Python is a popular programming language – it is used in many industries and used for a variety of things. I have written a bit about it before. See here when I was trying to understand if Python is easier to learn than R.
Python is known for its simplicity, readability, and versatility. Because it is a general-purpose language, it can be used for many different types of programming, including data analysis, web development, automation, and machine learning.
See below for a list of what Python can be used to accomplish.
Data Analysis, Data Visualization, and Model Building
This blog series will focus mostly on Python for data analysis and will cover a range of topics like analyzing data with Pandas and NumPy and creating visualizations with Matplotlib and Seaborn.
We will also go over some important topics in model building. Although Python can be used to develop AI models using Scikit-Learn and TensorFlow, we will focus on much simpler models in this series.
Python is also a great tool for automating repetitive tasks. However, we will not cover much of this. It is good to know that Python can writes scripts to automate things like renaming files or processing data.
Web development is another area where Python excels, as it can also be used to create websites and web apps with frameworks like Flask and Django.
Python is also widely used in penetration testing and cybersecurity (think ethical hacking). We will not cover this aspect at all.
Because of its versatility, applications of Python extend beyond data science and web development and can include:
- Game development with Pygame.
- Building desktop applications with Tkinter.
- Writing scripts for system administration and networking
Python is one of the most commonly used languages in data analysis, which makes it an excellent choice if you are just starting to work with data. It is also one of the better languages to learn if you are new to programming.
In the next post, we’ll go over how to install Python.
Recommended Python Books
- Fluent Python: Clear, Concise, and Effective Programming
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
- Learning Python: Powerful Object-Oriented Programming
- Python Crash Course
- Python Programming for Beginners
- Pandas Cookbook