Color mapping with colorbar in Matplotlib - Mini Project: Build & Apply

Color mapping with colorbar

📖 Scenario: You are working with temperature data for a week. You want to visualize the temperatures using colors so it is easy to see which days were hotter or colder.

🎯 Goal: Create a color map for the temperature values and display a colorbar to show the temperature scale.

📋 What You'll Learn

Create a list of temperatures for 7 days

Create a colormap using matplotlib

Map the temperatures to colors using the colormap

Display a colorbar showing the temperature scale

💡 Why This Matters

🌍 Real World

Color mapping with colorbars is used in weather maps, heatmaps, and scientific visualizations to show data intensity or value ranges clearly.

💼 Career

Data scientists and analysts use color mapping to make data easier to understand visually, helping in reports and presentations.

1

Create the temperature data list

Create a list called temperatures with these exact values: 22, 25, 19, 30, 28, 24, 20 representing temperatures for 7 days.

Matplotlib

# Create the list of temperatures for 7 days
# Your code here

Hint

Use square brackets to create a list and separate values with commas.

2

Create a colormap and normalization

Import matplotlib.pyplot as plt and Normalize from matplotlib.colors. Then create a Normalize object called norm with vmin=19 and vmax=30 to normalize the temperature range.

Matplotlib

temperatures = [22, 25, 19, 30, 28, 24, 20]
# Import matplotlib.pyplot as plt and Normalize from matplotlib.colors
# Create a Normalize object called norm with vmin=19 and vmax=30
# Your code here

Hint

Use import matplotlib.pyplot as plt and from matplotlib.colors import Normalize.

Create norm = Normalize(vmin=19, vmax=30).

3

Map temperatures to colors using a colormap

Create a variable called cmap and set it to plt.cm.viridis. Then create a list called colors by applying cmap(norm(temp)) for each temp in temperatures using a list comprehension.

Matplotlib

import matplotlib.pyplot as plt
from matplotlib.colors import Normalize

temperatures = [22, 25, 19, 30, 28, 24, 20]
norm = Normalize(vmin=19, vmax=30)
# Create cmap as plt.cm.viridis
# Create colors list by applying cmap(norm(temp)) for each temp in temperatures
# Your code here

Hint

Use plt.cm.viridis for the colormap.

Use a list comprehension to map each temperature to a color.

4

Display the colorbar with the temperature scale

Create a figure and axis using plt.subplots(). Then create a ScalarMappable object called sm with the norm and cmap. Use sm.set_array([]). Finally, add a colorbar to the axis with plt.colorbar(sm, ax=ax) and call plt.show() to display the plot.

Matplotlib

import matplotlib.pyplot as plt
from matplotlib.colors import Normalize

temperatures = [22, 25, 19, 30, 28, 24, 20]
norm = Normalize(vmin=19, vmax=30)
cmap = plt.cm.viridis
colors = [cmap(norm(temp)) for temp in temperatures]
# Create figure and axis with plt.subplots()
# Create ScalarMappable sm with norm and cmap
# Call sm.set_array([])
# Add colorbar with plt.colorbar(sm, ax=ax)
# Show the plot with plt.show()
# Your code here

Hint

Use ScalarMappable to create a mappable for the colorbar.

Call plt.colorbar with the mappable and axis.

Practice

1. What is the main purpose of adding a colorbar in a matplotlib plot with color mapping?

easy

A. To show the scale of colors representing data values

B. To change the plot title

C. To add grid lines to the plot

D. To save the plot as an image file

Solution

1. Step 1: Understand color mapping

   Color mapping assigns colors to data values to visualize differences.

2. Step 2: Role of colorbar

   The colorbar shows which colors correspond to which data values, acting as a legend.

3. Final Answer:

   To show the scale of colors representing data values -> Option A

4. Quick Check:

   Colorbar explains colors = B [OK]

Hint: Colorbar always explains color scale in plots [OK]

Common Mistakes:

• Thinking colorbar changes plot title
• Confusing colorbar with grid lines
• Assuming colorbar saves the plot

2. Which of the following is the correct way to add a colorbar to a scatter plot in matplotlib?

easy

A. plt.scatter(x, y); plt.colorbar(c=z)

B. plt.colorbar(); plt.scatter(x, y, c=z)

C. plt.scatter(x, y, c=z); plt.colorbar()

D. plt.scatter(x, y, c=z, colorbar=True)

Solution

1. Step 1: Plot with color mapping

   Use plt.scatter(x, y, c=z) to map colors to values z.

2. Step 2: Add colorbar after plotting

   Call plt.colorbar() after the scatter plot to add the color scale.

3. Final Answer:

   plt.scatter(x, y, c=z); plt.colorbar() -> Option C

4. Quick Check:

   Plot first, then colorbar [OK]

Hint: Call plt.colorbar() after plotting with color data [OK]

Common Mistakes:

• Calling plt.colorbar() before plotting
• Passing colorbar as a parameter to scatter
• Trying to pass c=z inside plt.colorbar()

3. What will the following code display?

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(5)
y = np.arange(5)
z = x * y
plt.scatter(x, y, c=z)
plt.colorbar()
plt.show()

medium

A. A scatter plot with points colored from 0 to 16 and a colorbar showing this range

B. A scatter plot with all points in the same color and no colorbar

C. A line plot connecting points with a colorbar

D. An error because colorbar needs a colormap argument

Solution

1. Step 1: Understand data and color mapping

   x and y are arrays 0 to 4, z is their product (0,1,4,9,16).

2. Step 2: Scatter plot with color mapping and colorbar

   Points colored by z values, colorbar shows color scale from min to max z.

3. Final Answer:

   A scatter plot with points colored from 0 to 16 and a colorbar showing this range -> Option A

4. Quick Check:

   Color mapped scatter with colorbar = D [OK]

Hint: Colorbar shows range of c= values in scatter [OK]

Common Mistakes:

• Thinking colorbar is missing
• Expecting a line plot instead of scatter
• Believing colorbar needs explicit colormap argument

4. Identify the error in this code snippet that tries to add a colorbar:

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5, 6]
plt.scatter(x, y)
plt.colorbar()
plt.show()

medium

A. Error because plt.show() is missing

B. No error; code runs fine and shows colorbar

C. Error because plt.colorbar() must be called before scatter

D. Error because scatter plot has no color mapping (c parameter missing)

Solution

1. Step 1: Check scatter plot parameters

   Scatter plot lacks c= parameter, so no color mapping exists.

2. Step 2: Understand colorbar requirement

   Colorbar needs a mappable object with colors; without c=, colorbar cannot be created.

3. Final Answer:

   Error because scatter plot has no color mapping (c parameter missing) -> Option D

4. Quick Check:

   Colorbar needs color data = A [OK]

Hint: Colorbar requires color mapping (c=) in plot [OK]

Common Mistakes:

• Calling colorbar before plotting
• Forgetting plt.show() (though optional)
• Assuming colorbar works without color data

5. You want to create a heatmap using imshow with a colorbar that shows values from 0 to 1. Which code snippet correctly sets the color limits and adds the colorbar?

hard

A. plt.imshow(data); plt.colorbar(vmin=0, vmax=1)

B. plt.imshow(data, vmin=0, vmax=1); plt.colorbar()

C. plt.imshow(data, colorbar=True, vmin=0, vmax=1)

D. plt.imshow(data); plt.colorbar(); plt.clim(0, 1)

Solution

1. Step 1: Set color limits in imshow

   Use vmin=0 and vmax=1 in imshow to fix color scale range.

2. Step 2: Add colorbar without extra arguments

   Call plt.colorbar() after plotting to add the colorbar matching the scale.

3. Final Answer:

   plt.imshow(data, vmin=0, vmax=1); plt.colorbar() -> Option B

4. Quick Check:

   Set vmin/vmax in imshow, then colorbar [OK]

Hint: Set vmin/vmax in imshow, then call plt.colorbar() [OK]

Common Mistakes:

• Passing vmin/vmax to plt.colorbar() instead of imshow
• Using colorbar=True inside imshow (not valid)
• Calling plt.clim() after colorbar (wrong order)