Appendix — Python Basics: Tutorial

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Python is a popular high-level programming language created by Guido van Rossum. It is widely used for:

• Web development (server-side)
• Software development
• Mathematics
• System scripting

Python is recommended for several reason:

• Platform independence: Works on multiple platforms (Windows, Mac, Linux, etc.).
• Readability and shallow learning curve: Has a simple and elegant syntax similar to English.
• Conciseness: Allows developers to write concise code.
• Rapid prototyping: Runs on an interpreter system, enabling quick prototyping.
• Versatility: Supports procedural, object-oriented, and functional programming styles.
• Accessibility: Comes pre-installed on many PCs and Macs.

One of Python’s key drawbacks is its performance. As an interpreted language, Python’s code is executed line by line, which can be slower compared to compiled languages like C++ or Java. This can be noticeable in computationally intensive tasks or large-scale applications.

However, it is important to note that Python’s speed has improved significantly in recent years. Additionally, for many applications, Python’s readability, versatility, and extensive libraries outweigh its performance limitations.

This tutorial is designed for absolute beginners with no prior programming experience who want to learn the foundational concepts of Python programming for social media analytics. By the end, you will be able to understand and potentially modify Python code related to social media data analysis.

The tutorial will guide you through installing Python and Anaconda, introduce the language’s syntax, variables, data types, and common control structures. You will also learn about functions, modules, and JSON data format.

Additionally, the tutorial explores popular Python libraries:

• Pandas: for data manipulation and analysis.
• Matplotlib: for creating basic visualizations.
• NumPy: for efficient numerical computing.
• Seaborn: a high-level library built on Matplotlib for more advanced data visualizations.

This tutorial includes hands-on coding exercises to help you solidify your understanding. It assumes you are using a basic code editor such as the Jupyter notebook, but the concepts apply to any Python development environment. Download this notebook to follow along with the Python implementation on Jupyter.

For a more comprehensive understanding of Python, you can refer to online resources like W3School’s Python Tutorial.

As you advance further, you should consider online forums like Stack Overflow or code-sharing platforms like Kaggle for answers to complex questions. Additionally, you might want to transition to Integrated Development Environments (IDEs) such as PyCharm, Sublime Text, or Atom. These provide features like code completion, debugging tools, and syntax highlighting to make development more efficient.

There’s another option to consider: conda. It’s gaining popularity in the scientific community because it simplifies dependency management, a common challenge when working with various software packages. Conda is an open-source package and environment manager that allows you to install different versions of software packages (and their dependencies) and easily switch between them. It supports Linux, macOS, and Windows, and while initially created for Python, it can package and distribute any software. This can be particularly useful for managing different Python environments with specific package versions for your projects.