As a developer, I’ve built up a toolkit of essential Python libraries that make scientific computing easier and more enjoyable. Here are the top picks for every new machine.
At the heart of most programming languages, including Python, lies Jupyter notebooks. These interactive tools combine code with text and graphics to provide an intuitive way to work on projects. While not specifically designed for scientific programming, Jupyter is widely used in stats due to its versatility.
Mamba is a great tool for setting up environments on new machines. It allows users to create custom packages and switch between them easily, ensuring their system environment remains intact.
NumPy is the backbone of most statistical computations in Python. Its numerical array functionality makes solving linear equations and performing statistical calculations straightforward.
SciPy offers more advanced functions, including statistical analysis tools like the mode calculation or access to popular distributions such as normal, binomial, and Student’s t. This library helps me quickly compute specific statistical values without having to dig through tables.
SymPy is a unique library that turns Python into a computer algebra system. It enables users to manipulate symbolic variables in much the same way calculators handle numbers. While it doesn’t account for most daily operations, SymPy has proven invaluable for deeper understanding of statistical concepts.
The pandas library is an extension of NumPy’s capabilities. Its DataFrames and methods provide easy data handling, statistics, and plotting functions – making it a top pick for any scientific computing task.
Seaborn acts as a front-end to Matplotlib, simplifying the process of generating common statistical plots. It offers an intuitive interface that allows users to easily create desired visualizations.
Pingouin and statsmodels provide tools for statistical analysis in an easy-to-use manner. These libraries cross-check results with other programs like R, ensuring the accuracy of my findings.
These essential Python libraries make data analysis more fun and accessible. Whether I’m working on a new machine or revisiting familiar projects, these tools will continue to be at the forefront of my workflow.
Source: https://www.howtogeek.com/i-install-these-python-tools-on-every-new-machine