Data Storage and Operations

This week we discussed how data is stored on disk and in memory, how that interacts with our visualization process, and we introduced the notion of a palette of operations you can apply to data to visualize it.

References

Also, more about numpy.reshape (and what the -1 means): https://stackoverflow.com/questions/18691084/what-does-1-mean-in-numpy-reshape

Optional Reading List (See syllabus for acronyms)

  1. VAD, Ch. 2: What: Data Abstraction
  2. FDV, Ch. 2: Visualizing data: Mapping data onto aesthetics
  3. VAD, Ch. 13: Reduce Items and Attributes
  4. FDV, Ch. 27: Understanding the most commonly used image file formats
  5. IS452’s intro to CSV files (bottom of page)
  6. IS452’s Intro to Dictionaries
  7. Pandas Docs & NumPy Docs

Lectures

Lecture 2.1 - Data storage & Operations, Image data

When we draw something on a screen, how do we represent that internally, and how is that translated into pixels? How are values transformed from 0's and 1's into values we can manipulate and understand?

Examples

Data