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} | 15 | 2013-10-03T22:46:08Z | 2016-10-12T23:04:15Z | 2013-10-04T17:19:18Z | CONTRIBUTOR | null | Previously, in `0.12` and earlier, I could quickly visualize `groupby` objects with a call to `.plot()`. Currently in master, the `.plot` method on a `groupby` object raises, with following error:
``` python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-63-3760a47a721b> in <module>()
----> 1 grp.kWh.plot()
/pandas/pandas/core/groupby.pyc in __getattr__(self, attr)
250
251 if hasattr(self.obj, attr) and attr != '_cache':
--> 252 return self._make_wrapper(attr)
253
254 raise AttributeError("%r object has no attribute %r" %
/pandas/pandas/core/groupby.pyc in _make_wrapper(self, name)
265 "using the 'apply' method".format(kind, name,
266 type(self).__name__))
--> 267 raise AttributeError(msg)
268
269 f = getattr(self.obj, name)
AttributeError: Cannot access callable attribute 'plot' of 'SeriesGroupBy' objects, try using the 'apply' method
```
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`df = DataFrame(index=["A","B","C"], columns = [1,2,3,4,5])
df2 = df.fillna(1)
df2.cumsum()`
Fails with the following error message:
```
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas/core/generic.py", line 827, in cumsum
mask = np.isnan(self.values)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
However, creating a DataFrame filled with ones using the following works just fine:
`df = DataFrame(np.ones((3,5)),index=["A","B","C"], columns = [1,2,3,4,5])`
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} | 15 | 2013-10-04T00:34:19Z | 2014-10-09T13:36:59Z | 2014-10-09T13:36:59Z | CONTRIBUTOR | null | In current master (and before the initial arithmetic refactor), `__iadd__` doesn't do anything, it's just a synonym for `__add__` (or not defined). (just for reference, `v0.12.0` had `__iadd__ = __add__` on `Series` and no `__add__` defined on `DataFrame`). It would be great to support in place ops on `+=` and friends. Is this possible to do?
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} | 8 | 2013-10-04T00:57:36Z | 2014-06-20T23:44:00Z | 2013-10-04T17:19:18Z | MEMBER | null | closes #5102
also fixes extra plots created by groupby plotting (couldn't find an issue), now there's a test that ensures that groupby only plots the number of axes/figures it's supposed to
removes an unused internal groupby parameter
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https://api.github.com/repos/pandas-dev/pandas/issues/5106 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5106/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5106/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5106/events | https://github.com/pandas-dev/pandas/pull/5106 | 20,493,251 | MDExOlB1bGxSZXF1ZXN0ODgyNzcxNQ== | 5,106 | BUG: read_html should convert array skiprows into a list | {
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} | 1 | 2013-10-04T01:23:01Z | 2014-07-11T22:34:05Z | 2013-10-04T02:56:27Z | MEMBER | null | not sure why this is only showing up here, but it should be converted to a `list` anyway:
https://travis-ci.org/cpcloud/pandas/jobs/12117482
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} | 25 | 2013-10-04T05:03:20Z | 2019-01-21T14:24:20Z | 2019-01-21T14:24:20Z | CONTRIBUTOR | null | ```
pd.read_html("http://www.sec.gov/Archives/edgar/data/1174922/000119312513329016/d548026d10q.htm",header=[1,2,3])
```
```
----> 1 pd.read_html("http://www.sec.gov/Archives/edgar/data/1174922/000119312513329016/d548026d10q.htm",header=[1,2,3])
/home/alex/git/pandas/pandas/io/html.pyc in read_html(io, match, flavor, header, index_col, skiprows, infer_types, attrs, parse_dates, tupleize_cols, thousands)
838 'data (you passed a negative value)')
839 return _parse(flavor, io, match, header, index_col, skiprows, infer_types,
--> 840 parse_dates, tupleize_cols, thousands, attrs)
/home/alex/git/pandas/pandas/io/html.pyc in _parse(flavor, io, match, header, index_col, skiprows, infer_types, parse_dates, tupleize_cols, thousands, attrs)
710 return [_data_to_frame(table, header, index_col, skiprows, infer_types,
711 parse_dates, tupleize_cols, thousands)
--> 712 for table in tables]
713
714
/home/alex/git/pandas/pandas/io/html.pyc in _data_to_frame(data, header, index_col, skiprows, infer_types, parse_dates, tupleize_cols, thousands)
600 skiprows=_get_skiprows(skiprows),
601 parse_dates=parse_dates, tupleize_cols=tupleize_cols,
--> 602 thousands=thousands)
603 df = tp.read()
604
/home/alex/git/pandas/pandas/io/parsers.pyc in TextParser(*args, **kwds)
1173 """
1174 kwds['engine'] = 'python'
-> 1175 return TextFileReader(*args, **kwds)
1176
1177
/home/alex/git/pandas/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
485 self.options['has_index_names'] = kwds['has_index_names']
486
--> 487 self._make_engine(self.engine)
488
489 def _get_options_with_defaults(self, engine):
/home/alex/git/pandas/pandas/io/parsers.pyc in _make_engine(self, engine)
601 elif engine == 'python-fwf':
602 klass = FixedWidthFieldParser
--> 603 self._engine = klass(self.f, **self.options)
604
605 def _failover_to_python(self):
/home/alex/git/pandas/pandas/io/parsers.pyc in __init__(self, f, **kwds)
1291 self.data = f
1292
-> 1293 self.columns = self._infer_columns()
1294
1295 # we are processing a multi index column
/home/alex/git/pandas/pandas/io/parsers.pyc in _infer_columns(self)
1485
1486 while self.pos <= hr:
-> 1487 line = self._next_line()
1488
1489 this_columns = []
/home/alex/git/pandas/pandas/io/parsers.pyc in _next_line(self)
1544 line = self.data[self.pos]
1545 except IndexError:
-> 1546 raise StopIteration
1547 else:
1548 while self.pos in self.skiprows:
StopIteration:
```
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} | 2 | 2013-10-04T15:28:52Z | 2018-07-06T20:21:02Z | 2017-07-10T21:09:02Z | CONTRIBUTOR | null | This is similar to Issue #1533 and I've adapted the example from there.
``` python
import numpy as np
import pandas as pd
a = pd.Panel(items=[1,2,3], major_axis=[11,22,33], minor_axis=[111,222,333])
b = pd.DataFrame(np.random.randn(2,3), index=[111,333], columns=[1,2,3])
c = pd.DataFrame(np.mat('1 2 3; 4 5 6'), index=[111,333], columns=[1,2,3])
# works now
print "\na.ix[...] before"
print a.ix[:, 22, [111, 333]]
a.ix[:, 22, [111, 333]] = b
print "\na.ix[...] after"
print a.ix[:, 22, [111, 333]]
tf = np.array([True, True, True])
tf2 = np.array([True, False, True])
# boolean indexing returns the same data
print "\na.ix[tf, ...] before"
print a.ix[tf, 22, tf2]
a.ix[tf, 22, tf2]
# but assigning to it fails
try:
a.ix[tf, 22, tf2] = c
print "\na.ix[tf, ...] after"
a.ix[tf, 22, tf2]
except Exception, e:
print e
# however if we first create view into the Panel
v = a.ix[:, 22, :]
print "\nv.ix[tf, ...] before"
print v.ix[tf2, tf]
# then assigning works
v.ix[tf2, tf] = c
print "\nv.ix[tf, ...] after"
print v.ix[tf2, tf]
print "\na.ix[tf, ...] after"
print a.ix[tf, 22, tf2]
```
produces output
```
a.ix[...] before
1 2 3
111 NaN NaN NaN
333 NaN NaN NaN
a.ix[...] after
1 2 3
111 0.329258 0.759436 -0.070773
333 0.528950 -1.276978 0.084436
a.ix[tf, ...] before
1 2 3
111 0.329258 0.759436 -0.070773
333 0.528950 -1.276978 0.084436
shape mismatch: objects cannot be broadcast to a single shape
v.ix[tf, ...] before
1 2 3
111 0.329258 0.759436 -0.070773
333 0.528950 -1.276978 0.084436
v.ix[tf, ...] after
1 2 3
111 1 2 3
333 4 5 6
a.ix[tf, ...] after
1 2 3
111 1 2 3
333 4 5 6
```
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} | 6 | 2013-10-04T16:38:08Z | 2013-10-04T16:54:53Z | 2013-10-04T16:54:53Z | MEMBER | null | Error message:
```
AmbiguousTimeError: Cannot infer dst time from Timestamp('2011-11-06 01:00:00', tz=None), try using the 'infer_dst' argument
```
pr on the way
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} | 1 | 2013-10-04T16:54:20Z | 2014-07-16T08:33:10Z | 2013-10-04T16:54:53Z | MEMBER | null | closes #5110.
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https://api.github.com/repos/pandas-dev/pandas/issues/5112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5112/events | https://github.com/pandas-dev/pandas/pull/5112 | 20,541,464 | MDExOlB1bGxSZXF1ZXN0ODg0ODM5MQ== | 5,112 | API: convert objects on fillna when object result dtype, related (GH5103) | {
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} | 0 | 2013-10-04T18:18:26Z | 2014-07-04T13:18:07Z | 2013-10-04T19:53:42Z | CONTRIBUTOR | null | related #5103
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https://api.github.com/repos/pandas-dev/pandas/issues/5113 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5113/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5113/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5113/events | https://github.com/pandas-dev/pandas/issues/5113 | 20,549,324 | MDU6SXNzdWUyMDU0OTMyNA== | 5,113 | virtualenv installation fails on mac OSX 10.8.5. virtualenv 1.8.4; python 2.7.2 | {
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} | [] | closed | false | null | [] | null | 4 | 2013-10-04T20:55:47Z | 2013-10-05T04:25:34Z | 2013-10-05T04:25:34Z | NONE | null | ```
$ bin/pip install pandas
Downloading/unpacking pandas
Downloading pandas-0.12.0.tar.gz (3.2MB): 3.2MB downloaded
Running setup.py egg_info for package pandas
Running from numpy source directory.
Traceback (most recent call last):
File "<string>", line 16, in <module>
File "/Users/timanovsky/pandas/build/pandas/setup.py", line 548, in <module>
**setuptools_kwargs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 112, in setup
_setup_distribution = dist = klass(attrs)
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 260, in __init__
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 284, in fetch_build_eggs
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 563, in resolve
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 799, in best_match
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 811, in obtain
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 327, in fetch_build_egg
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 446, in easy_install
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 476, in install_item
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 655, in install_eggs
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 930, in build_and_install
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 919, in run_setup
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 62, in run_setup
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 105, in run
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 64, in <lambda>
File "setup.py", line 214, in <module>
FULLVERSION += "-%s" % rev
File "setup.py", line 191, in setup_package
MICRO = 0
File "/var/folders/3z/0r_ycyx15l58th50t5trb1mc0000gn/T/easy_install-D6SLmc/numpy-1.7.1/numpy/distutils/core.py", line 25, in <module>
File "/var/folders/3z/0r_ycyx15l58th50t5trb1mc0000gn/T/easy_install-D6SLmc/numpy-1.7.1/numpy/distutils/command/build_ext.py", line 16, in <module>
File "/var/folders/3z/0r_ycyx15l58th50t5trb1mc0000gn/T/easy_install-D6SLmc/numpy-1.7.1/numpy/distutils/system_info.py", line 220, in <module>
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 203, in _open
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 199, in _violation
setuptools.sandbox.SandboxViolation: SandboxViolation: open('/dev/null', 'w') {}
The package setup script has attempted to modify files on your system
that are not within the EasyInstall build area, and has been aborted.
This package cannot be safely installed by EasyInstall, and may not
support alternate installation locations even if you run its setup
script by hand. Please inform the package's author and the EasyInstall
maintainers to find out if a fix or workaround is available.
Complete output from command python setup.py egg_info:
Running from numpy source directory.
Traceback (most recent call last):
File "<string>", line 16, in <module>
File "/Users/timanovsky/pandas/build/pandas/setup.py", line 548, in <module>
**setuptools_kwargs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 112, in setup
_setup_distribution = dist = klass(attrs)
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 260, in __init__
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 284, in fetch_build_eggs
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 563, in resolve
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 799, in best_match
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/pkg_resources.py", line 811, in obtain
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/dist.py", line 327, in fetch_build_egg
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 446, in easy_install
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 476, in install_item
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 655, in install_eggs
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 930, in build_and_install
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/command/easy_install.py", line 919, in run_setup
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 62, in run_setup
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 105, in run
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 64, in <lambda>
File "setup.py", line 214, in <module>
FULLVERSION += "-%s" % rev
File "setup.py", line 191, in setup_package
MICRO = 0
File "/var/folders/3z/0r_ycyx15l58th50t5trb1mc0000gn/T/easy_install-D6SLmc/numpy-1.7.1/numpy/distutils/core.py", line 25, in <module>
File "/var/folders/3z/0r_ycyx15l58th50t5trb1mc0000gn/T/easy_install-D6SLmc/numpy-1.7.1/numpy/distutils/command/build_ext.py", line 16, in <module>
File "/var/folders/3z/0r_ycyx15l58th50t5trb1mc0000gn/T/easy_install-D6SLmc/numpy-1.7.1/numpy/distutils/system_info.py", line 220, in <module>
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 203, in _open
File "/Users/timanovsky/pandas/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg/setuptools/sandbox.py", line 199, in _violation
setuptools.sandbox.SandboxViolation: SandboxViolation: open('/dev/null', 'w') {}
The package setup script has attempted to modify files on your system
that are not within the EasyInstall build area, and has been aborted.
This package cannot be safely installed by EasyInstall, and may not
support alternate installation locations even if you run its setup
script by hand. Please inform the package's author and the EasyInstall
maintainers to find out if a fix or workaround is available.
----------------------------------------
Command python setup.py egg_info failed with error code 1 in /Users/timanovsky/pandas/build/pandas
Storing complete log in /Users/timanovsky/.pip/pip.log
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5114 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5114/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5114/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5114/events | https://github.com/pandas-dev/pandas/issues/5114 | 20,554,335 | MDU6SXNzdWUyMDU1NDMzNQ== | 5,114 | MultiIndex DataFrame with tuple columns error? | {
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} | [] | closed | false | null | [] | null | 12 | 2013-10-04T22:54:48Z | 2013-10-08T11:48:45Z | 2013-10-08T11:48:45Z | NONE | null | Hi, I am using pandas 0.12.0 and numpy 1.7.1.
I get the below error when storing to a CSV a DataFrame with a multiIndex column composed of tuples and then loading it afterwards. Thanks for your help!
``` python
import pandas as pd
import numpy as np
tup = ('a','g','b')
arrays = [[tup] * 4, range(1,5)]
tuples = zip(*arrays)
multi_columns = pd.MultiIndex.from_tuples(tuples, names=['first','second'])
index = range(1,10)
df = pd.DataFrame(index = index, columns=multi_columns)
df.to_csv('test.csv', tupleize_cols=False)
df2 = pd.read_csv('test.csv', index_col = 0, parse_dates = True, header=[0,1], tupleize_cols=False)
df.columns[0][0]
('a', 'g', 'b')
df2.columns[0][0]
"('a', 'g', 'b')"
df2[df.columns[0][0],]
TypeError: not all arguments converted during string formatting
```
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In [35]: pd.date_range("2013-1-1", periods=4,freq='WOM-1SAT')
----> 1 pd.date_range("2013-1-1", periods=4,freq='WOM-1SAT')
/home/alex/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize, name)
1724 """
1725 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1726 freq=freq, tz=tz, normalize=normalize, name=name)
1727
1728
/home/alex/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
158 # if a passed freq is None, don't infer automatically
159 if freq != 'infer':
--> 160 freq = to_offset(freq)
161 else:
162 freq_infer = True
/home/alex/git/pandas/pandas/tseries/frequencies.pyc in to_offset(freqstr)
463 delta = delta + offset
464 except Exception:
--> 465 raise ValueError("Could not evaluate %s" % freqstr)
466
467 if delta is None:
ValueError: Could not evaluate WOM-1SAT
```
I will fix as part of #5004
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} | 7 | 2013-10-05T04:30:05Z | 2013-10-08T23:55:51Z | 2013-10-08T23:55:51Z | CONTRIBUTOR | null | Opening up a separate issue about some Canopy IPython bugs that pandas (or new BigQuery IO module) may be producing. @azbones, @jacobschaer, @sean-schaefer and others, let's work here to figure it out.
Related comments in #4140.
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} | 3 | 2013-10-05T16:02:04Z | 2014-07-16T08:33:14Z | 2013-10-05T19:43:22Z | CONTRIBUTOR | null | `Int64Index` repeated itself and actually reconstructed an ndarray twice when passed in data was an iterable. This simplifies it down but does not actually change any behavior. (you'll see that there was basically exactly the same code twice)
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} | 16 | 2013-10-05T16:18:46Z | 2013-10-16T12:56:19Z | 2013-10-16T12:56:19Z | CONTRIBUTOR | null | These two tests fail (note the warning that it generates, happens in one other place but the test passes).
```
pandas.computation.tests.test_eval.TestAlignment.test_basic_series_frame_alignment('numexpr', 'python') ... /Users/jtratner/scratch/pandas/pandas/core/index.py:900: RuntimeWarning: tp_compare didn't return -1 or -2 for exception
result.sort()
FAIL
pandas.computation.tests.test_eval.TestAlignment.test_basic_series_frame_alignment('numexpr', 'pandas') ... FAIL
```
Traceback:
```
FAIL: pandas.computation.tests.test_eval:TestAlignment.test_basic_series_frame_alignment('numexpr', 'python')
/usr/local/bin/vim +872 pandas/computation/tests/test_eval.py # check_basic_series_frame_alignment
testit(r_idx_type, c_idx_type, index_name)
/usr/local/bin/vim +867 pandas/computation/tests/test_eval.py # testit
assert_frame_equal(res, expected)
/usr/local/bin/vim +312 pandas/util/testing.py # assert_frame_equal
assert_index_equal(left.columns, right.columns)
/usr/local/bin/vim +192 pandas/util/testing.py # assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([1, 2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,
2, 4, 3, 0, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00], dtype=object)],
right [Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2000-01-06 00:00:00,
2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00, 0, 1, 2, 3, 4], dtype=object) object]
FAIL: pandas.computation.tests.test_eval:TestAlignment.test_basic_series_frame_alignment('numexpr', 'pandas')
/usr/local/bin/vim +872 pandas/computation/tests/test_eval.py # check_basic_series_frame_alignment
testit(r_idx_type, c_idx_type, index_name)
/usr/local/bin/vim +867 pandas/computation/tests/test_eval.py # testit
assert_frame_equal(res, expected)
/usr/local/bin/vim +312 pandas/util/testing.py # assert_frame_equal
assert_index_equal(left.columns, right.columns)
/usr/local/bin/vim +192 pandas/util/testing.py # assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([1, 2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00,
2, 4, 3, 0, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00], dtype=object)],
right [Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2000-01-06 00:00:00,
2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00, 0, 1, 2, 3, 4], dtype=object) object]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5119/events | https://github.com/pandas-dev/pandas/pull/5119 | 20,566,939 | MDExOlB1bGxSZXF1ZXN0ODg2MTk4Ng== | 5,119 | CLN: Remove leftover test.py file | {
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} | [] | closed | false | null | [] | null | 1 | 2013-10-05T16:25:24Z | 2014-07-16T08:33:15Z | 2013-10-05T16:32:34Z | CONTRIBUTOR | null | Extra test file at top level with a set_trace on it was checked in with
the json normalize PR. I'm assuming it shouldn't be here. @jreback
@cpcloud? I think it's causing the test suite to hang for me, since I
run it from within the pandas directory.
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} | 13 | 2013-10-05T20:03:05Z | 2013-12-20T17:21:48Z | 2013-12-20T17:06:46Z | NONE | null | It often happens that I print a MultiIndex object (whether on purpose or not) which leads to flooding my interpreter and makes it unresponsive for a couple of seconds since often the length of my indexers exceeds 500-1000k. I was thinking about adding a neat `__repr__` for Indexers that summarizes levels + length or just omits what's in the middle once a certain length is exceeded. It seems this has not been tackled so far when looking at the current master, but please feel free to close this issue if this is a dupe (checked but did not find anything related). Any pointers are welcome.
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} | 6 | 2013-10-05T22:34:34Z | 2014-06-24T19:40:22Z | 2013-10-05T23:28:25Z | MEMBER | null | Also add a test to make sure that the C parser options validation is actually
covered
example travis fail:
https://travis-ci.org/pydata/pandas/jobs/12171563
explanation (for the record):
if you print out `options` before this commit you'll see that they are all overwritten with a single value. Whatever this value is will depend on how the `_parser_defaults` dictionary happens to be ordered (which is random because of hashing).
This caused a bug because the value of `value` is retained from the loop over `_parser_defaults`, and `default` is _not_ reassigned in the two loops that follow the loop over `_parser_defaults`, so all the values end up being the same as the last argument that was iterated over from `_parser_defaults`.
magic explained :tada:
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} | 7 | 2013-10-06T08:18:32Z | 2014-06-16T01:40:59Z | 2013-10-14T01:40:48Z | CONTRIBUTOR | null | Added tests and a fix for unhandled numpy data types in the
Excel writers. Issue #3122.
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} | 4 | 2013-10-06T16:54:17Z | 2013-10-11T12:01:34Z | 2013-10-11T12:01:34Z | CONTRIBUTOR | null | ```
In [1]: from pandas import *
In [2]: idx = MultiIndex.from_tuples([(0, 'a'), (1, 'b'), (2, 'c')])
In [3]: pd.isnull(idx)
Segmentation fault: 11
(pandas-dev)dhcp80fff527:pandas tom$
```
This is on latest master (`'0.12.0-757-g69b67f3'`). Numpy 1.7.1.
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} | 21 | 2013-10-06T17:25:14Z | 2014-06-13T10:01:30Z | 2013-10-11T12:01:34Z | CONTRIBUTOR | null | Now raises NotImplementedError b/c not yet clear what it should return.
Fixes #5123.
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} | 9 | 2013-10-06T18:34:02Z | 2016-10-12T23:04:16Z | 2013-10-11T20:56:30Z | CONTRIBUTOR | null | `parse_dates` should have a little more documentation (I did look at docs on read_csv as well).
It is not clear to me what the default value of parse_dates is.
Alson on read_html is listed as "bool" whereas on read_csv as "boolean, list of ints or names, list of lists, or dict".
I am not sure what setting parse_dates=False does.
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I added a test for indexing with int-typed arrays containing single indices, which currently fails. I spent some time reading test_indexing, because my hunch is that it could be better integrated with the existing tests (e.g. using check_result), but anyhow - any test is better than none, I guess.
The failing test goes through `_iLocIndexer._getitem_axis()` -> `_NDFrameIndexer._get_loc()` -> `Series._ixs()` -> `index.get_value_at()` -> `util.get_value_at()`, but I don't see the point where that should've been different right now.
Sorry, but I lack time for solving this at the moment.
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} | 7 | 2013-10-06T19:42:15Z | 2014-06-29T09:44:06Z | 2013-12-22T21:02:16Z | CONTRIBUTOR | null | This is an initial patch to add PyExcelerate as an Excel writer engine.
**It isn't suitable for merge** yet since there is issue with date handling in PyExcelerate that I am working to resolve with the authors. I just want this to appear on the pandas timeline for now so I can refer to it in other PRs.
1. The requirements files are missing since the PR will require a PyExcelerate update and release. Also, the `print_versions.py` entry.
2. There is also a failing test due to versioning.
3. There is currently no merged cell support.
I'll fix all these before the final PR request.
John
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} | 23 | 2013-10-06T20:02:32Z | 2016-10-12T23:04:16Z | 2014-02-27T19:02:44Z | CONTRIBUTOR | null | ``` python
In [5]: pd.read_html("http://pastebin.com/raw.php?i=7mAF0Ei6",infer_types=False, header=[0,1])[0]
---------------------------------------------------------------------------
----> 1 pd.read_html("http://pastebin.com/raw.php?i=7mAF0Ei6",infer_types=False, header=[0,1])[0]
/home/alex/git/pandas/pandas/io/html.pyc in read_html(io, match, flavor, header, index_col, skiprows, infer_types, attrs, parse_dates, tupleize_cols, thousands)
838 'data (you passed a negative value)')
839 return _parse(flavor, io, match, header, index_col, skiprows, infer_types,
--> 840 parse_dates, tupleize_cols, thousands, attrs)
/home/alex/git/pandas/pandas/io/html.pyc in _parse(flavor, io, match, header, index_col, skiprows, infer_types, parse_dates, tupleize_cols, thousands, attrs)
710 return [_data_to_frame(table, header, index_col, skiprows, infer_types,
711 parse_dates, tupleize_cols, thousands)
--> 712 for table in tables]
713
714
/home/alex/git/pandas/pandas/io/html.pyc in _data_to_frame(data, header, index_col, skiprows, infer_types, parse_dates, tupleize_cols, thousands)
600 skiprows=_get_skiprows(skiprows),
601 parse_dates=parse_dates, tupleize_cols=tupleize_cols,
--> 602 thousands=thousands)
603 df = tp.read()
604
/home/alex/git/pandas/pandas/io/parsers.pyc in TextParser(*args, **kwds)
1171 """
1172 kwds['engine'] = 'python'
-> 1173 return TextFileReader(*args, **kwds)
1174
1175
/home/alex/git/pandas/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
481 self.options['has_index_names'] = kwds['has_index_names']
482
--> 483 self._make_engine(self.engine)
484
485 def _get_options_with_defaults(self, engine):
/home/alex/git/pandas/pandas/io/parsers.pyc in _make_engine(self, engine)
596 elif engine == 'python-fwf':
597 klass = FixedWidthFieldParser
--> 598 self._engine = klass(self.f, **self.options)
599
600 def _failover_to_python(self):
/home/alex/git/pandas/pandas/io/parsers.pyc in __init__(self, f, **kwds)
1294 if len(self.columns) > 1:
1295 self.columns, self.index_names, self.col_names, _ = self._extract_multi_indexer_columns(
-> 1296 self.columns, self.index_names, self.col_names)
1297 else:
1298 self.columns = self.columns[0]
/home/alex/git/pandas/pandas/io/parsers.pyc in _extract_multi_indexer_columns(self, header, index_names, col_names, passed_names)
734 # if we find 'Unnamed' all of a single level, then our header was too long
735 for n in range(len(columns[0])):
--> 736 if all([ 'Unnamed' in c[n] for c in columns ]):
737 raise _parser.CParserError("Passed header=[%s] are too many rows for this "
738 "multi_index of columns" % ','.join([ str(x) for x in self.header ]))
TypeError: argument of type 'float' is not iterable
```
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} | 19 | 2013-10-06T20:07:50Z | 2016-10-12T23:04:16Z | 2014-01-29T03:59:48Z | CONTRIBUTOR | null | Allow creating a `_LiberalLxmlFrameParser` (see conversation on: https://github.com/pydata/pandas/issues/4469#issuecomment-22142824)
For now I will factor out:
```
parser = HTMLParser(recover=False)
```
so that subclass can change the behavior.
Would there be an issue with adding `_LiberalLxmlFrameParser` to the Pandas codebase?
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} | 29 | 2013-10-06T20:51:31Z | 2014-07-21T06:35:36Z | 2013-11-01T00:23:43Z | CONTRIBUTOR | null | Closes #5130
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} | 0 | 2013-10-06T22:27:31Z | 2013-10-06T22:30:11Z | 2013-10-06T22:30:11Z | MEMBER | null | ```
In [67]: df
Out[67]:
counter
created
05-16-13 3
05-17-13 1
05-18-13 1
In [68]: print df.to_json('asdfasdf')
None
In [69]: print df.to_dict('series')
{'counter': created
05-16-13 3
05-17-13 1
05-18-13 1
Name: counter, dtype: int64}
In [70]: print df.counter.to_json('asdfasdf')
None
In [71]: print df.counter.to_json('index')
None
```
The "default" value of `'index'` (whose default is actually `None`) doesn't do what it says.
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} | 0 | 2013-10-06T23:55:10Z | 2014-06-18T14:51:20Z | 2013-10-07T00:10:08Z | CONTRIBUTOR | null | closes #5006
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} | 3 | 2013-10-07T00:29:03Z | 2013-10-13T23:33:01Z | 2013-10-13T23:33:01Z | CONTRIBUTOR | null | I have put boxes by the ones 2 look at, some/most of these were caused by #3482, but always worth taking a look.
- [x] frame_xs_col, miniscule
- [x] groupby_multi_python, #3482
- [x] concat_series_axis1, series construction overhead (so ok)
- [x] period_setitem, issue #5155, will need to be address in 0.14
- [x] frame_iloc_dups, miniscule
- [x] reshape_stack_simple, fixed after #5178
- [x] frame_iteritems, #3482
- [x] frame_fancy_lookup, series construction overhead (so ok)
- [x] sparse_frame_constructor, #3482
- [x] timeseries_period_downsample_mean, PR #5204
- [x] series_timestamp_compare, PR #5154 (was boxing isnull)
- [x] reindex_daterange_pad, PR #5178 (not using specialized indexer)
- [x] reindex_daterange_backfill, PR #5178 (not using specialized indexer)
The good
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
plot_timeseries_period | 54.3883 | 1061.3217 | 0.0512 |
timeseries_to_datetime_YYYYMMDD | 9.4270 | 42.3674 | 0.2225 |
write_store_table_mixed | 36.2830 | 116.2400 | 0.3121 |
packers_write_hdf_table | 29.6400 | 77.6550 | 0.3817 |
write_store_table_panel | 47.1393 | 88.3977 | 0.5333 |
write_store_table | 30.7643 | 56.7960 | 0.5417 |
read_csv_thou_vb | 17.3206 | 30.3980 | 0.5698 |
frame_getitem_single_column | 25.7526 | 40.0293 | 0.6433 |
```
The bad
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_xs_col | 0.0350 | 0.0233 | 1.5051 |
groupby_multi_python | 123.9320 | 81.8870 | 1.5135 |
concat_series_axis1 | 93.0324 | 60.6890 | 1.5329 |
period_setitem | 212.0950 | 138.1503 | 1.5352 |
frame_iloc_dups | 0.3070 | 0.1993 | 1.5403 |
reshape_stack_simple | 1.9557 | 1.1647 | 1.6792 |
frame_iteritems | 37.6317 | 19.2017 | 1.9598 |
frame_fancy_lookup | 3.4526 | 1.6307 | 2.1173 |
sparse_frame_constructor | 12.2224 | 5.3250 | 2.2953 |
timeseries_period_downsample_mean | 14.1153 | 5.4576 | 2.5863 |
series_timestamp_compare | 7.3427 | 1.8593 | 3.9491 |
reindex_daterange_pad | 2.8083 | 0.6460 | 4.3476 |
reindex_daterange_backfill | 2.8074 | 0.6456 | 4.3482 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [e6b99f4] : Merge pull request #5134 from jreback/list_index
BUG: Treat a list/ndarray identically for iloc indexing with list-like (GH5006)
Base [8c0a34f] : RLS: set released to True, edit release dates
```
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} | 0 | 2013-10-07T01:17:50Z | 2013-10-07T13:00:51Z | 2013-10-07T13:00:51Z | CONTRIBUTOR | null | The `fillna` should work with this type of replacement (_could_ just directly call `replace` if `method=None` in fact).
```
In [1]: df = DataFrame(dict(A = [1,np.nan]))
In [2]: df
Out[2]:
A
0 1
1 NaN
In [3]: df.fillna('')
ValueError: could not convert string to float:
In [4]: df.replace(np.nan,'')
Out[4]:
A
0 1
1
```
http://stackoverflow.com/questions/19215383/how-to-not-display-nan-in-ipython-notebook-html-table-of-pandas-dataframe
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} | 1 | 2013-10-07T03:03:53Z | 2014-06-12T23:21:59Z | 2013-10-07T12:27:06Z | CONTRIBUTOR | null | Adds `datetime.time` support to JSON serialiser. Note:
- times are encoded to strings and will be decoded to strings.
- `date_unit` and `date_format` params have no affect on `time` serialisation, they are always iso formatted.
Fixes #4873
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https://api.github.com/repos/pandas-dev/pandas/issues/5138 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5138/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5138/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5138/events | https://github.com/pandas-dev/pandas/pull/5138 | 20,594,119 | MDExOlB1bGxSZXF1ZXN0ODg3MzA2Ng== | 5,138 | ENH: json default_handler param | {
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} | 2 | 2013-10-07T06:02:37Z | 2014-06-15T16:16:36Z | 2013-10-07T12:36:33Z | CONTRIBUTOR | null | This adds a `default_handler` param to `to_json`, which if passed must be a callable which takes one argument (the object to convert) and returns a serialisable object. The `default_handler` will only be used if an object cannot otherwise be serialised.
Note right now the JSON serialiser has direct handling for:
- Python basic types `bool`, `int`, `long`, `float`.
- Python containers `dict`, `list`, `tuple` and `set`.
- Python byte and unicode strings.
- Python decimal.
- Python `datetime` and `date`.
- Python `None`.
- Numpy arrays.
- Numpy scalars
- `NaN` and `NaT`
- Pandas `Index`, `Series`, `DataFrame` and `Timestamp`
- any subclasses of the above.
If an object is not recognised the fallback behaviour with this PR would be:
1. if a `toDict` method is defined by the unrecognised object that
will be called and its returned `dict` will be JSON serialised.
2. if a `default_handler` has been passed to `to_json` that will
be called to convert the object.
3. otherwise an attempt is made to convert the object to a `dict` by
iterating over its attributes.
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} | 0 | 2013-10-07T12:48:49Z | 2014-06-26T11:25:23Z | 2013-10-07T13:00:51Z | CONTRIBUTOR | null | closes #5136 (just tests), as not a bug
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} | 2 | 2013-10-07T14:13:34Z | 2013-10-08T12:13:38Z | 2013-10-08T12:13:38Z | CONTRIBUTOR | null | endianness issue I guess. Seems to happen only with python2.6 on that box:
http://nipy.bic.berkeley.edu/builders/pandas-py2.6-wheezy-sparc/builds/149/steps/shell_4/logs/stdio
`schroot -c wheezy` would bring you into that env (for anyone with access)
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} | 1 | 2013-10-07T14:15:51Z | 2013-10-08T02:04:28Z | 2013-10-08T02:04:28Z | CONTRIBUTOR | null | http://nipy.bic.berkeley.edu/builders/pandas-py3.x-sid-sparc/builds/230/steps/shell_4/logs/stdio
```
======================================================================
ERROR: test_variable_width_unicode (pandas.io.tests.test_parsers.TestFwfColspaceSniffing)
----------------------------------------------------------------------
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-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/tests/test_parsers.py", line 2322, in test_variable_width_unicode
colspecs=[(0, 4), (5, 9)], header=None)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 433, in read_fwf
return _read(filepath_or_buffer, kwds)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 203, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 483, in __init__
self._make_engine(self.engine)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 598, in _make_engine
self._engine = klass(self.f, **self.options)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 2084, in __init__
PythonParser.__init__(self, f, **kwds)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 1291, in __init__
self.columns = self._infer_columns()
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 1523, in _infer_columns
line = self._next_line()
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 1550, in _next_line
line = next(self.data)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 2066, in next
line = next(self.f)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/encodings/ascii.py", line 26, in decode
return codecs.ascii_decode(input, self.errors)[0]
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd7 in position 0: ordinal not in range(128)
======================================================================
ERROR: test_BytesIO_input (pandas.io.tests.test_parsers.TestPythonParser)
----------------------------------------------------------------------
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-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/tests/test_parsers.py", line 2150, in test_BytesIO_input
result = pd.read_fwf(BytesIO("\u05e9\u05dc\u05d5\u05dd\n\u05e9\u05dc\u05d5\u05dd".encode('utf8')), widths=[2,2])
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 433, in read_fwf
return _read(filepath_or_buffer, kwds)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 203, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 483, in __init__
self._make_engine(self.engine)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 598, in _make_engine
self._engine = klass(self.f, **self.options)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 2084, in __init__
PythonParser.__init__(self, f, **kwds)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 1291, in __init__
self.columns = self._infer_columns()
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 1482, in _infer_columns
line = self._next_line()
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 1550, in _next_line
line = next(self.data)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/site-packages/pandas-0.12.0_755_ge6b99f4-py3.3-linux-sparc64.egg/pandas/io/parsers.py", line 2066, in next
line = next(self.f)
File "/home/buildslave/nd-bb-slave-sparc-sid/pandas-py3_x-sid-sparc/build/venv/lib/python3.3/encodings/ascii.py", line 26, in decode
return codecs.ascii_decode(input, self.errors)[0]
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd7 in position 0: ordinal not in range(128)
----------------------------------------------------------------------
Ran 4083 tests in 1851.237s
FAILED (SKIP=291, errors=2)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5142 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5142/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5142/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5142/events | https://github.com/pandas-dev/pandas/issues/5142 | 20,640,528 | MDU6SXNzdWUyMDY0MDUyOA== | 5,142 | doc generation fails on windows | {
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} | 23 | 2013-10-07T21:31:30Z | 2014-01-24T16:50:09Z | 2014-01-24T16:22:19Z | CONTRIBUTOR | null | ```
[...]
reading sources... [ 75%] io
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "c:\portabel\python27\lib\site-packages\sphinx\cmdline.py", line 246, in
main
app.build(force_all, filenames)
File "c:\portabel\python27\lib\site-packages\sphinx\application.py", line 212,
in build
self.builder.build_update()
File "c:\portabel\python27\lib\site-packages\sphinx\builders\__init__.py", lin
e 214, in build_update
'out of date' % len(to_build))
File "c:\portabel\python27\lib\site-packages\sphinx\builders\__init__.py", lin
e 234, in build
purple, length):
File "c:\portabel\python27\lib\site-packages\sphinx\builders\__init__.py", lin
e 134, in status_iterator
for item in iterable:
File "c:\portabel\python27\lib\site-packages\sphinx\environment.py", line 470,
in update_generator
self.read_doc(docname, app=app)
File "c:\portabel\python27\lib\site-packages\sphinx\environment.py", line 621,
in read_doc
raise SphinxError(str(err))
SphinxError: 'utf8' codec can't decode byte 0x84 in position 25: invalid start b
yte
```
The problem is the following line in io.rst:
```
data = 'word,length\nTr\xe4umen,7\nGr\xfc\xdfe,5'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5143 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5143/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5143/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5143/events | https://github.com/pandas-dev/pandas/issues/5143 | 20,641,179 | MDU6SXNzdWUyMDY0MTE3OQ== | 5,143 | Replace partial matches behavior | {
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} | 8 | 2013-10-07T21:43:36Z | 2016-10-12T23:04:16Z | 2013-10-08T04:35:21Z | CONTRIBUTOR | null | I ran into some unexpected behavior with `replace` today.
I want to replace the answer with a numeric value according to the dictionary `weights`
``` python
In [33]: df.answer.head()
Out[33]:
0 Strongly Agree
1 Agree
2 Neutral
3 Disagree
4 Strongly Disagree
Name: answer, dtype: object
In [34]: weights
Out[34]:
{'Agree': 4,
'Disagree': 2,
'Neutral': 3,
'Strongly Agree': 5,
'Strongly Disagree': 1}
In [35]: df.answer.replace(weights).head()
Out[35]:
0 4
1 4
2 3
3 2
4 2
dtype: int64
```
It looks like replace matches on the first part, `Agree`, or `Disagree` and doesn't make it through the dict to the `Strongly`s. Am I just being a noob with how regexes work, or is this a bug?
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} | 15 | 2013-10-07T23:24:10Z | 2014-06-24T07:44:28Z | 2013-10-08T04:35:21Z | MEMBER | null | This avoids the seeming passage of regular expressions
closes #5143.
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} | 18 | 2013-10-08T00:11:28Z | 2013-11-21T19:59:05Z | 2013-11-21T15:26:54Z | CONTRIBUTOR | null | Right now sometimes getting config options iterates over a list filtering on regular expressions, which seems like overdoing it when internal code knows exactly what it wants. Makes sense to add getitem and set item to bypass the checks (and then add an update method to set up everything).
https://github.com/pydata/pandas/commit/7aa7880f#diff-1e746a348e7db9596f3d053134d00e74R515
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} | 88 | 2013-10-08T00:39:32Z | 2014-10-23T17:15:44Z | 2014-07-28T14:59:20Z | CONTRIBUTOR | null | This PR is a work in progress
Fixes #5306 - Removing unused imports
Fixes #5082 - Added `inferred_freq_offset`
Fixes #5028 - fixed issues with `_offset_map`
Fixes #4878 - `freq` parameter for `Period` init supports full set of Offsets.
Fixes #5418 - raise `DateParseError` rather than incorrect `KeyError` for invalid frequencies
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} | 7 | 2013-10-08T02:47:41Z | 2016-10-12T23:04:16Z | 2013-10-08T03:41:58Z | CONTRIBUTOR | null | Testing on my local armel box (heh -- bringing now my own media box into the game): it seems to avoid #4473 but whines about
```
======================================================================
ERROR: test_parse_dates_combine (pandas.io.tests.test_html.TestReadHtml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/tests/test_html.py", line 580, in test_parse_dates_combine
index_col=1)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/html.py", line 840, in read_html
parse_dates, tupleize_cols, thousands, attrs)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/html.py", line 698, in _parse
parser = _parser_dispatch(flav)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/html.py", line 657, in _parser_dispatch
raise ImportError("lxml not found please install it")
ImportError: lxml not found please install it
======================================================================
ERROR: test_parse_dates_list (pandas.io.tests.test_html.TestReadHtml)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/tests/test_html.py", line 572, in test_parse_dates_list
res = read_html(expected, parse_dates=[0], index_col=0)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/html.py", line 840, in read_html
parse_dates, tupleize_cols, thousands, attrs)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/html.py", line 698, in _parse
parser = _parser_dispatch(flav)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/io/html.py", line 657, in _parser_dispatch
raise ImportError("lxml not found please install it")
ImportError: lxml not found please install it
```
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] | closed | false | null | [] | null | 7 | 2013-10-08T03:50:50Z | 2016-12-11T14:06:54Z | 2016-12-11T14:06:54Z | CONTRIBUTOR | null | if you give infer_freq 5 consecutive weekdays, it'll come back with 'D' as its inferred frequency. But if your actual frequency is `BDay`, then, when DatetimeIndex checks that the frequency matches, 'B' != 'D'. (note that verify_integrity=False skips this). This leads to a more general issue about `infer_freq` with ambiguous cases. I think it makes the most sense to move these sorts of checks to a method on offset that takes a frequence and an Index or ndarray, and determines whether it is compatible.
This matters because you can hit some edge cases when you pass freq and also datetime64[ns] to the DatetimeIndex constructor and more generally because comparing freqstr is probably not the best way to go about checking whether a frequency matches.
Default implementation could be:
``` python
def is_compatible(freqstr, arr=None):
return freqstr == self.freqstr
```
and then bday could do something like (and this is totally psuedocode)
``` python
def is_compatible(freqstr, arr=None):
if freqstr == self.freqstr: return True
if arr is not None and len(arr) <= 5:
if freqstr == 'D': # or other compatibles that ensure it's consecutive
return all(is_weekday(date) for date in arr)
```
This gets more complicated with multiplied offsets, but I think it's worth considering.
``` python
from datetime import datetime
import pandas as pd
dates = [datetime(2013, 10, 7), datetime(2013, 10, 8), datetime(2013, 10, 9)]
ind = pd.DatetimeIndex(dates, freq=pd.tseries.frequencies.BDay())
ind2 = pd.DatetimeIndex(ind.values, freq=pd.tseries.frequencies.BDay(),
verify_integrity=False)
ind3 = pd.DatetimeIndex(ind.values, freq=pd.tseries.frequencies.BDay())
```
produces this Traceback:
```
Traceback (most recent call last):
File "test2.py", line 8, in <module>
ind3 = pd.DatetimeIndex(ind.values, freq=pd.tseries.frequencies.BDay())
File "../pandas/tseries/index.py", line 280, in __new__
raise ValueError('Dates do not conform to passed '
ValueError: Dates do not conform to passed frequency
```
cc @cancan101 - this is what we need to deal with in adding your offsets. I believe that every other offset can be returned from infer_freq, so these offsets would be different and therefore could _never_ pass integrity checks. So either we'd need to change infer_freq and/or define some kind of is_compatible method that intelligently covers all the ways in which the frequency could be something different than its freqstr.
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} | 4 | 2013-10-08T04:01:18Z | 2016-08-22T20:44:51Z | 2016-08-22T13:10:34Z | CONTRIBUTOR | null | For example from docs (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html):
```
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to
```
But it is not clear what those default values are.
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} | 4 | 2013-10-08T13:04:26Z | 2014-07-16T08:33:41Z | 2013-10-08T13:14:12Z | CONTRIBUTOR | null | - API/CLN: provide isnull/notnull on NDFrame objects
- PERF: remove auto-boxing on isnull/notnull
related #5135
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} | 11 | 2013-10-08T13:50:21Z | 2014-08-07T11:34:54Z | 2014-08-07T11:34:54Z | CONTRIBUTOR | null | vbench already in timeseries/period_setitem
very inefficient because on the boxing of the PeriodIndex (to an object index), it recomputes the freq for each Period (even though it is passed), need a fastpath on this type of construction
```
In [1]: rng = period_range('1/1/1990', freq='S', periods=20000)
In [2]: df = DataFrame(index=range(len(rng)))
In [3]: def f():
...: df['col'] = rng
...:
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
40000 0.055 0.000 0.095 0.000 abc.py:128(__instancecheck__)
20000 0.043 0.000 0.345 0.000 period.py:66(__init__)
20000 0.037 0.000 0.381 0.000 period.py:683(<lambda>)
80031 0.037 0.000 0.131 0.000 {isinstance}
20000 0.034 0.000 0.223 0.000 frequencies.py:76(get_freq_code)
60003 0.029 0.000 0.029 0.000 _weakrefset.py:68(__contains__)
20000 0.028 0.000 0.063 0.000 frequencies.py:464(_base_and_stride)
20000 0.028 0.000 0.028 0.000 {method 'match' of '_sre.SRE_Pattern' objects}
20000 0.027 0.000 0.038 0.000 frequencies.py:767(_period_str_to_code)
40000 0.023 0.000 0.144 0.000 common.py:1730(is_integer)
1 0.016 0.016 0.398 0.398 {pandas.lib.map_infer}
40007 0.010 0.000 0.010 0.000 {getattr}
20000 0.009 0.000 0.012 0.000 frequencies.py:112(_get_freq_str)
60000 0.008 0.000 0.008 0.000 {method 'get' of 'dict' objects}
40000 0.006 0.000 0.006 0.000 {method 'group' of '_sre.SRE_Match' objects}
20000 0.004 0.000 0.004 0.000 {method 'lower' of 'str' objects}
20000 0.002 0.000 0.002 0.000 {method 'upper' of 'str' objects}
20015 0.002 0.000 0.002 0.000 {len}
```
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https://groups.google.com/forum/#!topic/pydata/KO-PmQdBUZI
```
In [1]: data = """c1\ntext11,text12\ntext21,text22"""
In [2]: read_csv(StringIO(data))
Out[2]:
c1
text11 text12
text21 text22
In [3]: read_csv(StringIO(data),header=0,names=list('abc'))
Bus error (core dumped)
```
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} | 0 | 2013-10-08T14:17:38Z | 2014-06-17T13:24:48Z | 2013-10-08T14:30:56Z | CONTRIBUTOR | null | closes #4928
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} | 2 | 2013-10-08T14:30:31Z | 2013-10-08T14:51:18Z | 2013-10-08T14:51:04Z | NONE | null | closes https://github.com/pydata/pandas/issues/4630
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} | 0 | 2013-10-08T17:05:16Z | 2014-07-16T08:33:46Z | 2013-10-08T17:26:45Z | CONTRIBUTOR | null | restore API before #5154
boxing via null/isnull is same as 0.12
(aside from now `isnull/notnull` are now available on `NDFrame` objs as well)
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} | 18 | 2013-10-08T20:54:57Z | 2014-06-18T10:12:24Z | 2013-10-14T18:18:38Z | MEMBER | null | closes #4790
I copied the approach of numpy: extending the autosummary class template to automatically generate all method stub files:
- https://github.com/numpy/numpy/blob/master/doc/source/_templates/autosummary/class.rst
- example output: http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html
I added `DataFrame`, `Series`, `Panel`, `Index` and `DatetimeIndex`. Others? (eg `Timestamp`) Or too much?
The only issue at the moment is that normally also a list of attributes should be included, which is not the case (only a list of methods)
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} | 5 | 2013-10-08T21:40:29Z | 2013-11-01T12:50:07Z | 2013-10-15T13:14:48Z | NONE | null | ```
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from pandas.tseries import offsets
```
```
# bug 1: the dtype of "id" changes from int to float, why?
df1 = DataFrame([[1, 10.0],
[2, 20.0]],
columns=['id', 'value'])
df2 = DataFrame([[2, -20.0],
[3, -30.0]],
columns=['id', 'value'])
df = pd.merge(df1, df2, on='id', how='outer', suffixes=['_a', '_b'])
print df1.dtypes
print df2.dtypes
print df.dtypes
# watch dtype of column "id"
print
```
```
# bug 2: MultiIndex is int32 instead of being int64
df1 = DataFrame([0,1], index=[2*[2L**32-1], ['1','2']], columns=['value'])
print df1.unstack(level=0) # OK
df2 = DataFrame([0,1], index=[2*[2L**32], ['1','2']], columns=['value'])
print df2.unstack(level=0) # FAILS
df3 = DataFrame([0,1], index=[2*[str(2L**32)], ['1','2']], columns=['value'])
print df3.unstack(level=0) # OK
```
```
# bug 3: inconsistent result for application of pd.tseries.offsets.MonthEnd()
t1_start = pd.Timestamp('02/01/2000')
t1_end = pd.Timestamp(t1_start+pd.tseries.offsets.MonthEnd())
# t1_end is the end of February
t2_start = pd.date_range('2000-02-01', '2000-03-01', freq='6M')
t2_end = t2_start+pd.tseries.offsets.MonthEnd()
# t2_end is the end of March, why?
print
```
```
# bug 4: unexpected behaviour for "group by"
data = DataFrame(\
{'A': [1,1,1,1,1, 2,2,2, 3 ],
'B': ['a',np.NaN,'b',np.NaN,'c', np.NaN,np.NaN,np.NaN, 'd'],
'C': [np.NaN,np.NaN,10,np.NaN,np.NaN, np.NaN,np.NaN,np.NaN, 30],
'D': [-1,np.NaN,np.NaN,np.NaN,-1, np.NaN,np.NaN,np.NaN, -3]})
data_1 = data.copy()
data_2 = data.copy()
grouped = data_1[['B','C']].groupby(data_1['A'])
data_1[['B','C']] = grouped.fillna(method='ffill')
data_1[['B','C']] = grouped.fillna(method='bfill')
grouped = data_2[['B','C']].groupby(data_2['A'])
data_2[['B','C']] = grouped.fillna(method='ffill')
grouped = data_2[['B','C']].groupby(data_2['A'])
data_2[['B','C']] = grouped.fillna(method='bfill')
# data_1 and data_2 differ, why? maybe I missunderstand something here?
```
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] | closed | false | null | [] | null | 7 | 2013-10-09T11:52:42Z | 2013-10-10T16:36:41Z | 2013-10-09T12:00:09Z | CONTRIBUTOR | null | It seemed that after DataFrame.copy(deep=True) does not copy the data of index but keeps the reference to it.
version: pandas-0.12.0
For example:
```
df1 = pd.DataFrame({'data':0}, index=pd.date_range('2001-01-01','2001-01-05'))
df1 = df1.tz_localize('UTC')
print 'DF1 initial'
print df1
df2 = df1.copy()
df2.index.tz = 'US/Eastern'
print 'DF2'
print df2
print 'DF1 was also updated'
print df1
```
results in:
```
DF1 initial
data
2001-01-01 00:00:00+00:00 0
2001-01-02 00:00:00+00:00 0
2001-01-03 00:00:00+00:00 0
2001-01-04 00:00:00+00:00 0
2001-01-05 00:00:00+00:00 0
DF2
data
2000-12-31 19:00:00-05:00 0
2001-01-01 19:00:00-05:00 0
2001-01-02 19:00:00-05:00 0
2001-01-03 19:00:00-05:00 0
2001-01-04 19:00:00-05:00 0
DF1 was also updated
data
2000-12-31 19:00:00-05:00 0
2001-01-01 19:00:00-05:00 0
2001-01-02 19:00:00-05:00 0
2001-01-03 19:00:00-05:00 0
2001-01-04 19:00:00-05:00 0
```
This particular example, indeed, has a simple workaround in the way how timezone should be converted:
```
df2 = df2.tz_convert('US/Eastern')
```
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} | 0 | 2013-10-09T12:22:46Z | 2020-04-27T02:52:26Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/19267029/why-pandas-transform-fails-if-you-only-have-a-single-column/19271841#19271841
Even though this is a correct error message, when it comes out of a groupby assignment via transform its 'unclear'. (as the assignment when the rhs has a single-column 'works')
```
In [48]: df = DataFrame(index=range(3))
In [49]: df
Out[49]:
Empty DataFrame
Columns: []
Index: [0, 1, 2]
In [50]: df['foo'] = DataFrame(index=range(3))
---------------------------------------------------------------------------
ValueError: Wrong number of items passed 1, indices imply 0
```
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Usecase / how to reproduce:
In ipython, type:
In [1]: x = pd.Series([1, 2])
In [2]: print x.dtype
Out[2]: dtype('int64')
In [3]: x.describe()
Out[3]:
count 2.000000
mean 1.500000
std 0.707107
min 1.000000
25% 1.250000
50% 1.500000
75% 1.750000
max 2.000000
dtype: float64 <----------------------- This is wrong
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} | 8 | 2013-10-10T01:24:53Z | 2017-10-31T00:32:07Z | 2017-10-31T00:32:07Z | CONTRIBUTOR | null | When you create a timestamp with a timezone and don't use a keyword argument, it fails silently:
```
>> pd.Timestamp('2012-01-01', 'US/Pacific')
Timestamp('2012-01-01 00:00:00', tz=None)
```
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} | 1 | 2013-10-10T02:21:30Z | 2014-07-16T08:33:51Z | 2013-10-11T20:56:30Z | MEMBER | null | closes #5126.
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} | [] | closed | false | null | [] | null | 3 | 2013-10-10T14:36:30Z | 2013-10-15T14:42:46Z | 2013-10-15T13:14:33Z | NONE | null | replacing single quotes with `\\'`seems to work.
This is with pandas 0.11 with pd.Term, seems like pandas is moving to string queries rather than pd.Term in the future?
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} | 6 | 2013-10-10T15:51:04Z | 2015-03-11T12:11:10Z | 2015-03-11T12:10:19Z | NONE | null | - [x] #5694
```
df = pd.DataFrame([0], index=[datetime(2012, 11, 4, 23, 0, 0)])
df = df.tz_localize('America/New_York')
df.resample(rule='D', how='sum')
```
raises a AmbiguousTimeError even though datetime(2012, 11, 4, 23, 0, 0) is not ambiguous.
- [x] #8601
```
idx = date_range('2014-10-08 00:00','2014-10-09 00:00', freq='D', tz='Europe/Berlin')
pd.Series(5, idx).resample('MS')
```
- [ ] #8744
```
index = pd.to_datetime(pd.Series([
'2014-10-26 07:35:49',
'2014-10-26 07:45:08',
'2014-10-26 08:04:58'
]))
df = pd.DataFrame(np.arange(len(index)), index=index)
df = df.tz_localize('Asia/Krasnoyarsk', ambiguous='NaT')
df.resample('D')
AmbiguousTimeError: Cannot infer dst time from Timestamp('2014-10-26 01:00:00'), try using the 'ambiguous' argument
```
- [x] #8653 (might be separate issue)
```
import datetime
import pytz as tz
import pandas as pd
rome = tz.timezone('Europe/Rome')
dr = []
for i in range(2):
dp = datetime.datetime(2014, 10, 25) + datetime.timedelta(days=i)
dr.append(rome.localize(dp))
series = {}
for i, ddr in enumerate(dr):
series[ddr] = i * 10
s1 = pd.Series(series)
s1 = s1.resample('D', how='mean')
```
- [x] #9119
- [ ] #9173
- [x] #9468
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} | 1 | 2013-10-10T16:33:06Z | 2014-04-09T12:19:40Z | 2014-04-09T12:19:40Z | NONE | null | I'm trying to create simple pivot
``` python
import pandas as pd
import numpy
import datetime
s1 = pd.Series([datetime.datetime.now() - datetime.timedelta(days=i) for i in range(5)])
s2 = pd.Series(['a1', 'a1', 'a2', 'a2', 'a3'])
s3 = pd.Series([i for i in range(5)])
frame = pd.DataFrame({'s1': s1, 's2': s1, 's3': s3})
pivot = pd.pivot_table(frame, rows='s2', cols='s1', values='s3', margins=True)
```
It happens if I set "margins=True" and cols for pivot is datetime.
```
Traceback (most recent call last):
File "./aa.py", line 12, in <module>
p = pd.pivot_table(b, rows='s2', cols='s1', values='s3', margins=True)
File "/usr/local/lib/python2.6/dist-packages/pandas/tools/pivot.py", line 135, in pivot_table
cols=cols, aggfunc=aggfunc)
File "/usr/local/lib/python2.6/dist-packages/pandas/tools/pivot.py", line 174, in _add_margins
piece[all_key] = margin[key]
File "/usr/local/lib/python2.6/dist-packages/pandas/core/frame.py", line 2119, in __setitem__
self._set_item(key, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/frame.py", line 2166, in _set_item
NDFrame._set_item(self, key, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/generic.py", line 679, in _set_item
self._data.set(key, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/internals.py", line 1781, in set
self.insert(len(self.items), item, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/internals.py", line 1801, in insert
new_items = self.items.delete(loc)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/index.py", line 2610, in delete
new_labels = [np.delete(lab, loc) for lab in self.labels]
File "/usr/local/lib/python2.6/dist-packages/numpy/lib/function_base.py", line 3456, in delete
"invalid entry")
ValueError: invalid entry
```
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} | 42 | 2013-10-10T16:49:41Z | 2013-10-28T22:58:08Z | 2013-10-28T19:52:51Z | MEMBER | null | ```
$ nosetests pandas/tests/test_generic.py:TestSeries.test_interp_quad
F
======================================================================
FAIL: test_interp_quad (pandas.tests.test_generic.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/phillip/Documents/code/py/pandas/pandas/tests/test_generic.py", line 339, in test_interp_quad
assert_series_equal(result, expected)
File "/home/phillip/Documents/code/py/pandas/pandas/util/testing.py", line 452, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "/home/phillip/Documents/code/py/pandas/pandas/util/testing.py", line 369, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "/home/phillip/Documents/code/py/pandas/pandas/util/testing.py", line 354, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int64') != dtype('float64')
----------------------------------------------------------------------
Ran 1 test in 0.041s
FAILED (failures=1)
```
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} | 35 | 2013-10-10T17:03:01Z | 2014-09-04T22:18:50Z | 2014-09-04T22:18:50Z | CONTRIBUTOR | null | I want next Monday:
```
def next_week_start(t):
return t + pd.DateOffset(weekday=0)
```
But for DST (fall back is Sunday 2013-11-03):
```
>> next_week_start(pd.Timestamp("2013-10-30", tz="US/Pacific"))
Timestamp('2013-11-03 23:00:00-0800', tz='US/Pacific')
```
which is Sunday at 11pm, not Monday.
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} | [] | closed | false | null | [] | null | 1 | 2013-10-10T17:14:06Z | 2013-10-15T14:41:15Z | 2013-10-15T14:40:55Z | NONE | null | Coming from http://stackoverflow.com/q/19300730/624829
<pre>
import pandas as pd
values = pd.Series(i for i in range(5))
rows = pd.Series(['a', 'b', 'a', 'a', 'b'])
columns = pd.date_range('20130101',periods=5)
df = pd.DataFrame({'values': values, 'rows': rows, 'columns': columns})
pivot = pd.pivot_table(
data=df,
rows='rows',
cols='columns',
values='values',
margins=True
)
</pre>
This call fails down to that traceback:
<pre>
Traceback (most recent call last):
File "./test.py", line 17, in <module>
margins=True
File "/usr/local/lib/python2.6/dist-packages/pandas/tools/pivot.py", line 135, in pivot_table
cols=cols, aggfunc=aggfunc)
File "/usr/local/lib/python2.6/dist-packages/pandas/tools/pivot.py", line 174, in _add_margins
piece[all_key] = margin[key]
File "/usr/local/lib/python2.6/dist-packages/pandas/core/frame.py", line 2119, in __setitem__
self._set_item(key, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/frame.py", line 2166, in _set_item
NDFrame._set_item(self, key, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/generic.py", line 679, in _set_item
self._data.set(key, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/internals.py", line 1781, in set
self.insert(len(self.items), item, value)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/internals.py", line 1801, in insert
new_items = self.items.delete(loc)
File "/usr/local/lib/python2.6/dist-packages/pandas/core/index.py", line 2610, in delete
new_labels = [np.delete(lab, loc) for lab in self.labels]
File "/usr/lib/pymodules/python2.6/numpy/lib/function_base.py", line 3339, in delete
"invalid entry")
ValueError: invalid entry
</pre>
Resetting the columns attribute on the df before workarounds the issue:
<pre>
df.columns = ['rows', 'columns', 'values']
</pre>
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} | 0 | 2013-10-10T18:45:36Z | 2014-07-16T08:33:56Z | 2013-10-10T19:54:28Z | CONTRIBUTOR | null | TST: update interpolate tests to account for correct dtype inferernce
closes #5174
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} | 0 | 2013-10-10T21:51:56Z | 2014-07-16T08:33:58Z | 2013-10-10T22:17:34Z | CONTRIBUTOR | null | better on reindexing
```
Invoked with :
--ncalls: 3
--repeats: 3
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_reindex_upcast | 12.3383 | 12.7693 | 0.9662 |
frame_reindex_both_axes | 36.2797 | 36.2826 | 0.9999 |
frame_reindex_both_axes_ix | 34.7884 | 34.7476 | 1.0012 |
frame_reindex_axis1 | 555.0433 | 553.5540 | 1.0027 |
frame_reindex_axis0 | 87.8770 | 82.8171 | 1.0611 |
reindex_fillna_pad | 0.6303 | 0.5744 | 1.0974 |
reindex_frame_level_align | 0.6430 | 0.5836 | 1.1017 |
reindex_fillna_backfill | 0.6286 | 0.5697 | 1.1035 |
reindex_frame_level_reindex | 0.5973 | 0.5407 | 1.1048 |
reindex_fillna_pad_float32 | 0.5320 | 0.4800 | 1.1083 |
reindex_fillna_backfill_float32 | 0.5307 | 0.4773 | 1.1119 |
dataframe_reindex | 0.4213 | 0.3650 | 1.1541 |
frame_reindex_columns | 0.3730 | 0.3120 | 1.1954 |
reindex_multiindex | 1.4537 | 1.1006 | 1.3208 |
reindex_daterange_pad | 1.2333 | 0.6417 | 1.9221 |
reindex_daterange_backfill | 1.2383 | 0.6430 | 1.9258 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [661adc5] : PERF: calling get_indexer twice when a specialized indexer (e.g. pad) could be called instead
Base [8c0a34f] : RLS: set released to True, edit release dates
```
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} | 9 | 2013-10-10T22:20:22Z | 2014-06-23T01:43:05Z | 2013-10-15T00:57:42Z | CONTRIBUTOR | null | Added some documentation for GBQ module
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} | 0 | 2013-10-11T12:14:26Z | 2014-07-16T08:34:03Z | 2013-10-11T12:26:22Z | CONTRIBUTOR | null | related #5045
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} | 2 | 2013-10-11T14:19:03Z | 2014-07-16T08:34:04Z | 2013-10-14T01:53:38Z | CONTRIBUTOR | null | replacement key for deprecated option `display.height` was set to `display.height` itself. I believe it should be `display.max_rows` instead.
This avoid a weird message in `describe_option()` :
```
display.height: [default: 60] [currently: 60]
: int
Deprecated.
(Deprecated, use `display.height` instead.)
```
(I spotted it while reading the online doc http://pandas.pydata.org/pandas-docs/dev/basics.html?highlight=display.height#working-with-package-options )
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} | 5 | 2013-10-11T15:14:39Z | 2013-10-14T21:51:32Z | 2013-10-12T01:29:49Z | CONTRIBUTOR | null | When tab completing in ipython notebook on `pd.options...`, nothing shows up, and on the backend (in iterm) I see a `KeyError: 'trait_names'`, with a longer traceback.
pd.version.version == '0.12.0'
python version == 3.3.2
IPython.version_info == (2, 0, 0, 'dev')
Pandas is latest from pip, and ipython is latest from git. It may be an IPython problem.
Traceback [here](https://gist.github.com/d10genes/6936334).
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} | 10 | 2013-10-11T16:26:28Z | 2014-01-24T21:43:37Z | 2014-01-24T21:43:37Z | CONTRIBUTOR | null | seems we need a public version of `tm.assert_frame_equal`
maybe `equals` on generic NDFrame objects
http://stackoverflow.com/questions/19322506/pandas-nan-comparison
seems good on the values comparison (should compare axes/types as well of course)
```
((df1 == df2) | ((df != df) & (df2 != df2))).values.all()
```
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} | 3 | 2013-10-11T18:08:44Z | 2017-06-09T20:36:06Z | 2017-06-09T20:35:59Z | CONTRIBUTOR | null | this might be interesting as it allows jit optimizations but w/o major deps (like blaze/numba).
could be useful as a back end for eval
potentially could be used in apply as well
http://www.parakeetpython.com/
http://www.phi-node.com/2013/01/just-in-time-compilers-for-number.html
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} | 3 | 2013-10-11T22:41:54Z | 2013-10-13T15:06:00Z | 2013-10-12T13:54:10Z | CONTRIBUTOR | null | I did this:
In [1]: df1 = DataFrame([1, 2, 3, 4, 5], index=[1, 2, 1, 2, 3])
In [2]: df2 = DataFrame([1, 2, 3], index=[1, 2, 3])
In [3]: df1.sub(df2)
Out[3]:
0
1 0
1 2
2 0
2 2
3 2
which is possible up to and including [this commit](https://github.com/jreback/pandas/commit/725b1951249a795fe01896dff4ce46bd9206021f). Then, [this "bug fix"](https://github.com/jreback/pandas/commit/3f76205f1d4a42ba971ff5be8770b52172b9f822#diff-0) seems to have broken the above usage. It now raises like so:
```
ValueError Traceback (most recent call last)
<ipython-input-12-e7e549a4e818> in <module>()
----> 1 df1.sub(df2)
/Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/ops.pyc in f(self, other, axis, level, fill_value)
710 def f(self, other, axis=default_axis, level=None, fill_value=None):
711 if isinstance(other, pd.DataFrame): # Another DataFrame
--> 712 return self._combine_frame(other, na_op, fill_value, level)
713 elif isinstance(other, pd.Series):
714 return self._combine_series(other, na_op, fill_value, axis, level)
/Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/frame.pyc in _combine_frame(self, other, func, fill_value, level)
2759
2760 def _combine_frame(self, other, func, fill_value=None, level=None):
-> 2761 this, other = self.align(other, join='outer', level=level, copy=False)
2762 new_index, new_columns = this.index, this.columns
2763
/Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/generic.pyc in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
2331 copy=copy, fill_value=fill_value,
2332 method=method, limit=limit,
-> 2333 fill_axis=fill_axis)
2334 elif isinstance(other, Series):
2335 return self._align_series(other, join=join, axis=axis, level=level,
/Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/generic.pyc in _align_frame(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
2362 left = self._reindex_with_indexers({0: [join_index, ilidx],
2363 1: [join_columns, clidx]},
-> 2364 copy=copy, fill_value=fill_value)
2365 right = other._reindex_with_indexers({0: [join_index, iridx],
2366 1: [join_columns, cridx]},
/Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/generic.pyc in _reindex_with_indexers(self, reindexers, method, fill_value, limit, copy, allow_dups)
1320 indexer = com._ensure_int64(indexer)
1321 new_data = new_data.reindex_indexer(index, indexer, axis=baxis,
-> 1322 fill_value=fill_value, allow_dups=allow_dups)
1323
1324 elif baxis == 0 and index is not None and index is not new_data.axes[baxis]:
/Users/danielallan/Documents/Repos/pandas-danielballan/pandas/core/internals.pyc in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups)
2884 # trying to reindex on an axis with duplicates
2885 if not allow_dups and not self.axes[axis].is_unique:
-> 2886 raise ValueError("cannot reindex from a duplicate axis")
2887
2888 if axis == 0:
ValueError: cannot reindex from a duplicate axis
```
I found this very handy. Should it really raise? Can you suggest what I should be doing instead? Thanks.
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} | [] | closed | false | null | [] | null | 0 | 2013-10-11T23:06:47Z | 2013-10-12T01:29:49Z | 2013-10-12T01:29:49Z | CONTRIBUTOR | null | `hasattr` expects `AttributeError` when looking up. Let's make it simple and backward compatible, replace `KeyError` with this class:
``` python
class OptionNotFound(AttributeError, KeyError):
"""Exception for pandas.options, backwards compatible with KeyError checks"""
```
cc @d10genes this should fix your problem.
simple example:
``` python
In [5]: class OptionNotFoundError(AttributeError, KeyError): pass
In [6]: class MyObject(self):
...:
KeyboardInterrupt
In [6]: class MyObject(object):
...: @property
...: def prop(self):
...: raise OptionNotFoundError("HEYO!")
...:
In [7]: a = MyObject()
In [8]: hasattr(a, "prop")
Out[8]: False
In [9]: OptionNotFoundError = KeyError
In [10]: class MyObject(object):
@property
def prop(self):
raise OptionNotFoundError("HEYO!")
....:
In [11]: a = MyObject()
In [12]: hasattr(a, "prop")
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-12-7205b5401c9b> in <module>()
----> 1 hasattr(a, "prop")
<ipython-input-10-7a717a2e41df> in prop(self)
2 @property
3 def prop(self):
----> 4 raise OptionNotFoundError("HEYO!")
5
KeyError: 'HEYO!'
In [13]:
```
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} | 0 | 2013-10-11T23:10:20Z | 2014-06-14T18:00:07Z | 2013-10-12T13:54:10Z | CONTRIBUTOR | null | closes #5185
```
In [1]: df1 = DataFrame([1, 2, 3, 4, 5], index=[1, 2, 1, 2, 3])
In [2]: df2 = DataFrame([1, 2, 3], index=[1, 2, 3])
In [3]: df1-df2
Out[3]:
0
1 0
1 2
2 0
2 2
3 2
In [4]: df1.sub(df2)
Out[4]:
0
1 0
1 2
2 0
2 2
3 2
```
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} | 5 | 2013-10-11T23:13:23Z | 2014-07-16T08:34:07Z | 2013-10-12T01:29:49Z | CONTRIBUTOR | null | Fixes #5182, closes #5186.
It's called OptionError now and it subclasses both AttributeError (for
hasattr) and KeyError (for backwards compatibility). Hurray!
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} | 36 | 2013-10-12T04:28:45Z | 2014-06-12T14:25:30Z | 2013-10-15T00:58:56Z | CONTRIBUTOR | null | Implements @wesm's suggestion and clears the way for new offsets.
Very simple to extend - map a string to your class in mapping, define a `from_name`
classmethod, then class gets passed everything after the `-` (if
anything) to its method `from_name()`. Caches to dict afterwards (right
now doesn't use cacheable, but could take advantage of that).
cc @cancan101
For reference, here are all the existing offset: output aliases (using
types because otherwise prints exactly the same).
[(None, NoneType),
('A-APR', pandas.tseries.offsets.YearEnd),
('A-AUG', pandas.tseries.offsets.YearEnd),
('A-DEC', pandas.tseries.offsets.YearEnd),
('A-FEB', pandas.tseries.offsets.YearEnd),
('A-JAN', pandas.tseries.offsets.YearEnd),
('A-JUL', pandas.tseries.offsets.YearEnd),
('A-JUN', pandas.tseries.offsets.YearEnd),
('A-MAR', pandas.tseries.offsets.YearEnd),
('A-MAY', pandas.tseries.offsets.YearEnd),
('A-NOV', pandas.tseries.offsets.YearEnd),
('A-OCT', pandas.tseries.offsets.YearEnd),
('A-SEP', pandas.tseries.offsets.YearEnd),
('AS-APR', pandas.tseries.offsets.YearBegin),
('AS-AUG', pandas.tseries.offsets.YearBegin),
('AS-DEC', pandas.tseries.offsets.YearBegin),
('AS-FEB', pandas.tseries.offsets.YearBegin),
('AS-JAN', pandas.tseries.offsets.YearBegin),
('AS-JUL', pandas.tseries.offsets.YearBegin),
('AS-JUN', pandas.tseries.offsets.YearBegin),
('AS-MAR', pandas.tseries.offsets.YearBegin),
('AS-MAY', pandas.tseries.offsets.YearBegin),
('AS-NOV', pandas.tseries.offsets.YearBegin),
('AS-OCT', pandas.tseries.offsets.YearBegin),
('AS-SEP', pandas.tseries.offsets.YearBegin),
('B', pandas.tseries.offsets.BusinessDay),
('BA-APR', pandas.tseries.offsets.BYearEnd),
('BA-AUG', pandas.tseries.offsets.BYearEnd),
('BA-DEC', pandas.tseries.offsets.BYearEnd),
('BA-FEB', pandas.tseries.offsets.BYearEnd),
('BA-JAN', pandas.tseries.offsets.BYearEnd),
('BA-JUL', pandas.tseries.offsets.BYearEnd),
('BA-JUN', pandas.tseries.offsets.BYearEnd),
('BA-MAR', pandas.tseries.offsets.BYearEnd),
('BA-MAY', pandas.tseries.offsets.BYearEnd),
('BA-NOV', pandas.tseries.offsets.BYearEnd),
('BA-OCT', pandas.tseries.offsets.BYearEnd),
('BA-SEP', pandas.tseries.offsets.BYearEnd),
('BAS-APR', pandas.tseries.offsets.BYearBegin),
('BAS-AUG', pandas.tseries.offsets.BYearBegin),
('BAS-DEC', pandas.tseries.offsets.BYearBegin),
('BAS-FEB', pandas.tseries.offsets.BYearBegin),
('BAS-JAN', pandas.tseries.offsets.BYearBegin),
('BAS-JUL', pandas.tseries.offsets.BYearBegin),
('BAS-JUN', pandas.tseries.offsets.BYearBegin),
('BAS-MAR', pandas.tseries.offsets.BYearBegin),
('BAS-MAY', pandas.tseries.offsets.BYearBegin),
('BAS-NOV', pandas.tseries.offsets.BYearBegin),
('BAS-OCT', pandas.tseries.offsets.BYearBegin),
('BAS-SEP', pandas.tseries.offsets.BYearBegin),
('BM', pandas.tseries.offsets.BusinessMonthEnd),
('BMS', pandas.tseries.offsets.BusinessMonthBegin),
('BQ', pandas.tseries.offsets.BQuarterEnd),
('BQ-APR', pandas.tseries.offsets.BQuarterEnd),
('BQ-AUG', pandas.tseries.offsets.BQuarterEnd),
('BQ-DEC', pandas.tseries.offsets.BQuarterEnd),
('BQ-FEB', pandas.tseries.offsets.BQuarterEnd),
('BQ-JAN', pandas.tseries.offsets.BQuarterEnd),
('BQ-JUL', pandas.tseries.offsets.BQuarterEnd),
('BQ-JUN', pandas.tseries.offsets.BQuarterEnd),
('BQ-MAR', pandas.tseries.offsets.BQuarterEnd),
('BQ-MAY', pandas.tseries.offsets.BQuarterEnd),
('BQ-NOV', pandas.tseries.offsets.BQuarterEnd),
('BQ-OCT', pandas.tseries.offsets.BQuarterEnd),
('BQ-SEP', pandas.tseries.offsets.BQuarterEnd),
('BQS', pandas.tseries.offsets.BQuarterBegin),
('BQS-APR', pandas.tseries.offsets.BQuarterBegin),
('BQS-AUG', pandas.tseries.offsets.BQuarterBegin),
('BQS-DEC', pandas.tseries.offsets.BQuarterBegin),
('BQS-FEB', pandas.tseries.offsets.BQuarterBegin),
('BQS-JAN', pandas.tseries.offsets.BQuarterBegin),
('BQS-JUL', pandas.tseries.offsets.BQuarterBegin),
('BQS-JUN', pandas.tseries.offsets.BQuarterBegin),
('BQS-MAR', pandas.tseries.offsets.BQuarterBegin),
('BQS-MAY', pandas.tseries.offsets.BQuarterBegin),
('BQS-NOV', pandas.tseries.offsets.BQuarterBegin),
('BQS-OCT', pandas.tseries.offsets.BQuarterBegin),
('BQS-SEP', pandas.tseries.offsets.BQuarterBegin),
('Q-APR', pandas.tseries.offsets.QuarterEnd),
('Q-AUG', pandas.tseries.offsets.QuarterEnd),
('Q-DEC', pandas.tseries.offsets.QuarterEnd),
('Q-FEB', pandas.tseries.offsets.QuarterEnd),
('Q-JAN', pandas.tseries.offsets.QuarterEnd),
('Q-JUL', pandas.tseries.offsets.QuarterEnd),
('Q-JUN', pandas.tseries.offsets.QuarterEnd),
('Q-MAR', pandas.tseries.offsets.QuarterEnd),
('Q-MAY', pandas.tseries.offsets.QuarterEnd),
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} | 32 | 2013-10-12T13:33:57Z | 2014-09-11T00:49:44Z | 2014-09-11T00:33:14Z | CONTRIBUTOR | null | related is #2094
related is #6847 (fixes kind and some arg ordering)
related is #7121 (make `sortlevel` a part of `sort_index` by adding level arg)
the sorting API is currently inconsistent and confusing. here is what exists:
Series:
- `sort`: calls `Series.order`, in-place, defaults `quicksort`
- `order`: do the sort on values, return a new object, defaults `mergesort`
- `sort_index`: sort by labels, returns new object
Frame:
- `sort`: calls `sort_index`
- `sort_index`: sorts by the index with no args, otherwise a nested sort of the passed columns
The semantics are different between `Series` and `DataFrame`. In `Series`, `sort` mean in-place, `order` returns a new object. `sort/order` sort on the values, while `sort_index` sorts on the index. For a `DataFrame`, `sort` and `sort_index` are the same and sort on a column/list of columns; `inplace` is a keyword.
Proposed signature of combined methods. We need to break a `Series` API here. because `sort` is an in-place method which is quite inconsistent with everything else.
```
def sort(self, by=None, axis=0, level=None, ascending=True, inplace=False,
kind='quicksort', na_last=True):
```
This is what I think we should do:
- make `Series.sort/order` be the same.
- by can take a column/list of columns (as it can now), or an index name / `index` to provide index sorting (which means sort by the specifiied axis)
- default is `inplace=False` (which is the same as now, except for `Series.sort`).
- `Series.sort_index` does `s.sort('index')`
- `DataFrame.sort_index` does `df.sort('index')`
- eventually deprecate `Series.order`
- add `DataFrame.sort_columns` to perform axis=1 sorting
This does switch the argument to the current `sort_index`, (e.g. axis is currently first), but I think then allows more natural syntax
- `df.sort()` or `df.sort_index()` or `df.sort_index('index')` sort on the index labels
- `df.sort(['A','B'],axis=1)` sort on these columns (allow 'index' here as well to sort on the index too)
- `df.sort_columns()` or `df.sort('columns')` sort on the column labels
- `df.sort_columns()` defaults `axis=1`, so `df.sort_columns(['A','B'])` is equiv of - - `df.sort(['A','B'],axis=1)`
- `s.sort()` sort on the values
- `s.sort('index')` or `s.sort_index()` sort on the series index
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} | 3 | 2013-10-12T16:37:13Z | 2013-10-12T17:45:28Z | 2013-10-12T17:45:28Z | NONE | null | ## Description
When building a DataFrame with specified column names and dtypes, one might expect one of two possible behaviours:
- The column names and dtypes specs are perfectly cromulent, and Pandas goes on to build the object.
- The column names or dtypes don't match the data shape, or the dtypes are badly specified, and Pandas gives an error message.
Instead, I have encountered a segmentation fault.
Now, it is unclear to me whether my column names spec and dtypes are correctly written and if my data is proper too (see example below). But in any case, it should not crash.
## Reproducing
To reproduce, please run:
``` python
import pandas as pd
import datetime as dt
import itertools as it
df_test = pd.DataFrame(data = list(it.repeat((dt.datetime(2001, 1, 1), "aa", 20), 9)),
columns=["A", "B", "C"],
dtype=[("A","datetime64[h]"), ("B","str"), ("C","int32")])
```
## Modes of failure
I have found that the above script always crashes on my machine (see next section for detailed configuration information). It does it in 2 possible ways:
### First mode of failure: hanging
```
Python 2.7.5 (default, Sep 6 2013, 09:55:21)
[GCC 4.8.1 20130725 (prerelease)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import datetime as dt
>>> import itertools as it
>>>
>>> df_test = pd.DataFrame(data = list(it.repeat((dt.datetime(2001, 1, 1), "aa", 20), 9)),
... columns=["A", "B", "C"],
... dtype=[("A","datetime64[h]"), ("B","str"), ("C","int32")])
*** Error in `python': corrupted double-linked list: 0x0000000001bfd8e0 ***
```
After that line, the terminal is dead.
### Second mode of failure: segfault
```
Python 2.7.5 (default, Sep 6 2013, 09:55:21)
[GCC 4.8.1 20130725 (prerelease)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import datetime as dt
>>> import itertools as it
>>>
>>> df_test = pd.DataFrame(data = list(it.repeat((dt.datetime(2001, 1, 1), "aa", 20), 9)),
... columns=["A", "B", "C"],
... dtype=[("A","datetime64[h]"), ("B","str"), ("C","int32")])
*** Error in `python2': double free or corruption (!prev): 0x00000000027161d0 ***
======= Backtrace: =========
/usr/lib/libc.so.6(+0x72ecf)[0x7f2bd7ab9ecf]
/usr/lib/libc.so.6(+0x7869e)[0x7f2bd7abf69e]
/usr/lib/libc.so.6(+0x79377)[0x7f2bd7ac0377]
/home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/numpy/core/multiarray.so(_field_transfer_data_free+0x2e)[0x7f2bd634d47e]
/home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/numpy/core/multiarray.so(+0x9a1c9)[0x7f2bd63a61c9]
/home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/numpy/core/multiarray.so(+0xa4a3a)[0x7f2bd63b0a3a]
/home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/numpy/core/multiarray.so(+0xab0a1)[0x7f2bd63b70a1]
/home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/numpy/core/multiarray.so(+0xb838b)[0x7f2bd63c438b]
/home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/numpy/core/multiarray.so(+0xb8643)[0x7f2bd63c4643]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x4c2f)[0x7f2bd80ec2ef]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x850)[0x7f2bd80ed290]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x4dc9)[0x7f2bd80ec489]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x850)[0x7f2bd80ed290]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x4dc9)[0x7f2bd80ec489]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x850)[0x7f2bd80ed290]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x4dc9)[0x7f2bd80ec489]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x850)[0x7f2bd80ed290]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x4dc9)[0x7f2bd80ec489]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x850)[0x7f2bd80ed290]
/usr/lib/libpython2.7.so.1.0(+0x6dbdd)[0x7f2bd807cbdd]
/usr/lib/libpython2.7.so.1.0(PyObject_Call+0x43)[0x7f2bd8058c13]
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/usr/lib/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x850)[0x7f2bd80ed290]
/usr/lib/libpython2.7.so.1.0(PyEval_EvalCode+0x32)[0x7f2bd80ed392]
/usr/lib/libpython2.7.so.1.0(+0xf708f)[0x7f2bd810608f]
/usr/lib/libpython2.7.so.1.0(PyRun_InteractiveOneFlags+0x140)[0x7f2bd8107fb0]
/usr/lib/libpython2.7.so.1.0(PyRun_InteractiveLoopFlags+0x4e)[0x7f2bd810819e]
/usr/lib/libpython2.7.so.1.0(PyRun_AnyFileExFlags+0x3e)[0x7f2bd81087fe]
/usr/lib/libpython2.7.so.1.0(Py_Main+0xc7f)[0x7f2bd8118c2f]
/usr/lib/libc.so.6(__libc_start_main+0xf5)[0x7f2bd7a68bc5]
python2[0x400741]
======= Memory map: ========
00400000-00401000 r-xp 00000000 08:11 1886483 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/bin/python2
00600000-00601000 r--p 00000000 08:11 1886483 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/bin/python2
00601000-00602000 rw-p 00001000 08:11 1886483 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/bin/python2
012d1000-029b7000 rw-p 00000000 00:00 0 [heap]
7f2bced0d000-7f2bced11000 r-xp 00000000 08:01 923895 /usr/lib/python2.7/lib-dynload/termios.so
7f2bced11000-7f2bcef10000 ---p 00004000 08:01 923895 /usr/lib/python2.7/lib-dynload/termios.so
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7f2bcef13000-7f2bcef26000 r-xp 00000000 08:11 57747 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/json.so
7f2bcef26000-7f2bcf125000 ---p 00013000 08:11 57747 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/json.so
7f2bcf125000-7f2bcf126000 r--p 00012000 08:11 57747 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/json.so
7f2bcf126000-7f2bcf127000 rw-p 00013000 08:11 57747 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/json.so
7f2bcf127000-7f2bcf171000 r-xp 00000000 08:11 57858 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/parser.so
7f2bcf171000-7f2bcf370000 ---p 0004a000 08:11 57858 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/parser.so
7f2bcf370000-7f2bcf371000 r--p 00049000 08:11 57858 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/parser.so
7f2bcf371000-7f2bcf376000 rw-p 0004a000 08:11 57858 /home/agravier/metahome/.local-common/share/python2.7/venvs/finance64/lib/python2.7/site-packages/pandas/parser.so
7f2bcf376000-7f2bcf377000 rw-p 00000000 00:00 0
7f2bcf377000-7f2bcf3d9000 r-xp 00000000 08:01 798526 /usr/lib/libssl.so.1.0.0
7f2bcf3d9000-7f2bcf5d8000 ---p 00062000 08:01 798526 /usr/lib/libssl.so.1.0.0
7f2bcf5d8000-7f2bcf5dc000 r--p 00061000 08:01 798526 /usr/lib/libssl.so.1.0.0
7f2bcf5dc000-7f2bcf5e3000 rw-p 00065000 08:01 798526 /usr/lib/libssl.so.1.0.0
7f2bcf5e3000-7f2bcf5eb000 r-xp 00000000 08:01 923889 /usr/lib/python2.7/lib-dynload/_ssl.soAborted (core dumped)
```
## Configuration information
Python:
```
Python 2.7.5
```
uname -a:
```
Linux agravier-archvm 3.10.10-1-ARCH #1 SMP PREEMPT Fri Aug 30 11:30:06 CEST 2013 x86_64 GNU/Linux
```
pip freeze --local:
```
QSTK==0.2.6
matplotlib==1.3.0
nose==1.3.0
numpy==1.7.1
pandas==0.12.0
pyparsing==2.0.1
python-dateutil==2.1
pytz==2013.7
scikit-learn==0.14.1
scipy==0.12.1
six==1.4.1
yolk==0.4.3
```
## Concluding remarks
Note that in the line that I use to create the data `list(it.repeat((dt.datetime(2001, 1, 1), "aa", 20), 9))`, the number of rows has an influence on whether Python crashes. If less than 9, there is the output:
```
Python 2.7.5 (default, Sep 6 2013, 09:55:21)
[GCC 4.8.1 20130725 (prerelease)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import datetime as dt
>>> import itertools as it
>>>
>>> df_test = pd.DataFrame(data = list(it.repeat((dt.datetime(2001, 1, 1), "aa", 20), 8)),
... columns=["A", "B", "C"],
... dtype=[("A","datetime64[h]"), ("B","str"), ("C","int32")])
>>> df_test
A B C
0 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
1 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
2 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
3 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
4 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
5 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
6 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
7 2001-01-01 00:00:00 (1972-11-04 17:00:00, , 0) (1970-01-01 20:00:00, , 20)
```
Now, this output doesn't make much sense to me, it doesn't seem to respect the dtype spec that I give, but it's very possible that I don't understand the dtype spec well and that it's actually perfectly sensible output.
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```
pd.Categorical(pd.Series([1,1,1,1])).describe()
```
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} | 0 | 2013-10-12T21:16:16Z | 2014-06-22T17:39:20Z | 2013-10-12T21:28:42Z | CONTRIBUTOR | null | BUG: Bug in describe on duplicate frames
related to #4421
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} | 5 | 2013-10-12T21:39:54Z | 2013-10-12T23:53:46Z | 2013-10-12T23:53:46Z | NONE | null | Hi,
I am using pandas 0.12.0 and numpy 1.7.1.
Not sure if this is the expected functionality or not. I thought if coerce = True or errors='ignore' I wouldn't get this result:
``` python
In [372]: td = pd.Series(['May 04', 'Jun 02', 'Dec 11'], index=[1,2,3])
In [373]: td.apply(pd.to_datetime, format='%b %y', errors='ignore', coerce=True)
Out[373]:
1 2004-05-01 00:00:00
2 2002-06-01 00:00:00
3 2011-12-01 00:00:00
dtype: datetime64[ns]
In [374]: td = pd.Series(['May 04', 'Jun 02', ''], index=[1,2,3])
In [375]: td.apply(pd.to_datetime, format='%b %y', errors='ignore', coerce=True)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-375-0c44a3c36f8e> in <module>()
----> 1 td.apply(pd.to_datetime, format='%b %y', errors='ignore', coerce=True)
C:\Python27\lib\site-packages\pandas\core\series.pyc in apply(self, func, convert_dtype, args, **kwds)
2534 values = lib.map_infer(values, lib.Timestamp)
2535
-> 2536 mapped = lib.map_infer(values, f, convert=convert_dtype)
2537 if isinstance(mapped[0], Series):
2538 from pandas.core.frame import DataFrame
C:\Python27\lib\site-packages\pandas\lib.pyd in pandas.lib.map_infer (pandas\lib.c:42320)()
C:\Python27\lib\site-packages\pandas\core\series.pyc in <lambda>(x)
2523
2524 if kwds or args and not isinstance(func, np.ufunc):
-> 2525 f = lambda x: func(x, *args, **kwds)
2526 else:
2527 f = func
C:\Python27\lib\site-packages\pandas\tseries\tools.pyc in to_datetime(arg, errors, dayfirst, utc, box, format, coerce, u
nit)
127 return _convert_listlike(arg, box=box)
128
--> 129 return _convert_listlike(np.array([ arg ]), box=box)[0]
130
131 class DateParseError(ValueError):
C:\Python27\lib\site-packages\pandas\tseries\tools.pyc in _convert_listlike(arg, box)
115 return DatetimeIndex._simple_new(values, None, tz=tz)
116 except (ValueError, TypeError):
--> 117 raise e
118
119 if arg is None:
ValueError: time data '' does not match format '%b %y'
In [376]:
```
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} | 2 | 2013-10-12T22:10:46Z | 2015-09-11T14:28:24Z | 2015-09-11T14:28:23Z | CONTRIBUTOR | null | e.g. here's wrong behavior on current master
``` python
In [1]: import pandas as pd
In [2]: ind = pd.Index([1, 2, 3], dtype='int64')
In [3]: ind
Out[3]: Index([1, 2, 3], dtype='object')
```
Problem is ordering of how passed dtypes work in Index constructor. Make sure this gets fixed in 0.14.
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} | 0 | 2013-10-12T22:13:49Z | 2014-06-24T06:53:46Z | 2013-10-12T23:53:46Z | CONTRIBUTOR | null | closes #5195
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} | 3 | 2013-10-12T22:20:12Z | 2019-07-29T14:38:21Z | 2014-03-17T14:18:12Z | CONTRIBUTOR | null | Me being dumb discovered this:
``` python
In [209]: bug = pd.DataFrame({"A": [1, 2], 'B': ['c', 'd']})
In [210]: bug
Out[210]:
A B
0 1 c
1 2 d
In [211]: bug.eval('A + B')
# stack trace truncated ...
RuntimeError: maximum recursion depth exceeded
```
cc @cpcloud
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} | 26 | 2013-10-12T22:26:09Z | 2017-04-05T02:05:43Z | 2013-10-17T20:49:58Z | CONTRIBUTOR | null | API: Series should respect index labels. This would be incompatible, but there hasn't been any official release since `isin` was merged.
Will close #4421. Still a WIP for now. Needs more testing.
Thoughts on adding an argument to ignore the index? i.e.
``` python
In [150]: df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, np.nan, 4, 4]}, index=['a','b','c','d'])
In [151]: s = pd.Series([1, 3, 11, 12], index=['a','b','c','d'])
In [152]: df.isin(s)
Out[152]:
A B
a True False
b False False
c False False
d False False
In [152]: df.isin(s, ignore_index=True)
Out[152]:
A B
a True False
b False False
c True False
d False False
```
I would say there's no need for an `ignore_index` since you could just do `df.isin(s.values)`.
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https://api.github.com/repos/pandas-dev/pandas/issues/5200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5200/events | https://github.com/pandas-dev/pandas/issues/5200 | 20,918,684 | MDU6SXNzdWUyMDkxODY4NA== | 5,200 | pd.date_range Attribute_Error | {
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} | [] | closed | false | null | [] | null | 9 | 2013-10-12T22:51:46Z | 2013-10-12T23:52:04Z | 2013-10-12T23:46:56Z | NONE | null | I'm using pandas 0.12 and numpy 1.7.1.
I'm trying to calculate the number of business days between two columns of dates. Not all rows have valid dates though, and pd.date_range does not have an ignore errors option, so I'm getting this error:
``` python
In [439]: td = pd.DataFrame({'a':[pd.Timestamp('2010-1-1'), pd.Timestamp('2010-2-1')], 'b':[pd.Timestamp('2010-1-10'), p
d.Timestamp('2010-2-9')]})
In [440]: td.apply(lambda row: len(pd.date_range(row['a'], row['b'], freq='B')), axis=1)
Out[440]:
0 6
1 7
dtype: int64
In [441]: td = pd.DataFrame({'a':[pd.Timestamp('2010-1-1'), pd.Timestamp('2010-2-1')], 'b':[pd.Timestamp('2010-1-10'), p
d.NaT]})
In [442]: td.apply(lambda row: len(pd.date_range(row['a'], row['b'], freq='B')), axis=1)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-442-30b1500c1594> in <module>()
----> 1 td.apply(lambda row: len(pd.date_range(row['a'], row['b'], freq='B')), axis=1)
C:\Python27\lib\site-packages\pandas\core\frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
4414 return self._apply_raw(f, axis)
4415 else:
-> 4416 return self._apply_standard(f, axis)
4417 else:
4418 return self._apply_broadcast(f, axis)
C:\Python27\lib\site-packages\pandas\core\frame.pyc in _apply_standard(self, func, axis, ignore_failures)
4489 # no k defined yet
4490 pass
-> 4491 raise e
4492
4493
AttributeError: ("'NaTType' object has no attribute 'tz'", u'occurred at index 1')
In [443]:
```
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