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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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} | 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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} | 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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} | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5207/events | https://github.com/pandas-dev/pandas/pull/5207 | 20,930,632 | MDExOlB1bGxSZXF1ZXN0OTA1NDk0MQ== | 5,207 | 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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} | 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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} | 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.
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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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} | 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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} | 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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} | 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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} | 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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} | [] | closed | false | null | [] | null | 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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} | 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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} | 0 | 2013-10-14T15:04:05Z | 2014-06-28T23:03:41Z | 2013-10-14T15:20:35Z | CONTRIBUTOR | null | closes #5216
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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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5220 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5220/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5220/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5220/events | https://github.com/pandas-dev/pandas/pull/5220 | 20,977,662 | MDExOlB1bGxSZXF1ZXN0OTA3ODk5NA== | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5221 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5221/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5221/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5221/events | https://github.com/pandas-dev/pandas/issues/5221 | 20,977,929 | MDU6SXNzdWUyMDk3NzkyOQ== | 5,221 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5222 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5222/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5222/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5222/events | https://github.com/pandas-dev/pandas/pull/5222 | 20,978,600 | MDExOlB1bGxSZXF1ZXN0OTA3OTUwMg== | 5,222 | BUG: Groupby filter maintains ordering, closes #4621 | {
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} | [] | closed | false | null | [] | null | 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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} | [] | closed | false | null | [] | null | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5224/events | https://github.com/pandas-dev/pandas/pull/5224 | 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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} | 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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} | 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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} | 0 | 2013-10-15T02:37:32Z | 2014-06-12T20:46:55Z | 2013-10-15T13:11:40Z | CONTRIBUTOR | null | closes #5226
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```
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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} | 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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] | closed | false | null | [] | null | 3 | 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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} | 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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} | 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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} | 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 | 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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} | 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:
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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} | 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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} | 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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} | [] | closed | false | null | [] | null | 3 | 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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} | 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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} | [] | closed | false | null | [] | null | 1 | 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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} | [] | closed | false | null | [] | null | 1 | 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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} | [] | closed | false | null | [] | null | 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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} | 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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} | [] | closed | false | null | [] | null | 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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``` python
df = pd.DataFrame(zip(range(10), range(10)))
df._data = 5
df + 2 # Maximum recursion depth error.
```
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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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} | 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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} | 0 | 2013-10-17T11:30:50Z | 2014-06-25T10:44:46Z | 2013-10-17T11:59:24Z | CONTRIBUTOR | null | closes #5248
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cf the recent issue with OLS.predict and issues with how. #5233
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closes #5234
closes #5250
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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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} | 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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} | [] | closed | false | null | [] | null | 6 | 2013-10-17T23:00:50Z | 2013-10-18T12:23:31Z | 2013-10-18T12:23:31Z | NONE | 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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} | [] | closed | false | null | [] | null | 1 | 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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} | 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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} | 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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} | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5264/events | https://github.com/pandas-dev/pandas/issues/5264 | 21,263,178 | MDU6SXNzdWUyMTI2MzE3OA== | 5,264 | 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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} | 63 | 2013-10-19T16:49:01Z | 2014-06-15T16:56:32Z | 2014-06-03T13:36:59Z | MEMBER | null | closes #5039
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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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} | 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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} | 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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} | 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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} | [] | closed | false | null | [] | null | 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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} | 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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```
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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5277/events | https://github.com/pandas-dev/pandas/pull/5277 | 21,279,434 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5278 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5278/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5278/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5278/events | https://github.com/pandas-dev/pandas/issues/5278 | 21,282,028 | MDU6SXNzdWUyMTI4MjAyOA== | 5,278 | drop_level does not show up as arg for DataFrame.xs on api page | {
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} | [] | closed | false | null | [] | null | 2 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5279 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5279/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5279/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5279/events | https://github.com/pandas-dev/pandas/issues/5279 | 21,282,215 | MDU6SXNzdWUyMTI4MjIxNQ== | 5,279 | New in version 0.13 for "Setting index metadata" | {
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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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https://api.github.com/repos/pandas-dev/pandas/issues/5280 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5280/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5280/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5280/events | https://github.com/pandas-dev/pandas/issues/5280 | 21,282,460 | MDU6SXNzdWUyMTI4MjQ2MA== | 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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https://api.github.com/repos/pandas-dev/pandas/issues/5281 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5281/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5281/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5281/events | https://github.com/pandas-dev/pandas/pull/5281 | 21,282,623 | MDExOlB1bGxSZXF1ZXN0OTIzOTI2OA== | 5,281 | DOC: Added versionadded for "Setting index metadata" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5282 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5282/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5282/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5282/events | https://github.com/pandas-dev/pandas/pull/5282 | 21,283,294 | MDExOlB1bGxSZXF1ZXN0OTIzOTUxNw== | 5,282 | DOC: add some missing entries to api docs | {
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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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} | 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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} | 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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} | 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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} | [] | closed | false | null | [] | null | 1 | 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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} | 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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``` 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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} | [] | closed | false | null | [] | null | 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:
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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} | 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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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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closes #4444
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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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https://github.com/pydata/pandas/pull/5199#issuecomment-26786681
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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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} | 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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} | 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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} | 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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