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https://api.github.com/repos/pandas-dev/pandas/issues/28613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28613/events | https://github.com/pandas-dev/pandas/issues/28613 | 498,260,111 | MDU6SXNzdWU0OTgyNjAxMTE= | 28,613 | combine_first changes datatype | {
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"de... | open | false | null | [] | null | 6 | 2019-09-25T12:40:05Z | 2021-10-17T17:43:02Z | null | CONTRIBUTOR | null | ```python
df_1 = pd.DataFrame(data = {'A' : [1, 2, 3], 'B' : [4, 5, 6]})
df_2 = pd.DataFrame(data = {'A' : [1, 20, 30], 'B' : [40, 50, 60], 'C' : [12, 34, 65]})
df_1.combine_first(df_2).dtypes
A int64
B int64
C float64
dtype: object
```
#### Problem description
The combine_first changes the datatype of the the column C to float64 from int64. Is this
an expected behavior
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.6.3
Cython : 0.29.2
pytest : 4.0.2
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28614/events | https://github.com/pandas-dev/pandas/pull/28614 | 498,331,749 | MDExOlB1bGxSZXF1ZXN0MzIxMjgzNjQx | 28,614 | CI: troubleshoot s3fs failures | {
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} | 1 | 2019-09-25T16:07:44Z | 2019-11-15T15:25:57Z | 2019-11-15T15:25:57Z | MEMBER | null | xref #28612, #28614.
CI was failing with newer versions of s3fs, so we pinned it.
Need to figure out if it's still failing with new versions and if so, why. | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-25T17:50:42Z | 2019-09-25T18:09:25Z | 2019-09-25T18:09:25Z | NONE | null | #### Problem Description
element-wise value comparison noticeably slower for large-scale dataframes, compared to corresponding numpy array value comparison (although in older pandas version, the operation speed was acceptable).
```python
import numpy as np
import pandas as pd
x = pd.DataFrame(np.random.rand(3000*4000).reshape((3000,-1)))
# pandas 0.25.1
%timeit x>0.0
1.72 s ± 3.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# pandas 0.17.1
%timeit x>0.0
115 ms ± 114 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit x.values>0.0
13.1 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
#### INSTALLED VERSIONS
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-80.7.2.el8_0.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2 | {
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"id": 241332... | open | false | null | [] | null | 2 | 2019-09-25T18:12:08Z | 2021-07-21T05:00:25Z | null | CONTRIBUTOR | null | As part of the backend callback, can / should we copy the docstring from the backend to the `DataFrame.plot` object (including methods like DataFrame.plot.scatter)?
There may be backend-specific notes or behavior. It'd be nice if those were transferred over.
cc @jsignell. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28619/events | https://github.com/pandas-dev/pandas/issues/28619 | 498,467,316 | MDU6SXNzdWU0OTg0NjczMTY= | 28,619 | Allow users to pass backend option to `.plot()` | {
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] | closed | false | null | [] | null | 1 | 2019-09-25T19:03:02Z | 2019-11-05T17:07:09Z | 2019-11-05T17:07:09Z | CONTRIBUTOR | null | Once you have installed another plotting library with a pandas.plotting entrypoint it would be nice to be able to toggle back and forth between different backends even within on session.
## What currently exists
```python
pd.options.plotting.backend = 'holoviews'
```
## Proposed additional API
```python
df.plot(backend='holoviews')
```
This would be particularly useful when a particular backend doesn't support a certain type of plot, or you need to use a backend to get some specific functionality but don't want to use if for your whole notebook.
@datapythonista - @TomAugspurger mentioned that you might have opinions on this.
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https://api.github.com/repos/pandas-dev/pandas/issues/28620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28620/events | https://github.com/pandas-dev/pandas/issues/28620 | 498,479,535 | MDU6SXNzdWU0OTg0Nzk1MzU= | 28,620 | dataframe index not showing up bold after conda update | {
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import numpy as np
import pandas as pd
%config InteractiveShell.ast_node_interactivity='all'
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df
```
#### Problem description
During my work today I decided to update conda when I started getting some random errors which I can't recall anymore. After the update I noticed that the indeces in the printouts of my dataframes were no longer bold. Its not a big deal, buts it is visually unappealing. If possible I'd like to fix this (if it is an issue with pandas), but I've never contributed a fix to anything before :) and I don't even know where to start hunting for the bug.
#### Expected Output
Indeces should be bold, just like column names---at least they've always were before the conda update.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-29-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
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 : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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} | 4 | 2019-09-25T19:32:53Z | 2019-10-03T06:56:14Z | 2019-10-03T06:56:11Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Deprecates ``Index.set_value``. This is a very little used and confusing method IMO.
For a given index ``idx``, array ``arr``, value ``idx_val`` in ``idx`` and a new value of ``val``, ``idx.set_value(arr, idx_val, val)`` is equivalent to ``arr[idx.get_loc(idx_val) = val``, which is more standard and should be used instead.
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} | 5 | 2019-09-25T19:42:15Z | 2019-11-05T17:07:14Z | 2019-11-05T17:07:09Z | CONTRIBUTOR | null | - [x] closes #28619
- [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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] | closed | false | null | [] | null | 11 | 2019-09-25T21:07:08Z | 2019-10-04T20:12:55Z | 2019-10-04T20:12:38Z | MEMBER | null | closes https://github.com/pandas-dev/pandas/issues/15556/
I want to propose that we start using the new bootstrap-based theme that is being developed at https://github.com/pandas-dev/pandas-sphinx-theme/ for the dev docs.
This way it will already be used for the docs at https://dev.pandas.io/docs/ so it can get some exposure before the next release.
How I did it in this PR is to install it from git master https://github.com/pandas-dev/pandas-sphinx-theme/ instead of moving the actual source into the pandas repo. I would prefer doing it like this for now, as that makes it easier to further iterate on the theme (the other repo is set up with a faster doc build (disabled ipython directive + smaller api) and automatic preview using doctr on travis). In a later stage, we can still move it here if we want (or move the pandas-specific customizations here).
A preview (of a subset, not all API pages) can be seen at https://dev.pandas.io/pandas-sphinx-theme/
There are still several "must todo's" to get the theme in a decent enough state for a release. There are some open issues on the theme repo, but I can also open an issue here to keep track of those.
And of course, feedback on the theme is very welcome.
I made two other changes:
- moved the install.rst into the getting_started directory (so it is not a top-level navigation item). Given that the new website will have a page with a quick install linking to this more advanced install page, I think that is fine (and also, on a reworked home page of the docs it could also get a prominent place without being in the navigation bar)
- Idem for the ecosystem page, as this will also get more exposure on the new markdown website (we actually need to remove that page / reconcile it with the website, but let's leave that for another PR) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28624/events | https://github.com/pandas-dev/pandas/pull/28624 | 498,594,101 | MDExOlB1bGxSZXF1ZXN0MzIxNDk0NDU0 | 28,624 | REF: separate out dispatch-centric ops functions | {
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} | 3 | 2019-09-26T01:03:07Z | 2019-09-26T15:42:46Z | 2019-09-26T15:39:54Z | MEMBER | null | Seems like a reasonable chunk of stuff to get out of `__init__`.
`dispatch_to_series` is not moved because it will end up using `array_ops` and I don't want to introduce circular dependencies. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28625/events | https://github.com/pandas-dev/pandas/issues/28625 | 498,602,428 | MDU6SXNzdWU0OTg2MDI0Mjg= | 28,625 | Date type output changes when using df.column.unique()[i] | {
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] | closed | false | null | [] | null | 5 | 2019-09-26T01:35:14Z | 2020-04-23T14:05:40Z | 2020-04-23T14:05:28Z | NONE | null | Apologies if this is somehow expected. I admit I don't know the intricacies of all date types and what each of these is designed to return, but I found the result surprising.
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
dates = [pd.Timestamp(year=2019, month=m, d=1) for m in range(1, 6)]
df = pd.DataFrame({'date': dates})
>>> df.iloc[2].date
Timestamp('2019-03-01 00:00:00')
>>> df.iloc[2].date.year
2019
>>> df.date.unique()[2]
numpy.datetime64('2019-03-01T00:00:00.000000000')
>>> df.date.unique()[2].year
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'numpy.datetime64' object has no attribute 'year'
```
#### Problem description
I was subsetting data based on unique dates in a data frame. Part of my code used a string formatter, assuming I could access `x.year` when I got the `AttributeError` above.
#### Expected Output
Intuitively, I would expect that any incantation of obtaining values from a data frame should give me the type that's there, not a new type I don't expect.
Again, not being familiar with the intricacies of the various types and what's going on behind the curtains, this could easily be a false assumption and somehow `pd.Timestamp` and `np.datetime64` are more closely related than I understand and the call to `.unique()` is expected to cast one to the other?
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.1-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-09-26T04:10:26Z | 2019-09-27T22:47:48Z | 2019-09-27T22:47:24Z | CONTRIBUTOR | null | Replaces a some frequently repeated lines with a function.
- [x] closes #28555
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry | {
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} | 2 | 2019-09-26T06:40:54Z | 2019-09-26T11:40:36Z | 2019-09-26T11:32:27Z | NONE | null | Backport PR #28357: Change conda channel order for Windows builds | {
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} | 6 | 2019-09-26T07:08:53Z | 2019-10-30T14:04:29Z | 2019-10-30T12:23:35Z | CONTRIBUTOR | null | If index is MultiIndex and level of 0 is PeriodIndex, loc function raise
exception if all input of loc does not match index values
Background: This bug only happens when MultiIndex's level is 3 and first level index is PeriodIndex. In this situation, if someone access one row using a '.loc' with a miss match key, then would not raise exception.
Someone already change what i try to do `parse_time_string` function in '_libs.tslibs.parsing'.
in the past,
```
def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None):
if not isinstance(arg, str):
return arg
```
What i try to do:
```
def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None):
if not isinstance(arg, str):
raise TypeError
```
now in master:
```
def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None):
if not isinstance(arg, str):
raise TypeError("parse_time_string argument must be str")
```
Just add tests for issue.
- [x] closes (#20684)
- [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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... | open | false | null | [] | null | 5 | 2019-09-26T08:12:06Z | 2020-09-21T21:35:23Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> type(pd.to_datetime(pd.Categorical(['2019-09-24 17:00:00']*50)))
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>
>>> type(pd.to_datetime(pd.Categorical(['2019-09-24 17:00:00']*51)))
<class 'pandas.core.indexes.category.CategoricalIndex'>
```
#### Problem description
The output container type is inconsistent for `pd.to_datetime` and flips once the series is above 50 rows. This is an issue since the `CategoricalIndex` class does not support DateTime-like operations (tz_localize, stftime, etc.).
Since the behaviour switches between differently sized Series objects this is particularly troublesome since code behaves quite different depending on the amount of data we feed it (e.g. unit test vs. production).
#### Expected Output
Consistent container type.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.184-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 39.1.0
Cython : None
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.0.5
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.5.4
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.0.5
</details>
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} | 14 | 2019-09-26T10:57:22Z | 2019-10-04T08:48:40Z | 2019-10-04T02:13:39Z | CONTRIBUTOR | null | @datapythonista kindly review
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版本:pandas == 0.24.2
```python
import pandas as pd
Data = [
["A", "A", "A"],
["B", "B", "B"],
["C", "C", "C"],
["D", "D", "D"],
]
test1 = pd.DataFrame(Data)
print(test1)
print("------------")
test1 = test1.reindex([0, 1, 2, 3, 4, 5], method="ffill", limit=1)
print(test1)
```
limit参数限制数据继续向后填充
版本:pandas==0.25.1
```python
import pandas as pd
Data = [
["A", "A", "A"],
["B", "B", "B"],
["C", "C", "C"],
["D", "D", "D"],
]
test1 = pd.DataFrame(Data)
print(test1)
print("------------")
test1 = test1.reindex([0, 1, 2, 3, 4, 5], method="ffill", limit=1)
print(test1)
```
limit参数并没有限制数据继续向后填充 | {
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} | 1 | 2019-09-26T12:18:52Z | 2019-10-19T01:22:56Z | 2019-10-07T15:01:03Z | CONTRIBUTOR | null | - [x] closes #28469
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
@gabriellm1 @hugoecarl | {
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} | 13 | 2019-09-26T12:52:58Z | 2019-11-04T13:42:42Z | 2019-11-04T13:42:42Z | MEMBER | null | I get an error while following the [contributing guide](https://dev.pandas.io/docs/development/contributing.html) on creating a python eviroment using pip.
when I run the command:
```
python -m pip install -e . --no-build-isolation
```
I get an error that says:
> error: invalid command 'bdist_wheel'
#### To repreduce this issue:
1. Install [Arch linux](https://www.archlinux.org/download/), September release(and upgrade to the latest version)
2. Install :
1. ```python3-venv```
2. ```python-pip```
3. Follow the [contributing guide](https://dev.pandas.io/docs/development/contributing.html#id12)
1. ```git clone https://github.com/your-user-name/pandas.git pandas-yourname```
2. ```cd pandas-yourname```
3. ```git remote add upstream https://github.com/pandas-dev/pandas.git```
4. ```python3 -m venv ~/virtualenvs/pandas-dev```
5. ```. ~/virtualenvs/pandas-dev/bin/activate```
6. ```python -m pip install -r requirements-dev.txt```
7. ```python setup.py build_ext --inplace -j 4```
8. ```python -m pip install -e . --no-build-isolation```
on line 8 I get an error.
P.S
This is my second time opening an issue(ever), please let me know if what else I should mention/include/change in order to be more productive.
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https://api.github.com/repos/pandas-dev/pandas/issues/28634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28634/events | https://github.com/pandas-dev/pandas/pull/28634 | 498,873,056 | MDExOlB1bGxSZXF1ZXN0MzIxNzEwMTI1 | 28,634 | BUG: value_counts can handle the case even with empty groups (#28479) | {
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} | 6 | 2019-09-26T13:06:48Z | 2019-11-07T21:20:01Z | 2019-11-07T21:19:58Z | CONTRIBUTOR | null | * If applying rep to recons_labels go fail, use ids which has no
consecutive duplicates instead.
- [x] closes #28479
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xuancong84 found that value_counts() crashes if `groupby` object contains empty groups.
However, even though I made the construction of DataFrame don't skip empty rows, it still crashed.
Till then, I already tried in many ways though, in this time I tried to correct the callee `self.grouper.recons_labels`. After several tests, I found that If freq of `Grouper` is too long so that it has empty groups in some periods then it crashes. And also have found that this is solved by using `ids` which has no consecutive duplicates instead of `self.grouper.recons_labels`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28635/events | https://github.com/pandas-dev/pandas/issues/28635 | 498,913,237 | MDU6SXNzdWU0OTg5MTMyMzc= | 28,635 | Resample on MultiIndex level takes much more time than on normal column | {
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"description": "res... | open | false | null | [] | null | 1 | 2019-09-26T14:13:47Z | 2021-07-21T05:03:38Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
n=80000
g=5
index = pd.MultiIndex.from_product([
np.arange(g),
pd.to_timedelta(np.arange(n), unit='s')
])
data = pd.DataFrame(
np.random.randint(0,1000,size=(len(index))),
index=index
)
%timeit data.groupby(level=0).resample('10s',level=1).mean()
# 3.93 s ± 295 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit data.reset_index(1).groupby(level=0).resample('10s',on='level_1').mean()
# 157 ms ± 3.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
#### Problem description
``resample`` seem to take much more time when resampling on a level of MultiIndex instead of normal data column. The second, faster approach is more convoluted and is not what first comes to mind.
#### Expected Output
Both operations should around the same amount of time with second possibly slightly more, because of additional reset_index operation. If the difference is expected than first operation should show warning hinting on optimal solution.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
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 : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-09-26T15:49:51Z | 2019-10-01T16:58:20Z | 2019-10-01T16:53:18Z | MEMBER | null | After this, the three Series methods in `ops.__init__` are just about in sync, the last holdout being alignment behavior in the comparison method. | {
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} | 1 | 2019-09-26T16:19:52Z | 2019-10-02T12:44:29Z | 2019-10-02T11:52:27Z | MEMBER | null | Next step after this will be to consolidate _construct_result calls. That hinges on some not-obvious alignment behaviors. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28639/events | https://github.com/pandas-dev/pandas/issues/28639 | 499,020,999 | MDU6SXNzdWU0OTkwMjA5OTk= | 28,639 | ENH: Add 'wheel' to requirements-dev.txt when user is using pip and Arch linux | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-26T17:32:46Z | 2019-09-26T18:37:48Z | 2019-09-26T18:37:48Z | MEMBER | null | I had [This issue](https://github.com/pandas-dev/pandas/issues/28633) and appendig the word 'wheel' to requirements-dev.txt solved it. | {
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] | closed | false | null | [] | null | 4 | 2019-09-26T17:34:36Z | 2019-09-26T19:06:44Z | 2019-09-26T19:06:44Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
x = pd.Series(list('abcdadcb'))
category = pd.Categorical(x.values, ordered=True, categories=list('abcd'))
x.astype(category)
```
#### Problem description
Trying to assign a variable an ordered category type failes with
`AttributeError: 'Categorical' object has no attribute '_ordered'`
#### Expected Output
The operations completes successfully and yields a series of categorical dtype that is ordered.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : 4.34.0
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 2 | 2019-09-26T17:41:22Z | 2020-06-15T22:53:27Z | 2020-06-15T22:53:27Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```ipython
In [1]: import pandas as pd
In [2]: pd.DataFrame({"A": [1997], "B": pd.Series(["b"], dtype="category").cat.as_ordered()}).groupby("A", as_index=True).agg({"B": "size"})
Out[2]:
B
A
1997 1
In [3]: pd.DataFrame({"A": [1997], "B": pd.Series(["b"], dtype="category").cat.as_ordered()}).groupby("A", as_index=True).agg({"B": "min"})
Out[3]:
B
0 b
```
#### Problem description
When aggregating `min`, `max` or `first` of a categorical column, `.agg()` returns a dataframe with a default index instead of the index returned by `groupby()`.
(This may be related to #13416 ... but I think it's a clear, well-defined bug so maybe easier to resolve?)
In my case, I think I can work around this problem with a hack: if I `.agg({"B": ["min", "size"]})` and then ignore the `("B", "size")` output column, Pandas will output a dataframe with the correct index.
#### Expected Output
```
Out[3]:
B
A
1997 b
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.11-200.fc30.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.1
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.5
pytest : 4.5.0
hypothesis : None
sphinx : 2.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : 0.2.1
gcsfs : None
lxml.etree : 4.2.5
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | 1 | 2019-09-26T17:56:50Z | 2019-09-27T14:23:02Z | 2019-09-27T11:48:49Z | MEMBER | null | 84 of these left. | {
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} | 2 | 2019-09-26T18:19:31Z | 2019-10-02T20:24:37Z | 2019-10-02T19:58:34Z | MEMBER | null | This is a PITA and I'm not 100% happy with the solution here, open to suggestions.
There is a call to `util.get_value_at(labels, i)` that raises `TypeError` if `labels` is not an `ndarray`. This fixes that by checking for non-ndarray and handling that case correctly.
There is _also_ a case in master where we get an `UnboundLocalError` by referencing `result` before it is assigned. This patches that to raise a `ValueError` instead, _but_ AFAICT fixing the ndarray bug above made it so that the UnboundLocalError case is no longer reached in the tests. i.e. this change is definitely more correct, but we don't have a test case specific to it.
I'm also not wild about the specific exception-catching on L297-307 in core.apply, but don't see a viable alternative. Suggestions welcome. | {
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} | 4 | 2019-09-26T19:38:55Z | 2019-09-27T17:03:49Z | 2019-09-27T15:53:47Z | CONTRIBUTOR | null | Documentation update fixing some of the methods with a PR06 error code which involved updating
- string to str
- integer to int
- boolean to bool
No tests required
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} | 13 | 2019-09-26T20:07:10Z | 2019-11-02T15:50:19Z | 2019-11-02T15:48:40Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-09-26T20:14:18Z | 2019-09-27T14:58:51Z | 2019-09-27T11:48:03Z | MEMBER | null | This removes some unused code in the internals `astype`. There is a small change in behaviour though, when passing the categories/ordered keyword you now get TypeError instead of ValueError. But since that is Python's default behaviour, I would say this is rather a good fix. | {
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} | 4 | 2019-09-26T22:09:45Z | 2019-11-19T04:59:16Z | 2019-11-19T04:59:10Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I ran into this while implementing the hvplot backend. In hvplot you can do:
```python
df.hvplot.hist(y='y', by='category')
```
but with the pandas version
```python
pd.options.plotting.backend= 'holoviews'
df.plot.hist(y='y', by='category')
```
will fail because `data = data[y]` is called before the plotting is passed off to the backend.
Basically it seems like backend writers should be free to get the passed pandas objects with as little interference as possible.
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} | 1 | 2019-09-26T23:46:05Z | 2019-09-27T14:24:36Z | 2019-09-27T11:45:46Z | MEMBER | null | The only relevant exception that gets raised by bottleneck AFAICT is a TypeError when we pass a `mask` kwarg that it doesn't accept. By avoiding this case at the beginning, we avoid having to catch the exception. | {
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} | 1 | 2019-09-27T03:36:56Z | 2019-10-01T13:43:40Z | 2019-10-01T13:07:53Z | MEMBER | null | - [x] closes #16959
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
In an unrelated branch I found that this exec-generated code is a PITA to debug. So this PR finally gets rid of it. | {
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} | 0 | 2019-09-27T07:32:14Z | 2020-01-01T16:21:27Z | 2020-01-01T16:21:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
a = pd.DataFrame([range(3)]*3,index=map(str, range(3))).T
a.iloc[:,0] = [10, 11, 11]
a.groupby('0').apply(lambda x: print(x.index))
indexes = a.groupby('0').apply(lambda x: x.index)
print(indexes)
index_lengths = a.groupby('0').apply(lambda x: len(x.index))
print(index_lengths)
indexes_lengths = a.groupby('0').apply(lambda x: x.index).apply(len)
print(indexes_lengths)
assert indexes_lengths.equals(index_lengths), "Apply indexes are returning the wrong index"
```
#### Problem description
Groupby indexing is returning the wrong value. It is returning the indexes of the last group for all the groups.
#### Expected Output
```
0
10 Int64Index([0], dtype='int64')
11 Int64Index([1, 2], dtype='int64')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.0-arch2-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C
LOCALE : None.None
pandas : 0.25.1
numpy : 1.15.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.28.5
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 2.2.2
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.1.5
scipy : 1.3.1
sqlalchemy : None
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28653 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28653/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28653/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28653/events | https://github.com/pandas-dev/pandas/issues/28653 | 499,296,367 | MDU6SXNzdWU0OTkyOTYzNjc= | 28,653 | ExcelWriter's append mode unable to write to existing worksheet | {
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The openpxyl engine for ExcelWriter's append mode allows us to write data into a new Worksheet and append it to an existing Workbook. However, when trying to append data into an existing Worksheet, it creates a new blank Worksheet (without any existing data) with the Worksheet's name suffixed numerically incremented.
#### Sample Code
```python
import pandas as pd
from openpyxl import load_workbook
excel_filename = 'excel_file.xlsx'
# Create initial excel file
initial_data = pd.DataFrame({'col1': ['A', 'B'], 'col2': [1, 2]})
with pd.ExcelWriter(excel_filename, engine='openpyxl', mode='w') as writer:
initial_data.to_excel(writer, sheet_name="sheet", index=False)
# Append some new rows into an existing worksheet
new_data = pd.DataFrame({'col1': ['C', 'D'], 'col2': [3, 4]})
workbook = load_workbook(excel_filename)
with pd.ExcelWriter(excel_filename, engine='openpyxl', mode='a') as writer:
new_data.to_excel(writer,
sheet_name="sheet",
startrow=workbook['sheet'].max_row,
header=False,
index=False)
```
#### Expected Output
We would expect the Excel file to contain a Worksheet named "sheet", with the following data:
| col1 | col2 |
| ---| --- |
| A | 1 |
| B | 2 |
| C | 3 |
| D | 4 |
#### Actual Output
However, the Excel file contains two Worksheets, namely "sheet" and "sheet1", with the following data:
`sheet`:
| col1 | col2 |
| ---| --- |
| A | 1 |
| B | 2 |
`sheet1`:
| | |
| ---| --- |
| | |
| | |
| C | 3 |
| D | 4 |
#### Explanation
When the `ExcelWriter` object is instantiated, it assumes a new Empty Workbook with no Worksheets (see ExcelWriter: line 701).
https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/io/excel/_base.py#L701
So when we try to write data, it always creates a new blank Worksheet instead (see _OpenpyxlWriter: line 400-403).
https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/io/excel/_openpyxl.py#L400-L403
In addition, the openpxyl library performs a check before writing to "avoid duplicate names" (see [openpxyl: line 18](https://bitbucket.org/openpyxl/openpyxl/src/15df72eae2f93bedc0c0c0cc8753b9107cc82d41/openpyxl/workbook/child.py#lines-18)), which numerically increment the suffix of the sheet name.
#### Existing solution
An existing solution is found in Stack Overflow (see [here](https://stackoverflow.com/questions/57932758/unable-to-append-pandas-dataframe-to-existing-excel-sheet/57941866#57941866)).
It is a simple fix with just one line to add all existing worksheets after the `ExcelWriter` object is instantiated (see Sample Code above):
```python
writer.sheets = dict((ws.title, ws) for ws in writer.book.worksheets)
```
It would be preferred if this is done in the backend, right after the workbook is loaded here:
https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/io/excel/_openpyxl.py#L23-L27
It will improve usability of the `DataFrame.to_excel` function.
However, some additional flags should be added to warn the user that existing data in existing worksheets may be overwritten (if the `startrow` or `startcol` parameter is stated wrongly by accident). Perhaps a new default parameter `overwrite=false` should be created for `DataFrame.to_excel` to only write data to empty regions (by performing bound checks for `startrow` and `startcol`).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.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: None.None
pandas: 0.24.2
pytest: 2.8.5
pip: 10.0.1
setuptools: 39.2.0
Cython: 0.23.4
numpy: 1.16.4
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.5.0
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
feather: None
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml.etree: 3.5.0
bs4: 4.4.1
html5lib: None
sqlalchemy: 1.0.11
pymysql: 0.8.0
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-09-27T12:57:20Z | 2019-09-27T20:59:50Z | 2019-09-27T20:59:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
pd.set_option("display.max_rows", 100)
print(display)
Displays this:
<img width="391" alt="Screen Shot 2019-09-27 at 6 55 04 AM" src="https://user-images.githubusercontent.com/5413726/65770716-e0850700-e0f3-11e9-904f-f13e73d17687.png">
I need to see more than 12 rows.
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} | 4 | 2019-09-27T12:57:33Z | 2019-10-01T18:51:36Z | 2019-09-30T16:22:42Z | CONTRIBUTOR | null | This relates to: [27977](https://github.com/pandas-dev/pandas/issues/27977). I have fixed the doc PR08 formatting issues for:
```
pandas.DataFrame.insert: Parameter "column" description should start with a capital letter
pandas.DataFrame.apply: Parameter "raw" description should start with a capital letter
pandas.DataFrame.pipe: Parameter "func" description should start with a capital letter
pandas.DataFrame.pipe: Parameter "args" description should start with a capital letter
pandas.DataFrame.pipe: Parameter "kwargs" description should start with a capital letter
pandas.DataFrame.corr: Parameter "method" description should start with a capital letter
pandas.DataFrame.corrwith: Parameter "axis" description should start with a capital letter
pandas.DataFrame.corrwith: Parameter "method" description should start with a capital letter
pandas.DataFrame.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.DataFrame.duplicated: Parameter "keep" description should start with a capital letter
pandas.DataFrame.idxmax: Parameter "axis" description should start with a capital letter
pandas.DataFrame.idxmin: Parameter "axis" description should start with a capital letter
pandas.DataFrame.unstack: Parameter "fill_value" description should start with a capital letter
pandas.DataFrame.plot: Parameter "kind" description should start with a capital letter
pandas.DataFrame.plot: Parameter "style" description should start with a capital letter
pandas.DataFrame.to_feather: Parameter "fname" description should start with a capital letter
pandas.DataFrame.to_msgpack: Parameter "path" description should start with a capital letter
pandas.DataFrame.to_msgpack: Parameter "append" description should start with a capital letter
pandas.DataFrame.to_msgpack: Parameter "compress" description should start with a capital letter
pandas.DataFrame.to_clipboard: Parameter "excel" description should start with a capital letter
```
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Will continue to work through all PR08 docstring errors. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28656 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28656/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28656/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28656/events | https://github.com/pandas-dev/pandas/issues/28656 | 499,447,561 | MDU6SXNzdWU0OTk0NDc1NjE= | 28,656 | DOC: Styler errors PR08 and PR09 | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-27T13:30:02Z | 2019-10-02T06:29:51Z | 2019-10-02T06:29:51Z | CONTRIBUTOR | null | ```python
python scripts/validate_docstrings.py pandas.io.formats.style.Styler --errors=PR08,PR09
```
#### Problem description
returns:
```
11 Errors found:
Parameters {table_attributes} not documented
Parameter "data" has no description
Parameter "precision" description should start with a capital letter
Parameter "precision" description should finish with "."
Parameter "table_styles" description should start with a capital letter
Parameter "table_styles" description should finish with "."
Parameter "uuid" description should start with a capital letter
Parameter "uuid" description should finish with "."
Parameter "caption" description should start with a capital letter
Parameter "caption" description should finish with "."
Missing description for See Also "DataFrame.style" reference
2 Warnings found:
No extended summary found
No examples section found
```
This is a subset of error messages from #28602 and #25232. Part of the work for #27977
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"description": "Non-arit... | open | false | null | [] | null | 3 | 2019-09-27T14:50:29Z | 2019-10-26T01:41:14Z | null | NONE | null | #### Code Sample
This is probably the fastest implementation I am aware of at the moment: https://rdrr.io/cran/WGCNA/man/bicor.html
However this is also available but considerably slower and not parallelized:
https://docs.astropy.org/en/stable/api/astropy.stats.biweight_midcorrelation.html
#### Problem description
Nothing popped up for the following searches:
biweight
https://github.com/pandas-dev/pandas/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+biweight+
bicor
https://github.com/pandas-dev/pandas/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aopen+bicor+
#### Expected Output
Fast implementation of biweight midcorrelation and extremely robust correlation metric
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} | 2 | 2019-09-27T16:45:55Z | 2019-10-02T06:43:09Z | 2019-10-02T06:43:09Z | NONE | null | #### Code Sample
I have found a strange corner case when adding strings to a Series. This is an operation that most of the times works well:
```python
import pandas as pd
# Adding a string to a Series, works as expected
pd.Series(['hello','world'])+'!'
```
output:
```
0 hello!
1 world!
dtype: object
```
But in the specific case of adding the string 'm', it raises an exception:
```python
# Adding the string 'm' to a Series
pd.Series(['hello','world'])+'m'
```
output:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-125-795dd84b51d1> in <module>
----> 1 pd.Series(['hello','world'])+'m'
~/PycharmProjects/nsa.notebooks-voice/virtual3/lib/python3.6/site-packages/pandas-0.25.1-py3.6-macosx-10.12-x86_64.egg/pandas/core/ops/__init__.py in wrapper(left, right)
1014 if is_scalar(right):
1015 # broadcast and wrap in a TimedeltaIndex
-> 1016 assert np.isnat(right)
1017 right = np.broadcast_to(right, left.shape)
1018 right = pd.TimedeltaIndex(right)
TypeError: ufunc 'isnat' is only defined for datetime and timedelta.
```
#### Problem description
It took me a while to figure this one out, but looking at the [source code ](https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/core/ops/__init__.py) of _arith_method_SERIES() in pandas/pandas/core/ops/__init__.py, it's apparently due to running is_timedelta64_dtype() as part of type checks for the scalar. String 'm' is incorrectly parsed as a timedelta64.
```python
pd.core.ops.is_timedelta64_dtype('m')
>True
```
This can be traced to a pandas_dtype() function:
```python
pd.core.dtypes.common.pandas_dtype('m')
>dtype('<m8')
```
Presumably there are other random strings with similar interactions with type checking functions that throw similar errors in this operation.
I get this error on build 25.1 . I haven't been able to test this on the latest master code, which introduces a lot of changes to /pandas/core/ops/__init__ , so I don't know if this is still a problem there.
#### Expected Output
Adding strings to a string Series shouldn't throw this exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 10.0.1
setuptools : 39.0.1
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
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 : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 8 | 2019-09-27T18:58:27Z | 2020-01-19T08:16:24Z | 2020-01-19T08:16:21Z | NONE | null | - [ ] closes #8193
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Line/bar plot accepts color by dictonary.
@elisamalzoni | {
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] | closed | false | null | [] | null | 2 | 2019-09-27T19:48:17Z | 2020-04-05T01:47:16Z | 2020-04-05T01:47:15Z | NONE | null | Python version 3.7.3 or 3.7.4
#### Code Sample, a copy-pastable example if possible
``` decoded = base64.b64decode(content_string)
try :
if 'dm.xpt' in filename :
# df_dict['dm'] = pd.read_sas(filename,encoding="latin1")
df_dict['dm'] = pd.read_sas(io.BytesIO(decoded),format='xport',encoding='latin1')
```
#### Problem description
'bytes' do not have attribute 'encode"
**How to fix:
commenting out L279-L282 in Python37\lib\site-packages\pandas\io\sas\sasreader.py**
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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"default"... | closed | false | null | [] | null | 3 | 2019-09-27T20:46:37Z | 2019-11-21T20:00:40Z | 2019-10-01T14:03:20Z | MEMBER | null | Cuts import time by about 23%, from 393ms to 303ms (note these measurements are pretty noisy). The percentage edges up to 26% if we ignore get_version, since that is negligible in a release.
Downside: we lose some docstrings on plotting methods. Will need to decide if it is worth a refactor to keep them in place. | {
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} | 11 | 2019-09-27T23:27:41Z | 2020-01-07T00:30:25Z | 2020-01-01T16:21:28Z | MEMBER | null | - [x] closes #28652
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Makes sure that the output of `groupby.apply` is built up by value instead of by reference in `reduction.pyx` to avoid the behavior from #28652. | {
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} | 6 | 2019-09-28T06:10:53Z | 2019-10-16T12:29:24Z | 2019-10-16T12:29:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
data = pd.read_csv("test.csv",encoding="gbk")
print(data)
html = data.to_html(col_space=40,justify="center")
```
#### Problem description
当我用to_html（）方法生成html的时候，中文显示乱码，后来一看该方法竟然没有encoding参数，希望作者能完善了，这样以后会方便很多人哈！
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import random
import numpy as np
Creating the time-series index
n=60
index = pd.date_range('01/13/2020', periods = 70,freq='D')
Creating the dataframe
df = pd.DataFrame({"A":np.random.uniform(low=0.5, high=13.3, size=(70,)),
"B":np.random.uniform(low=10.5, high=45.3, size=(70,)),
"C":np.random.uniform(low=70.5, high=85, size=(70,)),
"D":np.random.uniform(low=50.5, high=65.7, size=(70,))}, index = index)
df.pct_change(freq='BM')
```
#### Problem description
For a time-series data, the df.pct_change(freq='BM') doesn't works and throws following error.
ValueError: cannot reindex from a duplicate axis
#### Expected Output
df.asfreq('BM').pct_change()
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.15.4
pytz : 2017.2
dateutil : 2.6.1
pip : 18.0
setuptools : 39.1.0
Cython : 0.26.1
pytest : 5.1.2
hypothesis : None
sphinx : 1.6.3
blosc : None
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.1.0
html5lib : 0.9999999
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.9.6
IPython : 6.1.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.1.0
matplotlib : 2.1.0
numexpr : 2.6.2
odfpy : None
openpyxl : 2.4.8
pandas_gbq : None
pyarrow : 0.8.0
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.1.13
tables : 3.4.2
xarray : 0.10.7
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
</details>
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} | 5 | 2019-09-28T11:16:10Z | 2019-12-26T00:08:09Z | 2019-12-26T00:08:08Z | NONE | null | This produces a poor warning
```python
>>> import pandas as pd
>>> import numpy as np
>>> np.ptp(pd.Series([1, 2, 3]))
/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.
```
because `np.ptp` calls `Series.ptp`, which is deprecated. We should instruct the user
to convert the Series to an array before calling `ptp`.
```python
np.ptp(pd.Series([1, 2, 3]).array)
```
The warning is defined at https://github.com/pandas-dev/pandas/blob/20e4c186191859dcde2437edacaee43d8d34dc46/pandas/core/generic.py#L10197-L10199
---
In this case I am using numpy ptp, however the error messages states I am not
```
>>> dfagg = df.groupby(['idf']).agg({ 'f1' : [np.min, np.max, np.ptp] })
C:\test\lib\numpy\core\fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.
```
NO warning
`>>> dfagg = df.groupby(['idf']).agg({ 'f1' : [np.min, np.max, numpy.ptp] })`
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} | 1 | 2019-09-28T16:07:58Z | 2019-10-08T13:59:33Z | 2019-10-06T22:40:39Z | CONTRIBUTOR | null | - [x] closes #18878
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-09-28T17:14:14Z | 2019-10-02T04:06:48Z | 2019-10-02T04:06:43Z | CONTRIBUTOR | null | - [x] Updated the "See Also" section for the IndexOpsMixin '''_is_homogenous_type'''
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} | 2 | 2019-09-28T20:28:30Z | 2019-10-08T12:40:09Z | 2019-10-08T12:40:09Z | CONTRIBUTOR | null | Failure on merging on Categorical columns which include intervals.
For instance, the following raises `TypeError: data type not understood`
```python
bins = np.arange(0, 91, 30)
df1 = pd.DataFrame(np.array([[1, 22], [2, 35], [3, 82]]),
columns=['Id', 'Dist']).set_index('Id')
df1['DistGroup'] = pd.cut(df1['Dist'], bins)
idx = pd.IntervalIndex.from_breaks(bins)
df2 = pd.DataFrame(np.array(['g1', 'g2', 'g3']), columns=['GroupId'], index=idx)
df2.index.name = 'DistGroup'
res = pd.merge(df1, df2, left_on='DistGroup', right_index=True).reset_index()
```
#### Expected Output
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>Dist</th> <th>DistGroup</th> <th>GroupId</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>22</td> <td>(0, 30]</td> <td>g1</td> </tr> <tr> <th>1</th> <td>35</td> <td>(30, 60]</td> <td>g2</td> </tr> <tr> <th>2</th> <td>82</td> <td>(60, 90]</td> <td>g3</td> </tr> </tbody></table>'
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28669 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28669/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28669/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28669/events | https://github.com/pandas-dev/pandas/issues/28669 | 499,824,979 | MDU6SXNzdWU0OTk4MjQ5Nzk= | 28,669 | minor inconsistency in Categorical.remove_categories error message | {
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} | 12 | 2019-09-28T21:52:21Z | 2019-10-22T15:46:04Z | 2019-10-22T15:46:04Z | MEMBER | null | mypy error: `pandas\core\arrays\categorical.py:1125:13: error: Incompatible types in assignment (expression has type "List[Any]", variable has type "Set[Any]") [assignment]`
#### Code Sample, a copy-pastable example if possible
```python
Python 3.7.4 (default, Aug 9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> import pandas as pd
>>> pd.__version__
'0.26.0.dev0+537.gd2bc6a497.dirty'
>>>
>>> cat = pd.Categorical(["a", "b", "c", "a"], ordered=True)
>>>
>>> cat
[a, b, c, a]
Categories (3, object): [a < b < c]
>>>
>>> cat = cat.remove_categories(["c"])
>>> cat
[a, b, NaN, a]
Categories (2, object): [a < b]
>>>
>>> cat = cat.remove_categories(["c"])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas-simonjayhawkins\pandas\core\arrays\categorical.py", line 1130, in remove_categories
raise ValueError(msg.format(not_included=not_included))
ValueError: removals must all be in old categories: {'c'}
>>>
>>> cat = cat.remove_categories(["c", np.nan])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas-simonjayhawkins\pandas\core\arrays\categorical.py", line 1130, in remove_categories
raise ValueError(msg.format(not_included=not_included))
ValueError: removals must all be in old categories: ['c']
>>> cat
[a, b, NaN, a]
Categories (2, object): [a < b]
>>>
```
#### Problem description
The error message shows invalid removals as a set or a list depending on whether the removal list contains a null value.
#### Expected Output
consistent message. preferably as a list.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d2bc6a49778407a02f31c40408e8d62cb24e6b64
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+537.gd2bc6a497.dirty
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.2.1
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
>>>
</details>
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} | 1 | 2019-09-29T06:07:23Z | 2019-10-05T22:44:58Z | 2019-10-05T22:44:54Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR fixes incorrect doc for `pandas.to_datetime`. | {
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} | 4 | 2019-09-29T16:38:32Z | 2019-10-01T12:17:45Z | 2019-10-01T12:00:39Z | MEMBER | null | - [x] closes #28631
- [x] tests added / passed
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- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28672 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28672/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28672/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28672/events | https://github.com/pandas-dev/pandas/issues/28672 | 499,948,157 | MDU6SXNzdWU0OTk5NDgxNTc= | 28,672 | Pandas tries to do something with indexes while only supposed to work with values. | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-29T17:52:03Z | 2020-01-27T07:23:00Z | 2020-01-27T07:22:59Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
ra = pd.read_excel("Edata.xlsx", sheet_name="r-a").astype(float)
```
```python
d[i].min().min()
```
#### Problem description
Traceback (most recent call last):
File "E:/загрузки/учеба/лабы/H2lab/Search.py", line 4, in <module>
ra = pd.read_excel("Edata.xlsx", sheet_name="r-a").astype(float)
File "E:\python\lib\site-packages\pandas\core\generic.py", line 5882, in astype
dtype=dtype, copy=copy, errors=errors, **kwargs
File "E:\python\lib\site-packages\pandas\core\internals\managers.py", line 581, in astype
return self.apply("astype", dtype=dtype, **kwargs)
File "E:\python\lib\site-packages\pandas\core\internals\managers.py", line 438, in apply
applied = getattr(b, f)(**kwargs)
File "E:\python\lib\site-packages\pandas\core\internals\blocks.py", line 559, in astype
return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
File "E:\python\lib\site-packages\pandas\core\internals\blocks.py", line 643, in _astype
values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
File "E:\python\lib\site-packages\pandas\core\dtypes\cast.py", line 729, in astype_nansafe
return arr.astype(dtype, copy=True)
ValueError: could not convert string to float: '4→0'
---------------
Traceback (most recent call last):
File "E:/загрузки/учеба/лабы/H2lab/Search.py", line 23, in <module>
mins[i] = d[i].min().min()
File "E:\python\lib\site-packages\pandas\core\generic.py", line 11620, in stat_func
f, name, axis=axis, skipna=skipna, numeric_only=numeric_only
File "E:\python\lib\site-packages\pandas\core\series.py", line 4083, in _reduce
return op(delegate, skipna=skipna, **kwds)
File "E:\python\lib\site-packages\pandas\core\nanops.py", line 123, in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
File "E:\python\lib\site-packages\pandas\core\nanops.py", line 843, in reduction
result = getattr(values, meth)(axis)
File "E:\python\lib\site-packages\numpy\core\_methods.py", line 32, in _amin
return umr_minimum(a, axis, None, out, keepdims, initial)
TypeError: '<=' not supported between instances of 'str' and 'int'
I encountered this problem i new version of pandas. It seems like pandas when coverting values to float or searching for min, tries to operate with indexes(which strings) as well. I know it because "4→0" is a name of row or column. I can provide any fether info if you need it. It works ok with pandas 0.23.4
#### Expected Output
everything working without mistakes
#### Output of ``pd.show_versions()``
<details>
This convig works prety fine:
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: None
pip: 19.1.1
setuptools: 39.0.1
Cython: 0.29.7
numpy: 1.15.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.1
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: 2.6.3
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
------------------------------------------
This doesn't:
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.15.1
pytz : 2018.5
dateutil : 2.7.3
pip : 19.1.1
setuptools : 39.0.1
Cython : 0.29.7
pytest : None
hypothesis : None
sphinx : 1.8.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.0.0
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 1 | 2019-09-29T20:42:54Z | 2019-10-04T15:33:29Z | 2019-10-04T15:33:29Z | CONTRIBUTOR | null | This fixes validation errors PR09,PR08 for pandas.Timestamp, pandas.Timedelta (and also NaT) class:
```
pandas.Timestamp.ceil: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.ceil: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.ceil: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.day_name: Parameter "locale" description should start with a capital letter
pandas.Timestamp.day_name: Parameter "locale" description should finish with "."
pandas.Timestamp.floor: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.floor: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.floor: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.fromordinal: Parameter "ordinal" description should start with a capital letter
pandas.Timestamp.fromordinal: Parameter "ordinal" description should finish with "."
pandas.Timestamp.fromordinal: Parameter "freq" description should finish with "."
pandas.Timestamp.month_name: Parameter "locale" description should start with a capital letter
pandas.Timestamp.month_name: Parameter "locale" description should finish with "."
pandas.Timestamp.now: Parameter "tz" description should finish with "."
pandas.Timestamp.round: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.round: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.round: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.today: Parameter "tz" description should finish with "."
pandas.Timestamp.tz_localize: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.tz_localize: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.tz_localize: Parameter "errors" description should start with a capital letter
pandas.Timestamp.tz_localize: Parameter "errors" description should finish with "."
pandas.Timedelta.max: Parameter "unit" description should finish with "."
pandas.Timedelta.min: Parameter "unit" description should finish with "."
```
This is a subset of #28602 and #27977
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} | 6 | 2019-09-29T20:48:20Z | 2019-10-03T23:59:22Z | 2019-10-01T16:59:29Z | CONTRIBUTOR | null | This fixes PR09,PR08 errors in Timestamp, NaT, Timedelta classes
Example:
```
pandas.Timestamp.ceil: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.ceil: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.day_name: Parameter "locale" description should finish with "."
pandas.Timestamp.floor: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.floor: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.fromordinal: Parameter "ordinal" description should finish with "."
pandas.Timestamp.fromordinal: Parameter "freq" description should finish with "."
pandas.Timestamp.month_name: Parameter "locale" description should finish with "."
pandas.Timestamp.now: Parameter "tz" description should finish with "."
pandas.Timestamp.round: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.round: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.today: Parameter "tz" description should finish with "."
pandas.Timestamp.tz_localize: Parameter "ambiguous" description should finish with "."
pandas.Timestamp.tz_localize: Parameter "nonexistent" description should finish with "."
pandas.Timestamp.tz_localize: Parameter "errors" description should finish with "."
```
closes #28673
- [x] closes #xxxx
- [x] tests added / passed
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} | 6 | 2019-09-30T00:27:12Z | 2020-05-10T15:52:55Z | 2020-05-10T15:52:55Z | NONE | null | #### Minimal Example
```python
import datetime as dt
import numpy as np
import pandas as pd
def np_to_df(data, start_time):
index = pd.DatetimeIndex(
[start_time + dt.timedelta(milliseconds=t) for t in range(len(data))])
df = pd.DataFrame(data, index=index)
return df
# generate sample data
data = np.sin(np.arange(1000) / 30)
# create DataFrames with DateTimeIndices
df_1 = np_to_df(data, dt.datetime(2019, 9, 30, 9, 41))
df_2 = np_to_df(data, dt.datetime(2019, 9, 30, 9, 42))
# print difference before resampling
print("error_1-2:", np.mean(np.abs(df_1.values - df_2.values)))
# resample
df_1 = df_1.resample("19ms").mean()
df_2 = df_2.resample("19ms").mean()
# print difference after resampling
print("error_1-2:", np.mean(np.abs(df_1.values - df_2.values)))
```
Output:
```
error_1-2: 0.0
error_1-2: 0.04119868246404099
```
#### Problem description
When you give the exact same data to the resample function, it becomes non-deterministic if the `DateTimeIndex` has differing values - even though the frequency is the same.
#### Expected Output
The values of the two `DataFrames` should be exactly the same.
#### Output of ``pd.show_versions()``
<details>
- commit : None
- python : 3.6.8.final.0
- python-bits : 64
- OS : Linux
- OS-release : 4.15.0-51-generic
- machine : x86_64
- processor : x86_64
- byteorder : little
- LC_ALL : None
- LANG : C.UTF-8
- LOCALE : en_US.UTF-8
- pandas : 0.25.1
- numpy : 1.17.2
- pytz : 2019.2
- dateutil : 2.8.0
- pip : 9.0.1
- setuptools : 41.0.1
- Cython : None
- pytest : 4.4.0
- hypothesis : None
- sphinx : None
- blosc : None
- feather : None
- xlsxwriter : None
- lxml.etree : 4.3.3
- html5lib : 0.999999999
- pymysql : None
- psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
- jinja2 : 2.10.1
- IPython : 7.1.1
- pandas_datareader: None
- bs4 : None
- bottleneck : None
- fastparquet : None
- gcsfs : None
- lxml.etree : 4.3.3
- matplotlib : 3.1.1
- numexpr : None
- odfpy : None
- openpyxl : None
- pandas_gbq : None
- pyarrow : None
- pytables : None
- s3fs : None
- scipy : 1.3.1
- sqlalchemy : 1.3.7
- tables : None
- xarray : None
- xlrd : None
- xlwt : None
- xlsxwriter : None
</details>
Happy about any help, @jreback ? | {
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] | closed | false | null | [] | null | 3 | 2019-09-30T00:44:57Z | 2020-04-23T14:14:10Z | 2020-04-23T14:12:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df[col].value_counts(dropna=False, normalize=True)
```
#### Problem description
Doing df[col].value_counts(dropna=False, normalize=True) gives different results on different x64 machines. Updated both machines to latest version of Python (3.7.4), Numpy (1.17.2), Scipy (1.3.1) and Pandas (0.25.1), and the problem still occurs. Regardless of version updates, the two machines produce slightly different orderings of value counts when null-values are present, the only difference being hardware differences.
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} | 17 | 2019-09-30T02:34:07Z | 2019-10-22T15:46:10Z | 2019-10-22T15:46:04Z | CONTRIBUTOR | null | This pull request fixes minor inconsistency in Categorical.remove_categories error message
- [x] closes #28669
- [ ] tests added / passed
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
Changed the error message to show invalid removals as a set. Added tests for removal of null from the categories. Parameterized pytest. | {
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} | 1 | 2019-09-30T03:49:22Z | 2019-10-02T06:50:51Z | 2019-10-02T06:50:51Z | MEMBER | null | For some decreasing `RangeIndex`, the `get_indexer` method will indicate that all of it's own values are missing, and find matches for values not included in the index:
```python
In [2]: ri = pd.RangeIndex(10, 0, -3)
In [3]: ri.get_indexer(ri)
Out[3]: array([-1, -1, -1, -1])
In [4]: ri.get_indexer(ri - 1)
Out[4]: array([ 1, 2, 3, -1])
```
This will in turn result in methods like `Series.reindex` not working properly:
```python
In [5]: s = pd.Series(list('abcd'), index=ri)
In [6]: s
Out[6]:
10 a
7 b
4 c
1 d
dtype: object
In [7]: s.reindex([10, 9, 7])
Out[7]:
10 NaN
9 b
7 NaN
dtype: object
```
The issue appears to occur specifically for decreasing `RangeIndex` that are not in their canonical form. By canonical form, I mean when `stop` is the next valid value in the range that's not included, e.g. when you think of a more standard range like `range(1, 7, 1)`, 7 is the next valid value that's not present, but when the step is larger than 1 you lose uniqueness of representation with `stop` (i.e. `range(1, 6, 2) == range(1, 7, 2)`).
Note that the code above works properly for the equivalent `RangeIndex` in it's canonical form:
```python
In [8]: ri2 = pd.RangeIndex(start=10, stop=-2, step=-3)
In [9]: ri2.equals(ri)
Out[9]: True
In [10]: ri2.get_indexer(ri2)
Out[10]: array([0, 1, 2, 3])
In [11]: ri2.get_indexer(ri2 - 1)
Out[11]: array([-1, -1, -1, -1])
In [12]: s2 = pd.Series(list('abcd'), index=ri2)
In [13]: s2
Out[13]:
10 a
7 b
4 c
1 d
dtype: object
In [14]: s2.reindex([10, 9, 7])
Out[14]:
10 a
9 NaN
7 b
dtype: object
```
The cause of the issue appears to be that the code to determine `start`, `stop`, `step` when dealing with decreasing `RangeIndex` in `get_indexer` assumes `self.stop` is the canonical form:
https://github.com/pandas-dev/pandas/blob/c4489cbfa5f93e69301486e1ef867caed9f4eb9e/pandas/core/indexes/range.py#L386-L390
Instead of directly computing the reversed values ourselves, I think we should simply take the values from the reversed underlying `range` object:
```diff
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index 8783351cc..4c5904e5a 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -387,7 +387,8 @@ class RangeIndex(Int64Index):
start, stop, step = self.start, self.stop, self.step
else:
# Work on reversed range for simplicity:
- start, stop, step = (self.stop - self.step, self.start + 1, -self.step)
+ reverse = self._range[::-1]
+ start, stop, step = reverse.start, reverse.stop, reverse.step
target_array = np.asarray(target)
if not (is_integer_dtype(target_array) and target_array.ndim == 1):
``` | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-30T06:02:45Z | 2019-09-30T06:44:13Z | 2019-09-30T06:44:12Z | CONTRIBUTOR | null | the commit <Add Indent Support in to_json (#28130)> breaks the test. `pytest pandas/tests/io/json/test_pandas.py`.
<details>
/Users/donghojung/anaconda3/envs/pandas-dev/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)
INSTALLED VERSIONS
------------------
commit : c94eaeec0387eb61eff303c24fc5ada24e1c563c
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 : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.26.0.dev0+376.gc94eaeec0
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.2.1
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 4 | 2019-09-30T06:05:30Z | 2019-10-02T16:13:03Z | 2019-10-02T06:50:51Z | MEMBER | null | - [X] closes #28678
- [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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} | 8 | 2019-09-30T10:34:21Z | 2019-11-15T14:55:09Z | 2019-11-15T14:55:06Z | CONTRIBUTOR | null | - [x] closes #28664
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
pct_change didn't work when the freq is anchored(like `1W`, `1M`, `BM`)
so when the freq is anchored, use `data.asfreq(freq)` instead of the raw
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} | [] | closed | false | null | [] | null | 10 | 2019-09-30T11:16:29Z | 2019-10-04T16:56:21Z | 2019-10-04T10:18:41Z | NONE | null | I want to update data within a HDF table that has a multiindex.
This should be done in two steps:
- Remove the data from the HDF Store where the multiindex equals the multiindex of an update table (=> store.remove)
- Insert the new data into the HDF Store (=> store.append)
The problem is that I do not find a way to select the multiindex.
I would need something like this:
store.remove(‘table_name’, where="index in df_update.index")
Is there a way to select a multiindex from a HDF store? | {
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] | closed | false | null | [] | null | 3 | 2019-09-30T11:31:16Z | 2020-02-17T14:53:36Z | 2020-02-17T14:53:36Z | NONE | null | So I have very long text in excel cell, when I want to read this it only shows a few lines. How to get all text from a cell?
tekst=self.alco.loc[self.alco['Name']=='Vodka', 'description']
tekst=tekst.to_string(index=False)
print(tekst)
Output:
Vodka is a distilled beverage composed primari...
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- [X ] tests added / passed
- [ X ] passes `black pandas`
- [ X ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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python scripts/validate_docstrings.py pandas.api.extensions.ExtensionArray.view
python scripts/validate_docstrings.py pandas.api.extensions.ExtensionArray.fillna
python scripts/validate_docstrings.py pandas.api.extensions.ExtensionArray.shift
```
#### Problem description
pandas.api.extensions.ExtensionArray.view: Parameter "dtype" description should finish with "."
pandas.api.extensions.ExtensionArray.view: Return value has no description
pandas.api.extensions.ExtensionArray.fillna: Parameter "method" description should finish with "."
pandas.api.extensions.ExtensionArray.fillna: The first line of the returns section should contain only the type, unless multiple values are being returned
pandas.api.extensions.ExtensionArray.fillna: Return value has no description
pandas.api.extensions.ExtensionArray.shift: Parameter "fill_value" description should finish with "."
pandas.api.extensions.ExtensionArray.shift: The first line of the returns section should contain only the type, unless multiple values are being returned
pandas.api.extensions.ExtensionArray.shift: Return value has no description
```
This is a subset of #27977 and #28602 | {
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} | 6 | 2019-09-30T14:47:21Z | 2019-10-14T21:50:43Z | 2019-10-14T21:48:16Z | CONTRIBUTOR | null | - [x] closes #28685
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28687 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28687/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28687/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28687/events | https://github.com/pandas-dev/pandas/issues/28687 | 500,338,144 | MDU6SXNzdWU1MDAzMzgxNDQ= | 28,687 | DOC: Docstrings Formatting Error PR09,PR08 in pandas.plotting | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-30T15:04:31Z | 2019-10-01T15:50:52Z | 2019-10-01T15:50:52Z | CONTRIBUTOR | null | fix PR09,PR08 errors in `pandas.plotting`:
```
pandas.plotting.andrews_curves: Parameter "frame" description should finish with "."
pandas.plotting.andrews_curves: Parameter "color" description should finish with "."
pandas.plotting.andrews_curves: Parameter "kwds" description should finish with "."
pandas.plotting.autocorrelation_plot: Parameter "kwds" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "class_column" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "cols" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "ax" description should start with a capital letter
pandas.plotting.parallel_coordinates: Parameter "ax" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "color" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "use_columns" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "xticks" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "axvlines" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "axvlines_kwds" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "sort_labels" description should finish with "."
pandas.plotting.parallel_coordinates: Parameter "kwds" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "alpha" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "alpha" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "figsize" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "figsize" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "grid" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "grid" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "diagonal" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "diagonal" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "marker" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "hist_kwds" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "density_kwds" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "range_padding" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "range_padding" description should finish with "."
pandas.plotting.scatter_matrix: Parameter "kwds" description should finish with "."
pandas.plotting.table: Parameter "data" description should start with a capital letter
pandas.plotting.table: Parameter "data" description should finish with "."
pandas.plotting.table: Parameter "kwargs" description should start with a capital letter
```
This is a subset of #28602 and #27977
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"description": "rolling, ewma, expanding... | closed | false | null | [] | null | 1 | 2019-09-30T15:13:45Z | 2020-10-18T00:18:02Z | 2020-10-18T00:18:02Z | NONE | null | I'm experiencing a very weird bug with one very specific dataset - when I try to use pandas' rolling.std function on it.
Basically - the setup is -
* I have a dataframe with 1 column stored in float32 format
```
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2186 entries, 2010-11-30 16:00:00 to 2019-08-16 16:00:00
Data columns (total 1 columns):
high 2186 non-null float32
dtypes: float32(1)
memory usage: 25.6 KB
```
```python
ddf.head()
```
Here is a plot of the full timeseries (left column) along with a tail of 200 rows (right column)
Note the scale of the numbers here - it goes from 1e8 to 1e1.
Next I compute the rolling standard deviations using rolling means, as per:
```python
rs = np.sqrt((ddf.high ** 2).rolling(10).mean() - (ddf.high.rolling(10).mean() ) ** 2)
```
This is what the rolling std computed this way it looks like (and matches what I would expect):
But if I use
```python
rs = ddf.rolling(10).high.std()
```
this is what I get:
Something has gotten corrupted - as we can see in the tail of 200 rows in the right.
Now - however, if I rescale the data to make the numbers sit in a smaller range, compute the rolling std and scale it back up
```python
ddf = ddf.assign( high_rescaled=ddf.high / 1e8 )
rs = ddf.rolling(10).high_rescaled.std() * 1e8
```
This is what I get
which matches the output computed using rolling means !
Note - the original data was in np.float32 format. So I thought that this bug might be happening because of some overflow issues (which it really should not - the window is only 10 long !!).
So I converted the data to float64 to test this:
```python
ddf.high = ddf.high.astype(np.float64)
display( ddf.info() )
```
```
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2186 entries, 2010-11-30 16:00:00 to 2019-08-16 16:00:00
Data columns (total 1 columns):
high 2186 non-null float64
dtypes: float64(1)
memory usage: 34.2 KB
```
In this case - even applying rolling.std() to rescaled version of the data (which worked for float32) - is broken !!
#### Minimum Reproducible Example
Regarding generating an MRE - here is the rub.
The bug seems to be a function of the numerics specific to this dataset - and I cannot reproduce it using random data (since I don't know what feature of the numerics is causing this).
Now, if I save the data as a pickle file (using df.to_pickle) and load it back in - I can reproduce these results exactly.
However, if I save it as a csv file (for sharing here) - and load it back in - I get a whole new level of badness. The results look really bad for all cases after this round-tripping. This seems to indicate that there is some thing about the *exact numbers* of the dataset that is triggering some numerical problems with rolling.std.
[rs.zip](https://github.com/pandas-dev/pandas/files/3672047/rs.zip)
```python
from pylab import *
import pandas as pd
ddf = pd.read_csv( '/path/to/rs.csv')
ddf.high = ddf.high
display( ddf.info() )
figure()
subplot(121)
plot( ddf.high, '-r' )
subplot(122)
plot( ddf.high.tail(200), '-r' )
gcf().suptitle( 'original data' )
figure()
subplot(121)
rs = np.sqrt( (ddf.high ** 2).rolling(10).mean() - (ddf.high.rolling(10).mean() ) ** 2 )
plot( rs, '-b' )
subplot(122)
plot( rs.tail(200), '-b' )
gcf().suptitle( 'rolling std computed using rolling means on original data' )
figure()
subplot(121)
rs = ddf.rolling(10).high.std()
plot( rs, '-b' )
subplot(122)
plot( rs.tail(200), '-b' )
gcf().suptitle( 'rolling std computed on original data' )
figure()
subplot(121)
ddf = ddf.assign( high_rescaled=ddf.high / 1e8 )
rs = ddf.rolling(10).high_rescaled.std() * 1e8
plot( rs, '-b' )
subplot(122)
plot( rs.tail(200), '-b' )
gcf().suptitle( 'rolling std computed on rescaled data' )
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-9-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.3
setuptools: 41.2.0
Cython: None
numpy: 1.16.4
scipy: 1.3.1
pyarrow: 0.13.0
xarray: 0.13.0+6.g4617e68b
IPython: 7.8.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.4.1
bs4: 4.8.0
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: 0.3.0
</details>
@jreback | {
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} | 2 | 2019-09-30T16:11:59Z | 2019-10-01T18:14:49Z | 2019-10-01T15:50:52Z | CONTRIBUTOR | null | fixes the errors along with minor changes to standardize kwds -> **kwargs
closes #28687
verified this fixes PR09,PR08 errors with:
```
./scripts/validate_docstrings.py --errors=PR09,PR08 | grep "pandas.plotting"
```
- [x] closes #xxxx
- [x] tests added / passed | {
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} | 0 | 2019-09-30T16:19:30Z | 2020-12-17T23:40:05Z | 2020-12-17T23:40:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
This fails with `AttributeError: 'Index' object has no attribute 'codes'`:
```python
pd.DataFrame(columns=pd.CategoricalIndex([], categories=['A'])).reindex(columns=pd.Index(['A']))
```
The backtrace is:
```
c:\python36\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
195 @wraps(func)
196 def wrapper(*args, **kwargs):
--> 197 return func(*args, **kwargs)
198
199 if not PY2:
c:\python36\lib\site-packages\pandas\core\frame.py in reindex(self, *args, **kwargs)
3807 kwargs.pop('axis', None)
3808 kwargs.pop('labels', None)
-> 3809 return super(DataFrame, self).reindex(**kwargs)
3810
3811 @Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
c:\python36\lib\site-packages\pandas\core\generic.py in reindex(self, *args, **kwargs)
4354 # perform the reindex on the axes
4355 return self._reindex_axes(axes, level, limit, tolerance, method,
-> 4356 fill_value, copy).__finalize__(self)
4357
4358 def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
c:\python36\lib\site-packages\pandas\core\frame.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
3734 if columns is not None:
3735 frame = frame._reindex_columns(columns, method, copy, level,
-> 3736 fill_value, limit, tolerance)
3737
3738 index = axes['index']
c:\python36\lib\site-packages\pandas\core\frame.py in _reindex_columns(self, new_columns, method, copy, level, fill_value, limit, tolerance)
3756 new_columns, indexer = self.columns.reindex(new_columns, method=method,
3757 level=level, limit=limit,
-> 3758 tolerance=tolerance)
3759 return self._reindex_with_indexers({1: [new_columns, indexer]},
3760 copy=copy, fill_value=fill_value,
c:\python36\lib\site-packages\pandas\core\indexes\category.py in reindex(self, target, method, level, limit, tolerance)
521 else:
522
--> 523 codes = new_target.codes.copy()
524 codes[indexer == -1] = cats[missing]
525 new_target = self._create_from_codes(codes)
```
#### Problem description
This should not fail, but rather return an `0x1` DataFrame with the new columns. Other, closely-related, expressions **do** work correctly in this scenario e.g.:
```
pd.DataFrame(columns=pd.CategoricalIndex([], categories=['A'])).reindex(columns=pd.Index(['B']))
pd.DataFrame(columns=pd.CategoricalIndex([], categories=['A'])).reindex(columns=pd.CategoricalIndex(['A']))
pd.DataFrame(columns=pd.CategoricalIndex([], categories=['A'])).reindex(columns=pd.CategoricalIndex(['B']))
```
#### Expected Output
```
pd.DataFrame(columns=pd.Index(['A']))
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: 3.1.2
pip: 19.2.1
setuptools: 39.0.1
Cython: 0.29.6
numpy: 1.17.0
scipy: 1.2.1
pyarrow: 0.13.0
xarray: 0.12.1
IPython: 6.1.0
sphinx: 2.1.1
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.3.0.dev0
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 0.9999999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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- [x ] passes `black pandas`
- [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 8 | 2019-09-30T19:35:35Z | 2019-10-16T12:29:27Z | 2019-10-16T12:29:24Z | CONTRIBUTOR | null | - [x ] closes https://github.com/pandas-dev/pandas/issues/28663
- [x ] tests added / passed
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- [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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added encoding argument to DataFrame.to_html()
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} | 4 | 2019-09-30T20:10:08Z | 2019-10-07T15:58:05Z | 2019-10-06T22:02:43Z | CONTRIBUTOR | null | The bug is not present any more.
- [x] closes #21416
- [x] tests added / passed
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} | 2 | 2019-09-30T21:50:46Z | 2021-05-31T14:27:03Z | 2021-05-31T14:27:03Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
s = pd.Series([1, 2, 1, 3], ['first', 'b', 'second', 'c'])
print(s.sort_values(ascending=False, kind='mergesort'))
```
#### Output
```
c 3
b 2
second 1
first 1
dtype: int64
```
#### Expected Output
```
c 3
b 2
first 1
second 1
dtype: int64
```
#### Problem description
Mergesort should be stable irrespective of direction, but `ascending=False` (which is not stable) is just the reverse of `ascending=True` (which is stable). | {
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} | [] | closed | false | null | [] | null | 3 | 2019-09-30T22:03:25Z | 2019-12-09T18:04:36Z | 2019-12-09T18:04:36Z | CONTRIBUTOR | null | The solution uses the `nargsort` function, which was created to handle
in one place the intricacies of sorting. Not adapting Series.sort_values
to use may have been an oversight.
- [ ] closes #28697
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-09-30T23:29:06Z | 2019-10-10T02:05:01Z | 2019-10-10T02:05:01Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': range(10), 'b': range(10)})
df.to_hdf('/tmp/1.hdf', 'key2')
store = pd.HDFStore('/tmp/1.hdf', 'r')
try:
pd.read_hdf(store, 'key1')
except KeyError:
pass
pd.read_hdf(store, 'key2')
```
#### Problem description
Due to a recent change in pandas, the read_hdf function closes HDF5 stores if it fails to read from the file. In my opinion, this is the expected behavior if read_hdf opened the file itself, but shouldn't happen if it was passed a file that is already open. One use case for this is a series of try/except blocks that try to read a series of different keys from an HDF5 file that may or may not have the individual keys, as in the example above. The attempt to read key2 will fail because the first attempt will have closed the file.
This issue was introduced in pull #25863 to address issue #25766 where read_hdf wasn't closing files that it hadn't opened itself.
The fix is pretty easy, just add `if not isinstance(path_or_buf, HDFStore):` before `store.close()` at the end of read_hdf().
#### Expected Output
Should not raise an Exception when reading key2.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.27.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.utf8
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 9 | 2019-10-01T00:49:39Z | 2019-10-10T02:05:06Z | 2019-10-10T02:05:01Z | CONTRIBUTOR | null | - [ ] closes #28699
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This commit fixes #28699. I added a test for the bug. | {
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] | open | false | null | [] | null | 1 | 2019-10-01T01:58:16Z | 2019-10-16T18:51:51Z | null | CONTRIBUTOR | null | #### Problem description
Per [contributing_docstring#section-3-parameters](https://pandas.pydata.org/pandas-docs/stable/development/contributing_docstring.html#section-3-parameters), default values should be documented as such:
* `int, default 0`
* `int, default -1, meaning all cpus` (optional descriptions)
Currently, the `validate_docstrings.py` script does not check default values. This should be added to ensure valid docstrings.
#### Proposal
Add a new validation, "PR11", to the validate_docstrings scripts. "PR03" compares the ordering of the actual arguments to the documented arguments: this might be a good starting point. The validation would need to
* check which params have a default value
* check that the documentation for the parameter has the correct format for default value documentation
* check that the documented value matches the actual value
#### Questions
* If default values documentation is enforced, does it make sense to allow optional descriptions inline?
* Can it be verified that the parameter description includes the default value description?
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https://api.github.com/repos/pandas-dev/pandas/issues/28702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28702/events | https://github.com/pandas-dev/pandas/issues/28702 | 500,634,085 | MDU6SXNzdWU1MDA2MzQwODU= | 28,702 | DOC: Docstrings Formatting error PR09,PR08 in pandas.DataFrame | {
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] | closed | false | null | [] | null | 1 | 2019-10-01T03:13:30Z | 2021-07-21T05:07:12Z | 2021-07-21T05:07:12Z | CONTRIBUTOR | null | fix errors PR09,PR08 listed below:
```
pandas.DataFrame: Parameter "data" description should finish with "."
pandas.DataFrame: Parameter "index" description should finish with "."
pandas.DataFrame: Parameter "columns" description should finish with "."
pandas.DataFrame: Parameter "dtype" description should finish with "."
pandas.DataFrame: Parameter "copy" description should finish with "."
pandas.DataFrame.insert: Parameter "loc" description should finish with "."
pandas.DataFrame.insert: Parameter "column" description should start with a capital letter
pandas.DataFrame.insert: Parameter "column" description should finish with "."
pandas.DataFrame.lookup: Parameter "row_labels" description should finish with "."
pandas.DataFrame.lookup: Parameter "col_labels" description should finish with "."
pandas.DataFrame.corr: Parameter "method" description should finish with "."
pandas.DataFrame.corrwith: Parameter "method" description should finish with "."
pandas.DataFrame.mode: Parameter "axis" description should finish with "."
pandas.DataFrame.rank: Parameter "method" description should finish with "."
pandas.DataFrame.rank: Parameter "na_option" description should finish with "."
pandas.DataFrame.sem: Parameter "skipna" description should finish with "."
pandas.DataFrame.sem: Parameter "level" description should finish with "."
pandas.DataFrame.std: Parameter "skipna" description should finish with "."
pandas.DataFrame.std: Parameter "level" description should finish with "."
pandas.DataFrame.var: Parameter "skipna" description should finish with "."
pandas.DataFrame.var: Parameter "level" description should finish with "."
pandas.DataFrame.align: Parameter "axis" description should finish with "."
pandas.DataFrame.align: Parameter "level" description should finish with "."
pandas.DataFrame.align: Parameter "fill_value" description should finish with "."
pandas.DataFrame.align: Parameter "method" description should finish with "."
pandas.DataFrame.align: Parameter "fill_axis" description should finish with "."
pandas.DataFrame.align: Parameter "broadcast_axis" description should finish with "."
pandas.DataFrame.drop_duplicates: Parameter "subset" description should finish with "."
pandas.DataFrame.drop_duplicates: Parameter "inplace" description should finish with "."
pandas.DataFrame.duplicated: Parameter "subset" description should finish with "."
pandas.DataFrame.idxmax: Parameter "axis" description should finish with "."
pandas.DataFrame.idxmin: Parameter "axis" description should finish with "."
pandas.DataFrame.reindex: Parameter "index, columns" description should finish with "."
pandas.DataFrame.reindex: Parameter "method" description should finish with "."
pandas.DataFrame.reindex_like: Parameter "method" description should finish with "."
pandas.DataFrame.pivot_table: Parameter "aggfunc" description should finish with "."
pandas.DataFrame.pivot_table: Parameter "fill_value" description should finish with "."
pandas.DataFrame.pivot_table: Parameter "margins" description should finish with "."
pandas.DataFrame.pivot_table: Parameter "dropna" description should finish with "."
pandas.DataFrame.sort_values: Parameter "by" description should finish with "."
pandas.DataFrame.unstack: Parameter "level" description should finish with "."
pandas.DataFrame.unstack: Parameter "fill_value" description should finish with "."
pandas.DataFrame.asfreq: Parameter "method" description should finish with "."
pandas.DataFrame.asfreq: Parameter "how" description should finish with "."
pandas.DataFrame.asfreq: Parameter "normalize" description should finish with "."
pandas.DataFrame.slice_shift: Parameter "periods" description should finish with "."
pandas.DataFrame.tshift: Parameter "periods" description should finish with "."
pandas.DataFrame.tshift: Parameter "freq" description should finish with "."
pandas.DataFrame.tshift: Parameter "axis" description should finish with "."
pandas.DataFrame.tz_localize: Parameter "level" description should finish with "."
pandas.DataFrame.tz_localize: Parameter "copy" description should finish with "."
pandas.DataFrame.tz_localize: Parameter "ambiguous" description should finish with "."
pandas.DataFrame.tz_localize: Parameter "nonexistent" description should finish with "."
pandas.DataFrame.plot: Parameter "data" description should finish with "."
pandas.DataFrame.plot: Parameter "kind" description should finish with "."
pandas.DataFrame.plot: Parameter "use_index" description should finish with "."
pandas.DataFrame.plot: Parameter "grid" description should finish with "."
pandas.DataFrame.plot: Parameter "legend" description should finish with "."
pandas.DataFrame.plot: Parameter "style" description should finish with "."
pandas.DataFrame.plot: Parameter "logx" description should finish with "."
pandas.DataFrame.plot: Parameter "logy" description should finish with "."
pandas.DataFrame.plot: Parameter "loglog" description should finish with "."
pandas.DataFrame.plot: Parameter "rot" description should finish with "."
pandas.DataFrame.plot: Parameter "colorbar" description should finish with "."
pandas.DataFrame.plot: Parameter "position" description should finish with "."
pandas.DataFrame.plot: Parameter "mark_right" description should finish with "."
pandas.DataFrame.from_records: Parameter "index" description should finish with "."
pandas.DataFrame.from_records: Parameter "exclude" description should finish with "."
pandas.DataFrame.from_records: Parameter "columns" description should finish with "."
pandas.DataFrame.from_records: Parameter "coerce_float" description should finish with "."
pandas.DataFrame.from_records: Parameter "nrows" description should finish with "."
pandas.DataFrame.to_parquet: Parameter "partition_cols" description should finish with "."
pandas.DataFrame.to_pickle: Parameter "protocol" description should finish with "."
pandas.DataFrame.to_json: Parameter "orient" description should finish with "."
pandas.DataFrame.to_msgpack: Parameter "path" description should finish with "."
```
This is a subset of #28602 and #27977 | {
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} | 2 | 2019-10-01T04:15:09Z | 2019-10-12T17:09:57Z | 2019-10-12T17:09:57Z | MEMBER | null | We often ask people to add the GH Issue number as a comment to new tests, but that requirement isn't explicitly spelled out in the contributing guide. Might save some back and forth if that is added to the `Writing Tests` section:
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#writing-tests | {
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"description": "Requires discussion from... | open | false | null | [] | null | 8 | 2019-10-01T07:59:56Z | 2021-07-21T05:07:59Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
These both say `float64`:
```python
pd.DataFrame(np.zeros((0, 0), dtype=int), index=[]).values.dtype
pd.DataFrame(np.zeros((1, 0), dtype=int), index=['A']).values.dtype
```
All of the following examples instead say `int32` as expected:
```python
pd.DataFrame(np.zeros((0, 1), dtype=int), index=[]).values.dtype
pd.DataFrame(np.zeros(0, dtype=int), index=[]).values.dtype
pd.Series(np.zeros(0, dtype=int), index=[]).values.dtype
```
#### Problem description
I think in general it would be a nice property that if `X` is a 2D ndarray, then it should be the case that `pd.DataFrame(X).values.base is X`.
It would be great if pandas could not lose dtype information, because I sometimes wrap 2D arrays in DataFrames before extracting them (via `.values`) for use elsewhere. If that wrapping/unwrapping process changes dtype then it can lead to surprising failures (e.g. I roundtrip a 2D array of indexes and then find that I'm trying to index into another array with a 2D array of floats..)
#### Expected Output
`int32` or `int64` for all examples.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: 3.1.2
pip: 19.2.1
setuptools: 39.0.1
Cython: 0.29.6
numpy: 1.17.0
scipy: 1.2.1
pyarrow: 0.13.0
xarray: 0.12.1
IPython: 6.1.0
sphinx: 2.1.1
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.3.0.dev0
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 0.9999999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28705/events | https://github.com/pandas-dev/pandas/pull/28705 | 500,734,831 | MDExOlB1bGxSZXF1ZXN0MzIzMTQxNzY5 | 28,705 | Added the GH Issue number note to Writing Tests in Docs | {
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} | 2 | 2019-10-01T08:13:22Z | 2019-10-15T01:27:43Z | 2019-10-15T01:27:43Z | NONE | null | - [x] closes #28703
I've added the required note at the appropriate place in the documentation.
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https://api.github.com/repos/pandas-dev/pandas/issues/28706 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28706/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28706/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28706/events | https://github.com/pandas-dev/pandas/issues/28706 | 500,795,640 | MDU6SXNzdWU1MDA3OTU2NDA= | 28,706 | DOC: Fix PR08 issues in docstrings in pandas.core.groupby | {
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} | [] | closed | false | null | [] | null | 3 | 2019-10-01T10:07:55Z | 2019-10-01T11:58:25Z | 2019-10-01T11:58:25Z | CONTRIBUTOR | null | Current errors:
```
$ ./scripts/validate_docstrings.py --errors=PR08
pandas.Series.corr: Parameter "method" description should start with a capital letter
pandas.Series.quantile: Parameter "q" description should start with a capital letter
pandas.Series.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.Series.duplicated: Parameter "keep" description should start with a capital letter
pandas.Series.rename: Parameter "index" description should start with a capital letter
pandas.Series.reorder_levels: Parameter "order" description should start with a capital letter
pandas.Series.dt.tz_localize: Parameter "errors" description should start with a capital letter
pandas.Series.str.match: Parameter "flags" description should start with a capital letter
pandas.Series.str.replace: Parameter "case" description should start with a capital letter
pandas.Series.str.replace: Parameter "flags" description should start with a capital letter
pandas.Series.str.replace: Parameter "regex" description should start with a capital letter
pandas.Series.str.translate: Parameter "table" description should start with a capital letter
pandas.Series.cat.rename_categories: Parameter "new_categories" description should start with a capital letter
pandas.Series.hist: Parameter "xrot" description should start with a capital letter
pandas.Series.hist: Parameter "yrot" description should start with a capital letter
pandas.Series.hist: Parameter "figsize" description should start with a capital letter
pandas.Timestamp.ceil: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.day_name: Parameter "locale" description should start with a capital letter
pandas.Timestamp.floor: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.fromordinal: Parameter "ordinal" description should start with a capital letter
pandas.Timestamp.month_name: Parameter "locale" description should start with a capital letter
pandas.Timestamp.round: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.tz_localize: Parameter "errors" description should start with a capital letter
pandas.Period.to_timestamp: Parameter "how" description should start with a capital letter
pandas.arrays.IntervalArray.from_tuples: Parameter "copy" description should start with a capital letter
pandas.arrays.IntervalArray.from_breaks: Parameter "copy" description should start with a capital letter
pandas.read_msgpack: Parameter "iterator" description should start with a capital letter
pandas.io.json.build_table_schema: Parameter "primary_key" description should start with a capital letter
pandas.read_html: Parameter "skiprows" description should start with a capital letter
pandas.HDFStore.put: Parameter "format" description should start with a capital letter
pandas.HDFStore.append: Parameter "format" description should start with a capital letter
pandas.HDFStore.append: Parameter "append " description should start with a capital letter
pandas.HDFStore.append: Parameter "dropna " description should start with a capital letter
pandas.read_feather: Parameter "nthreads" description should start with a capital letter
pandas.read_feather: Parameter "use_threads" description should start with a capital letter
pandas.read_sql_table: Parameter "parse_dates" description should start with a capital letter
pandas.read_sql_query: Parameter "con" description should start with a capital letter
pandas.read_sql_query: Parameter "parse_dates" description should start with a capital letter
pandas.read_sql: Parameter "con" description should start with a capital letter
pandas.read_sql: Parameter "parse_dates" description should start with a capital letter
pandas.core.resample.Resampler.get_group: Parameter "name" description should start with a capital letter
pandas.core.resample.Resampler.get_group: Parameter "obj" description should start with a capital letter
pandas.core.resample.Resampler.pipe: Parameter "args" description should start with a capital letter
pandas.core.resample.Resampler.pipe: Parameter "kwargs" description should start with a capital letter
pandas.core.resample.Resampler.ffill: Parameter "limit" description should start with a capital letter
pandas.core.resample.Resampler.pad: Parameter "limit" description should start with a capital letter
pandas.core.resample.Resampler.sem: Parameter "ddof" description should start with a capital letter
pandas.core.resample.Resampler.var: Parameter "ddof" description should start with a capital letter
pandas.plotting.parallel_coordinates: Parameter "ax" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "alpha" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "figsize" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "grid" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "diagonal" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "range_padding" description should start with a capital letter
pandas.plotting.table: Parameter "data" description should start with a capital letter
pandas.plotting.table: Parameter "kwargs" description should start with a capital letter
pandas.api.types.union_categoricals: Parameter "to_union" description should start with a capital letter
pandas.merge_ordered: Parameter "how" description should start with a capital letter
pandas.merge_asof: Parameter "allow_exact_matches" description should start with a capital letter
pandas.to_numeric: Parameter "errors" description should start with a capital letter
pandas.to_datetime: Parameter "arg" description should start with a capital letter
pandas.to_datetime: Parameter "errors" description should start with a capital letter
pandas.to_datetime: Parameter "box" description should start with a capital letter
pandas.to_datetime: Parameter "format" description should start with a capital letter
pandas.to_datetime: Parameter "exact" description should start with a capital letter
pandas.to_datetime: Parameter "unit" description should start with a capital letter
pandas.to_timedelta: Parameter "box" description should start with a capital letter
pandas.to_timedelta: Parameter "errors" description should start with a capital letter
pandas.infer_freq: Parameter "index" description should start with a capital letter
pandas.util.hash_array: Parameter "encoding" description should start with a capital letter
pandas.util.hash_pandas_object: Parameter "index" description should start with a capital letter
pandas.util.hash_pandas_object: Parameter "encoding" description should start with a capital letter
pandas.Index.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.Index.is_: Parameter "other" description should start with a capital letter
pandas.Index.fillna: Parameter "downcast" description should start with a capital letter
pandas.Index.to_series: Parameter "index" description should start with a capital letter
pandas.Index.to_series: Parameter "name" description should start with a capital letter
pandas.Index.get_indexer: Parameter "method" description should start with a capital letter
pandas.Index.get_loc: Parameter "method" description should start with a capital letter
pandas.CategoricalIndex.rename_categories: Parameter "new_categories" description should start with a capital letter
pandas.IntervalIndex.from_tuples: Parameter "copy" description should start with a capital letter
pandas.IntervalIndex.from_breaks: Parameter "copy" description should start with a capital letter
pandas.IntervalIndex.get_loc: Parameter "method" description should start with a capital letter
pandas.IntervalIndex.get_indexer: Parameter "method" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "levels" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "level" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "inplace" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "verify_integrity" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "codes" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "level" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "inplace" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "verify_integrity" description should start with a capital letter
pandas.MultiIndex.get_loc_level: Parameter "drop_level" description should start with a capital letter
pandas.MultiIndex.get_indexer: Parameter "method" description should start with a capital letter
pandas.MultiIndex.get_level_values: Parameter "level" description should start with a capital letter
pandas.DatetimeIndex.indexer_at_time: Parameter "time" description should start with a capital letter
pandas.DatetimeIndex.indexer_between_time: Parameter "start_time, end_time" description should start with a capital letter
pandas.DatetimeIndex.tz_localize: Parameter "errors" description should start with a capital letter
pandas.TimedeltaIndex.to_series: Parameter "index" description should start with a capital letter
pandas.TimedeltaIndex.to_series: Parameter "name" description should start with a capital letter
pandas.PeriodIndex.asfreq: Parameter "freq" description should start with a capital letter
pandas.PeriodIndex.asfreq: Parameter "how" description should start with a capital letter
pandas.core.window.rolling.Rolling.apply: Parameter "raw" description should start with a capital letter
pandas.core.window.expanding.Expanding.apply: Parameter "raw" description should start with a capital letter
pandas.core.groupby.GroupBy.get_group: Parameter "name" description should start with a capital letter
pandas.core.groupby.GroupBy.get_group: Parameter "obj" description should start with a capital letter
pandas.Grouper: Parameter "key" description should start with a capital letter
pandas.Grouper: Parameter "level" description should start with a capital letter
pandas.Grouper: Parameter "sort" description should start with a capital letter
pandas.core.groupby.GroupBy.pipe: Parameter "args" description should start with a capital letter
pandas.core.groupby.GroupBy.pipe: Parameter "kwargs" description should start with a capital letter
pandas.core.groupby.GroupBy.bfill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.GroupBy.ffill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.GroupBy.nth: Parameter "n" description should start with a capital letter
pandas.core.groupby.GroupBy.nth: Parameter "dropna" description should start with a capital letter
pandas.core.groupby.GroupBy.rank: Parameter "method" description should start with a capital letter
pandas.core.groupby.GroupBy.rank: Parameter "na_option" description should start with a capital letter
pandas.core.groupby.GroupBy.sem: Parameter "ddof" description should start with a capital letter
pandas.core.groupby.GroupBy.std: Parameter "ddof" description should start with a capital letter
pandas.core.groupby.GroupBy.var: Parameter "ddof" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.bfill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.ffill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.filter: Parameter "dropna" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.rank: Parameter "method" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.rank: Parameter "na_option" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.shift: Parameter "periods" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.boxplot: Parameter "subplots" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.boxplot: Parameter "layout" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "precision" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "table_styles" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "uuid" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "caption" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "func" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "func" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "cond" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "value" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "other" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.set_properties: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.set_properties: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_max: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_max: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_min: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_min: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "cmap" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "low" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "high" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "text_color_threshold" description should start with a capital letter
pandas.io.formats.style.Styler.bar: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.use: Parameter "styles" description should start with a capital letter
pandas.api.extensions.ExtensionArray.argsort: Parameter "*args, **kwargs:" description should start with a capital letter
pandas.DataFrame.insert: Parameter "column" description should start with a capital letter
```
This is a subset of #27977 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28707 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28707/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28707/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28707/events | https://github.com/pandas-dev/pandas/pull/28707 | 500,804,513 | MDExOlB1bGxSZXF1ZXN0MzIzMTk3NTg2 | 28,707 | DOC: Fixed PR09 docstring errors in pandas.tseries (#27977) | {
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- [ ] tests added / passed
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- [x ] fixed minor issues in the couple of docstrings (ErrorCode: PR09) in pandas.tseries.offsets.py, namely added dots at the end of parameters {param_name} descriptions. Some of the ErrorCodes: PR09 still appear when running the validation script. This is due the script not covering some of the special cases (such as extra info in the bullet points format in the descriptions of parameters {param_name}), see #25786 and #25461.
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- 0 tests added
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- [x] fixed test_spam_url test by changing a url address to the same html page stored in the pandas github repo
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