# W1D02  Piscine AI - Data Science

## Pandas

The goal of this day is to understand practical usage of **Pandas**.
As **Pandas** in intensively used in Data Science, other days of the piscine will be dedicated to it.

Not only is the **Pandas** library a central component of the data science toolkit but it is used in conjunction with other libraries in that collection.

**Pandas** is built on top of the NumPy package, meaning a lot of the structure of NumPy is used or replicated in **Pandas**. Data in **Pandas** is often used to feed statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn.

Most of the topics we will cover today are explained and describes with examples in the first resource. The number of exercises is low on purpose: Take the time to understand the chapter 5 of the resource, even if there are 40 pages.

## Exercises of the day

- Exercice 0 Environment and libraries
- Exercise 1 Your first DataFrame
- Exercise 2 Electric power consumption
- Exercise 3 E-commerce purchases
- Exercise 4 Handling missing values

## Virtual Environment 
- Python 3.x
- NumPy
- Pandas
- Jupyter or JupyterLab

*Version of Pandas I used to do the exercises: 1.0.1*. 
I suggest to use the most recent one.

## Resources

- If I had to give you one resource it would be this one:

https://bedford-computing.co.uk/learning/wp-content/uploads/2015/10/Python-for-Data-Analysis.pdf

It contains ALL you need to know about Pandas.

- Pandas documentation:

  - https://pandas.pydata.org/docs/

  - https://jakevdp.github.io/PythonDataScienceHandbook/

  - https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

  - https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/

  - https://jakevdp.github.io/PythonDataScienceHandbook/03.04-missing-values.html

# Exercise 0 Environment and libraries

The goal of this exercise is to set up the Python work environment with the required libraries.

**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. 
- 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

1. Create a virtual environment named `ex00`, with a version of Python >= `3.8`, with the following libraries: `pandas`, `numpy` and `jupyter`.