.97.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=23, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 54628), raddr=('173.194.210.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=22, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 37618), raddr=('173.194.215.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=24, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 38812), raddr=('173.194.217.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=28, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 38824), raddr=('173.194.217.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85), ('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=27, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 54640), raddr=('173.194.210.95', 443)>"), '/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/html5lib/_utils.py', 85)]
../../../miniconda3/envs/pandas-dev/lib/python3.7/contextlib.py:119: AssertionError
```
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MDExOlB1bGxSZXF1ZXN0NDg5NjYxMzY5
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CI: xfail html test #36467
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closed
| false
| null |
[] | null | 6
| 2020-09-19T04:16:05Z
| 2020-09-23T19:37:34Z
| 2020-09-21T14:10:42Z
|
MEMBER
| null |
Part of #36467
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| 704,799,667
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MDU6SXNzdWU3MDQ3OTk2Njc=
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BUG: failed to setup dev environment (pip)
|
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[] | null | 8
| 2020-09-19T04:32:47Z
| 2021-08-13T02:59:27Z
| null |
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] 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 -m pip install -r requirements-dev.txt
```
#### Problem description
I followed the guide on the setting up the dev environment.
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#creating-a-python-environment-pip
The failure occurred when installing dependencies.
```
(venv) ~/code/pandas (master) $ git log -1
commit aed64e85eb17edb0e55013868b1aa4e44e977a36 (HEAD -> master, upstream/master)
Author: Hans <hanspagh@gmail.com>
Date: Sat Sep 19 04:14:25 2020 +0200
BUG: fix isin with nans and large arrays (#36266)
(venv) ~/code/pandas (master) $ python --version
Python 3.8.0
(venv) ~/code/pandas (master) $ python -m pip install -r requirements-dev.txt
Collecting git+https://github.com/pandas-dev/pydata-sphinx-theme.git@master (from -r requirements-dev.txt (line 77))
Cloning https://github.com/pandas-dev/pydata-sphinx-theme.git (to master) to /tmp/pip-k88ybarj-build
Collecting git+https://github.com/numpy/numpydoc (from -r requirements-dev.txt (line 78))
Cloning https://github.com/numpy/numpydoc to /tmp/pip-p42nydu6-build
Collecting numpy>=1.16.5 (from -r requirements-dev.txt (line 4))
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Using cached https://files.pythonhosted.org/packages/02/25/85675fd82b6272ed4d87941b826cec68352dd4cdfbe49b5644946dd3da61/pytest_xdist-2.1.0-py3-none-any.whl
Collecting pytest-asyncio (from -r requirements-dev.txt (line 39))
Using cached https://files.pythonhosted.org/packages/0b/82/b62efae871e8a543295c8c280b04a2da2ef3f6dd9bb830f6a1acc7145ad2/pytest_asyncio-0.14.0-py3-none-any.whl
Collecting seaborn (from -r requirements-dev.txt (line 40))
Using cached https://files.pythonhosted.org/packages/bc/45/5118a05b0d61173e6eb12bc5804f0fbb6f196adb0a20e0b16efc2b8e98be/seaborn-0.11.0-py3-none-any.whl
Collecting statsmodels (from -r requirements-dev.txt (line 41))
Using cached https://files.pythonhosted.org/packages/d6/97/d77cc2db06ab28f207269c2b6d3958c54cb131026f4aa92a49d76af31335/statsmodels-0.12.0-cp38-cp38-manylinux1_x86_64.whl
Collecting ipywidgets (from -r requirements-dev.txt (line 42))
Using cached https://files.pythonhosted.org/packages/56/a0/dbcf5881bb2f51e8db678211907f16ea0a182b232c591a6d6f276985ca95/ipywidgets-7.5.1-py2.py3-none-any.whl
Collecting nbformat (from -r requirements-dev.txt (line 43))
Using cached https://files.pythonhosted.org/packages/4d/d1/b568bd35f95321f152f594b3647cd080e96d3347843ff2fa34dce871b8bf/nbformat-5.0.7-py3-none-any.whl
Collecting notebook>=5.7.5 (from -r requirements-dev.txt (line 44))
Using cached https://files.pythonhosted.org/packages/cc/00/0db4005f8410c0c6c598d6beecd650846e00955a3bf800ea09872a9009f1/notebook-6.1.4-py3-none-any.whl
Requirement already satisfied: pip in ./venv/lib/python3.8/site-packages (from -r requirements-dev.txt (line 45))
Collecting blosc (from -r requirements-dev.txt (line 46))
Using cached https://files.pythonhosted.org/packages/1c/89/004eeb307ba3f56fca9727f47fbde09f8ae3936a1cb0dd6b7708f846d3fd/blosc-1.9.2.tar.gz
Complete output from command python setup.py egg_info:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-build-jg1zwlgb/blosc/setup.py", line 18, in <module>
from skbuild import setup
ModuleNotFoundError: No module named 'skbuild'
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-jg1zwlgb/blosc/
```
#### Expected Output
Successful installation.
#### Output of ``pd.show_versions()``
N/A
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MDU6SXNzdWU3MDQ4MjA4MjE=
| 36,470
|
BUG: groupby(..., dropna=False) excludes NA values when grouping on MultiIndex levels
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[] | null | 2
| 2020-09-19T06:58:30Z
| 2021-08-13T03:00:10Z
| 2021-08-13T03:00:10Z
|
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
In [3]: mi = pd.MultiIndex.from_tuples([('a',np.nan), ('b', 2)], names=['ltr','num'])
In [4]: df = pd.DataFrame({'foo': range(mi.size)}, index=mi)
In [5]: df
Out [5]:
foo
ltr num
a NaN 0
b 2 1
# grouping on multindex levels
In [6]: df.groupby(df.index.names, dropna=False).sum()
Out [6]:
foo
ltr num
b 2 1
# grouping on same information as columns
In [7]: df.reset_index().groupby(df.index.names, dropna=False).sum()
Out [7]:
foo
ltr num
a NaN 0
b 2.0 1
# grouping on multiindex levels for pd.Series
In [8]: df['foo'].groupby(s.index.names, dropna=False).sum()
Out [8]:
ltr num
b 2 1
Name: foo, dtype: int64
```
#### Problem description
The desired behavior of dropna=False, namely including NA values in the groups, does not work when grouping on MultiIndex levels, but does work when grouping on DataFrame columns.
#### Expected Output
```python
foo
ltr num
a NaN 0
b 2.0 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.193-149.317.amzn2.x86_64
Version : #1 SMP Thu Sep 3 19:04:44 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 : 1.1.2
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.7.4
pip : 20.1.1
setuptools : 40.4.3
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.0.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.2.12
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDg5NjgyMTE5
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|
CI: add pre-commit action, include pyupgrade
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| 14
| 2020-09-19T07:15:22Z
| 2020-09-25T18:27:43Z
| 2020-09-22T14:31:08Z
|
MEMBER
| null |
xref this comment https://github.com/pandas-dev/pandas/issues/36426#issuecomment-694238629 by @TomAugspurger
> I would recommend running the linting commands on CI through pre-commit. That way the versions in .pre-commit-config.yaml are used everywhere.
So, this PR does that.
It also adds pyupgrade as a pre-commit hook (xref #36450 ) and removes some unnecessary configurations from the `.pre-commit-config.yaml` file (all of these hooks already specify `language: python`, see e.g. https://github.com/psf/black/blob/master/.pre-commit-hooks.yaml )
TODO
----
update https://pandas.pydata.org/docs/development/contributing.html#code-standards (thanks Ali!)
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MDU6SXNzdWU3MDQ4MzA4MzE=
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|
BUG: Regex parameter from replace function doesn't work on string dtype
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| 2020-09-19T07:57:51Z
| 2020-09-20T04:47:44Z
| 2020-09-20T04:47:44Z
|
CONTRIBUTOR
| null |
>>>import pandas as pd
>>>print(pd.__version__)
1.1.1
>>>s1 = pd.Series(['a','b'], dtype='string')
>>>s2 = pd.Series(['a','b'], dtype='object')
>>>s1.replace(r'[a]',pd.NA,regex=True)
0 a
1 b
dtype: string
>>>s2.replace(r'[a]',pd.NA,regex=True)
0 <NA>
1 b
dtype: object
Hi, it seems that regex doesn't function well for the replace method on string dtype, but normal on object dtype.
Besides, I also find setting pd.NA as repl of str.replace method will raise error, both on string and object.
Yet I think it may be better not to raise this error because the pd.NA indeed could be a missing string value.
>>>pd.Series(['a','b'],dtype='string').str.replace('a',pd.NA)
TypeError: repl must be a string or callable
>>>pd.Series(['a','b'],dtype='object').str.replace('a',pd.NA)
TypeError: repl must be a string or callable
Thanks!
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Backport PR #36440 on branch 1.1.x (REGR: Series[numeric] comparison with str raising on numexpr path)
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| 1
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NONE
| null |
Backport PR #36440: REGR: Series[numeric] comparison with str raising on numexpr path
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Backport PR #36266:: BUG: fix isin with nans and large arrays
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| 1
| 2020-09-19T09:57:09Z
| 2020-09-19T11:14:46Z
| 2020-09-19T11:14:40Z
|
MEMBER
| null |
#36266
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TYP: FrameOrSeriesUnion
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| 2020-09-19T12:10:19Z
| 2020-09-20T17:06:01Z
| null |
MEMBER
| null |
From the discussion in PR #36409 it was mentioned that it is undesirable to type API functions as `NDFrame` as this may be confusing from a user perspective since it shows up in the docs. Currently the difference between `NDFrame` and `FrameOrSeriesUnion = Union[DataFrame, Series]` is that the former can be used in methods of NDFrame (and those called from NDFrame methods) whereas the latter cannot.
Is there a benefit to this restriction? If not, I propose changing `FrameOrSeriesUnion` to be an alias of `NDFrame`. This would then be user-friendly for the docs.
cc @simonjayhawkins @WillAyd @jreback
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MDExOlB1bGxSZXF1ZXN0NDg5NzI3Nzgw
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Turn on stale PR GitHub action
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| 2
| 2020-09-19T13:33:28Z
| 2020-09-19T20:04:59Z
| 2020-09-19T19:52:25Z
|
MEMBER
| null |
I think it makes sense to turn this on now (it's only labeling stale PRs for now, and after doing a lot of this manually it seems to have calmed down). Later can add some sort of friendly reminder to update the PR. Also rather than closing these stalled PRs I'm wondering if they could serve as starting points for new contributors who want to pick up where someone else left off.
Some recent workflows here (you can search for the string "Marking" in the logs to find PRs that would have been labeled): https://github.com/pandas-dev/pandas/actions?query=workflow%3A%22Stale+PRs%22
@simonjayhawkins You'd mentioned doing this more frequently in debug mode, would you prefer longer intervals when running live?
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Backport PR #36385 on branch 1.1.x (BUG: Always cast to Categorical in lexsort_indexer)
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NONE
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Backport PR #36385: BUG: Always cast to Categorical in lexsort_indexer
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CLN: aggregation.transform
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MEMBER
| null |
- [x] closes #36330
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Followup to #35964
- Moved whatsnew to reshaping
- Use is_list_like/is_dict_like in aggregate.transform
- Broke out dict-like and str/callable computations to own functions to split up the transform function
- Added/refined typing
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BUG: KeyError after groupby().size() when index is multiindex and involves nan
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| 2020-09-26T14:03:01Z
| 2020-09-26T14:03:01Z
|
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.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
data = [[pd.NA, 10], [pd.NA,10], [2, pd.NA]]
df = pd.DataFrame(data, columns=['A1', 'A2'])
print(df)
counts = df.groupby(['A1', 'A2'],dropna=False).size()
print(counts)
# If you uncomment the lines below, it works
# pk_file = 'tmp.df'
# counts.to_pickle(pk_file)
# counts = pd.read_pickle(pk_file)
idx = counts.index[0]
print(counts.loc[idx]) # KeyError: (2.0, nan)
```
#### Problem description
Unable to access the Series returned from groupby using apparently valid key
Line "print(counts.loc[idx]" raises KeyError.
If I uncomment the lines that store/load the data with pickle, then it works. No KeyError is raised.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
The Series returned from groupby is accessible with loc with valid keys
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 1
| 2020-09-19T17:41:23Z
| 2020-09-19T19:59:15Z
| 2020-09-19T19:59:11Z
|
CONTRIBUTOR
| null |
- [x] ref #36467
AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed <ssl.SSLSocket fd=18, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.20.0.43', 54722), raddr=('**172.217.204.95**', 443)>"), ),
**This is a google address** -> which leads me to believe this resource warning is coming from our `network` function in `_testing.py` since it calls `urllib.request.urlopen("https://www.google.com")`
Note: this doesn't solve the problem but we should use https. (Avoids a redirect)
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Commas edit
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Complex Dtype Support for Hashmap Algos
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| 22
| 2020-09-19T18:58:54Z
| 2021-09-04T14:25:00Z
| 2021-09-04T14:25:00Z
|
CONTRIBUTOR
| null |
- [x] fixes #17927 #26475
- [x] tests added / passed
- [x] passes black pandas
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff
Ref: https://github.com/pandas-dev/pandas/issues/18009
Based on https://github.com/pandas-dev/pandas/pull/27599
First set of tests for complex number handling + sensible results from functions that rely on hash tables.
Use generic object hashing for now.
@jbrockmendel you interested in reviewing?
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REF: share IntervalArray._validate_foo
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ENH: PandasArray ops use core.ops functions
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MEMBER
| null |
Needs tests, hopefully i can tweak fixture usage in tests.arithmetic
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TYP: core.missing; PERF for needs_i8_conversion
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Split off from a branch that shares `fillna` code between some of our EAs.
Preliminary to a PR that allows for an actually-inplace fillna implementation.
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| 10
| 2020-09-19T22:52:42Z
| 2020-09-22T23:24:07Z
| 2020-09-22T23:24:02Z
|
CONTRIBUTOR
| null |
…subframe
- [x] closes #36424
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDg5ODQyODA2
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Don't unlabel stale PR on update
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| 1
| 2020-09-20T01:25:08Z
| 2020-09-20T02:49:13Z
| 2020-09-20T02:13:55Z
|
MEMBER
| null |
Apparently the bot unlabels on any activity (not just activity from the owner), e.g., https://github.com/pandas-dev/pandas/pull/34584#issuecomment-693107104, so if you ask the person for an update and they don't respond, the PR is no longer stale. Probably better to do this manually for now.
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REF: share fillna
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| 0
| 2020-09-20T03:04:35Z
| 2021-02-21T22:42:04Z
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MEMBER
| null |
Near-identical implementations in DatetimeLikeArray and PandasArray
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MDExOlB1bGxSZXF1ZXN0NDg5ODUyNDQw
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Commas chaged
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| 2020-09-20T03:49:21Z
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NONE
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Commas Edited
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NONE
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MDExOlB1bGxSZXF1ZXN0NDg5ODU2MzQ4
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TST: DataFrame.to_parquet accepts pathlib.Path with partition_cols defined
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| 6
| 2020-09-20T04:44:49Z
| 2020-09-23T14:42:17Z
| 2020-09-23T11:20:14Z
|
MEMBER
| null |
- [x] closes #35902
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU3MDUwNDUzODI=
| 36,492
|
BUG: pd.NA in str.replace raises error
|
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| 2020-09-20T04:55:11Z
| 2020-09-20T06:20:52Z
| 2020-09-20T06:20:52Z
|
CONTRIBUTOR
| null |
>>>pd.__version__
1.1.1
I find setting pd.NA as repl of str.replace method will raise error, both on string and object.
Yet I think it may be better not to raise this error because the pd.NA indeed could be a missing string value.
>>>pd.Series(['a','b'],dtype='string').str.replace('a',pd.NA)
TypeError: repl must be a string or callable
>>>pd.Series(['a','b'],dtype='object').str.replace('a',pd.NA)
TypeError: repl must be a string or callable
And also, pd.NA can't be the pat of str.replace. Maybe reasonable to allow this.
>>>pd.Series([pd.NA,'a'],dtype='string').str.replace(pd.NA,'1')
TypeError: object of type 'NAType' has no len()
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|
CI: Update version of 'black'
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| 4
| 2020-09-20T06:57:43Z
| 2020-09-23T12:17:25Z
| 2020-09-22T13:37:59Z
|
MEMBER
| null |
closes #35925
Not many files left so am making a PR to close this off
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MDU6SXNzdWU3MDUwNjAwNzg=
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|
Update outdated instructions in scripts/generate_pip_deps_from_conda.py
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| 3
| 2020-09-20T07:14:28Z
| 2020-09-21T21:12:09Z
| 2020-09-21T21:11:46Z
|
MEMBER
| null |
It says
```
Usage:
Generate `requirements-dev.txt`
$ ./conda_to_pip
Compare and fail (exit status != 0) if `requirements-dev.txt` has not been
generated with this script:
$ ./conda_to_pip --compare
```
but it should be
```
Usage:
Generate `requirements-dev.txt`
$ python scripts/generate_pip_deps_from_conda.py
Compare and fail (exit status != 0) if `requirements-dev.txt` has not been
generated with this script:
$ python scripts/generate_pip_deps_from_conda.py --compare
```
No need to ask if you can work on this, feel free to just submit a PR
|
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REGR: fillna not filling NaNs after pivot without explicitly listing pivot values
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| 5
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| 2020-11-26T17:00:20Z
| 2020-11-26T17:00:20Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Hi,
I have this (simplified) piece of code which seems to have stopped working correctly in Pandas 1.1:
```python
import pandas as pd
data = [
[1, 1, 1, 1.],
[2, 2, 2, 2.],
[3, 3, 3, 3.],
]
df = pd.DataFrame(data, columns=['i1', 'i2', 'i3', 'f1'])
df = df.pivot('i1', 'i2')
df = df.fillna(0)
print(df)
```
#### Problem description
This is the output in Pandas 1.1.2:
```
i3 f1
i2 1 2 3 1 2 3
i1
1 1.0 NaN NaN 1.0 0.0 0.0
2 NaN 2.0 NaN 0.0 2.0 0.0
3 NaN NaN 3.0 0.0 0.0 3.0
```
It contains NaNs after using fillna. It can be fixed by explicitly providing remaining columns as values to pivot (which should be the default):
`df = df.pivot('i1', 'i2', values=['i3', 'f1'])`
It also works if all columns are of the same type, like when I change f1 to contain ints instead of floats. In that case I don't need to specify the values.
#### Expected Output
This is the output on Pandas 1.0.5 and 0.25.3:
```
i3 f1
i2 1 2 3 1 2 3
i1
1 1.0 0.0 0.0 1.0 0.0 0.0
2 0.0 2.0 0.0 0.0 2.0 0.0
3 0.0 0.0 3.0 0.0 0.0 3.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 32
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : None.None
pandas : 1.1.2
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 : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
None
</details>
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Update outdated instructions in scripts/generate_pip_deps_from_conda.py #36494
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CONTRIBUTOR
| null |
- [x] closes #36494
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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PERF: DataFrame initialisation with dict with single key/value pair
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|
CONTRIBUTOR
| null |
Faster initialisation of DataFrames, when the data is a single-item dict:
```python
>>> x = np.arange(1_000_000)
>>> %timeit pd.DataFrame(dict(a=x))
2.16 ms ± 66.7 µs per loop # master
282 µs ± 4.63 µs per loop # this PR
>>> x = np.array([str(u) for u in range(1_000_000)], dtype=object)
>>> %timeit pd.DataFrame(dict(a=x))
29.5 ms ± 131 µs per loop # master
12.6 ms ± 55.8 µs per loop # this PR
```
I don't think it's possible to optimize the case with more than 1 key/value pair, as concatenation will always take some time to perform.
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Maintain inplace operation on series
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CONTRIBUTOR
| null |
- [ ] closes #36373
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: concat series with NA in multiindex fail to combine rows of same index
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|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
names=['A1','A2']
idx1 = pd.MultiIndex.from_tuples([(1,1), (2, pd.NA)], names=names)
idx2 = pd.MultiIndex.from_tuples([(2, pd.NA), (3,3)], names=names)
s1 = pd.Series([11,12], index=idx1)
s2 = pd.Series([13,14], index=idx2)
s3 = pd.concat([s1, s2], axis=1)
print (s3)
# s3 fails to combine the two rows with index (2, NaN)
# 0 1
# A1 A2
# 1 1.0 11.0 NaN
# 2 NaN 12.0 13.0
# NaN 12.0 13.0
# 3 3.0 NaN 14.0
# Workaround is to use merge after reset index
s1 = s1.reset_index()
s2 = s2.reset_index()
s3 = pd.merge(s1,s2,on=names,how='outer')
s3 = s3.set_index(names)
print (s3)
# Now the result is expected
# A1 A2
# 1 1.0 11.0 NaN
# 2 NaN 12.0 13.0
# 3 3.0 NaN 14.0
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3MDUxMjkyNzc=
| 36,500
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BUG: concat series returns error result when indexes use tuples with NA in them
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| 2021-08-13T03:03:53Z
| 2021-08-13T03:03:52Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
names=['A1','A2']
idx1 = [(1,1), (2, pd.NA)]
idx2 = [(2, pd.NA), (3,3)]
s1 = pd.Series([11,12], index=idx1)
s2 = pd.Series([13,14], index=idx2)
s3 = pd.concat([s1, s2], axis=1)
print (s3)
# 0 1
# 1 1 11.0 NaN
# 2 NaN NaN NaN <= expect 12.0 13.0 as the values
# 3 3 NaN 14.0
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
Modify doc/source/whatsnew7
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| 4
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|
CONTRIBUTOR
| null |
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] modify files in doc/source/whatsnew 'v0.25.0.rst' 'v0.25.1.rst' 'v0.25.2.rst' 'v0.25.3.rst' 'v1.0.0.rst' 'v1.0.1.rst' 'v1.0.2.rst' 'v1.0.3.rst' 'v1.0.4.rst' 'v1.05.rst' 'v1.1.0.rst'
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BUG: to_numeric casts floats incorrectly
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| 2020-09-20T23:46:53Z
|
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
df = pd.DataFrame({'A': ['0.09', '0.63', '0.121909', '0.117863']})
df['B'] = pd.to_numeric(df['A'])
df['C'] = df['A'].astype(float)
```
#### Problem description
When floats as strings are passed to a column of a DataFrame, pd.to_numeric casts some of the given strings incorrectly.
In the above case df['C'] and df['A'] should have exactly the same values, but they differ in some decimals:
df['B'] = (0, 0.09) (1, 0.63) (2, 0.12190899999999999) (3, 0.11786300000000001)
df['C'] = (0, 0.09) (1, 0.63) (2, 0.121909) (3, 0.117863)
df['C'] is correct but df['B'] is incorrect.
The above issue does not occur when the same input is passed as list
w = pd.to_numeric(['0.09', '0.63', '0.121909', '0.117863'])
[0.09 0.63 0.121909 0.117863]
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 32
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.2
</details>
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MDExOlB1bGxSZXF1ZXN0NDg5OTM3MDIx
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|
BUG: alignment changing index on input series
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| 3
| 2020-09-20T18:08:38Z
| 2020-09-25T00:33:21Z
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|
MEMBER
| null |
- [x] closes #33671
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: Unify Series case in _wrap_applied_output
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| 3
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| 2020-09-26T12:00:06Z
| 2020-09-21T21:59:47Z
|
MEMBER
| null |
One block of _wrap_applied_output is only executed when `first_not_none` is a Series; moved it to the else clause.
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MDU6SXNzdWU3MDUxNjAzNDc=
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|
QST: Wish to know how to properly avoid SettingwithCopyWarning without changing dtype
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| 4
| 2020-09-20T18:30:27Z
| 2020-09-20T23:32:54Z
| 2020-09-20T23:30:57Z
|
NONE
| null |
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
The data dataframe's label column is dtype: float64, and consists of four numbers, `[ 0., 2., 1., 3., nan]`.
```python
data[label_column][data[label_column] != 2] = 0
```
produces a SettingWithCopyWarning as it should. However,
```python
data.loc[data[label_column] != 2, label_column] = 0
```
does not produce a SettingWithCopyWarning, but changes the column to dtype: object.
I recognize `.loc` tries to cast back to the original dtype on assignment and `[]` does not based on [the getting started docs](http://pandas-docs.github.io/pandas-docs-travis/getting_started/basics.html), but I would like to know if the expected behaviour in this situation is expected and what the proper syntax I should use to avoid the warning but preserve the dtype.
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MDExOlB1bGxSZXF1ZXN0NDg5OTQ2MTQ3
| 36,506
|
DOC: Update generate_pip_deps_from_conda.py #36494
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|
NONE
| null |
- [x] closes #36494
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
ENH: Allow as_index=False on SeriesGroupBy
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| 2020-09-20T19:29:31Z
| 2020-10-19T21:15:11Z
| null |
MEMBER
| null |
Would make the code:
s.to_frame(s.name).groupby(groups, as_index=False).sum()
Become:
s.groupby(groups, as_index=False).sum()
The change should just amount to calling a function like:
```
def adjust_for_as_index(self, result: FrameOrSeries) -> DataFrame:
if result.ndim == 1:
result = result.to_frame(result.name)
self._insert_inaxis_grouper_inplace(result)
result.index = np.arange(len(result))
```
in various methods of SeriesGroupBy (and could be used in DataFrameGroupBy).
This would close #13666 and make #35443 to be just about error reporting for the `axis=1` case.
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|
BUG: aggfunc use different default arguments in pivot_table
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| 2021-08-13T03:05:47Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': ['a', 'a'], 'X': [5, 9]})
# standard deviation of X:
np.std(df['X'])
# 2.0
# aggfunc returns the same standard deviation as np.std as expected with this trick
pd.pivot_table(df, index='A', values='X', aggfunc=lambda x: np.std(x))
# X
# A
# a 2
# aggfunc returns the unbiased standard deviation unexpectedly
pd.pivot_table(df, index='A', values='X', aggfunc=np.std)
# X
# A
# a 2.828427
```
#### Problem description
The default value of ddof for np.std is 0:
numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)
When np,std passed to aggfunc to calculate the standard deviation of X it returns the unbiased standard deviation, because it picks different ddof =1 and does not pick up the default ddof = 0.
On the other hand the expected behavior if we have a function f, when we pass it to aggfunc:
aggfunc=f and aggfunc=lambda x: f(x) must return exactly the same result.
#### Expected Output
1) When np.std is passed to aggfunc directly it picks up ddof=1 by default (this is unexpected as the default ddof for np.std is 0).
2) When np.std is passed to aggfunc by using lambda x: np.std(x), it picks up correctly the default ddof=0 from numpy as expected.
To summerize, the expected behavior is to use the function's default arguments when it is passed to aggregate values in pd.pivot_table.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 32
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.2
numpy : 1.19.2
</details>
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MultiIndex.from_product could accept MultiIndex
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[] | null | 2
| 2020-09-20T20:58:36Z
| 2021-08-13T03:06:16Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
The problem is in xarray.DataArray.to_dataframe() which uses MultiIndex.from_product() to build the index based on the array coordinates.
When an xarray has a MultiIndex as one of the coordinates, from_product is called with a Multiindex as one of the iterable. This raises an exception, as discussed here https://github.com/pydata/xarray/issues/3008
#### Describe the solution you'd like
I've implemented a solution in xarray [PR ##4442], this close the issue for xarray.
However, the solution could be ported to from_product to allow this method to accept MultiIndex. I'm not sure it is of great interest for the users, but at least the solution exists and is relatively simple.
#### API breaking implications
none breaking
#### Describe alternatives you've considered
none
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MDExOlB1bGxSZXF1ZXN0NDg5OTU5MjA3
| 36,510
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REF: dataframe formatters/outputs
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| 11
| 2020-09-20T21:09:09Z
| 2020-10-20T23:19:04Z
| 2020-10-20T23:18:59Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Partially addresses #36407
This is a continuation of https://github.com/pandas-dev/pandas/pull/36434.
- Separated ``StringFormatter`` (or better ``ConsoleFormatter``?) from ``DataFrameFormatter``. Placed it into the new module (subject to discussion).
- Used composition of ``DataFrameFormatter`` in ``HTMLFormatter``, ``LatexFormatter``, ``CSVFormatter`` and ``StringFormatter``. It turned out that the inheritance of each of the formatters from the base ``DataFrameFormatter`` was too complicated to comprehend. The composition seems to suit here better.
- Created new class ``DataFrameRenderer`` to keep methods for outputs in each of the formats.
This is not the ultimate refactoring, but just one more step.
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CI: troubleshoot segfault
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| 4
| 2020-09-20T21:25:49Z
| 2021-11-20T23:21:19Z
| 2020-09-21T12:28:24Z
|
MEMBER
| null |
I can't reproduce the CI failures locally, so just rolling back a few commits to narrow it down.
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|
DOC: default value for pd.DateOffset() is 24 hours instead of 1 calendar day
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| 7
| 2020-09-21T01:43:17Z
| 2020-10-31T23:00:18Z
| 2020-10-31T23:00:18Z
|
CONTRIBUTOR
| null |
```python
>>>pd.__version__
1.1.1
>>>ts = pd.Timestamp('2011-3-27 00:00:00', tz='Europe/Helsinki')
>>>ts + pd.DateOffset(days=1)
Timestamp('2011-03-28 00:00:00+0300', tz='Europe/Helsinki')
>>>ts + pd.DateOffset()
Timestamp('2011-03-28 01:00:00+0300', tz='Europe/Helsinki')
>>>ts + pd.DateOffset(hours=24)
Timestamp('2011-03-28 01:00:00+0300', tz='Europe/Helsinki')
```
The default action for pd.DateOffset seems to be equal to pd.DateOffset(hours=24) instead of pd.DateOffset(days=1).
But the user guide says DateOffset object defaults to 1 calendar day.
<img width="611" alt="捕获" src="https://user-images.githubusercontent.com/41546976/93727690-db677480-fbee-11ea-9439-24979ebb85ce.PNG">
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MDU6SXNzdWU3MDUyMzk3MjY=
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BUG: Series.replace does not work as intended when replacing values with None
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[] | null | 3
| 2020-09-21T02:15:15Z
| 2020-09-21T16:33:26Z
| 2020-09-21T16:33:26Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> pd.Series([1, 2,2 ,3, 3])
>>> s.replace(2, None)
0 1
1 1
2 1
3 3
4 3
dtype: int64
```
#### Problem description
This should replace all the 2's with None.
#### Expected Output
```
0 1.0
1 NaN
2 NaN
3 3.0
4 3.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.el7.x86_64
Version : #1 SMP Thu Nov 8 23:39:32 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0.post20200710
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.18.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : 0.10.1
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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CI/CLN: update travis
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| 5
| 2020-09-21T02:55:11Z
| 2020-10-03T05:41:40Z
| 2020-09-24T14:58:54Z
|
MEMBER
| null |
update and cleanup Travis
closes #36601
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| 705,253,577
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|
Update outdated instructions in scripts/generate_pip_deps_from_conda.py
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DOC: Correct inconsistent description on default DateOffset setting
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| 21
| 2020-09-21T04:20:50Z
| 2020-10-31T23:50:19Z
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|
CONTRIBUTOR
| null |
correct inconsistent doc description on default DateOffset setting
- [x] closes #36512
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