Branch-AI/piscine/week01/day01

W1D01 Piscine AI - Data Science

NumPy

The goal of this day is to understand practical usage of NumPy. NumPy is a commonly used Python data analysis package. By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood. NumPy was originally developed in the mid 2000s, and arose from an even older package called Numeric. This longevity means that almost every data analysis or machine learning package for Python leverages NumPy in some way.

Exercises of the day

• Exercise 0 Environment and libraries
• Exercise 1 Your first NumPy array
• Exercise 2 Zeros
• Exercise 3 Slicing
• Exercise 4 Random
• Exercise 5 Split, concatenate, reshape arrays
• Exercise 6 Broadcasting and Slicing
• Exercise 7 NaN
• Exercise 8 Wine
• Exercise 9 Football tournament

Virtual Environment

• Python 3.x
• NumPy
• Jupyter or JupyterLab

Version of NumPy I used to do the exercises: 1.18.1. I suggest to use the most recent one.

Ressources

• https://medium.com/fintechexplained/why-should-we-use-NumPy-c14a4fb03ee9
• https://numpy.org/doc/
• https://jakevdp.github.io/PythonDataScienceHandbook/