# HIDDEN # Clear previously defined variables %reset -f # Set directory for data loading to work properly import os os.chdir(os.path.expanduser('~/notebooks/06'))
There is a magic in graphs. The profile of a curve reveals in a ﬂash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.
― Henry D. Hubbard
Data visualization is an essential tool for data science at every step of analysis, from data cleaning to EDA to communicating conclusions and predictions. Because human minds are highly developed for visual perception, a well-chosen plot can often reveal trends and anomalies in the data much more efficiently than a textual description.
To effectively use data visualizations, you must be proficient with both the
programming tools to generate plots and the principles of visualization. In
this chapter we will introduce
matplotlib, our tools of choice
for creating plots. We will also learn how to spot misleading visualizations
and how to improve visualizations using data transformations, smoothing, and