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} | 81 | "2013-06-07T23:05:25Z" | "2014-07-16T08:12:32Z" | "2013-06-18T22:30:01Z" | CONTRIBUTOR | null | Add some shortcuts to make it easier to develop with pandas.
now there's a set of commands in the `Makefile` in the top-level pandas directory with the following functionality
- `make clean` will delete the `build` and `dist` directories + all `*.pyc` and `*.so` files
- `make clean_pyc` just removes `*.pyc` files
- `make build` will build extensions inplace
- `make develop` will install `pandas` in your environment but will place a link to the dev dir so that you can make changes and they will show up immediately
- `make doc` will build the documentation from scratch (erases generated and build directories)
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} | 95 | "2013-06-07T23:05:47Z" | "2014-06-13T20:06:58Z" | "2013-06-11T19:18:34Z" | CONTRIBUTOR | null | This is @wesm PR #3583 with this:
It builds now, and passes travis on py2 and py3, had 2 issues:
- clean was erasing the *.c files from ujson
- the module import didn't work because it was using the original init function
Converted to new io API: `to_json` / `read_json`
Docs added
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} | 53 | "2013-06-07T23:53:53Z" | "2014-06-17T05:35:45Z" | "2013-10-12T01:30:47Z" | CONTRIBUTOR | null | fixes #3788
Please check out whether you like the error message for `Performance Warning`.
Also, I'm not sure whether this means that groupby fails under certain conditions and not others (like when trying Cython, etc.).
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} | [] | closed | false | null | [] | null | 33 | "2013-06-08T01:51:13Z" | "2013-07-11T05:21:11Z" | "2013-06-10T03:05:15Z" | CONTRIBUTOR | null | Also adds an `__all__` to all the api files as well as a test case that
checks that everything in the api files ends up in the toplevel pandas
namespace. I personally find it hard to trace back the top level pandas
functions because of the `from xyz import *` calls. I changed everything
to be explicit. I think it's better, but it's your call.
The only large change with this is that the `pandas` module/package no
longer exports `numpy/np` at the top level.
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https://api.github.com/repos/pandas-dev/pandas/issues/3809 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3809/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3809/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3809/events | https://github.com/pandas-dev/pandas/pull/3809 | 15,304,767 | MDExOlB1bGxSZXF1ZXN0NjIwMzkyNQ== | 3,809 | DOC: turn off the ipython cache | {
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closes #3807.
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https://api.github.com/repos/pandas-dev/pandas/issues/3810 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3810/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3810/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3810/events | https://github.com/pandas-dev/pandas/issues/3810 | 15,306,482 | MDU6SXNzdWUxNTMwNjQ4Mg== | 3,810 | Feature Request: Dynamic Time Warping | {
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mlpy has something on these lines as well, though not as complete.
Since Pandas has a lot of timeseries stuff, adding DTW might be good. Or if not adding afresh, integrating with R or mlpy package maybe, or simply using rpy2 interface within Pandas and expand the sections here http://pandas.pydata.org/pandas-docs/dev/r_interface.html
References:
- http://stackoverflow.com/questions/5695388/dynamic-time-warping-in-python
- http://rdatamining.wordpress.com/2011/08/23/time-series-analysis-and-mining-with-r/
(specifically the section on clustering)
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} | 8 | "2013-06-08T19:21:31Z" | "2013-07-26T23:49:32Z" | "2013-07-26T23:44:15Z" | MEMBER | null | there's a few of these floating around basically to dispatch on different kinds of possibly ambiguous string/filename/url input. would be nice if there was something in `common` or `util` that just does what you want. this may be overly optimistic...
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} | 15 | "2013-06-09T00:30:24Z" | "2014-04-06T15:14:01Z" | "2013-06-22T19:03:38Z" | CONTRIBUTOR | null | I would like to propose an interface breaking refactoring to io/data options.
Here are the proposed major changes:
- replace month/year with datetime parameter (the current implementation does not support weekly options)
- add a call to list available expirations (I'm unsure how that is supported with Yahoo Finance data)
- add data_source to all functions (where applicable) and refactor to _yahoo functions
This is in preparation to hooking up Google Finance as an options data source. (#3822)
Please comment. Thanks
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} | 9 | "2013-06-09T03:41:50Z" | "2014-06-18T08:55:58Z" | "2013-06-13T19:12:08Z" | CONTRIBUTOR | null | to enable passing of integers or floats that are in an epoch unit of s, ms, us, ns
(e.g. unix timestamps or epoch s, with fracional seconds allowed)
closes #3540
```
In [5]: pd.to_datetime(Series([ 1370745748 + t for t in range(5) ]),unit='s')
Out[5]:
0 2013-06-09 02:42:28
1 2013-06-09 02:42:29
2 2013-06-09 02:42:30
3 2013-06-09 02:42:31
4 2013-06-09 02:42:32
dtype: datetime64[ns]
```
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``` bash
#snippet from nosetests
........................................................S.................................................................
ci/script.sh: line 13: 4513 Floating point exceptionnosetests --exe -w /tmp -A "$NOSE_ARGS" pandas
```
The exception occurs when `__mod__` is evaluated with str_rep of `%` (meaning `a_values % b_values` is passed to `numexpr.evaluate`), and it occurs in `test_expressions.py` under `test_mixed_floats_arithmetic` and `test_integer_with_zeros` in (for example) jtratner/pandas@e938da2c5e28171488392fa2605e4e18a47c65eb . Omitting `str_rep` from `__mod__` fixes the issues, clearly showing it's occurring with numexpr. Going to investigate more in the future.
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https://api.github.com/repos/pandas-dev/pandas/issues/3820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3820/events | https://github.com/pandas-dev/pandas/issues/3820 | 15,318,421 | MDU6SXNzdWUxNTMxODQyMQ== | 3,820 | Option for crosstab/pivot_table to include empty columns | {
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} | 4 | "2013-06-09T10:55:22Z" | "2013-07-10T13:20:05Z" | "2013-07-10T13:20:05Z" | CONTRIBUTOR | null | I don't think you can do this easily atm. _crosstab is just a [convenience for pivot_table]((https://github.com/pydata/pandas/blob/master/pandas/tools/pivot.py#L218)..._
http://stackoverflow.com/questions/17003034/missing-data-in-pandas-crosstab
```
a = np.array(['foo', 'foo', 'foo', 'bar', 'bar', 'foo', 'foo'], dtype=object)
b = np.array(['one', 'one', 'two', 'one', 'two', 'two', 'two'], dtype=object)
c = np.array(['dull', 'dull', 'dull', 'dull', 'dull', 'shiny', 'shiny'], dtype=object)
pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
b one two
c dull dull shiny
a
bar 1 1 0
foo 2 1 2
# but wants
b one two
c dull shiny dull shiny
a
bar 1 0 1 0
foo 2 0 1 2
```
Maybe we could have an option in pivot_table (and crosstab) to include empty columns.
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https://api.github.com/repos/pandas-dev/pandas/issues/3821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3821/events | https://github.com/pandas-dev/pandas/pull/3821 | 15,320,514 | MDExOlB1bGxSZXF1ZXN0NjIwODg0Ng== | 3,821 | FIX py3ing some print statements | {
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```
grep "print [^(]" . -r
```
I don't know how to deal with the `print >>buf, empty` etc. (or whether we need to, ~~I've only labelled~~ there's only the two of these).... ?
_I found one by trying to use py3 before building, where I got a `print e` syntax error._
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} | 13 | "2013-06-09T18:47:50Z" | "2014-06-20T06:53:44Z" | "2013-06-17T13:29:07Z" | CONTRIBUTOR | null | It should be more than 10 times faster than the old version.
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} | 0 | "2013-06-09T23:15:25Z" | "2014-07-16T08:13:04Z" | "2013-06-09T23:41:23Z" | CONTRIBUTOR | null | removed to io.clipboard (from io.parsers)
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} | 19 | "2013-06-10T00:21:25Z" | "2018-06-08T04:19:36Z" | "2018-06-08T04:19:35Z" | MEMBER | null | complete [bikeshedding](https://en.wikipedia.org/wiki/Parkinson's_law_of_triviality), but...might be nice, any ideas? i was thinking one of the plots shown on the main website might work.
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https://api.github.com/repos/pandas-dev/pandas/issues/3826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3826/events | https://github.com/pandas-dev/pandas/pull/3826 | 15,328,472 | MDExOlB1bGxSZXF1ZXN0NjIxMTM3MQ== | 3,826 | DOC: speedup io.rst doc build | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3827/events | https://github.com/pandas-dev/pandas/pull/3827 | 15,328,709 | MDExOlB1bGxSZXF1ZXN0NjIxMTQ0Mg== | 3,827 | CLN: remove relative imports | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3828/events | https://github.com/pandas-dev/pandas/pull/3828 | 15,330,028 | MDExOlB1bGxSZXF1ZXN0NjIxMTkzOA== | 3,828 | Add msgpack as submodule for pandas | {
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} | 3 | "2013-06-10T02:38:03Z" | "2014-07-13T14:07:50Z" | "2013-09-22T16:27:29Z" | MEMBER | null | Let's get the ball rolling on better high perf serialization. Note this is based on the 0.3.0 release of msgpack-python, should be updated whenever another stable release of the library is made.
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] | closed | false | null | [] | null | 4 | "2013-06-10T11:19:05Z" | "2013-06-11T01:49:43Z" | "2013-06-11T01:48:45Z" | NONE | null | I have a big DataFrame where i want to to overwrite values in a given column if the values in another column is not in a list. Value to be overwritten is from the checked column. The following code gives the expected results in 0.10.1, but gives unexpected results in 0.11.0
using numpy 1.7.1
code:
# df contains the original pandas DataFrame
accurate_list = ['SA EQUITY CFD', 'SA EQUITY', 'SA SSF']
# x is the df of items not matching the list in its Classification
x = df[~df.Classification.isin(accurate_list)]
# overwrites only incorrect Classifications
df.ix[x.index,'Industry Code'] = df['Classification']
I have been unable to recreate the error with smaller dataframes. Have double checked and the dataframe definitely has an unique index. No error message, just incorrect values written to the 'Industry Code' column
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https://api.github.com/repos/pandas-dev/pandas/issues/3830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3830/events | https://github.com/pandas-dev/pandas/issues/3830 | 15,345,918 | MDU6SXNzdWUxNTM0NTkxOA== | 3,830 | Bug in Panel Indexing | {
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} | 4 | "2013-06-10T13:12:36Z" | "2013-08-07T20:20:41Z" | "2013-08-07T20:20:41Z" | CONTRIBUTOR | null | related to #3777
It seems that assigning a DataFrame to a Panel slice doesn't work correctly.
Unless I'm mistaken the below test code should work:
``` python
a = np.zeros((2,4,5))
panel = pd.Panel(a, items=['a1','a2'])
df = pd.DataFrame(a[0,:,:])
np.testing.assert_equal(panel.loc['a1'].values, df.values)
np.testing.assert_equal(panel.loc['a2'].values, df.values)
# Assignment by Value Passes for 'a2'
panel.loc['a2'] = df.values
np.testing.assert_equal(panel.loc['a1'].values, df.values)
np.testing.assert_equal(panel.loc['a2'].values, df.values)
# Assignment by DataFrame Fails for 'a2'
panel.loc['a2'] = df
np.testing.assert_equal(panel.loc['a1'].values, df.values)
np.testing.assert_equal(panel.loc['a2'].values, df.values)
Traceback (most recent call last):
File "C:\Users\dhirschfeld\.spyder2\temp.py", line 41, in <module>
np.testing.assert_equal(panel.loc['a2'].values, df.values)
File "C:\dev\bin\Python27\lib\site-packages\numpy\testing\utils.py", line 257, in assert_equal
return assert_array_equal(actual, desired, err_msg, verbose)
File "C:\dev\bin\Python27\lib\site-packages\numpy\testing\utils.py", line 588, in chk_same_position
raise AssertionError(msg)
AssertionError:
Arrays are not equal
x and y nan location mismatch:
x: array([[ 0., 0., 0., 0., nan],
[ 0., 0., 0., 0., nan],
[ 0., 0., 0., 0., nan],
[ 0., 0., 0., 0., nan]])
y: array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3831/events | https://github.com/pandas-dev/pandas/pull/3831 | 15,348,632 | MDExOlB1bGxSZXF1ZXN0NjIyMDgzNQ== | 3,831 | ENH: support for msgpack serialization/deserialization | {
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} | 26 | "2013-06-10T14:10:57Z" | "2014-06-24T20:07:18Z" | "2013-10-01T13:54:09Z" | CONTRIBUTOR | null | extension of #3828
ToDo
- [x] remove use of `pytest` in test_msgpack
- [ ] PERF!
```
msgpack serialization/deserialization
support all pandas objects: Timestamp,Period,all index types,Series,DataFrame,Panel,Sparse suite
docs included (in io.rst)
iterator support
top-level api support
compression and direct calls to in-line msgpack (enabled via #3828) will wait for 0.13+
closes #686
```
```
Benchmarking: 50k rows of 2x columns of floats with a datetime index
In [4]: %timeit df.to_msgpack('foo')
100 loops, best of 3: 16.5 ms per loop
In [2]: %timeit df.to_pickle('foo')
10 loops, best of 3: 12.9 ms per loop
In [6]: %timeit df.to_csv('foo')
1 loops, best of 3: 470 ms per loop
In [11]: %timeit df.to_hdf('foo2','df',mode='w')
10 loops, best of 3: 20.1 ms per loop
In [13]: %timeit df.to_hdf('foo2','df',mode='w',table=True)
10 loops, best of 3: 81.4 ms per loop
```
```
In [5]: %timeit pd.read_msgpack('foo')
100 loops, best of 3: 16.3 ms per loop
In [3]: %timeit pd.read_pickle('foo')
1000 loops, best of 3: 1.28 ms per loop
In [7]: %timeit pd.read_csv('foo')
10 loops, best of 3: 46.1 ms per loop
In [12]: %timeit pd.read_hdf('foo2','df')
100 loops, best of 3: 5.64 ms per loop
In [14]: %timeit pd.read_hdf('foo2','df')
100 loops, best of 3: 9.39 ms per loop
```
```
In [1]: df = DataFrame(randn(10,2),
...: columns=list('AB'),
...: index=date_range('20130101',periods=10))
In [2]: pd.to_msgpack('foo.msg',df)
In [3]: pd.read_msgpack('foo.msg')
Out[3]:
A B
2013-01-01 0.676700 -1.702599
2013-01-02 -0.070164 -1.368716
2013-01-03 -0.877145 -1.427964
2013-01-04 -0.295715 -0.176954
2013-01-05 0.566986 0.588918
2013-01-06 -0.307070 1.541773
2013-01-07 1.302388 0.689701
2013-01-08 0.165292 0.273496
2013-01-09 -3.492113 -1.178075
2013-01-10 -1.069521 0.848614
```
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} | 2 | "2013-06-10T14:14:36Z" | "2016-06-06T15:08:14Z" | "2016-06-06T15:08:14Z" | CONTRIBUTOR | null | link to #3831
- compression is actually enabled (via blosc/zlib), pass `compress='blosc'`, but
need to pass this on packing AND unpacking, so defering this to 0.12
- maybe should make `blosc` an included library as well (as `blosc`
vastly outperforms `zlib`)
- direct calls to inline `msgpack` interface to increase pack/unpack perf
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} | 15 | "2013-06-10T14:46:15Z" | "2013-06-13T11:50:01Z" | "2013-06-12T14:47:58Z" | NONE | null | The exception handling code in _get_concatenated_data() relies on the exception not being generated in self._prepare_blocks() and can generate an exception with this non-informative error message:
```
Traceback (most recent call last):
File "<pyshell#5>", line 12, in <module>
df = pd.concat([activedf,closeddf])
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 873, in concat
return op.get_result()
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 957, in get_result
new_data = self._get_concatenated_data()
File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 1001, in _get_concatenated_data
new_data[item] = self._concat_single_item(rdata, item)
UnboundLocalError: local variable 'rdata' referenced before assignment
```
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```
AttributeError: Unknown property figsize
```
It would be extremely helpful for `hist` to be easily resizable, as the default layout for a column-wise set of histograms is rarely readable for even a moderate number of columns.
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} | 5 | "2013-06-10T16:00:50Z" | "2015-01-26T01:08:20Z" | "2015-01-26T01:08:20Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/17027470/pandas-groupby-and-multiindex
not sure how useful this is....but say we provide `dropna=True` (which is the current behavior), but if `dropna=False` then can reindex by all the combinations of each index level as a reindexer...
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https://api.github.com/repos/pandas-dev/pandas/issues/3836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3836/events | https://github.com/pandas-dev/pandas/issues/3836 | 15,356,080 | MDU6SXNzdWUxNTM1NjA4MA== | 3,836 | Strange results when slicing objects using ix | {
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] | closed | false | null | [] | null | 2 | "2013-06-10T16:21:32Z" | "2013-06-10T19:13:29Z" | "2013-06-10T19:13:29Z" | CONTRIBUTOR | null | Here's a weird one [from SO](http://stackoverflow.com/questions/17020763/pandas-0-10-1-to-0-11-0-is-method/). Bug in 0.11.0 (fixed in dev).
```
accurate_ICB = ['SA EQUITY CFD', 'SA EQUITY', 'SA SSF']
data = {'Classification': ['SA EQUITY CFD', 'bbb', 'SA EQUITY', 'SA SSF', 'aaa'],
'Random': [1,2,3,4,5],
'X': ['correct', 'wrong','correct', 'correct','wrong']}
df =pd.DataFrame(data)
x = df[~df.Classification.isin(accurate_ICB)]
df.ix[x.index,'X'] = df['Classification']
print df
```
In pandas 0.11.0 it produces:
```
Classification Random X
0 SA EQUITY CFD 1 correct
1 bbb 2 bbb
2 SA EQUITY 3 correct
3 SA SSF 4 correct
4 aaa 5 bbb
```
In pandas 0.11.1dev it produces:
```
Classification Random X
0 SA EQUITY CFD 1 correct
1 bbb 2 bbb
2 SA EQUITY 3 correct
3 SA SSF 4 correct
4 aaa 5 aaa
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3837/events | https://github.com/pandas-dev/pandas/issues/3837 | 15,357,764 | MDU6SXNzdWUxNTM1Nzc2NA== | 3,837 | to_clipboard() required xsel to be installed in Linux | {
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} | [] | closed | false | null | [] | null | 8 | "2013-06-10T16:59:14Z" | "2013-06-12T14:14:34Z" | "2013-06-12T14:14:34Z" | CONTRIBUTOR | null | Must be mentioned somewhere, or atleast the message should be more helpful. I saw the Popen failing and installed xsel and it worked.
The last error line was
```
OSError: [Errno 2] No such file or directory
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3838/events | https://github.com/pandas-dev/pandas/issues/3838 | 15,358,003 | MDU6SXNzdWUxNTM1ODAwMw== | 3,838 | read_clipboard() on master failing and not giving helpful message | {
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} | [] | closed | false | null | [] | null | 7 | "2013-06-10T17:04:32Z" | "2013-06-10T17:33:33Z" | "2013-06-10T17:33:33Z" | CONTRIBUTOR | null | The end user might need to know a better error. Strangely it worked for me once. I tried many times again after copying to clipboard the same thing and it didn't work.
```
In [34]: pd.read_clipboard()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-34-475cedd2a1b1> in <module>()
----> 1 pd.read_clipboard()
/usr/local/lib/python2.7/dist-packages/pandas-0.11.1.dev_9713e45-py2.7-linux-x86_64.egg/pandas/io/clipboard.pyc in read_clipboard(**kwargs)
12 from pandas.util.clipboard import clipboard_get
13 text = clipboard_get()
---> 14 return read_table(StringIO(text), **kwargs)
15
16
NameError: global name 'read_table' is not defined
```
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} | [] | closed | false | null | [] | null | 0 | "2013-06-10T19:13:04Z" | "2014-06-26T11:27:24Z" | "2013-06-10T19:13:17Z" | CONTRIBUTOR | null | cc #3836
Added test for example from OP (which is not working in 0.11 but is fixed in master).
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} | 2 | "2013-06-10T19:15:24Z" | "2014-07-16T08:13:15Z" | "2013-06-13T18:42:47Z" | CONTRIBUTOR | null | closes #3674
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} | [] | closed | false | null | [] | null | 0 | "2013-06-10T20:40:22Z" | "2014-07-07T04:45:28Z" | "2013-06-11T17:43:23Z" | CONTRIBUTOR | null | now, 999.0 (a float) will have: ['999','999.0'] added for matching, as well as matching a float value of 999.0 in a float column
closes #3611 again!
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} | 9 | "2013-06-11T01:48:55Z" | "2018-07-06T22:00:12Z" | "2018-07-06T22:00:12Z" | CONTRIBUTOR | null | Should I be able to [slice like](http://pandas.pydata.org/pandas-docs/dev/indexing.html#advanced-indexing-with-hierarchical-index) follows:
```
In [1]: df
Out[1]:
report_date item_id views category
0 2013-06-01 00:00:00 2 3 a
1 2013-06-01 00:00:00 2 2 b
2 2013-06-01 00:00:00 5 16 a
3 2013-06-01 00:00:00 2 4 c
4 2013-06-01 00:00:00 2 5 d
In [2]: pivot = df.pivot_table(values=['views'], rows=['report_date','item_id'], aggfunc='sum')
In [3]: pivot
Out[3]:
views
report_date item_id
2013-06-01 2 14
5 16
In [4]: pivot.ix[('2013-6-01', 3):('2013-06-1', 6)]
Out[4]:
views
report_date item_id
2013-06-01 5 16
```
But if I try and slice from outside any of the ranges, I get an exception:
```
In [21]: pivot.ix[('2013-6-01',3):('2013-06-02', 6)]
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-21-390c2ec15fd2> in <module>()
----> 1 pivot.ix[('2013-6-01',3):('2013-06-02', 6)]
/Users/234BroadWalk/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
46 return self._getitem_tuple(key)
47 else:
---> 48 return self._getitem_axis(key, axis=0)
49
50 def _get_label(self, label, axis=0):
/Users/234BroadWalk/pandas/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
390 labels = self.obj._get_axis(axis)
391 if isinstance(key, slice):
--> 392 return self._get_slice_axis(key, axis=axis)
393 elif _is_list_like(key) and not (isinstance(key, tuple) and
394 isinstance(labels, MultiIndex)):
/Users/234BroadWalk/pandas/pandas/core/indexing.pyc in _get_slice_axis(self, slice_obj, axis)
652 else:
653 try:
--> 654 indexer = labels.slice_indexer(start, stop, slice_obj.step)
655 except Exception:
656 if _is_index_slice(slice_obj):
/Users/234BroadWalk/pandas/pandas/core/index.pyc in slice_indexer(self, start, end, step)
1199 This function assumes that the data is sorted, so use at your own peril
1200 """
-> 1201 start_slice, end_slice = self.slice_locs(start, end)
1202
1203 # return a slice
/Users/234BroadWalk/pandas/pandas/core/index.pyc in slice_locs(self, start, end, strict)
2276 if not isinstance(end, tuple):
2277 end = end,
-> 2278 end_slice = self._partial_tup_index(end, side='right')
2279
2280 return start_slice, end_slice
/Users/234BroadWalk/pandas/pandas/core/index.pyc in _partial_tup_index(self, tup, side)
2293 if lab not in lev:
2294 if not lev.is_type_compatible(lib.infer_dtype([lab])):
-> 2295 raise Exception('Level type mismatch: %s' % lab)
2296
2297 # short circuit
Exception: Level type mismatch: 2013-06-02
```
see [this SO question](http://stackoverflow.com/questions/17035108/filtering-based-on-the-rows-data-after-creating-a-pivot-table-in-python-pandas).
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} | 15 | "2013-06-11T03:47:13Z" | "2014-01-19T00:47:05Z" | "2013-06-11T14:15:58Z" | NONE | null | ```
Hi,
Again the std issue
When I call numpy.std outside pandas I get the population std where as if I call inside it gives me sample std. How can I ask pandas to allow np.std to behave as it originally does?
wit best regards,
Sudheer
In [13]: ts[53153:53155]
Out[13]:
2007-09-15 15:30:00 30.1
2007-09-15 15:40:00 31.6
Freq: 10T
In [14]: np.std(ts[53153:53155])
Out[14]: 0.75
In [15]: ts[53153:53155].resample('D',how=np.std)
Out[15]:
2007-09-15 1.06066
Freq: D
```
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} | 7 | "2013-06-11T05:21:44Z" | "2014-07-16T08:13:23Z" | "2013-06-13T18:42:30Z" | CONTRIBUTOR | null | Added documentation for to_clipboard().
Closes #3784
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Apologies in advance if this is known, fixed, etc.--I am new to pandas/python/Git (trifecta...).
mask_missing appears to handle an all-NaN array (arr=[nan,...,nan]) poorly (discovered while trying to do a .replace()).
The problem point appears to be mask=arr==x.
If arr is all nan, then arr==x results in mask=False.
This promulgates downwards and causes mask_missing to return [False], which then causes higher-level functions (which expect mask_missing to return something the same size as arr) to error out.
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} | 20 | "2013-06-11T10:33:11Z" | "2014-07-16T08:13:24Z" | "2013-06-13T18:41:39Z" | CONTRIBUTOR | null | Use [pyperclip](http://coffeeghost.net/src/pyperclip.py) to manage copy and pasting.
Fixes #3837, also cc #3845.
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} | 0 | "2013-06-11T14:23:16Z" | "2013-09-25T12:31:36Z" | "2013-09-25T12:31:36Z" | CONTRIBUTOR | null | ```
In [34]: s = Series([30.1,31.6],index=[Timestamp('20070915 15:30:00'),Timestamp('20070915 15:40:00')])
In [35]: s
Out[35]:
2007-09-15 15:30:00 30.1
2007-09-15 15:40:00 31.6
dtype: float64
In [36]: s.resample('D',how=lambda x: np.std(x))
Out[36]:
2007-09-15 NaN
Freq: D, dtype: float64
In [37]: s.groupby(pd.TimeGrouper('D')).apply(lambda x: np.std(x))
Out[37]:
2007-09-15 0.75
dtype: float64
```
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} | 10 | "2013-06-11T14:51:47Z" | "2014-05-28T13:30:23Z" | "2014-05-28T13:30:23Z" | CONTRIBUTOR | null | seems lots of questions in regards to this simple, but very useful function
http://stackoverflow.com/questions/17046823/filter-pandas-dataframe-with-another-series
http://stackoverflow.com/questions/17223362/the-python-version-of-rs-in-function
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NumPy version: '1.7.1'
Replication of Error:
```
import pandas
pandas.read_csv('http://www.netfonds.no/quotes/tradedump.php?date=20130611&paper=AAPL.A&csv_format=csv')
```
Error Message:
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-5-e950a741cc55> in <module>()
----> 1 pandas.read_csv('http://www.netfonds.no/quotes/tradedump.php?date=20130611&paper=AAPL.A&csv_format=csv')
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
399 buffer_lines=buffer_lines)
400
--> 401 return _read(filepath_or_buffer, kwds)
402
403 parser_f.__name__ = name
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
214 return parser
215
--> 216 return parser.read()
217
218 _parser_defaults = {
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
631 # self._engine.set_error_bad_lines(False)
632
--> 633 ret = self._engine.read(nrows)
634
635 if self.options.get('as_recarray'):
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
955
956 try:
--> 957 data = self._reader.read(nrows)
958 except StopIteration:
959 if nrows is None:
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader.read (pandas\src\parser.c:6014)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_low_memory (pandas\src\parser.c:6231)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_rows (pandas\src\parser.c:6833)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._tokenize_rows (pandas\src\parser.c:6718)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.raise_parser_error (pandas\src\parser.c:17131)()
CParserError: Error tokenizing data. C error: Expected 1 fields in line 72, saw 3
```
I am trying to pull stock tick data from netfounds.com as you can see above. The URL is incorrect because the correct ticker symbol is supposed to be `AAPL.O` and not `AAPL.A` This issue seems to come and go, as some days it just returns a jumbled data frame when `AAPL.A` is queried, (this is what I want), and does not throw the `CParserError`. Having the incorrect query is only a convenience in my program but I'm trying to understand why pandas only throws this error sometimes.
More Info about the endings:
Typically I will iterate through a list of exchanges in this dict:
```
exchanges = {'Nasdaq': '.O', 'Nyse' : '.N', 'Amex': '.A'}
```
to arrive at the correct ticker 'ending` and get the tick data.
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} | 8 | "2013-06-11T21:12:45Z" | "2014-02-18T19:06:18Z" | "2014-02-18T19:06:18Z" | CONTRIBUTOR | null | There's nothing on the docs about these guys:
```
from pandas.io import data
data.get_components_yahoo data.get_data_fred data.get_data_yahoo data.get_quote_yahoo
data.get_data_famafrench data.get_data_google data.get_quote_google
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3855 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3855/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3855/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3855/events | https://github.com/pandas-dev/pandas/pull/3855 | 15,426,986 | MDExOlB1bGxSZXF1ZXN0NjI2MDA4Nw== | 3,855 | TST: Fix missing import in io/tests/test_json | {
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} | [] | closed | false | null | [] | null | 8 | "2013-06-11T22:55:48Z" | "2014-06-27T11:37:18Z" | "2013-06-12T00:54:34Z" | CONTRIBUTOR | null | Nose import is missing. If you get to the error at the last line, it throws an error because nose is never imported.
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https://api.github.com/repos/pandas-dev/pandas/issues/3856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3856/events | https://github.com/pandas-dev/pandas/pull/3856 | 15,427,404 | MDExOlB1bGxSZXF1ZXN0NjI2MDMwMQ== | 3,856 | ENH Prefer requests over urllib2 | {
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} | 19 | "2013-06-11T23:07:52Z" | "2014-06-24T19:26:04Z" | "2013-08-26T00:09:30Z" | CONTRIBUTOR | null | At the moment we use urllib2 for http requests, this branch prefers to use requests if it's installed. For one thing it means you can read gzipped json, which is more difficult in urllib2:
```
url = 'https://api.stackexchange.com/2.1/search?page=1&pagesize=10&order=desc&sort=activity&tagged=pandas&site=stackoverflow'
result = pd.read_json(url) # in master this raises ValueError: Expected object or value
```
cc #3804 @jreback
Thoughts?
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$ ci/print_versions.py
$ ci/after_script.sh
/home/travis/build.sh: line 208: ci/after_script.sh: No such file or directory
Done. Your build exited with 1.
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https://api.github.com/repos/pandas-dev/pandas/issues/3858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3858/events | https://github.com/pandas-dev/pandas/pull/3858 | 15,431,944 | MDExOlB1bGxSZXF1ZXN0NjI2MjcyOQ== | 3,858 | TST slicing regression test | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3859 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3859/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3859/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3859/events | https://github.com/pandas-dev/pandas/pull/3859 | 15,432,535 | MDExOlB1bGxSZXF1ZXN0NjI2Mjk5MQ== | 3,859 | DOC add to_datetime to api.rst | {
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} | 34 | "2013-06-12T02:54:37Z" | "2014-06-16T20:26:09Z" | "2013-06-21T00:07:10Z" | CONTRIBUTOR | null | Either I'm being thick or `to_datetime` isn't in the docs (does adding it like this add it?)
Should also put something in basics...?
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https://api.github.com/repos/pandas-dev/pandas/issues/3860 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3860/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3860/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3860/events | https://github.com/pandas-dev/pandas/pull/3860 | 15,437,239 | MDExOlB1bGxSZXF1ZXN0NjI2NTA1Nw== | 3,860 | Add colormap= argument to DataFrame plotting methods | {
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} | 30 | "2013-06-12T06:57:17Z" | "2014-06-18T19:57:31Z" | "2013-06-27T02:56:30Z" | CONTRIBUTOR | null | I frequently plot DataFrames with a large number of columns and generally have difficulty distinguishing series due to the short cycle length of the default color scheme.
Especially in cases where the ordering of columns has significant information, the ideal way to color the series would be with a matplotlib colormap that uniformly spaces colors. This is pretty straightforward with pyplot, but pretty annoying to have to repeatedly do.
This patch modifies DataFrame plotting functions to take a `colormap=` argument consisting of either a `str` name of a [matplotlib colormap](http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps) or a colormap object itself.
```
df.cumsum().plot(colormap='jet', figsize=(10,5))
```
KDE plot:
```
df.plot(kind='kde', colormap='jet', figsize=(10,5))
```
Some colormaps don't work as well on a white background (the 0 column is white):
df.cumsum().plot(colormap=cm.Greens, figsize=(10,5))
But work better for other graph types:
df.plot(kind='bar', colormap='jet', figsize=(10,5))
Parallel coordinates on the iris dataset:
```
parallel_coordinates(iris, 'Name', colormap='gist_rainbow')
```
Andrews curves (I'd appreciate someone double checking this one; don't think I have it quite right):
```
andrews_curves(iris, 'Name', colormap='winter')
```
I've included some test coverage and unified all the color creation code into one method `_get_standard_colors()`. I started adding to the documentation but ran into a weird issue with the sphinx plot output. When adding this to `visualization.rst`:
```
.. ipython:: python
from matplotlib import cm
df = DataFrame(randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()
plt.figure()
@savefig greens.png width=6in
df.plot(colormap=cm.Greens)
```
I get this output (the lines should be white->green):
My first thought was that it was the `options.display.mpl_style = 'default'`, but plots render fine in IPython with this setting. My guess is something in `@savefig`, but is anyone familiar with what might be happening here?
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} | 4 | "2013-06-12T10:22:25Z" | "2013-12-04T00:56:41Z" | "2013-06-18T23:29:04Z" | CONTRIBUTOR | null | We have trouble combining pandas with <a href="http://buildout.org">buildout</a>. It looks like pandas depends on numpy during the setup phase, but it doesn't it declare the `setup_requires` (<a href="http://pythonhosted.org/distribute/setuptools.html">ref</a>) directive in setup.py. Any reason why this feature remains unused, or shall I submit a pull request? Because I see the matplotlib and numexpr don't use it as well.
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} | 5 | "2013-06-12T13:39:21Z" | "2013-10-03T00:32:01Z" | "2013-08-16T19:27:34Z" | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.11.1.dev-45d298d'
In [3]: pd.Series(10)
Out[3]: 10
```
Ok, when setting index explicitly
```
In [4]: pd.Series(10, index=[0])
Out[4]:
0 10
dtype: int64
```
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} | 9 | "2013-06-12T14:16:48Z" | "2013-07-02T14:39:42Z" | "2013-07-02T14:39:42Z" | CONTRIBUTOR | null | ```
import pandas as pd
pd.Timestamp(pd.Timestamp.min)
```
Throws: ValueError: Out of bounds nanosecond timestamp: 1-01-01 00:00:00
Pandas version: 0.11.1_dev
Numpy version: 1.7.1
The the min and max methods on the Timestamp class return simple Python datetime objects, but more importantly they do not adhere to the limitations of Timestamps. They return the default datetime(1,1,1) and datetime(9999, 12, 31, 23, 59, 59, 999999) respectively - both of which are well outside of the valid Timestamp range.
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} | [] | closed | false | null | [] | null | 4 | "2013-06-12T14:24:44Z" | "2013-06-12T21:47:09Z" | "2013-06-12T21:47:09Z" | CONTRIBUTOR | null | https://travis-ci.org/gliptak/pandas/jobs/8002651
#
ERROR: test_options_warnings (pandas.io.tests.test_yahoo.TestYahoo)
---
Traceback (most recent call last):
File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/pandas-0.11.1.dev_e23f225-py2.6-linux-x86_64.egg/pandas/io/tests/test_yahoo.py", line 184, in test_options_warnings
(calls, puts) = aapl.get_options_data(month=month, year=year)
File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/pandas-0.11.1.dev_e23f225-py2.6-linux-x86_64.egg/pandas/io/data.py", line 521, in get_options_data
from lxml.html import parse
ImportError: No module named lxml.html
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https://api.github.com/repos/pandas-dev/pandas/issues/3866 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3866/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3866/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3866/events | https://github.com/pandas-dev/pandas/issues/3866 | 15,459,982 | MDU6SXNzdWUxNTQ1OTk4Mg== | 3,866 | BUG: Unexpected behaviour when reading large text files with mixed datatypes | {
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} | 4 | "2013-06-12T16:26:25Z" | "2013-09-29T19:28:09Z" | "2013-09-29T19:28:09Z" | NONE | null | read_csv gives unexpected behaviour with large files if a column contains both strings and integers. eg
``` python
>>> df=DataFrame({'colA':range(500000-1)+['apple', 'pear']+range(500000-1)})
len(set(df.colA))
500001
>>> df.to_csv('testpandas2.txt')
>>> df2=read_csv('testpandas2.txt')
>>> len(set(df2.colA))
762143
>>> pandas.__version__
'0.11.0'
```
It seems some of the integers are parsed as integers and others as strings.
``` python
>>> list(set(df2.colA))[-10:]
['282248', '282249', '282240', '282241', '282242', '15679', '282244', '282245', '282246', '282247']
>>> list(set(df2.colA))[:10]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3867 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3867/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3867/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3867/events | https://github.com/pandas-dev/pandas/issues/3867 | 15,465,735 | MDU6SXNzdWUxNTQ2NTczNQ== | 3,867 | json round trip exception | {
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} | [] | closed | false | null | [] | null | 13 | "2013-06-12T18:23:36Z" | "2013-06-13T18:05:22Z" | "2013-06-13T18:05:22Z" | CONTRIBUTOR | null | [This csv](https://raw.github.com/hayd/lahman2012/master/csvs/Teams.csv) (from the baseball database) reads ok to a DataFrame, pastes ok to a json.
```
In [6]: df = pd.read_csv('https://raw.github.com/hayd/lahman2012/master/csvs/Teams.csv')
In [7]: s = df.to_json()
In [8]: pd.read_json(s)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-ebde42cd0695> in <module>()
----> 1 pd.read_json(s)
/Users/234BroadWalk/pandas/pandas/io/json.pyc in read_json(path_or_buf, orient, typ, dtype, numpy, parse_dates, keep_default_dates)
158 obj = None
159 if typ == 'frame':
--> 160 obj = FrameParser(json, orient, dtype, numpy, parse_dates, keep_default_dates).parse()
161
162 if typ == 'series' or obj is None:
/Users/234BroadWalk/pandas/pandas/io/json.pyc in parse(self)
185
186 def parse(self):
--> 187 self._parse()
188 if self.obj is not None:
189 self._convert_axes()
/Users/234BroadWalk/pandas/pandas/io/json.pyc in _parse(self)
284 try:
285 if orient == "columns":
--> 286 args = loads(json, dtype=dtype, numpy=True, labelled=True)
287 if args:
288 args = (args[0].T, args[2], args[1])
TypeError: long() argument must be a string or a number, not 'NoneType'
```
cc #3804
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https://api.github.com/repos/pandas-dev/pandas/issues/3868 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3868/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3868/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3868/events | https://github.com/pandas-dev/pandas/pull/3868 | 15,466,125 | MDExOlB1bGxSZXF1ZXN0NjI3OTkxOQ== | 3,868 | BLD: remove after_script.sh from travis since it does not exist anymore | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3869 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3869/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3869/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3869/events | https://github.com/pandas-dev/pandas/issues/3869 | 15,467,834 | MDU6SXNzdWUxNTQ2NzgzNA== | 3,869 | BUG: Key Error: range exception when printing DataFrame | {
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} | 9 | "2013-06-12T19:04:13Z" | "2013-09-05T02:43:34Z" | "2013-09-05T02:18:00Z" | NONE | null | Here's a reprodu
```
df = pd.DataFrame({ 'A' : ['foo',"~:{range}:0"], 'B' : ['bar','bah'] })
df
A B
0 foo bar
1 ~:{range}:0 bah
df.set_index(['A']).info()
*** KeyError: 'range'
```
in core/index.py, check if `head/tail` is not already an instance of a str
```
def summary(self, name=None):
if len(self) > 0:
head = self[0]
if hasattr(head,'format'):
head = head.format()
tail = self[-1]
if hasattr(tail,'format'):
tail = tail.format()
```
Printing a DataFrame created from two Series objects (previously columns in other DataFrames) results in a "Key Error: 'range'" exception being raised. The DataFrame creation seems to work fine. Printing other DataFrames with the same "problematic" index also works okay.
Test code:
```
import pstats
import pandas as pd
# Import some cProfile data
run1 = pstats.Stats('run1.prof')
run2 = pstats.Stats('run2.prof')
# Utility function to convert pstats dict into a list of lists
def pstats_to_list( stats ):
plist = []
for key, value in stats.strip_dirs().stats.items():
filename, lineno, func_name = key
ccalls, ncalls, total_time, cum_time, callers = value
name = "{}:{}:{}".format( filename, func_name, lineno )
plist.append( [name, ncalls, total_time, cum_time] )
return plist
jit_list = pstats_to_list( run1 )
nojit_list = pstats_to_list( run2 )
# Create DataFrames for the profile run data
columns=['name','ncalls','ttime', 'ctime']
jdf = pd.DataFrame( jit_list, columns = columns )
ndf = pd.DataFrame( nojit_list, columns = columns )
# Set the 'name' column to be the index (for plotting)
jdf = jdf.set_index( 'name' )
ndf = ndf.set_index( 'name' )
# These DataFrames print fine
print jdf
print ndf
# Extract out the 'ttime' columns
x = ndf['ttime']
y = jdf['ttime']
# Create a new DataFrame using the 'ttime' Series from jdf and ndf
z = pd.DataFrame( {'jit': x, 'nojit': y } )
# Print some data.... this works
print z[0:10]
# Print some data.... this raises "KeyError: 'range'"
print z
```
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} | 2 | "2013-06-12T19:26:01Z" | "2013-10-02T21:04:46Z" | "2013-09-22T17:04:18Z" | NONE | null | might be related to #3849
This bug can be reproduced with 0.11.1.dev-0538c43 and 0.11.0.
The following code should keep the values on 2012-09-12, 2012-09-19, and 2012-09-26 and insert NaN between these dates. However, the values of all rows between 2012-09-12 to 2012-09-26 are NaN.
``` python
import pandas as pd
pd.DataFrame({'value':[1,2,3]},index= pd.Series([pd.Timestamp('2012-09-12 10:00:00'), pd.Timestamp('2012-09-19 10:00:00'), pd.Timestamp('2012-09-26 10:00:00')])).resample('D')
```
Output:
```
value
2012-09-12 NaN
2012-09-13 NaN
2012-09-14 NaN
2012-09-15 NaN
2012-09-16 NaN
2012-09-17 NaN
2012-09-18 NaN
2012-09-19 NaN
2012-09-20 NaN
2012-09-21 NaN
2012-09-22 NaN
2012-09-23 NaN
2012-09-24 NaN
2012-09-25 NaN
2012-09-26 NaN
```
The weird thing was, if I changed the time of one index slightly, the output became correct.
``` python
pd.DataFrame({'value':[1,2,3]},index= pd.Series([pd.Timestamp('2012-09-12 10:00:00'), pd.Timestamp('2012-09-19 10:00:00'), pd.Timestamp('2012-09-26 00:00:00')])).resample('D')
```
```
value
2012-09-12 1
2012-09-13 NaN
2012-09-14 NaN
2012-09-15 NaN
2012-09-16 NaN
2012-09-17 NaN
2012-09-18 NaN
2012-09-19 2
2012-09-20 NaN
2012-09-21 NaN
2012-09-22 NaN
2012-09-23 NaN
2012-09-24 NaN
2012-09-25 NaN
2012-09-26 3
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3871/events | https://github.com/pandas-dev/pandas/issues/3871 | 15,472,203 | MDU6SXNzdWUxNTQ3MjIwMw== | 3,871 | DOC: Timings/space of datatypes in the docs | {
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} | 13 | "2013-06-12T20:29:41Z" | "2015-03-06T00:42:13Z" | null | CONTRIBUTOR | null | Would it be useful to have a section in the docs discussing:
- how much space pandas objects take up (vaguely how many rows \* columns equates to size space, if I have an xGb csv how big will it be in pandas/HDF5/etc.).
- how quick are some standard operations are likely to be (e.g. read_csv in/merge/join/etc. vs data size).
- how do these compare to other platforms (?)
Probably distinct from comparing _functionality_ (although that may also be interesting) e.g. like numpy do it for _features_ against matlab here: http://wiki.scipy.org/NumPy_for_Matlab_Users) e.g. #3980
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} | 16 | "2013-06-12T20:56:48Z" | "2013-06-17T20:43:23Z" | "2013-06-17T20:22:51Z" | CONTRIBUTOR | null | The build succeeds if I change
void initObjToJSON(void)
to
int initObjToJSON(void)
in objToJSON.c, but I don't know if there are repercussions. I haven't tested with numpy 1.7 and lower.
---
$ git log
commit 51cc9d9d21554701f70ef24392bd78a3da189335
...
$ python3 -c "import numpy;print(numpy.**version**)"
1.7.1
(also fails with 1.8.0.dev-f7ea474)
$ python3 setup.py build
...
pandas/src/ujson/python/objToJSON.c:118:5: error: void function 'initObjToJSON' should not return a value [-Wreturn-type]
import_array();
^~~~~~~~~~~~~~
/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numpy/core/include/numpy/__multiarray_api.h:1693:144: note: expanded from macro 'import_array'
#define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NUMPY_IMPORT_ARRAY_RETVAL; } }
---
from generate_numpy_api.py
# if PY_VERSION_HEX >= 0x03000000
# define NUMPY_IMPORT_ARRAY_RETVAL NULL
# else
# define NUMPY_IMPORT_ARRAY_RETVAL
# endif
# define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NUMPY_IMPORT_ARRAY_RETVAL; } }
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} | 1 | "2013-06-12T21:17:03Z" | "2013-06-13T12:22:47Z" | "2013-06-13T12:22:47Z" | NONE | null | The following code gives incorrect result (using pandas 0.11):
``` python
import pandas
df = pandas.DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
print df[["a", "a"]].values # works
print list(df[["a", "a"]].itertuples()) # FAILS!
```
The last lines prints [(0, 'a', 'a'), (1, 'a', 'a')] instead of [(0, 1, 1), (1, 2, 2), (2, 3, 3)].
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} | 2 | "2013-06-13T00:09:07Z" | "2014-06-19T22:24:06Z" | "2013-06-15T12:26:49Z" | MEMBER | null | This is necessary because clang complains about the return type. There's a
call to the macro import_array() which injects a return statement into
wherever it's used.
closes #3872.
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] | null | 17 | "2013-06-13T00:52:37Z" | "2013-06-15T00:14:09Z" | "2013-06-15T00:14:09Z" | MEMBER | null | The following throws a `UnicodeDecodeError`.
```
In [8]: df = mkdf(10, 10, data_gen_f=lambda r,c:randn(), r_idx_type='dt', c_idx_type='u')
In [9]: s = Series(randn(5,), df.index[:5])
In [10]: s.index.join(df.columns, how='outer').join(s.index)
```
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} | 13 | "2013-06-13T02:53:57Z" | "2014-06-19T05:28:21Z" | "2013-06-13T19:14:10Z" | CONTRIBUTOR | null | revised argument structure for `read_json` to control dtype conversions, which are all on by default:
- `convert_axes` : if you for some reason want to turn off dtype conversion on the axes (only really necessary if you have string-like numbers)
- `dtype` : now accepts a dict of name -> dtype for specific conversions, or True to try to coerce all
- `convert_dates` : default True (in conjunction with `keep_default_dates` determines which columns to attempt date conversion)
DOC updates for all
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} | 0 | "2013-06-13T03:57:48Z" | "2013-06-13T17:44:38Z" | "2013-06-13T17:44:37Z" | MEMBER | null | I can't tell if this is bug or not. I think object indices should stay as object indices if they are originally mixed, but currently an index join converts something date-ish to dates...
```
In [9]: df = mkdf(10, 10, data_gen_f=lambda*args:randn(), r_idx_type='i', c_idx_type='dt')
In [10]: df.columns.join(df.index, how='outer').join(df.columns)
Out[10]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-03 00:00:00, ..., 1970-01-01 00:00:00.000000009]
Length: 20, Freq: None, Timezone: None
```
this could be an edge case, but it's a problem when trying to join indexes in a general way for upcoming `pandas.eval`...i.e., i can't just call `align`.
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} | 12 | "2013-06-13T04:35:16Z" | "2014-07-03T18:56:41Z" | "2013-06-13T17:44:38Z" | MEMBER | null | closes #3877.
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https://api.github.com/repos/pandas-dev/pandas/issues/3879 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3879/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3879/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3879/events | https://github.com/pandas-dev/pandas/pull/3879 | 15,489,563 | MDExOlB1bGxSZXF1ZXN0NjI5MTU4NA== | 3,879 | BUG: allow itertuples to work with frames with duplicate column names | {
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} | 1 | "2013-06-13T05:48:29Z" | "2014-06-26T11:26:17Z" | "2013-06-13T12:22:47Z" | MEMBER | null | closes #3873.
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} | 1 | "2013-06-13T07:28:08Z" | "2013-06-13T19:46:37Z" | "2013-06-13T19:46:37Z" | NONE | null | ```
In [1]: from pandas import DataFrame
In [2]: x = DataFrame([[1, 1], [1, 1]])
In [3]: x.index.name = 'index_name'
In [4]: x.iloc[[0, 1]].index.name
Out[4]: 'index_name'
In [5]: x.loc[[0, 1]].index.name
In [6]: x.loc[[0, 1]].index.name is None
Out[6]: True
```
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} | 0 | "2013-06-13T10:37:25Z" | "2014-02-14T03:24:56Z" | "2014-02-14T03:24:56Z" | CONTRIBUTOR | null | Groupbys from TGs seem to have some useful functionality suppressed/hidden (?)
```
import datetime as DT
df = pd.DataFrame({
'Branch' : 'A A A A A B'.split(),
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
DT.datetime(2013,1,1,13,0),
DT.datetime(2013,1,1,13,5),
DT.datetime(2013,10,1,20,0),
DT.datetime(2013,10,2,10,0),
DT.datetime(2013,12,2,12,0),
DT.datetime(2013,12,2,14,0),
]})
df = df.set_index('Date', drop=False)
g = df.groupby(pd.TimeGrouper('6M'))
In [104]: g.gr
g.group_keys g.grouper g.groups
In [104]: g.groups
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-104-b207e9b2ead3> in <module>()
----> 1 g.groups
/Users/234BroadWalk/pandas/pandas/core/groupby.pyc in __getattr__(self, attr)
235
236 raise AttributeError("'%s' object has no attribute '%s'" %
--> 237 (type(self).__name__, attr))
238
239 def __getitem__(self, key):
AttributeError: 'DataFrameGroupBy' object has no attribute 'groups'
```
cc #3794
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} | 12 | "2013-06-13T11:27:13Z" | "2013-06-13T18:49:52Z" | "2013-06-13T18:49:52Z" | CONTRIBUTOR | null | See this [SO answer](http://stackoverflow.com/a/15411073/1240268), they want to use memoisation.
OP points out this gets different results from (presumably it does it off id)
```
hash(pd.DataFrame([1,2,3]))
```
Should they be hashable or should hash raise? (does it defeat the point of hashing if hashing is expensive?) cc @cpcloud
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} | 0 | "2013-06-13T12:03:06Z" | "2016-09-29T22:44:20Z" | "2016-09-29T22:31:27Z" | CONTRIBUTOR | null | I feel having such a feature would help people get better at Pandas, learn some advanced skills and better ways to do things, from people who have more experience with it. Maybe sort of a community blog or twitter feed. As an end user i feel it would be appreciated.
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} | 3 | "2013-06-13T13:29:45Z" | "2014-06-21T16:50:42Z" | "2013-06-13T18:49:52Z" | CONTRIBUTOR | null | fixes #3882
raise TypeError if trying to hash a DataFrame (or Panel etc.).
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} | 21 | "2013-06-13T15:28:41Z" | "2013-09-28T19:56:47Z" | "2013-09-28T19:56:47Z" | MEMBER | null | would be nice as @jreback says to implement this since indices are supposed to be immutable. currently they try to hash the underlying `ndarray` which of course fails because it is mutable.
[succinct reasoning behind why you need immutability for hashables](http://stackoverflow.com/a/2671398/564538)
[python docs quote about using `^` (exclusive or) in the implementation](http://stackoverflow.com/a/2671402/564538)
but see [this answer](http://stackoverflow.com/questions/5386694/fast-way-to-hash-numpy-objects-for-caching) for a way to hash numpy arrays.
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https://api.github.com/repos/pandas-dev/pandas/issues/3886 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3886/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3886/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3886/events | https://github.com/pandas-dev/pandas/issues/3886 | 15,513,237 | MDU6SXNzdWUxNTUxMzIzNw== | 3,886 | DOC: date_range method instead of DateRange | {
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As a beginner, I'm not sure if the documentation is old or my version of pandas is. Also, I don't know how to tag this issue as "Docs"
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https://api.github.com/repos/pandas-dev/pandas/issues/3887 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3887/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3887/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3887/events | https://github.com/pandas-dev/pandas/pull/3887 | 15,523,077 | MDExOlB1bGxSZXF1ZXN0NjMwODQyMg== | 3,887 | BUG: (GH3880) index names are now propogated with loc/ix | {
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} | 0 | "2013-06-13T18:57:18Z" | "2014-07-03T09:04:12Z" | "2013-06-13T19:46:37Z" | CONTRIBUTOR | null | closes #3880
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} | 5 | "2013-06-13T19:06:36Z" | "2013-06-19T00:56:27Z" | "2013-06-19T00:56:27Z" | CONTRIBUTOR | null | It seems it returns different objects when passed array vs string:
```
In [1]: pd.to_datetime(['2012'])[0]
Out[1]: Timestamp('2012-01-01 00:00:00', tz=None)
In [2]: pd.to_datetime('2012')
Out[2]: datetime.datetime(2012, 1, 1, 0, 0)
```
Also worth mentioning (though different):
```
In [3]: pd.Timestamp('2012')
Out[3]: Timestamp('2012-06-13 00:00:00', tz=None)
```
Seems a little inconsistent... (cc #3859)
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} | 3 | "2013-06-13T19:10:25Z" | "2013-06-19T11:25:39Z" | "2013-06-19T11:25:39Z" | CONTRIBUTOR | null | https://www.travis-ci.org/pydata/pandas/jobs/8061364
https://www.travis-ci.org/jreback/pandas/jobs/8061960
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} | 2 | "2013-06-13T19:45:50Z" | "2014-06-17T13:26:30Z" | "2013-06-19T00:56:27Z" | CONTRIBUTOR | null | closes #3888
resolves the following inconsistencies in the Timestamp/to_datetime interface
Things that do the same thing will now _do the same thing_!
```
In [1]: to_datetime = pd.to_datetime
In [2]: to_datetime('')
Out[2]: NaT
In [3]: to_datetime(['', ''])
Out[3]:
<class 'pandas.tseries.index.DatetimeIndex'>
[NaT, NaT]
Length: 2, Freq: None, Timezone: None
In [4]: Timestamp(0)
Out[4]: Timestamp('1970-01-01 00:00:00', tz=None)
In [5]: to_datetime(0)
Out[5]: Timestamp('1970-01-01 00:00:00', tz=None)
In [6]: to_datetime(['2012'])[0]
Out[6]: Timestamp('2012-01-01 00:00:00', tz=None)
In [8]: to_datetime('2012')
Out[8]: Timestamp('2012-01-01 00:00:00', tz=None)
In [9]: array = ['20120101','20120101 12:01:01']
In [11]: list(to_datetime(array))
Out[11]:
[Timestamp('2012-01-01 00:00:00', tz=None),
Timestamp('2012-01-01 12:01:01', tz=None)]
In [13]: map(Timestamp,array)
Out[13]:
[Timestamp('2012-01-01 00:00:00', tz=None),
Timestamp('2012-01-01 12:01:01', tz=None)]
```
Note that the following is still inconsisten and will be fixed in a future PR
```
In [14]: Timestamp('2012')
Out[14]: Timestamp('2012-06-18 00:00:00', tz=None)
In [15]: to_datetime('2012')
Out[15]: Timestamp('2012-01-01 00:00:00', tz=None)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3891 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3891/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3891/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3891/events | https://github.com/pandas-dev/pandas/pull/3891 | 15,528,801 | MDExOlB1bGxSZXF1ZXN0NjMxMTAxMA== | 3,891 | BLD: install older versions of numexpr/pytables on fulldeps/2 build | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3892 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3892/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3892/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3892/events | https://github.com/pandas-dev/pandas/issues/3892 | 15,529,437 | MDU6SXNzdWUxNTUyOTQzNw== | 3,892 | TST: bus error on sparc: pandas/hashtable.c:7282 while pandas.io.tests.test_pytables:TestHDFStore.test_encoding | {
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} | 5 | "2013-06-13T20:51:11Z" | "2013-06-14T13:13:15Z" | "2013-06-14T13:13:15Z" | CONTRIBUTOR | null | v0.11.0-492-gb204e14
```
test_encoding (pandas.io.tests.test_pytables.TestHDFStore) ...
Program received signal SIGBUS, Bus error.
__pyx_pf_6pandas_9hashtable_14Int64HashTable_16map_locations (__pyx_v_values=<optimized out>, __pyx_v_self=0xf7cc0900) at pandas/hashtable.c:7282
7282 __pyx_v_val = (*__Pyx_BufPtrStrided1d(__pyx_t_5numpy_int64_t *, __pyx_pybuffernd_values.rcbuffer->pybuffer.buf, __pyx_t_3, __pyx_pybuffernd_values.diminfo[0].strides));
(gdb) bt
#0 __pyx_pf_6pandas_9hashtable_14Int64HashTable_16map_locations (__pyx_v_values=<optimized out>, __pyx_v_self=0xf7cc0900) at pandas/hashtable.c:7282
#1 __pyx_pw_6pandas_9hashtable_14Int64HashTable_17map_locations (__pyx_v_self=<pandas.hashtable.Int64HashTable at remote 0xf7cc0900>, __pyx_v_values=<optimized out>) at pandas/hashtable.c:7190
#2 0x0007c2f0 in PyCFunction_Call (func=<built-in method map_locations of pandas.hashtable.Int64HashTable object at remote 0xf7cc0900>, arg=(<numpy.ndarray at remote 0x145d610>,), kw=0x0)
at ../Objects/methodobject.c:101
#3 0x00031228 in PyObject_Call (func=<built-in method map_locations of pandas.hashtable.Int64HashTable object at remote 0xf7cc0900>, arg=(<numpy.ndarray at remote 0x145d610>,), kw=0x0) at ../Objects/abstract.c:2529
#4 0xf6536a00 in __pyx_f_6pandas_5index_11IndexEngine_initialize (__pyx_v_self=0x13515f0) at pandas/index.c:4804
#5 0xf6533124 in __pyx_f_6pandas_5index_11IndexEngine__ensure_mapping_populated (__pyx_v_self=0x13515f0) at pandas/index.c:4716
#6 __pyx_pf_6pandas_5index_11IndexEngine_2__contains__ (__pyx_v_val='columns', __pyx_v_self=0x13515f0) at pandas/index.c:2502
#7 __pyx_pw_6pandas_5index_11IndexEngine_3__contains__ (__pyx_v_self=<pandas.index.Int64Engine at remote 0x13515f0>, __pyx_v_val='columns') at pandas/index.c:2471
#8 0x000329e0 in PySequence_Contains (seq=<pandas.index.Int64Engine at remote 0x13515f0>, ob='columns') at ../Objects/abstract.c:2387
#9 0x000e96bc in cmp_outcome (w=<optimized out>, v='columns', op=<optimized out>) at ../Python/ceval.c:4463
#10 PyEval_EvalFrameEx (f=<optimized out>, throwflag=<optimized out>) at ../Python/ceval.c:2287
```
```
(gdb) l 7270
7265 /* "pandas/hashtable.pyx":429
7266 *
7267 * for i in range(n):
7268 * val = values[i] # <<<<<<<<<<<<<<
7269 * k = kh_put_int64(self.table, val, &ret)
7270 * self.table.vals[k] = i
7271 */
7272 __pyx_t_3 = __pyx_v_i;
7273 __pyx_t_4 = -1;
7274 if (__pyx_t_3 < 0) {
(gdb)
7275 __pyx_t_3 += __pyx_pybuffernd_values.diminfo[0].shape;
7276 if (unlikely(__pyx_t_3 < 0)) __pyx_t_4 = 0;
7277 } else if (unlikely(__pyx_t_3 >= __pyx_pybuffernd_values.diminfo[0].shape)) __pyx_t_4 = 0;
7278 if (unlikely(__pyx_t_4 != -1)) {
7279 __Pyx_RaiseBufferIndexError(__pyx_t_4);
7280 {__pyx_filename = __pyx_f[0]; __pyx_lineno = 429; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
7281 }
7282 __pyx_v_val = (*__Pyx_BufPtrStrided1d(__pyx_t_5numpy_int64_t *, __pyx_pybuffernd_values.rcbuffer->pybuffer.buf, __pyx_t_3, __pyx_pybuffernd_values.diminfo[0].strides));
7283
7284 /* "pandas/hashtable.pyx":430
(gdb)
7285 * for i in range(n):
7286 * val = values[i]
7287 * k = kh_put_int64(self.table, val, &ret) # <<<<<<<<<<<<<<
7288 * self.table.vals[k] = i
7289 *
7290 */
7291 __pyx_v_k = kh_put_int64(__pyx_v_self->table, __pyx_v_val, (&__pyx_v_ret));
7292
7293 /* "pandas/hashtable.pyx":431
7294 * val = values[i]
```
cython 0.19+git51-g3078752-1 (Debian build)
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https://api.github.com/repos/pandas-dev/pandas/issues/3894 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3894/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3894/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3894/events | https://github.com/pandas-dev/pandas/issues/3894 | 15,533,633 | MDU6SXNzdWUxNTUzMzYzMw== | 3,894 | TST: test_read_pickles_0_10_1 and test_read_pickles_0_11_0 failures on sparc | {
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} | 9 | "2013-06-13T22:28:01Z" | "2013-06-14T19:08:05Z" | "2013-06-14T19:07:45Z" | CONTRIBUTOR | null | was happening with 0.11 and now with v0.11.0-492-gb204e14:
```
======================================================================
FAIL: test_read_pickles_0_10_1 (pandas.io.tests.test_pickle.TestPickle)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pickle.py", line 64, in test_read_pickles_0_10_1
self.compare(vf)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pickle.py", line 57, in compare
comparator(result,expected)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 238, in assert_frame_equal
check_less_precise=check_less_precise)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 197, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 141, in assert_almost_equal
assert_almost_equal(a[i], b[i], check_less_precise)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 172, in assert_almost_equal
assert a == b, "%s != %s" % (a, b)
AssertionError: 1970-01-01T00:00:00.000000000+0000 != 2009-01-01T00:00:00.000000000+0000
======================================================================
FAIL: test_read_pickles_0_11_0 (pandas.io.tests.test_pickle.TestPickle)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pickle.py", line 71, in test_read_pickles_0_11_0
self.compare(vf)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pickle.py", line 57, in compare
comparator(result,expected)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 238, in assert_frame_equal
check_less_precise=check_less_precise)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 197, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 141, in assert_almost_equal
assert_almost_equal(a[i], b[i], check_less_precise)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 172, in assert_almost_equal
assert a == b, "%s != %s" % (a, b)
AssertionError: 1970-01-01T00:00:00.000000000+0000 != 2009-01-01T00:00:00.000000000+0000
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3895/events | https://github.com/pandas-dev/pandas/issues/3895 | 15,533,689 | MDU6SXNzdWUxNTUzMzY4OQ== | 3,895 | TST: test_typ FAILs on sparc | {
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} | 5 | "2013-06-13T22:29:34Z" | "2013-06-14T13:13:15Z" | "2013-06-14T13:13:15Z" | CONTRIBUTOR | null | v0.11.0-492-gb204e14
```
======================================================================
FAIL: test_typ (pandas.io.tests.test_json.test_pandas.TestPandasObjects)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_json/test_pandas.py", line 250, in test_typ
assert_series_equal(result,s)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 199, in assert_series_equal
assert(left.dtype == right.dtype)
AssertionError
```
in any case would be nice to replace this with assert_equal
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https://api.github.com/repos/pandas-dev/pandas/issues/3896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3896/events | https://github.com/pandas-dev/pandas/issues/3896 | 15,533,806 | MDU6SXNzdWUxNTUzMzgwNg== | 3,896 | TST: test_write_dta5 and test_write_dta6 failures on sparc | {
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} | 0 | "2013-06-13T22:32:51Z" | "2013-06-14T19:08:18Z" | "2013-06-14T19:08:18Z" | CONTRIBUTOR | null | v0.11.0-492-gb204e14
```
======================================================================
ERROR: test_write_dta5 (pandas.io.tests.test_stata.StataTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_stata.py", line 137, in test_write_dta5
written_and_read_again = self.read_dta(path)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_stata.py", line 39, in read_dta
return read_stata(file, convert_dates=True)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/stata.py", line 37, in read_stata
reader = StataReader(filepath_or_buffer, encoding)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/stata.py", line 302, in __init__
self._read_header()
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/stata.py", line 324, in _read_header
typlist = [ord(self.path_or_buf.read(1)) for i in range(self.nvar)]
TypeError: ord() expected a character, but string of length 0 found
======================================================================
ERROR: test_write_dta6 (pandas.io.tests.test_stata.StataTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_stata.py", line 146, in test_write_dta6
written_and_read_again = self.read_dta(path)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_stata.py", line 39, in read_dta
return read_stata(file, convert_dates=True)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/stata.py", line 37, in read_stata
reader = StataReader(filepath_or_buffer, encoding)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/stata.py", line 302, in __init__
self._read_header()
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/stata.py", line 328, in _read_header
self.dtyplist = [self.DTYPE_MAP[typ] for typ in typlist]
KeyError: 0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3897/events | https://github.com/pandas-dev/pandas/issues/3897 | 15,533,852 | MDU6SXNzdWUxNTUzMzg1Mg== | 3,897 | TST: please skip tests needing up-to-date xlrd | {
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} | 1 | "2013-06-13T22:34:16Z" | "2013-06-14T13:13:15Z" | "2013-06-14T13:13:15Z" | CONTRIBUTOR | null | ```
======================================================================
ERROR: test_excel_010_hemstring (pandas.io.tests.test_excel.ExcelTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_excel.py", line 836, in test_excel_010_hemstring
res = roundtrip(df)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_excel.py", line 826, in roundtrip
xf = pd.ExcelFile(path)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/excel.py", line 65, in __init__
raise ImportError("pandas requires xlrd >= 0.9.0 for excel support, current version "+xlrd.__VERSION__)
ImportError: pandas requires xlrd >= 0.9.0 for excel support, current version 0.6.1
======================================================================
ERROR: test_to_excel (pandas.tests.test_panel.TestPanel)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_panel.py", line 1378, in test_to_excel
reader = ExcelFile(path)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/excel.py", line 65, in __init__
raise ImportError("pandas requires xlrd >= 0.9.0 for excel support, current version "+xlrd.__VERSION__)
ImportError: pandas requires xlrd >= 0.9.0 for excel support, current version 0.6.1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3898/events | https://github.com/pandas-dev/pandas/pull/3898 | 15,534,686 | MDExOlB1bGxSZXF1ZXN0NjMxNDI4Mw== | 3,898 | TST: test fixes for various builds (debian) | {
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} | 0 | "2013-06-13T22:58:30Z" | "2014-07-01T15:50:54Z" | "2013-06-14T13:13:16Z" | CONTRIBUTOR | null | TST: json tests to int64 to avoid dtype issues, closes #3895
TST: skip tests if xlrd has lower than needed version, closes #3897
TST: skip pickle tests on not-little endianess , closes #3894
TST: skip test_encoding on non-little endian in test_pytables , closes #3892
TST: skip some stata tests on non-little endian, closes #3896
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 17 | "2013-06-14T00:11:08Z" | "2013-10-04T02:39:18Z" | "2013-10-04T02:39:18Z" | MEMBER | null | one should probably _not_ be trying to join these types of indices. even so, a stack overflow is unacceptable. there should at least be an error message...
code:
```
In [9]: df = mkdf(10, 10, data_gen_f=lambda *args: randint(2), c_idx_type='p', r_idx_type='dt')
In [10]: s = df.iloc[:5, 0]
In [11]: s.index.join(df.columns, how='outer')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/3900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3900/events | https://github.com/pandas-dev/pandas/pull/3900 | 15,536,905 | MDExOlB1bGxSZXF1ZXN0NjMxNTQzNg== | 3,900 | BUG: fix unicode -> str cast in tslib | {
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} | [] | closed | false | null | [] | null | 6 | "2013-06-14T00:11:29Z" | "2014-07-16T08:14:07Z" | "2013-06-15T00:14:09Z" | MEMBER | null | This should use format since calling str on a unicode string is a _bad_ idea
because it may or may not repr correctly.
closes #3875.
another error is created from fixing this issue.
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