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https://api.github.com/repos/pandas-dev/pandas/issues/101 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/101/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/101/comments | https://api.github.com/repos/pandas-dev/pandas/issues/101/events | https://github.com/pandas-dev/pandas/issues/101 | 1,409,178 | MDU6SXNzdWUxNDA5MTc4 | 101 | Enable .drop(...) to work with MultiIndex | {
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] | closed | false | null | [] | null | 0 | 2011-08-15T17:36:34Z | 2011-08-16T17:41:19Z | 2011-08-16T17:41:19Z | MEMBER | null | So we could do like df.drop(['foo']) on this:
```
A B C foo
foo one -0.659 0.7982 0.2346 bar
two 2.266 -1.157 0.04916 bar
three -1.786 2.029 0.4018 bar
bar one 0.4682 2.42 -1.073 bar
two -0.1343 -0.4565 -0.7504 bar
baz two -1.387 -0.9... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/102 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/102/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/102/comments | https://api.github.com/repos/pandas-dev/pandas/issues/102/events | https://github.com/pandas-dev/pandas/issues/102 | 1,417,365 | MDU6SXNzdWUxNDE3MzY1 | 102 | pandas.stats.interface.ols broken in 0.4 from 0.3 | {
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code:
WP = pandas.WidePanel.fromDict( AllPairSignals)
AllPairRets = WP.minor_xs('PairRet')
AllPairSigs = WP.minor_xs('Sig1')
AllPairModel = pandas.stats.interface.ols( y=AllPairRets, ... | {
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```
frame.groupby(level=[0,2])
```
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presentation in Github is as good as could be. Other
changes include some minor changes to wording and
added hyperlinks.
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In [44]: import pandas.rpy.common as com
In [45]: cw = com.load_data('ChickWeight')
Chick weight
1 0.0 10.5 41.4
2.0 10.5 47.25
4.0 10.11 56.47
6.0 10.11 66.79
8.0 10.11 79.68
10.0 10.11 93.05
12.0 10.11 108.5
14.0 9.778 123.4
16.0 9.471 144.6
... | {
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```
df.join([df2, df2, df3], how='outer')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/117 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/117/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/117/comments | https://api.github.com/repos/pandas-dev/pandas/issues/117/events | https://github.com/pandas-dev/pandas/pull/117 | 1,523,976 | MDExOlB1bGxSZXF1ZXN0Mjk5MzQ2 | 117 | Added baseball.csv | {
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arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = zip(*arrays)
tuples
index = MultiIndex.from_tuples(tuples)
s = Series(randn(8), index=index)
This does not yield the expected result:
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As an example:
from pandas.io.data import DataReader
gs = DataReader("GS", "yahoo")
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In [23]: import pandas.util.testing as tm
In [24]: df = tm.makeTimeDataFrame()
In [25]: df.groupby(lambda x: x.month).count()
Out[25]:
A B C D
1 21 21 21 21
2 9 9 9 9
In [26]: df.groupby(lambda x: x.month).apply(len)
Exception
you want to get a Series like:
1 21
2 9
```
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def apply(self, f, *args, **kwargs):
pass
```
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] | closed | false | null | [] | null | 2 | 2011-09-09T02:27:47Z | 2011-09-09T03:06:57Z | 2011-09-09T02:55:30Z | MEMBER | null | Chris Jordan-Squire said:
If I had some DataFrame with a bunch of columns, including, say
'char1', 'char2', and 'float' then
df.groupby('char1')['float'].agg([np.mean, np.std])
works but
df.groupby(['char1', 'char2'])['float'].agg([np.mean, np.std])
throws TypeError: 'list' object is not callable.
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https://api.github.com/repos/pandas-dev/pandas/issues/134 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/134/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/134/comments | https://api.github.com/repos/pandas-dev/pandas/issues/134/events | https://github.com/pandas-dev/pandas/issues/134 | 1,629,601 | MDU6SXNzdWUxNjI5NjAx | 134 | Partial multiple selection in advanced indexing | {
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] | closed | false | null | [] | {
"closed_at": "2012-03-16T16:11:29Z",
"closed_issues": 34,
"created_at": "2012-03-01T23:49:11Z",
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foo bar
one two three one two
A 0.1241 0.0009403 -0.8312 -0.8608 0.1198
B 0.3026 1.344 -2.37 0.5601 -1.064
C 0.5238 -0.7135 -1.861 -1.266 0.3329
```
Want to be able to do
```
df.ix[:, ['bar', 'foo']]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/135 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/135/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/135/comments | https://api.github.com/repos/pandas-dev/pandas/issues/135/events | https://github.com/pandas-dev/pandas/issues/135 | 1,629,620 | MDU6SXNzdWUxNjI5NjIw | 135 | Setting single column in mixed type frame throws exception | {
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] | closed | false | null | [] | {
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```
df = DataFrame(randn(5, 3), index=labels,
columns=['foo', 'bar', 'baz'])
df['str'] = 'value'
df.ix[::2, 'str'] = nan
```
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of datetime into version.py
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Compiles on Windows-7 64bit with just a few warnings remaining (of initially hundreds) that I cannot fix since they seem to be upstream in cython.
C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /MD ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/142 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/142/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/142/comments | https://api.github.com/repos/pandas-dev/pandas/issues/142/events | https://github.com/pandas-dev/pandas/pull/142 | 1,648,976 | MDExOlB1bGxSZXF1ZXN0MzMxMjM0 | 142 | ENH: Allow unstacking by level name | {
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The problem is that there is no header for the index column:
In [1]: import pandas
In [2]: d = pandas.DataFrame(randn(3, 2), range(3), ['a', 'b'])
In [3]: d.to_csv('tmp.csv')
In [4]: cat tmp.csv
a,b
0,-0.244543341458,-1.50542482731
1,-1.02524916375,0.279887233706
2,0.0657877607121,-... | {
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def dtypes(self):
return np.sort( Series( dict( (x,self[x].dtype ) for x in self.columns ) ) )
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https://api.github.com/repos/pandas-dev/pandas/issues/146 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/146/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/146/comments | https://api.github.com/repos/pandas-dev/pandas/issues/146/events | https://github.com/pandas-dev/pandas/pull/146 | 1,660,786 | MDExOlB1bGxSZXF1ZXN0MzM0ODIy | 146 | Minor change to csv reading | {
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Firstly, congrats on such an amazing project! I love prototyping in python / numpy / ipython, but I always envy some of R's features. I tried pandas out about 9 months ago and although it was interesting, it seemed very rough around the edges. Now, however, it is looking really polished and I've been using it f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/148 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/148/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/148/comments | https://api.github.com/repos/pandas-dev/pandas/issues/148/events | https://github.com/pandas-dev/pandas/issues/148 | 1,667,966 | MDU6SXNzdWUxNjY3OTY2 | 148 | Failing Test on Ambiguous Date/Time on Ubuntu 10.04 | {
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No milestone No one is assigned
Edit
I've installed the dependencies as well as Cython and am getting a failing test on Ubuntu 10.04:
FAIL: test_with_tzinfo_ambiguous_times (pandas.tests.test_daterange.TestDateRange)
Traceback (most recent call last):
File "/home/ry... | {
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FAIL: testOLSWithDatasets (pandas.stats.tests.test_ols.TestOLS)
Traceback (most recent call last):
File "/home/ryan/Code/pandas/pandas/stats/tests/test_ols.py", line 56, in testOLSWithDatasets
self.checkDataSet(datasets.c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/151 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/151/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/151/comments | https://api.github.com/repos/pandas-dev/pandas/issues/151/events | https://github.com/pandas-dev/pandas/pull/151 | 1,669,893 | MDExOlB1bGxSZXF1ZXN0MzM3NDM3 | 151 | ENH: improve DataFrame read_csv / to_csv for Index/MultiIndex | {
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1 ) If header and index are True, you should write something for an index header, even if index_level is None.
2) If given a MultiIndex, don't want to just call str() on each element.
Also fixed read_csv to take an iterable for index_col so that I could write tests.
I haven't looked at ... | {
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Should be careful to raise an exception when there are duplicate names and the level name is used
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I'm not familiar with the internals of numpy or pandas but it ... | {
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In [44]: foo = Series(np.random.randn(100000))
In [45]: bar = Series(np.random.randn(100000))
In [46]: foo[::3] = np.nan
In [47]: bar[::4] = np.nan
In [48]: sfoo = foo.to_sparse(kind='integer')
In [49]: sbar = bar.to_sparse(kind='integer')
In [52]: timeit sfoo + sbar
100 loops, best of 3: 15.8 ms per l... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/176 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/176/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/176/comments | https://api.github.com/repos/pandas-dev/pandas/issues/176/events | https://github.com/pandas-dev/pandas/issues/176 | 1,734,043 | MDU6SXNzdWUxNzM0MDQz | 176 | Join general speed optimizations | {
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- Take functions for each of the major dtypes: bool, object, float, integer
- Routines for joining together consolidated blocks using the above routines
- Get rid of putmask business
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https://api.github.com/repos/pandas-dev/pandas/issues/177 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/177/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/177/comments | https://api.github.com/repos/pandas-dev/pandas/issues/177/events | https://github.com/pandas-dev/pandas/issues/177 | 1,745,843 | MDU6SXNzdWUxNzQ1ODQz | 177 | Write Cython code generation for take routines once stabilized | {
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https://api.github.com/repos/pandas-dev/pandas/issues/178 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/178/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/178/comments | https://api.github.com/repos/pandas-dev/pandas/issues/178/events | https://github.com/pandas-dev/pandas/issues/178 | 1,748,967 | MDU6SXNzdWUxNzQ4OTY3 | 178 | DateRange.union speed shortcuts don't work in some cases | {
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``` python
In [15]: rng = DateRange('1/1/2000', periods=100000, offset=datetools.Minute())
In [16]: timeit rng.union(rng[:-1])
10 loops, best of 3: 93.9 ms per loop
In [17]: timeit rng.intersection(rng[:-1])
100 loops, best of 3: 12.2 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/179 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/179/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/179/comments | https://api.github.com/repos/pandas-dev/pandas/issues/179/events | https://github.com/pandas-dev/pandas/issues/179 | 1,749,992 | MDU6SXNzdWUxNzQ5OTky | 179 | Pre-epoch dates giving OverflowError in mktime with HDFStore | {
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Sample code is:
```
import numpy as np
from pandas import *
def panda_test():
# generate some data
data = np.random.rand(50,5)
# generate some dates
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https://api.github.com/repos/pandas-dev/pandas/issues/180 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/180/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/180/comments | https://api.github.com/repos/pandas-dev/pandas/issues/180/events | https://github.com/pandas-dev/pandas/issues/180 | 1,750,801 | MDU6SXNzdWUxNzUwODAx | 180 | import pandas | {
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/Library/Python/2.6/site-packages/pandas-0.4.1-py2.6-macosx-10.6-universal.egg/pandas/core/common.py:11: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
import pandas._tseries as _tseries
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https://api.github.com/repos/pandas-dev/pandas/issues/181 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/181/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/181/comments | https://api.github.com/repos/pandas-dev/pandas/issues/181/events | https://github.com/pandas-dev/pandas/issues/181 | 1,758,697 | MDU6SXNzdWUxNzU4Njk3 | 181 | as_index in GroupBy still not fully implemented | {
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df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : randn(8), 'D' : randn(8)})
df
grouped = df.groupby('A')
groupe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/182/events | https://github.com/pandas-dev/pandas/issues/182 | 1,765,150 | MDU6SXNzdWUxNzY1MTUw | 182 | DataFrame to_csv() ignores nanRep argument for float32 input | {
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"followers_url": "https://api.github.com/users/wesm/f... | 2 | 2011-09-28T15:03:45Z | 2011-10-02T11:56:59Z | 2011-10-01T19:24:10Z | NONE | null | import numpy as np
import pandas
d = np.random.randn(8, 4)
df = pandas.DataFrame(d)
df[1][:] = np.nan
df.to_csv('pandas-success.csv', nanRep='999')
d = np.asarray(np.random.randn(8, 4), dtype=np.float32)
df = pandas.DataFrame(d)
df[1][:] = np.nan
df.to_csv('pandas-fail.csv', nanRep='999')
After running this with pan... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/183/events | https://github.com/pandas-dev/pandas/issues/183 | 1,766,997 | MDU6SXNzdWUxNzY2OTk3 | 183 | nosetests failures and errors | {
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... | [] | closed | false | null | [] | null | 4 | 2011-09-28T18:24:37Z | 2011-09-28T19:50:47Z | 2011-09-28T19:50:47Z | NONE | null | I am trying to build the latest version of pandas on Mac OS X 10.6.8. I am using Enthought's python distribution: Python 2.7.2 |EPD 7.1-2 (64-bit).
I downloaded the source from github, used python setup.py install, and it appeared to build without error. When I run nosetests pandas, I get some errors and failures (out... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/184 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/184/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/184/comments | https://api.github.com/repos/pandas-dev/pandas/issues/184/events | https://github.com/pandas-dev/pandas/issues/184 | 1,771,005 | MDU6SXNzdWUxNzcxMDA1 | 184 | Consistently set group name on groupby groups | {
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https://api.github.com/repos/pandas-dev/pandas/issues/185 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/185/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/185/comments | https://api.github.com/repos/pandas-dev/pandas/issues/185/events | https://github.com/pandas-dev/pandas/issues/185 | 1,771,030 | MDU6SXNzdWUxNzcxMDMw | 185 | Advanced indexing .ix does not handle step with integer labels | {
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``` python
In [14]: import pandas.util.testing as tm
In [15]: df = tm.makeTimeDataFrame()
In [16]: df.index = np.arange(len(df))
In [17]: df.ix[:20:2, :]
Out[17]:
A B C D
0 0.7272 2.299 0.6004 -1.295
1 0.1992 -0.6554 -0.6646 2.679
2 -2.888 -0... | {
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```
I'm not sure if anyone else has come across this, but I use notnull
extensively for checking if individual values are valid, and when
switching from pandas 0.3 to 0.4, noticed an almost fivefold increase
in the runtime for notnull. I think this is because notnul... | {
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``` python
level_values = np.asarray(index.levels[i]).take(index.labels[i])
```
documentation and tests needed
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https://api.github.com/repos/pandas-dev/pandas/issues/189 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/189/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/189/comments | https://api.github.com/repos/pandas-dev/pandas/issues/189/events | https://github.com/pandas-dev/pandas/issues/189 | 1,790,293 | MDU6SXNzdWUxNzkwMjkz | 189 | Add fast pairwise rolling correlation function | {
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``` python
def rolling_corr_pairwise(df, window):
all_results = {}
for col, left in df.iteritems():
all_results[col] = col_results = {}
for col, right in df.iteritems():
col_results[col] = rolling_corr(left, right, window)
return Panel(all_results)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/194 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/194/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/194/comments | https://api.github.com/repos/pandas-dev/pandas/issues/194/events | https://github.com/pandas-dev/pandas/issues/194 | 1,810,877 | MDU6SXNzdWUxODEwODc3 | 194 | adding covariance to dataframe | {
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``` python
def cov(self):
"""
Compute pairwise covariance of columns
Returns
-------
y : DataFrame
"""
cols = self.columns
std = np.diag(self.std())
corr = self.corr()
result = np.dot(np.dot(std, corr), std)
return self._const... | {
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DateRange(date(1990,1,1), date(1990,12,31))
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https://api.github.com/repos/pandas-dev/pandas/issues/197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/197/events | https://github.com/pandas-dev/pandas/issues/197 | 1,814,974 | MDU6SXNzdWUxODE0OTc0 | 197 | Raise SkipTest if no xlrd | {
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https://api.github.com/repos/pandas-dev/pandas/issues/198 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/198/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/198/comments | https://api.github.com/repos/pandas-dev/pandas/issues/198/events | https://github.com/pandas-dev/pandas/issues/198 | 1,829,001 | MDU6SXNzdWUxODI5MDAx | 198 | MultiIndex Bugs (may be related to failure of get_tuple_index) | {
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Namely, you cannot add DataFrames or Series of different sizes, and you cannot use reindexing. This seems to be related to the fact that Index(list(index)) fails if index is an instance of MultiIndex.
> > > from pandas import *
> > > from numpy.ra... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/200/events | https://github.com/pandas-dev/pandas/pull/200 | 1,836,648 | MDExOlB1bGxSZXF1ZXN0MzgzMTk1 | 200 | Python 3 compatibility | {
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There's one test failure left, and I think that's something odd with numpy, specifically the `ndarray.searchsorted()` method. It returns seemingly random results when it's called in `panel.get_a... | {
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