Heatmap with plt.pcolormesh in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does plt.pcolormesh do in matplotlib?

It creates a heatmap by coloring a grid of cells based on the values in a 2D array. Each cell's color represents the data value at that position.

beginner

What type of data structure is typically used as input for plt.pcolormesh?

A 2D array or matrix of numerical values, where each value corresponds to the color intensity of a cell in the heatmap.

beginner

How can you add a color bar to a heatmap created with plt.pcolormesh?

Use plt.colorbar() after calling plt.pcolormesh to show a color scale that explains the mapping of colors to data values.

intermediate

What is the difference between plt.pcolormesh and plt.imshow for heatmaps?

plt.pcolormesh uses a grid of colored cells and can handle non-uniform grids, while plt.imshow displays an image from an array and is simpler but less flexible for irregular grids.

beginner

How do you control the color map used in plt.pcolormesh?

You can pass the cmap parameter with a colormap name like 'viridis', 'plasma', or 'coolwarm' to change the colors of the heatmap.

What input does plt.pcolormesh require to create a heatmap?

A A 2D array of numerical values

B A list of strings

C A single number

D A 1D array of numbers

plt.pcolormesh needs a 2D array to map each value to a colored cell in the heatmap.

Which function adds a color scale bar to a heatmap made with plt.pcolormesh?

A plt.grid()

B plt.legend()

C plt.title()

D plt.colorbar()

plt.colorbar() adds a color bar that shows how colors map to data values.

What parameter changes the color scheme in plt.pcolormesh?

A color

B cmap

C style

D palette

The cmap parameter sets the colormap for the heatmap colors.

Which is true about plt.pcolormesh compared to plt.imshow?

A <code>plt.pcolormesh</code> can handle irregular grids

B <code>plt.imshow</code> can handle irregular grids

C Both handle irregular grids equally

D Neither can handle irregular grids

plt.pcolormesh is more flexible for non-uniform grid data.

What does each colored cell in a plt.pcolormesh heatmap represent?

A A category label

B A date or time

C A numerical value from the data

D A text string

Each cell's color corresponds to a numerical value in the 2D data array.

Explain how to create a heatmap using plt.pcolormesh from a 2D data array.

Think about the steps from data to visual colors.

Describe the advantages of using plt.pcolormesh over plt.imshow for heatmaps.

Consider flexibility with grid shapes.

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