?eval方法可以直接利用c語(yǔ)言的速度，而不用分配中間數(shù)組，不需要中間內(nèi)存的占用.

如果包含多個(gè)步驟，每個(gè)步驟都要分配一塊內(nèi)存

            
              import numpy as np
import pandas as pd
import timeit


df = pd.DataFrame({'a': np.random.randn(10000000),
                   'b': np.random.randn(10000000),
                   'c': np.random.randn(10000000),
                   'x': 'x'})
# print df
start_time = timeit.default_timer()
df['a']/( df['b']+0.1)-df['c']
end_time = timeit.default_timer()
print (end_time - start_time)
print "___________________"
start_time = timeit.default_timer()
pd.eval("df['a']/( df['b']+0.1)-df['c']")
end_time = timeit.default_timer()
print (end_time - start_time)
            
          

運(yùn)行時(shí)間對(duì)比?

            
              0.136633455546
___________________
0.087637596342
            
          

As of version 0.13 (released January 2014), Pandas includes some experimental tools that allow you to directly access C-speed operations without costly allocation of intermediate arrays.