Share numpy function tips and examples to improve work efficiency

Sharing of numpy function tips and examples to improve work efficiency

Introduction:
In the fields of data processing and scientific computing, it is very common to use Python's numpy library . Numpy provides a series of powerful functions and tools that can easily perform large-scale data operations and calculations. This article will introduce some numpy function techniques to improve work efficiency and provide specific code examples.

1. Vectorization operation
The vectorization operation of numpy is one of its most powerful functions. Through vectorization operations, you can avoid using for loops to operate on each element, thus greatly improving the operation speed.

Sample code 1: Calculate the sum of rows and columns of a matrix

import numpy as np

m = np.random.rand(1000, 1000)

# 使用for循环
row_sum = np.zeros(1000)
col_sum = np.zeros(1000)
for i in range(1000):
    for j in range(1000):
        row_sum[i] += m[i][j]
        col_sum[j] += m[i][j]

# 使用矢量化操作
row_sum = np.sum(m, axis=1)
col_sum = np.sum(m, axis=0)

Sample code 2: Calculate the weighted average of two arrays

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
weights = np.array([0.2, 0.3, 0.5])

# 使用for循环
result = 0
for i in range(3):
    result += a[i] * b[i] * weights[i]

# 使用矢量化操作
result = np.dot(np.multiply(a, b), weights)

2. Broadcast
Broadcasting is a function in numpy that makes operations between arrays of different dimensions very convenient. By broadcasting, we can only operate on an array without explicit dimension matching.

Sample code 3: Calculate the mean square error of the array

import numpy as np

a = np.array([1, 2, 3])
mean = np.mean(a)
var = np.sqrt(np.mean((a - mean) ** 2))

Sample code 4: Subtract the mean of the corresponding row from each row of the matrix

import numpy as np

m = np.random.rand(1000, 1000)
mean = np.mean(m, axis=1)
m -= mean[:, np.newaxis]

3. Slicing and indexing skills
numpy provides a wealth of slicing and indexing techniques, which can easily intercept and filter arrays.

Sample code 5: Randomly extract some elements from the array

import numpy as np

a = np.arange(100)
np.random.shuffle(a)
selected = a[:10]

Sample code 6: Filter the elements in the array that meet the conditions

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6])
selected = a[a > 3]

4. General functions and aggregate functions
numpy provides a large number of general functions and aggregate functions, which can easily perform various mathematical and statistical operations on arrays.

Sample code 7: Take the absolute value of the elements of the array

import numpy as np

a = np.array([-1, -2, -3, 4, 5, 6])
abs_a = np.abs(a)

Sample code 8: Calculate the sum, average and maximum value of the array

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6])
sum_a = np.sum(a)
mean_a = np.mean(a)
max_a = np.max(a)

Summary:
This article introduces some numpy function tips to improve work efficiency and provides specific code examples. Through vectorization operations, broadcasting, slicing and indexing techniques, and the use of general and aggregate functions, we can use numpy more efficiently in data processing and scientific computing. I hope this article will be helpful to everyone’s work!

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