Pandas

The Ultimate Beginner’s Guide to Jupyter Notebooks

The Ultimate Beginner’s Guide to Jupyter Notebooks

Jupyter Notebooks offer a great way to write and iterate on your Python code. It is an incredibly powerful tool for interactively developing and presenting data science projects. A notebook integrates code and its output into a single document that combines visualisations, narrative text, mathematical equations, and other rich media. 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. Best of all, as part of the open source Project Jupyter, they are completely free.

Project Jupyter is the successor to an earlier project IPython Notebook, which was first published as a prototype in 2010. Jupyter Notebook is built off of IPython, an interactive way of running Python code in the terminal using the REPL model (Read-Eval-Print-Loop).

A Quick Introduction to Data Analysis With Pandas

A Quick Introduction to Data Analysis With Pandas

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas aims to integrate the functionality of NumPy and matplotlib to give you a convenient tool for data analytics and visualization. It does more than just integration — it makes the usage far more better.

In this blog, I’ll give you a list of useful pandas snippets that can be reused over and over again. These will definitely save you some time that you might need skimming through the comprehensive Pandas docs.