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} | 46 | 2013-12-15T20:15:09Z | 2014-06-24T01:58:21Z | 2014-01-15T02:57:34Z | CONTRIBUTOR | null | Closes #3401
Closes #5708
Closes #5912
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} | 1 | 2013-12-15T22:05:26Z | 2013-12-15T23:57:16Z | 2013-12-15T23:56:16Z | CONTRIBUTOR | null | ``` python
In [10]: df = pd.DataFrame({'A': [1., 2., 3., 4.]})
In [11]: df.dtypes
Out[11]:
A float64
dtype: object
In [12]: df.loc[:, 'A'] = df['A'].astype(np.int64)
In [13]: df.dtypes
Out[13]:
A float64
dtype: object
```
#4624 (which closed #4312) fixed it for `.iloc`
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} | 8 | 2013-12-15T23:16:17Z | 2013-12-16T16:23:30Z | 2013-12-16T16:23:30Z | CONTRIBUTOR | null | Test code:
```
import pandas as pd
print pd.__version__
s1 = pd.Series([np.nan])
s2 = pd.Series([1])
s1.fillna(s2)
```
Results:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-10-28706fea97bb> in <module>()
6 s2 = pd.Series([1])
7
----> 8 s1.fillna(s2)
/usr/local/anaconda/lib/python2.7/site-packages/pandas-0.13.0rc1_64_gceec8bf-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in fillna(self, value, method, axis, inplace, limit, downcast)
1917 continue
1918 obj = result[k]
-> 1919 obj.fillna(v, inplace=True)
1920 return result
1921 else:
AttributeError: 'numpy.float64' object has no attribute 'fillna'
0.13.0rc1-64-gceec8bf
```
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} | 4 | 2013-12-15T23:52:58Z | 2014-06-21T11:40:12Z | 2013-12-16T16:23:30Z | CONTRIBUTOR | null | closes #5703
```
In [1]: s1 = Series([np.nan])
In [2]: s2 = Series([1])
In [3]: s1.fillna(s2)
Out[3]:
0 1
dtype: float64
In [4]: s1.fillna({ 1 : 1})
Out[4]:
0 1
dtype: float64
In [5]: s1.fillna({ 1 : 2, 2 : 3})
ValueError: cannot fillna on a 1-dim object with more than one value
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5706 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5706/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5706/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5706/events | https://github.com/pandas-dev/pandas/issues/5706 | 24,325,698 | MDU6SXNzdWUyNDMyNTY5OA== | 5,706 | Sentinal -> Sentinel | {
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} | [] | closed | false | null | [] | null | 0 | 2013-12-16T02:12:18Z | 2013-12-16T13:43:16Z | 2013-12-16T13:43:16Z | CONTRIBUTOR | null | I was working on some typo fixes, and noticed that "Sentinel" is misspelled "Sentinal" in a few user-visible places (function names, arguments). Seems unlikely that anyone's relying on this, though, and it would produce a noisy and easily fixed error if so after the change.
```
./pandas/core/common.py:def sentinal_factory():
./pandas/core/common.py: class Sentinal(object):
./pandas/core/common.py: return Sentinal()
./pandas/core/format.py: sentinal = com.sentinal_factory()
./pandas/core/format.py: levels = self.columns.format(sparsify=sentinal, adjoin=False,
./pandas/core/format.py: level_lengths = _get_level_lengths(levels, sentinal)
./pandas/core/format.py: sentinal = com.sentinal_factory()
./pandas/core/format.py: levels = frame.index[:nrows].format(sparsify=sentinal,
./pandas/core/format.py: level_lengths = _get_level_lengths(levels, sentinal)
./pandas/core/format.py:def _get_level_lengths(levels, sentinal=''):
./pandas/core/format.py: if x != sentinal:
./pandas/core/index.py: sentinal = ''
./pandas/core/index.py: # use value of sparsify as sentinal, unless it's an obvious
./pandas/core/index.py: sentinal = sparsify
./pandas/core/index.py: sentinal=sentinal)
./pandas/core/index.py:def _sparsify(label_list, start=0, sentinal=''):
./pandas/core/index.py: sparse_cur.append(sentinal)
./doc/source/missing_data.rst:sentinal value that can be represented by numpy in a singular dtype (datetime64[ns]).
```
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} | [] | closed | false | null | [] | null | 1 | 2013-12-16T03:31:30Z | 2014-06-16T16:35:38Z | 2013-12-16T13:43:16Z | CONTRIBUTOR | null | Closes #5706 -- more a test that my git PR pipeline is working than because of its significance.
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} | 0 | 2013-12-16T03:54:27Z | 2014-01-15T02:57:34Z | 2014-01-15T02:57:34Z | CONTRIBUTOR | null | ```
In [12]: str(pd.NaT)
Out[12]: '0001-255-255 00:00:00'
```
where as:
```
In [13]: repr(pd.NaT)
Out[13]: 'NaT'
```
I will fix this as part of #5701
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} | 1 | 2013-12-16T10:08:26Z | 2014-06-16T04:21:15Z | 2013-12-18T00:50:14Z | CONTRIBUTOR | null | New pull request as replacement for PR-#5525
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} | [] | closed | false | null | [] | null | 2 | 2013-12-16T16:14:50Z | 2013-12-19T04:34:38Z | 2013-12-19T04:34:38Z | NONE | null | I have installed pandas-0.12.0 to a local directory that is on my PYTHONPATH. This appeared to install correctly (output too long to paste so truncated below). Howevere, upon trying to import paandas I get the following error:
[medlste@login1 config]$ python
Enthought Python Distribution -- www.enthought.com
Version: 7.3-1 (64-bit)
Python 2.7.3 |EPD 7.3-1 (64-bit)| (default, Apr 11 2012, 17:52:16)
[GCC 4.1.2 20080704 (Red Hat 4.1.2-44)] on linux2
Type "credits", "demo" or "enthought" for more information.
> > > import pandas
> > > /home/ufaserv1_m/medlste/bin/PyClone-0.12.3/lib/lib/python2.7/site-packages/pandas-0.12.0-py2.7-linux-x86_64.egg/pandas/hashtable.so: undefined symbol: _intel_fast_memset
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/home/ufaserv1_m/medlste/bin/PyClone-0.12.3/lib/lib/python2.7/site-packages/pandas-0.12.0-py2.7-linux-x86_64.egg/pandas/__init__.py", line 6, in <module>
> > > from . import hashtable, tslib, lib
> > > ImportError: /home/ufaserv1_m/medlste/bin/PyClone-0.12.3/lib/lib/python2.7/site-packages/pandas-0.12.0-py2.7-linux-x86_64.egg/pandas/hashtable.so: undefined symbol: _intel_fast_memset
Can anyone help?
Kind regards
Lucy
[medlste@login1 pandas-0.12.0]$ python setup.py install --prefix="~/bin/PyClone-0.12.3/lib"
running install
running bdist_egg
running egg_info
creating pandas.egg-info
writing requirements to pandas.egg-info/requires.txt
writing pandas.egg-info/PKG-INFO
writing top-level names to pandas.egg-info/top_level.txt
writing dependency_links to pandas.egg-info/dependency_links.txt
writing manifest file 'pandas.egg-info/SOURCES.txt'
reading manifest file 'pandas.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'TODO.rst'
warning: no files found matching 'setupegg.py'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '_.so' found anywhere in distribution
warning: no previously-included files matching '_.pyd' found anywhere in distribution
warning: no previously-included files matching '_.pyc' found anywhere in distribution
warning: no previously-included files matching '.git_' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
writing manifest file 'pandas.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
creating build
creating build/lib.linux-x86_64-2.7
creating build/lib.linux-x86_64-2.7/pandas
copying pandas/version.py -> build/lib.linux-x86_64-2.7/pandas
---
Installed /home/ufaserv1_m/medlste/bin/PyClone-0.12.3/lib/lib/python2.7/site-packages/pandas-0.12.0-py2.7-linux-x86_64.egg
Processing dependencies for pandas==0.12.0
Searching for numpy==1.6.1
Best match: numpy 1.6.1
Adding numpy 1.6.1 to easy-install.pth file
Using /apps/compilers/epd/7.3-1/1/default/lib/python2.7/site-packages
Searching for pytz==2011n
Best match: pytz 2011n
Adding pytz 2011n to easy-install.pth file
Using /apps/compilers/epd/7.3-1/1/default/lib/python2.7/site-packages
Searching for python-dateutil==1.5
Best match: python-dateutil 1.5
Adding python-dateutil 1.5 to easy-install.pth file
Using /apps/compilers/epd/7.3-1/1/default/lib/python2.7/site-packages
Finished processing dependencies for pandas==0.12.0
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} | 8 | 2013-12-16T16:57:22Z | 2013-12-17T09:38:53Z | 2013-12-17T01:18:26Z | NONE | null | This across all bits/pythons:
pandas.tests.test_indexing.TestIndexing.test_astype_assignment 16 ms 3
``` python
Error Message
attr is not equal [dtype]: dtype('int32') != dtype('int64')
Stacktrace
Traceback (most recent call last):
File "c:\Python26-AMD64\Lib\unittest.py", line 279, in run
testMethod()
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\tests\test_indexing.py", line 1387, in test_astype_assignment
assert_frame_equal(df,expected)
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 493, in assert_frame_equal
check_less_precise=check_less_precise)
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 441, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 425, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 408, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
```
pandas.tests.test_series.TestSeries.test_where_setitem_invalid
``` python
Error Message
attr is not equal [dtype]: dtype('int32') != dtype('int64')
Stacktrace
Traceback (most recent call last):
File "c:\Python26-AMD64\Lib\unittest.py", line 279, in run
testMethod()
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\tests\test_series.py", line 1348, in test_where_setitem_invalid
assert_series_equal(s, expected)
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 441, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 425, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\Program Files (x86)\Jenkins\workspace\pandas_tests\BITS\64\PYTHONVER\26\pandas\util\testing.py", line 408, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
```
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} | 5 | 2013-12-16T18:26:45Z | 2013-12-16T20:21:33Z | 2013-12-16T19:53:58Z | CONTRIBUTOR | null | I have used groupby.transform to compute a number of statistics on data that is grouped by date. It seems to fail on indexing
All of my tests are failing for 0.13 but work in 0.12.
Here is one example:
the following three lines work fine in 0.12 and fails in 0.13.
print cik_df.head()
cik_df["portfolio_rank"] = cik_df.groupby("ex_date")["value"].transform(lambda x: x.rank(ascending=False))
print cik_df.head()
Here is the output: https://gist.github.com/MichaelWS/7991767
(I sent this to the pydata group but was asked to post it here by a more active contributor)
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} | 0 | 2013-12-16T18:49:31Z | 2014-06-24T10:34:39Z | 2013-12-16T19:53:58Z | CONTRIBUTOR | null | closes #5712
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http://stackoverflow.com/questions/20617854/is-there-a-bug-in-pandas-dataframe-corrwith-function
as a test.
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```
$ ci/script.sh
inside ci/script.sh
Setting LC_ALL to zh_CN.GB18030
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 slow and not network pandas --with-xunit --xunit-file=/tmp/nosetests.xml
............................S.......................S........................................................................................./home/travis/virtualenv/python2.7_with_system_site_packages/local/lib/python2.7/site-packages/pandas-0.13.0rc1_77_g7fd33e3-py2.7-linux-x86_64.egg/pandas/tools/plotting.py:1175: UserWarning: bw_method was added in Scipy 0.11.0. Scipy version in use is 0.10.0.
warnings.warn(msg)
........
----------------------------------------------------------------------
Ran 150 tests in 302.127s
OK (SKIP=2)
```
it should obviously be failing.
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} | 0 | 2013-12-17T00:14:42Z | 2014-07-16T08:43:26Z | 2013-12-17T01:18:26Z | CONTRIBUTOR | null | DOC string updates for fillna
closes #5711
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} | 23 | 2013-12-17T00:36:14Z | 2013-12-19T16:14:16Z | 2013-12-17T02:35:54Z | CONTRIBUTOR | null | Is there any way to use Term to select membership in a large list for a Panel in HDFstore?
I have a hdfstore of the following format: https://gist.github.com/MichaelWS/7997225
My major axis is "id" and minor axis is "date". I am trying to give a large python list into Term and select if an id is in it. This works for a small list and does not work for a larger one. This fails for a list of 338 items.
```
store = pd.HDFStore(store_path, "r")
terms = []
if not members is None:
terms.append(Term("major_axis", "=", members))
if not additional_terms is None:
terms += additional_terms
if len(terms) > 0:
panel = store.select(country, where=terms)
else:
panel = store.select(country)
store.close()
```
Here is the traceback:
```
File "/home/michael/repos/franklin/factset/reference_data.py", line 1037, in get_frame
panel = store.select(country, where=terms)
File "/local/install/pandas_test/lib/python2.7/site- packages/pandas/io/pytables.py", line 637, in select
auto_close=auto_close).get_values()
File "/local/install/pandas_test/lib/python2.7/site-packages/pandas/io/pytables.py", line 1311, in get_values
results = self.func(self.start, self.stop)
File "/local/install/pandas_test/lib/python2.7/site-packages/pandas/io/pytables.py", line 626, in func
columns=columns, **kwargs)
File "/local/install/pandas_test/lib/python2.7/site-packages/pandas/io/pytables.py", line 3453, in read
sorted_values, items, tuple(N), take_labels)
File "/local/install/pandas_test/lib/python2.7/site-packages/pandas/core/reshape.py", line 1046, in block2d_to_blocknd
pvalues[i].flat[mask] = values[:, i]
IndexError: too many indices
```
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} | 0 | 2013-12-17T02:19:01Z | 2014-06-19T11:37:31Z | 2013-12-17T02:35:54Z | CONTRIBUTOR | null | closes #5717
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https://api.github.com/repos/pandas-dev/pandas/issues/5719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5719/events | https://github.com/pandas-dev/pandas/pull/5719 | 24,399,497 | MDExOlB1bGxSZXF1ZXN0MTA4ODI1NTA= | 5,719 | CLN: add diff to series/dataframe groupby dispatch whitelist | {
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} | [] | closed | false | null | [] | null | 1 | 2013-12-17T04:33:21Z | 2014-07-06T04:39:17Z | 2013-12-17T15:45:32Z | CONTRIBUTOR | null | Building on #5480, this PR adds `diff` to the groupby dispatch whitelist.
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https://api.github.com/repos/pandas-dev/pandas/issues/5720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5720/events | https://github.com/pandas-dev/pandas/issues/5720 | 24,402,760 | MDU6SXNzdWUyNDQwMjc2MA== | 5,720 | Empty dataframe corrupted by column add (0.13rc1) | {
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} | 4 | 2013-12-17T06:42:27Z | 2013-12-19T17:32:02Z | 2013-12-17T21:32:24Z | CONTRIBUTOR | null | Noticed the following difference in behavior between 0.12 and 0.13rc1 when adding a column to an empty dataframe. Obviously, a weird case, and can be worked around easily.
```
df = pd.DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
y = df[df.A > 5]
y['New'] = np.nan
print y
print y.values
```
```
(pandas-0.12)$ python test.py
Empty DataFrame
Columns: [A, B, New]
Index: []
[]
```
```
(pandas-master)$ python test.py
A B New
0 NaN NaN NaN
[1 rows x 3 columns]
Traceback (most recent call last):
File "do_fail.py", line 8, in <module>
print y.values
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/generic.py", line 1705, in values
return self.as_matrix()
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/generic.py", line 1697, in as_matrix
self._consolidate_inplace()
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/generic.py", line 1622, in _consolidate_inplace
self._data = self._protect_consolidate(f)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/generic.py", line 1660, in _protect_consolidate
result = f()
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/generic.py", line 1621, in <lambda>
f = lambda: self._data.consolidate()
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/internals.py", line 2727, in consolidate
bm = self.__class__(self.blocks, self.axes)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/internals.py", line 1945, in __init__
self._verify_integrity()
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/internals.py", line 2227, in _verify_integrity
tot_items, block.values.shape[1:], self.axes)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0rc1_82_g66934c2-py2.7-linux-i686.egg/pandas/core/internals.py", line 3561, in construction_error
tuple(map(int, [len(ax) for ax in axes]))))
ValueError: Shape of passed values is (3, 0), indices imply (3, 1)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5721/events | https://github.com/pandas-dev/pandas/pull/5721 | 24,429,826 | MDExOlB1bGxSZXF1ZXN0MTA4OTk1NjI= | 5,721 | DOC: trim CONTRIBUTING.MD | {
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} | [] | closed | false | null | [] | null | 0 | 2013-12-17T15:55:04Z | 2014-07-12T15:00:06Z | 2013-12-17T15:55:09Z | NONE | null | Still working on the 140 char version. maybe we should just use the sha1 instead. hmm.
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https://api.github.com/repos/pandas-dev/pandas/issues/5722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5722/events | https://github.com/pandas-dev/pandas/pull/5722 | 24,432,180 | MDExOlB1bGxSZXF1ZXN0MTA5MDA5NjY= | 5,722 | DOC: DatetimeIndex accepts name param | {
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} | 1 | 2013-12-17T16:26:49Z | 2014-07-16T08:43:35Z | 2013-12-17T17:36:22Z | CONTRIBUTOR | null | Just a docstring change to reflect `DatetimeIndex` taking a name parameter. I added a test since it wasn't explicitly tested anywhere.
``` python
In [1]: from pandas import DatetimeIndex
In [2]: idx = DatetimeIndex(start='2000-01-01', periods=1, freq='A', name='TEST')
In [3]: idx.name
Out[3]: 'TEST'
```
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} | 11 | 2013-12-17T22:22:32Z | 2019-07-11T14:12:49Z | 2017-01-18T16:14:35Z | NONE | null | See http://stackoverflow.com/questions/20625982/split-apply-combine-on-pandas-timedelta-column
related as well: http://stackoverflow.com/questions/20789976/python-pandas-dataframe-1st-line-issue-with-datetime-timedelta/20802902#20802902
I have a DataFrame with a column of timedeltas (actually upon inspection the dtype is `timedelta64[ns]` or `'<m8[ns]'`), and I'd like to do a split-combine-apply, but the timedelta column is being dropped:
``` python
import pandas as pd
import numpy as np
pd.__version__
Out[3]: '0.13.0rc1'
np.__version__
Out[4]: '1.8.0'
data = pd.DataFrame(np.random.rand(10, 3), columns=['f1', 'f2', 'td'])
data['td'] *= 10000000
data['td'] = pd.Series(data['td'], dtype='<m8[ns]')
data
Out[8]:
f1 f2 td
0 0.990140 0.948313 00:00:00.003066
1 0.277125 0.993549 00:00:00.001443
2 0.016427 0.581129 00:00:00.009257
3 0.048662 0.512215 00:00:00.000702
4 0.846301 0.179160 00:00:00.000396
5 0.568323 0.419887 00:00:00.000266
6 0.328182 0.919897 00:00:00.006138
7 0.292882 0.213219 00:00:00.008876
8 0.623332 0.003409 00:00:00.000322
9 0.650436 0.844180 00:00:00.006873
[10 rows x 3 columns]
data.groupby(data.index < 5).mean()
Out[9]:
f1 f2
False 0.492631 0.480118
True 0.435731 0.642873
[2 rows x 2 columns]
```
Or, forcing pandas to try the operation on the 'td' column:
``` python
data.groupby(data.index < 5)['td'].mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-12-88cc94e534b7> in <module>()
----> 1 data.groupby(data.index < 5)['td'].mean()
/path/to/lib/python3.3/site-packages/pandas-0.13.0rc1-py3.3-linux-x86_64.egg/pandas/core/groupby.py in mean(self)
417 """
418 try:
--> 419 return self._cython_agg_general('mean')
420 except GroupByError:
421 raise
/path/to/lib/python3.3/site-packages/pandas-0.13.0rc1-py3.3-linux-x86_64.egg/pandas/core/groupby.py in _cython_agg_general(self, how, numeric_only)
669
670 if len(output) == 0:
--> 671 raise DataError('No numeric types to aggregate')
672
673 return self._wrap_aggregated_output(output, names)
DataError: No numeric types to aggregate
```
However, taking the mean of the column works fine, so numeric operations should be possible:
``` python
data['td'].mean()
Out[11]:
0 00:00:00.003734
dtype: timedelta64[ns]
```
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} | 7 | 2013-12-18T03:10:21Z | 2013-12-19T12:33:09Z | 2013-12-18T20:30:27Z | CONTRIBUTOR | null | i have a dataframe with multi-index as
```
>>> df
val diffs
tag day
A 26 0.208105 NaN
37 2.709899 NaN
57 0.933981 NaN
B 75 0.624029 NaN
C 0 -1.645489 NaN
27 -1.749618 NaN
27 -0.725497 NaN
37 -0.381485 NaN
67 0.110426 NaN
82 1.253857 NaN
[10 rows x 2 columns]
```
however
```
>>> df.val[ 'A' ]
A NaN
Name: val, dtype: float64
>>> df.val[ 'X' ]
X NaN
Name: val, dtype: float64
>>> pd.__version__
'0.13.0rc1-92-gf6fd509'
```
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} | 8 | 2013-12-18T09:37:32Z | 2014-08-28T00:30:53Z | 2013-12-18T20:30:27Z | NONE | null | I am on `0.13.0rc1-64-gceec8bf` (master). I get the `IndexError` when trying to print large dataframe with MultiIndex full of NaNs. Following script raises the error
``` python
import pandas as pd
import numpy as np
n = 3500000
arrays = [range(n), np.empty(n)]
index = pd.MultiIndex.from_tuples(zip(*arrays))
s = pd.Series(np.zeros(n), index=index)
print(s)
```
And here is a Traceback
```
Traceback (most recent call last):
File "/home/mvinkler/Documents/analysis/models/test.py", line 12, in <module>
print(s)
File "/home/mvinkler/Downloads/pandas/pandas/core/base.py", line 35, in __str__
return self.__bytes__()
File "/home/mvinkler/Downloads/pandas/pandas/core/base.py", line 47, in __bytes__
return self.__unicode__().encode(encoding, 'replace')
File "/home/mvinkler/Downloads/pandas/pandas/core/series.py", line 857, in __unicode__
result = self._tidy_repr(min(30, max_rows - 4))
File "/home/mvinkler/Downloads/pandas/pandas/core/series.py", line 876, in _tidy_repr
head = self.iloc[:num]._get_repr(print_header=True, length=False,
File "/home/mvinkler/Downloads/pandas/pandas/core/generic.py", line 946, in _indexer
setattr(self, iname, indexer(self, name))
File "/home/mvinkler/Downloads/pandas/pandas/core/generic.py", line 1607, in __setattr__
elif name in self._info_axis:
File "/home/mvinkler/Downloads/pandas/pandas/core/index.py", line 2498, in __contains__
self.get_loc(key)
File "/home/mvinkler/Downloads/pandas/pandas/core/index.py", line 2986, in get_loc
return self._get_level_indexer(key, level=0)
File "/home/mvinkler/Downloads/pandas/pandas/core/index.py", line 3123, in _get_level_indexer
loc = level_index.get_loc(key)
File "/home/mvinkler/Downloads/pandas/pandas/core/index.py", line 1017, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "index.pyx", line 129, in pandas.index.IndexEngine.get_loc (pandas/index.c:3560)
File "index.pyx", line 138, in pandas.index.IndexEngine.get_loc (pandas/index.c:3314)
File "util.pxd", line 40, in util.get_value_at (pandas/index.c:13101)
IndexError: index out of bounds
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5728/events | https://github.com/pandas-dev/pandas/issues/5728 | 24,478,962 | MDU6SXNzdWUyNDQ3ODk2Mg== | 5,728 | DataFrame update issue | {
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} | [] | closed | false | null | [] | null | 1 | 2013-12-18T09:44:11Z | 2013-12-18T14:57:23Z | 2013-12-18T14:57:23Z | NONE | null | When generating inhomogeneous datasets containing dates we got DataFrames containing datetime.datetime in pandas version 0.10.0. Now we get numpy.datetime64 objects by defaults. This breaks old code. There should be a feature (flag etc.) that allows a smooth upgrade.
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} | 3 | 2013-12-18T14:17:22Z | 2017-09-18T13:10:32Z | 2017-09-18T11:00:01Z | NONE | null | ```
start = datetime.datetime(2013, 1, 1)
end = datetime.datetime(2013, 01, 27)
f=web.get_data_yahoo('AAPL',start, end)
f['Adj Close'].to_json(date_format='iso',orient='split')
```
The above code gives the following result:
```
'{"name":"Adj Close","index":["2013-01-02T00:00:00","2013-01-03T00:00:0
0","2013-01-04T00:00:00","2013-01-07T00:00:00","2013-01-08T00:00:00","2013-01-09
T00:00:00","2013-01-10T00:00:00","2013-01-11T00:00:00","2013-01-14T00:00:00","20
13-01-15T00:00:00","2013-01-16T00:00:00","2013-01-17T00:00:00","2013-01-18T00:00
:00","2013-01-22T00:00:00","2013-01-23T00:00:00","2013-01-24T00:00:00","2013-01-
25T00:00:00"],"data":[535.58,528.82,514.09,511.06,512.44,504.43,510.68,507.55,48
9.45,474.01,493.69,490.36,487.75,492.4,501.41,439.46,429.1]}'
```
What I want is:
```
'[{"index":"2013-01-02T00:00:00",value:535.58},{"index":"2013-01-04T00:00:00",value:528.82},...]'
```
This is useful when using D3JS, and potentially many more places.
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} | 0 | 2013-12-18T14:52:16Z | 2014-06-20T00:36:45Z | 2013-12-18T20:30:27Z | CONTRIBUTOR | null | closes #5727
closes #5725
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https://api.github.com/repos/pandas-dev/pandas/issues/5731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5731/events | https://github.com/pandas-dev/pandas/pull/5731 | 24,494,543 | MDExOlB1bGxSZXF1ZXN0MTA5MzczMzU= | 5,731 | DOC: add demo of factorize | {
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} | [] | closed | false | null | [] | null | 1 | 2013-12-18T14:57:56Z | 2014-07-16T08:43:44Z | 2013-12-18T15:15:45Z | CONTRIBUTOR | null | Here is some documentation of `factorize`, per [this request](http://stackoverflow.com/questions/20619851/pandas-equivalent-of-statas-encode/20619971?noredirect=1#comment30860849_20619971).
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```
repr(pd.Series([1], index=pd.Index(["a"], name="idx")))
```
```
idx
a 1
dtype: int64
```
but `to_string` does not:
```
pd.Series([1], index=pd.Index(["a"], name="idx")).to_string(name=True)
```
```
a 1
```
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files apple.xlsx, apple.xls are created in the run directory and not deleted
need to change to use `ensure_clean` context manager
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raise KeyError appropriately under py3 on mi series
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} | 0 | 2013-12-19T04:58:11Z | 2014-06-25T08:29:42Z | 2013-12-30T20:55:38Z | NONE | null | closes #5495
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} | 22 | 2013-12-19T05:42:17Z | 2014-06-12T17:49:32Z | 2014-02-17T13:59:56Z | CONTRIBUTOR | null | `DataFrame.dtypes` and `DataFrame.ftypes` values were inconsistent for empty dataframes:
``` python
In [2]: pd.DataFrame().dtypes
Out[2]:
Empty DataFrame
Columns: []
Index: []
[0 rows x 0 columns]
In [3]: pd.DataFrame().ftypes
Out[3]:
Empty DataFrame
Columns: []
Index: []
[0 rows x 0 columns]
In [4]: pd.DataFrame(columns=list("abc")).ftypes
Out[4]:
a NaN
b NaN
c NaN
dtype: float64
In [5]: pd.DataFrame(columns=list("abc")).dtypes
Out[5]:
a NaN
b NaN
c NaN
dtype: float64
In [6]: pd.__version__
Out[6]: '0.13.0rc1-92-gf6fd509'
```
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Good catch, @nmichaud.
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} | 15 | 2013-12-19T06:41:20Z | 2014-06-15T06:34:27Z | 2014-01-18T14:20:24Z | CONTRIBUTOR | null | I develop an application that does quite a bit of data manipulation. Being aware of `pandas` being functional-but-not-really-heavily-optimized I use it to maintain label consistency and for grouping/merging data, heavy-duty maths is usually done with `numpy` ufuncs. The application contains entities that have no data at the beginning and receive data over their lifetimes. Every once in a while an incoming data chunk will contain no data for a certain entity. Usually it's fine but if the entity was just created the following happens:
``` python
In [1]: pd.__version__
Out[1]: '0.13.0rc1-92-gf6fd509'
In [2]: data = pd.Series(dtype=np.float)
In [3]: chunk = pd.Series(dtype=np.float)
In [4]: pd.concat([data, chunk])
Out[4]: Series([], dtype: object)
```
After that ufuncs like `isnan` cease to work on `data.values` since its dtype has changed to `object`. This PR fixes it.
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} | 3 | 2013-12-19T12:01:41Z | 2017-11-24T16:52:16Z | 2017-11-24T16:52:16Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/20670370/pandas-and-unicode/20670901?noredirect=1#comment30966775_20670901
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} | 2 | 2013-12-19T14:22:56Z | 2013-12-19T17:32:02Z | 2013-12-19T15:20:21Z | NONE | null | I am using Pandas 0.13.0rc1-23-g286811a, Python 2.7 and Windows XP.
The space in column names is apparently causing a problem. On IPython,
```
df = pd.DataFrame(columns=['a', 'b', 'c c'])
df['d'] = 3
df['c c']
```
Error
```
C:\python_envs\prod2\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
1626 return self._getitem_multilevel(key)
1627 else:
-> 1628 return self._getitem_column(key)
1629
1630 def _getitem_column(self, key):
C:\python_envs\prod2\lib\site-packages\pandas\core\frame.pyc in _getitem_column(self, key)
1633 # get column
1634 if self.columns.is_unique:
-> 1635 return self._get_item_cache(key)
1636
1637 # duplicate columns & possible reduce dimensionaility
C:\python_envs\prod2\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
977 if res is None:
978 values = self._data.get(item)
--> 979 res = self._box_item_values(item, values)
980 cache[item] = res
981 res._cacher = (item, weakref.ref(self))
C:\python_envs\prod2\lib\site-packages\pandas\core\frame.pyc in _box_item_values(self, key, values)
1834 return self._constructor(values.T, columns=items, index=self.index)
1835 else:
-> 1836 return self._box_col_values(values, items)
1837
1838 def _box_col_values(self, values, items):
C:\python_envs\prod2\lib\site-packages\pandas\core\frame.pyc in _box_col_values(self, values, items)
1839 """ provide boxed values for a column """
1840 return self._constructor_sliced.from_array(values, index=self.index,
-> 1841 name=items, fastpath=True)
1842
1843 def __setitem__(self, key, value):
C:\python_envs\prod2\lib\site-packages\pandas\core\series.pyc in from_array(cls, arr, index, name, copy, fastpath)
233 cls = SparseSeries
234
--> 235 return cls(arr, index=index, name=name, copy=copy, fastpath=fastpath)
236
237 @property
C:\python_envs\prod2\lib\site-packages\pandas\core\series.pyc in __init__(self, data, index, dtype, name, copy, fastpath
)
130 # data is an ndarray, index is defined
131 if not isinstance(data, SingleBlockManager):
--> 132 data = SingleBlockManager(data, index, fastpath=True)
133 if copy:
134 data = data.copy()
C:\python_envs\prod2\lib\site-packages\pandas\core\internals.pyc in __init__(self, block, axis, do_integrity_check, fast
path)
3413 block = block[0]
3414 if not isinstance(block, Block):
-> 3415 block = make_block(block, axis, axis, ndim=1, fastpath=True)
3416
3417 else:
C:\python_envs\prod2\lib\site-packages\pandas\core\internals.pyc in make_block(values, items, ref_items, klass, ndim, dt
ype, fastpath, placement)
1893
1894 return klass(values, items, ref_items, ndim=ndim, fastpath=fastpath,
-> 1895 placement=placement)
1896
1897
C:\python_envs\prod2\lib\site-packages\pandas\core\internals.pyc in __init__(self, values, items, ref_items, ndim, fastp
ath, placement)
1297 super(ObjectBlock, self).__init__(values, items, ref_items, ndim=ndim,
1298 fastpath=fastpath,
-> 1299 placement=placement)
1300
1301 @property
C:\python_envs\prod2\lib\site-packages\pandas\core\internals.pyc in __init__(self, values, items, ref_items, ndim, fastp
ath, placement)
63 if len(items) != len(values):
64 raise ValueError('Wrong number of items passed %d, indices imply '
---> 65 '%d' % (len(items), len(values)))
66
67 self.set_ref_locs(placement)
ValueError: Wrong number of items passed 1, indices imply 0
```
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} | 6 | 2013-12-19T14:43:10Z | 2014-06-16T20:48:04Z | 2013-12-19T15:20:21Z | CONTRIBUTOR | null | closes #5744
related #5720
I think this is the correct behavior
```
In [1]: df = pd.DataFrame(columns=['a', 'b', 'c c'])
In [2]: df['d'] = 3
ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
In [3]: df['c c']
Out[3]: Series([], name: c c, dtype: object)
```
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] | closed | false | null | [] | null | 1 | 2013-12-19T17:18:13Z | 2014-01-10T12:15:43Z | 2014-01-10T12:15:43Z | NONE | null | So python 3.4 beta1 is out, and eventually travis will roll out 3.4 support.
Out test matrix is already bulky and partial at the same time.
I see travis [introduced](http://about.travis-ci.org/docs/user/caching/) a caching mechanism into their infrastructure. Previous attempts to do the same thing
using a network cache brought build times down to [2-3 minutes](https://travis-ci.org/y-p/pandas/builds/6422326) for most builds (i.e. those with no no cython changes).
We could squeeze in more test matrix coverage that way, while almost certainly making the CI more brittle.
Thoughts?
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} | 0 | 2013-12-19T17:32:02Z | 2014-06-21T16:49:50Z | 2013-12-19T18:04:07Z | CONTRIBUTOR | null | ```
allow assignment of a column in a frame with a scalar with no index (so adds to the columns),
instead of raising; this preservers 0.12 behavior
```
related #5720, #5744
going back to 0.12 behavior
effectively can add a column by assigning a scalar to a frame that doesn't have an index
need a more compelling reason to raise here
```
In [4]: df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
In [5]: y = df[df.A > 5]
In [6]: y
Out[6]:
Empty DataFrame
Columns: [A, B]
Index: []
[0 rows x 2 columns]
In [7]: y['New'] = np.nan
In [8]: y
Out[8]:
Empty DataFrame
Columns: [A, B, New]
Index: []
[0 rows x 3 columns]
```
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} | 1 | 2013-12-19T19:01:49Z | 2014-11-28T15:41:57Z | 2014-11-28T15:41:57Z | NONE | null | IIUC, we're advocating usage of those in lieu of a `pandasrc` #4907.
P.S.
`Good as first PR` doesn't mean "someone else has to do it", it just means wouldn't
it be nice if they did...
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} | 2 | 2013-12-19T20:30:14Z | 2014-01-16T14:27:39Z | 2014-01-16T14:27:39Z | CONTRIBUTOR | null | For a sample of data like this:
```
d = pd.Series([1] * 25)
```
Both of these throw an exception (during an attempt to divide by zero):
```
pd.rolling_skew(d, window=25)
pd.rolling_kurt(d, window=25)
```
The issue is in algos.pyx. There are no checks for what amounts to zero variance in the data. If one value occurs more times in a row than than the size of the window, the entire rolling computation fails, rather than just returning NaN for that one period (which is what I'd expect). For reference, scipy gives a kurtosis of -3 and a skewness of 0 (plus a warning) for this situation, which is not what I'd expect (since the higher moments are all zero, implying a division by zero).
```
>>> from scipy import stats
>>> stats.kurtosis([1,1,1,1,1,1,1])
-3.0
>>> stats.skew([1,1,1,1,1,1,1])
/usr/lib/python2.7/dist-packages/scipy/stats/stats.py:1067: RuntimeWarning: invalid value encountered in double_scalars
vals = np.where(zero, 0, m3 / m2**1.5)
0.0
```
Below is the approach I was taking to weed out any possible divide by zero issues. I'll submit a proper pull request tomorrow, in the meantime this is here in case I can get any feedback, preferably on whether these added conditions are enough (I think the kurtosis could still break) and how to add some tests for both of these.
```
diff --git a/pandas/algos.pyx b/pandas/algos.pyx
index 08ec707..78b619f 100644
--- a/pandas/algos.pyx
+++ b/pandas/algos.pyx
@@ -1160,7 +1160,7 @@ def roll_skew(ndarray[double_t] input, int win, int minp):
nobs -= 1
- if nobs >= minp:
+ if nobs >= minp and not (x == 0 and xx == 0) and nobs != 2:
A = x / nobs
B = xx / nobs - A * A
C = xxx / nobs - A * A * A - 3 * A * B
@@ -1227,7 +1227,7 @@ def roll_kurt(ndarray[double_t] input,
nobs -= 1
- if nobs >= minp:
+ if nobs >= minp and not (x == 0 and xx == 0) and nobs != 2:
A = x / nobs
R = A * A
B = xx / nobs - R
```
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} | 64 | 2013-12-19T21:03:19Z | 2016-08-29T07:44:34Z | 2016-04-25T15:31:12Z | CONTRIBUTOR | null | I would like to promote the idea of applying multiprocessing.Pool() execution of embarrassingly parallel tasks, e.g. the application of a function to a large number of columns.
Obviously, there's some overhead to set it up, so there will be a lower limit of number of columns from which on this only can be faster than the already fast cython approach. I will be adding my performance tests later to this issue.
I was emailing @jreback about this and he added the following remarks/complications:
- transferring numpy data is not that efficient - but still prob good enough
- you can transfer (pickle) lambda type functions so maybe need to look at using a library called dill ( which solves this problem) - possibly could slightly modify msgpack to do this though (and is already pretty efficient at transferring other types of objects)
- could also investigate joblib - I think statsmodels uses it [ed: That seems to be correct, I read in their group about joblib]
- I would create a new top level dir core/parallel for this type of stuff
- the strategy in this link could be a good way to follow: http://stackoverflow.com/questions/17785275/share-large-read-only-numpy-array-between-multiprocessing-processes
links:
http://stackoverflow.com/questions/13065172/multiprocess-python-numpy-code-for-processing-data-faster
http://docs.cython.org/src/userguide/parallelism.html
http://distarray.readthedocs.org/en/v0.5.0/
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} | 7 | 2013-12-19T22:42:42Z | 2014-06-14T10:26:45Z | 2013-12-31T00:49:22Z | NONE | null | <del>
Moving `option_context` to toplevel rather then making `set_option` a context manager means
a one line change instead of metaclasses and config_prefix subtlety and the rest of the ich.
When a given approach to something explodes in complexity I'll [bravely, bravely run away](http://www.youtube.com/watch?v=BZwuTo7zKM8) every single time.
</del>
closes #5618
replaces #5625.
@jtratner, your points about config_prefix being broken are valid, feel free to pick up in the future
if you're so inclined.
cc @jseabold
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} | 3 | 2013-12-19T22:54:03Z | 2014-06-27T10:14:01Z | 2013-12-20T15:55:34Z | NONE | null | closes #3391.
No idea what the default value should be. numpy equivalent default is 1000, but even 100 is too big IMO.
The default is primarily there to mitigate output bombs when displaying some pandas objects.
Still need to update the release notes on all these micro-PRs.
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} | 6 | 2013-12-20T04:50:51Z | 2013-12-20T16:09:03Z | 2013-12-20T14:08:05Z | CONTRIBUTOR | null | I have a long list of dataframes that concat fine in 0.12 but not in 0.13. Has anyone seen this error?
I am concatenating using the following
```
pd.concat(all_gics, ignore_index=True)
```
My traceback:
/usr/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)
925 keys=keys, levels=levels, names=names,
926 verify_integrity=verify_integrity)
--> 927 return op.get_result()
928
929
/usr/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in get_result(self)
1016 return tmpdf
1017 else:
-> 1018 new_data = self._get_concatenated_data()
1019 new_data = self._post_merge(new_data)
1020 return self.objs[0]._from_axes(new_data, self.new_axes)
/usr/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_concatenated_data(self)
1075 new_data = {}
1076 for item in self.new_axes[0]:
-> 1077 new_data[item] = self._concat_single_item(rdata, item)
1078
1079 return new_data
/usr/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in _concat_single_item(self, objs, item)
1184 elif isnull(v).all():
1185
-> 1186 alls.remove('other')
1187 alls.remove('object')
1188
KeyError: 'other'
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} | 19 | 2013-12-20T06:42:07Z | 2021-04-11T04:00:59Z | 2021-04-11T04:00:59Z | CONTRIBUTOR | null | Checklist:
- [x] head and tail act like filter
- [x] nth act like filter
- [x] nth on Series should dropna? (discuss?)
- [ ] something subtle with some functions in agg/apply (again)
- [ ] go through other methods to see if as_index acts correctly
- [ ] cythonized vs with apply #7002
- [ ] get_group with `as_index=False`, #5764
- [ ] returned group for a DataFrame.groupby.cumsum(), #5614 / https://github.com/jreback/pandas/commit/371a7bf6ce330bcfe24d9b39118d5f4dc1292cbf / just need to test for transformers (that they ignore `as_index`)
I've had this half-written for a while, but I wasn't sure actually explain it. I'm still not sure, but here goes.
Would be intersting to discuss what peoples views on the correctness of these was.
I think there's scope for cleaning up some possible inconsistencies (unfortunately this would mean breaking the API):
```
In [2]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
In [3]: g = df.groupby('A', )
In [4]: g1 = df.groupby('A', as_index=False)
In [5]: g.head(1) # I did a dirty hack to get the index like this!
Out[5]:
A B
A
1 0 1 2
5 2 5 6
In [6]: g1.head(1)
Out[6]:
A B
0 1 2
2 5 6
In [7]: g.nth(0)
Out[7]:
B
A
1 2
5 6
In [8]: g1.nth(0) # the index doesn't match the original
Out[8]:
A B
0 1 2
1 5 6
In [9]: g.filter(lambda x: x.B>0)
Out[9]:
A B
0 1 2
1 1 4
2 5 6
In [10]: g1.filter(lambda x: x.B>0)
Out[10]:
A B
0 1 2
1 1 4
2 5 6
In [13]: g.apply(lambda x: x.sum())
Out[13]:
A B
A
1 2 6
5 5 6
In [14]: g1.apply(lambda x: x.sum()) # but as_index=False
Out[14]:
A B
A
1 2 6
5 5 6
In [15]: g.apply(lambda x: x) # no index ?
Out[15]:
A B
0 1 2
1 1 4
2 5 6
In [16]: g1.apply(lambda x: x)
Out[16]:
A B
0 1 2
1 1 4
2 5 6
In [18]: g.agg(lambda x: x.sum())
Out[18]:
B
A
1 6
5 6
In [19]: g1.agg(lambda x: x.sum()) # Note: A is not summed
Out[19]:
A B
0 1 6
1 5 6
```
My opinion is that head/tail/nth should act like filter (i.e. both ignore the as_index).
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} | 0 | 2013-12-20T12:33:30Z | 2013-12-20T14:08:05Z | 2013-12-20T14:08:05Z | CONTRIBUTOR | null | Here's a snippet to show what I'm talking about:
``` python
In [1]: pd.__version__
Out[1]: '0.13.0rc1-95-gfec2ff6'
In [2]: pd.DataFrame(pd.Series())
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-31c6b8fd30b1> in <module>()
----> 1 pd.DataFrame(pd.Series())
/home/immerrr/sources/pandas/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
230 else:
231 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 232 copy=copy)
233 elif isinstance(data, (list, types.GeneratorType)):
234 if isinstance(data, types.GeneratorType):
/home/immerrr/sources/pandas/pandas/core/frame.py in _init_ndarray(self, values, index, columns, dtype, copy)
334
335 # zero len case (GH #2234)
--> 336 if not len(values) and len(columns):
337 values = np.empty((0, 1), dtype=object)
338
TypeError: object of type 'NoneType' has no len()
```
Apparently, it's a regression from 0.12.0, because
``` python
In [1]: pd.__version__
Out[1]: '0.12.0'
In [2]: pd.DataFrame(pd.Series())
Out[2]:
Empty DataFrame
Columns: [0]
Index: []
```
I'd guess that one of failing checks previously read `if smth ...`, but since implicit conversion to boolean for ndarrays are now forbidden, the check was replaced by `len(smth)`, but I didn't dig into this.
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} | 0 | 2013-12-20T13:06:10Z | 2014-07-02T10:56:26Z | 2013-12-20T14:08:05Z | CONTRIBUTOR | null | closes #5756, BUG: construction of DataFrame from empty Series regression
closes #5754, BUG: Row-wise concat of differeing dtypes failing in certain cases
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} | 1 | 2013-12-20T14:03:53Z | 2018-01-31T15:19:14Z | 2018-01-31T15:19:14Z | CONTRIBUTOR | null | Only happens when the user function mutates the input
Originally from: http://stackoverflow.com/questions/20691168/pandas-apply-to-data-frame-groupby/20705226#20705226
Could be auto-fixed or maybe just a better error report to the user (about the dups)
```
In [40]: df = DataFrame(dict(A = ['foo','foo','bar','bar'], B = np.random.randn(4)),index=[1,1,2,2])
In [41]: df
Out[41]:
A B
1 foo 0.971425
1 foo -1.151693
2 bar 1.265031
2 bar -0.219011
[4 rows x 2 columns]
In [42]: def f(x):
x['std'] = x['B'].std()
return x
....:
```
Cannot straight perform the apply
```
In [44]: df.groupby('A').apply(f)
ValueError: cannot reindex from a duplicate axis
```
By using unique indices everything is ok (so straightforward to fix)
```
In [45]: df.reset_index().groupby('A').apply(f).set_index('index')
Out[45]:
A B std
index
1 foo 0.971425 1.501271
1 foo -1.151693 1.501271
2 bar 1.265031 1.049376
2 bar -0.219011 1.049376
[4 rows x 3 columns]
```
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} | 0 | 2013-12-20T14:48:20Z | 2014-06-25T21:40:52Z | 2013-12-20T15:10:26Z | CONTRIBUTOR | null | related #5698
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} | 7 | 2013-12-20T19:35:08Z | 2014-06-20T06:05:10Z | 2014-01-16T14:27:39Z | CONTRIBUTOR | null | closes #5749
The rolling skewness and kurtosis in algos.pyx were modified to match
the testing logic in pandas/core/nanops.py. They now both return NaN
where they are not defined, which occurs where there are either too
few observations or where the variance is zero.
A set of tests was added to verify that Nan is returned in these cases
and that the computations continue to work correctly when the values
are defined.
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https://api.github.com/repos/pandas-dev/pandas/issues/5761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5761/events | https://github.com/pandas-dev/pandas/pull/5761 | 24,662,958 | MDExOlB1bGxSZXF1ZXN0MTEwMzM4NzA= | 5,761 | DOC: Flesh out the R comparison section of docs (GH3980) | {
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} | 12 | 2013-12-21T10:04:08Z | 2014-06-19T00:03:47Z | 2013-12-28T15:55:45Z | CONTRIBUTOR | null | Some additions to #3980.
I've done some more comparisons to the plyr functions located [here](http://nbviewer.ipython.org/gist/chappers/8066230/). More specifically [aaply](http://nbviewer.ipython.org/gist/chappers/8066230/aaply.ipynb), [alply](http://nbviewer.ipython.org/gist/chappers/8066230/alply.ipynb) don't really have much to do with pandas (its just list comprehension). and [dlply](http://nbviewer.ipython.org/gist/chappers/8066230/dlplyr.ipynb) is probably more suited to be in statsmodel example rather than pandas.
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https://api.github.com/repos/pandas-dev/pandas/issues/5762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5762/events | https://github.com/pandas-dev/pandas/issues/5762 | 24,664,987 | MDU6SXNzdWUyNDY2NDk4Nw== | 5,762 | groupby and aggregate passing different types to aggregating function | {
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} | [] | closed | false | null | [] | null | 2 | 2013-12-21T13:31:15Z | 2014-01-25T00:11:58Z | 2014-01-25T00:11:58Z | CONTRIBUTOR | null | it seems that when no column is selected, groupby and aggregate pass different types to the aggregating function. I am not sure if this is not a bug:
```
>>> pd.__version__
'0.13.0rc1-118-gb0c2aa4'
>>> df
tag val
0 A -0.621620
1 A 0.386178
2 B 0.449459
3 B 0.421856
4 B -0.083518
[5 rows x 2 columns]
>>> _ = df.groupby( 'tag' ).aggregate( print )
tag val
0 A -0.621620
1 A 0.386178
[2 rows x 2 columns]
[-0.6216203 0.38617806]
[ 0.44945865 0.42185594 -0.0835182 ]
>>> _ = df.groupby( 'tag' ).aggregate( lambda x: print ( type( x ) ) )
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.array.SNDArray'>
<class 'pandas.core.array.SNDArray'>
```
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} | 15 | 2013-12-22T01:24:46Z | 2014-06-12T11:50:46Z | 2014-02-07T10:31:42Z | NONE | null | closes #3190.
Provides conditional formatting and CSS styling of HTML tables for dataframes using code.
The plan is to clean up and package the functionality behind a nice API in some pandas namespace,
perhaps with a dotted API, and ship in 0.14.
Unlike my first attempt from 6 months ago which turned into a rabbit hole, this is a much simpler approach that came together in a few hours and supports everything I care about.
See the ipython notebook in the root directory, or on [nbviewer](http://nbviewer.ipython.org/github/y-p/pandas/blob/PR_html_conditional_formatting/pandas_HTML_styling.ipynb).
Feedback welcome.
- Related https://github.com/pydata/pandas/pull/5374. need to have format-specific escaping function as part of the process.
- Equivalent functionality to formatters
- Should be able to "freeze" constructed views and use them as functions of other df
(Example: highlight 3 largest values in "price" column for each frame in a panel).
- Use to define default style for html output (bootstrap-like for visual integration with ipnb).
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} | 5 | 2013-12-23T13:53:32Z | 2019-12-31T15:04:44Z | 2019-12-31T15:04:44Z | MEMBER | null | ```
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])
>>> df
A B
0 1 2
1 1 4
2 5 6
[3 rows x 2 columns]
```
Selecting a column of the GroupBy object, still returns all columns:
```
>>> g = df.groupby('A', as_index=False)['B']
>>> g.get_group(1)
A B
0 1 2
1 1 4
[2 rows x 2 columns]
>>> g = df.groupby('A', as_index=False)
>>> g.get_group(1)
A B
0 1 2
1 1 4
[2 rows x 2 columns]
>>> g.get_group(1)['B']
0 2
1 4
Name: B, dtype: int64
```
So an applied function with `apply` is applied on all columns:
```
>>> df.groupby('A', as_index=False)['B'].apply(lambda x: x.cumsum())
A B
0 1 2
1 2 6
2 5 6
[3 rows x 2 columns]
```
With `as_index=True` it works as expected:
```
>>> g = df.groupby('A')
>>> g.get_group(1)
A B
0 1 2
1 1 4
[2 rows x 2 columns]
>>> g = df.groupby('A')['B']
>>> g.get_group(1)
0 2
1 4
Name: B, dtype: int64
>>> df.groupby('A')['B'].apply(lambda x: x.cumsum())
0 2
1 6
2 6
dtype: int64
```
A more elaborate example where this turned out:
```
>>> s="""L1 L2 L3
... X 1 200
... X 2 100
... Z 1 15
... X 3 200
... Z 2 10
... Y 1 1
... Z 3 20
... Y 2 10
... Y 3 100"""
>>>
>>> df = pd.read_csv(StringIO(s), sep="\s+")
>>> df.groupby("L1")["L3"].apply(lambda x: x.order().cumsum()/x.sum())
L1
X 1 0.200000
0 0.600000
3 1.000000
Y 5 0.009009
7 0.099099
8 1.000000
Z 4 0.222222
2 0.555556
6 1.000000
dtype: float64
```
But if I don't want the X, Y, Z in the index:
```
>>> df.groupby("L1", as_index=False)["L3"].apply(lambda x: x.order().cumsum()/x.sum())
```
return an error as `x` is a dataframe.
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} | 16 | 2013-12-23T18:49:43Z | 2014-01-28T18:57:33Z | 2014-01-28T12:40:18Z | NONE | null | df.to_json() method seems consistently ~1.8x slower in version 0.13.x (and a few older 0.12.x versions in the master branch on git) than in 0.12.0.
Version 0.12.0:
``` python
Python 2.7.5+ (default, Sep 17 2013, 15:31:50)
In [1]: import pandas as pd, numpy as np
In [2]: df = pd.DataFrame(np.random.rand(100000,10))
In [3]: %timeit df.to_json(orient='split')
10 loops, best of 3: 96.1 ms per loop
In [4]: pd.__version__, np.__version__
Out[4]: ('0.12.0', '1.7.1')
```
Version 0.13.0rc1:
``` python
Python 2.7.5+ (default, Sep 17 2013, 15:31:50)
In [1]: import pandas as pd, numpy as np
In [2]: df = pd.DataFrame(np.random.rand(100000,10))
In [3]: %timeit df.to_json(orient='split')
10 loops, best of 3: 172 ms per loop
In [4]: pd.__version__, np.__version__
Out[4]: ('0.13.0rc1-119-g2485e09', '1.8.0')
```
The 1.8x factor seems to hold on my machine across Python versions (2.7.5 vs 3.3.2), dataframe sizes, orient values and dtypes (only tried floats and DatetimeIndex).
Was there some change in to_json() or have I goofed something up in my environment?
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} | 4 | 2013-12-23T20:57:58Z | 2013-12-25T02:05:13Z | 2013-12-25T02:05:13Z | CONTRIBUTOR | null | Example code:
``` python
from StringIO import StringIO
import pandas as pd
names = ["a","b","c"]
data = """\
0,1,2
3,4,5
6,7,8"""
# usecols works as expected if all columns are named
print pd.read_csv(StringIO(data), header=None, usecols=[1,2], names=names)
print pd.read_csv(StringIO(data), header=None, usecols=[0,1], names=names)
# naming only columns selected with usecols works when last column is included
print pd.read_csv(StringIO(data), header=None, usecols=[1,2], names=names[1:])
# causes IndexError
print pd.read_csv(StringIO(data), header=None, usecols=[0,1], names=names[:-1])
```
Output:
```
b c
0 1 2
1 4 5
2 7 8
[3 rows x 2 columns]
a b
0 0 1
1 3 4
2 6 7
[3 rows x 2 columns]
b c
0 1 2
1 4 5
2 7 8
[3 rows x 2 columns]
Traceback (most recent call last):
File "pandas_test2.py", line 18, in <module>
print pd.read_csv(StringIO(data), header=None, usecols=[0,1], names=names[:-1])
File "/home/weston/pandas/pandas/io/parsers.py", line 404, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/weston/pandas/pandas/io/parsers.py", line 212, in _read
return parser.read()
File "/home/weston/pandas/pandas/io/parsers.py", line 610, in read
ret = self._engine.read(nrows)
File "/home/weston/pandas/pandas/io/parsers.py", line 1050, in read
data = self._reader.read(nrows)
File "parser.pyx", line 727, in pandas.parser.TextReader.read (pandas/parser.c:6475)
File "parser.pyx", line 749, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:6695)
File "parser.pyx", line 824, in pandas.parser.TextReader._read_rows (pandas/parser.c:7517)
File "parser.pyx", line 902, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:8296)
File "parser.pyx", line 1139, in pandas.parser.TextReader._get_column_name (pandas/parser.c:11353)
IndexError: list index out of range
```
`print_versions.py` output:
```
INSTALLED VERSIONS
------------------
Python: 2.7.3.final.0
OS: Linux 3.2.0-51-generic #77-Ubuntu SMP Wed Jul 24 20:21:10 UTC 2013 i686
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.13.0rc1-119-g2485e09
Cython: 0.15.1
Numpy: 1.6.1
Scipy: 0.9.0
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 1.5
pytz: 2011k
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: 1.1.1rc
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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#5766
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} | 17 | 2013-12-24T00:13:06Z | 2019-06-16T14:30:39Z | 2019-06-16T14:30:39Z | CONTRIBUTOR | null | It would be nice to be able to import spss dant with read_spss and export it using to_spss.
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} | 4 | 2013-12-24T18:46:51Z | 2019-10-06T00:52:18Z | 2019-10-06T00:52:17Z | NONE | null | If I understand this right, the syntax is:
``` python
df.sort_index(..., inplace=False)
```
but is
``` python
df.reindex_axis(..., copy=True)
```
Any reason why we shouldn't just use the same keyword (either `copy` or `inplace`) in all functions to determine whether to modify the original object or make a new one?
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} | 2 | 2013-12-25T11:41:55Z | 2013-12-25T13:08:03Z | 2013-12-25T13:07:52Z | NONE | null | ``` python
import pandas as pd
a = pd.Series(index=[4,5,6], data=0)
print a.loc[4:5]
a.loc[4:5] += 1
```
Yields:
```
4 0
5 0
Traceback (most recent call last):
File "temp1.py", line 9, in <module>
dtype: int64
a.loc[4:5] += 1
File "lib\site-packages\pandas\core\indexing.py", line 88, in __setitem__
self._setitem_with_indexer(indexer, value)
File "lib\site-packages\pandas\core\indexing.py", line 177, in _setitem_with_indexer
value = self._align_series(indexer, value)
File "lib\site-packages\pandas\core\indexing.py", line 206, in _align_series
raise ValueError('Incompatible indexer with Series')
ValueError: Incompatible indexer with Series
```
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} | 0 | 2013-12-25T12:54:50Z | 2014-06-27T23:15:08Z | 2013-12-25T13:07:52Z | CONTRIBUTOR | null | closes #5771
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} | 1 | 2013-12-25T17:41:20Z | 2018-07-06T20:21:03Z | 2017-07-10T21:09:32Z | CONTRIBUTOR | null | This creates an IndexError.
``` python
idx = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'),
(2, 'two')])
df = DataFrame([[1, 'a', 1], [2, 'b', 1], [3, 'c', 1], [4, 'd', 1]],
columns=['A', 'B', 'C'], index=idx)
wp = Panel({'i1': df, 'i2': df})
wp.loc['i1', (1, 'one'), 'A'] = np.nan
```
Looks like its just with a Panel with a MultiIndex for one the axes (raises when following `take_split_path` in `core.indexing._setitem_with_indexer`)
``` python
In [19]: df.index = range(4)
In [20]: df
Out[20]:
A B C
0 1 a 1
1 2 b 1
2 3 c 1
3 4 d 1
[4 rows x 3 columns]
In [21]: wp = pd.Panel({'i1': df, 'i2': df})
In [22]: wp.loc['i1', 1, 'A'] = 3
In [23]: wp.loc['i1', 1, 'A']
Out[23]: 3 # correct
```
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} | 5 | 2013-12-26T00:38:27Z | 2017-01-06T12:15:29Z | 2017-01-06T12:14:59Z | NONE | null | Duplicate of #1918
----
Basically the problem is if you want to do a bar plot with time series
```
df.plot(kind='bar')
```
it will create a label for every date entry instead one clean label and more dates when you zoom in.
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} | [] | closed | false | null | [] | null | 6 | 2013-12-26T17:13:55Z | 2015-07-21T21:25:27Z | 2014-01-25T00:24:54Z | NONE | null | In a [tool that I'm building](https://github.com/jackmaney/ucc-pandas) that utilizes Pandas data frames, I'm adding a simple data validation layer to only accept numeric columns of a given data frame. I found a [Stack Overflow question](http://stackoverflow.com/questions/12725417/drop-non-numeric-columns-from-a-pandas-dataframe) from a bit over a year ago pointing out the private `_get_numeric_data()` method in the `DataFrame` class. Would it be possible to make this method part of the public API?
(And my apologies if this request has been made before; I did a few searches through the issues and didn't find anything.)
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] | closed | false | null | [] | null | 7 | 2013-12-26T18:39:32Z | 2014-09-18T17:52:48Z | 2013-12-27T18:10:17Z | CONTRIBUTOR | null | Hi, I'm testing out some code on the 0.13 release candidate and I've run into problems with a fairly common (for me) pattern. It's no longer possible to use a boolean Series to index a numpy array. E.g.:
```
import numpy as np
import pandas as pd
x = np.random.randn(30)
mask = pd.Series(np.random.rand(30) > .5)
x[mask].mean()
```
Raises:
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-27-73e40e756b4e> in <module>()
3 x = np.random.randn(30)
4 mask = pd.Series(np.random.rand(30) > .5)
----> 5 x[mask].mean()
IndexError: unsupported iterator index
```
Perhaps this is not a good idiom, but this change breaks quite a bit of existing code.
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} | [] | closed | false | null | [] | null | 4 | 2013-12-27T04:22:28Z | 2014-12-22T18:47:50Z | 2014-01-20T19:09:28Z | CONTRIBUTOR | null | This is a bit of an odd one, but when I run `python test_perf.py`, I get this traceback:
``` python
(pandas-dev) ~/E/p/l/p/s/p/vb_suite (to-frame-multi=) python test_perf.py -h
Traceback (most recent call last):
File "test_perf.py", line 55, in <module>
import pandas as pd
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_130_g0657e63-py2.7-macosx-10.8-x86_64.egg/pandas/__init__.py", line 37, in <module>
import pandas.core.config_init
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_130_g0657e63-py2.7-macosx-10.8-x86_64.egg/pandas/core/config_init.py", line 17, in <module>
from pandas.core.format import detect_console_encoding
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_130_g0657e63-py2.7-macosx-10.8-x86_64.egg/pandas/core/format.py", line 9, in <module>
from pandas.core.index import Index, MultiIndex, _ensure_index
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_130_g0657e63-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.py", line 11, in <module>
import pandas.index as _index
File "index.pyx", line 34, in init pandas.index (pandas/index.c:14961)
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pytz/__init__.py", line 35, in <module>
from pkg_resources import resource_stream
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pkg_resources.py", line 74, in <module>
import parser
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas/vb_suite/parser.py", line 1, in <module>
from vbench.api import Benchmark
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/vbench/__init__.py", line 2, in <module>
import vbench.log
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/vbench/vbench/__init__.py", line 2, in <module>
import vbench.log
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/vbench/log.py", line 34, in <module>
from vbench.config import is_interactive
File "/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/vbench/vbench/config.py", line 3, in <module>
TIME_ZONE = pytz.timezone('US/Eastern')
AttributeError: 'module' object has no attribute 'timezone'
```
I followed a lot of red herrings all over the place, but the actual problem seems to be some sort of namespace collision with the `parser` module in `/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pkg_resources.py`, which I'm assuming is the [standard library parser](http://docs.python.org/2/library/parser.html).
My reason for suspecting that it's some kind of namespace collision is that renaming `pandas/vb_suite/parser.py` to `pandas/vb_suite/parser_.py` (and changing `parser` to `parser_` in `pandas/vb_suite/suite.py`) fixes the problem.
I'm doing some reading on python's import machinery to figure out if this is a bug here and what a better solution would be.
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======================================================================
ERROR: test_read_write_dta11 (pandas.io.tests.test_stata.TestStata)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/tests/test_stata.py", line 247, in test_read_write_dta11
written_and_read_again = self.read_dta(path)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/tests/test_stata.py", line 41, in read_dta
return read_stata(file, convert_dates=True)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 47, in read_stata
reader = StataReader(filepath_or_buffer, encoding)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 343, in __init__
self._read_header()
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 468, in _read_header
for i in range(self.nvar)]
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 468, in <listcomp>
for i in range(self.nvar)]
TypeError: ord() expected a character, but string of length 0 found
======================================================================
ERROR: test_read_write_dta12 (pandas.io.tests.test_stata.TestStata)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/tests/test_stata.py", line 263, in test_read_write_dta12
written_and_read_again = self.read_dta(path)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/tests/test_stata.py", line 41, in read_dta
return read_stata(file, convert_dates=True)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 47, in read_stata
reader = StataReader(filepath_or_buffer, encoding)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 343, in __init__
self._read_header()
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 468, in _read_header
for i in range(self.nvar)]
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/io/stata.py", line 468, in <listcomp>
for i in range(self.nvar)]
TypeError: ord() expected a character, but string of length 0 found
```
cc @PKEuS
http://nipy.bic.berkeley.edu/builders/pandas-py3.x-sid-sparc/builds/491/steps/shell_4/logs/stdio
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======================================================================
ERROR: test_to_timedelta_on_missing_values (pandas.tseries.tests.test_timedeltas.TestTimedeltas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/tseries/tests/test_timedeltas.py", line 204, in test_to_timedelta_on_missing_values
expected = Series([np.timedelta64(1000000000, 'ns'), timedelta_NaT], dtype='<m8[ns]')
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/core/series.py", line 218, in __init__
raise_cast_failure=True)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/core/series.py", line 2542, in _sanitize_array
subarr = _try_cast(data, False)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/core/series.py", line 2503, in _try_cast
arr = _possibly_cast_to_datetime(arr, dtype)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/core/common.py", line 1579, in _possibly_cast_to_datetime
"cannot convert timedeltalike to dtype [%s]" % dtype)
TypeError: cannot convert timedeltalike to dtype [<m8[ns]]
======================================================================
FAIL: test_timedelta_ops_with_missing_values (pandas.tseries.tests.test_timedeltas.TestTimedeltas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/tseries/tests/test_timedeltas.py", line 281, in test_timedelta_ops_with_missing_values
assert_frame_equal(actual, dfn)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/util/testing.py", line 493, in assert_frame_equal
check_less_precise=check_less_precise)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.13.0rc1_124_g8cd34c6-py3.3-linux-sparc64.egg/pandas/util/testing.py", line 442, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2561)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1803)
File "testing.pyx", line 107, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2023)
AssertionError: First object is not null, second is null: numpy.timedelta64(9223372036854775807,'ns') != numpy.timedelta64('NaT','ns')
```
http://nipy.bic.berkeley.edu/builders/pandas-py3.x-sid-sparc/builds/491/steps/shell_4/logs/stdio
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TST: closes #5779, big endian compensation for datelike dtypes
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} | 8 | 2013-12-27T19:53:51Z | 2014-05-29T11:10:05Z | 2014-05-29T11:10:05Z | CONTRIBUTOR | null | related #5778
Investigate/fix whether the test failures (currently skipped on big-endian) are actual bugs or just an odd test.
cc @PKEuS
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} | 2 | 2013-12-28T03:55:12Z | 2013-12-29T23:35:34Z | 2013-12-29T23:35:06Z | NONE | null | ```
=============================================================
=========
FAIL: test_aggregate_item_by_item (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/buildslave/nd-bb-slave-sparc-wheezy/pandas-py3_x-wheezy-sparc/build/venv/lib/python3.2/site-packages/pandas-0.13.0rc1_128_g375a66e-py3.2-linux-sparc64.egg/pandas/tests/test_groupby.py", line 556, in test_aggregate_item_by_item
assert_almost_equal(result.xs('foo'), [foo] * K)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2554)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1796)
File "testing.pyx", line 113, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2077)
AssertionError: First object is numeric, second is not: 5.0 != 5
```
http://nipy.bic.berkeley.edu/builders/pandas-py3.x-wheezy-sparc
http://nipy.bic.berkeley.edu/builders/pandas-py3.x-wheezy-sparc/builds/330/steps/shell_4/logs/stdio
I've got a patch for a seperate unicode issue awaiting travis, with it and this fixed (cc @jreback), sparc should be green across the board.
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These checks were removed from the tests during a refactor to use ensure_clean
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} | 0 | 2013-12-28T04:19:45Z | 2014-06-27T23:28:24Z | 2013-12-28T04:19:58Z | NONE | null | @jtratner, guess it could fail after all. Windows doesn't support os.uname().
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] | null | 1 | 2013-12-28T16:55:32Z | 2013-12-28T16:57:39Z | 2013-12-28T16:57:39Z | CONTRIBUTOR | null | ndarray's ravel() allows an optional order keyword. For compat, `Series` ravel should either accept `order='C'` or just take `*args, **kwargs` and pass it on downwards.
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} | [] | closed | false | null | [] | null | 6 | 2013-12-28T22:18:05Z | 2013-12-28T23:49:11Z | 2013-12-28T23:49:11Z | MEMBER | null | Just saw an example in this [SO question](http://stackoverflow.com/questions/18879782/python-pandas-groupby-forloop-idxmax), the use of `idxmax()` on a groupby object:
```
df.groupby(...).idxmax()
```
This worked in 0.12, but not anymore in 0.13 as it is not in the whitelist.
- Should this be added to the whitelist? (idxmax/idxmin for SeriesGroupby) I think this is a usefull method on a groupby object.
- I got an AttributeError and not a DeprecationWarning. There was spoken of a deprecation first (#5480), but was it decided not to do it?
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} | [] | closed | false | null | [] | null | 1 | 2013-12-28T23:25:08Z | 2014-06-21T19:00:13Z | 2013-12-28T23:49:11Z | MEMBER | null | Closes #5786.
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} | 8 | 2013-12-28T23:33:51Z | 2020-01-19T23:46:46Z | 2013-12-29T16:23:22Z | MEMBER | null | With this dataframe:
```
import pandas as pd
from StringIO import StringIO
s="""2011.05.16,00:00,1.40893
2011.05.16,01:00,1.40760
2011.05.16,02:00,1.40750
2011.05.16,03:00,1.40649
2011.05.17,02:00,1.40893
2011.05.17,03:00,1.40760
2011.05.17,04:00,1.40750
2011.05.17,05:00,1.40649
2011.05.18,02:00,1.40893
2011.05.18,03:00,1.40760
2011.05.18,04:00,1.40750
2011.05.18,05:00,1.40649"""
df = pd.read_csv(StringIO(s), header=None, names=['date', 'time', 'value'], parse_dates=[['date', 'time']])
df = df.set_index('date_time')
```
applying an `idxmax`:
```
df.groupby(df.index.date).apply(lambda x: x.idxmax())
```
produces on master:
```
ValueError: Shape of passed values is (1, 3), indices imply (1, 3)
```
while this does work on 0.12
It has to do I think with difference between SeriesGroupby and DataFrameGroupby with one column, as `df.groupby(df.index.date)['value'].apply(lambda x: x.idxmax())` does work in master, but in 0.12 both ways work.
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} | 0 | 2013-12-28T23:44:21Z | 2013-12-29T16:23:22Z | 2013-12-29T16:23:22Z | MEMBER | null | (Encountered with same SO [question](http://stackoverflow.com/questions/20819702/hourly-frequency-count-with-python/20820166#20820166) as #5786 and #5788. With following dataframe:
```
from StringIO import StringIO
s="""2011.05.16,00:00,1.40893
2011.05.16,01:00,1.40760
2011.05.16,02:00,1.40750
2011.05.16,03:00,1.40649
2011.05.17,02:00,1.40893
2011.05.17,03:00,1.40760
2011.05.17,04:00,1.40750
2011.05.17,05:00,1.40649
2011.05.18,02:00,1.40893
2011.05.18,03:00,1.40760
2011.05.18,04:00,1.40750
2011.05.18,05:00,1.40649"""
df = pd.read_csv(StringIO(s), header=None, names=['date', 'time', 'value'])
```
where all dates or hours are strings. When doing a groupby on the date and then accessing the value in column `time` where `value` is maximal, the hour string is converted to a datetime:
```
In [21]: df.groupby('date').apply(lambda x: x['time'][x['value'].idxmax()])
Out[21]:
date
2011.05.16 2013-12-29 00:00:00
2011.05.17 2013-12-29 02:00:00
2011.05.18 2013-12-29 02:00:00
dtype: datetime64[ns]
```
while in 0.12 this remains a string:
```
In [63]: df.groupby('date').apply(lambda x: x['time'][x['value'].idxmax()])
Out[63]:
date
2011.05.16 00:00
2011.05.17 02:00
2011.05.18 02:00
dtype: object
```
I think in this case this conversion can be highly inappropriate (and you have a chance that just because a string is looking like a date/time component it is converted, while this will not always be your intention).
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} | 0 | 2013-12-29T03:59:24Z | 2020-01-19T23:27:51Z | 2013-12-29T16:23:22Z | CONTRIBUTOR | null | only when we have actual Timestamps in the data (GH5788,GH5789)
closes #5789
TST: tests for idxmax used in an apply
closes #5788
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} | 0 | 2013-12-29T23:34:43Z | 2014-06-19T10:35:39Z | 2013-12-29T23:35:06Z | CONTRIBUTOR | null | closes #5782
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} | [] | closed | false | null | [] | null | 1 | 2013-12-30T09:44:34Z | 2014-07-16T08:44:39Z | 2013-12-30T11:27:19Z | MEMBER | null | Two minor space-errors which caused a doc build warning.
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} | [] | closed | false | null | [] | null | 5 | 2013-12-30T10:11:13Z | 2014-01-31T04:02:19Z | 2014-01-31T04:02:19Z | NONE | null | Followup to 3922 where it was recognised a bug maybe to be fixed in Ver 12, I left it a few months and waiting for version 0.12.0 which is now installed on Mac with fully patched mavericks.
Old issue: https://github.com/pydata/pandas/issues/3922?source=c
Doing the example on http://pandas.pydata.org/pandas-docs/stable/reshaping.html, all works down to In [3] where I issue the command:
```
df.pivot(index='date', columns='variable', values='value')
```
Nothing happens. Press ctrl-d:
``` python
File "<stdin>", line 1, in <module>
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/frame.py", line 4182, in pivot
return pivot(self, index=index, columns=columns, values=values)
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/reshape.py", line 345, in pivot
return indexed.unstack(columns)
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/series.py", line 2439, in unstack
return unstack(self, level)
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/reshape.py", line 419, in unstack
unstacker = _Unstacker(obj.values, obj.index, level=level)
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/reshape.py", line 87, in __init__
self._make_sorted_values_labels()
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/reshape.py", line 98, in _make_sorted_values_labels
comp_index, obs_ids = get_compressed_ids(to_sort, sizes)
File "/Users/rmschne/.virtualenv/rmspython/lib/python2.7/site-packages/pandas/core/reshape.py", line 470, in get_compressed_ids
while com._long_prod(sizes[:i]) >= 2 ** 63:
```
System requirements (from 'pip') are:
Cython==0.19.1
Django==1.5.1
MySQL-python==1.2.4b4
distribute==0.6.28
django-ical==1.1
django-mailchimp==0.1.30
icalendar==3.1
ipython==0.13.2
mailsnake==1.6.1
matplotlib==1.2.1
nose==1.3.0
numpy==1.7.1
pandas==0.12.0
python-dateutil==2.1
pytz==2013.8
requests==0.14.2
scipy==0.11.0
simplejson==2.6.2
six==1.3.0
twitter==1.10.2
wsgiref==0.1.2
yolk==0.4.3
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https://api.github.com/repos/pandas-dev/pandas/issues/5795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5795/events | https://github.com/pandas-dev/pandas/issues/5795 | 24,888,652 | MDU6SXNzdWUyNDg4ODY1Mg== | 5,795 | Variable not changed from integer to float on conditional update | {
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} | 1 | 2013-12-30T15:54:19Z | 2013-12-30T16:00:13Z | 2013-12-30T16:00:02Z | CONTRIBUTOR | null | I want to create a dataframe variable, set all rows to zero, then conditionally update some rows with a floating point value. When I don't explicitly create the variable as a float, subsequent conditional operations produce unexpected output.
See the three examples below.
df['C'] is created as an integer -- result of calculation converts df['C'] to float:
```
df = pd.DataFrame({"A": np.arange(6), "B": np.arange(6)})
df['C'] = 0 # create as integer
df['C'] = df.A / df.B.sum() # update all
print df
A B C
0 0 0 0.000000
1 1 1 0.066667
2 2 2 0.133333
3 3 3 0.200000
4 4 4 0.266667
5 5 5 0.333333
```
df['C'] created as integer -- conditional update results in strange output:
```
df = pd.DataFrame({"A": np.arange(6), "B": np.arange(6)})
df['C'] = 0 # create as integer
df['C'][df.index > 2] = df.A / df.B.sum() # conditional update
print df
A B C
0 0 0 0
1 1 1 0
2 2 2 0
3 3 3 4596373779694328218
4 4 4 4598475459520434449
5 5 5 4599676419421066581
```
df['C'] created as float -- conditional update results as expected:
```
df = pd.DataFrame({"A": np.arange(6), "B": np.arange(6)})
df['C'] = 0.0 # create as float
df['C'][df.index > 2] = df.A / df.B.sum() # conditional update
print df
A B C
0 0 0 0.000000
1 1 1 0.000000
2 2 2 0.000000
3 3 3 0.200000
4 4 4 0.266667
5 5 5 0.333333
```
I'm using the Anaconda distribution with Pandas 0.12.0.
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} | 44 | 2013-12-30T17:06:28Z | 2014-01-05T02:57:02Z | 2013-12-30T19:55:34Z | NONE | null | @jreback , good to go?
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} | 9 | 2013-12-30T19:22:28Z | 2014-01-02T22:59:01Z | 2014-01-02T22:59:01Z | CONTRIBUTOR | null | I was working with financial data and was replacing dates that were not valid trading days. It seems that replace does not work on any dict between two timestamps. Here is an example. This simply throws an exception on 0.12.
```
import pandas.io.data as web
ibm = web.DataReader("IBM", "yahoo", "1993/01/01").reset_index()
next_date_dict = dict(zip(ibm["Date"], ibm["Date"].shift()))
ibm_next_date = ibm["Date"].replace(next_date_dict)
print (ibm_next_date == ibm["Date"]).all()
```
in 0.12 there is an exception:
https://gist.github.com/MichaelWS/8186689
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} | 3 | 2013-12-30T20:30:28Z | 2019-12-12T21:05:08Z | 2019-12-12T21:05:08Z | CONTRIBUTOR | null | I'm not really sure what should be done here. At least a better error message I suppose. Perhaps just skip non-string columns or maybe even try to turn them into strings? E.g., make match("\d", 123) work? Obviously could run into some problems for things that can't be converted (or do "unexpected" things) for turning into string/unicode.
``` python
>>> pd.version.version
>>> '0.12.0-1149-g141e93a'
(if columns are all numerical also bombs with different msg)
>>> pd.DataFrame(np.random.random((3,2)), columns=['STRING', 123]).filter(regex='STRING')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-427-e5acd93a79d6> in <module>()
----> 1 pd.DataFrame(np.random.random((3,2)), columns=['STRING', 123]).filter(regex='STRING')
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1149_g141e93a-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in filter(self, items, like, regex, axis)
1522 matcher = re.compile(regex)
1523 return self.select(lambda x: matcher.search(x) is not None,
-> 1524 axis=axis_name)
1525 else:
1526 raise TypeError('Must pass either `items`, `like`, or `regex`')
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1149_g141e93a-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in select(self, crit, axis)
1116 if len(axis_values) > 0:
1117 new_axis = axis_values[
-> 1118 np.asarray([bool(crit(label)) for label in axis_values])]
1119 else:
1120 new_axis = axis_values
/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1149_g141e93a-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in <lambda>(x)
1521 elif regex:
1522 matcher = re.compile(regex)
-> 1523 return self.select(lambda x: matcher.search(x) is not None,
1524 axis=axis_name)
1525 else:
TypeError: expected string or buffer
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5799/events | https://github.com/pandas-dev/pandas/issues/5799 | 24,900,456 | MDU6SXNzdWUyNDkwMDQ1Ng== | 5,799 | version.py version and short_version are inconsistent | {
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} | 1 | 2013-12-30T21:04:48Z | 2018-11-16T13:55:54Z | 2013-12-31T01:00:26Z | NONE | null | The new dev versioning scheme from #4046 introduced an inconsistency:
```
user1@localhost ~/src/pandas (git)-[master] % cat pandas/version.py
version = '0.13.0-1-gaf9c025'
short_version = '0.14.0'
```
https://github.com/pydata/pandas/issues/5796#issuecomment-31368550
short_version means we're in the 0.14.0 cycle, while version tells us how
many commits after the latest tag (0.13.0) we're at, giving an order on dev
versions. It's not intuitive, but it does what we need.
Not sure if this actually needs fixing or not.
**correction**: #5803
```
user1@localhost ~/src/pandas (git)-[master] % cat pandas/version.py
version = '0.13.0-9-gb3f6972'
short_version = '0.13.0'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5800/events | https://github.com/pandas-dev/pandas/pull/5800 | 24,901,377 | MDExOlB1bGxSZXF1ZXN0MTExNDkzMjk= | 5,800 | This fixes issue 5797 and allows replace using timestamps. | {
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} | 6 | 2013-12-30T21:28:59Z | 2014-07-25T08:41:04Z | 2013-12-31T01:25:48Z | CONTRIBUTOR | null | this fixes https://github.com/pydata/pandas/issues/5797
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