Python data analysis and application NumPy(1) (jupyter notebook)

Create array

import numpy as np

arr1=np.array([0.3,0.5,4.2])#Create a one-dimensional array
arr2=np.array([[3,4,5],[4,2,1]])#Create a two-dimensional array
print(arr1)
print(arr2)
print(type(arr1))

[0.3 0.5 4.2]
[[3 4 5]
 [4 2 1]]
<class 'numpy.ndarray'>

#View the basic properties of the array
print(arr1.shape)#returns a tuple
print(arr1.ndim)
print(arr1.dtype)#data type
print(arr2.shape)
print(arr2.ndim)#Return the dimension of the array
print(arr2.dtype)

(3,)
1
float64
(twenty three)
2
int32

#First introduction to the characteristics of arrays
list1=[0.3,0.5,4.2]
arr1=np.array([0.3,0.5,4.2])
print(list1)
print(arr1)
#list1**2
print([i**2 for i in list1])
arr1**2# square term
print(arr1**2)

[0.3, 0.5, 4.2]
[0.3 0.5 4.2]
[0.09, 0.25, 17.64]
[0.09 0.25 17.64]

arr3=np.arange(0,10)#
arr4=np.arange(10) #The default starting point is equal to 0
arr5=np.arange(0,1,0.1)#start stop step

print(arr3)
print(arr4)
print(arr5)

[0 1 2 3 4 5 6 7 8 9]
[0 1 2 3 4 5 6 7 8 9]
[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]

#All 0 array
arr7=np.zeros([3,4,3])#Three rows and four columns three-dimensional array
print(arr7)

[[[0. 0. 0.]
  [0. 0. 0.]
  [0. 0. 0.]
  [0. 0. 0.]]

 [[0. 0. 0.]
  [0. 0. 0.]
  [0. 0. 0.]
  [0. 0. 0.]]

 [[0. 0. 0.]
  [0. 0. 0.]
  [0. 0. 0.]
  [0. 0. 0.]]]

#Data type of array
arr8=np.array([3,4,5],dtype=np.float32)#Declare its data type when creating an array
print(arr8)
print(arr8.dtype)
arr8[0]=1.2
print(arr8)
print(np.int32(arr8))#Convert the array type of the array

#Generate random numbers
print(np.random.random(10))#Random number under unconstrained conditions
print(np.random.rand(3,4))#Generate uniformly distributed random numbers of the specified shape#That is, three rows and four columns
print(np.random.randn(2,2,2))#Generate normal distributed random numbers of specified shape#Three-dimensional structure

[3. 4. 5.]
float32
[1.2 4. 5. ]
[1 4 5]

[0.17106092 0.29044013 0.79911209 0.45078865 0.46598821 0.43567392
 0.58008144 0.95826806 0.9241146 0.19584847]
[[0.09034558 0.69409671 0.2933771 0.95821404]
 [0.60125074 0.54150608 0.99578956 0.64812296]
 [0.60906182 0.10291584 0.63418118 0.54207162]]
[[[-0.45568266 1.11536442]
  [-1.24810256 -1.33604114]]

 [[-1.51907174 0.24281556]
  [ 0.70003938 0.10203376]]]

random module commonly used random number generation function

seed determines the seed of the random number generator permutation returns a random permutation or range of a sequence shuffle randomly sorts a sequence

arr9=np.array([1,5,6,8,4])
print(np.random.shuffle(arr9))

array index

arr1=np.array([0.3,0.78,0.24,5,3.2])
print(arr1)
print(arr1[0])
print(arr1[-5])
#Multiple element indexes of one-dimensional arrays (slicing)
print(arr1[1:3])#Left closed and right opened
print(arr1[-4:-2])

res1=arr1[3]
res2=arr1[3:4]#Retain the original structure
print(res1,res1.shape)#0-dimensional array is a scalar
print(res2,res2.shape)#One-dimensional array, which is a vector

[0.3 0.78 0.24 5. 3.2]
0.3
0.3
[0.78 0.24]
[0.78 0.24]
5.0 ()
[5.] (1,)

Logical index

arr2=np.array([2.3,1.8,4.5])
print(arr2)
print(arr2[[False,False,True]])
arr2>2
index=arr2>2
print(index)
print(arr2[index])

[2.3 1.8 4.5]
[4.5]
[True False True]
[2.3 4.5]

Index of multi-dimensional array

arr3=np.arange(1,13).reshape([3,4])
print(arr3)
print(arr3[2,3])
print(arr3[2,0:])
print(arr3[:,0])
print(arr3[0:2,0])
print(arr3[2:,:])
print(arr3[arr3[:,0]>4,:])

[[ 1 2 3 4]
 [5 6 7 8]
 [9 10 11 12]]
12
[9 10 11 12]
[1 5 9]
[1 5]
[[ 9 10 11 12]]
[[5 6 7 8]
 [ 9 10 11 12]]

Modify elements in array

arr3=np.arange(1,13).reshape([3,4])
print(arr3)
arr3[0,0]=15
print(arr3)

[[ 1 2 3 4]
 [5 6 7 8]
 [9 10 11 12]]
[[15 2 3 4]
 [5 6 7 8]
 [ 9 10 11 12]]

Solve for the distance matrix

n=10
x=np.linspace(1,100,n)#arithmetic array#sample abscissa
y=np.linspace(1,100,n)#The ordinate of the sample
dist=np.zeros([n,n])
for i in range(n):
for j in range(n):
dist[i,j]=np.sqrt((x[i]-x[j])**2 + (y[i]-y[j])**2) #Calculate Euclidean distance
print(x)
print(y)
print(dist)

[ 1. 12. 23. 34. 45. 56. 67. 78. 89. 100.]
[1. 12. 23. 34. 45. 56. 67. 78. 89. 100.]
[[ 0. 15.55634919 31.11269837 46.66904756 62.22539674
   77.78174593 93.33809512 108.8944443 124.45079349 140.00714267]
 [ 15.55634919 0. 15.55634919 31.11269837 46.66904756
   62.22539674 77.78174593 93.33809512 108.8944443 124.45079349]
 [ 31.11269837 15.55634919 0. 15.55634919 31.11269837
   46.66904756 62.22539674 77.78174593 93.33809512 108.8944443 ]
 [46.66904756 31.11269837 15.55634919 0. 15.55634919
   31.11269837 46.66904756 62.22539674 77.78174593 93.33809512]
 [ 62.22539674 46.66904756 31.11269837 15.55634919 0.
   15.55634919 31.11269837 46.66904756 62.22539674 77.78174593]
 [77.78174593 62.22539674 46.66904756 31.11269837 15.55634919
    0. 15.55634919 31.11269837 46.66904756 62.22539674]
 [93.33809512 77.78174593 62.22539674 46.66904756 31.11269837
   15.55634919 0. 15.55634919 31.11269837 46.66904756]
 [108.8944443 93.33809512 77.78174593 62.22539674 46.66904756
   31.11269837 15.55634919 0. 15.55634919 31.11269837]
 [124.45079349 108.8944443 93.33809512 77.78174593 62.22539674
   46.66904756 31.11269837 15.55634919 0. 15.55634919]
 [140.00714267 124.45079349 108.8944443 93.33809512 77.78174593
   62.22539674 46.66904756 31.11269837 15.55634919 0. ]]