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https://api.github.com/repos/pandas-dev/pandas/issues/8601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8601/events | https://github.com/pandas-dev/pandas/issues/8601 | 46,499,065 | MDU6SXNzdWU0NjQ5OTA2NQ== | 8,601 | Exception during resample w/ tz | {
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Having a similar issue
idx = date_range('2014-10-08 00:00','2014-10-09 00:00', freq='D', tz='Europe/Berlin')
pd.Series(5, idx).resample('MS')
ValueError: Values falls after last bin
While if I remove the tz :
idx = date_range('2014-10-08 00:00','2014-10-09 00:00', freq='D')
pd.Series(5, idx).resample('M... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8602 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8602/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8602/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8602/events | https://github.com/pandas-dev/pandas/issues/8602 | 46,539,694 | MDU6SXNzdWU0NjUzOTY5NA== | 8,602 | pandas 0.15+ is not deployable via py2app with python 3.4.2 | {
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"followers_url": "https://api.github.com/users/jr... | 9 | 2014-10-22T17:54:20Z | 2014-11-17T00:34:21Z | 2014-11-17T00:34:21Z | CONTRIBUTOR | null | I'm deploying an PyQt(5.3.2) application using py2app. Everything works fine with 0.14.1, 0.15.0 fails at importing. As an example one can take the following `main.py`:
``` python
import pandas
def main():
x = pandas.Series([0, 1, 2])
print(x)
if __name__ == "__main__":
main()
```
and use the default `setup.p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8603/events | https://github.com/pandas-dev/pandas/issues/8603 | 46,543,695 | MDU6SXNzdWU0NjU0MzY5NQ== | 8,603 | BUG: HDFStore fails to save panel with mixed type minor axis when format='table' | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2014-10-22T18:30:26Z | 2018-07-06T20:21:05Z | 2017-07-11T09:56:56Z | CONTRIBUTOR | null | I'm trying to save a panel that has a mixed type minor axis using HDFStore. I can save the panel when using the fixed format (although I get a PerformanceWarning), but it fails for the table format. Example code:
``` python
import pandas as pd
import numpy as np
d1 = pd.DataFrame({1:pd.Series(np.random.randn(10)), 2:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8604/events | https://github.com/pandas-dev/pandas/pull/8604 | 46,550,068 | MDExOlB1bGxSZXF1ZXN0MjMxODI5MzA= | 8,604 | Fix typo in visualization.rst doc | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8605/events | https://github.com/pandas-dev/pandas/pull/8605 | 46,558,101 | MDExOlB1bGxSZXF1ZXN0MjMxODgwMTY= | 8,605 | DOC: read_html docstring update link to read_csv and infer_types mention | {
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] | closed | false | null | [] | null | 0 | 2014-10-22T20:43:13Z | 2014-10-27T15:39:02Z | 2014-10-27T15:39:02Z | MEMBER | null | - changed link to toplevel read_csv
- since `infer_types` is in essence removed, the comment made no sense anymore I think
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https://api.github.com/repos/pandas-dev/pandas/issues/8606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8606/events | https://github.com/pandas-dev/pandas/pull/8606 | 46,558,735 | MDExOlB1bGxSZXF1ZXN0MjMxODgzNjY= | 8,606 | DOC: reorg whatsnew files to subfolder | {
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@jreback What do you think?
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https://api.github.com/repos/pandas-dev/pandas/issues/8607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8607/events | https://github.com/pandas-dev/pandas/issues/8607 | 46,561,671 | MDU6SXNzdWU0NjU2MTY3MQ== | 8,607 | column datatype conversion impacts whole dataframe when using df.ix indexing | {
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"followers_url": "https://api.github.com/users/jre... | 8 | 2014-10-22T21:15:03Z | 2014-10-30T11:19:44Z | 2014-10-27T12:13:31Z | NONE | null | My dataframe has a timestamp column that is encoded as unix epoch. When I convert the column using named index selection it works fine, but when I use the '.ix' syntax it coerces the whole dataframe. Example:
```
import pandas as pd
df = pd.DataFrame(
{'timestamp':[1413840976, 1413842580, 1413760580],
'delt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8608/events | https://github.com/pandas-dev/pandas/issues/8608 | 46,576,866 | MDU6SXNzdWU0NjU3Njg2Ng== | 8,608 | Behavior of numeric ops on Index has changed | {
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```
In [7]: import pandas as pd
In [8]: import numpy as np
In [9]: a = np.zeros(5, dtype=np.float)
In [10]: df = pd.DataFrame({"foo": [10, 20, 30, 40, 50]})
In [11]: df.index
Out[11]: Int64Index([0, 1, 2, 3, 4], dtype='int64')
In [12]: df.index * 3.2
Out[12]: Int64Index([0.0, 3.2, 6.4,... | {
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Also added tests for other ndarray compat properties.
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https://api.github.com/repos/pandas-dev/pandas/issues/8610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8610/events | https://github.com/pandas-dev/pandas/issues/8610 | 46,583,106 | MDU6SXNzdWU0NjU4MzEwNg== | 8,610 | DOC: DataFrame.to_html() documentation isn't rendering properly | {
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] | closed | false | null | [] | null | 2 | 2014-10-23T02:41:56Z | 2014-10-25T02:28:09Z | 2014-10-25T02:27:17Z | CONTRIBUTOR | null | The arguments are showing in two lists.
[Link](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_html.html)
I'll fix this myself, if somebody doesn't get to it quickly. It's good for a first PR, and I've done a couple PRs myself.
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https://api.github.com/repos/pandas-dev/pandas/issues/8611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8611/events | https://github.com/pandas-dev/pandas/issues/8611 | 46,608,575 | MDU6SXNzdWU0NjYwODU3NQ== | 8,611 | Why DataFrame creation from ndarray record do not consist with other method, and how to overcome it | {
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"de... | closed | false | null | [] | null | 9 | 2014-10-23T10:11:10Z | 2014-10-27T02:09:26Z | 2014-10-25T00:40:01Z | NONE | null | In my understanding, DataFrame(in the way how it's displayed) could be treated as a **transposed** SQL table. A row represents a 'property' and a column represents a record. It's clear and consistent in creation using `dict of Series or dicts` and `dict of ndarrays / lists`. As below
``` py
In [32]: d = {'user1':['Jea... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8612/events | https://github.com/pandas-dev/pandas/issues/8612 | 46,610,143 | MDU6SXNzdWU0NjYxMDE0Mw== | 8,612 | pandas.io.data.Options is broken due to Yahoo options page change | {
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Are there plans for a fix in a future release?
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https://api.github.com/repos/pandas-dev/pandas/issues/8613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8613/events | https://github.com/pandas-dev/pandas/issues/8613 | 46,617,626 | MDU6SXNzdWU0NjYxNzYyNg== | 8,613 | API: label-based slicing with not-included labels | {
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Considering the following dataframe:
```
In [18]: df = pd.DataFrame(np.random.randn(5,2), index=pd.date_range('2012-01-01', periods=5))
In [19]: df
Out[19]:
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8614/events | https://github.com/pandas-dev/pandas/issues/8614 | 46,629,944 | MDU6SXNzdWU0NjYyOTk0NA== | 8,614 | Plotting of DatetimeIndex directly with matplotlib no longer gives datetime formatted axis (0.15) | {
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"followers_url": "https://api.github.com/users/jre... | 20 | 2014-10-23T14:20:45Z | 2014-11-05T14:34:26Z | 2014-11-05T14:34:26Z | MEMBER | null | From http://stackoverflow.com/questions/26526230/plotting-datetimeindex-on-x-axis-with-matplotlib-creates-wrong-ticks-in-pandas-0
```
df = pd.DataFrame({'RandomValues': np.random.randint(1, 50, 60)},
index=pd.date_range("2012-01-01", periods=60))
```
Plotting of course works:
```
In [79]: df.plot()... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8615/events | https://github.com/pandas-dev/pandas/issues/8615 | 46,654,477 | MDU6SXNzdWU0NjY1NDQ3Nw== | 8,615 | to_datetime conversion inconsistency in 0.15.0 | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-10-23T17:55:20Z | 2015-07-29T20:17:19Z | 2015-07-29T20:17:06Z | NONE | null | to_datetime is not converting input consistently between a pandas series or a python list of either strings or datetime objects. for example:
import pandas as pd
s = pd.Series(['102214','102314'])
df = pd.Series({'dates':['102214','102314']})
lst = ['102214','102314']
pd.to_datetime(s,format='%m%d%y') and pd.to_date... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8616/events | https://github.com/pandas-dev/pandas/issues/8616 | 46,656,558 | MDU6SXNzdWU0NjY1NjU1OA== | 8,616 | BUG: DatetimeIndex.shift(freq=...) raises near DST boundary | {
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"followers_url": "https://api.github.com/users/jre... | 3 | 2014-10-23T18:10:13Z | 2018-06-18T22:34:29Z | 2018-06-18T22:34:29Z | CONTRIBUTOR | null | xref #5694 #8531 (?)
xref #8817
This is presumably caused by the fact that pytz time zones internalize the offset of the current time.
``` python
In [1]: import pandas as pd
In [2]: idx = pd.date_range('2013-11-03', tz='America/Chicago',
...: periods=6, freq='H')
In [3]: pd.Series(index=idx)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8617 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8617/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8617/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8617/events | https://github.com/pandas-dev/pandas/issues/8617 | 46,657,546 | MDU6SXNzdWU0NjY1NzU0Ng== | 8,617 | BUG: Series.median() treats NaT as INT_MIN | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2014-10-23T18:19:04Z | 2016-09-01T07:28:07Z | 2016-09-01T07:28:07Z | CONTRIBUTOR | null | Fixing this will be easier once numpy/numpy#5222 is fixed.
``` python
In [1]: import pandas as pd
In [2]: pd.Series([0, pd.tslib.iNaT], dtype='m8[ns]')
Out[2]:
0 0 days
1 NaT
dtype: timedelta64[ns]
In [3]: _.median()
Out[3]:
0 -53375 days, 23:53:38.427388
dtype: timedelta64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8618/events | https://github.com/pandas-dev/pandas/pull/8618 | 46,678,000 | MDExOlB1bGxSZXF1ZXN0MjMyNjAxMjc= | 8,618 | Updating generic.py error message | {
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closes https://github.com/pydata/pandas/issues/8015
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https://api.github.com/repos/pandas-dev/pandas/issues/8619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8619/events | https://github.com/pandas-dev/pandas/pull/8619 | 46,682,249 | MDExOlB1bGxSZXF1ZXN0MjMyNjI4NzE= | 8,619 | Fix timezone comparison in DatetimeIndex._generate | {
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] | closed | false | null | [] | null | 4 | 2014-10-23T21:56:54Z | 2015-05-09T15:58:46Z | 2015-05-09T15:58:46Z | CONTRIBUTOR | null | This fix is just a band-aid-- a real fix will have to wait for PEP 431 to unite all the time zone implementations.
Also refine a bare `except:` in the same method.
Fixes #8616
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https://api.github.com/repos/pandas-dev/pandas/issues/8620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8620/events | https://github.com/pandas-dev/pandas/issues/8620 | 46,689,560 | MDU6SXNzdWU0NjY4OTU2MA== | 8,620 | Enable Sourcegraph | {
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] | closed | false | null | [] | null | 2 | 2014-10-23T23:25:44Z | 2014-11-10T14:28:58Z | 2014-10-27T18:34:56Z | NONE | null | I want to use [Sourcegraph code search and code review](https://sourcegraph.com) with pandas. A project maintainer needs to enable it to set up a webhook so the code is up-to-date there.
Could you please enable pandas on @Sourcegraph by going to https://sourcegraph.com/github.com/pydata/pandas and clicking on Settings... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8621/events | https://github.com/pandas-dev/pandas/issues/8621 | 46,702,731 | MDU6SXNzdWU0NjcwMjczMQ== | 8,621 | to_csv issue | {
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``` python
Python 3.4.2 (default, Oct 8 2014, 13:44:52)
[GCC 4.9.1 20140903 (prerelease)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versi... | {
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I centralized a bit of logic into tile.py's `_bins_to_cuts()` so that this change could be made in one place, hope that's okay.
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x=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']],
columns=['person_id','person_name'])
x['person_name']=pd.Categorical(x.person_name) # doing this breaks transform
g=x.groupby(['person_id'])
g.transform(lambda x:x)
AttributeError: 'ObjectBlock' object has no attribute '_holder'
```
using... | {
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In [29]: from sqlalchemy import create_engine
...: engine = create_engine('sqlite://')
...: df=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']],
...: columns=['person_id','person_name'])
...: df.to_sql('data1',engine)
...: df['person_name']=pd.Categorical(df.person_name)... | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2014-10-24T18:42:12Z | 2017-04-14T13:32:37Z | 2017-04-14T13:32:37Z | CONTRIBUTOR | null | xref #8595
something like: `Interval(left=(-1.5,'open),right=(0,'closed'))`
reprs to `(-1.5,0]`
can be used immediately in the index values for `pd.cut` and such (as an object index)
evenutally can form the basis for `IntervalIndex`
cc @shoyer
cc @JanSchulz
cc @rosnfeld
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https://api.github.com/repos/pandas-dev/pandas/issues/8626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8626/events | https://github.com/pandas-dev/pandas/issues/8626 | 46,778,842 | MDU6SXNzdWU0Njc3ODg0Mg== | 8,626 | BUG: DataFrame.to_records, DataFrame constructor broken for categoricals | {
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Broken (`TypeError: data type not understood`):
``` python
pd.DataFrame(list('abc'),dtype='category')
df=pd.DataFrame(list('abc'), columns=['A'])
df['A'] = df['A'].astype('c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8627/events | https://github.com/pandas-dev/pandas/issues/8627 | 46,780,098 | MDU6SXNzdWU0Njc4MDA5OA== | 8,627 | PERF: perform .str operations on categoricals | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8628/events | https://github.com/pandas-dev/pandas/issues/8628 | 46,788,945 | MDU6SXNzdWU0Njc4ODk0NQ== | 8,628 | ENH: decode for Categoricals | {
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That's slow and a `decode` ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8629/events | https://github.com/pandas-dev/pandas/issues/8629 | 46,798,130 | MDU6SXNzdWU0Njc5ODEzMA== | 8,629 | Add arithmetic to categoricals? | {
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> In contrast to statistical categorical variables, categorical data might have an order (e.g. ‘strongly agree’ vs ‘agree’ or ‘first observation’ vs. ‘second observation’), but numerical opera... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8630/events | https://github.com/pandas-dev/pandas/issues/8630 | 46,800,979 | MDU6SXNzdWU0NjgwMDk3OQ== | 8,630 | CI: osx testing on Travis | {
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... | 1 | 2014-10-25T03:38:49Z | 2015-06-17T00:52:18Z | 2015-06-17T00:52:18Z | CONTRIBUTOR | null | http://docs.travis-ci.com/user/multi-os/
http://docs.travis-ci.com/user/osx-ci-environment/
so might be nice to have say a. 2.7 and 3.3/4 build on osx
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https://api.github.com/repos/pandas-dev/pandas/issues/8631 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8631/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8631/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8631/events | https://github.com/pandas-dev/pandas/pull/8631 | 46,802,064 | MDExOlB1bGxSZXF1ZXN0MjMzMzU0NzY= | 8,631 | BUG: Fix pandas.io.data.Options for change in format of Yahoo Option page | {
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Will need a sample of a during the week HTML to make sure that the underlying price and quote time work with that format. I'll update for that on Monday.
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https://api.github.com/repos/pandas-dev/pandas/issues/8632 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8632/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8632/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8632/events | https://github.com/pandas-dev/pandas/issues/8632 | 46,805,605 | MDU6SXNzdWU0NjgwNTYwNQ== | 8,632 | pd.Catagorical breaks to_msgpack | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-10-25T07:46:11Z | 2016-12-17T14:32:41Z | 2014-10-25T12:51:38Z | NONE | null | ``` python
df=pd.DataFrame([[1,'John P. Doe'],[2,'Jane Dove'],[1,'John P. Doe']])
df[1]=pd.Categorical(df[1])
pd.read_msgpack(df.to_msgpack())
KeyError: 100
```
#8626, #8624
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https://api.github.com/repos/pandas-dev/pandas/issues/8633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8633/events | https://github.com/pandas-dev/pandas/issues/8633 | 46,812,561 | MDU6SXNzdWU0NjgxMjU2MQ== | 8,633 | ENH: categorical dataexport - graceful degradation | {
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Currently this is only the case for `to_sql` and `to_csv`, where the categories are exported, while `to_pickle` is the only option to persist categorical data
For Stata and HDF it is:
- `to_hdf`: `N... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8634/events | https://github.com/pandas-dev/pandas/pull/8634 | 46,815,245 | MDExOlB1bGxSZXF1ZXN0MjMzNDA0NDM= | 8,634 | BUG: bug in Float/Int index with ndarray for add/sub numeric ops (GH8608) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8635/events | https://github.com/pandas-dev/pandas/issues/8635 | 46,819,686 | MDU6SXNzdWU0NjgxOTY4Ng== | 8,635 | Can't display DataFrame imported from Excel or CSV in IPython notebook | {
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```
-----... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8636 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8636/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8636/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8636/events | https://github.com/pandas-dev/pandas/issues/8636 | 46,822,497 | MDU6SXNzdWU0NjgyMjQ5Nw== | 8,636 | Timezone conversion? Strange behaviour in read_csv | {
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"descript... | closed | false | null | [] | null | 3 | 2014-10-25T20:23:37Z | 2014-10-26T10:04:48Z | 2014-10-26T10:04:48Z | NONE | null | I have this dataset
```
datetime,CGI,numRecords,countryCode
2014-03-30 01:00:00,222-01-00149-19464,1,39
2014-03-30 02:00:00,222-01-00149-19464,3,39
2014-03-30 03:00:00,222-01-00149-19464,1,39
```
I have this code
```
import pandas as pd
import numpy as np
calls_df = pd.read_csv('222-01-00149-19464.csv', sep=',', enc... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8637/events | https://github.com/pandas-dev/pandas/issues/8637 | 46,827,027 | MDU6SXNzdWU0NjgyNzAyNw== | 8,637 | DatetimeIndex.tz_localize ignores 'ambiguous' keyword | {
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In [120]: pd.Timestamp('2014-10-26 01:01:20').tz_localize('Asia/Irkutsk')
---------------------------------------------------------------------------
AmbiguousTimeError: Cannot infer dst time from Timestamp('2014-10-26 01:01:20'), try using the 'ambiguous' argument
# ok
In [121]: pd.Timestamp('2014-10-26 01... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8638/events | https://github.com/pandas-dev/pandas/issues/8638 | 46,833,048 | MDU6SXNzdWU0NjgzMzA0OA== | 8,638 | Strange X limits | {
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] | closed | false | null | [] | null | 3 | 2014-10-26T05:31:40Z | 2014-10-29T02:31:53Z | 2014-10-29T02:31:53Z | NONE | null | Hey I'm having some weird behavior in the notebook... I just hacked together some code that supposed to poll a temp/humidity sensor 60 times and then plot the temp against time. Instead of getting a nicely formatted x axis I'm just getting a straight line, and then all the dates from 2010 to 2016 as the x axis. Any ide... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8639/events | https://github.com/pandas-dev/pandas/issues/8639 | 46,852,251 | MDU6SXNzdWU0Njg1MjI1MQ== | 8,639 | pandas-0.15.0 test failure test_string_index_alias_tz_aware (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil) | {
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## ERROR: test_string_index_alias_tz_aware (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.15.0/work/pandas-0.15.0-python3_3/lib/pandas/tseries/tests/test_timezones.py", line 606, in test_string_index_alias_tz_aware
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https://api.github.com/repos/pandas-dev/pandas/issues/8640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8640/events | https://github.com/pandas-dev/pandas/issues/8640 | 46,855,603 | MDU6SXNzdWU0Njg1NTYwMw== | 8,640 | API/ENH: dtype='string' / pd.String | {
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... | 61 | 2014-10-26T21:35:23Z | 2021-04-11T05:23:09Z | null | CONTRIBUTOR | null | *update for 2019-10-07: We have a StringDtype extension dtype. It's memory model is the same as the old implementation, an object-dtype ndarray of strings. The next step is to store & process it natively*.
---
xref #8627
xref #8643, #8350
Since we introduced `Categorical` in 0.15.0, I think we have found 2 m... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8641/events | https://github.com/pandas-dev/pandas/issues/8641 | 46,855,664 | MDU6SXNzdWU0Njg1NTY2NA== | 8,641 | ERR: concat of 2 Series categories does not preserve category dtype | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-10-26T21:37:29Z | 2014-11-09T22:10:39Z | 2014-11-09T22:10:39Z | CONTRIBUTOR | null | xref #8640
As this is inconcistent with how DataFrame concat's work (its a different code-path), so needs updating
```
s = Series(list('abc'),dtype='category')
s2 = Series(list('abd'),dtype='category')
# this should raise
pd.concat([s,s2])
# this should be a category
pd.concat([s,s])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8642 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8642/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8642/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8642/events | https://github.com/pandas-dev/pandas/issues/8642 | 46,856,865 | MDU6SXNzdWU0Njg1Njg2NQ== | 8,642 | drop_duplicates() does not drop duplicates of large datasets | {
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https://www.dropbox.com/s/mx3zqot352gdrhq/hg19.txt?dl=0
After reading this into a dataframe, and calling drop_duplicates, the dataframe still has the same number of rows. Though clearly, there are many duplicates contained in the file.
I am usi... | {
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... | 4 | 2014-10-26T23:45:41Z | 2016-11-11T11:13:28Z | 2016-11-11T11:13:28Z | CONTRIBUTOR | null | this should actually be relatively straightforward.
Incorporating this library: https://github.com/libdydn/dynd-python
as an optional dep, allows native Integer NA support (implemented
very similarly to how NaT is represented in `datetime64[ns]` dtypes ATM.
`dynd-python` are the python bindings for `libdynd` and have... | {
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======================================================================
FAIL: test_precise_conversion (pandas.io.tests.test_parsers.TestCParserLowMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.0/debian/tmp/usr/lib/pyth... | {
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======================================================================
FAIL: test_boxplot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
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```
======================================================================
ERROR: test_hist_df (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
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https://api.github.com/repos/pandas-dev/pandas/issues/8649 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8649/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8649/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8649/events | https://github.com/pandas-dev/pandas/issues/8649 | 46,865,110 | MDU6SXNzdWU0Njg2NTExMA== | 8,649 | UnboundLocalError: local variable 'store' referenced before assignment | {
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"followers_url": "https://api.github.com/users/jre... | 1 | 2014-10-27T02:45:30Z | 2014-10-30T11:19:44Z | 2014-10-27T12:37:35Z | CONTRIBUTOR | null | in ensure_clean_store on ubuntu 13.10 triggered by many tests, e.g.
```
======================================================================
ERROR: test_unicode_index (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
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https://api.github.com/repos/pandas-dev/pandas/issues/8650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8650/events | https://github.com/pandas-dev/pandas/issues/8650 | 46,865,188 | MDU6SXNzdWU0Njg2NTE4OA== | 8,650 | AttributeError: 'module' object has no attribute 'open_file' | {
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"followers_url": "https://api.github.com/users/jre... | 2 | 2014-10-27T02:47:33Z | 2014-10-30T11:19:44Z | 2014-10-27T12:37:35Z | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_frame_select_complex2 (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
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https://api.github.com/repos/pandas-dev/pandas/issues/8651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8651/events | https://github.com/pandas-dev/pandas/pull/8651 | 46,897,775 | MDExOlB1bGxSZXF1ZXN0MjMzNzg1NjI= | 8,651 | TST: skip tests on incompatible PyTables version (GH8649, GH8650) | {
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closes #8650
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https://api.github.com/repos/pandas-dev/pandas/issues/8652 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8652/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8652/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8652/events | https://github.com/pandas-dev/pandas/pull/8652 | 46,903,189 | MDExOlB1bGxSZXF1ZXN0MjMzODE4MTI= | 8,652 | BUG: various categorical fixes (GH8626) | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jre... | 0 | 2014-10-27T13:14:02Z | 2014-10-30T11:20:23Z | 2014-10-28T00:03:02Z | CONTRIBUTOR | null | BUG: coerce Categorical in record array creation (GH8626)
BUG: Categorical not created properly with to_frame() from Series (GH8626)
BUG: handle astype with passed pd.Categorical (GH8626)
closes #8626
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As a workaround, I'm currently try/except-ing the ValueError and eventually adding a np.nan datapoint at the end of the series (effectively "walking over"... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8655 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8655/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8655/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8655/events | https://github.com/pandas-dev/pandas/pull/8655 | 46,964,277 | MDExOlB1bGxSZXF1ZXN0MjM0MTkwNTA= | 8,655 | DOC: update docs on direct plotting with matplotlib (GH8614) | {
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"followers_url": "https://api.github.com/users/jre... | 6 | 2014-10-27T21:50:58Z | 2014-10-30T11:20:23Z | 2014-10-28T11:02:41Z | MEMBER | null | Closes #8614
- this adds a warning that you now have to use `to_pydatetime` as direct plotting with a DatetimeIndex does not work anymore (see #8614)
- I also removed for now the note on speed and explanation of the registered formatters. @agijsberts could you shed some light on this?
- "_The speed up for large data... | {
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] | closed | false | null | [] | null | 6 | 2014-10-27T23:24:58Z | 2014-10-28T11:23:00Z | 2014-10-28T00:37:54Z | NONE | null | Hi,
Is this a known issue?
Time offset on 22nd of Sept should be -4 hours in New York as below:
```
pandas.Timestamp('2014-09-22 09:30:00-0400', tz='EST5EDT').tz_convert('EST5EDT')
Timestamp('2014-09-22 09:30:00-0400', tz='EST5EDT')
```
but when providing this twice (-0400 and EST4EDT), the result becomes -0500:
`... | {
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Not sure if this is sensible, but I thought noteworthy.
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"description": "Categorical Data Type",
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``` python
In [1]: cat = pd.Categorical([1,2,3])
In [2]: cat > cat[0]
Out[2]: array([False, True, True], dtype=bool)
In [3]: cat[0] < cat
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipyt... | {
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... | closed | false | null | [] | null | 6 | 2014-10-28T14:42:51Z | 2020-11-19T21:13:15Z | 2020-11-19T21:13:14Z | NONE | null | Catchall for `rolling_*` issues:
- [x] #12537 exclude nuiscance columns
- [ ] #12536 fully support timedeltas
- [ ] #4130 (return type of `rolling_apply`)
- [x] #3185 (stop using `np.apply_along_axis`)
- [x] #5071, #12950 (pass frames, operate 2-D)
- [x] #9413 `rolling_quantile`
- [ ] #9481 `rolling_idxmax`
- [... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8660 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8660/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8660/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8660/events | https://github.com/pandas-dev/pandas/issues/8660 | 47,038,522 | MDU6SXNzdWU0NzAzODUyMg== | 8,660 | apply changes the datatypes: strange behaviour/bug? | {
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] | closed | false | null | [] | null | 8 | 2014-10-28T15:17:25Z | 2014-10-31T11:10:47Z | 2014-10-30T10:55:49Z | NONE | null | Hello. I tried to apply a function to a dataframe and it changes the column types. See this example: http://nbviewer.ipython.org/urls/dl.dropboxusercontent.com/u/1118905/Calls-bug.ipynb
Am I doing something wrong? Is it a bug?
pandas 0.15.0
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https://api.github.com/repos/pandas-dev/pandas/issues/8661 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8661/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8661/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8661/events | https://github.com/pandas-dev/pandas/issues/8661 | 47,048,971 | MDU6SXNzdWU0NzA0ODk3MQ== | 8,661 | read_csv problem with delim_whitespace, skiprows and trailing spaces in skipped rows | {
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```
skip1<lf>
skip2<lf>
0 1 2<lf>
3 4 5<lf>
```
reading with read_csv() in pandas 0.15.0-42-g20be789 and python 3.4.2 works
```
df = pd.read_csv('test.txt', skiprows=2, delim_whitespace=True, header=None)
df
0 1 2
0 0 1 2
1 3 4 5
```
If I... | {
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... | 7 | 2014-10-28T19:06:14Z | 2020-07-12T12:31:24Z | 2020-07-12T12:31:23Z | NONE | null | I'm grouping rows by column investor id. within apply, when a group has more than 1 row, i'm copying a value in the second row in the group into the a column of the first row in that group. I'm using groupby with apply but the assembled result is incorrect - all rows except row 1 show the same investor B - seems like a... | {
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Adds a flag which when True returns a DataFrame with columns being the index of the lists generated by the string splitting operation. When False, it returns a 1D numpy array, as before. Defaults to false to not break compatibility.
In the case with no splits, returns a single column DataFrame rather... | {
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``` python
df = pd.DataFrame({'a': [1, 2, 3], 'b': ['a', 'b', 'c']})
df.query('a=1')
```
That raises a ValueError. But `df` was modified.
``` python
In [15]: df
Out[15]:
a b
0 1 a
1 1 b
2 1 c
```
versions:
```
pandas: 0.15.0-6-g403f38d
bottleneck: No... | {
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] | closed | false | null | [] | null | 9 | 2014-10-28T22:04:34Z | 2014-10-29T20:53:52Z | 2014-10-29T20:53:52Z | NONE | null | I am seeing a new but consistently repeatable segmentation fault in our application test suite, running CPython 2.7.8, when calling `_uuid_generate_time(_buffer)` from uuid.py (somewhere inside the kombu library).
The salient difference that makes this segfault occur is if I import pandas before the test run. If I don... | {
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"descript... | closed | false | null | [] | null | 15 | 2014-10-28T22:05:18Z | 2015-01-29T16:55:47Z | 2014-10-30T22:12:47Z | NONE | null | I've downloaded large dataset from http://www.sigkdd.org/kdd-cup-2009-customer-relationship-prediction.
orange_large_train.data.chunk1.zip (52.7 Mbytes)
orange_large_train.data.chunk2.zip (52.7 Mbytes)
orange_large_train.data.chunk3.zip (52.6 Mbytes)
orange_large_train.data.chunk4.zip (52.5 Mbytes)
orange_large_train... | {
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```
import pandas as pd
from datetime import time
ser = pd.Series(index=pd.date_range('2000-1-1', periods=1000000, freq=pd.datetools.offsets.Second()))
ser.head(999999)[time(15,0)] # this is fine
ser[time(15,0)]... | {
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Enhances `pd.concat` to work with any iterable (except specifically undesired ones like pandas objects and strings). A new test is included covering tuples, lists, generator expressions, deques, and custom sequences, and all existing tests still pass. Finally, a "what's new" entry is included (not sure th... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8670 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8670/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8670/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8670/events | https://github.com/pandas-dev/pandas/issues/8670 | 47,098,687 | MDU6SXNzdWU0NzA5ODY4Nw== | 8,670 | BUG: Strange type conversions when assigning with `df.loc` | {
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``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3],'B': [np... | {
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"followers_url": "https://api.github.com/users/jre... | 1 | 2014-10-29T03:48:02Z | 2014-10-30T11:20:24Z | 2014-10-29T11:39:26Z | CONTRIBUTOR | null | closes #6961 this was probably fixed by #7907 but just adding an explicit test and release note.
(in a sense, it should really be the 0.15.0 release note, but that release has shipped...)
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I want to truncate (or even drop) an existing table in SQL server. I didn't find any dedicated function in pandas.io.sql. So I tried to create an empty data frame just to call:
```
df1 = pd.DataFrame()
df1.to_sql(out_tbl, engine, if_exists='replace', index=False)
```
This code does drop the table but then generat... | {
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"f... | 1 | 2014-10-30T00:08:18Z | 2018-07-08T14:33:38Z | 2018-07-06T22:08:31Z | CONTRIBUTOR | null | The `result_type` argument of `Series.str.split` has a default value of `'series'` for compatibility purposes. This should be deprecated in favor of the default `'frame'`.
See issue #8428 , PR #8663 for introduction of the argument and discussion of default value.
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If I use the default C engine with `read_table` I have to specify `skiprows=85` to properly load the table:
``` python
pd.read_table(
'co2_mm_mlo.txt.', sep=r'\s+', header=None, skiprows=8... | {
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The first commit adds `Index.is_monotonic_decreasing` and `Index.is_monotonic_increasing` (alias for `is_monotonic`).
`is_monotonic` will have a performance degradation (still O(n) time) in cases where the Index is decreasing monotonic. If necessary, we could work around this, but I think we can probably... | {
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pd.read_csv(filename, skiprows=100000000, iterator=True)
python crashes with a memory error. I have 32 gigs of memory and python eats up all that memory!!
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Use .get_values() instead of .values on the block,
this ensures that NonConsolidatable blocks (non-dense
blocks like categorical or sparse) are densified
instead of just returning the raw .values.
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```
2014-10-14 23:06:07.440000 6.44000
2014-10-14 23:06:07.761000 5.09600
2014-10-14 23:06:08.215000 6.44000
2014-10-14 23:06:08.486000 6.44000
2014-10-14 23:06:08.509000 5.20800
2014-10-14 23:06:08.789000 4.02842
2014-10-14 23:... | {
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"descript... | closed | false | null | [] | null | 9 | 2014-10-30T17:02:02Z | 2016-04-29T01:48:11Z | 2016-04-29T01:48:11Z | CONTRIBUTOR | null | The following code changes the index type for my series:
``` python
import arrow
start_time = arrow.get(2014, 10, 15, hour = 19, tzinfo='US/Eastern')
end_time = start_time.replace(seconds=400)
ts = pd.date_range(start=start_time.isoformat(), end=end_time.isoformat(), freq='2200L')
my_series = pd.read_pickle(...)
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import pandas as pd
pd.read_csv('https://raw.githubusercontent.com/YeoLab/shalek2013/master/expression.csv.gz', compression='gzip', index_col=0)
```
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-... | {
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used in _cat_compare_op, `pd.lib.isscalar` should be used instead.
``` python
In [1]: cat = pd.Categorical(pd.date_range('2014', periods=5))
In [2]: cat > cat[0]
---------------------------------------------------------------------------
Typ... | {
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https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=s390x&ver=0.15.0-1&stamp=1414699796
and others
https://buildd.debian.org/status/package.php?p=pandas
(some have other failures as well, yet to find a moment to "analyze", but e.g. hurd-i386 fails test_precise_conversion)
could/should all ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8689 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8689/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8689/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8689/events | https://github.com/pandas-dev/pandas/issues/8689 | 47,348,592 | MDU6SXNzdWU0NzM0ODU5Mg== | 8,689 | datetime operations on NaT produce dangerous results | {
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In [189]: ser = pd.Series([np.datetime64('nat')])
In [190]: ser
Out[190]:
0 NaT
dtype: datetime64[ns]
In [191]: ser.dt.hour
Out[191]:
0 -1
dtype: int64
```
While -1 is not a valid hour it is a very valid integer and could (has) caused hard to track bugs.
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... | 2 | 2014-10-31T12:58:26Z | 2019-01-04T07:00:57Z | 2019-01-04T07:00:26Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26660286/dealing-with-dst-time-with-set-levels-function-multiindex-pandas)
```
In [91]: def works(df):
....: return df.reset_index(level=1,drop=True).set_index(df.index.get_level_values(1).tz_localize('Europe/Paris', ambiguous = 'infer'),append=True).iloc[7151:7160]
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closes #8689
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https://api.github.com/repos/pandas-dev/pandas/issues/8696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8696/events | https://github.com/pandas-dev/pandas/issues/8696 | 47,417,789 | MDU6SXNzdWU0NzQxNzc4OQ== | 8,696 | API: fix milliseconds accessor in Timestamp/DatetimeIndex | {
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https://github.com/jreback/pandas/tree/milliseconds
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https://api.github.com/repos/pandas-dev/pandas/issues/8697 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8697/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8697/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8697/events | https://github.com/pandas-dev/pandas/issues/8697 | 47,425,133 | MDU6SXNzdWU0NzQyNTEzMw== | 8,697 | BUG: SQL get_schema generates incorrect DDL for specific dialect | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2014-10-31T17:52:37Z | 2014-11-17T08:31:24Z | 2014-11-17T08:31:12Z | NONE | null | I'm importing data from an Excel spreadsheet and I'd like to generate a CREATE TABLE statement for PostgreSQL that matches the data I've imported from Excel.
Here's what I've got so far:
``` python
>>> import pandas as pd
>>> from sqlalchemy import create_engine
>>> import psycopg2
>>> basedata = pd.read_excel('mydat... | {
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... | 7 | 2014-10-31T19:07:24Z | 2015-06-11T12:48:43Z | 2015-06-11T12:42:48Z | CONTRIBUTOR | null | So this as expected. Though I its pretty straightforward to add support for this directly to a Timedelta, after the notion is the same, _though_ a `time` object can support a time-zone so not sure if this is odd (or maybe we should add an optional tz to Timedelta).
```
In [5]: from datetime import time
In [6]: time(s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8699/events | https://github.com/pandas-dev/pandas/pull/8699 | 47,436,040 | MDExOlB1bGxSZXF1ZXN0MjM3MDA2ODU= | 8,699 | BUG: incorrect serialization of a CustomBusinessDay in HDF5 (GH8591) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8700/events | https://github.com/pandas-dev/pandas/pull/8700 | 47,446,437 | MDExOlB1bGxSZXF1ZXN0MjM3MDcxODc= | 8,700 | BUG: various Categorical fixes (GH8623) | {
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"followers_url": "https://api.github.com/users/jre... | 1 | 2014-10-31T21:29:04Z | 2014-11-01T13:11:51Z | 2014-10-31T22:35:39Z | CONTRIBUTOR | null | BUG: bug in selecting from a Categorical with iloc (GH8623)
BUG: bug in groupby-transform with a Categorical (GH8623)
BUG: bug in duplicated/drop_duplicates with a Categorical (GH8623)
closes #8623
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https://api.github.com/repos/pandas-dev/pandas/issues/8701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8701/events | https://github.com/pandas-dev/pandas/pull/8701 | 47,509,854 | MDExOlB1bGxSZXF1ZXN0MjM3MjEwNDI= | 8,701 | ENH: provide a null_counts keyword to df.info() to force showing of null counts | {
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"followers_url": "https://api.github.com/users/jre... | 0 | 2014-11-01T16:27:22Z | 2014-11-02T18:57:21Z | 2014-11-02T18:57:21Z | CONTRIBUTOR | null | Allows run-time control of showing of null-counts with `df.info()` (similar to what we allow with for example memory_usage in that this will override the default options for that call)
```
In [2]: df = DataFrame(1,columns=range(10),index=range(10))
In [3]: df.iloc[1,1] = np.nan
```
Default for a small frame (current... | {
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