Statistical plot enhancements in Matplotlib - Mini Project: Build & Apply

Statistical plot enhancements

📖 Scenario: You work as a data analyst for a small company. You have collected sales data for different products over several months. Your manager wants you to create a clear and informative bar chart that shows the sales numbers and highlights the highest sales month.

🎯 Goal: Build a bar chart using matplotlib that shows monthly sales data. You will add enhancements like colors, labels, and a title to make the chart easy to understand and visually appealing.

📋 What You'll Learn

Create a dictionary with monthly sales data

Set a variable for the month with the highest sales

Use a bar chart to plot the sales data

Highlight the highest sales month with a different color

Add labels for the x-axis, y-axis, and a chart title

Display the final plot

💡 Why This Matters

🌍 Real World

Data analysts often need to visualize sales or performance data clearly to help managers make decisions. Highlighting important data points like the highest sales month makes reports more useful.

💼 Career

Knowing how to create and enhance plots with matplotlib is a key skill for data scientists and analysts to communicate insights effectively.

1

Create monthly sales data

Create a dictionary called monthly_sales with these exact entries: 'January': 150, 'February': 200, 'March': 180, 'April': 220, 'May': 170.

Matplotlib

# Create the monthly_sales dictionary with the given data
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate each month and sales number with a colon.

2

Identify the highest sales month

Create a variable called highest_month that stores the month with the highest sales from the monthly_sales dictionary.

Matplotlib

monthly_sales = {'January': 150, 'February': 200, 'March': 180, 'April': 220, 'May': 170}
# Find the month with the highest sales and assign it to highest_month
# Your code here

Hint

Use the max() function with the key=monthly_sales.get argument to find the month with the highest sales.

3

Create a bar chart with color highlights

Use matplotlib.pyplot to create a bar chart of monthly_sales. Use a list comprehension to create a list of colors where the bar for highest_month is 'orange' and all others are 'blue'. Use this color list in the color parameter of plt.bar().

Matplotlib

monthly_sales = {'January': 150, 'February': 200, 'March': 180, 'April': 220, 'May': 170}
highest_month = max(monthly_sales, key=monthly_sales.get)
import matplotlib.pyplot as plt

# Create a list of colors with 'orange' for highest_month and 'blue' for others
# Create the bar chart using plt.bar with the colors
# Your code here

Hint

Use a list comprehension to assign colors. Then pass the colors list to the color argument in plt.bar().

4

Add labels and display the plot

Add an x-axis label 'Month', a y-axis label 'Sales', and a title 'Monthly Sales Data' to the plot using plt.xlabel(), plt.ylabel(), and plt.title(). Then display the plot using plt.show().

Matplotlib

monthly_sales = {'January': 150, 'February': 200, 'March': 180, 'April': 220, 'May': 170}
highest_month = max(monthly_sales, key=monthly_sales.get)
import matplotlib.pyplot as plt

colors = ['orange' if month == highest_month else 'blue' for month in monthly_sales.keys()]
plt.bar(monthly_sales.keys(), monthly_sales.values(), color=colors)

# Add x-axis label, y-axis label, and title
# Display the plot
# Your code here

Hint

Use plt.xlabel(), plt.ylabel(), and plt.title() to add labels and title. Use plt.show() to display the plot.

Practice

1. What is the main purpose of adding a legend to a matplotlib plot?

easy

A. To explain what different colors or markers represent

B. To change the plot's background color

C. To save the plot as an image file

D. To remove grid lines from the plot

Solution

1. Step 1: Understand what a legend does

   A legend shows labels for different plot elements like colors or markers.

2. Step 2: Match legend purpose to options

   Only To explain what different colors or markers represent describes explaining plot elements, which is the legend's role.

3. Final Answer:

   To explain what different colors or markers represent -> Option A

4. Quick Check:

   Legend = Explain plot elements [OK]

Hint: Legend explains plot symbols and colors [OK]

Common Mistakes:

• Confusing legend with grid or background settings
• Thinking legend saves the plot
• Assuming legend removes plot elements

2. Which of the following is the correct way to add a title to a matplotlib plot?

easy

A. plt.set_title('My Plot')

B. plt.add_title('My Plot')

C. plt.title('My Plot')

D. plt.plot_title('My Plot')

Solution

1. Step 1: Recall matplotlib title syntax

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

2. Step 2: Check options for correct function name

   Only plt.title('My Plot') uses plt.title('My Plot'), which is correct syntax.

3. Final Answer:

   plt.title('My Plot') -> Option C

4. Quick Check:

   Title function = plt.title() [OK]

Hint: Use plt.title() to add plot titles [OK]

Common Mistakes:

• Using incorrect function names like set_title or add_title
• Confusing title with label functions
• Missing parentheses in function call

3. What will the following code display?

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6], marker='o', color='red')
plt.grid(True)
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Sample Plot')
plt.show()

medium

A. A red line plot with circle markers, grid lines, and labeled axes with a title

B. A blue scatter plot without grid lines or labels

C. A red bar chart with no markers or grid

D. An empty plot with only axis labels

Solution

1. Step 1: Analyze plot function and parameters

   The code plots points [1,2,3] vs [4,5,6] with red color and circle markers.

2. Step 2: Check enhancements added

   Grid is enabled, x and y axes are labeled, and a title is set.

3. Final Answer:

   A red line plot with circle markers, grid lines, and labeled axes with a title -> Option A

4. Quick Check:

   Plot with markers, grid, labels, title = A red line plot with circle markers, grid lines, and labeled axes with a title [OK]

Hint: Look for markers, colors, grid, labels in code [OK]

Common Mistakes:

• Confusing plot type (line vs scatter vs bar)
• Ignoring grid or label commands
• Assuming default colors or no markers

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

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], label='Line 1')
plt.legend()
plt.show()

medium

A. The plot function is missing y-values

B. The legend function is called before plot

C. The label parameter is invalid in plot

D. There is no error; the code runs correctly

Solution

1. Step 1: Check plot function parameters

   The plot call has only one list, so it treats it as y-values with x as indices 0,1,2.

2. Step 2: Understand matplotlib behavior

   This is valid syntax; it plots y-values against default x-values. So no error here.

3. Step 3: Re-examine options carefully

   The plot function is missing y-values says missing y-values, but y-values are given. The legend function is called before plot is wrong order. The label parameter is invalid in plot label is valid. There is no error; the code runs correctly says no error.

4. Final Answer:

   There is no error; the code runs correctly -> Option D

5. Quick Check:

   Code runs fine with legend after plot [OK]

Hint: Check if plot syntax matches matplotlib docs [OK]

Common Mistakes:

• Assuming single list plot is invalid
• Thinking legend must come before plot
• Believing label is not accepted in plot

5. You want to create a scatter plot with blue triangles, add grid lines, and label axes as 'Height' and 'Weight'. Which code snippet correctly does this?

hard

A. plt.scatter(x, y, marker='s', color='red')
plt.grid(True)
plt.xlabel('Weight')
plt.ylabel('Height')

B. plt.scatter(x, y, marker='^', color='blue')
plt.grid(True)
plt.xlabel('Height')
plt.ylabel('Weight')

C. plt.plot(x, y, marker='o', color='green')
plt.grid(False)
plt.xlabel('Weight')
plt.ylabel('Height')

D. plt.plot(x, y, marker='^', color='blue')
plt.grid(True)
plt.xlabel('Height')
plt.ylabel('Weight')

Solution

1. Step 1: Identify scatter plot with blue triangles

   Use plt.scatter() with marker='^' and color='blue'.

2. Step 2: Check grid and axis labels

   Grid must be enabled with plt.grid(True), and axes labeled 'Height' and 'Weight' correctly.

3. Step 3: Match code snippet to requirements

   plt.scatter(x, y, marker='^', color='blue') plt.grid(True) plt.xlabel('Height') plt.ylabel('Weight') matches all requirements exactly.

4. Final Answer:

   plt.scatter(x, y, marker='^', color='blue') plt.grid(True) plt.xlabel('Height') plt.ylabel('Weight') -> Option B

5. Quick Check:

   Scatter + blue triangles + grid + correct labels = plt.scatter(x, y, marker='^', color='blue') plt.grid(True) plt.xlabel('Height') plt.ylabel('Weight') [OK]

Hint: Scatter uses plt.scatter(), triangles marker='^', grid True [OK]

Common Mistakes:

• Using plt.plot instead of plt.scatter for scatter plot
• Wrong marker symbol for triangles
• Swapping x and y axis labels
• Forgetting to enable grid lines