Python Basics (PB) Tutorial Series

Welcome to the Python Basics Tutorial Series! This repository contains a collection of Jupyter notebooks designed to help you learn the fundamental concepts and modules of Python programming together with Python's Data Science stack. These notebooks were written by yours truly, David Akman, and are my own work for the most part. They have been tested with Python 3.11.

Each notebook focuses on a specific topic and provides a hands-on approach to learning through code examples and exercises.

Notebooks Overview

1. PB1_nb_intro.ipynb

   • Introduction to Jupyter Notebooks: Learn the basics of using Jupyter Notebooks, including how to check for Python and module versions, spellchecking, and reading in CSV files. Understand the notebook essential features and package management in Python.

2. PB2_nb_markdown.ipynb

   • Markdown in Jupyter Notebooks: Explore how to use Markdown to format text in Jupyter Notebooks. Learn how to create headers, lists, links, images, and more to document your code effectively.

3. PB3_intro_to_python.ipynb

   • Introduction to Python: Get started with Python programming. This notebook covers the basic syntax, variables, data types, and control structures such as loops and conditionals.

4. PB4_numpy.ipynb

   • NumPy Basics: Dive into NumPy, the fundamental package for numerical computing in Python. Learn about arrays, array operations, and essential functions for scientific computing.

5. PB5_pandas.ipynb

   • Pandas Basics: Discover the power of Pandas for data manipulation and analysis. This notebook covers DataFrames, Series, data cleaning, and data transformation techniques.

6. PB6_matplotlib.ipynb

   • Matplotlib for Data Visualisation: Learn how to create visualisations using Matplotlib. This notebook covers basic plots, customisation options, and advanced plotting techniques.

7. PB7_seaborn.ipynb

   • Seaborn for Statistical Plots: Explore Seaborn, a Python visualisation library based on Matplotlib. Learn how to create attractive and informative statistical graphics.

8. PB8_python_vs_r.ipynb

   • Python vs. R: Compare Python and R, two popular languages for data analysis. If you are coming from an R programming background, you should definitely have a look at this tutorial for some key differences between these two languages at a basic level.

Getting Started

To get started with these notebooks, you need to have Jupyter notebook installed on your machine. If you haven't installed Jupyter notebook yet, you can do so by following the instructions on the Jupyter website.

Once Jupyter notebook is installed, you can clone this repository and open the notebooks in Jupyter Notebook or JupyterLab:

git clone <repository-url>
cd <repository-directory>
jupyter notebook