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MDU6SXNzdWU1OTcwNDE5NDM=
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DOC: lreshape and wide_to_long references
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| 2020-04-09T05:59:41Z
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|
CONTRIBUTOR
| null |
#### Location of the documentation
* [General functions](https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/general_functions.html): Does not list `lreshape`
* [DataFrame reshaping, sorting, transposing](https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/frame.html#reshaping-sorting-transposing): Does not list `lreshape` or `wide_to_long`
* [pd.melt](https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/api/pandas.melt.html#pandas.melt): Does not list `lreshape` or `wide_to_long` in the "See Also" section
* [pd.pivot](https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/api/pandas.pivot.html): Include more related functions in the "See Also" section
* [pd.pivot_table](): Include more related functions in the "See Also" section
* [pd.wide_to_long](https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/api/pandas.wide_to_long.html#pandas.wide_to_long): Does not have a "See Also" section
* [pd.lreshape](https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/api/pandas.lreshape.html): _Does not exist_
#### Documentation problem
There remains an outstanding question as to whether `lreshape` and/or `wide_to_long` should be deprecated/removed (#15003). Until that question is resolved, the pandas documentation should include `lreshape` and `wide_to_long`.
More links to and between the related pivoting/melting functions should be included in their respective "See Also" sections of their docstrings.
#### Suggested fix for documentation
Fairly self-explanatory. PR will contain the suggested additions.
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lreshape and wide_to_long documentation (Closes #33417)
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| 7
| 2020-04-09T06:39:07Z
| 2020-08-19T13:05:48Z
| 2020-08-19T13:05:41Z
|
CONTRIBUTOR
| null |
- [ ] closes #33417
- [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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pandas 1.0.0 conflict with Anaconda
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NONE
| null |
Trying to install or update from pandas 0.25.1 to 1.0.0 I'm getting into a long process of examining conflicts with existing packages:
```
conda install pandas=1.0.0
```
Installation log:
> Collecting package metadata (current_repodata.json): done
> Solving environment: failed with initial frozen solve. Retrying with flexible solve.
> Collecting package metadata (repodata.json): done
> Solving environment: failed with initial frozen solve. Retrying with flexible solve.
> Solving environment: |
> Found conflicts! Looking for incompatible packages.
> This can take several minutes. Press CTRL-C to abort
Followed by:
> Examining conflict for psutil parso spyder-kernels sphinxcontrib-websupport tblib urllib3 jupyter_core glob2 asn1crypto docutils sphinxcontrib boto sortedcollections matplotlib entrypoints markupsafe astropy...
And a conflict check against individual packages which takes a long time.
Working with pandas standalone or on virtual env is not an option as I need to run my function using a jupyter notebook. Do I need to write it with version 0.25.1 or is there a work around that I may be missing here?
|
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BUG: set_levels set wrong order levels for MutiIndex
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| 5
| 2020-04-09T07:43:43Z
| 2020-05-25T22:43:39Z
| 2020-05-25T22:43:28Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'one', 'one', 'one', 'one', 'one', 'one', 'one'],
['three', 'four', 'three', 'four', 'three', 'four', 'three', 'four'],
]
df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
df.index.set_levels([3, 4], level=2, inplace=True)
```
#### Problem description
The changed MutiIndex isn't the same order of assigned list.
```
0 1 2 3
bar one 4 -0.606998 -1.225794 0.284541 -0.395946
3 2.693403 0.803962 0.421412 -0.369766
baz one 4 0.182893 0.364850 0.499927 0.271245
3 -0.783458 -0.072504 -1.081301 -0.091093
foo one 4 2.630340 -0.442308 0.519715 -0.412296
3 -0.813574 0.323040 -0.356866 1.877196
qux one 4 -0.578158 0.511383 0.613484 -0.483777
3 -0.161618 -0.481930 0.012694 -1.001978
```
#### Expected Output
```
0 1 2 3
bar one 3 -0.606998 -1.225794 0.284541 -0.395946
4 2.693403 0.803962 0.421412 -0.369766
baz one 3 0.182893 0.364850 0.499927 0.271245
4 -0.783458 -0.072504 -1.081301 -0.091093
foo one 3 2.630340 -0.442308 0.519715 -0.412296
4 -0.813574 0.323040 -0.356866 1.877196
qux one 3 -0.578158 0.511383 0.613484 -0.483777
4 -0.161618 -0.481930 0.012694 -1.001978
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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API: More permissive conversion to StringDtype
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| 2020-04-09T13:05:15Z
| 2020-05-25T11:50:11Z
| 2020-04-09T13:23:31Z
|
CONTRIBUTOR
| null |
- [x] closes #31204
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a proposal to make using ``StringDtype`` more permissive and be usable inplace of ``dtype=str``.
ATM converting to StringDtype will only accept arrays that are ``str`` already, meaning you will often have to use ``astype(str).astype("string")`` to be sure not to get errors, which can be tedious. For example these fail in master and work in this PR:
```python
>>> pd.Series([1,2, np.nan], dtype="string")
0 1
1 2
2 <NA>
dtype: string
>>> pd.array([1,2, np.nan], dtype="string")
<StringArray>
['1', '2', <NA>]
Length: 3, dtype: string
>>> pd.Series([1,2, np.nan]).astype("string")
0 1.0
1 2.0
2 <NA>
dtype: string
>>> pd.Series([1,2, np.nan], dtype="Int64").astype("string")
0 1
1 2
2 <NA>
dtype: string
```
etc. now work. Previously the above all gave errors.
Obviously tests and doc updates are still missing, but I would appreciate feedback if this solution looks ok.
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|
MEMBER
| null |
Upcoming pyarrow 0.17 release will include an upgraded feather format.
This PRs updates pandas for that, more specifically ensures the new keywords can be passed through (the basics should keep working out of the box, since the public API did not change), and small update to the tests
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MEMBER
| null |
I believe this was supposed to be temporary, so quick follow up to #29431 @TomAugspurger
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CLN: remove unnecessary branches in Series.__setitem__
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| 0
| 2020-04-09T15:04:59Z
| 2021-11-20T23:23:12Z
| 2020-04-09T18:28:32Z
|
MEMBER
| null |
Eventually I'd like to get the structure of `__setitem__` to parallel the structure of `__getitem__`. Some big improvements to both will be available following #33404.
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| 0
| 2020-04-09T15:25:23Z
| 2020-04-10T17:47:25Z
| 2020-04-10T17:47:25Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print(pd.__version__)
sr = pd.Series(['a','b','c','d','e'])
others = pd.Index(['a','b','c','d','e'])
result = sr.str.cat(others=others)
print(result)
1.0.3
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
dtype: object
```
#### Problem description
The result should be the same as when `others` is a list or numpy array with the same values. The result is correct for pandas < 1.0.
#### Expected Output
```
0 aa
1 bb
2 cc
3 dd
4 ee
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-134-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.1.post20200322
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.7.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDU6SXNzdWU1OTczOTExNzc=
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BUG: core.missing._akima_interpolate will raise AttributeError
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| 8
| 2020-04-09T16:08:50Z
| 2020-04-26T19:57:38Z
| 2020-04-26T19:57:38Z
|
MEMBER
| null |
```
from scipy import interpolate
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der]
```
There is no `interpolate._isscalar` so this would raise if it were ever reached. AFAICT we have only one test that gets to this function, must always have `der == 0`
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CLN: avoid accessing private functions
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| 2020-04-10T18:02:12Z
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MEMBER
| null |
xref #33394
@simonjayhawkins thoughts on how to handle the mypy complaint?
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DOC: Data links in Pandas API Reference are broken 404
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| 7
| 2020-04-09T16:27:00Z
| 2020-04-14T09:58:30Z
| 2020-04-14T09:58:30Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/reference/api/pandas.plotting.parallel_coordinates.html
...probably many examples in other sections
#### Documentation problem
Results in 404 not found error
df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
'/pandas/tests/data/csv/iris.csv')
#### Suggested fix for documentation
The GitHub site should be "raw.githubusercontent.com"
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MDExOlB1bGxSZXF1ZXN0NDAxNTIzNDU2
| 33,429
|
DOC: Fix heading capitalization in doc/source/whatsnew - part3 (#32550)
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| 1
| 2020-04-09T16:29:36Z
| 2020-04-10T17:48:10Z
| 2020-04-10T17:48:06Z
|
CONTRIBUTOR
| null |
- [ ] Modify files v0.8.0.rst, v0.8.1.rst, v0.9.0.rst, v0.9.1.rst, v010.0.rst, v0.10.1.rst, v0.11.0.rst, v0.12.0.rst, v0.13.0.rst, v0.13.1.rst
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| 597,407,137
|
MDU6SXNzdWU1OTc0MDcxMzc=
| 33,430
|
DOC: Development guide giving 404
|
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| 2020-04-09T16:35:35Z
| 2020-04-09T18:18:42Z
| 2020-04-09T17:33:04Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/docs/development/index.html is giving a 404 right now. Haven't looked into why cc @datapythonista.
|
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MDExOlB1bGxSZXF1ZXN0NDAxNTM1NjY1
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Temporary fix for Py38 build issues on macOS
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| 1
| 2020-04-09T16:52:58Z
| 2020-04-09T18:27:01Z
| 2020-04-09T18:26:58Z
|
MEMBER
| null |
ref #33239
works around issues confirmed by @jbrockmendel and @TomAugspurger
Ideally could fix upstream, but may not be worth the effort given the level of nuance
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remove flake8-rst cap
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|
MEMBER
| null |
looks like this was capped a ways back in #24756 - tried locally and didn't see any issues so may be fine to lift the cap
@datapythonista
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BUG: Using `kwargs` in `Rolling.apply()`
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| 1
| 2020-04-09T17:07:56Z
| 2020-05-26T09:29:05Z
| 2020-05-15T14:11:39Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
from functools import partial
import numpy as np
import pandas as pd
def foo(x, par):
return np.sum(x + par)
df = pd.DataFrame({"gr": [1, 1], "a": [1, 2]})
# With `kwargs`
try:
print(df.groupby("gr")["a"].rolling(1).apply(foo, kwargs={"par": 10}))
except Exception as e:
print(e)
# Using `partial`
bar = partial(foo, par=10)
print(df.groupby("gr")["a"].rolling(1).apply(bar))
```
#### Problem description
**Expected**: using appropriate `kwargs` argument for `pandas.core.window.rolling.Rolling.apply()` should be equivalent to premodifying supplied function with `functools.partial()` (because it is stated in [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.rolling.Rolling.apply.html)).
**Current**: `kwargs` doesn't seem to be used at all, because it results into "missing 1 required positional argument" exception.
**Note**: This works as expected in pandas 0.25.3, but gives error as early as 1.0.0.
#### Expected Output
```
gr
1 0 11.0
1 12.0
Name: a, dtype: float64
gr
1 0 11.0
1 12.0
Name: a, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-29-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ru_UA.UTF-8
LOCALE : ru_UA.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1OTc0MzMxNjU=
| 33,434
|
tm.external_error_raised instead of tm.assertRaises in the documentation
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|
CONTRIBUTOR
| null |
I tried to use tm.assertRaises but got the following error:
with tm.assertRaises(ValueError):
AttributeError: module 'pandas.util.testing' has no attribute 'assertRaises'
After carefully assessing whether I had all the dependencies, I looked into the testing.py files and realised that there is no assertRaises function although the testing documentation advises to use the function.(https://github.com/pandas-dev/pandas/wiki/Testing) I believe the documentation should be updated to tm.external_error_raised.
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MDExOlB1bGxSZXF1ZXN0NDAxNTUyNDIy
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Support downcasting of nullable arrays
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NONE
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Addressing GH #33013. Supports downcasting of nullable dtypes by transferring elements in extension array to ndarray to avoid TypeError thrown by np.allclose. Enhancement documented on v1.1.0 in the "other enhancements" section. Unit test written in test_to_numeric.
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MDExOlB1bGxSZXF1ZXN0NDAxNTY5MTgz
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BUG: Fix bug when concatenating Index of strings
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MEMBER
| null |
- [ ] closes #33425
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: DataFrame constructor doesn't validate index/data lengths with EA data
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| 2020-08-07T02:20:03Z
| 2020-08-07T02:20:03Z
|
MEMBER
| null |
```
dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
>>> pd.DataFrame(dti, index=range(4))
0
0 2016-01-01 00:00:00-08:00
1 2016-01-02 00:00:00-08:00
2 2016-01-03 00:00:00-08:00
3
>>> pd.DataFrame(dti.tz_localize(None), index=range(4))
ValueError: Shape of passed values is (3, 1), indices imply (4, 1)
```
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MDU6SXNzdWU1OTc0ODEyODA=
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|
BUG: Inconsistent behavior of pd.DataFrame.drop
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| 2020-04-09T18:46:41Z
| 2020-08-21T17:05:58Z
| 2020-04-10T10:29:10Z
|
NONE
| null |
**Problem description**
Until an hour ago I thought that I can safely omit the use of inplace = True (which is not recommended, e.g., #30484) and instead use inplace = False and directly assign the result.
For example, I thought that
```python
df.drop(columns = ['a'], inplace = True)
```
can be replaced by
```python
df = df.drop(columns = ['a'])
```
where df is a pd.DataFrame.
However, this does not seem to be case when I use these operations within a function.
```python
# 1) direct assignment, .drop
df = pd.DataFrame([1])
def tfun(df):
df['a'] = 2
df = df.drop(columns = ['a'])
tfun(df)
print(df.columns)
>>> Index([0, 'a'], dtype='object')
```
Compare this with
```python
# 2) direct assignment, .drop with inplace (or del)
df = pd.DataFrame([1])
def tfun(df):
df['a'] = 2
df.drop(columns = ['a'], inplace = True) # using del df['a'] leads to the same result
tfun(df)
print(df.columns)
>>> Index([0], dtype='object')
```
The result of 2) is as expected (we add column 'a' and immediately remove it). However, I am very confused about the result of 1). The removal of column 'a' which is done inside tfun is not reflected in df outside after tfun is applied.
It gets even stranger when we use .assign to add column 'a' to df inside tfun:
```python
# 3) .assign, .drop
df= pd.DataFrame([1])
def tfun(df):
df= df.assign(a = 2)
df= df.drop(columns = ['a'])
tfun(df)
print(df.columns)
>>> RangeIndex(start=0, stop=1, step=1)
```
Now, column 'a' is removed, although the type of the remaining column is now a RangeIndex.
I definitely would expect that the result of 3) is equal to the result of 1). What is going on here?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDU6SXNzdWU1OTc0ODQ3MjM=
| 33,439
|
BUG: Mysterious Series.get() with Int64Index bug
|
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| 13
| 2020-04-09T18:53:17Z
| 2020-05-02T18:09:55Z
| 2020-05-01T00:26:36Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
def get_t_minus_n_val(n):
def f(x):
# assume index is days_from_origin
if n == 3 and x.index[-1] == 27:
import pdb; pdb.set_trace()
print(x.index[-1], n, x.get(x.index[-1] - n)) # for debugging
return x.get(x.index[-1] - n, np.NaN)
f.__name__ = f"t_minus_{n}_days"
return f
res = data_df.set_index("days_from_origin_").groupby("device").agg({"metric1": get_t_minus_n_val(3)})
> <ipython-input-452-9b903578cb56>(7)f()
-> print(x.index[-1], n, x.get(x.index[-1] - n)) # for debugging
(Pdb) x.get(24)
(Pdb) x.iloc[-5:]
23 60221064
24 232131096
25 46413584
26 133181464
27 229400712
Name: metric1, dtype: int64
(Pdb) 24 in x.index
False
(Pdb) 24 in x.index.values
True
Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27],
dtype='int64')
(Pdb) x.to_dict().get(24)
232131096
```
#### Problem description
I am not able to get a minimum repro of this as it is seems to be data dependent. Instead, I am capturing the bug by showing you my pdb debugging statements in hopes that someone that knows the code can figure out where the problem is.
Basically, I am doing a custom agg function which needs to grab an element from a Series object, and even though the value clearly exists in the index, it returns None. If I first convert to dict, then it does get the value.
I was not able to repro this by simply creating a new series and calling .get on it... that works just fine. And in fact, if I filter the dataframe for just that device, then it works just fine. It is definitely some sort of internal state issue which happens as a result of groupby having more records...
#### Expected Output
obviously I expect x.get(24) to return the correct value instead of None.
#### 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 : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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| 1
| 2020-04-09T19:41:52Z
| 2020-04-10T17:00:00Z
| 2020-04-10T16:59:56Z
|
MEMBER
| null |
- [x] closes #33173
- [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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| 597,513,940
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MDExOlB1bGxSZXF1ZXN0NDAxNjEyNDI0
| 33,441
|
BUG: Timedelta == ndarray[td64]
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| 2
| 2020-04-09T19:49:45Z
| 2020-04-10T18:06:09Z
| 2020-04-10T15:58:10Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xref #33346
Between the two of these, we should be able to get rid of this kludge: https://github.com/pandas-dev/pandas/blob/master/pandas/core/internals/managers.py#L629
IIRC there were some test_coercion xfails related to that kludge, will be worth revisiting.
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MDExOlB1bGxSZXF1ZXN0NDAxNjIyODA2
| 33,442
|
ENH: Add prod to masked_reductions
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| 6
| 2020-04-09T20:14:40Z
| 2020-04-12T23:21:57Z
| 2020-04-12T22:59:02Z
|
MEMBER
| null |
Adding prod to /core/array_algos/masked_reductions.py and using them for IntegerArray and BooleanArray. This also seems to offer a decent speedup over nanops like the other reductions:
```python
# Branch
[ins] In [3]: %timeit arr.prod() # arr = pd.Series([None, 0, 1, 2] * 10_000, dtype="Int64")
102 µs ± 379 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
[ins] In [5]: %timeit arr.prod() # arr = pd.Series([0, 0, 1, 2] * 10_000, dtype="Int64")
82.6 µs ± 752 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
# Master
[ins] In [4]: %timeit arr.prod() # arr = pd.Series([None, 0, 1, 2] * 10_000, dtype="Int64")
291 µs ± 6.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
[ins] In [6]: %timeit arr.prod() # arr = pd.Series([0, 0, 1, 2] * 10_000, dtype="Int64")
78.6 µs ± 2.5 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
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BUG: Low performance of DataFrame operations
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[] | null | 3
| 2020-04-09T20:56:23Z
| 2020-08-18T22:26:48Z
| 2020-08-18T22:26:48Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import timeit
import pandas as pd
import numpy as np
def div_simple(df):
return df.div(df.sum(axis=0), axis=1)
def div_transposed(df):
df = df.T
return df.div(df.sum(axis=1), axis=0).T
def div_python(df):
data = df.values.tolist()
for i in range(COLS):
s = sum(data[j][i] for j in range(ROWS))
for j in range(ROWS):
data[j][i] /= s
return data
COLS = 1000
ROWS = 10
df = pd.DataFrame(
np.random.randint(0,100,size=(ROWS, COLS)),
columns=list(range(COLS)),
)
for func in (div_simple, div_transposed, div_python):
print(f'{timeit.timeit(lambda: func(df.copy()), number=100):08.4f}s\t{func.__name__}')
```
#### Problem description
I've faced a strange performance issue. The pandas' version works much longer than a bare-python version.
```
100 x 100
001.6424s div_simple
000.0596s div_transposed
000.1856s div_python
```
```
10 x 1000
000.2322s div_simple
000.0576s div_transposed
000.1780s div_python
```
```
1000 x 10
015.3119s div_simple
000.0560s div_transposed
000.2327s div_python
```
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : ru_RU.UTF-8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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DOC: Fix heading capitalization in doc/source/whatsnew - part4 (#32550)
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| 6
| 2020-04-09T22:21:38Z
| 2020-04-17T02:55:52Z
| 2020-04-17T02:55:35Z
|
CONTRIBUTOR
| null |
Add exceptions to the list in 'scripts/validate_rst_title_capitalization.py'
- [ ] Modify files in doc/source/whatsnew: v0.14.0.rst, v0.14.1.rst, v0.15.0.rst, v0.15.1.rst, v0.15.2.rst, v0.16.0.rst, v0.16.1.rst, v0.16.2.rst, v0.17.0.rst, v0.17.1.rst
- [ ] Modify files in scripts: validate_rst_title_capitalization.py
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MDExOlB1bGxSZXF1ZXN0NDAxNzE5ODAz
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REF: remove replace_list kludge
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| 2020-04-10T20:57:33Z
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MEMBER
| null |
This sits on top of #33441
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MDExOlB1bGxSZXF1ZXN0NDAxNzI1MjE1
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BUG: Fix ValueError when grouping by read-only category (#33410)
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CONTRIBUTOR
| null |
- [x] closes #33410
- [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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REF: de-nest Series getitem/setitem checks
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| 2020-04-10T03:59:45Z
| 2020-04-14T03:17:57Z
| 2020-04-10T18:21:13Z
|
MEMBER
| null |
ATM the first checks we do in `Series.__getitem__` and `__setitem__` are for `get_loc` with some not-totally-straightforward try/except logic.
This moves the slice case and the com.is_bool_indexer case up, and aligns the ordering of the checks between getitem/setitem.
Getting the com.is_bool_indexer case out from the try/except in setitem is particularly nice.
That said, this _may_ not be what we want to do. This will very slightly improve performance in the cases moved up, and equally slightly harm performance in the scalar case. AFAIK the current ordering is not based on optimizing one case over the others.
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MDExOlB1bGxSZXF1ZXN0NDAxNzkxNzQ2
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|
Whitelist std and var for use with custom rolling windows
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| 11
| 2020-04-10T07:21:48Z
| 2020-04-17T06:27:03Z
| 2020-04-17T02:54:51Z
|
MEMBER
| null |
- [X] xref #32865
- [X] 0 tests added / 0 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
### The problem
While researching what funcitons are broken for #32865 , I added `std` and `var` to the list since their output didn't match numpy output. I have since discovered that this is because we default to the sample variance formula for all window calculations.
After closer examination, the algorithm itself turned out to be very robust against custom indexers. It is even resilient against non-monothonic window starts and ends.
There is nothing to do there, so we should revert blacklisting `std` and `var`. I don't think tests are necessary, since we aren't changing anything.
### Food for thought
Some background information to make our decision here more informed:
The reason I first believed these functions to be broken is because using the sample variance formula for sliding windows makes no sense to me from a statistical viewpoint. We use sample variance when the dataset is a sample drawn from a larger population. **A window is not a sample**. When we calculate sliding window variance, we aren't interested in getting the correct variance for some underlying general window, we are interested in computing it correctly for each window, and thus each window is the population.
However, as we discussed with @mroeschke:
1) Usually users are interested in calculating variance for large windows, and the difference between formulas for variance is proportional to `1 / (window_size - 1) - 1 / window_size`
2) We use sample variance as a default everywhere else in pandas.
3) The user can specify `rolling.var(ddof=0)` to set degrees of freedom to zero and get population variance, if they know what they want and are aware that pandas uses sample variance by default.
So the default doesn't make much sense, but it is consistent with the rest of our software, and the harm is negligible for most use cases. The harm in changing the default would be that people who know the package well might expect the sample variance default.
Apologies for the long read. It's a part of my job to find mistakes in data science models, so I'm sensitive to stuff like this.
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BUG: Series.any/all with level keyword should preserve boolean dtype
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| 2020-04-10T07:51:30Z
| 2021-04-13T17:12:16Z
| 2021-04-13T17:12:16Z
|
MEMBER
| null |
```
In [5]: s = pd.Series([False, False, True, True, False, True],
index=[0, 0, 1, 1, 2, 2], dtype="boolean")
In [6]: s.all(level=0)
Out[6]:
0 False
1 True
2 False
dtype: bool
```
The dtype of the result could also be boolean instead of bool.
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BUG: maximum of pd.Series([np.nan],dtype=ordered_category) raise
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| 7
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| 2020-05-26T09:33:58Z
| 2020-04-13T08:16:59Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [1]: pd.__version__
Out[1]: '1.0.3'
In [2]: pd.Series([np.nan],dtype=pd.CategoricalDtype([0,1],ordered=True)).max()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-5a47d189b696> in <module>
----> 1 pd.Series([np.nan],dtype=pd.CategoricalDtype([0,1],ordered=True)).max()
~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/generic.py in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
11213 return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
11214 return self._reduce(
> 11215 f, name, axis=axis, skipna=skipna, numeric_only=numeric_only
11216 )
11217
~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/series.py in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
3870
3871 if isinstance(delegate, Categorical):
-> 3872 return delegate._reduce(name, skipna=skipna, **kwds)
3873 elif isinstance(delegate, ExtensionArray):
3874 # dispatch to ExtensionArray interface
~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/arrays/categorical.py in _reduce(self, name, axis, **kwargs)
2123 if func is None:
2124 raise TypeError(f"Categorical cannot perform the operation {name}")
-> 2125 return func(**kwargs)
2126
2127 @deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
212 else:
213 kwargs[new_arg_name] = new_arg_value
--> 214 return func(*args, **kwargs)
215
216 return cast(F, wrapper)
~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/arrays/categorical.py in max(self, skipna)
2188 if not good.all():
2189 if skipna:
-> 2190 pointer = self._codes[good].max()
2191 else:
2192 return np.nan
~/.pyenv/versions/3.7.4/lib/python3.7/site-packages/numpy/core/_methods.py in _amax(a, axis, out, keepdims, initial, where)
28 def _amax(a, axis=None, out=None, keepdims=False,
29 initial=_NoValue, where=True):
---> 30 return umr_maximum(a, axis, None, out, keepdims, initial, where)
31
32 def _amin(a, axis=None, out=None, keepdims=False,
ValueError: zero-size array to reduction operation maximum which has no identity
```
In the older version, the same code didn't raise an error.
```python
In [10]: pd.__version__
Out[10]: '0.25.3'
In [11]: pd.Series([np.nan],dtype=pd.CategoricalDtype([0,1],ordered=True)).max()
...:
Out[11]: nan
```
#### Problem description
Because of this behavior, I failed to df.groupby().max() for ordered categories.
#### Expected Output
Expected output should be np.nan
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : 0.2.2
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : 0.8.5
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.2
numba : None
</details>
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DOC: Be explicit whether a view or copy is returned
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| 1
| 2020-04-10T10:27:07Z
| 2020-06-23T19:45:34Z
| 2020-06-23T19:44:19Z
|
NONE
| null |
#### Location of the documentation
For instance,
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html
#### Documentation problem
Sometimes the documentation is explicit that a copy is returned, e.g., https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html -> "Returns a new object ...", sometimes it is not or not explicitly stated, e.g., https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html -> "Drop specified labels from rows or columns."
#### Suggested fix for documentation
I wrongly assumed that pandas.DataFrame.drop returns a view because the first line of the docstring "Drop specified labels from rows or columns." suggests that, in my view. Later it is mentioned what happens if you set inplace = True. But you can only assume what happens if the default argument is used. Thus, the documentation is not clear w.r.t. this point and may be confusing to users, see #33438 or #30484.
I would suggest that the documentation reads `"Drop specified labels from rows or columns and return a new object"' and/or "inplace : bool, default False. If False, return a copy. If True, do operation inplace and return None." This also applies to other methods that have the inplace parameter.
Since the use of inplace = True seems to be discouraged (#30484) it would also make sense to mention that in the documentation?
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ENH: Use fsspec for reading/writing from/to S3, GCS, Azure Blob, etc.
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| 26
| 2020-04-10T10:42:26Z
| 2020-06-23T13:43:25Z
| 2020-06-23T13:43:25Z
|
NONE
| null |
#### Is your feature request related to a problem?
Currently pandas has some support for S3 and GCS using the pandas.io.{gcs,s3} modules, which are based on S3fs and gcsfs.
It seems like we could easily broaden the support for different filesystems by leveraging the fsspec library (https://pypi.org/project/fsspec/) and its interface implementations (see https://github.com/intake/filesystem_spec/blob/master/fsspec/registry.py for some examples) to read/write files in pandas.
This way, we would also be able to use filesystems such as Azure-based storage systems directly from pandas.
#### Describe the solution you'd like
I'd like to be able to use the different file systems supported by fsspec in pandas with something like:
```
import pandas as pd
df1 = pd.read_csv("abfs://my_container/my_file.csv")
df1.to_json("file:///some/local/path.json") # Also works without file:// prefix.
df2 = pd.read_csv("s3://my_bucket/my_file.csv")
...
```
#### API breaking implications
In principle, it looks as if we could cover most of the work by adapting get_filepath_or_buffer in pandas/io/common.py to use fsspec. We would of course have to test if fsspec doesn't break anything compared to the current implementations.
One challenge is that some storage systems require extra arguments (called storage options in fsspec). For example, Azure blob requires the user to pass two storage options (account_name and account_key) to be able to access the storage. We would need to consider how to pass these options to the correct methods, either by (a) setting these options globally for a given type of storage or (b) passing the options through the pd.read_* functions and pd.DataFrame.to_* methods.
#### Describe alternatives you've considered
This seems like a change that would have a small impact, as pandas already uses S3fs and gcsfs, which are both implementations of the broader fsspec interface. It should also provide support for a great number of different filesystems with minimal changes, compared to adding support for each filesystem ourselves. As such, I would think it is the preferred approach.
Another approach would be to add support for each additional filesystem as it comes along. This however would require adding and maintaining code for each filesystem, which seems less preferable.
#### Additional context
I recently implemented similar wrappers for pandas code at one of our clients, and am therefore somewhat familiar with fsspec etc. I would be happy to see if we can contribute these ideas + code to the pandas project.
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CLN/TYP: redundant casts and unused ignores
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Bump cython asv
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| 0
| 2020-04-10T13:07:26Z
| 2020-04-10T13:53:37Z
| 2020-04-10T13:53:34Z
|
CONTRIBUTOR
| null |
We were pulling in an older version, possibly from defaults.
I think we should plan a major update of all these (notably python 3.6 -> 3.8) but that'll require some care to not invalidate the results at pandas.pydata.org/speed/pandas.
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TYP: enable disallow-untyped-decorators in CI
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/33160#issuecomment-608707397
in order to ensure adding decorators or changing existing decorators don't lose types on our typed functions, we could enable this flag, after resolving the current violations. (expand details)
<details>
$ mypy pandas --disallow-untyped-decorators
pandas\core\window\numba_.py:58: error: Untyped decorator makes function "roll_apply" untyped
pandas\tseries\offsets.py:458: error: Untyped decorator makes function "freqstr" untyped
pandas\core\nanops.py:97: error: Untyped decorator makes function "f" untyped
pandas\core\nanops.py:477: error: Untyped decorator makes function "nansum" untyped
pandas\core\nanops.py:799: error: Untyped decorator makes function "nansem" untyped
pandas\core\nanops.py:849: error: Untyped decorator makes function "reduction" untyped
pandas\core\nanops.py:880: error: Untyped decorator makes function "nanargmax" untyped
pandas\core\nanops.py:916: error: Untyped decorator makes function "nanargmin" untyped
pandas\core\nanops.py:952: error: Untyped decorator makes function "nanskew" untyped
pandas\core\nanops.py:1036: error: Untyped decorator makes function "nankurt" untyped
pandas\core\nanops.py:1129: error: Untyped decorator makes function "nanprod" untyped
pandas\core\nanops.py:1309: error: Untyped decorator makes function "nancorr" untyped
pandas\core\nanops.py:1364: error: Untyped decorator makes function "nancov" untyped
pandas\core\apply.py:136: error: Untyped decorator makes function "dtypes" untyped
pandas\tseries\frequencies.py:333: error: Untyped decorator makes function "is_unique" untyped
pandas\tseries\frequencies.py:337: error: Untyped decorator makes function "is_unique_asi8" untyped
pandas\core\arrays\period.py:270: error: Untyped decorator makes function "dtype" untyped
pandas\core\base.py:198: error: Untyped decorator makes function "ndim" untyped
pandas\core\indexes\base.py:1619: error: Untyped decorator makes function "is_unique" untyped
pandas\core\indexes\base.py:1973: error: Untyped decorator makes function "inferred_type" untyped
pandas\core\indexes\base.py:1980: error: Untyped decorator makes function "is_all_dates" untyped
pandas\core\indexes\base.py:2021: error: Untyped decorator makes function "hasnans" untyped
pandas\core\indexes\base.py:3840: error: Untyped decorator makes function "array" untyped
pandas\core\arrays\integer.py:62: error: Untyped decorator makes function "is_signed_integer" untyped
pandas\core\arrays\integer.py:66: error: Untyped decorator makes function "is_unsigned_integer" untyped
pandas\core\arrays\integer.py:74: error: Untyped decorator makes function "numpy_dtype" untyped
pandas\core\arrays\integer.py:79: error: Untyped decorator makes function "kind" untyped
pandas\core\arrays\integer.py:83: error: Untyped decorator makes function "itemsize" untyped
pandas\core\arrays\integer.py:341: error: Untyped decorator makes function "dtype" untyped
pandas\core\arrays\categorical.py:497: error: Untyped decorator makes function "size" untyped
pandas\core\arrays\categorical.py:504: error: Untyped decorator makes function "itemsize" untyped
pandas\core\indexes\numeric.py:452: error: Untyped decorator makes function "is_unique" untyped
pandas\core\indexes\range.py:170: error: Untyped decorator makes function "_int64index" untyped
pandas\core\indexes\range.py:280: error: Untyped decorator makes function "nbytes" untyped
pandas\core\indexes\range.py:325: error: Untyped decorator makes function "is_monotonic_increasing" untyped
pandas\core\indexes\range.py:329: error: Untyped decorator makes function "is_monotonic_decreasing" untyped
pandas\core\indexes\multi.py:1133: error: Untyped decorator makes function "dtype" untyped
pandas\core\indexes\multi.py:1152: error: Untyped decorator makes function "nbytes" untyped
pandas\core\indexes\multi.py:1414: error: Untyped decorator makes function "inferred_type" untyped
pandas\core\indexes\multi.py:1450: error: Untyped decorator makes function "is_monotonic_increasing" untyped
pandas\core\indexes\multi.py:1474: error: Untyped decorator makes function "is_monotonic_decreasing" untyped
pandas\core\indexes\datetimes.py:279: error: Untyped decorator makes function "_is_dates_only" untyped
pandas\core\indexes\period.py:348: error: Untyped decorator makes function "_int64index" untyped
pandas\core\indexes\interval.py:384: error: Untyped decorator makes function "_multiindex" untyped
pandas\core\indexes\interval.py:388: error: Untyped decorator makes function "values" untyped
pandas\core\indexes\interval.py:430: error: Untyped decorator makes function "is_monotonic_decreasing" untyped
pandas\core\indexes\interval.py:1031: error: Untyped decorator makes function "intersection" untyped
pandas\core\groupby\ops.py:257: error: Untyped decorator makes function "is_monotonic" untyped
pandas\core\groupby\ops.py:270: error: Untyped decorator makes function "codes_info" untyped
pandas\core\groupby\ops.py:288: error: Untyped decorator makes function "ngroups" untyped
pandas\core\groupby\ops.py:298: error: Untyped decorator makes function "result_index" untyped
pandas\core\groupby\ops.py:818: error: Untyped decorator makes function "reconstructed_codes" untyped
pandas\core\groupby\grouper.py:395: error: Untyped decorator makes function "result_index" untyped
pandas\core\groupby\grouper.py:420: error: Untyped decorator makes function "groups" untyped
pandas\core\window\rolling.py:1802: error: Untyped decorator makes function "is_datetimelike" untyped
pandas\core\window\rolling.py:1808: error: Untyped decorator makes function "_on" untyped
pandas\tests\frame\methods\test_replace.py:14: error: Untyped decorator makes function "mix_ab" untyped
pandas\tests\frame\methods\test_replace.py:19: error: Untyped decorator makes function "mix_abc" untyped
Found 58 errors in 20 files (checked 987 source files)
</details>
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TYP: F used in decorators to _typing
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MEMBER
| null |
xref #27404
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MDU6SXNzdWU1OTc5MTI1MDk=
| 33,457
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REGR: setting column with setitem should not modify existing array inplace
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[] | null | 34
| 2020-04-10T14:18:57Z
| 2021-11-08T18:31:46Z
| null |
MEMBER
| null |
So consider this example of a small dataframe with a nullable integer column:
```
def recreate_df():
return pd.DataFrame({'int': [1, 2, 3], 'int2': [3, 4, 5],
'float': [.1, .2, .3],
'EA': pd.array([1, 2, None], dtype="Int64")
})
```
Assigning a new column with `__setitem__` (`df[col] = ...`) normally does not even preserve the dtype:
```
In [2]: df = recreate_df()
...: df['EA'] = np.array([1., 2., 3.])
...: df['EA'].dtype
Out[2]: dtype('float64')
In [3]: df = recreate_df()
...: df['EA'] = np.array([1, 2, 3])
...: df['EA'].dtype
Out[3]: dtype('int64')
```
When assigning a new nullable integer array, it of course keeps the dtype of the assigned values:
```
In [4]: df = recreate_df()
...: df['EA'] = pd.array([1, 2, 3], dtype="Int64")
...: df['EA'].dtype
Out[4]: Int64Dtype()
```
However, in this case you now also have the tricky side-effect of being in place:
```
In [5]: df = recreate_df()
...: original_arr = df.EA.array
...: df['EA'] = pd.array([1, 2, 3], dtype="Int64")
...: original_arr is df.EA.array
Out[5]: True
```
I don't think this behaviour should depend on the values being set, and setitem should always replace the array of the ExtensionBlock.
Because with the above way, you can unexpectedly alter the data with which you created the dataframe. See also a different example using Categorical of this at the original PR that introduced this: https://github.com/pandas-dev/pandas/pull/32831#issuecomment-611994512
cc @jbrockmendel
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TST: concat(..., copy=False) with datetime tz-aware data raises ValueError: cannot create a DatetimeTZBlock without a tz
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| 5
| 2020-04-10T15:30:26Z
| 2020-07-13T12:55:29Z
| 2020-07-13T12:55:12Z
|
CONTRIBUTOR
| null |
…n version 0.24.1 but passes on current version
- [x] closes #25257
- [x] tests added / passed
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| 597,962,466
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MDExOlB1bGxSZXF1ZXN0NDAxOTY2NDAy
| 33,459
|
asv config to use `build_ext` instead of `build`
|
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| 2020-04-10T15:59:30Z
| 2020-04-10T19:27:31Z
| 2020-04-10T19:26:29Z
|
MEMBER
| null |
Not 100% sure on this change, feel free to close at any time.
---
I have not seen a single mention in the docs of ```python setup.py build``` there was only mention of ```python setup.py build_ext```, so I thought that asv should follow that as well (I could be wrong here).
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MDU6SXNzdWU1OTc5NzgzMTM=
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BUG: When using apply on a DataFrame with SparseDtype, passing raw=True doesn't have any effect.
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| 2020-04-10T16:33:45Z
| 2020-08-21T17:05:58Z
| 2020-04-10T18:50:39Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
pd.DataFrame(
[[0, 1, 0, 2, 0, 3, 0, 4]],
dtype=pd.SparseDtype(np.dtype("int")),
).apply(lambda x: type(x), raw=True, axis=1)
```
Output:
`0 <class 'pandas.core.series.Series'>`
#### Problem description
When using apply on a DataFrame with SparseDtype, passing raw=True doesn't have any effect.
#32425 is related but did not solve it.
#### Expected Output
`0 <class 'numpy.ndarray'>`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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CI: fix lint issue
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MEMBER
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https://github.com/pandas-dev/pandas/runs/577244968
Linting .py code
##[error]./pandas/core/internals/blocks.py:11:1:F401:'pandas._libs.tslibs.Timedelta' imported but unused
Linting .py code DONE
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BUG: None converted to NaN after groupby first and last
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| 1
| 2020-04-10T17:48:02Z
| 2020-05-26T09:36:35Z
| 2020-04-17T02:53:39Z
|
CONTRIBUTOR
| null |
- [x] closes #32800
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR solves #32800. In #32124 `not checknull()` was introduced to `groupfirst()` and `grouplast()`. `None` is not caught in this conditional and it's being converted to `nan` because of that.
I'm a newbie and not sure whether I should add whatsnew entry or not. Should I?
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merge_asof(left_index=True, right_on=...) overwrites left index with right index
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| 4
| 2020-04-10T18:05:25Z
| 2021-04-12T14:41:14Z
| 2021-04-12T14:41:14Z
|
NONE
| null |
Building from an example in the docs...
```
left = pd.DataFrame({'left_val': ['a', 'b', 'c'], 'left_time': [1, 5, 10]}, index=[1, 5, 10])
right = pd.DataFrame({'right_val': [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
```
```
>>> pd.merge_asof(left, right, left_index=True, right_index=True)
left_val left_time right_val
1 a 1 1
5 b 5 3
10 c 10 7
```
Returned index is the index of left.
```
>>> pd.merge_asof(left, right, left_on='left_time', right_index=True)
left_val left_time right_val
1 a 1 1
5 b 5 3
10 c 10 7
```
Returned index is the index of left.
```
>>> pd.merge_asof(left, right, left_on='left_time', right_on='right_val')
left_val left_time right_val
0 a 1 1
1 b 5 3
2 c 10 7
```
Return value has index reset (I assume expected?)
```
>>> pd.merge_asof(left, right, left_index=True, right_on='right_val')
left_val left_time right_val
1 a 1 1
3 b 5 3
7 c 10 7
```
Returned index is the index of _right_.
Perhaps I'm misunderstanding something about how this is supposed to work, but it seems to me that the behavior in the last example is incorrect.
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 8
| 2020-04-10T19:50:25Z
| 2020-05-27T05:56:38Z
| 2020-05-26T22:20:21Z
|
CONTRIBUTOR
| null |
- [x] closes #31204
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a proposal to make using ``StringDtype`` more permissive and be usable inplace of ``dtype=str``.
ATM converting to StringDtype will only accept arrays that are ``str`` already, meaning you will often have to use ``astype(str).astype("string")`` to be sure not to get errors, which can be tedious. For example these fail in master and work in this PR:
```python
>>> pd.Series([1,2, np.nan], dtype="string")
0 1
1 2
2 <NA>
dtype: string
>>> pd.array([1,2, np.nan], dtype="string")
<StringArray>
['1', '2', <NA>]
Length: 3, dtype: string
>>> pd.Series([1,2, np.nan]).astype("string")
0 1.0
1 2.0
2 <NA>
dtype: string
>>> pd.Series([1,2, np.nan], dtype="Int64").astype("string")
0 1
1 2
2 <NA>
dtype: string
```
etc. now work. Previously the above all gave errors.
## The proposed solution:
``ExtensionArray._from_sequence`` is in master explicitly stated to expect a sequence of scalars of the *correct* type. This makes it not usable for type conversion.
I've therefore added a new ``ExtensionArray._from_sequence_of_any_type`` that accepts scalars of any type and may change the scalars to the correct type before passing them on to ``_from_sequence``. Currently it just routes everything through to ``ExtensionArray._from_sequence``, except in ``StringArray._from_sequence_of_any_type``, where it massages the input scalars into strings before passing them on.
Obviously tests and doc updates are still missing, but I would appreciate feedback if this solution looks ok and I'll add tests and doc updates if this is ok.
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BUG: margin=True is affected by NaN in other columns in pivot_table
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| 2021-07-31T04:16:46Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> data = {'A': [6, 7, None], 'B': [None, 5, 8], 'name': ['Alice', 'Alice', 'Alice']}
>>> df = pd.DataFrame(data)
>>> df.pivot_table(index='name', values=['A', 'B'], aggfunc=['min', 'max'], margins=True)
```
Output:
```python
min max
A B A B
name
Alice 6.0 5.0 7.0 8.0
All 7.0 5.0 7.0 5.0
```
#### Problem description
The summary line 'All' shows min for A as 7.0 and max for B as 5.0 although min of A is 6.0 and max of B is 8.0
#### Expected Output
```python
min max
A B A B
name
Alice 6.0 5.0 7.0 8.0
All 6.0 5.0 7.0 8.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 6bc8a49f2595daca6f6d0391020981d536747b23
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
Version : Darwin Kernel Version 19.3.0: Thu Jan 9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1225.g6bc8a49f2
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 3.0.1
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MEMBER
| null |
Just a smattering of things that I doubt anyone is using
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MDExOlB1bGxSZXF1ZXN0NDAyMDY5MTYw
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Groupby test_apply_mutate
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MEMBER
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@jreback comment in https://github.com/pandas-dev/pandas/pull/33072#pullrequestreview-391657128
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API: __getitem__[int] vs __setitem__[int] with Float64Index
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| 2021-07-04T21:38:46Z
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|
MEMBER
| null |
```
ser = pd.Series([1, 2, 3, 4], index=[1.1, 2.1, 3.0, 4.1])
>>> ser[5] # <--we are treating 5 as a label
KeyError: 5
>>> ser[5] = 5 # < --we are treating 5 as position
IndexError: index 5 is out of bounds for axis 0 with size 4
>>> ser[3] = 5 # <-- we are treating 3 as a label
>>> ser
1.1 1
2.1 2
3.0 5
4.1 4
dtype: int64
```
The `ser[5] = 5` case is an outlier because instead of having its label-vs-positional behavior determined by `ser.index._should_fallback_to_positional`, it is defermined by `if is_integer(key) and not self.index.inferred_type == "integer":`
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DOC: timezone conversion example added to pandas.Series.astype doc #33399
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| 2020-04-13T01:58:00Z
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|
CONTRIBUTOR
| null |
…3399
- [x] closes #33399
Have added an example in the documentation page of pandas.Series.astype for datetime type with timezone.
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REF: Simplify __getitem__ by doing positional-int check first
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| 1
| 2020-04-10T23:22:34Z
| 2020-04-12T20:31:02Z
| 2020-04-12T20:29:57Z
|
MEMBER
| null |
This gets rid of the last non-test usage of `Index.get_value`, so that can be deprecated, xref #19728
This focuses only on `__getitem__`; `__setitem__` is tougher for a few reasons, including #33469.
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MDU6SXNzdWU1OTgxNTQzNzU=
| 33,472
|
DOC: unwanted line of text
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[] | null | 0
| 2020-04-11T00:38:12Z
| 2020-08-21T17:05:59Z
| 2020-04-11T02:44:37Z
|
NONE
| null |
#### Location of the documentation
[./doc/source/development/contributing.rst](https://github.com/pandas-dev/pandas/blob/master/doc/source/development/contributing.rst#bug-reports-and-enhancement-requests)
#### Documentation problem
There seems to be an [unwanted line of text](https://github.com/pandas-dev/pandas/blob/master/doc/source/development/contributing.rst) at the top of the document: "{{ header }}"
#### Suggested fix for documentation
[I fixed it in my fork](https://github.com/benji1123/pandas/blob/master/doc/source/development/contributing.rst) and can make a PR (into master?).
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| 598,166,106
|
MDExOlB1bGxSZXF1ZXN0NDAyMTIzMzcw
| 33,473
|
BUG: Series.__setitem__[listlike_of_integers] with IntervalIndex
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| 5
| 2020-04-11T01:54:00Z
| 2020-04-17T19:44:12Z
| 2020-04-17T18:09:59Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDAyMTMwMDU3
| 33,474
|
REF: unstack
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| 1
| 2020-04-11T03:00:36Z
| 2020-12-02T17:58:46Z
| 2020-04-12T20:33:38Z
|
MEMBER
| null |
cc @jreback this moves a small amount of the unstack logic up the call stack, which is worthwhile, but the big wins available are if you can help me figure out the questions in inline comments.
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MDU6SXNzdWU1OTgxODU5Njk=
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BUG: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead. import pandas.util.testing as tm
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| 2020-04-11T04:12:16Z
| 2020-08-21T17:05:59Z
| 2020-04-13T23:49:54Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
```
#### Problem description
I'm getting this future warning when I import pandas as pd. It does not affect pandas' performance, but I think this is something that should be looked at. I have already looked into reports for similar issues, but the circumstances are different. It is also important to note that I am working in Google Colab.
#### Expected Output
There should be no warning.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
pandas : 1.0.3
</details>
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| 33,476
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BUG: read_excel: incorrect multi-index values - 1 (empty or equal values)
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| 0
| 2020-04-11T10:09:19Z
| 2020-06-09T18:34:03Z
| 2020-06-04T21:56:55Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.read_excel('df.xlsx', sheet_name='Sheet1', index_col=0, header=[0, 1])
```
Note: `engine`s could be different.
#### Problem description
Exception on `pd.read_excel()`:
```
$ ~/miniconda3/bin/python read.py
Traceback (most recent call last):
File "read.py", line 4, in <module>
df = pd.read_excel('df_empty.xlsx', engine='openpyxl', sheet_name='Sheet1', index_col=0, header=[0, 1])
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 521, in parse
].columns.set_names(header_names)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1325, in set_names
idx._set_names(names, level=level)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1239, in _set_names
raise ValueError(f"Length of new names must be 1, got {len(values)}")
ValueError: Length of new names must be 1, got 2
```
on XLSX like:
(C2 is empty, [df_empty.xlsx](https://github.com/pandas-dev/pandas/files/4464392/df_empty.xlsx))
or this:
(B2 == C2, [df_equals.xlsx](https://github.com/pandas-dev/pandas/files/4464393/df_equals.xlsx))
#### Expected Output
No exceptions.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.25-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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| 598,244,751
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MDExOlB1bGxSZXF1ZXN0NDAyMTc1Njc5
| 33,477
|
Send None parameter to pandas-gbq to set no progress bar
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| 13
| 2020-04-11T11:12:16Z
| 2020-06-04T18:10:02Z
| 2020-06-04T17:41:35Z
|
CONTRIBUTOR
| null |
- [x] closes #33360
- [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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| 598,261,974
|
MDU6SXNzdWU1OTgyNjE5NzQ=
| 33,478
|
CLN remove unreachable code in pandas/core/groupby/generic.py::DataFrameGroupBy::_wrap_applied_output
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[] | null | 21
| 2020-04-11T13:02:10Z
| 2020-10-04T06:43:35Z
| 2020-10-04T06:43:34Z
|
MEMBER
| null |
NOTE: I'd originally [opened a PR](https://github.com/pandas-dev/pandas/pull/32583) to address this, but am now busy with other obligations + other PRs to respond to. So I'm opening it up as a good first issue for now, and will return to work on it if nobody takes it.
---------
In `pandas/core/groupby/generic.py::DataFrameGroupBy::_wrap_applied_output`, it seems this branch is never reached:
```
else:
# Handle cases like BinGrouper
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
```
Two things need to be done here:
1. refactor `_wrap_applied_output` by making a helper function out of some of its internals (it's currently very long, and changes are hard to review)
2. Remove this unused `else` branch
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|
Added option to ignore file paths
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MEMBER
| null |
This is needed both for #32942 and to #33394 to ignore the ```pandas/tests``` directory
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BUG: Add unordered option to pandas.cut (#33141)
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| 5
| 2020-04-11T14:07:04Z
| 2020-05-01T00:31:53Z
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|
CONTRIBUTOR
| null |
Added tests and error message when there are no labels and `ordered=False`.
Issue: Pandas cut raises error if labels are non-unique (Closes #33141)
- [x] closes #33141
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 33,481
|
CI: Github actions error
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[] | null | 1
| 2020-04-11T14:26:33Z
| 2020-04-11T14:34:20Z
| 2020-04-11T14:34:20Z
|
MEMBER
| null |
Noticed on #33479 and #33480
Seems like github actions have an internal error.
The error message:
```
This check failed
```
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MDExOlB1bGxSZXF1ZXN0NDAyMTk4MzQ3
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|
TST: Don't use deprecated version of read_html
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| 4
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| 2020-04-12T21:48:37Z
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|
CONTRIBUTOR
| null |
Usage of function read_html is marked as deprecated when used with
positional arguments. In case when we do not explicitly check the
warning message with assert_produces_warning we should use non-deprecated
version.
This will resolve #33397
To identify candidates to change I modified decorator to raise an `Exception`
```diff
--- a/pandas/util/_decorators.py
+++ b/pandas/util/_decorators.py
@@ -294,6 +294,7 @@ def deprecate_nonkeyword_arguments(
"Starting with Pandas version {version} all arguments of {funcname}"
"{except_args} will be keyword-only"
).format(version=version, funcname=func.__name__, except_args=arguments)
+ raise Exception("keyword argument test")
warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
return func(*args, **kwargs)
```
Related test build https://dev.azure.com/piotrnielacny/piotrnielacny/_build/results?buildId=2&view=ms.vss-test-web.build-test-results-tab
- [x] closes #33397
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] ~whatsnew entry~
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BUG: Clipping a data frame after selecting only numeric columns does not work
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| 5
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| 2020-04-12T01:06:29Z
| 2020-04-12T01:06:23Z
|
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
df1 = pd.DataFrame({'A': [100] * 10, 'B': [100] * 10, 'C': ['abc'] * 10})
df1.loc[:, df1.select_dtypes(include=[np.number]).columns].clip(-90, 90, inplace=True)
```
#### Problem description
I am trying to clip the values of all numeric columns in the dataframe.
Initially, the data frame is
```
A B C
0 100 100 abc
1 100 100 abc
2 100 100 abc
3 100 100 abc
4 100 100 abc
5 100 100 abc
6 100 100 abc
7 100 100 abc
8 100 100 abc
9 100 100 abc
```
The numeric columns are A and B, so
`df1.loc[:, df1.select_dtypes(include=[np.number]).columns]` selects the required columns.
`df1.loc[:, df1.select_dtypes(include=[np.number]).columns].clip(-90, 90, inplace=True)` should clip values in A and B to 90.
Dataframe after clipping is:
```
A B C
0 100 100 abc
1 100 100 abc
2 100 100 abc
3 100 100 abc
4 100 100 abc
5 100 100 abc
6 100 100 abc
7 100 100 abc
8 100 100 abc
9 100 100 abc
```
Clearly, the values are not getting clipped.
#### Expected Output
```
A B C
0 90 90 abc
1 90 90 abc
2 90 90 abc
3 90 90 abc
4 90 90 abc
5 90 90 abc
6 90 90 abc
7 90 90 abc
8 90 90 abc
9 90 90 abc
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.15
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
numba : 0.45.1
</details>
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BUG: pd.Series.replace(to_replace=...) does not preserve the original dtype of the series
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| 4
| 2020-04-11T16:48:18Z
| 2020-09-25T09:30:12Z
| null |
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> s = pd.Series(["one", "two"], dtype="string")
>>> expected = pd.Series(["1", "2"], dtype="string")
>>> result = s.replace(to_replace={"one": "1", "two": "2"})
>>> expected
0 1
1 2
dtype: string
>>> result
0 1
1 2
dtype: object
```
#### Problem description
The `pandas.Series.replace` does not return a series with the same `dtype` as the the original series. This behaviour is produced only when the replace function is called using the argument `to_replace`.
Notice: Similar behaviour is produced with other dtypes as well, this issue is not limited to string dtype.
The investigation should probably start here: `/pandas/core/generic.py`
#### Expected Output
We would expect the `pandas.Series.replace(to_replace=...)` to preserve the dtype of the series.
In the above code sample, both of the series should have a dtype of string.
#### Output of ``pd.show_versions()``
<details>
<h2>INSTALLED VERSIONS</h2>
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 598,319,411
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MDExOlB1bGxSZXF1ZXN0NDAyMjI1Njcw
| 33,485
|
PERF: Cythonize `from_nested_dict`
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| 8
| 2020-04-11T18:00:50Z
| 2020-04-23T16:34:57Z
| 2020-04-23T16:34:57Z
|
MEMBER
| null |
I did try to cythonize this function, but the problem is that it also accepting a ```collections.OrderedDict```, which cannot be ```cdef``` as ```dict```, but have to ```cdef``` as ```object```.
---
I don't see much of a performance boost, I have ran the full benchmark suite and ```asv``` says that the ```BENCHMARKS NOT SIGNIFICANTLY CHANGED.```
Maybe just better to remove the ```TODO``` note, thoughts?
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MDExOlB1bGxSZXF1ZXN0NDAyMjQ2MTUy
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REF: remove Block.concat_same_type
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| 2020-04-11T20:56:32Z
| 2020-04-15T19:34:21Z
| 2020-04-15T19:22:47Z
|
MEMBER
| null |
All the relevant logic lives in concat_compat already.
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MDExOlB1bGxSZXF1ZXN0NDAyMjQ4Nzcx
| 33,487
|
API: dont infer freq in DTA/TDA arithmetic ops
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| 0
| 2020-04-11T21:20:39Z
| 2020-04-17T22:03:53Z
| 2020-04-17T21:42:53Z
|
MEMBER
| null |
- [x] closes #23216
freq inference is pure overhead for the Series/DataFrame op because freq gets discarded.
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REF: _convert_to_index_sliceable as DataFrame method
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| 2020-04-11T21:57:07Z
| 2021-07-16T16:08:20Z
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|
MEMBER
| null |
It really doesnt make sense as anything other than a method.
|
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| 598,392,551
|
MDU6SXNzdWU1OTgzOTI1NTE=
| 33,489
|
BUG: weird resample behavior between very similar cases of Period vs Datetimes
|
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[] | null | 4
| 2020-04-12T02:21:10Z
| 2021-10-26T15:38:05Z
| 2020-07-29T10:42:28Z
|
MEMBER
|
resolved
|
- [X] I have checked that this issue has not already been reported. (as far as I know...)
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Create `result_date`:
```python
series = pd.Series(1, index=pd.date_range(start="2000-01-01", periods=100))
result_date = series.resample("2M").count()
result_date
```
Outputs:
```
2000-01-31 31
2000-03-31 60
2000-05-31 9
Freq: 2M, dtype: int64
```
Create `result_period`:
```python
series = pd.Series(1, index=pd.period_range(start="2000-01-01", periods=100))
result_period = series.resample("2M").count()
result_period
```
Outputs:
```
2000-01 60
2000-03 40
Freq: 2M, dtype: int64
```
#### Problem description
I would have expect that `result_period` and `result_date` would have been equals.
I have noticed this behavior when trying to debug `pandas/tests/resample/test_period_index.py::test_resample_count` for #31809. (see https://github.com/pandas-dev/pandas/pull/31809#discussion_r407133957)
After the debug, I realised that maybe another bug was there... but I am not sure, I am not familiar enough with the use of Period. So. Is this a bug? If that's not the case, could someone explain why the two outputs are different?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13-arch2-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDAyMjgwODk1
| 33,490
|
Implement pd.NA checking function
|
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| 2020-04-12T03:30:26Z
| 2020-04-14T18:04:30Z
| 2020-04-14T18:04:26Z
|
MEMBER
| null |
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This may already exist in some form, but if not I think it could be useful to have a fast function for checking specifically for the presence of pd.NA (e.g. if we're looking at an arbitrary array and don't know if we'll have access to a _mask attribute, or if we're handling some other data type that uses pd.NA for missing values but doesn't have this attribute).
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MDExOlB1bGxSZXF1ZXN0NDAyMjg0MzA3
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|
REF: de-duplicate code in libperiod
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| 2
| 2020-04-12T04:16:46Z
| 2020-04-15T19:38:19Z
| 2020-04-15T19:20:55Z
|
MEMBER
| null |
discussed a few months ago, this gets rid of `date_info_from_days_and_time` and instead re-uses `pandas_datetime_to_datetimestruct`
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|
BUG: apply for series of numpy arrays falis with np functions
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| 2020-04-12T12:01:30Z
| 2021-07-31T04:18:48Z
| 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({"col": [np.array([.1, .2], dtype=float)]})
np.sin(df['col'].iloc[0]) # This works fine
df["col"].apply(np.sin) # This breaks
```
Raises:
```python
AttributeError: 'numpy.ndarray' object has no attribute 'sin'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "~/main.py", line 6, in <module>
df["col"].apply(np.sin)
File "~/anaconda3/envs/pandas-issue/lib/python3.7/site-packages/pandas/core/series.py", line 3840, in apply
return f(self)
File "~/anaconda3/envs/pandas-issue/lib/python3.7/site-packages/pandas/core/series.py", line 679, in __array_ufunc__
result = getattr(ufunc, method)(*inputs, **kwargs)
TypeError: loop of ufunc does not support argument 0 of type numpy.ndarray which has no callable sin method
```
#### Problem description
When I have a pd.Series where each row is a numerical numpy array, I can't apply numpy functions to them, I tried np.sin, np.cos, np.exp, but I think it's a general problem. I can apply them to individual rows manually and it works fine.
Moreover, If I import tqdm with tqdm.pandas and use progress_apply instead of apply it also works just fine!
#### Expected Output
```
0 [0.09983341664682815, 0.19866933079506122]
Name: col, dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-46-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG: groupby.hist legend should use group keys
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| 15
| 2020-04-12T13:55:43Z
| 2020-07-11T16:02:04Z
| 2020-06-22T15:30:56Z
|
MEMBER
| null |
- [x] closes #6279
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
cc @TomAugspurger
Added argument "legend" to histogram backend. This adds the ability to display a legend even when not using a groupby.
````
df = pd.DataFrame(np.random.randn(30, 2), columns=['A', 'B'])
df['C'] = 15 * ['a'] + 15 * ['b']
df = df.set_index('C')
df.groupby('C')['A'].hist(legend=True)
````
produces
I went off the tests that already existed in test_hist_method and test_groupby, but perhaps more stringent tests should be added. I've been manually checking with the following code:
````
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame(np.random.randn(30, 2), columns=['A', 'B'])
df['C'] = 15 * ['a'] + 15 * ['b']
df = df.set_index('C')
df.groupby('C')['A'].hist(legend=False)
plt.show()
df.groupby('C')['A'].hist(legend=True)
plt.show()
df.A.hist(by='C', legend=False)
plt.show()
df.A.hist(by='C', legend=True)
plt.show()
plt.show()
df.hist(by='C', legend=False)
plt.show()
df.hist(by='C', legend=True)
plt.show()
df.hist(by='C', column='B', legend=False)
plt.show()
df.hist(by='C', column='B', legend=True)
plt.show()
df.groupby('C')['A'].hist(label='D', legend=False)
plt.show()
df.groupby('C')['A'].hist(label='D', legend=True)
plt.show()
df.A.hist(by='C', label='D', legend=False)
plt.show()
df.A.hist(by='C', label='D', legend=True)
plt.show()
df.hist(by='C', label='D', legend=False)
plt.show()
df.hist(by='C', label='D', legend=True)
plt.show()
df.hist(by='C', column='B', label='D', legend=False)
plt.show()
df.hist(by='C', column='B', label='D', legend=True)
plt.show()
````
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MDU6SXNzdWU1OTg1MDMyNjg=
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|
BUG: MultiIndex drop produces wrong output if not lexicographically sorted
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| 0
| 2020-04-12T14:56:04Z
| 2020-12-02T23:28:04Z
| 2020-12-02T23:28:04Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
Latest Pandas version my system installs (via `anaconda update pandas`), which is 0.25.3.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df1 = pd.DataFrame(
[
[4, 5, 6],
[5, 4, 6],
[4, 5, 6],
[3, 7, 5],
[5, 9, 0],
[1, 2, 3],
],
columns=["a", "b", "c"],
)
df2 = pd.DataFrame(
[
[1, 2, 3],
[4, 5, 6],
[1, 6, 0],
],
columns=["a", "b", "c"],
)
df1mi = pd.MultiIndex.from_frame(df1)
df2mi = pd.MultiIndex.from_frame(df2)
ismi = df1mi.intersection(df2mi) # intersection of df1 and df2
# df1mi, _ = df1mi.sortlevel(sort_remaining=True)
complement = df1mi.drop(ismi).to_frame().reset_index(drop=True)
print(df1, "\n")
print(df2, "\n")
print(complement)
```
#### Problem description
Above code computes the multi-set complement A-B for two multi-sets A and B (only difference to sets is that duplicates in A that are not in B are preserved).
Without the commented-out line, pandas issues a warning (`sys:1: PerformanceWarning: indexing past lexsort depth may impact performance.`). However, the result is wrong: Row 1 from df1 is missing in the complement.
With the commented-out line, no warning is issued and the result is correct.
The bug is that without the sorting, the drop command malfunctions.
#### Expected Output
Correct output (with explicit sort):
```
a b c
0 3 7 5
1 5 4 6
2 5 9 0
```
Wrong output (without explicit sort):
```
a b c
0 3 7 5
1 5 9 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDAyMzU5NDk4
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CLN: remove ensure_categorical
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| 1
| 2020-04-12T15:56:39Z
| 2020-04-17T02:55:17Z
| 2020-04-17T02:48:49Z
|
MEMBER
| null |
only used in two places, its a misnomer because it really only ensures categorical dtype, and it complicates the dependency structure
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BUG: Allow apply of ufunc over Series of list-like
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| 2020-04-12T16:25:50Z
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|
MEMBER
| null |
- [ ] closes #33492
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDAyMzY0NzE5
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CLN: assorted cleanups, annotations, de-privatizing
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MDExOlB1bGxSZXF1ZXN0NDAyMzY2MTQ4
| 33,498
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BUG: Fix a bug in 'timedelta_range' that produced an extra point on a edge case (fix #30353)
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| 7
| 2020-04-12T16:44:54Z
| 2020-05-09T20:41:27Z
| 2020-05-09T19:23:30Z
|
MEMBER
| null |
The issue from #30353 came actually from `timedelta_range`.
```python
import pandas as pd
def mock_timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None):
epoch = pd.Timestamp(0)
if start is not None:
start = epoch + pd.Timedelta(start)
if end is not None:
end = epoch + pd.Timedelta(end)
res = pd.date_range(start=start, end=end, periods=periods, freq=freq, name=name, closed=closed)
res -= epoch
res.freq = freq
return res
print(mock_timedelta_range("1day", "10day", freq="2D"))
print(pd.timedelta_range("1day", "10day", freq="2D"))
```
The outputs from `mock_timedelta_range` and from `pd.timedelta_range` are supposed to equivalent, but are not on pandas v1.0.0:
Outputs without this PR:
```
TimedeltaIndex(['1 days', '3 days', '5 days', '7 days', '9 days'], dtype='timedelta64[ns]', freq='2D')
TimedeltaIndex(['1 days', '3 days', '5 days', '7 days', '9 days', '11 days'], dtype='timedelta64[ns]', freq='2D')
```
Outputs with this PR:
```
TimedeltaIndex(['1 days', '3 days', '5 days', '7 days', '9 days'], dtype='timedelta64[ns]', freq='2D')
TimedeltaIndex(['1 days', '3 days', '5 days', '7 days', '9 days'], dtype='timedelta64[ns]', freq='2D')
```
----
It also solve an issue (that fail on master) related to this comment: https://github.com/pandas-dev/pandas/issues/13022#issuecomment-238382933
```python
import pandas as pd
rng = pd.timedelta_range(start='1d', periods=10, freq='d')
df = pd.Series(range(10), index=rng)
df.resample('2D').count()
```
Outputs with this PR:
```
1 days 2
3 days 2
5 days 2
7 days 2
9 days 2
Freq: 2D, dtype: int64
```
----
On the firsts commits I just fixed the issue in `_generate_regular_range`, then I decided to do a refactor and to use the same code that generate ranges in `core/arrays/_ranges.py` for `date_range` and `timedelta_range`.
Linked with #10887.
----
- [X] closes #30353
closes #13022
closes #7687 (for loffset when resampling Timedelta)
- [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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| 598,543,542
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MDExOlB1bGxSZXF1ZXN0NDAyMzc5MjA1
| 33,499
|
ENH: Adds round_decimals=None to Series.value_counts() to use with normalize=True
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| 2020-04-12T18:24:42Z
| 2020-06-19T11:31:07Z
| 2020-06-19T11:31:07Z
|
NONE
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
When using `value_counts` with `normalize=True` with a **Series,** the result contains the relative frequencies of the unique values, and therefore sums up to 1.0. If then you need to round this frequencies to some decimals, it is not a so trivial task [[1]](https://stackoverflow.com/questions/13483430/how-to-make-rounded-percentages-add-up-to-100)[[2]](https://stackoverflow.com/questions/5227215/how-to-deal-with-the-sum-of-rounded-percentage-not-being-100)[[3]](https://revs.runtime-revolution.com/getting-100-with-rounded-percentages-273ffa70252b). So when this new **int** `round_decimals` argument is **not None**, the method applies the [Largest_remainder_method](https://en.wikipedia.org/wiki/Largest_remainder_method) to round the values to the number of decimals selected, but also to sum up to 1.0 in total.
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BUG: Empty dataframe returns weird aggregation results with custom aggregation functions
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| 1
| 2020-04-12T18:41:11Z
| 2020-07-27T20:46:43Z
| 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
df = pd.DataFrame( columns=["item", "att1", "att2"])
df.agg(("count",))
# item att1 att2
# count 0 0 0
def count_not_null(series: pd.Series) -> int:
return series.notnull().astype(int).sum()
df.agg((count_not_null,))
# Empty DataFrame
# Columns: [(item, count_not_null), (att1, count_not_null), (att2, count_not_null)]
# Index: []
def count_all(series: pd.Series) -> int:
""" count all the values (regardless if they are null or nan) """
return len(series)
df.agg((count_not_null, count_all))
# Empty DataFrame
# Columns: [(item, count_not_null), (item, count_all), (att1, count_not_null), (att1, count_all),
# (att2, count_not_null), (att2, count_all)]
# Index: []
```
#### Problem description
This issue only occurs when using custom aggregation function on empty dataframes. it works as expected with non empty dataframe. I would expect a similar output as the one obtained when using `count` aggregation function.
Workaround: if I use only one aggregation pass only the specific function to `agg`.
This is definetely a very dark specific bug that I found by toying around with pandas for a small library that I am writing. I don't see big effect in the daily lives of users (we probably don't want to aggregate on empty dataframes!) :).
#### Expected Output
```
# item att1 att2
# count 0 0 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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CONTRIBUTOR
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- [x] closes #33215
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Allows `Series.update()` to accept objects that can be coerced into a `Series`, like a `dict`. This mirrors the existing behavior of `DataFrame.update()`.
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CONTRIBUTOR
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- [x] ref https://github.com/pandas-dev/pandas/issues/33395
- In a very similar fashion to @jbrockmendel work for 3.8 - might be useful to have start a 3.9 build so we can start eliminating the issues @TomAugspurger mentioned in https://github.com/pandas-dev/pandas/issues/33395
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| 12
| 2020-04-12T22:57:21Z
| 2020-05-10T01:01:13Z
| 2020-05-09T23:26:57Z
|
CONTRIBUTOR
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/33395
- In a very similar fashion to @jbrockmendel work for 3.8 - might be useful to have start a 3.9 build so we can start eliminating the issues @TomAugspurger mentioned in https://github.com/pandas-dev/pandas/issues/33395.
This is using currently using `Python 3.9.0a5+`
Currently failing with
```
ImportError while loading conftest '/home/travis/build/pandas-dev/pandas/pandas/conftest.py'.
pandas/conftest.py:1123: in <module>
("period", [pd.Period(2013), pd.NaT, pd.Period(2018)]),
pandas/_libs/tslibs/period.pyx:2470: in pandas._libs.tslibs.period.Period.__new__
freq = Resolution.get_freq(reso)
pandas/_libs/tslibs/resolution.pyx:240: in pandas._libs.tslibs.resolution.Resolution.get_freq
return cls._reso_freq_map[resostr]
E AttributeError: type object 'type' has no attribute '_reso_freq_map'
The command "ci/run_tests.sh" exited with 4.
cache.2
store build cache
```
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|
Removed xfail for issue #31883
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| 2
| 2020-04-12T23:05:53Z
| 2020-04-14T15:01:00Z
| 2020-04-14T15:00:53Z
|
CONTRIBUTOR
| null |
I removed the xfail in lines 412-414 as discussed in https://github.com/pandas-dev/pandas/issues/31883.
- [x] closes #31883
- [x] 0 tests added / 0 passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1OTg2MDE4NDk=
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|
CI: Numpy Dev Build Failing
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| 2020-04-13T00:17:50Z
| 2020-04-27T23:10:34Z
| 2020-04-27T23:10:34Z
|
CONTRIBUTOR
| null |
Looks like our numpy dev build also uses a pre-release (alpha) of cython.
Is this the intended behaviour?
Think this is potentially what is causing the test failures:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=33245&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=4d05ed0e-1ed3-5bff-dd63-1e957f2766a9
```
attrs 19.3.0 py_0
ca-certificates 2020.1.1 0
certifi 2020.4.5.1 py37_0
**cython 3.0a1 pypi_0 pypi**
execnet 1.7.1 py_0
hypothesis 5.5.4 py_0
importlib_metadata 1.5.0 py37_0
ld_impl_linux-64 2.33.1 h53a641e_7
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libgcc-ng 9.1.0 hdf63c60_0
libstdcxx-ng 9.1.0 hdf63c60_0
more-itertools 8.2.0 py_0
ncurses 6.2 he6710b0_0
numpy 1.19.0.dev0+8f7adad pypi_0 pypi
openssl 1.1.1f h7b6447c_0
packaging 20.3 py_0
pandas 1.1.0.dev0+1243.gd3ff12cff dev_0 <develop>
pip 20.0.2 py37_1
pluggy 0.13.1 py37_0
py 1.8.1 py_0
pyparsing 2.4.6 py_0
pytest 5.4.1 py37_0
pytest-azurepipelines 0.8.0 py_0
pytest-forked 1.1.3 py_0
pytest-xdist 1.31.0 py_0
python 3.7.7 hcf32534_0_cpython
**python-dateutil 2.8.2.dev25+gc175137 pypi_0 pypi**
pytz 2019.3 py_0
readline 8.0 h7b6447c_0
scipy 1.5.0.dev0+f614064 pypi_0 pypi
setuptools 46.1.3 py37_0
six 1.14.0 py37_0
sortedcontainers 2.1.0 py37_0
sqlite 3.31.1 h7b6447c_0
tk 8.6.8 hbc83047_0
wcwidth 0.1.9 py_0
wheel 0.34.2 py37_0
xz 5.2.4 h14c3975_4
zipp 2.2.0 py_0
zlib 1.2.11 h7b6447c_3
```
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|
Added two tests for issue #29697
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| 6
| 2020-04-13T00:28:11Z
| 2020-04-26T20:24:21Z
| 2020-04-26T20:24:18Z
|
CONTRIBUTOR
| null |
I added two basic tests for `merge` as requested in #29697, one for `how="outer"` and one for `how="right"`.
I'm not sure if the way I'm creating duplicate columns for the `expected` dataframe is the recommended way but I could not find anything on this. Please let me know if there's a better way to do this.
- [x] closes #29697
- [x] 2 tests added / 2 passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDAyNDIzMTc5
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ENH: clearer error message if read_hdf loads upsupported HDF file (GH9539)
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| 1
| 2020-04-13T00:37:04Z
| 2020-04-20T04:18:38Z
| 2020-04-17T18:08:36Z
|
CONTRIBUTOR
| null |
- [x] closes #9539
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The error message changed since the original bug report but still could be made more clear.
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NONE
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- [ ] passes `black pandas`
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CLN: remove unused util.hashing functions
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| 1
| 2020-04-13T03:06:31Z
| 2020-04-15T01:35:56Z
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MEMBER
| null |
The only place where _any_ of util.hashing is used internally is in `CategoricalDtype.__hash__`. It's pretty ugly dependency-tree wise. This just prunes a couple of entirely unused functions.
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MEMBER
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Fixes one small error in a test, and otherwise tries to remove some code duplication
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MEMBER
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- [ ] closes #33450
- [ ] tests added / passed
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MDExOlB1bGxSZXF1ZXN0NDAyNDU2NjY1
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DOC: Add missing descriptions in See Also section
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| 2020-04-13T03:41:51Z
| 2020-05-22T10:29:05Z
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|
CONTRIBUTOR
| null |
Fixed SR04 for `pandas/core/window/rolling.py`
- [X] xref #28792
- [ ] 0 tests added / 0 passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: DataError: No numeric types to aggregate during pd.pivot_table
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[] | null | 3
| 2020-04-13T04:47:05Z
| 2021-04-27T07:06:51Z
| 2020-05-02T09:29:53Z
|
NONE
| null |
I am using pandas 1.0.3 in anaconda python 3.6.6 64 bits on win7 64 bits
the following code
```python
pd.pivot_table(df, index=['time', 'goods'], values=['price',])
```
# says something like https://github.com/pandas-dev/pandas/issues/17382
```
DataError Traceback (most recent call last)
<ipython-input-62-dc402f731fc3> in <module>()
----> 1 pd.pivot_table(df.iloc[:5,:], index=['time', 'goods'], values=['price',])
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\reshape\pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
99
100 grouped = data.groupby(keys, observed=observed)
--> 101 agged = grouped.agg(aggfunc)
102 if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):
103 agged = agged.dropna(how="all")
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
926 func = _maybe_mangle_lambdas(func)
927
--> 928 result, how = self._aggregate(func, *args, **kwargs)
929 if how is None:
930 return result
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
309
310 if isinstance(arg, str):
--> 311 return self._try_aggregate_string_function(arg, *args, **kwargs), None
312
313 if isinstance(arg, dict):
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\base.py in _try_aggregate_string_function(self, arg, *args, **kwargs)
265 if f is not None:
266 if callable(f):
--> 267 return f(*args, **kwargs)
268
269 # people may try to aggregate on a non-callable attribute
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\groupby\groupby.py in mean(self, *args, **kwargs)
1223 nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
1224 return self._cython_agg_general(
-> 1225 "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
1226 )
1227
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\groupby\generic.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
992 ) -> DataFrame:
993 agg_blocks, agg_items = self._cython_agg_blocks(
--> 994 how, alt=alt, numeric_only=numeric_only, min_count=min_count
995 )
996 return self._wrap_agged_blocks(agg_blocks, items=agg_items)
L:\prg\py\Anaconda3_64\lib\site-packages\pandas\core\groupby\generic.py in _cython_agg_blocks(self, how, alt, numeric_only, min_count)
1093
1094 if not (agg_blocks or split_frames):
-> 1095 raise DataError("No numeric types to aggregate")
1096
1097 if split_items:
DataError: No numeric types to aggregate
```
# but if I do
```
df.to_excel('r:/5.xls')
dfm = pd.read_excel('r:/5.xls')
pd.pivot_table(dfm, index=['time', 'goods'], values=['price',])
```
then I get the pivot table as I expected
# the question
so you may find that I can't make a minimal excel file to let you reproduce the `DataError`. On the other hand, my original `df` is made by `concat`ing some privacy excel files, which I can't share too.
Can anyone give me some hints? Thanks
```
df = pd.concat(
[pd.read_excel(i, convertor={'ID2': int, 'pin': int}) for i in someXlsFilenames]
)
df.head(1)
```
| _ | time | ID1 | ID2 | goods | pin | owener | price | weight | from | to | material | source |
| ---: | ----------: | ----------: | ------: | ---------------: | ---------: | --------: | -----: | -----: | ------: | ---: | -------: | ----------- |
| 152 | 2013-2014_1 | BS080003123 | 1311225 | Data Transfomers | 1200830216 | Jack Chan | 65 | 1.0 | England | USA | wood | 13-14-1.xls |
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MDExOlB1bGxSZXF1ZXN0NDAyNDk4MjQ5
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add a new feature sample() into groupby
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| 2020-04-13T06:34:37Z
| 2020-05-13T18:33:52Z
| 2020-05-13T18:33:52Z
|
NONE
| null |
- [ ] closes #33175
- [ ] 0 tests added / 0 passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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