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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

Exercise 0 Environment and libraries

The goal of this exercise is to set up the Python work environment with the required libraries and to learn to launch a jupyter notebook. Jupyter notebooks are very convenient as they allow to write and test code within seconds. However, it really easy to implement instable and not reproducible code using notebooks. Keep the notebook and the underlying code clean. An article below detail when the Notebook should be used. Notebook can be used for most of the exercices of the piscine as the goal is to experiment A LOT. But no worries, you'll be asked to build a more robust structure for all the projects.

Note: For each quest, your first exercice will be to set up the virtual environment with the required libraries.

I recommend to use:

the last stable versions of Python. However, for educational purpose you will install a specific version of Python in this exercise.
the virtual environment you're the most confortable with. virtualenv and conda are the most used in Data Science.
one of the most recents versions of the libraries required

Create a virtual environment named ex00, with Python 3.8, with the following libraries: numpy, jupyter.
Launch a jupyter notebook on port 8891 and create a notebook named Notebook_ex00. JupyterLab can be used instead of Jupyter Notebook here.
Put the text H1 TITLE as heading level 1 and H2 TITLE as heading level 2 in the first cell.
Run print("Buy the dip ?") in the second cell

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