Exploration

Exploration involves being able to interactively slice and dice your data to try and make quick discoveries. Exploration can include various tasks such as:

• Examining how variables relate to each other
• Determining how the data is distributed
• Finding and excluding outliers
• Creating quick visualizations
• Quickly creating new data representations or models to feed into more permanent and detailed modeling processes

Exploration is one of the great strengths of pandas. While exploration can be performed in most programming languages, each has its own level of ceremony—how much non-exploratory effort must be performed—before actually getting to discoveries.

When used with the read-eval-print-loop (REPL) nature of IPython and/or Jupyter notebooks, pandas creates an exploratory environment that is almost free of ceremony. The expressiveness of the syntax of pandas lets you describe complex data manipulation constructs succinctly, and the result of every action you take upon your data is immediately presented for your inspection. This allows you to quickly determine the validity of the action you just took without having to recompile and completely rerun your programs.