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BUG/ENH: Index constructor with dtype now delegates
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6
2013-10-12T22:52:19Z
2014-07-12T17:10:21Z
2013-10-13T00:23:35Z
CONTRIBUTOR
null
...to Index subclass (if possible). Simplifies the Index constructor slightly too, which is a nice side bonus. Will even better after Index refactor. Fixes #5196.
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ENH: Period ops NaT & timedelta ops
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4
2013-10-12T23:09:40Z
2018-10-25T03:25:41Z
2018-10-25T03:25:41Z
CONTRIBUTOR
null
see related https://github.com/pydata/pandas/issues/5200, #4551, #4056 seem that a direct subtraction of PeriodIndexes would accomplish this, but don't seem to support those types of ops... This is currently an intersection (with a '-'), but want to do a timedelta like op ``` In [40]: pd.PeriodIndex(start='20130101',end='20130501',freq='M').sub( pd.PeriodIndex(start='20130201',end='20130401',freq='M')) ``` Partially supported actuallly ``` In [5]: pd.PeriodIndex(start='20130101',end='20130501',freq='M').to_series()-pd.PeriodIndex(start='20130205',end='20130405',freq='M').to_series() Out[5]: 2013-01 NaN 2013-02 0 2013-03 0 2013-04 0 2013-05 NaN Freq: M, dtype: float64 In [6]: pd.PeriodIndex(start='20130101',end='20130201',freq='B').to_series()-pd.Period('20130101',freq='B') TypeError: unsupported operand type(s) for -: 'int' and 'Period' ```
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5,203
BUG: period on weekends rolling to odd business days
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18
2013-10-12T23:49:29Z
2013-10-15T13:28:58Z
2013-10-15T13:28:58Z
CONTRIBUTOR
null
For biz days, seems saturday is rolling to monday, but sunday to friday looks like a bug (though it might just be ambiguous/undefined behavior). ``` In [11]: pd.Period('2013-10-4', freq='B') Out[11]: Period('2013-10-04', 'B') In [12]: pd.Period('2013-10-5', freq='B') Out[12]: Period('2013-10-07', 'B') In [13]: pd.Period('2013-10-6', freq='B') Out[13]: Period('2013-10-04', 'B') In [14]: pd.Period('2013-10-7', freq='B') Out[14]: Period('2013-10-07', 'B') ```
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improved daytimes conversion performance in period.c
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1
2013-10-13T08:14:17Z
2014-01-12T07:38:07Z
2013-10-13T23:31:31Z
CONTRIBUTOR
null
Especially when using repeated conversion with period_asfreq_arr of tslib the code introduced with #3060 was about two to three times slower than before. This PR improves the performance issues by moving global calculations out of the loop.
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API: rename _prop_attributes to __finalize__ (in NDFrame)
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7
2013-10-13T15:06:14Z
2014-06-15T14:41:17Z
2013-10-15T00:58:25Z
CONTRIBUTOR
null
- rename _propogate_attributes to _metadata (in NDFrame) - change methods which directly pass metadata to use **finalize** These provide a simple mechanism for meta data propagation, which can be overriden in subclasses if desired. Currently these only do non-ambiguous propagation. makes #60, #2485 easier
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ENH/BUG: broadcast a lower-dim assignment if possible
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0
2013-10-13T15:36:29Z
2013-10-13T23:12:39Z
2013-10-13T23:12:39Z
CONTRIBUTOR
null
http://stackoverflow.com/questions/19346033/how-to-collectively-set-the-values-of-multiple-columns-for-certain-selected-rows/19346925#19346925 This does not work as we are using a rhs of a series which is not broadcast to the assignment ``` df.loc[df['A'],['A','B']] = df['C'] ``` This works, but a tad cumbersome ``` In [17]: df = DataFrame(dict(A = [1,2,0,0,0],B=[0,0,0,10,11],C=[3,4,5,6,7])) In [18]: df Out[18]: A B C 0 1 0 3 1 2 0 4 2 0 0 5 3 0 10 6 4 0 11 7 In [19]: mask = df['A'] == 0 In [20]: for col in ['A','B']: ....: df.loc[mask,col] = df['C'] ....: In [21]: df Out[21]: A B C 0 1 0 3 1 2 0 4 2 5 5 5 3 6 6 6 4 7 7 7 ```
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DOC: Note -> Notes (numpydoc docstring section)
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1
2013-10-13T18:23:00Z
2014-07-13T12:48:43Z
2013-10-14T02:13:48Z
MEMBER
null
While adding functions/methods to the docs in #5160, I noticed an error in the build output mentioning an unknown 'Note' section. This has to be 'Notes' for numpydoc. So I searched for all occurences and adjusted them.
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DOC: make api docs reflect api usage (#4539)
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4
2013-10-13T19:03:15Z
2015-04-23T21:47:53Z
2013-10-14T06:37:31Z
MEMBER
null
Closes #4539. This removes all use of `.. currentmodule:: pandas.some.submodule` (in favor of `.. currentmodule:: pandas`) in api.rst. Rationale is that docs should reflect the user api usage. Only exception are `pandas.io.sql.read_frame/write_frame` and `pandas.io.stata.StataReader` methods, because they are not available in the top-level pandas namespace. All old links will remain valid because they are still generated because I added all those in a comment at the bottom in api.rst (only not for Index, because this was only added very recently in api.rst). The **downside** with this approach is that you now get warnings when building the docs (`document isn't included in any toctree` for these functions). I am not sure how I can prevent this while keeping the old links but not including it visually in api.rst.
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CLN/BUG: Better validation of levels/labels/names
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11
2013-10-13T20:30:51Z
2014-07-16T08:34:45Z
2013-10-14T01:43:01Z
CONTRIBUTOR
null
Improved validation of levels, labels and names (plus fleshed out test cases). Fixes #4794. Changed an assert_almost_equal to assert_series_equal to better reflect actual return value. Also makes set_levels, set_labels and set_names match behavior of rename to _not_ return self if inplace.
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20,935,047
MDExOlB1bGxSZXF1ZXN0OTA1Njc0NA==
5,210
BUG: Bug in loc setting with multiple indexers and a rhs of a Series that needs broadcasting (GH5206)
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0
2013-10-13T23:12:23Z
2014-07-16T08:34:49Z
2013-10-13T23:12:39Z
CONTRIBUTOR
null
closes #5206
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5,211
ENH: Add usecols option to python parser.
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8
2013-10-13T23:14:00Z
2014-06-24T14:37:52Z
2013-10-17T14:18:58Z
CONTRIBUTOR
null
Closes #4335 This ticket felt a little bit like whack-a-mole. The key is that names can have two meanings: if it matches length of usecols then it is the name to apply to the (numeric) usecols. If it matches the length of the original columns, it is the set of column names and string values in usecols refer to it.
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5,212
Validate that MultiIndex labels are Int64 type when not called internally.
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2
2013-10-14T01:44:30Z
2021-04-11T03:43:45Z
2021-04-11T03:43:44Z
CONTRIBUTOR
null
Right now you could create MI with weird types and it doesn't error out. More difficult to do effectively when MI is an ndarray subclass (because there are so many different ways to instantiate a MultiIndex). Will fix in Index refactor (just want to remember to do so).
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MultiIndex needs to validate that levels and labels are compatible.
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4
2013-10-14T01:49:31Z
2013-10-16T02:07:46Z
2013-10-16T02:07:46Z
CONTRIBUTOR
null
Urp: ``` n [1]: import pandas as pd In [2]: mi = pd.MultiIndex(labels = [[1.25, 0.25, 3.1], [3.2, 1.2, 4.1]], levels=[['a'], ['a', 'b', 'c']]) In [3]: mi Out[3]: MultiIndex(levels=[[u'a'], [u'a', u'b', u'c']], labels=[[1, 0, 3], [3, 1, 4]]) In [4]: mi.values --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-4-e9591d864c48> in <module>() ----> 1 mi.values /Users/jtratner/projects/python/pandas/pandas/core/index.py in values(self) 2163 values = [] 2164 for lev, lab in zip(self.levels, self.labels): -> 2165 taken = com.take_1d(lev.values, lab) 2166 # Need to box timestamps, etc. 2167 if hasattr(lev, '_box_values'): /Users/jtratner/projects/python/pandas/pandas/core/common.py in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill) 630 func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, 631 axis=axis, mask_info=mask_info) --> 632 func(arr, indexer, out, fill_value) 633 return out 634 /Users/jtratner/projects/python/pandas/pandas/algos.so in pandas.algos.take_1d_object_object (pandas/algos.c:68489)() IndexError: Out of bounds on buffer access (axis 0) In [5]: mi Out[5]: MultiIndex(levels=[[u'a'], [u'a', u'b', u'c']], labels=[[1, 0, 3], [3, 1, 4]]) In [6]: mi.levels Out[6]: FrozenList([[u'a'], [u'a', u'b', u'c']]) In [7]: mi.labels Out[7]: FrozenList([[1, 0, 3], [3, 1, 4]]) ``` Labels.max() needs to be <= len(levels) for each pair of levels and labels.
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20,937,719
MDExOlB1bGxSZXF1ZXN0OTA1NzkwNQ==
5,214
BUG/CLN: MI now checks level & label compatibility
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5
2013-10-14T02:29:10Z
2014-06-12T18:48:21Z
2013-10-16T02:07:46Z
CONTRIBUTOR
null
Via verify_integrity kwarg. Internals all pass False.I don't think I've missed anything / passed verify_integrity=True where it ought not to be. Fixes #5213. Clearly this adds a (likely small) perf hit - but should only occur from external MI calls that specifically call the constructor with levels and labels (for example, doesn't occur with `from_arrays` or `from_tuples`). For 0.14 I'll have to do it differently, but this should work for 0.13.
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BUG/CLN: Clear _tuples on setting MI levels/labels
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1
2013-10-14T04:01:33Z
2014-06-22T19:15:39Z
2013-10-14T04:19:20Z
CONTRIBUTOR
null
Previously, setting levels and labels didn't reset `_tuple`, which meant that it would return wrong values. (and this was ocurring in v0.12 and earlier too).
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BUG: AttributeError when trying to assign a value to an element of a MultiIndex DataFrame
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1
2013-10-14T13:40:58Z
2013-12-12T12:44:16Z
2013-10-14T15:20:35Z
CONTRIBUTOR
null
Code snipet producing the (unexpected) ValueError (versions of pandas and numpy printed after the traceback) ``` python import numpy as np import pandas as pd print('pandas : %s' % pd.__version__) print('numpy : %s' % np.__version__) a = np.random.rand(10, 3) df = pd.DataFrame(a, columns=['x', 'y', 'z']) tuples = [(i, j) for i in range(5) for j in range(2)] index = pd.MultiIndex.from_tuples(tuples) df.index = index df.loc[0]['z'].iloc[0] = 1. ``` output: ``` python --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-9-8152e4c5a53b> in <module>() 8 index = pd.MultiIndex.from_tuples(tuples) 9 df.index = index ---> 10 df.loc[0]['z'].iloc[0] = 1. /usr/local/lib/python2.7/dist-packages/pandas-0.12.0_852_gb76b265-py2.7-linux-i686.egg/pandas/core/indexing.pyc in __setitem__(self, key, value) 92 indexer = self._convert_to_indexer(key, is_setter=True) 93 ---> 94 self._setitem_with_indexer(indexer, value) 95 96 def _has_valid_type(self, k, axis): /usr/local/lib/python2.7/dist-packages/pandas-0.12.0_852_gb76b265-py2.7-linux-i686.egg/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value) 364 365 self.obj._data = self.obj._data.setitem(indexer,value) --> 366 self.obj._maybe_update_cacher(clear=True) 367 368 def _align_series(self, indexer, ser): /usr/local/lib/python2.7/dist-packages/pandas-0.12.0_852_gb76b265-py2.7-linux-i686.egg/pandas/core/generic.pyc in _maybe_update_cacher(self, clear) 944 cacher = getattr(self,'_cacher',None) 945 if cacher is not None: --> 946 cacher[1]()._maybe_cache_changed(cacher[0],self) 947 if clear: 948 self._clear_item_cache() AttributeError: 'NoneType' object has no attribute '_maybe_cache_changed' pandas : 0.12.0-852-gb76b265 numpy : 1.8.0.dev-d310678 ```
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BUG: don't try to deal with a dead cache referant (GH5216)
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0
2013-10-14T15:04:05Z
2014-06-28T23:03:41Z
2013-10-14T15:20:35Z
CONTRIBUTOR
null
closes #5216
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20,975,147
MDU6SXNzdWUyMDk3NTE0Nw==
5,218
DOC: documenting cython class (eg Timestamp): "cyfunction is not a python function"
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8
2013-10-14T19:00:15Z
2017-11-15T16:44:34Z
2017-11-15T16:40:02Z
MEMBER
null
Follows from discussion in PR #5160. When adding `Timestamp` in api.rst to document this class and its methods (as eg done in this commit https://github.com/jorisvandenbossche/pandas/commit/3a8a41dda11d47d337e3f916567e8af2772598c2), you get warnings when building the docs like for example: ``` WARNING: error while formatting arguments for pandas.Timestamp.to_period: <cyfunction Timestamp.to_period at 0x00000000057616C0> is not a Python function. ``` `Timestamp` is a python class in a cython file (so I suppose then it is a cython class). You get the warnings for all methods that are defined in this class (defined here https://github.com/pydata/pandas/blob/master/pandas/tslib.pyx#L130), but not for inherited functions of _Timestamp or datetime. It seems that the generated docstring pages are built (so with the docstring rendered), but that Sphinx could not detect the signature. Does somebody know how to let Sphinx handle cython classes/functions?
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5,219
TST/PERF: Re-write assert_almost_equal() in cython #4398
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24
2013-10-14T19:34:42Z
2014-06-19T14:48:35Z
2013-10-21T21:35:44Z
CONTRIBUTOR
null
closes #4398 Add a testing.pyx cython file, and port assert_almost_equal() from python to cython. This also fixes a few minor bugs that were in the python version of assert_almost_equal() and adds more test cases to test_tests.py On my machine this brings a modest gain to the suite of "not slow" tests (160s -> 140s), but on assert_almost_equal() heavy tests, like test_expressions.py, it shows a large improvement (14s -> 4s).
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5,220
fix business day calculation
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2
2013-10-14T19:48:51Z
2014-06-22T14:38:02Z
2013-10-15T13:28:59Z
CONTRIBUTOR
null
This will fix the so far broken calculation of business day periods and the corresponding tests reported closes #5203. The expected behaviour found in the present tests is that the date is always rolling forward to the next business day: ``` Friday: Period('2012-3-09', freq='B') => '2012-03-09' Saturday: Period('2012-3-10', freq='B') => '2012-03-12' Sunday: Period('2012-3-11', freq='B') => '2012-03-12' Monday: Period('2012-3-12', freq='B') => '2012-03-12' ``` Can anyone confirm this?
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DOC: use Sphinx extension directly from numpy/ipython instead of maintaining our own version
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20
2013-10-14T19:53:53Z
2014-02-24T12:56:13Z
2014-02-24T12:56:13Z
MEMBER
null
Summary: proposal to not maintain our own version of Sphinx extension in pandas repo but instead use the extensions directly from the project it maintains. The pandas docs are using some Sphinx extensions that are not included in Sphinx itself: `numpydoc` and ipythons `ipython_directive` and `ipython_console_highlighting` (also matplotlibs `plot_directive` is imported in conf.py, but this is not included in the pandas repo). These extension are located here: https://github.com/pydata/pandas/tree/master/doc/sphinxext. I think that ideally pandas does not have to care about them, but just uses them by importing the extension from the respective project it lives under (numpy/ipython). Some recent issue with the doc building also arised form outdated versions of these extensions in pandas repo (see part of the commits in PR #5160 and #4165). The original extensions are living here: - ipython sphinxext in ipython itself: https://github.com/ipython/ipython/tree/master/IPython/sphinxext - numpydoc in its own repo: https://github.com/numpy/numpydoc **Options** we could do: - ipython extensions can just be imported from there in conf.py (because ipython is already a dependency) - numpydoc could be added as a submodule (this is how eg numpy itself does it: https://github.com/numpy/numpy/tree/master/doc, 'sphinxext' links to numpydoc), or it can be added as a doc build dependency. Possible **problems** with this is that the version in the pandas repo has converged from the version in numpy/ipython: - I think for `numpydoc` this will not be a problem. Following the history it was updated 3 years ago, and then only recently for python 3 compatibility. - For `ipython_directive` this seems more of a problem, looking at the history: https://github.com/pydata/pandas/commits/master/doc/sphinxext/ipython_directive.py there seems to be work done on this specifically for the pandas docs. If this is the case that is has converged from the ipython one, then ideally this could be contributed back to ipython (if the feature is not available there), but this will be more work to adapt.
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20,978,600
MDExOlB1bGxSZXF1ZXN0OTA3OTUwMg==
5,222
BUG: Groupby filter maintains ordering, closes #4621
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12
2013-10-14T20:07:10Z
2014-07-01T06:01:20Z
2013-10-15T00:55:05Z
CONTRIBUTOR
null
Simply adds `np.sort`. Includes tests for SeriesGroupBy and DataFrameGroupBy, using indexes that are sequentially increasing, sequentially decreasing, and shuffled. Also, this PR removes all ad hoc sorting involved in the original filter tests -- now no longer needed. Much better!
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20,979,100
MDU6SXNzdWUyMDk3OTEwMA==
5,223
Slicing using .loc fails when either boundary falls outside the index
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3
2013-10-14T20:17:29Z
2013-10-15T13:15:07Z
2013-10-15T13:15:07Z
CONTRIBUTOR
null
Using version 0.12, is throwing an exception the intended behavior? ``` >>> s = pd.Series(np.arange(10)) >>> s.loc[:15] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-37-e6f70b3a4e46> in <module>() ----> 1 s.loc[:15] /Users/sleepingpills/anaconda/envs/research/python.app/Contents/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key) 697 return self._getitem_tuple(key) 698 else: --> 699 return self._getitem_axis(key, axis=0) 700 701 def _getitem_axis(self, key, axis=0): /Users/sleepingpills/anaconda/envs/research/python.app/Contents/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis) 773 774 if isinstance(key, slice): --> 775 self._has_valid_type(key,axis) 776 return self._get_slice_axis(key, axis=axis) 777 elif com._is_bool_indexer(key): /Users/sleepingpills/anaconda/envs/research/python.app/Contents/lib/python2.7/site-packages/pandas/core/indexing.pyc in _has_valid_type(self, key, axis) 744 if key.stop is not None: 745 if key.stop not in ax: --> 746 raise KeyError("stop bound [%s] is not in the [%s]" % (key.stop,self.obj._get_axis_name(axis))) 747 748 elif com._is_bool_indexer(key): KeyError: 'stop bound [15] is not in the [index]' ``` The .ix indexer works as expected here (and returns the items contained in the slice).
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20,982,597
MDExOlB1bGxSZXF1ZXN0OTA4MTg1NA==
5,224
Redefine match
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36
2013-10-14T21:24:20Z
2014-07-16T08:35:09Z
2013-10-31T02:15:28Z
CONTRIBUTOR
null
closes #5075 How does this look? If the docstring and the tests reflect our consensus, I'll take a stab at the docs. This is the gist of it: Default behavior is unchanged, but issues a warning. ``` In [1]: Series(['aa', 'bb']).str.match('(a)(a)') pandas/core/strings.py:333: UserWarning: This usage of match will be removed in an upcoming version of pandas. Consider using extract instead. UserWarning) Out[1]: 0 (a, a) 1 [] dtype: object ``` New, more useful behavior is available through `as_indexer`. ``` In [2]: Series(['aa', 'bb']).str.match('(a)(a)', as_indexer=True) Out[2]: 0 True 1 False dtype: bool ``` P.S. There's a stray commit in here. Not sure why 88716e4 got lumped in....
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20,983,448
MDU6SXNzdWUyMDk4MzQ0OA==
5,225
BUG: MI needs to invalidate all of its cached properties when levels or labels are set.
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2
2013-10-14T21:39:58Z
2013-10-16T22:32:15Z
2013-10-16T22:32:15Z
CONTRIBUTOR
null
Whack-a-mole! @jreback is there some kind of standardized method for resetting the `_cache` attribute? I was thinking we could add a `_reset_cache` method to `PandasObject` that resets the attribute (so we only have to change it in one or two places).
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20,992,677
MDU6SXNzdWUyMDk5MjY3Nw==
5,226
New appending behavior doesn't work on an empty DataFrame
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1
2013-10-15T01:54:31Z
2013-10-23T14:51:25Z
2013-10-15T13:11:40Z
CONTRIBUTOR
null
Consider this working example from the docs: ``` In [9]: df1 = DataFrame(np.arange(6).reshape(3,2), ...: columns=['A','B']) In [10]: df1.loc[3] = [6, 7] In [11]: df1 Out[11]: A B 0 0 1 1 2 3 2 4 5 3 6 7 ``` And now watch what happens when the DataFrame is empty: ``` In [12]: df2 = DataFrame(columns=['A','B']) In [13]: df2.loc[3] = [6, 7] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-13-53d84383e8d4> in <module>() ----> 1 df2.loc[3] = [6, 7] /Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/indexing.pyc in __setitem__(self, key, value) 90 indexer = self._convert_tuple(key, is_setter=True) 91 else: ---> 92 indexer = self._convert_to_indexer(key, is_setter=True) 93 94 self._setitem_with_indexer(indexer, value) /Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter) 821 if is_setter: 822 if obj >= len(self.obj) and not isinstance(labels, MultiIndex): --> 823 raise ValueError("cannot set by positional indexing with enlargement") 824 825 return obj ValueError: cannot set by positional indexing with enlargement ``` I haven't read the discussion that generated this new behavior of loc/ix. Perhaps there is a good reason why this can't work. But I can't think of one. (This was prompted by [this SO question](http://stackoverflow.com/questions/19365513/how-to-add-an-extra-row-to-a-pandas-dataframe/19368360?noredirect=1#comment28705229_19368360).)
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20,993,723
MDExOlB1bGxSZXF1ZXN0OTA4NzkzOA==
5,227
BUG: allow enlargement to work with empty objects (GH5226)
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0
2013-10-15T02:37:32Z
2014-06-12T20:46:55Z
2013-10-15T13:11:40Z
CONTRIBUTOR
null
closes #5226
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5,228
ERROR: Failure: ImportError (No module named pandas)
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2
2013-10-15T03:54:59Z
2013-10-15T10:56:03Z
2013-10-15T10:56:03Z
CONTRIBUTOR
null
see: https://travis-ci.org/cancan101/pandas/jobs/12551987 ``` cannot import name hashtable Traceback (most recent call last): File "<string>", line 1, in <module> File "pandas/__init__.py", line 9, in <module> from . import hashtable, tslib, lib ImportError: cannot import name hashtable nosetests --exe -w /tmp -A not slow pandas --show-skipped E ====================================================================== ERROR: Failure: ImportError (No module named pandas) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/nose/loader.py", line 402, in loadTestsFromName module = resolve_name(addr.module) File "/home/travis/virtualenv/python2.6_with_system_site_packages/lib/python2.6/site-packages/nose/util.py", line 311, in resolve_name module = __import__('.'.join(parts_copy)) ImportError: No module named pandas ```
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5,229
Yahoo finance data fails on market cap
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4
2013-10-15T08:43:21Z
2014-11-07T11:48:09Z
2014-11-07T11:48:09Z
CONTRIBUTOR
null
Market cap returns a "B" etc. that needs to be parsed before the result is coerced into a DataFrame. https://groups.google.com/forum/#!topic/pydata/KOdYtiw7cE8
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5,230
Series.diff incorrect values with uint64
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2013-10-15T16:45:01Z
2013-10-15T19:03:37Z
2013-10-15T16:58:51Z
NONE
null
Series.diff appears to get things off with unsigned ints ``` python s=pd.Series([1, 2, 3, 4, 5, 6], dtype='uint') print s print s.shift(-1) print s.diff(-1) # this result is wrong, some conversion issues print s.map(int) print s.map(int).diff(-1) # this result is right 0 1 1 2 2 3 3 4 4 5 5 6 dtype: uint64 0 2 1 3 2 4 3 5 4 6 5 NaN dtype: float64 0 1.844674e+19 1 1.844674e+19 2 1.844674e+19 3 1.844674e+19 4 1.844674e+19 5 NaN dtype: float64 0 1 1 2 2 3 3 4 4 5 5 6 dtype: int64 0 -1 1 -1 2 -1 3 -1 4 -1 5 NaN dtype: float64 ``` (this appears to have worked correctly in an older version of pandas 0.8 and doesn't in most recent 0.12)
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EHN/FIX: Add na_last parameter to DataFrame.sort. Fixes GH3917
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71
2013-10-15T17:15:17Z
2014-09-10T00:22:49Z
2014-03-27T22:23:50Z
CONTRIBUTOR
null
closes #3917 This is an attempt to fix the Nested Sort with NaN bug (https://github.com/pydata/pandas/issues/3917). I've added tests to `test_frame.py` and `test_hashtable.py` to demonstrate the problem. `hashtable.Factorizer.factorize` has been modified to map `nan` to `na_sentinel`. Before it was mapping `nan` to a label which was already being used. This, I believe is the origin of the bug. My first idea was to mimick/reuse code from `Series.order`, since this method already handles `nan`s nicely, and allows the user to choose if nans should be placed at the beginning or the end of the sort via the `na_last` parameter. Although I found a solution using code from `Series.order`, I eventually abandoned this when I realized this patches the problem at too high a level and that it could be handled more generally with a modification of `factorize`. I retained the idea that `df.sort` should have a `na_last` parameter, however. To that end, `groupby._lexsort_indexer` has been modified to handle all possible combinations of `na_last` and `orders` settings. There are four tests (assertions) in `test_frame.py` to exercise the possibilities, one of which demonstrates that `df.sort(['A','B'])` now behaves correctly for the DataFrame shown in GH3917.
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5,232
read_csv dtype argument not working when there is a footer
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12
2013-10-15T17:16:46Z
2016-08-02T09:56:06Z
2016-08-02T09:56:06Z
NONE
null
xref #7141 ## Datafile test.csv col1|col2 a|438087272980 b|399432587827 c|592706116147 d|1584843561523 footer 1 ## Command print pd.read_csv('test.csv', sep='|', skipfooter=1, dtype={'col2':'object'}).dtypes ## Output col1 object col2 int64 dtype: object ## Expected Output col1 object col2 object dtype: object ## Platform Windows XP, Python 2.7, Pandas version 0.11.0
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5,233
BUG: OLS.predict() fails when given a Series
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1
2013-10-15T19:44:55Z
2013-10-17T13:07:34Z
2013-10-17T13:07:34Z
CONTRIBUTOR
null
OLS.predict() makes a call to dropna() with an argument when Series has no such argument. I believe the fix here is to just move the DataFrame creation above the "if fill_value" clause. ``` orig_x = x if fill_value is None and fill_method is None: x = x.dropna(how='any') else: x = x.fillna(value=fill_value, method=fill_method, axis=axis) if isinstance(x, Series): x = DataFrame({'x': x}) if self._intercept: x['intercept'] = 1. ```
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5,234
FIX: OLS.predict() - cast input to DataFrame from Series before performing operations
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5
2013-10-15T20:14:08Z
2014-06-16T18:51:25Z
2013-10-17T13:07:34Z
CONTRIBUTOR
null
Closes #5233. Simply cast to a Series to a DataFrame before dropping NAs or filling.
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5,235
Issue with Excel writers when column names are duplicated
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7
2013-10-15T22:19:45Z
2013-11-04T01:38:29Z
2013-10-16T18:50:39Z
CONTRIBUTOR
null
There appears to be an issue with Excel writers when DataFrame column names are duplicated. This issue that was initially reported on [StackOverflow](http://stackoverflow.com/questions/19363779/problems-writing-to-file-in-pandas). For example consider the following program: ``` python import pandas as pd from pandas import DataFrame df = DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) df.columns = ['A', 'B', 'B'] # !!! df.to_csv('output.csv') df.to_excel('output.xlsx') ``` Note the duplicated column name. The `df` for this looks like this: ``` python >>> df A B B 0 1 2 3 1 1 2 3 2 1 2 3 ``` The corresponding output of the CSV is as expected: ``` $ cat output.csv ,A,B,B 0,1,2,3 1,1,2,3 2,1,2,3 ``` However, the output of the any of the Excel writers is incorrect: ![screenshot](https://f.cloud.github.com/assets/94267/1338347/b42803a2-35e5-11e3-912e-4923d3382f3d.png) The issue appears to be in `pandas/core/format.py`. The output data is gathered based on column names, as shown below, which causes issues with duplicate names. ``` python def _format_regular_rows(self): ... for colidx, colname in enumerate(self.columns): series = self.df[colname] ... ``` I initially thought that this might be the correct behaviour and that column names shouldn't be duplicated but given that the output is different to the csv writer it looks like a bug. I'll write a test case but I'm not sure of the best way to fix the issue.
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Add cache resetting method to PandasObject
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0
2013-10-15T22:33:55Z
2013-12-04T00:44:11Z
2013-10-16T22:32:15Z
CONTRIBUTOR
null
Going to use this to fix MI, but also worth it anywhere we use cached_readonly and only hardcode cache name and implementation in a few places. (even on objects which supposedly immutable).
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5,237
BUG/TST: Fix Excel writers with duplicated column names.
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6
2013-10-15T23:27:24Z
2014-06-18T11:05:10Z
2013-10-16T18:50:14Z
CONTRIBUTOR
null
Test and fix for issue #5235
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5,238
CLN: Add _reset_cache method to PandasObject
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2013-10-16T01:06:04Z
2014-06-13T16:31:31Z
2013-10-16T22:32:15Z
CONTRIBUTOR
null
I think this is the right way to do it. MI needs this for _engine and friends. I think there are other objects that use `cache_readonly` that might want to reset too. I also moved the local dir methods off of StringMixin to PandasObject (where they ought to have been). Fixes #5236 and fixes #5225.
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na_filter=False ignored when index_col set
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3
2013-10-16T02:24:42Z
2017-11-06T20:33:44Z
2017-11-06T20:33:44Z
CONTRIBUTOR
null
Given the following CSV file: ``` u1,u2,u3,d1,d2,d3,d4 Good Things,C,,1,1,1,1 Good Things,R,,1,1,1,1 Bad Things,C,,1,1,1,1 Bad Things,T,,1,1,1,1 Okay Things,N,B,1,1,1,1 Okay Things,N,D,1,1,1,1 Okay Things,B,,1,1,1,1 Okay Things,D,,1,1,1,1 ``` First I parse with `na_filter=True`: ``` In [13]: pd.read_csv("/home/alex/nan_issue.csv", na_filter=True) Out[13]: u1 u2 u3 d1 d2 d3 d4 0 Good Things C NaN 1 1 1 1 1 Good Things R NaN 1 1 1 1 2 Bad Things C NaN 1 1 1 1 3 Bad Things T NaN 1 1 1 1 4 Okay Things N B 1 1 1 1 5 Okay Things N D 1 1 1 1 6 Okay Things B NaN 1 1 1 1 7 Okay Things D NaN 1 1 1 1 ``` then I parse with `na_filter=False`: ``` In [12]: pd.read_csv("/home/alex/nan_issue.csv", na_filter=False) Out[12]: u1 u2 u3 d1 d2 d3 d4 0 Good Things C 1 1 1 1 1 Good Things R 1 1 1 1 2 Bad Things C 1 1 1 1 3 Bad Things T 1 1 1 1 4 Okay Things N B 1 1 1 1 5 Okay Things N D 1 1 1 1 6 Okay Things B 1 1 1 1 7 Okay Things D 1 1 1 1 ``` then `index_cols` set: ``` In [11]: pd.read_csv("/home/alex/nan_issue.csv", na_filter=False,index_col=[0,1,2],) Out[11]: d1 d2 d3 d4 u1 u2 u3 Good Things C NaN 1 1 1 1 R NaN 1 1 1 1 Bad Things C NaN 1 1 1 1 T NaN 1 1 1 1 Okay Things N B 1 1 1 1 D 1 1 1 1 B NaN 1 1 1 1 D NaN 1 1 1 1 ``` Finally setting `na_values=[], keep_default_na=False` seems to fix the issue: ``` In [14]: pd.read_csv("/home/alex/nan_issue.csv", na_filter=False,index_col=[0,1,2],na_values=[], keep_default_na=False) Out[14]: d1 d2 d3 d4 u1 u2 u3 Good Things C 1 1 1 1 R 1 1 1 1 Bad Things C 1 1 1 1 T 1 1 1 1 Okay Things N B 1 1 1 1 D 1 1 1 1 B 1 1 1 1 D 1 1 1 1 ```
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5,240
CLN/ENH: Make names getattr'able in core/ops +PEP8.
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2013-10-16T02:41:28Z
2014-06-14T14:18:48Z
2013-10-16T22:31:48Z
CONTRIBUTOR
null
Now the name passed to a method can be used to get that method from the NDFrame (previously you had things like `or_ [|]`. better to just have them all be named exactly what they are s.t. subclasses can do `getattr(super(self, MyClass), name)(*args, **kwargs)`.
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5,241
DOC: fix building of gbq docs
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2013-10-16T07:08:09Z
2014-07-16T08:35:32Z
2013-10-16T12:30:54Z
MEMBER
null
This fixes some build warnings in de gbq docs after merging #5179 (``` is markdown syntax + wrong internal reference). And in the same time I corrected some minor numpydoc docstring things.
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5,242
Fixed assertion error message
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7
2013-10-16T12:03:03Z
2014-06-21T09:23:33Z
2013-10-16T12:20:16Z
CONTRIBUTOR
null
This is a pretty straightforward fix, message was simply inverted.
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5,243
API: add excel kw to to_clipboard to preserve compat behavior (related GH5070)
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2
2013-10-16T18:29:04Z
2014-07-16T08:35:35Z
2013-10-16T19:54:25Z
CONTRIBUTOR
null
related #5070
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5,244
API/CLN: consolidate truncate into NDFrame (panel had a separate method)
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1
2013-10-16T21:36:53Z
2014-07-16T08:35:37Z
2013-10-17T11:29:15Z
CONTRIBUTOR
null
API: truncate error if before > after is now a ValueError (rather than AssertionError) (related #5242)
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21,116,498
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5,245
Make doc compiling under windows workable
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3
2013-10-16T22:47:17Z
2014-06-18T13:37:40Z
2013-10-21T21:53:30Z
CONTRIBUTOR
null
The doc generation failed under windows due to problems with sphinx and encoded umlauts in code (see links in https://github.com/pydata/pandas/issues/5142). The workaround is to replace the offending text with one which does not fail (but which makes the example a bit pointless), build the docs and restore the old text. Closes: #5142
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5,246
API: Should _data be settable?
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2013-10-16T22:57:58Z
2013-10-17T12:12:57Z
2013-10-17T12:12:57Z
CONTRIBUTOR
null
right now you can seriously mess things up by doing: ``` python df = pd.DataFrame(zip(range(10), range(10))) df._data = 5 df + 2 # Maximum recursion depth error. ```
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5,247
ENH: Add inplace option to drop and dropna
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5
2013-10-17T01:37:45Z
2014-06-25T15:32:58Z
2013-10-28T02:06:38Z
CONTRIBUTOR
null
And appropriately invalidates caches as necessary BUT _still_ makes a copy. @jreback and I decided that it shouldn't call `__finalize__` a second time in `_update_inplace` (so if metadata is supposed to change, it's not going to be updated, but subclass can always override it). Closes #1960, related #2325 (doesn't actually fix because still makes a copy). Adds `_update_inplace` method that takes a result and invalidates the internal cache then reindexes. --- @jreback - would you mind taking a look? It's almost there, I think I'm missing one cache reference or something... (or I'm just using reindex wrong).
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Series.drop regression and unexpected behaviour
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6
2013-10-17T09:54:14Z
2013-10-17T13:08:34Z
2013-10-17T11:59:24Z
CONTRIBUTOR
null
On 0.12 stable this works: ``` >>> s = pandas.Series([1,1,2],index=["one","one","two"]) >>> s.drop(["one"]) two 2 dtype: int64 ``` but a singleton silently fails: ``` >>> s.drop("one") one 1 one 1 two 2 dtype: int64 ``` except when index is unique: ``` >>> s = pandas.Series([1,2],index=["one","two"]) >>> s.drop("one") two 2 dtype: int64 ``` according to the docs, the input to drop should be array like, so i'm not sure this should be permissible. On head, with unique index, it matches, but with non-unique: ``` >>> pandas.__version__ '0.12.0-892-g6830600' >>> s = pandas.Series([1,1,2],index=["one","one","two"]) >>> s2 = s.drop(["one"]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pandas/core/generic.py", line 1133, in drop return self.ix[tuple(slicer)] File "pandas/core/indexing.py", line 53, in __getitem__ return self._getitem_tuple(key) File "pandas/core/indexing.py", line 558, in _getitem_tuple return self._multi_take(tup) File "pandas/core/indexing.py", line 598, in _multi_take raise self._exception KeyError ```
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5,249
BUG: fixed issue with drop on a non-unique index with Series (GH5248)
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0
2013-10-17T11:30:50Z
2014-06-25T10:44:46Z
2013-10-17T11:59:24Z
CONTRIBUTOR
null
closes #5248
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MDU6SXNzdWUyMTE0ODU3NA==
5,250
ENH: Unify API for drop and dropna for NDFrame
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3
2013-10-17T12:12:08Z
2013-10-17T13:07:34Z
2013-10-17T13:07:34Z
CONTRIBUTOR
null
This can at least be done for Series and DataFrame (for dropna, thresh and how would just have no effect and axis must always be 0). Need to see whether it's possible for Panel. cf the recent issue with OLS.predict and issues with how. #5233
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5,251
API: allow Series/Panel dropna to accept other args for compat with DataFrame (GH5233/GH5250)
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0
2013-10-17T12:42:10Z
2014-07-16T08:35:47Z
2013-10-17T13:07:34Z
CONTRIBUTOR
null
closes #5233 closes #5234 closes #5250
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21,151,302
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5,252
DOC: core/generic/drop docs from array-like to list-like
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11
2013-10-17T13:08:01Z
2014-06-25T08:44:44Z
2013-10-17T13:53:10Z
CONTRIBUTOR
null
related #5248
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MDExOlB1bGxSZXF1ZXN0OTE3NDUxMA==
5,253
DOC/TST: core/generic/drop doc-string and tests
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0
2013-10-17T14:18:36Z
2014-07-16T08:35:50Z
2013-10-17T14:31:21Z
CONTRIBUTOR
null
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5,254
ENH: Better handling of MultiIndex with Excel
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50
2013-10-17T21:48:20Z
2014-02-07T16:03:42Z
2013-11-06T01:42:23Z
CONTRIBUTOR
null
I'm going to put something together this weekend, even if I can only make it work with one engine. The current MI output for columns is too hard to use. Would be really nice to round-trip it, but I'm less concerned about that. @jmcnamara if you have any tips they'd certainly be appreciated - I may only do it for Xlsxwriter this weekend. (existing MI summary form can judge where to put whitespace to show that one index covers multiple values. Just need to do the same thing but rather than blank cells, should be a merged cell). @cancan101 were you interested in this too?
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BUG: Google BigQuery API Change
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4
2013-10-17T22:17:00Z
2013-10-21T18:00:29Z
2013-10-21T18:00:29Z
CONTRIBUTOR
null
This is a placeholder for a commit I'm working on to resolve a recently introduced API bug with `pandas.io.gbq`. Basically, the module fails to handle result pagination properly, so result sets are limied to 10k rows. When #4140 was pulled, we were told by Google that this would be a backend fix that would not require changes on our parts. Unfortunately, there were some issues with bq.py (v 2.0.15) which we listed as a requirement for this package, so we will be needing to up that to v2.0.16 and fix some compatibility bugs around new code introduced. To reiterate, the only mandatory change at this point is upping the bq.py requirement `easy_install --upgrade bigquery`, though the 10k result set bug is crippling in our opinion so we're making it a high priority to fix. See: http://stackoverflow.com/questions/19145587/bq-py-not-paging-results/19260506?noredirect=1#comment28771950_19260506
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21,191,260
MDU6SXNzdWUyMTE5MTI2MA==
5,256
use of iloc with heterogeneous DataFrame coerces dtype?
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6
2013-10-17T23:00:50Z
2013-10-18T12:23:31Z
2013-10-18T12:23:31Z
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null
I'm using pandas 0.11.0 and numpy 1.7.1 in Anaconda Python 1.6.0. The `iloc` method seems to coerce dtype of values in some cases. For example: ``` x5 = pd.DataFrame(index=['A','B','C'],columns=['foo','bar','baz']) x5.foo = np.int16([-1,-2,-3]) x5.bar = np.uint16([1,2,3]) x5.baz = np.uint32([100000,200000,300000]) print x5 print x5.dtypes foo bar baz A -1 1 100000 B -2 2 200000 C -3 3 300000 foo int16 bar uint16 baz uint32 dtype: object ``` So far, so good. Now using `iloc`, it seems to cause type coercion when a single row is selected: ``` print x5.iloc[1:2] foo bar baz B -2 2 200000 print x5.iloc[1] foo 4294967294 bar 2 baz 200000 Name: B, dtype: uint32 ```
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21,197,081
MDU6SXNzdWUyMTE5NzA4MQ==
5,257
Column Slicing using Boolean Array
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2013-10-18T02:14:27Z
2013-10-18T02:21:35Z
2013-10-18T02:21:35Z
CONTRIBUTOR
null
These function as expected: Rows: ``` In [5]: pd.DataFrame({"A":[1,2],"B":[20,40]}).loc[[1]] Out[5]: A B 1 2 40 In [6]: pd.DataFrame({"A":[1,2],"B":[20,40]}).loc[[False, True]] Out[6]: A B 1 2 40 ``` and these work for columns: ``` In [7]: pd.DataFrame({"A":[1,2],"B":[20,40]})[["B"]] Out[7]: B 0 20 1 40 In [8]: pd.DataFrame({"A":[1,2],"B":[20,40]})[[1]] Out[8]: B 0 20 1 40 ``` but this does not work (as I would expect it to): ``` In [9]: pd.DataFrame({"A":[1,2],"B":[20,40]})[[False, True]] Out[9]: A B 1 2 40 ``` Is is possible to slice on columns using a boolean array?
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21,201,782
MDExOlB1bGxSZXF1ZXN0OTE5ODcxOA==
5,258
DOC: changed the term `Vern` to `Versions` to be clearer
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3
2013-10-18T05:51:55Z
2014-07-02T17:29:56Z
2013-10-20T12:12:22Z
NONE
null
based on this SO question: http://stackoverflow.com/q/19438309/1301710
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21,207,823
MDU6SXNzdWUyMTIwNzgyMw==
5,259
DOC: docs on website not building
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7
2013-10-18T09:05:54Z
2013-12-07T14:16:33Z
2013-12-07T14:16:33Z
MEMBER
null
The docs of the development version are not updated since a week or so. I don't exactly know how it is arranged, but are they supposed to build daily?
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21,235,033
MDU6SXNzdWUyMTIzNTAzMw==
5,260
DataFrame.groupby() causes loss of precision on large integer values
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4
2013-10-18T18:03:40Z
2015-03-03T01:41:06Z
2015-03-03T01:41:06Z
NONE
null
Here's what I've in a dataframe to start with: ``` python In [298]: g Out[298]: ts level 33 1382016600011617669 32 34 1382016600011625872 32 44 1382016600013590377 43 45 1382016600013598606 43 ``` Running groupby() causes my 'ts' value to change and causes some loss of precision in the smallest units. My guess would be somewhere internally this column is converted to a np.float64 and then returned back as an np.int64. ``` python In [299]: g.groupby('level').last() Out[299]: ts level 32 1382016600011625984 43 1382016600013598720 ``` Type information: ``` python In [300]: g.dtypes Out[300]: ts int64 level int64 dtype: object ``` Version info: ``` python In [303]: pd.__version__ Out[303]: '0.12.0' In [304]: np.__version__ Out[304]: '1.7.1' ```
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21,252,795
MDU6SXNzdWUyMTI1Mjc5NQ==
5,261
Support for numpy strings
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10
2013-10-19T00:25:06Z
2020-08-06T18:01:55Z
2020-08-06T18:01:54Z
CONTRIBUTOR
null
This is more of a question than an issue: why does Pandas use object type rather than the native fixed length numpy strings? For example: ``` In [24]: np.array(["foo", "baz"]) Out[24]: array(['foo', 'baz'], dtype='|S3') ``` but: ``` In [29]: pd.DataFrame({"a":np.array(["foo", "baz"])}).dtypes Out[29]: a object dtype: object ```
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5,262
BUG: Pagination in pandas.io.gbq
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12
2013-10-19T01:16:07Z
2014-07-16T08:35:55Z
2013-10-21T18:00:12Z
CONTRIBUTOR
null
In light of some last minute API changes in Google BigQuery, I have updated our code to function properly. In particular, this fixes a known bug that limited result sets to 10,000. Hopefully we'll have an entirely new version soon that will make better use of Google's reference code and thus be more future proof. Note that bigquery v2.0.17 is new as of today... 2.0.16 tested fine, but they fixed a few important backend things so we went ahead and made the change mandatory in light of the pandas release candidate. See: closes https://github.com/pydata/pandas/issues/5255
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Styling groupby boxplots
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4
2013-10-19T09:42:14Z
2017-07-09T02:32:37Z
2017-07-09T02:32:37Z
NONE
null
related #4264 The normal matplotlib boxplot command in Python returns a dictionary with keys for the boxes, median, whiskers, fliers, and caps. This makes styling really easy. Pandas.groupby boxplots, however, return an AxesSubplot object. This makes styling the plots more difficult. I posted this question recently on [Stack Overflow](http://stackoverflow.com/questions/19453994/styling-of-pandas-groupby-boxplots) and eventually came to this solution. I hope it will be useful to others. ``` from numpy.random import rand import matplotlib.pyplot as plt import pandas as pd #2 columns produces an array of 2 matplotlib.axes.AxesSubplot objects df2 = pd.DataFrame(rand(10,2), columns=['Col1', 'Col2'] ) df2['X'] = pd.Series(['A','B','A','B','A','B','A','B','A','B']) #1 column produces a single matplotlib.axes.AxesSubplot object df1 = pd.DataFrame(rand(10), columns=['Col1'] ) df1['X'] = pd.Series(['A','B','A','B','A','B','A','B','A','B']) def stylable_groupby_boxplot(df, by): ''' If you plot only one column, boxplot returns a single AxesSubplot object. If there are several columns, boxplot returns an array of several AxesSubplot objects. ''' bp = df.boxplot(by=by, grid=False) bptype = str(type(bp)) if bptype == "<class 'matplotlib.axes.AxesSubplot'>": cl = bp.get_children() cl=[item for item in cl if isinstance(item, matplotlib.lines.Line2D)] bpdict = {} groups = df.groupby(by).groups.keys() for i in range(len(groups)): bpdict[groups[i]] = {'boxes': [], 'caps': [], 'fliers': [], 'medians': [], 'whiskers': []} bpdict[groups[i]]['boxes'] = [cl[4+8*i]] bpdict[groups[i]]['caps'] = [cl[2+8*i], cl[3+8*i]] bpdict[groups[i]]['fliers'] = [cl[6+8*i], cl[7+8*i]] bpdict[groups[i]]['medians'] = [cl[5+8*i]] bpdict[groups[i]]['whiskers'] = [cl[0+8*i], cl[1+8*i]] else: bpdict = {} groups = df.groupby(by).groups.keys() keys = range(len(bp)) for i in keys: bpdict[keys[i]] = {} cl = bp[i].get_children() cl=[item for item in cl if isinstance(item, matplotlib.lines.Line2D)] for j in range(len(groups)): bpdict[keys[i]][groups[j]] = {'boxes': [], 'caps': [], 'fliers': [], 'medians': [], 'whiskers': []} bpdict[keys[i]][groups[j]]['boxes'] = [cl[4+8*j]] bpdict[keys[i]][groups[j]]['caps'] = [cl[2+8*j], cl[3+8*j]] bpdict[keys[i]][groups[j]]['fliers'] = [cl[6+8*j], cl[7+8*j]] bpdict[keys[i]][groups[j]]['medians'] = [cl[5+8*j]] bpdict[keys[i]][groups[j]]['whiskers'] = [cl[0+8*j], cl[1+8*j]] return bpdict bp2 = stylable_groupby_boxplot(df2, by="X") bp1 = stylable_groupby_boxplot(df1, by="X") #2 column styling plt.suptitle("") plt.setp(bp2[0]['A']['boxes'], color='blue') plt.setp(bp2[0]['A']['medians'], color='red') plt.setp(bp2[0]['A']['whiskers'], color='blue') plt.setp(bp2[0]['A']['fliers'], color='blue') plt.setp(bp2[0]['A']['caps'], color='blue') plt.setp(bp2[0]['B']['boxes'], color='red') plt.setp(bp2[0]['B']['medians'], color='blue') plt.setp(bp2[0]['B']['whiskers'], color='red') plt.setp(bp2[0]['B']['fliers'], color='red') plt.setp(bp2[0]['B']['caps'], color='red') plt.setp(bp2[1]['A']['boxes'], color='green') plt.setp(bp2[1]['A']['medians'], color='purple') plt.setp(bp2[1]['A']['whiskers'], color='green') plt.setp(bp2[1]['A']['fliers'], color='green') plt.setp(bp2[1]['A']['caps'], color='green') plt.setp(bp2[1]['B']['boxes'], color='purple') plt.setp(bp2[1]['B']['medians'], color='green') plt.setp(bp2[1]['B']['whiskers'], color='purple') plt.setp(bp2[1]['B']['fliers'], color='purple') plt.setp(bp2[1]['B']['caps'], color='purple') #1 column styling plt.suptitle("") plt.setp(bp1['A']['boxes'], color='blue') plt.setp(bp1['A']['medians'], color='red') plt.setp(bp1['A']['whiskers'], color='blue') plt.setp(bp1['A']['fliers'], color='blue') plt.setp(bp1['A']['caps'], color='blue') plt.setp(bp1['B']['boxes'], color='red') plt.setp(bp1['B']['medians'], color='blue') plt.setp(bp1['B']['whiskers'], color='red') plt.setp(bp1['B']['fliers'], color='red') plt.setp(bp1['B']['caps'], color='red') ```
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BUG: groupby sub-selection ignored with some methods
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30
2013-10-19T14:59:21Z
2014-04-11T14:57:13Z
2014-04-11T14:57:13Z
CONTRIBUTOR
null
related #6512, #6524, #6346 _Update from @hayd I think these should reference `_selected_obj` rather than `obj`._ Looking through some others, looks these also ignore the selection - [x] count #6570 - [x] corr #6570 - [x] cummax #6570 - [x] cummin #6570 - [x] cumsum #6570 - [x] cumprod #6570 - [x] describe #6570 - [x] fillna #6570 - [x] quantile #6570 - [x] head #6533 - [x] hist? the output is ok but the plots have all - [ ] ohlc? possibly fixed with #6570 (resample with ohlc is tested), should this method exist? see #6594 - [x] plot - [x] rank #6570 - [x] tail #6533 - [x] filter, #6570 (tested in #6593) - [x] resample, #6570 - [x] nth #6569 - [x] diff/shift, #6570 - [x] all/any #6570 - [x] ffill, #6570 - [x] pct_change #6570 - [x] idxmin/idxmax, #6570 - [x] dtypes #6570 - [ ] apply #6570 (could be tested more / different paths?) Aggregation functions like (they already _kind_ of do, but they allow bad selections ie column names not in columns, may be sep issue?): - [x] sum/max/min/median/mean/var/std/.. (not tested) - [x] agg (not tested) (these "work" with the described bug) Atm selecting a column not in df doesn't raise: - [ ] it should raise a Key Error, #6578 what about `iloc/loc/ix` (current all disabled)? - [x] iloc (very similar to head/tail) - [ ] loc/ix (maybe push off for now, this is pretty tricky) - [x] iterate over all (whitelisted) functions to check they adhere to this The column selection on a groupby object is being ignored when `.quantile()` is called. So it computes the quantile on all the (numeric) columns and returns the full DataFrame. ``` python In [92]: t = pd.DataFrame(np.random.randn(10, 4)); t[0] = np.hstack([np.ones(5), np.zeros(5)]) In [95]: t.groupby(0)[[1, 2]].quantile() # shows other cols Out[95]: 0 1 2 3 0 0 0 0.127152 0.108908 0.369601 1 1 -0.321279 0.265550 -0.382398 In [96]: t[[1, 2]].groupby(t[0]).quantile() # Should be equivalent to: Out[96]: 1 2 0 0 0.127152 0.108908 1 -0.321279 0.265550 ``` Seeing all these, I'm wondering if this is a bug or just how some of the methods are implementer. The [docs](http://pandas.pydata.org/pandas-docs/dev/groupby.html#dataframe-column-selection-in-groupby) don't mention anything about only supporting some methods though. version: '0.12.0-883-g988d4be'
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TST: test_fast.sh and test_multi.sh should skip network tests
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2013-10-19T16:44:20Z
2014-07-16T08:35:57Z
2013-10-19T19:38:22Z
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5,266
BUG: union should not try to sort inplace because platform impls differ as to when sorting occurs for objects that cannot be compared
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63
2013-10-19T16:49:01Z
2014-06-15T16:56:32Z
2014-06-03T13:36:59Z
MEMBER
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closes #5039
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5,267
BUG: groupby get_group should be more datelike friendly
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0
2013-10-19T18:51:04Z
2014-04-21T12:22:23Z
2014-04-11T22:33:18Z
CONTRIBUTOR
null
http://stackoverflow.com/questions/19458361/python-pandas-groupby-date-and-accessing-each-group-by-timestamp ``` In [2]: df= pd.DataFrame({'DATE' : ['10-Oct-2013', '10-Oct-2013', '10-Oct-2013', '11-Oct-2013', '11-Oct-2013', '11-Oct-2013'],'VAL' : [1,2,3,4,5,6]}) In [3]: df['DATE'] = pd.to_datetime(df['DATE']) In [4]: df Out[4]: DATE VAL 0 2013-10-10 00:00:00 1 1 2013-10-10 00:00:00 2 2 2013-10-10 00:00:00 3 3 2013-10-11 00:00:00 4 4 2013-10-11 00:00:00 5 5 2013-10-11 00:00:00 6 In [5]: g = df.groupby('DATE') In [8]: key = g.groups.keys()[0] In [9]: key Out[9]: numpy.datetime64('2013-10-09T20:00:00.000000000-0400') In [10]: g.indices Out[10]: {1381363200000000000L: array([0, 1, 2]), 1381449600000000000L: array([3, 4, 5])} In [11]: g.get_group(key.astype('i8')) Out[11]: DATE VAL 0 2013-10-10 00:00:00 1 1 2013-10-10 00:00:00 2 2 2013-10-10 00:00:00 3 ``` so works, but not very friendly in addition on windows, the following is needed for the selection to succeed `g.get_group(long(key.astype('i8')))`
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5,268
BUG: Fixed issue #5156: segfault on read_csv
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1
2013-10-19T20:05:43Z
2014-07-06T15:02:43Z
2013-10-19T21:13:49Z
CONTRIBUTOR
null
closes #5156 Our c parser was not checking array bounds. Example: ``` In [1]: from StringIO import StringIO; import pandas as pd In [2]: data = """\ 1,2,3 4,,6 7,8,9 10,11,12\n""" In [3]: df = pd.read_csv(StringIO(data), header=0, names=['a', 'b', 'c', 'd'], engine='c') CParserError: Too many columns specified: expected 4 and found 3 ``` Previously, this would segfault.
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5,269
Fix unhelpful "length mismatch" error message on set_axis
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0
2013-10-19T21:08:11Z
2013-10-23T12:35:47Z
2013-10-23T12:35:47Z
CONTRIBUTOR
null
When trying to change the columns of a DataFrame, I sometimes mess up the number of elements. I was getting this kind of message a few times too often. ``` >>> df = DataFrame({0:[1,2,3], 1:[1,2,3]}) >>> df.columns = df.columns = ['a', 'b', 'c'] Exception: Length mismatch (3 vs 2) ``` When the lists were bigger, I had to consult the source code or do some debugging to remember that the left side of the 'vs' is the length of the new column list. So I wrote a fix...
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CLN: Fix unhelpful "length mismatch" error message on set_axis
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4
2013-10-19T21:11:25Z
2014-07-24T02:22:25Z
2013-10-23T12:35:47Z
CONTRIBUTOR
null
closes #5269
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21,270,476
MDExOlB1bGxSZXF1ZXN0OTIzNDg5MA==
5,271
Added handling for v3 advanced segment ids which aren't just ints as of July 15th
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21
2013-10-19T23:02:49Z
2014-06-14T07:09:34Z
2013-10-22T04:06:16Z
CONTRIBUTOR
null
The old int based advanced segment ids are deprecated. The new segment ids are alphanumeric. I know from experimentation that their form includes alphanumeric and alphanumeric+hyphen. Updated code for both cases. Reference: https://developers.google.com/analytics/devguides/reporting/core/v3/changelog
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5,272
BUG: Selecting a column from a data frame sometimes returns a series and sometimes returns a data frame
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6
2013-10-19T23:33:22Z
2013-10-20T05:14:54Z
2013-10-20T05:14:54Z
NONE
null
With pandas version 0.12: In [3]: pandas.version.version Out[3]: '0.12.0' ## Code to reproduce: ``` python import collections import pandas as pd import numpy as np cols = map(str, np.unique(np.random.randint(0, 1000000, 200))) index = map(str, np.unique(np.random.randint(0, 1000000, 5000))) df = pd.DataFrame(np.random.randint(0,5,(len(index), len(cols))), index=index, columns=cols) types = [] for _ in xrange(1000): subcols = np.random.choice(cols, 10) subindex = np.random.choice(index, 400) t = df.loc[subindex, subcols] if type(t[subcols[0]]) == pd.core.series.Series: types.append('Series') elif type(t[subcols[0]]) == pd.core.frame.DataFrame: types.append('Frame') else: raise Exception() print collections.Counter(types) ``` --- About 20% of the time the result is a DataFrame and the other 80% is a Series
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21,271,692
MDU6SXNzdWUyMTI3MTY5Mg==
5,273
Change names of MultiIndex Index of a Dataframe
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4
2013-10-20T00:54:23Z
2014-04-09T12:27:47Z
2014-04-09T12:27:47Z
CONTRIBUTOR
null
How do I change the names of the various levels on a MultiIndex? How do I do so NOT in place (i.e returning a new Dataframe)? For example I have: ``` d1 d2 d3 d4 u1 u2 u3 Good Things C 1 1 1 1 R 1 1 1 1 Bad Things C 1 1 1 1 T 1 1 1 1 Okay Things N B 1 1 1 1 D 1 1 1 1 B 1 1 1 1 D 1 1 1 1 ``` and I want to change the u1, u2, u3. Currently I can change the names in place using: ``` df.index.names = ['a1', 'a2', 'a3'] ```
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21,272,705
MDU6SXNzdWUyMTI3MjcwNQ==
5,274
Finding the max value for a hierarchy level over another level
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1
2013-10-20T02:32:51Z
2013-10-20T21:57:12Z
2013-10-20T21:57:12Z
CONTRIBUTOR
null
Let's say I have a DataFrame like this: ``` l1 1 2 l2 1 13 99 89 0 1 2 2 1 ``` and for each value of l1, I want to find the corresponding max value amongst the l2 values under that l1 value. Is there an easy way to do this using groupby? so in the above example it would be 1->2 and 2->99
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21,273,355
MDU6SXNzdWUyMTI3MzM1NQ==
5,275
reset_index for columns
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5
2013-10-20T03:50:55Z
2021-03-21T15:51:01Z
2021-03-21T15:50:10Z
CONTRIBUTOR
null
Is there a reset_index equivalent for the column headings? In other words, if the column names are an MultiIndex, how would I drop one of the levels?
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21,274,142
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5,276
CLN: Remove unused ValuesProperty class.
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1
2013-10-20T05:18:48Z
2014-07-16T08:36:09Z
2013-10-20T22:46:04Z
CONTRIBUTOR
null
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MDExOlB1bGxSZXF1ZXN0OTIzODAzMw==
5,277
DOC: changed the term "Vern" to "Version" to be clearer
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1
2013-10-20T12:33:05Z
2014-07-10T12:11:16Z
2014-01-01T03:20:48Z
NONE
null
Second attempt to change a single word
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21,282,028
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5,278
drop_level does not show up as arg for DataFrame.xs on api page
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2013-10-20T15:44:57Z
2013-10-20T16:05:53Z
2013-10-20T16:05:53Z
CONTRIBUTOR
null
On the API page (http://pandas.pydata.org/pandas-docs/dev/api.html) `drop_level` does not show up as an argument to `DataFrame.xs`. It does show up here: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.xs.html#pandas.DataFrame.xs
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5,279
New in version 0.13 for "Setting index metadata"
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4
2013-10-20T15:55:59Z
2013-10-20T17:09:48Z
2013-10-20T16:19:02Z
CONTRIBUTOR
null
This section: http://pandas.pydata.org/pandas-docs/dev/indexing.html#setting-index-metadata-name-s-levels-labels makes no mention of "new in version 0.13". I believe that many of those methods were recently added. For example on v12: ``` AttributeError: 'MultiIndex' object has no attribute 'set_names' ```
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5,280
DOC: MultiIndex Indexing Using Slices
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10
2013-10-20T16:09:30Z
2014-03-30T14:46:04Z
2014-03-30T14:46:04Z
CONTRIBUTOR
null
I have the following `DataFrame`: ``` t1 t2 b1 b1 i1 1 1 ``` which I can index like this: ``` In [139]: df["t1"] Out[139]: b1 i1 1 In [140]: df["t1","b1"] Out[140]: i1 1 Name: (t1, b1), dtype: int64 In [141]: df["t1"]["b1"] Out[141]: i1 1 Name: b1, dtype: int64 ``` I would expect this to work, but ti does not: ``` In [142]: df[:]["b1"] KeyError: 'b1' ``` as well as this (which also raises an unhelpful error message): ``` In [143]: df[:,"b1"] TypeError: ```
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5,281
DOC: Added versionadded for "Setting index metadata"
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2013-10-20T16:17:39Z
2014-07-12T14:44:36Z
2013-10-20T16:19:02Z
CONTRIBUTOR
null
Closes #5279
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5,282
DOC: add some missing entries to api docs
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2
2013-10-20T16:59:55Z
2014-07-16T08:36:12Z
2013-10-20T21:36:23Z
MEMBER
null
Thanks to @JanSchulz for his notebook.
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MDExOlB1bGxSZXF1ZXN0OTIzOTc5Ng==
5,283
API: Add equals method to NDFrames.
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85
2013-10-20T17:49:45Z
2014-10-01T22:01:28Z
2014-01-24T21:43:37Z
CONTRIBUTOR
null
Also adds `array_equivalent`, which is similar to `np.array_equal` except that it handles object arrays and treats NaNs in corresponding locations as equal. closes https://github.com/pydata/pandas/issues/5183
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21,284,853
MDU6SXNzdWUyMTI4NDg1Mw==
5,284
Binary operators between DataFrame and Series object doesn't seem to work
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27
2013-10-20T18:38:26Z
2019-10-05T23:03:29Z
2019-10-05T23:03:29Z
NONE
null
related similar operation http://stackoverflow.com/questions/19484344/how-do-i-use-a-specific-columns-value-in-a-pandas-dataframe-where-clause/19494873#19494873 http://stackoverflow.com/questions/19507088/filtering-a-pandas-dataframe-without-removing-rows/19516869#19516869 http://stackoverflow.com/q/21627926/190597 This should be a bit more intuitive ``` In [59]: data = """ A B C D 1/1 0 1 0 1 1/2 2 1 1 1 1/3 3 0 1 0 1/4 1 0 1 2 1/5 1 0 1 1 1/6 2 0 2 1 1/7 3 5 2 3""" In [60]: df = read_csv(StringIO(data),sep='\s+') In [61]: df Out[61]: A B C D 1/1 0 1 0 1 1/2 2 1 1 1 1/3 3 0 1 0 1/4 1 0 1 2 1/5 1 0 1 1 1/6 2 0 2 1 1/7 3 5 2 3 In [62]: df.where((df>df.shift(1)).values & DataFrame(df.D==1).values) Out[62]: A B C D 1/1 NaN NaN NaN NaN 1/2 2 NaN 1 NaN 1/3 NaN NaN NaN NaN 1/4 NaN NaN NaN NaN 1/5 NaN NaN NaN NaN 1/6 2 NaN 2 NaN 1/7 NaN NaN NaN NaN ``` Given that normal binary operators like addition or logical `and` work well between a pair of Series objects, or between a pair of DataFrame objects (returning a element-wise addition/conjuction), I found it surprising that I cannot do the same between a Series object and a DataFrame object. Here's a demonstration of what doesn't work now and what would be the expected result: http://nbviewer.ipython.org/urls/dl.dropboxusercontent.com/u/52886258/000-qdoqud/Untitled0.ipynb
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21,287,109
MDU6SXNzdWUyMTI4NzEwOQ==
5,285
pandas.DataFrame.sort doc missing column param
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5
2013-10-20T20:51:34Z
2014-04-09T12:29:18Z
2014-04-09T12:29:13Z
CONTRIBUTOR
null
The `column` param is undocumented for `pandas.DataFrame.sort`: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.sort.html
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21,287,388
MDU6SXNzdWUyMTI4NzM4OA==
5,286
Phantom Index when NaN in MultiIndex
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13
2013-10-20T21:07:01Z
2019-12-26T00:25:35Z
2019-12-26T00:25:35Z
CONTRIBUTOR
null
Using the following: ``` csv_str = """u1,u2,u3,d1,d2,d3,d4 Good Things,C,,1,1,1,1 Good Things,R,,1,1,1,1 Bad Things,C,,1,1,1,1 Bad Things,T,,1,1,1,1 Okay Things,N,B,1,1,1,1 Okay Things,N,D,1,1,1,1 Okay Things,B,,1,1,1,1 Okay Things,D,,1,1,1,1""" d_nan = pd.read_csv(StringIO.StringIO(csv_str), na_filter=False,index_col=[0,1,2]) d_no_nan = pd.read_csv(StringIO.StringIO(csv_str), na_filter=False,index_col=[0,1,2],na_values=[], keep_default_na=False) ``` If I have the following DataFrame: ``` In [63]: d_nan Out[63]: d1 d2 d3 d4 u1 u2 u3 Good Things C NaN 1 1 1 1 R NaN 1 1 1 1 Bad Things C NaN 1 1 1 1 T NaN 1 1 1 1 Okay Things N B 1 1 1 1 D 1 1 1 1 B NaN 1 1 1 1 D NaN 1 1 1 1 ``` and then I try to use `loc` on the index: ``` In [64]: d_nan.loc["Good Things"] Out[64]: d1 d2 d3 d4 u2 u3 C NaN 1 1 1 1 R NaN 1 1 1 1 ``` When I try to `loc` the last level, a mysterious D appears: ``` In [65]: d_nan.loc["Good Things"].loc['C'] Out[65]: d1 d2 d3 d4 u3 D 1 1 1 1 ``` if d instead looks like (no NaNs): ``` In [67]: d_no_nan Out[67]: d1 d2 d3 d4 u1 u2 u3 Good Things C 1 1 1 1 R 1 1 1 1 Bad Things C 1 1 1 1 T 1 1 1 1 Okay Things N B 1 1 1 1 D 1 1 1 1 B 1 1 1 1 D 1 1 1 1 ``` then I have no problems: ``` In [68]: d_no_nan.loc["Good Things"].loc['C'] Out[68]: d1 d2 d3 d4 u3 1 1 1 1 ```
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21,290,775
MDExOlB1bGxSZXF1ZXN0OTI0MjYyMg==
5,287
DOC: expand JSON docs
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2013-10-21T00:33:13Z
2014-07-16T08:36:20Z
2013-10-21T00:41:26Z
CONTRIBUTOR
null
closes #4703 Expanded JSON docs to add more detail about the different orient options and the use of the numpy param when decoding. Feedback welcome.
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MDU6SXNzdWUyMTI5NDg1OA==
5,288
API: Poor error message on colspec validation.
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5
2013-10-21T04:09:17Z
2014-02-19T17:41:28Z
2014-02-19T17:41:28Z
CONTRIBUTOR
null
AssertionError bubbling up to the user - ruroh: ``` python from io import StringIO data = StringIO(u""" 121301234 121300123 121300012 """) pd.read_fwf(data, colspecs=[4, 5]) ``` Ends up with an AssertionError: ``` /opt/anaconda/envs/np17py27-1.5/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, colspecs, filler, thousands) 1771 assert isinstance(colspecs, (tuple, list)) 1772 for colspec in colspecs: -> 1773 assert isinstance(colspec, (tuple, list)) 1774 assert len(colspec) == 2 1775 assert isinstance(colspec[0], int) AssertionError: ``` Low priority but we should fix at some point. Really could be as simple as this in `read_fwf`. `if not all(isinstance(colspec, (tuple, list) and len(colspec) == 2 and isinstance(colspec[0], int) for colspec in colspecs): raise ValueError("colspecs must be 2-tuples of integers")`
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21,295,319
MDU6SXNzdWUyMTI5NTMxOQ==
5,289
User-facing AssertionError with groupby on list of empty list.
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5
2013-10-21T04:35:44Z
2019-03-07T21:59:40Z
2019-03-07T21:59:40Z
CONTRIBUTOR
null
I hesitate to post this, but grouping on a list of empty list yields an AssertionError. It's so edge case I'm not sure it's worth bring up though: ``` python df = pd.DataFrame(zip(range(10), range(10)), columns=['apple', 'b']) df.groupby([[]]) # Long traceback yields: AssertionError: Grouper and axis must be same length ``` Came from this SO issue (but it's really only related because of the error message) - http://stackoverflow.com/questions/19483991/how-to-make-grouper-and-axis-the-same-length
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21,316,891
MDU6SXNzdWUyMTMxNjg5MQ==
5,290
Series.replace replaces wrong value
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4
2013-10-21T13:59:08Z
2013-10-21T15:26:16Z
2013-10-21T15:26:16Z
NONE
null
I'm running into an issue with `replace` whereby one of the strings that I am using as a key in a replacement dict is not being replaced with the corresponding value. Here is a screen shot: ![screen shot 2013-10-21 at 8 57 34 am](https://f.cloud.github.com/assets/81476/1372831/c40df93a-3a58-11e3-9420-4f6d03d148be.png) Notice that the second value of 0.5 is not in the replacement Series, even though the element is in the original. I have checked for whitespace issues. I have also tried using integer values instead of floats, but that did not work either. [Here is the Series I am using](http://cl.ly/S4WZ)
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21,341,709
MDU6SXNzdWUyMTM0MTcwOQ==
5,291
Segmentation fault or UnicodeDecodeError when reading csv-file depending on chunksize.
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12
2013-10-21T20:28:00Z
2017-11-06T17:42:08Z
2017-11-06T17:42:08Z
NONE
null
I have encountered an issue with the csv parser, pandas.io.parser.read_csv. I get segmentation fault or UnicodeDecodeError when reading a csv-file in chunks and it seems like that the problem depends on the size of the chunks. Consider the following code: ``` python import codecs import csv import pandas as pd def create_csv_file(columns, rows): csv_file_name = 'csv_test_file.csv' with codecs.open(csv_file_name, mode='w', encoding='utf_8') as csv_file: csv_writer = csv.writer(csv_file, delimiter=',') for row in xrange(rows): csv_writer.writerow( [float(row)] * columns) return csv_file_name def main(): """ """ columns = 20 rows = 10000 chunksize = 999 csv_file_name = create_csv_file(columns, rows) reader = pd.io.parsers.read_csv(csv_file_name, header=None, chunksize=chunksize, encoding='utf_8') for x, dataframe in enumerate(reader, 1): print x * chunksize if __name__ == "__main__": main() ``` I get segmentation fault from the attached code, when the chunksize is 999 rows. If the chunksize is decreased to 998 rows, I instead get an UnicodeDecodeError. If the chunksize is increased to 1000 rows there are no problems of reading the csv-file. My first guess was that the problem appears when the last chunk include too few rows, but I was surprised when reading of the csv-file with the following setting, ``` python columns = 20 rows = 1000 chunksize = 99 ``` worked properly.
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5,292
BUG: DateOffset weekday around DST produces unexpected results. fixes #5175
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32
2013-10-21T21:04:06Z
2014-09-10T17:46:09Z
2014-09-04T22:18:50Z
CONTRIBUTOR
null
closes #5175 Wound up working on this in preparation for the DST change next week. Here's a first pass at a solution and a test.
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5,293
DOC add get_dummies to reshaping.rst
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3
2013-10-21T21:05:33Z
2014-07-16T08:36:23Z
2013-10-21T22:23:25Z
CONTRIBUTOR
null
examples shamelessly from Wes' book closes #4444
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21,348,883
MDU6SXNzdWUyMTM0ODg4Mw==
5,294
BLD: make all cython imports be name _* (so they are uniform)
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6
2013-10-21T22:19:32Z
2018-10-25T03:24:44Z
2018-10-25T03:24:44Z
CONTRIBUTOR
null
or could move everything to '_*.so'
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5,295
BLD: 0 13 pickles
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7
2013-10-21T23:03:54Z
2014-06-27T17:57:16Z
2013-10-23T12:32:55Z
CONTRIBUTOR
null
closes #4796
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5,296
DOC: Remove iloc example.
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2013-10-22T13:40:07Z
2014-06-27T12:40:04Z
2013-10-22T13:53:21Z
CONTRIBUTOR
null
Forgot to remove the `isin(iloc=True)` example from indexing.rst. https://github.com/pydata/pandas/pull/5199#issuecomment-26786681
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5,297
io.html.read_html from UTF-8 file-like object (OS X)
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3
2013-10-22T20:44:35Z
2013-10-23T11:40:44Z
2013-10-23T07:50:00Z
CONTRIBUTOR
null
Running on Python 3.3.2 and OS X 10.8.5 For faster testing, I read *.html files from disk instead of URL with `io.html.read_html()`. Now these files are utf-8 encoded and I can read them directly with ``` open('some.html', encoding='UTF-8').read() ``` However, using `read_html()` with the file name as string gives TB ``` UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 859: ordinal not in range(128) ``` Is it planned for `read_html()` to have an argument for this? I thought `lxml` or `bs4` would take care of that - as is the case when the URL is passed to `read_html()`. Am I missing something obvious? EDIT: I tested this with a Windows 7 machine and it works over there.
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5,298
BUG: parser can handle a common_format multi-column index (no row index cols), (GH4702)
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12
2013-10-22T23:40:12Z
2014-06-22T16:42:49Z
2013-10-24T13:39:19Z
CONTRIBUTOR
null
closes #4702, should help with #5254 Will handle the format generated by `to_csv` (which has a an 'extra' line for the index names) and the more 'common' format ``` In [4]: data = """,a,a,a,b,c,c ...: ,q,r,s,t,u,v ...: one,1,2,3,4,5,6 ...: two,7,8,9,10,11,12""" In [6]: read_csv(StringIO(data),header=[0,1],index_col=0) Out[6]: a b c q r s t u v one 1 2 3 4 5 6 two 7 8 9 10 11 12 ``` equiv to ``` df = DataFrame([[1,2,3,4,5,6],[7,8,9,10,11,12]], index=['one','two'], columns=MultiIndex.from_tuples([('a','q'),('a','r'),('a','s'), ('b','t'),('c','u'),('c','v')])) ``` no index col ``` In [1]: data = """a,a,a,b,c,c ...: q,r,s,t,u,v ...: 1,2,3,4,5,6 ...: 7,8,9,10,11,12""" In [2]: read_csv(StringIO(data),header=[0,1],index_col=None) Out[2]: a b c q r s t u v 0 1 2 3 4 5 6 1 7 8 9 10 11 12 ```
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21,447,128
MDU6SXNzdWUyMTQ0NzEyOA==
5,299
doc: DataFrame.apply can return several columns
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6
2013-10-23T11:15:46Z
2018-03-05T14:38:48Z
2018-03-05T14:38:41Z
NONE
null
Might be worth mentioning in the docs that if "func" returns a Series, the result is a DataFrame. It took me a while to figure this out (tried with tuples and a dict).
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21,452,380
MDU6SXNzdWUyMTQ1MjM4MA==
5,300
More tolerant dataframe drop method for multiple columns deletion
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5
2013-10-23T13:09:37Z
2015-04-08T14:35:57Z
2015-04-08T14:35:57Z
NONE
null
Current dataframe.drop will raise error for below code for 'non_exist_in_df_col' : ``` df = df.drop(['col_1', 'col_2', 'non_exist_in_df_col'], axis=1) ``` But below is better for it can accept it. ``` def drop_cols(df, del_cols): for col in (set(del_cols) & set(df.columns)): df = df.drop([col], axis=1) return df DataFrame.drop_cols = drop_cols ``` And it will be better to add an 'inplace' option to speed up the repeatly df self-assignment.
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