Data visualization with Python

This chapter provides an introduction to data visualization with Python, specifically for the purpose of Exploratory Data Analysis (EDA) and presenting the results from a data science project in an interactive document (dashboard, app).

Exploratory Data Analysis

The term Exploratory Data Analysios (EDA) is mostly attributed to Tukey (1977). In EDA, one’s aim is not to draw conclusions on predefined research questions (be it the construction of a model, the estimation of parameters, or the confirmation or rejection of a hypothesis); in fact, EDA in problem solving is often applied to data collected without well-defined hypotheses. In EDA, one screens the data for clues that could inspire ideas and hypotheses.

_ de Mast & Kemper (2009)

Read the paper by de Mast & Kemper (2009) to understand what the purpose of EDA and how it fits in the context of a data science project.

Interactive data visualization

Interactive data visualization makes it possible to present results in interactive online documents. Read and follow along with the exercises in the introduction notebook to get started.

Altair as the library of choice

Altair is chosen as the main library to demonstrate how an interactive Grammar of Graphics helps you in reasoning about the process of creating interactive data visualization.