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} | 1 | "2012-04-05T18:54:21Z" | "2012-04-09T14:25:01Z" | "2012-04-09T14:25:01Z" | NONE | null | If the row and/or column labels are not present in the DataFrame, the lookup() method returns ... whatever it wants, apparently. I would expect either a NaN or an exception, personally - but whatever the result, it shouldn't be the wrong piece of data.
> > > df = pandas.DataFrame({'A': pandas.Series(['A', 'B', 'C', 'D', 'E'], index=range(5))})
> > >
> > > df.lookup([2], ['A']) # Correct
> > > array([C], dtype=object)
> > >
> > > df.lookup([10], ['A']) # 10 is not present in the index, so we get .. the last value in A?
> > > array([E], dtype=object)
> > >
> > > df.lookup([3], ['B']) # There is no column 'B', so we get ... the value from the last column (A) in that row?
> > > array([C], dtype=object)
> > >
> > > df.lookup([10], ['B']) # Neither row or column label is valid, so we get ... I can't rationalize this one.
> > > array([D], dtype=object)
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} | 0 | "2012-04-06T01:18:37Z" | "2012-04-06T04:39:46Z" | "2012-04-06T04:39:46Z" | MEMBER | null | The way that matplotlib is being told about the x ticks causes autofmt_xdate to now work. Figure out how to have sane, attractive default tick labeling in time series plots
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} | 0 | "2012-04-06T14:34:41Z" | "2014-06-19T01:32:15Z" | "2012-04-07T14:40:01Z" | CONTRIBUTOR | null | factory function for HDFStore usable in with statement and auto-closes
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} | 1 | "2012-04-06T18:32:47Z" | "2014-06-13T23:56:36Z" | "2012-04-07T14:29:33Z" | CONTRIBUTOR | null | Hi,
The default branch relates to rpy2 2.3.x for some time. It is safer to stick to the 2.2.x series if you developed code against it.
L.
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} | 1 | "2012-04-07T18:27:31Z" | "2014-06-24T10:17:36Z" | "2012-04-10T03:53:33Z" | CONTRIBUTOR | null | I added a try/except catch for the lack of an installed openpyxl. The user gets a nice message that she needs to install it or to save the .xlsx file as .xls.
I also added a test for it and a test.xlsx file that I saved from test.xls.
Furthermore I noticed an ugly break of the DRY principle in ExcelFile.parse and gave an alternative with a nice dictionary that provides the handle for the required method depending on the 'use_xlsx' flag.
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} | 0 | "2012-04-07T20:43:40Z" | "2012-04-12T00:47:25Z" | "2012-04-12T00:46:33Z" | CONTRIBUTOR | null | new function should take in new values for exogenous variables and produced predicted y values.
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"""
Hello All,
Working on 0.7.2.
I have a DataFrame with a MultiIndex (2 levels).
Because my second level contains non ascii character I can't use the
methods ;
- DataFrame.index.levels
- DataFrame.get_level_values(1)
Is it a known issue?
Thanks!
"""
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} | 0 | "2012-04-07T21:34:28Z" | "2012-04-10T03:51:04Z" | "2012-04-10T03:51:04Z" | CONTRIBUTOR | null | df = DataFrame(np.random.randn(100, 5)*1000000)
df.plot() looks the same as df.plot(logy=True)
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} | 1 | "2012-04-08T16:25:36Z" | "2012-04-11T22:09:58Z" | "2012-04-11T22:09:58Z" | CONTRIBUTOR | null | Right now DataFrame.hist plots histogram matrix and axis labels all overlap.
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} | 0 | "2012-04-08T16:46:29Z" | "2012-04-10T18:29:12Z" | "2012-04-10T18:29:12Z" | CONTRIBUTOR | null | per pydata email:
---
I created a dataframe with multi index like this:
a b c
y m d
2000 1 1 1 2 3
2 7 8 9
3 4 5 6
...
it doesn't work to do this: df.ix[(2000,1,1), ['a','b']]
## it gives KeyError = 1
in _NDFrameIndexer._getitem_tuple, the return value of the first ix._getitem_axis(key, axis=i) call returns a Series so the second call with axis=1 fails with KeyError=1
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} | 0 | "2012-04-09T04:14:44Z" | "2014-02-16T21:28:19Z" | "2014-02-16T21:28:19Z" | MEMBER | null | something like
`array([82.87, 82.1, 84.25, 84.53, 85.9, 85.9, 86.95, 88.13, 89.49, 89.76,
90.76, 88.99, 89.42, 89.0, 89.49, 88.95, 89.09, 88.65, 88.52, 88.98], dtype=object)`
from the mailing list
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} | 0 | "2012-04-10T12:46:41Z" | "2012-04-10T16:04:07Z" | "2012-04-10T16:04:07Z" | CONTRIBUTOR | null | In [68]: a = Series(['a', 'b', 'c'])
In [69]: b = Series(['b', 'a'])
In [70]: a > b
Warning: Program '/home/chang/epd/bin/ipython' crashed.
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} | 0 | "2012-04-10T15:24:30Z" | "2012-04-10T15:24:30Z" | "2012-04-10T16:19:33Z" | CONTRIBUTOR | null | additional test coverage for release
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```
In [33]: DataFrame({'a': [0, 1], 'b': [True, False]}).groupby('a').agg(any)
Out[33]:
b
a
0 True
1 True
```
pandas.**version** == '0.7.2'
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https://api.github.com/repos/pandas-dev/pandas/issues/1024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1024/events | https://github.com/pandas-dev/pandas/issues/1024 | 4,063,797 | MDU6SXNzdWU0MDYzNzk3 | 1,024 | Aggregation with 'any' always returns True | {
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} | [] | closed | false | null | [] | null | 1 | "2012-04-11T12:28:58Z" | "2012-04-11T13:36:30Z" | "2012-04-11T13:36:30Z" | CONTRIBUTOR | null | Using 'any' as aggregation function (and 'all' as well) to find groups where at least one value is True seems like an intuitive way, but it doesn't work right now:
```
In [12]: DataFrame({'a': [0, 1], 'b': [True, False]}).groupby('a').agg(any)
Out[12]:
b
a
0 True
1 True
```
This is the case where this issue came up: I have a dataset with seasons (continuous integers) and a boolean column (e.g. called 'success'). Now I want to select all data from seasons that have at least one True in the boolean column. Right now the best way to do it that I can think of would be:
1. Select rows where success == True.
2. Group them by season and aggregate with a function returning True regardless of the input.
3. Join the result back into the dataset on season.
4. Perform fillna(False).
Being able to just apply 'any' would cut the process down to two steps. Also, if there is another, better way to do this operation, I’d be grateful to hear about it.
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} | 2 | "2012-04-11T15:50:35Z" | "2014-06-17T01:00:55Z" | "2012-04-11T22:09:57Z" | CONTRIBUTOR | null | TST: test cases for both Series and DataFrame histogram
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} | 1 | "2012-04-11T16:42:23Z" | "2012-05-07T18:02:34Z" | "2012-05-07T18:02:34Z" | NONE | null | Although it is possible to index MultiIndexed DataFrames with multiple index columns, one or more of which have a compound type, it is not possible to index an Indexed DataFrame with a compound type for its column, nor is it possible to index a MultiIndexed Dataframe with a single column that has a compound type.
tl;dr - I can't index a DataFrame with a namedtuple, even though I can create one.
In the first example, I try to index a dataframe with a namedtuple with a regular Index, which fails.
In the second example, I index a dataframe with a tuple of namedtuples (MultiIndex), which succeeds.
In the third example, I try to index a dataframe with a length-1 tuple of namedtuples, again with a MultiIndex, which fails.
```
from collections import namedtuple
import pandas
# First example
"""
>>> IndexType = namedtuple("IndexType", ["a", "b"])
>>> idx1 = IndexType("foo", "bar")
>>> idx2 = IndexType("baz", "bof")
>>> index = pandas.Index([idx1, idx2], name="composite_index")
>>> index
Index([IndexType(a='foo', b='bar'), IndexType(a='baz', b='bof')], dtype=object)
>>> df = pandas.DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"])
>>> df
A B
composite_index..................
IndexType(a='foo', b='bar') 1 2
IndexType(a='baz', b='bof') 3 4
>>> df.ix[IndexType("foo", "bar")]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._getitem_tuple(key)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._getitem_lowerdim(tup)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
section = self._getitem_axis(key, axis=i)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._get_label(idx, axis=0)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self.obj.xs(label, axis=axis, copy=True)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
loc = self.index.get_loc(key)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._engine.get_loc(key)
File "engines.pyx", line 101, in pandas._engines.DictIndexEngine.get_loc (pandas/src/engines.c:2498)
File "engines.pyx", line 108, in pandas._engines.DictIndexEngine.get_loc (pandas/src/engines.c:2460)
KeyError: 'foo'
"""
# Second example
"""
>>> mult_index = pandas.MultiIndex.from_tuples([(idx1, idx2)], names=["comp_1", "comp_2"])
>>> mult_index
MultiIndex([(IndexType(a='foo', b='bar'), IndexType(a='baz', b='bof'))], dtype=object)
>>> df = pandas.DataFrame([(1, 2, 3, 4)], index=mult_index, columns=["A", "B", "C", "D"])
>>> df
A B C D
comp_1 comp_2.................................
IndexType(a='foo', b='bar') IndexType(a='baz', b='bof') 1 2 3 4
>>> df.ix[(IndexType("foo", "bar"), IndexType("baz", "bof"))]
A 1
B 2
C 3
D 4
Name: (IndexType(a='foo', b='bar'), IndexType(a='baz', b='bof'))
"""
# Third example
"""
>>> index = pandas.MultiIndex.from_tuples([(IndexType("foo", "bar"),), (IndexType("baz", "bof"),)], names=["ind#
>>> index
Index([IndexType(a='foo', b='bar'), IndexType(a='baz', b='bof')], dtype=object
>>> df = pandas.DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"])
>>> df
A B
index............................
IndexType(a='foo', b='bar') 1 2
IndexType(a='baz', b='bof') 3 4
>>> df.ix[IndexType("foo", "bar")]
Traceback (most recent call last):
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._getitem_tuple(key)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._getitem_lowerdim(tup)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
section = self._getitem_axis(key, axis=i)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._get_label(idx, axis=0)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self.obj.xs(label, axis=axis, copy=True)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
loc = self.index.get_loc(key)
File "/Network/Cluster/home/echlebek/.virtualenvs/pandas/lib/python2.6/site-packages/pandas-0.7.3.dev_3d4d5af#
return self._engine.get_loc(key)
File "engines.pyx", line 101, in pandas._engines.DictIndexEngine.get_loc (pandas/src/engines.c:2498)
File "engines.pyx", line 108, in pandas._engines.DictIndexEngine.get_loc (pandas/src/engines.c:2460)
KeyError: 'foo'
>>> df.ix[(IndexType("foo", "bar"),)]
A B
foo NaN NaN
bar NaN NaN
"""
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1027 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1027/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1027/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1027/events | https://github.com/pandas-dev/pandas/pull/1027 | 4,070,154 | MDExOlB1bGxSZXF1ZXN0MTE0NTMzMQ== | 1,027 | ENH: added OLS.predict method | {
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GH #1008
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https://api.github.com/repos/pandas-dev/pandas/issues/1034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1034/events | https://github.com/pandas-dev/pandas/issues/1034 | 4,085,610 | MDU6SXNzdWU0MDg1NjEw | 1,034 | test_xlsx_table failure | {
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```
======================================================================
FAIL: test_xlsx_table (pandas.io.tests.test_parsers.TestParsers)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/build-area/pandas-0.7.2+git111-gf7b9139/debian/python-pandas/usr/lib/python2.6/dist-packages/pandas/io/tests/test_parsers.py", line 262, in test_xlsx_table
assert_frame_equal(df, df2)
File "/home/yoh/deb/gits/pkg-exppsy/build-area/pandas-0.7.2+git111-gf7b9139/debian/python-pandas/usr/lib/python2.6/dist-packages/pandas/util/testing.py", line 131, in assert_frame_equal
assert_series_equal(series, right[col])
File "/home/yoh/deb/gits/pkg-exppsy/build-area/pandas-0.7.2+git111-gf7b9139/debian/python-pandas/usr/lib/python2.6/dist-packages/pandas/util/testing.py", line 124, in assert_series_equal
assert(left.index.equals(right.index))
AssertionError
----------------------------------------------------------------------
Ran 1544 tests in 149.729s
FAILED (SKIP=8, failures=1)
```
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floats ints strings bools objects
0 -1.478427 0 foo False 2000-01-03 00:00:00
1 0.524988 1 bar True 2000-01-04 00:00:00
2 0.404705 2 foo True 2000-01-05 00:00:00
3 0.577046 3 bar True 2000-01-06 00:00:00
4 -1.715002 4 foo False 2000-01-07 00:00:00
5 -1.039268 5 bar False 2000-01-10 00:00:00
6 -0.370647 6 foo False 2000-01-11 00:00:00
7 -1.157892 7 bar False 2000-01-12 00:00:00
8 -1.344312 8 foo False 2000-01-13 00:00:00
9 0.844885 9 bar True 2000-01-14 00:00:00
In [67]: foo.reindex(range(15))
Out[67]:
floats ints strings bools objects
0 -1.478427 0 foo False 9.468576e+14
1 0.524988 1 bar True 9.469440e+14
2 0.404705 2 foo True 9.470304e+14
3 0.577046 3 bar True 9.471168e+14
4 -1.715002 4 foo False 9.472032e+14
5 -1.039268 5 bar False 9.474624e+14
6 -0.370647 6 foo False 9.475488e+14
7 -1.157892 7 bar False 9.476352e+14
8 -1.344312 8 foo False 9.477216e+14
9 0.844885 9 bar True 9.478080e+14
10 NaN NaN NaN NaN NaN
11 NaN NaN NaN NaN NaN
12 NaN NaN NaN NaN NaN
13 NaN NaN NaN NaN NaN
14 NaN NaN NaN NaN NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1039/events | https://github.com/pandas-dev/pandas/pull/1039 | 4,091,335 | MDExOlB1bGxSZXF1ZXN0MTE1NDU0Nw== | 1,039 | BUG: Allow indexing with namedtuple, close #1026 | {
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} | [] | closed | false | null | [] | null | 1 | "2012-04-12T20:32:05Z" | "2014-06-18T10:56:30Z" | "2012-04-14T18:22:07Z" | CONTRIBUTOR | null | This patch allow indexing with namedtuple and other tuple subclasses while retaining the comma-separated **getitem** functionality.
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https://api.github.com/repos/pandas-dev/pandas/issues/1040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1040/events | https://github.com/pandas-dev/pandas/issues/1040 | 4,092,330 | MDU6SXNzdWU0MDkyMzMw | 1,040 | TestGroupBy failures and docs don't build on Mac OS X Lion | {
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} | [] | closed | false | null | [] | null | 5 | "2012-04-12T21:25:41Z" | "2012-04-13T14:07:09Z" | "2012-04-13T13:29:46Z" | CONTRIBUTOR | null | I just installed pandas from git (9d0949396a8533d9fb9b0a6d0fb90a9f0ac16b23) on Mac OS X Lion (XCode 4.3).
I'm using Macports python 2.7, numpy 1.7.0.dev-3503c5f and cython 449e3b58a9fe4826179b7208241ee991e7360a7b
There's one test error and two failures (see details below).
Also I can't generate the docs (see details below).
During the build the clang compiler was used and there were over 100 warnings for tseries.
I think this is unrelated, but I can paste a build log if you are interested.
```
======================================================================
ERROR: test_cython_fail_agg (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/tests/test_groupby.py", line 1126, in test_cython_fail_agg
summed = grouped.sum()
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 307, in sum
return self.aggregate(lambda x: np.sum(x, axis=self.axis))
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 928, in aggregate
result = self._aggregate_named(func_or_funcs, *args, **kwargs)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 989, in _aggregate_named
output = func(grp, *args, **kwargs)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 307, in <lambda>
return self.aggregate(lambda x: np.sum(x, axis=self.axis))
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/numpy/core/fromnumeric.py", line 1489, in sum
return sum(axis=axis, dtype=dtype, out=out)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/series.py", line 972, in sum
return nanops.nansum(self.values, skipna=skipna)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/nanops.py", line 43, in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/nanops.py", line 67, in _nansum
the_sum = values.sum(axis)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/numpy/core/_methods.py", line 18, in _sum
out=out, skipna=skipna, keepdims=keepdims)
TypeError: unsupported operand type(s) for +: 'bool' and 'str'
======================================================================
FAIL: test_series_agg_multi_pure_python (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/tests/test_groupby.py", line 466, in test_series_agg_multi_pure_python
result = data.groupby(['A', 'B']).agg(bad)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 245, in agg
return self.aggregate(func, *args, **kwargs)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 1177, in aggregate
return self._python_agg_general(arg, *args, **kwargs)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 333, in _python_agg_general
result, counts = self.grouper.agg_series(obj, agg_func)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 619, in agg_series
return self._aggregate_series_pure_python(obj, func)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 649, in _aggregate_series_pure_python
res = func(group)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/core/groupby.py", line 327, in <lambda>
agg_func = lambda x: func(x, *args, **kwargs)
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/tests/test_groupby.py", line 463, in bad
assert(len(x.base) == len(x))
AssertionError
======================================================================
FAIL: test_wrap_aggregated_output_multindex (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/tests/test_groupby.py", line 1024, in test_wrap_aggregated_output_multindex
self.assertEqual(len(agged2.columns) + 1, len(df.columns))
AssertionError: 9 != 10
----------------------------------------------------------------------
Ran 1544 tests in 291.297s
FAILED (SKIP=7, errors=1, failures=2)
```
```
$ python make.py html
Running Sphinx v1.1.3
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sphinx/cmdline.py", line 188, in main
warningiserror, tags)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sphinx/application.py", line 102, in __init__
confoverrides or {}, self.tags)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sphinx/config.py", line 216, in __init__
exec code in config
File "/Users/deil/code/pandas/doc/source/conf.py", line 71, in <module>
import pandas
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/__init__.py", line 10, in <module>
import pandas._tseries as lib
File "numpy.pxd", line 177, in init pandas._tseries (pandas/src/tseries.c:119198)
ValueError: numpy.ndarray has the wrong size, try recompiling
> /Users/deil/code/pandas/doc/numpy.pxd(177)init pandas._tseries (pandas/src/tseries.c:119198)()
(Pdb)
```
Next I installed statsmodels and tried to build the docs there.
This fails complaining about not finding pandas._tseries ...
```
reading sources... [ 29%] dev/internal
Exception occurred:
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/__init__.py", line 10, in <module>
import pandas._tseries as lib
AttributeError: 'module' object has no attribute '_tseries'
```
... although it is there:
``` bash
-rwxr-xr-x 1 deil staff 278B Apr 12 22:36 /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/_tseries.py
-rw-r--r-- 1 deil staff 682B Apr 12 22:36 /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/_tseries.pyc
-rwxr-xr-x 1 deil staff 1.4M Apr 12 22:36 /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.7.3-py2.7-macosx-10.7-x86_64.egg/pandas/_tseries.so
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1043 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1043/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1043/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1043/events | https://github.com/pandas-dev/pandas/issues/1043 | 4,096,540 | MDU6SXNzdWU0MDk2NTQw | 1,043 | Add first / last optimized groupby functions | {
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|
https://api.github.com/repos/pandas-dev/pandas/issues/1044 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1044/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1044/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1044/events | https://github.com/pandas-dev/pandas/issues/1044 | 4,096,836 | MDU6SXNzdWU0MDk2ODM2 | 1,044 | Make partial slicing work with DatetimeIndex | {
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```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/Dropbox/talks/notebooks/<ipython-input-31-6c807131b1d0> in <module>()
----> 1 close_px.ix['2000':'2003']
/home/wesm/code/pandas/pandas/core/indexing.pyc in __getitem__(self, key)
33 return self._getitem_tuple(key)
34 else:
---> 35 return self._getitem_axis(key, axis=0)
36
37 def _get_label(self, label, axis=0):
/home/wesm/code/pandas/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
165 labels = self.obj._get_axis(axis)
166 if isinstance(key, slice):
--> 167 return self._get_slice_axis(key, axis=axis)
168 elif _is_list_like(key) and not (isinstance(key, tuple) and
169 isinstance(labels, MultiIndex)):
/home/wesm/code/pandas/pandas/core/indexing.pyc in _get_slice_axis(self, slice_obj, axis)
361 else:
362 try:
--> 363 i, j = labels.slice_locs(start, stop)
364 slicer = slice(i, j, slice_obj.step)
365 except Exception:
/home/wesm/code/pandas/pandas/core/index.pyc in slice_locs(self, start, end)
909 else:
910 try:
--> 911 end_slice = self.get_loc(end) + 1
912 except KeyError:
913 if self.is_monotonic:
TypeError: unsupported operand type(s) for +: 'slice' and 'int'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1045 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1045/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1045/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1045/events | https://github.com/pandas-dev/pandas/issues/1045 | 4,097,701 | MDU6SXNzdWU0MDk3NzAx | 1,045 | NumPy reductions don't work with convert / TimeGrouper | {
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} | 0 | "2012-04-13T04:37:55Z" | "2012-04-14T20:09:47Z" | "2012-04-14T20:09:47Z" | MEMBER | null | ```
(1 + returns).convert('BM', how=np.cumprod)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/code/rapidquant/<ipython-input-22-c8b05a51e175> in <module>()
----> 1 (1 + returns).convert('BM', how=np.cumprod)
/home/wesm/code/pandas/pandas/core/generic.pyc in convert(self, rule, method, how, axis, as_index, closed, label)
176 how = translate_grouping(how)
177
--> 178 result = grouped.agg(how)
179 else:
180 # upsampling
/home/wesm/code/pandas/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
245 See docstring for aggregate
246 """
--> 247 return self.aggregate(func, *args, **kwargs)
248
249 def _iterate_slices(self):
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs)
1195 return self._python_agg_general(func_or_funcs, *args, **kwargs)
1196 except Exception:
-> 1197 result = self._aggregate_named(func_or_funcs, *args, **kwargs)
1198
1199 index = Index(sorted(result), name=self.grouper.names[0])
/home/wesm/code/pandas/pandas/core/groupby.pyc in _aggregate_named(self, func, *args, **kwargs)
1266 result = {}
1267
-> 1268 for name in self.grouper:
1269 grp = self.get_group(name)
1270 grp.name = name
/home/wesm/code/pandas/pandas/core/groupby.pyc in __iter__(self)
448
449 def __iter__(self):
--> 450 return iter(self.indices)
451
452 def numkeys(self):
/home/wesm/code/pandas/pandas/_tseries.so in pandas._tseries.cache_readonly.__get__ (pandas/src/tseries.c:111266)()
/home/wesm/code/pandas/pandas/core/groupby.pyc in indices(self)
491 @cache_readonly
492 def indices(self):
--> 493 if len(self.groupings) == 1:
494 return self.groupings[0].indices
495 else:
AttributeError: 'TimeGrouper' object has no attribute 'groupings'
(1 + returns).convert('BM', how=np.cumprod)
```
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} | 0 | "2012-04-13T04:53:29Z" | "2012-04-13T05:22:06Z" | "2012-04-13T05:22:06Z" | MEMBER | null | Improperly boxing time stamps:
```
%prun -s cumulative result = left + right
1000435 function calls (1000434 primitive calls) in 4.870 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 4.870 4.870 <string>:1(<module>)
1 0.000 0.000 4.870 4.870 series.py:62(wrapper)
1 0.000 0.000 4.866 4.866 series.py:1824(align)
1 0.000 0.000 4.856 4.856 index.py:1561(join)
1 0.001 0.001 4.856 4.856 index.py:710(join)
1 0.254 0.254 4.778 4.778 index.py:1542(union)
1 0.076 0.076 4.524 4.524 index.py:418(union)
1 0.000 0.000 2.767 2.767 index.py:1830(__iter__)
1 0.000 0.000 2.767 2.767 index.py:1464(asobject)
1 0.203 0.203 2.767 2.767 datetools.py:34(_dt_box_array)
500000 0.215 0.000 2.564 0.000 datetools.py:41(<lambda>)
500000 2.349 0.000 2.349 0.000 datetools.py:28(_dt_box)
1 0.000 0.000 0.901 0.901 index.py:473(_wrap_union_result)
1 0.010 0.010 0.901 0.901 index.py:1197(__new__)
18 0.890 0.049 0.890 0.049 {numpy.core.multiarray.array}
16 0.000 0.000 0.890 0.056 numeric.py:167(asarray)
1 0.743 0.743 0.743 0.743 {method 'sort' of 'list' objects}
3 0.000 0.000 0.108 0.036 index.py:597(get_indexer)
3 0.106 0.035 0.106 0.035 {method 'get_indexer' of 'pandas._engines.DatetimeEngine' objects}
2 0.000 0.000 0.010 0.005 series.py:1860(_reindex_indexer)
2 0.000 0.000 0.009 0.005 common.py:170(take_1d)
2 0.007 0.004 0.007 0.004 {pandas._tseries.take_1d_float64}
1 0.004 0.004 0.004 0.004 {method 'nonzero' of 'numpy.ndarray' objects}
2 0.000 0.000 0.003 0.001 index.py:1866(equals)
2 0.003 0.001 0.003 0.001 numeric.py:1927(array_equal)
2 0.000 0.000 0.002 0.001 common.py:666(_ensure_int32)
2 0.002 0.001 0.002 0.001 {method 'astype' of 'numpy.ndarray' objects}
1 0.000 0.000 0.002 0.002 series.py:47(na_op)
1 0.002 0.002 0.002 0.002 {operator.add}
13 0.000 0.000 0.001 0.000 index.py:1858(dtype)
18 0.000 0.000 0.001 0.000 _internal.py:180(_datetimestring)
18 0.001 0.000 0.001 0.000 {method 'match' of '_sre.SRE_Pattern' objects}
1 0.001 0.001 0.001 0.001 {method 'take' of 'numpy.ndarray'
```
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} | 4 | "2012-04-13T11:27:57Z" | "2018-02-01T08:10:24Z" | "2012-06-25T00:04:40Z" | NONE | null | I'd like to be able to write csv to temporary files and not have to worry about cleaning them up after they are sent to a user. Is it possible to have an optional file parameter on the to_csv methods, which would be used instead of the string filename? This could then be any file-like object (or even any suitable io object?)
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} | 2 | "2012-04-13T12:50:06Z" | "2016-10-12T23:02:53Z" | "2012-05-07T16:36:05Z" | CONTRIBUTOR | null | I get a segfault when running:
pandas.DataFrame([1,2,3]).groupby([]).groups
As an aside, in my scenario (which I don't know how general it is) the user can supply the grouping. If no grouping should take place (i.e. groupby([])) I would like .groupby() to return the whole data, instead there is a:
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _get_compressed_labels(self)
547 group_index = get_group_index(all_labels, self.shape)
548 else:
--> 549 group_index = all_labels[0]
550 comp_ids, obs_group_ids = _compress_group_index(group_index)
551 return comp_ids, obs_group_ids
IndexError: list index out of range
I'm not sure whether it would be sensible to change the behavior so that the whole DataFrame is returned, but to me it would be more useful than raising an error.
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} | 2 | "2012-04-13T16:51:19Z" | "2012-09-18T14:44:43Z" | "2012-09-18T14:44:43Z" | NONE | null | Tested with Pandas 0.7.3
I have a IPython notebook that explores this issue (basically it only seems to occur when you add a float32 series to the DataFrame **after** creating it, no other datatype I tested seems to trigger this bug). Please contact me if you want the notebook since I don't seem to be able to attach files to this issue. The simplest way to replicate it is via the following lines:
```
import numpy as np
import pandas
a = np.random.randint(0, 5, 100)
df = pandas.DataFrame({'a': a})
s = pandas.Series(np.random.random(5), name='md')
df.join(s, on='a') # this is OK
df['b'] = np.random.randint(0, 5, 100)
df.join(s, on='a') # this is still OK
df['c'] = np.random.randint(0, 5, 100).astype('f')
df.join(s, on='a') # this fails
```
The traceback is:
```
ValueError Traceback (most recent call last)
C:\Users\brad\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Enthought\<ipython-input-1-454edb5f6546> in <module>()
8 df.join(s, on='a') # this is still OK
9 df['c'] = np.random.randint(0, 5, 100).astype('f')
---> 10 df.join(s, on='a') # this fails
11
C:\Python27\lib\site-packages\pandas\core\frame.pyc in join(self, other, on, how, lsuffix, rsuffix, sort)
3285 # For SparseDataFrame's benefit
3286 return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
-> 3287 rsuffix=rsuffix, sort=sort)
3288
3289 def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
C:\Python27\lib\site-packages\pandas\core\frame.pyc in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)
3298 return merge(self, other, left_on=on, how=how,
3299 left_index=on is None, right_index=True,
-> 3300 suffixes=(lsuffix, rsuffix), sort=sort)
3301 else:
3302 if on is not None:
C:\Python27\lib\site-packages\pandas\tools\merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
29 right_index=right_index, sort=sort, suffixes=suffixes,
30 copy=copy)
---> 31 return op.get_result()
32 if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame'
33
C:\Python27\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
80 copy=self.copy)
81
---> 82 result_data = join_op.get_result()
83 result = DataFrame(result_data)
84
C:\Python27\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
495 for klass in kinds:
496 klass_blocks = [mapping.get(klass) for mapping in blockmaps]
--> 497 res_blk = self._get_merged_block(klass_blocks)
498 result_blocks.append(res_blk)
499
C:\Python27\lib\site-packages\pandas\tools\merge.pyc in _get_merged_block(self, blocks)
509
510 if len(to_merge) > 1:
--> 511 return self._merge_blocks(to_merge)
512 else:
513 unit, block = to_merge[0]
C:\Python27\lib\site-packages\pandas\tools\merge.pyc in _merge_blocks(self, merge_chunks)
545 com.take_fast(blk.values, unit.indexer,
546 None, False,
--> 547 axis=self.axis, out=out_chunk)
548
549 sofar += len(blk)
C:\Python27\lib\site-packages\pandas\core\common.pyc in take_fast(arr, indexer, mask, needs_masking, axis, out, fill_value)
264 return take_2d(arr, indexer, out=out, mask=mask,
265 needs_masking=needs_masking,
--> 266 axis=axis, fill_value=fill_value)
267
268 result = arr.take(indexer, axis=axis, out=out)
C:\Python27\lib\site-packages\pandas\core\common.pyc in take_2d(arr, indexer, out, mask, needs_masking, axis, fill_value)
236 out = np.empty(out_shape, dtype=arr.dtype)
237 take_f = _get_take2d_function(dtype_str, axis=axis)
--> 238 take_f(arr, indexer, out=out, fill_value=fill_value)
239 return out
240 else:
C:\Python27\lib\site-packages\pandas\_tseries.pyd in pandas._tseries.take_2d_axis1_float64 (pandas\src\tseries.c:49365)()
ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1051 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1051/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1051/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1051/events | https://github.com/pandas-dev/pandas/issues/1051 | 4,116,038 | MDU6SXNzdWU0MTE2MDM4 | 1,051 | nosetests fails one test | {
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} | 2 | "2012-04-14T06:07:16Z" | "2012-08-20T17:26:58Z" | "2012-07-12T21:47:19Z" | NONE | null | Hi Wes,
I have just watched your 3+ hour talk on pandas and got enthusiastic. So, I am exploring pandas for my scientific work on high-frequency finance data. Thanks for all the good stuff.
There is one issue you might want to know about. This morning I upgraded to the latest Enthought python distribution and then tried to install pandas as indicated in your manual. But, trying separately on two machines, I keep getting the following failure (see below). It seems so far to go unreported so I thought I should let you know about it (I tried it on a MB Air and a Mac Pro which both have Mac OS Lion installed (and I always run Mac software upgrades)).
Keep up the good work! Greetings from Amsterdam.
Cheers, Albert
## FAIL: test_timeseries_preepoch (pandas.io.tests.test_pytables.TesttHDFStore)
Traceback (most recent call last):
File "/Users/albertjmenkveld/Downloads/pandas/pandas/io/tests/test_pytables.py", line 216, in test_timeseries_preepoch
self._check_roundtrip(ts, tm.assert_series_equal)
File "/Users/albertjmenkveld/Downloads/pandas/pandas/io/tests/test_pytables.py", line 459, in _check_roundtrip
comparator(retrieved, obj)
File "/Users/albertjmenkveld/Downloads/pandas/pandas/util/testing.py", line 124, in assert_series_equal
assert(left.index.equals(right.index))
AssertionError
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} | 5 | "2012-04-14T11:56:24Z" | "2013-03-09T14:25:29Z" | "2013-03-09T14:25:29Z" | CONTRIBUTOR | null | First of all, thank you very much for the great library! I really like the scatter plot in the latest release. I am very new to the library, but I am enjoying it very much.
However, I am really uneasy about the fallback behavior of the `.ix` attribute. Why can't we have a purely label based version of `.ix` and a purely integer index version of `.ix`? I want pandas to raise an error when a data frame does not have integer label I pass rather than fallback to integer index based indexing, because "explicit is better than implicit".
I can write a pull request if it is likely to be pulled. Or maybe you should write it because it is a big API change.
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https://api.github.com/repos/pandas-dev/pandas/issues/1053 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1053/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1053/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1053/events | https://github.com/pandas-dev/pandas/issues/1053 | 4,117,718 | MDU6SXNzdWU0MTE3NzE4 | 1,053 | Bug in DataFrame.to_html with MultiIndex | {
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] | closed | false | null | [] | null | 2 | "2012-04-14T13:38:21Z" | "2016-10-12T23:02:54Z" | "2012-04-16T19:33:22Z" | CONTRIBUTOR | null | ``` python
DataFrame(
rand(2, 4),
columns=MultiIndex.from_tuples(zip('aabb', 'xyxy'))
).to_html()
```
generates
<table border="1">
<thead>
<tr>
<th><table><tbody><tr><td>a</td></tr><tr><td>x</td></tr></tbody></table></th>
<th><table><tbody><tr><td>a</td></tr><tr><td>y</td></tr></tbody></table></th>
<th><table><tbody><tr><td>b</td></tr><tr><td>x</td></tr></tbody></table></th>
<th><table><tbody><tr><td>b</td></tr><tr><td>y</td></tr></tbody></table></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>0</strong></td>
<td> 0.280664</td>
<td> 0.465508</td>
<td> 0.616751</td>
<td> 0.246239</td>
</tr>
<tr>
<td><strong>1</strong></td>
<td> 0.124957</td>
<td> 0.867052</td>
<td> 0.504789</td>
<td> 0.178313</td>
</tr>
</tbody>
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https://api.github.com/repos/pandas-dev/pandas/issues/1056 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1056/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1056/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1056/events | https://github.com/pandas-dev/pandas/pull/1056 | 4,120,095 | MDExOlB1bGxSZXF1ZXN0MTE2NTU2OQ== | 1,056 | A few minor tweaks to plotting | {
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} | 0 | "2012-04-14T21:12:57Z" | "2014-06-26T12:56:03Z" | "2012-05-01T18:23:47Z" | CONTRIBUTOR | null | ENH: call subplots_adjust on all DataFrame plot types to make it more friendly for figure reuse.
ENH: boxplot can now take a Series (wrapped as columns 'x' in a DataFrame)
ENH: set xaxis and yaxis for empty subplots to invisible
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https://api.github.com/repos/pandas-dev/pandas/issues/1057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1057/events | https://github.com/pandas-dev/pandas/issues/1057 | 4,120,319 | MDU6SXNzdWU0MTIwMzE5 | 1,057 | Pure python apply broken with TimeGrouper | {
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} | 0 | "2012-04-14T21:56:52Z" | "2013-06-07T13:15:13Z" | "2012-04-14T22:20:02Z" | MEMBER | null | ```
In [12]: s.convert('D', how='count')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-12-52597b6f97c4> in <module>()
----> 1 s.convert('D', how='count')
/home/wesm/code/pandas/pandas/core/generic.pyc in convert(self, rule, method, how, axis, as_index, closed, label)
179 how = translate_grouping(how)
180
--> 181 result = grouped.agg(how)
182 else:
183 # upsampling
/home/wesm/code/pandas/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
246 See docstring for aggregate
247 """
--> 248 return self.aggregate(func, *args, **kwargs)
249
250 def _iterate_slices(self):
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs)
1263 """
1264 if isinstance(func_or_funcs, basestring):
-> 1265 return getattr(self, func_or_funcs)(*args, **kwargs)
1266
1267 if hasattr(func_or_funcs,'__iter__'):
/home/wesm/code/pandas/pandas/core/groupby.pyc in wrapper(*args, **kwargs)
178 return self.apply(curried_with_axis)
179 except Exception:
--> 180 return self.apply(curried)
181
182 return wrapper
/home/wesm/code/pandas/pandas/core/groupby.pyc in apply(self, func, *args, **kwargs)
237 applied : type depending on grouped object and function
238 """
--> 239 return self._python_apply_general(func, *args, **kwargs)
240
241 def aggregate(self, func, *args, **kwargs):
/home/wesm/code/pandas/pandas/core/groupby.pyc in _python_apply_general(self, func, *args, **kwargs)
409
410 return self._wrap_applied_output(result_keys, result_values,
--> 411 not_indexed_same=not_indexed_same)
412
413 def _wrap_applied_output(self, *args, **kwargs):
/home/wesm/code/pandas/pandas/core/groupby.pyc in _wrap_applied_output(self, keys, values, not_indexed_same)
1335 not_indexed_same=not_indexed_same)
1336 else:
-> 1337 return Series(values, index=_get_index())
1338
1339 def _aggregate_named(self, func, *args, **kwargs):
/home/wesm/code/pandas/pandas/core/groupby.pyc in _get_index()
1319 index = MultiIndex.from_tuples(keys, names=key_names)
1320 else:
-> 1321 ping = self.grouper.groupings[0]
1322 if len(keys) == ping.ngroups:
1323 index = ping.group_index
AttributeError: 'TimeGrouper' object has no attribute 'groupings'
In [13]: s.convert('D', how='count')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1062/events | https://github.com/pandas-dev/pandas/issues/1062 | 4,128,128 | MDU6SXNzdWU0MTI4MTI4 | 1,062 | KeyError raised out of DatetimeEngine formatted as int64 | {
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} | 0 | "2012-04-16T03:08:29Z" | "2012-04-22T02:02:50Z" | "2012-04-22T02:02:50Z" | MEMBER | null | ```
In [12]:
aapl[datetime(2011, 12, 7)]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/home/wesm/code/teaching/ga/<ipython-input-12-d32cb44b8294> in <module>()
----> 1 aapl[datetime(2011, 12, 7)]
/home/wesm/code/pandas/pandas/core/series.pyc in __getitem__(self, key)
379 def __getitem__(self, key):
380 try:
--> 381 return self.index.get_value(self, key)
382 except InvalidIndexError:
383 pass
/home/wesm/code/pandas/pandas/core/index.pyc in get_value(self, series, key)
1747 pass
1748
-> 1749 return self._engine.get_value(series, to_timestamp(key))
1750
1751 def get_loc(self, key):
/home/wesm/code/pandas/pandas/_engines.so in pandas._engines.IndexEngine.get_value (pandas/src/engines.c:16263)()
/home/wesm/code/pandas/pandas/_engines.so in pandas._engines.IndexEngine.get_value (pandas/src/engines.c:16129)()
/home/wesm/code/pandas/pandas/_engines.so in pandas._engines.DatetimeEngine.get_loc (pandas/src/engines.c:20163)()
/home/wesm/code/pandas/pandas/_engines.so in pandas._engines.Int64HashTable.get_item (pandas/src/engines.c:5498)()
/home/wesm/code/pandas/pandas/_engines.so in pandas._engines.Int64HashTable.get_item (pandas/src/engines.c:5452)()
KeyError: 1323216000000000
```
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} | 0 | "2012-04-16T03:32:17Z" | "2012-04-22T00:14:11Z" | "2012-04-22T00:14:11Z" | MEMBER | null | ```
In [46]: ts[:5]
Out[46]:
2000-01-01 00:00:00 1.126328
2000-01-01 01:00:00 -1.551378
2000-01-01 02:00:00 -0.353657
2000-01-01 03:00:00 1.447303
2000-01-01 04:00:00 -0.423588
In [45]: ts[:5].shift(1, offset=pd.datetools.Hour(4))
/home/wesm/code/pandas/pandas/core/series.py:2517: FutureWarning: 'timeRule' and 'offset' parameters are deprecated, please use 'freq' instead
FutureWarning)
Out[45]:
2000-01-01 01:00:00 1.126328
2000-01-01 02:00:00 -1.551378
2000-01-01 03:00:00 -0.353657
2000-01-01 04:00:00 1.447303
2000-01-01 05:00:00 -0.423588
```
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} | 3 | "2012-04-16T10:58:16Z" | "2012-04-23T17:46:38Z" | "2012-04-23T17:46:20Z" | NONE | null | x = DataFrame(np.arange(9).reshape(3,3))
x['test']=0
x['fl']= [1.3,1.5,1.6]
x.groupby('test').agg({'fl':'sum',2:'size'}).dtypes
Out[]:
2 int64
fl object
(version 0.8)
Output in 0.7.3
Out[]:
2 int64
fl float64
The output for x['test'] = [1,2,3] works fine
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First, the compile failed because of the deprecated "basetring" used on line 48 of pandas/src/datetime.pyx.
After fixing that, pandas built, but failed when I tried to import it:
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/home/emarkley/tmp/<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/__init__.py in <module>()
8
9 try:
---> 10 import pandas._tseries as lib
11 except Exception as e: # pragma: no cover
12 if 'No module named' in str(e):
ImportError: /usr/local/lib/python3.2/site-packages/pandas-0.8.0.dev-py3.2-linux-x86_64.egg/pandas/_tseries.cpython-32m.so: undefined symbol: PyInt_AsLong
```
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} | 14 | "2012-04-16T15:49:48Z" | "2019-03-13T16:43:02Z" | "2013-10-03T21:00:21Z" | MEMBER | null | ```
Is there a simple way of grabbing nested keys when constructing a Pandas Dataframe from JSON. Using the example JSON from below, how would I build a Dataframe that uses this column_header = ['id_str', 'text', 'user.screen_name'], (i.e. how do I get the 'screen_name' from the 'user' key without flattening the JSON).
Thanks,
Vishal
{ u'_id': ObjectId('4f8b95e8a504d022e2000000'),
u'contributors': None,
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} | 0 | "2012-04-16T20:35:37Z" | "2012-04-16T20:45:45Z" | "2012-04-16T20:45:45Z" | MEMBER | null | ```
In [47]: rets.ix['2000']
Out[47]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 261 entries, 2000-01-03 00:00:00 to 2001-01-01 00:00:00
Data columns:
AAPL 261 non-null values
GOOG 261 non-null values
MSFT 261 non-null values
dtypes: float64(3)
```
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} | 3 | "2012-04-16T20:52:04Z" | "2014-04-28T20:06:48Z" | "2012-05-07T18:04:50Z" | NONE | null | `DataFrame` should behave consistently with `Series` and NumPy arrays when used in boolean comparison. Additionally, should provide consistent `all`/`any` behaviour.
```
>>> from pandas import *
>>> s = Series([1, 2, 3])
>>> bool(s)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
>>> d = DataFrame([[1,2,3]])
>>> bool(d)
True # should throw ValueError as well
>>> all(s)
True
>>> all(d)
False # should be True
```
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https://api.github.com/repos/pandas-dev/pandas/issues/1070 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1070/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1070/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1070/events | https://github.com/pandas-dev/pandas/pull/1070 | 4,147,340 | MDExOlB1bGxSZXF1ZXN0MTE3NjAxNQ== | 1,070 | fixing the error in io/sql.py | {
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} | 5 | "2012-04-17T05:14:25Z" | "2014-07-04T03:14:05Z" | "2012-05-07T14:40:37Z" | CONTRIBUTOR | null | Not able to find an testing module in for io/sql.py. And I don't have time to create one now. So I paste the testing code below, which can be added into pandas later on.
```
#!/usr/bin/env python
from pymysql import connect
from pandas.io.sql import read_frame
con = connect(host='genome-mysql.cse.ucsc.edu', user='genome', db='mm9')
sql = 'select geneName as symbol, name as refSeq, chrom, strand, txStart, txEnd from refFlat limit 5'
df = read_frame(sql, con)
print df
```
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} | 3 | "2012-04-17T13:46:33Z" | "2015-12-05T19:29:09Z" | "2015-12-05T19:29:09Z" | CONTRIBUTOR | null | ``` python
from pandas import DataFrame
import quantities as pq
df = DataFrame([[pq.Quantity(i, pq.s)] for i in range(3)])
df[1:] - df[:-1]
```
raises: `ValueError: Unable to convert between units of "dimensionless" and "s"`
Note that the following works w/o error because pandas "strips" dimension:
``` python
df = DataFrame(pq.Quantity(np.array([0,1,2]).T, pq.s))
df[1:] - df[:-1]
```
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} | 3 | "2012-04-17T15:02:08Z" | "2012-04-20T17:47:11Z" | "2012-04-20T17:47:11Z" | CONTRIBUTOR | null | Try:
```
wget http://pypi.python.org/packages/source/p/pandas/pandas-0.7.3.tar.gz
tar tzf pandas-0.7.3.tar.gz | grep flymake
```
I don't mind but I happen to find it, so...
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} | 1 | "2012-04-17T18:05:17Z" | "2014-06-19T05:28:48Z" | "2012-05-07T18:02:35Z" | NONE | null | Calling bool() on DataFrame raises issues described in #1069. This
change causes DataFrame to raise a ValueError when called with bool(),
and instead provides a new property, dataframe.empty.
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} | 3 | "2012-04-18T15:06:23Z" | "2012-05-07T14:21:19Z" | "2012-05-07T14:17:05Z" | CONTRIBUTOR | null | Here's the code:
```
In [23]: d = DataFrame({'a': [np.nan, False], 'b': [True, True]})
In [24]: d['a'] | d['b']
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (1025, 0))
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (1036, 0))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/media/DATA/Code/Epidemic/<ipython-input-25-f18359aab663> in <module>()
----> 1 d['a'].fillna(False) | d['b']
/usr/lib/python2.7/site-packages/pandas/core/series.pyc in wrapper(self, other)
143 if isinstance(other, Series):
144 name = _maybe_match_name(self, other)
--> 145 return Series(na_op(self.values, other.values),
146 index=self.index, name=name)
147 elif isinstance(other, DataFrame):
/usr/lib/python2.7/site-packages/pandas/core/series.pyc in na_op(x, y)
132
133 if isinstance(y, np.ndarray):
--> 134 result = lib.vec_binop(x, y, op)
135 else:
136 result = lib.scalar_binop(x, y, op)
/usr/lib/python2.7/site-packages/pandas/_tseries.so in pandas._tseries.vec_binop (pandas/src/tseries.c:9600)()
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'double'
```
This seems to happen when performing any kind of boolean operation on a column containing a NaN. Calling .fillna() on it before the operation doesn't help (i.e. "d['a'].fillna(False) | d['b']" crashes as well). Also, "d['a'] & True" will fail too, but without the messages about "tokenizing input".
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Searching for pandas
Reading http://pypi.python.org/simple/pandas/
Reading http://pandas.pydata.org
Reading http://pandas.sourceforge.net
Best match: pandas 0.7.3
Downloading http://pypi.python.org/packages/source/p/pandas/pandas-0.7.3.zip#md5=1c06999a379ee4b8765291bc1519bec9
Processing pandas-0.7.3.zip
Running pandas-0.7.3/setup.py -q bdist_egg --dist-dir /tmp/easy_install-6D8TYW/pandas-0.7.3/egg-dist-tmp-InWfu5
warning: no files found matching 'setupegg.py'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '_.so' found anywhere in distribution
warning: no previously-included files matching '_.pyd' found anywhere in distribution
warning: no previously-included files matching '_.pyc' found anywhere in distribution
warning: no previously-included files matching '.git_' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
unable to execute gcc: No such file or directory
error: Setup script exited with error: command 'gcc' failed with exit status 1
Any help?
Also, how would I download 0.8.0 from repo and install.......
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} | 1 | "2012-04-18T16:49:44Z" | "2017-01-25T21:00:52Z" | "2017-01-25T15:49:35Z" | MEMBER | null | To facilitate groupby-type operations, from mailing list:
``````
With the following:
df # predefined
df['E'] = pandas.Series(0.)
for grp_name, grp_data in df.groupby(['C','D']):
# snipped code here; returns a scalar x that is dependent on
grp_data
grp_data['E'] = x # NB df['E'] not updated
I was wondering if there was a way of internally capturing child-
parent relationships between dataframes so that each 'grp_data'
dataframe knows that its parent is dataframe 'df' and knows how to
propagate assignments back to the parent dataframe using column names
and row masks. I suppose this would require that grp_data is an
instance of a subclass of pandas.DataFrame.```
Not sure how feasible this is, but an idea.
``````
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} | 2 | "2012-04-18T18:46:54Z" | "2016-10-12T23:02:54Z" | "2012-05-03T22:35:31Z" | CONTRIBUTOR | null | ``` python
randn = np.random.randn
from pandas import *
index = MultiIndex.from_tuples([('a', 0), ('b', 'y')])
df = DataFrame(randn(3, 2), columns=index)
store = HDFStore('/tmp/testpandas.hdf5')
store['multi'] = df
```
raises `ValueError: invalid literal for long() with base 10: 'y'`
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} | 2 | "2012-04-18T20:16:09Z" | "2012-05-08T19:13:42Z" | "2012-05-08T19:13:42Z" | CONTRIBUTOR | null | > import pandas
> import numpy
> df = pandas.DataFrame(numpy.random.randn(8, 3), index=range(8),columns=['A', 'B', 'C'])
> None == df
raises: pandas.core.common.PandasError: DataFrame constructor not properly called!
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https://api.github.com/repos/pandas-dev/pandas/issues/1081 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1081/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1081/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1081/events | https://github.com/pandas-dev/pandas/issues/1081 | 4,188,643 | MDU6SXNzdWU0MTg4NjQz | 1,081 | duplicated time series index entries after hdf5 store (dayligth saving time related) | {
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} | 3 | "2012-04-19T10:00:10Z" | "2012-05-14T15:24:50Z" | "2012-05-14T15:24:50Z" | NONE | null | I have a data frame like this
```
>> df
<class 'pandas.core.frame.DataFrame'>
Index: 596520 entries, 2006-04-14 00:00:00 to 2011-12-31 23:55:00
Data columns:
g_m_pyr__0 596520 non-null values
e_wr__0 596520 non-null values
p_nenn_sg__0 596520 non-null values
flaeche_sg__0 596520 non-null values
dtypes: float64(4)
>> df.index[0].timetuple()
time.struct_time(tm_year=2006, tm_mon=4, tm_mday=14, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=4, tm_yday=104, tm_isdst=-1)
>> df.index.get_duplicates()
[]
```
saving this dataframe to hdf5 and reloading it results in duplicated index entries:
```
>> store = pd.io.pytables.HDFStore('store.h5')
>> store['df'] = df
>> df2 = store['df']
>> df2.index.get_duplicates()
[datetime.datetime(2007, 3, 25, 3, 0),
datetime.datetime(2007, 3, 25, 3, 5),
datetime.datetime(2007, 3, 25, 3, 10),
datetime.datetime(2007, 3, 25, 3, 15),
datetime.datetime(2007, 3, 25, 3, 20),
datetime.datetime(2007, 3, 25, 3, 25),
datetime.datetime(2007, 3, 25, 3, 30),
datetime.datetime(2007, 3, 25, 3, 35),
datetime.datetime(2007, 3, 25, 3, 40),
datetime.datetime(2007, 3, 25, 3, 45),
datetime.datetime(2007, 3, 25, 3, 50),
datetime.datetime(2007, 3, 25, 3, 55),
datetime.datetime(2008, 3, 30, 3, 0),
datetime.datetime(2008, 3, 30, 3, 5),
datetime.datetime(2008, 3, 30, 3, 10),
datetime.datetime(2008, 3, 30, 3, 15),
datetime.datetime(2008, 3, 30, 3, 20),
datetime.datetime(2008, 3, 30, 3, 25),
datetime.datetime(2008, 3, 30, 3, 30),
datetime.datetime(2008, 3, 30, 3, 35),
datetime.datetime(2008, 3, 30, 3, 40),
datetime.datetime(2008, 3, 30, 3, 45),
datetime.datetime(2008, 3, 30, 3, 50),
datetime.datetime(2008, 3, 30, 3, 55),
datetime.datetime(2009, 3, 29, 3, 0),
datetime.datetime(2009, 3, 29, 3, 5),
datetime.datetime(2009, 3, 29, 3, 10),
datetime.datetime(2009, 3, 29, 3, 15),
datetime.datetime(2009, 3, 29, 3, 20),
datetime.datetime(2009, 3, 29, 3, 25),
datetime.datetime(2009, 3, 29, 3, 30),
datetime.datetime(2009, 3, 29, 3, 35),
datetime.datetime(2009, 3, 29, 3, 40),
datetime.datetime(2009, 3, 29, 3, 45),
datetime.datetime(2009, 3, 29, 3, 50),
datetime.datetime(2009, 3, 29, 3, 55),
datetime.datetime(2010, 3, 28, 3, 0),
datetime.datetime(2010, 3, 28, 3, 5),
datetime.datetime(2010, 3, 28, 3, 10),
datetime.datetime(2010, 3, 28, 3, 15),
datetime.datetime(2010, 3, 28, 3, 20),
datetime.datetime(2010, 3, 28, 3, 25),
datetime.datetime(2010, 3, 28, 3, 30),
datetime.datetime(2010, 3, 28, 3, 35),
datetime.datetime(2010, 3, 28, 3, 40),
datetime.datetime(2010, 3, 28, 3, 45),
datetime.datetime(2010, 3, 28, 3, 50),
datetime.datetime(2010, 3, 28, 3, 55),
datetime.datetime(2011, 3, 27, 3, 0),
datetime.datetime(2011, 3, 27, 3, 5),
datetime.datetime(2011, 3, 27, 3, 10),
datetime.datetime(2011, 3, 27, 3, 15),
datetime.datetime(2011, 3, 27, 3, 20),
datetime.datetime(2011, 3, 27, 3, 25),
datetime.datetime(2011, 3, 27, 3, 30),
datetime.datetime(2011, 3, 27, 3, 35),
datetime.datetime(2011, 3, 27, 3, 40),
datetime.datetime(2011, 3, 27, 3, 45),
datetime.datetime(2011, 3, 27, 3, 50),
datetime.datetime(2011, 3, 27, 3, 55)]
```
I get the same duplicates with other time series os the same kind and the duplicates are in march for every dataframe.
Is this a pandas problem?
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https://api.github.com/repos/pandas-dev/pandas/issues/1082 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1082/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1082/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1082/events | https://github.com/pandas-dev/pandas/issues/1082 | 4,194,354 | MDU6SXNzdWU0MTk0MzU0 | 1,082 | OpenSuse 12.1: install error......easy_install | {
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} | [] | closed | false | null | [] | null | 8 | "2012-04-19T15:46:13Z" | "2013-12-04T00:43:27Z" | "2012-04-19T17:25:00Z" | NONE | null | continued from 1075....
0.8.0 wont compile.
> python.h are there
> numpy dev are there.
error below.... any help?
pandas/src/tseries.c:125721:14: warning: variable ‘__pyx_bshape_0_left’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125720:14: warning: variable ‘__pyx_bstride_0_left’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125718:14: warning: variable ‘__pyx_bshape_0_left_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125717:14: warning: variable ‘__pyx_bstride_0_left_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125712:14: warning: variable ‘__pyx_bshape_0_right_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125711:14: warning: variable ‘__pyx_bstride_0_right_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125700:14: warning: variable ‘__pyx_bshape_0_right’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:125699:14: warning: variable ‘__pyx_bstride_0_right’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c: In function ‘__pyx_pf_6pandas_8_tseries_230full_outer_join’:
pandas/src/tseries.c:126907:14: warning: variable ‘__pyx_bshape_0_left’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126906:14: warning: variable ‘__pyx_bstride_0_left’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126901:14: warning: variable ‘__pyx_bshape_0_left_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126900:14: warning: variable ‘__pyx_bstride_0_left_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126895:14: warning: variable ‘__pyx_bshape_0_right_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126894:14: warning: variable ‘__pyx_bstride_0_right_sorter’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126889:14: warning: variable ‘__pyx_bshape_0_right’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:126888:14: warning: variable ‘__pyx_bstride_0_right’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c: In function ‘__pyx_pf_6pandas_8_tseries_232join_sorter’:
pandas/src/tseries.c:128059:14: warning: variable ‘__pyx_bshape_0_where’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:128056:14: warning: variable ‘__pyx_bshape_0_counts’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:128053:14: warning: variable ‘__pyx_bshape_0_result’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c:128050:14: warning: variable ‘__pyx_bshape_0_index’ set but not used [-Wunused-but-set-variable]
pandas/src/tseries.c: In function ‘__pyx_pf_6pandas_8_tseries_19maybe_booleans_to_slice’:
pandas/src/tseries.c:8484:15: warning: ‘__pyx_v_end’ may be used uninitialized in this function [-Wuninitialized]
pandas/src/tseries.c:8482:15: warning: ‘__pyx_v_start’ may be used uninitialized in this function [-Wuninitialized]
pandas/src/tseries.c: In function ‘__pyx_pf_6pandas_8_tseries_89group_ohlc’:
pandas/src/tseries.c:52915:147: warning: ‘__pyx_v_vclose’ may be used uninitialized in this function [-Wuninitialized]
pandas/src/tseries.c:52764:22: warning: ‘__pyx_v_vlow’ may be used uninitialized in this function [-Wuninitialized]
pandas/src/tseries.c:52786:22: warning: ‘__pyx_v_vhigh’ may be used uninitialized in this function [-Wuninitialized]
pandas/src/tseries.c:52882:147: warning: ‘__pyx_v_vopen’ may be used uninitialized in this function [-Wuninitialized]
gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -fmessage-length=0 -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector -funwind-tables -fasynchronous-unwind-tables -g -fPIC -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/np_datetime.c -o build/temp.linux-x86_64-2.7/pandas/src/np_datetime.o
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1973:0,
from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from pandas/src/np_datetime.c:11:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -fmessage-length=0 -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector -funwind-tables -fasynchronous-unwind-tables -g -fPIC -I/usr/lib64/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/np_datetime_strings.c -o build/temp.linux-x86_64-2.7/pandas/src/np_datetime_strings.o
In file included from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1973:0,
from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib64/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:15,
from pandas/src/np_datetime_strings.c:17:
/usr/lib64/python2.7/site-packages/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
pandas/src/np_datetime_strings.c: In function ‘get_datetime_iso_8601_strlen’:
pandas/src/np_datetime_strings.c:911:14: error: ‘NPY_FR_B’ undeclared (first use in this function)
pandas/src/np_datetime_strings.c:911:14: note: each undeclared identifier is reported only once for each function it appears in
pandas/src/np_datetime_strings.c:888:5: warning: enumeration value ‘NPY_FR_GENERIC’ not handled in switch [-Wswitch]
error: command 'gcc' failed with exit status 1
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https://api.github.com/repos/pandas-dev/pandas/issues/1083 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1083/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1083/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1083/events | https://github.com/pandas-dev/pandas/issues/1083 | 4,195,622 | MDU6SXNzdWU0MTk1NjIy | 1,083 | DataFrame stores complex number as object sometimes | {
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} | 1 | "2012-04-19T16:56:09Z" | "2012-05-15T00:10:06Z" | "2012-05-15T00:10:06Z" | CONTRIBUTOR | null | ``` python
>>> DataFrame([[0.0 + 1j, 0.0 + 2j]]).values
array([[1j, 2j]], dtype=object)
>>> DataFrame([np.array([1j, 2j])]).values
array([[ 0.+1.j, 0.+2.j]])
```
I think these two examples should yield the same result.
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https://api.github.com/repos/pandas-dev/pandas/issues/1084 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1084/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1084/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1084/events | https://github.com/pandas-dev/pandas/pull/1084 | 4,200,196 | MDExOlB1bGxSZXF1ZXN0MTE5ODM5MA== | 1,084 | datetime stuff on python 3 | {
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In [8]: to_datetime('')
Out[8]: datetime.datetime(2012, 4, 19, 0, 0)
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} | 1 | "2012-04-20T15:42:53Z" | "2012-04-22T14:51:03Z" | "2012-04-22T14:51:03Z" | NONE | null | Hello,
It seems possible to have negative variances due to numerical inaccuracies. This is because nanops.py, line 120 does not take the absolute value of the result. Having negative values will cause std() to return NaN when it should be 0.
The code below should [probabilistically] recreate the problem. It could also be turned into a unit test.
Thanks!
from pandas import DataFrame
import numpy as np
random_repeated_rows = np.array( [np.random.random((10000,)),] \* 10 )
my_var = DataFrame( random_repeated_rows ).var()
len( my_var[ my_var < 0 ] ) # returns a negative slightly less than half of the time
np.min( DataFrame( random_repeated_rows ).var() ) # returns a tiny negative -9.8686491077791697e-16
np.min( DataFrame( random_repeated_rows ).values.var(axis=0) ) # returns 0
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} | 0 | "2012-04-20T20:38:32Z" | "2012-04-22T01:49:53Z" | "2012-04-22T01:49:53Z" | MEMBER | null | noticed in test failure:
```
======================================================================
ERROR: test_shift_multiple_of_same_base (__main__.TestLegacySupport)
----------------------------------------------------------------------
Traceback (most recent call last):
File "pandas/tests/test_timeseries.py", line 624, in test_shift_multiple_of_same_base
ts = Series(np.random.randn(5), index=date_range('1/1/2000', freq='H'))
File "/home/wesm/code/pandas/pandas/core/daterange.py", line 65, in date_range
freq=freq, tz=tz, normalize=normalize)
File "/home/wesm/code/pandas/pandas/core/index.py", line 1279, in __new__
b, e = Timestamp(start), Timestamp(end)
File "datetime.pyx", line 59, in pandas._tseries.Timestamp.__new__ (pandas/src/tseries.c:27720)
AttributeError: 'NoneType' object has no attribute 'dtval'
----------------------------------------------------------------------
```
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} | 0 | "2012-04-20T20:42:23Z" | "2012-04-23T17:21:00Z" | "2012-04-23T17:20:59Z" | MEMBER | null | ```
In [1]: index = DatetimeIndex(['1/1/2000', '1/2/2000'])
In [2]: index
Out[2]: DatetimeIndex([2000-01-01 00:00:00, 2000-01-02 00:00:00], dtype=datetime64[us])
In [3]: index + datetools.Hour(2)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-3-6142ead163c9> in <module>()
----> 1 index + datetools.Hour(2)
/home/wesm/code/pandas/pandas/core/index.pyc in wrapper(self, other)
1142 else:
1143 func = getattr(super(DatetimeIndex, self), opname)
-> 1144 return func(other)
1145 return wrapper
1146
/home/wesm/code/pandas/pandas/core/index.pyc in __add__(self, other)
412 return self.union(other)
413 else:
--> 414 return Index(self.view(np.ndarray) + other)
415
416 __eq__ = _indexOp('__eq__')
TypeError: ufunc 'add' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule 'safe'
```
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} | 8 | "2012-04-20T20:52:40Z" | "2012-04-29T19:16:36Z" | "2012-04-26T19:41:58Z" | NONE | null | Hi all,
I can't find an existing ticket for this, hopefully I didn't miss it. If I understand correctly, asfreq currently only does decimation to change the frequency of a timeseries. I would like to suggest that we make this point more clear in the docstring and offer additional methods to aggregate values such as mean for example, but we could think of others as well, and even a custom function. I am happy to take a stab at the implementation.
Best,
Jonathan
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} | 5 | "2012-04-20T22:37:23Z" | "2014-06-16T23:23:36Z" | "2012-05-15T00:09:58Z" | CONTRIBUTOR | null | This fixes #1083. I checked that the number of failing tests (SKIP=38, errors=5, failures=34) do not increase with these change sets.
BTW, in form_blocks why not simply do
``` python
if float_dict:
...
```
instead of calculating length?:
``` python
if len(float_dict):
...
```
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} | 0 | "2012-04-21T17:39:34Z" | "2012-04-22T02:54:41Z" | "2012-04-22T02:54:41Z" | MEMBER | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/1100 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/1100/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/1100/comments | https://api.github.com/repos/pandas-dev/pandas/issues/1100/events | https://github.com/pandas-dev/pandas/issues/1100 | 4,227,095 | MDU6SXNzdWU0MjI3MDk1 | 1,100 | Datetime formatting for year < 1900 | {
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} | 0 | "2012-04-22T02:07:48Z" | "2012-04-23T18:09:05Z" | "2012-04-23T18:08:57Z" | MEMBER | null | ```
In [1]: rng = date_range('1/1/1800', '1/1/2000')
In [2]: rng
Out[2]:
<class 'pandas.tseries.index.DatetimeIndex'>
freq: <1 Day>, timezone: None
[1800-01-01 00:00:00, ..., 2000-01-01 00:00:00]
length: 73049
In [3]: ts = Series(randn(len(rng)), index=rng)
In [4]: ts
Out[4]: ---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/Users/wesm/code/pandas/<ipython-input-4-a03e77341a8b> in <module>()
----> 1 ts
/Users/wesm/code/repos/ipython/IPython/core/displayhook.pyc in __call__(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
/Users/wesm/code/repos/ipython/IPython/core/displayhook.pyc in compute_format_data(self, result)
148 MIME type representation of the object.
149 """
--> 150 return self.shell.display_formatter.format(result)
151
152 def write_format_data(self, format_dict):
/Users/wesm/code/repos/ipython/IPython/core/formatters.pyc in format(self, obj, include, exclude)
124 continue
125 try:
--> 126 data = formatter(obj)
127 except:
128 # FIXME: log the exception
/Users/wesm/code/repos/ipython/IPython/core/formatters.pyc in __call__(self, obj)
445 type_pprinters=self.type_printers,
446 deferred_pprinters=self.deferred_printers)
--> 447 printer.pretty(obj)
448 printer.flush()
449 return stream.getvalue()
/Users/wesm/code/repos/ipython/IPython/lib/pretty.pyc in pretty(self, obj)
358 if callable(meth):
359 return meth(obj, self, cycle)
--> 360 return _default_pprint(obj, self, cycle)
361 finally:
362 self.end_group()
/Users/wesm/code/repos/ipython/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle)
478 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
479 # A user-provided repr.
--> 480 p.text(repr(obj))
481 return
482 p.begin_group(1, '<')
/Users/wesm/code/pandas/pandas/core/series.pyc in __repr__(self)
699 else fmt.print_config.max_rows)
700 if len(self.index) > max_rows:
--> 701 result = self._tidy_repr(min(30, max_rows - 4))
702 elif len(self.index) > 0:
703 result = self._get_repr(print_header=True,
/Users/wesm/code/pandas/pandas/core/series.pyc in _tidy_repr(self, max_vals)
712 num = max_vals // 2
713 head = self[:num]._get_repr(print_header=True, length=False,
--> 714 name=False)
715 tail = self[-(max_vals - num):]._get_repr(print_header=False,
716 length=False,
/Users/wesm/code/pandas/pandas/core/series.pyc in _get_repr(self, name, print_header, length, na_rep, float_format)
762 length=length, na_rep=na_rep,
763 float_format=float_format)
--> 764 return formatter.to_string()
765
766 def __str__(self):
/Users/wesm/code/pandas/pandas/core/format.pyc in to_string(self)
98 return ''
99
--> 100 fmt_index, have_header = self._get_formatted_index()
101 fmt_values = self._get_formatted_values()
102
/Users/wesm/code/pandas/pandas/core/format.pyc in _get_formatted_index(self)
84 else:
85 have_header = index.name is not None
---> 86 fmt_index = index.format(name=True)
87 return fmt_index, have_header
88
/Users/wesm/code/pandas/pandas/core/index.pyc in format(self, name)
333 if dt.time() != zero_time or dt.tzinfo is not None:
334 return header + ['%s' % x for x in self]
--> 335 result.append(dt.strftime("%Y-%m-%d"))
336 return header + result
337
ValueError: year=1800 is before 1900; the datetime strftime() methods require year >= 1900
```
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