numpy1

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NumPy Introduction 1

Array

First step: Import numpy

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import numpy as np

Multi-dimensions array

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np.array([range(i,i+3)for i in[2,4,6]])

Out[2]:array([[2, 3, 4],
              [4, 5, 6],
              [6, 7, 8]])

Creating repeated array

create an array with length10,and all the values are 0.

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np.zeros(10,dtype=int)

Out[3]:array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

create an array which is 3x5,type is float,and all the values are 1.

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np.ones((3,5),dtype=float)

Out[5]:array([[1., 1., 1., 1., 1.],
              [1., 1., 1., 1., 1.],
              [1., 1., 1., 1., 1.]])

create an array which is 3x5,and all the values are 3.14.

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np.full((3,5),3.14)

Out[6]:array([[3.14, 3.14, 3.14, 3.14, 3.14],
              [3.14, 3.14, 3.14, 3.14, 3.14],
              [3.14, 3.14, 3.14, 3.14, 3.14]])

create an array with 5 numbers from 0~1 uniformly

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np.linspace(0,1,5)

Out[7]:array([0.  , 0.25, 0.5 , 0.75, 1.  ])

create an array which is 3x3 with random number

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np.random.random((3,3))
 # 创建一个3×3的、均值为0、方差为1的
 # 正态分布的随机数数组
Out[8]:array([[0.47224148, 0.23125945, 0.95002522],
              [0.09738343, 0.88182864, 0.38184937],
              [0.99395495, 0.42838151, 0.92674988]])

create an array which is 3x3 with random normal number from 0~1

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np.random.normal(0,1,(3,3))

Out[9]:array([[-0.55049464, -0.35293695,  0.75417556],
              [-0.63125306,  0.31639634, -2.24209815],
              [ 1.44175974, -0.69736733,  0.30666294]])

create an array which is 3x3 with random integer between1and 10

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np.random.randint(0,10,(3,3))

Out[10]:array([[1, 4, 4],
               [3, 5, 6],
               [6, 3, 3]])

create an array which is 4x4 ,its numbers have only one digit

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np.eye(4)

Out[11]:array([[1., 0., 0., 0.],
               [0., 1., 0., 0.],
               [0., 0., 1., 0.],
               [0., 0., 0., 1.]])

create an array with 3 random number from the storage

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np.empty(3)

Out[12]:array([1., 1., 1.])

The factors of array

ndim(the dimension),shape and size

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np.random.seed(0)
x1=np.random.randint(10,size=6)
x2=np.random.randint(10,size=(3,4))
x3=np.random.randint(10,size=(3,4,5))
x1.ndim

Out[13]:1
    
    
print('x3 ndim: ', x3.ndim)
print('x3 shape: ', x3.shape)
print('x3.size: ' , x3.size)

Out[14]:x3 ndim:  3
        x3 shape:  (3, 4, 5)
        x3.size:  60

            
print('dtype: ',x3.dtype)

Out[15]:dtype:  int32

Get one element

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x1

Out[16]:array([5, 0, 3, 3, 7, 9])
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x1[0]

Out[17]:5

From the end

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x1[-1]

Out[18]:9

Use the comma

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x2

Out[19]:array([[3, 5, 2, 4],
               [7, 6, 8, 8],
               [1, 6, 7, 7]])
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x2[0,0]

Out[20]:3
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x2[2,-1]

Out[21]:7

Change element(x1=（[5, 0, 3, 3, 7, 9]）)

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x1[0]=3.14
x1

Out[22]:array([3, 0, 3, 3, 7, 9]) #float->integer

slice

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x = np.arange(10)  
x  
#Out: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])  

x[:5] # 前五个元素 
#Out:array([0, 1, 2, 3, 4])  

x[5:] # 索引五之后的元素 
#Out:array([5, 6, 7, 8, 9])  

x[4:7] # 中间的子数组 
#Out:array([4, 5, 6])  

x[::2] # 每隔一个元素 
#Out:array([0, 2, 4, 6, 8])  

x[1::2] # 每隔一个元素，从索引1开始 
#Out:array([1, 3, 5, 7, 9])

multi-dimension:

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x2=array([[12, 5, 2, 4],  
		  [ 7, 6, 8, 8],  
          [ 1, 6, 7, 7]])  
x2[:2, :3] # 两行，三列 
#Out:array([[12, 5, 2],  
#			[ 7, 6, 8]])  

x2[:3, ::2] # 所有行，每隔一列 
#Out:array([[12, 2],  
#   		[ 7, 8],  
#    		[ 1, 7]])

x2[::-1, ::-1] 
#Out:array([[ 7, 7, 6, 1], 
#		    [ 8, 8, 6, 7], 
#           [ 4, 2, 5, 12]])

.copy()

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x2_sub_copy = x2[:2, :2].copy()  
print(x2_sub_copy)  
#out:[[99 5]  
#     [ 7 6]]


x2_sub_copy[0, 0] = 42 
print(x2_sub_copy) 
#out:[[42 5] 
#     [ 7 6]]

.reshape()

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grid = np.arange(1, 10).reshape((3, 3))  
print(grid)  

#out:[[1 2 3]  
#     [4 5 6]  
#     [7 8 9]]
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x = np.array([1, 2, 3])  # 通过变形获得的行向量 
x.reshape((1, 3))  
#Out:array([[1, 2, 3]])  

# 通过newaxis获得的行向量 
x[np.newaxis, :]  
#Out:array([[1, 2, 3]])  

# 通过变形获得的列向量 
x.reshape((3, 1))  
#Out:array([[1],  
#			[2],  
#			[3]])  

# 通过newaxis获得的列向量 
x[:, np.newaxis]  
#Out:array([[1],  
#			[2],  
#			[3]])

.concatenate() (combine arrays)

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x = np.array([1, 2, 3])  
y = np.array([3, 2, 1])  
np.concatenate([x, y])  

#Out:array([1, 2, 3, 3, 2, 1])
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z = [99, 99, 99]  
print(np.concatenate([x, y, z]))  
#out:[ 1 2 3 3 2 1 99 99 99] 

#two dimension
grid = np.array([[1, 2, 3],  
				 [4, 5, 6]])  
# 沿着第一个轴拼接 
np.concatenate([grid, grid])  
#Out:array([[1, 2, 3],  
#			[4, 5, 6],  
#			[1, 2, 3],  
#			[4, 5, 6]])  

# 沿着第二个轴拼接（从0开始索引） 
np.concatenate([grid, grid], axis=1)  
#out: array([[1, 2, 3, 1, 2, 3],  
#            [4, 5, 6, 4, 5, 6]]) 



#np.vstack（垂直栈）和 np.hstack（水平栈）
x = np.array([1, 2, 3])  
grid = np.array([[9, 8, 7],  
				 [6, 5, 4]])  
# 垂直栈数组 
np.vstack([x, grid])  
#Out:array([[1, 2, 3],  
#           [9, 8, 7],  
#           [6, 5, 4]])   

y = np.array([[99],  
              [99]])  
np.hstack([grid, y])  
#out: array([[ 9, 8, 7, 99],  
#            [ 6, 5, 4, 99]])

.split()

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x = [1, 2, 3, 99, 99, 3, 2, 1]  
x1, x2, x3 = np.split(x, [3, 5])  
print(x1, x2, x3)  

#out:[1 2 3] [99 99] [3 2 1]

np.hsplit & np.vsplit

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grid = np.arange(16).reshape((4, 4))  
grid  
#Out:array([[ 0, 1, 2, 3],  
#			[ 4, 5, 6, 7],  
#			[ 8, 9, 10, 11],  
#			[12, 13, 14, 15]])  

upper, lower = np.vsplit(grid, [2])  
print(upper)  
print(lower)  
#out:[[0 1 2 3]  
#	  [4 5 6 7]]  
#	 [[ 8 9 10 11]  
#     [12 13 14 15]]  

left, right = np.hsplit(grid, [2])  
print(left)  
print(right)  
#out:[[ 0 1]  
#     [ 4 5]  
#     [ 8 9]  
#     [12 13]]  
#    [[ 2 3]  
#     [ 6 7]  
#     [10 11]  
#     [14 15]]