Real Python

Linear Regression in Python

2024-12-07T14:00:00+00:00

Linear regression is a foundational statistical tool for modeling the relationship between a dependent variable and one or more independent variables. It’s widely used in data science and machine learning to predict outcomes and understand relationships between variables. In Python, implementing linear regression can be straightforward with the help of third-party libraries such as scikit-learn and statsmodels.

By the end of this tutorial, you’ll understand that:

Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation.
Implementing linear regression in Python involves using libraries like scikit-learn and statsmodels to fit models and make predictions.
The formula for linear regression is 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀, representing the linear relationship between variables.
Simple linear regression involves one independent variable, whereas multiple linear regression involves two or more.
The scikit-learn library provides a convenient and efficient interface for performing linear regression in Python.

To implement linear regression in Python, you typically follow a five-step process: import necessary packages, provide and transform data, create and fit a regression model, evaluate the results, and make predictions. This approach allows you to perform both simple and multiple linear regressions, as well as polynomial regression, using Python’s robust ecosystem of scientific libraries.

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Interactive Quiz

Linear Regression in Python

In this quiz, you'll test your knowledge of linear regression in Python. Linear regression is one of the fundamental statistical and machine learning techniques, and Python is a popular choice for machine learning.

Regression

Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them.

What Is Regression?

Regression searches for relationships among variables. For example, you can observe several employees of some company and try to understand how their salaries depend on their features, such as experience, education level, role, city of employment, and so on.

This is a regression problem where data related to each employee represents one observation. The presumption is that the experience, education, role, and city are the independent features, while the salary depends on them.

Similarly, you can try to establish the mathematical dependence of housing prices on area, number of bedrooms, distance to the city center, and so on.

Generally, in regression analysis, you consider some phenomenon of interest and have a number of observations. Each observation has two or more features. Following the assumption that at least one of the features depends on the others, you try to establish a relation among them.

In other words, you need to find a function that maps some features or variables to others sufficiently well.

The dependent features are called the dependent variables, outputs, or responses. The independent features are called the independent variables, inputs, regressors, or predictors.

Regression problems usually have one continuous and unbounded dependent variable. The inputs, however, can be continuous, discrete, or even categorical data such as gender, nationality, or brand.

It’s a common practice to denote the outputs with 𝑦 and the inputs with 𝑥. If there are two or more independent variables, then they can be represented as the vector 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of inputs.

When Do You Need Regression?

Typically, you need regression to answer whether and how some phenomenon influences the other or how several variables are related. For example, you can use it to determine if and to what extent experience or gender impacts salaries.

Regression is also useful when you want to forecast a response using a new set of predictors. For example, you could try to predict electricity consumption of a household for the next hour given the outdoor temperature, time of day, and number of residents in that household.

Regression is used in many different fields, including economics, computer science, and the social sciences. Its importance rises every day with the availability of large amounts of data and increased awareness of the practical value of data.

Linear Regression

Linear regression is probably one of the most important and widely used regression techniques. It’s among the simplest regression methods. One of its main advantages is the ease of interpreting results.

Problem Formulation

When implementing linear regression of some dependent variable 𝑦 on the set of independent variables 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of predictors, you assume a linear relationship between 𝑦 and 𝐱: 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀. This equation is the regression equation. 𝛽₀, 𝛽₁, …, 𝛽ᵣ are the regression coefficients, and 𝜀 is the random error.

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

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Socket Programming in Python (Guide)

2024-12-07T14:00:00+00:00

Socket programming is essential for network communication, enabling data exchange across different devices. In Python, sockets allow for inter-process communication (IPC) over networks. This tutorial provides a comprehensive guide on creating socket servers and clients, handling multiple connections, and managing errors in Python’s socket module.

By the end of this tutorial, you’ll understand that:

A socket in Python is an endpoint for sending or receiving data across a network using the socket API.
Socket programming in Python involves using sockets to establish communication between a server and clients over a network.
A simple echo server in Python can be created using sockets to listen for client connections and echo back received messages.
Handling multiple clients with Python sockets can be achieved using non-blocking sockets and the selectors module for concurrent connections.
Connection errors in socket programs in Python can be managed by implementing error handling and using exceptions like OSError.

Along the way, you’ll learn about the main functions and methods in Python’s socket module that let you write your own client-server applications based on TCP sockets. You’ll learn how to reliably send messages and data between endpoints and handle multiple connections simultaneously.

Networking and sockets are large subjects. Literal volumes have been written about them. If you’re new to sockets or networking, it’s completely normal if you feel overwhelmed with all of the terms and pieces. To get the most out of this tutorial, it’s best to download the source code and have it on hand for reference while reading:

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Interactive Quiz

Socket Programming in Python

In this quiz, you'll test your understanding of Python sockets. With this knowledge, you'll be able to create your own client-server applications, handle multiple connections simultaneously, and send messages and data between endpoints.

Historical Background

Sockets have a long history. Their use originated with ARPANET in 1971 and later became an API in the Berkeley Software Distribution (BSD) operating system released in 1983 called Berkeley sockets.

When the Internet took off in the 1990s with the World Wide Web, so did network programming. Web servers and browsers weren’t the only applications taking advantage of newly connected networks and using sockets. Client-server applications of all types and sizes came into widespread use.

Today, although the underlying protocols used by the socket API have evolved over the years, and new ones have developed, the low-level API has remained the same.

The most common type of socket applications are client-server applications, where one side acts as the server and waits for connections from clients. This is the type of application that you’ll be creating in this tutorial. More specifically, you’ll focus on the socket API for Internet sockets, sometimes called Berkeley or BSD sockets. There are also Unix domain sockets, which can only be used to communicate between processes on the same host.

Python Socket API Overview

Python’s socket module provides an interface to the Berkeley sockets API. This is the module that you’ll use in this tutorial.

The primary socket API functions and methods in this module are:

socket()
.bind()
.listen()
.accept()
.connect()
.connect_ex()
.send()
.recv()
.close()

Python provides a convenient and consistent API that maps directly to system calls, their C counterparts. In the next section, you’ll learn how these are used together.

As part of its standard library, Python also has classes that make using these low-level socket functions easier. Although it’s not covered in this tutorial, you can check out the socketserver module, a framework for network servers. There are also many modules available that implement higher-level Internet protocols like HTTP and SMTP. For an overview, see Internet Protocols and Support.

TCP Sockets

You’re going to create a socket object using socket.socket(), specifying the socket type as socket.SOCK_STREAM. When you do that, the default protocol that’s used is the Transmission Control Protocol (TCP). This is a good default and probably what you want.

Why should you use TCP? The Transmission Control Protocol (TCP):

Is reliable: Packets dropped in the network are detected and retransmitted by the sender.
Has in-order data delivery: Data is read by your application in the order it was written by the sender.

In contrast, User Datagram Protocol (UDP) sockets created with socket.SOCK_DGRAM aren’t reliable, and data read by the receiver can be out-of-order from the sender’s writes.

Why is this important? Networks are a best-effort delivery system. There’s no guarantee that your data will reach its destination or that you’ll receive what’s been sent to you.

Network devices, such as routers and switches, have finite bandwidth available and come with their own inherent system limitations. They have CPUs, memory, buses, and interface packet buffers, just like your clients and servers. TCP relieves you from having to worry about packet loss, out-of-order data arrival, and other pitfalls that invariably happen when you’re communicating across a network.

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

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How to Round Numbers in Python

2024-12-07T14:00:00+00:00

Rounding numbers in Python is an essential task, especially when dealing with data precision. Python’s built-in round() function uses the rounding half to even strategy, which rounds numbers like 2.5 to 2 and 3.5 to 4. This method helps minimize rounding bias in datasets. To round numbers to specific decimal places, you can use the round() function with a second argument specifying the number of decimals.

For more advanced rounding strategies, you can explore Python’s decimal module or use NumPy and pandas for data science applications. NumPy arrays and pandas DataFrames offer methods for rounding numbers efficiently. In NumPy, you can use functions like np.round(), np.ceil(), np.floor(), and np.trunc() to apply different rounding strategies. For pandas, the df.round() method allows rounding of entire DataFrames or specific columns.

By the end of this tutorial, you’ll understand that:

Python uses the rounding half to even strategy, where ties round to the nearest even number.
Python’s default rounding strategy minimizes rounding bias in large datasets.
You can round numbers to specific decimal places using Python’s round() function with a second argument.
Different rounding strategies can be applied using Python’s decimal module or custom functions for precision control.
NumPy and pandas provide methods for rounding numbers in arrays and DataFrames, offering flexibility in data manipulation.

You won’t get a treatise on numeric precision in computing, although you’ll touch briefly on the subject. Only a familiarity with the fundamentals of Python is necessary, and the math should feel familiar if you’ve had high school algebra.

You’ll start by looking at Python’s built-in rounding mechanism.

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Interactive Quiz

Rounding Numbers in Python

Test your knowledge of rounding numbers in Python.

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Python’s Built-in `round()` Function

Python has a built-in round() function that takes two numeric arguments, n and ndigits, and returns the number n rounded to ndigits. The ndigits argument defaults to zero, so leaving it out results in a number rounded to an integer. As you’ll see, round() may not work quite as you expect.

The way most people are taught to round a number goes something like this:

Round the number n to p decimal places by first shifting the decimal point in n by p places. To do that, multiply n by 10ᵖ (10 raised to the p power) to get a new number, m.
Then look at the digit d in the first decimal place of m. If d is less than 5, round m down to the nearest integer. Otherwise, round m up.
Finally, shift the decimal point back p places by dividing m by 10ᵖ.

It’s an algorithm! For example, the number 2.5 rounded to the nearest whole number is 3. The number 1.64 rounded to one decimal place is 1.6.

Now open up an interpreter session and round 2.5 to the nearest whole number using Python’s built-in round() function:

Python
      
>>> round(2.5)
2

Gasp!

Check out how round() handles the number 1.5:

Python
      
>>> round(1.5)
2

So, round() rounds 1.5 up to 2, and 2.5 down to 2!

Before you go raising an issue on the Python bug tracker, rest assured you that round(2.5) is supposed to return 2. There’s a good reason why round() behaves the way it does.

In this tutorial, you’ll learn that there are more ways to round a number than you might expect, each with unique advantages and disadvantages. round() behaves according to a particular rounding strategy—which may or may not be the one you need for a given situation.

You might be wondering, Can the way I round numbers really have that much of an impact? Next up, take a look at just how extreme the effects of rounding can be.

How Much Impact Can Rounding Have?

Suppose you have an incredibly lucky day and find $100 on the ground. Rather than spending all your money at once, you decide to play it smart and invest your money by buying some shares of different stocks.

The value of a stock depends on supply and demand. The more people there are who want to buy a stock, the more value that stock has, and vice versa. In high-volume stock markets, the value of a particular stock can fluctuate on a second-by-second basis.

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

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Python GUI Programming With Tkinter

2024-12-07T14:00:00+00:00

Tkinter is Python’s standard GUI framework, making it convenient for developing graphical user interfaces. As a cross-platform library, Tkinter ensures your applications appear native across Windows, macOS, and Linux. Despite criticisms of its outdated appearance, Tkinter remains a practical choice for quickly creating functional and cross-platform GUI applications.

By the end of this tutorial, you’ll understand that:

GUI refers to graphical user interfaces, and Tkinter is Python’s built-in library for creating them.
Tkinter is included with most Python installations, so separate installation is often unnecessary.
Tkinter is still a relevant choice for building simple, cross-platform GUI applications.
Widgets in a Tkinter application can be arranged using geometry managers like .pack(), .place(), and .grid().
Interactive GUI applications with Tkinter are created by binding events, such as button clicks, to Python functions.

You’ll cover getting started with Tkinter, managing widgets, and creating interactive applications. Once you’ve mastered these skills by working through the exercises at the end of each section, you’ll tie everything together by building two applications. The first is a temperature converter, and the second is a text editor. It’s time to dive right in and learn how to build an application with Tkinter!

Note: This tutorial is adapted from the chapter “Graphical User Interfaces” of Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell. In this tutorial, references to IDLE have been removed in favor of more general language.

The bulk of the material in this tutorial has been left unchanged, and you should have no problems running the example code from the editor and environment of your choice.

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Interactive Quiz

Python GUI Programming With Tkinter

In this quiz, you'll test your understanding of Python GUI Programming With Tkinter, the de facto Python GUI framework. Check your knowledge of GUI programming concepts such as widgets, geometry managers, and event handlers.

Building Your First Python GUI Application With Tkinter

The foundational element of a Tkinter GUI is the window. Windows are the containers in which all other GUI elements live. These other GUI elements, such as text boxes, labels, and buttons, are known as widgets. Widgets are contained inside of windows.

First, create a window that contains a single widget. Start up a new Python shell session and follow along!

Note: The code examples in this tutorial have all been tested on Windows, macOS, and Ubuntu Linux 20.04 with Python version 3.10.

If you’ve installed Python with the official installers available for Windows and macOS from python.org, then you should have no problem running the sample code. You can safely skip the rest of this note and continue with the tutorial!

If you haven’t installed Python with the official installers, or there’s no official distribution for your system, then here are some tips for getting up and going.

Python on macOS with Homebrew:

The Python distribution for macOS available on Homebrew doesn’t come bundled with the Tcl/Tk dependency required by Tkinter. The default system version is used instead. This version may be outdated and prevent you from importing the Tkinter module. To avoid this problem, use the official macOS installer.

Ubuntu Linux 20.04:

To conserve memory space, the default version of the Python interpreter that comes pre-installed on Ubuntu Linux 20.04 has no support for Tkinter. However, if you want to continue using the Python interpreter bundled with your operating system, then install the following package:

Shell
      
$ sudo apt-get install python3-tk

This installs the Python GUI Tkinter module.

Other Linux Flavors:

If you’re unable to get a working Python installation on your flavor of Linux, then you can build Python with the correct version of Tcl/Tk from the source code. For a step-by-step walk-through of this process, check out the Python 3 Installation & Setup Guide. You may also try using pyenv to manage multiple Python versions.

With your Python shell open, the first thing you need to do is import the Python GUI Tkinter module:

Python
      
>>> import tkinter as tk

A window is an instance of Tkinter’s Tk class. Go ahead and create a new window and assign it to the variable window:

Python
      
>>> window = tk.Tk()

When you execute the above code, a new window pops up on your screen. How it looks depends on your operating system:

Throughout the rest of this tutorial, you’ll see Windows screenshots.

Now that you have a window, you can add a widget. Use the tk.Label class to add some text to a window. Create a Label widget with the text "Hello, Tkinter" and assign it to a variable called greeting:

Python
      
>>> greeting = tk.Label(text="Hello, Tkinter")

The window you created earlier doesn’t change. You just created a Label widget, but you haven’t added it to the window yet. There are several ways to add widgets to a window. Right now, you can use the Label widget’s .pack() method:

Python
      
>>> greeting.pack()

The window now looks like this:

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

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When to Use a List Comprehension in Python

2024-12-07T14:00:00+00:00

List comprehensions in Python provide a concise way to create lists by embedding a loop and optional conditional logic in a single line. You use a list comprehension to transform and filter elements from an iterable efficiently. It allows you to replace complex loops and map() functions with more readable and often faster expressions. By understanding list comprehensions, you can optimize your code for better performance and clarity.

You can add conditional logic to a list comprehension in Python by appending an if statement to the end of the expression. This allows you to filter elements based on a condition, effectively replacing the need for filter() in many cases. Additionally, you can use conditional expressions at the beginning to choose between different outcomes, enhancing the versatility of your list comprehensions.

By the end of this tutorial, you’ll understand that:

A list comprehension in Python is a tool for creating lists by iterating over an iterable and optionally applying a condition.
You should use list comprehensions instead of loops when you want concise, readable code that performs transformations or filtering.
You add conditional logic to a list comprehension by including an if statement within the comprehension.
A list comprehension can be faster than a for loop because it’s optimized for performance by Python’s internal mechanisms.
A Python list comprehension is not lazy—it generates and stores the entire list in memory eagerly.
The difference between list comprehensions and map() is that the former creates a list, while the latter returns a map object, which is iterable.
To optimize performance with list comprehensions, use them for small to medium-sized lists and profile different approaches to choose the fastest one.

In this tutorial, you’ll explore how to leverage list comprehensions to simplify your code. You’ll also gain an understanding of the trade-offs that come with using them so that you can determine when other approaches are preferable.

Get Your Code: Click here to download the free code that shows you how and when to use list comprehensions in Python.

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Interactive Quiz

When to Use a List Comprehension in Python

In this quiz, you'll test your understanding of Python list comprehensions. You'll revisit how to rewrite loops as list comprehensions, how to choose between comprehensions and loops, and how to use conditional logic in your comprehensions.

Transforming Lists in Python

There are a few different ways to create and add items to a lists in Python. In this section, you’ll explore for loops and the map() function to perform these tasks. Then, you’ll move on to learn about how to use list comprehensions and when list comprehensions can benefit your Python program.

Use `for` Loops

The most common type of loop is the for loop. You can use a for loop to create a list of elements in three steps:

Instantiate an empty list.
Loop over an iterable or range of elements.
Append each element to the end of the list.

If you want to create a list containing the first ten perfect squares, then you can complete these steps in three lines of code:

Python
      
        
      
    
>>> squares = []
>>> for number in range(10):
...     squares.append(number * number)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Here, you instantiate an empty list, squares. Then, you use a for loop to iterate over range(10). Finally, you multiply each number by itself and append the result to the end of the list.

Work With `map` Objects

For an alternative approach that’s based in functional programming, you can use map(). You pass in a function and an iterable, and map() will create an object. This object contains the result that you’d get from running each iterable element through the supplied function.

As an example, consider a situation in which you need to calculate the price after tax for a list of transactions:

Python
      
        
      
    
>>> prices = [1.09, 23.56, 57.84, 4.56, 6.78]
>>> TAX_RATE = .08
>>> def get_price_with_tax(price):
...     return price * (1 + TAX_RATE)
...

>>> final_prices = map(get_price_with_tax, prices)
>>> final_prices
<map object at 0x7f34da341f90>

>>> list(final_prices)
[1.1772000000000002, 25.4448, 62.467200000000005, 4.9248, 7.322400000000001]

Here, you have an iterable, prices, and a function, get_price_with_tax(). You pass both of these arguments to map() and store the resulting map object in final_prices. Finally, you convert final_prices into a list using list().

Leverage List Comprehensions

List comprehensions are a third way of making or transforming lists. With this elegant approach, you could rewrite the for loop from the first example in just a single line of code:

Python
      
>>> squares = [number * number for number in range(10)]
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Rather than creating an empty list and adding each element to the end, you simply define the list and its contents at the same time by following this format:

Python Syntax
      
new_list = [expression for member in iterable]

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

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The Real Python Podcast – Episode #231: Good Python Programming Practices When New to the Language

2024-12-06T12:00:00+00:00

What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Expression vs Statement in Python: What's the Difference?

2024-12-04T14:00:00+00:00

After working with Python for a while, you’ll eventually come across two seemingly similar terms: expression and statement. When you browse the official documentation or dig through a Python-related thread on an online forum, you may get the impression that people use these terms interchangeably. That’s often true, but confusingly enough, there are cases when the expression vs statement distinction becomes important.

So, what’s the difference between expressions and statements in Python?

Get Your Code: Click here to download the free sample code you’ll use to learn about the difference between expressions and statements.

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Interactive Quiz

Expression vs Statement in Python: What's the Difference?

In this quiz, you'll test your understanding of Python expressions vs statements. Knowing the difference between these two is crucial for writing efficient and readable Python code.

In Short: Expressions Have Values and Statements Cause Side Effects

When you open the Python glossary, you’ll find the following two definitions:

Expression: A piece of syntax which can be evaluated to some value. (…) (Source)

Statement: A statement is part of a suite (a “block” of code). A statement is either an expression or one of several constructs with a keyword, (…) (Source)

Well, that isn’t particularly helpful, is it? Fortunately, you can summarize the most important facts about expressions and statements in as little as three points:

All instructions in Python fall under the broad category of statements.
By this definition, all expressions are also statements—sometimes called expression statements.
Not every statement is an expression.

In a technical sense, every line or block of code is a statement in Python. That includes expressions, which represent a special kind of statement. What makes an expression special? You’ll find out now.

Expressions: Statements With Values

Essentially, you can substitute all expressions in your code with the computed values, which they’d produce at runtime, without changing the overall behavior of your program. Statements, on the other hand, can’t be replaced with equivalent values unless they’re expressions.

Consider the following code snippet:

Python
      
>>> x = 42
>>> y = x + 8
>>> print(y)
50

In this example, all three lines of code contain statements. The first two are assignment statements, while the third one is a call to the print() function.

When you look at each line more closely, you can start disassembling the corresponding statement into subcomponents. For example, the assignment operator (=) consists of the parts on the left and the right. The part to the left of the equal sign indicates the variable name, such as x or y, and the part on the right is the value assigned to that variable.

The word value is the key here. Notice that the variable x is assigned a literal value, 42, that’s baked right into your code. In contrast, the following line assigns an arithmetic expression, x + 8, to the variable y. Python must first calculate or evaluate such an expression to determine the final value for the variable when your program is running.

Arithmetic expressions are just one example of Python expressions. Others include logical expressions, conditional expressions, and more. What they all have in common is a value to which they evaluate, although each value will generally be different. As a result, you can safely substitute any expression with the corresponding value:

Python
      
>>> x = 42
>>> y = 50
>>> print(y)
50

This short program gives the same result as before and is functionally identical to the previous one. You’ve calculated the arithmetic expression by hand and inserted the resulting value in its place.

Note that you can evaluate x + 8, but you can’t do the same with the assignment y = x + 8, even though it incorporates an expression. The whole line of code represents a pure statement with no intrinsic value. So, what’s the point of having such statements? It’s time to dive into Python statements and find out.

Statements: Instructions With Side Effects

Statements that aren’t expressions cause side effects, which change the state of your program or affect an external resource, such as a file on disk. For example, when you assign a value to a variable, you define or redefine that variable somewhere in Python’s memory. Similarly, when you call print(), you effectively write to the standard output stream (stdout), which, by default, displays text on the screen.

Note: While statements encompass expressions, most people use the word statement informally when they refer to pure statements or instructions with no value.

Okay. You’ve covered statements that are expressions and statements that aren’t expressions. From now on, you can refer to them as pure expressions and pure statements, respectively. But it turns out there’s a middle ground here.

Read the full article at https://realpython.com/python-expression-vs-statement/ »

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Handling or Preventing Errors in Python: LBYL vs EAFP

2024-12-03T14:00:00+00:00

Dealing with errors and exceptional situations is a common requirement in programming. You can either prevent errors before they happen or handle errors after they’ve happened. In general, you’ll have two coding styles matching these strategies: look before you leap (LBYL), and easier to ask forgiveness than permission (EAFP). In this video course, you’ll dive into the questions and considerations surrounding LBYL vs EAFP in Python.

By learning about Python’s LBYL and EAFP coding styles, you’ll be able to decide which strategy and coding style to use when you’re dealing with errors in your code.

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

Use the LBYL and EAFP styles in your Python code
Understand the pros and cons of LBYL vs EAFP
Decide when to use either LBYL or EAFP

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Basic Input and Output in Python

2024-12-02T14:00:00+00:00

For a program to be useful, it often needs to communicate with the outside world. In Python, the input() function allows you to capture user input from the keyboard, while you can use the print() function to display output to the console.

These built-in functions allow for basic user interaction in Python scripts, enabling you to gather data and provide feedback. If you want to go beyond the basics, then you can even use them to develop applications that are not only functional but also user-friendly and responsive.

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

Take user input from the keyboard with input()
Display output to the console with print()
Use readline to improve the user experience when collecting input on UNIX-like systems
Format output using the sep and end keyword arguments of print()

To get the most out of this tutorial, you should have a basic understanding of Python syntax and familiarity with using the Python interpreter and running Python scripts.

Get Your Code: Click here to download the free sample code that you’ll use to learn about basic input and output in Python.

Take the Quiz: Test your knowledge with our interactive “Basic Input and Output in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Basic Input and Output in Python

In this quiz, you'll test your understanding of Python's built-in functions for user interaction, namely input() and print(). These functions allow you to capture user input from the keyboard and display output to the console, respectively.

Reading Input From the Keyboard

Programs often need to obtain data from users, typically through keyboard input. In Python, one way to collect user input from the keyboard is by calling the input() function:

The input() function pauses program execution to allow you to type in a line of input from the keyboard. Once you press the Enter key, all characters typed are read and returned as a string, excluding the newline character generated by pressing Enter.

If you add text in between the parentheses, effectively passing a value to the optional prompt argument, then input() displays the text you entered as a prompt:

Python
      
>>> name = input("Please enter your name: ")
Please enter your name: John Doe
>>> name
'John Doe'

Adding a meaningful prompt will assist your user in understanding what they’re supposed to input, which makes for a better user experience.

The input() function always reads the user’s input as a string. Even if you type characters that resemble numbers, Python will still treat them as a string:

Python
      
        
      
    
>>> number = input("Enter a number: ")
Enter a number: 50

>>> type(number)
<class 'str'>

>>> number + 100
Traceback (most recent call last):
  File "<python-input-1>", line 1, in <module>
    number + 100
    ~~~~~~~^~~~~
TypeError: can only concatenate str (not "int") to str

In the example above, you wanted to add 100 to the number entered by the user. However, the expression number + 100 on line 7 doesn’t work because number is a string ("50") and 100 is an integer. In Python, you can’t combine a string and an integer using the plus (+) operator.

You wanted to perform a mathematical operation using two integers, but because input() always returns a string, you need a way to read user input as a numeric type. So, you’ll need to convert the string to the appropriate type:

Python
      
>>> number = int(input("Enter a number: "))
Enter a number: 50

>>> type(number)
<class 'int'>

>>> number + 100
150

In this updated code snippet, you use int() to convert the user input to an integer right after collecting it. Then, you assign the converted value to the name number. That way, the calculation number + 100 has two integers to add. The calculation succeeds and Python returns the correct sum.

Note: When you convert user input to a numeric type using functions like int() in a real-world scenario, it’s crucial to handle potential exceptions to prevent your program from crashing due to invalid input.

The input() function lets you collect information from your users. But once your program has calculated a result, how do you display it back to them? Up to this point, you’ve seen results displayed automatically as output in the interactive Python interpreter session.

However, if you ran the same code from a file instead, then Python would still calculate the values, but you wouldn’t see the results. To display output in the console, you can use Python’s print() function, which lets you show text and data to your users.

Writing Output to the Console

In addition to obtaining data from the user, a program will often need to present data back to the user. In Python, you can display data to the console with the print() function.

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

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Python Exceptions: An Introduction

2024-12-01T14:00:00+00:00

Python exceptions provide a mechanism for handling errors that occur during the execution of a program. Unlike syntax errors, which are detected by the parser, Python raises exceptions when an error occurs in syntactically correct code. Knowing how to raise, catch, and handle exceptions effectively helps to ensure your program behaves as expected, even when encountering errors.

In Python, you handle exceptions using a try … except block. This structure allows you to execute code normally while responding to any exceptions that may arise. You can also use else to run code if no exceptions occur, and the finally clause to execute code regardless of whether an exception was raised.

By the end of this tutorial, you’ll understand that:

Exceptions in Python occur when syntactically correct code results in an error.
You can handle exceptions using the try, except, else, and finally keywords.
The try … except block lets you execute code and handle exceptions that arise.
Python 3 introduced more built-in exceptions compared to Python 2, making error handling more granular.
It’s bad practice to catch all exceptions at once using except Exception or the bare except clause.
Combining try, except, and pass allows your program to continue silently without handling the exception.
Using try … except is not inherently bad, but you should use it judiciously to handle only known issues appropriately.

In this tutorial, you’ll get to know Python exceptions and all relevant keywords for exception handling by walking through a practical example of handling a platform-related exception. Finally, you’ll also learn how to create your own custom Python exceptions.

Get Your Code: Click here to download the free sample code that shows you how exceptions work in Python.

Take the Quiz: Test your knowledge with our interactive “Python Exceptions: An Introduction” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python Exceptions: An Introduction

In this quiz, you'll test your understanding of Python exceptions. You'll cover the difference between syntax errors and exceptions and learn how to raise exceptions, make assertions, and use the try and except block.

Understanding Exceptions and Syntax Errors

Syntax errors occur when the parser detects an incorrect statement. Observe the following example:

Python Traceback
      
    
>>> print(0 / 0))
  File "<stdin>", line 1
    print(0 / 0))
                ^
SyntaxError: unmatched ')'

The arrow indicates where the parser ran into the syntax error. Additionally, the error message gives you a hint about what went wrong. In this example, there was one bracket too many. Remove it and run your code again:

Python
      
>>> print(0 / 0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ZeroDivisionError: division by zero

This time, you ran into an exception error. This type of error occurs whenever syntactically correct Python code results in an error. The last line of the message indicates what type of exception error you ran into.

Instead of just writing exception error, Python details what type of exception error it encountered. In this case, it was a ZeroDivisionError. Python comes with various built-in exceptions as well as the possibility to create user-defined exceptions.

Raising an Exception in Python

There are scenarios where you might want to stop your program by raising an exception if a condition occurs. You can do this with the raise keyword:

You can even complement the statement with a custom message. Assume that you’re writing a tiny toy program that expects only numbers up to 5. You can raise an error when an unwanted condition occurs:

Pythonlow.py
      
    
number = 10
if number > 5:
    raise Exception(f"The number should not exceed 5. ({number=})")
print(number)

In this example, you raised an Exception object and passed it an informative custom message. You built the message using an f-string and a self-documenting expression.

When you run low.py, you’ll get the following output:

Python Traceback
      
    
Traceback (most recent call last):
  File "./low.py", line 3, in <module>
    raise Exception(f"The number should not exceed 5. ({number=})")
Exception: The number should not exceed 5. (number=10)

The program comes to a halt and displays the exception to your terminal or REPL, offering you helpful clues about what went wrong. Note that the final call to print() never executed, because Python raised the exception before it got to that line of code.

With the raise keyword, you can raise any exception object in Python and stop your program when an unwanted condition occurs.

Debugging During Development With `assert`

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

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Python String Formatting: Available Tools and Their Features

2024-12-01T14:00:00+00:00

String formatting is essential in Python for creating dynamic and well-structured text by inserting values into strings. This tutorial covers various methods, including f-strings, the .format() method, and the modulo operator (%). Each method has unique features and benefits for different use cases. The string formatting mini-language provides additional control over the output format, allowing for aligned text, numeric formatting, and more.

F-strings provide an intuitive and efficient way to embed expressions inside string literals. The .format() method offers flexibility for lazy interpolation and is compatible with Python’s formatting mini-language. The modulo operator is an older technique still found in legacy code. Understanding these methods will help you choose the best option for your specific string formatting needs.

By the end of this tutorial, you’ll understand that:

String formatting in Python involves inserting and formatting values within strings using interpolation.
Python supports different types of string formatting, including f-strings, the .format() method, and the modulo operator (%).
F-strings are generally the most readable and efficient option for eager interpolation in Python.
Python’s string formatting mini-language offers features like alignment, type conversion, and numeric formatting.
While f-strings are more readable and efficient compared to .format() and the % operator, the .format() method supports lazy evaluation.

To get the most out of this tutorial, you should be familiar with Python’s string data type and the available string interpolation tools. Having a basic knowledge of the string formatting mini-language is also a plus.

Get Your Code: Click here to download the free sample code you’ll use to learn about Python’s string formatting tools.

Take the Quiz: Test your knowledge with our interactive “Python String Formatting: Available Tools and Their Features” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python String Formatting: Available Tools and Their Features

You can take this quiz to test your understanding of the available tools for string formatting in Python, as well as their strengths and weaknesses. These tools include f-strings, the .format() method, and the modulo operator.

Interpolating and Formatting Strings in Python

String interpolation involves generating strings by inserting other strings or objects into specific places in a base string or template. For example, here’s how you can do some string interpolation using an f-string:

Python
      
>>> name = "Bob"

>>> f"Hello, {name}!"
'Hello, Bob!'

In this quick example, you first have a Python variable containing a string object, "Bob". Then, you create a new string using an f-string. In this string, you insert the content of your name variable using a replacement field. When you run this last line of code, Python builds a final string, 'Hello, Bob!'. The insertion of name into the f-string is an interpolation.

Note: To dive deeper into string interpolation, check out the String Interpolation in Python: Exploring Available Tools tutorial.

When you do string interpolation, you may need to format the interpolated values to produce a well-formatted final string. To do this, you can use different string interpolation tools that support string formatting. In Python, you have these three tools:

The first two tools support the string formatting mini-language, a feature that allows you to fine-tune your strings. The third tool is a bit old and has fewer formatting options. However, you can use it to do some minimal formatting.

Note: The built-in format() function is yet another tool that supports the format specification mini-language. This function is typically used for date and number formatting, but you won’t cover it in this tutorial.

In the following sections, you’ll start by learning a bit about the string formatting mini-language. Then, you’ll dive into using this language, f-strings, and the .format() method to format your strings. Finally, you’ll learn about the formatting capabilities of the modulo operator.

Using F-Strings to Format Strings

Python 3.6 added a string interpolation and formatting tool called formatted string literals, or f-strings for short. As you’ve already learned, f-strings let you embed Python objects and expressions inside your strings. To create an f-string, you must prefix the string with an f or F and insert replacement fields in the string literal. Each replacement field must contain a variable, object, or expression:

Python
      
>>> f"The number is {42}"
'The number is 42'

>>> a = 5
>>> b = 10
>>> f"{a} plus {b} is {a + b}"
'5 plus 10 is 15'

In the first example, you define an f-string that embeds the number 42 directly into the resulting string. In the second example, you insert two variables and an expression into the string.

Formatted string literals are a Python parser feature that converts f-strings into a series of string constants and expressions. These are then joined up to build the final string.

Using the Formatting Mini-Language With F-Strings

When you use f-strings to create strings through interpolation, you need to use replacement fields. In f-strings, you can define a replacement field using curly brackets ({}) as in the examples below:

Python
      
>>> debit = 300.00
>>> credit = 450.00

>>> f"Debit: ${debit}, Credit: ${credit}, Balance: ${credit - debit}"
'Debit: $300, Credit: $450.0, Balance: $150.0'

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

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How to Check if a Python String Contains a Substring

2024-12-01T14:00:00+00:00

To check if a string contains another string in Python, use the in membership operator. This is the recommended method for confirming the presence of a substring within a string. The in operator is intuitive and readable, making it a straightforward way to evaluate substring existence.

Additionally, you can use string methods like .count() and .index() to gather more detailed information about substrings, such as their frequency and position. For more complex substring searches, use regular expressions with the re module. When you’re dealing with tabular data, then pandas provides efficient tools for searching for substrings within DataFrame columns.

By the end of this tutorial, you’ll understand that:

The in membership operator is the recommended way to check if a Python string contains a substring.
Converting input text to lowercase generalizes substring checks by removing case sensitivity.
The .count() method counts occurrences of a substring, while .index() finds the first occurrence’s position.
Regular expressions in the re module allow for advanced substring searches based on complex conditions.
The .str.contains() method in pandas identifies which DataFrame entries contain a specific substring.

Understanding these methods and tools enables you to effectively check for substrings in Python strings, catering to various needs from simple checks to complex data analysis.

Get Your Code: Click here to download the free sample code that you’ll use to check if a string contains a substring.

How to Confirm That a Python String Contains Another String

If you need to check whether a string contains a substring, use Python’s membership operator in. In Python, this is the recommended way to confirm the existence of a substring in a string:

Python
      
>>> raw_file_content = """Hi there and welcome.
... This is a special hidden file with a SECRET secret.
... I don't want to tell you The Secret,
... but I do want to secretly tell you that I have one."""

>>> "secret" in raw_file_content
True

The in membership operator gives you a quick and readable way to check whether a substring is present in a string. You may notice that the line of code almost reads like English.

Note: If you want to check whether the substring is not in the string, then you can use not in:

Python
      
>>> "secret" not in raw_file_content
False

Because the substring "secret" is present in raw_file_content, the not in operator returns False.

When you use in, the expression returns a Boolean value:

True if Python found the substring
False if Python didn’t find the substring

You can use this intuitive syntax in conditional statements to make decisions in your code:

Python
      
>>> if "secret" in raw_file_content:
...    print("Found!")
...
Found!

In this code snippet, you use the membership operator to check whether "secret" is a substring of raw_file_content. If it is, then you’ll print a message to the terminal. Any indented code will only execute if the Python string that you’re checking contains the substring that you provide.

Note: Python considers empty strings always as a substring of any other string, so checking for the empty string in a string returns True:

Python
      
>>> "" in "secret"
True

This may be surprising because Python considers emtpy strings as false, but it’s an edge case that is helpful to keep in mind.

The membership operator in is your best friend if you just need to check whether a Python string contains a substring.

However, what if you want to know more about the substring? If you read through the text stored in raw_file_content, then you’ll notice that the substring occurs more than once, and even in different variations!

Which of these occurrences did Python find? Does capitalization make a difference? How often does the substring show up in the text? And what’s the location of these substrings? If you need the answer to any of these questions, then keep on reading.

Generalize Your Check by Removing Case Sensitivity

Python strings are case sensitive. If the substring that you provide uses different capitalization than the same word in your text, then Python won’t find it. For example, if you check for the lowercase word "secret" on a title-case version of the original text, the membership operator check returns False:

Python
      
>>> title_cased_file_content = """Hi There And Welcome.
... This Is A Special Hidden File With A Secret Secret.
... I Don't Want To Tell You The Secret,
... But I Do Want To Secretly Tell You That I Have One."""

>>> "secret" in title_cased_file_content
False

Despite the fact that the word secret appears multiple times in the title-case text title_cased_file_content, it never shows up in all lowercase. That’s why the check that you perform with the membership operator returns False. Python can’t find the all-lowercase string "secret" in the provided text.

Humans have a different approach to language than computers do. This is why you’ll often want to disregard capitalization when you check whether a string contains a substring in Python.

Read the full article at https://realpython.com/python-string-contains-substring/ »

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The Real Python Podcast – Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python

2024-11-29T12:00:00+00:00

What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

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Managing Dependencies With Python Poetry

2024-11-26T14:00:00+00:00

When your Python project relies on external packages, you need to make sure you’re using the right version of each package. After an update, a package might not work as it did before. A dependency manager like Python Poetry helps you specify, install, and resolve external packages in your projects. This way, you can be sure that you always work with the correct dependency version on every machine.

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

Create a new project using Poetry
Add Poetry to an existing project
Configure your project through pyproject.toml
Pin your project’s dependency versions
Install dependencies from a poetry.lock file
Run basic Poetry commands using the Poetry CLI

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The Real Python Podcast – Episode #229: The Joy of Tinkering & Python Free-Threading Performance

2024-11-22T12:00:00+00:00

What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Quiz: Expression vs Statement in Python: What's the Difference?

2024-11-21T12:00:00+00:00

In this quiz, you’ll test your understanding of Expression vs Statement in Python: What’s the Difference?

By working through this quiz, you’ll revisit the key differences between expressions and statements in Python, and how to use them effectively in your code.

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Quiz: Interacting With Python

2024-11-21T12:00:00+00:00

In this quiz, you’ll test your understanding of the different ways you can interact with Python.

By working through this quiz, you’ll revisit key concepts related to Python interaction in interactive mode using the Read-Eval-Print Loop (REPL), through Python script files, and within Integrated Development Environments (IDEs) and code editors.

You’ll also test your knowledge of some other options that may be useful, such as Jupyter Notebooks.

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Quiz: NumPy Practical Examples: Useful Techniques

2024-11-20T12:00:00+00:00

In this quiz, you’ll test your understanding of the techniques covered in the tutorial NumPy Practical Examples: Useful Techniques.

By working through the questions, you’ll review your understanding of NumPy arrays and also expand on what you learned in the tutorial.

You’ll need to do some research outside of the tutorial to answer all the questions. Embrace this challenge and let it take you on a learning journey.

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Working With TOML and Python

2024-11-19T14:00:00+00:00

TOML—Tom’s Obvious Minimal Language—is a reasonably new configuration file format that the Python community has embraced over the last couple of years. TOML plays an essential part in the Python ecosystem. Many of your favorite tools rely on TOML for configuration, and you’ll use pyproject.toml when you build and distribute your own packages.

In this video course, you’ll learn more about TOML and how you can use it. In particular, you’ll:

Learn and understand the syntax of TOML
Use tomli and tomllib to parse TOML documents
Use tomli_w to write data structures as TOML
Use tomlkit when you need more control over your TOML files

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The Real Python Podcast – Episode #228: Maintaining the Foundations of Python & Cautionary Tales

2024-11-15T12:00:00+00:00

How do you build a sustainable open-source project and community? What lessons can be learned from Python's history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

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Quiz: Namespaces and Scope in Python

2024-11-14T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Namespaces and Scope.

You’ll revisit how Python organizes symbolic names and objects in namespaces, when Python creates a new namespace, how namespaces are implemented, and how variable scope determines symbolic name visibility.

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Quiz: Basic Input and Output in Python

2024-11-13T12:00:00+00:00

In this quiz, you’ll test your understanding of how to use Python’s built-in functions input() and print() for basic input and output operations.

You’ll also revisit how to use readline to improve the user experience when collecting input, and how to format output using the sep and end keyword arguments of print().

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Quiz: Python Dictionary Comprehensions: How and When to Use Them

2024-11-13T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Dictionary Comprehensions: How and When to Use Them.

Understanding dictionary comprehensions is crucial for you as a Python developer because they provide a Pythonic tool for dictionary manipulation and can be a valuable addition to your programming toolkit.

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Formatting Floats Inside Python F-Strings

2024-11-12T14:00:00+00:00

You’ll often need to format and round a Python float to display the results of your calculations neatly within strings. In earlier versions of Python, this was a messy thing to do because you needed to round your numbers first and then use either string concatenation or the old string formatting technique to do this for you.

Since Python 3.6, the literal string interpolation, more commonly known as a formatted string literal or f-string, allows you to customize the content of your strings in a more readable way.

An f-string is a literal string prefixed with a lowercase or uppercase letter f and contains zero or more replacement fields enclosed within a pair of curly braces {...}. Each field contains an expression that produces a value. You can calculate the field’s content, but you can also use function calls or even variables.

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The Real Python Podcast – Episode #227: New PEPs: Template Strings & External Wheel Hosting

2024-11-08T12:00:00+00:00

Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Introduction to Web Scraping With Python

2024-11-05T14:00:00+00:00

Web scraping is the process of collecting and parsing raw data from the Web, and the Python community has come up with some pretty powerful web scraping tools.

The Internet hosts perhaps the greatest source of information on the planet. Many disciplines, such as data science, business intelligence, and investigative reporting, can benefit enormously from collecting and analyzing data from websites.

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

Parse website data using string methods and regular expressions
Parse website data using an HTML parser
Interact with forms and other website components

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Quiz: Variables in Python: Usage and Best Practices

2024-11-05T12:00:00+00:00

In this quiz, you’ll test your understanding of Variables in Python: Usage and Best Practices.

By working through this quiz, you’ll revisit how to create and assign values to variables, change a variable’s data type dynamically, use variables to create expressions, counters, accumulators, and Boolean flags, follow best practices for naming variables, and create, access, and use variables in their scopes.

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The Real Python Podcast – Episode #226: PySheets: Spreadsheets in the Browser Using PyScript

2024-11-01T12:00:00+00:00

What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book "Communication for Engineers."

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Python's Magic Methods in Classes

2024-10-29T14:00:00+00:00

As a Python developer who wants to harness the power of object-oriented programming, you’ll love to learn how to customize your classes using special methods, also known as magic methods or dunder methods. A special method is a method whose name starts and ends with a double underscore. These methods have special meanings in Python.

Python automatically calls magic methods as a response to certain operations, such as instantiation, sequence indexing, attribute managing, and much more. Magic methods support core object-oriented features in Python, so learning about them is fundamental for you as a Python programmer.

In this video course, you’ll:

Learn what Python’s special or magic methods are
Understand the magic behind magic methods in Python
Customize different behaviors of your custom classes with special methods

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Quiz: Using .repr() vs .str() in Python

2024-10-29T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s .__repr__() and .__str__() special methods. These methods allow you to control how a program displays an object, making your classes more readable and easier to debug and maintain.

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Quiz: Beautiful Soup: Build a Web Scraper With Python

2024-10-28T12:00:00+00:00

In this quiz, you’ll test your understanding of web scraping with Python, Requests, and Beautiful Soup.

By working through this quiz, you’ll revisit how to inspect the HTML structure of your target site with your browser’s developer tools, decipher data encoded in URLs, use Requests and Beautiful Soup for scraping and parsing internet data, and gain an understanding of what a web scraping pipeline looks like.

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

The Real Python Podcast – Episode #225: Python Getting Faster and Leaner & Ideas for Django Projects

2024-10-25T12:00:00+00:00

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Quiz: How to Reset a pandas DataFrame Index

2024-10-25T12:00:00+00:00

In this quiz, you’ll test your understanding of how to reset a pandas DataFrame index.

By working through the questions, you’ll review your knowledge of indexing and also expand on what you learned in the tutorial.

You’ll need to do some research outside of the tutorial to answer all the questions. Embrace this challenge and let it take you on a learning journey.

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

Understanding Python's Global Interpreter Lock (GIL)

2024-10-22T14:00:00+00:00

The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.

This means that only one thread can be in a state of execution at any point in time. The impact of the GIL isn’t visible to developers who execute single-threaded programs, but it can be a performance bottleneck in CPU-bound and multi-threaded code.

Since the GIL allows only one thread to execute at a time even in a multi-threaded architecture with more than one CPU core, the GIL has gained a reputation as an “infamous” feature of Python.

In this video course you’ll learn how the GIL affects the performance of your Python programs, and how you can mitigate the impact it might have on your code.

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

The Real Python Podcast – Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

2024-10-18T12:00:00+00:00

How does a Python tool support all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals.

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

Using Type Hints for Multiple Return Types in Python

2024-10-15T14:00:00+00:00

In Python, type hinting is an optional yet useful feature for making your code easier to read, reason about, and debug. With type hints, you let other developers know the expected data types for variables, function arguments, and return values. As you write code for applications that require greater flexibility, you may need to specify multiple return types to make your code more robust and adaptable to different situations.

You’ll encounter different use cases where you may want to annotate multiple return types within a single function in Python. In other words, the data returned can vary in type. In this video course, you’ll walk through examples of how to specify multiple return types for a function that parses a string from an email address to grab the domain name.

In addition, you’ll see examples of how to specify type hints for callback functions or functions that take another function as input. With these examples, you’ll be ready to express type hints in functional programming.

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

The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13

2024-10-11T12:00:00+00:00

Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, "Python 3.13: Cool New Features for You to Try." Christopher's video course "What's New in Python 3.13" covers the topics from the article and shows the new features in action.

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

What's New in Python 3.13

2024-10-08T14:00:00+00:00

Python 3.13 was published on October 7, 2024. This new version is a major step forward for the language, although several of the biggest changes are happening under the hood and won’t be immediately visible to you.

In a sense, Python 3.13 is laying the groundwork for some future improvements, especially to the language’s performance. As you watch the course, you’ll learn more about the background for this and dive into some new features that are fully available now.

In this video course, you’ll learn about some of the improvements in the new version, including:

Improvements made to the interactive interpreter (REPL)
Clearer error messages that can help you fix common mistakes
Advancements done in removing the global interpreter lock (GIL) and making Python free-threaded
The implementation of an experimental Just-In-Time (JIT) compiler
A host of minor upgrades to Python’s static type system

In this video course, you’ll explore these changes and see how this new version of Python can work for you.

If you want to try any of the examples in this video course, then you’ll need to use Python 3.13. The Python 3 Installation & Setup Guide and How Can You Install a Pre-Release Version of Python? walk you through several options for adding a new version of Python to your system.

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

Differences Between Python's Mutable and Immutable Types

2024-10-01T14:00:00+00:00

As a Python developer, you’ll have to deal with mutable and immutable objects sooner or later. Mutable objects are those that allow you to change their value or data in place without affecting the object’s identity. In contrast, immutable objects don’t allow this kind of operation. You’ll just have the option of creating new objects of the same type with different values.

In Python, mutability is a characteristic that may profoundly influence your decision when choosing which data type to use in solving a given programming problem. Therefore, you need to know how mutable and immutable objects work in Python.

In this video course, you’ll:

Understand how mutability and immutability work under the hood in Python
Explore immutable and mutable built-in data types in Python
Identify and avoid some common mutability-related gotchas
Understand and control how mutability affects your custom classes

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

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

2024-09-27T12:00:00+00:00

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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

Real Python

Linear Regression in Python

Regression

What Is Regression?

When Do You Need Regression?

Linear Regression

Problem Formulation

Socket Programming in Python (Guide)

Historical Background

Python Socket API Overview

TCP Sockets

How to Round Numbers in Python

Python’s Built-in round() Function

How Much Impact Can Rounding Have?

Python GUI Programming With Tkinter

Building Your First Python GUI Application With Tkinter

Adding a Widget

When to Use a List Comprehension in Python

Transforming Lists in Python

Use for Loops

Work With map Objects

Leverage List Comprehensions

The Real Python Podcast – Episode #231: Good Python Programming Practices When New to the Language

Expression vs Statement in Python: What's the Difference?

In Short: Expressions Have Values and Statements Cause Side Effects

Expressions: Statements With Values

Statements: Instructions With Side Effects

Handling or Preventing Errors in Python: LBYL vs EAFP

Basic Input and Output in Python

Reading Input From the Keyboard

Writing Output to the Console

Python Exceptions: An Introduction

Understanding Exceptions and Syntax Errors

Raising an Exception in Python

Debugging During Development With assert

Python String Formatting: Available Tools and Their Features

Interpolating and Formatting Strings in Python

Using F-Strings to Format Strings

Using the Formatting Mini-Language With F-Strings

How to Check if a Python String Contains a Substring

How to Confirm That a Python String Contains Another String

Generalize Your Check by Removing Case Sensitivity

The Real Python Podcast – Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python

Managing Dependencies With Python Poetry

The Real Python Podcast – Episode #229: The Joy of Tinkering & Python Free-Threading Performance

Quiz: Expression vs Statement in Python: What's the Difference?

Quiz: Interacting With Python

Quiz: NumPy Practical Examples: Useful Techniques

Working With TOML and Python

The Real Python Podcast – Episode #228: Maintaining the Foundations of Python & Cautionary Tales

Quiz: Namespaces and Scope in Python

Quiz: Basic Input and Output in Python

Quiz: Python Dictionary Comprehensions: How and When to Use Them

Formatting Floats Inside Python F-Strings

The Real Python Podcast – Episode #227: New PEPs: Template Strings & External Wheel Hosting

Introduction to Web Scraping With Python

Quiz: Variables in Python: Usage and Best Practices

The Real Python Podcast – Episode #226: PySheets: Spreadsheets in the Browser Using PyScript

Python's Magic Methods in Classes

Quiz: Using .__repr__() vs .__str__() in Python

Quiz: Beautiful Soup: Build a Web Scraper With Python

The Real Python Podcast – Episode #225: Python Getting Faster and Leaner & Ideas for Django Projects

Quiz: How to Reset a pandas DataFrame Index

Understanding Python's Global Interpreter Lock (GIL)

The Real Python Podcast – Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries

Using Type Hints for Multiple Return Types in Python

The Real Python Podcast – Episode #223: Exploring the New Features of Python 3.13

What's New in Python 3.13

Differences Between Python's Mutable and Immutable Types

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

Python’s Built-in `round()` Function

Use `for` Loops

Work With `map` Objects

Debugging During Development With `assert`

Quiz: Using .repr() vs .str() in Python