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} | 3 | 2013-06-23T04:14:42Z | 2013-06-28T23:06:17Z | 2013-06-28T23:06:17Z | MEMBER | null | - [x] remove `isinstance(x, str)` checks
- [x] handle urls with context managers
- [x] get rid of `Exception` and use appropriate exception types
- [x] pep8
- [x] remove redundant parens in conditionals
- [x] remove `\` style line joins
- [x] `range` -> `xrange`
- [x] functions should not exit after a warning with `None` they should raise an exception
- [x] turn all the str `+` style concats into format strings
- [x] remove redundant calls to `str`
- [x] remove exception handlers with no exception type
- [x] replace calls to `__setattr__` and `__getattribute__` with `setattr` and `getattr`
- [x] code coverage at 99%
This should remove the connection reset by peer testing error and I thought I would perform a little housekeeping while I was at it.
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} | 18 | 2013-06-23T05:20:26Z | 2014-06-27T11:41:37Z | 2013-06-28T23:06:17Z | MEMBER | null | closes #4001
closes #3982
closes #4028
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} | 8 | 2013-06-23T13:09:33Z | 2016-12-10T21:35:41Z | 2016-12-10T21:35:41Z | NONE | null | See http://stackoverflow.com/questions/17236852/pandas-crosstab-double-counting-when-using-two-aggregate-functions for discussion.
To reproduce:
Create a test dataframe:
``` python
df = DataFrame({'A': ['one', 'one', 'two', 'three'] * 6, 'B': ['A', 'B', 'C'] * 8, 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4, 'D': np.random.randn(24), 'E': np.random.randn(24)})
```
Crosstab gives the expected results:
``` python
crosstab(rows=[df['A'],df['B']], cols=[df['C']], margins=True)
C bar foo All
A B
one A 2 2 4
B 2 2 4
C 2 2 4
three A 2 0 2
B 0 2 2
C 2 0 2
two A 0 2 2
B 2 0 2
C 0 2 2
All 12 12 24
```
However, if you try to get mean and count in the same crosstab, specifying values for the mean, the crosstab will double count the elements when calculating the margin total:
``` python
crosstab(rows=[df['A'],df['B']], cols=[df['C']], margins=True, aggfunc=[np.size, np.mean], values=df['D'])
size mean
C bar foo All bar foo All
A B
one A 2 2 4 0.245998 0.076366 0.161182
B 2 2 4 -0.739757 0.137780 -0.300988
C 2 2 4 -1.555759 -1.446554 -1.501157
three A 2 NaN 2 1.216109 NaN 1.216109
B NaN 2 2 NaN 0.255482 0.255482
C 2 NaN 2 0.732448 NaN 0.732448
two A NaN 2 2 NaN -0.273747 -0.273747
B 2 NaN 2 -0.001649 NaN -0.001649
C NaN 2 2 NaN 0.685422 0.685422
All 24 24 24 -0.017102 -0.094208 -0.055655
```
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} | 9 | 2013-06-23T17:26:13Z | 2014-06-25T21:40:36Z | 2013-06-24T09:57:50Z | CONTRIBUTOR | null | DataFrame rename doesn't rename a MultiIndex column and it flattens the columns. Closes https://github.com/pydata/pandas/issues/3165 which is a fix for index but not columns.
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} | 2 | 2013-06-23T23:21:51Z | 2013-09-22T17:12:48Z | 2013-09-22T17:12:48Z | CONTRIBUTOR | null | ```
>>> s = pd.Series(pd.date_range('2000-1-1', '2000-1-2'))
>>> s
0 2000-01-01 00:00:00
1 2000-01-02 00:00:00
```
This works:
```
>>> s[0] = pd.NaT
>>> s
0 NaT
1 2000-01-02 00:00:00
```
This works:
```
>>> s.iloc[0] = pd.NaT.value
>>> s
0 NaT
1 2000-01-02 00:00:00
dtype: datetime64[ns]
```
This doens't:
```
>>> s.iloc[0] = pd.NaT
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/indexing.py", line 88, in __setitem__
self._setitem_with_indexer(indexer, value)
File "pandas/core/indexing.py", line 185, in _setitem_with_indexer
values[indexer] = value
ValueError: Invalid date (-1,-1,-1) when converting to NumPy datetime
```
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} | 4 | 2013-06-23T23:27:32Z | 2014-02-18T18:54:28Z | 2014-02-18T18:54:28Z | CONTRIBUTOR | null | ```
>>> s = pd.Series([None, 'a', 'b'])
>>> s
0 None
1 a
2 b
dtype: object
>>> s.min()
inf
```
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} | 18 | 2013-06-23T23:45:57Z | 2014-06-15T09:36:14Z | 2013-10-03T21:00:21Z | MEMBER | null | closes #1067.
This isn't a fast function but has quite a bit of functionality. Take a look at the unit tests. Comments on the API welcome and this could use a few iterations. Various ad hoc things like concatenating "key paths" to metadata
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} | [] | closed | false | null | [] | null | 4 | 2013-06-24T01:18:39Z | 2013-06-24T13:38:29Z | 2013-06-24T01:29:39Z | CONTRIBUTOR | null | Just updated to pydata/pandas@master and now have a failing test:
``` python
======================================================================
ERROR: test_raise_on_no_columns (pandas.io.tests.test_parsers.TestCParserLowMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../pandas/pandas/io/tests/test_parsers.py", line 2337, in test_raise_on_no_columns
self.assertRaises(ValueError, self.read_csv, StringIO(data))
File "/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 471, in assertRaises
callableObj(*args, **kwargs)
File "/.../pandas/pandas/io/tests/test_parsers.py", line 2026, in read_csv
return read_csv(*args, **kwds)
File "/.../pandas/pandas/io/parsers.py", line 396, in parser_f
return _read(filepath_or_buffer, kwds)
File "/.../pandas/pandas/io/parsers.py", line 194, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/.../pandas/pandas/io/parsers.py", line 475, in __init__
self._make_engine(self.engine)
File "/.../pandas/pandas/io/parsers.py", line 582, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/.../pandas/pandas/io/parsers.py", line 953, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "parser.pyx", line 480, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4508)
ZeroDivisionError: integer division or modulo by zero
----------------------------------------------------------------------
```
ff5797959ef5d78682d6e85d97356407f9d63de6 creates the problem (though I'm not sure if that test existed before that commit). Thoughts on what's going on?
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} | 39 | 2013-06-24T01:52:30Z | 2014-06-18T13:38:53Z | 2013-07-03T12:50:07Z | CONTRIBUTOR | null | Changed double decorated tests previously `network` and `slow` so they actually get run + stored usda_data such that the tests don't fail on network access.
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https://api.github.com/repos/pandas-dev/pandas/issues/4010 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4010/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4010/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4010/events | https://github.com/pandas-dev/pandas/pull/4010 | 15,912,607 | MDExOlB1bGxSZXF1ZXN0NjQ5Nzc0NA== | 4,010 | CLN: Added *.dta to the .gitignore. Fixed typo in comment. Removed import. | {
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] | closed | false | null | [] | null | 22 | 2013-06-24T13:35:31Z | 2013-06-24T15:55:52Z | 2013-06-24T15:55:17Z | NONE | null | I apparently successfully installed pandas using pip (there were a large number of warnings generated), but when I import pandas I get:
dlopen(/Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/hashtable.so, 2): Symbol not found: _get_c_string
Referenced from: /Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/hashtable.so
Expected in: flat namespace
in /Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/hashtable.so
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/__init__.py", line 6, in <module>
from . import hashtable, tslib, lib
ImportError: dlopen(/Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/hashtable.so, 2): Symbol not found: _get_c_string
Referenced from: /Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/hashtable.so
Expected in: flat namespace
in /Ureka/1.0beta5/Ureka/variants/scipy2013/lib/python2.7/site-packages/pandas/hashtable.so
I'm using Mac OSX 10.8.4 with the latest version of Xcode (Version 4.6.3 (4H1503)), Python 2.7.3 and numpy 1.6.2.
Please let me know if there's any more information I can provide.
Many thanks,
Emma :)
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} | 12 | 2013-06-24T16:41:40Z | 2014-02-18T02:26:41Z | 2014-02-18T02:26:41Z | NONE | null | Issue valid ONLY for numpy < 1.7, need to astype to object the datetime64[ns] first, then to_records
To recreate:
``` python
import pandas as pd
import datetime
current = datetime.datetime.combine(datetime.date.today(), datetime.time())
date_list = [('test{0}'.format(i), current + datetime.timedelta(days=i)) for i in xrange(5)]
# convert this to pandas df
df1 = pd.DataFrame(date_list, columns=['id', 'day1'])
print "Data Frame"
print df1
dfr = df1.to_records(index=False)
print dfr
for r in dfr:
print r[1], type(r[1])
Output with 0.10.1 (Good)
Data Frame
id day1
0 test0 2013-06-24 00:00:00
1 test1 2013-06-25 00:00:00
2 test2 2013-06-26 00:00:00
3 test3 2013-06-27 00:00:00
4 test4 2013-06-28 00:00:00
[('test0', datetime.datetime(2013, 6, 24, 0, 0))
('test1', datetime.datetime(2013, 6, 25, 0, 0))
('test2', datetime.datetime(2013, 6, 26, 0, 0))
('test3', datetime.datetime(2013, 6, 27, 0, 0))
('test4', datetime.datetime(2013, 6, 28, 0, 0))]
2013-06-24 00:00:00 <type 'datetime.datetime'>
2013-06-25 00:00:00 <type 'datetime.datetime'>
2013-06-26 00:00:00 <type 'datetime.datetime'>
2013-06-27 00:00:00 <type 'datetime.datetime'>
2013-06-28 00:00:00 <type 'datetime.datetime'>
```
Output with 0.11 (Bad)
``` python
Data Frame
id day1
0 test0 2013-06-24 00:00:00
1 test1 2013-06-25 00:00:00
2 test2 2013-06-26 00:00:00
3 test3 2013-06-27 00:00:00
4 test4 2013-06-28 00:00:00
[('test0', datetime.datetime(1970, 1, 16, 128, 0))
('test1', datetime.datetime(1970, 1, 16, 152, 0))
('test2', datetime.datetime(1970, 1, 16, 176, 0))
('test3', datetime.datetime(1970, 1, 16, 200, 0))
('test4', datetime.datetime(1970, 1, 16, 224, 0))]
1970-01-16 128:00:00 <type 'numpy.datetime64'>
1970-01-16 152:00:00 <type 'numpy.datetime64'>
1970-01-16 176:00:00 <type 'numpy.datetime64'>
1970-01-16 200:00:00 <type 'numpy.datetime64'>
1970-01-16 224:00:00 <type 'numpy.datetime64'>
```
1. Notice the datetime value. This is just not a display issue. The object has the incorrect value.
2. 0.10.1 use to return datetime.datetime type, while 0.11 returns numpy.datetime64.
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} | 2 | 2013-06-24T17:22:36Z | 2016-10-12T23:03:55Z | 2014-03-28T23:31:24Z | NONE | null | When applying different functions to columns with a MultiIndex by supplying a mapping to groupby.agg(), the top-level name of the columns get lost.
I believe this is a bug, because the names of the columns are unchanged (the total number of columns might be smaller, if not all columns are in the mapping, though).
In the example here I am using groupby.agg(), even though technically speaking I want to do a transformation. However, groupby.agg() seems to be the only apply-like method that allows the usage of a mapping for different functions per column. What would be the recommended way?
``` python
In [2]: df = pd.DataFrame({
...: 'exp' : ['A']*6 + ['B']*6,
...: 'obj' : [1,1,1,2,2,2]*2,
...: 'rep' : [1,2,3] * 4,
...: 'var1' : range(12),
...: 'var2' : range(12,24),
...: 'var3' : range(24,36),
...: })
In [3]: df = df.set_index(['exp', 'obj', 'rep'])
In [4]: df = df.sort_index()
In [5]: df.columns.name = 'vars'
In [6]: print('before unstack: ', df.columns.names)
('before unstack: ', ['vars'])
In [7]: df = df.unstack('rep')
In [8]: print('after unstack: ', df.columns.names)
('after unstack: ', ['vars', 'rep'])
In [9]: funcs = {
...: 'var1' : lambda x: x - x.median(),
...: 'var2' : lambda y: y - y.mean(),
...: 'var3' : lambda y: y - y.sum(),
...: }
In [10]: df1 = df.groupby(level=0).agg(funcs)
In [11]: print('after groupby.agg: ', df1.columns.names)
('after groupby.agg: ', [None, 'rep'])
```
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} | [] | closed | false | null | [] | null | 2 | 2013-06-24T17:38:53Z | 2019-11-26T14:09:18Z | 2013-06-24T22:08:25Z | NONE | null | ``` python
In [24]: df = pd.DataFrame({
....: 'exp' : ['A']*3 + ['B']*3,
....: 'var1' : range(6),
....: })
In [25]: df = df.set_index(['exp'])
In [26]: df.index.names
Out[27]: ['exp']
In [28]: df
Out[28]:
var1
exp
A 0
A 1
A 2
B 3
B 4
B 5
In [29]: try:
....: df.groupby(level='exp').size()
....: except ValueError as e:
....: print('index can not be accessed by name')
....: print(e)
....:
index can not be accessed by name
level > 0 only valid with MultiIndex
# at this point I would argue that the level 'exp' is level 0,
# and therefore this should work.
In [29]: df1 = df.groupby(level=0).size()
In [30]: df1
Out[30]:
exp
A 3
B 3
dtype: int64
```
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} | [] | closed | false | null | [] | null | 2 | 2013-06-24T20:15:42Z | 2014-07-16T08:15:48Z | 2013-06-24T22:08:25Z | CONTRIBUTOR | null | Fixes #4014.
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} | 6 | 2013-06-24T22:18:22Z | 2013-08-07T20:20:41Z | 2013-08-07T20:20:41Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
ind = pd.date_range(start="2000", freq="D", periods=1000)
df = pd.DataFrame(np.random.randn(len(ind), 5), index=ind, columns=list('ABCDE'))
panel = pd.Panel({'frame_'+c:df for c in list('ABC')})
test1 = panel.ix[:, "2002"]
test1.ndim # 3
type(test1) # pandas.core.frame.DataFrame
test2 = panel.ix[:, "2002":"2002-12-31"]
test2.ndim # 3
type(test2) # pandas.core.panel.Panel
print pd.__version__
#0.11.1.dev-d7fe745
```
When trying to grab all the data for year 2002, I get back a DataFrame. If I use a range for the major axis, then it returns a Panel.
http://nbviewer.ipython.org/5853887
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} | 1 | 2013-06-24T23:00:55Z | 2013-06-28T13:18:24Z | 2013-06-26T18:15:38Z | CONTRIBUTOR | null | I encountered some strange behavior from df.iloc today (today's master). Here's an example. First, create a 300,000 element df with ascending integer index and then select 3000 rows from it:
from pandas import *
import numpy as np
df = DataFrame({'A' : [0.1] \* 300000, 'B' : [1] \* 300000})
idx = array(range(3000)) \* 99
%timeit df.A.iloc[idx]
> 1000 loops, best of 3: 256 µs per loop
This seems quite reasonable. Now do the same thing, but this time with a df whose index is not ascending:
df2 = DataFrame({'A' : [0.1] \* 100000, 'B' : [1] \* 100000})
df2 = concat([df, 2_df, 3_df])
%timeit df2.A.iloc[idx]
After waiting several _minutes_, I get the output:
---
AssertionError Traceback (most recent call last)
<ipython-input-47-8f3d2b974bc2> in <module>()
1 df2 = DataFrame({'A' : [0.1] \* 100000, 'B' : [1] \* 100000})
2 df2 = concat([df, 2_df, 3_df])
----> 3 get_ipython().magic(u'timeit df2.A.iloc[idx]')
/RHS/packages/anaconda/ipython/IPython/core/interactiveshell.pyc in magic(self, arg_s)
2180 magic_name, _, magic_arg_s = arg_s.partition(' ')
2181 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2182 return self.run_line_magic(magic_name, magic_arg_s)
2183
2184 #-------------------------------------------------------------------------
/RHS/packages/anaconda/ipython/IPython/core/interactiveshell.pyc in run_line_magic(self, magic_name, line)
2101 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2102 with self.builtin_trap:
-> 2103 result = fn(_args,_*kwargs)
2104 return result
2105
/RHS/packages/anaconda/ipython/IPython/core/magics/execution.pyc in timeit(self, line, cell)
/RHS/packages/anaconda/ipython/IPython/core/magic.pyc in <lambda>(f, _a, *_k)
190 # but it's overkill for just that one bit of state.
191 def magic_deco(arg):
--> 192 call = lambda f, _a, *_k: f(_a, *_k)
193
194 if callable(arg):
/RHS/packages/anaconda/ipython/IPython/core/magics/execution.pyc in timeit(self, line, cell)
885 number = 1
886 for i in range(1, 10):
--> 887 if timer.timeit(number) >= 0.2:
888 break
889 number *= 10
/Users/stanton/anaconda/lib/python2.7/timeit.pyc in timeit(self, number)
193 gc.disable()
194 try:
--> 195 timing = self.inner(it, self.timer)
196 finally:
197 if gcold:
<magic-timeit> in inner(_it, _timer)
/RHS/packages/anaconda/pandas/pandas/core/indexing.pyc in **getitem**(self, key)
686 return self._getitem_tuple(key)
687 else:
--> 688 return self._getitem_axis(key, axis=0)
689
690 def _getitem_axis(self, key, axis=0):
/RHS/packages/anaconda/pandas/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
838 raise ValueError("Cannot index by location index with a non-integer key")
839
--> 840 return self._get_loc(key,axis=axis)
841
842 def _convert_to_indexer(self, obj, axis=0):
/RHS/packages/anaconda/pandas/pandas/core/indexing.pyc in _get_loc(self, key, axis)
62
63 def _get_loc(self, key, axis=0):
---> 64 return self.obj._ixs(key, axis=axis)
65
66 def _slice(self, obj, axis=0, raise_on_error=False):
/RHS/packages/anaconda/pandas/pandas/core/series.pyc in _ixs(self, i, axis)
599 label = self.index[i]
600 if isinstance(label, Index):
--> 601 return self.reindex(label)
602 else:
603 return _index.get_value_at(self, i)
/RHS/packages/anaconda/pandas/pandas/core/series.pyc in reindex(self, index, method, level, fill_value, limit, copy)
2660 level=level, limit=limit)
2661 new_values = com.take_1d(self.values, indexer, fill_value=fill_value)
-> 2662 return Series(new_values, index=new_index, name=self.name)
2663
2664 def reindex_axis(self, labels, axis=0, **kwargs):
/RHS/packages/anaconda/pandas/pandas/core/series.pyc in **new**(cls, data, index, dtype, name, copy)
487 else:
488 subarr = subarr.view(Series)
--> 489 subarr.index = index
490 subarr.name = name
491
/RHS/packages/anaconda/pandas/pandas/lib.so in pandas.lib.SeriesIndex.**set** (pandas/lib.c:29483)()
AssertionError: Index length did not match values
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https://api.github.com/repos/pandas-dev/pandas/issues/4018 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4018/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4018/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4018/events | https://github.com/pandas-dev/pandas/pull/4018 | 15,955,258 | MDExOlB1bGxSZXF1ZXN0NjUxOTQ5OA== | 4,018 | BUG: GH4017, efficiently support non-unique indicies with iloc | {
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} | 12 | 2013-06-25T00:14:19Z | 2014-06-12T13:14:25Z | 2013-06-26T18:15:38Z | CONTRIBUTOR | null | closes #4017
This was a bug because the iloc was dealing with a non-unique index (and was
reindexing which is not correct in this situation, instead can effectively
take)
```
In [1]: df= DataFrame({'A' : [0.1] * 300000, 'B' : [1] * 300000})
In [2]: idx = np.array(range(3000)) * 99
In [3]: expected = df.iloc[idx]
In [4]: df2 = DataFrame({'A' : [0.1] * 100000, 'B' : [1] * 100000})
In [5]: df2 = pd.concat([df2, 2*df2, 3*df2])
In [6]: %timeit df2.iloc[idx]
1000 loops, best of 3: 221 us per loop
In [7]: %timeit df2.loc[idx]
10 loops, best of 3: 25.6 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4019/events | https://github.com/pandas-dev/pandas/issues/4019 | 15,958,645 | MDU6SXNzdWUxNTk1ODY0NQ== | 4,019 | BUG(?): Stack dropna being ignored | {
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`stack` takes an argument `dropna`:
dropna : boolean, default True
Whether to drop rows in the resulting Frame/Series with no valid
values
It seems like that argument may be getting ignored. That or I'm confused and the docstring may need a rewrite.
Here's some code to try it on.
``` python
df = pd.DataFrame({'A': ['loc_a'] * 12 + ['loc_b'],
'B': ['group_a'] * 7 + ['group_b'] * 3 + ['group_c'] * 2 + ['group_a'],
'Date': ["2013-06-11",
"2013-07-02",
"2013-07-09",
"2013-07-30",
"2013-08-06",
"2013-09-03",
"2013-10-01",
"2013-07-09",
"2013-08-06",
"2013-09-03",
"2013-07-09",
"2013-09-03",
"2013-10-01"],
'Value': [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3]})
df.Date = pd.to_datetime(df.Date)
df = df.set_index(['A', 'B', 'Date'])
In [75]: df.unstack(['A', 'B']).stack(['A', 'B'], dropna=False)
Out[75]:
Value
Date A B
2013-06-11 loc_a group_a 22
2013-07-02 loc_a group_a 35
2013-07-09 loc_a group_a 14
group_b 4
group_c 1
2013-07-30 loc_a group_a 9
2013-08-06 loc_a group_a 4
group_b 2
2013-09-03 loc_a group_a 40
group_b 5
group_c 2
2013-10-01 loc_a group_a 18
loc_b group_a 3
# Same
In [76]: df.unstack(['A', 'B']).stack(['A', 'B'], dropna=True)
Out[76]:
Value
Date A B
2013-06-11 loc_a group_a 22
2013-07-02 loc_a group_a 35
2013-07-09 loc_a group_a 14
group_b 4
group_c 1
2013-07-30 loc_a group_a 9
2013-08-06 loc_a group_a 4
group_b 2
2013-09-03 loc_a group_a 40
group_b 5
group_c 2
2013-10-01 loc_a group_a 18
loc_b group_a 3
# Zeros are where NaNs should show up with dropna=False?
In [77]: df.unstack(['A', 'B']).fillna(0).stack(['A', 'B'])
Out[77]:
Value
Date A B
2013-06-11 loc_a group_a 22
group_b 0
group_c 0
loc_b group_a 0
2013-07-02 loc_a group_a 35
group_b 0
group_c 0
loc_b group_a 0
2013-07-09 loc_a group_a 14
group_b 4
group_c 1
loc_b group_a 0
2013-07-30 loc_a group_a 9
group_b 0
group_c 0
loc_b group_a 0
2013-08-06 loc_a group_a 4
group_b 2
group_c 0
loc_b group_a 0
2013-09-03 loc_a group_a 40
group_b 5
group_c 2
loc_b group_a 0
2013-10-01 loc_a group_a 18
group_b 0
group_c 0
loc_b group_a 3
In [78]: pd.__version__
Out[78]: '0.11.1.dev-3406a54'
```
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} | 25 | 2013-06-25T02:53:55Z | 2021-04-11T02:10:32Z | null | MEMBER | null | most plotting functions return an `Artist` instance whereas a few return something else. for example, `boxplot` returns a `dict` and `scatter` returns a `tuple`. they should at least return a `matplotlib.artist.Artist` subclass (usually either a `Figure` or `Axes`) or an array of `Artist`s.
this has been brought up before but it would be nice to make a decision here.
[mailing list thread](https://groups.google.com/d/topic/pystatsmodels/biNlCvJPNNY/discussion) and #3474.
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} | 9 | 2013-06-25T03:36:39Z | 2014-07-16T08:15:54Z | 2013-06-28T15:18:57Z | MEMBER | null | cc @fonnesbeck
here's the original issue: https://github.com/pydata/pandas/commit/51cc9d9d21554701f70ef24392bd78a3da189335#commitcomment-3494266
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} | 1 | 2013-06-25T03:52:50Z | 2013-07-25T03:52:47Z | 2013-06-26T00:01:04Z | MEMBER | null | We should only be using the parallel version of this.
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https://api.github.com/repos/pandas-dev/pandas/issues/4023 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4023/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4023/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4023/events | https://github.com/pandas-dev/pandas/issues/4023 | 15,960,481 | MDU6SXNzdWUxNTk2MDQ4MQ== | 4,023 | rename multiindex bug in DataFrame | {
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} | 6 | 2013-06-25T04:11:12Z | 2016-10-12T23:03:56Z | 2014-09-08T13:47:55Z | NONE | null | related / same as #4160
```
from datetime import datetime, time
from pandas import date_range, Series, DataFrame
time_ind = date_range(datetime(2013, 1, 1), datetime(2013, 1, 10), freq='1min')
s = Series(1, index=time_ind)
s_g = s.groupby([lambda x: x.hour, lambda x: x.minute]).sum()
s_g = s_g.rename(lambda (h, m): time(h, m, 0))
df = DataFrame(1, index=time_ind, columns=['a', 'b'])
df_g = df.groupby([lambda x: x.hour, lambda x: x.minute]).sum()
df_g = df_g.rename(lambda (h, m): time(h, m, 0))
```
Works fine on Series, but throws error on DataFrame:
```
Traceback (most recent call last):
File "<ipython-input-25-4e51131ae70d>", line 2, in <module>
df_g = df_g.rename(lambda (h, m): time(h, m, 0))
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 3539, in rename
result._rename_index_inplace(index_f)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 3548, in _rename_index_inplace
self._data = self._data.rename_axis(mapper, axis=1)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1734, in rename_axis
new_axis = MultiIndex.from_tuples([tuple(mapper(y) for y in x) for x in index], names=index.names)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1734, in <genexpr>
new_axis = MultiIndex.from_tuples([tuple(mapper(y) for y in x) for x in index], names=index.names)
File "<ipython-input-25-4e51131ae70d>", line 2, in <lambda>
df_g = df_g.rename(lambda (h, m): time(h, m, 0))
TypeError: 'long' object is not iterable
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4024/events | https://github.com/pandas-dev/pandas/pull/4024 | 15,961,493 | MDExOlB1bGxSZXF1ZXN0NjUyMjU4Mg== | 4,024 | BUG: Make secondary_y work properly for bar plots GH3598 | {
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} | 9 | 2013-06-25T05:06:27Z | 2014-06-25T08:05:07Z | 2013-06-26T13:38:27Z | CONTRIBUTOR | null | As far as I'm aware this seems to work properly, but I'm not sure if this was done the previous way for a reason I don't understand.
Fixes: https://github.com/pydata/pandas/issues/3598
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https://api.github.com/repos/pandas-dev/pandas/issues/4025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4025/events | https://github.com/pandas-dev/pandas/issues/4025 | 15,961,889 | MDU6SXNzdWUxNTk2MTg4OQ== | 4,025 | ER: Better error reporting in get_data_yahoo | {
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} | 20 | 2013-06-25T05:27:58Z | 2014-02-14T13:20:06Z | 2014-02-14T13:20:06Z | MEMBER | null | reported by a book reader
```
When processing a long list of symbols using get_data_yahoo it is essential
that you be able to see which symbol is causing the problem. I was
importing a supposedly cleaned list of all the Russell 2000 stocks. When I
used your example (page 139), it worked. When I used my 'short list' it
worked. When I used my full list is failed: HTTP Error 404: Not Found.
The only way to find the error was to add the stocks one by one until the
error occurred. So what we need is something that kicks out a list of 'bad
symbols'. When I looked at your code it seemed that you had an attempt at
that:
in def dl_mult_symbols(symbol), i see:
try:
stocks[sym] = etc.
except:
warnings.warn('Error with sym: ' + sym + ' ...skipping'
But this is not being invoked. All I am getting is the 404 warning with no
indication of which stock in the list caused the error. I am not familiar
enough (or good enough) to read the code and make the necessary
corrections.
When I added the symbol 'ABVT' I got an error. I checked it out: ABVT was
AboveNet, Inc. which is now Zayo Group But obviously we need something
which kicks out a list of bad symbols, but keeps loading the good ones.
Below I give the traceback from trying to call ABVT.
I am making a list of errors in the book (have only found a few) which I
will be glad to provide at any point.
HTTPError Traceback (most recent call last)
<ipython-input-110-daf6874a2577> in <module>()
----> 1 web.get_data_yahoo('ABVT','1/1/2013','3/1/2013')
/Users/GJA/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/data.pyc
in get_data_yahoo(symbols, start, end, retry_count, pause, adjust_price,
ret_index, chunksize, **kwargs)
328 if isinstance(symbols, (str, int)):
329 sym = symbols
--> 330 hist_data = _get_hist_yahoo(sym, start=start, end=end)
331 #Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
332 elif isinstance(symbols, DataFrame):
/Users/GJA/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/data.pyc
in _get_hist_yahoo(sym, start, end, retry_count, pause, **kwargs)
160
161 for _ in range(retry_count):
--> 162 resp = urllib2.urlopen(url)
163 if resp.code == 200:
164 lines = resp.read()
/Applications/Canopy.app/appdata/canopy-1.0.0.1160.macosx-x86_64/Canopy.app/Contents/lib/python2.7/urllib2.pyc
in urlopen(url, data, timeout)
124 if _opener is None:
125 _opener = build_opener()
--> 126 return _opener.open(url, data, timeout)
127
128 def install_opener(opener):
/Applications/Canopy.app/appdata/canopy-1.0.0.1160.macosx-x86_64/Canopy.app/Contents/lib/python2.7/urllib2.pyc
in open(self, fullurl, data, timeout)
404 for processor in self.process_response.get(protocol, []):
405 meth = getattr(processor, meth_name)
--> 406 response = meth(req, response)
407
408 return response
/Applications/Canopy.app/appdata/canopy-1.0.0.1160.macosx-x86_64/Canopy.app/Contents/lib/python2.7/urllib2.pyc
in http_response(self, request, response)
517 if not (200 <= code < 300):
518 response = self.parent.error(
--> 519 'http', request, response, code, msg, hdrs)
520
521 return response
/Applications/Canopy.app/appdata/canopy-1.0.0.1160.macosx-x86_64/Canopy.app/Contents/lib/python2.7/urllib2.pyc
in error(self, proto, *args)
442 if http_err:
443 args = (dict, 'default', 'http_error_default') +
orig_args
--> 444 return self._call_chain(*args)
445
446 # XXX probably also want an abstract factory that knows when it
makes
/Applications/Canopy.app/appdata/canopy-1.0.0.1160.macosx-x86_64/Canopy.app/Contents/lib/python2.7/urllib2.pyc
in _call_chain(self, chain, kind, meth_name, *args)
376 func = getattr(handler, meth_name)
377
--> 378 result = func(*args)
379 if result is not None:
380 return result
/Applications/Canopy.app/appdata/canopy-1.0.0.1160.macosx-x86_64/Canopy.app/Contents/lib/python2.7/urllib2.pyc
in http_error_default(self, req, fp, code, msg, hdrs)
525 class HTTPDefaultErrorHandler(BaseHandler):
526 def http_error_default(self, req, fp, code, msg, hdrs):
--> 527 raise HTTPError(req.get_full_url(), code, msg, hdrs, fp)
528
529 class HTTPRedirectHandler(BaseHandler):
HTTPError: HTTP Error 404: Not Found
ref:_00D708xG6._50070UAdZw:ref
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4026/events | https://github.com/pandas-dev/pandas/issues/4026 | 15,966,993 | MDU6SXNzdWUxNTk2Njk5Mw== | 4,026 | CLN: Remove deprecated 'height' display option. | {
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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 | 2013-06-25T08:41:53Z | 2013-07-28T21:05:53Z | 2013-07-28T21:05:53Z | CONTRIBUTOR | null | Hi,
I've got some Deprecation Warning messages about the 'height' display option (e.g. in `format.get_console_size()` function). See commit b036a7e316426fe4a312f8df4fb246512165812b which introduced this deprecation.
This message occurs when I display a `DataFrame` in my IPython console.
Best regards,
Damien G.
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} | 7 | 2013-06-25T08:47:33Z | 2014-07-10T08:53:26Z | 2013-07-28T21:05:10Z | CONTRIBUTOR | null | Hi,
See the related issue at https://github.com/pydata/pandas/issues/4026
Best regards,
Damien
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cc @gliptak
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} | 3 | 2013-06-25T14:01:58Z | 2014-01-25T15:47:38Z | 2013-06-25T16:39:45Z | CONTRIBUTOR | null | is this random?
https://travis-ci.org/jreback/pandas/jobs/8423251
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} | 9 | 2013-06-25T14:12:48Z | 2014-07-16T08:15:59Z | 2013-06-26T02:12:04Z | CONTRIBUTOR | null | I'm having this error in master branch:
> > > import pandas
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "pandas/**init**.py", line 33, in <module>
> > > from pandas.util.testing import debug
> > > File "pandas/util/testing.py", line 17, in <module>
> > > import nose
> > > ImportError: No module named nose
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} | 8 | 2013-06-25T18:19:52Z | 2014-06-20T04:13:38Z | 2013-06-29T14:50:54Z | CONTRIBUTOR | null | closes #4050. The current version defaults to a very sane default, but sometimes for presentation I prefer them all to be all in a row, or all in a column, or some non-balanced mix of the two. This keyword gives the flexibility for all of those options.
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} | 4 | 2013-06-25T20:56:57Z | 2013-06-26T14:02:11Z | 2013-06-26T14:02:11Z | CONTRIBUTOR | null | The code below causes the DataFrame internals to get confused and throws an AssertionError on `print df.values`. The code looks contrived, but its just a simplified version of something I was trying to do. It doesn't seem to matter whether any columns are actually renamed so I simplified that also, but the rename calls do contribute to the problem.
```
import pandas as pd
df = pd.DataFrame({'a': [1, 2],
'b': [3, 4],
'c': [5, 6]})
df = df.set_index(['a', 'b'])
df = df.unstack()
df = df.rename(columns={'a': 'd'})
df = df.reset_index()
print df._data
df = df.rename(columns={})
print df._data
print df.values
```
```
BlockManager
Items: MultiIndex
[(u'a', u''), (u'c', 3), (u'c', 4)]
Axis 1: Int64Index([0, 1], dtype=int64)
FloatBlock: [(c, 3), (c, 4)], 2 x 2, dtype float64
IntBlock: [(a, )], 1 x 2, dtype int64
BlockManager
Items: MultiIndex
[(u'a', u''), (u'c', 3), (u'c', 4)]
Axis 1: Int64Index([0, 1], dtype=int64)
FloatBlock: [(a, ), (c, 3)], 2 x 2, dtype float64
IntBlock: [(a, )], 1 x 2, dtype int64
Traceback (most recent call last):
File "/home/mtk/bug.py", line 13, in <module>
print df.values
File "/Users/mtk/Source/pandas/pandas/core/frame.py", line 1779, in as_matrix
return self._data.as_matrix(columns).T
File "/Users/mtk/Source/pandas/pandas/core/internals.py", line 1513, in as_matrix
mat = self._interleave(self.items)
File "/Users/mtk/Source/pandas/pandas/core/internals.py", line 1549, in _interleave
raise AssertionError('Some items were not contained in blocks')
AssertionError: Some items were not contained in blocks
```
```
>>> pd.__version__
'0.11.1.dev-8a242d2'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4033/events | https://github.com/pandas-dev/pandas/issues/4033 | 16,006,455 | MDU6SXNzdWUxNjAwNjQ1NQ== | 4,033 | write_frame fails to replace table (io.sql) | {
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] | closed | false | null | [] | null | 1 | 2013-06-25T22:54:58Z | 2013-06-29T15:54:02Z | 2013-06-29T15:54:02Z | NONE | null | For the option if_exists='replace' in write_frame the table is dropped but not re-created.
Simple error in the logic:
```
#create or drop-recreate if necessary
create = None
if exists and if_exists == 'replace':
create = "DROP TABLE %s" % name
elif not exists:
create = get_schema(frame, name, flavor)
if create is not None:
cur = con.cursor()
cur.execute(create)
cur.close()
```
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] | closed | false | null | [] | null | 1 | 2013-06-25T23:10:12Z | 2013-06-29T15:52:42Z | 2013-06-29T15:52:42Z | NONE | null | At least connections via pyodbc or ceODBC do not accept NaN values in INSERT statements.
If I replace the NaN with None it is working as expected, i.e. these are NULLs in the data base:
```
frame2 = frame.where((pd.notnull(frame)), None)
data = [tuple(x) for x in frame2.values]
cur.executemany(insert_query, data)
```
It is pretty expensive to copy the data frame. There is probably a better solution.
I have tested this only with SQL Server for which I have written a small extension to sql.py.
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} | 15 | 2013-06-26T00:22:06Z | 2020-12-23T01:12:39Z | null | NONE | null | related #4036, #4116
from SO with 0.14.0 multi-index slicers: http://stackoverflow.com/questions/24126542/pandas-multi-index-slices-for-level-names/24126676#24126676
Here's the example from there:
```
In [11]: midx = pd.MultiIndex.from_product([list(range(3)),['a','b','c'],pd.date_range('20130101',periods=3)],names=['numbers','letters','dates'])
In [12]: midx.names.index('letters')
Out[12]: 1
In [13]: midx.names.index('dates')
Out[13]: 2
```
Here's a complete example
```
In [18]: df = DataFrame(np.random.randn(len(midx),1),index=midx)
In [19]: df
Out[19]:
0
numbers letters dates
0 a 2013-01-01 0.261092
2013-01-02 -1.267770
2013-01-03 0.008230
b 2013-01-01 -1.515866
2013-01-02 0.351942
2013-01-03 -0.245463
c 2013-01-01 -0.253103
2013-01-02 -0.385411
2013-01-03 -1.740821
1 a 2013-01-01 -0.108325
2013-01-02 -0.212350
2013-01-03 0.021097
b 2013-01-01 -1.922214
2013-01-02 -1.769003
2013-01-03 -0.594216
c 2013-01-01 -0.419775
2013-01-02 1.511700
2013-01-03 0.994332
2 a 2013-01-01 -0.020299
2013-01-02 -0.749474
2013-01-03 -1.478558
b 2013-01-01 -1.357671
2013-01-02 0.161185
2013-01-03 -0.658246
c 2013-01-01 -0.564796
2013-01-02 -0.333106
2013-01-03 -2.814611
```
This is your dict of level names -> slices
```
In [20]: slicers = { 'numbers' : slice(0,1), 'dates' : slice('20130102','20130103') }
```
This creates an indexer that is empty (selects everything)
```
In [21]: indexer = [ slice(None) ] * len(df.index.levels)
```
Add in your slicers
```
In [22]: for n, idx in slicers.items():
indexer[df.index.names.index(n)] = idx
```
And select (this has to be a tuple, but was a list to start as we had to modify it)
```
In [23]: df.loc[tuple(indexer),:]
Out[23]:
0
numbers letters dates
0 a 2013-01-02 -1.267770
2013-01-03 0.008230
b 2013-01-02 0.351942
2013-01-03 -0.245463
c 2013-01-02 -0.385411
2013-01-03 -1.740821
1 a 2013-01-02 -0.212350
2013-01-03 0.021097
b 2013-01-02 -1.769003
2013-01-03 -0.594216
c 2013-01-02 1.511700
2013-01-03 0.994332
```
I use hierarchical indices regularly with pandas DataFrames and Series objects. It is invaluable to be able to partially select subsets of rows based on a set of arbitrary index values, and retain the index information for subsequent groupby operations etc.
I am looking for an elegant way to pass an ordered tuple (with possibly empty slices) or an arbitrary dict of {index_level_name:value,...} pairs to select rows matching the passed index:value pairs. Note: I am aware that with Boolean indexing on data columns and nested np.logical_and() statements you can construct such a Boolean select index. I'm looking for an elegant solution using indexes & levels to avoid repeatedly using df.reset_index and building Boolean arrays. Also, df.xs() does not work in every situation (see below) and does not exist for Series with MultiIndex.
To explain this lets create a DataFrame with 5 index levels:
``` python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: print pd.__version__
0.10.0
In [4]: # Generate Test DataFrame
...: NUM_ROWS = 100000
...:
In [5]: NUM_COLS = 10
In [6]: col_names = ['A'+num for num in map(str,np.arange(NUM_COLS).tolist())]
In [7]: index_cols = col_names[:5]
In [8]: # Set DataFrame to have 5 level Hierarchical Index.
...: # The dtype does not matter try str or np.int64 same results.
...: # Sort the index!
...: df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names)
...:
In [9]: df = df.set_index(index_cols).sort_index()
In [10]: df
Out[10]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 100000 entries, (0, 0, 0, 0, 0) to (4, 4, 4, 4, 4)
Data columns:
A5 100000 non-null values
A6 100000 non-null values
A7 100000 non-null values
A8 100000 non-null values
A9 100000 non-null values
dtypes: int64(5)
In [11]: df.index.names
Out[11]: ['A0', 'A1', 'A2', 'A3', 'A4']
```
Now index on every level and we get back the rows we want :) I love that I get back the complete index too because it may be useful later.
``` python
In [12]: df.ix[(0,1,2,3,4)]
Out[12]: A5 A6 A7 A8 A9
A0 A1 A2 A3 A4
0 1 2 3 4 1 3 1 1 3
4 4 3 4 2 4
4 0 2 3 1 3
...
4 1 1 3 4 3
4 0 1 2 4 1
```
Now if we index on the first 4 levels we get back something different, a data frame with the first 4 index levels dropped. It would be nice to have the option to keep all index levels even though they are repetitive (like above).
``` python
In [13]: df.ix[(0,1,2,3)]
Out[13]: <class 'pandas.core.frame.DataFrame'>
Int64Index: 144 entries, 0 to 4
Data columns:
A5 144 non-null values
A6 144 non-null values
A7 144 non-null values
A8 144 non-null values
A9 144 non-null values
dtypes: int64(5)
```
Now comes the tricky part. What if I only want to index on the first and last 2 index levels, and want everything from the 3rd level? Empty slicing is not supported.
``` python
In [14]: df.ix[(0,1,:,3,4)]
File "<ipython-input-14-7e44e59fa5b1>", line 1
df.ix[(0,1,:,3,4)]
^
SyntaxError: invalid syntax
In [15]: df.ix[(0,1,slice(None),3,4)]
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
<ipython-input-15-0e4517ae7fc5> in <module>()
----> 1 df.ix[(0,1,slice(None),3,4)]
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
30 pass
31
---> 32 return self._getitem_tuple(key)
33 else:
34 return self._getitem_axis(key, axis=0)
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_tuple(self, tup)
212 for i, key in enumerate(tup):
213 if i >= self.obj.ndim:
--> 214 raise IndexingError('Too many indexers')
215
216 if _is_null_slice(key):
IndexingError: Too many indexers
```
df.xs can somewhat help here but its useless for MultiIndex on a series. And it drops the indexed levels leaving you unsure to what fixed index levels you have drilled to. :(
``` python
In [16]: df.xs((0,2,3),level=df.index.names[::2])
Out[16]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 805 entries, (0, 0) to (4, 4)
Data columns:
A5 805 non-null values
A6 805 non-null values
A7 805 non-null values
A8 805 non-null values
A9 805 non-null values
dtypes: int64(5)
```
Interestingly df.xs() is not consistant, because you cannot explicitly index on every level giving it the list of all level names:
``` python
In [17]: df.xs((0,1,2,3,4), level=df.index.names)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-19-d0e373dfcd5f> in <module>()
----> 1 df.xs((0,1,2,3,4), level=df.index.names)
C:\Python27\lib\site-packages\pandas\core\frame.pyc in xs(self, key, axis, level, copy)
2233 labels = self._get_axis(axis)
2234 if level is not None:
-> 2235 loc, new_ax = labels.get_loc_level(key, level=level)
2236
2237 if not copy and not isinstance(loc, slice):
C:\Python27\lib\site-packages\pandas\core\index.pyc in get_loc_level(self, key, level)
2193
2194 result = loc if result is None else result & loc
-> 2195 return result, _drop_levels(result, level)
2196
2197 level = self._get_level_number(level)
C:\Python27\lib\site-packages\pandas\core\index.pyc in _drop_levels(indexer, levels)
2177 levels = [self._get_level_number(i) for i in levels]
2178 for i in sorted(levels, reverse=True):
-> 2179 new_index = new_index.droplevel(i)
2180 return new_index
2181
AttributeError: 'Int64Index' object has no attribute 'droplevel'
```
However df.xs without the level attribute on all index levels works as expected...
``` ptthon
In [18]: df.xs((0,1,2,3,4))
Out[18]: A5 A6 A7 A8 A9
A0 A1 A2 A3 A4
0 1 2 3 4 1 3 1 1 3
4 4 3 4 2 4
4 0 2 3 1 3
...
4 1 1 3 4 3
4 0 1 2 4 1
```
Thoughts:
One (somewhat limiting) solution could be allowing df.ix[(0,1,3,:,4)] to take an empty slice for an index level and return the data frame indexed on only the the passed index levels that are known. Today this capability does not exist, although an ordered partial list of index levels works.
The next and more general approach could be to pass a dict of df.ix[{index_level:value}] pairs and return the rows where the specified index levels equal the passed values. Unspecified levels are not filtered down and we have the option to return all index levels.
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} | 74 | 2013-06-26T00:34:16Z | 2014-06-12T09:31:29Z | 2013-07-08T18:16:51Z | MEMBER | null | closes #3393.
This is a ways from being mergeable but I thought I would put up the PR for people to play with and to get some other eyes on it. Tire-kick at will. A consolidated list of things to be completed to come.
# `eval` Roadmap
## Documentation
- [ ] enhancing performance section
- [x] `eval` docstring
- [ ] Warn users that this is _not_ a magic bullet
- E.g., `x = 3; y = 2; pd.eval('x // y', engine='python')` is 1000 times slower than the same operation in actual Python.
## Engines
- [x] python engine for testing (basically a restricted eval, should make sure nothing dangerous a la `eval('os.system("sudo rm -rf /")')`) is possible here
- [x] numexpr engine
- [x] pytables engine (#4155)
Should python be the default engine for small frames?
## Functions
- [x] toplevel `isexpr`
- [x] move `eval` to top-level pandas
## Error Handling
- [x] syntax error handling
- [x] name error handling
## Alignment
- Algorithm
1. flatten the tree of terms
2. resolve the variables, special casing only numpy arrays/scalars/single unary operations (probably room for more abstraction here)
3. align by joining on the indices using the current broadcasting behavior
4. update the original namespace with the newly aligned version of the underlying numpy array
5. reconstruct the final object with the aforementioned joined indices and type
- [ ] punt to `'python'` engine when a `Series` and `DataFrame` both have `DatetimeIndexes` (I would rather not do this and just enforce the to-be-deprecated index-aligning-if-both-indices-are-datetimeindex behavior)
- [ ] `PeriodIndex`es don't work (well, they won't pass my tests) because of a stack overflow bug when joining a `DatetimeIndex` and a `PeriodIndex` (that bug is slated for 0.13, but a design decision needs to be made)
- [ ] alignment for `NDFrame` (is this really necessary?)
- [x] unary alignment
- [x] binary alignment
## Scope
- [x] deal with global scoping issues (not sure if this is tested well enough...which means probably not)
- [x] allow `Expr` objects to be passed to `eval` (`Expr` will pull in the local and global variables from the calling stack frame)
## Miscellaneous
- [ ] replace current `evaluate` function
- [ ] `Expr` in `NDFrame.__getitem__` cc @jreback
- [x] reconstruct the final object to return the expected output type, e.g., `df + df2` should return a `DataFrame` if `df` and `df2` are both `DataFrame` objects.
- [x] change `Expression` to `Expr`
- [x] add `truediv` keyword to `Expr`, default to `True`
## Operators
- [x] `%`
- [x] `**`
- [x] unary ops don't play well when they are operands of binary ops
## Tests
- [ ] `DatetimeIndex` and `PeriodIndex` objects are untested because of a few join and alignment issues.
- [ ] rewrite a few tests in the python evaluator to make sure the `//` operator works as it should (it works, but there may be edge cases that I haven't though of yet)
- [ ] `//` `numexpr` only supports scalar evaluation here: variables are not allowed so this will always eval in python space (half-assed it a bit in the tests here need to fix that)
- [x] truediv keyword tests
- [x] add tests for nans
- [x] fix `**` associativity issue in testing
## Near Future
- allow `and`, `or` and `not` aliases for the operators `&`, `|` and `~`, respectively
- attribute accessing e.g., `df.A` syntax
- reductions (`sum` and `prod`)
- math ops (sin, cos, tan, sqrt, etc.)
## Far Future
- non-python indexing (or just punt to python)
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} | 154 | 2013-06-26T01:22:53Z | 2017-07-09T02:26:19Z | 2013-08-11T20:01:02Z | CONTRIBUTOR | null | This PR covers:
Fixes: #4202, #3714, #3742 (there are might be some others, but I've blanked on them...)
Bug fixes:
- `MultiIndex` preserves names as much as possible and it's now harder to overwrite index metadata by making changes down the line.
- `set_values` no longer messes up names.
External API Changes:
- Names, levels and labels are now validated each time and 'mostly' immutable.
- names, levels and labels produce containers that are immutable (using new containers `FrozenList` and `FrozenNDArray`)
- `MultiIndex` now shallow copies levels and labels before storing them.
- Adds `astype` method to `MultiIndex` to resolve issue with `set_values` in `NDFrame`
- Direct setting of levels and labels is "deprecated" with a setter that raises a DeprecationWarning (but still functions)
- New `set_names`, `set_labels`, and `set_levels` methods allow setting of these attributes and take an `inplace=True` keyword argument to mutate in place.
- `Index` has a `rename` method that works similarly to the `set_*` methods.
- Improved exceptions on `Index` methods to be more descriptive / more specific (e.g., replacing `Exception` with `ValueError`, etc.)
- `Index.copy()` now accepts keyword arguments (`name=`,`names=`, `levels=`, `labels=`,) which return a new copy with those attributes set. It also accepts `deep`, which is there for compatibility with other `copy()` methods, but doesn't actually change what copy does (though, for MultiIndex, it makes the copy operation slower)
Internal changes:
- `MultiIndex` now uses `_set_levels`, `_get_levels`, `_set_labels`, `_get_labels` internally to handle labels and levels (and uses that directly in `__array_finalize__` and `__setstate__`, etc.)
- `MultiIndex.copy(deep=True)` will deepcopy levels, labels, and names.
- `Index` objects handle names with `_set_names` and `_get_names`.
- `Index` now inherits from `FrozenNDArray` which (mostly) blocks mutable methods (except for `view()` and `reshape()`)
- `Index` now actually copies ndarrays when copy=True is passed to constructor and dtype=None
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} | 4 | 2013-06-26T02:04:10Z | 2013-09-24T01:07:20Z | 2013-09-24T01:06:33Z | MEMBER | null | It would be nice to have the `.. versionadded:: x.y.z` where and whenever a new feature was introduced.
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} | 17 | 2013-06-26T03:17:56Z | 2014-06-27T04:35:19Z | 2014-02-26T22:25:41Z | CONTRIBUTOR | null | - Added 'to_julian_date()' to Timestamp
- Added 'to_julian_date()' to DatetimeIndex
- Added tests for both methods.
Toying with the idea of using pandas for my astronomy and tidal analysis packages and I need the astronomical Julian Date. So here is an implementation and tests. I think I put these in the right place. Really wanted to have the same function for the Timestamp and DatetimeIndex so that both would benefit from improvements, but couldn't figure out how to make that work.
Kindest regards,
Tim
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} | 19 | 2013-06-26T05:17:01Z | 2014-06-19T16:05:37Z | 2013-07-20T01:45:17Z | NONE | null | ujson has trouble with very big, and especially very small, floats:
esnme/ultrajson#69
esnme/ultrajson#83
esnme/ultrajson#90
From: https://github.com/pydata/pandas/pull/1263#issuecomment-20024006
```
>>> import ujson
>>> ujson.dumps(1e-40)
'0.0'
>>> ujson.dumps(1e-40, double_precision=17)
'0.0'
```
I believe this makes ujson inappropriate for inclusion in pandas.
This pull requests adds tests that ujson should, but won't, completely pass. I don't feel yet sufficiently familiar with pandas' design to substitute in (e.g.) simplejson.
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} | 0 | 2013-06-26T13:01:35Z | 2014-07-16T08:16:15Z | 2013-06-26T14:02:11Z | CONTRIBUTOR | null | closes #4032
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Modified DataReader slightly to accept "ff" anyway.
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} | 3 | 2013-06-26T14:11:59Z | 2017-05-04T13:42:24Z | 2017-05-04T13:42:23Z | CONTRIBUTOR | null | The behavior I'm experiencing is non-deterministic. DataFrames with different data but constructed at the same way behave differently.
The example from the documentation on indexing works:
``` python
df.loc[("foo", "one")]
0 1 2 3
foo one -1.465106 0.518888 0.901505 -0.250815
```
However in some occasions doing .loc with 3 or more keys gives me a Series with the index as name:
``` python
dataframe.loc[("chr1", 27106293, "Test")]
file NaN
gene NaN
reference NaN
mutated_base NaN
mutated_base_reads 0
reference_base_reads 0
other_reads 0
total_reads 0
mutated_percentage NaN
Name: (chr1, 27106293, Test), dtype: object
```
A similarly-constructed DataFrame but with different data yields instead a DataFrame.
I can't paste the affected DataFrame to this bug tracker, but I can provide it privately if need be.
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} | 14 | 2013-06-26T17:27:03Z | 2013-12-30T21:04:48Z | 2013-07-25T02:04:08Z | NONE | null | currently:
```
0.12.0.dev-73de5de
```
[PEP386](http://www.python.org/dev/peps/pep-0386/) details 3 version format conventions extent in the python world and this format is invalid in all of them.
In practice it's caused no problems( I know of), probably because the tooling is lenient, but one real drawback is that lexi. sorting of the version string does not properly order the revisions, which some tools rely on. Something which
```
0.12.0pre234(-73de5de)
```
would provide, where 234 is the commit count since the last release. We could add a pd.version.commit field to compensate for omitting the commit in the version string.
As always it'll break something for sombody, but being a dev-only thing we're probably entitled.
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} | 13 | 2013-06-26T18:13:55Z | 2014-07-05T21:27:17Z | 2013-06-27T13:02:34Z | MEMBER | null | closes #4028, #3982
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} | 7 | 2013-06-26T18:26:14Z | 2014-06-30T12:16:55Z | 2013-06-26T19:17:51Z | CONTRIBUTOR | null | closes #4028
just a reversal of the warnings - seem ok now
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http://stackoverflow.com/questions/17326973/is-there-a-way-to-auto-adjust-excel-column-widths-with-pandas-excelwriter
It would be a nice feature to have the columns in the excel file auto-adjusted to the length of the longest entry in that column.
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} | 0 | 2013-06-26T22:54:44Z | 2013-06-29T14:50:54Z | 2013-06-29T14:50:54Z | CONTRIBUTOR | null | Right now, the layout of the subplots for a dataframe's histogram is out of the control of the user, it picks the (very sane) default of trying to balance out the rows and columns.
This is great, but sometimes I want the histograms to all be in a row, or all in a column, or some combination for better presentation.
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} | 22 | 2013-06-27T02:45:45Z | 2014-06-23T17:35:57Z | 2013-07-24T03:38:32Z | CONTRIBUTOR | null | Resolves #3765.
#### This is going to be split into two parts and then rebased on top of #3482.
Tasks:
- [x] Move arithmetic operations to separate file (e.g., `ops.py`).
- [x] Explore combining series/panel/frame operation methods.
- [x] Add rand, rxor, ror, etc...
This adds:
1. numexpr acceleration to `Series`, `Panel`, `Panel4D`, and `DataFrame` (`SparsePanel` has to opt-out b/c doesn't respond to shape)
2. Adds a test mechanism to numexpr to check that it actually is accelerated.
3. Moves all arithmetic operators to core/generic and binds them at import to reduce code duplication.
4. Makes all of `Series`, `Panel`, `Panel4D`, `DataFrame`, `SparsePanel` support the entire range of arithmetic operators [previously some were missing].
5. Cleans up core/expressions slightly
6. Test cases for all operations.
7. Improves a few test utilities to be better (error message for isnull and better error for isinstance checks in util/testing)
@cpcloud - I think this would be very easy to add into your current changes with eval. (mostly just changes `if hasattr` --> `getattr` to be cleaner).
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} | 82 | 2013-06-27T10:39:19Z | 2016-08-25T14:26:13Z | 2016-02-20T18:40:31Z | CONTRIBUTOR | null | It would be really convenient to be able to at least import SAS tables into pandas dataframe. Is this planned ? Are they insurmountable issues ?
Thanks
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} | 6 | 2013-06-27T13:02:01Z | 2013-07-05T18:10:49Z | 2013-07-05T18:10:49Z | CONTRIBUTOR | null | Hi,
I could really do with using some of the new features in 0.12-dev on my Windows machine, but the Windows builds linked from the homepage (at http://pandas.pydata.org/pandas-build/dev/) don't seem to have been updated since April.
Are these still being built but put elsewhere? Or is there a problem with the automated build process?
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} | 8 | 2013-06-27T13:03:15Z | 2014-06-13T01:01:55Z | 2013-06-28T14:21:53Z | MEMBER | null | closes #4028, #3982. for good.
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https://api.github.com/repos/pandas-dev/pandas/issues/4055 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4055/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4055/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4055/events | https://github.com/pandas-dev/pandas/issues/4055 | 16,086,641 | MDU6SXNzdWUxNjA4NjY0MQ== | 4,055 | Printing large DataFrame changes spacing for all subsequent output in ipython qtconsole | {
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The main problem is that after printing the dataframe, all other commands are printed with excessive spacing. I just used the %hist command as a simple example, but all other printing commands (eg. printing a numpy array) also give excessive spacing - which makes numpy arrays even harder to read! There are a few other issues too: the title of the dataframe printout has < and > rather than < and > as in the previous version.
I suspect this is a problem introduced by the addition of the ability of pandas to produce tables for use in the IPython notebook, but I don't know my way around the pandas code well enough to find where the problem is.
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} | 0 | 2013-06-27T13:48:50Z | 2014-02-18T01:35:30Z | 2014-02-18T01:35:30Z | CONTRIBUTOR | null | related #5202
You can do it but its akward
```
In [142]: pd.period_range('2011-07','2012-05',freq='M').values.min()
Out[142]: 498
In [143]: pd.period_range('2011-07','2012-05',freq='M').values.max()
Out[143]: 508
In [146]: pd.period_range(pd.Period(ordinal=498,freq='M'),pd.Period(ordinal=508,freq='M'),freq='M')
Out[146]:
<class 'pandas.tseries.period.PeriodIndex'>
freq: M
[2011-07, ..., 2012-05]
length: 11
```
This fails
```
In [150]: pd.period_range('2011-07','2012-05',freq='M').min()
AttributeError: 'PeriodIndex' object has no attribute 'freq'
```
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} | 2 | 2013-06-27T13:53:22Z | 2014-07-16T08:16:26Z | 2013-06-27T15:09:46Z | CONTRIBUTOR | null | inserting columns at the end was extremely slow
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} | 0 | 2013-06-27T14:36:19Z | 2016-09-30T12:35:16Z | 2016-09-29T22:32:47Z | CONTRIBUTOR | null | It is not clear if this should raise on construction or not
http://stackoverflow.com/questions/17338148/pandas-duplicate-datetimeindex-entries-lead-to-odd-exception?noredirect=1#comment25166923_17338148
```
In [3]: from collections import OrderedDict
In [4]: import datetime as dt
In [5]: a = [1371215933513120, 1371215933513121, 1371215933513122, 1371215933513122]
In [6]: b = [1,2,3,4]
In [7]: df = pd.DataFrame({'a':a, 'b':b, 'c':[dt.datetime.fromtimestamp(t/1000000.) for t in a]})
In [8]: df.index=pd.DatetimeIndex(df['c'])
In [9]: d = OrderedDict()
In [10]: d['x'] = df
In [11]: p = pd.Panel(d)
In [12]: for y in p.major_axis:
....: print y
....: print p.major_xs(y)
....:
2013-06-14 09:18:53.513120
x
a 1371215933513120
b 1
c 2013-06-14 09:18:53.513120
2013-06-14 09:18:53.513121
x
a 1371215933513121
b 2
c 2013-06-14 09:18:53.513121
2013-06-14 09:18:53.513122
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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related #414
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} | 0 | 2013-06-27T19:22:28Z | 2013-06-28T19:47:40Z | 2013-06-28T19:47:40Z | MEMBER | null | invocation error due to pickle import. this happens after recreating the cache
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} | 5 | 2013-06-27T19:53:37Z | 2014-06-16T02:42:59Z | 2013-06-28T19:47:40Z | MEMBER | null | In pandas.io.pickle the following statement will not allow python to import
anything due to the following rewrite by 2to3
import pickle -> from . import pickle
This makes the import try to import itself ad infinitum and thus fails.
closes #4062
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} | 23 | 2013-06-28T01:56:07Z | 2013-10-07T23:01:29Z | 2013-10-07T23:00:26Z | CONTRIBUTOR | null | The following should work, but since it doesn't should raise an error...
In [116]: pd.Timestamp(np.datetime64('1677-01-01'),'D')
Out[116]: Timestamp('2261-07-22 23:34:33.709551616', tz=None)
In [117]: pd.Timestamp(np.datetime64('1678-01-01'),'D')
Out[117]: Timestamp('1678-01-01 00:00:00', tz=None)
In [121]: pd.**version**
Out[121]: '0.11.1.dev-06cd915'
In [125]: sys.version
Out[125]: '3.3.2 (default, May 21 2013, 15:40:45) \n[GCC 4.8.0 20130502 (prerelease)]'
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} | 12 | 2013-06-28T02:09:14Z | 2019-07-23T23:08:31Z | 2019-07-23T23:08:31Z | CONTRIBUTOR | null | A daily frequency np.datetime64 should have a range of [2.5e16 BC, 2.5e16 AD]. Instead it follows the range for nanosecond [ 1678 AD, 2262 AD].
Ranges taken from http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html
In [126]: pd.Timestamp(np.datetime64('1677-01-01'),'D')
Out[126]: Timestamp('2261-07-22 23:34:33.709551616', tz=None)
In [127]: pd.Timestamp(np.datetime64('1678-01-01'),'D')
Out[127]: Timestamp('1678-01-01 00:00:00', tz=None)
In [128]: pd.Timestamp(np.datetime64('2262-01-01'),'D')
Out[128]: Timestamp('2262-01-01 00:00:00', tz=None)
In [129]: pd.Timestamp(np.datetime64('2263-01-01'),'D')
Out[129]: Timestamp('1678-06-12 00:25:26.290448384', tz=None)
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```
In [1]: from pandas import DataFrame
In [2]: df = DataFrame([[1,2],[3,4]], index=[0,0], columns=[0,0])
In [3]: df
Out[3]:
0 0
0 1 2
0 3 4
In [4]: df.index=[0,1]
In [5]: df
Out[5]:
0 0
0 1 2
1 3 4
In [6]: df.columns=[0,1]
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-6-538dde7e36b8> in <module>()
----> 1 df.columns=[0,1]
C:\Python27\lib\site-packages\pandas\core\frame.pyc in __setattr__(self, name, value)
2016 existing = getattr(self, name)
2017 if isinstance(existing, Index):
-> 2018 super(DataFrame, self).__setattr__(name, value)
2019 elif name in self.columns:
2020 self[name] = value
C:\Python27\lib\site-packages\pandas\lib.pyd in pandas.lib.AxisProperty.__set__ (pandas\lib.c:28444)()
C:\Python27\lib\site-packages\pandas\core\generic.pyc in _set_axis(self, axis, labels)
557
558 def _set_axis(self, axis, labels):
--> 559 self._data.set_axis(axis, labels)
560 self._clear_item_cache()
561
C:\Python27\lib\site-packages\pandas\core\internals.pyc in set_axis(self, axis, value)
920 if axis == 0:
921 for block in self.blocks:
--> 922 block.set_ref_items(self.items, maybe_rename=True)
923
924 # make items read only for now
C:\Python27\lib\site-packages\pandas\core\internals.pyc in set_ref_items(self, ref_items, maybe_rename)
72 raise AssertionError('block ref_items must be an Index')
73 if maybe_rename:
---> 74 self.items = ref_items.take(self.ref_locs)
75 self.ref_items = ref_items
76
C:\Python27\lib\site-packages\pandas\core\internals.pyc in ref_locs(self)
57 def ref_locs(self):
58 if self._ref_locs is None:
---> 59 indexer = self.ref_items.get_indexer(self.items)
60 indexer = com._ensure_platform_int(indexer)
61 if (indexer == -1).any():
C:\Python27\lib\site-packages\pandas\core\index.pyc in get_indexer(self, target, method, limit)
847
848 if not self.is_unique:
--> 849 raise Exception('Reindexing only valid with uniquely valued Index '
850 'objects')
851
Exception: Reindexing only valid with uniquely valued Index objects
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4068 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4068/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4068/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4068/events | https://github.com/pandas-dev/pandas/pull/4068 | 16,128,672 | MDExOlB1bGxSZXF1ZXN0NjYwOTQxOQ== | 4,068 | DOC: read_frame doc enhancement | {
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"description": "2014-02-3: previous version released",
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} | 1 | 2013-06-28T12:28:50Z | 2014-03-30T15:23:19Z | 2014-03-30T15:23:19Z | NONE | null | ``` python
>>> import datetime as dt
>>> from pandas.tseries.offsets import *
>>> d = dt.datetime(2013, 6, 27, 8, 10)
>>> d + BMonthEnd()
datetime.datetime(2013, 6, 28, 0, 0) # correct!!!
>>> d = dt.datetime(2013, 6, 28, 8, 10)
>>> d + BMonthEnd()
datetime.datetime(2013, 7, 31, 0, 0) ## should 6/28 not 7/31...day not over
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4070 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4070/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4070/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4070/events | https://github.com/pandas-dev/pandas/pull/4070 | 16,141,160 | MDExOlB1bGxSZXF1ZXN0NjYxNTk3OA== | 4,070 | PERF: optimize iloc for unique case | {
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} | [] | closed | false | null | [] | null | 0 | 2013-06-28T13:18:24Z | 2014-07-01T20:48:51Z | 2013-06-28T15:35:50Z | CONTRIBUTOR | null | related #4017, #4018
optimize the iloc case (which should be the same for unique and non-unique indicies)
```
In [1]: df = DataFrame({'A' : [0.1] * 30000000, 'B' : [1] * 30000000})
In [6]: idx = np.array(range(30000)) * 99
In [7]: df2 = DataFrame({'A' : [0.1] * 10000000, 'B' : [1] * 10000000})
In [8]: df2 = concat([df2, df2, df2])
In [9]: %timeit df.iloc[idx]
1000 loops, best of 3: 1.49 ms per loop
In [10]: %timeit df2.iloc[idx]
1000 loops, best of 3: 1.41 ms per loop
```
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} | 6 | 2013-06-28T14:02:03Z | 2016-10-12T23:03:56Z | 2013-06-28T18:41:48Z | NONE | null | Using a DataFrame of bools to mask some values in another dataframe works great:
``` python
In [84]: pd.DataFrame([[1,2],[3,4]]).mask(pd.DataFrame([[True,False],[False, True]]))
Out[84]:
0 1
0 NaN 2
1 3 NaN
```
but not if we try on a dataframe with a single index:
``` python
In [85]: pd.DataFrame([[1,2],]).mask(pd.DataFrame([[True,False],]))
AssertionError: Number of Block dimensions (1) must equal number of axes (2)
```
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} | 0 | 2013-06-28T14:54:32Z | 2014-07-02T22:48:55Z | 2013-06-28T18:41:48Z | MEMBER | null | closes #4071.
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https://api.github.com/repos/pandas-dev/pandas/issues/4074 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4074/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4074/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4074/events | https://github.com/pandas-dev/pandas/issues/4074 | 16,145,727 | MDU6SXNzdWUxNjE0NTcyNw== | 4,074 | Append pd.Series in HDF5 files | {
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} | [] | closed | false | null | [] | null | 2 | 2013-06-28T15:04:21Z | 2013-06-28T15:35:27Z | 2013-06-28T15:35:27Z | CONTRIBUTOR | null | I tried to google to see if it supposed to be implemented or not, but did not find anything.
The `append` function does not work for `pd.Series` object. For instance, the following code:
```
ss = pd.Series([1,2,3,4,5])
h5store = pd.HDFStore('tmpstore.h5')
h5store.append('ss',ss)
h5store.close()
```
fails with the error:
```
TypeError: cannot properly create the storer for: [group->ss,value-><class 'pandas.core.series.Series'>]
```
However storing the series like this:
```
store['ss']=ss
```
or appending DataFrames works well.
My version of pandas: 0.11.0, pytables: 3.0.0
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} | [] | closed | false | null | [] | null | 0 | 2013-06-28T16:05:24Z | 2014-07-16T08:16:36Z | 2013-06-28T17:30:14Z | CONTRIBUTOR | null | perf regression introduced in 1585aa4976ee492f05410b70014bad74cb7414b6
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} | 10 | 2013-06-28T17:53:55Z | 2016-10-12T23:03:57Z | 2014-03-23T13:49:21Z | NONE | null | I've got a pandas data frame defined like this, using pandas 0.11.0:
```
last_4_weeks_range = pandas.date_range(
start=datetime.datetime(2001, 5, 4), periods=28)
last_4_weeks = pandas.DataFrame(
[{'REST_KEY': 1, 'DLY_TRN_QT': 80, 'DLY_SLS_AMT': 90,
'COOP_DLY_TRN_QT': 30, 'COOP_DLY_SLS_AMT': 20}] * 28 +
[{'REST_KEY': 2, 'DLY_TRN_QT': 70, 'DLY_SLS_AMT': 10,
'COOP_DLY_TRN_QT': 50, 'COOP_DLY_SLS_AMT': 20}] * 28,
index=last_4_weeks_range.append(last_4_weeks_range))
last_4_weeks.sort(inplace=True)
```
and when I go to resample it:
```
In [265]: last_4_weeks.resample('7D', how='sum')
Out[265]:
COOP_DLY_SLS_AMT COOP_DLY_TRN_QT DLY_SLS_AMT DLY_TRN_QT REST_KEY
2001-05-04 280 560 700 1050 21
2001-05-11 280 560 700 1050 21
2001-05-18 280 560 700 1050 21
2001-05-25 280 560 700 1050 21
2001-06-01 0 0 0 0 0
```
I end up with an extra empty bin I wouldn't expect to see -- 2001-06-01. I wouldn't expect that bin to be there, as my 28 days are evenly divisible into the 7 day resample I'm performing. I've tried messing around with the closed kwarg, but I can't escape that extra bin. This seems like a bug, and it messes up my mean calculations when I try to do
```
In [266]: last_4_weeks.groupby('REST_KEY').resample('7D', how='sum').mean(level=0)
Out[266]:
COOP_DLY_SLS_AMT COOP_DLY_TRN_QT DLY_SLS_AMT DLY_TRN_QT REST_KEY
REST_KEY
1 112 168 504 448 5.6
2 112 280 56 392 11.2
```
as the numbers are being divided by 5 rather than 4. (I also wouldn't expect REST_KEY to show up in the aggregation columns as it's part of the groupby, but that's really a smaller problem.)
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} | 3 | 2013-06-28T17:56:09Z | 2013-06-28T18:41:53Z | 2013-06-28T18:40:04Z | MEMBER | null | Also fixed stable sorting test failure that presents on some platforms
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} | 9 | 2013-06-28T22:22:58Z | 2013-10-02T20:54:59Z | 2013-10-02T20:54:59Z | MEMBER | null | as it stands
```
In [24]: tuples = [(randint(10), randint(10)) for _ in range(10)]
In [25]: mi = MultiIndex.from_tuples(tuples)
In [26]: df = DataFrame(index=mi, columns=mi)
```
fails, whereas single level indexes work fine
```
In [27]: df = DataFrame(index=range(10), columns=range(10))
In [28]: df
Out[28]:
0 1 2 3 4 5 6 7 8 9
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
```
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} | 9 | 2013-06-29T00:04:45Z | 2013-06-29T15:52:40Z | 2013-06-29T15:10:36Z | MEMBER | null | fixes yet another bug with yahoo/google testing
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} | 15 | 2013-06-29T01:58:32Z | 2013-08-16T19:27:26Z | 2013-08-16T19:27:26Z | NONE | null | ``` python
import pandas as pd
df = pd.DataFrame(dict((c, [1,2,3]) for c in ['a', 'b', 'c']))
df.set_index(['a', 'b', 'c'], inplace=True)
s = pd.Series([1], index=[(2,2,2)])
df['val'] = 0
df
df['val'].update(s)
df
```
on my machine (pandas 0.11.1.dev-45d298d, linux) i get the rather unexpected output
```
val
a b c
1 1 1 0
2 2 2 4607182418800017408
3 3 3 0
```
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} | 11 | 2013-06-29T14:47:23Z | 2014-06-28T10:42:55Z | 2013-07-25T17:20:23Z | CONTRIBUTOR | null | closes #4080
essentially a variation on #3970
here block consolidation and cache is not the issue,
rather the dtype of the series is int, and as a result of the update (directly on the series)
changes dtype to float (which is fine)
BUT the parent still has this in an Int Block
this PR essentially creates an Observer Pattern thru a weakref
that allows notification of chanes (propogated back to the BlockManager)
which can then take action if needed
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} | 14 | 2013-06-29T15:05:00Z | 2020-04-04T18:29:56Z | null | MEMBER | null | this would be useful for reference purposes and also so that @jreback doesn't have to fix almost every non-trivial bug that pops up :). i would be happy to start writing this (with the help of others who know these things better than i do), i think it would be an excellent way to gain a deeper understanding of the internals.
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} | 1 | 2013-06-29T16:26:35Z | 2013-07-26T20:39:52Z | 2013-07-26T20:39:52Z | CONTRIBUTOR | null | already do this in `read_json`
first check if its a valid filename (which is safe in python)
if so, then open and process
else try to use as a `StringIO(buffer)`
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} | 1 | 2013-06-29T21:50:22Z | 2018-07-06T21:43:37Z | 2018-07-06T21:43:37Z | CONTRIBUTOR | null | The numpy way
```
In [183]: x
Out[183]:
one two
0 1 a
2 2 c
3 3 d
4 4 d
5 5 d
In [184]: np.unique(x['two'].values,return_inverse=True)
Out[184]: (array(['a', 'c', 'd'], dtype=object), array([0, 1, 2, 2, 2]))
```
The pandas way - maybe provide a better API to this
maybe: `uniques, indexer = Index(x['two']).get_uniques()` ??
```
In [186]: uniques = x['two'].unique()
In [187]: uniques
Out[187]: array(['a', 'c', 'd'], dtype=object)
In [188]: Index(uniques).get_indexer_non_unique(x['two'])
Out[188]: (Int64Index([0, 1, 2, 2, 2], dtype=int64), array([], dtype=int64))
```
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} | 5 | 2013-06-30T14:36:02Z | 2014-12-07T00:18:23Z | 2014-12-07T00:18:23Z | CONTRIBUTOR | null | starting PR is here: https://github.com/pydata/pandas/pull/6647
By reading the docs on `Dataframe.reindex()`, I would have guessed (not sure what "broadcast" is...) that this works:
``` python
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = zip(*arrays)
index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = DataFrame({"a":list("abcdefgh"), "b":list("abcdefgh")}, index=index)
order = ["baz", "bar", "foo", "qux"]
df.reindex(order, level=0) # still returns the same ordering
```
It works that way on a normal index (without the level argument):
``` python
df2 = DataFrame({"a":list("ABCD")}, index=['bar', 'baz', 'foo', 'qux'])
df2.reindex(order) # new ordering
```
It also works when I pass a list of tuples:
``` python
_new_index = []
for _a in order:
for _b in df.index.levels[1]:
_new_index.append((_a, _b))
df.reindex(_new_index) # new ordering
```
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} | 13 | 2013-06-30T23:35:16Z | 2016-10-12T23:03:57Z | 2013-07-01T03:28:07Z | NONE | null | [Here is an example](http://nbviewer.ipython.org/urls/gist.github.com/fonnesbeck/5897459/raw/6181f066f245c7fb489b68f88e4b4731c77a238b/King%27s+Bay+Survey+Analysis.ipynb) of the `hist` method called as a multi-panel plot via the `by` argument, with `sharey=True` and `sharex=True`. As you can observe in the plot at the bottom of the notebook, neither the x nor the y axis is shared among the subplots.
Running 0.11.1 on Python 2.7.2 (OS X 10.8.4).
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https://api.github.com/repos/pandas-dev/pandas/issues/4090 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4090/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4090/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4090/events | https://github.com/pandas-dev/pandas/issues/4090 | 16,195,732 | MDU6SXNzdWUxNjE5NTczMg== | 4,090 | CLN: Refactor string special methods to common decorator + safe unicode everywhere | {
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} | 13 | 2013-07-01T00:32:33Z | 2013-07-02T01:42:28Z | 2013-07-02T01:42:28Z | CONTRIBUTOR | null | I was implementing some new objects for another PR and noticed that string methods are duplicated throughout (particularly on objects that don't inherit from each other). Wrote this up on this branch - https://github.com/jtratner/pandas/tree/refactor_string_magic_methods .
If you thing this is worthwhile, I'll split it up a little, add a bit better documentation and submit.
This is used multiple times:
``` python
def __str__(self):
"""
Return a string representation for a particular object.
Invoked by str(obj) in both py2/py3.
Yields Bytestring in Py2, Unicode String in py3.
"""
if py3compat.PY3:
return self.__unicode__()
return self.__bytes__()
def __bytes__(self):
"""
Return a string representation for a particular object.
Invoked by bytes(obj) in py3 only.
Yields a bytestring in both py2/py3.
"""
from pandas.core.config import get_option
encoding = get_option("display.encoding")
return self.__unicode__().encode(encoding, 'replace')
def __repr__(self):
"""
Return a string representation for a particular object.
Yields Bytestring in Py2, Unicode String in py3.
"""
return str(self)
```
Unicode tends to vary, but often is like this:
``` python
def __unicode__(self):
"""
Return a string representation for a particular object.
Invoked by unicode(obj) in py2 only. Yields a Unicode String in both
py2/py3.
"""
prepr = pprint_thing(self, escape_chars=('\t', '\r', '\n'), quote_strings=True)
return '%s(%s)' % (type(self).__name__, prepr)
```
Additionally, a number of objects aren't using a unicode-safe representation of themselves, so this would resolve that as well. Would this be useful to include?
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} | 2 | 2013-07-01T01:26:14Z | 2014-07-16T08:16:48Z | 2013-07-01T03:28:07Z | MEMBER | null | closes #4089
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} | 31 | 2013-07-01T02:47:14Z | 2014-06-14T19:01:58Z | 2013-07-02T01:42:28Z | CONTRIBUTOR | null | closes #4090, #3231
- The internal `pandas` class hierarchy has changed (slightly). The
previous `PandasObject` now is called `PandasContainer` and a new
`PandasObject` has become the baseclass for `PandasContainer` as well
as `Index`, `Categorical`, `GroupBy`, `SparseList`, and
`SparseArray` (+ their base classes). Currently, `PandasObject`
provides string methods (from `StringMixin`).
- New `StringMixin` that, given a `__unicode__` method, gets python 2 and
python 3 compatible string methods (`__str__`, `__bytes__`, and
`__repr__`). Now employed in many places throughout the pandas library.
- Tried to improve safe unicode handling (e.g., where user/external input is
used in the string or repr methods of a library).
I'm hoping to use this for the new objects in the MultiIndex naming PR, so
hopefully it's okay to merge for v0.12.
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} | 9 | 2013-07-01T12:17:52Z | 2014-02-14T03:48:34Z | 2014-02-14T03:48:34Z | MEMBER | null | I have some (admittedly, slightly less general than what would be need for pandas) code sitting around that reads spreadsheets from Google Drive, would it be useful to have this in io?
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} | 5 | 2013-07-01T13:00:04Z | 2020-02-07T00:15:57Z | null | CONTRIBUTOR | null | more examples in #13957
http://stackoverflow.com/questions/17398216/unwanted-type-conversion-in-pandas-dataframe-update
```
df = pd.DataFrame({'int': [1, 2], 'float': [np.nan, np.nan]})
print('Integer column:')
print(df['int'])
for _, df_sub in df.groupby('int'):
df_sub['float'] = float(df_sub['int'])
df.update(df_sub)
print('NO integer column:')
print(df['int'])
```
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} | 22 | 2013-07-01T13:38:27Z | 2016-04-22T18:42:43Z | 2015-11-16T12:38:10Z | CONTRIBUTOR | null | - [x] shift [here](http://stackoverflow.com/questions/17401197/how-to-speed-up-pandas-multilevel-dataframe-shift-by-group) and [here](http://stackoverflow.com/questions/25298659/faster-alternative-to-grouby-shift)
- [x] [cumsum](http://stackoverflow.com/questions/28453238/cumsum-in-pandas-groupby-is-slow)
- [x] [cumprod](http://stackoverflow.com/questions/32138067/quick-pandas-groupby-calculations)
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} | 4 | 2013-07-01T17:53:33Z | 2013-11-07T19:56:18Z | 2013-07-01T21:27:26Z | CONTRIBUTOR | null | There seems to be a strange random bug in saving dataframes with integer columns to HDF5 files. The error sounds like:
```
ValueError: invalid combinate of [values_axes] on appending data [name->values_block_1,cname->values_block_1,axis->None,pos->2,kind->integer] vs current table [name->values_block_1,cname->values_block_1,axis->None,pos->2,kind->integer]
```
and the full traceback:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-91a60d72b1ca> in <module>()
25 print raw.dtypes
26 store = pd.HDFStore('possible_bug9.h5')
---> 27 store.append('raw', raw)
28 store.close()
/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in append(self, key, value, columns, **kwargs)
608 raise Exception("columns is not a supported keyword in append, try data_columns")
609
--> 610 self._write_to_group(key, value, table=True, append=True, **kwargs)
611
612 def append_to_multiple(self, d, value, selector, data_columns=None, axes=None, **kwargs):
/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, index, table, append, complib, **kwargs)
869 raise ValueError('Compression not supported on non-table')
870
--> 871 s.write(obj = value, append=append, complib=complib, **kwargs)
872 if s.is_table and index:
873 s.create_index(columns = index)
/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, **kwargs)
2738 # create the axes
2739 self.create_axes(axes=axes, obj=obj, validate=append,
-> 2740 min_itemsize=min_itemsize, **kwargs)
2741
2742 if not self.is_exists:
/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
2482 # validate the axes if we have an existing table
2483 if validate:
-> 2484 self.validate(existing_table)
2485
2486 def process_axes(self, obj, columns=None):
/Users/v/.virtual_envs/system/lib/python2.7/site-packages/pandas/io/pytables.pyc in validate(self, other)
2096 oax = ov[i]
2097 if sax != oax:
-> 2098 raise ValueError("invalid combinate of [%s] on appending data [%s] vs current table [%s]" % (c,sax,oax))
2099
2100 # should never get here
ValueError: invalid combinate of [values_axes] on appending data [name->values_block_1,cname->values_block_1,axis->None,pos->2,kind->integer] vs current table [name->values_block_1,cname->values_block_1,axis->None,pos->2,kind->integer]
```
Interestingly, the error is not deterministic and depends at least on (i) set of other (non-integer) columns in dataframe and (ii) size of dataframe.
Unfortunately I was unable to narrow it down to "just-code" report, so I have to attach a piece of data and an ipython notebook. This is a minimal code in which I was able to reproduce the bug.
https://www.dropbox.com/s/myo03sbqbulzvaj/pandas_possible_bug.zip
the pandas version is 0.11.0, pytables version: 3.0.0
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} | [] | closed | false | null | [] | null | 2 | 2013-07-01T19:05:14Z | 2013-07-01T20:09:48Z | 2013-07-01T20:09:08Z | NONE | null | > > > import pandas
> > > from pandas.tseries.offsets import *
> > > import datetime as dt
> > > d = dt.datetime(2013, 6, 28, 0, 0)
> > > d + BMonthEnd()
> > > datetime.datetime(2013, 7, 31, 0, 0)
> > > d = dt.datetime(2013, 6, 28, 12, 0)
> > > d + BMonthEnd()
> > > datetime.datetime(2013, 7, 31, 0, 0)
At noon on 6/28 end of business month is 23:59, not the start of the
day... Bug, I think????
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} | 0 | 2013-07-01T19:27:05Z | 2013-07-01T20:39:08Z | 2013-07-01T20:39:08Z | CONTRIBUTOR | null | ```
In [1]: df = DataFrame(dict(A = Series(xrange(3), index=date_range('2000-1-1',periods=3,freq='H', tz='US/Eastern'))))
In [2]: df.index
Out[2]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00, ..., 2000-01-01 02:00:00]
Length: 3, Freq: H, Timezone: US/Eastern
In [3]: df.to_hdf('tz.h5','df',mode='w')
In [4]: pd.read_hdf('tz.h5','df').index
Out[4]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00, ..., 2000-01-01 02:00:00]
Length: 3, Freq: H, Timezone: US/Eastern
In [5]: df.to_hdf('tz.h5','df',mode='w',table=True)
In [6]: pd.read_hdf('tz.h5','df').index
Out[6]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00, ..., 2000-01-01 02:00:00]
Length: 3, Freq: H, Timezone: US/Eastern
```
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} | 0 | 2013-07-01T20:09:36Z | 2014-06-25T15:42:21Z | 2013-07-01T20:39:08Z | CONTRIBUTOR | null | closes #4098
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} | 0 | 2013-07-01T20:34:46Z | 2014-07-16T08:17:10Z | 2013-07-01T21:27:26Z | CONTRIBUTOR | null | closes #4096
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