Python numpy模块用法 Python中numpy模块常见用法demo实例小结

import numpy as np

arr = np.array([[1,2,3], [2,3,4]])
print(arr)
print(type(arr))
print('number of dim:', arr.ndim)
print('shape:', arr.shape)
print('size:', arr.size)

a32 = np.array([1,23,456], dtype=np.int)
print(a32.dtype)
a64 = np.array([1,23,456], dtype=np.int64)
print(a64.dtype)
f64 = np.array([1,23,456], dtype=np.float)
print(f64.dtype)

z = np.zeros((3, 4))
print(z)
print(z.dtype)
print()
one = np.ones((3, 4), dtype=int)
print(one)
print(one.dtype)
print()
emt = np.empty((3, 4), dtype=int)
print(emt)
print(emt.dtype)
print()
ran = np.arange(12).reshape((3,4))
print(ran)
print(ran.dtype)
print()
li = np.linspace(1, 10, 6).reshape(2, 3)
print(li)
print(li.dtype)

a = np.array([10,20,30,40])
b = np.arange(4)
print(a)
print(b)
print()
print(a+b)
print(a-b)
print(a*b)
print()
print(a**b)
print()
print(10*np.sin(a))
print()
print(b<3)
print()

a = np.array([[1,2], [3,4]])
b = np.arange(4).reshape(2, 2)
print(a)
print(b)
print()
print(a * b)
print(np.dot(a, b)) #矩阵乘法，或下面：
print(a.dot(b))
print()

a = np.random.random((2, 4))
print(a)
print(np.sum(a))
print(np.min(a))
print(np.max(a))
print()
print(np.sum(a, axis=1)) #返回每一行的和。 axis=1代表行
print(np.min(a, axis=0)) #返回每一列的最小值。 axis=0代表列
print(np.mean(a, axis=1)) #返回每一行的平均值

A = np.arange(2, 14).reshape(3, 4)
print(A)
print(np.argmin(A)) #最小索引
print(np.argmax(A)) #最大索引
print()
print(A.mean())
print(np.median(A)) #中位数
print(A.cumsum()) #累加值
print(np.diff(A)) #相邻差值
print()

A = np.array([[1,0], [0,3]])
print(A)
print(A.nonzero()) #分别输出非零元素的行和列值
print(np.sort(A)) #逐行排序后的矩阵
print(np.sort(A, axis=0)) #逐列排序的矩阵
print(np.sort(A).nonzero())
print()
B = np.arange(14, 2, -1).reshape(3, 4)
print(B)
print(B.transpose()) #转置
print((B.T).dot(B)) #转置
print()
print(np.clip(B, 5, 9)) #B中将范围限定，大于9的数都为9，小于5的都为5，之间的数不变
print()

A = np.arange(3, 7)
print(A)
print(A[2])
print()
B = np.arange(3, 15).reshape(3, 4)
print(B)
print(B[2])
print(B[2][1])
print(B[2, 1])
print()
print(B[2, 2:])
print(B[1:, 2:])
print()
for row in B:
  print(row)
print()
for col in B.T:
  print(col)
print()
print(B.flatten())
for elm in B.flat:
  print(elm)

#矩阵合并
A = np.array([1,1,1])
B = np.array([2,2,2])
C = np.vstack((A, B, A, B))
print(C)
print(A.shape, (A.T).shape)
print(C.shape)
print()
D = np.hstack((A, B))
print(D)
print()
print(A[np.newaxis, :])
print(A[:, np.newaxis])
print(np.hstack((A[:, np.newaxis], B[:, np.newaxis])))
print()
print(np.stack((A,B), axis=0))
print(np.stack((A,B), axis=1))
#print(np.concatenate((A,B,B,A), axis=0))
#print(np.concatenate((A,B,B,A), axis=1))

A = np.arange(12).reshape(3, 4)
print(A)
print(np.split(A, 2, axis=1))
print(np.split(A, 3, axis=0))
print()
print(np.array_split(A, 3, axis=1)) #不等分割
print()
print(np.hsplit(A, 2))
print(np.vsplit(A, 1))

A = np.arange(4)
B = A
C = B
D = A.copy()
print(A, B, C, D)
A[0] = 5
print(A, B, C, D)
print(id(A), id(B), id(C), id(D)) #id返回指针的值（内存地址）
print()