Real Python

Inheritance and Composition: A Python OOP Guide

2020-04-14T14:00:00+00:00

In this course, you’ll explore inheritance and composition in Python. Inheritance and composition are two important concepts in object oriented programming that model the relationship between two classes. They are the building blocks of object oriented design, and they help programmers to write reusable code.

By the end of this course, you’ll know how to:

Use inheritance in Python
Model class hierarchies using inheritance
Use multiple inheritance in Python and understand its drawbacks
Use composition to create complex objects
Reuse existing code by applying composition
Change application behavior at run-time through composition

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Combining Data in Pandas With merge(), .join(), and concat()

2020-04-13T14:00:00+00:00

Pandas’ Series and DataFrame objects are powerful tools for exploring and analyzing data. Part of their power comes from a multifaceted approach to combining separate datasets. With Pandas, you can merge, join, and concatenate your datasets, allowing you to unify and better understand your data as you analyze it.

In this tutorial, you’ll learn how and when to combine your data in Pandas with:

merge() for combining data on common columns or indices
.join() for combining data on a key column or an index
concat() for combining DataFrames across rows or columns

If you have some experience using DataFrame and Series objects in Pandas and you’re ready to learn how to combine them, then this tutorial will help you do exactly that. If you want a quick refresher on DataFrames before proceeding, then Pandas DataFrames 101 will get you caught up in no time.

You can follow along with the examples in this tutorial using the interactive Jupyter Notebook available at the link below:

Get the Jupyter Notebook: Click here to get the Jupyter Notebook you'll use to learn about Pandas merge(), .join(), and concat() in this tutorial.

Note: The techniques you’ll learn about below will generally work for both DataFrame and Series objects. But for simplicity and conciseness, the examples will use the term dataset to refer to objects that can be either DataFrames or Series.

Pandas `merge()`: Combining Data on Common Columns or Indices

The first technique you’ll learn is merge(). You can use merge() any time you want to do database-like join operations. It’s the most flexible of the three operations you’ll learn.

When you want to combine data objects based on one or more keys in a similar way to a relational database, merge() is the tool you need. More specifically, merge() is most useful when you want to combine rows that share data.

You can achieve both many-to-one and many-to-many joins with merge(). In a many-to-one join, one of your datasets will have many rows in the merge column that repeat the same values (such as 1, 1, 3, 5, 5), while the merge column in the other dataset will not have repeat values (such as 1, 3, 5).

As you might have guessed, in a many-to-many join, both of your merge columns will have repeat values. These merges are more complex and result in the Cartesian product of the joined rows.

This means that, after the merge, you’ll have every combination of rows that share the same value in the key column. You’ll see this in action in the examples below.

What makes merge() so flexible is the sheer number of options for defining the behavior of your merge. While the list can seem daunting, with practice you’ll be able to expertly merge datasets of all kinds.

When you use merge(), you’ll provide two required arguments:

The left DataFrame
The right DataFrame

After that, you can provide a number of optional arguments to define how your datasets are merged:

how: This defines what kind of merge to make. It defaults to 'inner', but other possible options include 'outer', 'left', and 'right'.
on: Use this to tell merge() which columns or indices (also called key columns or key indices) you want to join on. This is optional. If it isn’t specified, and left_index and right_index (covered below) are False, then columns from the two DataFrames that share names will be used as join keys. If you use on, then the column or index you specify must be present in both objects.
left_on and right_on: Use either of these to specify a column or index that is present only in the left or right objects that you are merging. Both default to None.
left_index and right_index: Set these to True to use the index of the left or right objects to be merged. Both default to False.
suffixes: This is a tuple of strings to append to identical column names that are not merge keys. This allows you to keep track of the origins of columns with the same name.

These are some of the most important parameters to pass to merge(). For the full list, see the Pandas documentation.

Note: In this tutorial, you’ll see that examples always specify which column(s) to join on with on. This is the safest way to merge your data because you and anyone reading your code will know exactly what to expect when merge() is called. If you do not specify the merge column(s) with on, then Pandas will use any columns with the same name as the merge keys.

How to `merge()`

Before getting into the details of how to use merge(), you should first understand the various forms of joins:

inner
outer
left
right

Note: Even though you’re learning about merging, you’ll see inner, outer, left, and right also referred to as join operations. For this tutorial, you can consider these terms equivalent.

You’ll learn about these in detail below, but first take a look at this visual representation of the different joins:

Visual Representation of Join Types

Read the full article at https://realpython.com/pandas-merge-join-and-concat/ »

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The Real Python Podcast – Episode #4: Learning Python Through Errors

2020-04-10T12:00:00+00:00

This week Christopher interviews Martin Breuss, about a couple of the video training courses he's created for Real Python. They discuss his course on getting started with Django, and how to learn Python through errors, and how errors really are your friends. Martin talks about his work with Coding Nomads, and teaching Python around the world. He also provides some tips on debugging and writing good questions. This episode was recorded at an earlier date, and because of recent events Martin came back to discuss a new #StayAtHome Mentorship Program he's working on. The program is meant not only for learners but also for those who want to try their hand at being a mentor. We also answer our first listener submitted question.

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How to Provide Test Fixtures for Django Models in Pytest

2020-04-08T14:00:00+00:00

If you’re working in Django, pytest fixtures can help you create tests for your models that are uncomplicated to maintain. Writing good tests is a crucial step in sustaining a successful app, and fixtures are a key ingredient in making your test suite efficient and effective. Fixtures are little pieces of data that serve as the baseline for your tests.

As your test scenarios change, it can be a pain to add, modify, and maintain your fixtures. But don’t worry. This tutorial will show you how to use the pytest-django plugin to make writing new test cases and fixtures a breeze.

In this tutorial, you’ll learn:

How to create and load test fixtures in Django
How to create and load pytest fixtures for Django models
How to use factories to create test fixtures for Django models in pytest
How to create dependencies between test fixtures using the factory as fixture pattern

The concepts described in this tutorial are suited for any Python project using pytest. For convenience, the examples use the Django ORM, but the results can be reproduced in other types of ORMs and even in projects that don’t use an ORM or a database.

Free Bonus: Click here to get access to a free Django Learning Resources Guide (PDF) that shows you tips and tricks as well as common pitfalls to avoid when building Python + Django web applications.

Fixtures in Django

To get started, you’re going to set up a fresh Django project. Throughout this tutorial, you’ll write some tests using the built-in authentication module.

Setting Up a Python Virtual Environment

When you create a new project, it’s best to also create a virtual environment for it. A virtual environment allows you to isolate the project from other projects on your computer. This way, different projects can use different versions of Python, Django, or any other package without interfering with each other.

Here’s how you can create your virtual environment in a new directory:

$ mkdir django_fixtures
$ cd django_fixtures
django_fixtures $ python -m venv venv

For step-by-step instructions on how to create a virtual environment, check out Python Virtual Environments: A Primer.

Running this command will create a new directory called venv. This directory will store all the packages you install inside the virtual environment.

Setting Up a Django Project

Now that you have a fresh virtual environment, it’s time to set up a Django project. In your terminal, activate the virtual environment and install Django:

$ source venv/bin/activate
$ pip install django

Now that you have Django installed, you can create a new Django project called django_fixtures:

$ django-admin startproject django_fixtures

After running this command, you’ll see that Django created new files and directories. For more about how to start a new Django project, check out Starting a Django Project.

To finish setting up your Django project, apply the migrations for the built-in modules:

$ cd django_fixtures
$ python manage.py migrate
Operations to perform:
  Apply all migrations: admin, auth, contenttypes, sessions
Running migrations:
  Applying contenttypes.0001_initial... OK
  Applying auth.0001_initial... OK
  Applying admin.0001_initial... OK
  Applying admin.0002_logentry_remove_auto_add... OK
  Applying admin.0003_logentry_add_action_flag_choices... OK
  Applying contenttypes.0002_remove_content_type_name... OK
  Applying auth.0002_alter_permission_name_max_length... OK
  Applying auth.0003_alter_user_email_max_length... OK
  Applying auth.0004_alter_user_username_opts... OK
  Applying auth.0005_alter_user_last_login_null... OK
  Applying auth.0006_require_contenttypes_0002... OK
  Applying auth.0007_alter_validators_add_error_messages... OK
  Applying auth.0008_alter_user_username_max_length... OK
  Applying auth.0009_alter_user_last_name_max_length... OK
  Applying auth.0010_alter_group_name_max_length... OK
  Applying auth.0011_update_proxy_permissions... OK
  Applying sessions.0001_initial... OK

The output lists all the migrations Django applied. When starting a new project, Django applies migrations for built-in apps such as auth, sessions, and admin.

Now you’re ready to start writing tests and fixtures!

Creating Django Fixtures

Read the full article at https://realpython.com/django-pytest-fixtures/ »

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Arduino With Python: How to Get Started

2020-04-07T14:00:00+00:00

Microcontrollers have been around for a long time, and they’re used in everything from complex machinery to common household appliances. However, working with them has traditionally been reserved for those with formal technical training, such as technicians and electrical engineers.

The emergence of Arduino has made electronic application design much more accessible to all developers. In this course, you’ll discover how to use Arduino with Python to develop your own electronic projects.

You’ll cover the basics of Arduino with Python and learn how to:

Set up electronic circuits
Set up the Firmata protocol on Arduino
Write basic applications for Arduino in Python
Control analog and digital inputs and outputs
Integrate Arduino sensors and switches with higher-level apps
Trigger notifications on your PC and send emails using Arduino

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How to Make an Instagram Bot With Python and InstaPy

2020-04-06T14:00:00+00:00

What do SocialCaptain, Kicksta, Instavast, and many other companies have in common? They all help you reach a greater audience, gain more followers, and get more likes on Instagram while you hardly lift a finger. They do it all through automation, and people pay them a good deal of money for it. But you can do the same thing—for free—using InstaPy!

In this tutorial, you’ll learn how to build a bot with Python and InstaPy, a library by Tim Großmann which automates your Instagram activities so that you gain more followers and likes with minimal manual input. Along the way, you’ll learn about browser automation with Selenium and the Page Object Pattern, which together serve as the basis for InstaPy.

In this tutorial, you’ll learn:

How Instagram bots work
How to automate a browser with Selenium
How to use the Page Object Pattern for better readability and testability
How to build an Instagram bot with InstaPy

You’ll begin by learning how Instagram bots work before you build one.

Important: Make sure you check Instagram’s Terms of Use before implementing any kind of automation or scraping techniques.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you'll need to take your Python skills to the next level.

How Instagram Bots Work

How can an automation script gain you more followers and likes? Before answering this question, think about how an actual person gains more followers and likes.

They do it by being consistently active on the platform. They post often, follow other people, and like and leave comments on other people’s posts. Bots work exactly the same way: They follow, like, and comment on a consistent basis according to the criteria you set.

The better the criteria you set, the better your results will be. You want to make sure you’re targeting the right groups because the people your bot interacts with on Instagram will be more likely to interact with your content.

For example, if you’re selling women’s clothing on Instagram, then you can instruct your bot to like, comment on, and follow mostly women or profiles whose posts include hashtags such as #beauty, #fashion, or #clothes. This makes it more likely that your target audience will notice your profile, follow you back, and start interacting with your posts.

How does it work on the technical side, though? You can’t use the Instagram Developer API since it is fairly limited for this purpose. Enter browser automation. It works in the following way:

You serve it your credentials.
You set the criteria for who to follow, what comments to leave, and which type of posts to like.
Your bot opens a browser, types in https://instagram.com on the address bar, logs in with your credentials, and starts doing the things you instructed it to do.

Next, you’ll build the initial version of your Instagram bot, which will automatically log in to your profile. Note that you won’t use InstaPy just yet.

How to Automate a Browser

For this version of your Instagram bot, you’ll be using Selenium, which is the tool that InstaPy uses under the hood.

First, install Selenium. During installation, make sure you also install the Firefox WebDriver since the latest version of InstaPy dropped support for Chrome. This also means that you need the Firefox browser installed on your computer.

Now, create a Python file and write the following code in it:

 1 from time import sleep
 2 from selenium import webdriver
 3 
 4 browser = webdriver.Firefox()
 5 
 6 browser.get('https://www.instagram.com/')
 7 
 8 sleep(5)
 9 
10 browser.close()

Run the code and you’ll see that a Firefox browser opens and directs you to the Instagram login page. Here’s a line-by-line breakdown of the code:

Lines 1 and 2 import sleep and webdriver.
Line 4 initializes the Firefox driver and sets it to browser.
Line 6 types https://www.instagram.com/ on the address bar and hits Enter.
Line 8 waits for five seconds so you can see the result. Otherwise, it would close the browser instantly.
Line 10 closes the browser.

This is the Selenium version of Hello, World. Now you’re ready to add the code that logs in to your Instagram profile. But first, think about how you would log in to your profile manually. You would do the following:

Go to https://www.instagram.com/.
Click the login link.
Enter your credentials.
Hit the login button.

The first step is already done by the code above. Now change it so that it clicks on the login link on the Instagram home page:

Read the full article at https://realpython.com/instagram-bot-python-instapy/ »

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The Real Python Podcast – Episode #3: Effective Python and Python at Google Scale

2020-04-03T12:00:00+00:00

In this episode, Christopher interviews Brett Slatkin about the 2nd edition of his book Effective Python. Brett talks about the revisions he made for the book, and updating it for the newest versions of Python 3. Christopher asks who is the intended developer for the book. Brett also discusses working on Google App Engine, and what it's like to develop and maintain Python applications at Google Scale. Brett mentions a brief anecdote about working with Guido van Rossum, while they both worked at Google. He also provides advice about maintaining a large and aging Python code base.

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Linked Lists in Python: An Introduction

2020-04-01T14:00:00+00:00

Linked lists are like a lesser-known cousin of lists. They’re not as popular or as cool, and you might not even remember them from your algorithms class. But in the right context, they can really shine.

In this article, you’ll learn:

What linked lists are and when you should use them
How to use collections.deque for all of your linked list needs
How to implement your own linked lists
What the other types of linked lists are and what they can be used for

If you’re looking to brush up on your coding skills for a job interview, or if you want to learn more about Python data structures besides the usual dictionaries and lists, then you’ve come to the right place!

You can follow along with the examples in this tutorial by downloading the source code available at the link below:

Get the Source Code: Click here to get the source code you'll use to learn about linked lists in this tutorial.

Understanding Linked Lists

Linked lists are an ordered collection of objects. So what makes them different from normal lists? Linked lists differ from lists in the way that they store elements in memory. While lists use a contiguous memory block to store references to their data, linked lists store references as part of their own elements.

Main Concepts

Before going more in depth on what linked lists are and how you can use them, you should first learn how they are structured. Each element of a linked list is called a node, and every node has two different fields:

Data contains the value to be stored in the node.
Next contains a reference to the next node on the list.

Here’s what a typical node looks like:

Node

A linked list is a collection of nodes. The first node is called the head, and it’s used as the starting point for any iteration through the list. The last node must have its next reference pointing to None to determine the end of the list. Here’s how it looks:

Linked List

Now that you know how a linked list is structured, you’re ready to look at some practical use cases for it.

Practical Applications

Linked lists serve a variety of purposes in the real world. They can be used to implement (spoiler alert!) queues or stacks as well as graphs. They’re also useful for much more complex tasks, such as lifecycle management for an operating system application.

Queues or Stacks

Queues and stacks differ only in the way elements are retrieved. For a queue, you use a First-In/First-Out (FIFO) approach. That means that the first element inserted in the list is the first one to be retrieved:

Queue

In the diagram above, you can see the front and rear elements of the queue. When you append new elements to the queue, they’ll go to the rear end. When you retrieve elements, they’ll be taken from the front of the queue.

For a stack, you use a Last-In/Fist-Out (LIFO) approach, meaning that the last element inserted in the list is the first to be retrieved:

Stack

In the above diagram you can see that the first element inserted on the stack (index 0) is at the bottom, and the last element inserted is at the top. Since stacks use the LIFO approach, the last element inserted (at the top) will be the first to be retrieved.

Read the full article at https://realpython.com/linked-lists-python/ »

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Comparing Python Objects the Right Way: "is" vs "=="

2020-03-31T14:00:00+00:00

There’s a subtle difference between the Python identity operator (is) and the equality operator (==). Your code can run fine when you use the Python is operator to compare numbers, until it suddenly doesn’t. You might have heard somewhere that the Python is operator is faster than the == operator, or you may feel that it looks more Pythonic. However, it’s crucial to keep in mind that these operators don’t behave quite the same.

The == operator compares the value or equality of two objects, whereas the Python is operator checks whether two variables point to the same object in memory. In the vast majority of cases, this means you should use the equality operators == and !=, except when you’re comparing to None.

In this course, you’ll learn:

What the difference is between object equality and identity
When to use equality and identity operators to compare objects
What these Python operators do under the hood
Why using is and is not to compare values leads to unexpected behavior
How to write a custom __eq__() class method to define equality operator behavior

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How to Use any() in Python

2020-03-30T14:00:00+00:00

As a Python programmer, you’ll frequently deal with Booleans and conditional statements—sometimes very complex ones. In those situations, you may need to rely on tools that can simplify logic and consolidate information. Fortunately, any() in Python is such a tool. It looks through the elements in an iterable and returns a single value indicating whether any element is true in a Boolean context, or truthy.

In this tutorial, you’ll learn:

How to use any()
How to decide between any() and or

Let’s dive right in!

Python Pit Stop: This tutorial is a quick and practical way to find the info you need, so you’ll be back to your project in no time!

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

How to Use `any()` in Python

Imagine that you’re writing a program for your employer’s recruiting department. You might want to schedule interviews with candidates who meet any of the following criteria:

Know Python already
Have five or more years of developer experience
Have a degree

One tool you could use to write this conditional expression is or:

# recruit_developer.py
def schedule_interview(applicant):
    print(f"Scheduled interview with {applicant['name']}")

applicants = [
    {
        "name": "Devon Smith",
        "programming_languages": ["c++", "ada"],
        "years_of_experience": 1,
        "has_degree": False,
        "email_address": "devon@email.com",
    },
    {
        "name": "Susan Jones",
        "programming_languages": ["python", "javascript"],
        "years_of_experience": 2,
        "has_degree": False,
        "email_address": "susan@email.com",
    },
    {
        "name": "Sam Hughes",
        "programming_languages": ["java"],
        "years_of_experience": 4,
        "has_degree": True,
        "email_address": "sam@email.com",
    },
]
for applicant in applicants:
    knows_python = "python" in applicant["programming_languages"]
    experienced_dev = applicant["years_of_experience"] >= 5

    meets_criteria = (
        knows_python
        or experienced_dev
        or applicant["has_degree"]
    )
    if meets_criteria:
        schedule_interview(applicant)

In the above example, you check each applicant’s credentials and schedule an interview if the applicant meets any of your three criteria.

Technical Detail: Python’s any() and or aren’t limited to evaluating Boolean expressions. Instead, Python performs a truth value test on each argument, evaluating whether the expression is truthy or falsy. For example, nonzero integer values are considered truthy and zero is considered falsy:

>>>

>>> 1 or 0
1

In this example, or evaluated the nonzero value 1 as truthy even though it’s not of type Boolean. or returned 1 and didn’t need to evaluate the truthiness of 0. Later in this tutorial, you’ll learn more about the return value and argument evaluation of or.

If you execute this code, then you’ll see that Susan and Sam will get interviews:

$ python recruit_developer.py
Scheduled interview with Susan Jones
Scheduled interview with Sam Hughes

Read the full article at https://realpython.com/any-python/ »

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The Real Python Podcast – Episode #2: Learn Python Skills While Creating Games

2020-03-27T12:00:00+00:00

In this episode, Christopher interviews Jon Fincher from the Real Python Team. Jon talks about his recent articles on PyGame and Arcade. They discuss if game programming is a good way to develop your Python programming skills, and if a game would make a good portfolio piece. He compares the two popular Python game libraries of Arcade and PyGame, and discusses about how to find assets for your own creations.

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SimPy: Simulating Real-World Processes With Python

2020-03-25T14:00:00+00:00

The real world is full of systems, like airports and highways, that frequently experience congestion and delay. When these systems are not optimized, their inefficiency can lead to countless unhappy customers and hours of wasted time. In this tutorial, you’ll learn how to use Python’s simpy framework to create virtual simulations that will help you solve problems like these.

In this tutorial, you’ll learn how to:

Use a simulation to model a real-world process
Create a step-by-step algorithm to approximate a complex system
Design and run a real-world simulation in Python with simpy

In this tutorial, you’ll create a simulation for a local movie theater. Your goal is to provide the manager with a script to help find the optimal number of employees to have on staff. You can download the source code for this script by clicking on the link below:

Download Code: Click here to download the code you'll use to learn about SimPy in this tutorial.

What Simulation Is

A simulation is a representation of a real-world system. One can use mathematical or computational models of this system to study how it works, or what happens when parts of it are changed. Simulations are used in airports, restaurants, mechanics, government agencies, and many other systems where poor resource allocation can lead to congestion, customer dissatisfaction, and critical transportation delays.

A system can be any environment where things happen. Examples of real-world systems include car washes, banks, manufacturing plants, airports, post offices, call centers, and more. These systems have agents that undergo processes within them. For instance:

A car wash will have cars go through the washing process.
An airport will have passengers go through the security check process.
A call center will have customers go through the process of speaking with a telemarketer.

This relationship is summed up in the table below:

System	Agent	Process
Car wash	Car	Wash
Airport	Passenger	Security check
Call center	Customer	Speak with a telemarketer

Understanding the processes that agents go through within a system is an important component of logistical planning, especially for large-scale organizations. For example, an airport can see passenger wait times at a security checkpoint skyrocket if there aren’t enough workers that day. Similarly, time-sensitive mail can be delayed by days (or even weeks) if it isn’t routed properly.

These instances of congestion can have real-life consequences on time and money, so it’s important to be able to model these processes beforehand. This gives you an idea of where the system might run into problems and how resources should be allocated ahead of time to solve those problems in the most efficient way possible.

How Simulation Works

In Python, you can use the simpy framework for event simulation. First, take a quick look at how a simulated process would run in Python. Below is a code snippet from a simulation of a security checkpoint system. The following three lines of code set up the environment, pass all necessary functions, and run the simulation:

# Set up the environment
env = simpy.Environment()

# Assume you've defined checkpoint_run() beforehand
env.process(checkpoint_run(env, num_booths, check_time, passenger_arrival))

# Let's go!
env.run(until=10)

The first line of code above establishes the environment. You’ll do this by assigning simpy.Environment() to the desired variable. Here, it’s simply named env. This tells simpy to create an environment object named env that will manage the simulation time and move the simulation through each subsequent time step.

Once you have your environment established, you’ll pass in all of the variables that will act as your parameters. These are the things you can vary to see how the system will react to changes. For this security checkpoint system, you’re using the following parameters:

env: the environment object to schedule and process events
num_booths: the number of ID check booths
check_time: the length of time it takes to check a passenger’s ID
passenger_arrival: the rate at which passengers arrive at the queue

Then, it’s time to run the simulation! You can do this by calling env.run() and specifying how long you want the simulation to run for. The simulation runs in minutes, so this sample code will run the simulation for 10 real-time minutes.

Note: Don’t worry! You won’t have to wait 10 actual minutes for the simulation to finish. Because simulation gives you a virtual look at a real-time process, those 10 minutes will pass in mere seconds on the computer.

To recap, here are the three steps to running a simulation in Python:

Establish the environment.
Pass in the parameters.
Run the simulation.

But there’s a lot more going on underneath the hood! You’ll need to understand how to choose those parameters, and you’ll have to define all the functions that will be called when the simulation is run.

Let’s get started!

Read the full article at https://realpython.com/simpy-simulating-with-python/ »

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Using NumPy's np.arange() Effectively

2020-03-24T14:00:00+00:00

NumPy is the fundamental Python library for numerical computing. Its most important type is an array type called ndarray. NumPy offers a lot of array creation routines for different circumstances. arange() is one such function based on numerical ranges. It’s often referred to as np.arange() because np is a widely used abbreviation for NumPy.

Creating NumPy arrays is important when you’re working with other Python libraries that rely on them, like SciPy, Pandas, Matplotlib, scikit-learn, and more. NumPy is suitable for creating and working with arrays because it offers useful routines, enables performance boosts, and allows you to write concise code.

By the end of this course, you’ll know:

What np.arange() is
How to use np.arange()
How np.arange() compares to the Python built-in class range
Which routines are similar to np.arange()

Let’s see np.arange() in action!

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The Python math Module: Everything You Need to Know

2020-03-23T14:00:00+00:00

In this article, you’ll learn all about Python’s math module. Mathematical calculations are an essential part of most Python development. Whether you’re working on a scientific project, a financial application, or any other type of programming endeavor, you just can’t escape the need for math.

For straightforward mathematical calculations in Python, you can use the built-in mathematical operators, such as addition (+), subtraction (-), division (/), and multiplication (*). But more advanced operations, such as exponential, logarithmic, trigonometric, or power functions, are not built in. Does that mean you need to implement all of these functions from scratch?

Fortunately, no. Python provides a module specifically designed for higher-level mathematical operations: the math module.

By the end of this article, you’ll learn:

What the Python math module is
How to use math module functions to solve real-life problems
What the constants of the math module are, including pi, tau, and Euler’s number
What the differences between built-in functions and math functions are
What the differences between math, cmath, and NumPy are

A background in mathematics will be helpful here, but don’t worry if math isn’t your strong suit. This article will explain the basics of everything you need to know.

So let’s get started!

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Getting to Know the Python `math` Module

The Python math module is an important feature designed to deal with mathematical operations. It comes packaged with the standard Python release and has been there from the beginning. Most of the math module’s functions are thin wrappers around the C platform’s mathematical functions. Since its underlying functions are written in CPython, the math module is efficient and conforms to the C standard.

The Python math module offers you the ability to perform common and useful mathematical calculations within your application. Here are a few practical uses for the math module:

Calculating combinations and permutations using factorials
Calculating the height of a pole using trigonometric functions
Calculating radioactive decay using the exponential function
Calculating the curve of a suspension bridge using hyperbolic functions
Solving quadratic equations
Simulating periodic functions, such as sound and light waves, using trigonometric functions

Since the math module comes packaged with the Python release, you don’t have to install it separately. Using it is just a matter of importing the module:

>>>

>>> import math

You can import the Python math module using the above command. After importing, you can use it straightaway.

Constants of the `math` Module

The Python math module offers a variety of predefined constants. Having access to these constants provides several advantages. For one, you don’t have to manually hardcode them into your application, which saves you a lot of time. Plus, they provide consistency throughout your code. The module includes several famous mathematical constants and important values:

Pi
Tau
Euler’s number
Infinity
Not a number (NaN)

In this section, you’ll learn about the constants and how to use them in your Python code.

Pi

Pi (π) is the ratio of a circle’s circumference (c) to its diameter (d):

π = c/d

This ratio is always the same for any circle.

Pi is an irrational number, which means it can’t be expressed as a simple fraction. Therefore, pi has an infinite number of decimal places, but it can be approximated as 22/7, or 3.141.

Interesting Fact: Pi is the most recognized and well-known mathematical constant in the world. It has its own celebration date, called Pi Day, which falls on March 14th (3/14).

Read the full article at https://realpython.com/python-math-module/ »

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The Real Python Podcast Is Here!

2020-03-20T20:18:40+00:00

The Real Python Podcast is finally live! Tune in for interesting guests, interviews with expert Pythonistas, and lots of behind-the-scenes with the Real Python Team.

Today we’re officially launching the Real Python Podcast, a new (and freely accessible) podcast for Pythonistas like you.

This has been in the making for a while, and both Christopher and I are super proud to finally release the first episode to you this week.

We’ll have a roster of interesting guests for you, interviews with expert Pythonistas, and lots of behind-the-scenes with the Real Python Team.

Here’s more info about the show and how you can listen to it:

What Is the Real Python Podcast?

Read the full article at https://realpython.com/real-python-podcast-launch/ »

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The Real Python Podcast – Episode #1: Python Decorators and Writing for Real Python

2020-03-20T14:00:00+00:00

In this first episode, Christopher interviews Geir Arne Hjelle from the Real Python Team. You’ll learn about Geir Arne’s background as a Pythonista and PyCon speaker, the tutorials he’s written for the site, how Python decorators can help you write better code, and what Real Python’s tutorial publishing process looks like behind the scenes.

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Python Scope & the LEGB Rule: Resolving Names in Your Code

2020-03-18T14:00:00+00:00

The concept of scope rules how variables and names are looked up in your code. It determines the visibility of a variable within the code. The scope of a name or variable depends on the place in your code where you create that variable. The Python scope concept is generally presented using a rule known as the LEGB rule.

The letters in the acronym LEGB stand for Local, Enclosing, Global, and Built-in scopes. This summarizes not only the Python scope levels but also the sequence of steps that Python follows when resolving names in a program.

In this tutorial, you’ll learn:

What scopes are and how they work in Python
Why it’s important to know about Python scope
What the LEGB rule is and how Python uses it to resolve names
How to modify the standard behavior of Python scope using global and nonlocal
What scope-related tools Python offers and how you can use them

With this knowledge at hand, you can take advantage of Python scopes to write more reliable and maintainable programs. Using Python scope will help you avoid or minimize bugs related to name collision as well as bad use of global names across your programs.

You’ll get the most out of this tutorial if you’re familiar with intermediate Python concepts like classes, functions, inner functions, variables, exceptions, comprehensions, built-in functions, and standard data structures.

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Understanding Scope

In programming, the scope of a name defines the area of a program in which you can unambiguously access that name, such as variables, functions, objects, and so on. A name will only be visible to and accessible by the code in its scope. Several programming languages take advantage of scope for avoiding name collisions and unpredictable behaviors. Most commonly, you’ll distinguish two general scopes:

Global scope: The names that you define in this scope are available to all your code.
Local scope: The names that you define in this scope are only available or visible to the code within the scope.

Scope came about because early programming languages (like BASIC) only had global names. With this kind of name, any part of the program could modify any variable at any time, so maintaining and debugging large programs could become a real nightmare. To work with global names, you’d need to keep all the code in mind at the same time to know what the value of a given name is at any time. This was an important side-effect of not having scopes.

Some languages like Python use scope to avoid this kind of problem. When you use a language that implements scope, there’s no way for you to access all the variables in a program at all locations in that program. In this case, your ability to access a given name will depend on where you’ve defined that name.

Note: You’ll be using the term name to refer to the identifiers of variables, constants, functions, classes, or any other object that can be assigned a name.

The names in your programs will have the scope of the block of code in which you define them. When you can access the value of a given name from someplace in your code, you’ll say that the name is in scope. If you can’t access the name, then you’ll say that the name is out of scope.

Names and Scopes in Python

Since Python is a dynamically-typed language, variables in Python come into existence when you first assign them a value. On the other hand, functions and classes are available after you define them using def or class, respectively. Finally, modules exist after you import them. As a summary, you can create Python names through one of the following operations:

Operation	Statement
Assignments	`x = value`
Import operations	`import module` or `from module import name`
Function definitions	`def my_func(): ...`
Argument definitions in the context of functions	`def my_func(arg1, arg2,... argN): ...`
Class definitions	`class MyClass: ...`

All these operations create or, in the case of assignments, update new Python names because all of them assign a name to a variable, constant, function, class, instance, module, or other Python object.

Note: There’s an important difference between assignment operations and reference or access operations. When you reference a name, you’re just retrieving its content or value. When you assign a name, you’re either creating that name or modifying it.

Python uses the location of the name assignment or definition to associate it with a particular scope. In other words, where you assign or define a name in your code determines the scope or visibility of that name.

For example, if you assign a value to a name inside a function, then that name will have a local Python scope. In contrast, if you assign a value to a name outside of all functions—say, at the top level of a module—then that name will have a global Python scope.

Python Scope vs Namespace

In Python, the concept of scope is closely related to the concept of the namespace. As you’ve learned so far, a Python scope determines where in your program a name is visible. Python scopes are implemented as dictionaries that map names to objects. These dictionaries are commonly called namespaces. These are the concrete mechanisms that Python uses to store names. They’re stored in a special attribute called .__dict__.

Names at the top level of a module are stored in the module’s namespace. In other words, they’re stored in the module’s .__dict__ attribute. Take a look at the following code:

>>>

>>> import sys
>>> sys.__dict__.keys()
dict_keys(['__name__', '__doc__', '__package__',..., 'argv', 'ps1', 'ps2'])

Read the full article at https://realpython.com/python-scope-legb-rule/ »

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Make a 2D Side-Scroller Game With PyGame

2020-03-17T14:00:00+00:00

In this course, you’ll learn about creating games using Python and the library PyGame.

By the end of this course, you’ll be able to:

Draw items on your screen
Play sound effects and music
Handle user input
Implement event loops
Describe how game programming differs from standard procedural Python programming

This primer assumes you have a basic understanding of writing Python programs, including user-defined functions, imports, loops, and conditionals. You should also be familiar with how to open files on your platform. A basic understanding of object-oriented Python is helpful as well. pygame works with most versions of Python, but Python 3.6 is recommended and used throughout this article.

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How to Do a Binary Search in Python

2020-03-16T14:00:00+00:00

Binary search is a classic algorithm in computer science. It often comes up in programming contests and technical interviews. Implementing binary search turns out to be a challenging task, even when you understand the concept. Unless you’re curious or have a specific assignment, you should always leverage existing libraries to do a binary search in Python or any other language.

In this tutorial, you’ll learn how to:

Use the bisect module to do a binary search in Python
Implement a binary search in Python both recursively and iteratively
Recognize and fix defects in a binary search Python implementation
Analyze the time-space complexity of the binary search algorithm
Search even faster than binary search

This tutorial assumes you’re a student or an intermediate programmer with an interest in algorithms and data structures. At the very least, you should be familiar with Python’s built-in data types, such as lists and tuples. In addition, some familiarity with recursion, classes, data classes, and lambdas will help you better understand the concepts you’ll see in this tutorial.

Below you’ll find a link to the sample code you’ll see throughout this tutorial, which requires Python 3.7 or later to run:

Get Sample Code: Click here to get the sample code you'll use to learn about binary search in Python in this tutorial.

Benchmarking

In the next section of this tutorial, you’ll be using a subset of the Internet Movie Database (IMDb) to benchmark the performance of a few search algorithms. This dataset is free of charge for personal and non-commercial use. It’s distributed as a bunch of compressed tab-separated values (TSV) files, which get daily updates.

To make your life easier, you can use a Python script included in the sample code. It’ll automatically fetch the relevant file from IMDb, decompress it, and extract the interesting pieces:

$ python download_imdb.py
Fetching data from IMDb...
Created "names.txt" and "sorted_names.txt"

Be warned that this will download and extract approximately 600 MB of data, as well as produce two additional files, which are about half of that in size. The download, as well as the processing of this data, might take a minute or two to complete.

Download IMDb

To manually obtain the data, navigate your web browser to https://datasets.imdbws.com/ and grab the file called name.basics.tsv.gz, which contains the records of actors, directors, writers, and so on. When you decompress the file, you’ll see the following content:

nconst     primaryName      birthYear  deathYear  (...)
nm0000001  Fred Astaire     1899       1987       (...)
nm0000002  Lauren Bacall    1924       2014       (...)
nm0000003  Brigitte Bardot  1934       \N         (...)
nm0000004  John Belushi     1949       1982       (...)

It has a header with the column names in the first line, followed by data records in each of the subsequent lines. Each record contains a unique identifier, a full name, birth year, and a few other attributes. These are all delimited with a tab character.

There are millions of records, so don’t try to open the file with a regular text editor to avoid crashing your computer. Even specialized software such as spreadsheets can have problems opening it. Instead, you might take advantage of the high-performance data grid viewer included in JupyterLab, for example.

Read Tab-Separated Values

There are a few ways to parse a TSV file. For example, you can read it with Pandas, use a dedicated application, or leverage a few command-line tools. However, it’s recommended that you use the hassle-free Python script included in the sample code.

Note: As a rule of thumb, you should avoid parsing files manually because you might overlook edge cases. For example, in one of the fields, the delimiting tab character could be used literally inside quotation marks, which would break the number of columns. Whenever possible, try to find a relevant module in the standard library or a trustworthy third-party one.

Ultimately, you want to end up with two text files at your disposal:

names.txt
sorted_names.txt

One will contain a list of names obtained by cutting out the second column from the original TSV file:

Fred Astaire
Lauren Bacall
Brigitte Bardot
John Belushi
Ingmar Bergman
...

The second one will be the sorted version of this.

Once both files are ready, you can load them into Python using this function:

Read the full article at https://realpython.com/binary-search-python/ »

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Defining Main Functions in Python

2020-03-10T14:00:00+00:00

Many programming languages have a special function that is automatically executed when an operating system starts to run a program. This function is usually called main() and must have a specific return type and arguments according to the language standard. On the other hand, the Python interpreter executes scripts starting at the top of the file, and there is no specific function that Python automatically executes.

Nevertheless, having a defined starting point for the execution of a program is useful for understanding how a program works. Python programmers have come up with several conventions to define this starting point.

By the end of this course, you’ll understand:

What the special __name__ variable is and how Python defines it
Why you would want to use a main() in Python
What conventions there are for defining main() in Python
What the best practices are for what code to put into your main()

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The Real Python Podcast – Episode #0: About the Show

2020-03-06T00:00:00+00:00

A weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community. The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us to hear what's new in the world of Python programming and become a better coder.

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How to Implement a Python Stack

2020-03-03T14:00:00+00:00

Have you heard of stacks and wondered what they are? Do you have a general idea but are wondering how to implement a Python stack? You’ve come to the right place!

In this course, you’ll learn:

How to recognize when a stack is a good choice for a data structure
How to decide which implementation is best for your program
What extra considerations to make about stacks in a threading or multiprocessing environment

This course is for Pythonistas who are comfortable running scripts, know what a list is and how to use it, and are wondering how to implement Python stacks.

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How to Work With a PDF in Python

2020-02-25T14:00:00+00:00

The Portable Document Format or PDF is a file format that can be used to present and exchange documents reliably across operating systems. While the PDF was originally invented by Adobe, it is now an open standard that is maintained by the International Organization for Standardization (ISO). You can work with a preexisting PDF in Python by using the PyPDF2 package.

PyPDF2 is a pure-Python package that you can use for many different types of PDF operations.

By the end of this course, you’ll know how to:

Extract document information from a PDF in Python
Rotate pages
Merge PDFs
Split PDFs
Add watermarks
Encrypt a PDF

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Finding the Perfect Python Code Editor

2020-02-18T14:00:00+00:00

Find your perfect Python development setup with this review of Python IDEs and code editors. Writing Python using IDLE or the Python REPL is great for simple things, but not ideal for larger programming projects. With this course you’ll get an overview of the most common Python coding environments to help you make an informed decision.

By the end of this course, you’ll know how to:

Choose the Python editing environment that’s right for you
Perform common tasks like creating, running, and debugging code
Dig deeper into optimizing your favorite editing setup

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Playing and Recording Sound in Python

2020-02-11T14:00:00+00:00

In this course, you’ll learn how to play and record sound in Python using some of the most popular audio libraries. You’ll learn about the most straightforward methods for playing and recording sound first, and then you’ll learn about some libraries that offer some more functionality in exchange for a few extra lines of code.

By the end of this course, you’ll know how to:

Play MP3 and WAV files, as well as a range of other audio formats
Play NumPy and Python arrays containing sound
Record sound using Python
Save your recordings or audio files in a range of different file formats

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Real Python

Inheritance and Composition: A Python OOP Guide

Combining Data in Pandas With merge(), .join(), and concat()

Pandas merge(): Combining Data on Common Columns or Indices

How to merge()

The Real Python Podcast – Episode #4: Learning Python Through Errors

How to Provide Test Fixtures for Django Models in Pytest

Fixtures in Django

Setting Up a Python Virtual Environment

Setting Up a Django Project

Creating Django Fixtures

Arduino With Python: How to Get Started

How to Make an Instagram Bot With Python and InstaPy

How Instagram Bots Work

How to Automate a Browser

The Real Python Podcast – Episode #3: Effective Python and Python at Google Scale

Linked Lists in Python: An Introduction

Understanding Linked Lists

Main Concepts

Practical Applications

Queues or Stacks

Comparing Python Objects the Right Way: "is" vs "=="

How to Use any() in Python

How to Use any() in Python

The Real Python Podcast – Episode #2: Learn Python Skills While Creating Games

SimPy: Simulating Real-World Processes With Python

What Simulation Is

How Simulation Works

Using NumPy's np.arange() Effectively

The Python math Module: Everything You Need to Know

Getting to Know the Python math Module

Constants of the math Module

Pi

The Real Python Podcast Is Here!

What Is the Real Python Podcast?

The Real Python Podcast – Episode #1: Python Decorators and Writing for Real Python

Python Scope & the LEGB Rule: Resolving Names in Your Code

Understanding Scope

Names and Scopes in Python

Python Scope vs Namespace

Make a 2D Side-Scroller Game With PyGame

How to Do a Binary Search in Python

Benchmarking

Download IMDb

Read Tab-Separated Values

Defining Main Functions in Python

The Real Python Podcast – Episode #0: About the Show

How to Implement a Python Stack

How to Work With a PDF in Python

Finding the Perfect Python Code Editor

Playing and Recording Sound in Python

Pandas `merge()`: Combining Data on Common Columns or Indices

How to `merge()`

How to Use `any()` in Python

Getting to Know the Python `math` Module

Constants of the `math` Module