Python is a versatile programming language used for a range of applications, including web development, data analysis, artificial intelligence, and scientific computing. To work effectively with Python, you will need to install the base Python interpreter and an Integrated Development Environment (IDE). As we’re interested in using Python for data analysis, we’ll be using Miniconda to manage our Python environment and Spyder to interact with python (or our ‘Integrated Development Environment’).
Miniconda is a free and minimal installer for conda – a package, dependency and environment management system for any language. It is lightweight and ideal for users who just want to install the essential packages and avoid unnecessary bloat. You can install Miniconda on Windows, Mac, or Linux by following the respective ‘Download and Install’ links at https://conda.io/projects/conda/en/stable/user-guide/install/download.html
Spyder, developed by Spyder Project Contributors, is an ‘Integrated Development Environment’ (IDE) that is well suited to using Python for data analysis. By default Spyder comes with a variety of key libraries that are used for data analysis, like Matplotlib, NumPy, and SciPy. However, in most cases it’s a good approach to use miniconda’s ‘Python Intepreter’ so you can install any necessary libraries using miniconda’s ‘Anaconda prompt’. You can tell Spyder to use Miniconda’s version of Python by selecting ‘Tools’ > ‘Preferences’ and selecting ‘Python interpreter’ in Spyder’s file menu. For windows, you can then specify the location of Miniconda’s python.exe file, e.g. “C:\Users\User\miniconda3\python.exe”