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https://api.github.com/repos/pandas-dev/pandas/issues/601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/601/events | https://github.com/pandas-dev/pandas/issues/601 | 2,778,822 | MDU6SXNzdWUyNzc4ODIy | 601 | DataFrame.groupby bug | {
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} | 2 | "2012-01-10T02:32:50Z" | "2012-01-10T03:52:04Z" | "2012-01-10T03:52:04Z" | NONE | null | pandas latest commit: When groupby using multiple columns, I get a TypeError. Furthermore, the TypeError is different on 64bit and 32bit Linux machines.
In [1]: import pandas
In [2]: a = pandas.DataFrame(randn(2, 3))
In [3]: a
Out[3]:
0 1 2
0 -0.7262 0.9849 0.1685
1 -1.0241 -2.3102 -1.1348
In [4]: g = a.groupby([0, 1])
In [5]: g.sum()
Out[5]:
# on Ubuntu64 11.10; numpy 1.6.1
TypeError: object of type 'int' has no len()
# on sles10 32bit; numpy 1.5.1
TypeError: array cannot be safely cast to required type
---
# The following works fine on the 64bit machine, but not on the 32bit:
In [6]: a = pandas.DataFrame(randn(2, 3), columns=['a', 'b', 'c'])
In [7]: a
Out[7]:
a b c
0 0.04682 0.1323 0.5046
1 -0.15613 0.4555 0.4553
In [8]: g = a.groupby(['a', 'b'])
In [9]: g.sum()
Out[9]:
# on sles10 32bit; numpy 1.5.1
TypeError: array cannot be safely cast to required type
---
This bug was not present in the pypi version of pandas. However, that version has a Series index overflow bug on the 32bit machine:
In [10]: a = pandas.Series(randn(2))
In [11]: a
Out[11]:
0 0.08557
1 -0.3659
In [12]: a[100]
Out[12]: 8.318223500663e-312
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} | 2 | "2012-01-10T07:48:16Z" | "2012-01-12T20:23:57Z" | "2012-01-12T20:23:57Z" | NONE | null | I Installed the NumPy Version 1.6.1, My python is 2.5. Could anybody tell me what's the problem? Thanks very much !
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https://api.github.com/repos/pandas-dev/pandas/issues/603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/603/events | https://github.com/pandas-dev/pandas/issues/603 | 2,780,724 | MDU6SXNzdWUyNzgwNzI0 | 603 | When reading data with read_table, columns with ints and NAs are turned into floats | {
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} | [] | closed | false | null | [] | null | 3 | "2012-01-10T08:31:18Z" | "2012-01-10T15:39:33Z" | "2012-01-10T14:07:32Z" | CONTRIBUTOR | null | Minimal case:
ID Data
1 45.0
NA NA
2 34
I use read_clipboard in this example, but it affects also read_table:
``` python
In [4]: pandas.read_clipboard(sep="\s+")
Out[4]:
ID Data
0 1.0 45.
1 NaN NaN
2 2.0 34.
In [9]: pandas.read_clipboard(sep="\s+").dtypes
Out[9]:
ID float64
Data float64
```
Is this working as intended? I ended up writing a wrapper to pandas.read_table to work around this issue (I tried writing a converter function, but it fails with the NAs).
As a clarification, the behavior I'm mentioning is the one in the ID column (the Data column contains floats).
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https://api.github.com/repos/pandas-dev/pandas/issues/604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/604/events | https://github.com/pandas-dev/pandas/pull/604 | 2,780,992 | MDExOlB1bGxSZXF1ZXN0NjcwNjYy | 604 | Use new GlobalPrintConfig also when setting engineering float formatter. | {
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} | 0 | "2012-01-10T09:03:42Z" | "2014-06-16T01:04:25Z" | "2012-01-10T14:27:45Z" | CONTRIBUTOR | null | Before, the following global variables in pandas.core.common were used to store print options:
- _float_format
- _column_space
- _precision
- _max_rows
- _max_columns
Now these are stored in pandas.core.common.GlobalPrintConfig, when setting the engineering float formatter it should alter GlobalPrintConfig and not the above variables (which i commented out by the way sine i think they are no longer used). Otherwise it has no effect.
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} | 0 | "2012-01-10T14:38:07Z" | "2012-01-10T14:49:57Z" | "2012-01-10T14:49:57Z" | CONTRIBUTOR | null | For column alignment. And release note for it.
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https://api.github.com/repos/pandas-dev/pandas/issues/607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/607/events | https://github.com/pandas-dev/pandas/pull/607 | 2,805,314 | MDExOlB1bGxSZXF1ZXN0Njc2ODU1 | 607 | ENH/TST: eng float formatter | {
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https://api.github.com/repos/pandas-dev/pandas/issues/608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/608/events | https://github.com/pandas-dev/pandas/pull/608 | 2,807,968 | MDExOlB1bGxSZXF1ZXN0Njc3ODUx | 608 | DOC: added a note for behavior of append | {
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} | 4 | "2012-01-11T18:25:07Z" | "2014-06-14T18:06:16Z" | "2012-01-11T18:30:05Z" | CONTRIBUTOR | null | it took me some time to realize what is happening since my python-oriented mind was expecting operation in-place on the original dataframe like Python does on lists... imho it is worth a note in documentation. unfortunately blunt attempt to build docs failed, so I don't know if it builds nicely, and may be utilizes intersphinx to point to list.append doc. Here is what I get upon make.py:
```
$> cd doc
build/ data/ make.py* plots/ source/ sphinxext/
3 41392.....................................:Wed 11 Jan 2012 01:23:38 PM EST:.
(git)novo:~exppsy/pandas[enh/doc-append]doc
$> ./make.py
Running Sphinx v1.0.8
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "/usr/lib/pymodules/python2.7/sphinx/cmdline.py", line 187, in main
warningiserror, tags)
File "/usr/lib/pymodules/python2.7/sphinx/application.py", line 116, in __init__
self.setup_extension(extension)
File "/usr/lib/pymodules/python2.7/sphinx/application.py", line 253, in setup_extension
err)
ExtensionError: Could not import extension ipython_directive (exception: cannot import name Config)
> /usr/lib/pymodules/python2.7/sphinx/application.py(253)setup_extension()
-> err)
(Pdb)
```
quick grepping didn't help in my attempt to find how to build docs correctly (I think I recall some discussion on the list a while ago but don't remember details now), and
```
$> python setup.py build_sphinx
setup.py:26: UserWarning: Module paste was already imported from None, but /usr/lib/python2.7/dist-packages is being added to sys.path
import pkg_resources
running build_sphinx
creating build/sphinx
creating build/sphinx/doctrees
creating build/sphinx/html
Running Sphinx v1.0.8
Traceback (most recent call last):
File "setup.py", line 402, in <module>
**setuptools_kwargs)
File "/usr/lib/python2.7/distutils/core.py", line 152, in setup
dist.run_commands()
File "/usr/lib/python2.7/distutils/dist.py", line 953, in run_commands
self.run_command(cmd)
File "/usr/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/usr/lib/pymodules/python2.7/sphinx/setup_command.py", line 136, in run
freshenv=self.fresh_env)
File "/usr/lib/pymodules/python2.7/sphinx/application.py", line 116, in __init__
self.setup_extension(extension)
File "/usr/lib/pymodules/python2.7/sphinx/application.py", line 253, in setup_extension
err)
sphinx.errors.ExtensionError: Could not import extension ipython_directive (exception: cannot import name Config)
```
gives the same result
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} | 0 | "2012-01-11T20:09:10Z" | "2012-05-13T04:32:12Z" | "2012-05-13T04:32:12Z" | MEMBER | null | Majorly slow, especially in the multi-key case. Need to identify realistic use cases and vbench then fix
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} | 0 | "2012-01-11T20:42:35Z" | "2012-05-14T20:28:22Z" | "2012-05-14T20:28:22Z" | MEMBER | null | from @stig
I'd love an option to maintain the column order that I've specified. At the moment I do this:
```
print df.groupby('Status')['Duration'].agg({ 'Min': np.min,
'Max':np.max,
'Mean': np.mean,
'Median': np.median,
'Stddev':np.std,
})
```
but the column order come out like this:
```
Max Mean Median Min Stddev
Error 0.141 0.09967 0.093 0.072 0.02796
Success 12.07 5.879 11.05 0.27 5.41
```
Which is not ideal. I'd love to pass an OrderedDict and have the order be respected.
```
d = OrderedDict([('Min', np.min),
('Max', np.max),
('Mean', np.mean),
('Median', np.median),
('Stddev', np.std),
])
```
... or, the method could just accept a list of tuples and respect that order.
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} | 2 | "2012-01-11T21:25:49Z" | "2016-10-12T23:02:44Z" | "2012-01-12T18:50:33Z" | MEMBER | null | From user email
Hi there,
Just a quick bug report. I'm using psycopg2 with a NamedTupleCursor, which returns namedtuples for rows. I'm trying to use DataFrame.from_records like so:
df = DataFrame.from_records(rows, rows[0]._fields)
I get the following error:
```
TypeError Traceback (most recent call last)
/Users/kdevens/source/(etc.) <ipython-input-46-3e705b917abc> in <module>()
----> 1 df = DataFrame.from_records(rows, rows[0]._fields)
/Library/Python/2.6/site-packages/pandas-0.6.1-py2.6-macosx-10.6-universal.egg/pandas/core/frame.pyc in from_records(cls, data, index, exclude, columns, names)
533 columns, sdict = _rec_to_dict(data)
534 else:
--> 535 sdict, columns = _list_to_sdict(data, columns)
536
537 if exclude is None:
/Library/Python/2.6/site-packages/pandas-0.6.1-py2.6-macosx-10.6-universal.egg/pandas/core/frame.pyc in _list_to_sdict(data, columns)
3547 def _list_to_sdict(data, columns):
3548 if isinstance(data[0], tuple):
-> 3549 content = list(lib.to_object_array_tuples(data).T)
3550 else:
3551 # list of lists
/Library/Python/2.6/site-packages/pandas-0.6.1-py2.6-macosx-10.6-universal.egg/pandas/_tseries.so in pandas._tseries.to_object_array_tuples (pandas/src/tseries.c:52160)()
TypeError: Expected tuple, got Record
```
So it looks like in tseries.c it's doing something like "type(row) == tuple" instead of "isinstance(row, tuple)". For what it's worth, "isinstance(rows[0], tuple)" is true, so the tuples returned by psycopg2 are definitely real namedtuples. Additionally, the following works fine:
```
In [49]: dictrows = [r._asdict() for r in rows]
In [50]: df = DataFrame.from_dict(dictrows)
```
So there's no problem with the data, or with DataFrame, just the type check in tseries.c.
Let me know if I can provide any more information! Thanks.
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https://api.github.com/repos/pandas-dev/pandas/issues/612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/612/events | https://github.com/pandas-dev/pandas/issues/612 | 2,812,325 | MDU6SXNzdWUyODEyMzI1 | 612 | Pure python multi-key groupby can't handle non-numeric results | {
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} | 1 | "2012-01-12T00:23:58Z" | "2012-01-12T00:46:30Z" | "2012-01-12T00:46:30Z" | MEMBER | null | reported by @yarikoptic, could occur fairly easily in 0.6.1, now very pathological to reproduce
```
data = DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B' : ['one', 'one', 'one', 'two',
'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C' : ['dull', 'dull', 'shiny', 'dull',
'dull', 'shiny', 'shiny', 'dull',
'shiny', 'shiny', 'shiny'],
'D' : np.random.randn(11),
'E' : np.random.randn(11),
'F' : np.random.randn(11)})
def bad(x):
assert(len(x.base) == len(x))
return 'foo'
data.groupby(['A', 'B']).agg(bad)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/code/pandas/vb_suite/<ipython-input-23-745606b0da15> in <module>()
----> 1 data.groupby(['A', 'B']).agg(bad)
/home/wesm/code/pandas/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
283 See docstring for aggregate
284 """
--> 285 return self.aggregate(func, *args, **kwargs)
286
287 def _iterate_slices(self):
/home/wesm/code/pandas/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
963 else:
964 if len(self.groupings) > 1:
--> 965 return self._python_agg_general(arg, *args, **kwargs)
966 else:
967 result = self._aggregate_generic(arg, *args, **kwargs)
/home/wesm/code/pandas/pandas/core/groupby.pyc in _python_agg_general(self, func, *args, **kwargs)
393 try:
394 result, counts = self._aggregate_series(obj, agg_func,
--> 395 comp_ids, max_group)
396 output[name] = result
397 except TypeError:
/home/wesm/code/pandas/pandas/core/groupby.pyc in _aggregate_series(self, obj, func, group_index, ngroups)
411 return _aggregate_series_fast(obj, func, group_index, ngroups)
412 except Exception:
--> 413 return self._aggregate_series_pure_python(obj, func, ngroups)
414
415 def _aggregate_series_pure_python(self, obj, func, ngroups):
/home/wesm/code/pandas/pandas/core/groupby.pyc in _aggregate_series_pure_python(self, obj, func, ngroups)
422 continue
423 counts[label] = group.shape[0]
--> 424 result[label] = func(group)
425
426 return result, counts
ValueError: could not convert string to float: foo
```
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} | 10 | "2012-01-12T02:52:14Z" | "2012-07-15T00:26:44Z" | "2012-01-12T19:58:32Z" | NONE | null | Working through the tutorial labeled pandas 0.7.0.dev-1139a8d documentation, I was having a problem with the list of dicts example
---
In [1]: from pandas import *
In [2]: import numpy as np
In [14]: pandas.version
Out[14]: <module 'pandas.version' from '/Library/Frameworks/Python.framework/Versions/7.2/lib/python2.7/site-packages/pandas-0.6.1-py2.7-macosx-10.5-i386.egg/pandas/version.pyc'>
# Prove that I have everything installed correctly
In [15]: data = np.zeros((2,),dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])
In [16]: data[:] = [(1,2.,'Hello'),(2,3.,"World")]
In [17]: DataFrame(data)
Out[17]:
A B C
0 1 2 Hello
1 2 3 World
# List of dicts example that fails
In [18]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]
In [19]: DataFrame(data2)
Out[19]:
0
0 {'a': 1, 'b': 2}
1 {'a': 5, 'c': 20, 'b': 10}
In [20]: DataFrame(data2, index=['first', 'second'])
Out[20]:
0
first {'a': 1, 'b': 2}
second {'a': 5, 'c': 20, 'b': 10}
In [21]: DataFrame(data2, columns=['a', 'b', 'c'])
## AssertionError:
Looking at the mailing list, you almost have the solution in your post on 9/28/2011 in response to "Re: [pystatsmodels] Getting "stuff" into dataframes..."
In [23]: row_data2 = dict(enumerate(data2))
In [24]: DataFrame.from_dict(row_data2, orient='index'))
Out[24]:
a b c
0 1 2 NaN
1 5 10 20
(which matches the result in the documentation)
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} | 0 | "2012-01-12T05:10:01Z" | "2012-01-12T05:38:31Z" | "2012-01-12T05:38:31Z" | MEMBER | null | ```
In [3]: data.ix[4]
Out[3]:
A bar
B one
C NaN
D 0.1949
E 0.8223
F 1.335
Name: 4
```
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} | 1 | "2012-01-12T09:36:26Z" | "2012-01-12T14:59:58Z" | "2012-01-12T14:59:58Z" | CONTRIBUTOR | null | I don`t have bottleneck, this little change allows me to import trunk again
nosetests running fine
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} | 6 | "2012-01-12T17:28:30Z" | "2012-01-17T20:34:00Z" | "2012-01-17T20:34:00Z" | CONTRIBUTOR | null | I have tried to find related issue but failed... so pardon me if it is a duplicate:
ATM if groupping doesn't result in actually all possible combinations, the pandas spits out non-informative
```
/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/groupby.py in _aggregate_series_pure_python(self, obj, func, ngroups)
431 raise ValueError('function does not reduce')
432
--> 433 counts[label] = group.shape[0]
434 result[label] = res
435
IndexError: index out of bounds
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/groupby.py(433)_aggregate_series_pure_python()
432
--> 433 counts[label] = group.shape[0]
434 result[label] = res
```
imho there could be an option to still handle those but place NaNs for those entries, OR at least spit out an informative exception something like "combination f1='x', f2='y' doesn't have data entries in the original data, or smth like that
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} | 1 | "2012-01-12T20:07:45Z" | "2012-01-12T20:30:24Z" | "2012-01-12T20:30:24Z" | NONE | null | The description of ignore_index does not appear in the parameter list in `concat`'s docstring.
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} | 2 | "2012-01-12T20:42:57Z" | "2012-07-15T22:50:48Z" | "2012-07-15T22:50:48Z" | MEMBER | null | cc @hammer, @arthurgerigk. Not sure when will be able to make this happen, but this would be a very nice addition. I've often found myself doing stuff like:
```
df[col].map(lambda x: x[:10])
```
or various other forms of string munging / regex-processing.
Obviously that would fail if any of `df[col]` is NA. And having to write this kinda sucks:
```
df[col].map(lambda x: x[:10] if notnull(x) else x)
```
If multiple columns were involved in some kind of string processing exercise, you'd just want the whole operation to short circuit and be NA if an NA is encountered.
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https://api.github.com/repos/pandas-dev/pandas/issues/621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/621/events | https://github.com/pandas-dev/pandas/issues/621 | 2,823,711 | MDU6SXNzdWUyODIzNzEx | 621 | DataFrame constructor speed | {
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} | 2 | "2012-01-12T21:13:05Z" | "2012-01-12T21:52:54Z" | "2012-01-12T21:52:54Z" | CONTRIBUTOR | null | Hi, i was playing around with constructing DataFrame's from nested dicts, and noticed that things have gotten a bit slower since v0.6.1.
Here's some sample code i was playing with:
import time
import pandas
print pandas.**version**
data = dict((i,dict((j,float(j)) for j in xrange(100))) for i in xrange(5000))
t0 = time.time(); df = pandas.DataFrame(data); t1 = time.time(); print t1 - t0
With version 0.6.1, the printed time is about 0.21s on my machine, with a little help from git bisect,
i found that:
commit f3ca67dc371ce46589e6a1598168cddcc1b8cb6b takes it from 0.21s to 0.44s
commit 9d65e8e77b675ce4774b6f394acd8f74bd40af78 takes it from 0.44s to 0.54s
It's possible that some of these were unavoidable considering they may have been necessary bugfixes, but i wanted to
see if anyone else is seeing this too.
environment info: 64bit ubuntu 11.10, python2.7, numpy 1.6.1, cython 0.15.1
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} | 14 | "2012-01-13T14:11:51Z" | "2013-02-10T16:23:57Z" | "2013-02-10T16:23:57Z" | CONTRIBUTOR | null | Is this intended? I had hoped no copying at all would happen in that case.
In [65]: a = np.random.randint(10, size=1e6)
In [66]: a.dtype
Out[66]: dtype('int32')
In [67]: b = np.random.randint(2, size=1e6)
In [68]: df = pandas.DataFrame({'a': a, 'b': b})
In [69]: df.dtypes
Out[69]:
a int64
b int64
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2000-01-03 NaN
2000-01-04 0.6095
2000-01-05 NaN
2000-01-06 1.24
2000-01-07 NaN
2000-01-10 1.43
2000-01-11 NaN
2000-01-12 0.6033
2000-01-13 NaN
2000-01-14 -0.2555
2000-01-17 NaN
2000-01-18 0.4684
2000-01-19 NaN
2000-01-20 -1.825
2000-01-21 NaN
2000-01-24 1.023
2000-01-25 NaN
2000-01-26 0.09094
2000-01-27 NaN
2000-01-28 0.218
2000-01-31 NaN
2000-02-01 -1.741
2000-02-02 NaN
2000-02-03 1.437
2000-02-04 NaN
2000-02-07 -2.42
2000-02-08 NaN
2000-02-09 0.2374
2000-02-10 NaN
2000-02-11 0.06525
Name: ts
```
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} | 1 | "2012-01-13T21:32:16Z" | "2014-07-06T10:27:56Z" | "2012-01-13T22:52:29Z" | CONTRIBUTOR | null | I'm using ipython inside Emacs. Detection of the console width and height is not working with the current implementation. ioctl seems to return (0,0). Therefore, performing an explicit check for cr == (0,0) (in addition to the check for not cr) before falling back to environment variables is a quick fix that I think shouldn't make any negative impact on non-emacs users.
I've also added the import of os.environ line since env previously wasn't defined.
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} | 4 | "2012-01-14T17:13:20Z" | "2012-05-25T21:26:17Z" | "2012-05-25T21:26:17Z" | CONTRIBUTOR | null | This shall serve as meta issue for inclusion of the most needed functions from scikits.timeseries.
fucntions, use cases and scenarios shall be listed at:
https://github.com/wesm/pandas/wiki/Time-Series-Manipulation
It is an enhancement request/wish.
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} | 2 | "2012-01-14T20:51:20Z" | "2012-02-09T21:18:16Z" | "2012-02-09T21:18:16Z" | CONTRIBUTOR | null | How to get involved:
- git basics
- possible areas for improvement in the library
- short notes on best practices
Basically a version of http://statsmodels.sourceforge.net/dev/index.html
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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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} | 1 | "2012-01-15T02:48:07Z" | "2013-09-21T12:36:42Z" | "2013-09-21T12:36:42Z" | MEMBER | null | Selecting a level other than `level=0` results in `O(n)` work
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https://api.github.com/repos/pandas-dev/pandas/issues/634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/634/events | https://github.com/pandas-dev/pandas/issues/634 | 2,853,008 | MDU6SXNzdWUyODUzMDA4 | 634 | Errors in example plots in documentation | {
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http://pandas.sourceforge.net/visualization.html#basic-plotting-plot
The following page talks about version 0.4 and this new feature so perhaps it should be updated now that 0.7 has been released.
http://pandas.sourceforge.net/indexing.html#indexing-hierarchical
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} | 0 | "2012-01-16T16:46:33Z" | "2012-01-16T19:49:05Z" | "2012-01-17T18:14:57Z" | CONTRIBUTOR | null | Addresses issue #568
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} | 0 | "2012-01-16T17:44:30Z" | "2012-01-17T18:39:38Z" | "2012-01-17T18:39:38Z" | MEMBER | null | ```
test_crosstab_margins (__main__.TestCrosstab) ... > /home/wesm/code/pandas/generated.pyx(132)pandas._tseries.merge_indexer_object (pandas/src/tseries.c:28324)()
(Pdb) u
> /home/wesm/code/pandas/pandas/core/index.py(1548)get_indexer()
-> indexer = self._merge_indexer(target_index, self_index.indexMap)
(Pdb) target_index
Int64Index([ 4, 4, 1, 2, 4, 3, 7, 5, 4, 3, 1, 1, 0,
6, 5, 2, 2, 3, 2, 5, 5, 1, 4, 3, 5, 4,
4, 3, 4, 3, 100])
(Pdb) self
MultiIndex([(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7),
(0, 8), (0, 9), (1, 0), (1, 1), (1, 3), (1, 4), (1, 5), (1, 6),
(1, 7), (1, 8), (1, 9), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4),
(2, 5), (2, 6), (2, 7), (2, 8), (2, 9), ('All', '')], dtype=object)
(Pdb) c
ERROR
======================================================================
ERROR: test_crosstab_margins (__main__.TestCrosstab)
----------------------------------------------------------------------
Traceback (most recent call last):
File "pandas/tools/tests/test_pivot.py", line 201, in test_crosstab_margins
exp_rows = exp_rows.reindex(all_rows).fillna(0)
File "/home/wesm/code/pandas/pandas/core/series.py", line 1585, in reindex
level=level)
File "/home/wesm/code/pandas/pandas/core/index.py", line 1569, in reindex
indexer = self.get_indexer(target, method=method)
File "/home/wesm/code/pandas/pandas/core/index.py", line 1548, in get_indexer
indexer = self._merge_indexer(target_index, self_index.indexMap)
File "generated.pyx", line 132, in pandas._tseries.merge_indexer_object (pandas/src/tseries.c:28324)
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
----------------------------------------------------------------------
```
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https://api.github.com/repos/pandas-dev/pandas/issues/637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/637/events | https://github.com/pandas-dev/pandas/issues/637 | 2,867,208 | MDU6SXNzdWUyODY3MjA4 | 637 | The sort option of groupby does not work as stated | {
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} | [] | closed | false | null | [] | null | 2 | "2012-01-17T11:09:51Z" | "2013-12-04T01:20:16Z" | "2012-01-17T14:18:59Z" | NONE | null | In the documentation (http://pandas.sourceforge.net/groupby.html) step 349, the sort option does not work as stated. If one repeats the steps he gets:
```
In [38]: df2 = DataFrame({'X' : ['B', 'B', 'A', 'A'], 'Y' : [1, 2, 3, 4]})
In [39]: df2.groupby(['X'], sort=True).sum()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/tcebrian/<ipython console> in <module>()
TypeError: groupby() got an unexpected keyword argument 'sort'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/638/events | https://github.com/pandas-dev/pandas/issues/638 | 2,868,567 | MDU6SXNzdWUyODY4NTY3 | 638 | NaN in a join-result despite know value | {
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} | 3 | "2012-01-17T13:43:08Z" | "2012-01-18T20:27:49Z" | "2012-01-18T19:32:12Z" | CONTRIBUTOR | null | When performing a join on a data frame with a data series, the last entry of the new column is sometimes NaN. It only seems to happen when the last item in the column the join is done on is found in more than one row. Here is a minimal example:
```
In [1]: df = DataFrame({'a': [1, 1]})
In [2]: ds = Series([2], index=[1], name='b')
In [3]: df.join(ds, on='a')
Out[3]:
a b
0 1 2
1 1 NaN
```
Tested with 0.6.1 and 0.5.0 with identical results.
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https://api.github.com/repos/pandas-dev/pandas/issues/639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/639/events | https://github.com/pandas-dev/pandas/pull/639 | 2,871,739 | MDExOlB1bGxSZXF1ZXN0NzAwMDgx | 639 | FIX: multiindex error #636 | {
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} | 0 | "2012-01-17T17:24:34Z" | "2012-01-17T17:25:14Z" | "2012-01-17T18:39:21Z" | CONTRIBUTOR | null | Closes issue #636
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} | 0 | "2012-01-17T18:58:03Z" | "2012-01-17T21:24:27Z" | "2012-01-17T21:24:27Z" | MEMBER | null | User-reported crash
```
import cPickle
from pandas import DataFrame
df = DataFrame()
with open('df', 'wb') as f:
cPickle.dump(df, f)
with open('df', 'rb') as f:
df2 = cPickle.load(f)
print df2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/641/events | https://github.com/pandas-dev/pandas/issues/641 | 2,873,675 | MDU6SXNzdWUyODczNjc1 | 641 | print fails in current dev version under Windows 32-bit | {
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} | 4 | "2012-01-17T19:55:35Z" | "2012-01-18T22:08:04Z" | "2012-01-17T20:33:37Z" | CONTRIBUTOR | null | I get the following Traceback:
Traceback (most recent call last):
File "C:\src\OSR\OSR\flowering_start.py", line 171, in <module>
df_x = execute(x,df,conn)
File "C:\src\OSR\OSR\flowering_start.py", line 92, in execute
print result['Date']
File "C:\src\pandas\pandas\core\series.py", line 566, in **str**
return repr(self)
File "C:\src\pandas\pandas\core\series.py", line 521, in **repr**
result = self._tidy_repr(min(30, max_rows - 4))
File "C:\src\pandas\pandas\core\series.py", line 533, in _tidy_repr
head = self[:num]._get_repr(print_header=True, length=False,
File "C:\src\pandas\pandas\core\series.py", line 431, in __getslice__
return self.**getitem**(slobj)
File "C:\src\pandas\pandas\core\series.py", line 299, in **getitem**
return self._get_with(key)
File "C:\src\pandas\pandas\core\series.py", line 304, in _get_with
indexer = self.ix._convert_to_indexer(key, axis=0)
File "C:\src\pandas\pandas\core\indexing.py", line 230, in _convert_to_indexer
i, j = labels.slice_locs(obj.start, obj.stop)
File "C:\src\pandas\pandas\core\index.py", line 782, in slice_locs
beg_slice = self.get_loc(start)
File "C:\src\pandas\pandas\core\index.py", line 478, in get_loc
return self._engine.get_loc(key)
File "engines.pyx", line 100, in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2328)
File "engines.pyx", line 107, in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2291)
KeyError: 0
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} | 1 | "2012-01-17T21:37:40Z" | "2012-05-14T21:10:27Z" | "2012-05-14T21:10:27Z" | MEMBER | null | cc @arthurgerigk
Something of this nature would be nice:
```
df.groupby(['A', 'B']).agg({'C' : [np.mean, np.std]})
```
Would also be nice to add some options to agg for what to do with the input dict (by default targets columns)
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} | 3 | "2012-01-17T23:28:02Z" | "2012-06-18T16:58:20Z" | "2012-04-05T00:23:03Z" | NONE | null | We are having a problem when loading daily historical dividend data into a pandas Series(). Because companies sometimes declare two different dividends on the same day, we have an issue when trying to index this series
The following
```
from pandas.core.api import Series
import datetime
data_seq = [.10, .23]
date_seq = [datetime.datetime(2011, 1, 1, 0, 0), datetime.datetime(2011, 1, 1, 0, 0)]
series = Series(data_seq, index=date_seq)
series[datetime.datetime(2011, 1, 1, 0, 0)]
```
Returns
```
Exception: Index values are not unique
```
We're hoping for a function that will go through a pandas a series and produce a new series w/o duplicate dates via some sort of combination function. In this case, it makes the most sense to combine the two dividends by adding them together.
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https://api.github.com/repos/pandas-dev/pandas/issues/644 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/644/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/644/comments | https://api.github.com/repos/pandas-dev/pandas/issues/644/events | https://github.com/pandas-dev/pandas/issues/644 | 2,882,783 | MDU6SXNzdWUyODgyNzgz | 644 | "Applying multiple functions at once" example from the docs broken | {
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} | [] | closed | false | null | [] | null | 9 | "2012-01-18T14:10:24Z" | "2012-01-18T15:19:10Z" | "2012-01-18T14:16:06Z" | CONTRIBUTOR | null | Using `agg` with a list of functions as parameter on a `groupby` object doesn't work as described:
```
df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C' : randn(8), 'D' : randn(8)})
grouped = df.groupby('A')
grouped.agg([np.sum, np.mean, np.std])
```
leads to this (with 0.6.1 stable and master):
---
AssertionError Traceback (most recent call last)
/home/ilja/env/<ipython-input-11-f7f54864ac69> in <module>()
----> 1 grouped.agg([np.sum, np.mean, np.std])
/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in agg(self, func, _args, *_kwargs)
295 See docstring for aggregate
296 """
--> 297 return self.aggregate(func, _args, *_kwargs)
298
299 def _get_names(self):
/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in aggregate(self, arg, _args, *_kwargs)
954 return self._python_agg_general(arg, _args, *_kwargs)
955 else:
--> 956 result = self._aggregate_generic(arg, _args, *_kwargs)
957
958 if not self.as_index:
/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in _aggregate_generic(self, func, _args, *_kwargs)
982 result[name] = func(data, _args, *_kwargs)
983 except Exception:
--> 984 return self._aggregate_item_by_item(func, _args, *_kwargs)
985 else:
986 for name in self.indices:
/usr/lib/python2.7/site-packages/pandas/core/groupby.pyc in _aggregate_item_by_item(self, func, _args, *_kwargs)
1027 result_columns = result_columns.drop(cannot_agg)
1028
-> 1029 return DataFrame(result, columns=result_columns)
1030
1031 def _wrap_aggregated_output(self, output, mask):
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in **init**(self, data, index, columns, dtype, copy)
191 mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)
192 elif isinstance(data, dict):
--> 193 mgr = self._init_dict(data, index, columns, dtype=dtype)
194 elif isinstance(data, ma.MaskedArray):
195 mask = ma.getmaskarray(data)
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
260
261 # consolidate for now
--> 262 mgr = BlockManager(blocks, axes)
263 return mgr.consolidate()
264
/usr/lib/python2.7/site-packages/pandas/core/internals.pyc in **init**(self, blocks, axes, do_integrity_check)
274
275 if do_integrity_check:
--> 276 self._verify_integrity()
277
278 @property
/usr/lib/python2.7/site-packages/pandas/core/internals.pyc in _verify_integrity(self)
353 mgr_shape = self.shape
354 for block in self.blocks:
--> 355 assert(block.values.shape[1:] == mgr_shape[1:])
356 tot_items = sum(len(x.items) for x in self.blocks)
357 assert(len(self.items) == tot_items)
AssertionError:
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} | [] | closed | false | null | [] | null | 1 | "2012-01-18T15:48:02Z" | "2016-10-12T23:02:45Z" | "2012-01-18T16:08:04Z" | NONE | null | python-dateutil is at 2.0, so the > 2 requirement fails.
```
'install_requires': ['python-dateutil >= 2','numpy'],
```
in setup.py does the trick for me.
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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 | "2012-01-18T16:35:57Z" | "2013-09-22T00:05:44Z" | "2013-09-22T00:05:44Z" | MEMBER | null | {
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} | [] | closed | false | null | [] | null | 0 | "2012-01-18T16:39:50Z" | "2012-01-23T16:03:03Z" | "2012-01-23T16:03:03Z" | CONTRIBUTOR | null | I have found useful cummax and cummin functions in R, so I propose to add them to pandas.
At fabriziop/pandas there is a forked branch with these changes, commit 660d5ddf05c49c55fabce072a41e5d5b5096e529 .
The new functions are dumbly derived from the cumsum function.
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```
Traceback (most recent call last):
[...]
from pandas import DataFrame
File "/usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/pandas-0.7.0.dev_c65a098-py3.2-macosx-10.4-x86_64.egg/pandas/__init__.py", line 10, in <module>
import pandas._tseries as lib
ImportError: dlopen(/usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/pandas-0.7.0.dev_c65a098-py3.2-macosx-10.4-x86_64.egg/pandas/_tseries.so, 2): Symbol not found: _PyString_AsString
Referenced from: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/pandas-0.7.0.dev_c65a098-py3.2-macosx-10.4-x86_64.egg/pandas/_tseries.so
Expected in: dynamic lookup
```
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} | 4 | "2012-01-19T16:59:07Z" | "2013-09-30T16:17:12Z" | "2012-01-19T20:53:50Z" | CONTRIBUTOR | null | To still allow default matplotlib size for the figure I added handling of figsize="default"... may be altogether it should just drop custom default size here?
also another commit on **kwds -- depends on what was intended, but at least figsize should be passed
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} | 0 | "2012-01-20T01:59:31Z" | "2012-05-23T22:31:00Z" | "2012-05-23T22:31:00Z" | MEMBER | null | Like DataFrame.add, but gt, lt, ge, le, etc.
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} | 2 | "2012-01-20T03:18:28Z" | "2013-09-30T16:17:12Z" | "2012-01-20T03:45:54Z" | CONTRIBUTOR | null | pre-evaluated numpy version condition for "efficiency" ;-)
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} | 2 | "2012-01-20T10:28:02Z" | "2012-01-23T23:45:16Z" | "2012-01-23T23:45:16Z" | NONE | null | > > > from pandas import *
> > > import numpy as np
> > > x=DataFrame(np.arange(9).reshape(3,3))
> > > x[0].iget([0,1])
> > > 0 0
> > > 1 3
> > > x.icol([0,1])
> > > 0 1
> > > 0 0 1
> > > 1 3 4
> > > 2 6 7
> > > x.irow([0,1])
Traceback (most recent call last):
File "<pyshell#5>", line 1, in <module>
x.irow([0,1])
File "C:\Python27\lib\site-packages\pandas-0.7.0rc1-py2.7-win32.egg\pandas\core\frame.py", line 1238, in irow
return self.xs(label)
File "C:\Python27\lib\site-packages\pandas-0.7.0rc1-py2.7-win32.egg\pandas\core\frame.py", line 1498, in xs
loc = self.index.get_loc(key)
File "C:\Python27\lib\site-packages\pandas-0.7.0rc1-py2.7-win32.egg\pandas\core\index.py", line 478, in get_loc
return self._engine.get_loc(key)
File "engines.pyx", line 100, in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2497)
File "engines.pyx", line 102, in pandas._engines.DictIndexEngine.get_loc (pandas\src\engines.c:2409)
TypeError
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} | 2 | "2012-01-20T15:13:15Z" | "2012-01-20T16:20:12Z" | "2012-01-20T15:57:20Z" | CONTRIBUTOR | null | Could not stand it any more ;)
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} | 1 | "2012-01-20T17:24:11Z" | "2012-01-23T00:33:48Z" | "2012-01-23T00:33:48Z" | NONE | null | Series in 0.7 RC1 have some weird behaviour when selecting by index.
> > > y
> > > a 0
> > > 0 1
> > > 1 2
> > > Name: a
> > > y[0]
> > > 1 # expected
> > > y[[0,1]]
> > > a 0 # wrong?
> > > 0 1
> > > Name: a
> > > y[[1,2]]
> > > 0 1
> > > 1 2
> > > Name: a
> > > y.ix[[0,1]] # this also doesnt work
> > > a 0
> > > 0 1
> > > Name: a
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} | 1 | "2012-01-21T23:00:12Z" | "2012-01-23T21:41:45Z" | "2012-01-23T21:41:45Z" | MEMBER | null | from SO:
```
# In [4]: type(pd)
# Out[4]: pandas.sparse.frame.SparseDataFrame
memid = unique(pd.Member)
pan = {}
for mem in memid:
pan[mem] = pd[pd.Member==mem]
goal = pandas.Panel(pan)
```
comments
```
What are the dimensions (pd.shape) and density (pd.density) of the SparseDataFrame? Any change you can e-mail me a pickle of the object (pd.save(file_path)) for me to have a look to try to diagnose what's up? BTW these questions would be better posed on the mailing list than SO. – wesm 29 mins ago
pd.shape = (2668990, 232) – user1162837 23 mins ago
pd.density = 0.12814551216649045 The file is too large to send through email. – user1162837 22 mins ago
Also, pandas is a great library. :) – user1162837 14 mins ago
```
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} | 0 | "2012-01-22T04:56:02Z" | "2012-01-22T04:56:02Z" | "2012-01-22T19:33:20Z" | CONTRIBUTOR | null | week is defined above as week = Week(). Using it as the iterator here was overwriting this.
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} | 2 | "2012-01-22T13:39:45Z" | "2012-02-05T19:45:43Z" | "2012-01-25T21:16:25Z" | CONTRIBUTOR | null | Here is an attempt at fully resolving name conflict in merges when `left_on` or `right_on` keys are passed explicitly.
I am not sure my fix is correct but I include a new non-regression test to highlight the issue and the resolution does not introduce any regression findable by running `nosetests`.
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http://stackoverflow.com/questions/8957175/dataframe-to-panel-indexed-by-nonunique-column-with-pandas
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} | 0 | "2012-01-23T23:34:56Z" | "2012-01-24T19:06:12Z" | "2012-01-24T19:06:12Z" | MEMBER | null | Not sure about this
```
In [4]: df = tm.makeTimeDataFrame()
In [5]: df
Out[5]:
A B C D
2000-01-03 0.5694 1.15202 1.269872 -0.08819
2000-01-04 -0.3278 -0.39915 -0.591330 0.81658
2000-01-05 0.8175 0.15806 0.751025 0.61545
2000-01-06 0.2541 2.25491 -0.536671 -0.43782
2000-01-07 -1.4400 -0.12882 0.077768 -0.40887
2000-01-10 -0.3598 1.74354 -1.388002 0.49269
2000-01-11 -0.1260 -0.86097 1.028045 -0.15667
2000-01-12 1.9286 1.42076 1.780311 1.78580
2000-01-13 0.6148 0.28743 0.660499 0.94072
2000-01-14 -0.7055 0.34959 0.538657 0.90766
2000-01-17 -0.4395 0.19145 -0.413462 1.35715
2000-01-18 0.6851 -1.04204 1.721110 0.67047
2000-01-19 1.2581 -1.36096 -0.854228 0.02768
2000-01-20 0.8928 -0.93538 -0.810424 1.64063
2000-01-21 0.5252 -1.09177 -1.060109 0.44210
2000-01-24 0.2277 -0.06904 -0.029423 0.36855
2000-01-25 0.3063 -0.38531 0.597502 -0.24924
2000-01-26 -1.5342 -0.17552 0.033193 0.88376
2000-01-27 1.3528 -1.28998 0.378057 -1.15563
2000-01-28 0.7619 -0.18432 0.476671 -0.08385
2000-01-31 0.3166 0.17006 -0.470700 -0.22658
2000-02-01 -0.4439 0.94540 -0.090201 0.97781
2000-02-02 -0.1776 2.30224 0.225779 -0.52249
2000-02-03 -1.6860 0.10163 0.009537 -0.44277
2000-02-04 -0.7501 1.19757 -2.191548 -0.96064
2000-02-07 -0.5917 0.81859 0.140187 1.42304
2000-02-08 1.4421 0.15792 -0.085060 0.39537
2000-02-09 0.6675 -0.59396 -1.012737 -1.67261
2000-02-10 -0.2199 0.77754 -0.319754 0.24299
2000-02-11 -0.4806 -0.84326 0.664512 0.91148
In [6]: df.dtypes
Out[6]:
A float64
B float64
C float64
D float64
In [7]: df['B'] = 0
In [8]: df.dtypes
Out[8]:
A float64
B int64
C float64
D float64
```
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update: was actually slicing
```
s[:5.0]
```
may fail in some cases (or it might have been fixed, need to test)
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} | 2 | "2012-01-24T02:15:27Z" | "2012-01-24T03:08:05Z" | "2012-01-24T03:08:05Z" | CONTRIBUTOR | null | bugs, or expected?
```
Q E A B C D
F G
0 0 x51CohYbqL aJBXfDaxvl -0.21474 -0.07802 0.37657 0.519663
1 2 Q07yrbfCLo RvNdHB9dl3 -0.16727 -1.26608 1.21477 -1.559552
2 4 uihKvB6NBA 5DjiMCJ8Sw -0.43737 0.10384 0.50846 1.413200
3 6 1qgFB8QlJS 2fUK7jD4Vb 1.06883 0.39375 -0.90719 -1.280366
4 8 9cmUdqYSor 93UZeCZOG8 0.28849 0.20656 0.14076 -1.350762
5 10 upb4NIS9Im V2v36EBTbW 1.42492 -0.81992 -0.58840 1.297900
6 12 EI1mcWNhQc lOi1LZoa9d -0.43579 -0.55900 -0.38126 -1.996946
7 14 P4svFjVstF KRdANeSKc1 -0.06271 -0.73894 -1.61319 0.015665
8 16 8TnT3assPy 8GMHH7cwIM -0.16219 -1.37595 0.26222 0.387206
9 18 ZpQAdGJGqn aOyyiwsKe5 -1.48009 0.20925 1.66069 -0.165002
10 20 GsgxBj1X6H IjKfhf7s0t -0.90167 0.42439 0.15507 1.423653
11 22 HvPp46Lsw7 6eRfG5cO5i 0.44674 0.35359 -1.03449 -1.224809
12 24 KZFZq4N2x6 WoZU0DDsZt -1.04287 0.50613 -0.37356 0.412353
13 26 BeXkOEK1vp KisdEjCbGD -0.68967 1.75701 0.83935 2.449815
14 28 xIaBEjA3KJ cYu48PoJcE 2.33759 0.80017 -0.49848 0.184022
15 30 SLC9Xq9WJr aAZeWu4Cuz -1.12675 -1.43130 0.67064 -0.004682
16 32 3L68qvWgWr JicCNHSE0W 1.39660 0.43552 0.42691 -0.576866
17 34 d5WumlvFj0 dvyPF9hbpr -1.44074 0.71225 -0.31398 -0.332095
18 36 s7JvczBxzY tdBT7QIsea -0.08437 0.37257 -0.96486 0.598225
19 38 G4vFpFXvRC NiOLw70EzR 1.53051 0.11435 -0.88396 0.765056
20 40 wW6VWMgyVo Yar4UkKxFU 0.16342 -0.22157 -0.28934 -0.563170
21 42 pGSClBDzVm mnfNHAv4rO 0.43092 1.20629 -0.43644 0.381052
22 44 I72WAD7jmE LLxBVrAYSJ -0.12854 -0.08114 -0.51555 0.030052
23 46 8CZLWGObmb EJwNMl0JGu -0.81029 -0.91152 -0.13883 0.195795
24 48 gMEDr2vJpl MEFjWFHtAA -1.10904 0.23615 0.15525 -0.187997
25 50 lzhkv8AZih kQYVmuHnBR 0.11009 -2.23447 -0.08848 -0.354509
26 52 YzPLrcENot XYpjlEAPaO -0.04009 -1.95039 0.45737 -2.303422
27 54 VltHl29NXr NpveZNu6dQ 0.77100 0.09994 -0.36891 0.643359
28 56 Xdt4lbmTDV YFPDKFbdrL 0.65604 -0.41013 2.08534 1.749054
29 58 J6SbXUfABj 0r1IxqyAZj -1.64618 -0.33196 -0.91242 -0.527971
In [68]: df.ix[(0,0), :]
ERROR: An unexpected error occurred while tokenizing input
In [69]: df.ix[[(0,0)], :]
Out[69]:
Q E A B C D
F G
0 0 x51CohYbqL aJBXfDaxvl -0.2147 -0.07802 0.3766 0.5197
In [71]: df.ix[[0,0], :]
Q E A B C D
F G
0 0 x51CohYbqL aJBXfDaxvl -0.2147 -0.07802 0.3766 0.5197
0 x51CohYbqL aJBXfDaxvl -0.2147 -0.07802 0.3766 0.5197
```
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} | 2 | "2012-01-24T02:55:33Z" | "2012-01-24T17:36:07Z" | "2012-01-24T17:36:07Z" | NONE | null | When argument is boxed in a TimeSeries, the 'axis' argument is respected:
In [108]: a = DataFrame(reshape(range(9),(3,3)))
In [109]: a
Out[109]:
0 1 2
0 0 1 2
1 3 4 5
2 6 7 8
In [110]: a.mul(TimeSeries(range(3)))
Out[110]:
0 1 2
0 0 1 4
1 0 4 10
2 0 7 16
In [111]: a.mul(TimeSeries(range(3)), axis='index')
Out[111]:
0 1 2
0 0 0 0
1 3 4 5
2 12 14 16
but when it is unboxed, the axis argument is silently ignored:
In [112]: a.mul(range(3))
Out[112]:
0 1 2
0 0 1 4
1 0 4 10
2 0 7 16
In [113]: a.mul(range(3), axis='index')
Out[113]:
0 1 2
0 0 1 4
1 0 4 10
2 0 7 16
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https://api.github.com/repos/pandas-dev/pandas/issues/673 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/673/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/673/comments | https://api.github.com/repos/pandas-dev/pandas/issues/673/events | https://github.com/pandas-dev/pandas/issues/673 | 2,946,103 | MDU6SXNzdWUyOTQ2MTAz | 673 | Series([])[0] returns garbage | {
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Out[12]: 6.9265549260634e-310
ideally it should throw a KeyError or something
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https://api.github.com/repos/pandas-dev/pandas/issues/674 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/674/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/674/comments | https://api.github.com/repos/pandas-dev/pandas/issues/674/events | https://github.com/pandas-dev/pandas/issues/674 | 2,949,311 | MDU6SXNzdWUyOTQ5MzEx | 674 | plot test not working in current master | {
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} | [] | closed | false | null | [] | null | 10 | "2012-01-24T12:22:34Z" | "2012-03-12T10:40:08Z" | "2012-03-12T10:40:08Z" | CONTRIBUTOR | null | test_upsample_max (pandas.tests.test_functions.TestReductions) ... ok
test_upsample_mean (pandas.tests.test_functions.TestReductions) ... ok
test_upsample_min (pandas.tests.test_functions.TestReductions) ... ok
test_boxplot (pandas.tests.test_graphics.TestDataFramePlots) ... ERROR
test_hist (pandas.tests.test_graphics.TestDataFramePlots) ... ERROR
test_plot (pandas.tests.test_graphics.TestDataFramePlots) ... ERROR
test_plot_bar (pandas.tests.test_graphics.TestDataFramePlots) ... ERROR
test_plot_int_columns (pandas.tests.test_graphics.TestDataFramePlots) ... ERROR
test_hist (pandas.tests.test_graphics.TestSeriesPlots) ... ERROR
test_plot (pandas.tests.test_graphics.TestSeriesPlots) ... ERROR
test_agg_apply_corner (pandas.tests.test_groupby.TestGroupBy) ... ok
test_agg_must_agg (pandas.tests.test_groupby.TestGroupBy) ... ok
test_agg_python_multiindex (pandas.tests.test_groupby.TestGroupBy) ... ok#
see the build recipe log for the daily Ubuntu package:
https://launchpadlibrarian.net/90884761/buildlog_ubuntu-precise-i386.pandas_0.7.0-ubuntu0~1714~precise1_FAILEDTOBUILD.txt.gz
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} | 3 | "2012-01-24T15:03:39Z" | "2012-01-27T21:34:42Z" | "2012-01-24T17:26:15Z" | NONE | null | from pandas import DataFrame
df = DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object)
df.mean(1)
Expected:
In [33]: df.mean(1)
Out[33]:
0 NaN
1 3.0
Actual:
In [31]: df.mean(1)
Out[31]:
0 NaN
1 NaN
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df.groupby('A').agg({('C', 'D'): lambda x, y: (x-y)**2})
Which would call the lambda function with row pairs of C and D for x and y, respectively. Obviously a toy example.
Apologies if this already works in some way I am not familiar with.
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} | 2 | "2012-01-24T17:50:13Z" | "2012-01-24T23:24:33Z" | "2012-01-24T23:24:33Z" | CONTRIBUTOR | null | I found an issue related to having an Int64Index and using pivot_table.
Here's the code that illustrates the problem:
# Start code
import itertools
import datetime
import pandas
d = datetime.date.min
data = list(itertools.product(['foo', 'bar'], ['A', 'B', 'C'],['x1', 'x2'],[d + datetime.timedelta(i) for i in xrange(20)], [1.0]))
print data[0]
df = pandas.DataFrame(data)
df2 = pandas.pivot_table(df, values=4, rows=[0,1,3],cols=[2])
print df2.columns
df = df.rename(columns=lambda x: str(x))
df2 = pandas.pivot_table(df, values='4', rows=['0','1','3'],cols=['2'])
print df2.columns
# end code
I would expect the columns of the pivoted table to be the same regardless of whether the column index of the original table
is ints or strings. I'm finding that this is not the case.
thanks in advance for looking into it.
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} | 1 | "2012-01-24T21:55:59Z" | "2012-01-24T23:00:30Z" | "2012-01-24T23:00:30Z" | MEMBER | null | This used to work (cc @craustin):
```
from pandas import Series
s = Series({'T1': 5})
print s.groupby({'T1': 'T2'}).agg(sum)
```
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} | 29 | "2012-01-25T00:14:20Z" | "2012-01-27T00:23:56Z" | "2012-01-25T23:21:02Z" | NONE | null | from pandas import DataFrame
df = DataFrame({u'c/\u03c3':[1,2,3]})
Try typing 'df' in IPython:
UnicodeEncodeError: 'ascii' codec can't encode character u'\u03c3' in position 5: ordinal not in range(128)
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} | 5 | "2012-01-25T13:42:22Z" | "2012-01-25T17:43:24Z" | "2012-01-25T15:35:55Z" | CONTRIBUTOR | null | Not sure if it's a bug or just me doing something wrong, but the code below crashes after about two seconds of 100% CPU utilization:
```
from pandas import DataFrame
df = DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df2 = DataFrame({'c': [2, 2], 'd': [3, 3]})
gp2 = df2.groupby('c')['d']
df.reindex(gp2.index)
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', (2494, 0))
[...]
TypeError: unhashable type: 'numpy.ndarray'
```
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Currently:
DataFrame.asfreq(MonthEnd(0)) goes into infinite loop. I believe this is the case for all the Date *End subclasses
Desired:
Exception should be raised
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} | 2 | "2012-01-25T19:14:36Z" | "2012-01-26T20:02:04Z" | "2012-01-26T19:46:42Z" | CONTRIBUTOR | null | This fixes up the unicode situation on Python 3.
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} | 1 | "2012-01-25T22:05:17Z" | "2012-01-25T22:25:56Z" | "2012-01-25T22:25:56Z" | NONE | null | Contrary to the documentation, the SparseSeries code, when doing a combine_first, actually assumes the other series is a SparseSeries. It performs a other.to_dense(), which fails on a Series.
```
def combine_first(self, other):
"""
Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
y : Series
"""
dense_combined = self.to_dense().combine_first(other.to_dense())
return dense_combined.to_sparse(fill_value=self.fill_value)
```
I changed my code locally to the following, which seems to work (though I'm sure there is a more elegant way):
```
def combine_first(self, other):
"""
Combine Series values, choosing the calling Series's values
first. Result index will be the union of the two indexes
Parameters
----------
other : Series
Returns
-------
y : Series
"""
if isinstance(other, SparseSeries):
dense_combined = self.to_dense().combine_first(other.to_dense())
elif isinstance(other, Series):
dense_combined = self.to_dense().combine_first(other)
return dense_combined.to_sparse(fill_value=self.fill_value)
```
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} | 0 | "2012-01-25T22:45:04Z" | "2012-01-25T22:59:57Z" | "2012-01-25T23:17:04Z" | NONE | null | In LICENSE, I updated the copyright notice & policy for 2012, as well as added a description of copyright holder. In the docs overview I clarified the development team, getting support, and credits, and included the LICENSE file.
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} | [] | closed | false | null | [] | null | 5 | "2012-01-26T15:59:50Z" | "2012-01-27T12:31:39Z" | "2012-01-27T12:31:39Z" | CONTRIBUTOR | null | ```
df2
Out[62]:
A B C D
level1a 0 foo one -0.3364 -1.212780
1 bar one 1.5044 0.832076
level1b 2 foo two 0.1210 -0.547690
3 bar three -0.6163 0.948797
level1c 4 foo two 0.4783 -1.295089
5 bar two -0.6558 -1.515122
level1d 6 foo one -1.1923 -0.884387
7 foo three -0.6004 0.003952
pd.concat([df2[0:4], df2[4:8]], keys=['level0a', 'level0b'])
Out[66]:
A B C D
level0a level1a 0 foo one -0.3364 -1.212780
1 bar one 1.5044 0.832076
level0a level1b 2 foo two 0.1210 -0.547690
3 bar three -0.6163 0.948797
level0b level1c 4 foo two 0.4783 -1.295089
5 bar two -0.6558 -1.515122
level0b level1d 6 foo one -1.1923 -0.884387
7 foo three -0.6004 0.003952
```
While I would expect that there are only one of level0a and level0b at the first level.
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} | 2 | "2012-01-26T16:04:43Z" | "2012-01-26T16:51:35Z" | "2012-01-26T16:43:05Z" | NONE | null | import numpy as np
from pandas import Series
s = Series(range(3))
s2 = s.map(lambda x: np.where(x == 0, 0, 1))
print s2.dtype
In pandas 0.6.0, this returned 'float64', but now it returns 'object'. This change requires us to explicitly specify some dtypes to pandas constructors as a workaround. If this is the desired behavior, we can live with it - but wanted to alert you guys.
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} | 0 | "2012-01-26T18:34:16Z" | "2012-01-26T20:14:37Z" | "2012-01-26T20:14:37Z" | CONTRIBUTOR | null | data = {"title" : ['foobar','bar','foobar'] + ['foobar'] \* 17 , "cruft" : np.random.random(20)}
df = pandas.DataFrame(data)
ix = df[df['title'] == 'bar'].index
# this doesn't work with a list of variables
df.ix[ix, ['title']] = 'foobar'
df.ix[ix, ['cruft']] = 0
print df
# but this does
df.ix[ix, 'title'] = 'foobar'
df.ix[ix, 'cruft'] = 0
print df
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```
In [11]: df
Out[11]:
a b c d
one two three
foo bar baz -0.2732 1 -0.2732 1
In [12]: df.groupby(level='three')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/wesm/code/pandas/<ipython-input-12-17ba46f6aa67> in <module>()
----> 1 df.groupby(level='three')
/home/wesm/code/pandas/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort)
120 from pandas.core.groupby import groupby
121 return groupby(self, by, axis=axis, level=level, as_index=as_index,
--> 122 sort=sort)
123
124 def select(self, crit, axis=0):
/home/wesm/code/pandas/pandas/core/groupby.pyc in groupby(obj, by, **kwds)
520 raise TypeError('invalid type: %s' % type(obj))
521
--> 522 return klass(obj, by, **kwds)
523
524 def _get_axes(group):
/home/wesm/code/pandas/pandas/core/groupby.pyc in __init__(self, obj, grouper, axis, level, groupings, exclusions, column, as_index, sort)
112 if groupings is None:
113 groupings, exclusions = _get_groupings(obj, grouper, axis=axis,
--> 114 level=level, sort=sort)
115
116 self.groupings = groupings
/home/wesm/code/pandas/pandas/core/groupby.pyc in _get_groupings(obj, grouper, axis, level, sort)
720 name = gpr
721 gpr = obj[gpr]
--> 722 ping = Grouping(group_axis, gpr, name=name, level=level, sort=sort)
723 if ping.name is None:
724 ping.name = 'key_%d' % i
/home/wesm/code/pandas/pandas/core/groupby.pyc in __init__(self, index, grouper, name, level, sort)
589 level_values = index.levels[level].take(inds)
590 if grouper is not None:
--> 591 self.grouper = level_values.map(self.grouper)
592 else:
593 self._was_factor = True
/home/wesm/code/pandas/pandas/core/index.pyc in map(self, mapper)
580
581 def map(self, mapper):
--> 582 return self._arrmap(self.values, mapper)
583
584 def _get_method(self, method):
/home/wesm/code/pandas/pandas/_tseries.so in pandas._tseries.arrmap_object (pandas/src/tseries.c:41721)()
TypeError: 'numpy.ndarray' object is not callable
```
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} | 3 | "2012-01-26T23:16:02Z" | "2012-12-28T15:08:25Z" | "2012-12-28T15:08:21Z" | MEMBER | null | Should just silently fail otherwise if the PyTables version in use does not have the indexing functionality. Should be fairly simple to add, only affects Table formatted objects
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} | 1 | "2012-01-27T00:31:34Z" | "2012-01-27T03:40:15Z" | "2012-01-27T03:40:15Z" | MEMBER | null | cc @craustin per e-mail discussion
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