At some point in your Python journey, you’ll definitely need to work with dates, times, or both. Learn all the basics of date and time in Python with this short tutorial. Need to calculate how long it’s been since a given date? Working with date and time objects in Python? In this beginner’s guide, we’ll take a look at how to write programs with these two key data types in Python.
Even if you’re just starting your Python journey, you’ve most likely come across Python sets. But do you know how to use them effectively? This article aims to cover all the main Python set operations to give you a better feel for when and how to use this important data structure. What Are Python Sets? First, let’s start with the basics. A set is a built-in type in Python that has a number of important characteristics:
You already have some foundational knowledge of Python for data science. But do you write your code efficiently? Check out these tips and tricks to supercharge your Python skills. How to Write Efficient Python Code In this article, we’ll take a look at some tricks that will help you write fast and efficient Python code. I’ll start with how to optimize code that involves the pandas library. If you want to refresh your knowledge of pandas, check out our Introduction to Python for Data Science course.
You’ve spent hours studying Python, and you may even have several successful projects in your portfolio. But do you write your Python code like a pro? Let’s review some important guidelines to help you clean up your code. What Is the Pythonic Way of Writing Code? There are often several ways to do something in Python; naturally, some are better than others. But you should always prefer code that is not only syntactically correct but also in alignment with coding best practices and the way the language ought to be used.
Looking for some advice to build a data science portfolio that will put you ahead of other aspiring data scientists? Don’t miss these useful tips. Why Have a Portfolio at All? Even though the demand for data scientists is high, the competition for entry-level positions in this field is tough. It should come as no surprise that companies prefer to hire people with at least some real-world experience in data science.
When you already have some experience with Python, building your own portfolio of data science projects is the best way to showcase your skills to potential employers. But where do you begin with developing your very first Python project? First, Why Develop a Data Science Project? There are a number of career development benefits to creating your own data science project in a language such as Python: Studying.
Looking for a data science job? Then you’ve probably noticed that most positions require applicants to have some level of Python programming skills. But how are they going to test this? What are they going to ask? Let’s prepare you for some interview questions! Why Do Data Scientists Need Python? Data science goes beyond simple data analysis and requires that you be able to work with more advanced tools. Thus, if you work with big data and need to perform complex computations or create aesthetically pleasing and interactive plots, Python is one of the most efficient solutions out there.
Heat maps are a great way to visualize patterns in data, but some businesses avoid them because creating them seems challenging and time consuming. Well, it’s not. Do you know what the most popular programming language currently is? According to the PYPL Index, it’s—you guessed it—Python. And our serpentine friend was also crowned the best programming language in 2018 by Linux Journal readers. Why all the buzz? Because Python is simple and easy to learn.