如何将CSV数据读入NumPy中的记录数组?

你可以用Numpy的 genfromtxt() 方法,通过设置 delimiter

from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')

有关该函数的更多信息,请参见相应的 documentation

我推荐 read_csv 从 pandas 图书馆:

import pandas as pd
df=pd.read_csv('myfile.csv', sep=',',header=None)
df.values
array([[ 1. ,  2. ,  3. ],
       [ 4. ,  5.5,  6. ]])

DataFrame -允许 many useful data manipulation functions which are not directly available with numpy record arrays

DataFrame是一种二维标记的数据结构,包含 可能是不同的类型。你可以把它想象成电子表格或者 SQL表。。。

我也建议 genfromtxt . 然而,既然这个问题要求 record array ,与普通数组相反 dtype=None 参数需要添加到 电话:

给定一个输入文件, myfile.csv :

1.0, 2, 3
4, 5.5, 6

import numpy as np
np.genfromtxt('myfile.csv',delimiter=',')

给出一个数组:

array([[ 1. ,  2. ,  3. ],
       [ 4. ,  5.5,  6. ]])

和

np.genfromtxt('myfile.csv',delimiter=',',dtype=None)

给出一个记录数组:

array([(1.0, 2.0, 3), (4.0, 5.5, 6)], 
      dtype=[('f0', '<f8'), ('f1', '<f8'), ('f2', '<i4')])

multiple data types (including strings) can be easily imported .

我计时了

from numpy import genfromtxt
genfromtxt(fname = dest_file, dtype = (<whatever options>))

import csv
import numpy as np
with open(dest_file,'r') as dest_f:
    data_iter = csv.reader(dest_f,
                           delimiter = delimiter,
                           quotechar = '"')
    data = [data for data in data_iter]
data_array = np.asarray(data, dtype = <whatever options>)

在460万行(约70列)上,发现NumPy路径耗时2分16秒,csv列表理解方法耗时13秒。

你也可以试试 recfromcsv() 它可以猜测数据类型并返回格式正确的记录数组。

• 更快
• 减少CPU使用
• 与NumPy genfromtxt相比,使用了1/3的RAM

$ for f in test_pandas.py test_numpy_csv.py ; do  /usr/bin/time python $f; done
2.94user 0.41system 0:03.05elapsed 109%CPU (0avgtext+0avgdata 502068maxresident)k
0inputs+24outputs (0major+107147minor)pagefaults 0swaps

23.29user 0.72system 0:23.72elapsed 101%CPU (0avgtext+0avgdata 1680888maxresident)k
0inputs+0outputs (0major+416145minor)pagefaults 0swaps

测试数量_csv.py文件

from numpy import genfromtxt
train = genfromtxt('/home/hvn/me/notebook/train.csv', delimiter=',')

from pandas import read_csv
df = read_csv('/home/hvn/me/notebook/train.csv')

du -h ~/me/notebook/train.csv
 59M    /home/hvn/me/notebook/train.csv

NumPy和pandas的版本:

$ pip freeze | egrep -i 'pandas|numpy'
numpy==1.13.3
pandas==0.20.2

import numpy as np
csv = np.genfromtxt('test.csv', delimiter=",")
print(csv)

我建议用桌子( pip3 install tables ). 你可以省下你的钱 .csv 文件到 .h5 pip3 install pandas

import pandas as pd
data = pd.read_csv("dataset.csv")
store = pd.HDFStore('dataset.h5')
store['mydata'] = data
store.close()

然后,您就可以轻松地、用更少的时间(即使是处理大量数据)将数据加载到 NumPy阵列

import pandas as pd
store = pd.HDFStore('dataset.h5')
data = store['mydata']
store.close()

# Data in NumPy format
data = data.values

使用 numpy.loadtxt

很简单的方法。但它要求所有元素都是float(int等)

import numpy as np 
data = np.loadtxt('c:\\1.csv',delimiter=',',skiprows=0)  

这是最简单的方法:

import csv
with open('testfile.csv', newline='') as csvfile:
    data = list(csv.reader(csvfile))

我试过这个:

import pandas as p
import numpy as n

closingValue = p.read_csv("<FILENAME>", usecols=[4], dtype=float)
print(closingValue)

import csv
with open("data.csv", 'r') as f:
    data = list(csv.reader(f, delimiter=";"))

import numpy as np
data = np.array(data, dtype=np.float)

In [329]: %time my_data = genfromtxt('one.csv', delimiter=',')
CPU times: user 19.8 s, sys: 4.58 s, total: 24.4 s
Wall time: 24.4 s

In [330]: %time df = pd.read_csv("one.csv", skiprows=20)
CPU times: user 1.06 s, sys: 312 ms, total: 1.38 s
Wall time: 1.38 s