| title | layout |
|---|---|
Lecture 6 |
lecture |
- What is the visualization trying to show?
- What are its methods?
- What are the strengths / weaknesses?
- https://gizmodo.com/observatories-across-the-world-announce-groundbreaking-1819500578
- https://gizmodo.com/let-s-break-down-what-that-monumental-neutron-star-coll-1819613829
- Transformations
- Colors and color mapping
- HSV/RGB/etc
- Image visualization
- Importing modules
- Choose an accessible, appropriate colormap
- "Am I showing different things?"
- "Can these things be compared directly?"
- "Do I want to demonstrate deviation or gradiation?"
- Mapping between "data space" and "color space" requires normalization and
color mapping
- Normalization:
$f(v) => v' \in [0, 1]$ - Color mapping:
$g(v) => RGBA$
- Normalization:
- Showing Composition
- Comparing Datasets
- More Pandas and some Seaborn
Don't use a pie chart.
- Hierarchical data
- Stacked bar or area
- Among Items
- One Variable, Few Categories: Column, or collection of bars
- Two Variables: Variable Width Column Chart
- Many variables: Embedded table or charts
- Changing Over Time
- Many Periods, non-cyclical: Line chart
- Few Periods: Column or Line (depending on number of categories)
- pandas.pydata.org
- Support for indexing, multi-indexing
- Data structures
- Series
- DataFrame
- Panel
- Groupby, select, aggregation features
- IO features
- Reading/writing CSV, HDF5
- Loading data from remote sources
Today we are going to build comparisons with our (virtual) hands.
- A Bit More Pandas
- Load a CSV file in the fast way
- Make sure the dates are correctly read in
- Aggregate by a characteristic
- A Bit More Matplotlib
- Patches and elements
- "Projections"
- Polar projections
- Build a sunburst
- Brainstorm and implement other cyclical visualizations