# 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/