Real Python

Get Started With Django Part 2: Django User Management

2020-07-01T14:00:00+00:00

If you finished the first part of this series, then you may already have a lot of ideas for your own Django applications. At some point, you might decide to extend them with user accounts. In this step-by-step tutorial, you’ll learn how to work with Django user management and add it to your program.

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

Create an application where users can register, log in, and reset and change passwords on their own
Edit the default Django templates responsible for user management
Send password reset emails to actual email addresses
Authenticate using an external service

Let’s get started!

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Set Up a Django Project

This tutorial uses Django 3.0 and Python 3.6. It focuses on user management, so you won’t use any advanced or responsive styling. It also doesn’t deal with groups and permissions, only with creating and managing user accounts.

It’s a good idea to use a virtual environment when working with Python projects. That way, you can always be sure that the python command points to the right version of Python and that the modules required by your project have correct versions. To read more about it, check out Python Virtual Environments: A Primer.

To set up a virtual environment on Linux and macOS, run these commands:

$ python3 -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install --upgrade pip
(venv) $ python -m pip install django

To activate a virtual environment on Windows, run this command:

C:\> venv\Scripts\activate

Now that the environment is ready, you can create a new project and an application to store all your user management code:

(venv) $ django-admin startproject awesome_website
(venv) $ cd awesome_website
(venv) $ python manage.py startapp users

In this example, your application is called users. Keep in mind that you need to install it by adding it to INSTALLED_APPS:

# awesome_website/settings.py

INSTALLED_APPS = [
    "users",
    "django.contrib.admin",
    "django.contrib.auth",
    "django.contrib.contenttypes",
    "django.contrib.sessions",
    "django.contrib.messages",
    "django.contrib.staticfiles",
]

Next, apply the migrations and run the server:

(venv) $ python manage.py migrate
(venv) $ python manage.py runserver

This will create all user-related models in the database and start your application at http://localhost:8000/.

Note: In this tutorial, you’ll be using Django’s built-in user model. In practice, you would more likely create a custom user model, extending the functionality offered by Django. You can read more about customizing the default user model in Django’s documentation.

There’s one more thing you should do for this setup. By default, Django enforces strong passwords to make user accounts less prone to attacks. But you’re going to change passwords very often during the course of this tutorial, and figuring out a strong password each time would be very inconvenient.

You can solve this issue by disabling password validators in settings. Just comment them out, leaving an empty list:

# awesome_website/settings.py

AUTH_PASSWORD_VALIDATORS = [
    # {
    #     "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
    # },
    # {
    #     "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
    # },
    # {
    #     "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
    # },
    # {
    #     "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
    # },
]

Now Django will allow you to set passwords like password or even pass, making your work with the user management system much easier. Just remember to enable the validators in your actual application!

For this tutorial, it would also be useful to have access to the admin panel so you can track newly created users and their passwords. Go ahead and create an admin user:

Read the full article at https://realpython.com/django-user-management/ »

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Unicode in Python: Working With Character Encodings

2020-06-30T14:00:00+00:00

Python’s Unicode support is strong and robust, but it takes some time to master. There are many ways of encoding text into binary data, and in this course you’ll learn a bit of the history of encodings. You’ll also spend time learning the intricacies of Unicode, UTF-8, and how to use them when programming Python. You’ll practice with multiple examples and see how smooth working with text and binary data in Python can be!

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

What an encoding is
What ASCII is
How binary displays as octal and hex values
How UTF-8 encodes a code point
How to combine code points into a single glyph
Which built-in functions can help you

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Python's reduce(): From Functional to Pythonic Style

2020-06-29T14:00:00+00:00

Python’s reduce() is a function that implements a mathematical technique called folding or reduction. reduce() is useful when you need to apply a function to an iterable and reduce it to a single cumulative value. Python’s reduce() is popular among developers with a functional programming background, but Python has more to offer.

In this tutorial, you’ll cover how reduce() works and how to use it effectively. You’ll also cover some alternative Python tools that can be more Pythonic, readable, and efficient than reduce().

In this tutorial, you’ll learn:

How Python’s reduce() works
What the more common reduction use cases are
How to solve these use cases using reduce()
What alternative Python tools are available to solve these same use cases

With this knowledge, you’ll be able to decide which tools to use when it comes to solving reduction or folding problems in Python.

For a better understanding of Python’s reduce(), it would be helpful to have some previous knowledge of how to work with Python iterables, especially how to loop over them using a for loop.

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Exploring Functional Programming in Python

Functional programming is a programming paradigm based on breaking down a problem into a set of individual functions. Ideally, every function only takes a set of input arguments and produces an output.

In functional programming, functions don’t have any internal state that affects the output that they produce for a given input. This means that anytime you call a function with the same set of input arguments, you’ll get the same result or output.

In a functional program, input data flows through a set of functions. Each function operates on its input and produces some output. Functional programming tries to avoid mutable data types and state changes as much as possible. It works with the data that flow between functions.

Other core features of functional programming include the following:

The use of recursion rather than loops or other structures as a primary flow control structure
A focus on lists or arrays processing
A focus on what is to be computed rather than on how to compute it
The use of pure functions that avoid side effects
The use of higher-order functions

There are several important concepts in this list. Here’s a closer look to some of them:

Recursion is a technique in which functions call themselves, either directly or indirectly, in order to loop. It allows a program to loop over data structures that have unknown or unpredictable lengths.
Pure functions are functions that have no side effects at all. In other words, they’re functions that do not update or modify any global variable, object, or data structure in the program. These functions produce an output that depends only on the input, which is closer to the concept of a mathematical function.
Higher-order functions are functions that operate on other functions by taking functions as arguments, returning functions, or both, as with Python decorators.

Since Python is a multi-paradigm programming language, it provides some tools that support a functional programming style:

Functions as first-class objects
Recursion capabilities
Anonymous functions with lambda
Iterators and generators
Standard modules like functools and itertools
Tools like map(), filter(), reduce(), sum(), len(), any(), all(), min(), max(), and so on

Even though Python isn’t heavily influenced by functional programming languages, back in 1993 there was a clear demand for some of the functional programming features listed above.

In response, several functional tools were added to the language. According to Guido van Rossum, they were contributed by a community member:

Python acquired lambda, reduce(), filter() and map(), courtesy of (I believe) a Lisp hacker who missed them and submitted working patches. (Source)

Over the years, new features such as list comprehensions, generator expressions, and built-in functions like sum(), min(), max(), all(), and any() were viewed as Pythonic replacements for map(), filter(), and reduce(). Guido planned to remove map(), filter(), reduce(), and even lambda from the language in Python 3.

Luckily, this removal didn’t take effect, mainly because the Python community didn’t want to let go of such popular features. They’re still around and still widely used among developers with a strong functional programming background.

In this tutorial, you’ll cover how to use Python’s reduce() to process iterables and reduce them to a single cumulative value without using a for loop. You’ll also learn about some Python tools that you can use in place of reduce() to make your code more Pythonic, readable, and efficient.

Getting Started With Python’s `reduce()`

Python’s reduce() implements a mathematical technique commonly known as folding or reduction. You’re doing a fold or reduction when you reduce a list of items to a single cumulative value. Python’s reduce() operates on any iterable—not just lists—and performs the following steps:

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

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The Real Python Podcast – Episode #15: Python Regular Expressions, Views vs Copies in Pandas, and More

2020-06-26T12:00:00+00:00

Have you wanted to learn Regular Expressions in Python, but don't know where to start? Have you stumbled into the dreaded pink SettingWithCopyWarning in Pandas? This week on the show, we have David Amos from the Real Python team to discuss a recent two-part series on Regex in Python. We also talk about another recent article on the site about views vs copies in Pandas. David also brings a few other articles and projects from the wider Python community for us to discuss.

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The Python heapq Module: Using Heaps and Priority Queues

2020-06-24T14:00:00+00:00

Heaps and priority queues are little-known but surprisingly useful data structures. For many problems that involve finding the best element in a dataset, they offer a solution that’s easy to use and highly effective. The Python heapq module is part of the standard library. It implements all the low-level heap operations as well as some high-level common uses for heaps.

A priority queue is a powerful tool that can solve problems as varied as writing an email scheduler, finding the shortest path on a map, or merging log files. Programming is full of optimization problems in which the goal is to find the best element. Priority queues and the functions in the Python heapq module can often help with that.

In this tutorial, you’ll learn:

What heaps and priority queues are and how they relate to each other
What kinds of problems can be solved using a heap
How to use the Python heapq module to solve those problems

This tutorial is for Pythonistas who are comfortable with lists, dicts, sets, and generators and are looking for more sophisticated data structures.

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What Are Heaps?

Heaps are concrete data structures, whereas priority queues are abstract data structures. An abstract data structure determines the interface, while a concrete data structure defines the implementation.

Heaps are commonly used to implement priority queues. They’re the most popular concrete data structure for implementing the priority queue abstract data structure.

Concrete data structures also specify performance guarantees. Performance guarantees define the relationship between the size of the structure and the time operations take. Understanding those guarantees allows you to predict how much time the program will take as the size of its inputs change.

Data Structures, Heaps, and Priority Queues

Abstract data structures specify operations and the relationships between them. The priority queue abstract data structure, for example, supports three operations:

is_empty checks whether the queue is empty.
add_element adds an element to the queue.
pop_element pops the element with the highest priority.

Priority queues are commonly used for optimizing task execution, in which the goal is to work on the task with the highest priority. After a task is completed, its priority is lowered, and it’s returned to the queue.

There are two different conventions for determining the priority of an element:

The largest element has the highest priority.
The smallest element has the highest priority.

These two conventions are equivalent because you can always reverse the effective order. For example, if your elements consist of numbers, then using negative numbers will flip the conventions around.

The Python heapq module uses the second convention, which is generally the more common of the two. Under this convention, the smallest element has the highest priority. This might sound surprising, but it’s often quite useful. In the real-life examples you’ll see later, this convention will simplify your code.

Note: The Python heapq module, and the heap data structure in general, is not designed to allow finding any element except the smallest one. For retrieval of any element by size, a better option is a binary search tree.

Concrete data structures implement the operations defined in an abstract data structure and further specify performance guarantees.

The heap implementation of the priority queue guarantees that both pushing (adding) and popping (removing) elements are logarithmic time operations. This means that the time it takes to do push and pop is proportional to the base-2 logarithm of the number of elements.

Logarithms grow slowly. The base-2 logarithm of fifteen is about four, while the base-2 logarithm of a trillion is about forty. This means that if an algorithm is fast enough on fifteen elements, then it’s going to be only ten times slower on a trillion elements and will probably still be fast enough.

In any discussion of performance, the biggest caveat is that these abstract considerations are less meaningful than actually measuring a concrete program and learning where the bottlenecks are. General performance guarantees are still important for making useful predictions about program behavior, but those predictions should be confirmed.

Implementation of Heaps

A heap implements a priority queue as a complete binary tree. In a binary tree, each node will have at most two children. In a complete binary tree, all levels except possibly the deepest one are full at all times. If the deepest level is incomplete, then it will have the nodes as far to the left as possible.

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

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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!

Free Bonus: Get a sample chapter from Python Basics: A Practical Introduction to Python 3 to see how you can go from beginner to intermediate in Python with a complete curriculum, up-to-date for Python 3.8.

`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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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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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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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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`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

Get Started With Django Part 2: Django User Management

Set Up a Django Project

Unicode in Python: Working With Character Encodings

Python's reduce(): From Functional to Pythonic Style

Exploring Functional Programming in Python

Getting Started With Python’s reduce()

The Real Python Podcast – Episode #15: Python Regular Expressions, Views vs Copies in Pandas, and More

The Python heapq Module: Using Heaps and Priority Queues

What Are Heaps?

Data Structures, Heaps, and Priority Queues

Implementation of Heaps

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

A Beginner's Guide to Pip

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

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

Getting Started With Python’s `reduce()`

Example of a `SettingWithCopyWarning`

`re` Module Functions