Python Numpy
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一些Numpy常用的概念和方法
1.数组
# 1D Arraya = np.array([0, 1, 2, 3, 4])b = np.array((0, 1, 2, 3, 4))c = np.arange(5)d = np.linspace(0, 2*np.pi, 5) #在0到2*np.pi之间平均生成5个数print(a) # >>>[0 1 2 3 4]print(b) # >>>[0 1 2 3 4]print(c) # >>>[0 1 2 3 4]print(d) # >>>[ 0. 1.57079633 3.14159265 4.71238898 6.28318531]print(a[3]) # >>>3下面是二维数组
# MD Array,a = np.array([[11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25], [26, 27, 28 ,29, 30], [31, 32, 33, 34, 35]])print(a[2,4]) # >>>25
2.数组切片
print(a[0, 1:4]) # >>>[12 13 14]print(a[1:4, 0]) # >>>[16 21 26]print(a[::2,::2]) # >>>[[11 13 15] # [21 23 25] # [31 33 35]]print(a[:, 1]) # >>>[12 17 22 27 32]这幅图很好的解释了切片操作
3.数组操作
a = np.arange(25)a = a.reshape((5, 5))b = np.array([10, 62, 1, 14, 2, 56, 79, 2, 1, 45, 4, 92, 5, 55, 63, 43, 35, 6, 53, 24, 56, 3, 56, 44, 78])b = b.reshape((5,5))print(a + b) #两个数组对应元素进行+-*/print(a - b)print(a * b)print(a / b)print(a ** 2)print(a < b) print(a > b)#返回布尔值print(a.dot(b))#点积
a = np.arange(10)print(a.sum())print(a.min()) print(a.max())a = np.arange(0, 100, 10)b = a[:5]c = a[a >= 50]print(b) # >>>[ 0 10 20 30 40]print(c) # >>>[50 60 70 80 90]
这里具体说一下reshapenumpy.reshape(a, newshape, order='C')#a就是要被reshape的数组，newshape是新数组形状int or tuple of ints.
例子：
>>> a = np.arange(6).reshape((3, 2))>>> aarray([[0, 1], [2, 3], [4, 5]])>>> a = np.array([[1,2,3], [4,5,6]])>>> np.reshape(a, 6)array([1, 2, 3, 4, 5, 6])>>> np.reshape(a, 6, order='F')array([1, 4, 2, 5, 3, 6])>>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2array([[1, 2], [3, 4], [5, 6]])
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