问题描述
我正在尝试加快几个大型多级数据帧的总和.
I am trying to speed up the sum for several big multilevel dataframes.
以下是示例:
df1 = mul_df(5000,30,400) # mul_df to create a big multilevel dataframe
#let df2, df3, df4 = df1, df1, df1 to minimize the memory usage,
#they can also be mul_df(5000,30,400)
df2, df3, df4 = df1, df1, df1
In [12]: timeit df1+df2+df3+df4
1 loops, best of 3: 993 ms per loop
我对993ms感到不满意，有什么办法可以加快速度吗? cython可以提高性能吗?如果是，如何编写cython代码?谢谢.
I am not satisfy with the 993ms, Is there any way to speed up ? Can cython improve the performance ? If yes, how to write the cython code ? Thanks.
注意:mul_df()是用于创建演示多层数据帧的功能.
Note:mul_df() is the function to create the demo multilevel dataframe.
import itertools
import numpy as np
import pandas as pd
def mul_df(level1_rownum, level2_rownum, col_num, data_ty='float32'):
''' create multilevel dataframe, for example: mul_df(4,2,6)'''
index_name = ['STK_ID','RPT_Date']
col_name = ['COL'+str(x).zfill(3) for x in range(col_num)]
first_level_dt = [['A'+str(x).zfill(4)]*level2_rownum for x in range(level1_rownum)]
first_level_dt = list(itertools.chain(*first_level_dt)) #flatten the list
second_level_dt = ['B'+str(x).zfill(3) for x in range(level2_rownum)]*level1_rownum
dt = pd.DataFrame(np.random.randn(level1_rownum*level2_rownum, col_num), columns=col_name, dtype = data_ty)
dt[index_name[0]] = first_level_dt
dt[index_name[1]] = second_level_dt
rst = dt.set_index(index_name, drop=True, inplace=False)
return rst
更新:
我的Pentium双核T4200 @ 2.00GHZ，3.00GB RAM，WindowXP，Python 2.7.4，Numpy 1.7.1，Pandas 0.11.0，numexpr 2.0.1(Anaconda 1.5.0(32位))上的数据)
Data on my Pentium Dual-Core [email protected], 3.00GB RAM, WindowXP, Python 2.7.4, Numpy 1.7.1, Pandas 0.11.0, numexpr 2.0.1 (Anaconda 1.5.0 (32-bit))
In [1]: from pandas.core import expressions as expr
In [2]: import numexpr as ne
In [3]: df1 = mul_df(5000,30,400)
In [4]: df2, df3, df4 = df1, df1, df1
In [5]: expr.set_use_numexpr(False)
In [6]: %timeit df1+df2+df3+df4
1 loops, best of 3: 1.06 s per loop
In [7]: expr.set_use_numexpr(True)
In [8]: %timeit df1+df2+df3+df4
1 loops, best of 3: 986 ms per loop
In [9]: %timeit DataFrame(ne.evaluate('df1+df2+df3+df4'),columns=df1.columns,index=df1.index,dtype='float32')
1 loops, best of 3: 388 ms per loop
推荐答案
方法1:在我的机器上还不错(禁用了numexpr)
method 1: On my machine not so bad (with numexpr disabled)
In [41]: from pandas.core import expressions as expr
In [42]: expr.set_use_numexpr(False)
In [43]: %timeit df1+df2+df3+df4
1 loops, best of 3: 349 ms per loop
方法2:使用numexpr(如果已安装numexpr，则默认启用)
method 2: Using numexpr (which is by default enabled if numexpr is installed)
In [44]: expr.set_use_numexpr(True)
In [45]: %timeit df1+df2+df3+df4
10 loops, best of 3: 173 ms per loop
方法3:直接使用numexpr
In [34]: import numexpr as ne
In [46]: %timeit DataFrame(ne.evaluate('df1+df2+df3+df4'),columns=df1.columns,index=df1.index,dtype='float32')
10 loops, best of 3: 47.7 ms per loop
使用numexpr可以实现这些加速，因为:
These speedups are achieved using numexpr because:
- 避免使用中间临时数组(在您出现的情况下，这可能是 numpy效率很低，我怀疑这是像
((df1+df2)+df3)+df4那样进行评估的 - 使用可用的多核
- avoids using intermediate temporary arrays (which in the case you are presenting is probably quite inefficient in numpy, I suspect this is being evaluated like
((df1+df2)+df3)+df4 - uses multi-cores as available
正如我在上面暗示的那样，pandas在某些类型的操作(例如0.11)中使用numexpr作为背景. df1 + df2将以这种方式求值，但是您在此处给出的示例将导致多次调用numexpr(这是方法2比方法1更快).使用直接(方法3)ne.evaluate(...)可以实现更大的加速.
As I hinted above, pandas uses numexpr under the hood for certain types of ops (in 0.11), e.g. df1 + df2 would be evaluated this way, however the example you are giving here will result in several calls to numexpr (this is method 2 is faster than method 1.). Using the direct (method 3) ne.evaluate(...) achieves even more speedups.
请注意，在熊猫0.13(本周将发布0.12)中，我们实现了一个函数pd.eval，该函数实际上将执行上述示例中的操作.请继续关注(如果您喜欢冒险，它将很快成为大师: https://github.com/pydata/pandas/pull /4037 )
Note that in pandas 0.13 (0.12 will be released this week), we are implemented a function pd.eval which will in effect do exactly what my example above does. Stay tuned (if you are adventurous this will be in master somewhat soon: https://github.com/pydata/pandas/pull/4037)
In [5]: %timeit pd.eval('df1+df2+df3+df4')
10 loops, best of 3: 50.9 ms per loop
最后一个回答您的问题，cython根本无济于事； numexpr在这类问题上非常有效(也就是说，在 情况下，cython很有帮助)
Lastly to answer your question, cython will not help here at all; numexpr is quite efficient at this type of problem (that said, there are situation where cython is helpful)
一个警告:为了使用直接的Numexpr方法，帧应该已经对齐(Numexpr在numpy数组上运行，并且对索引一无所知).而且它们应该是单个dtype
One caveat: in order to use the direct Numexpr method the frames should be already aligned (Numexpr operates on the numpy array and doesn't know anything about the indices). also they should be a single dtype
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