我发布了这个问题,因为我想知道我是否做错了什么严重的事情才能获得此结果。
我有一个中等大小的csv文件,我尝试使用numpy加载它。为了说明,我使用python制作了文件:
import timeit
import numpy as np
my_data = np.random.rand(1500000, 3)*10
np.savetxt('./test.csv', my_data, delimiter=',', fmt='%.2f')
然后,我尝试了两种方法:numpy.genfromtxt,numpy.loadtxt
setup_stmt = 'import numpy as np'
stmt1 = """\
my_data = np.genfromtxt('./test.csv', delimiter=',')
"""
stmt2 = """\
my_data = np.loadtxt('./test.csv', delimiter=',')
"""
t1 = timeit.timeit(stmt=stmt1, setup=setup_stmt, number=3)
t2 = timeit.timeit(stmt=stmt2, setup=setup_stmt, number=3)
结果表明 t1 = 32.159652940464184,t2 = 52.00093725634724 。
但是,当我尝试使用matlab时:
tic
for i = 1:3
my_data = dlmread('./test.csv');
end
toc
结果显示:经过的时间为 3.196465秒。
我了解加载速度可能会有一些差异,但是:
任何输入将不胜感激。在此先多谢!
最佳答案
是的,将csv
文件读入numpy
很慢。代码路径上有很多纯Python。这些天,即使当我使用纯numpy
时,我仍然对IO使用pandas
:
>>> import numpy as np, pandas as pd
>>> %time d = np.genfromtxt("./test.csv", delimiter=",")
CPU times: user 14.5 s, sys: 396 ms, total: 14.9 s
Wall time: 14.9 s
>>> %time d = np.loadtxt("./test.csv", delimiter=",")
CPU times: user 25.7 s, sys: 28 ms, total: 25.8 s
Wall time: 25.8 s
>>> %time d = pd.read_csv("./test.csv", delimiter=",").values
CPU times: user 740 ms, sys: 36 ms, total: 776 ms
Wall time: 780 ms
另外,在这种简单的情况下,您可以使用Joe Kington编写的类似here的代码:
>>> %time data = iter_loadtxt("test.csv")
CPU times: user 2.84 s, sys: 24 ms, total: 2.86 s
Wall time: 2.86 s
如果
pandas
过于依赖,还有Warren Weckesser的textreader库:>>> import textreader
>>> %time d = textreader.readrows("test.csv", float, ",")
readrows: numrows = 1500000
CPU times: user 1.3 s, sys: 40 ms, total: 1.34 s
Wall time: 1.34 s
关于python - numpy的csv TOO与Matlab相比慢,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/18259393/