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24,318,425
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5,701
Smarter formatting of timedelta and datetime columns
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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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BUG: astype assignment with loc not working
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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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5,703
Series.fillna doesn't work with series input
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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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BUG: loc assignment with astype buggy, (GH5702)
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2013-12-15T23:18:37Z
2014-06-21T03:16:05Z
2013-12-15T23:56:16Z
CONTRIBUTOR
null
closes #5702
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BUG: Bug in fillna with Series and a passed series/dict (GH5703)
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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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24,325,698
MDU6SXNzdWUyNDMyNTY5OA==
5,706
Sentinal -> Sentinel
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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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5,707
Correct "sentinel" spelling.
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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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5,708
str for NaT is garbage
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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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StataWriter: Replace non-isalnum characters in variable names by _ instead of integral represantation of replaced character. Eliminate duplicates created by replacement.
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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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hashtable.so: undefined symbol: _intel_fast_memset
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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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5,711
Failing tests on win/jenkins
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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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MDU6SXNzdWUyNDM2ODU2NQ==
5,712
groupby.transform failing in 0.13
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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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24,369,950
MDExOlB1bGxSZXF1ZXN0MTA4NjYxNzU=
5,713
BUG: Bug in groupby transform with a datetime-like grouper (GH5712)
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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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24,372,056
MDExOlB1bGxSZXF1ZXN0MTA4NjczOTA=
5,714
Add test for DataFrame.corrwith and np.corrcoef compatibility
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1
2013-12-16T19:22:20Z
2014-06-21T02:58:03Z
2013-12-16T19:54:23Z
CONTRIBUTOR
null
Add apparently-once-failing code taken from http://stackoverflow.com/questions/20617854/is-there-a-bug-in-pandas-dataframe-corrwith-function as a test.
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Travis exception on LOCALE_OVERRIDE builds
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9
2013-12-16T20:43:38Z
2014-01-26T23:05:42Z
2014-01-26T23:05:42Z
NONE
null
https://travis-ci.org/pydata/pandas/jobs/15544827 ``` $ 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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TST/DOC: close win32 tests issues (GH5711)
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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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5,717
Problem selecting major_axis of a panel in HDFstore from a list if data_columns are used.
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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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5,718
BUG: In a HDFStore, correctly handle data_columns with a Panel (GH5717)
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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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5,719
CLN: add diff to series/dataframe groupby dispatch whitelist
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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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Empty dataframe corrupted by column add (0.13rc1)
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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 ``` &nbsp; ``` (pandas-0.12)$ python test.py Empty DataFrame Columns: [A, B, New] Index: [] [] ``` &nbsp; ``` (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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5,721
DOC: trim CONTRIBUTING.MD
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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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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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BUG: don't use partial setting with scalars (GH5720)
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0
2013-12-17T20:36:18Z
2014-06-21T16:51:31Z
2013-12-17T21:32:24Z
CONTRIBUTOR
null
closes #5720
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5,724
groupby.mean, etc, doesn't recognize timedelta64
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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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5,725
inconsistent indexing behaviour with multi index
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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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24,476,667
MDExOlB1bGxSZXF1ZXN0MTA5MjcwNjU=
5,726
DOC: small doc build warning: Note -> Notes
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1
2013-12-18T08:52:58Z
2014-07-16T08:43:40Z
2013-12-18T14:57:35Z
MEMBER
null
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https://api.github.com/repos/pandas-dev/pandas/issues/5727
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24,478,638
MDU6SXNzdWUyNDQ3ODYzOA==
5,727
IndexError: index out of bounds for large Series with MultiIndex
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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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24,478,962
MDU6SXNzdWUyNDQ3ODk2Mg==
5,728
DataFrame update issue
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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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24,492,096
MDU6SXNzdWUyNDQ5MjA5Ng==
5,729
New orientation for to_json method
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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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24,494,182
MDExOlB1bGxSZXF1ZXN0MTA5MzcxMjk=
5,730
CLN/BUG: indexing fixes (GH5725, GH5727)
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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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5,731
DOC: add demo of factorize
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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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5,732
read_csv/read_table have a prefix argument which is not respected
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0
2013-12-18T15:28:47Z
2014-01-03T01:09:56Z
2014-01-03T01:09:56Z
CONTRIBUTOR
null
According to the docs, `prefix` can either be a `str` or None. If it is a string then it is used as the prefix of the column name. However, no matter what string is passed, the prefix used is 'X'.
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5,733
Fix prefix argument for read_csv/read_table
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7
2013-12-18T15:29:56Z
2014-06-14T13:17:44Z
2014-01-03T01:09:56Z
CONTRIBUTOR
null
Closes #5732
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MDU6SXNzdWUyNDUwNTA0Nw==
5,734
Series to_string does not show index name
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12
2013-12-18T17:27:32Z
2015-04-14T14:32:08Z
2015-04-14T14:32:08Z
CONTRIBUTOR
null
The standard `repr` for a `Series` shows the index name: ``` 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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5,735
TST: excel not completely cleaning up test files
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1
2013-12-18T19:33:33Z
2013-12-19T12:46:33Z
2013-12-19T12:46:33Z
CONTRIBUTOR
null
https://github.com/pydata/pandas/blob/master/pandas/io/tests/test_excel.py#L1063 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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TST: Cleanup temp files in Excel test.
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2
2013-12-19T00:30:15Z
2014-06-30T06:58:37Z
2013-12-19T00:31:32Z
CONTRIBUTOR
null
Fix for issue #5735
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5,737
BUG: raise KeyError if missing value in py3 on multi-index (GH5725), revisted
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0
2013-12-19T01:04:09Z
2014-06-19T17:03:00Z
2013-12-19T01:24:38Z
CONTRIBUTOR
null
#5725 raise KeyError appropriately under py3 on mi series
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2
2013-12-19T04:31:31Z
2014-06-18T11:08:50Z
2013-12-31T01:24:12Z
NONE
null
Putting some pieces in place.
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2013-12-19T04:58:11Z
2014-06-25T08:29:42Z
2013-12-30T20:55:38Z
NONE
null
closes #5495
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BUG: return Series as DataFrame.dtypes/ftypes for empty dataframes
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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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5,741
BUG: Fixed issue #5732. prefix option works in csv
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3
2013-12-19T05:54:44Z
2014-06-20T00:02:17Z
2013-12-19T16:28:40Z
CONTRIBUTOR
null
Closes issue https://github.com/pydata/pandas/issues/5732 Good catch, @nmichaud.
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BUG: don't lose dtypes when concatenating empty array-likes
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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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API: json should be returning string (if possible) in py2 rather than unicode
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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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BUG: Column indexing does not work in this case
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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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MDExOlB1bGxSZXF1ZXN0MTA5NzU4OTY=
5,745
BUG: don't allow an empty dataframe to have scalar assignment succeed (GH5744)
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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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5,746
Travis, python 3.4 and caching
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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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API/REGRESS: partial revert of f8b6208675b5b10d73a74f50478fa5e37b43fc02 (GH5720,GH5744)
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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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DOC: add section about using python/ipython startup files to set options to FAQ
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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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Rolling skewness and kurtosis fail on a sample of all equal values
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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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DOC: added missing argument in Series.apply
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3
2013-12-19T20:57:33Z
2015-04-25T23:33:13Z
2013-12-20T01:55:41Z
CONTRIBUTOR
null
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5,751
Parallelization for embarrassingly parallel tasks
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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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ENH: expose option_context as a top-level API GH5618
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7
2013-12-19T22:42:42Z
2014-06-14T10:26:45Z
2013-12-31T00:49:22Z
NONE
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<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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ENH: set display.max_seq_items default != None
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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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pandas concat working in 0.12 but not 0.13
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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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Consistency with groupby as_index
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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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BUG: empty Series construction (GH5756), concat issues (GH5754)
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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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BUGI: groupby issue with user function with mutation, and duplicate indices
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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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API: Series.ravel compat with ndarray
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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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Update rolling skew & kurtosis to handle cases where they aren't defined
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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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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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groupby and aggregate passing different types to aggregating function
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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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24,674,046
MDExOlB1bGxSZXF1ZXN0MTEwMzgxMzU=
5,763
WIP: df rendering using templates + conditional formatting for HTML
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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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MDU6SXNzdWUyNDcwNjkxMA==
5,764
BUG: Applying function on column of Groupby object with as_index=False does not select column
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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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24,719,039
MDU6SXNzdWUyNDcxOTAzOQ==
5,765
df.to_json() slower in 0.13.x vs 0.12.0
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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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5,766
BUG: IndexError on read_csv/read_table when using usecols/names parameters and omitting last column
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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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BUG: IndexError on read_csv/read_table when using usecols/names parameters and omitting last column
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1
2013-12-23T23:16:54Z
2014-06-19T21:11:04Z
2013-12-25T01:59:56Z
CONTRIBUTOR
null
This addresses an issue in read_csv/read_table where there is no header and both usecols and names are assigned but the last column is not included. This caused an IndexError after reaching the last column specified in usecols. Existing test cases for `usecols` were modified to expose and address this specific issue. #5766
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MDU6SXNzdWUyNDcyOTgwOA==
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Read and write spss data format
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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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Inconsistent name for keyword that determines whether new object is generated
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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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BUG: regression in read_csv parser handling of usecols GH5766
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1
2013-12-25T01:58:46Z
2014-06-29T15:36:16Z
2013-12-25T01:58:59Z
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Cannot increment after loc indexing
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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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24,768,166
MDExOlB1bGxSZXF1ZXN0MTEwODYwNzM=
5,772
BUG: setitem for iloc/loc with a slice on a Series (GH5771)
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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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5,773
BUG: Panel, MultiIndex, _setitem_with_indexer
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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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24,774,891
MDU6SXNzdWUyNDc3NDg5MQ==
5,774
Enhance: Time series for different types of graphs using plot fuction
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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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5,775
Add `_get_numeric_data()` to the public API?
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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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MDU6SXNzdWUyNDc5NjIxMw==
5,776
Using boolean Series to mask array broken in 0.13
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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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24,807,711
MDU6SXNzdWUyNDgwNzcxMQ==
5,777
BUG? import name collision in vb_suite/parser
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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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24,827,358
MDU6SXNzdWUyNDgyNzM1OA==
5,778
Failing stata tests on debian SPARC buildbot
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4
2013-12-27T17:34:50Z
2013-12-27T21:14:53Z
2013-12-27T20:08:13Z
NONE
null
``` ====================================================================== 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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5,779
Failing timeseries tests on debian sparc buildbot
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5
2013-12-27T17:36:11Z
2013-12-27T21:06:44Z
2013-12-27T20:08:13Z
NONE
null
``` ====================================================================== 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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24,828,007
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5,780
TST: close sparc test failures
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4
2013-12-27T17:57:54Z
2014-07-16T08:44:26Z
2013-12-27T20:08:13Z
CONTRIBUTOR
null
TST: closes #5778, failing tests on non-little endian for stata (spac) TST: closes #5779, big endian compensation for datelike dtypes
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24,831,581
MDU6SXNzdWUyNDgzMTU4MQ==
5,781
TST/BUG: stata failures on big endian
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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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test_aggregate_item_by_item fails on SPARC py3
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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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TST: ensure_clean skips test when fs doesn't support unicode (sparc)
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0
2013-12-28T04:16:49Z
2014-07-16T08:44:30Z
2013-12-28T04:17:46Z
NONE
null
http://nipy.bic.berkeley.edu/builders/pandas-py2.x-sid-sparc/builds/365/steps/shell_4/logs/stdio These checks were removed from the tests during a refactor to use ensure_clean
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24,840,956
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5,784
BLD: print_versions get uname() via cross-platform API
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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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5,785
Series ravel() doesn't accept order keyword
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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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5,786
idxmax() not available on groupby anymore
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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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5,787
Add idxmax/idxmin to groupby dispatch whitelist (#5786)
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1
2013-12-28T23:25:08Z
2014-06-21T19:00:13Z
2013-12-28T23:49:11Z
MEMBER
null
Closes #5786.
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BUG: apply idxmax on one-column DataFrameGroupby generates ValueError
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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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BUG: hour string automatically converted to datetime in groupby apply
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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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BUG: dont' always coerce reductions in a groupby always to datetimes
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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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TST: aggregate_item_by_item test failure (GH5782)
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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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DOC: fix minor doc build warnings
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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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Related to Issue 3922: Panda still crashes with pivot() Function
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2013-12-30T10:11:13Z
2014-01-31T04:02:19Z
2014-01-31T04:02:19Z
NONE
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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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0
2013-12-30T14:55:50Z
2014-06-26T12:05:55Z
2013-12-30T14:55:55Z
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null
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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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5,797
Series replace values using timestamps in a dict
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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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regex filter on non-string columns raises an exception
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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
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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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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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