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Learning Python

Learning Python

By : Romano
4.1 (21)
Buy this Book
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Learning Python

Learning Python

4.1 (21)
By: Romano
Buy this Book

Overview of this book

Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction and First Steps – Take a Deep Breath
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1. Introduction and First Steps – Take a Deep Breath
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A proper introduction
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Enter the Python
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About Python
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What are the drawbacks?
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Who is using Python today?
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Setting up the environment
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Installing Python
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How you can run a Python program
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How is Python code organized
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Python's execution model
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Guidelines on how to write good code
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The Python culture
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A note on the IDEs
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Summary
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2. Built-in Data Types
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2. Built-in Data Types
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Everything is an object
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Mutable or immutable? That is the question
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Numbers
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Immutable sequences
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Mutable sequences
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Set types
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Mapping types – dictionaries
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The collections module
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Final considerations
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Summary
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3. Iterating and Making Decisions
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3. Iterating and Making Decisions
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Conditional programming
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Looping
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Putting this all together
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A quick peek at the itertools module
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Summary
4
4. Functions, the Building Blocks of Code
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4. Functions, the Building Blocks of Code
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Why use functions?
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Scopes and name resolution
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Input parameters
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Return values
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A few useful tips
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Recursive functions
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Anonymous functions
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Function attributes
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Built-in functions
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One final example
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Documenting your code
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Importing objects
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Summary
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5. Saving Time and Memory
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5. Saving Time and Memory
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map, zip, and filter
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Comprehensions
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Generators
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Some performance considerations
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Don't overdo comprehensions and generators
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Name localization
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Generation behavior in built-ins
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One last example
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Summary
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6. Advanced Concepts – OOP, Decorators, and Iterators
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6. Advanced Concepts – OOP, Decorators, and Iterators
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Decorators
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Object-oriented programming
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Writing a custom iterator
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Summary
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7. Testing, Profiling, and Dealing with Exceptions
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7. Testing, Profiling, and Dealing with Exceptions
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Testing your application
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Test-driven development
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Exceptions
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Profiling Python
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Summary
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8. The Edges – GUIs and Scripts
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8. The Edges – GUIs and Scripts
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First approach – scripting
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Second approach – a GUI application
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Where do we go from here?
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Summary
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9. Data Science
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9. Data Science
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IPython and Jupyter notebook
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Dealing with data
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Where do we go from here?
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Summary
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10. Web Development Done Right
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10. Web Development Done Right
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What is the Web?
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How does the Web work?
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The Django web framework
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A regex website
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The future of web development
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Summary
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11. Debugging and Troubleshooting
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11. Debugging and Troubleshooting
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Debugging techniques
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Troubleshooting guidelines
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Summary
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12. Summing Up – A Complete Example
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12. Summing Up – A Complete Example
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The challenge
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Our implementation
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Implementing the Django interface
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Implementing the Falcon API
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Where do you go from here?
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Summary
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A word of farewell
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Index
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Index

One final example

Before we finish off this chapter, how about a final example? I was thinking we could write a function to generate a list of prime numbers up to a limit. We've already seen the code for this so let's make it a function and, to keep it interesting, let's optimize it a bit.

It turns out that you don't need to divide it by all numbers from 2 to N-1 to decide if a number N is prime. You can stop at . Moreover, you don't need to test the division for all numbers from 2 to , you can just use the primes in that range. I'll leave it to you to figure out why this works, if you're interested. Let's see how the code changes:

primes.py 

from math import sqrt, ceil

def get_primes(n):
    """Calculate a list of primes up to n (included). """
    primelist = []
    for candidate in range(2, n + 1):
        is_prime = True
        root = int(ceil(sqrt(candidate)))  # division limit
        for prime in primelist:  # we try only the primes
            if prime > root:  # no need to check...
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Tech Concepts
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Programming languages
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