Heatmap with plt.pcolormesh in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of plt.pcolormesh with given data

What will be the shape of the array used to create the heatmap with plt.pcolormesh in this code?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

Z = np.array([[1, 2, 3], [4, 5, 6]])
plt.pcolormesh(Z)
plt.close()  # Close plot to avoid display
print(Z.shape)

A (2, 2)

B (3, 2)

C (3, 3)

D (2, 3)

💡 Hint

Remember that the shape of the array Z is rows by columns.

The array Z has 2 rows and 3 columns, so its shape is (2, 3). This shape is used by plt.pcolormesh to create the heatmap grid.

❓ data_output

intermediate

Data values shown by plt.pcolormesh

Given this code, what is the color value at position (1, 1) in the heatmap data array?

Matplotlib

import numpy as np
Z = np.array([[10, 20, 30], [40, 50, 60]])
value = Z[1, 1]
print(value)

A 50

B 40

C 20

D 60

💡 Hint

Remember that indexing starts at 0 for rows and columns.

Z[1, 1] accesses the second row and second column, which is 50.

❓ visualization

advanced

Effect of shading='auto' in plt.pcolormesh

What visual difference does setting shading='auto' make in plt.pcolormesh when plotting a 2x2 array?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

Z = np.array([[1, 2], [3, 4]])
plt.figure(figsize=(4, 3))
plt.pcolormesh(Z, shading='auto')
plt.close()

A The heatmap cells have gaps between them.

B The heatmap cells align exactly with the data points without gaps.

C The heatmap uses a different color map automatically.

D The heatmap cells are plotted as circles instead of squares.

💡 Hint

shading='auto' adjusts the grid to match the data shape.

Using shading='auto' makes the cells align perfectly with the data points, avoiding gaps or overlaps.

🔧 Debug

advanced

Identify the error in plt.pcolormesh usage

What error will this code raise when run?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

Z = np.array([[1, 2], [3, 4]])
plt.pcolormesh(Z, cmap='viridis', shading='invalid')
plt.close()

A ValueError: shading must be 'flat', 'gouraud', or 'auto'

B TypeError: cmap must be a Colormap instance

C SyntaxError: invalid syntax in plt.pcolormesh call

D No error, plot displays normally

💡 Hint

Check the allowed values for the shading parameter.

The shading parameter only accepts 'flat', 'gouraud', or 'auto'. 'invalid' causes a ValueError.

🚀 Application

expert

Creating a heatmap with custom X and Y coordinates

Which option correctly creates a heatmap with plt.pcolormesh using custom X and Y coordinates for a 2x2 data array?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

Z = np.array([[1, 2], [3, 4]])
X = np.array([0, 1, 2])
Y = np.array([0, 1, 2])
# Fill in the correct plt.pcolormesh call below

A

plt.pcolormesh(Z, X, Y)
plt.close()

B

plt.pcolormesh(X, Y, Z.T)
plt.close()

C

plt.pcolormesh(X, Y, Z)
plt.close()

D

plt.pcolormesh(Z.T, X, Y)
plt.close()

💡 Hint

X and Y must be 1D arrays with length one more than Z's dimensions.

X and Y define the grid edges and must have length one more than Z's shape. Z is passed as is without transpose.

Practice

1. What does plt.pcolormesh do in matplotlib?

easy

A. It creates a scatter plot with colored dots.

B. It plots a line graph connecting points.

C. It creates a heatmap by coloring a grid based on data values.

D. It draws bar charts with colored bars.

Solution

1. Step 1: Understand the function purpose

   plt.pcolormesh colors a grid of cells based on the values in a 2D array, making a heatmap.

2. Step 2: Compare with other plot types

   Line graphs connect points, scatter plots show dots, and bar charts show bars, none of which match pcolormesh.

3. Final Answer:

   It creates a heatmap by coloring a grid based on data values. -> Option C

4. Quick Check:

   Heatmap = pcolormesh = A [OK]

Hint: pcolormesh colors grids for heatmaps, not lines or bars [OK]

Common Mistakes:

• Confusing pcolormesh with line or scatter plots
• Thinking pcolormesh plots points instead of colored cells
• Assuming pcolormesh creates bar charts

2. Which of these is the correct way to add a colorbar to a heatmap created with plt.pcolormesh?

easy

A. plt.colorbar()

B. plt.add_colorbar()

C. plt.show_colorbar()

D. plt.colorbar_add()

Solution

1. Step 1: Recall matplotlib colorbar syntax

   The correct function to add a colorbar is plt.colorbar().

2. Step 2: Check other options

   Functions like plt.add_colorbar(), plt.show_colorbar(), or plt.colorbar_add() do not exist in matplotlib.

3. Final Answer:

   plt.colorbar() -> Option A

4. Quick Check:

   Colorbar function = plt.colorbar() = B [OK]

Hint: Use plt.colorbar() exactly to add color legend [OK]

Common Mistakes:

• Using non-existent functions like plt.add_colorbar()
• Forgetting to add a colorbar after pcolormesh
• Trying to add colorbar before plotting heatmap

3. What will the following code display?

import matplotlib.pyplot as plt
import numpy as np

Z = np.array([[1, 2], [3, 4]])
plt.pcolormesh(Z)
plt.colorbar()
plt.show()

medium

A. An error because Z is not a 1D array.

B. A line plot connecting points 1, 2, 3, 4.

C. A scatter plot with 4 points colored by values.

D. A 2x2 heatmap with colors representing values 1 to 4 and a colorbar.

Solution

1. Step 1: Understand the input array Z

   Z is a 2x2 numpy array with values from 1 to 4.

2. Step 2: What does plt.pcolormesh(Z) do?

   It creates a heatmap grid colored by the values in Z, so 4 colored squares.

3. Step 3: Adding plt.colorbar()

   This adds a color scale legend showing how colors map to values 1 to 4.

4. Final Answer:

   A 2x2 heatmap with colors representing values 1 to 4 and a colorbar. -> Option D

5. Quick Check:

   2x2 heatmap + colorbar = C [OK]

Hint: pcolormesh with 2D array shows colored grid matching array shape [OK]

Common Mistakes:

• Thinking pcolormesh needs 1D array only
• Confusing heatmap with line or scatter plots
• Forgetting colorbar shows value-color mapping

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
import numpy as np

Z = np.array([[1, 2], [3, 4]])
plt.pcolormesh(Z)
plt.colorbar
plt.show()

medium

A. Missing parentheses after plt.colorbar causing no colorbar to show.

B. Z should be a 1D array, not 2D.

C. plt.show() must come before plt.pcolormesh().

D. plt.pcolormesh() requires x and y coordinates explicitly.

Solution

1. Step 1: Check plt.colorbar usage

   The code uses plt.colorbar without parentheses, so the function is not called.

2. Step 2: Effect of missing parentheses

   Without calling the function, no colorbar appears on the plot.

3. Final Answer:

   Missing parentheses after plt.colorbar causing no colorbar to show. -> Option A

4. Quick Check:

   Call plt.colorbar() with () to show colorbar [OK]

Hint: Always add () after plt.colorbar to display it [OK]

Common Mistakes:

• Forgetting () after plt.colorbar
• Thinking 2D array is invalid input
• Calling plt.show() too early
• Assuming x,y needed for simple heatmap

5. You want to create a heatmap of a 3x3 matrix with values from 0 to 8 using plt.pcolormesh. Which code correctly sets the x and y axis labels to 'X Axis' and 'Y Axis' and adds a colorbar?

hard

A. import matplotlib.pyplot as plt
import numpy as np
Z = np.arange(9).reshape(3,3)
plt.pcolormesh(Z)
plt.set_xlabel('X Axis')
plt.set_ylabel('Y Axis')
plt.colorbar()
plt.show()

B. import matplotlib.pyplot as plt
import numpy as np
Z = np.arange(9).reshape(3,3)
plt.pcolormesh(Z)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.colorbar()
plt.show()

C. import matplotlib.pyplot as plt
import numpy as np
Z = np.arange(9).reshape(3,3)
plt.pcolormesh(Z)
plt.xlabel = 'X Axis'
plt.ylabel = 'Y Axis'
plt.colorbar()
plt.show()

D. import matplotlib.pyplot as plt
import numpy as np
Z = np.arange(9).reshape(3,3)
plt.pcolormesh(Z)
plt.axis_labels('X Axis', 'Y Axis')
plt.colorbar()
plt.show()

Solution

1. Step 1: Check axis label functions

   The correct functions to label axes are plt.xlabel() and plt.ylabel().

2. Step 2: Verify colorbar and show usage

   plt.colorbar() must be called with parentheses, and plt.show() displays the plot.

3. Step 3: Identify incorrect options

   import matplotlib.pyplot as plt import numpy as np Z = np.arange(9).reshape(3,3) plt.pcolormesh(Z) plt.set_xlabel('X Axis') plt.set_ylabel('Y Axis') plt.colorbar() plt.show() uses non-existent plt.set_xlabel(), C assigns strings instead of calling functions, D uses a non-existent plt.axis_labels().

4. Final Answer:

   import matplotlib.pyplot as plt import numpy as np Z = np.arange(9).reshape(3,3) plt.pcolormesh(Z) plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.colorbar() plt.show() -> Option B

5. Quick Check:

   Use plt.xlabel(), plt.ylabel(), plt.colorbar() [OK]

Hint: Use plt.xlabel() and plt.ylabel() to label axes [OK]

Common Mistakes:

• Using plt.set_xlabel() instead of plt.xlabel()
• Assigning labels instead of calling functions
• Forgetting parentheses on plt.colorbar()
• Using non-existent axis_labels() function