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

• Python:
  • IS452’s Intro to Reading & Writing CSV files (scroll down on page)
  • IS452’s Dictionary week
• Colors: RGB color triplets (we will also cover this extensively next week)

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. VAD, Ch. 13: Reduce Items and Attributes
3. IS452’s intro to CSV files (bottom of page)
4. IS452’s Intro to Dictionaries
5. Pandas Docs & NumPy Docs