Part 01 – Getting started with Python

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Choosing the distribution

There are many Python distribution available out there for various needs and use cases. For starting your journey with Python, I recommend starting with one of the below

  1. CPython available from
  2. Anaconda from Continuum Analytics

CPython is vanilla build of python where you will need to install 3rd party libraries or modules using pip install command

Anaconda is a python distribution targeted towards data analysis use case. Therefore, many packages required for data analysis are bundled in this distribution.

pip is python package manager it is present in most distributions. However, some distributions like Anaconda also include their own package manager

Choosing an IDEs

An integrated development environment (IDE) is a software application that aids the coders in writing and debugging complex code. Most IDE have following features to empower a coder

  1. Syntax Highlighting
  2. Autocomplete
  3. Build tools to prepare a executable
  4. Debugging
  5. Code navigation for large code base with multiple classes spread across various files and folders

There are many IDEs that can work with Python. Chances are your favorite IDE supports python as well!

Below is a list of a few IDEs available for python:

  1. IDLE
  2. PyCharm
  3. Visual Studio Code
  4. Jupyter
  5. Spyder

What IDE to use depends on use case and what you are comfortable with.  

  • For beginners IDLE the inbuilt python IDE might be sufficient. Most distributions of python are shipped with IDLE by default.
  • For Automation and Scripting, you may choose to use PyCharm or Visual Studio Code. PyCharm is marginally powerful for Python as compared to Visual Studio Code. However, if you work with many languages Visual Studio Code could be a better choice.
  • For analytics Jupyter notebook can be one of the preferred choices. Jupyter notebook allows you to execute code per block with ability to hold the results in memory or displayed on screen. The output generated is easily shareable as file or using cloud services. The ability to execute the code per block is especially power full if you are working with a large dataset.

Part 02 – Installation of Python on a Windows system