Heatmap with plt.pcolormesh in Matplotlib - Mini Project: Build & Apply

Heatmap with plt.pcolormesh

📖 Scenario: You work as a data analyst and want to visualize temperature data collected over a week in different cities. A heatmap is a great way to see patterns and differences quickly.

🎯 Goal: Create a heatmap using plt.pcolormesh to visualize the temperature data for 3 cities over 7 days.

📋 What You'll Learn

Create a 2D list called temperatures with exact temperature values for 3 cities over 7 days

Create a list called days with day names from Monday to Sunday

Create a list called cities with 3 city names

Use plt.pcolormesh to plot the heatmap with correct axis labels

Add a color bar to show the temperature scale

Print the heatmap plot

💡 Why This Matters

🌍 Real World

Heatmaps are used in weather forecasting, sales analysis, and many fields to quickly spot patterns in data.

💼 Career

Data analysts and scientists use heatmaps to visualize complex data simply and communicate insights effectively.

1

Create the temperature data

Create a 2D list called temperatures with these exact values: [[22, 21, 23, 24, 25, 26, 27], [18, 19, 20, 21, 22, 23, 24], [30, 29, 28, 27, 26, 25, 24]] representing temperatures for 3 cities over 7 days.

Matplotlib

# Your code here

Hint

Use a list of lists to represent rows (cities) and columns (days).

2

Create the days and cities labels

Create a list called days with these exact values: ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']. Also create a list called cities with these exact values: ['CityA', 'CityB', 'CityC'].

Matplotlib

temperatures = [[22, 21, 23, 24, 25, 26, 27], [18, 19, 20, 21, 22, 23, 24], [30, 29, 28, 27, 26, 25, 24]]
# Your code here

Hint

Use simple lists with the exact names and values.

3

Plot the heatmap using plt.pcolormesh

Import matplotlib.pyplot as plt. Use plt.pcolormesh to plot the heatmap of temperatures. Set the x-axis ticks to days and y-axis ticks to cities. Use plt.colorbar() to add a color bar.

Matplotlib

temperatures = [[22, 21, 23, 24, 25, 26, 27], [18, 19, 20, 21, 22, 23, 24], [30, 29, 28, 27, 26, 25, 24]]
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
cities = ['CityA', 'CityB', 'CityC']
# Your code here

Hint

Use shading='auto' in plt.pcolormesh for better color alignment.

4

Display the heatmap

Use plt.show() to display the heatmap plot.

Matplotlib

import matplotlib.pyplot as plt

temperatures = [[22, 21, 23, 24, 25, 26, 27], [18, 19, 20, 21, 22, 23, 24], [30, 29, 28, 27, 26, 25, 24]]
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
cities = ['CityA', 'CityB', 'CityC']

plt.pcolormesh(temperatures, shading='auto')
plt.xticks(ticks=range(len(days)), labels=days)
plt.yticks(ticks=range(len(cities)), labels=cities)
plt.colorbar()
# Your code here

Hint

Use plt.show() to see the heatmap window.

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