A straightforward approach to learning Python that emphasizes hands-on skills you can apply immediately! Based on the renowned mthree training program, this book will get you up to speed in the basics of Python, loops and data structures, object-oriented programming, and data processing

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Python

Preface

Setting Up a Python Programming Environment

This lesson provides an overview of general concepts related to the world of computer programming. The information presented will help you understand basic concepts that are common to all types of programming, regardless of the language you use to write computer code, as well as give you some basic tools that you can use to design your own programs.

Understanding Programming Basics

In this lesson, we will start programming Python. We will begin with an introductory look at Python syntax, comments, variables, user input, and data types.

Exploring Basic Python Syntax

Python supports a variety of basic data types that determine how content in a program will work and interact. This lesson covers variables typed as numbers and Booleans. The lesson also introduces operators and how to use them to change the value of a variable to produce an updated output.

Working with Basic Python Data Types

Python supports a variety of control structures that determine how content in a program will work and interact. This module explores those control structures that make creating algorithms possible.

Using Python Control Statements

In this lesson, we pull together many of the concepts from the previous lessons. Instead of presenting new information on Python, we will walk through a real-world application that uses what you've already learned. In this code-along, we will walk through the steps to create a calculator that determines a person's income tax based on their income.

Pulling It All Together: Income Tax Calculator

Controlling Program Flow with Loops

In addition to storing a single value in a variable, Python supports data structures that allow us to store multiple elements in a single variable. In this lesson, we will introduce several such structures and then take a closer look at lists. Python uses lists and these other structures as basic data types with specific features associated with each.

Understanding Basic Data Structures: Lists

In this lesson, we continue to look at understanding basic data types in Python; however, we shift from lists to tuples. In the previous lesson, you learned about lists, which store a sequence of data values. You learned that these values stored in a list did not have to be unique and that you could add and remove them. In this lesson, the focus shifts to tuples, which are a collection of values, which will still allow you to have duplicates; however, the values are ordered and unchangeable.

Understanding Basic Data Structures: Tuples

We've seen how to use lists and tuples to store data, where we can use automatically assigned index values to identify specific values in the collection. Python also supports the use of dictionaries, which give us the ability to define the index values ourselves, and sets, which are unorganized and unindexed. We will discuss dictionaries in this lesson and sets in Lesson 11.

Diving Deeper into Data Structures: Dictionaries

We've seen how to use lists, tuples, and dictionaries to store data. In this lesson, we tackle one final basic data structure: sets. A set is an unordered collection of items in which every set element is unique and must be immutable (cannot be changed). A set itself, however, is mutable. We can add or remove items from it.

Diving Deeper into Data Structures: Sets

In this lesson, we'll put everything we've learned in the book so far into a single program, including declaring variables, storing values into variables, accepting user input, and displaying input values to the user. We will also, of course, use proper Python syntax along the way as well as take advantage of data types we've seen.

Pulling It All Together: Prompting for an Address

A function is a block of code that is executed only when it's called. While Python includes many built-in functions, we can also write new, custom functions to create reusable components that we can use every time we need them.

Organizing with Functions

PART III Object-Oriented Programming in Python

Incorporating Object-Oriented Programming

As we learned in the previous lesson, object-oriented programming uses objects, classes, and methods to solve problems. Developers who work in this programming paradigm must also be familiar with polymorphism and inheritance. Inheritance allows us to inherit attributes and methods from other classes. Polymorphism uses those inherited methods to perform different tasks. This lesson focuses on inheritance.

Including Inheritance

In this lesson, we will build an application to pull together many of the concepts we've learned up to this point related to object-oriented programming using Python. Specifically, we'll focus on building an application that accepts a customer's order at a burger shop.

Pulling It All Together: Building a Burger Shop

Working with Dates and Times

In many cases, when we need to analyze the contents of text for a specific purpose, the text already exists in a file, so it can be efficient to have Python read the text directly from the source file. Not only is this faster than manually inputting the text at runtime, but it also means that Python can look at a file whose contents may change over time. This allows Python to automatically perform the analysis on the most recent version of the file.

Processing Text Files

In the previous lesson, we covered working with basic text files. In this lesson, we continue learning about working with files but transition to working with CSV files ( .csv ), which include records organized into columns like tables and are often generated by structured databases. We can use Python scripts to read these files, add (or append) content to an existing CSV file, and create new CSV files.

Processing CSV Files

This is the third lesson on working with files using Python. In the previous two lessons, we learned about working with basic text files and with CSV files. In this lesson, the focus shifts to working with JSON files, which can represent unstructured datasets and with key:value pairs. Like with the other file formats, we can use Python scripts to read JSON files, add (or append) content to an existing file, and create new files.

Processing JSON Files

PART V Data Analysis and Exception Handling

Using Lambdas

Errors detected during the program execution are called exceptions . Specifically, an exception is an event that occurs during the execution of a program, disrupting the normal flow of the program's instructions. Error detection is distinguished from detection of syntax errors (like incorrect indents or missing colons), which prevent the code from running at all.

Handling Exceptions

In this lesson, we will apply what we've learned up to this point to perform a common text analysis process using Python. Specifically, we will build a project that takes a dataset and analyzes it to calculate the number of times each word appears.

Pulling It All Together: Word Analysis in Python

In this and the next lesson, we will build on everything we've learned throughout this book and create a Python script we can use to automate processes related to extracting data from one source, transforming that data to meet the needs of a data scientist, and load the data into a data source accessible to the data scientist.

Extracting, Transforming, and Loading with ETL Scripting

From Lesson 24, we have a basic ETL script that can be used to extract data from a variety of sources, perform basic transformations on the extracted data, and load the transformed data into a variety of target datasets. In this lesson, we will tweak the existing script to make it more efficient and easier to use.

Improving ETL Scripting

Appendix A: Flowcharts

Along with flowcharts, which we covered in Appendix A, developers frequently use pseudocode as a way to plan out and describe the instructions for a computer program before starting to write code for that program.

Appendix B: Creating Pseudocode

MySQL is a relational database management system ( RDBMS ) that runs as a server providing multiuser access to multiple databases. Many Python programmers use MySQL with their Python applications. This appendix provides instructions for installing MySQL. By the end of this appendix, you should be able to have a MySQL database server and client working on your local system.

Appendix C: Installing MySQL

The Vinyl DB is a database that is used in the ETL program presented in Lessons 24 and 25 of this book. This appendix provides you with structure for the database and the code for a script you can use to create the SQL database.

Appendix D: Installing Vinyl DB

This appendix provides instructions for downloading, installing, and running MongoDB using MongoDB Community Server. The instructions that follow assume that you are using Windows. If you are using Mac OS or Linux, please select the appropriate options for that OS.

Appendix E: Installing MongoDB

In this appendix, we will learn how to use the mongoimport utility to move the contents of a JSON file into a collection in MongoDB. Specifically, we will import a dataset that contains user reviews for an ecommerce store to a MongoDB collection.

Appendix F: Importing to MongoDB

A a argument, 404 abbreviation: name pairs, 229 absolute path, 401 abstraction defined, 314 as a principle of OOP, 314 account balance, summing, 483–484 Add file icon (Replit), 12 add() method example of, 297–298 within a set, 262, 263 add_condiment method, 353 adding constants to lib.py file, 522 content to text files, 412–415 dictionary objects to CSV files, 441–442 drinks to order, 360–362 exception handler to functions, 517 exception handling to extract class, 594–601 extract.fromCSV method, 534–535 fromMONGODB method, 542–543 methods to Person class, 320–321 multiple exceptions, 496 sides to order, 362–363 transactions via dictionaries, 442–443 Adding Attributes exercise, 344–345 Adding New Names exercise, 280 algorithms defined, 613 purpose of, 102 use of, 31–32 Alice's Adventures in Wonderland files, 400 All About Cars exercise, 446 All About You exercise, 203 Anaconda Distribution about, 14–15 Jupyter Notebook installation, 15–16 and logic operation, 92 And the Total Is… exercise, 169 anonymous functions, 467 append() method within lists, 189 tuples and, 214 appending data, 436–439 applying timestamps, 384–387 Are You Rich? exercise, 115 area of a circle, hard-coded constants within, 44 arguments arbitrary, 304–306 defined, 296 keyword, 306–308 arithmetic, dates and, 387–388 * (asterisk), within functions, 304 attributes adding, 344–345 as a component of classes, 315 datetime , 379–380 defined, 314 Person class with multiple, 318–319 average opening price, calculating, 429–430 

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Haythem Balti

Kimberley A. Weiss

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Job Ready Python