In Python, an assertion is a statement that confirms something about the state of your program. For example, if you write a createUser function and you are sure that the user needs to be older than 18, you assert that the age field is greater than or equal to 18. You can think of an assert statement like a unit test that is performed at runtime.
While lists aren’t the most efficient data structure if you’ll be doing lots of deleting from the middle, there are definitely good ways to accomplish the task. The built-in remove() method should be your first option. Let’s go over some examples.
Developers love concise code that’s easy to read, and that’s exactly what ternary operators are for. The ternary operator in Python lets you perform a small if/else statement in a single line. Let’s take a look at a few examples.
Let’s go over a few idiomatic ways to remove duplicates from lists in Python. Method #1 - Create a new list (simplest) This is the easiest algorithm to code, but because it requires creating a new list, also requires more memory and is a bit slower.
When working with files in Python, you’ll often need to check if a file exists before you do anything else with it, such as reading from or writing to it. Luckily, the Python standard library makes this a piece of cake.
It’s either a blessing or a curse when choosing to learn Python or C++ because there couldn’t be two more opposing languages to compare.
PHP famously claims to be the backend programming language for just under 80% of the Internet. However, if you look at the popularity rankings of programming languages, Python is consistently far ahead of PHP. How can that be? Both languages can be used for backend web development, and PHP was even specifically made for web development.
A ruby is a beautiful red gemstone; a python is a beautiful green snake. Aside from that, they’re both very popular programming languages. They’re popular for different reasons, and they’re good at different things. Before you choose between Ruby vs. Python, make sure you ask yourself these 10 questions.
Everyone wants to know the best way to learn to code Python nowadays. It’s a great language, as I’ve written about before, with great career prospects and tons of useful features.
I’ve made no secret of the fact that I love Python, and that I believe one of the best ways to learn Python is by finding an awesome project you care about to work on. There’s nothing more stimulating than passion and curiosity, and that makes the best projects for Python beginners usually something a little grittier than the standard tutorials.
Rust and Python contrast in a unique way because while they both provide back-end web support, Python thrives as a flexible and consistent language, while Rust makes a name for itself with raw power and speed.
If you’re thinking about dipping your toe into the finance sector for your career and you stumble across this article, you may be wondering, “How can Python help in finance?”
Comparing Python and Java is a bit like comparing boats and cars. They both get you from point A to B and are fun to show off, but that’s where the similarities end. And the same with Python and Java, they’re both programming languages and known for powerful server-side coding, but after that, they become completely different tools.
Simple to learn, endlessly versatile, and perennially in demand by employers paying higher and higher salaries every year — it feels like there’s a million reasons to learn Python.
Python and R made a name for themselves as top-end competitors in the world of data science for their ability to seamlessly navigate and handle data. But what sets these languages apart from each other?
From the outset, comparing JavaScript and Python seems a bit like comparing apples and oranges. Surprisingly however, there are quite a few similarities hidden beneath their popular respective exteriors.
Read this if you don’t know where to start learning data science Data science is a vast field with tons of entry points, depending on where and how you want to start. I started learning basic data science with a language called R, until I ran into one of its many limitations. Python has definitely won the battle of R vs Python for data science, as I learned. When I wanted to take the next step in my data science journey, I leaned on Python. Learning Python for data science is one of the fastest, easiest, and most fun ways to get into data science.
A red-black tree is a kind of self-balancing binary search tree. Each node stores an extra bit, which we will call the color, red or black. The color ensures that the tree remains approximately balanced during insertions and deletions. When the tree is modified, the new tree is rearranged and repainted to restore the coloring properties that constrain how unbalanced the tree can become in the worst case.
I’ve been wanting to expand boot.dev’s curriculum, and one of the most requested programming languages has been Python. Because my courses allow students to write and execute code right in the web browser, I decided to look into existing projects that allow a Python interpreter to run in the browser using Web Assembly. I settled on a tool called Pyodide, which does just that.
It’s a fairly common scenario to subscribe to a Rabbit queue and process messages before acknowledging receipt. The pika package for dealing with RabbitMQ in Python however is only single-threaded out of the box. If we want to make a network or database call before each acknowledgment our subscribers can get really slow.
Want to encrypt text with a password or private key in Python? AES-256 is a solid symmetric cipher that is commonly used to encrypt data for oneself. In other words, the same person who encrypts the data also decrypts it, the way personal password managers work.
Building a from-scratch server or using a lightweight framework is empowering. With that power comes responsibility, specifically the responsibility to securely store user’s passwords.