day1-9-numpy读写

In [1]:

import numpy as np

 

使用Numpy读写数据¶

In [3]:

%%writefile test.txt
1 2 3 4 5 6
2 3 5 8 7 9

 

Writing test.txt

In [4]:

data = []
with open('test.txt') as f:
    for line in f.readlines():
        fileds = line.split()
        cur_data = [float(x) for x in fileds]
        data.append(cur_data)
data = np.array(data)
data

Out[4]:

array([[1., 2., 3., 4., 5., 6.],
       [2., 3., 5., 8., 7., 9.]])

In [5]:

data = np.loadtxt('test.txt')
data

Out[5]:

array([[1., 2., 3., 4., 5., 6.],
       [2., 3., 5., 8., 7., 9.]])

In [6]:

%%writefile test2.txt
1,2,3,4,5,6
2,3,5,8,7,9

 

Writing test2.txt

In [7]:

data = np.loadtxt('test2.txt',delimiter = ',')
data

Out[7]:

array([[1., 2., 3., 4., 5., 6.],
       [2., 3., 5., 8., 7., 9.]])

In [8]:

%%writefile test2.txt
x,y,z,w,a,b
1,2,3,4,5,6
2,3,5,8,7,9

 

Overwriting test2.txt

In [10]:

data = np.loadtxt('test2.txt',delimiter = ',',skiprows = 1)
data

Out[10]:

array([[1., 2., 3., 4., 5., 6.],
       [2., 3., 5., 8., 7., 9.]])

 

• 'test2.txt':路径最好放到和代码一起
• skiprows : 去掉几行
• delimiter = ',' :分隔符
• usecols = (0,1,4) ：指定使用哪几列

In [12]:

array1 = np.array([[1,2,3],[4,5,6]])

In [13]:

np.savetxt('test4.txt',array1)

In [15]:

np.savetxt('test4.txt',array1,fmt='%d')

In [18]:

np.savetxt('test4.txt',array1,fmt='%d',delimiter = ',')

In [20]:

np.savetxt('test4.txt',array1,fmt='%.2f',delimiter = ',')

 

读写array结构¶

In [22]:

array1 = np.array([[1,2,3],[4,5,6]])
np.save('array1.npy',array1)

In [23]:

tmp = np.load('array1.npy')
tmp

Out[23]:

array([[1, 2, 3],
       [4, 5, 6]])

In [24]:

array2 = np.arange(10)
array2

Out[24]:

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [25]:

np.savez('array1.npz',a=array1,b=array2)

In [22]:

tmp = np.load('array1.npz')
tmp

Out[22]:

<numpy.lib.npyio.NpzFile at 0x23390391ac0>

In [24]:

tmp.keys()

Out[24]:

KeysView(<numpy.lib.npyio.NpzFile object at 0x0000023390391AC0>)

In [26]:

tmp['a']

Out[26]:

array([[1, 2, 3],
       [4, 5, 6]])

In [27]:

tmp['b']

Out[27]:

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [ ]: