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https://api.github.com/repos/pandas-dev/pandas/issues/11303 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11303/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11303/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11303/events | https://github.com/pandas-dev/pandas/pull/11303 | 111,046,628 | MDExOlB1bGxSZXF1ZXN0NDc0NTkzOTQ= | 11,303 | DOC Use plot.<kind> instead of plot(kind=<kind>) GH11043 | {
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I modified the visualization documentation to use plot.<kind> instead of plot(kind=<kind>) in the examples as explained by [issue 11043](https://github.com/pydata/pandas/issues/11043).
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... | closed | false | null | [] | null | 18 | 2015-10-12T21:40:48Z | 2021-03-25T20:38:06Z | 2021-03-25T20:38:06Z | CONTRIBUTOR | null | In below screenshot, I am scanning the database for a categorical called `classification_id` with the value `50ef44b795e6e42cd2000001` but I am getting a data-row where the categorical has the value 50ef44b795e6e42cd6000001`.
How is this possible? Note that my list of categorical is huge, more than 4 million entries, ... | {
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```
items = [str(i) for i in range(40000)]
s = pd.Series(items, dtype='category')
df = pd.DataFrame({'C':s, 'data':np.random.randn(40000)})
data = df[df.C=='20']
str(data.C)
```
This renders the string of all of the categories before chopping them, see [here](https://github.com/pydata/pandas/blob/master/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11307/events | https://github.com/pandas-dev/pandas/issues/11307 | 111,109,131 | MDU6SXNzdWUxMTExMDkxMzE= | 11,307 | import pandas.io.ga as ga, Credentials, python-gflags-2.0, run_flow() or run()? | {
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] | closed | false | null | [] | null | 4 | 2015-10-13T05:40:09Z | 2016-05-27T01:53:08Z | 2015-10-14T11:44:49Z | NONE | null | I am trying to get [Google Analytics](http://pandas.pydata.org/pandas-docs/version/0.17.0/remote_data.html#remote-data-ga) pulling in data but am experiencing many problems. I'll outline them here.
**My Setup**: I am using Pandas '0.17.0' and Python 3.4.2 32-bit.
1. The Google Analytics console credentials screen no ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11308 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11308/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11308/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11308/events | https://github.com/pandas-dev/pandas/issues/11308 | 111,160,235 | MDU6SXNzdWUxMTExNjAyMzU= | 11,308 | DEPR: google-analytics | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-10-13T11:24:31Z | 2015-11-23T05:17:15Z | 2015-10-14T00:09:21Z | CONTRIBUTOR | null | not supported / tested in quite some time. this should be spun off into a separate package.
let's deprecate at least in 0.17.1.
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] | closed | false | null | [] | null | 1 | 2015-10-13T12:18:19Z | 2017-09-19T18:52:04Z | 2017-09-19T18:52:04Z | NONE | null | While working with cyclic data, Pandas failed to calculate the right limits for the plot. I am not sure which exact characteristic of my data causes the problem, but I will observe if further when I have time for it. Until then, here is the notebook in which the error occurred:
http://nbviewer.ipython.org/gist/AKueder... | {
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"de... | closed | false | null | [] | null | 9 | 2015-10-13T12:42:06Z | 2015-10-13T14:39:19Z | 2015-10-13T13:08:17Z | NONE | null | ```
dic = {'01/31/2015': {'A': 5, 'B': 10, 'C': 15},
'02/28/2015': {'A': 1, 'B': 20, 'C': 2}}
df = pd.DataFrame.from_dict(dic, orient='index')
df * 100 / df.sum(axis=1)
Out[33]:
01/31/2015 02/28/2015 A B C
01/31/2015 NaN NaN NaN NaN NaN
02/28/2015 NaN NaN N... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11312/events | https://github.com/pandas-dev/pandas/issues/11312 | 111,180,628 | MDU6SXNzdWUxMTExODA2Mjg= | 11,312 | ENH: interpolation of datetime values (NaT) | {
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... | 3 | 2015-10-13T13:33:32Z | 2020-03-31T05:30:09Z | null | MEMBER | null | I didn't directly found an open issue about it, but is there a reason we do not implement interpolation of datetime _values_ (so not in the index)?
Example:
```
In [14]: s = pd.Series(pd.date_range('2012-01-01', periods=5))
In [16]: s[2] = np.nan
In [17]: s
Out[17]:
0 2012-01-01
1 2012-01-02
2 NaT
3 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11313/events | https://github.com/pandas-dev/pandas/pull/11313 | 111,204,801 | MDExOlB1bGxSZXF1ZXN0NDc1NDgzNzQ= | 11,313 | Correct typo in error message. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11314/events | https://github.com/pandas-dev/pandas/issues/11314 | 111,228,832 | MDU6SXNzdWUxMTEyMjg4MzI= | 11,314 | set_index does not set 'freq' | {
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Example:
```
import pandas
import numpy
import datetime
df = pandas.DataFrame(numpy.random.randn(24, 1), columns=['a'], index=pandas.date_range(datetime.datetime(2015, 10, 1), datetime.datetime(2015,10,1,23), freq='H',... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11315/events | https://github.com/pandas-dev/pandas/pull/11315 | 111,241,583 | MDExOlB1bGxSZXF1ZXN0NDc1NzEwMTk= | 11,315 | Fixed typo | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-10-13T20:46:47Z | 2015-10-23T16:45:56Z | 2015-10-14T18:51:30Z | CONTRIBUTOR | null | If the multi-line headers come from Excel, and the header was not a string, the line all(['Unnamed' in c[n] for c in columns]): will fail because c[n] is an int and not iterable. So either force the headers to be strings (the proposed change) or come up with some other test when looking for 'Unnamed'
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https://api.github.com/repos/pandas-dev/pandas/issues/11319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11319/events | https://github.com/pandas-dev/pandas/pull/11319 | 111,295,360 | MDExOlB1bGxSZXF1ZXN0NDc2MDM1NjY= | 11,319 | DEPR: deprecate `engine` keyword from to_csv #11274 | {
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```
testdf = pd.DataFrame({"rna": (1.5,2.2,3.2,4.5), -1000: [11,21,36,40],0: [10,22,43,34], 1000:[0, 10, 20, 30]})
testdf[[1000]] #this works
testdf[[-1000]] #this does not
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11321/events | https://github.com/pandas-dev/pandas/issues/11321 | 111,360,328 | MDU6SXNzdWUxMTEzNjAzMjg= | 11,321 | Can't import pandas. Matplotlib and distutils conflict. | {
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] | closed | false | null | [] | null | 1 | 2015-10-14T09:40:50Z | 2015-10-14T10:41:13Z | 2015-10-14T10:40:51Z | NONE | null | Trying to import pandas
``` python
In [1]: import pandas
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/usr/local/lib/python2.7/site-packages/pa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11322 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11322/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11322/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11322/events | https://github.com/pandas-dev/pandas/pull/11322 | 111,373,902 | MDExOlB1bGxSZXF1ZXN0NDc2NDQxNjg= | 11,322 | BUG: Bug in list-like indexing with a mixed-integer Index, #11320 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11323/events | https://github.com/pandas-dev/pandas/issues/11323 | 111,377,818 | MDU6SXNzdWUxMTEzNzc4MTg= | 11,323 | BUG: merge on should accept Series | {
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... | 1 | 2015-10-14T11:19:01Z | 2018-06-28T11:39:08Z | 2018-06-28T11:39:08Z | CONTRIBUTOR | null | Prob need a better example, but xref #2996
these should accept Series (and align)!
```
In [47]: x = DataFrame({'A' : list('aabb'), 'B' : [1,2,3,4]})
In [48]: x.merge(x, left_on=['A','B'], right_on=['A',x['B']], how='left')
TypeError: 'Series' does not have the buffer interface
In [49]: x.merge(x, left_on=['A','B']... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11324 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11324/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11324/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11324/events | https://github.com/pandas-dev/pandas/issues/11324 | 111,382,182 | MDU6SXNzdWUxMTEzODIxODI= | 11,324 | groupby.apply datetime bug affecting 0.17 | {
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a) the original dataframe has a datetime column
b) the groupby.apply function returns a series object with a new datetime column
Code to reproduce:
```
import pandas as pd
import datetime
df = pd.DataFrame([['1', datetime.datetime.today()],
['2', datetime.datetime.today()... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11325/events | https://github.com/pandas-dev/pandas/pull/11325 | 111,393,019 | MDExOlB1bGxSZXF1ZXN0NDc2NTU2MTE= | 11,325 | ENH: itertuples() returns namedtuples (closes #11269) | {
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This will make itertuples return namedtuples. I'm not sure about tests, here. Since `namedtuple` is a drop-in replacement for ordinary tuples (once they are created) I naively expect things to work.
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```
>>> df = extended_df
Traceback (most recent call last):
File "/Users/josh/anaconda/envs/Openfolio/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-14-3f216cf6821e>", line 1, in <module>
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https://api.github.com/repos/pandas-dev/pandas/issues/11327 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11327/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11327/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11327/events | https://github.com/pandas-dev/pandas/pull/11327 | 111,433,893 | MDExOlB1bGxSZXF1ZXN0NDc2ODA3MjY= | 11,327 | BUG: Fix to_dict() problem when using datetime DataFrame #11247 | {
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This is my first time contributing through PR so forgive me if I make any silly mistake.
The issue can be referred to in GH11247
I followed @jreback 's suggestion to box things up in `to_dict()` but I am not sure if this is what he means because I do not know exactly when `_maybe_datetime()` should be us... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11328/events | https://github.com/pandas-dev/pandas/pull/11328 | 111,434,155 | MDExOlB1bGxSZXF1ZXN0NDc2ODA5MDA= | 11,328 | Fix for BUG: multi-index excel header fails if all numeric | {
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closes #11408
This includes updates to 3 Excel files, plus a test in test_excel.py,
plus the fix in parsers.py
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xref #11153
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https://api.github.com/repos/pandas-dev/pandas/issues/11330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11330/events | https://github.com/pandas-dev/pandas/pull/11330 | 111,518,566 | MDExOlB1bGxSZXF1ZXN0NDc3MzA0MjU= | 11,330 | CLN: GH11271 move _get_handle, UTF encoders to io.common | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11331/events | https://github.com/pandas-dev/pandas/issues/11331 | 111,520,449 | MDU6SXNzdWUxMTE1MjA0NDk= | 11,331 | BUG: read_excel doesn't respect string data | {
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"description": "IO issues that don't fit... | closed | false | null | [] | null | 7 | 2015-10-15T00:22:32Z | 2015-12-30T15:21:05Z | 2015-12-30T15:21:00Z | CONTRIBUTOR | null | From [SO](http://stackoverflow.com/questions/33137686/python-loading-zip-codes-into-a-dataframe-as-strings/33137797#33137797)
``` python
In [16]: df = pd.DataFrame({'a': ['001','002']})
In [17]: df.to_excel('temp.xlsx')
In [18]: pd.read_excel('temp.xlsx')
Out[18]:
a
0 1
1 2
```
I think it would probably make ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11332/events | https://github.com/pandas-dev/pandas/issues/11332 | 111,572,539 | MDU6SXNzdWUxMTE1NzI1Mzk= | 11,332 | test_int64_overflow failure | {
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"d... | closed | false | null | [] | null | 4 | 2015-10-15T08:57:20Z | 2015-11-05T18:38:37Z | 2015-11-05T18:38:37Z | NONE | null | Testing pandas 0.17:
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 17.1.1
Cython: 0.22
nump... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11333/events | https://github.com/pandas-dev/pandas/issues/11333 | 111,604,874 | MDU6SXNzdWUxMTE2MDQ4NzQ= | 11,333 | DataFrame.hist / Series.hist argument inconsistency | {
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... | open | false | null | [] | null | 4 | 2015-10-15T12:18:39Z | 2021-04-20T05:36:43Z | null | NONE | null | Series.hist accepts arguments such as xlim, ylim because it uses Series.plot internally, but DataFrame.hist doesn't, as it forwards the arguments to pyplot.hist directly.
I don't know about other arguments, but I expected at least xlim/ylim to work.
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https://api.github.com/repos/pandas-dev/pandas/issues/11334 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11334/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11334/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11334/events | https://github.com/pandas-dev/pandas/issues/11334 | 111,640,703 | MDU6SXNzdWUxMTE2NDA3MDM= | 11,334 | Series.str.cat(',') without "sep" keyword arg raises unintuitive `TypeError: len() of unsized object` | {
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"color": "5319... | closed | false | null | [] | {
"closed_at": "2016-03-12T16:19:08Z",
"closed_issues": 469,
"created_at": "2015-07-19T20:18:45Z",
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"followers_url": "https://api.github.com/users/jr... | 6 | 2015-10-15T15:20:48Z | 2016-02-12T15:01:29Z | 2016-02-12T15:01:29Z | CONTRIBUTOR | null | I think this easy-to-make mistake should raise a more descriptive error... maybe something like
``` python
ValueError: Did you mean to supply a 'sep' keyword?
```
I might be missing the main usage of `str.cat()`, but all the uses I've found involve concatenating series together with a sep character.
``` python
In [... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11335 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11335/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11335/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11335/events | https://github.com/pandas-dev/pandas/pull/11335 | 111,681,780 | MDExOlB1bGxSZXF1ZXN0NDc4MjU0NjY= | 11,335 | Use Service Account JSON Key File | {
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API calls from server. See [this issue comment](https://github.com/pydata/pandas/issues/8489#issuecomment-148466759) for details.
Docs and tests are not yet updated. DO NOT MERGE.
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https://api.github.com/repos/pandas-dev/pandas/issues/11336 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11336/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11336/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11336/events | https://github.com/pandas-dev/pandas/issues/11336 | 111,683,943 | MDU6SXNzdWUxMTE2ODM5NDM= | 11,336 | 0.17 changes behavior of division of DataFrame by Series in presence of DateTimeIndex | {
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] | closed | false | null | [] | null | 5 | 2015-10-15T18:55:32Z | 2015-10-16T15:15:09Z | 2015-10-15T19:48:03Z | CONTRIBUTOR | null | Older versions up to 0.16.2:
``` python
import pandas as pd
print(pd.__version__)
index = pd.date_range(start='2000-1-1', end='2000-1-3')
pd.DataFrame({1: [3, 3, 3], 2: [6, 6, 6]}, index=index) / pd.Series([2, 2, 2], index=index)
```
```
0.14.0rc1-33-g8c1f550
1 2
2000-01-01 1.5 3
2000-01-02 1.5 3
2000-01-03 1.5 ... | {
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] | closed | false | null | [] | null | 6 | 2015-10-15T19:37:04Z | 2015-10-15T20:15:07Z | 2015-10-15T19:57:41Z | NONE | null | When I am trying to do `rows.sort_index(inplace = True)` I get SettingWithCopyWarning:
`A value is trying to be set on a copy of a slice from a DataFrame`
`rows = rows.sort_index()` can be used as a workaround to avoid the warning.
Pandas version 0.17.0 installed by pip.
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...currently `itertuples` is building a new full objects in memory and then creating an iterator over those objects. Because it's building a new tuple for each row, these aren't views (I think). Should th... | {
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```
>>> import pandas as pd
>>> pd.__version__
u'0.17.0'
>>> s = pd.Series([(1,1),(1,2)])
>>> s==(1,2)
0 False
1 False
dtype: bool
```
Meanwhile in 0.16.2:
```
>>> import pandas as pd
>>> pd.__version__
'0.16.2'
>>> s = pd.Series... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11340 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11340/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11340/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11340/events | https://github.com/pandas-dev/pandas/pull/11340 | 111,747,790 | MDExOlB1bGxSZXF1ZXN0NDc4NjU4MjQ= | 11,340 | TST: tests for list skiprows in read_excel | {
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This seemed to be fixed already, so just adding tests
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https://api.github.com/repos/pandas-dev/pandas/issues/11341 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11341/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11341/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11341/events | https://github.com/pandas-dev/pandas/issues/11341 | 111,836,710 | MDU6SXNzdWUxMTE4MzY3MTA= | 11,341 | Numpy FutureWarning in DataFrame.unstack() | {
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... | closed | false | null | [] | null | 1 | 2015-10-16T13:53:42Z | 2015-10-16T14:36:51Z | 2015-10-16T14:36:42Z | NONE | null | Unstacking with an integer level seems to raise a FutureWarning from numpy. I'm using pandas version 0.17.0 and numpy version 1.10.1.
```
In[14]: d.unstack('ticker')
Out[14]:
factor close open
ticker BMO TD BMO TD
date
2005-08-01 61.10 55.90 ... | {
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... | closed | false | null | [] | null | 3 | 2015-10-16T14:18:04Z | 2015-10-21T10:29:46Z | 2015-10-21T10:29:46Z | NONE | null | I'm having problems with building a package depending on Pandas behind proxy, because setup_requires is specified in setup.py.
"pip install" calls setup.py egg_info, which in turn wants numpy, which it cannot find. Numpy is installed along with Pandas by pip, but setup.py can't know about this.
The same issue would al... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11343 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11343/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11343/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11343/events | https://github.com/pandas-dev/pandas/pull/11343 | 111,842,312 | MDExOlB1bGxSZXF1ZXN0NDc5MTY0NTM= | 11,343 | ENH: Add Index.fillna | {
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- `value` can only accept scalar.
- `MultiIndex.fillna` raises `NotImplementedError` because `isnull` is not defined for MI.
- Moved `hasnans` and related properties to base `Index`.
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```
import pandas as pd
with open('/tmp/file.json') as fileh:
text = pd.json.load(fileh)
```
It breaks with the following output
```
Error in `python3': double free or corruption (out): 0x00007ffe082171f0 ***
```
I can load the file with Pytho... | {
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"c... | closed | false | null | [] | null | 12 | 2015-10-16T16:41:59Z | 2016-03-01T17:33:08Z | 2015-10-16T16:53:50Z | NONE | null | Strange behaviour that appears only with a large number of rows.
``` python
import pandas as pd
import numpy as np
np.random.seed(0)
data = pd.DataFrame(np.random.rand(500000, 4))
def sum_products(data):
data_np = sum(data.values[:,0] * data.values[:,1] * data.values[:,2] * data.values[:,3])
data_pd = sum(da... | {
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"descript... | closed | false | null | [] | null | 4 | 2015-10-16T23:22:46Z | 2015-10-18T02:32:14Z | 2015-10-17T15:11:24Z | CONTRIBUTOR | null | I don't know if this is possible with the current pytables implementation/interface, but I would find it useful to be able to do filtering like this on disk:
``` python
df = pd.read_hdf(dbname, 'df', where='colname in ["a","b"]')
```
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-10-16T23:35:39Z | 2015-12-31T13:46:31Z | 2015-12-31T13:46:31Z | CONTRIBUTOR | null | Consider the following Series:
```
object_id
0CKVYKjyFn 76 days
0CrPL2QKH3 -15 days
0CrVStlVrg 23 days
0Cc5ZvS67u 76 days
0CTOk5OdtI 76 days
0CTSWtTzBa 76 days
0CwBqVeNCX 76 days
0CIRJFIOcD 58 days
0CRQPCxzQe 350 days
0CAq4m9Nru 15 days
0C617yvXBj 76 days
0CzUUJNKX9 -16 days
Name: days... | {
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"de... | open | false | null | [] | null | 0 | 2015-10-17T00:16:25Z | 2021-04-20T05:37:17Z | null | MEMBER | null | Create `IndexFormatter` like `Series` and `DataFrame`.
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"description": "T... | closed | false | null | [] | null | 5 | 2015-10-17T00:29:24Z | 2018-06-29T05:24:34Z | 2018-06-29T05:24:34Z | MEMBER | null | Currently there are some inconsistent results. We should clarify the policy first.
```
import pandas as pd
s = pd.Series([pd.Timestamp('2011-01-01'), pd.NaT])
s
#0 2011-01-01
#1 NaT
# dtype: datetime64[ns]
```
#### 1. coerced to object dtype
I prefer this behavior.
```
s.fillna(pd.Timestamp('2011-01-02',... | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-10-17T01:20:27Z | 2015-10-21T22:41:46Z | 2015-10-17T15:08:44Z | CONTRIBUTOR | null | Adds new functions to check for infinity rather than calling `np.isposinf` and `np.isneginf`, which were (surprising to me) a significant drag on `to_excel`.
I also added `xlsxwriter` to the asv build configuration. `openpyxl` is still failing, I'm assuming something do with the specific version on conda?
```
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"color": "00... | closed | false | null | [] | null | 1 | 2015-10-17T05:06:03Z | 2015-10-17T14:50:42Z | 2015-10-17T14:50:34Z | NONE | null | After creating a dataframe from excel and then deleting some rows by column value like
```
df = df[df.Name != "TBC"]
```
when need to iterate through the value of a column it throws key error as shown below.
It works fine if I do not delete the rows.
"
---> 38 print df["Country"][19]
/Library/Frameworks/Python.f... | {
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```
In [56]: s = pd.Series(range(6), pd.MultiIndex.from_product([['A', 'B'], [1,2,3]], names=['lev1', 'lev2']), name='Amount')
In [57]: s
Out[57]:
lev1 lev2
A 1 0
2 1
3 2
B 1 3
2 4
3 5
Name: Amount, d... | {
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... | 8 | 2015-10-17T15:02:57Z | 2018-02-14T00:54:07Z | null | CONTRIBUTOR | null | xref #11352
- [ ] use `to_native_types` by block to yield already formatted cells
- [ ] yield cells in row-oriented way https://github.com/pydata/pandas/pull/11144#issuecomment-141966626
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"followers_url": "https://api.github.com/users/jr... | 0 | 2015-10-17T16:30:40Z | 2015-10-18T23:52:37Z | 2015-10-18T23:52:37Z | CONTRIBUTOR | null | closes #8537
- avoid numpy comparison to `None` warnings
- change null indexing from `ValueError` to `TypeError` (more consistent with other errors)
- elementwise comparisons with index names xref #11162
- elementwise comparisons vs string-likes
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``` python
In [100]: df = pd.DataFrame({0: range(5), 1:range(5)})
In [101]: df.to_excel('tmp.xlsx')
In [102]: df.to_csv('tmp.csv')
In [103]: pd.read_excel('tmp.xlsx', index_col=0).columns
Out[103]: Int64Index([0, 1], dtype='int64')
In [106]: pd.read_csv('tmp.csv', index_col=0).columns
Out[... | {
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http://stackoverflow.com/questions/25714549/indexing-and-data-columns-in-pandas-pytables/25715005?noredirect=1#comment53932720_25715005
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https://api.github.com/repos/pandas-dev/pandas/issues/11359 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11359/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11359/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11359/events | https://github.com/pandas-dev/pandas/issues/11359 | 111,998,788 | MDU6SXNzdWUxMTE5OTg3ODg= | 11,359 | pandas.io.gbq verify_schema seems to be too strict. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11360 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11360/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11360/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11360/events | https://github.com/pandas-dev/pandas/issues/11360 | 111,999,543 | MDU6SXNzdWUxMTE5OTk1NDM= | 11,360 | memory issues when doing inplace operations and dataframe wide operations | {
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"default"... | closed | false | null | [] | null | 6 | 2015-10-18T03:55:45Z | 2015-10-28T21:06:12Z | 2015-10-18T13:34:42Z | NONE | null | Hi,
Whenever I do inplace operations in pandas, especially when performing operations like replace or astype. The memory blows up close or greater than 8GB(esp on values).
Same happens when using values or concatenation of larger dataframes.
The workaroud is to do a per column operation, even on concatenation(usi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11361 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11361/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11361/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11361/events | https://github.com/pandas-dev/pandas/pull/11361 | 112,000,265 | MDExOlB1bGxSZXF1ZXN0NDgwMDE0NDg= | 11,361 | DOC: added exp weighting clarifications from #8861 | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2015-10-18T04:23:02Z | 2015-11-01T10:28:53Z | 2015-11-01T10:27:53Z | CONTRIBUTOR | null | I integrated the comments in #8861 from @seth-p into the exponential weighted moment functions docs.
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https://api.github.com/repos/pandas-dev/pandas/issues/11362 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11362/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11362/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11362/events | https://github.com/pandas-dev/pandas/pull/11362 | 112,007,885 | MDExOlB1bGxSZXF1ZXN0NDgwMDQyNzE= | 11,362 | fixed pathlib tests on windows | {
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i can’t test on windows unfortunately.
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https://api.github.com/repos/pandas-dev/pandas/issues/11363 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11363/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11363/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11363/events | https://github.com/pandas-dev/pandas/issues/11363 | 112,041,617 | MDU6SXNzdWUxMTIwNDE2MTc= | 11,363 | Add support for DataFrame(Categorical) | {
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... | 1 | 2015-10-18T21:13:54Z | 2019-12-18T22:20:12Z | 2019-12-18T22:20:12Z | CONTRIBUTOR | null | This would be in symmetry with the existing support of DataFrame(np.array) and DataFrame(Series).
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https://api.github.com/repos/pandas-dev/pandas/issues/11364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11364/events | https://github.com/pandas-dev/pandas/issues/11364 | 112,041,917 | MDU6SXNzdWUxMTIwNDE5MTc= | 11,364 | inconisitent behaviour of hdf5 store.append for MultiIndex vs simple Index | {
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```
#Create example:
col_nums = [0]
df = pd.DataFrame({"V1":["a","b","c","d","e", "aaaah!!!"],
"W":["c","d","c","d","c","c"],
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https://api.github.com/repos/pandas-dev/pandas/issues/11365 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11365/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11365/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11365/events | https://github.com/pandas-dev/pandas/issues/11365 | 112,047,644 | MDU6SXNzdWUxMTIwNDc2NDQ= | 11,365 | BUG: .loc assignment of datetime with tz is coercing to naive | {
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"followers_url": "https://api.github... | 2 | 2015-10-18T22:44:37Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | CONTRIBUTOR | null | xref #12502 (example there as well)
xref #14148
```
In [3]: idx = pd.date_range('20130101',periods=3,tz='US/Eastern')
In [4]: df = DataFrame({'A' : [1,2,3]})
In [5]: df['B'] = idx
In [6]: df.dtypes
Out[6]:
A int64
B datetime64[ns, US/Eastern]
dtype: object
In [7]: df.loc[[True,False,Tr... | {
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"id... | open | false | null | [] | null | 1 | 2015-10-19T05:50:28Z | 2020-04-26T20:54:46Z | null | CONTRIBUTOR | null | ```
>>> import pandas as pd
>>> pd.eval('not True')
-2
```
Not sure if this was intentional, since `'not'` maps to `op.invert` in https://github.com/pydata/pandas/blob/master/pandas/computation/ops.py#L458. After fixing that `numexpr` still `invert`s rather than `not`s the operand.
```
INSTALLED VERSIONS
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https://api.github.com/repos/pandas-dev/pandas/issues/11368 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11368/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11368/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11368/events | https://github.com/pandas-dev/pandas/pull/11368 | 112,080,497 | MDExOlB1bGxSZXF1ZXN0NDgwMzQ5MDM= | 11,368 | interpreting index name in min_itemsize specification #11364, #10381 | {
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... | closed | false | null | [] | null | 8 | 2015-10-19T06:23:48Z | 2015-11-18T23:56:16Z | 2015-11-18T20:16:52Z | NONE | null | closes #10381
The fix replaces named index columns in [`Table.index_axes` with their names](https://github.com/pydata/pandas/compare/master...DSLituiev:master#diff-1b15d1477da3a0548d2dd72a5d023d00L3323) and [corrects `min_itemsize` if it contains 'index' item which is not in `Table.index_axes`](https://github.com/py... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11369 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11369/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11369/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11369/events | https://github.com/pandas-dev/pandas/issues/11369 | 112,123,949 | MDU6SXNzdWUxMTIxMjM5NDk= | 11,369 | Panel partial index is transposed when minor_index is MultiIndex | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-10-19T11:14:03Z | 2018-07-06T20:21:08Z | 2017-07-10T21:07:50Z | NONE | null | I can index a Panel to get a data frame, e.g. by major_axis:
```
>>> p = pd.Panel(np.arange(8).reshape(2,2,2),items=('A1','B1'),major_axis=('A2','B2'),minor_axis=('A3','B3'))
>>> p.loc[:,'A2',:]
A1 B1
A3 0 4
B3 1 5
```
But if I do the same index when the minor_axis is a MultiIndex the resulting data fra... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11370 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11370/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11370/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11370/events | https://github.com/pandas-dev/pandas/issues/11370 | 112,124,264 | MDU6SXNzdWUxMTIxMjQyNjQ= | 11,370 | Vectorised addition of MonthOffset(n=0) returns different values to item-by-item addition | {
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``` python
import pandas as pd
from pandas.util.testing import assert_index_equal
pd.show_versions()
offsets = [
pd.offsets.Day, pd.offsets.MonthBegin,
pd.offsets.QuarterBegin, pd.offsets.YearBegin,
]
dates = pd.date_range('2011-01-01', '2011-01-05... | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2015-10-19T11:39:15Z | 2015-10-20T20:47:02Z | 2015-10-20T17:29:50Z | CONTRIBUTOR | null | closes #10993
replaces #10989
So issue #10993 involves the insertion of a key into a multi-index that has as one of its levels a `CategoricalIndex`. This causes the semantics to break down because we are inserting an new key.
Existing
```
In [16]: df = DataFrame({'A' : [1,2], 'B' : [3,4] })
In [17]: df.columns = ... | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2015-10-19T14:57:26Z | 2015-10-27T11:17:08Z | 2015-10-27T11:17:08Z | CONTRIBUTOR | null | This code is broken in `0.17.0` but not in `0.15.2`:
``` python
import pandas as pd
import numpy as np
np.random.seed(1)
from itertools import product
from pandas.util.testing import assert_frame_equal
pd.show_versions()
idx = pd.MultiIndex.from_tuples(
list(
product(['A', 'B', 'C'],
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In case of identical or almost identical values of `index` and `columns`, it can be annoying to remember the assignment of the two data-axes to index and columns respectively. This could be solved by assigning na... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11374 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11374/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11374/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11374/events | https://github.com/pandas-dev/pandas/issues/11374 | 112,199,788 | MDU6SXNzdWUxMTIxOTk3ODg= | 11,374 | "TypeError: 'set' object does not support indexing" using na_values in read_csv() | {
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``` python
user@host:~$ python3
Python 3.4.3+ (default, Oct 14 2015, 16:03:50)
[GCC 5.2.1 20151010] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from io import StringIO
>>> import pandas as pd
>>> src = """first, second
... 0,0.1
... 1,1.1
... """
>>> df = pd.read_csv... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11375 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11375/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11375/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11375/events | https://github.com/pandas-dev/pandas/issues/11375 | 112,217,845 | MDU6SXNzdWUxMTIyMTc4NDU= | 11,375 | Pandas should accept ISO 8601 duration | {
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"f... | 15 | 2015-10-19T19:43:54Z | 2018-03-23T22:21:16Z | 2018-03-23T22:21:16Z | NONE | null | Hello,
ISO 8601 defines duration.
https://en.wikipedia.org/wiki/ISO_8601#Durations
see also usage in XML Schema http://www.w3.org/TR/xmlschema-2/#d0e11648
Duration can't be defined as `timedelta` (or `timedelta64`) because
- months don't always have 30 days
- years don't always have 365 days
It will be nice if Pan... | {
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```
import pandas as pd
raw_data = {'x': [7,6,3,3,4,8,0],'y': [0,6,5,5,9,1,2]}
df = pd.DataFrame(raw_data, columns = ['x', 'y'])
print("Before:", df)
df = df.drop_duplicates()
print("After:", df)
```
When run under python 2,... | {
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"color": "53... | closed | false | null | [] | null | 2 | 2015-10-19T23:13:25Z | 2015-11-15T18:41:09Z | 2015-10-19T23:20:00Z | CONTRIBUTOR | null | closes #11365
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https://api.github.com/repos/pandas-dev/pandas/issues/11378 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11378/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11378/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11378/events | https://github.com/pandas-dev/pandas/issues/11378 | 112,262,970 | MDU6SXNzdWUxMTIyNjI5NzA= | 11,378 | Irregular TimedeltaIndex points being spaced regularly along x-axis | {
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``` python
import pandas as pd
clock_times = pd.to_datetime(['12:00:00','12:23:00','12:30:00','12:45:00','... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11379 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11379/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11379/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11379/events | https://github.com/pandas-dev/pandas/issues/11379 | 112,277,133 | MDU6SXNzdWUxMTIyNzcxMzM= | 11,379 | Feature suggestion for pandas.io.gbq: option to ignore unknown values | {
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This is especially useful if working with data that changes in structure without notice.
To make pandas more comprehensively reflecting the functionality of GBQ I would suggest to add an optional parameter in to_gbq that prepends the body (e.g. [her... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11380 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11380/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11380/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11380/events | https://github.com/pandas-dev/pandas/issues/11380 | 112,283,229 | MDU6SXNzdWUxMTIyODMyMjk= | 11,380 | Numpy 1.10 compatibility with Pandas types | {
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] | closed | false | null | [] | null | 1 | 2015-10-20T04:10:36Z | 2015-10-20T10:05:29Z | 2015-10-20T10:03:41Z | NONE | null | Previously when using Pandas 0.16.2 and Numpy 1.9.2 one could run various numpy funcs on a pandas Series and have the result returned back typed as a Series (instead of an NDarray). Example:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 1))
dfn = np.nan_to_num(df[0])
print "... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11381 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11381/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11381/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11381/events | https://github.com/pandas-dev/pandas/issues/11381 | 112,291,827 | MDU6SXNzdWUxMTIyOTE4Mjc= | 11,381 | BUG: missing pandas.version submodule | {
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] | closed | false | null | [] | null | 2 | 2015-10-20T05:46:17Z | 2015-10-20T20:17:40Z | 2015-10-20T09:58:51Z | NONE | null | The public `pandas.version` submodule seems to have been removed without any deprecation warning beforehand in gh-10370. This causes a number of test failures for statsmodels.
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https://api.github.com/repos/pandas-dev/pandas/issues/11382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11382/events | https://github.com/pandas-dev/pandas/issues/11382 | 112,326,787 | MDU6SXNzdWUxMTIzMjY3ODc= | 11,382 | truncate() raises IndexError on large timeseries | {
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I found a bug, tested on pandas 0.16 and 0.17. On large time series, the `truncate()` method throws an IndexError. Here's code that reproduces the bug:
``` python
>>> import pandas as pd
>>> # Works fine on small timeseries:
>>> small = pd.Series(data=range(int(2e3)), index=range(int(2e3)))
>>> small.truncate(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11383 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11383/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11383/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11383/events | https://github.com/pandas-dev/pandas/issues/11383 | 112,359,321 | MDU6SXNzdWUxMTIzNTkzMjE= | 11,383 | ValueError: numpy.dtype has the wrong size, try recompiling | {
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```
File "/root/.virtualenvs/myproject/local/lib/python2.7/site-packages/pandas/__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
File "pan... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11384 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11384/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11384/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11384/events | https://github.com/pandas-dev/pandas/issues/11384 | 112,375,843 | MDU6SXNzdWUxMTIzNzU4NDM= | 11,384 | String indice error with pandas 0.17.0 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11385/events | https://github.com/pandas-dev/pandas/issues/11385 | 112,405,320 | MDU6SXNzdWUxMTI0MDUzMjA= | 11,385 | str.extract raises ValueError with group named "name" | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-10-20T16:15:02Z | 2016-04-10T01:46:47Z | 2016-04-10T01:46:47Z | CONTRIBUTOR | null | For a Series `S`, I find the `S.str.extract` method very useful. It is great how you implemented naming the resulting DataFrame columns according to the names specified in the capturing groups of the regular expression.
However there seems to be a bug when there is a capture group named "name" for example
``` python
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11386 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11386/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11386/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11386/events | https://github.com/pandas-dev/pandas/pull/11386 | 112,410,622 | MDExOlB1bGxSZXF1ZXN0NDgyMjY1NjI= | 11,386 | ENH: str.extractall for several matches | {
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"followers_url": "https://api.github.com/users/jr... | 57 | 2015-10-20T16:42:01Z | 2016-02-09T22:22:32Z | 2016-02-09T22:20:53Z | CONTRIBUTOR | null | For a series `S`, the excellent `S.str.extract` method returns the first match in each subject of the series:
``` python
>>> import re
>>> import pandas as pd
>>> import numpy as np
>>> data = {
... 'Dave': 'dave@google.com',
... 'multiple': 'rob@gmail.com some text steve@gmail.com',
... 'none': np.nan,
..... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11387 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11387/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11387/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11387/events | https://github.com/pandas-dev/pandas/issues/11387 | 112,431,691 | MDU6SXNzdWUxMTI0MzE2OTE= | 11,387 | pandas ewmcov with nan gives inconsistent results | {
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] | closed | false | null | [] | null | 2 | 2015-10-20T18:35:39Z | 2018-11-21T17:00:19Z | 2018-11-21T17:00:19Z | NONE | null | I have a dataframe, and I want to calculate ewmcov. Sometimes the dataframe has missing values.
I've noticed that using the same dataframe, I get two different results with two separate calls of ewmcov.
Anyone have an idea on why this could be?
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https://api.github.com/repos/pandas-dev/pandas/issues/11388 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11388/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11388/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11388/events | https://github.com/pandas-dev/pandas/issues/11388 | 112,436,132 | MDU6SXNzdWUxMTI0MzYxMzI= | 11,388 | ERROR: test_constructor_unsortable (pandas.tests.test_categorical.TestCategorical) | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-10-20T18:53:08Z | 2015-11-13T22:25:13Z | 2015-11-13T22:25:13Z | CONTRIBUTOR | null | ``` python
======================================================================
ERROR: test_constructor_unsortable (pandas.tests.test_categorical.TestCategorical)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.0... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11389 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11389/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11389/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11389/events | https://github.com/pandas-dev/pandas/issues/11389 | 112,474,207 | MDU6SXNzdWUxMTI0NzQyMDc= | 11,389 | Plot method ignores upper and lower error bar arguments | {
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] | closed | false | null | [] | null | 4 | 2015-10-20T22:35:14Z | 2021-04-20T05:41:32Z | 2021-04-20T05:41:32Z | CONTRIBUTOR | null | This seems to be specific to Pandas as opposed to matplotlib. Worked as desired as of a few months ago when I last updated from master.
This is the desired behavior in matplotlib:
``` python
plt.barh([0,1], [.3, .5], xerr=[[.1,.2], [.3,.4]],
color = ['red','green'],
ecolor='black')
```
![image](https://cl... | {
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] | closed | false | null | [] | null | 2 | 2015-10-20T23:11:05Z | 2015-10-20T23:18:23Z | 2015-10-20T23:16:24Z | NONE | null | With MultiIndex, stats.api.ols returns prediction of a different shape than outcome variable:
```
"set up the example"
x1 = np.arange(0,4)
x2 = 2*np.random.rand(4)
y = x1+x2 + .2*np.random.randn(4)
df = pd.DataFrame( np.c_[y,x1, x2] ,
columns = ["y", "x1","x2"],
index = pd.MultiIndex.from_t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11393 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11393/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11393/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11393/events | https://github.com/pandas-dev/pandas/pull/11393 | 112,491,547 | MDExOlB1bGxSZXF1ZXN0NDgyNzc2ODA= | 11,393 | BUG: GH11344 in pandas.json when file to read is big | {
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taken from esnme/ultrajson#145
test code
```
import json
import pandas
from pandas.compat import zip, range
SIZE = 5*10**7
FILENAME = 'generated.json'
with open(FILENAME, 'w') as fileh:
json.dump(dict(zip(range(SIZE), range(SIZE))), fileh)
with open(FILENAME) as fileh:
pandas.json.load(fileh)
`... | {
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For example:
new_data=pd.DataFrame()
option = Options('AA', 'yahoo')... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11397 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11397/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11397/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11397/events | https://github.com/pandas-dev/pandas/pull/11397 | 112,500,001 | MDExOlB1bGxSZXF1ZXN0NDgyODE4NzU= | 11,397 | Revert "BLD: conda" | {
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closes #11394
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https://api.github.com/repos/pandas-dev/pandas/issues/11399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11399/events | https://github.com/pandas-dev/pandas/pull/11399 | 112,525,924 | MDExOlB1bGxSZXF1ZXN0NDgyOTU1Nzg= | 11,399 | ENC: better pandas typesetting in ipython nbconvert --to latex (updated) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11400/events | https://github.com/pandas-dev/pandas/pull/11400 | 112,537,936 | MDExOlB1bGxSZXF1ZXN0NDgzMDIxNzc= | 11,400 | BUG: using .ix with a multi-index indexer | {
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Add an optional argument to _NDFrameIndexer to indicate
if the indexer is from a MultiIndex
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https://api.github.com/repos/pandas-dev/pandas/issues/11401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11401/events | https://github.com/pandas-dev/pandas/pull/11401 | 112,564,903 | MDExOlB1bGxSZXF1ZXN0NDgzMTc2ODE= | 11,401 | ENH: Improve the error message in to_gbq when schema does not match #11359 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11402 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11402/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11402/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11402/events | https://github.com/pandas-dev/pandas/issues/11402 | 112,658,581 | MDU6SXNzdWUxMTI2NTg1ODE= | 11,402 | Series.sort_index(inplace=True) always returns a new Series | {
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``` python
vals = range(10, 0, -1)
x = pd.Series(vals)
# Returns a new Series with the sorted index instead of
# changing x in place
y = x.sort_index(asc... | {
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