numpy创建ndarray对象的三种方法
In [8]: import numpy as np
In [9]: a = [1,2,3,4]
In [10]: x1 = np.array(a)
In [11]: x1
Out[11]: array([1, 2, 3, 4])
In [12]: type(x1)
Out[12]: numpy.ndarray
In [13]: x2 = np.arange(11)
In [14]: x2
Out[14]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
01.csv文件如下
使用numpy的loadtxt方法打开
x = np.loadtxt('01.csv',delimiter=',',skiprows=1,usecols=(1,4,6),unpack=False)
显示结果
In [18]: x.shape
Out[18]: (242, 3)
把每列分开保存
In [24]: open,close,volume = np.loadtxt('01.csv',delimiter=',',skiprows=1,usecols=(1,4,6),unpack=True)
结果:
In [26]: open.shape
Out[26]: (242,)
In [36]: c = np.random.randint(1,100,10)
In [37]: c
Out[37]: array([44, 26, 40, 87, 32, 82, 20, 70, 62, 14])
In [38]: c.min()
Out[38]: 14
In [39]: c.max()
Out[39]: 87
In [40]: c.mean()
Out[40]: 47.7
In [43]: y = np.sort(c)
In [44]: y
Out[44]: array([14, 20, 26, 32, 40, 44, 62, 70, 82, 87])