While third-party packages can be hosted in different locations, the most popular and comprehensive repository is the Python Package Index (PyPi). If you want to know more about packages in Python and how to develop your own packages, check out our Developing Python Packages Course. Fortunately, the internet is a huge store where you can find hundreds of thousands of packages developed by Python developers for all kinds of purposes.Īnd the best thing? The wide majority of these packages are free for use. In these cases, you will need to get new tools elsewhere. However, sometimes you will not find the tool you are looking for in Python or its Standard Library. You just have to “import” them to your working space (more on this coming up later). Since these packages come bundled in modern Python distributions, you can use them without any additional installation required. The so-called Python Standard Library is an extensive set of built-in packages that provides standardized solutions for many problems that occur in everyday programming.
When you buy (install) Python on your computer, it comes with a wide collection of additional tools (packages) that you can use anytime. Imagine Python is a nice and balanced toolbox with the essential items you will need to code. Let’s use a metaphor to understand what pip is. Pip is a powerful tool that will allow you to leverage and manage the many Python packages that you will come across as a data professional and a programmer. In this tutorial, you will be introduced to the world of packages in Python and pip, the standard package installer for Python. Where are these libraries hosted? How can you install and manage the packages of your interest? One of the key strengths of Python is its wide catalog of well-documented and comprehensive libraries. A package is a collection of related files, modules, and dependencies that can be used repeatedly in different applications and problems. While Python itself alone is already capable of many cool things, data professionals –and, more broadly, software developers– often make use of additional packages –also known as libraries– to make their life easier.
Python is an open-source, general-purpose, and powerful programming language, with applications in many software domains, such as web development, game development, and, of course, data science. Probably the most popular programming language for data science is Python. Programming languages are the key tools that allow data professionals to analyze and extract meaningful insights from vast amounts of data. Data professionals spend a great deal of their time coding. If you are considering becoming a data scientist, the sooner you start learning how to code, the better.
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