与Matlab相比,Numpy加载csv TOO变慢

Question

我发布了这个问题,因为我想知道我是否做了一些非常错误的结果.

我有一个中等大小的csv文件,我试图使用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)

结果显示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:经过的时间是3.196465秒.

我知道加载速度可能会有一些差异,但是:

1. 这远远超出我的预期;
2. 是不是np.loadtxt应该比np.genfromtxt更快？
3. 我还没有尝试过python csv模块,因为加载csv文件是我经常做的事情,并且使用csv模块,编码有点冗长......但是我很乐意尝试它,如果这是唯一的方法.目前我更担心的是我做错了什么.

任何输入将不胜感激.非常感谢提前!

Answer 1

是的,读取csv文件numpy非常慢.代码路径中有很多纯Python.这些天,即使我使用纯numpy我仍然使用pandasIO:

>>> 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

或者,在这个简单的情况下,你可以使用像Joe Kington这样写的东西:

>>> %time data = iter_loadtxt("test.csv")
CPU times: user 2.84 s, sys: 24 ms, total: 2.86 s
Wall time: 2.86 s

还有Warren Weckesser的文本阅读器库,以防万一pandas依赖:

>>> 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

Answer 2

我已经使用perfplot（我的一个小项目）对建议的解决方案进行了性能测试，发现

pandas.read_csv(filename)

确实是最快的解决方案（如果读取超过 2000 个条目，则在此之前所有内容都在毫秒范围内）。它的性能比 numpy 的变体高出大约 10 倍。（这里的 numpy.fromfile 只是为了比较，它无法读取实际的 csv 文件。）

重现情节的代码：

import numpy
import pandas
import perfplot

numpy.random.seed(0)
filename = "a.txt"


def setup(n):
    a = numpy.random.rand(n)
    numpy.savetxt(filename, a)
    return None


def numpy_genfromtxt(data):
    return numpy.genfromtxt(filename)


def numpy_loadtxt(data):
    return numpy.loadtxt(filename)


def numpy_fromfile(data):
    out = numpy.fromfile(filename, sep=" ")
    return out


def pandas_readcsv(data):
    return pandas.read_csv(filename, header=None).values.flatten()


def kington(data):
    delimiter = " "
    skiprows = 0
    dtype = float

    def iter_func():
        with open(filename, "r") as infile:
            for _ in range(skiprows):
                next(infile)
            for line in infile:
                line = line.rstrip().split(delimiter)
                for item in line:
                    yield dtype(item)
        kington.rowlength = len(line)

    data = numpy.fromiter(iter_func(), dtype=dtype).flatten()
    return data


b = perfplot.bench(
    setup=setup,
    kernels=[numpy_genfromtxt, numpy_loadtxt, numpy_fromfile, pandas_readcsv, kington],
    n_range=[2 ** k for k in range(23)],
)
b.save("out.png")

Answer 3

如果你想保存并读取一个numpy数组,最好将它保存为二进制或压缩二进制文件,具体取决于大小:

my_data = np.random.rand(1500000, 3)*10
np.savetxt('./test.csv', my_data, delimiter=',', fmt='%.2f')
np.save('./testy', my_data)
np.savez('./testz', my_data)
del my_data

setup_stmt = 'import numpy as np'
stmt1 = """\
my_data = np.genfromtxt('./test.csv', delimiter=',')
"""
stmt2 = """\
my_data = np.load('./testy.npy')
"""
stmt3 = """\
my_data = np.load('./testz.npz')['arr_0']
"""

t1 = timeit.timeit(stmt=stmt1, setup=setup_stmt, number=3)
t2 = timeit.timeit(stmt=stmt2, setup=setup_stmt, number=3)
t3 = timeit.timeit(stmt=stmt3, setup=setup_stmt, number=3)

genfromtxt 39.717250824
save 0.0667860507965
savez 0.268463134766