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Python 3 Performance

Python developers love Python's high productivity. Unfortunately, many Python applications suffer from slow performance. This course is about concrete approaches for improving the performance of your Python applications.

Are your Python applications running slowly? If yes, this Python 3 Performance course is going to give you concrete approaches to increase the speed of your Python applications. First, you will learn how to measure the performance of Python applications. Next, you will discover how to write faster Python code by using the right data structures. Finally, you will learn how to speed up your applications using threads, asyncio, and more processes. Take this course and make your Python applications faster!

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What's inside

Syllabus

Course Overview

Measuring Performance

Using the Right Data Structures

Optimizing Python Code

Using More Threads

Using Asynchronous Code

Using More Processes

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores concrete approaches to enhance the performance of your Python applications, a vital skill for developers

Led by Dan Tofan, a recognized expert in Python performance optimization

Covers crucial topics, including performance measurement, data structure optimization, and concurrency techniques

Provides hands-on practice with real-world examples, enhancing your understanding and practical implementation abilities

May require prior experience in Python programming for optimal comprehension

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python 3 Performance with these activities:

Review basic Python data structures

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Strengthen your foundation by revisiting the fundamental concepts of Python data structures.

Browse courses on Data Structures

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Go over Python data structures: lists, tuples, dictionaries, and sets.
Understand their properties, uses, and performance characteristics.

Revisit Python performance concepts

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Refresh your knowledge of Python performance optimization concepts and techniques.

Browse courses on Performance Optimization

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Review common performance bottlenecks in Python applications.
Recall techniques for optimizing code performance, such as using efficient data structures and algorithms.

Review 'Python Performance Optimization' by Michael Kennedy

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Gain insights and best practices for optimizing Python code performance from an expert in the field.

View Gloria Swanson: Hollywood's First Glamour Queen on Amazon

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Read the book thoroughly.
Take notes and highlight key concepts.
Implement the techniques in your own projects.

Five other activities

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Practice performance benchmark testing

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Benchmark the performance of your code regularly to monitor progress and identify areas for improvement.

Browse courses on Performance Optimization

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Choose a benchmarking tool.
Run the benchmark on your code.
Analyze the results and identify bottlenecks.
Make changes to your code to improve performance.
Rerun the benchmark to verify the improvements.

Explore 'Optimizing Python Code' tutorial

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Follow the structured guidance in the tutorial to learn best practices for optimizing code performance.

Browse courses on Code Optimization

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Find the 'Optimizing Python Code' tutorial.
Go through the tutorial step-by-step.
Try out the techniques in your own code.

Join a Python performance optimization study group

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Collaborate and learn from peers with diverse perspectives on performance optimization.

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Find or create a study group focused on Python performance optimization.
Participate in regular discussions and Q&A sessions.
Share knowledge, resources, and experiences with other members.

Create a blog post on performance optimization

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Deepen your understanding by explaining the concepts of performance optimization to others and reflecting on your learning.

Browse courses on Performance Optimization

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Choose a specific performance optimization technique.
Write a blog post explaining the technique in detail.
Share your blog post and engage with feedback.

Develop a performance-optimized Python application

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Apply your knowledge of performance optimization to build a practical application.

Browse courses on Performance Optimization

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Identify a problem or task that requires performance optimization.
Research different performance optimization techniques.
Design and develop your application using the techniques.
Test and benchmark your application to ensure optimal performance.

Career center

Learners who complete Python 3 Performance will develop knowledge and skills that may be useful to these careers:

Python Developer

A Python Developer may be interested in the Python 3 Performance course because it can help them write faster and more efficient code. The course covers topics like writing optimized code, choosing the right data structures, and using threads and asynchronous programming. This knowledge can help Python Developers create more scalable and performant Python applications.

See salaries and explore the career path for Python Developer

Performance Engineer

Performance Engineers will appreciate the Python 3 Performance course as it covers advanced topics such as using more threads, using asynchronous code, and using more processes, which are essential for writing performant systems. The course also covers measuring performance, which is crucial for identifying and fixing performance bottlenecks.

See salaries and explore the career path for Performance Engineer

Software Developer

A Software Developer may wish to consider the Python 3 Performance course if they are interested in learning how to make their Python applications run faster. The course covers measuring application performance, and optimizing code using the right data structures, threads, and processes. This knowledge can help Software Developers create more efficient and performant Python applications.

See salaries and explore the career path for Software Developer

Full-Stack Developer

The topics covered in the Python 3 Performance course, such as optimizing code for speed and using concurrency, are valuable for Full Stack Developers who want to create high-performance Python applications. The course can help Full Stack Developers write more efficient and scalable Python code, which can lead to improved performance for both the frontend and backend of their applications.

See salaries and explore the career path for Full-Stack Developer

Backend Developer

A Backend Developer may consider the Python 3 Performance course if they want to learn how to optimize the performance of their Python backend applications. The course covers topics like choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Backend Developers create more efficient and scalable Python backend applications.

See salaries and explore the career path for Backend Developer

Software Architect

Python 3 Performance is a great course for Software Architects who want to learn how to design and implement high-performance Python applications. The course covers topics such as choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Software Architects create scalable and efficient Python applications.

See salaries and explore the career path for Software Architect

Web Developer

Python 3 Performance is a suitable course for Web Developers who want to learn how to optimize the performance of their Python web applications. The course covers topics such as choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Web Developers create more efficient and scalable Python web applications.

See salaries and explore the career path for Web Developer

Data Analyst

A Data Analyst may wish to consider the Python 3 Performance course if they are interested in learning how to measure and optimize the performance of their Python code. The course covers using the right data structures, threads, and processes to improve code speed. This can be valuable for Data Analysts who need to process large amounts of data efficiently and quickly.

See salaries and explore the career path for Data Analyst

DevOps Engineer

For those working as a DevOps Engineer, the Python 3 Performance course may help build a foundation in Python performance optimization techniques. The course covers topics like measuring performance, optimizing code, and using threads and asynchronous programming. This knowledge can help DevOps Engineers identify and fix performance bottlenecks in Python applications.

See salaries and explore the career path for DevOps Engineer

Data Engineer

For those working in the field of Data Engineering, the Python 3 Performance course may be helpful for learning how to optimize the performance of Python data engineering pipelines. The course covers topics like choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Data Engineers create more efficient and scalable Python data engineering pipelines.

See salaries and explore the career path for Data Engineer

Data Scientist

A Data Scientist may wish to consider the Python 3 Performance course to learn how to make their Python code more efficient. The course covers topics such as choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Data Scientists create more efficient and scalable Python data science applications.

See salaries and explore the career path for Data Scientist

Mobile Developer

A Mobile Developer may find the Python 3 Performance course beneficial if they want to learn how to optimize the performance of their Python mobile applications. The course covers topics such as choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Mobile Developers create more efficient and scalable Python mobile applications.

See salaries and explore the career path for Mobile Developer

Cloud Engineer

Taking the Python 3 Performance course may be useful for Cloud Engineers who want to learn how to optimize the performance of Python applications running in the cloud. The course covers topics like choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Cloud Engineers create more efficient and scalable Python cloud applications.

See salaries and explore the career path for Cloud Engineer

Machine Learning Engineer

The Python 3 Performance course can be useful for Machine Learning Engineers who want to learn how to optimize the performance of their Python machine learning models and applications. The course covers topics such as choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Machine Learning Engineers create more efficient and scalable Python machine learning applications.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

A Quantitative Analyst may wish to consider the Python 3 Performance course to learn how to optimize the performance of their Python quantitative finance models and applications. The course covers topics such as choosing the right data structures, optimizing code for speed, and using concurrency. This knowledge can help Quantitative Analysts create more efficient and scalable Python quantitative finance applications.

See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python 3 Performance.

Python Microservices Development

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Covers advanced techniques for optimizing Python code, including techniques for profiling, memory management, and concurrency. It provides a deep dive into the Python interpreter and the underlying CPython implementation.

Python Microservices Development: Build efficient...

Kindle Edition

Expert Python Programming

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Provides a comprehensive overview of Python performance optimization techniques. It covers topics such as profiling, data structures, algorithms, and concurrency and includes practical examples and case studies.

Python 3 Performance

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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