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https://api.github.com/repos/pandas-dev/pandas/issues/2401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2401/events | https://github.com/pandas-dev/pandas/pull/2401 | 8,884,939 | MDExOlB1bGxSZXF1ZXN0MzE5ODMxMw== | 2,401 | Pytables support for hierarchical keys | {
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} | 5 | "2012-12-01T04:34:23Z" | "2014-06-27T02:54:09Z" | "2012-12-01T20:45:18Z" | CONTRIBUTOR | null | GH #2391
support for keys of the form: foo/bar/bah
includes docs & tests
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https://api.github.com/repos/pandas-dev/pandas/issues/2402 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2402/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2402/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2402/events | https://github.com/pandas-dev/pandas/issues/2402 | 8,904,373 | MDU6SXNzdWU4OTA0Mzcz | 2,402 | import pandas fails with ImportError: cannot import name Series | {
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> > > import pandas
> > > cannot import name Series
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/Library/Python/2.7/site-packages/pandas/**init**.py", line 10, in <module>
> > > import pandas.lib as lib
> > > File "/Library/Python/2.7/site-packages/pandas/pandas/**init**.py", line 33, in <module>
> > > from pandas.io.pytables import HDFStore
> > > File "/Library/Python/2.7/site-packages/pandas/io/pytables.py", line 12, in <module>
> > > from pandas import (
> > > ImportError: cannot import name Series
I have looked for a solution, but am unable to find one. Can someone please help?
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https://api.github.com/repos/pandas-dev/pandas/issues/2403 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2403/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2403/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2403/events | https://github.com/pandas-dev/pandas/issues/2403 | 8,906,315 | MDU6SXNzdWU4OTA2MzE1 | 2,403 | BUG: Consolidate not converting datetimes? | {
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on 0.10 dev
```
import pandas
import pandas.util.testing as tm
from pandas import Timestamp
df = tm.makeDataFrame()
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['bool1'] = df['A'] > 0
df['bool2'] = df['B'] > 0
df['bool3'] = True
df['int1'] = 1
df['int2'] = 2
df['datetime'] = Timestamp('20010101')
df = df.consolidate()
In [16]: df.get_dtype_counts()
Out[16]:
bool 3
float64 4
int64 2
object 3
In [18]: df.convert_objects().get_dtype_counts()
Out[18]:
bool 3
datetime64[ns] 1
float64 4
int64 2
object 2
```
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} | 3 | "2012-12-02T20:30:31Z" | "2017-01-25T21:01:57Z" | "2017-01-25T15:52:33Z" | MEMBER | null | Not sure if this can be trimmed much, but...
```
In [5]: timeit pandas.tseries.tools.dateutil_parse('20000104', datetime(1, 1, 1))
10000 loops, best of 3: 76.6 us per loop
```
Testing should be higher, at the `parse_time_string` level
Time spent mostly in dateutil:
```
In [8]: timeit dateutil.parser.parse('20000104')
10000 loops, best of 3: 66.5 us per loop
```
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Both README.rst and the docs
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} | 2 | "2012-12-03T01:00:29Z" | "2014-06-12T10:20:37Z" | "2012-12-07T16:06:56Z" | CONTRIBUTOR | null | updated whatsnew/RELEASE
added boolean comparison methods to panel.py (didn't exist before)
all tests pass
need to update the link to dsintro-panel4d section in the whatsnew docs....
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scipy and matplotlib tests.
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} | 2 | "2012-12-03T18:05:05Z" | "2013-02-22T19:43:43Z" | "2013-02-22T19:43:43Z" | CONTRIBUTOR | null | thinking of:
lots of parts of align
comparison ops,
parts of apply (and put special cases in the sub-classes)
pop
align & where
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```
In [3]: url = 'http://brianckeegan.com/data/candidates-spanish.txt'
In [4]: pd.read_table(url, sep='\t', encoding='utf-16')
Segmentation fault: 11 (core dumped)
```
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} | 1 | "2012-12-03T23:05:29Z" | "2016-09-21T10:14:28Z" | "2016-09-21T10:13:26Z" | MEMBER | null | http://stackoverflow.com/questions/13692418/does-pandas-cache-values-on-ix-call
I believe this will be mostly remedied when the equivalent of the `range-index` branch is merged.
```
import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.cbook as mc
import inspect
print 'pandas', pd.__version__
li_list = []
for i in range(50):
li_list.append(pd.DataFrame(data=np.random.randn(50, 17000)))
def first_element():
a = 0
for i in range(50):
b = li_list[0] #Only access first element
for j in b.columns:
a += b.ix[i, j]
def all_in_list():
a = 0
for i in range(50):
b = li_list[i] #Access all in list
for j in b.columns:
a += b.ix[i, j]
print(inspect.getabsfile(b.__class__))
break
if __name__ == '__main__':
# first_element()
all_in_list()
print(mc.report_memory())
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2422 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2422/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2422/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2422/events | https://github.com/pandas-dev/pandas/issues/2422 | 8,970,810 | MDU6SXNzdWU4OTcwODEw | 2,422 | what does print_config.digits do? | {
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} | 2 | "2012-12-04T01:33:23Z" | "2014-06-26T15:04:44Z" | "2012-12-06T02:37:29Z" | NONE | null | I'm documenting the existing options in the former print_config, and `digits`
is undocumented, nor have I found any use of it anywhere in the code.
Anyone know why it's there and if there's a reason not to chuck it?
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https://api.github.com/repos/pandas-dev/pandas/issues/2423 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2423/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2423/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2423/events | https://github.com/pandas-dev/pandas/issues/2423 | 8,972,989 | MDU6SXNzdWU4OTcyOTg5 | 2,423 | BUG/TST: Panel4d comparison operator | {
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} | 2 | "2012-12-04T03:57:07Z" | "2012-12-04T04:50:34Z" | "2012-12-04T04:50:34Z" | NONE | null | pandas.tests.test_panel4d:TestPanel4d. test_setitem fails on 3.x with
``` python
Traceback (most recent call last):
File "/home/travis/virtualenv/python3.1/lib/python3.1/site-packages/pandas-0.10.0.dev_1b7ceed-py3.1-linux-i686.egg/pandas/tests/test_panel4d.py", line 368, in test_setitem
self.panel4d['lP'] = self.panel4d['l1'] > 0
TypeError: unorderable types: Panel() > int()
```
It only passes on 2.x because it's more liberal with respect to bool coercion,
so you get the rather unhelpful:
``` python
In [44]: p['l1']
Out[44]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 30 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-02-11 00:00:00
Minor_axis axis: A to D
In [45]: p['l1'] > 1
Out[45]: True
```
Probably not the intended result.
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} | 2 | "2012-12-04T13:30:15Z" | "2012-12-09T22:41:49Z" | "2012-12-09T22:41:49Z" | CONTRIBUTOR | null | {
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|
https://api.github.com/repos/pandas-dev/pandas/issues/2425 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2425/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2425/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2425/events | https://github.com/pandas-dev/pandas/issues/2425 | 8,990,807 | MDU6SXNzdWU4OTkwODA3 | 2,425 | Allow level wildcard via slice(None) in df.ix[] with MultiIndex | {
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} | 5 | "2012-12-04T17:01:51Z" | "2013-12-18T19:59:09Z" | "2013-12-18T19:59:09Z" | CONTRIBUTOR | null | ```
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = zip(*arrays)
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = DataFrame(randn(3, 8), index=['A', 'B', 'C'], columns=index)
df = df.T
```
The following works:
```
df.A.ix[slice(None), 'two']
first
bar 0.233442
baz -1.551486
foo 0.175177
qux 1.524440
Name: A
```
It is very possible that this is something about a MultiIndex DataFrame that I don't understand, but I think the following should work:
```
df.ix[slice(None), 'two']
```
Instead of the expected:
```
first
bar 0.233442 0.359638 2.061794
baz -1.551486 -1.888951 0.172784
foo 0.175177 -1.085777 -0.988670
qux 1.524440 0.070535 1.006851
```
I get a KeyError:
...
/sjr/beodata/local/python_linux/lib/python2.6/site-packages/pandas/core/internals.pyc in _check_have(self, item)
949 def _check_have(self, item):
950 if item not in self.items:
--> 951 raise KeyError('no item named %s' % com.pprint_thing(item))
952
953 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: u'no item named two'
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} | 4 | "2012-12-04T17:21:05Z" | "2014-03-07T18:47:08Z" | "2014-03-07T18:47:08Z" | NONE | null | could you switch to calling urllib2.open (instead of urllib.open) in this function, so that it works seamlessly with http proxies?
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https://api.github.com/repos/pandas-dev/pandas/issues/2427 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2427/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2427/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2427/events | https://github.com/pandas-dev/pandas/issues/2427 | 8,995,335 | MDU6SXNzdWU4OTk1MzM1 | 2,427 | Potential bug resulting in MemoryError | {
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} | [] | closed | false | null | [] | null | 9 | "2012-12-04T19:24:07Z" | "2012-12-05T02:17:24Z" | "2012-12-05T02:17:24Z" | NONE | null | I'm performing the following set of consecutive `merge` operations in pandas on a few small tables (100k rows by ~15 columns) that are read from file using `read_table`. Although the tables are small and I run it on a machine with over 60 GB of RAM, I consistently get `MemoryError` from the `merge` operation, and the traceback points to:
```
File "/home/yarden/.local/lib/python2.6/site-packages/pandas-0.9.0-py2.6-linux-x86_64.egg/pandas/tools/merge.py", line 437, in _get_join_indexers
return join_func(left_group_key, right_group_key, max_groups)
File "join.pyx", line 105, in pandas.lib.left_outer_join (pandas/src/tseries.c:118232)
File "join.pyx", line
```
The following zip contains the short script (`make_table.py`) and the necessary text files to reproduce the bugs:
http://genes.mit.edu/burgelab/yarden/merge_error_pandas.zip
To generate the error, simply run:
`
python make_table.py
`
which for me yields the output:
```
Merging known to Ensembl...
- Merge took 0.48 secs
Merging kgXref...
Traceback (most recent call last):
File "make_table.py", line 137, in <module>
main()
File "make_table.py", line 124, in main
right_on=["kgID"])
File "/home/yarden/.local/lib/python2.6/site-packages/pandas-0.9.0-py2.6-linux-x86_64.egg/pandas/tools/merge.py", line 33, in merge
return op.get_result()
File "/home/yarden/.local/lib/python2.6/site-packages/pandas-0.9.0-py2.6-linux-x86_64.egg/pandas/tools/merge.py", line 180, in get_result
join_index, left_indexer, right_indexer = self._get_join_info()
File "/home/yarden/.local/lib/python2.6/site-packages/pandas-0.9.0-py2.6-linux-x86_64.egg/pandas/tools/merge.py", line 255, in _get_join_info
sort=self.sort, how=self.how)
File "/home/yarden/.local/lib/python2.6/site-packages/pandas-0.9.0-py2.6-linux-x86_64.egg/pandas/tools/merge.py", line 437, in _get_join_indexers
return join_func(left_group_key, right_group_key, max_groups)
File "join.pyx", line 105, in pandas.lib.left_outer_join (pandas/src/tseries.c:118232)
File "join.pyx", line 190, in pandas.lib._get_result_indexer (pandas/src/tseries.c:119829)
MemoryError
```
I'm happy to provide any information that might be needed and/or test code that might fix this. Any insights on what might be causing this will be greatly appreciated.
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} | 0 | "2012-12-04T22:32:26Z" | "2012-12-07T15:41:51Z" | "2012-12-07T15:41:51Z" | CONTRIBUTOR | null | Just a small update to the gotchas page to reference a potential
gotcha using the reindex_like functionality.
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} | 2 | "2012-12-04T23:22:47Z" | "2013-01-20T01:44:59Z" | "2013-01-20T01:44:59Z" | MEMBER | null | ```
Pandas Version: 0.10.0.dev-e80d24e
I get the following error when reading a CSV file that was previously correctly read with the old Pandas 0.09 read_csv() code.
CParserError: Error tokenizing data. C error: Expected 41 fields in line 7123, saw 40
Looks like the tokenizer will fail if it encounters a line with missing data at the end if there are no trialling commas for the last missing column(s):
A,B,C,D
1,2,3,4 <-OK
1,3,3, <-OK
1,4,5 <-Fail :(
I know this is not a clean CSV but having this feature may save some headaches when reading in files, especially since some CSV writers do not add the trailing commas.
Thanks,
-Gagi
```
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} | 2 | "2012-12-05T00:50:14Z" | "2012-12-05T01:53:42Z" | "2012-12-05T01:53:42Z" | NONE | null | Ran into this while trying some dropna and merge type operations
on a pickled data frame I loaded back i, It died with an `AttributeError`
on `self._known_consolidated`.
``` python
df = pd.DataFrame( [[1,2]])
df.save("/tmp/1.pickle")
print df._data._known_consolidated
df2=df.load("/tmp/1.pickle")
print df2._data._known_consolidated
```
looking at `internals.BLockManager.__setstate__` and `__getstate__` [Here](https://github.com/pydata/pandas/blob/master/pandas/core/internals.py#L547), those
attributes are not saved off.
a quick fix would be to just init to false on load, But is there a legacy reason
why getstate drops everything beyond the 3rd arg or can that be extended?
is there a versioning mechanism?
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} | 2 | "2012-12-05T02:19:33Z" | "2013-12-21T19:52:10Z" | "2013-12-21T19:21:36Z" | NONE | null | see #2427.
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} | 8 | "2012-12-05T17:53:20Z" | "2020-03-23T08:45:22Z" | "2020-03-23T08:45:22Z" | NONE | null | I noticed that when using a boolean comparison operator on a column with null values, Pandas does not propogate the null values to the result. For example, if I do the following:
```
df.foo > 0
```
the resulting series includes False entries where there were missing values in `foo`.
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} | 2 | "2012-12-05T21:27:50Z" | "2012-12-07T17:13:13Z" | "2012-12-07T17:13:13Z" | NONE | null | Using pandas 0.9
get_loc in tseries/index.py is getting called a lot more than necessary, and its fairly slow given the attempts at converting strings to dates in the exception handler. if you breakpoint get_loc and try things like truncate with a datetime range, calling as_matrix, or even just pulling slices, you'll notice it getting called with a key value for all types of magic methods and object variables at frame construction and slicing time.
As an example, when constructing a dataframe, the superclass calls:
self._item_cache = {}
That in turns calls __setattr_ which is overriden and it ends up calling get_loc off the index with a key of _item_cache (only to get back a KeyError).
Based off some cprofile results on the same set of code, my reindex calls using the repository codebase (for 0.9) results in 238534 calls to reindex that take 155.451 seconds. If I modify get_loc with the statement below, it drops to 50.54 seconds. This is closer to older versions of pandas that I had used. Similar improvements are found in many major operations (transpose, as_matrix, etc).
Hack code I added to test speedup - add this at beginning of get_loc:
```
if isinstance(key, str) and key[0] == '_':
raise KeyError(key)
```
There's probably a better way to solve this directly in the **setattr** though.
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} | 2 | "2012-12-06T01:22:47Z" | "2014-06-14T05:45:22Z" | "2012-12-07T23:09:01Z" | CONTRIBUTOR | null | by default no effect,
set_option('print.expand_frame_repr', True) to have it print out wider DataFrames instead of just info repr.
by default uses 80 characters as the width of the line and can be set like set_option('print.line_width', 120)
also adds set_option('test.interactive', False) option for testing purposes
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} | 5 | "2012-12-06T12:09:25Z" | "2014-11-20T16:45:49Z" | "2013-03-22T14:58:02Z" | CONTRIBUTOR | null | This was mentioned on [StackOverflow](http://stackoverflow.com/questions/13737992/indexing-timeseries-by-date-string), I thought I ought to post it here. I'm not sure whether or not this is a bug:
You can select by a date string in an ordered tseries, but not in an ordered one:
```
import pandas as pd
from numpy.random import randn
from random import shuffle
rng = pd.date_range(start='2011-01-01', end='2011-12-31')
rng2 = list(rng)
shuffle(rng2)
```
```
ts = pd.Series(randn(len(rng)), index=rng)
ts2 = pd.Series(randn(len(rng)), index=rng2)
```
```
ts.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-01-01 00:00:00, ..., 2011-12-31 00:00:00]
Length: 365, Freq: D, Timezone: None
ts['2011-01-01']
# -1.1454418070543406
```
```
ts2.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-04-16 00:00:00, ..., 2011-03-10 00:00:00]
Length: 365, Freq: None, Timezone: None
ts2['2011-01-01'] # same for ts2.ix['2011-01-01']
```
```
TimeSeriesError Traceback (most recent call last)
<ipython-input-112-051e81424a0d> in <module>()
----> 1 ts2['2011-01-01']
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/core/series.pyc in __getitem__(self, key)
462 def __getitem__(self, key):
463 try:
--> 464 return self.index.get_value(self, key)
465 except InvalidIndexError:
466 pass
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/tseries/index.pyc in get_value(self, series, key)
1015
1016 try:
-> 1017 loc = self._get_string_slice(key)
1018 return series[loc]
1019 except (TypeError, ValueError, KeyError):
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/tseries/index.pyc in _get_string_slice(self, key)
1062 asdt, parsed, reso = parse_time_string(key, freq)
1063 key = asdt
-> 1064 loc = self._partial_date_slice(reso, parsed)
1065 return loc
1066
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/tseries/index.pyc in _partial_date_slice(self, reso, parsed)
977 def _partial_date_slice(self, reso, parsed):
978 if not self.is_monotonic:
--> 979 raise TimeSeriesError('Partial indexing only valid for ordered time'
980 ' series')
981
TimeSeriesError: Partial indexing only valid for ordered time series
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2438/events | https://github.com/pandas-dev/pandas/issues/2438 | 9,054,525 | MDU6SXNzdWU5MDU0NTI1 | 2,438 | Certain slices of Series can cause problems with matplotlib boxplot | {
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} | 5 | "2012-12-06T14:07:40Z" | "2014-06-08T12:04:07Z" | "2014-06-08T01:40:08Z" | NONE | null | We just encountered a problem when boxplotting a slice of a Series that does not contain the first element (i.e., the element with index 0). Here is an example:
``` python
plt.boxplot(pd.Series([1,2,3,4])[1:])
```
Which gives the following output:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-29-b88183a29efb> in <module>()
----> 1 boxplot(pd.Series([1,2,3,4])[1:])
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/pyplot.pyc in boxplot(x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, hold)
2425 positions=positions, widths=widths,
2426 patch_artist=patch_artist, bootstrap=bootstrap,
-> 2427 usermedians=usermedians, conf_intervals=conf_intervals)
2428 draw_if_interactive()
2429 finally:
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axes.pyc in boxplot(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals)
5744 if hasattr(x, 'shape'):
5745 if len(x.shape) == 1:
-> 5746 if hasattr(x[0], 'shape'):
5747 x = list(x)
5748 else:
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
468 def __getitem__(self, key):
469 try:
--> 470 return self.index.get_value(self, key)
471 except InvalidIndexError:
472 pass
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
676 """
677 try:
--> 678 return self._engine.get_value(series, key)
679 except KeyError, e1:
680 if len(self) > 0 and self.inferred_type == 'integer':
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.IndexEngine.get_value (pandas/src/tseries.c:123878)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.IndexEngine.get_value (pandas/src/tseries.c:123693)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.IndexEngine.get_loc (pandas/src/tseries.c:124430)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.Int64HashTable.get_item (pandas/src/tseries.c:18340)()
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.Int64HashTable.get_item (pandas/src/tseries.c:18284)()
KeyError: 0
```
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} | 25 | "2012-12-06T15:48:14Z" | "2016-10-16T10:40:13Z" | "2012-12-06T16:38:04Z" | NONE | null | I just forked and cloned the master branch (last commit from 6th December), and building fails with the following error :
```
> python setup.py build_ext --inplace
running build_ext
building 'pandas.index' extension
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -I/home/spiq/pandas/env/local/lib/python2.7/site-packages/numpy/core/include -Ipandas/src/klib -Ipandas/src -I/usr/include/python2.7 -c pandas/index.c -o build/temp.linux-i686-2.7/pandas/index.o
gcc: error: pandas/index.c: No such file or directory
gcc: fatal error: no input files
compilation terminated.
error: command 'gcc' failed with exit status 4
```
Any idea ?
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} | 12 | "2012-12-06T16:26:59Z" | "2018-04-23T10:20:41Z" | "2018-04-23T10:20:41Z" | NONE | null | Hello,
I've run into a nasty bug as I try to work with dataframes and threads.
The problem is that one thread modifies a local dataframe by removing a column but in doing so somehow corrupts the dataframe of the other thread.
My input data is a dataframe of multiple data types (float and integer in the example). This dataframe is grouped according to column AA. Each group is placed into a synchronised Queue (from the standard library). Two threads will consume the items from the queue and place the results in a standard python List (whose append method is atomic). The thread internally creates an instance of a FrameProcessor and then enters an infinite job processing loop until the input queue is empty. The FrameProcessor is a callable object. It takes a dataframe as input and processes it in two levels. To process the first level it selects the rows of the input dataframe that match a condition and passes the resulting dataframe to a method that undertakes the second level processing. While processing the second level, the input dataframe is modified by removing a column and creating a new dataframe from the results of some calculations.
The code breaks in two places:
```
measurements = df[ df['BB'] == tag ] #from method _processFirstLevel
```
```
cc = df.pop('CC') #from method _processSecondLevel
```
and for different reasons (last line of trace shown):
```
File "C:\Python26\lib\site-packages\pandas\core\internals.py", line 570, in _verify_integrity
assert(len(self.items) == tot_items)
AssertionError
```
```
File "C:\Python26\lib\site-packages\pandas\core\internals.py", line 26, in __init__
assert(len(items) == len(values))
AssertionError
```
```
File "C:\Python26\lib\site-packages\pandas\core\index.py", line 315, in __getitem__
return arr_idx[key]
IndexError: index out of bounds
```
```
File "C:\Python26\lib\site-packages\numpy\lib\function_base.py", line 3336, in delete
"invalid entry")
ValueError: invalid entry
```
As much I can tell is that it seems that although two different objects (one for each thread) are working on two different dataframes (from the groups) they corrupt each other's local variables by removing the column.
Maybe the problem is because of my implementation (the way I set up the threads, jobs, etc.). However, I suspect the problem lies withing the deep support layers of the DataFrame class (such as BlockManager, etc.).
I am using
- Python 2.6.6
- Pandas 0.9.0
- NumPy 1.6.1
- Windows 7 Professional 64-bit
Why is data corruption ocurring in threads working with no shared resources?
Below is the code that you can use to reproduce the bug. Bear in mind that because of the threads, the bug may not happen on the first run, or you wont get the same effect every time.
```
import threading
import numpy as np
import pandas as pd
import Queue
class FrameProcessor(object):
def __call__(self, *args, **kwargs):
chunk = args[0]
result = self._processFirstLevel(chunk)
return result
def _processFirstLevel(self, df):
second_level_tags = list(df['BB'].unique())
results = []
while len(second_level_tags) > 0:
tag = second_level_tags.pop()
measurements = df[ df['BB'] == tag ]
result = self._processSecondLevel(measurements)
result['BB'] = tag
results.append(result)
result_Frame = pd.concat(results, axis=0)
return result_Frame
def _processSecondLevel(self, df):
cc = df.pop('CC')
result_row = {
'cc_avg': cc.mean()
,'measurements_in_avg':len(df)}
result = pd.DataFrame([result_row])
return result
def test_():
num_of_groups = 10
group_size = 30
sub_group_size = 5
rows = group_size*num_of_groups
a = []
[a.extend(np.repeat(x,group_size)) for x in range(num_of_groups)]
a = np.array(a)
b = np.array(np.tile(range(sub_group_size),rows/sub_group_size))
c = np.random.randn(rows)
p = np.random.random_integers(0,10,rows)
q = np.random.randn(rows)
x = np.vstack((a,b,c,p,q))
dates = np.asarray(pd.date_range('1/1/2000', periods=rows))
df = pd.DataFrame(x.T, index=dates, columns=['AA', 'BB', 'CC','P','Q'])
results = []
inbox = Queue.Queue()
group_by_columns = ['AA']
groups = df.groupby(group_by_columns)
for name, group in groups:
inbox.put(groups.get_group(name))
def workerShell():
processor = FrameProcessor()
while True:
try:
job = inbox.get(False)
result = processor(job)
results.append(result)
inbox.task_done()
print '{0} job done. Jobs left: {1}'.format(id(processor),inbox.qsize())
except Queue.Empty:
break
thread1 = threading.Thread(target=workerShell)
thread2 = threading.Thread(target=workerShell)
thread1.start()
thread2.start()
inbox.join()
thread1.join()
thread2.join()
df = pd.concat(results, axis=0)
return df
if __name__ == '__main__':
print 'pandas',pd.__version__
print 'numpy',np.__version__
for i in range(5):
print '--------------test:',i
test_()
```
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} | 0 | "2012-12-06T21:00:21Z" | "2012-12-07T22:34:25Z" | "2012-12-07T22:34:25Z" | NONE | null | import pandas as pd
from StringIO import StringIO
txt = """time,id,value
2012-10-20,1,1.5
2012-10-20,1,2.5
2012-10-21,1,1.5
"""
xxx = pd.read_csv(StringIO(txt), parse_dates=0)
print(xxx.set_index(["time"])[xxx.id == 1])
print(xxx.set_index(["time", "id"]).to_panel().value)
It prints out:
id value
time
2012-10-20 1 1.5
2012-10-20 1 2.5
2012-10-21 1 1.5
id 1
time
2012-10-20 1.5
2012-10-21 2.5
The value for panel on 2012-10-21 is incorrect.
I am using pandas 0.9.0
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} | 10 | "2012-12-06T22:15:57Z" | "2020-02-11T02:23:48Z" | "2012-12-10T21:22:21Z" | MEMBER | null | xref http://stackoverflow.com/questions/13719946/python-pandas-trailing-delimiter-confuses-read-csv
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`result = Timestamp('2002-06-28T01:00:00.000000000+0100')`
from http://stackoverflow.com/questions/13703720/converting-between-datetime-timestamp-and-datetime64
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} | 1 | "2012-12-06T22:49:15Z" | "2014-05-28T22:53:39Z" | "2014-05-28T22:53:39Z" | MEMBER | null | This was interesting: http://stackoverflow.com/questions/13675749/resample-searies-dataframe-with-frequency-anchored-to-specific-time
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} | 7 | "2012-12-06T23:43:15Z" | "2014-06-18T17:17:37Z" | "2012-12-09T18:31:02Z" | NONE | null | ``` python
import pandas as pd
df = pd.DataFrame([tuple([i for i in range(200)]) for i in range(10000)], columns=["%s" % i for i in range(200)])
df.to_excel('test.xls')
python testlarge.py ⏎
Traceback (most recent call last):
File "testlarge.py", line 9, in <module>
df.to_excel('test.xls')
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.10.0.dev_67ef67f-py2.7-macosx-10.8-x86_64.egg/pandas/core/frame.py", line 1445, in to_excel
startrow=startrow, startcol=startcol)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.10.0.dev_67ef67f-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.py", line 2068, in write_cells
self._writecells_xls(cells, sheet_name, startrow, startcol)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/pandas-0.10.0.dev_67ef67f-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.py", line 2131, in _writecells_xls
val, style)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Worksheet.py", line 1032, in write
self.row(r).write(c, label, style)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Row.py", line 236, in write
style_index = self.__parent_wb.add_style(style)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Workbook.py", line 303, in add_style
return self.__styles.add(style)
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Style.py", line 90, in add
return self._add_style(style)[1]
File "/Volumes/Locodrive/Dev/.virtualenvs/pandasdev/lib/python2.7/site-packages/xlwt/Style.py", line 149, in _add_style
raise ValueError("More than 4094 XFs (styles)")
V
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2448 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2448/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2448/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2448/events | https://github.com/pandas-dev/pandas/issues/2448 | 9,090,272 | MDU6SXNzdWU5MDkwMjcy | 2,448 | TimeSeries shift in plot when plotting different frequencies on same axis | {
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} | 2 | "2012-12-07T14:55:11Z" | "2012-12-09T17:55:59Z" | "2012-12-09T17:55:59Z" | NONE | null | using pandas.TimeSeries.plot() causes a shift in the plotted timeseries when plotting two different TimeSeries objects on the same axis. When plotting in subplots or plotting using pylab/matplotlib directly it behaves as expected. Issue remains if objects are DataFrames instead of Series.
Version 0.9.1
Simple code to reproduce:
import pandas as pd
import pylab as pl
ts_ind=pd.date_range('2012-01-01 13:00', '2012-01-02', freq='H')
ts_data=pl.random(12)
# hourly timeseries
ts=pd.TimeSeries(ts_data, index=ts_ind)
# minute frequency timeseries
ts2=ts.asfreq('T').interpolate()
# using TimeSeries.plot()
pl.figure()
ts.plot()
ts2.plot(style='r')
# using pylab.plot()
pl.figure()
pl.plot(ts.index, ts.values)
pl.plot(ts2.index, ts2.values, '-r')
# using TimeSeries.plot() on different axes
pl.figure()
pl.subplot(211)
ts.plot()
pl.subplot(212)
ts2.plot(style='r')
# the two timeseries objects are correct, problem lies with the plotting:
ts.index[ts==ts.max()]
ts2.index[ts2==ts2.max()]
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} | 2 | "2012-12-07T17:46:50Z" | "2012-12-07T21:05:26Z" | "2012-12-07T21:05:26Z" | CONTRIBUTOR | null | I was wondering if it was possible to elegantly extract the values of a MultiIndex which contains dates as datetime (and not an ndarray of datetime64[ns] which screws things up)?
```
>>> print df
numtrades
date id
2012-01-03 11891 1
14259 2
2012-01-05 14259 1
2012-01-06 11891 7
2012-01-09 11891 2
2012-01-10 11891 12
14259 4
10304 2
2012-01-11 11891 11
14259 8
>>> df.index[0]
(<Timestamp: 2012-01-03 00:00:00>, 11891)
>>> df.index.get_level_values('date')
array([1970-01-16 16:00:00, 1970-01-16 16:00:00, 1970-01-16 64:00:00,
1970-01-16 88:00:00, 1970-01-16 160:00:00, 1970-01-16 184:00:00,
1970-01-16 184:00:00, 1970-01-16 184:00:00, 1970-01-16 208:00:00,
1970-01-16 208:00:00], dtype=datetime64[ns])
```
Of course, we can always use `to_datetime` and then `to_pydatetime`, is that the preferred method? Am I missing something?
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} | 6 | "2012-12-07T20:46:39Z" | "2014-03-15T14:34:20Z" | "2014-03-15T14:34:20Z" | MEMBER | null | Using the `TimeGrouper` is both undocumented and not necessarily intended for average users. A cleaner, simpler API would be nice cc @dalejung related to bug in #2300
related to #3794
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} | 2 | "2012-12-07T23:37:59Z" | "2012-12-08T00:16:32Z" | "2012-12-08T00:16:32Z" | NONE | null | That's the shortest example I could find. Data gets somehow corrupted when creating new columns while iterating over the index. Pre-generating initial values for columns solved my problem (commented out part)
I am using pandas 0.9.0
``` python
df = pd.DataFrame(index=[0,1])
df[0] = nan
wasCol = {}
# uncommenting these makes the results match
#for col in xrange(100, 200):
# wasCol[col] = 1
# df[col] = nan
for i, dt in enumerate(df.index):
for col in xrange(100, 200):
if not col in wasCol:
wasCol[col] = 1
df[col] = nan
df[col][dt] = i
myid = 100
print(len(df.ix[isnan(df[myid]), [myid]]))
print(len(df.ix[isnan(df[myid]), [myid]]))
```
Outputs:
```
0
1
```
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} | 0 | "2012-12-08T02:49:18Z" | "2012-12-09T18:18:41Z" | "2012-12-09T18:18:41Z" | CONTRIBUTOR | null | typos in io.rst
moved some api changes docs to the proper place
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} | 1 | "2012-12-08T03:34:59Z" | "2012-12-08T22:36:17Z" | "2012-12-08T22:36:17Z" | CONTRIBUTOR | null | It would have the nice effect of having
``` python
ohlc = OrderedDict()
ohlc['open'] = 'first'
ohlc['high'] = 'max'
ohlc['low'] = 'min'
ohlc['close'] = 'last'
gdf = grouped.agg(ohlc)
```
come out with the proper column order.
core/groupby.py
``` python
1593 else:
1594 result = DataFrame(result)
```
result is ordered correctly before being passed to DataFrame constructor.
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} | 0 | "2012-12-08T18:20:21Z" | "2012-12-09T19:42:15Z" | "2012-12-09T19:42:15Z" | MEMBER | null | like the `lineterminator` dialect option
ref: http://stackoverflow.com/questions/13779308/how-to-parse-delimited-data-in-python-pandas-to-create-dataframe
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} | 11 | "2012-12-09T00:44:31Z" | "2014-07-20T08:52:27Z" | "2012-12-09T14:34:57Z" | CONTRIBUTOR | null | When I put this into a ipython notebook cell, an exception is thrown.
``` python
import pandas
data2 = [u"test", u"ß", u"ä", u"á"]
df2 = pandas.DataFrame({"a":data2})
print(df2["a"][1])
df2
```
```
ß
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-5-0bb7d6f7d515> in <module>()
2 df2 = pandas.DataFrame({"a":data2})
3 print(df2["a"][1])
----> 4 df2
C:\portabel\Python27\lib\site-packages\IPython\core\displayhook.pyc in __call__(self, result)
244 self.update_user_ns(result)
245 self.log_output(format_dict)
--> 246 self.finish_displayhook()
247
248 def flush(self):
C:\portabel\Python27\lib\site-packages\IPython\zmq\displayhook.pyc in finish_displayhook(self)
59 sys.stdout.flush()
60 sys.stderr.flush()
---> 61 self.session.send(self.pub_socket, self.msg, ident=self.topic)
62 self.msg = None
63
C:\portabel\Python27\lib\site-packages\IPython\zmq\session.pyc in send(self, stream, msg_or_type, content, parent, ident, buffers, track, header, metadata)
576
577 buffers = [] if buffers is None else buffers
--> 578 to_send = self.serialize(msg, ident)
579 to_send.extend(buffers)
580 longest = max([ len(s) for s in to_send ])
C:\portabel\Python27\lib\site-packages\IPython\zmq\session.pyc in serialize(self, msg, ident)
484 content = self.none
485 elif isinstance(content, dict):
--> 486 content = self.pack(content)
487 elif isinstance(content, bytes):
488 # content is already packed, as in a relayed message
C:\portabel\Python27\lib\site-packages\IPython\zmq\session.pyc in <lambda>(obj)
78 _version_info_list = list(IPython.version_info)
79 # ISO8601-ify datetime objects
---> 80 json_packer = lambda obj: jsonapi.dumps(obj, default=date_default)
81 json_unpacker = lambda s: extract_dates(jsonapi.loads(s))
82
C:\portabel\Python27\lib\site-packages\zmq\utils\jsonapi.pyc in dumps(o, **kwargs)
70 kwargs['separators'] = (',', ':')
71
---> 72 return _squash_unicode(jsonmod.dumps(o, **kwargs))
73
74 def loads(s, **kwargs):
C:\portabel\Python27\lib\site-packages\simplejson\__init__.pyc in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, encoding, default, use_decimal, namedtuple_as_object, tuple_as_array, bigint_as_string, sort_keys, item_sort_key, **kw)
332 sort_keys=sort_keys,
333 item_sort_key=item_sort_key,
--> 334 **kw).encode(obj)
335
336
C:\portabel\Python27\lib\site-packages\simplejson\encoder.pyc in encode(self, o)
235 # exceptions aren't as detailed. The list call should be roughly
236 # equivalent to the PySequence_Fast that ''.join() would do.
--> 237 chunks = self.iterencode(o, _one_shot=True)
238 if not isinstance(chunks, (list, tuple)):
239 chunks = list(chunks)
C:\portabel\Python27\lib\site-packages\simplejson\encoder.pyc in iterencode(self, o, _one_shot)
309 Decimal=Decimal)
310 try:
--> 311 return _iterencode(o, 0)
312 finally:
313 key_memo.clear()
UnicodeDecodeError: 'utf8' codec can't decode byte 0xdf in position 22: invalid continuation byte
```
This also happens when I have umlauts in csv files and specify an encoding:
```
df3 = pandas.read_csv("file_with_unlauts.csv", encoding="iso-8859-15")
print(df3.head(10)["name"][8]) # prints the right chars
df3.head(10) # throws the above error
```
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} | 8 | "2012-12-09T02:21:15Z" | "2012-12-09T18:04:08Z" | "2012-12-09T18:04:08Z" | NONE | null | ```
>>> pandas.read_csv(StringIO('1,2'), names=['a', 'b'])
Empty DataFrame
Columns: [a, b]
Index: []
>>> pandas.read_csv(StringIO('1,2'), names=['a', 'b'], header=None)
a b
0 1 2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2460/events | https://github.com/pandas-dev/pandas/issues/2460 | 9,118,488 | MDU6SXNzdWU5MTE4NDg4 | 2,460 | ENH: df.grep(col,pat) and df.dselect(col,"expr") | {
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 39 | "2012-12-09T07:40:01Z" | "2014-01-10T17:21:54Z" | "2013-04-09T01:17:00Z" | NONE | null | wes, how would you feel about adding something like the following as
a Dataframe method? especially with method chaining I would find this
useful. Will add handling for datatypes and so on.
``` ipython
In [32]: from pandas.util.testing import makeCustomDataframe as mkdf
In [33]: def grep(df,col,pat):
...: vals=df[col]
...: return df.ix[[bool(re.search(pat,unicode(x))) for x in vals]]
In [34]: df=mkdf(20,2)
...: print df
C0 C_l0_g0 C_l0_g1 \
R0
R_l0_g0 R0C0 R0C1
R_l0_g1 R1C0 R1C1
R_l0_g10 R2C0 R2C1
R_l0_g11 R3C0 R3C1
R_l0_g12 R4C0 R4C1
R_l0_g13 R5C0 R5C1
R_l0_g14 R6C0 R6C1
R_l0_g15 R7C0 R7C1
R_l0_g16 R8C0 R8C1
R_l0_g17 R9C0 R9C1
R_l0_g18 R10C0 R10C1
R_l0_g19 R11C0 R11C1
R_l0_g2 R12C0 R12C1
R_l0_g20 R13C0 R13C1
R_l0_g3 R14C0 R14C1
R_l0_g4 R15C0 R15C1
R_l0_g5 R16C0 R16C1
R_l0_g6 R17C0 R17C1
R_l0_g7 R18C0 R18C1
R_l0_g8 R19C0 R19C1
In [35]: grep(df,"C_l0_g0","R.*6")
Out[35]:
C0 C_l0_g0 C_l0_g1 \
R0
R_l0_g14 R6C0 R6C1
R_l0_g5 R16C0 R16C1
```
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} | 10 | "2012-12-09T09:24:40Z" | "2016-10-12T23:03:23Z" | "2014-01-10T12:24:30Z" | NONE | null | ``` python
hash(df.index)
> /home/user1/src/pandas/pandas/core/index.py(346)__hash__()
345 def __hash__(self):
--> 346 return hash(self.view(np.ndarray))
347
/home/user1/src/pandas/pandas/core/index.pyc in __hash__(self)
344
345 def __hash__(self):
--> 346 return hash(self.view(np.ndarray))
347
348 def __setitem__(self, key, value):
TypeError: unhashable type: 'numpy.ndarray'
```
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} | 1 | "2012-12-09T09:36:21Z" | "2012-12-09T16:43:42Z" | "2012-12-09T16:43:42Z" | NONE | null | The c-parser branch was merged in, however I don't see anything about this in the release notes.
Did I miss it?
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```
2011, 1, 10, 1000000
2011, 1, 11, 998785
2011, 1, 12, 1002940
2011, 1, 13, 1004815
2011, 1, 14, 1009415
...
2011, 12, 14, 1115020
2011, 12, 15, 1113532
2011, 12, 16, 1116016
2011, 12, 19, 1117444
2011, 12, 20, 1133860
```
read in via `numpy.loadtxt(...)` and converted to a DataFrame as follows:
``` python
import numpy as np
from pandas.core.frame import DataFrame as df
import datetime as dt
resultsFile = 'values.csv'
dtResults = np.dtype([('year',np.int),('month',np.int),('day',np.int),('value',np.float)])
results = np.loadtxt(resultsFile, dtype=dtResults, delimiter=",")
dates = [dt.datetime(r[0], r[1], r[2], 16) for r in results]
results = df([{'Date':d,'Portfolio':r[3]} for d,r in zip(dates,results)]).set_index('Date')
```
when performing the following simple daily returns calculation:
``` python
returns = results.diff()[1:]/results
returns.ix[0] = np.zeros(returns.shape[1])
```
I end up with the following results:
``` python
Portfolio
Date
2011-01-10 16:00:00 0.
2011-01-11 16:00:00 -0.001216
2011-01-12 16:00:00 0.004143
2011-01-13 16:00:00 0.001866
2011-01-14 16:00:00 0.004557
...
2011-12-14 16:00:00 -0.009245
2011-12-15 16:00:00 -0.001336
2011-12-16 16:00:00 0.002226
2011-12-19 16:00:00 0.001278
2011-12-20 16:00:00 0.014478
```
which are inaccurate.
Results should be:
``` python
array([[ 0. ],
[-0.001215 ],
[ 0.00416005],
[ 0.0018695 ],
[ 0.00457796],
...
[-0.00916 ],
[-0.00133451],
[ 0.00223074],
[ 0.00127955],
[ 0.01469067]])
```
as calculated using np array methods via the following:
``` python
from copy import deepcopy
vals = np.asarray(deepcopy(results))
vals[1:,] = (vals[1:,]-vals[:-1,])/vals[:-1,]
vals[0,] = np.zeros(vals.shape[1])
```
My environment is:
- PC: Mac OS X 10.8.2 on 2.6 GHz Intel Core i7 MacBook Pro
- Python: 2.7.3
- Pandas: 0.9.1
- Numpy: 1.6.2
Update:
I have been playing around with the code, and realised that:
``` python
returns = results.diff()[1:]/results
returns.ix[0] = np.zeros(returns.shape[1])
```
and
``` python
returns = results.diff()/results
returns.ix[0] = np.zeros(returns.shape[1])
```
oddly give the exact same results...so it is clear I bad assumptions about pandas DataFrame indexing, or there is an issue in indexing during division operation.
Additionally, why do I get the following results when evaluating?
``` python
>>>returns[1:]-returns[:-1]
Portfolio
Date
2011-01-10 16:00:00 NaN
2011-01-11 16:00:00 0
2011-01-12 16:00:00 0
2011-01-13 16:00:00 0
2011-01-14 16:00:00 0
...
2011-12-14 16:00:00 0
2011-12-15 16:00:00 0
2011-12-16 16:00:00 0
2011-12-19 16:00:00 0
2011-12-20 16:00:00 NaN
```
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} | 0 | "2012-12-09T15:09:15Z" | "2012-12-12T15:56:04Z" | "2012-12-12T15:56:04Z" | CONTRIBUTOR | null | Here is a small example:
<pre>
In [58]:
df = pd.DataFrame(0., index=[0,1,2], columns=[0,1,'A','B'])
df.filter(like='A')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-58-123fe8a23e8b> in <module>()
1 # BUG in pandas: cannot filter with like keyword if columns contain integers and string
2 df = pd.DataFrame(0., index=[0,1,2], columns=[0,1,'A','B'])
----> 3 df.filter(like='A')
/snfs2/HOME/abie/ENV/lib/python2.7/site-packages/pandas-0.10.0.dev_85b4b11-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in filter(self, items, like, regex)
2864 return self.reindex(columns=[r for r in items if r in self])
2865 elif like:
-> 2866 return self.select(lambda x: like in x, axis=1)
2867 elif regex:
2868 matcher = re.compile(regex)
/snfs2/HOME/abie/ENV/lib/python2.7/site-packages/pandas-0.10.0.dev_85b4b11-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in select(self, crit, axis)
318
319 if len(axis) > 0:
--> 320 new_axis = axis[np.asarray([crit(label) for label in axis])]
321 else:
322 new_axis = axis
/snfs2/HOME/abie/ENV/lib/python2.7/site-packages/pandas-0.10.0.dev_85b4b11-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in <lambda>(x)
2864 return self.reindex(columns=[r for r in items if r in self])
2865 elif like:
-> 2866 return self.select(lambda x: like in x, axis=1)
2867 elif regex:
2868 matcher = re.compile(regex)
TypeError: argument of type 'int' is not iterable
</pre>
This may have a simple solution, by making all of the labels into strings on line 320 of generic.py, I will investigate.
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} | 0 | "2012-12-09T16:22:39Z" | "2012-12-09T21:27:05Z" | "2012-12-09T21:27:05Z" | MEMBER | null | e.g. usecols=['b', 'd', 'e'] vs. usecols=[1, 2, 4]
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} | 0 | "2012-12-10T09:13:40Z" | "2015-05-05T22:27:56Z" | "2015-05-05T22:27:56Z" | NONE | null | in order to use pandas with Bloomberg time series data, I would like ability to set a time series with
i) hourly frequency for business days
ii) a given start and end time to that day
For the first part, using
bdate_range(startdate,enddate,freq='h')
doesn't do the right thing. Using 'freq=h' overrides the business days such that the resulting time series includes weekends too. The frequency alias 'bh' doesn't exist in the same way 'bm' does for example. Would it be possible to add this functionality? I could write something manually to go back and remove weekends, but this messes up the Frequency label on the time series such that later functions don't work (such as EWMVAR etc)
For the second part, this is useful functionality when calculating volatility/variance to be able to specify time ranges for each day. So a Business Hourly frequency starting at say 7am and ending 7pm on a day.
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} | 2 | "2012-12-10T15:39:06Z" | "2016-10-12T23:03:24Z" | "2012-12-10T17:35:36Z" | MEMBER | null | example from #2262
```
In [8]: df2['ts'][0].to_datetime()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-0663d65dd92c> in <module>()
----> 1 df2['ts'][0].to_datetime()
/home/wesm/code/pandas/pandas/tslib.so in pandas.tslib._Timestamp.to_datetime (pandas/tslib.c:8590)()
/home/wesm/code/pandas/pandas/tslib.so in pandas.tslib._Timestamp.to_datetime (pandas/tslib.c:8454)()
/home/wesm/code/pandas/pandas/tslib.so in pandas.tslib.convert_to_tsobject (pandas/tslib.c:10284)()
AttributeError: 'tzoffset' object has no attribute 'normalize'
In [9]: df2['ts'][0]
Out[9]: <Timestamp: 2012-11-15 10:38:04-0500, tz=tzoffset(None, -18000)>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2472 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2472/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2472/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2472/events | https://github.com/pandas-dev/pandas/issues/2472 | 9,146,459 | MDU6SXNzdWU5MTQ2NDU5 | 2,472 | read in a csv column as object fails | {
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```
authors = pandas.read_csv(csv_authors, encoding="iso-8859-15", converters={'author_id': object})
```
But it fails with this error stack:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-36-dbf3918f89b6> in <module>()
1 #%%timeit -n1 -r1
----> 2 authors = pandas.read_csv(csv_authors, encoding="iso-8859-15", converters={'author_id': object})
C:\portabel\Python27\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, header, index_col, names, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
365 buffer_lines=buffer_lines)
366
--> 367 return _read(filepath_or_buffer, kwds)
368
369 parser_f.__name__ = name
C:\portabel\Python27\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
190 return parser
191
--> 192 return parser.read()
193
194 _parser_defaults = {
C:\portabel\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
595 # self._engine.set_error_bad_lines(False)
596
--> 597 ret = self._engine.read(nrows)
598
599 if self.options.get('as_recarray'):
C:\portabel\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
902
903 try:
--> 904 data = self._reader.read(nrows)
905 except StopIteration:
906 if nrows is None:
C:\portabel\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader.read (pandas\src\parser.c:5640)()
C:\portabel\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_low_memory (pandas\src\parser.c:5857)()
C:\portabel\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_rows (pandas\src\parser.c:6725)()
C:\portabel\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._convert_column_data (pandas\src\parser.c:7601)()
C:\portabel\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser._apply_converter (pandas\src\parser.c:18118)()
TypeError: object.__new__() takes no parameters
```
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} | 1 | "2012-12-10T15:49:57Z" | "2014-06-29T00:33:46Z" | "2012-12-11T20:20:50Z" | CONTRIBUTOR | null | added generic docstrings for sub-classes so that they show the correct object type/axes for that class
update/keys methods corrected for sub-classess
more tests
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} | 1 | "2012-12-10T16:37:37Z" | "2013-07-17T15:31:35Z" | "2012-12-10T17:08:23Z" | CONTRIBUTOR | null | Apply to a DataFrame using lambda with a index/key (although that is in the DataFrame's index throws an error (a KeyError or IndexError respectively), perhaps it should exit more gracefully?
```
In [1]: x = pd.DataFrame(index=["a"])
In [2]: x
Out[2]:
Empty DataFrame
Columns: array([], dtype=object)
Index: array([a], dtype=object)
In [3]: x.apply(lambda x: x["a"], axis=1)
```
``` ---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-3-80540f6c1d18> in <module>()
----> 1 x.apply(lambda x: x["a"], axis=1)
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
3750 if not broadcast:
3751 if not all(self.shape):
-> 3752 is_reduction = not isinstance(f(_EMPTY_SERIES),
3753 np.ndarray)
3754 if is_reduction:
<ipython-input-447-80540f6c1d18> in <lambda>(x)
----> 1 x.apply(lambda x: x["a"], axis=1)
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/core/series.pyc in __getitem__(self, key)
462 def __getitem__(self, key):
463 try:
--> 464 return self.index.get_value(self, key)
465 except InvalidIndexError:
466 pass
/Library/Python/2.7/site-packages/pandas-0.9.0-py2.7-macosx-10.7-intel.egg/pandas/core/index.pyc in get_value(self, series, key)
689 raise InvalidIndexError(key)
690 else:
--> 691 raise e1
692 except Exception: # pragma: no cover
693 raise e1
KeyError: 'a'
```
_I am trying to wrangle data from a huge number of Excel sheets (converting each sheet into a DataFrame but need to clean them before they are merged into something more sensible... I wanted to apply functions across these without worrying which are empty..._
(same error in 0.9.1.)
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} | 4 | "2012-12-10T18:05:52Z" | "2012-12-17T17:12:09Z" | "2012-12-17T17:12:09Z" | CONTRIBUTOR | null | Also using to keep milestone open
- inf_as_na API change
- Google Analytics API example
- DONE Any additional C Parser notes etc
- Excel?
- DONE options API? @y-p
- plotting bugs
- scatter and grouped hist
- DONE wide frame repr
- PeriodIndex fixes
- groupby memory optimization
- DONE Resample API change !!!
- DONE centered moving window functions
- pivot annual hourly
- DONE Fillna API change
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} | 8 | "2012-12-10T18:54:38Z" | "2014-01-10T12:13:11Z" | "2014-01-10T12:13:03Z" | NONE | null | Initial implementation was #2370 by @locojay, which was very nice but suffered from
several corner-cases and ambiguity when the files were read back in.
f0aa065 was a stopgap measure to roll back the functionality so 0.10 was not delayed.
The enhancements should be brought back in 0.11, enahnced with parsing of
multiindex on both axes, support for index names everywhere, decisions on best
defaults, arguments and so on.
@locojay, are you still interested in completing this after a bit more discussion?
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} | 3 | "2012-12-10T19:17:46Z" | "2012-12-10T21:28:11Z" | "2012-12-10T21:28:11Z" | CONTRIBUTOR | null | - indexable columns were created and named in a legacy format, now named like the
indexable in the object, e.g. 'index' for DataFrame, or 'major_axis'/'minor_axis' for Panel
- fixes an issue if we want to support say a 'column' oriented Table
(e.g. instead of storing the transpose,
store with a 'column' indexables) - not implemented yet, but supported now
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} | 3 | "2012-12-11T03:00:26Z" | "2014-06-12T14:50:46Z" | "2012-12-11T20:15:53Z" | CONTRIBUTOR | null | wes...if you have a moment....mixed type concentation (with p4d) is broken...not excactly sure how this works....
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} | 1 | "2012-12-11T03:01:25Z" | "2014-07-09T07:04:52Z" | "2012-12-11T18:43:15Z" | CONTRIBUTOR | null | ENH: AppendableNDimTable (supporting Panel4D)
Term: only allow term querys that have matching axes
e.g. index/columns supported for DataFrames, items,major_axis,minor_axis for Panels
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} | 84 | "2012-12-11T06:21:40Z" | "2020-04-03T04:48:58Z" | "2020-04-03T04:48:57Z" | NONE | null | related:
https://github.com/pydata/pandas/issues/39 (column descriptions)
https://github.com/pydata/pandas/issues/686 (serialization concerns)
https://github.com/pydata/pandas/issues/447#issuecomment-11152782 (Feature request, implementation variant)
Ideas and issues:
- is pickle stable enough?
- serialization req. potentially couples this with storage format
- allow arbitrary python objects?
- impl. option: JSONables-only + type tag + load/save callback hook
- getter/setter or attribute style access (limits naming)?
- flat or hirarchical?
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} | 2 | "2012-12-11T08:05:05Z" | "2012-12-11T21:53:54Z" | "2012-12-11T21:53:54Z" | NONE | null | shouldn't the last two examples remove the innermost index level?
``` python
In [69]: from pandas.util.testing import makeCustomDataframe as mkdf
...: mkdf(4,2,r_idx_nlevels=1).set_index("C_l0_g0",append=True,drop=False)
Out[69]:
C0 C_l0_g0 C_l0_g1
R0 C_l0_g0
R_l0_g0 R0C0 R0C0 R0C1
R_l0_g1 R1C0 R1C0 R1C1
R_l0_g2 R2C0 R2C0 R2C1
R_l0_g3 R3C0 R3C0 R3C1
In [70]: from pandas.util.testing import makeCustomDataframe as mkdf
...: mkdf(4,2,r_idx_nlevels=1).set_index("C_l0_g0",append=True,drop=False).reset_index(0)
Out[70]:
C0 R0 C_l0_g0 C_l0_g1
C_l0_g0
R0C0 R_l0_g0 R0C0 R0C1
R1C0 R_l0_g1 R1C0 R1C1
R2C0 R_l0_g2 R2C0 R2C1
R3C0 R_l0_g3 R3C0 R3C1
```
``` python
In [71]: from pandas.util.testing import makeCustomDataframe as mkdf
...: mkdf(4,2,r_idx_nlevels=1).set_index("C_l0_g0",append=True,drop=False).reset_index(1)
/home/user1/src/pandas/pandas/core/internals.pyc in insert(self, loc, item, value)
956 def insert(self, loc, item, value):
957 if item in self.items:
--> 958 raise Exception('cannot insert %s, already exists' % item)
959
960 new_items = self.items.insert(loc, item)
Exception: cannot insert C_l0_g0, already exists
```
``` python
In [72]: from pandas.util.testing import makeCustomDataframe as mkdf
...: mkdf(4,2,r_idx_nlevels=1).set_index("C_l0_g0",append=True,drop=False).reset_index(-1)
/home/user1/src/pandas/pandas/core/internals.pyc in insert(self, loc, item, value)
956 def insert(self, loc, item, value):
957 if item in self.items:
--> 958 raise Exception('cannot insert %s, already exists' % item)
959
960 new_items = self.items.insert(loc, item)
Exception: cannot insert C_l0_g0, already exists
```
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} | 0 | "2012-12-11T08:21:44Z" | "2012-12-14T03:42:07Z" | "2012-12-14T03:42:07Z" | NONE | null | is there a reason to be so strict in the predicate return type?
notably python3 is much less cavalier about coercing things to bool.
``` python
In [75]: from pandas.util.testing import makeCustomDataframe as mkdf
...: mkdf(4,2,r_idx_nlevels=2).select(lambda x: bool(re.search("g1",x[0])))
Out[75]:
C0 C_l0_g0 C_l0_g1
R0 R1
R_l0_g1 R_l1_g1 R1C0 R1C1
```
``` python
In [76]: from pandas.util.testing import makeCustomDataframe as mkdf
...: mkdf(4,2,r_idx_nlevels=2).select(lambda x: re.search("g1",x[0]))
...:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-76-c172e2763471> in <module>()
1 from pandas.util.testing import makeCustomDataframe as mkdf
----> 2 mkdf(4,2,r_idx_nlevels=2).select(lambda x: re.search("g1",x[0]))
3
/home/user1/src/pandas/pandas/core/generic.pyc in select(self, crit, axis)
316
317 if len(axis) > 0:
--> 318 new_axis = axis[np.asarray([crit(label) for label in axis])]
319 else:
320 new_axis = axis
/home/user1/src/pandas/pandas/core/index.pyc in __getitem__(self, key)
1727 return tuple(retval)
1728 else:
-> 1729 if com._is_bool_indexer(key):
1730 key = np.asarray(key)
1731 sortorder = self.sortorder
/home/user1/src/pandas/pandas/core/common.pyc in _is_bool_indexer(key)
651 if not lib.is_bool_array(key):
652 if isnull(key).any():
--> 653 raise ValueError('cannot index with vector containing '
654 'NA / NaN values')
655 return False
ValueError: cannot index with vector containing NA / NaN values
```
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} | 1 | "2012-12-11T10:11:16Z" | "2012-12-11T21:00:41Z" | "2012-12-11T21:00:41Z" | NONE | null | one more trouble case with duplicate column names
the exception could be nicer (sort ambiguous because of duplicate column or similar) or it could sort by the first? column with this name (although this would not be nice in case the column is not duplicate but has in fact different content with the same name and the user not being aware of this).
```
import pandas as pd
df = pd.DataFrame([(1,2), (3,4)], columns = ['a', 'a'])
df.sort('a')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-251abbb5c77e> in <module>()
1 import pandas as pd
2 df = pd.DataFrame([(1,2), (3,4)], columns = ['a', 'a'])
----> 3 df.sort('a')
/usr/local/lib/python2.7/dist-packages/pandas-0.10.0.dev_74ab638-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in sort(self, columns, column, axis, ascending, inplace)
3062 columns = column
3063 return self.sort_index(by=columns, axis=axis, ascending=ascending,
-> 3064 inplace=inplace)
3065
3066 def sort_index(self, axis=0, by=None, ascending=True, inplace=False):
/usr/local/lib/python2.7/dist-packages/pandas-0.10.0.dev_74ab638-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in sort_index(self, axis, by, ascending, inplace)
3125 self._clear_item_cache()
3126 else:
-> 3127 return self.take(indexer, axis=axis)
3128
3129 def sortlevel(self, level=0, axis=0, ascending=True, inplace=False):
/usr/local/lib/python2.7/dist-packages/pandas-0.10.0.dev_74ab638-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in take(self, indices, axis)
2853 new_values = com.take_2d(self.values,
2854 com._ensure_int64(indices),
-> 2855 axis=axis)
2856 if axis == 0:
2857 new_columns = self.columns
/usr/local/lib/python2.7/dist-packages/pandas-0.10.0.dev_74ab638-py2.7-linux-x86_64.egg/pandas/core/common.pyc in take_2d(arr, indexer, out, mask, needs_masking, axis, fill_value)
414 out = np.empty(out_shape, dtype=arr.dtype)
415 take_f = _get_take2d_function(dtype_str, axis=axis)
--> 416 take_f(arr, _ensure_int64(indexer), out=out, fill_value=fill_value)
417 return out
418 elif dtype_str in ('float64', 'object', 'datetime64[ns]'):
/usr/local/lib/python2.7/dist-packages/pandas-0.10.0.dev_74ab638-py2.7-linux-x86_64.egg/pandas/algos.so in pandas.algos.take_2d_axis0_int64 (pandas/algos.c:73957)()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
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} | 1 | "2012-12-11T10:35:11Z" | "2012-12-14T04:27:26Z" | "2012-12-14T04:27:26Z" | NONE | null | In the version 9.1, when we are concatenating pandas.Series, we would expect the columns name of the created DataFrame to be the ones of the Series which were concatenated together. Nevertheless the series' names are lost, and the columns are simply 0, 1, 2 ...
Error shown below:
``` python
pd.__version__
Out[14]: '0.9.1rc1'
s1 = pd.TimeSeries(randn(5), index=pd.date_range('01-Jan-2013', periods=5, freq='D'), name='A')
s2 = pd.TimeSeries(randn(5), index=pd.date_range('01-Jan-2013', periods=5, freq='D'), name='B')
series = [s1, s2]
pd.concat(series, axis=1)
_32.columns = map(lambda s: s.name, series)
_32
```
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} | 42 | "2012-12-11T15:16:51Z" | "2012-12-14T16:45:32Z" | "2012-12-14T16:45:32Z" | NONE | null | I'm getting sporadic failures in
FAIL: test_rolling_std (pandas.stats.tests.test_moments.TestMoments)
FAIL: test_rolling_sum (pandas.stats.tests.test_moments.TestMoments)
FAIL: test_rolling_kurt (pandas.stats.tests.test_moments.TestMoments)
FAIL: test_rolling_max (pandas.stats.tests.test_moments.TestMoments)
and related
git bisected to: 915d261d298c54e887
I've upgraded to statsmodels '0.5.0.dev-c9062e4', did a clean/develop
but am still getting failures.
any ideas? @changhiskhan
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related #2368
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Related #2488
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} | 7 | "2012-12-11T21:36:29Z" | "2021-04-13T18:27:10Z" | "2015-12-05T19:29:19Z" | NONE | null | I want to use the quantities package and pandas to process scientific data. However, pandas strips the unit(s) of the data stored in numpy arrays if I create a dataframe out of them:
``` python
a = np.random.rand(10)*pq.s
b = np.random.rand(10)*pq.A
df = pd.DataFrame({'current':b, 't':a}, columns=['t','current'])
```
``` python
In [1]: df
Out[1]:
t current
0 0.663397 0.435423
1 0.038498 0.101763
2 0.960983 0.091785
3 0.262863 0.364734
4 0.154440 0.274169
5 0.953129 0.052678
6 0.389961 0.272535
7 0.961604 0.559451
8 0.747192 0.438268
9 0.789207 0.568685
```
What do you think, should pandas have support for writing the unit of each column in the corresponding columnname if the column is a quantities array, or should it be part of the quantities package.
``` python
In [1]: df
Out[1]:
t [s] current [A]
0 0.663397 0.435423
1 0.038498 0.101763
2 0.960983 0.091785
....
```
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} | 0 | "2012-12-11T22:06:51Z" | "2014-06-27T13:41:32Z" | "2012-12-13T14:58:37Z" | CONTRIBUTOR | null | also..have a hanging ref in N Dimensional Panels (Experimental) section to the dsintro-panel4d docs which is not working....thanks!
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} | 5 | "2012-12-12T01:34:45Z" | "2016-10-12T23:03:24Z" | "2012-12-14T03:42:57Z" | NONE | null | I tried creating using the from_dict() method to create a dataframe recently and noticed that orient='index' failed, even with trivial data.
> > > a={'hi': [32, 3, 3], 'there': [3, 5, 3]}
> > >
> > > df=DataFrame.from_dict(a)
> > >
> > > df2=DataFrame.from_dict(a, orient='index')
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/usr/local/EPD7.3/lib/python2.7/site-packages/pandas/core/frame.py", line 756, in from_dict
> > > for col, v in s.iteritems():
> > > AttributeError: 'list' object has no attribute 'iteritems'
It also failed with tuple values in the dictionary.
My current workaround is just to take the dataframe and perform a transpose, no big deal really.
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} | 21 | "2012-12-12T03:16:49Z" | "2014-06-22T17:19:33Z" | "2012-12-13T22:57:00Z" | CONTRIBUTOR | null | - ENH: changes axes_to_index keyword in table creation to axes
- ENH: allow passing of numeric or named axes (e.g. 0 or 'minor_axis') in axes
- BUG: create_axes now checks for current table scheme; raises if this indexing scheme is violated
- TST: added many p4d tests for appending/selection/partial selection/and axis permuation
- TST: added addition Term tests to include p4d
- BUG: add **eq** operators to IndexCol/DataCol/Table to comparisons
- DOC: updated docs with Panel4D saving & issues relating to threading
- ENH: supporting non-regular indexables:
- can index a Panel4D on say [labels,major_axis,minor_axis], rather
than the default of [items,major_axis,minor_axis]
- support column oriented DataFrames (e.g. queryable by the columns
- REMOVED: support metadata serializing via 'meta' attribute on an object
- ENH: supporting a data versioning scheme, warns if this is non-existant or old (only if trying a query)
as soon as the data is written again, this won't be warned again
- BUG: ixed PyTables version checking (and now correctly raises the error msg)
- BUG/DOC: added min_itemsize for strings in the values block (added TST and BUG fixes)
- BUG: handle int/float in indexes in the Term specification correctly
- ENH/DOC: allow changing of the table indexing scheme by calling create_table_index with new parameters
turned off memory testing: enable big_table test to turn it on
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} | 0 | "2012-12-12T06:37:10Z" | "2014-07-14T09:43:09Z" | "2012-12-13T14:54:16Z" | NONE | null | better not introduce API changes just yet
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https://api.github.com/repos/pandas-dev/pandas/issues/2499 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2499/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2499/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2499/events | https://github.com/pandas-dev/pandas/issues/2499 | 9,207,710 | MDU6SXNzdWU5MjA3NzEw | 2,499 | is pytz a hard dep? | {
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 2 | "2012-12-12T07:07:16Z" | "2012-12-14T23:38:44Z" | "2012-12-14T23:38:44Z" | NONE | null | the README states not, but setup.py has it in setup_requires.
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https://api.github.com/repos/pandas-dev/pandas/issues/2500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2500/events | https://github.com/pandas-dev/pandas/pull/2500 | 9,208,541 | MDExOlB1bGxSZXF1ZXN0MzMzMzMxOQ== | 2,500 | BLD: travis deps (stats,timeseries, pytables on py2.x), clean setup.py, nose tricks | {
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were previously skipped due to no scikit.timeseries on travis.
I picked the current statsmodels 0.5 git master commit hash as the
version to be installed, since pypi 0.5.0-beta raises a failing test in WLS
see http://nose.readthedocs.org/en/latest/plugins/testid.html
for nose-fu tidbit.
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