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https://api.github.com/repos/pandas-dev/pandas/issues/12604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12604/events | https://github.com/pandas-dev/pandas/pull/12604 | 140,443,940 | MDExOlB1bGxSZXF1ZXN0NjI2NjExNTk= | 12,604 | CLN GH10559 Rename sheetname variable to sheet_name for consistency | {
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] | closed | false | null | [] | null | 7 | 2016-03-13T04:11:25Z | 2016-09-27T14:32:11Z | 2016-05-07T18:50:37Z | NONE | null | - [x] closes #10559
- [x] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/12605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12605/events | https://github.com/pandas-dev/pandas/pull/12605 | 140,480,663 | MDExOlB1bGxSZXF1ZXN0NjI2Njk1ODA= | 12,605 | Chooses eval engine automatically depending on whether numexpr is installed | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12606/events | https://github.com/pandas-dev/pandas/pull/12606 | 140,481,098 | MDExOlB1bGxSZXF1ZXN0NjI2Njk3MTg= | 12,606 | Improved docs for infer_datetime_format | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12607/events | https://github.com/pandas-dev/pandas/pull/12607 | 140,491,180 | MDExOlB1bGxSZXF1ZXN0NjI2NzI3MTU= | 12,607 | WIP: ENH: pivot/groupby index with nan | {
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I've identified where to work and put in place a first, crude solution. I need to do more tests.
- [ ] closes #3729.
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
xref #9941, #5456, #6992, #443
---
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https://api.github.com/repos/pandas-dev/pandas/issues/12608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12608/events | https://github.com/pandas-dev/pandas/pull/12608 | 140,494,061 | MDExOlB1bGxSZXF1ZXN0NjI2NzMyNjU= | 12,608 | added pd as namespace | {
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] | closed | false | null | [] | null | 8 | 2016-03-13T15:08:29Z | 2016-05-26T16:15:03Z | 2016-05-26T16:15:03Z | CONTRIBUTOR | null | - [x ] closes https://github.com/pydata/pandas/pull/12268#discussion_r55924987
This is the msg:
more consistent with code in the rest of the document
added more namespace
Generally, also numpy's randn should used as
np.random.randn
isolated namespace changes
Note:
I hope I got the rebase & merge right.
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https://api.github.com/repos/pandas-dev/pandas/issues/12609 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12609/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12609/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12609/events | https://github.com/pandas-dev/pandas/issues/12609 | 140,506,490 | MDU6SXNzdWUxNDA1MDY0OTA= | 12,609 | group_keys=False stopped working with groupby().resample() in v0.18.0 | {
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```
>>> data = pd.DataFrame([1, 2, 3, 4], index=pd.date_range("20000101", periods=4))
>>> data.groupby(data.index, group_keys=False).resample('4H', how='last')
```
0.17.1:
```
0
2000-01-01 1
2000-01-02 2
2000-01-03 3
2000-01-04 4
```
0.18.0:
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```
import pandas as pd
import numpy as np
ndarr = np.array([1,2,3])
series1 = pd.Series(ndarr, name=['series1'])
series2 = pd.Series([1,2,3], name='series2')
series1.name
##['series1']
result_df = pd.concat([series1, series2], axis=1)
result_df
##give **type error**
##also
series3 = pd.Series(nd... | {
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```
$ nosetests-2.7 pandas.io.tests.test_parsers:TestCParserHighMemory.test_yy_format
F
======================================================================
FAIL: test_yy_format (pandas.io.tests.test_parsers.TestCParserHighMemory)
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 and since Python 2.6 we can use the abstract base class collections.Hashable.
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https://api.github.com/repos/pandas-dev/pandas/issues/12613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12613/events | https://github.com/pandas-dev/pandas/pull/12613 | 140,554,123 | MDExOlB1bGxSZXF1ZXN0NjI2OTE5Nzg= | 12,613 | COMPAT: dateutil 2.5.0 bug fix | {
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COMPAT: dateutil 2.5.0 bugfix causes some tests to fail, skip for now
xref #12611
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https://api.github.com/repos/pandas-dev/pandas/issues/12614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12614/events | https://github.com/pandas-dev/pandas/pull/12614 | 140,594,122 | MDExOlB1bGxSZXF1ZXN0NjI3MDc4ODk= | 12,614 | BUG: Crosstab margins ignoring dropna | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12615/events | https://github.com/pandas-dev/pandas/pull/12615 | 140,598,950 | MDExOlB1bGxSZXF1ZXN0NjI3MDk4MTc= | 12,615 | BUG: Mixed period cannot be displayed with ValueError | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
```
# we can create Series contains Periods with mixed freq
s = pd.Series([pd.Period('2011-01', freq='M'), pd.Period('2011-02-01', freq='D')])
# print the created Series raises Val... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12616/events | https://github.com/pandas-dev/pandas/pull/12616 | 140,604,031 | MDExOlB1bGxSZXF1ZXN0NjI3MTIyMTg= | 12,616 | TST: Holiday tests/docs | {
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"d... | closed | false | null | [] | null | 4 | 2016-03-14T08:12:58Z | 2016-05-07T18:49:59Z | 2016-05-07T18:49:59Z | NONE | null | ```
>>> from pandas.tseries.holiday import next_monday_or_tuesday
>>> from pandas.tseries.holiday import AbstractHolidayCalendar
>>>from pandas.tseries.holiday import Holiday
>>>class WrongBoxingDay(AbstractHolidayCalendar):
rules = [
Holiday('xmas', month=12, day=25, observance=next_monday_or... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12617 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12617/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12617/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12617/events | https://github.com/pandas-dev/pandas/issues/12617 | 140,604,650 | MDU6SXNzdWUxNDA2MDQ2NTA= | 12,617 | BUG: Index.str.partition gives ValueError while trying to compute index name (expand=False) | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-03-14T08:17:11Z | 2016-04-10T14:26:28Z | 2016-04-10T14:26:28Z | CONTRIBUTOR | null | `Index.str.partition`'s behaviour changed in 3ab35b40274276e4f9b40918a7e260f1abb941c1 in a backwards incompatible way which doesn't seem intentional.
#### Code Sample, a copy-pastable example if possible
```
python3 -c 'import pandas; pandas.Index(["a,b", "c,d"], name="hello").str.partition(",")'
```
``` pytb
Traceba... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12618/events | https://github.com/pandas-dev/pandas/issues/12618 | 140,630,530 | MDU6SXNzdWUxNDA2MzA1MzA= | 12,618 | Feature request: read_csv/read_table/read_fwf - read multiple files with the same structure, applying the same parameters (`skiprows`, `skipfooter`, `nrows`) | {
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"followers_url": "https://api.github.com/users/jorisva... | 5 | 2016-03-14T10:14:47Z | 2021-01-27T11:51:37Z | 2017-05-22T17:05:18Z | CONTRIBUTOR | null | Hello,
I think it would make sense to make `read_csv()`, `read_table()`, `read_fwf()` able to read multiple files with the same structure. It might be tricky to read multiple files into one string, especially when all of them have header line(s) and when you want to use the following parameters: `skiprows`, `skipfoot... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12619/events | https://github.com/pandas-dev/pandas/issues/12619 | 140,638,909 | MDU6SXNzdWUxNDA2Mzg5MDk= | 12,619 | API: Should extended dtype work as the same as np.dtype? | {
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"followers_url": "https://api.github... | 3 | 2016-03-14T10:50:19Z | 2018-07-02T23:44:12Z | 2018-07-02T23:44:12Z | MEMBER | null | We can create / convert using datetimetz dtype, but it doesn't work some cases.
```
s = pd.Series([pd.Timestamp('2011-01-31', tz='US/Eastern')])
s
#0 2011-01-31 00:00:00-05:00
# dtype: datetime64[ns, US/Eastern]
# OK
s.astype('datetime64[ns, Asia/Tokyo]')
#0 2011-01-31 14:00:00+09:00
# dtype: datetime64[ns, Asia/... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-03-14T10:56:01Z | 2016-03-17T14:17:46Z | 2016-03-17T14:17:46Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
pd.concat([pd.Series([pd.Timestamp('2011-01-01', tz='US/Eastern')]),
pd.Series([pd.Timedelta('1 day')])])
# AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize'
```
#### Expected Output
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12621/events | https://github.com/pandas-dev/pandas/issues/12621 | 140,693,948 | MDU6SXNzdWUxNDA2OTM5NDg= | 12,621 | DataFrame.groupby no longer works after upgrading to latest pandas and numpy | {
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... | closed | false | null | [] | null | 2 | 2016-03-14T14:43:49Z | 2016-03-14T16:38:21Z | 2016-03-14T16:38:21Z | NONE | null | #### Code Sample
```
import pandas as pd
df = pd.DataFrame({'group': 'abacb', 'value': [1, 2, 3, 4, 5]})
df.groupby('group').sum()
# Expected output - hopefully obvious :)
```
#### Exception raised
```
---------------------------------------------------------------------------
TypeError ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12622/events | https://github.com/pandas-dev/pandas/issues/12622 | 140,709,464 | MDU6SXNzdWUxNDA3MDk0NjQ= | 12,622 | BUG: pandas.Timestamp misbehaves in deeply nested pprint on Python 3 | {
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] | closed | false | null | [] | null | 2 | 2016-03-14T15:35:02Z | 2016-03-25T13:25:14Z | 2016-03-25T13:25:14Z | MEMBER | null | I'm not sure if we can fix this in pandas, but I ran into this recently:
``` python
In [10]: d = {'foo': 1, 'bar': [{'what': {'footttttttttttttttttttttttttttttttttt': pd.Timestamp('2011-01-01')}}] * 10}
In [11]: pprint.pprint(d)
<SNIP>
lib/python3.5/pprint.py in _format_dict_items(self, items, stream, indent, allowan... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12623/events | https://github.com/pandas-dev/pandas/issues/12623 | 140,777,005 | MDU6SXNzdWUxNDA3NzcwMDU= | 12,623 | BUG: Series.rename treats Series as list-like instead of mapping | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2016-03-14T19:38:08Z | 2016-03-17T14:06:48Z | 2016-03-17T14:06:48Z | CONTRIBUTOR | null | ``` python
#### Code Sample, a copy-pastable example if possible
In [6]: s = pd.Series([0, 1])
In [5]: s.rename(pd.Series({0: 10, 1: 20}))
Out[5]:
0 0
1 1
Name: [10, 20], dtype: int64
```
Problem is [here](https://github.com/pydata/pandas/blob/4f5099b4ee5f5c32f6b299de93902c9a074ac228/pandas/core/series.py#L233... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12624/events | https://github.com/pandas-dev/pandas/issues/12624 | 140,782,639 | MDU6SXNzdWUxNDA3ODI2Mzk= | 12,624 | BUG: Panel.fillna() ignores inplace=True | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-03-14T20:01:35Z | 2016-03-17T13:54:50Z | 2016-03-17T13:54:50Z | CONTRIBUTOR | null | I'm not sure of `Panel` is still being supported, but `Panel.fillna()` seems to ignore `inplace=True`:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [5]: p1 = pd.Panel([[[0,1],[2,np.nan]], [[10,11],[12,np.nan]]], items=['a','b'], minor_axis=['x','y'])
In [7]: p1['a']
Out[7]:
x y
0 0.0 1.0
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12625/events | https://github.com/pandas-dev/pandas/issues/12625 | 140,802,885 | MDU6SXNzdWUxNDA4MDI4ODU= | 12,625 | pandas_datareader failing to get all options for '^ndx' | {
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```
In [1]: from pandas_datareader import data as pddata
In [2]: foo = pddata.Options('^ndx', 'yahoo')
In [3]: foo.expiry_dates
Out[3]:
[datetime.date(2016, 3, 18),
datetime.date(2016, 3, 24),
datetime.date(2016, 4, 1),
datetime.date(2016, 4, 8... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12626/events | https://github.com/pandas-dev/pandas/pull/12626 | 140,812,027 | MDExOlB1bGxSZXF1ZXN0NjI4MjYyNzM= | 12,626 | BUG: .rename* not treating Series as mapping | {
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I added com.is_dict_like in https://github.com/pydata/pandas/pull/11980
and failed to use it for the `rename` method. Using that now and did
some refactoring while I was in there. Added more tests for rename.
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https://api.github.com/repos/pandas-dev/pandas/issues/12627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12627/events | https://github.com/pandas-dev/pandas/issues/12627 | 140,821,232 | MDU6SXNzdWUxNDA4MjEyMzI= | 12,627 | Write CSV without index as default | {
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Most of the time when I write a csv to file I don't need the index in the CSV output. I end up writing `to_csv('file/path.csv', index=False)` basically everytime. I am wondering if `Index=False` should be the default.
#### Expected Output
`pd.to_csv('file/path.csv... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12628/events | https://github.com/pandas-dev/pandas/issues/12628 | 140,830,606 | MDU6SXNzdWUxNDA4MzA2MDY= | 12,628 | Use DataFrame.join with just some levels of a multiindex | {
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"de... | closed | false | null | [] | null | 1 | 2016-03-14T23:59:18Z | 2016-03-15T00:07:12Z | 2016-03-15T00:06:54Z | CONTRIBUTOR | null | It would be nice if there was some way to join based on just some levels of a MultiIndex. For example, given a DataFrame like:
```
>>> df = pd.DataFrame({"a": [1, 1, 2], "b": [11, 12, 13], "c": [21, 22, 23]}).set_index(["a", "b"])
>>> df
c
a b
1 11 21
12 22
2 13 23
```
I'd like to join it to another ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12629/events | https://github.com/pandas-dev/pandas/pull/12629 | 140,851,430 | MDExOlB1bGxSZXF1ZXN0NjI4NDYxMjc= | 12,629 | BUG: GH12622 where pprint of Timestamp in nested structure fails | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2016-03-15T02:18:14Z | 2016-03-25T13:36:27Z | 2016-03-25T13:25:14Z | CONTRIBUTOR | null | - [x] closes #12622
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I suspect this is a bug in Cython (I don't think `type(A).func` should be an `instancemethod`). Not sure if we should use this workaround as we encounter issues in the wild, or do this for all t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12630/events | https://github.com/pandas-dev/pandas/pull/12630 | 140,870,902 | MDExOlB1bGxSZXF1ZXN0NjI4NTQ0Mjk= | 12,630 | BUG: #12624 where Panel.fillna() ignores inplace=True | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes #12624
Author: Pedro M Duarte pmd323@gmail.com
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https://api.github.com/repos/pandas-dev/pandas/issues/12631 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12631/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12631/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12631/events | https://github.com/pandas-dev/pandas/issues/12631 | 140,933,378 | MDU6SXNzdWUxNDA5MzMzNzg= | 12,631 | API: Index.take inconsistently handle fill_value | {
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```
pd.Index([1, 2, 3]).take(np.array([1, 0, -1]), fill_value=np.nan)
# Int64Index([2, 1, 3], dtype='int64')
```
But `DatetimeIndex` does (used in reshape ops at least).
```
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"d... | closed | false | null | [] | null | 4 | 2016-03-15T15:38:33Z | 2021-04-23T01:17:30Z | 2021-04-23T01:17:30Z | CONTRIBUTOR | null | We now have `codecov` [here](https://codecov.io/github/pydata/pandas) running on every build. You can get the browser extension to look at coverage in [github](https://github.com/codecov/browser-extension#codecov-extension)
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@jreback 's comment on #12620:
> should be handled at a slightly higher level.
I added number of `dtype` check logic in `tseries/common/_concat_compat`. But it should work on `base/common/_conc... | {
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- [x] closes #12574
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It seems misleading to have the `out` parameter in the function signature when it is not used:
```
pd.Series.argmin(self, axis=None, out=None, skipna=True)
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https://api.github.com/repos/pandas-dev/pandas/issues/12639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12639/events | https://github.com/pandas-dev/pandas/issues/12639 | 141,253,114 | MDU6SXNzdWUxNDEyNTMxMTQ= | 12,639 | Color by factor/group when plotting | {
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"description": nul... | closed | false | null | [] | null | 5 | 2016-03-16T12:05:05Z | 2019-08-26T06:54:18Z | 2019-08-26T06:54:18Z | NONE | null | Hi everyone!
Sorry if this is not the correct place to write this, I'm new to GitHub.
I'm really enjoying pandas but I think there is a basic feature it does not implement, which is
easily color by factor when plotting a dataframe. At least I didn't be able to find how this could be done in a "Pythonic" way.
Say we ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12640/events | https://github.com/pandas-dev/pandas/issues/12640 | 141,268,316 | MDU6SXNzdWUxNDEyNjgzMTY= | 12,640 | API: add top-level functions as method | {
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"description": "Concat, ... | open | false | null | [] | null | 9 | 2016-03-16T13:04:54Z | 2021-04-23T01:18:20Z | null | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/12578
so its pretty easy to add (and eventually deprecate) certain top-level functions that could/should be methods. Promotes a cleaner syntax and method chaining.
In 0.18.1 would propose adding these, deprecating in 0.19.0.
This would eliminate the ability to use dir... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12641/events | https://github.com/pandas-dev/pandas/issues/12641 | 141,269,331 | MDU6SXNzdWUxNDEyNjkzMzE= | 12,641 | API: why are we importing pandas.tseries.period.pnow into the pd namespace? | {
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if someone can take a look and see when this was added?
I don't think this is useful at all. Let's just deprecate and remove this function entirely. Could always add a `Period.now()`.
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https://api.github.com/repos/pandas-dev/pandas/issues/12642 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12642/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12642/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12642/events | https://github.com/pandas-dev/pandas/issues/12642 | 141,272,624 | MDU6SXNzdWUxNDEyNzI2MjQ= | 12,642 | BUG: crosstab with margins=True and dropna=False | {
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```
In [1]: df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan],'b': [3, 3, 4, 4, 4, 4]})
In [2]: pd.crosstab(df.a, df.b, margins=True, dropna=True)
Out[2]:
b 3 4 All
a
1.0 1 0 1
2.0 1 3 4
All 2 3 5
In [3]: pd.crosstab(df.a, df.b, margins=True, dropna=False)
KeyError: 'Leve... | {
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- [+] tests added / passed
- [+] passes `git diff upstream/master | flake8 --diff`
- [+] whatsnew entry
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xref #12614. Independent of issue #12642.
#### Code Sample
```
a = np.array(['foo', 'foo', 'foo', 'bar', 'bar', 'foo', 'foo'], dtype=object)
b = np.array(['one', 'one', 'two', 'one', 'two', 'two', 'two'], dtype=object)
c = np.array(['dull', 'dull', 'dull', 'dull... | {
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#### Code Sample, a copy-pastable example if possible
df = pd.read... | {
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df = pd.read_sas('infilename.sas7bdat')
#### Expected Output
to read a sas7bdat file into a pandas data frame.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
p... | {
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... | 39 | 2016-03-16T23:09:33Z | 2021-04-23T01:19:29Z | null | NONE | null | Unless I am missing something obvious, we can do this:
`df.groupby('x').cumcount()`
but not:
`df.cumcount()`
Obviously, there are workarounds, but seems like we should be able to do on a dataframe/series if we can do on a groupby object?
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... | 26 | 2016-03-17T02:28:38Z | 2021-05-22T18:24:52Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
s = pd.Series(['2012-01-01 10:00', '2012-02-10 23:20', '2012-03-22 12:40', '2012-05-02 02:00', '2012-06-11 15:20', '2012-07-22 04:40','2012-08-31 18:00', '2012-10-11 07:20', '2012-11-20 20:40', '2012-12-31 10:00'])
print s
0 2012-01-01 10... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12650/events | https://github.com/pandas-dev/pandas/pull/12650 | 141,460,762 | MDExOlB1bGxSZXF1ZXN0NjMxNjUwODQ= | 12,650 | BUG: pivot_table dropna=False drops columns/index names | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2016-03-17T02:29:44Z | 2016-04-17T14:31:42Z | 2016-04-17T14:31:40Z | CONTRIBUTOR | null | - [X] closes #12133
- [X] closes #12642, closes #12327 (replace PR)
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
Bug fixed from pull request #12327.
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-03-17T02:42:05Z | 2016-12-14T14:54:21Z | 2016-12-14T14:54:21Z | CONTRIBUTOR | null | xref #12648
- cross-link doc-strings for .cum\* to the .expanding().\* (for sum,prod,max,min, which are only supported).
- show an example (in computation.rst wher eexpanding is shown? or basics.rst (where cum\* are shown)) how these are related (w.r.t. NaN filling).
```
In [7]: s = Series([1,2,np.nan,3,np.nan,4])
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#### Code Sample, a copy-pasteable example if possible
```
import pandas as pd
df = pd.DataFrame({
('C', 'julian'): [0.258185, 0.52591899999999991, 0.17491099999999998, 0.94083099999999997, 0.7019370000000... | {
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... | 7 | 2016-03-17T03:13:19Z | 2021-09-19T08:32:04Z | 2021-09-19T08:32:03Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/8662
xref #12652
This makes pandas code jump thru hoops and is a complete anti-pattern for pandas. let's nuke it. users should _never_ do this.
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1. `pd.read_sas()` doesn't read SAS date variable correctly (this is noted in the doc). Dates are read as `numpy.float64`. Note in SAS, dates are recorded as numbers relative to 1960-1-1. It would be helpful to allow some sort of arguments... | {
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> pd.read_\* functions can now also accept pathlib.Path, or py._path.local.LocalPath objects for the filepath_or_buffer argument. (#11033)
Doing so with read_excel gives `ValueError: Must explicitly set engine if not passing in buffer or path for io.`
See also #11773
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closes #12654
closes #12647
closes #12809
Major performance improvements through use of Cython
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It seems that `Series.replace` is orders of magnitude slower than `Series.map` when called with a dict, and consumes enormous amounts of RAM:
``` python
>>> np.random.seed(0)
>>> s = pd.Series(np.random.randint(0, 10000, 1000000))
>>> r = {np.random.randint(0, 10000): np.random.randi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12658 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12658/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12658/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12658/events | https://github.com/pandas-dev/pandas/pull/12658 | 141,583,872 | MDExOlB1bGxSZXF1ZXN0NjMyMjI0MzM= | 12,658 | BUG: fixed SAS7BDATReader._get_properties | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2016-03-17T13:30:36Z | 2016-04-22T15:14:34Z | 2016-04-22T15:14:34Z | CONTRIBUTOR | null | this fixes a crasher I had in one of my files using SAS7BDATReader.
cc: @kshedden
self._path_or_buf.read is an obvious mistake
also added + total_align to _os_name_offset and _os_make_offset as they are
present in the original code from Jared Hobbs:
https://bitbucket.org/jaredhobbs/sas7bdat/src/da1faa90d0b15c2c97a2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12659 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12659/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12659/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12659/events | https://github.com/pandas-dev/pandas/issues/12659 | 141,588,734 | MDU6SXNzdWUxNDE1ODg3MzQ= | 12,659 | BUG: Read SAS fails | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-03-17T13:50:50Z | 2016-04-22T15:09:57Z | 2016-04-22T15:09:57Z | CONTRIBUTOR | null | cc: @kshedden
#### Code Sample, a copy-pastable example if possible
https://github.com/pydata/pandas/blob/8857008178eb90b59ca67874507ef860b3faf88f/pandas/io/tests/sas/data/test17.sas7bdat
Do you want a PR with this test case?
#### Expected Output
pandas reads the file without crashing :)
I tracked the problem to _pr... | {
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I know it is [well documented](http://pandas.pydata.org/pandas-docs/stable/advanced.html#the-need-for-sortedness-with-multiindex) that MultiIndex DataFrames need to be sorted to use slicing, and that is fine. Even if you forget this, in most cases (for example when using .loc with a slicer) Pandas gives a helpf... | {
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"where" is mentioned but not used, should it be df.where(df > 0)? It's the same in this case but anyway..
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This issue was identified while fixing an issue for `v0.18.1` (xref #124... | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2016-03-17T20:16:28Z | 2019-12-03T15:03:32Z | 2016-03-23T17:36:38Z | CONTRIBUTOR | null | The `copy` method of `pd.Series` and `pd.DataFrame` has a parameter `deep` which claims to [Make a deep copy, i.e. also copy data](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.copy.html). The example below seems to show that this isn't a truly deep copy (as in `from copy import deepcopy`) I can't... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12664 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12664/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12664/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12664/events | https://github.com/pandas-dev/pandas/pull/12664 | 141,775,069 | MDExOlB1bGxSZXF1ZXN0NjMzMjc3OTU= | 12,664 | Updated docstring for copy() method | {
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Added note that a copy with `deep=True` does not copy the data of objects contained in underlying arrays with `dtype=object`.
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a) change `CParserError` to an actual `ValueError`. this is used in a very small number of cases in the `engine='c'` parser. (in #12551 it will inherit from `ValueError`)
b) So we either change ALL `ValueError` to `ParserError(ValueError)` (e.g. a custom exception) an... | {
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``` python
import pandas as PD
df = PD.read_csv('test_csv.txt',
index_col=0,
converters={'xyinrad': com... | {
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... | 9 | 2016-03-18T18:49:39Z | 2019-10-21T19:34:17Z | null | NONE | null | Date parsing is slow on dates of the form "%m.%d.%Y" compared to "%Y-%m-%d". I am using 0.17.1 so maybe it's improved in 0.18 but I didn't see anything about it. I provide a benchmark below to compare the two datetime formats.
Some observations:
- Letting pandas figure it out is unacceptably slow. In small files it... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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```
In [15]: pd.DataFrame(np.arange(10)).rolling(2., center=True).median()
---------------------------------------------------------------------------
TypeE... | {
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```
Exception: Cython-generated file 'pandas/index.c' not found.
Cython is required t... | {
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Improvement in the documentation w.r.t to issue #12661
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#12651
commit for the .cum\* in .expanding
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#12561 : wrote the `.expanding` function int the `basics.rst`
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- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
#11558 is partially fixed by #12531, this PR let `.describe()` to preserve `CategoricalIndex` info. And added explicit test cases derived from #11558.
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https://api.github.com/repos/pandas-dev/pandas/issues/12676 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12676/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12676/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12676/events | https://github.com/pandas-dev/pandas/pull/12676 | 142,174,261 | MDExOlB1bGxSZXF1ZXN0NjM0OTg0MjM= | 12,676 | API: Index.take inconsistently handle fill_value | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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"color": "5319... | closed | false | null | [] | {
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#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import datetime
# should print True and does
print pd.Timestamp(datetime.date(2010,1... | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2016-03-20T20:46:05Z | 2016-04-06T19:17:26Z | 2016-04-06T19:17:26Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/12551 and comment: https://github.com/pydata/pandas/pull/12512#discussion_r56771668
`usecols=['a',1]` should raise
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I'm trying to make a new DataFrame that contains the value counts of a column of an existing DataFrame ([spreadsheet.xlsx](https://github.com/pydata/pandas/files/181629/spreadsheet.xlsx)), but I want the rows in the new DataFrame to be in the same order as the old one.
When I do:
``` python
import pandas as p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12680 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12680/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12680/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12680/events | https://github.com/pandas-dev/pandas/issues/12680 | 142,257,146 | MDU6SXNzdWUxNDIyNTcxNDY= | 12,680 | Issue passing a Timestamp to Series.between_time() | {
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I have an issue with passing pandas.Timestamp objects as arguments to pandas.Series.between_time(start, end).
It appears that pandas.tseries.index.DatetimeIndex.indexer_between_time() tries to convert start_time and end_time arguments as strings.
The following lines give a temporary fix:
```
if not isinstance(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12681 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12681/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12681/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12681/events | https://github.com/pandas-dev/pandas/issues/12681 | 142,336,169 | MDU6SXNzdWUxNDIzMzYxNjk= | 12,681 | Misleading Documentation for panel.tz_localize() | {
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"descript... | closed | false | null | [] | null | 2 | 2016-03-21T12:41:39Z | 2016-03-21T12:44:03Z | 2016-03-21T12:43:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> panel.tz_localize(tz = pytz.UTC, axis=1)
>>> pp.pprint(panel.axes[1].tzinfo)
```
``` python
[Index([u'ddhm', u'ffhm', u'nnhv', u'pshv', u'tahv', u'uuhv', u'rrhs'], dtype='object'),
DatetimeIndex(['2016-03-09 12:05:05'], dtype='datetime64[ns]', name... | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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... | closed | false | null | [] | null | 11 | 2016-03-21T14:11:44Z | 2017-05-23T03:37:02Z | 2016-03-21T14:19:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
conda create -n pandas18 python=3.5 pandas xlrd ipython
```
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.18.0'
In [3]: url='https://github.com/chris1610/pbpython/raw/master/data/sales-feb-2014.xlsx'
In [4]: pd.read_excel(url, par... | {
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`print pd.date_range(start=datetime.datetime(2016,3,18,0,0), end=datetime.datetim... | {
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``` python
>>> dft = pd.DataFrame(np.random.randn(100000,1),columns=['A'],index=pd.date_range('20130101',periods=100000,freq='T'))
>>> dft2 = pd.DataFrame(np.random.randn(200000,1),columns=['A'],index=pd.MultiIndex.from_product([dft.index, ['a', 'b']]))
>>> dft2.lo... | {
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... | open | false | null | [] | null | 22 | 2016-03-22T14:11:51Z | 2021-04-23T01:23:48Z | null | CONTRIBUTOR | null | ## Known differences between Python & C engines
Update [here](http://pandas.pydata.org/pandas-docs/stable/io.html#specifying-the-parser-engine)
### Features supported in the Python engine only
- [ ] `skipfooter` / `skip_footer` (#13349) - num of lines at the bottom of the file to skip
- [ ] sniffing (`sep=None`) ... | {
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xref #12603
#12644
[numpy issue 7325](https://github.com/numpy/numpy/pull/7325)
cc @gfyoung
cc @njsmith
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"followers_url": "https://api.github.com/users/jr... | 5 | 2016-03-22T14:35:26Z | 2016-12-13T23:12:17Z | 2016-12-13T23:11:38Z | CONTRIBUTOR | null | we have 3 copies of the compression inference routines in `parser.py, parser.py, and io/common.py`. See how much we can reduce this (_some_ is necessary as sometimes we need to actually return a file handle or need to read in and re-wrap).
xref #12175
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`df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))`
We can test this data frame :
```
df is None
False
```
However we cannot test its presence in a list :
```
df in [None]
TypeError: Cou... | {
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Rounding errors occur for timedeltas initialized using fractional microsecond values:
```
>>> pd.Timedelta('12.312us') == pd.Timedelta(12312)
False
>>> pd.Timedelta(12.312, unit='us') == pd.Timedelta(12312)
False
>>> pd.Timedelta('12.312us') == pd.Timedelta(12311)
True
```
Additio... | {
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... | 3 | 2016-03-22T15:26:26Z | 2021-04-23T01:24:54Z | null | NONE | null | I initially posted this on #12136, as I intended to fix it with the same patch, but I think it deserves its own issue as my initial idea for a fix may in fact change the behaviour for strings suffixed by capital `M`. Previously, those would be interpreted as minutes because of the `unit.lower()` in https://github.com/p... | {
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... | 3 | 2016-03-22T17:32:44Z | 2021-04-23T01:25:56Z | null | NONE | null | IN:
```
import pandas as pd
df = pd.DataFrame([[{'a', 'b'}], [{'b','c'}], [{'b', 'a'}]])
df
```
OUT:
```
0
0 {a, b}
1 {c, b}
2 {a, b}
```
IN:
```
df.duplicated()
```
OUT:
```
---------------------------------------------------------------------------
TypeError Traceback ... | {
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... | 12 | 2016-03-22T18:11:20Z | 2021-04-23T02:45:41Z | null | CONTRIBUTOR | null | It appears that `Series.argsort()` is implemented as `s.dropna().argsort().reindex(s.index, fill_value=-1) = np.argsort(s.dropna()).reindex(s.index, fill_value=-1)`.
There are two problems with this:
(a) Since the result represents integer indices into the original series `s`, the result should not have the same `inde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12695 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12695/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12695/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12695/events | https://github.com/pandas-dev/pandas/issues/12695 | 142,741,569 | MDU6SXNzdWUxNDI3NDE1Njk= | 12,695 | RangeIndex not generated with pd.concat with ignore_index as True | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-03-22T18:41:01Z | 2016-04-10T14:22:42Z | 2016-04-10T14:22:42Z | NONE | null | With version 0.18.0 introducing RangeIndex, should RangeIndex be applied for concat when ignore_index is set to True?
`df = pd.concat([df_a, df_b], ignore_index=True)`
df.index is an instance of Int64Index, not RangeIndex currently in 0.18.0
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https://api.github.com/repos/pandas-dev/pandas/issues/12696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12696/events | https://github.com/pandas-dev/pandas/pull/12696 | 142,776,886 | MDExOlB1bGxSZXF1ZXN0NjM3OTA5MjA= | 12,696 | Fix #12695 : RangeIndex generated with pd.concat with ignore_index as True | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Replaced generated indexes with RangeIndex, test still needed
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-03-22T23:55:33Z | 2017-01-29T21:52:56Z | 2017-01-29T21:52:56Z | NONE | null | When I try to slice a multi indexed data frame, the slice interval is ignored for all the entries of the first level index except the last one. This only happens if the length of the slice is beyond a particular value (around 30 in the cases that I observed, but it is not the same across data frames).
This bug seems t... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/12699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12699/events | https://github.com/pandas-dev/pandas/issues/12699 | 142,872,513 | MDU6SXNzdWUxNDI4NzI1MTM= | 12,699 | Appending categorical data should be more flexible | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12700/events | https://github.com/pandas-dev/pandas/issues/12700 | 142,962,194 | MDU6SXNzdWUxNDI5NjIxOTQ= | 12,700 | DOC: update comparison_with_sas docs to include new read_sas(format='sas7bdat') | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12701/events | https://github.com/pandas-dev/pandas/issues/12701 | 142,975,915 | MDU6SXNzdWUxNDI5NzU5MTU= | 12,701 | DataFrame.drop() does nothing for non-unique MultiIndex when attempting to drop from a level with DatetimeIndex | {
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there seems to be a bug in DataFrame.drop() when the DataFrame has a non-unique MultiIndex and one of the levels is a DatetimeIndex (labels of which I would like to pass to the drop-method)
#### Code Sample
```
import pandas as pd
from pandas import DataFrame
import numpy as np
# Prepare DataFrame
idx = pd.Index(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12702/events | https://github.com/pandas-dev/pandas/pull/12702 | 143,019,857 | MDExOlB1bGxSZXF1ZXN0NjM5MTY3MDc= | 12,702 | BUG: concatenation with a coercable dtype was too aggressive | {
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closes #12045
closes #11594
closes #10571
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"node_id": "MDU6TGFiZWw4OTM1MzEx",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Performance"
},
{
"color": "02d7e1",
"default"... | closed | false | null | [] | null | 1 | 2016-03-23T17:28:20Z | 2016-03-23T17:50:50Z | 2016-03-23T17:50:38Z | NONE | null | It seems like the Dataframe sample function is much slower than directly selecting a randomly indexed row:
import pandas as pd
import numpy as np
from time import time
> > > test_df = pd.DataFrame(np.random.randint(0, 100, size = (400, 600)))
> > > def f():
> > > ... start = time()
> > > ... for _ in range(100):
> ... | {
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"url": "https://api.github.com/repos/pandas-dev/pandas/issues/12703/reactions"
} | https://api.github.com/repos/pandas-dev/pandas/issues/12703/timeline | null | null | null |
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