Python文件读取

读取文件Advertising.csv，文件内容类似于：

,TV,Radio,Newspaper,Sales
1,230.1,37.8,69.2,22.1
2,44.5,39.3,45.1,10.4
3,17.2,45.9,69.3,9.3
4,151.5,41.3,58.5,18.5
5,180.8,10.8,58.4,12.9
6,8.7,48.9,75,7.2
7,57.5,32.8,23.5,11.8
8,120.2,19.6,11.6,13.2
9,8.6,2.1,1,4.8
10,199.8,2.6,21.2,10.6
11,66.1,5.8,24.2,8.6
12,214.7,24,4,17.4
13,23.8,35.1,65.9,9.2
14,97.5,7.6,7.2,9.7
15,204.1,32.9,46,19
16,195.4,47.7,52.9,22.4
17,67.8,36.6,114,12.5
18,281.4,39.6,55.8,24.4
19,69.2,20.5,18.3,11.3
20,147.3,23.9,19.1,14.6

手动读取：

path = '8.Advertising.csv'
f = file(path)
    x = []
    y = []
    for i, d in enumerate(f):
        if i == 0: #第一行是标题栏
            continue
        d = d.strip() #去除首位空格
        if not d:
            continue
        d = map(float, d.split(',')) #每个数据都变为float
        x.append(d[1:-1])
        y.append(d[-1])

python自带库：

f = file(path, 'rb')
    print f
    d = csv.reader(f)
    for line in d:
        print line
    f.close()

numpy:

 p = np.loadtxt(path, delimiter=',', skiprows=1)
    print p

pandas:

data = pd.read_csv(path)    # TV、Radio、Newspaper、Sales
    x = data[['TV', 'Radio', 'Newspaper']]
    # x = data[['TV', 'Radio']]
    y = data['Sales']

 使用sklearn作文件预处理：

from sklearn.preprocessing import StandardScaler
 le = preprocessing.LabelEncoder()
    le.fit(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
    print le.classes_
    y = le.transform(y)