search
HomeBackend DevelopmentPython TutorialHow to perform Numpy broadcast with dynamic array using Python?

How to perform Numpy broadcast with dynamic array using Python?

Sep 15, 2023 am 09:13 AM
pythondynamic arraynumpy broadcast

How to perform Numpy broadcast with dynamic array using Python?

"Broadcasting” refers to how NumPy handles arrays of different dimensions during arithmetic operations. The smaller array is "broadcast" across the larger array, subject to certain limits, to ensure that their shapes are consistent. Broadcasting allows you to vectorize array operations, allowing you to loop in C rather than Python."

This is accomplished without the need for unnecessary data copies, resulting in efficient algorithm implementations. In some cases, broadcasting is a negative idea since it results in wasteful memory utilization, which slows down the computation.

In this article, we will show you how to perform broadcasting with NumPy arrays using python.

在给定数组上执行广播的步骤-

  • Step 1. Create two arrays of compatible dimensions

  • Step 2. Print the given array

  • Step 3. Perform arithmetic operation with the two arrays

  • Step 4. Print the result array

添加两个不同维度的数组

使用arange()函数创建一个由0到n-1的数字组成的numpy数组(arange()函数返回在给定区间内均匀间隔的值。在半开区间[start,stop]内生成值),并将某个常数值加到其中。

Example

import numpy as np
# Getting list of numbers from 0 to 7
givenArray = np.arange(8)

# Adding a number to the numpy array 
result_array = givenArray + 9
print("The input array",givenArray)
print("Result array after adding 9 to the input array",result_array)

输出

The input array [0 1 2 3 4 5 6 7]
Result array after adding 9 to the input array [ 9 10 11 12 13 14 15 16] 

给定的数组有一个维度(轴),长度为8,而9是一个没有维度的简单整数。由于它们的维度不同,Numpy尝试沿着某个轴广播(只是拉伸)较小的数组,使其适用于数学运算。

将具有兼容维度的两个数组相加

Creating two NumPy arrays from 0 to n-1 using the arange() function and reshaping it with reshape() function(reshapes an array without affecting its data). The two arrays are with compatible dimensions (3,4) and (3,1) and adding the corresponding elements of both the arrays.

Example

import numpy as np
# Getting the list of numbers from 0 to 11 and reshaping it to 3 rows and 4 columns
givenArray_1 = np.arange(12).reshape(3, 4)

# Printing the shape(rowsize, columnsize) of array
print("The shape of Array_1 = ", givenArray_1.shape)
 
# Getting list of numbers from 0 to 2 and reshaping it to 3 rows and 1 columns
givenArray_2 = np.arange(3).reshape(3, 1)
print("The shape of Array_2 = ", givenArray_2.shape)

# Summing both the arrays
print("Input array 1 \n",givenArray_1)
print("Input array 2 \n",givenArray_2)
print("Summing both the arrays:")
print(givenArray_1 + givenArray_2)

输出

The shape of Array_1 =  (3, 4)
The shape of Array_2 =  (3, 1)
Input array 1 
 [[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11] ]
Input array 2 
 [[0]
  [1]
  [2]]
Summing both the arrays:
[[ 0  1  2  3]
 [ 5  6  7  8]
 [10 11 12 13]]

The givenArray_2 is expanded along the second dimension to match the dimension of givenArray_1. As the dimensions of both the arrays are compatible this can be made possible.

将具有不兼容维度的两个数组求和

Creating two NumPy arrays with INCOMPATIBLE dimensions (6, 4) and (6, 1). When we try to add the corresponding elements of both the arrays it raises an ERROR as shown below.

Example

import numpy as np
# Getting a list of numbers from 0 to 11 and reshaping it to 3 rows and 4 columns
givenArray_1 = np.arange(20).reshape(6, 4)

# Printing the shape(rowsize, columnsize) of array
print("The shape of Array_1 = ", givenArray_1.shape)
 
# Getting list of numbers from 0 to 5 and reshaping it to 3 rows and 1 columns
givenArray_2 = np.arange(6).reshape(6, 1)
print("The shape of Array_2 = ", givenArray_2.shape)

# Summing both the arrays
print("Summing both the arrays:")
print(givenArray_1 + givenArray_2)

输出

Traceback (most recent call last):
  File "main.py", line 3, in 
    givenArray_1 = np.arange(20).reshape(6, 4)
ValueError: cannot reshape array of size 20 into shape (6,4)

行数为6,列数为4。

It cannot be inserted in a matrix of size 20 (it requires a matrix of size 6*4 = 24).

Summing Numpy Multidimensional Array and Linear Array

Create an multidimensional array using the arange() function and reshape it to some random number of rows and columns using the reshape() function. Create Another linear array using the arange() function and sum both these arrays.

Example 1

import numpy as np
# Getting list of numbers from 0 to 14 and reshaping it to 5 rows and 3 columns
givenArray_1 = np.arange(15).reshape(5, 3)

# Printing the shape(rowsize, columnsize) of array
print("The shape of Array_1 = ", givenArray_1.shape)
 
# Getting list of numbers from 0 to 2
givenArray_2 = np.arange(3)
print("The shape of Array_2 = ", givenArray_2.shape)

# Summing both the arrays
print("Array 1 \n",givenArray_1)
print("Array 2 \n",givenArray_2)
print("Summing both the arrays: \n",givenArray_1 + givenArray_2)

输出

The shape of Array_1 =  (5, 3)
The shape of Array_2 =  (3,)
Array 1 
 [[ 0  1  2]
  [ 3  4  5]
  [ 6  7  8]
  [ 9 10 11]
  [12 13 14]]
Array 2 
 [0 1 2]
Summing both the arrays: 
 [[ 0  2  4]
  [ 3  5  7]
  [ 6  8 10]
  [ 9 11 13]
  [12 14 16]]
 

给定的线性数组被扩展以匹配给定数组1(多维数组)的维度。由于两个数组的维度是兼容的,这是可能的。

Example 2

import numpy as np
givenArray_1 = np.arange(240).reshape(6, 5, 4, 2)
print("The shape of Array_1: ", givenArray_1.shape)
 
givenArray_2 = np.arange(20).reshape(5, 4, 1)
print("The shape of Array_2: ", givenArray_2.shape)
 
# Summing both the arrays and printing the shape of it
print("Summing both the arrays and printing the shape of it:")
print((givenArray_1 + givenArray_2).shape)

输出

The shape of Array_1:  (6, 5, 4, 2)
The shape of Array_2:  (5, 4, 1)
Summing both the arrays and printing the shape of it:
(6, 5, 4, 2)

It is critical to understand that multiple arrays can be propagated along several dimensions. Array1 has dimensions (6, 5, 4, 2), whereas array2 has dimensions (5, 4, 1). The dimension array is formed by stretching array1 along the third dimension and array2 along the first and second dimensions(6, 5, 4, 2).

结论

Numpy广播比在数组上循环更快。从第一个示例开始。用户可以通过循环遍历数组,将相同的数字添加到数组中的每个元素,而不是使用广播方法。这种方式之所以慢,有两个原因:循环需要与Python循环进行交互,这会减慢C实现的速度。其次,NumPy使用步幅而不是循环。将步幅设置为0允许您无限循环遍历组件,而不会产生内存开销。

The above is the detailed content of How to perform Numpy broadcast with dynamic array using Python?. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:tutorialspoint. If there is any infringement, please contact admin@php.cn delete
Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python vs. C  : Memory Management and ControlPython vs. C : Memory Management and ControlApr 19, 2025 am 12:17 AM

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python for Scientific Computing: A Detailed LookPython for Scientific Computing: A Detailed LookApr 19, 2025 am 12:15 AM

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Python and C  : Finding the Right ToolPython and C : Finding the Right ToolApr 19, 2025 am 12:04 AM

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python for Data Science and Machine LearningPython for Data Science and Machine LearningApr 19, 2025 am 12:02 AM

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Learning Python: Is 2 Hours of Daily Study Sufficient?Learning Python: Is 2 Hours of Daily Study Sufficient?Apr 18, 2025 am 12:22 AM

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python for Web Development: Key ApplicationsPython for Web Development: Key ApplicationsApr 18, 2025 am 12:20 AM

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python vs. C  : Exploring Performance and EfficiencyPython vs. C : Exploring Performance and EfficiencyApr 18, 2025 am 12:20 AM

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

SublimeText3 English version

SublimeText3 English version

Recommended: Win version, supports code prompts!

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools

EditPlus Chinese cracked version

EditPlus Chinese cracked version

Small size, syntax highlighting, does not support code prompt function

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)