import numpy as np
import matplotlib.pyplot as plt
# numpy预览
a = [1, 2, 3, 4]
print(a)
a = np.array(a)
print(a)
a = a + 1
print(a)
a = a + a
print(a)
a = np.array([1, 2, 1, 4])
b = np.array([2, 3, 4, 5])
print(a * b)
# [ 2 6 4 20] 对应元素乘法
print(a ** b)
# [ 1 8 1 1024] 对应元素乘方
print(b[:2]) # 前两个元素
print(b[-2:]) # 后两个元素
# b = np.array([2, 3, 4, 5])
a = np.array([[1, 2], [3, 4], [5, 6]])
print(a.shape)
# a.shape = x, y 把a分成 x行 y列 也就是 x组 每组有y个元素 -1为自适应
# 每个行中有两个元素
a.shape = -1, 2
print(' a.shape = -1 ,2')
print(a)
# 每个行中有一个元素
a.shape = -1, 1
print('a.shape = -1,1')
print(a)
# 一共有3 行
a.shape = 3, -1
print('a.shape = 3, -1')
print(a)
a = np.array([1, 2, 3, 4])
b = np.array([2, 3, 4, 5])
a.shape = b.shape = 2, -1
""""
a = [[1,2] b = [ [2,3]
[3,4] [4,5]
] ]
"""
print(a + b) # 矩阵加法
print(a * b) # 矩阵乘法
"""
a+b = [[3,5] b = [ [2,6]
[7,9] [12,20]
] ]
"""
a = np.linspace(0, 2 * np.pi, 21)
# np.linspace(x, y, conunt) 在x,y中等距生成 count个数据
print(a)
b = np.sin(a) # 取正弦值
print(b)
plt.plot(a, b) # plot(x,y) 函数 绘图
plt.show() # 展示
mask = b >= 0
print(a[mask]) # 输入A对应mask中true值 正值
plt.plot(a[mask], b[mask], 'ro')
plt.show() # 作图
# numpy预览
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转载自blog.csdn.net/SpringQAQ/article/details/81269395
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