New to pandas here. A (trivial) problem: hosts, operations, execution times. I want to group by host, then by host+operation, calculate std deviation for execution time per host, then by host+operation pair. Seems simple?

It works for grouping by a single column:

df
Out[360]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 132564 entries, 0 to 132563
Data columns (total 9 columns):
datespecial    132564  non-null values
host           132564  non-null values
idnum          132564  non-null values
operation      132564  non-null values
time           132564  non-null values
...
dtypes: float32(1), int64(2), object(6)byhost = df.groupby('host')byhost.std()
Out[362]:datespecial         idnum      time
host
ahost1.test  11946.961952  40367.033852  0.003699
host1.test   15484.975077  38206.578115  0.008800
host10.test           NaN  37644.137631  0.018001
...

Nice. Now:

byhostandop = df.groupby(['host', 'operation'])byhostandop.std()
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-364-2c2566b866c4> in <module>()
----> 1 byhostandop.std()/home/username/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in std(self, ddof)386         # todo, implement at cython level?387         if ddof == 1:
--> 388             return self._cython_agg_general('std')389         else:390             f = lambda x: x.std(ddof=ddof)/home/username/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)16151616     def _cython_agg_general(self, how, numeric_only=True):
-> 1617         new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)1618         return self._wrap_agged_blocks(new_blocks)1619/home/username/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)1653                 values = com.ensure_float(values)1654
-> 1655             result, _ = self.grouper.aggregate(values, how, axis=agg_axis)16561657             # see if we can cast the block back to the original dtype/home/username/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in aggregate(self, values, how, axis)838                 if is_numeric:839                     result = lib.row_bool_subset(result,
--> 840                                                  (counts > 0).view(np.uint8))841                 else:842                     result = lib.row_bool_subset_object(result,/home/username/anaconda/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.row_bool_subset (pandas/lib.c:16540)()ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'

Huh?? Why do I get this exception?

More questions:

• how do I calculate std deviation on dataframe.groupby([several columns])?

• how can I limit calculation to a selected column? E.g. it obviously doesn't make sense to calculate std dev on dates/timestamps here.

It's important to know your version of Pandas / Python. Looks like this exception could arise in Pandas version < 0.10 (see ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'). To avoid this, you can cast your float columns to float64:

df.astype('float64')

To calculate std() on selected columns, just select columns :)

>>> df = pd.DataFrame({'a':range(10), 'b':range(10,20), 'c':list('abcdefghij'), 'g':[1]*3 + [2]*3 + [3]*4})
>>> dfa   b  c  g
0  0  10  a  1
1  1  11  b  1
2  2  12  c  1
3  3  13  d  2
4  4  14  e  2
5  5  15  f  2
6  6  16  g  3
7  7  17  h  3
8  8  18  i  3
9  9  19  j  3
>>> df.groupby('g')[['a', 'b']].std()a         b
g                    
1  1.000000  1.000000
2  1.000000  1.000000
3  1.290994  1.290994

update

As far as it goes, it looks like std() is calling aggregation() on the groupby result, and a subtle bug (see here - Python Pandas: Using Aggregate vs Apply to define new columns). To avoid this, you can use apply():

byhostandop['time'].apply(lambda x: x.std())