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Python Code Optimization: Pro Techniques To Boost Code Speed
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 871.44 MB | Duration: 2h 59m

Speed Up Python: Hands-On Case Studies for Faster Code Using Profiling, Data Structures, and Parallel Processing

[b]What you'll learn[/b]

Optimize code performance with advanced techniques, creating faster and more efficient solutions in Python

Apply advanced optimization techniques in real-world scenarios through hands-on case studies, strengthening practical problem-solving skills in Python

Identify and analyze Python performance bottlenecks using profiling tools, gaining insights into code execution efficiency

Choose and implement optimal data structures to improve code speed and memory usage

Create parallel processing workflows in Python to maximize code efficiency and reduce execution time in data-intensive applications

[b]Requirements[/b]

A working Python environment

Good grasp of basic Python coding

[b]Description[/b]

Unlock Lightning-Fast Python Performance and Transform Your Code Today!Are you ready to enhance your Python applications and achieve exceptional performance? "Python Code Optimization: Pro Techniques to Boost Code Speed" is the definitive Udemy course crafted to elevate your Python coding skills. Whether you're a junior developer or an experienced programmer, this course will empower you with advanced techniques to optimize your Python code, ensuring maximum efficiency and speed.Why Enroll in This Course?Master Performance Bottlenecks: Gain a deep understanding of Python's performance limitations and learn how to identify and eliminate bottlenecks that slow down your applications.Advanced Profiling Techniques: Learn to measure and profile your code effectively. Apply these skills to real-world scenarios with detailed case studies.Optimized Data Structures: Discover the power of optimized data structures and learn how to leverage them to handle large datasets efficiently.Parallel Processing: Unlock the potential of concurrency and parallelism in Python. Master multiprocessing and futures to build scalable and high-performance applications.What You'll LearnIntroduction to Code Optimization: Establish a solid foundation in Python code optimization techniques.Understanding Python Performance: Identify and analyze performance bottlenecks in your Python code.Measuring and Profiling: Learn to measure code execution and profile your applications using cProfile and dis.Optimizing Data Structures: Optimize lists, tuples, NumPy arrays, and Pandas DataFrames for faster data processing.Time Complexity: Grasp the principles of time complexity to write more efficient code.Parallel and Concurrent Processing: Implement multiprocessing and futures to enhance your code's performance.General Optimization Tips: Utilize comprehensions, generators, and optimize module imports to streamline your code.Real-World Case Studies: Apply your knowledge through multiple case studies that demonstrate effective code optimization strategies.Who Should Take This Course?Python Developers looking to enhance their code's performance and efficiency.Software Engineers aiming to optimize applications for better speed and scalability.Data Scientists who need to process large datasets efficiently.Anyone Interested in writing high-performance Python code and understanding deep optimization strategies.Enroll Now and Start Optimizing Your Python Code for Maximum Performance!Don't let inefficient code limit your potential. Enroll now and take your Python skills to the next level. Unlock the secrets to writing faster, more efficient code and distinguish yourself as a Python expert!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Understanding Python Performance

Lecture 2 Python's Performance Bottlenecks

Lecture 3 Measuring Code Execution

Lecture 4 Code Profiling with cProfile and dis

Lecture 5 Case Study: Identifying Performance Bottlenecks

Lecture 6 Line-by-Line Profiling

Section 3: Data Structures

Lecture 7 Lists and Tuples

Lecture 8 Short Intermission

Lecture 9 NumPy Arrays

Lecture 10 Pandas Series and DataFrame

Lecture 11 Time Complexity

Lecture 12 Multiprocessing and Futures

Section 4: General Tips

Lecture 13 Comprehensions and Generators

Lecture 14 Module Importing Optimization

Section 5: Parallel Processing

Lecture 15 Concurrency VS Parallelism

Lecture 16 Parallel Processing Workflow

Section 6: Case Studies

Lecture 17 Code Optimization Case Study 1

Lecture 18 Code Optimization Case Study 2

Lecture 19 Code Optimization Case Study 3

Section 7: Outro

Lecture 20 Congratulations!

Lecture 21 Bonus Lecture - Courses Links

Junior-level to advanced Python Developers who want to improve the performance and efficiency of their code in real-world applications,Data Analysts and Scientists experienced in Python, aiming to optimize data-heavy processes and reduce computation time in their workflows,Software Engineers Transitioning to High-Performance Code who already know Python and want to deepen their understanding of performance tuning and optimization strategies

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