Getting Productive – Modern Python
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Getting Productive with Modern Python
This is the code repository for [Getting Productive with Modern Python Video, published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
About the Video Course
Python is simple, but it isn’t easy. Python emphasizes code readability, using indentation and whitespaces to create code blocks. This makes it simpler than C++ or Java, where curly braces and keywords are scattered across the code. Python is high-level, which allows programmers like you to create logic with fewer lines of code. This course follows a problem-solution format to tackle common roadblocks in Python programming. How can we handle large datasets and files, processing them in Python efficiently? How can we address performance issues for long-running tasks? There is no other course that can transform every corner of your Python code. After going through the course, you will be confident enough to use Python for your large-scale applications and will perform tasks faster and more effective.
What You Will Learn
- Persist your work and data across time with Python databases
- Create faster and more compact applications with advanced comprehensions and containers
- Understand your program’s speed and bottlenecks with profiling
- Improve the speed at which you interface with input-output channels via concurrency
- Leverage modern multithreaded CPUs with parallelism to enhance performance
- Package your Python code so you can distribute it and share it with other developers
Instructions and Navigation
Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:
Basic knowledge of Python programming
Technical Requirements
This course has the following software requirements:
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit
Processor: Intel Core i7 or equivalent
Memory: 16 GB RAM
Storage: 35 GB available space
Software Requirements
OS: Windows 7 or above
Browser: Google Chrome, latest version
Code editor: Atom IDE, latest version (Jupyter Notebook or Jupyter Lab)
Others: Python3 installed using the Anaconda package or equivalent
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