这里的熊猫是新来的。一个(小)问题:主机、操作、执行时间。我想按主机分组,然后按主机+操作分组,计算每个主机的执行时间的std偏差,然后按主机+操作对分组。看起来很简单?
它适用于按单个列分组:
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
...
很好。现在:
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)
1615
1616 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)
1656
1657 # 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'
嗯??为什么我会有这个例外?
更多问题:
如何计算dataframe.groupby([several columns])
上的std偏差?
如何将计算限制为选定列?E、 显然,在这里计算日期/时间戳上的std dev是没有意义的。
了解Pandas/Python的版本很重要。看起来在Pandas版本<;0.10中可能会出现此异常(请参见ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float')。为了避免这种情况,可以将
float
列强制转换为float64
:要计算选定列上的
std()
,只需选择列:)更新
就目前而言,似乎
std()
正在对groupby
结果调用aggregation()
,这是一个微妙的错误(请参见-Python Pandas: Using Aggregate vs Apply to define new columns)。为了避免这种情况,可以使用apply()
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