How often do you think you’re touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I’ll explain what data science means to me. “Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information.”
Python’s pandas library is frequently used to import, manage, and analyze datasets in a variety of formats. In this article, we’ll use it to analyze Amazon’s stock prices and perform some basic time series operations.
One of the most important factors driving Python’s popularity as a statistical modeling language is its widespread use as the language of choice in data science and machine learning.
Close your eyes. Now imagine a perfect data world. What do you see? What do you wish to see? Exactly, me too. A flawlessly balanced dataset. A collection of data whose labels form a magnificent 1:1 ratio: 50% of this, 50% of that; not a bit to the left, nor a bit to the right. Just perfectly balanced, as all things should be. Now open your eyes, and come back to the real world.
You probably found this tutorial because you want to send emails using Python. Perhaps you want to receive email reminders from your code, send a confirmation email to users when they create an account, or send emails to members of your organization to remind them to pay their dues. Sending emails manually is a time-consuming and error-prone task, but it’s easy to automate with Python.
The set class is one of the key data structures in Python. It is an unordered collection of elements without duplicates. It represents, to a certain degree, a mathematical set, and many of the commonly used mathematical operations for sets exist in Python. Often the operations for sets are much faster than alternative operations with lists, thus, to write effective code, sets are essential. In this article, I will explain the ins and outs of the set class. Let’s get to it.
In programming, the term constant refers to names representing values that don’t change during a program’s execution. Constants are a fundamental concept in programming, and Python developers use them in many cases. However, Python doesn’t have a dedicated syntax for defining constants. In practice, Python constants are just variables that never change.
This tutorial looks at how to speed up CPU-bound and IO-bound operations with multiprocessing, threading, and AsyncIO.
Here we'll look at how to use Pyenv to manage and install different versions of Python, and Poetry to manage packages and virtual environments.
In this article, you'll glue everything together as you develop a single project from start to finish. After developing the basic project, you'll: Wire up CI/CD with GitHub Actions, Configure coverage reporting with CodeCov, Publish the package to PyPi and the docs to Read the Docs, Update PyPI and Read the Docs via GitHub Actions
Virtual Environments are isolated Python environments that have their own site-packages. Basically, it means that each virtual environment has its own set of dependencies to third-party packages usually installed from PyPI.
The Python HTTP library requests is probably my favourite HTTP utility in all the languages I program in. It's simple, intuitive and ubiquitous in the Python community. Most of the programs that interface with HTTP use either requests or urllib3 from the standard library.
Decorators are wrappers around Python functions (or classes) that change how these classes work. A decorator abstracts its own functioning as far away as possible. The Decorator notation is designed to be as minimally invasive as possible. A developer can develop his code within his domain as he is used to and only use the decorator to extend the functionality. Because this sounds very abstract, let’s look at some examples.
The ProcessPoolExecutor allows you to create and manage process pools in Python. Although the ProcessPoolExecutor has been available since Python 3.2, it is not widely used, perhaps because of misunderstandings of the capabilities and limitations of Processes and Threads in Python.
Python is a very dynamic language by nature. Variables do not need to be declared and can be added as attributes almost everywhere.
In Python, some objects like strs or lists can sliced. For example, you can get the first element of a list or a string.