Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.

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Table of Contents (13 chapters)
Preface
Preface
About the Course
Free Chapter
1
1. Vital Python – Math, Strings, Conditionals, and Loops
1. Vital Python – Math, Strings, Conditionals, and Loops
Introduction
Vital Python
Numbers: Operations, Types, and Variables
Python as a Calculator
Strings: Concatenation, Methods, and input()
String Interpolation
String Indexing and Slicing
Slicing
Booleans and Conditionals
Loops
Summary
2
2. Python Structures
2. Python Structures
Introduction
The Power of Lists
Matrix Operations
List Methods
Dictionary Keys and Values
Dictionary Methods
Tuples
A Survey of Sets
Choosing Types
Summary
3
3. Executing Python – Programs, Algorithms, and Functions
3. Executing Python – Programs, Algorithms, and Functions
Introduction
Python Scripts and Modules
Python Algorithms
Basic Functions
Iterative Functions
Recursive Functions
Dynamic Programming
Helper Functions
Variable Scope
Lambda Functions
Summary
4
4. Extending Python, Files, Errors, and Graphs
4. Extending Python, Files, Errors, and Graphs
Introduction
Reading Files
Writing Files
Preparing for Debugging (Defensive Code)
Plotting Techniques
The Don'ts of Plotting Graphs
Summary
5
5. Constructing Python – Classes and Methods
5. Constructing Python – Classes and Methods
Introduction
Classes and Objects
Defining Classes
The __init__ method
Methods
Properties
Inheritance
Summary
6
6. The Standard Library
6. The Standard Library
Introduction
The Importance of the Standard Library
Dates and Times
Interacting with the OS
Using the subprocess Module
Logging
Collections
Functools
Summary
7
7. Becoming Pythonic
7. Becoming Pythonic
Introduction
Using List Comprehensions
Set and Dictionary Comprehensions
Default Dictionary
Iterators
Itertools
Generators
Regular Expressions
Summary
8
8. Software Development
8. Software Development
Introduction
Debugging
Automated Testing
Creating a PIP Package
Creating Documentation the Easy Way
Source Management
Summary
9
9. Practical Python – Advanced Topics
9. Practical Python – Advanced Topics
Introduction
Developing Collaboratively
Dependency Management
Deploying Code into Production
Multiprocessing
Parsing Command-Line Arguments in Scripts
Performance and Profiling
Profiling
Summary
10
10. Data Analytics with pandas and NumPy
10. Data Analytics with pandas and NumPy
Introduction
NumPy and Basic Stats
Matrices
The pandas Library
Data
Null Values
Visual Analysis
Summary
11
11. Machine Learning
11. Machine Learning
Introduction
Introduction to Linear Regression
Cross-Validation
Regularization: Ridge and Lasso
K-Nearest Neighbors, Decision Trees, and Random Forests
Classification Models
Boosting Methods
Summary
Appendix
Appendix
1. Vital Python – Math, Strings, Conditionals, and Loops
2. Python Structures
3. Executing Python – Programs, Algorithms, Functions
4. Extending Python, Files, Errors, and Graphs
5. Constructing Python – Classes and Methods
6. The Standard Library
7. Becoming Pythonic
8. Software Development
9. Practical Python – Advanced Topics
10. Data Analytics with pandas and NumPy
11. Machine Learning

Helper Functions

A helper function performs part of the computation of another function. It allows you to reuse common code without repeating ourselves. For instance, suppose you had a few lines of code that printed out the elapsed time at various points in a function:

import time
def do_things():
    start_time = time.perf_counter()
    for i in range(10):
        y = i ** 100
        print(time.perf_counter() - start_time, "seconds elapsed")
    x = 10**2
    print(time.perf_counter() - start_time, "seconds elapsed")
    return x
   
do_things()

You should get the following output:

Figure 3.29: Timing our helper functions

The print statement is repeated twice in the preceding code, and will be better expressed as a helper function, as follows...

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