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Panel({k : df})
```
will cast everything to object if `df` is a mixed-type DataFrame
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} | 2 | "2011-10-27T14:45:53Z" | "2011-10-28T18:08:43Z" | "2011-10-28T15:13:43Z" | CONTRIBUTOR | null | After inserting a column of NaNs into a preexisting DataFrame its contents cannot be filled using advanced indexing. See test case below.
This only appears to happen if the existing DataFrame is filled with floats, i.e. it works fine for ints.
Tested with pandas 0.5 and latest github snapshot.
``` python
from pandas import DataFrame
import numpy as np
print '\n***RESULT\n'
df = DataFrame({'x': [1.1, 2.1, 3.1, 4.1], 'y': [5.1, 6.1, 7.1, 8.1]}, index=[0,1,2,3])
df.insert(2, 'z', np.nan)
print df
df.ix[2:, 'z'] = 42
print df
print '\n***EXPECTED\n'
df = DataFrame({'x': [1.1, 2.1, 3.1, 4.1], 'y': [5.1, 6.1, 7.1, 8.1], 'z':[np.nan]*4}, index=[0,1,2,3])
print df
df.ix[2:, 'z'] = 42
print df
```
The output I get is
```
***RESULT
x y z
0 1.1 5.1 NaN
1 2.1 6.1 NaN
2 3.1 7.1 NaN
3 4.1 8.1 NaN
x y z
0 1.1 5.1 NaN
1 2.1 6.1 NaN
2 3.1 7.1 NaN
3 4.1 8.1 NaN
***EXPECTED
x y z
0 1.1 5.1 NaN
1 2.1 6.1 NaN
2 3.1 7.1 NaN
3 4.1 8.1 NaN
x y z
0 1.1 5.1 NaN
1 2.1 6.1 NaN
2 3.1 7.1 42
3 4.1 8.1 42
```
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https://api.github.com/repos/pandas-dev/pandas/issues/307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/307/events | https://github.com/pandas-dev/pandas/issues/307 | 2,086,152 | MDU6SXNzdWUyMDg2MTUy | 307 | ENH: Add .topK(col, k) to DataFrame | {
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`````` df.groupby(col)[col].agg(len).order(ascending=False).head()```.
I added a convenience method to frame.py so that I could write ```df.topK(col)``` instead:
```python
def topK(self, col, k=5):
"""Returns top k most frequently occurring values in column col
"""
return self.groupby(col)[col].agg(len).order(ascending=False).head(k)
``````
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} | 1 | "2011-10-31T02:34:05Z" | "2011-11-16T20:21:19Z" | "2011-11-02T19:11:23Z" | CONTRIBUTOR | null | ... pandas/pandas/**init**.py:12: DeprecationWarning: BaseException.message has been deprecated as
of Python 2.6
Shows up when I edit the code base with omnicompletion in vim.
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https://api.github.com/repos/pandas-dev/pandas/issues/313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/313/events | https://github.com/pandas-dev/pandas/pull/313 | 2,113,332 | MDExOlB1bGxSZXF1ZXN0NDUzMjEx | 313 | ENH: Added the level arguments to the series and frame math operations | {
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} | 1 | "2011-11-01T18:56:52Z" | "2011-11-03T21:31:38Z" | "2011-11-03T21:31:38Z" | NONE | null | Example below of it returning a NumPy array. This seems unexpected, given the documentation in the function.
Thanks!
Example:
Code:
import pandas as ps
import numpy as np
test = ps.Series(np.linspace(start=0, stop=1, num=20))
print test
print test.apply(lambda x: np.round(x, 1))
Note : this is equivalent to np.round(test, 1)
Output:
> > > 0 0.0
> > > 1 0.0526315789474
> > > 2 0.105263157895
> > > 3 0.157894736842
> > > 4 0.210526315789
> > > 5 0.263157894737
> > > 6 0.315789473684
> > > 7 0.368421052632
> > > 8 0.421052631579
> > > 9 0.473684210526
> > > 10 0.526315789474
> > > 11 0.578947368421
> > > 12 0.631578947368
> > > 13 0.684210526316
> > > 14 0.736842105263
> > > 15 0.789473684211
> > > 16 0.842105263158
> > > 17 0.894736842105
> > > 18 0.947368421053
> > > 19 1.0
> > > Name: None, Length: 20
> > > [ 0. 0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.7
> > > 0.8 0.8 0.9 0.9 1. ]
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https://api.github.com/repos/pandas-dev/pandas/issues/315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/315/events | https://github.com/pandas-dev/pandas/issues/315 | 2,114,312 | MDU6SXNzdWUyMTE0MzEy | 315 | Support boolean data in Cythonized groupby | {
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```
In [146]: frame = DataFrame({'a': np.random.randint(0, 5, 10), 'b': np.random.ra
ndint(0, 2, 10).astype('bool')})
In [147]: print frame
a b
0 1 False
1 2 True
2 2 True
3 1 True
4 3 True
5 3 True
6 3 True
7 1 True
8 2 True
9 1 False
In [150]: frame.groupby('a')['b'].mean()
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
c:\users\brad\projects\sane\src\sane\scripts\<ipython-input-150-fceb5b21892e> in
<module>()
----> 1 frame.groupby('a')['b'].mean()
C:\Python27\lib\site-packages\pandas\core\groupby.pyc in mean(self)
297 For multiple groupings, the result index will be a MultiIndex
298 """
--> 299 return self._cython_agg_general('mean')
300
301 def size(self):
C:\Python27\lib\site-packages\pandas\core\groupby.pyc in _cython_agg_general(sel
f, how)
347 output[name] = result[mask]
348
--> 349 return self._wrap_aggregated_output(output, mask)
350
351 def _get_multi_index(self, mask):
UnboundLocalError: local variable 'mask' referenced before assignment
Note that agg() does work:
In [153]: frame.groupby('a')['b'].agg(np.mean)
Out[153]:
1 0.5
2 1.0
3 1.0
Name: None, Length: 3
```
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} | 0 | "2011-11-01T21:19:23Z" | "2011-11-03T21:39:18Z" | "2011-11-03T21:39:18Z" | MEMBER | null | More aesthetically pleasing to me anyway
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https://api.github.com/repos/pandas-dev/pandas/issues/318 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/318/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/318/comments | https://api.github.com/repos/pandas-dev/pandas/issues/318/events | https://github.com/pandas-dev/pandas/issues/318 | 2,115,249 | MDU6SXNzdWUyMTE1MjQ5 | 318 | Fix DataFrame.join docs to note that a Series with a name can be passed | {
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https://api.github.com/repos/pandas-dev/pandas/issues/319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/319/events | https://github.com/pandas-dev/pandas/issues/319 | 2,116,414 | MDU6SXNzdWUyMTE2NDE0 | 319 | Add new function to remove duplicate rows from a DataFrame | {
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} | 3 | "2011-11-01T23:20:43Z" | "2011-11-07T02:43:57Z" | "2011-11-07T02:43:57Z" | MEMBER | null | Should be reasonably performant, probably just use sets + `np.apply_along_axis`
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https://api.github.com/repos/pandas-dev/pandas/issues/320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/320/events | https://github.com/pandas-dev/pandas/issues/320 | 2,117,483 | MDU6SXNzdWUyMTE3NDgz | 320 | read_csv fails to import data | {
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} | [] | closed | false | null | [] | null | 3 | "2011-11-02T02:15:02Z" | "2011-11-02T03:02:31Z" | "2011-11-02T03:02:31Z" | CONTRIBUTOR | null | I'm using pandas 0.5.0 with python 2.7 on archlinux.
This example file seems pretty standard and it opens well in Libreoffice: http://dl.dropbox.com/u/40028/countrycode.csv
``` python
import pandas
cc = pandas.read_csv('countrycode.csv', index_col=1)
cc
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', (1224, 0))
Out[31]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/[path/to/file/anonymized/manually/by/me]/<ipython-input-31-870c06c00566> in <module>()
----> 1 cc
/usr/lib/python2.7/site-packages/IPython/core/displayhook.py in __call__(self, result)
300 self.start_displayhook()
301 self.write_output_prompt()
--> 302 format_dict = self.compute_format_data(result)
303 self.write_format_data(format_dict)
304 self.update_user_ns(result)
/usr/lib/python2.7/site-packages/IPython/core/displayhook.py in compute_format_data(self, result)
213 MIME type representation of the object.
214 """
--> 215 return self.shell.display_formatter.format(result)
216
217 def write_format_data(self, format_dict):
/usr/lib/python2.7/site-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
120 continue
121 try:
--> 122 data = formatter(obj)
123 except:
124 # FIXME: log the exception
/usr/lib/python2.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
441 type_pprinters=self.type_printers,
442 deferred_pprinters=self.deferred_printers)
--> 443 printer.pretty(obj)
444 printer.flush()
445 return stream.getvalue()
/usr/lib/python2.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
349 if hasattr(obj_class, '_repr_pretty_'):
350 return obj_class._repr_pretty_(obj, self, cycle)
--> 351 return _default_pprint(obj, self, cycle)
352 finally:
353 self.end_group()
/usr/lib/python2.7/site-packages/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
467 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
468 # A user-provided repr.
--> 469 p.text(repr(obj))
470 return
471 p.begin_group(1, '<')
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in __repr__(self)
263 buf = StringIO()
264 if len(self.index) < 500 and len(self.columns) <= 10:
--> 265 self.to_string(buf=buf)
266 else:
267 self.info(buf=buf, verbose=self._verbose_info)
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in to_string(self, buf, columns, colSpace, na_rep, formatters, float_format, sparsify, nanRep, index_names)
569 float_format=float_format,
570 sparsify=sparsify,
--> 571 index_names=index_names)
572
573 if buf is None:
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, frame, buf, columns, col_space, na_rep, formatters, float_format, sparsify, index_names)
2943 self.column_filter = frame.columns if columns is None else set(columns)
2944
-> 2945 self._write_to_buffer()
2946
2947 def _write_to_buffer(self):
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in _write_to_buffer(self)
2956 else:
2957 # may include levels names also
-> 2958 str_index = self._get_formatted_index()
2959 str_columns = self._get_formatted_column_labels()
2960
/usr/lib/python2.7/site-packages/pandas/core/frame.pyc in _get_formatted_index(self)
3035 names=show_index_names)
3036 else:
-> 3037 fmt_index = [index.format(name=show_index_names)]
3038
3039 adjoined = adjoin(1, *fmt_index).split('\n')
TypeError: format() got an unexpected keyword argument 'name'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/321/events | https://github.com/pandas-dev/pandas/pull/321 | 2,123,611 | MDExOlB1bGxSZXF1ZXN0NDU2ODUz | 321 | Added a QuarterEnd class to datetools module. | {
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Hoping to get this in to the public version so we don't have to keep a separate local copy. If its, a no go, just let me know and we'll work around.
Thanks,
Nathan Pinger
YCharts.com
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] | closed | false | null | [] | null | 4 | "2011-11-02T18:05:41Z" | "2012-04-10T05:07:43Z" | "2012-04-10T05:07:38Z" | CONTRIBUTOR | null | I love where pandas is going. A use-case for which R's csv capabilities still win, though, is in editing a large csv file that's used as a poor-mans database.
R will keep a column of integers as integers, pandas changes some of them to floats, some to strings! I'm not sure why there's inconsistency there, but I figure I'll kick the general issue your way and I'm happy to clarify as needed.
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https://api.github.com/repos/pandas-dev/pandas/issues/323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/323/events | https://github.com/pandas-dev/pandas/issues/323 | 2,124,754 | MDU6SXNzdWUyMTI0NzU0 | 323 | [BUG] DataFrame.describe fails if no numeric columns | {
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} | 3 | "2011-11-02T18:34:47Z" | "2011-11-05T00:00:46Z" | "2011-11-05T00:00:46Z" | NONE | null | DataFrame.describe function fails with the following when called on a
Data Frame with no numeric columns:
/usr/lib64/python2.7/site-packages/pandas/core/
frame.py(2324)describe()
2322 '25%', '50%', '75%', 'max']
2323
-> 2324 data = [tmp.count(), tmp.mean(), tmp.std(), tmp.min(),
2325 tmp.quantile(.25), tmp.median(),
2326 tmp.quantile(.75), tmp.max()]
/usr/lib64/python2.7/site-packages/pandas/core/frame.py(2361)count()
2359 frame = self
2360
-> 2361 result = DataFrame.apply(frame, Series.count,
axis=axis)
2362
2363 # what happens with empty DataFrame
/usr/lib64/python2.7/site-packages/pandas/core/frame.py(1984)apply()
1982 else:
1983 if not broadcast:
-> 1984 return self._apply_standard(func, axis)
1985 else:
1986 return self._apply_broadcast(func, axis)
> /usr/lib64/python2.7/site-packages/pandas/core/frame.py(2000)_apply_standar d()
1998 results[k] = func(target[k])
1999
-> 2000 if hasattr(results.values()[0], '**iter**'):
2001 result = self._constructor(data=results,
index=target.index,
2002
#
The problem seems to be that in the function DataFrame.describe, tmp
is empty if there are no numeric columns:
cols = self._get_numeric_columns()
tmp = self.reindex(columns=cols)
Nonetheless, Series.describe itself works on non-numeric values.
-- Marius
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} | 5 | "2011-11-02T20:30:48Z" | "2011-11-03T21:04:46Z" | "2011-11-03T21:04:37Z" | CONTRIBUTOR | null | It would be very helpful if DataFrame.plot() function would also provide a legend with column names
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} | 3 | "2011-11-02T21:17:27Z" | "2011-11-05T00:32:56Z" | "2011-11-05T00:32:56Z" | NONE | null | d = {'a': [1,2,3], 'b':[4,5,6]}
from pandas import DataFrame
df = DataFrame(d)
df.to_string()
Returns:
a b
0 1 4
1 2 5
2 3 6
df.to_string(columns=['b','a'])
Returns:
a b
0 4 1
1 5 2
2 6 3
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} | 0 | "2011-11-02T21:57:40Z" | "2011-11-03T21:03:07Z" | "2011-11-03T21:03:07Z" | MEMBER | null | Fails at least with matplotlib 1.1. Should always stringify the columns
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} | 1 | "2011-11-02T21:58:12Z" | "2011-11-03T21:45:22Z" | "2011-11-03T21:45:22Z" | MEMBER | null | Only true anecdotally (I've witnessed `np.unique(s.values)` being a lot faster but didn't have time to profile)
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https://api.github.com/repos/pandas-dev/pandas/issues/328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/328/events | https://github.com/pandas-dev/pandas/issues/328 | 2,132,016 | MDU6SXNzdWUyMTMyMDE2 | 328 | Fundamental issue with ix | {
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} | 3 | "2011-11-03T12:44:21Z" | "2012-01-07T01:29:06Z" | "2012-01-07T01:29:06Z" | NONE | null | Using square bracket indexing [x] on a Series where x is an integer
seems to return:
- the item at the index x if such index is present
- the x-th item if the index does not exist
To be precise:
In [190]: ss = pandas.Series(range(10), index = range(0, 20, 2))
...: print ss
...:
0 0
2 1
4 2
6 3
8 4
10 5
12 6
14 7
16 8
18 9
Name: None, Length: 10
In [191]: ss[0]
Out[191]: 0
In [192]: ss[1]
Out[192]: 1
In [193]: ss[2]
Out[193]: 1
In [194]: ss[3]
Out[194]: 3
In [195]: ss[4]
Out[195]: 2
In [196]: ss[17]
---
KeyError Traceback (most recent call
last)
/home/marius/Code/ccapital/<ipython-input-196-065c9192fad1> in
<module>()
----> 1 ss[17]
/usr/lib64/python2.7/site-packages/pandas/core/series.pyc in
**getitem**(self, key)
267 except Exception, _:
268 pass
--> 269 raise e1
270 except TypeError:
271 pass
KeyError: 17
In [197]: ss[18]
Out[197]: 9
#
Is this behaviour intended? To me it seems like a fundamental bug.
From the documentation:
Select row by location (int): df.ix[loc] Series
Which is not what ix seems to be doing.
If the labels are made strings, the behaviour is utterly different
(correct according to the documentation):
ss = pandas.Series(range(10), index = map(str, range(0, 20, 2)))
print ss
0 0
2 1
4 2
6 3
8 4
10 5
12 6
14 7
16 8
18 9
Name: None, Length: 10
ss[0]
Out[220]: 0
ss[1]
Out[221]: 1
ss[2]
Out[222]: 2
Many thanks,
-- Marius
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https://api.github.com/repos/pandas-dev/pandas/issues/329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/329/events | https://github.com/pandas-dev/pandas/issues/329 | 2,137,852 | MDU6SXNzdWUyMTM3ODUy | 329 | [Bug] pandas.io.data.DataReader Yahoo dates bug | {
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} | 4 | "2011-11-03T21:25:02Z" | "2011-11-05T01:08:44Z" | "2011-11-05T01:08:44Z" | NONE | null | Yahoo finance seems to set its historical data URLs with the month variables set back minus one, and DataReader doesn't seem to accommodate this.
For example, running:
ticker = '^GSPC'
start_dt = datetime(2008, 6, 30)
end_dt = datetime(2010, 12, 31)
data = DataReader(ticker, 'yahoo', start=start_dt, end=end_dt)
Then:
In[27]: data.index[0]
Out[27]: datetime.datetime(2008, 7, 30, 0, 0)
data.index[-1]
Out[28]: datetime.datetime(2011, 11, 2, 0, 0)
Shows that the pull started in July, not June as intended, and ended on the latest available day instead of December 31, 2010.
Looking at the source, DataReader looks like it constructs the following URL for the download:
http://ichart.yahoo.com/table.csv?s=^GSPC&a=6&b=30&c=2008&d=12&e=31&f=2010&g=d&ignore=.csv
If I go get this data from Yahoo manually, I get:
http://ichart.finance.yahoo.com/table.csv?s=^GSPC&a=05&b=30&c=2008&d=11&e=31&f=2010&g=d&ignore=.csv
(Whether you use .finance or not doesn't seem to matter).
The hack to get around this isn't straightforward, since you have to pass a datetime to DataReader, and you can't create datetime(2010, 11, 31). I'm assuming Yahoo is giving me all data to today because there is no month where d=12 in the URL (December is 11, January is 0).
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https://api.github.com/repos/pandas-dev/pandas/issues/330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/330/events | https://github.com/pandas-dev/pandas/pull/330 | 2,137,879 | MDExOlB1bGxSZXF1ZXN0NDYxOTE4 | 330 | ENH: small corrections to Series repr | {
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} | 0 | "2011-11-04T16:36:19Z" | "2011-11-04T16:43:36Z" | "2011-11-04T16:43:36Z" | MEMBER | null | An oversight in the join refactoring recently
```
create two objects:
a=DataFrame(randn(30,2),columns=['a','b'])
c=Series(randn(30))
now add c as column of a:
a['c']=c
create a third object:
d=DataFrame(randn(30,1),columns=['d'])
try to join with a:
a.join(d)
```
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After having gotten pandas to build and run on 10.7 previously, an updated build based on the current Github source yields the following AttributeError, also related to _tseries (which appears to be "lib" in this case):
```
In [1]: from pandas import *
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/Users/fonnescj/<ipython-input-1-7463a510993d> in <module>()
----> 1 from pandas import *
/Library/Python/2.7/site-packages/pandas-0.5.1.dev_5abb534_20111104-py2.7-macosx-10.7-x86_64.egg/pandas/__init__.py in <module>()
20 from pandas.info import __doc__
21
---> 22 from pandas.core.api import *
23 from pandas.core.common import set_printoptions
24 from pandas.core.common import set_eng_float_format
/Library/Python/2.7/site-packages/pandas-0.5.1.dev_5abb534_20111104-py2.7-macosx-10.7-x86_64.egg/pandas/core/api.py in <module>()
6 import pandas.core.datetools as datetools
7
----> 8 from pandas.core.common import isnull, notnull, set_printoptions, save, load
9 from pandas.core.index import Index, Int64Index, Factor, MultiIndex
10 from pandas.core.daterange import DateRange
/Library/Python/2.7/site-packages/pandas-0.5.1.dev_5abb534_20111104-py2.7-macosx-10.7-x86_64.egg/pandas/core/common.py in <module>()
111
112 _take1d_dict = {
--> 113 'float64' : lib.take_1d_float64,
114 'int32' : lib.take_1d_int32,
115 'int64' : lib.take_1d_int64,
AttributeError: 'module' object has no attribute 'take_1d_float64'
```
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import pandas as ps
x = ps.DataFrame(index=[1,2,3], columns=None, data=None)
s = ps.Series(index=[1,2,3], data=[10,20,30])
x['a']=s
x['b']=s_2
y = ps.DataFrame(index=[1,2,3], columns=None, data=None)
y['c']=s
y['d']=s_2
z = x.join(y, how='outer')
Error:
z = x.join(y, how='outer')
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2191, in join
return self._join_index(other, how, lsuffix, rsuffix)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2226, in _join_index
merged_data = join_managers(thisdata, otherdata, axis=1, how=how)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 983, in join_managers
op = _JoinOperation(left, right, axis=axis, how=how)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 997, in __init__
assert(left.is_consolidated())
AssertionError
If I add self._consolidate_inplace() to _set_item it no longer breaks, but I am not sure if this is where you would want to add it.
Thanks!
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DataFrame({'VIX':vix['close'],'VXV':vxv['close']})
However, it would be more convenient to use .join method, but to do this I have to hack around with a rename function:
def renameTs(series, name=None):
'''
return a renamed copy of time series
'''
s = series.copy()
s.name =name
return s
and use it as follows:
df.join(renameTs(series,'BAR'))
this all would become much cleaner by using
df.join(df['close'], newColName='VIX')
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```
df[cols] = transform(df[cols])
```
should be possible in a mixed-type DataFrmae, per the mailing list discussion
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https://api.github.com/repos/pandas-dev/pandas/issues/344 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/344/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/344/comments | https://api.github.com/repos/pandas-dev/pandas/issues/344/events | https://github.com/pandas-dev/pandas/issues/344 | 2,160,228 | MDU6SXNzdWUyMTYwMjI4 | 344 | library not found for -lbundle1.o | {
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} | [] | closed | false | null | [] | null | 2 | "2011-11-07T07:15:04Z" | "2011-11-07T16:42:38Z" | "2011-11-07T16:42:38Z" | CONTRIBUTOR | null | I'll begin by saying that I believe the problem with this could be unique to my machine. I'm running OSX Lion on a MacBookPro.
Attempting to easy_install pandas I get:
```
gcc: pandas/src/tseries.c
gcc -bundle -undefined dynamic_lookup -g -arch i386 build/temp.macosx-10.5-i386-2.7/pandas/src/tseries.o -o build/lib.macosx-10.5-i386-2.7/pandas/_tseries.so
ld: library not found for -lbundle1.o
```
A little searching led me to clone the git repo and make the following patch to setup.py:
```
~/git/pandas(master) $ git diff
diff --git a/setup.py b/setup.py
index e28cfbd..ff214a7 100755
--- a/setup.py
+++ b/setup.py
@@ -285,14 +285,22 @@ if suffix == '.pyx':
else:
tseries_depends = None
+
+if sys.platform=='darwin':
+ EXTRA_LINK_ARGS = ['-L/Developer/SDKs/MacOSX10.5.sdk/usr/lib']
+else:
+ EXTRA_LINK_ARGS = []
+
tseries_ext = Extension('pandas._tseries',
depends=tseries_depends,
sources=[srcpath('tseries', suffix=suffix)],
- include_dirs=[np.get_include()])
+ include_dirs=[np.get_include()],
+ extra_link_args=EXTRA_LINK_ARGS)
sparse_ext = Extension('pandas._sparse',
sources=[srcpath('sparse', suffix=suffix)],
- include_dirs=[np.get_include()])
+ include_dirs=[np.get_include()],
+ extra_link_args=EXTRA_LINK_ARGS)
extensions = [tseries_ext,
sparse_ext]
```
...which gave me a good build.
I can try an install on my iMac at the office tomorrow to see if it's just a problem with my libraries/paths on this machine, but I wanted to document this here before I lost it.
BTW, I'm installing it into a recent (latest?) Enthought Python Distribution:
Python 2.7.2 |EPD 7.1-1 (32-bit)| (default, Jul 3 2011, 15:40:35)
Best,
Travis
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Thanks for taking the QuarterEnd class as-is. Here are the accompanying unit tests you wanted, along with the original commit.
Best,
Nathan
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} | 1 | "2011-11-07T16:37:40Z" | "2011-11-08T04:46:30Z" | "2011-11-08T04:46:30Z" | MEMBER | null | per conversation with @mforthmann
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https://api.github.com/repos/pandas-dev/pandas/issues/347 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/347/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/347/comments | https://api.github.com/repos/pandas-dev/pandas/issues/347/events | https://github.com/pandas-dev/pandas/issues/347 | 2,164,532 | MDU6SXNzdWUyMTY0NTMy | 347 | Implement logical operators on DataFrame, &, |, ^, ~ | {
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} | 0 | "2011-11-07T16:41:38Z" | "2011-11-08T04:30:37Z" | "2011-11-08T04:30:37Z" | MEMBER | null | code example from ML
```
from pandas import *
df1 = {'a': {'a': True, 'b': False, 'c': False, 'd': True, 'e': True},
'b': {'a': False, 'b': True, 'c': False, 'd': False, 'e': False},
'c': {'a': False, 'b': False, 'c': True, 'd': False, 'e': False},
'd': {'a': True, 'b': False, 'c': False, 'd': True, 'e': True},
'e': {'a': True, 'b': False, 'c': False, 'd': True, 'e': True}}
df2 = {'a': {'a': True, 'b': False, 'c': True, 'd': False, 'e': False},
'b': {'a': False, 'b': True, 'c': False, 'd': False, 'e': False},
'c': {'a': True, 'b': False, 'c': True, 'd': False, 'e': False},
'd': {'a': False, 'b': False, 'c': False, 'd': True, 'e': False},
'e': {'a': False, 'b': False, 'c': False, 'd': False, 'e': True}}
df1 = DataFrame(df1)
df2 = DataFrame(df2)
df3 = (df1 and df2)
```
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} | [] | closed | false | null | [] | null | 2 | "2011-11-08T01:59:02Z" | "2014-06-14T08:07:19Z" | "2011-11-14T00:57:16Z" | CONTRIBUTOR | null | This is just a first stab at it, comments are very welcome, plus feel free to say you'd rather not add it at all.
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} | 9 | "2011-11-08T09:56:11Z" | "2012-05-14T15:06:42Z" | "2012-05-14T15:05:24Z" | CONTRIBUTOR | null | Although I have already produced code for this (see below) I'm posting this as an issue rather than a pull request to discuss the design, because there are some issues open in my code:
- Series of dtype object need an explicit cast or rpy2's numpy conversion will treat them improperly
- The performance has not been profiled
- Probably some room for optimizations
- Proper name for the function
- The generation of an intermediate OrdDict object may cause problems in case of very large datasets
The code in the current form is posted below. If there is interest, I will work towards integrating it in pandas.rpy.common and add unit tests.
``` python
import numpy as np
import rpy2.robjects as robjects
import rpy2.robjects.numpy2ri as numpy2ri
from rpy2.robjects.packages import importr
import rpy2.rlike.container as rlc
def dataset_to_data_frame(dataset, strings_as_factors=True):
# Activate conversion for numpy objects
robjects.conversion.py2ri = numpy2ri.numpy2ri
robjects.numpy2ri.activate()
base = importr("base")
columns = rlc.OrdDict()
for column in dataset:
value = dataset[column]
# object type requires explicit cast
if value.dtype == np.object:
value = robjects.StrVector(value)
#FIXME: how to generalize it?
if not strings_as_factors:
value = base.I(value)
columns[column] = value
dataframe = robjects.DataFrame(columns)
dataframe.rownames = robjects.StrVector(dataset.index)
# To prevent side-effects in other code
robjects.conversion.pi2ri = robjects.default_py2ri
return dataframe
```
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} | 2 | "2011-11-08T21:34:02Z" | "2011-11-10T03:19:38Z" | "2011-11-10T03:17:05Z" | NONE | null | - When you use .join with a MultiIndex, it combines the DataFrames improperly.
- When you use .join(how='outer'), the index is destroyed (turned into integers).
Example below.
Thanks,
Ryan
index1=MultiIndex.from_arrays([['a','a','a','b','b','b'], [1,2,3,1,2,3]])
index2=MultiIndex.from_arrays([['b','b','b','c','c','c'], [1,2,3,1,2,3]])
index1
MultiIndex([('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3)], dtype=object)
index2
MultiIndex([('b', 1), ('b', 2), ('b', 3), ('c', 1), ('c', 2), ('c', 3)], dtype=object)
df1 = DataFrame(data=numpy.random.randn(6), index=index1, columns=['var X'])
df2 = DataFrame(data=numpy.random.randn(6), index=index2, columns=['var Y'])
df1=df1.sortlevel(0)
df2=df2.sortlevel(0)
df1
```
var X
```
a 1 0.8658
2 -0.9211
3 -0.6389
b 1 -0.6716
2 -0.6799
3 1.98
df2
```
var Y
```
b 1 -0.6978
2 -0.6186
3 0.117
c 1 -0.4577
2 0.7812
3 -1.691
df1.join(df2) #(a1, a2, a3 should be Nan for 'var Y', the values shown under a1, a2, a3 should appear under b1, b2, b3)
```
var X var Y
```
a 1 0.8658 -0.6978
2 -0.9211 -0.6186
3 -0.6389 0.117
b 1 -0.6716 -0.4577
2 -0.6799 0.7812
3 1.98 -1.691
df1.join(df2, how='outer') #The index is destroyed, the index is not expanded, and the data is incorrectly alligned
var X var Y
0 0.8658 -0.6978
1 -0.9211 -0.6186
2 -0.6389 0.117
3 -0.6716 -0.4577
4 -0.6799 0.7812
5 1.98 -1.691
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} | 2 | "2011-11-09T10:48:44Z" | "2011-11-10T01:50:25Z" | "2011-11-10T01:50:25Z" | NONE | null | The first line of the file setup.py is as follows:
```
#/usr/bin/env python
```
(ie. missing a ! symbol)
Attempting to eecute the file with the command `./setup.py` results in some strange errors. A work-around is to execute the file using `python setup.py` instead.
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} | 0 | "2011-11-09T21:49:54Z" | "2011-11-10T02:09:06Z" | "2011-11-10T02:09:06Z" | MEMBER | null | dispatch order is wrong
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https://api.github.com/repos/pandas-dev/pandas/issues/354 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/354/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/354/comments | https://api.github.com/repos/pandas-dev/pandas/issues/354/events | https://github.com/pandas-dev/pandas/issues/354 | 2,199,548 | MDU6SXNzdWUyMTk5NTQ4 | 354 | pivot_table should fail if duplicate entries exist | {
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} | [] | closed | false | null | [] | null | 2 | "2011-11-10T16:14:03Z" | "2011-11-10T20:06:57Z" | "2011-11-10T20:06:57Z" | NONE | null | This looks like #147, but I don't have permission to re-open that.
Consider the following situation:
```
In [3]: dt
Out[3]:
foo bar baz
0 1 2 3
1 4 5 6
2 1 2 4
In [4]: pandas.pivot_table(dt, values="baz", rows=["foo"], cols=["bar"])
Out[4]:
bar 2 5
foo
1 3.5 NaN
4 NaN 6
```
Notice the column for `foo == 1 && bar == 2` shows a value of `3.5`. Either this should be in the resulting table twice, or the command should fail with an error.
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https://api.github.com/repos/pandas-dev/pandas/issues/355 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/355/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/355/comments | https://api.github.com/repos/pandas-dev/pandas/issues/355/events | https://github.com/pandas-dev/pandas/pull/355 | 2,199,773 | MDExOlB1bGxSZXF1ZXN0NDgyNjI3 | 355 | Vectorize pandas.Series.apply | {
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} | [] | closed | false | null | [] | null | 4 | "2011-11-10T16:33:30Z" | "2014-06-14T16:19:02Z" | "2011-11-11T21:05:56Z" | CONTRIBUTOR | null | When profiling my code I noticed that numpy.vectorize was a bit faster than pandas.Series.apply.
Execution Times:
numpy.vectorize: 3.3 seconds
pandas.Series.apply: 4.4 seconds.
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https://api.github.com/repos/pandas-dev/pandas/issues/356 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/356/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/356/comments | https://api.github.com/repos/pandas-dev/pandas/issues/356/events | https://github.com/pandas-dev/pandas/issues/356 | 2,200,747 | MDU6SXNzdWUyMjAwNzQ3 | 356 | read_csv error | {
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} | [] | closed | false | null | [] | null | 2 | "2011-11-10T17:54:40Z" | "2011-11-10T18:04:55Z" | "2011-11-10T18:04:55Z" | CONTRIBUTOR | null | I get the following traceback when trying to read a csv_file:
Traceback (most recent call last):
File "C:\src\OSR\OSR\run.py", line 127, in <module>
pheno_data = read_data(base_dir+'pheno_data_num_id.csv')
File "C:\src\OSR\OSR\run.py", line 41, in read_data
pheno_data = pandas.read_csv(filename,index_col=0,na_values='')
File "C:\src\pandas\pandas\io\parsers.py", line 64, in read_csv
return parser.get_chunk()
File "C:\src\pandas\pandas\io\parsers.py", line 405, in get_chunk
data = _convert_to_ndarrays(data, self.na_values)
File "C:\src\pandas\pandas\io\parsers.py", line 449, in _convert_to_ndarrays
result[c] = _convert_types(values, na_values)
File "C:\src\pandas\pandas\io\parsers.py", line 456, in _convert_types
lib.sanitize_objects(values, na_values)
TypeError: sanitize_objects() takes exactly one argument (2 given)
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} | 0 | "2011-11-10T21:50:56Z" | "2011-11-11T18:37:45Z" | "2011-11-11T18:37:45Z" | MEMBER | null | ```
In [2]: paste
df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
## -- End pasted text --
In [3]: df
Out[3]:
A B C D
0 foo one -2.096 0.4961
1 bar one -0.8738 -1.074
2 foo two -0.01376 -0.956
3 bar three 0.996 0.368
4 foo two -1.396 -0.3736
5 bar two 0.7779 0.4091
6 foo one -0.1221 1.26
7 foo three -0.9718 -0.4734
In [4]: df.groupby('A').mean()
Out[4]:
C D
bar 0.3001 -0.09914
foo -0.9199 -0.00939
In [5]: df.groupby(['A', 'B']).mean()
Out[5]:
C D
A B
bar one -0.8738 -1.074
three 0.996 0.368
two 0.7779 0.4091
foo one -1.109 0.878
three -0.9718 -0.4734
two -0.7048 -0.6648
```
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} | 1 | "2011-11-11T14:13:36Z" | "2011-11-11T18:37:54Z" | "2011-11-11T17:23:27Z" | CONTRIBUTOR | null | in the example below, "a" is a mixed type dataframe. Once included in the Panel, all columns become of type object:
In [249]: a=DataFrame(randn(30,3),columns=('a','b','c'))
In [250]: b=DataFrame(randn(30,3),columns=('a','b','c'))
In [251]: a['b']=a['b'].astype(object)
In [252]: a.dtypes
Out[252]:
a float64
b object
c float64
In [253]: b.dtypes
Out[253]:
a float64
b float64
c float64
In [254]: Panel({'1':a,'2':b})['1'].dtypes
Out[254]:
a object
b object
c object
In [255]: Panel({'1':a,'2':b})['2'].dtypes
Out[255]:
a float64
b float64
c float64
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1. Series.cov
2. DataFrame.cov
3. test_cov in test_series.py
4. test_cov in test_frame.py
Modified:
1. DataFrame.sort
2. DataFrame.sort_index
3. test_sort in test_frame.py
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} | 0 | "2011-11-14T18:59:02Z" | "2011-11-17T18:26:12Z" | "2011-11-17T18:26:12Z" | MEMBER | null | Major source of difficulty are quoted strings-- not doing this for now, will need to have a state machine-type parser (Cython?) later
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https://api.github.com/repos/pandas-dev/pandas/issues/365 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/365/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/365/comments | https://api.github.com/repos/pandas-dev/pandas/issues/365/events | https://github.com/pandas-dev/pandas/issues/365 | 2,238,764 | MDU6SXNzdWUyMjM4NzY0 | 365 | rolling_* with min_periods=0 | {
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} | [] | closed | false | null | [] | null | 2 | "2011-11-15T00:58:46Z" | "2011-11-16T20:02:10Z" | "2011-11-16T20:02:10Z" | CONTRIBUTOR | null | I ran into a minor gotcha with the rolling_\* methods, below is some ipython output to illustrate.
I set min_periods to 0, which in retrospect i realize is not a reasonable value, but i would either
expect the same behavior as min_periods=1 or maybe an error. For rolling_max, the issue is
with line 421 in moments.pyx:
"for i from minp - 1 <= i < N:"
so if minp==0 then the first value of i = -1, which will wreak all kinds of havoc.
In [10]: df
Out[10]:
0
0 7
1 9
2 8
3 20
In [11]: pandas.rolling_max(df,window=df.shape[0],min_periods=0)
Out[11]:
0
0 20
1 20
2 20
3 20
In [12]: pandas.rolling_max(df,window=df.shape[0],min_periods=1)
Out[12]:
0
0 7
1 9
2 9
3 20
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https://api.github.com/repos/pandas-dev/pandas/issues/366 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/366/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/366/comments | https://api.github.com/repos/pandas-dev/pandas/issues/366/events | https://github.com/pandas-dev/pandas/issues/366 | 2,242,259 | MDU6SXNzdWUyMjQyMjU5 | 366 | Traceback when calling df.as_matrix() | {
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} | 2 | "2011-11-15T11:09:50Z" | "2011-11-16T08:35:13Z" | "2011-11-16T08:35:13Z" | NONE | null | I have a DataFrame object that was created by importing some data in list form, pivoting it, then adding totals columns using `df.append(pandas.DataFrame(some_matrix, columns=df.columns, index=[some_tuple])` and `df[some_other_tuple] = some_array` respectively. The resulting DataFrame object looks like this:
```
>>> df
baz1 baz2 Totals
spam1 spam2 spam3 spam1 spam2 spam3
foo bar
foo1 bar1 NaN 20 NaN 50 NaN NaN NaN
bar2 NaN NaN 30 NaN NaN NaN NaN
foo2 bar2 NaN 40 NaN NaN NaN NaN NaN
foo3 bar1 NaN NaN NaN 60 NaN NaN NaN
Totals NaN NaN NaN NaN NaN NaN NaN
```
Calling `df.as_matrix()` fails with the below traceback:
```
>>> df.as_matrix()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/frame.py", line 671, in as_matrix
self._consolidate_inplace()
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/generic.py", line 267, in _consolidate_inplace
self._data = self._data.consolidate()
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 516, in consolidate
new_blocks = _consolidate(self.blocks, self.items)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 938, in _consolidate
new_block = _merge_blocks(list(group_blocks), items)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 951, in _merge_blocks
do_integrity_check=True)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 211, in make_block
do_integrity_check=do_integrity_check)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 34, in __init__
self._check_integrity()
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 40, in _check_integrity
return (self.ref_locs[1:] > self.ref_locs[:-1]).all()
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/pandas/core/internals.py", line 47, in ref_locs
assert((indexer != -1).all())
AssertionError
```
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} | 2 | "2011-11-15T15:53:22Z" | "2011-11-16T08:50:45Z" | "2011-11-16T08:50:45Z" | CONTRIBUTOR | null | In [23]: a=DataFrame(randn(5,2),index=[1,2,3,6,7],columns=['a','b'])
In [24]: b=Series([6,7,8,9],index=[0,1,2,3],name='c')
In [25]: c=Series([6,7,8,9],name='c')
In [26]: a
Out[26]:
a b
1 -0.268 -0.1305
2 0.2243 0.4644
3 0.4767 -0.5677
6 0.1806 0.6276
7 0.9767 0.3687
In [27]: b
Out[27]:
0 6
1 7
2 8
3 9
Name: c
In [28]: c
Out[28]:
0 6
1 7
2 8
3 9
Name: c
In [29]: a.join(b,how='outer')
Out[29]:
a b c
0 NaN NaN 6
1 -0.268 -0.1305 7
2 0.2243 0.4644 8
3 0.4767 -0.5677 9
6 0.1806 0.6276 NaN
7 0.9767 0.3687 NaN
## In [30]: a.join(c,how='outer')
ValueError Traceback (most recent call last)
/Users/c.prinoth/<ipython-input-30-ec7cd102d8a5> in <module>()
----> 1 a.join(c,how='outer')
/Library/Python/2.6/site-packages/pandas/core/frame.pyc in join(self, other, on, how, lsuffix, rsuffix)
2303 return self._join_on(other, on, how, lsuffix, rsuffix)
2304 else:
-> 2305 return self._join_index(other, how, lsuffix, rsuffix)
2306
2307 def _join_on(self, other, on, how, lsuffix, rsuffix):
/Library/Python/2.6/site-packages/pandas/core/frame.pyc in _join_index(self, other, how, lsuffix, rsuffix)
2340
2341 # this will always ensure copied data
-> 2342 merged_data = join_managers(thisdata, otherdata, axis=1, how=how)
2343 return self._constructor(merged_data)
2344
/Library/Python/2.6/site-packages/pandas/core/internals.pyc in join_managers(left, right, axis, how, copy)
991
992 def join_managers(left, right, axis=1, how='left', copy=True):
--> 993 op = _JoinOperation(left, right, axis=axis, how=how)
994 return op.get_result(copy=copy)
995
/Library/Python/2.6/site-packages/pandas/core/internals.pyc in **init**(self, left, right, axis, how)
1015 (self.join_index,
1016 self.lindexer,
-> 1017 self.rindexer) = laxis.join(raxis, how=how, return_indexers=True)
1018
1019 # do NOT sort
/Library/Python/2.6/site-packages/pandas/core/index.pyc in join(self, other, how, return_indexers)
491 if self.is_monotonic and other.is_monotonic:
492 return self._join_monotonic(other, how=how,
--> 493 return_indexers=return_indexers)
494
495 if how == 'left':
/Library/Python/2.6/site-packages/pandas/core/index.pyc in _join_monotonic(self, other, how, return_indexers)
530 join_index = Index(join_index)
531 elif how == 'outer':
--> 532 join_index, lidx, ridx = lib.outer_join_indexer_object(self, other)
533 join_index = Index(join_index)
534 else: # pragma: no cover
/Library/Python/2.6/site-packages/pandas/_tseries.so in pandas._tseries.outer_join_indexer_object (pandas/src/tseries.c:43677)()
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
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} | 1 | "2011-11-15T21:56:19Z" | "2013-09-29T00:03:46Z" | "2011-11-23T17:41:20Z" | MEMBER | null | per mailing list discussion @lodagro
- Return ax or axes
- Handling of [col] and multiple columns
- Plot when only single column passed not correct
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} | 7 | "2011-11-16T10:54:19Z" | "2012-01-17T07:49:03Z" | "2012-01-16T22:00:56Z" | CONTRIBUTOR | null | based on [mailing list discussion](https://groups.google.com/forum/#!searchin/pystatsmodels/wildcard/pystatsmodels/vxUE3NXWpVM/sYnBKkYZOLgJ), adding here for better traceability.
Is there a way to use wild cards when using MultiIndex?
As an example, let`s work with the following DataFrame:
``` python
In [10]: df
Out[10]:
0 1 2 3 4
x y z -0.02688 1.236 -1.174 -1.026 1.508
a b c 0.9156 1.742 0.4483 1.005 0.3641
p q r 0.2019 -0.3895 -0.8484 0.9992 -0.2274
```
Selecting all rows where level 0 of the MultiIndex == 'a' is easy:
``` python
In [11]: df.ix['a']
Out[11]:
0 1 2 3 4
b c 0.9156 1.742 0.4483 1.005 0.3641
```
But what if i want to select all rows where level 3 of the MultiIndex == 'c', and i don`t care what the other levels are?
This is what can be done:
``` python
In [12]: df.swaplevel(0, 2).swaplevel(1, 2).ix['c']
Out[12]:
0 1 2 3 4
a b 0.9156 1.742 0.4483 1.005 0.3641
```
Wes idea is that this merits a new API function or adding to an existing one.
e.g. to modify DataFrame.xs to work like:
``` python
df.xs('c', axis=0, level=2)
```
Other idea is to do something something similar as in numpy, and use : as wildcard.
For example in numpy:
``` python
In [7]: y = np.arange(27).reshape(3,3,3)
In [8]: y
Out[8]: array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]],
[[18, 19, 20],
[21, 22, 23],
[24, 25, 26]]])
In [9]: y[:, :, 0]
Out[9]: array([[ 0, 3, 6],
[ 9, 12, 15],
[18, 21, 24]])
```
y[:, :, 0] indexes all elements with "level 2" == 0.
This would translate to df.ix[:, :, 'c'] on the first example.
Another example on a DataFrames with MultIndex with a lot of levels and selecting on more than one level: df.ix[:, :, 'c', :, 10]
This approach looks familiar, and no other api method is needed for indexing when using wildcard.
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https://api.github.com/repos/pandas-dev/pandas/issues/372 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/372/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/372/comments | https://api.github.com/repos/pandas-dev/pandas/issues/372/events | https://github.com/pandas-dev/pandas/issues/372 | 2,257,153 | MDU6SXNzdWUyMjU3MTUz | 372 | Unable to build from HEAD | {
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```
error: pandas/src/cppsandbox.pyx: No such file or directory
```
Did you forget to add this file to the repository?
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} | 3 | "2011-11-16T17:15:52Z" | "2011-11-17T09:00:26Z" | "2011-11-16T19:28:29Z" | CONTRIBUTOR | null | If I do:
a=Series([1,2,3],index=['a','b','c'],name='x')
b=DataFrame(a)
I would expect b to have a single column 'x' with indices a,b,c; instead only a.values is retained.
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} | 0 | "2011-11-16T19:36:08Z" | "2011-12-28T22:16:48Z" | "2011-12-28T22:16:48Z" | MEMBER | null | Rolling median is slow as a result of too much Python
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} | 2 | "2011-11-17T12:21:36Z" | "2011-11-18T06:08:34Z" | "2011-11-18T06:08:34Z" | NONE | null | Seen on HEAD. This problem is new since 0.5.0.
```
In [3]: data
Out[3]:
foo bar baz spam data
0 foo1 bar1 baz1 spam2 20
1 foo1 bar2 baz1 spam3 30
2 foo2 bar2 baz1 spam2 40
3 foo1 bar1 baz2 spam1 50
4 foo3 bar1 baz2 spam1 60
In [4]: pandas.pivot_table(data, values="data", rows=["foo", "bar"], cols=["baz", "spam"])
Out[4]:
baz baz1 baz2
spam spam1 spam1
foo bar
foo1 bar1 20 50
bar2 30 NaN
foo2 bar2 40 NaN
foo3 bar1 NaN 60
```
As you can see, the `("baz1", "spam2")`, `("baz2", "spam2")`, `("baz1", "spam3")` and `("baz2", "spam3")` columns have disappeared and their contents have been aggregated into the remaining two columns.
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} | 2 | "2011-11-17T17:34:22Z" | "2011-11-17T17:57:48Z" | "2011-11-17T17:57:48Z" | CONTRIBUTOR | null | I have a situation where joining objects of different types creates object where info() and dtypes disagree, and generally all datatypes revert to object:
In [630]: a=DataFrame(['aa','bb','cc','dd','ee'],index=[1,2,3,4,5],columns=['a'])
In [631]: b=DataFrame(randn(5,1),index=[1,2,3,4,5],columns=['b'])
In [632]: a.dtypes
Out[632]: a object
In [633]: b.dtypes
Out[633]: b float64
In [636]: a.info()
<class 'pandas.core.frame.DataFrame'>
Index: 5 entries, 1 to 5
Data columns:
a 5 non-null values
dtypes: object(1)
In [637]: b.info()
<class 'pandas.core.frame.DataFrame'>
Index: 5 entries, 1 to 5
Data columns:
b 5 non-null values
dtypes: float64(1)
In [638]: b['b']
Out[638]:
1 0.182455296775
2 0.341883955748
3 -1.47860506557
4 0.227912752553
5 -0.393452694936
Name: b
In [639]: b['b'].dtype
Out[639]: dtype('float64')
In [640]: a['b']=b['b']
In [641]: a
Out[641]:
a b
1 aa 0.1825
2 bb 0.3419
3 cc -1.479
4 dd 0.2279
5 ee -0.3935
In [642]: a.dtypes
Out[642]:
a object
b object
In [643]: a.info()
<class 'pandas.core.frame.DataFrame'>
Index: 5 entries, 1 to 5
Data columns:
a 5 non-null values
b 5 non-null values
dtypes: float64(1), object(1)
In [644]:
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} | 2 | "2011-11-17T18:52:42Z" | "2011-11-18T06:48:05Z" | "2011-11-18T06:48:05Z" | NONE | null | from pandas import DataFrame
df = DataFrame({},columns=['time','item','value'])
df.pivot('time','item','value')
pandas\core\frame.pyc in pivot(self, index, columns, values)
1953 """
1954 from pandas.core.reshape import pivot
-> 1955 return pivot(self, index=index, columns=columns, values=values)
1956
1957 def stack(self, level=-1, dropna=True):
pandas\core\reshape.pyc in pivot(self, index, columns, values)
220 stacked = stacked.sortlevel(level=0)
221
--> 222 unstacked = stacked.unstack()
223 if values is not None:
224 unstacked.columns = unstacked.columns.droplevel(0)
pandas\core\frame.pyc in unstack(self, level)
2017 return result
2018 else:
-> 2019 return unstack(self, level)
2020
2021 def delevel(self):
pandas\core\reshape.pyc in unstack(obj, level)
289 unstacker = _Unstacker(obj.values, obj.index, level=level,
290 value_columns=columns)
--> 291 return unstacker.get_result()
292
293 def stack(frame, level=-1, dropna=True):
pandas\core\reshape.pyc in get_result(self)
120
121 # filter out missing levels
--> 122 values = values[:, mask]
123 columns = columns[mask]
124
IndexError: invalid index
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import numpy as np
def func(a, b):
return a + b
df = DataFrame({'US':[1,2,3]})
f = np.frompyfunc(func, 2, 1)
print f.accumulate(df)
In pre-0.4.0 pandas, this printed:
US
0 1
1 3
2 6
Now, this:
ValueError: could not find a matching type for func (vectorized).accumulate, requested type has type code 'q'
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} | 0 | "2011-11-18T00:07:02Z" | "2011-11-23T03:41:21Z" | "2011-11-23T03:41:21Z" | MEMBER | null | ```
data = dict()
data['a'] = [-0.00049987540199591344, -0.0016467257772919831, 0.00067695870775883013]
data['b'] = [-0, -0, 0.0]
data['c'] = [0.00031111847529610595, 0.0014902627951905339, -0.00094099200035979691]
AA = DataFrame( data , index = ['20110801', '20110802', '20110803'] , dtype = ‘object’ )
```
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https://api.github.com/repos/pandas-dev/pandas/issues/385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/385/events | https://github.com/pandas-dev/pandas/pull/385 | 2,278,271 | MDExOlB1bGxSZXF1ZXN0NTA3Njk1 | 385 | resolve issue 368 | {
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} | [] | closed | false | null | [] | null | 3 | "2011-11-18T09:21:29Z" | "2014-07-04T00:18:53Z" | "2011-11-18T17:35:29Z" | CONTRIBUTOR | null | two commits to resolve [issue#368](https://github.com/wesm/pandas/issues/368)
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https://api.github.com/repos/pandas-dev/pandas/issues/386 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/386/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/386/comments | https://api.github.com/repos/pandas-dev/pandas/issues/386/events | https://github.com/pandas-dev/pandas/issues/386 | 2,278,399 | MDU6SXNzdWUyMjc4Mzk5 | 386 | DataFrame.dtypes reports all object dtypes even if items aren't object dtype | {
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} | [] | closed | false | null | [] | null | 1 | "2011-11-18T09:38:17Z" | "2011-11-19T19:18:47Z" | "2011-11-19T19:18:47Z" | CONTRIBUTOR | null | I'm not sure if this is just an error on my part, but I have
``` python
>>> dataset
<class 'pandas.core.frame.DataFrame'>
Index: 17788 entries, 10000 to AFFX-r2-P1-cre-5
Data columns:
ID 17788 non-null values
Sample 1 17788 non-null values
Sample 2 17788 non-null values
Sample 3 17788 non-null values
dtypes: float64(3), object(1)
>>> dataset.dtypes
ID object
Sample 1 object
Sample 2 object
Sample 3 object
```
As it can be seen, "dtypes" reports "object" for all, but the columns aren't really all of dtype object. Is this intended behavior?
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https://api.github.com/repos/pandas-dev/pandas/issues/387 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/387/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/387/comments | https://api.github.com/repos/pandas-dev/pandas/issues/387/events | https://github.com/pandas-dev/pandas/pull/387 | 2,278,521 | MDExOlB1bGxSZXF1ZXN0NTA3Nzcy | 387 | DataFrame.to_html() | {
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} | [] | closed | false | null | [] | null | 2 | "2011-11-18T09:53:38Z" | "2014-06-17T21:46:53Z" | "2011-11-18T17:44:30Z" | CONTRIBUTOR | null | DataFrame.to_html() -- Render a DataFrame to a html table.
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https://api.github.com/repos/pandas-dev/pandas/issues/389 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/389/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/389/comments | https://api.github.com/repos/pandas-dev/pandas/issues/389/events | https://github.com/pandas-dev/pandas/issues/389 | 2,283,388 | MDU6SXNzdWUyMjgzMzg4 | 389 | DataFrame.apply returns Series | {
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} | 5 | "2011-11-18T18:59:34Z" | "2011-11-22T18:38:03Z" | "2011-11-22T18:38:03Z" | NONE | null | from pandas import DataFrame, DateRange
dr = DateRange('2011-11-01','2011-11-15')
df = DataFrame(index=dr)
df_or_so_youd_think = df.apply(lambda x:x)
print df_or_so_youd_think
This returns an empty Series.
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https://api.github.com/repos/pandas-dev/pandas/issues/390 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/390/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/390/comments | https://api.github.com/repos/pandas-dev/pandas/issues/390/events | https://github.com/pandas-dev/pandas/issues/390 | 2,283,732 | MDU6SXNzdWUyMjgzNzMy | 390 | Result of asfreq should have DateRange index | {
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as always, comments welcome.
thanks
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