Anaconda is the most widely used Python distribution for data science and comes pre-loaded with all the most popular libraries and tools. The easiest way for a beginner to get started with Jupyter Notebooks is by installing Anaconda. First, let's go ahead and install Jupyter. We've gone ahead and created a CSV of the data you can use here.Īs we shall demonstrate, Jupyter Notebooks are perfectly suited for this investigation. You find a data set of Fortune 500 companies spanning over 50 years since the list's first publication in 1955, put together from Fortune's public archive.
So, let's say you're a data analyst and you've been tasked with finding out how the profits of the largest companies in the US changed historically. We will walk through a sample analysis, to answer a real-life question, so you can see how the flow of a notebook makes the task intuitive to work through ourselves, as well as for others to understand when we share it with them. Indeed, this article is a Jupyter Notebook! Everything here was written in the Jupyter Notebook environment and you are viewing it in a read-only form.Įxample data analysis in a Jupyter Notebook Explore how easily notebooks can be shared and published online.Delve deeper and learn all the important terminology.Cover the basics of installing Jupyter and creating your first notebook.
Jupyter Notebooks can also act as a flexible platform for getting to grips with pandas and even Python, as it will become apparent in this article. That said, if you have experience with another language, the Python in this article shouldn't be too cryptic and pandas should be interpretable. To get the most out of this tutorial you should be familiar with programming, specifically Python and pandas specifically. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case. The Jupyter project is the successor to the earlier IPython Notebook, which was first published as a prototype in 2010. Best of all, as part of the open source Project Jupyter, they are completely free. The intuitive workflow promotes iterative and rapid development, making notebooks an increasingly popular choice at the heart of contemporary data science, analysis, and increasingly science at large.
A notebook integrates code and its output into a single document that combines visualisations, narrative text, mathematical equations, and other rich media. The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects.
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