Appendix A — Setting up Python and an introduction

[Python] is a popular and beginner-friendly programming language known for its simplicity and readability. It’s widely used in web development, data science, automation, artificial intelligence, and more. Whether you’re analyzing data, building a website, or creating software, Python is a powerful and versatile tool to learn.

A.1 Laptop setup

If you prefer to have [Python] installed on your laptop, then do the following:

  1. Go to the official Python website: https://www.python.org/downloads
  2. Click the download button for your operating system (Windows, macOS, or Linux).
  3. Run the installer.
  4. Important: Make sure to check the box that says “Add Python to PATH” during installation.
  5. Follow the prompts to complete the installation.

To verify the installation, open a terminal or command prompt and type:

python --version

To install an IDE for working with your code, you may install Visual Studio Code (VSCode):

  1. Go to the VSCode website: https://code.visualstudio.com

  2. Download and install the version for your operating system.

  3. Open VSCode after installation.

  4. Install the Python extension in VSCode:

    • Go to the Extensions tab (or press Ctrl+Shift+X)
    • Search for “Python” and install the one published by Microsoft

You’re now ready to start coding in Python!

A.2 Learning Python

If you are new to Python you may have a look at some [DataCamp] Courses. First you HAVE TO SIGNUP using [this link][datacamp-signup]. Afterwards have a look at these courses:

  1. Introduction to Python - Learn the basics of Python programming, including variables, data types, and control flow.

  2. Intermediate Python - Build on your basic knowledge with functions, loops, and working with Python libraries.

  3. Data Manipulation with pandas - pandas is the world’s most popular Python library, used for everything from data manipulation to data analysis.

A.3 R and Python packages

If you are used to do data transformation in R then this table may be useful.

R Package Purpose Python Equivalent(s) Notes
dplyr Data manipulation (filter, mutate, etc.) pandas, dfply, siuba pandas is standard; dfply and siuba mimic dplyr syntax with pipes and tidy verbs
ggplot2 Data visualization (Grammar of Graphics) plotnine Closest match in syntax and philosophy; uses + for layers like ggplot2
tidyr Data tidying (pivoting, reshaping) pandas, pyjanitor pandas handles pivot, melt; pyjanitor adds more tidy-like helpers
readr Read/write CSV pandas Use read_csv(), to_csv()
jsonlite Read/write JSON json (standard), pandas json for raw files; pandas.read_json() for tabular JSON
readxl / writexl Read/write Excel pandas, openpyxl, xlsxwriter pandas integrates with Excel libraries for reading/writing .xlsx
googlesheets4 Google Sheets I/O gspread, gspread-pandas Python requires Google API setup; gspread-pandas integrates with DataFrames