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https://api.github.com/repos/pandas-dev/pandas/issues/2301 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2301/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2301/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2301/events | https://github.com/pandas-dev/pandas/issues/2301 | 8,505,773 | MDU6SXNzdWU4NTA1Nzcz | 2,301 | Holiday calendar aware date offsets | {
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} | 3 | "2012-11-20T14:15:41Z" | "2013-07-25T23:03:53Z" | "2013-06-21T12:40:12Z" | CONTRIBUTOR | null | for business freq (B, BQ, BQS, BM, BMS, BA, BAS)
xref: http://stackoverflow.com/questions/13462802/dateoffset-in-pandas-with-a-holiday-calendar
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https://api.github.com/repos/pandas-dev/pandas/issues/2302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2302/events | https://github.com/pandas-dev/pandas/issues/2302 | 8,506,993 | MDU6SXNzdWU4NTA2OTkz | 2,302 | New Feature Request: Export plots to excel/pdf | {
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} | 10 | "2012-11-20T14:49:46Z" | "2014-01-18T05:29:36Z" | "2014-01-17T13:44:10Z" | NONE | null | The current functionality to export dataframes to excel is excellent:
i.e. df.to_excel('filename')
In the process of introducing python to my company, I realized I needed to not only export the dataframes but also plots. I searched but did not find this functionality. This is more of a reporting need, but would be very useful.
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https://api.github.com/repos/pandas-dev/pandas/issues/2303 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2303/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2303/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2303/events | https://github.com/pandas-dev/pandas/issues/2303 | 8,509,577 | MDU6SXNzdWU4NTA5NTc3 | 2,303 | New function in DataFrame: to_series | {
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(analagous to to_frame from a panel to a frame)
```
def to_series(self, concentate_labels = None):
"""
Transform a frame via ravelling it
Parameters
----------
concentate_labels if not None provides a string to concentate the index/column labels
for each entry
Returns
-------
y : Series
"""
N, K = self.shape
# use this as the concentation character
if concentate_labels is not None:
index = []
for l in self.index:
index.extend([ "%s%s%s" % (l,concentate_labels,c) for c in self.columns ])
index = Index(index)
else:
index = Index(np.array(self.index).repeat(K))
return Series(self.values.ravel(), index=index)
```
example:
```
import numpy as np
import pandas as pd
In [4]: df = pd.DataFrame(np.random.randn(3,5), columns = ['A','B','C','D','E'])
In [5]: df
Out[5]:
A B C D E
0 -0.197072 0.120001 -2.164021 1.179995 -0.590823
1 -1.047157 -0.683838 -0.244762 1.068976 -0.257587
2 0.049067 -0.896830 0.689987 0.970743 -0.334476
In [6]: df.to_series()
Out[6]:
0 -0.197072
0 0.120001
0 -2.164021
0 1.179995
0 -0.590823
1 -1.047157
1 -0.683838
1 -0.244762
1 1.068976
1 -0.257587
2 0.049067
2 -0.896830
2 0.689987
2 0.970743
2 -0.334476
In [7]: df.to_series(concentate_labels = '_')
Out[7]:
0_A -0.197072
0_B 0.120001
0_C -2.164021
0_D 1.179995
0_E -0.590823
1_A -1.047157
1_B -0.683838
1_C -0.244762
1_D 1.068976
1_E -0.257587
2_A 0.049067
2_B -0.896830
2_C 0.689987
2_D 0.970743
2_E -0.334476
```
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} | 3 | "2012-11-20T16:23:59Z" | "2014-04-05T19:32:40Z" | "2012-11-24T07:59:47Z" | NONE | null | Guys,
It seems to be a problem with broadcasting when having a datetime.date index, do you know what may be causing this?
Example:
``` python
index = index=[datetime.date(2012, 1, 1), datetime.date(2012, 1, 2)]
columns=['a', 'b']
df = pandas.DataFrame([[1., 2.], [3., 4.]], index, columns)
>>> print( df.mean(1) )
2012-01-01 1.5
2012-01-02 3.5
>>> print( (df - df.mean(1)).to_string() )
a b 2012-01-01 2012-01-02
2012-01-01 NaN NaN NaN NaN
2012-01-02 NaN NaN NaN NaN
```
The columns somehow end up being the union of the inital columns and the index. The above code works OK when using datetime.datetime instead.
Many thanks,
Marius
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} | 10 | "2012-11-20T21:36:29Z" | "2012-12-02T17:12:13Z" | "2012-12-02T17:12:13Z" | NONE | null | ```
rng = date_range('2012-01-01', periods=1000, freq='12H')
vals = randn(1000)
df = DataFrame(vals, index=rng)
```
`df.ix['2012']` and `df['2012-05']` (selection by year or year and month) work as expected. However:
```
In [314]: df.ix['2012-01-01']
Out[314]:
0 0.107819
Name: 2012-01-01 00:00:00
```
The output from indexing by year/month/day is unexpected - we should receive either all values matching the day or an error (if indexing by day has not been implemented), instead we get only the first match.
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} | 2 | "2012-11-21T02:39:29Z" | "2012-11-24T00:24:55Z" | "2012-11-24T00:24:55Z" | NONE | null | Title says it all. core/frame.py, combine() method at line 3448:
```
if other.empty:
return self.copy()
```
If other is a DataFrame which has indices but no columns, a combine_first() should add the indices with every column set to NaN.
Sorry for not submitting a patch myself - I'm very new to the pandas codebase, so I'm not sure whether this should be resolved in combine() for all combination attempts, or only in combine_first().
...Maybe empty should check whether the DataFrame has indices? Though that could silently break a lot of old code...
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https://api.github.com/repos/pandas-dev/pandas/issues/2311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2311/events | https://github.com/pandas-dev/pandas/issues/2311 | 8,535,571 | MDU6SXNzdWU4NTM1NTcx | 2,311 | Wish: Input / output for Open Document Spreadsheet (ODS) | {
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} | 27 | "2012-11-21T09:00:06Z" | "2019-07-03T22:36:47Z" | "2019-07-03T21:45:44Z" | CONTRIBUTOR | null | It would be nice to have, along with XLS and XLSX, also support for ODS (used notably by OpenOffice.org and Libreoffice, but not only that).
The main problem I saw is that there's a plethora of libraries to handle that: however not sure which is the "best" one (with a licensing that would fit with pandas).
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https://api.github.com/repos/pandas-dev/pandas/issues/2312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2312/events | https://github.com/pandas-dev/pandas/issues/2312 | 8,541,348 | MDU6SXNzdWU4NTQxMzQ4 | 2,312 | Shouldn't index in dataframes be called lines? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2313/events | https://github.com/pandas-dev/pandas/issues/2313 | 8,546,374 | MDU6SXNzdWU4NTQ2Mzc0 | 2,313 | DatetimeIndex.values yields unexpected results | {
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} | [] | closed | false | null | [] | null | 1 | "2012-11-21T14:59:35Z" | "2012-11-21T15:53:38Z" | "2012-11-21T15:53:38Z" | NONE | null | ```
import pandas as pd
dates = pd.date_range('1997-12-31', periods=5)
print('Iterating through DatetimeIndex:')
for date in dates:
print(date)
print('Iterating through DatetimeIndex.values:')
for date in dates.values:
print(date)
```
> Iterating through DatetimeIndex:
> 1997-12-31 00:00:00
> 1998-01-01 00:00:00
> 1998-01-02 00:00:00
> 1998-01-03 00:00:00
> 1998-01-04 00:00:00
> Iterating through DatetimeIndex.values:
> 1970-01-11 48:00:00
> 1970-01-11 72:00:00
> 1970-01-11 96:00:00
> 1970-01-11 120:00:00
> 1970-01-11 144:00:00
Is this the way it should be?
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} | 4 | "2012-11-21T16:14:10Z" | "2016-10-12T23:03:21Z" | "2012-12-02T01:05:56Z" | NONE | null | Hi,
here is an example that demonstrates the error I'm getting (using pandas 0.9.2.dev-61766ec):
Example script:
``` python
from pandas import DataFrame, MultiIndex
index_columns = list("abc")
df = DataFrame([[0, 1, 0, "x"], [0, 0, 1, "y"]],
columns=index_columns + ["data"])
df = df.set_index(index_columns)
print "Test DataFrame:"
print df
print
query_index = df.index[:1] # first index
print "MultiIndex to index the DataFrame through .ix[...]:"
print repr(query_index)
print
print "This works:"
print df.ix[query_index]["data"]
print
print "This doesn't:"
print df.ix[query_index, "data"]
print
```
which outputs:
```
Test DataFrame:
data
a b c
0 1 0 x
0 1 y
MultiIndex to index the DataFrame through .ix[...]:
MultiIndex
[(0, 1, 0)]
This works:
a b c
0 1 0 x
Name: data
This doesn't:
Traceback (most recent call last):
File "pandas_bug_github.py", line 20, in <module>
print df.ix[query_index, "data"]
File "/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_61766ec-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 32, in __getitem__
return self._getitem_tuple(key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_61766ec-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 202, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_61766ec-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 287, in _getitem_lowerdim
loc = ax0.get_loc(tup[0])
File "/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_61766ec-py2.7-linux-x86_64.egg/pandas/core/index.py", line 2080, in get_loc
return self._get_level_indexer(key, level=0)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_61766ec-py2.7-linux-x86_64.egg/pandas/core/index.py", line 2182, in _get_level_indexer
loc = level_index.get_loc(key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_61766ec-py2.7-linux-x86_64.egg/pandas/core/index.py", line 674, in get_loc
return self._engine.get_loc(key)
File "engines.pyx", line 113, in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:124894)
File "engines.pyx", line 115, in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:124554)
TypeError
```
The funny thing is that if, for instance, we swap the rows in the dataframe ( [[0, 0, 1, "y"], [0, 1, 0, "x"]] instead of [[0, 1, 0, "x"], [0, 0, 1, "y"]] ) it works just fine.
Regards.
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} | 6 | "2012-11-21T16:38:28Z" | "2014-05-29T06:25:10Z" | "2014-05-29T06:25:10Z" | MEMBER | null | xref: http://stackoverflow.com/questions/13478597/arithmetic-on-date-series-not-an-index-in-pandas
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} | 7 | "2012-11-21T16:56:42Z" | "2012-11-26T19:09:42Z" | "2012-11-26T18:56:49Z" | CONTRIBUTOR | null | See the below examples. My use case started out as trying to use apply on a Series (and having the applied function return a Series) in order to get a DataFrame - but seeing some inconsistencies of how the returned function is accumulated (obviously can do this by iterating over the series in a list comprehension and using concat too).
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: print pd.__version__
0.8.1
In [4]:
In [4]: s = pd.Series(np.random.rand(5))
In [5]: s
Out[5]:
0 0.681552
1 0.299908
2 0.212221
3 0.365597
4 0.779190
In [6]: print type(s)
<class 'pandas.core.series.Series'>
In [7]: print type(s[0])
<type 'numpy.float64'>
```
base case
```
In [6]: y = s.apply(lambda x: x)
In [7]: print type(y)
<class 'pandas.core.series.Series'>
In [8]: print type(y[0])
<type 'numpy.float64'>
In [9]: print y[0]
0.681551901344
```
this looks like it is doing some sort of conversion
e.g. using the Series values to construct the applied series
```
In [10]: y = s.apply(lambda x: pd.Series(x))
In [11]: print type(y)
<class 'pandas.core.series.Series'>
In [12]: print type(y[0])
<type 'numpy.float64'>
In [13]: print y[0]
0.681551901344
```
This is a bit inconsistent with the prior example;
Should this be upconverting to a DataFrame?
```
In [14]: y = s.apply(lambda x: pd.Series(x, index = ['foo']))
In [15]: print type(y)
<class 'pandas.core.series.Series'>
In [16]: print type(y[0])
<class 'pandas.core.series.Series'>
In [17]: print y[0]
foo 0.681552
```
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} | 2 | "2012-11-21T20:11:38Z" | "2012-11-24T02:24:44Z" | "2012-11-24T00:50:56Z" | NONE | null | When joining two DataFrames with localized indexes, the resulting DataFrame has erroneous Timestamps. (It appears that at some point the timestamps are interpreted at UTC without a correct timezone correction?)
Sample code:
```
In [94]: test1 = pandas.DataFrame(numpy.zeros((6,3)),index=pandas.tseries.index.date_range("2012-11-15 00:00:00", periods=6, freq="100L", tz="US/Central"))
In [95]: test2 = pandas.DataFrame(numpy.zeros((3,3)),index=pandas.tseries.index.date_range("2012-11-15 00:00:00", periods=3, freq="250L", tz="US/Central"), columns=range(3,6))
In [96]: test1
Out[96]:
0 1 2
2012-11-15 00:00:00-06:00 0 0 0
2012-11-15 00:00:00.100000-06:00 0 0 0
2012-11-15 00:00:00.200000-06:00 0 0 0
2012-11-15 00:00:00.300000-06:00 0 0 0
2012-11-15 00:00:00.400000-06:00 0 0 0
2012-11-15 00:00:00.500000-06:00 0 0 0
In [97]: test2
Out[97]:
3 4 5
2012-11-15 00:00:00-06:00 0 0 0
2012-11-15 00:00:00.250000-06:00 0 0 0
2012-11-15 00:00:00.500000-06:00 0 0 0
In [98]: test1.join(test2, how="outer")
Out[98]:
0 1 2 3 4 5
2012-11-15 06:00:00-06:00 0 0 0 0 0 0
2012-11-15 06:00:00.100000-06:00 0 0 0 NaN NaN NaN
2012-11-15 06:00:00.200000-06:00 0 0 0 NaN NaN NaN
2012-11-15 06:00:00.250000-06:00 NaN NaN NaN 0 0 0
2012-11-15 06:00:00.300000-06:00 0 0 0 NaN NaN NaN
2012-11-15 06:00:00.400000-06:00 0 0 0 NaN NaN NaN
2012-11-15 06:00:00.500000-06:00 0 0 0 0 0 0
```
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} | 5 | "2012-11-21T20:33:19Z" | "2012-11-26T22:59:06Z" | "2012-11-26T22:59:06Z" | NONE | null | see
https://launchpadlibrarian.net/123637321/buildlog_ubuntu-raring-powerpc.pandas_0.9.1-1ubuntu1_FAILEDTOBUILD.txt.gz
https://buildd.debian.org/status/package.php?p=pandas&suite=experimental
```
======================================================================
ERROR: test_fperr_robustness (pandas.stats.tests.test_moments.TestMoments)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.9.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/stats/tests/test_moments.py", line 175, in test_fperr_robustness
result = mom.rolling_sum(arr, 2)
File "/build/buildd/pandas-0.9.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/stats/moments.py", line 458, in f
freq=freq, time_rule=time_rule, **kwargs)
File "/build/buildd/pandas-0.9.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/stats/moments.py", line 261, in _rolling_moment
result = np.apply_along_axis(calc, axis, values)
File "/usr/lib/python2.7/dist-packages/numpy/lib/shape_base.py", line 80, in apply_along_axis
res = func1d(arr[tuple(i.tolist())],*args)
File "/build/buildd/pandas-0.9.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/stats/moments.py", line 258, in <lambda>
calc = lambda x: func(x, window, minp=minp, **kwargs)
File "/build/buildd/pandas-0.9.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/stats/moments.py", line 456, in call_cython
return func(arg, window, minp, **kwds)
File "moments.pyx", line 156, in pandas.lib.roll_sum (pandas/src/tseries.c:77996)
ValueError: Little-endian buffer not supported on big-endian compiler
======================================================================
FAIL: test_from_M8_structured (pandas.tseries.tests.test_timeseries.TestLegacySupport)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/buildd/pandas-0.9.1/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_timeseries.py", line 1917, in test_from_M8_structured
self.assertEqual(df['Date'][0], dates[0][0])
AssertionError: <Timestamp: 1976-02-12 15:25:34.986240> != datetime.datetime(2012, 9, 9, 0, 0)
```
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```
Index(np.arange(100000))
```
This might have happened in the `pprint_thing` refactoring, will have to take a look
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} | 3 | "2012-11-21T22:41:16Z" | "2014-06-14T08:30:00Z" | "2012-11-23T04:55:23Z" | NONE | null | ``` python
In [2]: pd.Index(np.arange(10000))
Out[2]: Int64Index([0, 1, 2, ..., 9997, 9998, 9999], dtype=int64)
```
broken by 95678ebf.
configurable via `np.set_printoptions (threshold)`, like the original version which used np.ndarray.**repr**.
closes #2319
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} | 3 | "2012-11-21T23:56:33Z" | "2014-06-12T06:37:53Z" | "2012-11-24T23:05:22Z" | CONTRIBUTOR | null | Cleans up SparseSeries and SparseDataFrame initialization code so no objects are created unnecessarily. Currently if you run the (potentially useless) statement:
```
SparseDataFrame(columns=np.arange(100000), index=np.arange(100000))
```
pandas will consume memory and time filling in empty Series objects only to then compress them away.
Added to test to ensure SparseDataFrame always initializes with SparseSeries. Otherwise functionality should be preserved.
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https://github.com/travis-ci/travis-ci.github.com/pull/157
a scipy perspective would be beneficial.
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} | 6 | "2012-11-22T15:25:19Z" | "2013-10-24T02:08:22Z" | "2013-07-02T14:22:24Z" | NONE | null | You can have a large frame in memory and want to drop a small percentage of labels with NA data just to clean things up. If you use drop() or dropna(), you will get a new object and effectively double the memory footprint. Favoring immutability is great, but under memory constraints this can be a killer.
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} | 1 | "2012-11-22T16:40:14Z" | "2012-12-28T15:22:47Z" | "2012-12-28T15:22:47Z" | CONTRIBUTOR | null | I often use scatterplots to show data and if these data are over a range of values it is useful to have a log-log scale. Right now the default df.plot() function supports a logy=True argument but not a logx=True.
I think a logx argument in the plotting function would be a useful addition.
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} | 4 | "2012-11-22T17:26:09Z" | "2014-06-25T18:33:51Z" | "2012-11-29T20:32:03Z" | NONE | null | Note that the semantics of `col_space` are different in each case, characters vs. pixels,
but that's reasonable.
I hope this doesn't presage more html configuration via kwd arguments, yonder way
madness lies.
also, there's a discrepency between the docstring and [io.rst](https://github.com/pydata/pandas/blame/master/doc/source/io.rst#L671) (github doesn't seem to jump to the right line)
the first defines it as the width of the columns, the latter as the number of spaces between columns,
I adopted the former, since space between columns does not translate as easily to html.
_sigh_, also, `colSpace` has already been deprecated in favor of `col_space`, so
I would feel bad about deprecating `col_space` in favor of `min_col_width`.
I'll leave that decision to braver, fearless souls.
closes #1000.
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} | 3 | "2012-11-22T18:24:36Z" | "2012-11-23T20:01:48Z" | "2012-11-23T20:01:48Z" | CONTRIBUTOR | null | I'm not sure if this is related to the datetime64 issues listed I've seen posted here. Still haven't really figured out exactly what is going but the date gets mangled when it moved to an index.
```
In [93]: import datetime, pandas
In [94]: df = pandas.DataFrame([[datetime.datetime.today(), 12.1]], columns=['Date', 'Value'])
In [95]: df = df.set_index('Date')
In [96]: df
Out[96]:
Value
Date
2012-11-22 12:12:40.905739 12.1
In [97]: df.index
Out[97]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2190-12-31 02:18:44.941732032]
Length: 1, Freq: None, Timezone: None
In [98]: df = df.reset_index()
In [99]: df
Out[99]:
Date Value
0 2190-12-31 02:18:44.941732032 12.1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2330/events | https://github.com/pandas-dev/pandas/pull/2330 | 8,586,161 | MDExOlB1bGxSZXF1ZXN0MzEwMDUwNA== | 2,330 | add check for endianess in major constructors | {
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} | 8 | "2012-11-22T19:48:44Z" | "2014-07-04T18:49:30Z" | "2013-02-08T11:03:02Z" | NONE | null | apropos #2318
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} | 22 | "2012-11-22T19:56:44Z" | "2016-10-12T23:03:21Z" | "2012-12-02T13:05:12Z" | NONE | null | on ubuntu 13.04 several tests fail with 3.3 while they suceed with python2
13.04 has python 3.3 and numpy 1.7
current pandas head 61766ec5f898c89
```
======================================================================
FAIL: test_quoting (pandas.io.tests.test_parsers.TestParsers)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jtaylor/tmp/pandas-0.9.1/build/lib.linux-x86_64-3.3/pandas/io/tests/test_parsers.py", line 528, in test_quoting
sep='\t')
AssertionError: Exception not raised by read_table
======================================================================
FAIL: test_cant_compare_tz_naive_w_aware (pandas.tseries.tests.test_timeseries.TestTimestamp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jtaylor/tmp/pandas-0.9.1/build/lib.linux-x86_64-3.3/pandas/tseries/tests/test_timeseries.py", line 2349, in test_cant_compare_tz_naive_w_aware
self.assertRaises(Exception, a.__eq__, b.to_pydatetime())
AssertionError: Exception not raised by __eq__
----------------------------------------------------------------------
======================================================================
FAIL: test_more_flexible_frame_multi_function (__main__.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jtaylor/tmp/pandas-0.9.1/build/lib.linux-x86_64-3.3/pandas/tests/test_groupby.py", line 1909, in test_more_flexible_frame_multi_function
assert_frame_equal(result, expected)
File "/home/jtaylor/tmp/pandas-0.9.1/build/lib.linux-x86_64-3.3/pandas/util/testing.py", line 167, in assert_frame_equal
assert(left.columns.equals(right.columns))
AssertionError
----------------------------------------------------------------------
======================================================================
ERROR: test_yahoo (pandas.io.tests.test_yahoo.TestYahoo)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/tests/test_yahoo.py", line 25, in test_yahoo
pd.DataReader("F", 'yahoo', start, end)['Close'][-1],
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/data.py", line 58, in DataReader
retry_count=retry_count, pause=pause)
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/data.py", line 149, in get_data_yahoo
parse_dates=True)[::-1]
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/parsers.py", line 364, in parser_f
return _read(filepath_or_buffer, kwds)
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/parsers.py", line 195, in _read
return parser.read()
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/parsers.py", line 592, in read
ret = self._engine.read(nrows)
File "/tmp/pandas/build/lib.linux-x86_64-3.3/pandas/io/parsers.py", line 844, in read
data = self._reader.read(nrows)
File "parser.pyx", line 597, in pandas._parser.TextReader.read (pandas/src/parser.c:5342)
File "parser.pyx", line 619, in pandas._parser.TextReader._read_low_memory (pandas/src/parser.c:5562)
File "parser.pyx", line 668, in pandas._parser.TextReader._read_rows (pandas/src/parser.c:6143)
File "parser.pyx", line 655, in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:6027)
File "parser.pyx", line 1385, in pandas._parser.raise_parser_error (pandas/src/parser.c:14807)
pandas._parser.CParserError: Error tokenizing data. C error: Expected 7 fields in line 106, saw 3
```
```
test_more_flexible_frame_multi_function (__main__.TestGroupBy) ... > /home/jtaylor/tmp/pandas-0.9.1/build/lib.linux-x86_64-3.3/pandas/util/testing.py(167)assert_frame_equal()
-> assert(left.columns.equals(right.columns))
(Pdb) p left.columns
[(D, mean), (D, std), (C, mean), (C, std)]
(Pdb) p right.columns
MultiIndex
[(C, mean), (C, std), (D, mean), (D, std)]
```
this one is random probably related to hash randomization
the yahoo one might be due to missing internet connection?
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https://api.github.com/repos/pandas-dev/pandas/issues/2332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2332/events | https://github.com/pandas-dev/pandas/pull/2332 | 8,586,709 | MDExOlB1bGxSZXF1ZXN0MzEwMDcxMA== | 2,332 | df.combine_first() with empty frame should still yield unioned index | {
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} | 1 | "2012-11-22T20:29:50Z" | "2014-07-14T03:26:39Z" | "2012-11-24T00:24:50Z" | NONE | null | closes #2307
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} | 5 | "2012-11-23T00:18:11Z" | "2012-12-09T21:50:57Z" | "2012-12-09T21:50:57Z" | MEMBER | null | I don't know if there's an easy strategy to handle cases like this gracefully:
http://stackoverflow.com/questions/13454909/missing-data-in-pandas-read-csv
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https://api.github.com/repos/pandas-dev/pandas/issues/2334 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2334/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2334/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2334/events | https://github.com/pandas-dev/pandas/pull/2334 | 8,592,355 | MDExOlB1bGxSZXF1ZXN0MzEwMjE1Mw== | 2,334 | Fix seconds parsing #2209 | {
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} | 3 | "2012-11-23T03:26:32Z" | "2014-06-24T22:55:32Z" | "2012-11-30T23:59:17Z" | CONTRIBUTOR | null | Creating a milliseconds entry by doing math on the seconds entry:
```
msecs = round(float(seconds[i] % 1)*10**6)
```
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} | 0 | "2012-11-23T12:20:14Z" | "2012-11-23T21:10:35Z" | "2012-11-23T21:10:35Z" | NONE | null | catch the regression noted in #2273 next time.
```
frame_iteritems 24.85 3.12 7.98
Columns: test_name | head_time [ms] | baseline_time [ms] | ratio
- a Ratio of 1.30 means HEAD is 30% slower then the Baseline.
Head [59d318c] : revert
Baseline [bdbca8e] : BUG: fix borked data-copying iteritems performance affecting DataFrame and SparseDataFrame. close #2273
```
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} | 1 | "2012-11-23T19:56:54Z" | "2012-11-28T00:11:13Z" | "2012-11-28T00:11:13Z" | MEMBER | null | Should investigate some of these ones at the bottom. The pivot benchmark is related to refactoring I made in the unstack code, and is not so simple to fix:
```
Results:
t_head t_baseline ratio
name
indexing_panel_subset 3.2588 1997.1172 0.0016
groupby_frame_median 10.2211 181.5090 0.0563
datetimeindex_add_offset 0.3107 2.2951 0.1354
read_csv_thou_vb 41.6154 205.6429 0.2024
frame_constructor_ndarray 0.0409 0.0888 0.4604
read_csv_vb 23.5146 50.1700 0.4687
read_csv_standard 14.4659 29.6830 0.4873
reindex_multiindex 1.4252 2.7399 0.5202
groupby_frame_singlekey_integer 3.8736 6.6471 0.5828
groupby_first 7.2411 12.2543 0.5909
groupby_last 7.6421 12.7714 0.5984
panel_from_dict_same_index 30.3418 49.3051 0.6154
panel_from_dict_equiv_indexes 30.5921 49.1175 0.6228
groupby_simple_compress_timing 53.7012 85.8531 0.6255
reindex_frame_level_reindex 1.8341 2.7030 0.6786
append_frame_single_homogenous 0.3622 0.4860 0.7453
timeseries_add_irregular 22.9419 30.2414 0.7586
write_csv_standard 359.9060 458.5490 0.7849
stat_ops_level_series_sum 3.7688 4.6419 0.8119
groupby_series_simple_cython 6.5816 7.9662 0.8262
dataframe_reindex_columns 0.3087 0.3718 0.8303
groupby_multi_size 39.3789 45.9909 0.8562
frame_get_numeric_data 0.0842 0.0981 0.8589
reshape_stack_simple 2.5648 2.9615 0.8660
frame_reindex_axis1 3.3879 3.8442 0.8813
timeseries_asof_nan 8.5555 9.6283 0.8886
timeseries_asof 9.2042 10.3367 0.8904
stat_ops_level_frame_sum 4.6151 5.0289 0.9177
frame_reindex_both_axes_ix 0.7154 0.7768 0.9209
indexing_dataframe_boolean_rows 0.2306 0.2500 0.9225
stats_rank_average_int 24.2966 26.2871 0.9243
frame_to_csv 474.3190 511.4419 0.9274
sort_level_zero 6.2975 6.7836 0.9283
reindex_frame_level_align 1.8621 1.9765 0.9421
dataframe_reindex_daterange 0.3935 0.4141 0.9503
frame_ctor_nested_dict_int64 135.1471 142.0300 0.9515
join_dataframe_index_single_key_bigger 8.2120 8.6290 0.9517
timeseries_large_lookup_value 0.0248 0.0260 0.9526
join_dataframe_index_single_key_small 7.7202 8.0596 0.9579
indexing_dataframe_boolean_rows_object 0.4611 0.4768 0.9670
timeseries_sort_index 21.2128 21.9074 0.9683
frame_boolean_row_select 0.2998 0.3096 0.9683
read_table_multiple_date 2367.1391 2435.5052 0.9719
read_table_multiple_date_baseline 1086.0040 1116.9188 0.9723
series_align_left_monotonic 17.5190 17.9150 0.9779
groupby_multi_series_op 19.7853 20.2238 0.9783
series_align_int64_index 37.7741 38.5680 0.9794
panel_from_dict_two_different_indexes 99.2441 101.2650 0.9800
frame_fillna_many_columns_pad 16.8927 17.2210 0.9809
reindex_daterange_backfill 0.1794 0.1813 0.9894
groupby_multi_cython 22.6835 22.9242 0.9895
reindex_daterange_pad 0.1835 0.1853 0.9901
reindex_fillna_pad 0.1220 0.1228 0.9940
frame_fancy_lookup 2.1757 2.1861 0.9953
index_int64_intersection 38.0053 38.1207 0.9970
index_int64_union 88.7940 89.0169 0.9975
groupby_multi_python 67.7776 67.8983 0.9982
frame_ctor_nested_dict 96.4491 96.5813 0.9986
stat_ops_series_std 0.2840 0.2827 1.0044
series_value_counts_int64 2.6949 2.6773 1.0066
timeseries_timestamp_downsample_mean 5.9724 5.9310 1.0070
sort_level_one 6.2288 6.1651 1.0103
groupby_apply_dict_return 40.3767 39.8300 1.0137
stats_rank2d_axis0_average 26.6424 26.1975 1.0170
concat_series_axis1 73.4706 72.2370 1.0171
groupby_frame_cython_many_columns 5.5423 5.4274 1.0212
stats_rank_average 35.6590 34.8816 1.0223
join_dataframe_index_multi 28.8276 28.1839 1.0228
reindex_fillna_backfill 0.1190 0.1160 1.0259
frame_fillna_inplace 19.4959 18.9305 1.0299
timeseries_asof_single 0.0586 0.0567 1.0333
stats_rank2d_axis1_average 18.5875 17.8737 1.0399
join_dataframe_index_single_key_bigger 21.5043 20.6628 1.0407
groupby_pivot_table 24.8929 23.6659 1.0518
timeseries_slice_minutely 0.0583 0.0553 1.0549
timeseries_timestamp_tzinfo_cons 0.0177 0.0168 1.0590
groupby_multi_different_functions 19.3980 18.1736 1.0674
read_csv_comment_vb 106.1771 99.0222 1.0723
match_strings 0.4124 0.3835 1.0753
series_constructor_ndarray 0.0110 0.0103 1.0773
groupby_indices 11.7223 10.7914 1.0863
timeseries_1min_5min_ohlc 0.6142 0.5541 1.1086
append_frame_single_mixed 1.7186 1.5501 1.1087
reshape_unstack_simple 3.3168 2.9830 1.1119
frame_ctor_list_of_dict 109.4160 98.0361 1.1161
groupby_multi_different_numpy_functions 20.6145 18.4240 1.1189
timeseries_1min_5min_mean 0.6310 0.5584 1.1300
frame_reindex_axis0 3.0153 2.5576 1.1790
frame_reindex_both_axes 0.7285 0.6088 1.1967
series_ctor_from_dict 3.7974 3.1420 1.2086
stat_ops_level_frame_sum_multiple 12.9655 10.3905 1.2478
sparse_series_to_frame 208.4851 146.2591 1.4255
stat_ops_level_series_sum_multiple 12.6471 8.4403 1.4984
panel_from_dict_all_different_indexes 181.9479 104.8920 1.7346
reshape_pivot_time_series 318.2459 148.5360 2.1426
frame_fancy_lookup_all 125.4880 36.3095 3.4561
Columns: test_name | head_time [ms] | baseline_time [ms] | ratio
- a Ratio of 1.30 means HEAD is 30% slower then the Baseline.
Head [5af2f95] : ENH: accelerate label compression. accept >= 10 micros in test_perf
Baseline [a901af2] : RLS: version 0.8.0
```
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} | 6 | "2012-11-23T21:42:36Z" | "2014-06-12T16:27:48Z" | "2012-11-24T05:20:52Z" | CONTRIBUTOR | null | add where and mask methods for Series, analagous to DataFrame methods added for 0.9.1
passes all tests
where is equivalent to: `s[cond].reindex_like(s).fillna(other)`
```
In [7]: s = pd.Series(np.random.rand(5))
In [8]: s
Out[8]:
0 0.638664
1 0.574688
2 0.460510
3 0.641840
4 0.044129
In [10]: s[0:2] = -s[0:2]
In [11]: s
Out[11]:
0 -0.638664
1 -0.574688
2 0.460510
3 0.641840
4 0.044129
```
boolean selection
```
In [12]: s[s>0]
Out[12]:
2 0.460510
3 0.641840
4 0.044129
In [13]: s.where(s>0)
Out[13]:
0 NaN
1 NaN
2 0.460510
3 0.641840
4 0.044129
In [14]: s.where(s>0,-s)
Out[14]:
0 0.638664
1 0.574688
2 0.460510
3 0.641840
4 0.044129
In [15]: s.mask(s<=0)
Out[15]:
0 NaN
1 NaN
2 0.460510
3 0.641840
4 0.044129
```
support setting as well (though not used anywhere explicity)
```
In [16]: s2 = s.copy()
In [17]: s2.where(s2>0,inplace=True)
Out[17]:
0 NaN
1 NaN
2 0.460510
3 0.641840
4 0.044129
In [18]: s2
Out[18]:
0 NaN
1 NaN
2 0.460510
3 0.641840
4 0.044129
```
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|
https://api.github.com/repos/pandas-dev/pandas/issues/2339 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2339/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2339/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2339/events | https://github.com/pandas-dev/pandas/pull/2339 | 8,621,290 | MDExOlB1bGxSZXF1ZXN0MzExMDc2Mg== | 2,339 | Update doc/source/r_interface.rst | {
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} | 2 | "2012-11-24T02:17:59Z" | "2012-11-25T05:33:38Z" | "2012-11-25T05:33:38Z" | CONTRIBUTOR | null | rpy2-2.3.0 is becoming the current release.
Changes to the API are relatively few.
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https://api.github.com/repos/pandas-dev/pandas/issues/2340 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2340/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2340/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2340/events | https://github.com/pandas-dev/pandas/issues/2340 | 8,623,214 | MDU6SXNzdWU4NjIzMjE0 | 2,340 | to_excel() removes existing sheet | {
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> > > df.to_excel('C:/FM_temp.xls','sheet1')
> > > df.to_excel('C:/FM_temp.xls','sheet2')
Pandas 0.8.1 deletes the existing 'sheet1' when it saves data to 'sheet2' .
I want to keep both sheets. Can 'to_excel()' be changed to accept 'append' mode instead of 'replace' mode?
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} | 6 | "2012-11-24T11:51:39Z" | "2012-11-28T13:59:56Z" | "2012-11-25T05:29:09Z" | NONE | null | The generated tseries.c included in the 0.9.0 and 0.9.1 distributions contains two integer constants that are to large for 32bit values and need an LL suffix. Note that the LL suffix is present in the cython source.
```
--- pandas/src/tseries.c.orig 2012-11-15 00:20:20.000000000 +0000
+++ pandas/src/tseries.c 2012-11-20 22:58:37.000000000 +0000
@@ -151203,7 +151203,7 @@
* cdef int64_t _NS_UPPER_BOUND = -9223372036854775807LL
*
*/
- __pyx_v_6pandas_3lib__NS_LOWER_BOUND = -9223285636854775809;
+ __pyx_v_6pandas_3lib__NS_LOWER_BOUND = -9223285636854775809LL;
/* "/home/wesm/code/pandas/pandas/src/datetime.pyx":665
*
@@ -151212,7 +151212,7 @@
*
* cdef inline _check_dts_bounds(int64_t value, pandas_datetimestruct *dts):
*/
- __pyx_v_6pandas_3lib__NS_UPPER_BOUND = -9223372036854775807;
+ __pyx_v_6pandas_3lib__NS_UPPER_BOUND = -9223372036854775807LL;
/* "/home/wesm/code/pandas/pandas/src/datetime.pyx":745
* raise ValueError('Unable to parse %s' % str(val))
```
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} | 1 | "2012-11-24T13:48:00Z" | "2012-11-28T19:16:31Z" | "2012-11-28T19:16:31Z" | NONE | null | I have Python 2.7.3 and IPython 0.13.1. Do this in IPython Notebook:
```
%pylab inline
```
```
import numpy as np
from pandas import Index, DataFrame
```
```
test = DataFrame(np.random.randn(10, 2), index=Index(range(10), name='bins'), columns=['x', 'y'])
```
The following works perfectly fine:
```
test.plot(figsize=(16, 8), title='Test')
```
But the following doesn't - figsize and title to not take effect:
```
test.plot(x='x', y='y', figsize=(16, 8), title='Test')
```
The output is the same as for
```
test.plot(x='x', y='y')
```
Here is another strange behavior I observe. The following works fine and fixes the above issue:
```
plt.figure(figsize=(16, 8))
test.plot(x='x', y='y')
plt.title('Test')
```
But the following doesn't work - figsize does not take effect even though title does:
```
plt.figure(figsize=(16, 8))
test.plot()
plt.title('Test')
```
This is very confusing. Are these pandas issues or do I have some problems in my local configuration?
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https://api.github.com/repos/pandas-dev/pandas/issues/2343 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2343/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2343/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2343/events | https://github.com/pandas-dev/pandas/pull/2343 | 8,630,616 | MDExOlB1bGxSZXF1ZXN0MzExMjAzOA== | 2,343 | updated docs for indexing to incorporate where and mask | {
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} | [] | closed | false | null | [] | null | 1 | "2012-11-24T15:05:53Z" | "2012-11-24T22:16:02Z" | "2012-11-24T22:16:02Z" | CONTRIBUTOR | null | updated docs in the indexing section for new Series/DataFrame methods where and mask
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} | 7 | "2012-11-24T16:22:15Z" | "2012-12-02T16:51:13Z" | "2012-12-02T16:51:13Z" | NONE | null | I just installed pandas on my "new" Windows 8 system using pandas-0.9.1.win32-py2.7.exe and had some trouble doing it: Python is installed to Program Files\Python27 (nothing unusual I think), so the installer needs admin permissions to write.
The installer didn't ask for that permission (no UAC), instead it crashed without useful explanation:
``` Problemsignatur:
Problemereignisname: BEX
Anwendungsname: pandas-0.9.1.win32-py2.7.exe
Anwendungsversion: 0.0.0.0
Anwendungszeitstempel: 49819995
Fehlermodulname: pandas-0.9.1.win32-py2.7.exe
Fehlermodulversion: 0.0.0.0
Fehlermodulzeitstempel: 49819995
Ausnahmeoffset: 000086dc
Ausnahmecode: c0000417
Ausnahmedaten: 00000000
Betriebsystemversion: 6.2.9200.2.0.0.256.103
Gebietsschema-ID: 1031
Zusatzinformation 1: 77fb
Zusatzinformation 2: 77fb7957d074ff18aeec8cdb7c477500
Zusatzinformation 3: d8a8
Zusatzinformation 4: d8a89b4ddc9e0cb1601da46380c6451f
```
Starting the installer as administrator solves the problem.
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} | 2 | "2012-11-24T20:47:44Z" | "2014-06-19T02:01:31Z" | "2012-11-28T00:31:20Z" | CONTRIBUTOR | null | Should be completely backwards compatible
mainly bug fixes, code clean, and much updated docs
should merge cleanly
1. added **str** (to do **repr**)
2. added **delitem** to support store deletion syntatic sugar
3. row removal in tables is much faster if rows are consecutive
(remove also returns the number of rows removed if a table)
4. added Term class, refactored Selection (this is backwards compatible)
Term is a concise way of specifying conditions for queries, e.g.
```
Term(dict(field = 'index', op = '>', value = '20121114'))
Term('index', '20121114')
Term('index', '>', '20121114')
Term('index', ['20121114','20121114'])
Term('index', datetime(2012,11,14))
Term('index>20121114')
added alias to the Term class; you can specify the nomial indexers
(e.g. index in DataFrame, major_axis/minor_axis or alias in Panel)
```
this should close GH #1996
5. added Col class to manage the column conversions
6. BUG: added min_itemsize parameter and checks in pytables to allow setting of indexer columns minimum size (current implemenation will truncate indexer columns that are too long in a subsequent append - loss of info)
7. added indexing support via method create_table_index (requires 2.3 in PyTables)
btw now works quite well as Int64 indicies are used as opposed to the Time64Col which has a bug); includes a check on the pytables version requirement
this should close GH #698
8. signficantlly updated docs for pytables to reflect all changes; added docs for Table sections
9. BUG: a store would fail if appending but the put had not been done before (see test_append)
this the result of incompatibility testing on the index_kind
10. BUG: minor change to select and remove: require a table ONLY if where is also provided (and not None)
all tests pass; tests added for new features
I have some implementation changes to make to Tables to make writing quite a bit faster
but will do in a future commit
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} | 0 | "2012-11-24T21:41:06Z" | "2012-11-25T19:17:43Z" | "2012-11-25T19:17:43Z" | NONE | null | ``` python
import pandas as pd
df=pd.DataFrame({0:[0,1],1:[0,1],2:[4,5]})
df.columns=["a","b","a"]
res=df.pop("a")
...
/home/user1/src/pandas/pandas/core/internals.py in _delete_from_block(self, i, item)
971 """
972 block = self.blocks.pop(i)
--> 973 new_left, new_right = block.split_block_at(item)
974
975 if new_left is not None:
/home/user1/src/pandas/pandas/core/internals.py in split_block_at(self, item)
195 return None, None
196
--> 197 if loc == 0:
198 # at front
199 left_block = None
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 4 | "2012-11-24T21:48:01Z" | "2012-11-26T16:36:21Z" | "2012-11-25T03:38:08Z" | CONTRIBUTOR | null | Works fine on v0.9.0. With master
```
rossi = pandas.read_csv("http://www.ams.jhu.edu/~dan/550.400/datasets/recidivism/Rossi%20data.csv")
*** glibc detected *** /usr/bin/python: malloc(): memory corruption: 0x0000000003c26680 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x7ba9a)[0x7f498b804a9a]
/lib/x86_64-linux-gnu/libc.so.6(+0x7ce32)[0x7f498b805e32]
/lib/x86_64-linux-gnu/libc.so.6(realloc+0xf9)[0x7f498b807839]
/usr/bin/python(PyList_Append+0xd6)[0x46e336]
/usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so(+0x99763)[0x7f497f8cf763]
/usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so(+0x998d9)[0x7f497f8cf8d9]
/usr/bin/python[0x47ac88]
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_f64ccf0-py2.7-linux-x86_64.egg/pandas/_parser.so(+0xb955)[0x7f4976c5a955]
/usr/bin/python(PyObject_Call+0x44)[0x45fed4]
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_f64ccf0-py2.7-linux-x86_64.egg/pandas/_parser.so(+0x2418d)[0x7f4976c7318d]
/usr/bin/python(PyObject_Call+0x44)[0x45fed4]
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_f64ccf0-py2.7-linux-x86_64.egg/pandas/_parser.so(+0x17ae1)[0x7f4976c66ae1]
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_f64ccf0-py2.7-linux-x86_64.egg/pandas/_parser.so(+0x142f3)[0x7f4976c632f3]
/usr/local/lib/python2.7/dist-packages/pandas-0.9.2.dev_f64ccf0-py2.7-linux-x86_64.egg/pandas/_parser.so(+0xf5bd)[0x7f4976c5e5bd]
/usr/bin/python(PyEval_EvalFrameEx+0x361)[0x499061]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalFrameEx+0xb2f)[0x49982f]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalCode+0x32)[0x4eeb92]
/usr/bin/python(PyEval_EvalFrameEx+0x37d5)[0x49c4d5]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalFrameEx+0x802)[0x499502]
/usr/bin/python(PyEval_EvalCodeEx+0x145)[0x49fd55]
/usr/bin/python(PyEval_EvalCode+0x32)[0x4eeb92]
/usr/bin/python[0x4ff7f4]
/usr/bin/python(PyRun_FileExFlags+0x90)[0x42cdd0]
/usr/bin/python(PyRun_SimpleFileExFlags+0x2dd)[0x42d798]
/usr/bin/python(Py_Main+0xad3)[0x418db5]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xff)[0x7f498b7a7eff]
/usr/bin/python[0x4c91b1]
======= Memory map: ========
00400000-0062f000 r-xp 00000000 08:05 12064965 /usr/bin/python2.7
0082e000-0082f000 r--p 0022e000 08:05 12064965 /usr/bin/python2.7
0082f000-00897000 rw-p 0022f000 08:05 12064965 /usr/bin/python2.7
00897000-008a9000 rw-p 00000000 00:00 0
00c80000-03e97000 rw-p 00000000 00:00 0 [heap]
7f496b8fd000-7f496b914000 r-xp 00000000 08:05 13107724 /lib/x86_64-linux-gnu/libresolv-2.13.so
7f496b914000-7f496bb14000 ---p 00017000 08:05 13107724 /lib/x86_64-linux-gnu/libresolv-2.13.so
7f496bb14000-7f496bb15000 r--p 00017000 08:05 13107724 /lib/x86_64-linux-gnu/libresolv-2.13.so
7f496bb15000-7f496bb16000 rw-p 00018000 08:05 13107724 /lib/x86_64-linux-gnu/libresolv-2.13.so
7f496bb16000-7f496bb18000 rw-p 00000000 00:00 0
7f496bb18000-7f496bb1f000 r-xp 00000000 08:05 13107879 /lib/x86_64-linux-gnu/libnss_dns-2.13.so
7f496bb1f000-7f496bd1e000 ---p 00007000 08:05 13107879 /lib/x86_64-linux-gnu/libnss_dns-2.13.so
7f496bd1e000-7f496bd1f000 r--p 00006000 08:05 13107879 /lib/x86_64-linux-gnu/libnss_dns-2.13.so
7f496bd1f000-7f496bd20000 rw-p 00007000 08:05 13107879 /lib/x86_64-linux-gnu/libnss_dns-2.13.so
7f496bd20000-7f496bd22000 r-xp 00000000 08:05 13107270 /lib/libnss_mdns4_minimal.so.2
7f496bd22000-7f496bf21000 ---p 00002000 08:05 13107270 /lib/libnss_mdns4_minimal.so.2
7f496bf21000-7f496bf22000 r--p 00001000 08:05 13107270 /lib/libnss_mdns4_minimal.so.2
7f496bf22000-7f496bf23000 rw-p 00002000 08:05 13107270 /lib/libnss_mdns4_minimal.so.2
7f496bf23000-7f496bf2f000 r-xp 00000000 08:05 13107883 /lib/x86_64-linux-gnu/libnss_files-2.13.so
7f496bf2f000-7f496c12e000 ---p 0000c000 08:05 13107883 /lib/x86_64-linux-gnu/libnss_files-2.13.so
7f496c12e000-7f496c12f000 r--p 0000b000 08:05 13107883 /lib/x86_64-linux-gnu/libnss_files-2.13.so
7f496c12f000-7f496c130000 rw-p 0000c000 08:05 13107883 /lib/x86_64-linux-gnu/libnss_files-2.13.so
7f496c130000-7f496c136000 r-xp 00000000 08:05 12194961 /usr/lib/python2.7/lib-dynload/_multiprocessing.so
7f496c136000-7f496c335000 ---p 00006000 08:05 12194961 /usr/lib/python2.7/lib-dynload/_multiprocessing.so
7f496c335000-7f496c336000 r--p 00005000 08:05 12194961 /usr/lib/python2.7/lib-dynload/_multiprocessing.so
7f496c336000-7f496c337000 rw-p 00006000 08:05 12194961 /usr/lib/python2.7/lib-dynload/_multiprocessing.so
7f496c337000-7f496c347000 r-xp 00000000 08:05 5117216 /usr/local/lib/python2.7/dist-packages/sklearn/utils/murmurhash.so
7f496c347000-7f496c546000 ---p 00010000 08:05 5117216 /usr/local/lib/python2.7/dist-packages/sklearn/utils/murmurhash.so
7f496c546000-7f496c547000 r--p 0000f000 08:05 5117216 /usr/local/lib/python2.7/dist-packages/sklearn/utils/murmurhash.so
7f496c547000-7f496c549000 rw-p 00010000 08:05 5117216 /usr/local/lib/python2.7/dist-packages/sklearn/utils/murmurhash.so
7f496c549000-7f496c54b000 r-xp 00000000 08:05 5117217 /usr/local/lib/python2.7/dist-packages/sklearn/__check_build/_check_build.so
7f496c54b000-7f496c74a000 ---p 00002000 08:05 5117217 /usr/local/lib/python2.7/dist-packages/sklearn/__check_build/_check_build.so
7f496c74a000-7f496c74b000 r--p 00001000 08:05 5117217 /usr/local/lib/python2.7/dist-packages/sklearn/__check_build/_check_build.soAborted
```
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} | 8 | "2012-11-24T21:51:27Z" | "2014-06-12T16:15:38Z" | "2012-11-25T19:17:47Z" | NONE | null | This touches some delicate functions to be messing with, so I've split off the
underlying logic into a function in common and added an exhaustive test.
Would welcome review none the less.
Also, I tried to test for nagative perf. There must something
wrong with the test since comparing the commit after just adding a test to the baseline,
shows 50% degredation in a bunch of things. As I said, I don't trust this, ideas?
```
timeseries_asof_nan 23.4131 15.7471 1.4868
datetimeindex_normalize 1153.1918 774.8010 1.4884
timeseries_asof 22.3342 14.9964 1.4893
reshape_unstack_simple 4.8106 3.2289 1.4899
read_table_multiple_date_baseline 980.0920 657.3641 1.4909
timeseries_1min_5min_mean 0.8087 0.5422 1.4915
timeseries_timestamp_tzinfo_cons 0.0210 0.0141 1.4922
match_strings 0.5718 0.3817 1.4982
timeseries_large_lookup_value 0.0301 0.0201 1.4998
reindex_fillna_pad 0.1828 0.1217 1.5018
timeseries_to_datetime_iso8601 5.6048 3.7270 1.5038
read_table_multiple_date 2169.5440 1441.5460 1.5050
reindex_daterange_pad 0.2608 0.1731 1.5066
timeseries_1min_5min_ohlc 0.8046 0.5331 1.5092
reindex_daterange_backfill 0.2466 0.1634 1.5094
period_setitem 1166.9910 772.4509 1.5108
reindex_fillna_backfill 0.1790 0.1183 1.5130
timeseries_asof_single 0.0656 0.0434 1.5136
append_frame_single_mixed 2.0747 1.3697 1.5147
timeseries_slice_minutely 0.0762 0.0501 1.5208
Columns: test_name | target_duration [ms] | baseline_duration [ms] | ratio
(01bc3e0 against 81169f9)
- a Ratio of 1.30 means the target commit is 30% slower then the baseline.
```
closes #2347
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} | 0 | "2012-11-25T04:28:10Z" | "2012-11-25T05:19:30Z" | "2012-11-25T05:19:30Z" | MEMBER | null | I would expect this to use almost no memory. Instead, > 2GB on my box.
`SparseDataFrame(columns=np.arange(100), index=np.arange(1e6))`
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} | 8 | "2012-11-25T05:39:05Z" | "2012-12-01T20:50:02Z" | "2012-12-01T20:50:02Z" | CONTRIBUTOR | null | I am in desperate need of converting my datetime values into something R can use so I that I can plot some data with ggplot. It appears that rpy2 supports this conversion but I am not sure how to go from a column with a datetime field (stored as a `numpy.datetime64`) into a `time.struct_time` value.
The relevant information about rpy2 is here: http://rpy.sourceforge.net/rpy2/doc-2.2/html/vector.html?highlight=intvector#rpy2.robjects.vectors.POSIXlt
I see that there need to be some minor changes [on line 244 of `common.py`](https://github.com/pydata/pandas/blob/master/pandas/rpy/common.py#L244) but basically I am stuck on trying to figure out how to convert from `numpy.datetime64` into the `struct_time`. I see that there is already functionality in Pandas, specifically the `to_pydatetime` function, for converting from this numpy format into a `datetime` object.
If someone could lead me over this hump I'd be happy to submit the pull request.
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} | 2 | "2012-11-25T06:59:06Z" | "2014-08-11T23:16:20Z" | "2012-12-01T20:49:06Z" | CONTRIBUTOR | null | Check to see if the values we're looking at are numpy datetime64
objects. If they are, we can assume we're looking at a Timestamp object,
which subclasses Python's datetime class, and we return a struct_time
which is understood by rpy2's POSIXct class.
Attempt at a fix for #2351
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} | 4 | "2012-11-25T15:20:29Z" | "2016-03-08T14:40:57Z" | "2016-03-08T14:40:57Z" | NONE | null | Currently the abilities of "pandas" seem to outstrip the documentation massively. for example, there is no link to "tools.plotting" in the whole documentation, and commands like "scatter_plot" don't show up in the documentation at all. To make pandas usable for "outsiders", the documentation should be made more complete.
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} | 0 | "2012-11-25T18:16:16Z" | "2012-11-28T02:47:33Z" | "2012-11-28T02:47:33Z" | MEMBER | null | ref: http://stackoverflow.com/questions/13550940/python-pandas-insert-long-integer
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df = pd.read_csv('foo.csv',parse_dates=[0]) #parse the first column as datetime
df = df.rename(columns={'Unnamed: 0':'date'}) # rename the column
df.set_index('date', drop=False) #set the renamed column as the index
df
Out[205]:
date AA AAPL GE
0 2011-10-03 00:00:00 8.90 374.60 14.69
1 2011-10-04 00:00:00 9.12 372.50 14.86
2 2011-10-05 00:00:00 9.37 378.25 15.27
3 2011-10-06 00:00:00 9.88 377.37 15.53
4 2011-10-07 00:00:00 9.71 369.80 15.50
5 2011-10-10 00:00:00 10.09 388.81 16.14
6 2011-10-11 00:00:00 10.30 400.29 16.14
7 2011-10-12 00:00:00 10.05 402.19 16.40
8 2011-10-13 00:00:00 10.10 408.43 16.22
9 2011-10-14 00:00:00 10.26 422.00 16.60
df.plot()
Out[204]:
## <matplotlib.axes.AxesSubplot at 0x61fc430>
RuntimeError Traceback (most recent call last)
G:\Python2732\lib\site-packages\IPython\zmq\pylab\backend_inline.pyc in show(close)
100 try:
101 for figure_manager in Gcf.get_all_fig_managers():
--> 102 send_figure(figure_manager.canvas.figure)
This is all in the ipython notebook, using **version** 0.9.1 pandas
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} | 2 | "2012-11-25T22:23:04Z" | "2012-11-26T05:47:57Z" | "2012-11-26T05:47:57Z" | NONE | null | Below is data parsed from a csv file. Next I'm trying the set_index with drop=False. Everything seems to work correctly until I attempt plot(). plot() works ok if I use drop=True.
Now that I'm looking at it more,
df2['date'].plot() works ok with drop=False, and the Y axis ticks are dates. Perhaps this is just my lack of knowledge of when a secondary Y axis is required.
This is all in the ipython notebook, using **version** 0.9.1 pandas
In [228]:
df2 = pd.read_csv('foo2.csv',parse_dates=['date'] ) #parse the first column as datetime
df2
Out[228]:
date AA AAPL GE
0 2011-10-03 00:00:00 8.90 374.60 14.69
1 2011-10-04 00:00:00 9.12 372.50 14.86
2 2011-10-05 00:00:00 9.37 378.25 15.27
3 2011-10-06 00:00:00 9.88 377.37 15.53
4 2011-10-07 00:00:00 9.71 369.80 15.50
5 2011-10-10 00:00:00 10.09 388.81 16.14
6 2011-10-11 00:00:00 10.30 400.29 16.14
7 2011-10-12 00:00:00 10.05 402.19 16.40
8 2011-10-13 00:00:00 10.10 408.43 16.22
9 2011-10-14 00:00:00 10.26 422.00 16.60
29]:
df2 = df2.set_index('date', drop=False)
df2
Out[229]:
date AA AAPL GE
date
2011-10-03 2011-10-03 00:00:00 8.90 374.60 14.69
2011-10-04 2011-10-04 00:00:00 9.12 372.50 14.86
2011-10-05 2011-10-05 00:00:00 9.37 378.25 15.27
2011-10-06 2011-10-06 00:00:00 9.88 377.37 15.53
2011-10-07 2011-10-07 00:00:00 9.71 369.80 15.50
2011-10-10 2011-10-10 00:00:00 10.09 388.81 16.14
2011-10-11 2011-10-11 00:00:00 10.30 400.29 16.14
2011-10-12 2011-10-12 00:00:00 10.05 402.19 16.40
2011-10-13 2011-10-13 00:00:00 10.10 408.43 16.22
2011-10-14 2011-10-14 00:00:00 10.26 422.00 16.60
Out[230]:
## <matplotlib.axes.AxesSubplot at 0x8c9cf30>
RuntimeError Traceback (most recent call last)
G:\Python2732\lib\site-packages\IPython\zmq\pylab\backend_inline.pyc in show(close)
100 try:
101 for figure_manager in Gcf.get_all_fig_managers():
--> 102 send_figure(figure_manager.canvas.figure)
103 finally:
104 show._to_draw = []
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} | 5 | "2012-11-25T22:26:35Z" | "2014-06-19T00:06:44Z" | "2012-11-27T02:34:22Z" | NONE | null | fixes all failing tests mentioned in #2331, perhaps others lurking.
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} | 0 | "2012-11-26T16:39:11Z" | "2012-11-28T05:42:33Z" | "2012-11-28T05:42:33Z" | MEMBER | null | And yes/no/YES/NO should not be recognized as boolean by default. Revert default behavior from #1691, #1295
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} | 0 | "2012-11-26T21:02:40Z" | "2012-11-26T22:31:02Z" | "2012-11-26T22:31:02Z" | NONE | null | pandas.read_table() does not handle stderr consistently.
error_bad_lines=True writes to stderr (which is correct), but warn_bad_lines=True writes to stdout (which is wrong).
This is with commit ae1b8a888149f6474c13141f8194da322dccc466
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} | 1 | "2012-11-26T21:26:08Z" | "2014-06-27T22:56:15Z" | "2012-11-27T00:02:13Z" | CONTRIBUTOR | null | as a follow-up to this [SO post](http://stackoverflow.com/questions/13572576/using-or-to-select-data-in-pandas/13572798#13572798) I thought it might be nice to have an example in the docs
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https://api.github.com/repos/pandas-dev/pandas/issues/2364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2364/events | https://github.com/pandas-dev/pandas/pull/2364 | 8,704,584 | MDExOlB1bGxSZXF1ZXN0MzEzMjU2Mw== | 2,364 | doc updates for whatsnew v0.10.0 | {
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} | 1 | "2012-11-27T03:11:11Z" | "2014-07-08T19:39:42Z" | "2012-11-27T22:25:41Z" | CONTRIBUTOR | null | added whatsnew for 0.10.0
added new apply functionality to whatsnew and flexible apply in groupby
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} | 3 | "2012-11-27T06:36:31Z" | "2013-03-26T12:52:12Z" | "2012-11-27T06:51:37Z" | NONE | null | note that py3 is now tested against numpy recent git, not pypi, just until
the travis people sort things out.
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```
$ python setup.py -v install --dry-run --install-lib "~/python"
```
does not do nothing (as --dry-run should imply), but seems to run the full install process.
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} | 4 | "2012-11-27T19:02:24Z" | "2013-12-04T00:40:40Z" | "2012-12-14T01:02:37Z" | CONTRIBUTOR | null | DataFrame.to_excel(headers=True) ignores "\n" in headers when columns are multiline strings
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} | 17 | "2012-11-27T20:23:26Z" | "2014-06-16T03:26:35Z" | "2012-11-29T19:32:13Z" | NONE | null | adds to export dataframe for excel:
- multiindex (merge cells similar to htmlformatter)
- border
- bold header
- ability to add dataframe in same sheet (startrow, startcol)
http://cl.ly/image/2r102L0E1l23
solves Issue #2294
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} | 7 | "2012-11-27T21:30:00Z" | "2012-11-29T16:47:29Z" | "2012-11-29T00:40:18Z" | CONTRIBUTOR | null | Refactor of PyTables support to allow multiple table types.
This commit allows for support of multiple table types in a pytables hdf file,
supporting the existing infrastructure in a backwards compatible manner (LegacyTable)
while extending to a slightly modified format to support AppendableTables and future support of WORMTables
AppendableTables are implementations of the current table format with two enhancements:
- mixed dtype support
- writing routines in cython for enhanced performance
WORMTables (not implemented - but pretty straightforward)
these tables can support a fixed 'table' (meaning not-appendable), that is searchable via queries
this would have greatly enhanced write performance compared with AppendableTables, and a similar read performance profile
In addition, the tables allow for arbitrary axes to be indexed (e.g. you could save a panel that allows indexing on major_axis,minor_axis AND items),
so all dimensions are queryable (currently only major/minor axes allow this query)
all tests pass (with 1 exception)
a frame table round-trip - will fail on a comparison of a sorted index of the frame vs the index of the table (which is as written), not sure why this should be the case?
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} | 2 | "2012-11-28T03:03:09Z" | "2014-07-21T09:13:49Z" | "2012-11-28T04:35:39Z" | NONE | null | A seperate 2.7 env is spawned, which is allowed to fail without
failing the entire build. The results are posted to a public gist as
a json file, and the gist url is printed in the log.
A helper function in vb_suite/perf_HEAD.py accepts a travis
build number, It then locates the VBENCH job within the build, fetches the log
through the Travis API, extract the gist raw_url from the log, fetches the results
as json and converts it into a pandas dataframe.
It remains to be seen whether the results are consistent
enough between builds over time to actually make this useful.
Here's how to gather the results from a few consecutive test builds I ran:
``` python
%cd vb_suite
import pandas as pd
from perf_HEAD import get_build_results
builds=[3393087,3393105,3393122,3393125,3393130]
dfs=[get_build_results(x) for x in builds]
dfs2=[x[['timing']] for x in dfs]
for df,b in zip(dfs2,builds):
df.columns=[str(b)]
df = dfs2[0]
for other in dfs2[1:]:
df=df.join(other,how='outer')
df[:20]
3393087 3393105 3393122 3393125 3393130
name
append_frame_single_homogenous 0.641988 0.340924 0.524198 0.400884 0.373756
append_frame_single_mixed 2.159144 1.354964 1.639278 1.421034 1.301402
concat_series_axis1 131.367922 56.542015 92.097044 75.879002 71.159506
ctor_index_array_string 0.009129 0.004975 0.005761 0.006048 0.007652
dataframe_get_value 0.001885 0.001201 0.001403 0.001305 0.001331
dataframe_getitem_scalar 0.005543 0.003445 0.003394 0.003432 0.003307
dataframe_reindex_columns 0.494599 0.233001 0.370013 0.334528 0.316710
dataframe_reindex_daterange 0.683607 0.329319 0.552313 0.506124 0.439592
datamatrix_getitem_scalar 0.004519 0.003790 0.003355 0.003436 0.003317
datetimeindex_add_offset 0.439327 0.227953 0.327404 0.410341 0.312527
datetimeindex_normalize 73.160005 51.629400 60.980701 59.092712 58.182502
frame_boolean_row_select 0.769411 0.596314 0.723754 0.564437 0.653430
frame_constructor_ndarray 0.064187 0.028872 0.033200 0.033250 0.042343
frame_ctor_list_of_dict 140.382051 86.547899 114.851952 127.656937 114.904165
frame_ctor_nested_dict 124.281883 73.726416 97.840071 106.689930 111.264944
frame_ctor_nested_dict_int64 229.266882 130.098104 143.503904 183.116913 152.652025
frame_fancy_lookup 2.752171 2.173202 2.223320 1.820600 2.148161
frame_fancy_lookup_all 135.908127 113.823605 117.978096 108.165979 113.741159
frame_fillna_inplace 25.430799 16.801000 20.938993 24.916101 21.809912
frame_fillna_many_columns_pad 23.508501 16.926599 20.899606 19.706178 17.867088
```
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} | 5 | "2012-11-28T14:55:22Z" | "2012-12-07T21:41:26Z" | "2012-12-07T21:32:06Z" | NONE | null | from datetime import datetime
from pandas import DataFrame
df = DataFrame({'x1': [datetime(1996,1,1)]})
df2 = df.applymap(lambda x: x)
Issue:
type(df['x1'][0]) == datetime.datetime
type(df2['x1'][0]) == pandas.lib.Timestamp
This repros in the 11/27/2012 cut of 0.10.0 - but not in 0.9.0 release.
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} | 0 | "2012-11-28T21:06:12Z" | "2012-11-29T16:44:55Z" | "2012-11-29T16:44:55Z" | NONE | null | - Only 4 functions are now defined `get`, `set`, `reset`, `describe`
- All functions accept a regexp as input and will
try to do the right thing accordingly.
For example: get_option("max_rows") now returns the value of print_config.max_rows.
- keys are now case-insensitive.
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} | 0 | "2012-11-28T23:51:15Z" | "2012-11-30T23:48:02Z" | "2012-11-30T23:48:01Z" | NONE | null | http://docs.python.org/3/whatsnew/3.3.html#datetime
This should leave just one error specific to 3.3 from those mentioned in #2331,
and that's parser related.
Don't know if other parts of the package depend on the exception being raised.
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} | 0 | "2012-11-28T23:56:22Z" | "2012-11-29T16:52:08Z" | "2012-11-28T23:59:48Z" | NONE | null | 1.6.2 and git master have diverged quite a bit, best to narrow down the chance
of spurious errors..
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} | 2 | "2012-11-29T00:03:44Z" | "2016-12-28T11:15:21Z" | "2016-12-28T11:15:14Z" | NONE | null | http://en.wikipedia.org/wiki/Day_count_convention
add day count convention to timeseries : busdayconvention, daycountconversion
assign : locojay
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https://api.github.com/repos/pandas-dev/pandas/issues/2381 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2381/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2381/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2381/events | https://github.com/pandas-dev/pandas/issues/2381 | 8,795,049 | MDU6SXNzdWU4Nzk1MDQ5 | 2,381 | Add DataFrame.conj/conjugate | {
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} | 5 | "2012-11-29T03:07:29Z" | "2012-12-16T16:08:44Z" | "2012-12-16T16:08:44Z" | CONTRIBUTOR | null | From mailing list
I noticed that Series has methods .conj() and .conjugate() but dataframe does not. Doing a search on complex numbers in pandas, I found this issue.
https://github.com/pydata/pandas/issues/1083
Is there any hope of getting .conj() onto the dataframe? Or should I convert to an np-array, then convert back to a df?
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On the page http://pandas.pydata.org/developers.html, in the section titled "Performance performance with vbench", the title should probably be "Performance testing with vbench", and the sentence
```
We created the vbench library library to enable easily monitoring the performance of critical pandas operations.
```
should probably read
```
We created the vbench library library to enable easy monitoring of the performance of critical pandas operations.
```
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} | 0 | "2012-11-29T05:02:31Z" | "2014-06-20T21:46:40Z" | "2012-11-29T20:24:49Z" | CONTRIBUTOR | null | In response to #2326
(Am I doing this right?)
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} | 2 | "2012-11-29T08:45:25Z" | "2014-02-28T23:57:53Z" | "2012-12-01T15:35:28Z" | CONTRIBUTOR | null | ```
ts = Series(randn(10))
ts[2:-2] = nan
sts = ts.to_sparse()
print sts
print sts.density
```
AttributeError: 'SparseSeries' object has no attribute 'density'
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} | 16 | "2012-11-29T09:56:57Z" | "2014-02-22T13:40:07Z" | "2014-02-21T22:44:05Z" | CONTRIBUTOR | null | concat thows an `AssertionError` when passed a ciollection of mixed Series and Dataframes e.g.
``` python
s1 = pd.TimeSeries(np.sin(linspace(0, 2*pi, 100)),
index=pd.date_range('01-Jan-2013',
periods=100, freq='H'))
s2 = pd.TimeSeries(np.cos(linspace(0, 2*pi, 100)),
index=pd.date_range('01-Jan-2013',
periods=100, freq='H'))
df = pd.DataFrame(np.cos(linspace(0, 2*pi,
100)).reshape(-1,1),
index=pd.date_range('01-Jan-2013',
periods=100, freq='H'))
In [23]: pd.concat([df,df], axis=1).shape
Out[23]: (100, 2)
In [24]: pd.concat([s1,s2], axis=1).shape
Out[24]: (100, 2)
In [25]: pd.concat([s1,s2,s1], axis=1).shape
Out[25]: (100, 3)
In [26]: pd.concat([s1,df,s2], axis=1).shape
Traceback (most recent call last):
File "<ipython-input-9-4512588e71f2>", line 1, in <module>
pd.concat([s1,df,s2], axis=1).shape
File "c:\dev\code\pandas\pandas\tools\merge.py", line 881, in concat
return op.get_result()
File "c:\dev\code\pandas\pandas\tools\merge.py", line 960, in get_result
columns=self.new_axes[1])
File "c:\dev\code\pandas\pandas\core\frame.py", line 376, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "c:\dev\code\pandas\pandas\core\frame.py", line 505, in _init_dict
dtype=dtype)
File "c:\dev\code\pandas\pandas\core\frame.py", line 5181, in _arrays_to_mgr
mgr = BlockManager(blocks, axes)
File "c:\dev\code\pandas\pandas\core\internals.py", line 499, in __init__
self._verify_integrity()
File "c:\dev\code\pandas\pandas\core\internals.py", line 584, in _verify_integrity
raise AssertionError('Block shape incompatible with manager')
AssertionError: Block shape incompatible with manager
In [27]: pd.__version__
Out[27]: '0.10.0.dev-fbd77d5'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2386 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2386/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2386/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2386/events | https://github.com/pandas-dev/pandas/issues/2386 | 8,812,502 | MDU6SXNzdWU4ODEyNTAy | 2,386 | integer overflow in pandas.DataFrame.unstack() | {
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} | 2 | "2012-11-29T14:39:56Z" | "2012-12-02T16:38:52Z" | "2012-12-02T16:38:52Z" | NONE | null | Pandas version 0.9.1
There is an integer overflow problem in pandas.DataFrame.unstack()
When unstack() is called on a relatively large data set (1 million rows) with four-column MultiIndex an exception is raised: "ValueError: negative dimensions are not allowed".
Stack trace is below,
The actual problem is in method pandas.core.reshape._Unstacker._make_sorted_values_labels(self)
line 91: max_groups = np.prod(sizes)
Each of the sizes fits in int32, but their product doesn't.
So max_groups becomes negative.
```
C:\Python27\lib\site-packages\pandas\core\frame.pyc in unstack(self, level)
3712 """
3713 from pandas.core.reshape import unstack
-> 3714 return unstack(self, level)
3715
3716 #----------------------------------------------------------------------
C:\Python27\lib\site-packages\pandas\core\reshape.pyc in unstack(obj, level)
361 if isinstance(obj, DataFrame):
362 if isinstance(obj.index, MultiIndex):
--> 363 return _unstack_frame(obj, level)
364 else:
365 return obj.T.stack(dropna=False)
C:\Python27\lib\site-packages\pandas\core\reshape.pyc in _unstack_frame(obj, level)
399 else:
400 unstacker = _Unstacker(obj.values, obj.index, level=level,
--> 401 value_columns=obj.columns)
402 return unstacker.get_result()
403
C:\Python27\lib\site-packages\pandas\core\reshape.pyc in __init__(self, values, index, level, value_columns)
77 self.full_shape = np.prod(lshape[:v] + lshape[v + 1:]), lshape[v]
78
---> 79 self._make_sorted_values_labels()
80 self._make_selectors()
81
C:\Python27\lib\site-packages\pandas\core\reshape.pyc in _make_sorted_values_labels(self)
96 comp_index, ngroups = group_index, max_groups
97
---> 98 indexer = lib.groupsort_indexer(comp_index, ngroups)[0]
99 indexer = _ensure_platform_int(indexer)
100
C:\Python27\lib\site-packages\pandas\lib.pyd in pandas.lib.groupsort_indexer (pandas\src\tseries.c:51018)()
ValueError: negative dimensions are not allowed
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2387 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2387/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2387/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2387/events | https://github.com/pandas-dev/pandas/pull/2387 | 8,813,428 | MDExOlB1bGxSZXF1ZXN0MzE3MzUwOQ== | 2,387 | minor doc updates for pytablesv4 | {
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} | 0 | "2012-11-29T15:02:00Z" | "2012-11-29T16:39:30Z" | "2012-11-29T16:39:30Z" | CONTRIBUTOR | null | added a min_itemsize example to the docs
min_itemsize can be passed as a dict
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https://api.github.com/repos/pandas-dev/pandas/issues/2388 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2388/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2388/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2388/events | https://github.com/pandas-dev/pandas/issues/2388 | 8,818,074 | MDU6SXNzdWU4ODE4MDc0 | 2,388 | Unable to use Series created with reindex_like with numpy.logical_and() | {
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} | 12 | "2012-11-29T16:32:04Z" | "2012-12-02T01:24:23Z" | "2012-12-02T01:24:23Z" | CONTRIBUTOR | null | I'm using numpy 1.6.2, pandas 0.9.1, and Python 2.7.2. I see strange behavior when using `numpy.logical_and()` depending on how I create a Series object. For example:
``` python
>>> import numpy
>>> import pandas
>>> series = pandas.Series([1, 2, 3])
>>> x = pandas.Series([True]).reindex_like(series).fillna(True)
>>> y = pandas.Series(True, index=series.index)
>>> series
0 1
1 2
2 3
>>> x
0 True
1 True
2 True
>>> y
0 True
1 True
2 True
>>> numpy.logical_and(series, y)
0 True
1 True
2 True
>>> numpy.logical_and(series, x)
Traceback (most recent call last):
File "<ipython-input-10-e2050a2015bf>", line 1, in <module>
numpy.logical_and(series, x)
AttributeError: logical_and
```
What is the difference between `x` and `y` here that is causing the `AttributeError`?
Also, I originally posted this as a [question on stackoverflow](http://stackoverflow.com/questions/13628860/attributeerror-when-using-numpy-logical-and-with-pandas-series-object). There are comments saying this works with pandas 0.9.0 and numpy 1.8. I haven't verified this for myself yet. However, my scenario is using the most recent stable releases of both projects.
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https://api.github.com/repos/pandas-dev/pandas/issues/2389 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2389/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2389/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2389/events | https://github.com/pandas-dev/pandas/pull/2389 | 8,818,332 | MDExOlB1bGxSZXF1ZXN0MzE3NTIxMQ== | 2,389 | DOC: pytables what's new tweaks. | {
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} | 12 | "2012-11-29T17:20:14Z" | "2016-07-26T08:15:20Z" | "2016-07-25T23:37:46Z" | CONTRIBUTOR | null | ## open (not in any particular order)
1. add support for other dtypes in table columns (datetime,date,unicode)
2. Implement variable length strings in a parallel VLArray (and synchronize): https://github.com/PyTables/PyTables/issues/198
3. revisit Term syntax - can we do better / more readability?
3a. implement `or` in Terms (maybe use pyparsing like syntax)
4. implement WORMTable
5. one big area is to test whether data columns really are slower; it thus may make sense to make data columns = True the default (but not necessarily index them). see https://groups.google.com/forum/m/?fromgroups#!topic/pydata/cmw1F3OFJSc - see the end of this post for some perf tests, so this is prob not a good idea after all
6. add `export` function, to export to different PyTables formats(an easy to read table for R (partially done), and output a GenericTable)
7. provide better access to columns that are data_columns (as we can directly select them) - see `read_column`, expand this to the entire table (if possible), allows one to avoid selecting all columns in a table (and then reindexing), this works if `columns` argument is provided to select or inferred from the where.
8. add out-of-core computation support (see my comment about 1/2 down in #622), this is partially supported now that we have an iterator (#3078)
9. add a method to create a table structure (`create_table`)?, w/o actually appending, so don't have to add parms in each call to append.
10. Support a better mechanism for table splitting `Splitter`? that a user can specify how to split (rather than a dict); then store this object, so can automatically recreate the resulting table (enable for both Storer and Table objects)
11. Optimize table appending, I think we can do better! (GH #3537) makes some improvements
12. allow `itemsize='truncate'` to allow subsquent appends to proceed with string truncation (on specific columns)
13. allow where in `select_column`, return a properly indexed Series, add option to include the index (`use_index=True?`)
14. Better deal with a very long list as input to a `Term`, but running multiple `or` sub-queries
15. Add support for coulumn oriented tables, dep is `carray`, http://carray.pytables.org/docs/manual/
## done
1. DONE (GH #2401): access store paths via path notation / dot notation (GH #2755)
2. DONE (GH #2497): add to docs (GH #2397) - issues about reading/writing concurrently in threads/processes
http://sourceforge.net/mailarchive/message.php?msg_id=30190886
3. DONE (GH #2497): support panelnd (GH #2242)
4. DONE (GH #2561): Should DataFrames be automagically indexed on 'index' (prob yes), but then should have a flag in append/put, and enable passing of the indexing options
5. DONE (GH #2497): Check if create_table_index changes the current index if different options are passed
6. DONE (GH #2561): for writing add chunk keyword to select to provide generator like behavior - each call to return the next chunk of data
7. DONE (GH #2561): support multi indexes on tables
5a. DONE real dtype integration is coming on PR #2708 (eg even though 0.10.1 will actually read/write float32 columns u can't really do much with them w/o having them upcasted) - in any event I think HDFStore will accommodate this already. but more testing needed
8. DONE iterator support in `select`, http://stackoverflow.com/questions/14614512/merging-two-tables-with-millions-of-rows-in-python (GH #3078)
9. DONE (GH #3531) support timezones in datelike columns (index should be ok already) (scott?), (GH #2852)
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} | 1 | "2012-11-29T17:30:38Z" | "2014-06-29T08:04:31Z" | "2012-11-30T23:30:50Z" | CONTRIBUTOR | null | works locally but how to put the qtconsole check in a test suite? #2275
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(Changes are now PEP8-compliant!)
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} | 4 | "2012-11-30T07:51:26Z" | "2016-10-12T23:03:22Z" | "2012-12-01T15:26:24Z" | NONE | null | ```
./test_rebuild.sh
running clean
running build_ext
cythoning pandas/src/generated.pyx to pandas/src/generated.c
building 'pandas._algos' extension
....
E
======================================================================
ERROR: Failure: ImportError (No module named xlwt)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/net/home4/bmueller/.virtualenvs/myenv/lib/python2.6/site-packages/nose/loader.py", line 390, in loadTestsFromName
addr.filename, addr.module)
File "/net/home4/bmueller/.virtualenvs/myenv/lib/python2.6/site-packages/nose/importer.py", line 39, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/net/home4/bmueller/.virtualenvs/myenv/lib/python2.6/site-packages/nose/importer.py", line 86, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/scratch/pandas/pandas/__init__.py", line 33, in <module>
from pandas.io.parsers import (read_csv, read_table, read_clipboard,
File "/scratch/pandas/pandas/io/parsers.py", line 9, in <module>
import xlwt
ImportError: No module named xlwt
...
Ran 1 test in 0.002s
FAILED (errors=1)
In [3]: pandas.version.version
Out[3]: '0.10.0.dev-504c7f1'
```
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} | 39 | "2012-11-30T10:08:17Z" | "2014-02-07T14:35:24Z" | "2012-12-10T18:55:03Z" | CONTRIBUTOR | null | An example:
``` python
In [9]: data = pandas.ExcelFile("sample.xls") # generated with df.to_excel()
In [10]: data.parse(data.sheet_names[0], index_col=0)
Out[10]:
Value
ID NaN
ID1 38.625700
ID2 44.691054
```
To be more clear, `ID` is the original index name, but it is shifted down one row when saving to Excel, and then it is picked up as a regular row by the Excel parser.
This kinds of complicates parsing if I'm expecting to read something generated by to_excel()...
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} | 6 | "2012-11-30T19:14:39Z" | "2012-12-28T15:08:46Z" | "2012-12-28T15:08:40Z" | NONE | null | ``` python
import threading
import pandas as pd
import time
def foo():
store = pd.HDFStore('my_hdf_file.h5')
store['foo']
store.close()
def main():
threading.Thread(target=foo).start()
threading.Thread(target=foo).start()
time.sleep(2)
if __name__ == '__main__':
main()
```
Crashes for me (Windows 7 using pytables 2.4.0 and pandas 0.9.1 from http://www.lfd.uci.edu/~gohlke/pythonlibs/). I can't get the stack trace easily, I can try harder if necessary. Simply using tables.openFile and reading a few values seems to work fine.
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} | 2 | "2012-11-30T20:13:06Z" | "2014-07-02T10:35:21Z" | "2012-12-10T15:24:12Z" | CONTRIBUTOR | null | use `center` keyword and then `shift` if True.
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} | 0 | "2012-11-30T22:55:01Z" | "2012-11-30T23:16:36Z" | "2012-11-30T23:16:36Z" | MEMBER | null | I'll add a vbench so this lights up next time
http://pastebin.com/f0Si46z3
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} | 2 | "2012-11-30T23:17:19Z" | "2012-12-01T18:00:48Z" | "2012-12-01T18:00:48Z" | CONTRIBUTOR | null | run test_multi.sh
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