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Handle passing Index.name in union
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Add R-like match function to API
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| 4 | 2011-12-19T17:55:28Z | 2011-12-20T03:19:41Z | 2011-12-20T03:19:41Z |
CONTRIBUTOR
| null |
Just an FYI, when compiling with MSVC on 64bit windows:
building 'pandas._sandbox' extension
C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\Bin\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC:\Python27\lib\site-packages\numpy\core\include -IC:\Python27\include -IC:\Python27\PC /Tcpandas\src\sandbox.c /Fobuild\temp.win-amd64-2.7\Release\pandas\src\sandbox.obj
sandbox.c
pandas\src\sandbox.c(240) : fatal error C1083: Cannot open include file: 'stdint.h': No such file or directory
I think this is (again) because MSVC is not C99 compliant. Some searching seems to indicate that you can find portable versions of stdint.h.
I downloaded one from here:
http://www.azillionmonkeys.com/qed/pstdint.h
There is also
http://code.google.com/p/msinttypes/
and added the path to it in the include_dirs for the sandbox extension, then everything compiled fine.
thanks!
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Pull in the Best Parts of Orange?
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[] | null | 7 | 2011-12-19T19:18:34Z | 2011-12-21T16:50:00Z | 2011-12-21T16:50:00Z |
CONTRIBUTOR
| null |
I assume you know about Orange http://orange.biolab.si/ . I am still in the midst of trying to get my data analysis workflow to be as lazy as possible, and like to use parts of that codebase as well. However it has many _issues_, and there might be some room to use the best parts of their code in pandas.
If this is appealing, let's discuss it!
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skiprows in read_csv not working?
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| 4 | 2011-12-19T20:32:56Z | 2013-12-04T00:43:44Z | 2011-12-21T04:49:42Z |
NONE
| null |
Hi Wes,
I'm trying to read in a csv file with few header lines at the beginning of the file starting w/ a '#'. I tried to use skiprows specifying a list of the first n rows to skip as [0,1,2,3,..,n] but I keep getting Index has duplicates ['#'] error. Is this a bug or I'm doing something wrong? Also, will this be the best place to post such issues?
I'm using pandas v 0.6.0 with python 2.7.2
Thanks for the great work you are doing w/ pandas. I'm finding it very useful and saves me a lot of time analyzing time series data.
-Solomon
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Add option to not normalize dates with Business Day offset in DateRange
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| 0 | 2011-12-19T22:49:35Z | 2012-04-14T21:23:31Z | 2012-04-14T21:23:31Z |
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Set up pandas so pip installs dependencies
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pandas.tests.test_index:TestMultiIndex.test_legacy_pickle error
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| 1 | 2011-12-20T09:37:41Z | 2011-12-21T04:15:32Z | 2011-12-21T04:15:32Z |
CONTRIBUTOR
| null |
When running nosetests (not inplace, but from the installed pandas package) pandas.tests.test_index:TestMultiIndex.test_legacy_pickle fails because it can not find the pickle file, simply because the pandas/tests/data directory is not in the installed package.
```
(lodagro_pandas)[1185][i] nosetests --with-progressive pandas
ERROR: pandas.tests.test_index:TestMultiIndex.test_legacy_pickle
gvim +700 <...>/lib/python2.7/site-packages/pandas/tests/test_index.py # test_legacy_pickle
obj = pickle.load(open(ppath, 'r'))
IOError: [Errno 2] No such file or directory: '<...>/lib/python2.7/site-packages/pandas/tests/data/multiindex_v1.pickle'
1145 tests, 0 failures, 1 error, 29 skips in 33.5s
```
Running nosetests in-place works fine.
```
(lodagro_pandas)[1192][i] nosetests --with-progressive pandas
OK! 1145 tests, 0 failures, 0 errors, 29 skips in 32.6s
```
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Problem with Series and Groupby
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| 4 | 2011-12-20T14:51:52Z | 2011-12-20T18:52:29Z | 2011-12-20T18:52:20Z |
NONE
| null |
When I do:
In [5]: w=cPickle.load (open('w.pkl'))
In [6]: w.groupby( lambda k: k<=37 ).count()
Out[6]:
key_0
False 4397
True 16
In [7]: len(w[w <= 37])
Out[7]: 805
When I inspect the data, there are 805 elements that satisfy w<=37, so the groupby is not counting correctly and reports only 16.
Am I doing this wrong?
BTW, I would like to attach the data file to this, but I can't see how.
Thanks!
|
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| 5 | 2011-12-20T16:47:22Z | 2011-12-21T15:46:57Z | 2011-12-21T03:57:15Z |
NONE
| null |
Is this desired?
import numpy as np
from pandas import Series
s2 = Series({'A': np.float64(5.0), 'B': np.float64(0.0)})
print type(s2['A'])
(type 'float')
In 0.4.0, this returned numpy.float64. That seems more expected.
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MDU6SXNzdWUyNjE1Mzc2
| 511 |
Certain DataFrame cannot be pickled to string
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| 9 | 2011-12-20T17:19:47Z | 2012-01-09T19:33:57Z | 2012-01-09T19:33:57Z |
NONE
| null |
import cPickle
import numpy as np
from pandas import Series
s = Series({'a' : None})
s['a'] = np.inf
cPickle.loads(cPickle.dumps(s))
ValueError: could not convert string to float
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HDFStore silently coerces number to string in 'where' clause but not in put
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| 1 | 2011-12-20T19:02:14Z | 2012-12-28T15:08:05Z | 2012-12-28T15:08:05Z |
NONE
| null |
Thanks for pandas! Once written, tables with numerical values in the index are unselectable. Perhaps either they should be coerced to string upon store.put, or alternately they should not be coerced in store.select:
``` python
store = HDFStore('test.h5')
store.put('test', DataFrame([0, 1, 2], [10, 11, 12], ['col1']), table=True)
store.select('test', where=[{'field' : 'index','op' : '>=','value' : 11}])
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
/home/bshanks/prog/atr/<ipython console> in <module>()
/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in select(self, key, where)
235 raise Exception('can only select on objects written as tables')
236 if group is not None:
--> 237 return self._read_group(group, where)
238
239 def put(self, key, value, table=False, append=False,
/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in _read_group(self, group, where)
619 kind = _LEGACY_MAP.get(kind, kind)
620 handler = self._get_handler(op='read', kind=kind)
--> 621 return handler(group, where)
622
623 def _read_series(self, group, where=None):
/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in _read_frame_table(self, group, where)
646
647 def _read_frame_table(self, group, where=None):
--> 648 return self._read_panel_table(group, where)['value']
649
650 def _read_panel_table(self, group, where=None):
/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in _read_panel_table(self, group, where)
655 # create the selection
656 sel = Selection(table, where)
--> 657 sel.select()
658 fields = table._v_attrs.fields
659
/usr/local/lib/python2.7/dist-packages/pandas/io/pytables.pyc in select(self)
861 """
862 if self.the_condition:
--> 863 self.values = self.table.readWhere(self.the_condition)
864
865 else:
/usr/lib/python2.7/dist-packages/tables/table.pyc in readWhere(self, condition, condvars, field, start, stop, step)
1271
1272 coords = [ p.nrow for p in
-> 1273 self._where(condition, condvars, start, stop, step) ]
1274 self._whereCondition = None # reset the conditions
1275 return self.readCoordinates(coords, field)
/usr/lib/python2.7/dist-packages/tables/table.pyc in _where(self, condition, condvars, start, stop, step)
1225 # Compile the condition and extract usable index conditions.
1226 condvars = self._requiredExprVars(condition, condvars, depth=3)
-> 1227 compiled = self._compileCondition(condition, condvars)
1228
1229 # Can we use indexes?
/usr/lib/python2.7/dist-packages/tables/table.pyc in _compileCondition(self, condition, condvars)
1101 indexedcols = frozenset(indexedcols)
1102 # Now let ``compile_condition()`` do the Numexpr-related job.
-> 1103 compiled = compile_condition(condition, typemap, indexedcols, copycols)
1104
1105 # Check that there actually are columns in the condition.
/usr/lib/python2.7/dist-packages/tables/conditions.pyc in compile_condition(condition, typemap, indexedcols, copycols)
154 except NotImplementedError, nie:
155 # Try to make this Numexpr error less cryptic.
--> 156 raise _unsupported_operation_error(nie)
157 params = varnames
158
NotImplementedError: unsupported operand types for *ge*: long, str
```
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| 513 |
HDFStore loses data because it silently loses microseconds in datetime index conversion
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| 1 | 2011-12-20T19:24:06Z | 2012-02-28T17:35:07Z | 2012-02-28T17:35:07Z |
NONE
| null |
``` python
store = HDFStore('test.h5')
store.put('test', DataFrame([0, 1, 2], [datetime.utcnow(), datetime.utcnow(),datetime.utcnow(),], ['col1']), table=True)
store['test']
Duplicate entries in table, taking most recently appended
Out[50]:
col1
2011-12-20 19:20:19 2
```
This happens because HDFStore uses .timetuple() to serialize, but two datetimes can be unique yet have the same .timetuple(), because .timetuple discards microseconds (slightly related discussion: http://bugs.python.org/issue2736 ; i suppose that whatever they decided to do to convert datetime to datetime64 might work?).
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Implement Cython type-check converter to replace _ensure_index
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| 1 | 2011-12-20T22:58:49Z | 2011-12-21T16:49:36Z | 2011-12-21T16:49:32Z |
MEMBER
| null |
Could squeeze out a little more speed
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various HDFStore fixes regarding tables
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[] | null | 5 | 2011-12-21T07:58:45Z | 2014-07-03T05:02:23Z | 2012-09-08T21:27:14Z |
NONE
| null |
commit d1f439c: added optional keyword argument 'itemsize' to HDFStore.put and HDFStore.append to control string length; default 255
it sets the string length of strings in the index and columns for the table (rather than the previous behavior, which was to take the max string length from the longest index/column string in the dataset that is used to create the table). Default is 255.
commit 2b13848: nicer error message for store.put('ccc', DataFrame(['hi'], ['a'], ['col']), table=True); also fixes a bug in d1f439
commit 2bce685: before this commit, the following would discard column1:
store.put('eee', DataFrame([0], ['a'], ['column1'])); store.append('eee', DataFrame([1], ['a'], ['column2'])); store['eee']
even with this commit, strings such as column names which are longer than "itemsize" and which are .appended in the future will be silently truncated. I recommend that a warning be raised when this happens, but i didn't add that myself because i couldn't easily find the place where the truncation occurs, or how to extract the current string length.
i'm having trouble building so i won't be able to try writing unit tests. however, here are tests for these commits:
from pandas import *; store = HDFStore('test.h5')
store.put('ccc', DataFrame(['hi'], ['a'], ['col']), table=True);
# should raise "TypeError: At this time, Pandas HDFStore tables only support floating point values."
store.put('fff', DataFrame(['0'], ['a'], ['column1'])); store.append('fff', DataFrame([1], ['a'], ['column2']));
# should raise "ValueError: Can only append to Tables"
store.put('fff', DataFrame([0], ['a'], ['column1']), table=True); store.append('fff', DataFrame([1], ['a'], ['column2'])); store['fff']
# should yield
# column1 column2
# a 0 1
# (this was true even before the present commits)
store.append('j', DataFrame([0], ['a'], ['column1'])); store.append('j', DataFrame([1], ['a'], ['column2'])); store['j']
# should yield:
# column1 column2
# a 0 1
# (this was true even before the present commits)
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pandas/io/sql.py default value of indexField
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| 2 | 2011-12-21T14:39:14Z | 2011-12-21T19:50:23Z | 2011-12-21T16:48:42Z |
NONE
| null |
In pandas/io/sql.py, Could you change the default value of frame_query indexField from "Time" to None?
That way, queries are more likely to 'just work'.
Current: def frame_query(sql, con, indexField='Time')
Requested: def frame_query(sql, con, indexField=None)
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friendlier setup.py errors when numpy is missing.
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| 1 | 2011-12-21T19:59:06Z | 2013-12-04T00:43:44Z | 2012-01-06T05:59:33Z |
CONTRIBUTOR
| null |
(I have a patch for this coming in, see if you like where it goes)
issues:
1) need numpy for dependency compilation
2) no easy way to tell `setup.py` to get `numpy` and work with it first. (or is there?)
3) `$ pip install numpy pandas` still isn't enough.
So the best I can do I give a friendlier message. Patch inbound.
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#517 friendlier setup message with no numpy
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| 1 | 2011-12-21T20:07:42Z | 2014-06-18T18:02:34Z | 2011-12-25T05:07:36Z |
CONTRIBUTOR
| null |
If there is a nicer way to do this, I am all ears :)
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Filtering dataframe rows by inclusion in a set/list
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[] | null | 7 | 2011-12-21T21:06:15Z | 2011-12-21T23:40:42Z | 2011-12-21T21:40:59Z |
NONE
| null |
I've got a dataframe that has text for one of the columns. I'd like to filter by all values that are in a set.
Ie:
```
beatles = people[people['name'] in ['ringo', 'john', 'paul', 'george']]
```
I can't seem to do this and would like to know the canonical way. kthxbye :)
|
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Autodep
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| 2 | 2011-12-21T21:43:35Z | 2012-01-04T16:13:54Z | 2012-01-04T16:13:54Z |
CONTRIBUTOR
| null |
still need numpy for setup, but at least we get the right version of `python-dateutil`
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(1) add a get() method like dicts have, (2) throw KeyError, not Exception, when a column isn't found
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| 1 | 2011-12-21T22:31:36Z | 2014-06-13T12:41:35Z | 2011-12-26T02:42:00Z |
NONE
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Misbehaved reindexing with NaN labels
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| 1 | 2011-12-21T22:33:55Z | 2012-05-07T16:42:33Z | 2012-05-07T16:42:33Z |
MEMBER
| null |
from user report
```
In [116]: df = pandas.DataFrame([[1,2], [3,4], [numpy.nan,numpy.nan], [7,8], [9,10]], columns=['a', 'b'], index=[100.0, 101.0, numpy.nan, 102.0, 103.0])
In [117]: print df
--------> print(df)
a b
100.0 1 2
101.0 3 4
nan NaN NaN
102.0 7 8
103.0 9 10
#referencing anything after the nan in the index results in getting nans!
In [118]: df.reindex(index=[101.0, 102.0, 103.0])
Out[118]:
a b
101.0 3 4
102.0 NaN NaN
103.0 NaN NaN
#after nan = NaN again!
In [119]: df.reindex(index=[103.0])
Out[119]:
a b
103.0 NaN NaN
#before nan looks good though
In [120]: df.reindex(index=[101.0])
Out[119]:
a b
101.0 3 4
```
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Implement reflexive comparison ops in Series/DataFrame
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| 8 | 2011-12-21T23:27:21Z | 2011-12-28T00:47:44Z | 2011-12-22T20:19:47Z |
MEMBER
| null |
Raised by convo with @mattharrison in #519
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More 0.5.0 documentation
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[] | null | 1 | 2011-12-22T16:27:09Z | 2011-12-22T21:35:28Z | 2011-12-22T21:35:28Z |
CONTRIBUTOR
| null |
Added more v0.5.0 docs
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Panel.to_frame does not preserve index names
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DataFrame with 'list of dicts' behaviour proposal
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| 6 | 2011-12-22T19:48:24Z | 2011-12-23T00:41:17Z | 2011-12-23T00:41:17Z |
CONTRIBUTOR
| null |
Sketch of proposed behaviour... make 'list of dicts' create a (potentially) 'ragged' array, with autoguessed column names, and sensible default values, when the keys don't exist in all dicts.
Current behaviour:
In [215]: pandas.DataFrame([dict(a=1),dict(a=2)],columns=['a'])
Out[215]:
a
0 {'a': 1}
1 {'a': 2}
(I happen to find this very surprising/useless behaviour!)
(one) Proposed behaviour...
> > > print DataFrame2([dict(a=1,c=1,d=True),dict(b=2,c='abc')])
> > > a c d
> > > 0 1 1 True
> > > 1 NaN abc NaN
I have a straw implementation at: https://gist.github.com/1511578
(there is a lot to comment on!... should it use the set of keys? Do we need more args? Documentation? Is this just a recipe?)
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DataFrame.delevel drops the column names, is this right?
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| 0 | 2011-12-22T20:41:46Z | 2011-12-23T23:35:04Z | 2011-12-23T23:35:04Z |
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MDU6SXNzdWUyNjQxNjcw
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Micro-optimizations with DataFrame.index/columns properties
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| 0 | 2011-12-22T20:48:43Z | 2011-12-23T17:56:18Z | 2011-12-23T17:56:18Z |
MEMBER
| null |
Can Cythonize / generalize to get better lookup times
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Handling of mis-matched columns in DataFrame incorrect except for combine_first
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| 0 | 2011-12-22T20:54:42Z | 2011-12-23T18:27:39Z | 2011-12-23T18:27:39Z |
MEMBER
| null |
Probably should align on both rows and columns and it will solve the problem
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MDU6SXNzdWUyNjQzNDgy
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fast_unique_multiple that consumes generator, smaller memory footprint
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DataFrame.stack() DataFrame.unstack() documentation/behavior
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| 3 | 2011-12-23T11:12:32Z | 2019-06-17T14:00:08Z | 2012-01-09T22:44:44Z |
CONTRIBUTOR
| null |
`DataFrame.stack()` and `DataFrame.unstack()` methods require docstring update.
`DataFrame.stack()` docstring mentions that method converts DataFrame to Series, and returns a Series. This is only the case when _columns_ has only one level. In case _columns_ has multiple levels, method returns a DataFrame.
`DataFrame.unstack()` docstring is mainly a copy of the `Series.unstack()` docstring, example should operate on a DataFrame and not a Series.
Concerning the behavior, when the DataFrame index is a single level one, _unstack()_ raises:
``` python
AttributeError: `Index` object has no attribute `levels`
```
`DataFrame.unstack()` assumes a DataFrame with a multilevel index, it can not operate on single level index, maybe the method could return a Series in case _unstack()_ operates on DataFrame with single level index, similar as _stack()_ does in case of single level _columns_?
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Doc: fix keyerror in ipython session demonstrating console output formatting.
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Doc: indexing correct swaplevels --> swaplevel.
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[] | null | 1 | 2011-12-23T15:13:21Z | 2011-12-25T04:44:41Z | 2011-12-25T04:44:41Z |
CONTRIBUTOR
| null |
Fix error in ipython session demonstrating `DataFrame.swaplevel()`
Maybe the paragraph where i did this correction can be removed, it is very similar to paragraph **Swapping levels with swaplevel** a bit higher.
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adding DataFrame.reorder_levels()
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[] | null | 1 | 2011-12-23T15:38:51Z | 2014-06-12T20:15:44Z | 2011-12-26T02:32:15Z |
CONTRIBUTOR
| null |
Convenience method to reorder the levels of a DataFrame hierarchical axis.
It uses `MultiIndex.reorder_levels()`. Neither of them can work with levels referenced by key, in contrast to _swaplevel()_ method.
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Use nanops in Panel methods
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Use nanops in Panel methods
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Address problems surrounding mutability of index.name field (e.g. sharing of objects)
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DOC: Fixed typo in dsintro.rst.
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Optimize DataFrame creation from nested dict + vbench
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vbench boolean row selection in DataFrame
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| 0 | 2011-12-24T20:23:40Z | 2011-12-24T20:29:48Z | 2011-12-24T20:29:48Z |
MEMBER
| null |
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| 2,655,528 |
MDU6SXNzdWUyNjU1NTI4
| 542 |
Broadcasting option for flex arith methods
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| 1 | 2011-12-25T17:38:47Z | 2011-12-29T04:45:09Z | 2011-12-29T04:45:09Z |
MEMBER
| null |
For, e.g., demeaning within a level, say, something like:
```
df.subtract(df.sum(level=0, broadcast=True))
```
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Craft API for 3D (Panel) <-> 2D (DataFrame) reshaping operations
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| 1 | 2011-12-26T03:28:09Z | 2018-07-06T20:20:59Z | 2016-09-29T22:10:36Z |
MEMBER
| null |
cc @lodagro, moving further discussion here from the mailing list
```
Panel was my first idea to use, but i got stuck rather quickly :-) I am unfamiliar with to_long() and LongPanel,
What you suggested is definitely more pathonic (pandas way of doing).
Concerning 3D<->2D manipulations API. First thing i looked for in Panel API was stack and unstack since these handle 2D<->1D manipulations on DataFrame,.
So i would reuse these method names. Just an idea...
Panel.stack(self, stack_ax=None, columns_ax=None, index_ax=None, dropna=True)
stack_ax: int, panel ax that will be used to reduce from 3D<->2D, defaults to ...
columns_ax: int, panel ax that will be used to create columns in resulting DataFrame, defaults to ...
index_ax: int, panel ax that will be used to create index in resulting DataFrame, defaults to ...
Maybe better to use only two of the three axes, since one can only choose two freely.
Being able to select from which ax to stack or take column and index data avoids the need to swap axes before stacking.
```
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DOC basics: last ipython code example broke
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[] | null | 1 | 2011-12-26T10:38:44Z | 2011-12-26T14:24:05Z | 2011-12-26T14:24:05Z |
CONTRIBUTOR
| null |
Under Console Output Formatting:
In [134]: set_eng_float_format(precision=3, use_eng_prefix=True)
## In [135]: df[0]/1.e3
KeyError Traceback (most recent call last)
/home/wesm/code/pandas/doc/<ipython-input-135-f536fd1de18b> in <module>()
----> 1 df[0]/1.e3
/home/wesm/code/pandas/pandas/core/frame.pyc in **getitem**(self, key)
1055 return self._getitem_multilevel(key)
1056 else:
-> 1057 return self._get_item_cache(key)
1058
1059 def _getitem_array(self, key):
/home/wesm/code/pandas/pandas/core/generic.pyc in _get_item_cache(self, item)
280 return cache[item]
281 except Exception:
--> 282 values = self._data.get(item)
283 res = self._box_item_values(item, values)
284 cache[item] = res
/home/wesm/code/pandas/pandas/core/internals.pyc in get(self, item)
557
558 def get(self, item):
--> 559 _, block = self._find_block(item)
560 return block.get(item)
561
/home/wesm/code/pandas/pandas/core/internals.pyc in _find_block(self, item)
633
634 def _find_block(self, item):
--> 635 self._check_have(item)
636 for i, block in enumerate(self.blocks):
637 if item in block:
/home/wesm/code/pandas/pandas/core/internals.pyc in _check_have(self, item)
640 def _check_have(self, item):
641 if item not in self.items:
--> 642 raise KeyError('no item named %s' % str(item))
643
644 def reindex_axis(self, new_axis, method=None, axis=0):
KeyError: 'no item named 0'
## In [136]: df[0]/1.e6
KeyError Traceback (most recent call last)
/home/wesm/code/pandas/doc/<ipython-input-136-96207214b710> in <module>()
----> 1 df[0]/1.e6
/home/wesm/code/pandas/pandas/core/frame.pyc in **getitem**(self, key)
1055 return self._getitem_multilevel(key)
1056 else:
-> 1057 return self._get_item_cache(key)
1058
1059 def _getitem_array(self, key):
/home/wesm/code/pandas/pandas/core/generic.pyc in _get_item_cache(self, item)
280 return cache[item]
281 except Exception:
--> 282 values = self._data.get(item)
283 res = self._box_item_values(item, values)
284 cache[item] = res
/home/wesm/code/pandas/pandas/core/internals.pyc in get(self, item)
557
558 def get(self, item):
--> 559 _, block = self._find_block(item)
560 return block.get(item)
561
/home/wesm/code/pandas/pandas/core/internals.pyc in _find_block(self, item)
633
634 def _find_block(self, item):
--> 635 self._check_have(item)
636 for i, block in enumerate(self.blocks):
637 if item in block:
/home/wesm/code/pandas/pandas/core/internals.pyc in _check_have(self, item)
640 def _check_have(self, item):
641 if item not in self.items:
--> 642 raise KeyError('no item named %s' % str(item))
643
644 def reindex_axis(self, new_axis, method=None, axis=0):
KeyError: 'no item named 0'
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| 0 | 2011-12-26T23:41:12Z | 2011-12-29T03:58:07Z | 2011-12-29T03:58:07Z |
MEMBER
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read_csv converters "Buffer dtyle mismatch"
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| 7 | 2011-12-27T18:21:23Z | 2011-12-29T05:33:52Z | 2011-12-29T04:57:47Z |
NONE
| null |
Am trying to convert a string column to seconds in a dataframe. It looks like there is some sort of off by one error.
Here's my csv
```
"id","Time to Calculate"
1,"19:31:15"
2,"19:18:17"
3,"19:31:15"
4,"19:27:42"
5,"19:27:42"
6,"19:28:25"
7,"19:28:25"
8,"19:28:25"
9,"19:28:04"
10,"19:28:04"
11,"19:28:04"
12,"19:25:56"
13,"19:25:56"
14,"19:25:57"
15,"19:25:57"
16,"19:26:41"
17,"19:26:41"
18,"19:26:08"
19,"19:26:08"
20,"1 day, 1:04:33"
21,"1 day, 1:04:33"
22,"1 day, 1:04:33"
23,"2 days, 2:14:33"
```
Here's my code
```
from pandas import read_csv
def sec_to_calc(time_to_calc):
print "TIMETD", time_to_calc, type(time_to_calc)
total = 0
days = 0
if 'days' in time_to_calc:
days, time_to_calc = time_to_calc.split(" days, ")
days = int(days)
elif 'day' in time_to_calc:
days = 1
_, time_to_calc = time_to_calc.split(" day, ")
hours, min, sec = [int(x) for x in time_to_calc.split(':')]
return days + 24 * 60 * 60 + hours *60 * 60 + min * 60 + sec
filename = '/tmp/test.csv'
df = read_csv(filename, converters={'Time to Calculate': sec_to_calc})
```
Here's my error:
```
File "/tmp/test.py", line 19, in <module>
df = read_csv(filename, converters={'Time to Calculate': sec_to_calc})
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/io/parsers.py", line 64, in read_csv
return parser.get_chunk()
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/io/parsers.py", line 418, in get_chunk
data = _convert_to_ndarrays(data, self.na_values)
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/io/parsers.py", line 462, in _convert_to_ndarrays
result[c] = _convert_types(values, na_values)
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/io/parsers.py", line 470, in _convert_types
lib.sanitize_objects(values, na_values)
File "parsing.pyx", line 220, in pandas._tseries.sanitize_objects (pandas/src/tseries.c:54380)
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
```
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| 2,666,087 |
MDU6SXNzdWUyNjY2MDg3
| 547 |
Proper inner/left/right/outer join ops on a level with MultiIndex
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| 0 | 2011-12-27T20:37:10Z | 2011-12-29T18:17:00Z | 2011-12-29T18:17:00Z |
MEMBER
| null |
e.g.
```
idx = Index(['a', 'b', 'c'])
midx = MultiIndex(levels=[['one', 'two'], ['a', 'b']], labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
idx.join(midx, level=1)
```
Not totally trivial as would have to do a "remapping" of the labels for the level. But it's the most proper thing to do
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| 548 |
Series.corr failure
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| 0 | 2011-12-27T20:57:13Z | 2011-12-28T22:29:44Z | 2011-12-28T22:29:44Z |
MEMBER
| null |
The zero-length check in nancorr is in the wrong place, see below:
```
from pandas import Series
import numpy as np
s1 = Series( index = range(3) , data = [np.NaN , np.NaN , np.NaN] )
s2 = Series( index = range(3) , data = [np.NaN , np.NaN , np.NaN] )
s2.corr(s1)
```
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| 549 |
Add function to create empty DataFrame/Panel/etc with given dtypes for items
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| 0 | 2011-12-27T22:41:51Z | 2018-11-10T18:02:30Z | 2018-11-10T18:02:30Z |
MEMBER
| null |
Would result in some non-trivial speedups in places (like conversion of mixed-type DataFrame -> Panel)
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| 550 |
element-wise iteration over subgroups fails
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| 3 | 2011-12-28T00:06:20Z | 2012-01-10T02:18:37Z | 2012-01-10T02:18:37Z |
NONE
| null |
Am trying to iterate over elements in a subgroup of a data frame and get a keyerror:
```
(Pdb) [x for x in group.ix]
*** KeyError: 0
(Pdb) len(group)
7
```
Element-wise iteration over the parent data frame works fine.
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| 551 |
Setting elements with MultiIndex through ix can fail
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| 1 | 2011-12-28T14:14:20Z | 2011-12-29T05:15:50Z | 2011-12-29T05:15:50Z |
MEMBER
| null |
reported by @CRP
```
Hi, I am currently trying to access and change elements of a multiindexed DataFrame with .ix. This works on read but not on write:
In [342]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
In [343]: tuples = zip(*arrays)
In [344]: index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
In [345]: a=DataFrame(randn(6, 3), index=index[:6])
In [346]: a.ix[('bar','two'),1]
Out[346]: -0.574310975217078
In [347]: a.ix[('bar','two'),1]=999
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/Users/c.prinoth/<ipython-input-347-755b7b16a7da> in <module>()
----> 1 a.ix[('bar','two'),1]=999
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
56 raise IndexingError('only tuples of length <= %d supported',
57 self.ndim)
---> 58 indexer = self._convert_tuple(key)
59 else:
60 indexer = self._convert_to_indexer(key)
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_tuple(self, key)
65 keyidx = []
66 for i, k in enumerate(key):
---> 67 idx = self._convert_to_indexer(k, axis=i)
68 keyidx.append(idx)
69 return _maybe_convert_ix(*keyidx)
/usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis)
239 mask = indexer == -1
240 if mask.any():
--> 241 raise KeyError('%s not in index' % objarr[mask])
242
243 return indexer
KeyError: '[bar two] not in index'
Is this a bug or am I using the index incorrectly?
```
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| 552 |
LongPanel removal
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[] | null | 0 | 2011-12-28T16:16:20Z | 2014-06-15T20:58:33Z | 2011-12-28T16:28:08Z |
MEMBER
| null |
Got rid of deprecated LongPanel class and added a bunch of new features to DataFrame and elsewhere to make up for its absence
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straw ``jsonmangle`` for reference / discussion
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| 2 | 2011-12-28T19:53:07Z | 2014-05-28T23:11:13Z | 2014-05-28T23:11:13Z |
CONTRIBUTOR
| null |
If you are interested, comment:
I am thinking about "JSON-Path", and other solutions for querying (and transforming) simple-ish nested data structures. (mostly to 'table-ize' them for R)
this is my straw attempt: https://gist.github.com/1529358
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[] | null | 1 | 2011-12-29T15:09:19Z | 2014-06-12T16:08:38Z | 2012-01-02T20:18:11Z |
CONTRIBUTOR
| null |
Also support IPython tab completion for Panel items
Obviously nothing important here, just some syntactic sugar to facilitate interactive access.
thanks!
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vbench: DataFrame sort by column(s)
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Series constructor ought to take MaskedArray as data argument
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| 0 | 2011-12-29T23:09:47Z | 2012-01-03T19:56:11Z | 2012-01-03T19:56:11Z |
CONTRIBUTOR
| null |
For consistency with DataFrame constructor
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DataFrame.drop_duplicates is unreliable in the presence of NA values
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vbench: DataFrame drop_duplicates
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Docs
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Added a whole bunch of docs and cross-references for v0.6.0
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Improve performance of set logic for primitive types
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Test coverage improvements
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CONTRIBUTOR
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- a few modules up to 100%
- a few small bugfixes on non-obvious codepaths
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Add support for MaskedArray in Series constructor, with test
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See GH #556
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Mixing Series/list + ndarray should not generate exception if everything right length in DataFrame constructor
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More testcoverage -
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CONTRIBUTOR
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Got a few more modules to 100%
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Optimize join-on-MultiIndex inner/outer join cases in merge
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| 567 |
Problem getting set difference OR isnotin
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[] | null | 2 | 2012-01-03T20:17:33Z | 2012-01-03T20:20:36Z | 2012-01-03T20:19:24Z |
NONE
| null |
I'm using `isin` and want `isnotin`, so I figured this would give it to me:
```
inside = df[df['Col'].isin(good_vals)]
not_in = df - inside
```
I'm getting the following error:
```
File "sensor.py", line 572, in wrapped
not_in = df - inside
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/core/frame.py", line 104, in f
return self._combine_frame(other, func, fill_value)
File "/home/mharrison/work/pandas/env/lib/python2.6/site-packages/pandas/core/frame.py", line 1862, in _combine_frame
result = func(this_vals, other_vals)
TypeError: unsupported operand type(s) for -: 'str' and 'float'
```
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ipython_directive should raise Exceptions to the user when building docs
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| 569 |
More release docs for v0.7
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[] | null | 1 | 2012-01-03T22:51:35Z | 2014-07-17T01:41:49Z | 2012-01-03T23:23:05Z |
CONTRIBUTOR
| null |
Whats new section, cross-referenced with additional docs for version 0.7
|
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Add index and header options to DataFrame.to_string()
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[] | null | 1 | 2012-01-04T08:46:51Z | 2016-10-12T23:02:43Z | 2012-01-06T05:39:17Z |
CONTRIBUTOR
| null |
Request from mailing list [discussion](https://groups.google.com/d/topic/pystatsmodels/XPuDEfFKU8U/discussion).
`DataFrame.to_csv()` have _index_ and _header_ argument, request is to add this functionality also to `DataFrame.to_string()`.
Maybe do this also for `DataFrame.to_html()`?
|
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DataFrame.to_string() remove extra white space between columns
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| 4 | 2012-01-04T08:50:43Z | 2016-10-12T23:02:43Z | 2012-01-08T00:14:59Z |
CONTRIBUTOR
| null |
`DataFrame.to_string()` uses one extra white space between columns for sign alignment.
Remove it, for user who need exact column width, how to do this nicely?
see also mailing list [discussion](https://groups.google.com/d/topic/pystatsmodels/XPuDEfFKU8U/discussion)
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more changes to setup.py
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[] | null | 7 | 2012-01-04T16:22:59Z | 2014-06-12T23:43:47Z | 2012-01-06T05:24:07Z |
CONTRIBUTOR
| null |
Maybe consider just making people use either `setuptools` or `distribute`?
|
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flatten, multiIndex, agg, groupby
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[] | null | 5 | 2012-01-04T22:00:15Z | 2012-01-04T22:20:03Z | 2012-01-04T22:14:14Z |
CONTRIBUTOR
| null |
Maybe this is even simpler, but how does one turn a MultiIndex back into a bunch of columns?
```
aaa = subby.groupby(['fxVersion','operatingSystem','updateChannel'])['isCompatible'].agg(dict(sum=np.sum,pct=lambda x: 100*np.mean(x),count=lambda x: len(x)))
```
So far, this is the best I have:
```
pandas.DataFrame(map(list,aaa.index.get_tuple_index()),columns=aaa.index.names)
```
Maybe it is just me, but I just don't get what MultiIndex is trying to do :) I have them after groupby().agg. When `as_level=False` is on, some types of `agg` don't work. Something here doesn't quite gel for me!
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| 574 |
implemented description property
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[] | null | 3 | 2012-01-04T23:07:19Z | 2012-09-09T01:09:58Z | 2012-09-09T01:09:58Z |
NONE
| null |
Hi,
This is a basic implementation of description and unit properties in DataFrame.
https://github.com/wesm/pandas/issues/39
I used dict to store both description and unit. I thought about using tuples but dict would be better if a user wants to modify individual descripition/unit.
One can directly access description/unit, and also DataFrame.info() will print out those if they exist.
I haven't touched at the storing part yet so it is very rudimentary now (no way to save the descriptions), but I wanted to know what you think. Please let me know.
And also, is this (topic branch) right way to submit a pull request? I'm still learning about git. :)
Happy new year!
-Joon
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| 575 |
enh request: ``describe`` for Factor, string fields, etc
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| 2 | 2012-01-05T18:30:40Z | 2012-05-12T18:16:53Z | 2012-05-12T18:16:53Z |
CONTRIBUTOR
| null |
I wonder if `Factor` and things that are Factor-coercible should have some of the methods that `collections.counter` items have (in particular, `most_common`).
The R `summary` output on such a thing is:
```
id
```
id1: 45577
id2: 16582
id3: 12446
(Other): 340115
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ENH: closing issues #395, #449, #570, #571
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[] | null | 2 | 2012-01-05T18:49:06Z | 2014-06-13T05:52:41Z | 2012-01-06T05:34:24Z |
CONTRIBUTOR
| null |
Fix column string formatting; index and header options to to_string; decorator for docstrings; floating point prettier printing. Also added to release notes & documentation. Address #395, #449, #570, #571
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enh: api cleanup... tostring, to_string, to_html, etc.
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| 11 | 2012-01-05T19:42:53Z | 2016-09-30T12:35:15Z | 2016-09-29T22:11:30Z |
CONTRIBUTOR
| null |
There is a lot of semi-duplication / differences in the `Series()` and `DataFrame()` methods.
Maybe it's worth taking a look through them and regularising.
Fwiw, I prefer `tostring` vs. `to_string`
Also, would be nice to have `tohtml` on things, though expanding it is beyond the scope of this.
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ENH: setup.py fixes incorporating PR #572
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[] | null | 1 | 2012-01-05T22:27:37Z | 2014-06-12T05:19:33Z | 2012-01-06T05:23:26Z |
CONTRIBUTOR
| null |
Also hopefully closing GH #192
|
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apply over groups api for DataFrames
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| 1 | 2012-01-05T22:32:56Z | 2014-05-28T23:16:48Z | 2014-05-28T23:16:48Z |
CONTRIBUTOR
| null |
I am not sure what the right solution is, but
```
dfext.head(10).groupby(['id','name']).apply(lambda g: DataFrame({'i':[g.isCompatible.sum()],}).ix[0])
```
to get a 'select' over multiple variables seems wrong/fragile/complex.
I know we are busy here trying to reinvent sql badly, but maybe this should get sugar:
dfext.groupby([vars]).select({k1:op1, k2:op2})
where some magic can happen in there.
Even simplifying down the _this is a one row data frame_ part would remove a lot of the snarl.
(Use case: typically, I group, and I want multiple stats on multiple columns, or some stat that is a combination of multiple columns.... pandas.DataFrame is like `itertools.groupby` on steroids and with less hassle!)
It is possible of course that I might want Pivots or something. If so, correct me.
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| 580 |
closing tag of 'tbody' was 'body'
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New numeric display adds sometimes unwanted trailing zeros
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| 1 | 2012-01-06T15:19:23Z | 2012-01-09T22:55:11Z | 2012-01-09T22:55:06Z |
CONTRIBUTOR
| null |
New numeric display adds sometimes unwanted trailing zeros.
One should also be careful when adding trailing zeros. In the example below, pi is represented up to the default precision of three and trailing zeros are added. I'm not in favor of this. To write pi=3.142 is ok, pi=3.1420000 is not ok.
``` python
In [3]: df = pandas.DataFrame({'A': [np.pi, np.pi*100000]})
In [4]: df
Out[4]:
A
0 3.1420000
1 3.142e+05
In [5]: pandas.set_printoptions(precision=5)
In [6]: df
Out[6]:
A
0 3.14160000
1 3.1416e+05
```
In this example -1 and 1 become -10000 and 10000.
``` python
In [5]: df = pandas.DataFrame({'A': [-1, 1, -1e-1, 1e-1, -1e-2, 1e-2, -1e-3, 1e-3]})
In [6]: df
Out[6]:
A
0 -10000
1 10000
2 -0.100
3 0.100
4 -0.010
5 0.010
6 -0.001
7 0.001
In [7]: df['A'][0]
Out[7]: -1.0
```
When enabling the engineering float formatter, things get messy.
``` python
In [8]: pandas.set_eng_float_format(use_eng_prefix=True)
In [9]: df
Out[9]:
A
0 -1.000000
1 1.0000000
2 -100.000m
3 100.000m0
4 -10.000m0
5 10.000m00
6 -1.000m00
7 1.000m000
In [10]: pandas.set_eng_float_format(precision=1, use_eng_prefix=True)
In [11]: df
Out[11]:
A
0 -1.0000
1 1.00000
2 -100.0m
3 100.0m0
4 -10.0m0
5 10.0m00
6 -1.0m00
7 1.0m000
```
Maybe easier to replace added trailing zeros with white space?
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FIX: corner case(s) of #395, added test
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| 0 | 2012-01-06T15:33:36Z | 2012-01-06T16:09:16Z | 2012-01-06T16:09:16Z |
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Converter returning string passed to read_csv causes exception
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| 0 | 2012-01-06T20:13:10Z | 2012-01-06T20:22:30Z | 2012-01-06T20:22:30Z |
MEMBER
| null |
for example:
```
data = """Id;Number1;Number2;Text1;Text2;Number3
1;1521,1541;187101,9543;ABC;poi;4,738797819
2;121,12;14897,76;DEF;uyt;0,377320872
3;878,158;108013,434;GHI;rez;2,735694704"""
f = lambda x : x.replace(",", ".")
converter = {'Number1':f,'Number2':f, 'Number3':f}
df2 = read_csv(StringIO(data), sep=';',converters=converter)
```
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Handle European decimal formats in parsers at a lower level
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FIX, ENH: #395 got float formatting working
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Add option to bold row labels in to_html
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| 587 |
Current Version doesn't build with dev numpy
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[] | null | 1 | 2012-01-06T22:14:17Z | 2012-01-06T22:20:53Z | 2012-01-06T22:20:53Z |
NONE
| null |
Can't build current pandas with dev version of numpy. I get the following error:
```
In file included from pandas/src/tseries.c:258:0: pandas/src/skiplist.h:31:28: error: redefinition of ‘__npy_nanf' /usr/local/lib/python2.7/site-packages/numpy/core/include/numpy/npy_math.h:19:25: note: previous definition of ‘__npy_nanf’ was here
```
This seems odd as a previous commit removed
#include <numpy/npy_math.h>
Commenting the offending function in `skiplist.h` seems to work, but that can't be the correct solution.
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DOC: added stack/unstack docstrings per #531
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[] | null | 1 | 2012-01-06T22:20:42Z | 2014-07-22T17:20:55Z | 2012-01-09T22:44:14Z |
CONTRIBUTOR
| null |
Updated the docstrings. Did not change unstack behavior in case of single-level index, which is to raise AttributeError. Thoughts on whether to allow full unstacking to Series representing first row of dataframe?
|
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import pandas fails on trunk
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[] | null | 6 | 2012-01-07T19:25:32Z | 2012-01-07T22:03:29Z | 2012-01-07T22:03:29Z |
CONTRIBUTOR
| null |
Just upgraded to trunk, import pandas fails now.
``` python
In [1]: import pandas
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<...>/<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
<...>/lib/python2.7/site-packages/pandas-0.7.0.dev_063ff4b-py2.7-linux-i686.egg/pandas/__init__.py in <module>()
8
9 try:
---> 10 import pandas._tseries as lib
11 except Exception, e: # pragma: no cover
12 if 'No module named' in str(e):
<...>/inference.pyx in init pandas._tseries (pandas/src/tseries.c:87762)()
AttributeError: 'module' object has no attribute 'float16'
In [2]:
```
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NA float format bug
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| 2 | 2012-01-07T19:50:07Z | 2012-01-08T02:46:00Z | 2012-01-07T23:59:43Z |
MEMBER
| null |
Need to put a leading space on NaN by default...I will fix this weekend if no one else gets to it @adamklein
```
D E F
count 3.00000 3.000000 3.00000
mean 0.45674 0.522317 0.15975
std 1.41366 1.268866 0.95044
min -1.02552 -0.414997 -0.77343
25% -0.20989 -0.199634 -0.32366
50% 0.60573 0.015730 0.12611
75% 1.19786 0.990974 0.62634
max 1.78999 1.966219 1.12657
count 1.00000 1.000000 1.00000
mean -1.17800 -0.124118 0.19188
std NaN NaN NaN
min -1.17800 -0.124118 0.19188
25% -1.17800 -0.124118 0.19188
50% -1.17800 -0.124118 0.19188
75% -1.17800 -0.124118 0.19188
max -1.17800 -0.124118 0.19188
count 4.00000 4.000000 4.00000
mean -0.09697 -0.723308 0.18293
std 1.07386 0.870480 0.39080
min -1.07445 -1.923554 -0.15451
25% -0.55308 -1.080760 -0.03626
50% -0.37468 -0.480612 0.07309
75% 0.08143 -0.123160 0.29228
max 1.43593 -0.008454 0.74007
count 3.00000 3.000000 3.00000
mean 0.39914 -0.110401 -0.82227
std 0.22223 1.146990 1.42853
min 0.17519 -1.022600 -1.78633
25% 0.28890 -0.754235 -1.64286
50% 0.40260 -0.485871 -1.49940
75% 0.51111 0.345698 -0.34025
max 0.61961 1.177267 0.81891
```
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MDU6SXNzdWUyNzU4NTgx
| 591 |
Docstring decorators confuse pdb, any way to fix?
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| 5 | 2012-01-07T23:45:04Z | 2012-01-13T03:01:01Z | 2012-01-13T03:00:57Z |
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| 592 |
Disable integer-based slicing with integer indexes
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| 0 | 2012-01-08T00:00:33Z | 2012-01-13T15:59:34Z | 2012-01-13T15:59:34Z |
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| 593 |
Add column justification as configuration option in set_printoptions
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| 0 | 2012-01-08T00:24:22Z | 2016-10-12T23:02:44Z | 2012-01-10T00:12:38Z |
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| 594 |
more Windows XP build problems
|
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[] | null | 4 | 2012-01-08T19:50:05Z | 2012-01-09T14:42:16Z | 2012-01-09T14:42:16Z |
NONE
| null |
```
building 'pandas._sandbox' extension
C:\Program Files\Microsoft Visual Studio 9.0\VC\BIN\link.exe /DLL /nologo /INCREMENTAL:NO /LIBPATH:C:\Python26\libs /LIBPATH:C:\Python26\PCbuild /EXPORT:init_sa
ndbox build\temp.win32-2.6\Release\pandas\src\sandbox.obj /OUT:build\lib.win32-2.6\pandas\_sandbox.pyd /IMPLIB:build\temp.win32-2.6\Release\pandas\src\_sandbox.
lib /MANIFESTFILE:build\temp.win32-2.6\Release\pandas\src\_sandbox.pyd.manifest Creating library build\temp.win32-2.6\Release\pandas\src\_sandbox.lib and obj
ect build\temp.win32-2.6\Release\pandas\src\_sandbox.exp
sandbox.obj : error LNK2019: unresolved external symbol _get_base_ndarray referenced in function ___pyx_pf_6pandas_8_sandbox_9foo3
build\lib.win32-2.6\pandas\_sandbox.pyd : fatal error LNK1120: 1 unresolved externals
error: command '"C:\Program Files\Microsoft Visual Studio 9.0\VC\BIN\link.exe"'
failed with exit status 1120
```
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| 595 |
Add option to not sort within groups in GroupBy
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| 5 | 2012-01-09T06:28:40Z | 2015-08-13T10:50:13Z | 2012-01-11T20:41:17Z |
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Build error on 32-bit windows for current development version
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[] | null | 2 | 2012-01-09T14:07:49Z | 2012-01-09T14:51:59Z | 2012-01-09T14:43:00Z |
CONTRIBUTOR
| null |
I get the following error when I try to use "python setup.py build_ext --inplace" (I'm using numpy 1.5.1):
pandas\src\sandbox.c: At top level:
C:\Python26\lib\site-packages\numpy\core\include/numpy/__ufunc_api.h:196:1: warn
ing: '_import_umath' defined but not used
writing build\temp.win32-2.6\Release\pandas\src_sandbox.def
C:\Python26\Scripts\gcc.exe -mno-cygwin -shared -s build\temp.win32-2.6\Release\
pandas\src\sandbox.o build\temp.win32-2.6\Release\pandas\src_sandbox.def -LC:\P
ython26\libs -LC:\Python26\PCbuild -lpython26 -lmsvcr90 -o C:\src\pandas\pandas\
_sandbox.pyd
build\temp.win32-2.6\Release\pandas\src\sandbox.o:sandbox.c:(.text+0x12d1): unde
fined reference to `get_base_ndarray'
collect2: ld returned 1 exit status
error: command 'gcc' failed with exit status 1
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ENH: enable unstack to create Series (#477)
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[] | null | 0 | 2012-01-09T19:45:19Z | 2014-07-26T10:10:54Z | 2012-01-09T22:40:14Z |
CONTRIBUTOR
| null |
Should close #477
|
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ENH: #593, add col header justification option
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| 1 | 2012-01-09T21:10:59Z | 2012-01-09T22:33:12Z | 2012-01-09T22:33:12Z |
CONTRIBUTOR
| null |
Also refactored globals into a single global class instance of format config parameters. Maybe first step to externalizing into config file?
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Enable concatenation of Series with concat function
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| 0 | 2012-01-09T22:49:48Z | 2012-01-09T23:40:32Z | 2012-01-09T23:40:32Z |
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concat with dicts
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| 0 | 2012-01-09T22:49:56Z | 2012-01-09T23:38:38Z | 2012-01-09T23:38:38Z |
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