[Data Analysis - Basic Introduction to NumPy⑥] - NumPy Case Consolidation and Strengthening

foreword

Hello everyone! I am the original heart. This issue brings you NumPy case consolidation and strengthening exercises. There are 17 questions in total. Personal test.

1. NumPy basic training

1.1 Create a one-dimensional ndarray object with a length of 10 and all zeros, and let the fifth element be 1

n1 = np.zeros(10,dtype=np.int16)
n1[4] = 1
n1

1.2 Create an ndarray object with elements from 10 to 49

n2 = np.arange(10,50)
n2

1.3 Reverse the position of all elements in question 2

n2[::-1]

1.4 Create a 10*10 ndarray object and print the largest and smallest elements

n4 = np.random.random((10,10))
print(np.max(n4))
print(np.min(n4))

1.5 Create a 10*10 ndarray object, and the matrix boundary is all 1, and the inside is all 0

n5 = np.zeros((10,10),dtype=np.int16)
n5[[0,9]] = 1
n5[:,[0,9]] = 1
print(n5)

1.6 Create a 5*5 matrix with each row ranging from 0 to 4

n6 = np.array(range(0,5))
n6

1.7 Create an arithmetic sequence of length 12 ranging from 0-1

n7 = np.linspace(0,1,num=12)
n7

1.8 Create a random array of length 10 and sort it

n8 = np.random.random(10)
np.sort(n8)

1.9 Create a random array of length 10 and replace the maximum value with 0

n9 = np.random.random(10)
n9[np.argmax(n9)] = 0
print(n9)

Two, NumPy intensive training

2.1 Given a 4-dimensional matrix, find the sum of the last two dimensions

n1 = np.random.randint(1,10,(2,3,4,5)) # 四维数组
display(n1)
np.sum(n1,(2,3))
# axis = 0 表示第一个维度
# axis = 1 表示第二个维度
# axis = 2 表示第三个维度
# axis = 3 表示第四个维度

2.2 Given an array [1,2,3,4,5], insert 3 0s after each element

n = np.arange(1,6)
display(n)
n2 = np.zeros(17,dtype=np.int16)
display(n2)
n2[::4] = n
n2

2.3 Given a two-dimensional matrix, swap two rows of elements

n = np.random.randint(1,10,(3,3))
display(n)
n = n[[1,0,2]] # 交换第一行和第二行
display(n)

2.4 Create a random array with a length of 100,000, use two methods to find the third power, and compare the time taken

n = np.random.randint(0,10,100000)
%timeit n ** 3

%timeit np.power(n,3)

2.5 Create a 5 * 3 random matrix and a 3 * 2 random matrix, and find the matrix product

n1 = np.random.randint(0,10,(5,3))
n2 = np.random.randint(0,10,(3,2))
display(n1,n2)
np.dot(n1,n2)

2.6 The elements of each row of the matrix are subtracted from the average value of the row

n = np.random.randint(0,10,(3,4))
display(n)
# 行平均值
n2 = np.mean(n,axis=1).reshape(3,1)
display(n2)
n - n2

2.7 Print the following matrix

n = np.zeros((8,8),dtype = np.int16)
display(n)
n[::2,1::2] = 1
n[1::2,0::2] = 1
display(n)

2.8 Regularize a 5*5 random matrix

n = np.random.randint(0,10,(5,5))
display(n)
min1 = np.min(n)
max1 = np.max(n)
n = (n - min1) / (max1 - min1)
display(n)

Note: The topic material comes from——"Qianfeng Education"

epilogue

This issue is to share with you these topics! I hope that everyone can do more practical exercises to strengthen and consolidate, so as to better master NumPy.

Related reading

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Review of last issue	[Data Analysis - Basic Introduction to NumPy⑤] - Basic Operation of NumPy - 2
Next issue notice	[Data Analysis - Basic Introduction to Pandas ①] - Pandas Basic Introduction - 1