Question

我有一个标准格式的csv输入文件，带有一个我正在剥离的杂乱标题，然后是一个包含35列和8760行的数组。除第6列（文本）外，所有这些数据都是数字。我已尝试允许genfromtxt()自行解决此问题，但最后该列转向nan s，我相信因为没有引号。

目前，我正在按如下方式阅读此数组：

WeaData = np.genfromtxt(FileIn, delimiter=",", skip_header=8)

我尝试使用

手动指定列类型

WeaData = np.genfromtxt(FileIn, delimiter=",", skip_header=8, dtype=(float,float,float,float,float,str,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float,float))

和

WeaData = np.genfromtxt(FileIn, delimiter=",", skip_header=8, dtype=[float for n in range(5)]+['S10']+[float for n in range(29)])

但没有运气。我相信我的语法在第一个选项中是错误的，第二个选项返回一个空洞数组。有没有一种简单的方法可以做到这一点，最好不指定35种列类型？

以下是我的csv文件的三行供参考，在我不关心的标题之后。

1966,1,1,1,60,A7A7A7A7*0?0?0?0?0?0?0?0A7A7A7A7A7A7F8F8A7E7,3.9,1.7,86,102400,0,0,264,0,0,0,0,0,0,0,230,2.1,0,0,24.1,77777,0,999999999,8,0.1000,0,88,0.000,0.0,0.0
1966,1,1,2,60,A7A7A7A7*0?0?0?0?0?0?0?0A7A7A7A7A7A7F8F8A7E7,4.4,0.0,73,102500,0,0,265,0,0,0,0,0,0,0,270,3.6,0,0,24.1,77777,0,999999999,8,0.1000,0,88,0.000,0.0,0.0
1966,1,1,3,60,A7A7A7A7*0?0?0?0?0?0?0?0A7A7A7A7A7A7F8F8A7E7,2.8,-0.6,79,102500,0,0,258,0,0,0,0,0,0,0,310,2.1,0,0,24.1,77777,0,999999999,8,0.1000,0,88,0.000,0.0,0.0

我正在使用Python V2.7。

Answer 1

使用numpy.loadtxt和参数usecols来仅选择包含浮点数的列。

>>> import numpy as np
>>> cols = range(0,5) + range(6,35)
>>> data = np.loadtxt("data.txt", delimiter=",", usecols=cols, dtype=np.float)
>>> data
[[  1.96600000e+03   1.00000000e+00   1.00000000e+00   1.00000000e+00
    6.00000000e+01   3.90000000e+00   1.70000000e+00   8.60000000e+01
    1.02400000e+05   0.00000000e+00   0.00000000e+00   2.64000000e+02
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   2.30000000e+02
    2.10000000e+00   0.00000000e+00   0.00000000e+00   2.41000000e+01
    7.77770000e+04   0.00000000e+00   9.99999999e+08   8.00000000e+00
    1.00000000e-01   0.00000000e+00   8.80000000e+01   0.00000000e+00
    0.00000000e+00   0.00000000e+00]
 [  1.96600000e+03   1.00000000e+00   1.00000000e+00   2.00000000e+00
    6.00000000e+01   4.40000000e+00   0.00000000e+00   7.30000000e+01
    1.02500000e+05   0.00000000e+00   0.00000000e+00   2.65000000e+02
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   2.70000000e+02
    3.60000000e+00   0.00000000e+00   0.00000000e+00   2.41000000e+01
    7.77770000e+04   0.00000000e+00   9.99999999e+08   8.00000000e+00
    1.00000000e-01   0.00000000e+00   8.80000000e+01   0.00000000e+00
    0.00000000e+00   0.00000000e+00]
 [  1.96600000e+03   1.00000000e+00   1.00000000e+00   3.00000000e+00
    6.00000000e+01   2.80000000e+00  -6.00000000e-01   7.90000000e+01
    1.02500000e+05   0.00000000e+00   0.00000000e+00   2.58000000e+02
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   3.10000000e+02
    2.10000000e+00   0.00000000e+00   0.00000000e+00   2.41000000e+01
    7.77770000e+04   0.00000000e+00   9.99999999e+08   8.00000000e+00
    1.00000000e-01   0.00000000e+00   8.80000000e+01   0.00000000e+00
    0.00000000e+00   0.00000000e+00]]

如果要包含第6列，则必须将矩阵作为对象加载，不能将浮点数与字符串混合。

>>> data = np.loadtxt("data.txt", delimiter=",", dtype=np.object)

因此，如果您需要此列，请单独加载。

读入csv文件，中间有一列字符串

1 个答案: