是的，读取 csv numpy 很慢。沿着代码路径有很多纯Python。这些天，即使我使用纯 numpy ，我仍然使用 pandas 作为IO：

Yeah, reading csv files into numpy is pretty slow. There's a lot of pure Python along the code path. These days, even when I'm using pure numpy I still use pandas for IO:

>>> 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，在像这样简单的情况下，你可以使用像Joe Kington在这里写道：

Alternatively, in a simple enough case like this one, you could use something like what Joe Kington wrote here:

>>> %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 textreader 库， pandas $ c>是太重的依赖：

There's also Warren Weckesser's textreader library, in case pandas is too heavy a dependency:

>>> 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