Building Blocks of Visualization: 1. Data 2. Transformation 3. Marks 4. Encoding – mapping from fields to mark properties 5. Scale – functions that map data to visual scales 6. Guides – visualization of scales (axes, legends, etc.)
@jakevdp Jake VanderPlas import matplotlib.pyplot as plt import numpy as np x = np.random.randn(1000) y = np.random.randn(1000) color = np.arange(1000) plt.scatter(x, y, c=color) plt.colorbar() Plotting with Matplotlib
@jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy For more on the historical perspective, see https://speakerdeck.com/jakevdp/pydata-101
@jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort)
@jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort) - Well-tested, standard tool for 15 years
@jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot with a bit of effort - Well-tested, standard tool for 15 years Weaknesses: - API is imperative & often overly verbose - Poor/no support for interactive/web graphs
@jakevdp Jake VanderPlas import matplotlib.pyplot as plt import numpy as np x = np.random.randn(1000) y = np.random.randn(1000) color = np.arange(1000) plt.scatter(x, y, c=color) plt.colorbar() Plotting with Matplotlib
@jakevdp Jake VanderPlas from vega_datsets import data iris = data('iris') iris.head() Data in column-oriented format; i.e. rows are samples, columns are features Statistical Visualization
@jakevdp Jake VanderPlas Toward a well-motivated Declarative Visualization Declarative - Specify What should be done. - Separates Specification from Execution - “Map to a position, and to a color” Imperative - Specify How something should be done. - Specification & Execution intertwined. - “Put a red circle here and a blue circle here” Declarative visualization lets you think about data and relationships, rather than incidental details.
@jakevdp Jake VanderPlas Toward a well-motivated Declarative Visualization Declarative - Specify What should be done. - Separates Specification from Execution - “Map to a position, and to a color” Imperative - Specify How something should be done. - Specification & Execution intertwined. - “Put a red circle here and a blue circle here” Declarative visualization lets you think about data and relationships, rather than incidental details.
@jakevdp Jake VanderPlas Altair for Statistical Visualization import altair as alt from vega_datasets import data iris = data.iris() alt.Chart(iris).mark_point().encode( x='petalLength', y='sepalWidth', color='species' )
@jakevdp Jake VanderPlas Encodings are Flexible: import altair as alt from vega_datasets import data iris = data.iris() alt.Chart(iris).mark_point().encode( x='petalLength', y='sepalWidth', color='species', column='species' )
@jakevdp Jake VanderPlas Altair is Interactive import altair as alt from vega_datasets import data iris = data.iris() alt.Chart(iris).mark_point().encode( x='petalLength', y='sepalWidth', color='species' ).interactive()
@jakevdp Jake VanderPlas Basics of an Altair Chart import altair as alt from vega_datasets import data iris = data.iris() alt.Chart(iris).mark_point().encode( x='petalLength:Q', y='sepalWidth:Q', color='species:N' )
@jakevdp Jake VanderPlas import altair as Chart from vega_datasets import data iris = data.iris() alt.Chart(iris).mark_point().encode( x='petalLength:Q', y='sepalWidth:Q', color='species:N' ) Anatomy of an Altair Chart mark_point() Chart uses one of several pre-defined marks: - point - line - bar - area - rect - geoshape - text - circle - square - rule - tick
@jakevdp Jake VanderPlas import altair as Chart from vega_datasets import data iris = data.iris() alt.Chart(iris).mark_point().encode( x='petalLength:Q', y='sepalWidth:Q', color='species:N' ) Basics of an Altair Chart - Encodings map visual channels to data columns, - Channels are automatically adjusted based on data type (N, O, Q, T) Available channels: - Position (x, y) - Facet (row, column) - color - shape - size - text - opacity - stroke - fill - latitude/longitude encode( x='petalLength:Q', y='sepalWidth:Q', color='species:N'
Suitable for ordered data (also length, area, volume, etc.) Suitable for unordered data (Also transparancy, blur/focus, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967)
Suitable for unordered data (Also transparancy, blur/focus, etc.) Suitable for ordered data (also length, area, volume, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967)
Suitable for unordered data (Also transparancy, blur/focus, etc.) Suitable for ordered data (also length, area, volume, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967) Order & Quantity
Suitable for unordered data (Also transparancy, blur/focus, etc.) Suitable for ordered data (also length, area, volume, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967) Order & Quantity (less so)
Suitable for unordered data (Also transparancy, blur/focus, etc.) Suitable for ordered data (also length, area, volume, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967) Order... Quantity?
Suitable for ordered data (also length, area, volume, etc.) Suitable for unordered data (Also transparancy, blur/focus, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967) Order, Quantity
Suitable for ordered data (also length, area, volume, etc.) Suitable for unordered data (Also transparancy, blur/focus, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967) Order, Quantity
Suitable for ordered data (also length, area, volume, etc.) Suitable for unordered data (Also transparancy, blur/focus, etc.) 2D Position Size Color Value Texture Color Hue Angle Shape Bertin’s Semiology of Graphics (1967) Bertin’s “Levels of Organization” Position N O Q Size N O Q Color Value N O Q Texture N O Color Hue N Angle N Shape N N = Nominal (named category) O = Ordinal (ordered category) Q = Quantitative (ordered continuous)
Key: Visualization concepts should map directly to visualization implementation. Great resource is Jeff Heer’s viz course: https://courses.cs.washington.edu/courses/cse512/16sp/
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