Real Python

The Real Python Podcast – Episode #142: Orchestrating Large and Small Projects With Apache Airflow

2023-01-27T12:00:00+00:00

Have you worked on a project that needed an orchestration tool? How do you define the workflow of an entire data pipeline or a messaging system with Python? This week on the show, Calvin Hendryx-Parker is back to talk about using Apache Airflow and orchestrating Python projects.

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The Python Standard REPL: Try Out Code and Ideas Quickly

2023-01-25T14:00:00+00:00

The Python standard shell, or REPL (Read-Eval-Print Loop), allows you to run Python code interactively while working on a project or learning the language. This tool is available in every Python installation, so you can use it at any moment.

As a Python developer, you’ll spend a considerable part of your coding time in a REPL session because this tool allows you to test new ideas, explore and experiment with new tools and libraries, refactor and debug your code, and try out examples.

In this tutorial, you’ll learn how to:

Run the Python standard REPL, or interactive shell
Write and execute Python code in an interactive session
Quickly edit, modify, and reuse code in a REPL session
Get help and introspect your code in an interactive session
Tweak some features of the standard REPL
Identify the standard REPL’s missing features

You’ll also learn about available feature-rich REPLs, such as IDLE, IPython, bpython, and ptpython.

To get the most out of this tutorial, you should be familiar with your operating system’s command line, or terminal. You should also know the basics of using the python command to run your code.

Free Sample Code: Click here to download the free sample code that you’ll use to explore the capabilities of Python’s standard REPL.

Getting to Know the Python Standard REPL

In computer programming, you’ll find two kinds of programming languages: compiled and interpreted languages. Compiled programming languages like C and C++ will have a compiler program, which takes care of translating the language’s code into machine code.

This machine code is typically saved into an executable file. Once you have an executable file, you can run your program on any compatible computer system without needing the compiler or the source code.

In contrast, interpreted languages like Python need an interpreter program. This means that you need to have a Python interpreter installed to run Python code on your computer. Some may consider this characteristic a drawback because it can make your code distribution process much more difficult.

However, in Python, having an interpreter offers one significant advantage that comes in handy during your development and testing process. The Python interpreter allows for what’s known as an interactive REPL (Read-Eval-Print Loop), or shell, which reads a piece of code, evaluates it, and then prints the result to the console in a loop.

Note: In this tutorial, you’ll learn about the CPython standard REPL, which is available in all the installers of this Python distribution. If you don’t have CPython yet, then check out Python 3 Installation & Setup Guide for detailed instructions.

The Python interpreter can execute Python code in two modes:

Script, or program
Interactive, or REPL

In script mode, you use the interpreter to run a source file as an executable program. In this case, Python loads the file content and runs the code line by line, following the script or program’s execution flow. Alternatively, interactive mode is when you launch the interpreter and use it as a platform to run code that you type in directly.

Note: The name Python is commonly used to denote two different things: the language itself, and the interpreter. In this tutorial, you’ll find the explicit term Python interpreter only in situations where ambiguity can arise.

In this tutorial, you’ll learn how to use the Python standard REPL to run code interactively, which allows you to try ideas and test concepts when using and learning Python. Are you ready to take a closer look at the Python REPL? Keep reading!

What Is Python’s Interactive Shell or REPL?

When you run the Python interpreter in interactive mode, you open an interactive shell, also known as an interactive or a REPL session. In this shell, your keyboard is the input source, and your screen is the output destination.

Note: In this tutorial, you’ll find the terms interactive shell, interactive session, interpreter session, and REPL session used interchangeably.

The input consists of Python code, which the interpreter parses and evaluates. After that’s done, the interpreter automatically displays the result on your screen, and the process starts again as a loop.

So, Python’s REPL is an interactive way to talk to your computer using the Python language. It’s like live chat. The whole process is known as a REPL because it goes through four steps that run under the hood:

Reading your input, which consists of Python code as expressions and statements
Evaluating your Python code, which generates a result or causes side effects
Printing any output so that you can check your code’s results and get immediate feedback
Looping back to step one to continue the interaction

This feature of Python is a powerful tool that you’ll wind up needing in your Python coding adventure, especially when you’re learning the language or when you’re in the early stages of a development process. That’s because the REPL offers several benefits, which you’ll learn about next.

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

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Python Basics: Object-Oriented Programming

2023-01-24T14:00:00+00:00

OOP, or object-oriented programming, is a method of structuring a program by bundling related properties and behaviors into individual objects.

Conceptually, objects are like the components of a system. Think of a program as a factory assembly line of sorts. At each step of the assembly line, a system component processes some material, ultimately transforming raw material into a finished product.

An object contains both data, like the raw or preprocessed materials at each step on an assembly line, and behavior, like the action that each assembly line component performs.

In this video course, you’ll learn how to:

Create a class, which is like a blueprint for creating an object
Use classes to create new objects
Instantiate classes with attributes and methods

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

Note that you’ll be using IDLE to interact with Python throughout this course. If you’re just getting started, then you might want to check out Python Basics: Setting Up Python before diving into this course.

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Linear Algebra in Python: Matrix Inverses and Least Squares

2023-01-23T14:00:00+00:00

Linear algebra is an important topic across a variety of subjects. It allows you to solve problems related to vectors, matrices, and linear equations. In Python, most of the routines related to this subject are implemented in scipy.linalg, which offers very fast linear algebra capabilities.

In particular, linear models play an important role in a variety of real-world problems, and scipy.linalg provides tools to compute them in an efficient way.

In this tutorial, you’ll learn how to:

Study linear systems using determinants and solve problems using matrix inverses
Interpolate polynomials to fit a set of points using linear systems
Use Python to solve linear regression problems
Use linear regression to predict prices based on historical data

This is the second part of a series of tutorials on linear algebra using scipy.linalg. So, before continuing, make sure to take a look at the first tutorial of the series before reading this one.

Free Source Code: Click here to download the free code and dataset that you’ll use to work with linear systems and algebra in Python with scipy.linalg.

Now you’re ready to get started!

Getting Started With Linear Algebra in Python

Linear algebra is a branch of mathematics that deals with linear equations and their representations using vectors and matrices. It’s a fundamental subject in several areas of engineering, and it’s a prerequisite to a deeper understanding of machine learning.

To work with linear algebra in Python, you can count on SciPy, which is an open-source Python library used for scientific computing, including several modules for common tasks in science and engineering.

Of course, SciPy includes modules for linear algebra, but that’s not all. It also offers optimization, integration, interpolation, and signal processing capabilities. It’s part of the SciPy stack, which includes several other packages for scientific computing, such as NumPy, Matplotlib, SymPy, IPython, and pandas.

scipy.linalg includes several tools for working with linear algebra problems, including functions for performing matrix calculations, such as determinants, inverses, eigenvalues, eigenvectors, and the singular value decomposition.

In the previous tutorial of this series, you learned how to work with matrices and vectors in Python to model practical problems using linear systems. You solved these problems using scipy.linalg.

In this tutorial, you’re going a step further, using scipy.linalg to study linear systems and build linear models for real-world problems.

In order to use scipy.linalg, you have to install and set up the SciPy library. Besides that, you’re going to use Jupyter Notebook to run the code in an interactive environment. SciPy and Jupyter Notebook are third-party packages that you need to install. For installation, you can use the conda or pip package manager. Revisit Working With Linear Systems in Python With scipy.linalg for installation details.

Note: Using Jupyter Notebook to run the code isn’t mandatory, but it facilitates working with numerical and scientific applications.

For a refresher on working with Jupyter Notebooks, take a look at Jupyter Notebook: An Introduction.

Next, you’ll go through some fundamental concepts of linear algebra and explore how to use Python to work with these concepts.

Understanding Vectors, Matrices, and the Role of Linear Algebra

A vector is a mathematical entity used to represent physical quantities that have both magnitude and direction. It’s a fundamental tool for solving engineering and machine learning problems. So are matrices, which are used to represent vector transformations, among other applications.

Note: In Python, NumPy is the most used library for working with matrices and vectors. It uses a special type called ndarray to represent them. As an example, imagine that you need to create the following matrix:

With NumPy, you can use np.array(), to create it, providing a nested list containing the elements of each row of the matrix:

>>>

In [1]: import numpy as np

In [2]: np.array([[1, 2], [3, 4], [5, 6]])
Out[2]:
array([[1, 2],
       [3, 4],
       [5, 6]])

NumPy provides several functions to facilitate working with vector and matrix computations. You can find more information on how to use NumPy to represent vectors and matrices and perform operations with them in the previous tutorial in this series.

A linear system or, more precisely, a system of linear equations, is a set of equations linearly relating to a set of variables. Here’s an example of a linear system relating to the variables x₁ and x₂:

Here you have two equations involving two variables. In order to have a linear system, the values that multiply the variables x₁ and x₂ must be constants, like the ones in this example. It’s common to write linear systems using matrices and vectors. For example, you can write the previous system as the following matrix product:

Comparing the matrix product form with the original system, you can notice the elements of matrix A correspond to the coefficients that multiply x₁ and x₂. Besides that, the values in the right-hand side of the original equations now make up vector b.

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

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The Real Python Podcast – Episode #141: Exploring Python With bpython & Formalizing f-String Grammar

2023-01-20T12:00:00+00:00

Have you used the Python Read-Eval-Print Loop (REPL) to explore the language and learn about how it operates? Would it help if it provided syntax highlighting, definitions, and code completion and behaved more like an IDE? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Working With Linear Systems in Python With scipy.linalg

2023-01-18T14:00:00+00:00

Linear algebra is widely used across a variety of subjects, and you can use it to solve many problems once you organize the information using concepts like vectors and linear equations. In Python, most of the routines related to this subject are implemented in scipy.linalg, which offers very fast linear algebra capabilities.

In particular, linear systems play an important role in modeling a variety of real-world problems, and scipy.linalg provides tools to study and solve them in an efficient way.

In this tutorial, you’ll learn how to:

Apply linear algebra concepts to practical problems using scipy.linalg
Work with vectors and matrices using Python and NumPy
Model practical problems using linear systems
Solve linear systems using scipy.linalg

Once you’ve gotten to know linear systems, you’ll be ready to explore matrices and least squares in the next tutorial in the series. For now, read on to get started with scipy.linalg.

Free Source Code: Click here to download the free code and dataset that you’ll use to work with linear systems and algebra in Python with scipy.linalg.

Getting Started With `scipy.linalg`

SciPy is an open-source Python library used for scientific computing, including several modules for common tasks in science and engineering, such as linear algebra, optimization, integration, interpolation, and signal processing. It’s part of the SciPy stack, which includes several other packages for scientific computing, such as NumPy, Matplotlib, SymPy, IPython, and pandas.

In this tutorial, you’re going to use some of the functions from scipy.linalg to work on practical problems involving linear systems. In order to use scipy.linalg, you have to install and set up the SciPy library, which you can do by using the Anaconda Python distribution and the conda package and environment management system.

Note: To learn more about Anaconda and conda, check out Setting Up Python for Machine Learning on Windows.

To begin, create a conda environment and activate it:

$ conda create --name linalg
$ conda activate linalg

After you activate the conda environment, your prompt will show its name, linalg. Then you can install the necessary packages inside the environment:

(linalg) $ conda install scipy jupyter

After you execute this command, it should take a while for the system to figure out the dependencies and proceed with the installation.

Note: Besides using SciPy, you’re also going to use Jupyter Notebook to run the code in an interactive environment. Doing so isn’t mandatory, but it facilitates working with numerical and scientific applications.

For a refresher on working with Jupyter Notebooks, take a look at Jupyter Notebook: An Introduction.

If you prefer to follow along with the tutorial using a different Python distribution and the pip package manager, then expand the collapsible section below to see how to set up your environment:

First, you should create a virtual environment in which you’ll install the packages. Assuming you have Python installed, you can create and activate a virtual environment named linalg:

After you activate the virtual environment, your prompt will show its name, linalg. Then you can install the necessary packages inside the environment using pip:

(linalg) $ python -m pip install scipy jupyter

The system will take a while to figure out the dependencies and proceed with the installation. After the command finishes, you’re all set to use scipy.linalg and Jupyter.

Before opening Jupyter Notebook, you need to register the linalg environment so that you can create Notebooks using it as the kernel. To do that, with the linalg environment activated, run the following command:

(linalg) $ python -m ipykernel install --user --name linalg

Now you can open Jupyter Notebook by running the following command:

$ jupyter notebook

After Jupyter loads in your browser, create a new Notebook by clicking New → linalg, as shown below:

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

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Python Basics Exercises: File System Operations

2023-01-17T14:00:00+00:00

In Python Basics: File System Operations, you learned how to use Python to work with files and folders. As a programmer, you’ll use the pathlib and shutil modules to complete file system operations without relying on your graphical user interface (GUI).

While you already got lots of hands-on practice with file system operations, programmers never stop training! The more you use your new skills, the more comfortable you’ll be when it’s time to put them to work in your own coding. That’s why it’s a great idea to complete exercises to reinforce and test your knowledge of the file system.

In this video course, you’ll practice:

Creating a directory
Iterating over the contents of a directory
Searching for files using wildcards
Moving and deleting files and folders

Along the way, you’ll also get some insight into how to tackle coding challenges in general, which can be a great way to level up as a developer.

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

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Python's Assignment Operator: Write Robust Assignments

2023-01-16T14:00:00+00:00

Python’s assignment operators allow you to define assignment statements. This type of statement lets you create, initialize, and update variables throughout your code. Variables are a fundamental cornerstone in every piece of code, and assignment statements give you complete control over variable creation and mutation.

Learning about the Python assignment operator and its use for writing assignment statements will arm you with powerful tools for writing better and more robust Python code.

In this tutorial, you’ll:

Use Python’s assignment operator to write assignment statements
Take advantage of augmented assignments in Python
Explore assignment variants, like assignment expressions and managed attributes
Become aware of illegal and dangerous assignments in Python

You’ll dive deep into Python’s assignment statements. To get the most out of this tutorial, you should be comfortable with several basic topics, including variables, built-in data types, comprehensions, functions, and Python keywords. Before diving into some of the later sections, you should also be familiar with intermediate topics, such as object-oriented programming, constants, imports, type hints, properties, descriptors, and decorators.

Free Source Code: Click here to download the free assignment operator source code that you’ll use to write assignment statements that allow you to create, initialize, and update variables in your code.

Assignment Statements and the Assignment Operator

One of the most powerful programming language features is the ability to create, access, and mutate variables. In Python, a variable is a name that refers to a concrete value or object, allowing you to reuse that value or object throughout your code.

The Assignment Statement Syntax

To create a new variable or to update the value of an existing one in Python, you’ll use an assignment statement. This statement has the following three components:

A left operand, which must be a variable
The assignment operator (=)
A right operand, which can be a concrete value, an object, or an expression

Here’s how an assignment statement will generally look in Python:

variable = expression

Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value.

To execute an assignment statement like the above, Python runs the following steps:

Evaluate the right-hand expression to produce a concrete value or object. This value will live at a specific memory address in your computer.
Store the object’s memory address in the left-hand variable. This step creates a new variable if the current one doesn’t already exist or updates the value of an existing variable.

The second step shows that variables work differently in Python than in other programming languages. In Python, variables aren’t containers for objects. Python variables point to a value or object through its memory address. They store memory addresses rather than objects.

This behavior difference directly impacts how data moves around in Python, which is always by reference. In most cases, this difference is irrelevant in your day-to-day coding, but it’s still good to know.

The Assignment Operator

The central component of an assignment statement is the assignment operator. This operator is represented by the = symbol, which separates two operands:

A variable
A value or an expression that evaluates to a concrete value

Operators are special symbols that perform mathematical, logical, and bitwise operations in a programming language. The objects (or object) on which an operator operates are called operands.

Unary operators, like the not Boolean operator, operate on a single object or operand, while binary operators act on two. That means the assignment operator is a binary operator.

Note: Like C, Python uses == for equality comparisons and = for assignments. Unlike C, Python doesn’t allow you to accidentally use the assignment operator (=) in an equality comparison.

Equality is a symmetrical relationship, and assignment is not. For example, the expression a == 42 is equivalent to 42 == a. In contrast, the statement a = 42 is correct and legal, while 42 = a isn’t allowed. You’ll learn more about illegal assignments later on.

The right-hand operand in an assignment statement can be any Python object, such as a number, list, string, dictionary, or even a user-defined object. It can also be an expression. In the end, expressions always evaluate to concrete objects, which is their return value.

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

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The Real Python Podcast – Episode #140: Speeding Up Your DataFrames With Polars

2023-01-13T12:00:00+00:00

How can you get more performance from your existing data science infrastructure? What if a DataFrame library could take advantage of your machine's available cores and provide built-in methods for handling larger-than-RAM datasets? This week on the show, Liam Brannigan is here to discuss Polars.

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Python Folium: Create Web Maps From Your Data

2023-01-11T14:00:00+00:00

If you’re working with geospatial data in Python, then you might want to quickly visualize that data on a map. Python’s Folium library gives you access to the mapping strengths of the Leaflet JavaScript library through a Python API. It allows you to create interactive geographic visualizations that you can share as a website.

You’ll build the web map shown below, which displays the ecological footprint per capita of many countries and is based on a similar map on Wikipedia. Along the way, you’ll learn the basics of using Folium for data visualization.

In this tutorial, you’ll:

Create an interactive map using Folium and save it as an HTML file
Choose from different web map tiles
Anchor your map to a specific geolocation
Bind data to a GeoJSON layer to create a choropleth map
Style the choropleth map

If you work through the tutorial, then your interactive map will look like this in the end:

Countries by raw ecological footprint per capita

In this tutorial, you’ll create HTML files that you can serve online at a static web hosting service.

An alternative workflow is to use Folium inside of a Jupyter notebook. In that case, the Folium library will render your maps directly in the Jupyter notebook, which gives you a good opportunity to visually explore a geographical dataset or include a map in your data science report.

If you click below to download the associated materials to this tutorial, then you’ll also get a Jupyter notebook set up with the code of this tutorial. Run the notebook to see how well Folium and Jupyter can play together:

Free Source Code: Click here to download the free source code for building web maps in Python with Folium.

Take the Quiz: Test your knowledge with our interactive “Python Folium: Create Web Maps From Your Data” quiz. Upon completion you will receive a score so you can track your learning progress over time:

Take the Quiz »

Install Folium

To get started, create and activate a virtual environment and install folium and pandas. You can use the platform switcher below to see the relevant commands for your operating system:

You can use many features of Folium without pandas. However, in this tutorial you’ll eventually create a choropleth map using folium.Choropleth, which takes a pandas DataFrame or Series object as one of its inputs.

Create and Style a Map

A useful and beginner-friendly feature of Folium is that you can create a map with only three lines of code. The map looks good by default because the underlying Leaflet JavaScript library works well with a number of different tile providers, which provide high-quality map backgrounds for your web maps.

Note: A tile for a web map is an image or vector data file that represents a specific geographical area. Tiled web maps seamlessly join multiple tiles to present a geographical area that’s larger than a single tile.

Additionally, the library boasts attractive default styles for map features and gives you many options to customize the map to fit your needs exactly.

Display Your Web Map Tiles—in Style!

You want to show data on a world map, so you don’t even need to worry about providing any specific geolocation yet. Open up a new Python file in your favorite text editor and create a tiled web map with three lines of code:

import folium

m = folium.Map()
m.save("footprint.html")

When you run your script, Python creates a new HTML file in your working directory that displays an empty world map with the default settings provided by Folium. Open the file in your browser by double-clicking on it and take a look:

Read the full article at https://realpython.com/python-folium-web-maps-from-data/ »

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Using the Terminal on macOS

2023-01-10T14:00:00+00:00

The terminal can be intimidating to work with when you’re used to working with graphical user interfaces. However, it’s an important tool that you need to get used to in your journey as a Python developer. Even though you can substitute some workflows in the terminal with apps that contain a graphical user interface (GUI), you may need to open the terminal at some point in your life as a Python developer.

In this Code Conversation, you’ll follow a chat between Philipp and Martin as they perform common tasks in the terminal on macOS, such as:

Showing the current working directory
Listing the contents of a folder
Adding text to files without opening them
Displaying the content of a file

Along the way, you’ll learn how to:

Find the terminal on your operating system
Open the terminal for the first time
Navigate your filesystem with basic commands
Create files and folders with the terminal
Run Python files on macOS

If you’ve never worked with the terminal on macOS before or you want to see some interesting use cases to incorporate the terminal into your workflow, then this video course is the perfect start for you.

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Learn From 2022's Most Popular Python Tutorials and Courses

2023-01-09T14:00:00+00:00

2022 was a great year for Python. The new Python 3.11 is 10 to 60 percent faster than 3.10, thanks to the efforts of the ongoing Faster CPython project. Python continues to be popular and has spent the year as the number one programming language on the TIOBE index. At the same time, Python has become easier to run in your browser. Python’s WebAssembly port, Pyodide, has matured, and the PyScript framework has showcased some of its possibilities.

Over the last year, we’ve published more than a hundred written tutorials and video courses here at Real Python. These cover evergreen topics about the fundamentals of the language, as well as newer features and developments. In this article, you’ll learn more about the most popular content from 2022. You can also listen to the 2022 Real Python Tutorial & Video Course Wrap Up podcast episode.

Join Now: Click here to join the Real Python Newsletter and you'll never miss another Python tutorial, course update, or post.

Get Your Questions Answered

You’ll find that all Real Python tutorials are well researched, with in-depth information. But sometimes, you’re in the thick of a project, and you need a quick answer before going back to coding. Throughout 2022, you’ve seen several tutorials that focus on getting you quickly up to speed on one topic.

How-to and question-and-answer articles are some of our new formats. The following are some of the popular examples from the last year:

But maybe, instead of asking questions, you’re studying the fundamentals. There are plenty of tutorials to get you started on your Python journey!

Learn the Basics

The latest edition of the Python Basics book was published in 2021. This practical introduction to Python covers all the fundamentals from scratch, including how to set up Python and write your first program. You’ll also learn about strings, numbers, lists, dictionaries, and how you use functions, loops, and conditional logic to control the flow of your program.

Note: Full access to all Real Python video courses is one of the benefits that you get from joining Real Python. You can learn more about the Real Python membership and see if it’s for you.

We’re adapting the Python Basics book into a series of video courses. Many of these courses are already available, including these three:

You can find all the Python Basics video courses in their designated learning path. There, you’ll also find quizzes so that you can check your understanding of key concepts. The list of courses will continue to grow in 2023, so remember to check back later.

Go Deep

Sometimes one article just isn’t enough. One recurring topic in 2022 has been the development of Python 3.11 and its new features, which you can read about in the Python 3.11 preview series. Additionally, you can explore how to work with a Python REST API or how to build a Twitter-like social network with Django in two other tutorial series.

Read the full article at https://realpython.com/popular-python-tutorials-2022/ »

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The Real Python Podcast – Episode #139: Surveying Comprehension Constructs & Python Parallelism Infighting

2023-01-06T12:00:00+00:00

Have you embraced the use of comprehensions in your Python journey? Are you familiar with all the varieties of comprehension constructs? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Discover bpython: A Python REPL With IDE-Like Features

2023-01-04T14:00:00+00:00

The standard Python interpreter lets you run scripts from files or interactively execute code on the fly in a so-called read-evaluate-print loop (REPL). While this is a powerful tool for exploring the language and discovering its libraries through instant feedback on your code inputs, the default REPL shipped with Python has several limitations. Luckily, alternatives like bpython offer a much more programmer-friendly and convenient experience.

You can use bpython to experiment with your code or quickly test an idea without switching contexts between different programs, just like in an integrated development environment (IDE). In addition, bpython may be a valuable teaching tool in either a virtual or physical classroom.

In this tutorial, you’ll learn how to:

Install and use bpython as your alternative Python REPL
Boost your productivity thanks to bpython’s unique features
Tweak bpython’s configuration and its color theme
Use common keyboard shortcuts to code more quickly
Contribute to bpython’s open-source project on GitHub

Before starting this tutorial, make sure you’re already familiar with Python basics and know how to start the standard Python REPL in the command line. In addition, you should be able to install packages with pip, ideally into a virtual environment.

To download the configuration files and sample scripts that you’ll use in this tutorial, click the link below:

Source Code: Click here to download the free source code that you’ll use to harness the power of bpython.

Get Started With bpython

Unlike stand-alone Python distributions, such as CPython, PyPy, or Anaconda, bpython is merely a pure-Python package serving as a lightweight wrapper around a chosen Python interpreter. Therefore, you can use bpython on top of any particular Python distribution, version, or even a virtual environment, which gives you plenty of flexibility.

Note: The letter b in bpython stands for Bob Farrell, who’s the original author and maintainer of the tool.

At the same time, bpython remains a familiar Python REPL with only a few essential features, such as syntax highlighting and auto-completion, borrowed from the full-fledged Python IDEs. This minimalistic approach contrasts with tools like IPython, which is yet another alternative to the standard Python REPL, popular in the data science community. IPython introduces a lot of custom commands and other extras that are unavailable in vanilla Python.

There are a few ways to get bpython on your computer. Package managers like Homebrew or APT offer pre-built versions of bpython for your operating system. However, they’re likely obsolete and hardwired to the system-wide Python interpreter. While you can build the latest bpython version from its source code by hand, it’s better to install it into a virtual environment with pip:

(venv) $ python -m pip install bpython

It’s common to have bpython installed in several copies across many virtual environments, and that’s fine. This allows you to wrap bpython around the specific Python interpreter that you used to create the virtual environment in the first place.

Note: Unfortunately, bpython isn’t natively supported on Windows because it depends on the curses library, which is only available on Unix-like systems, such as macOS and Linux. The official documentation mentions a work-around, which relies on an unofficial binary for Windows, but it seems not to work anymore. If you’re on Windows, then your best bet is to install the Windows Subsystem for Linux (WSL) and use bpython from there.

Once it’s installed, you can start bpython using either of these two commands:

bpython
python -m bpython

It’s preferable to choose the more explicit second command, which invokes bpython as a runnable Python module. This way, you’ll ensure that you’re running the bpython program installed into the currently active virtual environment.

On the other hand, using the bare bpython command could silently fall back to the program installed globally, if there is one. It could also be aliased to a different executable in your shell, taking precedence over the local bpython module.

Here’s an example illustrating the use of bpython against a few different Python interpreters encapsulated within isolated virtual environments:

(py2.7) $ python -m bpython
bpython version 0.20.1 on top of Python 2.7.18
 ⮑ /home/realpython/py2.7/bin/python
WARNING: You are using `bpython` on Python 2. Support for Python 2
 ⮑ has been deprecated in version 0.19 and might disappear
 ⮑ in a future version.
>>> import platform
>>> platform.python_version()
'2.7.18'
>>> platform.python_implementation()
'CPython'

(py3.11) $ python -m bpython
bpython version 0.23 on top of Python 3.11.0
 ⮑ /home/realpython/py3.11/bin/python
>>> import platform
>>> platform.python_version()
'3.11.0'
>>> platform.python_implementation()
'CPython'

(pypy) $ python -m bpython
bpython version 0.23 on top of Python 3.9.12
 ⮑ /home/realpython/pypy/bin/python
>>> import platform
>>>> platform.python_version()
'3.9.12'
>>> platform.python_implementation()
'PyPy'

Notice that you use the same command to run bpython from different virtual environments. Each highlighted line indicates the interpreter version and a path to the Python executable that bpython wraps in the current REPL session. You can confirm the Python version and its implementation through the platform module from the standard library.

Note: The Django web framework can detect bpython if it’s installed in your virtual environment. The framework will automatically run bpython when you execute the shell command to bring up the Python interactive interpreter with your project files on the module search path.

Okay, now that you’ve learned how to install and run bpython as an alternative Python REPL, it’s time to explore its key features. Over the next few sections, you’ll discover several ways that bpython can increase your productivity as a Python programmer, regardless of your skill level.

Spot Typos at a Glance

Read the full article at https://realpython.com/bpython-alternative-python-repl/ »

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Build Cross-Platform GUI Apps With Kivy

2023-01-03T14:00:00+00:00

These days, developers are highly likely to be working on a mobile or web application. Python doesn’t have built-in mobile development capabilities, but you can create mobile applications by using libraries like Kivy, PyQt, or even Beeware’s Toga library.

These libraries are all major players in the Python mobile space. However, there are some benefits that you’ll enjoy if you choose to create mobile applications with Kivy. Not only will your application look the same on all platforms, but you also won’t need to compile your code after every change. What’s more, you’ll be able to use Python’s clear syntax to build your applications.

In this video course, you’ll learn how to:

Work with Kivy widgets
Lay out the user interface (UI)
Add events
Use the KV language
Create a calculator application
Package your application for Windows, Linux, and macOS
Research the tools to package for iOS and Android

To complete this video course, you’ll need to be familiar with object-oriented programming. If you’re not, then check out Object-Oriented Programming (OOP) in Python 3 as either a written tutorial or a video course.

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Natural Language Processing With spaCy in Python

2023-01-02T14:00:00+00:00

If you want to do natural language processing (NLP) in Python, then look no further than spaCy, a free and open-source library with a lot of built-in capabilities. It’s becoming increasingly popular for processing and analyzing data in the field of NLP.

Unstructured text is produced by companies, governments, and the general population at an incredible scale. It’s often important to automate the processing and analysis of text that would be impossible for humans to process. To automate the processing and analysis of text, you need to represent the text in a format that can be understood by computers. spaCy can help you do that.

In this tutorial, you’ll learn how to:

Implement NLP in spaCy
Customize and extend built-in functionalities in spaCy
Perform basic statistical analysis on a text
Create a pipeline to process unstructured text
Parse a sentence and extract meaningful insights from it

If you’re new to NLP, don’t worry! Before you start using spaCy, you’ll first learn about the foundational terms and concepts in NLP. You should be familiar with the basics in Python, though. The code in this tutorial contains dictionaries, lists, tuples, for loops, comprehensions, object oriented programming, and lambda functions, among other fundamental Python concepts.

Free Source Code: Click here to download the free source code that you’ll use for natural language processing (NLP) in spaCy.

Introduction to NLP and spaCy

NLP is a subfield of artificial intelligence, and it’s all about allowing computers to comprehend human language. NLP involves analyzing, quantifying, understanding, and deriving meaning from natural languages.

Note: Currently, the most powerful NLP models are transformer based. BERT from Google and the GPT family from OpenAI are examples of such models.

Since the release of version 3.0, spaCy supports transformer based models. The examples in this tutorial are done with a smaller, CPU-optimized model. However, you can run the examples with a transformer model instead. All Hugging Face transformer models can be used with spaCy.

NLP helps you extract insights from unstructured text and has many use cases, such as:

spaCy is a free, open-source library for NLP in Python written in Cython. spaCy is designed to make it easy to build systems for information extraction or general-purpose natural language processing.

Installation of spaCy

In this section, you’ll install spaCy into a virtual environment and then download data and models for the English language.

You can install spaCy using pip, a Python package manager. It’s a good idea to use a virtual environment to avoid depending on system-wide packages. To learn more about virtual environments and pip, check out Using Python’s pip to Manage Your Projects’ Dependencies and Python Virtual Environments: A Primer.

First, you’ll create a new virtual environment, activate it, and install spaCy. Select your operating system below to learn how:

With spaCy installed in your virtual environment, you’re almost ready to get started with NLP. But there’s one more thing you’ll have to install:

(venv) $ python -m spacy download en_core_web_sm

There are various spaCy models for different languages. The default model for the English language is designated as en_core_web_sm. Since the models are quite large, it’s best to install them separately—including all languages in one package would make the download too massive.

Once the en_core_web_sm model has finished downloading, open up a Python REPL and verify that the installation has been successful:

>>>

>>> import spacy
>>> nlp = spacy.load("en_core_web_sm")

If these lines run without any errors, then it means that spaCy was installed and that the models and data were successfully downloaded. You’re now ready to dive into NLP with spaCy!

The `Doc` Object for Processed Text

Read the full article at https://realpython.com/natural-language-processing-spacy-python/ »

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The Real Python Podcast – Episode #138: 2022 Real Python Tutorial & Video Course Wrap Up

2022-12-23T12:00:00+00:00

It's been another year of changes at Real Python! The Real Python team has written, edited, curated, illustrated, and produced a mountain of Python material this year. We added some new members to the team, updated the site's features, and created new styles of tutorials and video courses.

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Generate Images With DALL·E 2 and the OpenAI API

2022-12-21T14:00:00+00:00

Describe any image, then let a computer create it for you. What sounded futuristic only a few years ago has become reality with advances in neural networks and latent diffusion models (LDM). DALL·E by OpenAI has made a splash through the amazing generative art and realistic images that people create with it.

OpenAI now allows access to DALL·E through their API, which means that you can incorporate its functionality into your Python applications.

In this tutorial, you’ll:

Get started using the OpenAI Python library
Explore API calls related to image generation
Create images from text prompts
Create variations of your generated image
Convert Base64 JSON responses to PNG image files

You’ll need some experience with Python, JSON, and file operations to breeze through this tutorial. You can also study up on these topics while you go along, as you’ll find relevant links throughout the text.

If you haven’t played with the web user interface (UI) of DALL·E before, then try it out before coming back to learn how to use it programmatically with Python.

Source Code: Click here to download the free source code that you’ll use to generate stunning images with DALL·E 2 and the OpenAI API.

Complete the Setup Requirements

If you’ve seen what DALL·E can do and you’re eager to make its functionality part of your Python applications, then you’re in the right spot! In this first section, you’ll quickly walk through what you need to do to get started using DALL·E’s image creation capabilities in your own code.

Install the OpenAI Python Library

Confirm that you’re running Python version 3.7.1 or higher, create and activate a virtual environment, and install the OpenAI Python library:

PS> python --version
Python 3.11.0
PS> python -m venv venv
PS> .\venv\Scripts\activate
(venv) PS> python -m pip install openai

$ python --version
Python 3.11.0
$ python -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install openai

The openai package gives you access to the full OpenAI API. In this tutorial, you’ll focus on the Image class, which you can use to interact with DALL·E to create and edit images from text prompts.

Get Your OpenAI API Key

You need an API key to make successful API calls. Sign up for the OpenAI API and create a new API key by clicking on the dropdown menu on your profile and selecting View API keys:

On this page, you can manage your API keys, which allow you to access the service that OpenAI offers through their API. You can create and delete secret keys.

Click on Create new secret key to create a new API key, and copy the value shown in the pop-up window:

Always keep this key secret! Copy the value of this key so you can later use it in your project. You’ll only see the key value once.

Save Your API Key as an Environment Variable

A quick way to save your API key and make it available to your Python scripts is to save it as an environment variable. Select your operating system to learn how:

Read the full article at https://realpython.com/generate-images-with-dalle-openai-api/ »

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Python Basics: File System Operations

2022-12-20T14:00:00+00:00

So far, you’ve written programs that get their input from one of two sources: the user or the program itself. Program output has been limited to displaying some text in IDLE’s interactive window.

These input and output strategies aren’t useful in several common scenarios, such as when:

The input values are unknown while writing the program.
The program requires more data than a user can be expected to type in by themselves.
Output must be shared with others after the program runs.

This is where files come in. You’ve likely been working with computer files for a long time. Even so, there are some things that programmers need to know about files that general users do not. Specifically, you’ll want to master some file system operations using the pathlib and shutil modules.

In this video course, you’ll learn how to:

Create files and directories
Iterate over the contents of a directory
Search for files and folders using wildcards
Move and delete files and folders

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

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Python's "in" and "not in" Operators: Check for Membership

2022-12-19T14:00:00+00:00

Python’s in and not in operators allow you to quickly determine if a given value is or isn’t part of a collection of values. This type of check is common in programming, and it’s generally known as a membership test in Python. Therefore, these operators are known as membership operators.

In this tutorial, you’ll learn how to:

Perform membership tests using the in and not in operators
Use in and not in with different data types
Work with operator.contains(), the equivalent function to the in operator
Provide support for in and not in in your own classes

To get the most out of this tutorial, you’ll need basic knowledge of Python, including built-in data types, such as lists, tuples, ranges, strings, sets, and dictionaries. You’ll also need to know about Python generators, comprehensions, and classes.

Source Code: Click here to download the free source code that you’ll use to perform membership tests in Python with in and not in.

Getting Started With Membership Tests in Python

Sometimes you need to find out whether a value is present in a collection of values or not. In other words, you need to check if a given value is or is not a member of a collection of values. This kind of check is commonly known as a membership test.

Arguably, the natural way to perform this kind of check is to iterate over the values and compare them with the target value. You can do this with the help of a for loop and a conditional statement.

Consider the following is_member() function:

>>>

>>> def is_member(value, iterable):
...     for item in iterable:
...         if value is item or value == item:
...             return True
...     return False
...

This function takes two arguments, the target value and a collection of values, which is generically called iterable. The loop iterates over iterable while the conditional statement checks if the target value is equal to the current value. Note that the condition checks for object identity with is or for value equality with the equality operator (==). These are slightly different but complementary tests.

If the condition is true, then the function returns True, breaking out of the loop. This early return short-circuits the loop operation. If the loop finishes without any match, then the function returns False:

>>>

>>> is_member(5, [2, 3, 5, 9, 7])
True

>>> is_member(8, [2, 3, 5, 9, 7])
False

The first call to is_member() returns True because the target value, 5, is a member of the list at hand, [2, 3, 5, 9, 7]. The second call to the function returns False because 8 isn’t present in the input list of values.

Membership tests like the ones above are so common and useful in programming that Python has dedicated operators to perform these types of checks. You can get to know the membership operators in the following table:

Operator	Description	Syntax
`in`	Returns `True` if the target value is present in a collection of values. Otherwise, it returns `False`.	`value in collection`
`not in`	Returns `True` if the target value is not present in a given collection of values. Otherwise, it returns `False`.	`value not in collection`

As with Boolean operators, Python favors readability by using common English words instead of potentially confusing symbols as operators.

Note: Don’t confuse the in keyword when it works as the membership operator with the in keyword in the for loop syntax. They have entirely different meanings. The in operator checks if a value is in a collection of values, while the in keyword in a for loop indicates the iterable that you want to draw from.

Like many other operators, in and not in are binary operators. That means you can create expressions by connecting two operands. In this case, those are:

Left operand: The target value that you want to look for in a collection of values
Right operand: The collection of values where the target value may be found

The syntax of a membership test looks something like this:

value in collection

value not in collection

In these expressions, value can be any Python object. Meanwhile, collection can be any data type that can hold collections of values, including lists, tuples, strings, sets, and dictionaries. It can also be a class that implements the .__contains__() method or a user-defined class that explicitly supports membership tests or iteration.

If you use the in and not in operators correctly, then the expressions that you build with them will always evaluate to a Boolean value. In other words, those expressions will always return either True or False. On the other hand, if you try and find a value in something that doesn’t support membership tests, then you’ll get a TypeError. Later, you’ll learn more about the Python data types that support membership tests.

Because membership operators always evaluate to a Boolean value, Python considers them Boolean operators just like the and, or, and not operators.

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

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The Real Python Podcast – Episode #137: Start Using a Build System & Continuous Integration in Python

2022-12-16T12:00:00+00:00

What advantages can a build system provide for a Python developer? What new skills are required when working with a team of developers? This week on the show, Benjy Weinberger from Toolchain is here to discuss the Pants build system and getting started with continuous integration (CI).

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Context Managers and Python's with Statement

2022-12-13T14:00:00+00:00

The with statement in Python is a quite useful tool for properly managing external resources in your programs. It allows you to take advantage of existing context managers to automatically handle the setup and teardown phases whenever you’re dealing with external resources or with operations that require those phases.

What’s a context manager? It’s a block of code that has side effects upon entering and exiting. The context management protocol allows you to create your own context managers so you can customize the way you deal with system resources.

In this video course, you’ll learn:

How context managers work
Some common context managers in the Python standard library
How to write a custom context manager

With this knowledge, you’ll write more expressive code and avoid resource leaks in your programs.

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The Real Python Podcast – Episode #136: Package Python Code With pyproject.toml & Listing Files With pathlib

2022-12-09T12:00:00+00:00

How do you start packaging your code with pyproject.toml? Would you like to join a conversation that gently walks you through setting up your Python projects to share? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Python Basics: Dictionaries

2022-12-06T14:00:00+00:00

In plain English, a dictionary is a book containing the definitions of words. Each entry in a dictionary has two parts: the word being defined, and its definition.

Python dictionaries, like lists and tuples, store a collection of objects. However, instead of storing objects in a sequence, dictionaries hold information in pairs of data called key-value pairs. That is, each object in a dictionary has two parts: a key and a value. Each key is assigned a single value, which defines the relationship between the two sets.

In this video course, you’ll learn:

What a dictionary is and how it’s structured
How dictionaries differ from other data structures
How to define and use dictionaries in your own code

This video course is part of the Python Basics series, which accompanies Python Basics: A Practical Introduction to Python 3. You can also check out the other Python Basics courses.

Note that you’ll be using IDLE to interact with Python throughout this course.

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The Real Python Podcast – Episode #135: Preparing Data to Measure True Machine Learning Model Performance

2022-12-02T12:00:00+00:00

How do you prepare a dataset for machine learning (ML)? How do you go beyond cleaning the data and move toward measuring how the model performs? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to talk about strategies for better ML model performance.

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Using Python's pathlib Module

2022-11-29T14:00:00+00:00

Have you struggled with file path handling in Python? With the pathlib module, the struggle is now over! You no longer need to scratch your head over code like this:

>>>

>>> path.rsplit('\\', maxsplit=1)[0]

And you don’t have to cringe at the verbosity of something like this:

>>>

>>> os.path.isfile(os.path.join(os.path.expanduser('~'), 'realpython.txt'))

In this video course, you’ll learn how to:

Work with file paths in Python
Read and write files in new ways
Manipulate paths and the underlying file system
List files and iterate over them

Using the pathlib module, the two examples above can be rewritten using elegant, readable, and Pythonic code:

>>>

>>> path.parent
>>> (pathlib.Path.home() / 'realpython.txt').is_file()

That’s what you’ll master in this video course!

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The Real Python Podcast – Episode #134: Building Python REST APIs With Flask & Structuring Pull Requests

2022-11-25T12:00:00+00:00

How do you build a REST API using the Flask web framework? How can you quickly add endpoints while automatically generating documentation? This week on the show, Real Python author Philipp Acsany is here to discuss his tutorial series "Python REST APIs With Flask, Connexion, and SQLAlchemy." Christopher Trudeau is also here with another batch of PyCoder's Weekly articles and projects.

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Everyday Project Packaging With pyproject.toml

2022-11-22T14:00:00+00:00

Packaging is often associated with publishing a project on PyPI, which can make you bypass packaging as something you don’t need to worry about at the start of your project. Python packaging has also had a long history and can be overwhelming for all levels of programmers. But it doesn’t have to be a scary process!

In this Code Conversation, you’ll follow a chat between Ian and Geir Arne demonstrating the relatively new, officially sanctioned way of setting up your Python projects using a pyproject.toml file and installing your package with pip. This offers nice benefits, such as:

Being able to call your project from anywhere
Playing on the same team as the import system
Allowing for consistent imports
Having one file that’ll work for many build systems

Along the way, you’ll:

Structure files and folders in your project
Understand different ways to run your script
Explore how the import system works
Explore the Python packaging world
Write a pyproject.toml file to configure your package
Install your pacakge with pip

You’ll also dive into various rabbit holes along the way as Ian and Geir Arne talk about all the aspects of the process.

To get the most out of this Code Conversation, you should have a handle on Python basics, be able to create a virtual environment and install packages, and have some exposure to the import system. Ideally, you have some experience fighting against the import system and thinking that there must be a better way.

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The Real Python Podcast – Episode #133: Moving Projects Away From Passwords With WebAuthn and Python

2022-11-18T12:00:00+00:00

What if you didn't have to worry about managing user passwords as a Python developer? That's where the WebAuthn protocol and new hardware standards are heading. This week on the show, Dan Moore from FusionAuth returns to discuss a password-less future.

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Deploy a Django App With Gunicorn and Nginx

2022-11-15T14:00:00+00:00

Taking a Django app from development to production is a demanding but rewarding process. In this video course, you’ll go through that process step by step. You’ll start at square one with a no-frills Django application then add in Gunicorn, Nginx, and domain registration. After working through this video course, you’ll be better equipped to take your Django app into production and serve it to the world.

In this video course, you’ll learn:

How you can take your Django app from development to production
How you can host your app on a real-world public domain
How to introduce Gunicorn and Nginx into the request and response chain

To make the most out of this video course, you should have an introductory-level understanding of Python, Django, and the high-level mechanics of HTTP requests.

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Real Python

The Real Python Podcast – Episode #142: Orchestrating Large and Small Projects With Apache Airflow

The Python Standard REPL: Try Out Code and Ideas Quickly

Getting to Know the Python Standard REPL

What Is Python’s Interactive Shell or REPL?

Python Basics: Object-Oriented Programming

Linear Algebra in Python: Matrix Inverses and Least Squares

Getting Started With Linear Algebra in Python

Understanding Vectors, Matrices, and the Role of Linear Algebra

The Real Python Podcast – Episode #141: Exploring Python With bpython & Formalizing f-String Grammar

Working With Linear Systems in Python With scipy.linalg

Getting Started With scipy.linalg

Python Basics Exercises: File System Operations

Python's Assignment Operator: Write Robust Assignments

Assignment Statements and the Assignment Operator

The Assignment Statement Syntax

The Assignment Operator

The Real Python Podcast – Episode #140: Speeding Up Your DataFrames With Polars

Python Folium: Create Web Maps From Your Data

Install Folium

Create and Style a Map

Display Your Web Map Tiles—in Style!

Using the Terminal on macOS

Learn From 2022's Most Popular Python Tutorials and Courses

Get Your Questions Answered

Learn the Basics

Go Deep

The Real Python Podcast – Episode #139: Surveying Comprehension Constructs & Python Parallelism Infighting

Discover bpython: A Python REPL With IDE-Like Features

Get Started With bpython

Spot Typos at a Glance

Build Cross-Platform GUI Apps With Kivy

Natural Language Processing With spaCy in Python

Introduction to NLP and spaCy

Installation of spaCy

The Doc Object for Processed Text

The Real Python Podcast – Episode #138: 2022 Real Python Tutorial & Video Course Wrap Up

Generate Images With DALL·E 2 and the OpenAI API

Complete the Setup Requirements

Install the OpenAI Python Library

Get Your OpenAI API Key

Save Your API Key as an Environment Variable

Python Basics: File System Operations

Python's "in" and "not in" Operators: Check for Membership

Getting Started With Membership Tests in Python

The Real Python Podcast – Episode #137: Start Using a Build System & Continuous Integration in Python

Context Managers and Python's with Statement

The Real Python Podcast – Episode #136: Package Python Code With pyproject.toml & Listing Files With pathlib

Python Basics: Dictionaries

The Real Python Podcast – Episode #135: Preparing Data to Measure True Machine Learning Model Performance

Using Python's pathlib Module

The Real Python Podcast – Episode #134: Building Python REST APIs With Flask & Structuring Pull Requests

Everyday Project Packaging With pyproject.toml

The Real Python Podcast – Episode #133: Moving Projects Away From Passwords With WebAuthn and Python

Deploy a Django App With Gunicorn and Nginx

Getting Started With `scipy.linalg`

The `Doc` Object for Processed Text