Real Python

Creating a Discord Bot in Python

2020-06-23T14:00:00+00:00

In a world where video games are so important to so many people, communication and community around games are vital. Discord offers both of those and more in one well-designed package. In this course, you’ll learn how to make a Discord bot in Python so that you can make the most of this fantastic platform.

By the end of this course you’ll learn:

What Discord is and why it’s so valuable
How to make a Discord bot through the Developer Portal
How to create Discord connections
How to handle events
How to accept commands and validate assumptions
How to interact with various Discord APIs

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Hands-On Linear Programming: Optimization With Python

2020-06-22T14:00:00+00:00

Linear programming is a set of techniques used in mathematical programming, sometimes called mathematical optimization, to solve systems of linear equations and inequalities while maximizing or minimizing some linear function. It’s important in fields like scientific computing, economics, technical sciences, manufacturing, transportation, military, management, energy, and so on.

The Python ecosystem offers several comprehensive and powerful tools for linear programming. You can choose between simple and complex tools as well as between free and commercial ones. It all depends on your needs.

In this tutorial, you’ll learn:

What linear programming is and why it’s important
Which Python tools are suitable for linear programming
How to build a linear programming model in Python
How to solve a linear programming problem with Python

You’ll first learn about the fundamentals of linear programming. Then you’ll explore how to implement linear programming techniques in Python. Finally, you’ll look at resources and libraries to help further your linear programming journey.

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Linear Programming Explanation

In this section, you’ll learn the basics of linear programming and a related discipline, mixed-integer linear programming. In the next section, you’ll see some practical linear programming examples. Later, you’ll solve linear programming and mixed-integer linear programming problems with Python.

What Is Linear Programming?

Imagine that you have a system of linear equations and inequalities. Such systems often have many possible solutions. Linear programming is a set of mathematical and computational tools that allows you to find a particular solution to this system that corresponds to the maximum or minimum of some other linear function.

What Is Mixed-Integer Linear Programming?

Mixed-integer linear programming is an extension of linear programming. It handles problems in which at least one variable takes a discrete integer rather than a continuous value. Although mixed-integer problems look similar to continuous variable problems at first sight, they offer significant advantages in terms of flexibility and precision.

Integer variables are important for properly representing quantities naturally expressed with integers, like the number of airplanes produced or the number of customers served.

A particularly important kind of integer variable is the binary variable. It can take only the values zero or one and is useful in making yes-or-no decisions, such as whether a plant should be built or if a machine should be turned on or off. You can also use them to mimic logical constraints.

Why Is Linear Programming Important?

Linear programming is a fundamental optimization technique that’s been used for decades in science- and math-intensive fields. It’s precise, relatively fast, and suitable for a range of practical applications.

Mixed-integer linear programming allows you to overcome many of the limitations of linear programming. You can approximate non-linear functions with piecewise linear functions, use semi-continuous variables, model logical constraints, and more. It’s a computationally intensive tool, but the advances in computer hardware and software make it more applicable every day.

Often, when people try to formulate and solve an optimization problem, the first question is whether they can apply linear programming or mixed-integer linear programming.

Some use cases of linear programming and mixed-integer linear programming are illustrated in the following articles:

The importance of linear programming, and especially mixed-integer linear programming, has increased over time as computers have gotten more capable, algorithms have improved, and more user-friendly software solutions have become available.

Linear Programming With Python

The basic method for solving linear programming problems is called the simplex method, which has several variants. Another popular approach is the interior-point method.

Mixed-integer linear programming problems are solved with more complex and computationally intensive methods like the branch-and-bound method, which uses linear programming under the hood. Some variants of this method are the branch-and-cut method, which involves the use of cutting planes, and the branch-and-price method.

There are several suitable and well-known Python tools for linear programming and mixed-integer linear programming. Some of them are open source, while others are proprietary. Whether you need a free or paid tool depends on the size and complexity of your problem as well as on the need for speed and flexibility.

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

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The Real Python Podcast – Episode #14: Going Serverless with Python

2020-06-19T12:00:00+00:00

Would you like to run your Python code in the cloud without having to become an infrastructure engineer? Do you want to have Python functions that run when triggered by specific events? This week on the show we have Anthony Chu to discuss serverless computing and running python functions in the cloud. Anthony Chu is program manager for Microsoft's Azure Functions.

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PySimpleGUI: The Simple Way to Create a GUI With Python

2020-06-17T14:00:00+00:00

Creating a simple graphical user interface (GUI) that works across multiple platforms can be complicated. But it doesn’t have to be that way. You can use Python and the PySimpleGUI package to create nice-looking user interfaces that you and your users will enjoy! PySimpleGUI is a new Python GUI library that has been gaining a lot of interest recently.

In this tutorial, you’ll learn how to:

Install the PySimpleGUI package
Create basic user interface elements with PySimpleGUI
Create applications, such as a PySimpleGUI image viewer
Integrate PySimpleGUI with Matplotlib
Use computer vision in PySimpleGUI
Package your PySimpleGUI application for Windows

Now it’s time to get started!

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Getting Started With PySimpleGUI

PySimpleGUI was launched in 2018, so it’s a relatively new package compared with the likes of wxPython or PyQt.

PySimpleGUI has four ports:

PySimpleGUI wraps portions of each of these other packages and makes them easier to use. However, each of the ports has to be installed separately.

PySimpleGUI wraps the entirety of Tkinter, which comes with Python. PySimpleGUI has wrapped most of PySide2, but only a small portion of wxPython. When you install PySimpleGUI, you get the Tkinter variant by default. For more information about Tkinter, check out Python GUI Programming With Tkinter.

Depending on which variant of PySimpleGUI you use, applications that you create with PySimpleGUI may not look native to their platform. But don’t let this stop you from giving PySimpleGUI a try. PySimpleGUI is still quite powerful and can get most things done with a little work.

Installing PySimpleGUI

Installing PySimpleGUI is easy if you use pip. For the purposes of this tutorial, you’ll learn how to install the regular PySimpleGUI port, which is the Tkinter variant.

Here’s how to do it:

$ python -m pip install pysimplegui

This will install PySimpleGUI to whatever your system Python is set to. You can also install PySimpleGUI to a Python virtual environment. If you’re unfamiliar with Python virtual environments, then you should read Python Virtual Environments: A Primer.

If you prefer to try the PyQt variant, then you can use pip install PySimpleGUIQt instead. Now that you have PySimpleGUI installed, it’s time to find out how to use it!

Creating Basic UI Elements in PySimpleGUI

If you’ve ever used a GUI toolkit before, then you may have heard the term widgets. A widget is a generic term used to describe the elements that make up the user interface (UI), such as buttons, labels, windows, and more. In PySimpleGUI, widgets are referred to as elements, which you may sometimes see capitalized elsewhere as Elements.

One of the basic building blocks of PySimpleGUI is the Window(). To create a Window(), you can do the following:

# hello_world.py

import PySimpleGUI as sg

sg.Window(title="Hello World", layout=[[]], margins=(100, 50)).read()

Window() takes lots of different arguments—too many to be listed here. However, for this example you can give the Window() a title and a layout and set the margins, which is how big the UI window will be in pixels.

read() returns any events that are triggered in the Window() as a string as well as a values dictionary. You’ll learn more about these in later sections of this tutorial.

When you run this code, you should see something like this:

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

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Python Generators 101

2020-06-16T14:00:00+00:00

Have you ever had to work with a dataset so large that it overwhelmed your machine’s memory? Or maybe you have a complex function that needs to maintain an internal state every time it’s called, but the function is too small to justify creating its own class. In these cases and more, generators and the Python yield statement are here to help.

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

What generators are and how to use them
How to create generator functions and expressions
How the Python yield statement works
How to use multiple Python yield statements in a generator function
How to use advanced generator methods
How to build data pipelines with multiple generators

If you’re a beginner or intermediate Pythonista and you’re interested in learning how to work with large datasets in a more Pythonic way, then this is the course for you.

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Python Keywords: An Introduction

2020-06-15T14:00:00+00:00

Every programming language has special reserved words, or keywords, that have specific meanings and restrictions around how they should be used. Python is no different. Python keywords are the fundamental building blocks of any Python program.

In this article, you’ll find a basic introduction to all Python keywords along with other resources that will be helpful for learning more about each keyword.

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

Identify Python keywords
Understand what each keyword is used for
Work with keywords programmatically using the keyword module

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Python Keywords

Python keywords are special reserved words that have specific meanings and purposes and can’t be used for anything but those specific purposes. These keywords are always available—you’ll never have to import them into your code.

Python keywords are different from Python’s built-in functions and types. The built-in functions and types are also always available, but they aren’t as restrictive as the keywords in their usage.

An example of something you can’t do with Python keywords is assign something to them. If you try, then you’ll get a SyntaxError. You won’t get a SyntaxError if you try to assign something to a built-in function or type, but it still isn’t a good idea. For a more in-depth explanation of ways keywords can be misused, check out Invalid Syntax in Python: Common Reasons for SyntaxError.

As of Python 3.8, there are thirty-five keywords in Python. Here they are with links to the relevant sections throughout the rest of this article:

You can use these links to jump to the keywords you’d like to read about, or you can continue reading for a guided tour.

Note: Two keywords have additional uses beyond their initial use cases. The else keyword is also used with loops as well as with try and except. The as keyword is also used with the with keyword.

How to Identify Python Keywords

The list of Python keywords has changed over time. For example, the await and async keywords weren’t added until Python 3.7. Also, both print and exec were keywords in Python 2.7 but have been turned into built-in functions in Python 3+ and no longer appear in the list of keywords.

In the sections below, you’ll learn several ways to know or find out which words are keywords in Python.

Use an IDE With Syntax Highlighting

There are a lot of good Python IDEs out there. All of them will highlight keywords to differentiate them from other words in your code. This will help you quickly identify Python keywords while you’re programming so you don’t use them incorrectly.

Use Code in a REPL to Check Keywords

In the Python REPL, there are a number of ways you can identify valid Python keywords and learn more about them.

Note: Code examples in this article use Python 3.8 unless otherwise indicated.

You can get a list of available keywords by using help():

>>>

>>> help("keywords")

Here is a list of the Python keywords.  Enter any keyword to get more help.

False               class               from                or
None                continue            global              pass
True                def                 if                  raise
and                 del                 import              return
as                  elif                in                  try
assert              else                is                  while
async               except              lambda              with
await               finally             nonlocal            yield
break               for                 not

Next, as indicated in the output above, you can use help() again by passing in the specific keyword that you need more information about. You can do this, for example, with the pass keyword:

>>>

>>> help("pass")
The "pass" statement
********************

   pass_stmt ::= "pass"

"pass" is a null operation — when it is executed, nothing happens. It
is useful as a placeholder when a statement is required syntactically,
but no code needs to be executed, for example:

   def f(arg): pass    # a function that does nothing (yet)

   class C: pass       # a class with no methods (yet)

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

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The Real Python Podcast – Episode #13: PDFs in Python and Projects on the Raspberry Pi

2020-06-12T12:00:00+00:00

Have you wanted to work with PDF files in Python? Maybe you want to extract text, merge and concatenate files, or even create PDFs from scratch. Are you interested in building hardware projects using a Raspberry Pi? This week on the show we have David Amos from the Real Python team to discuss his recent article on working with PDFs. David also brings a few other articles from the wider Python community for us to discuss.

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SettingWithCopyWarning in Pandas: Views vs Copies

2020-06-10T14:00:00+00:00

NumPy and Pandas are very comprehensive, efficient, and flexible Python tools for data manipulation. An important concept for proficient users of these two libraries to understand is how data are referenced as shallow copies (views) and deep copies (or just copies). Pandas sometimes issues a SettingWithCopyWarning to warn the user of a potentially inappropriate use of views and copies.

In this article, you’ll learn:

What views and copies are in NumPy and Pandas
How to properly work with views and copies in NumPy and Pandas
Why the SettingWithCopyWarning happens in Pandas
How to avoid getting a SettingWithCopyWarning in Pandas

You’ll first see a short explanation of what the SettingWithCopyWarning is and how to avoid it. You might find this enough for your needs, but you can also dig a bit deeper into the details of NumPy and Pandas to learn more about copies and views.

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Prerequisites

To follow the examples in this article, you’ll need Python 3.7 or 3.8, as well as the libraries NumPy and Pandas. This article is written for NumPy version 1.18.1 and Pandas version 1.0.3. You can install them with pip:

$ python -m pip install -U "numpy==1.18.*" "pandas==1.0.*"

If you prefer Anaconda or Miniconda distributions, you can use the conda package management system. To learn more about this approach, check out Setting Up Python for Machine Learning on Windows. For now, it’ll be enough to install NumPy and Pandas in your environment:

$ conda install numpy=1.18.* pandas=1.0.*

Now that you have NumPy and Pandas installed, you can import them and check their versions:

>>>

>>> import numpy as np
>>> import pandas as pd

>>> np.__version__
'1.18.1'
>>> pd.__version__
'1.0.3'

That’s it. You have all the prerequisites for this article. Your versions might vary slightly, but the information below will still apply.

Note: This article requires you to have some prior Pandas knowledge. You’ll also need some knowledge of NumPy for the later sections.

To refresh your NumPy skills, you can check out the following resources:

To remind yourself about Pandas, you can read the following:

Now you’re ready to start learning about views, copies, and the SettingWithCopyWarning!

Example of a `SettingWithCopyWarning`

If you work with Pandas, chances are that you’ve already seen a SettingWithCopyWarning in action. It can be annoying and sometimes hard to understand. However, it’s issued for a reason.

The first thing you should know about the SettingWithCopyWarning is that it’s not an error. It’s a warning. It warns you that you’ve probably done something that’s going to result in unwanted behavior in your code.

Let’s see an example. You’ll start by creating a Pandas DataFrame:

>>>

>>> data = {"x": 2**np.arange(5),
...         "y": 3**np.arange(5),
...         "z": np.array([45, 98, 24, 11, 64])}

>>> index = ["a", "b", "c", "d", "e"]

>>> df = pd.DataFrame(data=data, index=index)
>>> df
    x   y   z
a   1   1  45
b   2   3  98
c   4   9  24
d   8  27  11
e  16  81  64

This example creates a dictionary referenced by the variable data that contains:

The keys "x", "y", and "z", which will be the column labels of the DataFrame
Three NumPy arrays that hold the data of the DataFrame

You create the first two arrays with the routine numpy.arange() and the last one with numpy.array(). To learn more about arange(), check out NumPy arange(): How to Use np.arange().

The list attached to the variable index contains the strings "a", "b", "c", "d", and "e", which will be the row labels for the DataFrame.

Finally, you initialize the DataFrame df that contains the information from data and index. You can visualize it like this:

Read the full article at https://realpython.com/pandas-settingwithcopywarning/ »

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Getting the Most Out of a Python Traceback

2020-06-09T14:00:00+00:00

Python prints a traceback when an exception is raised in your code. The traceback output can be a bit overwhelming if you’re seeing it for the first time or you don’t know what it’s telling you. But the Python traceback has a wealth of information that can help you diagnose and fix the reason for the exception being raised in your code. Understanding what information a Python traceback provides is vital to becoming a better Python programmer.

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

Make sense of the next traceback you see
Recognize some of the more common tracebacks
Log a traceback successfully while still handling the exception

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Python Community Interview With Kattni Rembor

2020-06-08T14:00:00+00:00

This week, I’m joined by Kattni Rembor, a creative engineer at Adafruit Industries. Kattni’s role is varied, as she covers embedded software, hardware design, technical writing, and community leadership.

In this interview, we talk about her work developing CircuitPython and the role mentorship has played in her career to date. She also shares her advice for anyone looking to start their first hardware project using CircuitPython.

Ricky: Welcome to Real Python, Kattni. I’m so happy that you could join me for this interview. Let’s start in the same manner we do with all our guests, with the inevitable question: How’d you get into programming, and when did you start using Python?

Kattni: Those two questions are one and the same for me. I started with programming in July 2017, and I started with Python. I was unemployed and had a lot of time on my hands.

I decided to try to learn Python. I looked around and figured the official tutorial was the best place to start. It turns out that it’s written for programmers, not beginners. I got to section four, hit a wall, and gave up.

The hardware side of things began with a sale on Raspberry Pi Zero Ws and a friend of mine giving me one. I immediately did the thing you always do after getting a Raspberry Pi, and that is to buy all the things to go with your Raspberry Pi.

I found an accessory called Sense HAT that had a bunch of sensors and LEDs built into it, but it wasn’t designed for the Raspberry Pi Zero W, so I tried to recreate it with individual sensors, which got expensive quickly.

I discovered something called Circuit Playground Express that also had a lot of sensors and LEDs built into it and figured that would work. I ordered one, having no idea that it wasn’t compatible with the Raspberry Pi. It turns out it was a microcontroller.

I got it home and took one look at it, decided it was entirely too complicated, and put it aside for two weeks before finally picking it up and plugging it in. I remember thinking, I will never write anything as cool as this demo. It was a rainbow swirl on the LEDs that played a tone for each LED that lit up.

I looked into what I could do with the Circuit Playground Express. There were three options. Arduino went right over my head, and I had no desire whatsoever to try to learn it. MakeCode was just simple enough to be frustrating. I found a single mention of something called CircuitPython and thought, Hey, I’m trying to learn Python—this is perfect!

I found an Adafruit video on getting started, got it installed, and within a very short period of time, I had a blinking LED. As simple as it sounds, nothing I had done with Python up to that point hooked me as much as that moment. For the first time since attempting to learn Python, I felt a connection to what I was doing. I had found my passion.

Ricky: Adafruit’s microcontrollers are famously embedded with CircuitPython. For those who aren’t familiar with CircuitPython, how is this different from other microcontrollers? And how has this impacted the way you instruct in workshops and with Adafruit’s Learn tutorials?

Kattni: The difference depends on which microcontrollers you’re referring to. Arduino is basically C++, often uses an IDE, and requires being compiled before you can load it onto a board. MicroPython, while a version of Python, requires complicated extra steps to get going.

Read the full article at https://realpython.com/interview-kattni-rembor/ »

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The Real Python Podcast – Episode #12: Web Scraping in Python: Tools, Techniques, and Legality

2020-06-05T12:00:00+00:00

Do you want to get started with web scraping using Python? Are you concerned about the potential legal implications? What are the tools required and what are some of the best practices? This week on the show we have Kimberly Fessel to discuss her excellent tutorial created for PyCon 2020 online titled "It's Officially Legal so Let's Scrape the Web."

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Regular Expressions: Regexes in Python (Part 2)

2020-06-03T14:00:00+00:00

In the previous tutorial in this series, you covered a lot of ground. You saw how to use re.search() to perform pattern matching with regexes in Python and learned about the many regex metacharacters and parsing flags that you can use to fine-tune your pattern-matching capabilities.

But as great as all that is, the re module has much more to offer.

In this tutorial, you’ll:

Explore more functions, beyond re.search(), that the re module provides
Learn when and how to precompile a regex in Python into a regular expression object
Discover useful things that you can do with the match object returned by the functions in the re module

Ready? Let’s dig in!

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`re` Module Functions

In addition to re.search(), the re module contains several other functions to help you perform regex-related tasks.

Note: You saw in the previous tutorial that re.search() can take an optional <flags> argument, which specifies flags that modify parsing behavior. All the functions shown below, with the exception of re.escape(), support the <flags> argument in the same way.

You can specify <flags> as either a positional argument or a keyword argument:

re.search(<regex>, <string>, <flags>)
re.search(<regex>, <string>, flags=<flags>)

The default for <flags> is always 0, which indicates no special modification of matching behavior. Remember from the discussion of flags in the previous tutorial that the re.UNICODE flag is always set by default.

The available regex functions in the Python re module fall into the following three categories:

Searching functions
Substitution functions
Utility functions

The following sections explain these functions in more detail.

Searching Functions

Searching functions scan a search string for one or more matches of the specified regex:

Function	Description
`re.search()`	Scans a string for a regex match
`re.match()`	Looks for a regex match at the beginning of a string
`re.fullmatch()`	Looks for a regex match on an entire string
`re.findall()`	Returns a list of all regex matches in a string
`re.finditer()`	Returns an iterator that yields regex matches from a string

As you can see from the table, these functions are similar to one another. But each one tweaks the searching functionality in its own way.

re.search(<regex>, <string>, flags=0)

Scans a string for a regex match.

If you worked through the previous tutorial in this series, then you should be well familiar with this function by now. re.search(<regex>, <string>) looks for any location in <string> where <regex> matches:

>>>

>>> re.search(r'(\d+)', 'foo123bar')
<_sre.SRE_Match object; span=(3, 6), match='123'>
>>> re.search(r'[a-z]+', '123FOO456', flags=re.IGNORECASE)
<_sre.SRE_Match object; span=(3, 6), match='FOO'>

>>> print(re.search(r'\d+', 'foo.bar'))
None

The function returns a match object if it finds a match and None otherwise.

re.match(<regex>, <string>, flags=0)

Looks for a regex match at the beginning of a string.

Read the full article at https://realpython.com/regex-python-part-2/ »

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Parallel Iteration With Python's zip() Function

2020-06-02T14:00:00+00:00

Python’s zip() function creates an iterator that will aggregate elements from two or more iterables. You can use the resulting iterator to quickly and consistently solve common programming problems, like creating dictionaries. In this course, you’ll discover the logic behind the Python zip() function and how you can use it to solve real-world problems.

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

How zip() works in both Python 3 and Python 2
How to use the Python zip() function for parallel iteration
How to create dictionaries on the fly using zip()

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Build Physical Projects With Python on the Raspberry Pi

2020-06-01T14:00:00+00:00

The Raspberry Pi is one of the leading physical computing boards on the market. From hobbyists building DIY projects to students learning to program for the first time, people use the Raspberry Pi every day to interact with the world around them. Python comes built in on the Raspberry Pi, so you can take your skills and start building your own Raspberry Pi projects today.

In this tutorial, you’ll learn to:

Set up a new Raspberry Pi
Run Python on the Raspberry Pi using the Mu editor or remotely over SSH
Read input from physical sensors connected to the Raspberry Pi
Send output to external components using Python
Create unique projects with Python on the Raspberry Pi

Let’s get started!

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Getting to Know the Raspberry Pi

The Raspberry Pi is a single-board computer developed by the Raspberry Pi Foundation, a UK-based charity organization. Originally designed to provide young people with an affordable computing option to learn how to program, it has developed a massive following in the maker and DIY communities because of its compact size, full Linux environment, and general-purpose input–output (GPIO) pins.

With all the features and capabilities that are packed into this small board, there’s no shortage of projects and use cases for the Raspberry Pi.

Some example projects include the following:

If you can think of a project that would benefit from having a credit card–sized computer attached to it, then someone has probably used a Raspberry Pi to do it. The Raspberry Pi is a fantastic way to bring your Python project ideas to life.

Raspberry Pi Board Overview

The Raspberry Pi comes in a variety of form factors for different use cases. In this tutorial, you’ll be looking at the most recent version, the Raspberry Pi 4.

Below is the board layout of the Raspberry Pi 4. While this layout is slightly different from previous models of the Raspberry Pi, most of the connections are the same. The setup described in the next section should be the same for both a Raspberry Pi 3 and a Raspberry Pi 4:

The Raspberry Pi 4 board contains the following components:

General-purpose input–output pins: These pins are used to connect the Raspberry Pi to electronic components.
Ethernet port: This port connects the Raspberry Pi to a wired network. The Raspberry Pi also has WiFi and Bluetooth built in for wireless connections.
Two USB 3.0 and two USB 2.0 ports: These USB ports are used to connect peripherals like a keyboard or mouse. The two black ports are USB 2.0 and the two blue ports are USB 3.0.
AV jack: This AV jack allows you to connect speakers or headphones to the Raspberry Pi.
Camera Module port: This port is used to connect the official Raspberry Pi Camera Module, which enables the Raspberry Pi to capture images.
HDMI ports: These HDMI ports connect the Raspberry Pi to external monitors. The Raspberry Pi 4 features two micro HDMI ports, allowing it to drive two separate monitors at the same time.
USB power port: This USB port powers the Raspberry Pi. The Raspberry Pi 4 has a USB Type-C port, while older versions of the Pi have a micro-USB port.
External display port: This port is used to connect the official seven-inch Raspberry Pi touch display for touch-based input on the Raspberry Pi.
microSD card slot (underside of the board): This card slot is for the microSD card that contains the Raspberry Pi operating system and files.

A little later in this tutorial, you’ll use the components above to set up your Raspberry Pi.

Raspberry Pi vs Arduino

People often wonder what the difference is between a Raspberry Pi and an Arduino. The Arduino is another device that is widely used in physical computing. While there is some overlap in the capabilities of the Arduino and the Raspberry Pi, there are some distinct differences.

The Arduino platform provides a hardware and software interface for programming microcontrollers. A microcontroller is an integrated circuit that allows you to read input from and send output to electronic components. Arduino boards generally have limited memory, so they’re often used to repeatedly run a single program that interacts with electronics.

The Raspberry Pi is a general-purpose, Linux-based computer. It has a full operating system with a GUI interface that is capable of running many different programs at the same time.

The Raspberry Pi comes with a variety of software preinstalled, including a web browser, an office suite, a terminal, and even Minecraft. The Raspberry Pi also has built-in WiFi and Bluetooth to connect to the Internet and external peripherals.

For running Python, the Raspberry Pi is often the better choice, as you get a full-fledged Python installation out of the box without any configuration.

Setting Up the Raspberry Pi

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

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The Real Python Podcast – Episode #11: Advice on Getting Started With Testing in Python

2020-05-29T12:00:00+00:00

Have you wanted to get started with testing in Python? Maybe you feel a little nervous about diving in deeper than just confirming your code runs. What are the tools needed and what would be the next steps to level up your Python testing? This week on the show we have Anthony Shaw to discuss his article on this subject. Anthony is a member of the Real Python team and has written several articles for the site.

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How to Write an Installable Django App

2020-05-27T14:00:00+00:00

In the Django framework, a project refers to the collection of configuration files and code for a particular website. Django groups business logic into what it calls apps, which are the modules of the Django framework. There’s plenty of documentation on how to structure your projects and the apps within them, but when it comes time to package an installable Django app, information is harder to find.

In this tutorial, you’ll learn how to take an app out of a Django project and package it so that it’s installable. Once you’ve packaged your app, you can share it on PyPI so that others can fetch it through pip install.

In this tutorial, you’ll learn:

What the differences are between writing stand-alone apps and writing apps inside of projects
How to create a setup.cfg file for publishing your Django app
How to bootstrap Django outside of a Django project so you can test your app
How to test across multiple versions of Python and Django using tox
How to publish your installable Django app to PyPI using Twine

Be sure to follow along with the examples by downloading the source code at the link below:

Download the sample code: Click here to get the code you'll use to learn how to write an installable Django app in this tutorial.

Prerequisites

This tutorial requires some familiarity with Django, pip, PyPI, pyenv (or an equivalent virtual environment tool), and tox. To learn more about these topics, check out:

Starting a Sample Django App in a Project

This tutorial includes a working package to help guide you through the process of making an installable Django app. You can download the source code at the link below:

Download the sample code: Click here to get the code you'll use to learn how to write an installable Django app in this tutorial.

Even if you originally intend to make your Django app available as a package, you’re likely to start inside a project. To demonstrate the process of moving from Django project to installable Django app, I’ve made two branches available in the repo. The project branch is the starting state of an app inside of a Django project. The master branch is the finished installable app.

You can also download the finished app at the PyPI realpython-django-receipts package page. You can install the package by running pip install realpython-django-receipts.

The sample app is a short representation of the line items on a receipt. In the project branch, you’ll find a directory named sample_project that contains a working Django project. The directory looks like this:

sample_project/
│
├── receipts/
│   ├── fixtures/
│   │   └── receipts.json
│   │
│   ├── migrations/
│   │   ├── 0001_initial.py
│   │   └── __init__.py
│   │
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── tests.py
│   ├── urls.py
│   └── views.py
│
├── sample_project/
│   ├── __init__.py
│   ├── asgi.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
│
├── db.sqlite3
├── manage.py
├── resetdb.sh
└── runserver.sh

The most current version of Django at the time this tutorial was written was 3.0.4, and all testing was done with Python 3.7. None of the steps outlined in this tutorial should be incompatible with earlier versions of Django—I’ve used these techniques since Django 1.8. However, some changes are necessary if you’re using Python 2. To keep the examples simple, I’ve assumed Python 3.7 across the code base.

Creating the Django Project From Scratch

The sample project and receipts app were created using the Django admin command and some small edits. To start, run the following code inside of a clean virtual environment:

$ python -m pip install Django
$ django-admin startproject sample_project
$ cd sample_project
$ ./manage.py startapp receipts

This creates the sample_project project directory structure and a receipts app subdirectory with template files that you’ll use to create your installable Django app.

Next, the sample_project/settings.py file needs a few modifications:

Add '127.0.0.1' to the ALLOWED_HOSTS setting so you can test locally.
Add 'receipts' to the INSTALLED_APPS list.

You’ll also need to register the receipts app’s URLs in the sample_project/urls.py file. To do so, add path('receipts/', include('receipts.urls')) to the url_patterns list.

Exploring the Receipts Sample App

Read the full article at https://realpython.com/installable-django-app/ »

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A Beginner's Guide to Pip

2020-05-26T14:00:00+00:00

What is pip? pip is the standard package manager for Python. It allows you to install and manage additional packages that are not part of the Python standard library. This course is an introduction to pip for new Pythonistas.

In this course, you’ll learn about:

Installing additional packages not included with the standard Python distribution
Finding packages published to the Python Package Index (PyPI)
Managing requirements for your scripts and applications
Uninstalling packages and their dependencies

As you’ll see, the Python community is very active and has created some neat alternatives to pip that you’ll learn about later in this course.

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Create and Modify PDF Files in Python

2020-05-25T14:00:00+00:00

It’s really useful to know how to create and modify PDF files in Python. The PDF, or Portable Document Format, is one of the most common formats for sharing documents over the Internet. PDFs can contain text, images, tables, forms, and rich media like videos and animations, all in a single file.

This abundance of content types can make working with PDFs difficult. There are a lot of different kinds of data to decode when opening a PDF file! Fortunately, the Python ecosystem has some great packages for reading, manipulating, and creating PDF files.

In this tutorial, you’ll learn how to:

Read text from a PDF
Split a PDF into multiple files
Concatenate and merge PDF files
Rotate and crop pages in a PDF file
Encrypt and decrypt PDF files with passwords
Create a PDF file from scratch

Note: This tutorial is adapted from the chapter “Creating and Modifying PDF Files” in Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you will see occasional references to IDLE throughout this article. However, you should have no problems running the example code from the editor and environment of your choice.

Along the way, you’ll have several opportunities to deepen your understanding by following along with the examples. You can download the materials used in the examples by clicking on the link below:

Download the sample materials: Click here to get the materials you'll use to learn about creating and modifying PDF files in this tutorial.

Extracting Text From a PDF

In this section, you’ll learn how to read a PDF file and extract the text using the PyPDF2 package. Before you can do that, though, you need to install it with pip:

$ python3 -m pip install PyPDF2

Verify the installation by running the following command in your terminal:

$ python3 -m pip show PyPDF2
Name: PyPDF2
Version: 1.26.0
Summary: PDF toolkit
Home-page: http://mstamy2.github.com/PyPDF2
Author: Mathieu Fenniak
Author-email: biziqe@mathieu.fenniak.net
License: UNKNOWN
Location: c:\\users\\david\\python38-32\\lib\\site-packages
Requires:
Required-by:

Pay particular attention to the version information. At the time of writing, the latest version of PyPDF2 was 1.26.0. If you have IDLE open, then you’ll need to restart it before you can use the PyPDF2 package.

Opening a PDF File

Let’s get started by opening a PDF and reading some information about it. You’ll use the Pride_and_Prejudice.pdf file located in the practice_files/ folder in the companion repository.

Open IDLE’s interactive window and import the PdfFileReader class from the PyPDF2 package:

>>>

>>> from PyPDF2 import PdfFileReader

To create a new instance of the PdfFileReader class, you’ll need the path to the PDF file that you want to open. Let’s get that now using the pathlib module:

>>>

>>> from pathlib import Path
>>> pdf_path = (
...     Path.home()
...     / "creating-and-modifying-pdfs"
...     / "practice_files"
...     / "Pride_and_Prejudice.pdf"
... )

The pdf_path variable now contains the path to a PDF version of Jane Austen’s Pride and Prejudice.

Note: You may need to change pdf_path so that it corresponds to the location of the creating-and-modifying-pdfs/ folder on your computer.

Now create the PdfFileReader instance:

>>>

>>> pdf = PdfFileReader(str(pdf_path))

You convert pdf_path to a string because PdfFileReader doesn’t know how to read from a pathlib.Path object.

Recall from chapter 12, “File Input and Output,” that all open files should be closed before a program terminates. The PdfFileReader object does all of this for you, so you don’t need to worry about opening or closing the PDF file!

Read the full article at https://realpython.com/creating-modifying-pdf/ »

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The Real Python Podcast – Episode #10: Python Job Hunting in a Pandemic

2020-05-22T12:00:00+00:00

Do you know someone in the Python community who recently was let go from their job due to the pandemic? What does the job landscape currently look like? What are skills and techniques that will help you in your job search? This week we have Kyle Stratis on the show to discuss how he is managing his job search after just being let go from his data engineering job. Kyle is a member of the Real Python team and has written several articles for the site.

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Python Community Interview With Russell Keith-Magee

2020-05-20T14:00:00+00:00

Today I’m joined by Russell Keith-Magee. Russell is a Django core developer and the founder and maintainer of the BeeWare project. In the interview, we talk about his mission to help Python become a feasible option for writing and packaging mobile applications as well as his passion for open source projects. We also touch on his PyCon 2020 talk, which has since been recorded and uploaded to the PyCon YouTube channel.

So, without further ado, let’s get into it!

Ricky: Welcome to Real Python, Russell. Thanks for joining me for this interview. Let’s start the same way we always do. How’d you get into programming, and when did you start using Python?

Russell: My father has always been an early adopter of technology, and as a result, when I was eight or nine, he brought home one of the very first Commodore 64s imported into Australia.

As with many computers of that era, you couldn’t avoid programming with the Commodore 64 because when you turned it on, you were dropped directly into a BASIC programming environment.

There were also lots of children’s books around at the time that included program code you could type in. Those books would sell themselves as being SUPER EXCITING SPACE GAMES, but they were really just guess-the-random-number games with really good book art. However, that was enough to pique my interest.

Putting an exact date on when I started using Python is difficult. I remember coming across it in the late 1990s, around the Python 1.5 time frame, as a language being mostly used for Red Hat configuration management. My serious usage started around 2003, when I was starting to get into web programming. By the time I was a Django contributor, my day job became full-time Python—and it has been ever since.

Interestingly, my origin story and my Python background collided recently. Usborne, the publisher of some of my favorite Commodore-era programming books, open sourced the contents of their books, so I started a bit of a fun project to port the books to Python.

Ricky: Even though you’re one of Django’s core developers, would it be fair to say that you spend most of your open source time these days on the BeeWare project? For those who have yet to hear of BeeWare, what is it and what inspired you to start the project?

Read the full article at https://realpython.com/interview-russell-keith-magee/ »

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Convert a Python String to int

2020-05-19T14:00:00+00:00

Integers are whole numbers. In other words, they have no fractional component. Two data types you can use to store an integer in Python are int and str. These types offer flexibility for working with integers in different circumstances. In this course, you’ll learn how you can convert a Python string to an int. You’ll also learn how to convert an int to a string.

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

Store integers using str and int
Convert a Python string to an int
Convert a Python int to a string

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The Real Python Podcast – Episode #9: Leveling Up Your Python Literacy and Finding Python Projects to Study

2020-05-15T12:00:00+00:00

In your quest to become a better developer, how do you find Python code that is at your reading level? What are good code bases or projects to study? What are the things holding you back from leveling up your Python literacy? This week we have Cecil Phillip on the show to discuss all of these common questions. Cecil is a Senior Cloud Advocate at Microsoft.

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Improve Your Tests With the Python Mock Object Library

2020-05-12T14:00:00+00:00

When you’re writing robust code, tests are essential for verifying that your application logic is correct, reliable, and efficient. However, the value of your tests depends on how well they demonstrate these criteria. Obstacles such as complex logic and unpredictable dependencies make writing valuable tests difficult. The Python mock object library, unittest.mock, can help you overcome these obstacles.

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

Create Python mock objects using Mock
Assert that you’re using objects as you intended
Inspect usage data stored on your Python mocks
Configure certain aspects of your Python mock objects
Substitute your mocks for real objects using patch()
Avoid common problems inherent in Python mocking

You’ll begin by seeing what mocking is and how it will improve your tests!

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The Real Python Podcast – Episode #8: Docker + Python for Data Science and Machine Learning

2020-05-08T12:00:00+00:00

Docker is a common tool for Python developers creating and deploying applications, but what do you need to know if you want to use Docker for data science and machine learning? What are the best practices if you want to start using containers for your scientific projects? This week we have Tania Allard on the show. She is a Sr. Developer Advocate at Microsoft focusing on Machine Learning, scientific computing, research and open source.

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The Python print() Function: Go Beyond the Basics

2020-05-05T14:00:00+00:00

If you’re like most Python users, including us, then you probably started your Python journey by learning about print(). It helped you write your very own Hello Horld one-liner. You can use it to display formatted messages onto the screen and perhaps find some bugs. But if you think that’s all there is to know about Python’s print(), then you’re missing out on a lot!

Keep reading to take full advantage of this underappreciated little function. This course will get you up to speed with using Python print() effectively. Prepare for a deep dive as you go through the sections. You may be surprised how much print() has to offer!

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

Avoid common mistakes with Python’s print()
Deal with newlines, character encodings, and buffering
Write text to files
Mock print() in unit tests
Build advanced user interfaces in the terminal

Note: print() was a major addition to Python 3, in which it replaced the old print statement available in Python 2.

There were a number of good reasons for that, as you’ll see shortly. Although this tutorial focuses on Python 3, it does show the old way of printing in Python for reference.

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The Real Python Podcast – Episode #7: AsyncIO + Music, Origins of Black, and Managing Python Releases

2020-05-01T12:00:00+00:00

Want to learn more about AsyncIO in Python, with an example where you can see and hear events being triggered in real-time? This week we have Łukasz Langa on the show. Łukasz has created a talk for PyCon 2020 online about using AsyncIO with Music.

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Structuring a Python Application

2020-04-28T14:00:00+00:00

Python, though opinionated on syntax and style, is surprisingly flexible when it comes to structuring your applications. On the one hand, this flexibility is great: it allows different use cases to use structures that are necessary for those use cases. On the other hand, though, it can be very confusing to the new developer. The Internet isn’t a lot of help either. There are as many opinions as there are Python blogs!

In this course, you’ll:

Walk through a dependable Python application layout reference guide that you can refer to for most use cases
See examples of common Python application structures, including command-line applications (CLI apps), one-off scripts, installable packages, and web application layouts with popular frameworks like Flask and Django

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The Real Python Podcast – Episode #6: Python REST APIs and The Well-Grounded Python Developer

2020-04-24T12:00:00+00:00

Are you interested in building REST APIs with Flask and SQLAlchemy? This week we have Doug Farrell on the show. We talk about his four-part Real Python article series on Python REST APIs.

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Python Coding Interviews: Tips & Best Practices

2020-04-21T14:00:00+00:00

You’ve made it past the phone call with the recruiter, and now it’s time to show that you know how to solve problems with actual code. Whether it’s a HackerRank exercise, a take-home assignment, or an onsite whiteboard interview, this is your moment to prove your coding interview skills.

But interviews aren’t just about solving problems: they’re also about showing that you can write clean production code. This means that you have a deep knowledge of Python’s built-in functionality and libraries. This knowledge shows companies that you can move quickly and won’t duplicate functionality that comes with the language just because you don’t know it exists.

At Real Python, we’ve put our heads together and discussed what tools we’re always impressed to see in coding interviews. This course will walk you through the best of that functionality, starting with Python built-ins, then Python’s native support for data structures, and finally Python’s powerful (and often underappreciated) standard library.

In this course, you’ll learn how to:

Use enumerate() to iterate over both indices and values
Debug problematic code with breakpoint()
Format strings effectively with f-strings
Sort lists with custom arguments
Use generators instead of list comprehensions to conserve memory
Define default values when looking up dictionary keys
Count hashable objects with the collections.Counter class
Use the standard library to get lists of permutations and combinations

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The Real Python Podcast – Episode #5: Exploring CircuitPython

2020-04-17T12:00:00+00:00

Have you ever wanted to explore using Python with electronics? CircuitPython is a great platform to get started with. This week we have Thea Flowers on the show. Thea has been creating several hardware projects based around CircuitPython, and she talks about getting started on the platform.

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`False`	`await`	`else`	`import`	`pass`
`None`	`break`	`except`	`in`	`raise`
`True`	`class`	`finally`	`is`	`return`
`and`	`continue`	`for`	`lambda`	`try`
`as`	`def`	`from`	`nonlocal`	`while`
`assert`	`del`	`global`	`not`	`with`
`async`	`elif`	`if`	`or`	`yield`

Real Python

Creating a Discord Bot in Python

Hands-On Linear Programming: Optimization With Python

Linear Programming Explanation

What Is Linear Programming?

What Is Mixed-Integer Linear Programming?

Why Is Linear Programming Important?

Linear Programming With Python

The Real Python Podcast – Episode #14: Going Serverless with Python

PySimpleGUI: The Simple Way to Create a GUI With Python

Getting Started With PySimpleGUI

Installing PySimpleGUI

Creating Basic UI Elements in PySimpleGUI

Python Generators 101

Python Keywords: An Introduction

Python Keywords

How to Identify Python Keywords

Use an IDE With Syntax Highlighting

Use Code in a REPL to Check Keywords

The Real Python Podcast – Episode #13: PDFs in Python and Projects on the Raspberry Pi

SettingWithCopyWarning in Pandas: Views vs Copies

Prerequisites

Example of a SettingWithCopyWarning

Getting the Most Out of a Python Traceback

Python Community Interview With Kattni Rembor

The Real Python Podcast – Episode #12: Web Scraping in Python: Tools, Techniques, and Legality

Regular Expressions: Regexes in Python (Part 2)

re Module Functions

Searching Functions

Parallel Iteration With Python's zip() Function

Build Physical Projects With Python on the Raspberry Pi

Getting to Know the Raspberry Pi

Raspberry Pi Board Overview

Raspberry Pi vs Arduino

Setting Up the Raspberry Pi

The Real Python Podcast – Episode #11: Advice on Getting Started With Testing in Python

How to Write an Installable Django App

Prerequisites

Starting a Sample Django App in a Project

Creating the Django Project From Scratch

Exploring the Receipts Sample App

A Beginner's Guide to Pip

Create and Modify PDF Files in Python

Extracting Text From a PDF

Opening a PDF File

The Real Python Podcast – Episode #10: Python Job Hunting in a Pandemic

Python Community Interview With Russell Keith-Magee

Convert a Python String to int

The Real Python Podcast – Episode #9: Leveling Up Your Python Literacy and Finding Python Projects to Study

Improve Your Tests With the Python Mock Object Library

The Real Python Podcast – Episode #8: Docker + Python for Data Science and Machine Learning

The Python print() Function: Go Beyond the Basics

The Real Python Podcast – Episode #7: AsyncIO + Music, Origins of Black, and Managing Python Releases

Structuring a Python Application

The Real Python Podcast – Episode #6: Python REST APIs and The Well-Grounded Python Developer

Python Coding Interviews: Tips & Best Practices

The Real Python Podcast – Episode #5: Exploring CircuitPython

Example of a `SettingWithCopyWarning`

`re` Module Functions