你可以尝试这样的事情:
mean_a = np.sum(np.array([d['a'] for d in data]) * np.array([d['n'] for d in data])) / len(data)
编辑:实际上,@mgilson 的上述方法更快:
import numpy as np
from operator import itemgetter
from pandas import *
data=[]
for i in range(100000):
data.append({'a':np.random.random(), 'n':np.random.random(), 'o':np.random.random(), 'u':np.random.random()})
def func1(data):
x = n = o = u = 0
items = itemgetter('a','n','o','u')
for entry in data:
A,N,O,U = items(entry)
x += A*N # n - number of data points
n += N
o += O #don't know what you're doing with O or U, but I'll leave them
u += U
average = int(round(x / n)), n, o, u
return average
def func2(data):
mean_a = np.sum(np.array([d['a'] for d in data]) * np.array([d['n'] for d in data])/len(data)
return (mean_a,
np.sum([d['n'] for d in data]),
np.sum([d['o'] for d in data]),
np.sum([d['u'] for d in data])
)
def func3(data):
dframe = DataFrame(data)
return np.sum((dframe["a"]*dframe["n"])) / dframe.shape[0], np.sum(dframe["n"]), np.sum(dframe["o"]), np.sum(dframe["u"])
In [3]: %timeit func1(data)
10 loops, best of 3: 59.6 ms per loop
In [4]: %timeit func2(data)
10 loops, best of 3: 138 ms per loop
In [5]: %timeit func3(data)
10 loops, best of 3: 129 ms per loop
如果您正在对数据进行其他操作,我肯定会考虑使用 Pandas 包。它的 DataFrame 对象与您正在使用的字典列表非常匹配。我认为大部分开销是将数据放入 numpy 数组或 DataFrame 对象的 IO 操作。