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Python & Data Analysis

Guide and content relate

Python & Jupyter Notebooks


Standard Python files are usually saved as a ".py" filetype.  However, for researchers and analysts, Jupyter "notebooks" are an immensely popular interface for working Python scripts.  Jupyter itself is a web-based interactive development environment and notebook files are saved as ".ipynb" filetypes.  There are multiple reasons why Jupyter notebooks are extremely popular with data analysts, data scientists, and researchers, and why it may be useful for you:

  • Segmenting and testing small components of Python code
  • Teaching and learning Python
  • Displaying outputs and visualizations alongside and between blocks of code
  • Publishing and sharing code via hosted webpages (e.g. Google Colab, Github)

While you can download and install Jupyter directly, some users may find it easier to use Jupyter alongside one of the platforms and/or Python distributions listed below.

Jupyter Official Homepage:

Jupyter Logo


Free Platforms & Software Packages


There are multiple services on the web that will allow users to create and execute Python code in a cloud environment.  In many cases these software platforms are expensive or unstable.  However, there are a few platforms that are notably robust, reliable, and sufficiently powerful for many users.  These platforms allow users to upload their data and execute analyses without needing a powerful personal computer.

The two examples listed below enable users to create, test, and share Jupyter notebook files.  Both include "free" versions which many users will find sufficiently powerful.  There are also options available for integrating these tools with other cloud storage platforms (e.g. Google Drive)

Google Colab:


JetBrains - DataLore:

DataLore Logo


There are many software options available that will bundle Python and 3rd party-packages (e.g. Jupyter) together.  These are sometimes referred to as "distributions" and many of these are free.  Choosing the "correct" download options is entirely dependent on the needs of individual users.  However, the two software packages listed below are among the most popular in the Python and Data Science communities.


Anaconda Logo


JetBrains - PyCharm: