Colormaps (sequential, diverging, qualitative) in Matplotlib - Step-by-Step Execution

Concept Flow - Colormaps (sequential, diverging, qualitative)

Choose Data Type

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Select Colormap Type

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Sequential

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Apply Colors to Data

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Visualize with Matplotlib

Start by choosing the data type, then pick a colormap type (sequential, diverging, or qualitative), apply colors accordingly, and finally visualize the data.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Plot with sequential colormap
plt.scatter(x, y, c=y, cmap='viridis')
plt.colorbar()
plt.show()

This code plots points colored by their y-value using a sequential colormap 'viridis'.

Execution Table

Step	Action	Data Values	Colormap Type	Color Applied	Output
1	Generate x and y data	x: 0 to 10, y: sin(x)	N/A	N/A	Data arrays created
2	Choose colormap	N/A	Sequential ('viridis')	Colors from dark purple to yellow	Colormap selected
3	Map y values to colors	y values between -1 and 1	Sequential	Colors mapped from low to high y	Colors assigned to points
4	Plot scatter with colors	x and y arrays	Sequential	Colors shown on points	Scatter plot displayed
5	Add colorbar	N/A	Sequential	Color scale shown	Colorbar displayed
6	Show plot	N/A	Sequential	Colors visible	Plot window opens
7	End	N/A	N/A	N/A	Execution complete

💡 Plot displayed and program ends

Variable Tracker

Variable	Start	After Step 1	After Step 3	Final
x	undefined	array from 0 to 10	unchanged	unchanged
y	undefined	sin(x) values	unchanged	unchanged
cmap	undefined	undefined	'viridis'	'viridis'
colors	undefined	undefined	mapped colors from y	unchanged

Key Moments - 3 Insights

Why do we use different colormap types for different data?

Sequential colormaps are for ordered data from low to high (see Step 2 and 3 in execution_table). Diverging colormaps highlight deviation around a midpoint, and qualitative colormaps are for categorical data without order.

What happens if you use a qualitative colormap for continuous data?

Colors will not represent order or magnitude well, causing confusion. The execution_table shows sequential mapping for continuous y values, which qualitative colormaps do not support.

How does the colorbar relate to the colormap?

The colorbar (Step 5) visually shows how data values map to colors in the colormap, helping interpret the plot colors.

Visual Quiz - 3 Questions

Look at the execution_table at Step 3. What type of data is being mapped to colors?

A Categorical data

B Continuous numerical data

C Boolean data

D Text data

💡 Hint

Check the 'Data Values' column at Step 3 showing y values between -1 and 1.

At which step is the colormap chosen in the execution_table?

A Step 2

B Step 1

C Step 4

D Step 5

💡 Hint

Look at the 'Action' column for when the colormap is selected.

If you replaced 'viridis' with a qualitative colormap, what would change in the 'Color Applied' column at Step 3?

A Colors would represent a smooth gradient

B Colors would be grayscale

C Colors would be distinct blocks without order

D Colors would be random

💡 Hint

Refer to the 'Colormap Type' and 'Color Applied' columns at Step 3.

Concept Snapshot

Colormaps assign colors to data values.
Sequential: for ordered data from low to high.
Diverging: for data with a meaningful midpoint.
Qualitative: for categorical data without order.
Use matplotlib's cmap parameter to apply.
Add colorbar to show color scale.

Full Transcript

This visual execution shows how to use colormaps in matplotlib. First, data arrays x and y are created. Then, a sequential colormap 'viridis' is chosen to map continuous y values to colors. The colors are applied to points in a scatter plot. A colorbar is added to explain the color scale. The plot is displayed, showing how colors represent data values. Key points include choosing the right colormap type for your data and understanding how colors map to values.