# Ch0: numpy
###### tags: `AI` `Python`

## 註記

此部份節錄自章節1-3

Numpy相關概念對於TensorFlow可是很重要的
可以的話也回去翻一下矩陣運算以及線性代數的書

## Sample Code

import numpy as np

print("create an array with all valued zero, sized 3, 5, 2\n")
a = np.zeros((3, 5, 2)) # Create an array, sized 3, 5, 2, valued all zero
print("a : ")
print(a) # print x
print("dimension of a : ")
print(a.ndim) # print dimensions of a
print("shape of a : ")
print(a.shape) # print shape of a
print("data type of a : ")
print(a.dtype) # print datatype of data of a

print("\n\n")

print("create an array, [[1, 2], [3, 4]] in this content")
print("b : ")
b = np.array([[1, 2], [3, 4]])
print(b)

print("\n\n")

print("create an array with all valued zero , sized 2, data type uint8")
c = np.zeros(2, dtype='uint8')
print("c : ")
print(c)
print(c.dtype)

print("\n\n")

print("create an array with all valued one, sized 2, 3")
print("d : ")
d = np.ones((2, 3)) # Create an array, sized 2, 3, valued all one
print(d)

print("\n\n")

print("create an array with all valued seven, sized 2, 3")
print("e : ")
e = np.full((2, 3), 7) # Create an array, sized 2, 3, valued all seven
print(e)

print("\n\n")

print("create an array, value from 0 to 4 (less than 5)")
print("f : ")
f = np.arange(5) # Create an array, value from 0-4
print(f)

print("\n\n")

print("copy array, or create an array sized same as other array")
print("g : ")
g = np.copy(f) # g copied array f
print(g)

print("\n\n")

print("create an array, shaped same as f, value all zero")
h = np.zeros(f.shape)
print("h : ")
print(h)

print("\n\n")

print("create an array, shaped same as f, value all one")
i = np.ones(f.shape)
print("i : ")
print(i)

print("\n\n")

print("get part of array")
j = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("j : ")
print(j)
print(j[1, 2])
print(j[1:3, 1:3])

print("\n\n")

print("reshape array")
k = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
l = np.reshape(k, (2, 4))
print("k : ")
print(k)
print("l : ")
print(l)

print("\n\n")

print("transpose array")
m = np.array([[1, 2, 3], [4, 5, 6]])
print("m : ")
print(m)
print("transpose array from m : ")
print(np.transpose(m))

print("\n\n")

print("array calculating")
print("np.array([[1, 2], [3, 4]])+np.array([[1, 2], [3, 4]]) : ")
print(np.array([[1, 2], [3, 4]])+np.array([[1, 2], [3, 4]]))
print("np.array([1, 2])*np.array([3, 4]) : ")
print(np.array([1, 2])*np.array([3, 4]))

print("\n\n")

print("array index")
n = np.array([0, 1, 2, 3, 4])
print(n)
print("set index[1, 3]")
idx = [1, 3]
n[idx] = 7
print("after giving this index value 7, n : ")
print(n)
n[idx] = (8, 9)
print("after giving this index value 8, 9, n : ")
print(n)
o = n[idx]
print("after giving value to array o, array o : ")
print(o)
print("print o[[1, 1, 0, 1]]")
print(o[[1, 1, 0, 1]])
p = np.array([0, 1, 2])
print(p)
IDX = np.array([True, False, True])
print("after giving it index : ")
print(p[IDX])
p[IDX] = 7
print(p)

print("\n\n")

print("max, min, sum, mean etc... at specific axis of array")
q = np.array([[1, 2], [3, 4]])
print("np.max(q) : ")
print(np.max(q))
print("np.max(q, axis=0)")
print(np.min(q, axis=0))
print("np.min(q, axis=1)")
print(np.max(q, axis=1))
print("np.sum(q, axis=0)")
print(np.sum(q, axis=0))
print("np.mean(q, axis=0)")
print(np.mean(q, axis=0))

## source
* tf.Keras 深度學習攻略手冊
* 精通Python