Labels, titles, and legends in Data Analysis Python - Mini Project: Build & Apply

Labels, titles, and legends

📖 Scenario: You are analyzing sales data for a small store. You want to create a simple bar chart to show the sales of different products. To make the chart clear, you will add labels to the axes, a title, and a legend.

🎯 Goal: Create a bar chart using Python's matplotlib library that shows product sales. Add an x-axis label, a y-axis label, a chart title, and a legend to explain the colors.

📋 What You'll Learn

Create a dictionary called sales with product names as keys and sales numbers as values.

Create a variable called colors that assigns a color to each product.

Use matplotlib to plot a bar chart with the sales data.

Add an x-axis label called Product.

Add a y-axis label called Sales.

Add a chart title called Sales by Product.

Add a legend that shows which color belongs to which product.

💡 Why This Matters

🌍 Real World

Adding labels, titles, and legends to charts helps people understand data clearly. This is important when sharing reports or presentations.

💼 Career

Data scientists and analysts often create charts to explain their findings. Knowing how to label charts properly is a key skill for clear communication.

1

Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'Apples': 30, 'Bananas': 45, 'Cherries': 25, 'Dates': 40.

Data Analysis Python

# Create the sales dictionary with product names and sales numbers
# Your code here

Hint

Use curly braces {} to create the dictionary with the exact keys and values.

2

Create the colors list

Create a list called colors with these exact values in order: 'red', 'yellow', 'pink', 'brown'.

Data Analysis Python

sales = {'Apples': 30, 'Bananas': 45, 'Cherries': 25, 'Dates': 40}
# Create the colors list matching the products order
# Your code here

Hint

Use square brackets [] to create the list with the exact colors in the order of the products.

3

Plot the bar chart with labels, title, and legend

Import matplotlib.pyplot as plt. Use plt.bar() to plot the sales data with product names on the x-axis and sales numbers on the y-axis. Use the colors list for the bar colors. Add an x-axis label 'Product', a y-axis label 'Sales', and a title 'Sales by Product'. Add a legend that shows each product with its color.

Data Analysis Python

sales = {'Apples': 30, 'Bananas': 45, 'Cherries': 25, 'Dates': 40}
colors = ['red', 'yellow', 'pink', 'brown']
# Import matplotlib.pyplot as plt
# Plot the bar chart with sales and colors
# Add x-axis label 'Product'
# Add y-axis label 'Sales'
# Add title 'Sales by Product'
# Add legend with product names and colors
# Your code here

Hint

Use plt.bar() with color=colors. Use plt.xlabel(), plt.ylabel(), and plt.title() to add labels and title. Use plt.legend() with the bars and product names.

4

Display the chart

Use plt.show() to display the bar chart with labels, title, and legend.

Data Analysis Python

import matplotlib.pyplot as plt

sales = {'Apples': 30, 'Bananas': 45, 'Cherries': 25, 'Dates': 40}
colors = ['red', 'yellow', 'pink', 'brown']

products = list(sales.keys())
sales_values = list(sales.values())

bars = plt.bar(products, sales_values, color=colors)

plt.xlabel('Product')
plt.ylabel('Sales')
plt.title('Sales by Product')

plt.legend(bars, products)
# Display the plot
# Your code here

Hint

Call plt.show() to display the chart window.

Practice

1. What is the main purpose of adding a title to a chart in data visualization?

easy

A. To give the chart a clear heading that explains what it shows

B. To label the x-axis with numbers

C. To add colors to the chart

D. To describe the data source

Solution

1. Step 1: Understand the role of a title in charts

   A title provides a clear heading that tells the viewer what the chart is about.

2. Step 2: Compare options with the title's purpose

   Only To give the chart a clear heading that explains what it shows correctly describes the purpose of a title as a clear heading.

3. Final Answer:

   To give the chart a clear heading that explains what it shows -> Option A

4. Quick Check:

   Title = Clear heading [OK]

Hint: Titles are headings that explain the whole chart [OK]

Common Mistakes:

• Confusing titles with axis labels
• Thinking titles add colors
• Mixing titles with legends

2. Which of the following is the correct way to add a legend to a Matplotlib plot in Python?

easy

A. plt.xlabel('Legend')

B. plt.title('Legend')

C. plt.legend()

D. plt.legend('show')

Solution

1. Step 1: Recall the Matplotlib function for legends

   The function plt.legend() adds a legend to the plot.

2. Step 2: Check other options for correctness

   Options A and B set x-axis label and title, not legend. plt.legend('show') uses incorrect syntax.

3. Final Answer:

   plt.legend() -> Option C

4. Quick Check:

   Legend function = plt.legend() [OK]

Hint: Use plt.legend() to show legends in Matplotlib [OK]

Common Mistakes:

• Using plt.title() to add legend
• Passing strings inside plt.legend() incorrectly
• Confusing xlabel with legend

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], label='Line 1')
plt.plot([3, 2, 1], label='Line 2')
plt.title('Sample Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

medium

A. A plot with two lines, a title, axis labels, and a legend showing 'Line 1' and 'Line 2'

B. A plot with no title and no legend

C. A plot with only one line and no labels

D. An error because legend labels are missing

Solution

1. Step 1: Analyze the plot commands

   Two lines are plotted with labels 'Line 1' and 'Line 2'. Title and axis labels are set.

2. Step 2: Understand legend and show functions

   Calling plt.legend() displays the legend with the given labels. plt.show() displays the plot.

3. Final Answer:

   A plot with two lines, a title, axis labels, and a legend showing 'Line 1' and 'Line 2' -> Option A

4. Quick Check:

   Legend and labels appear correctly [OK]

Hint: Labels in plot() + plt.legend() show legends automatically [OK]

Common Mistakes:

• Forgetting to call plt.legend()
• Assuming legend appears without labels
• Confusing axis labels with legend

4. Identify the error in this code that prevents the legend from showing:

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

medium

A. plt.legend() is called before plt.plot()

B. Missing labels in plot() calls for legend

C. plt.show() is missing

D. plt.title() is required for legend

Solution

1. Step 1: Check plot() calls for labels

   Both plot calls lack the label argument, so legend has no text to display.

2. Step 2: Verify legend and show usage

   Calling plt.legend() without labels results in an empty legend. plt.show() is present and plt.title() is not required.

3. Final Answer:

   Missing labels in plot() calls for legend -> Option B

4. Quick Check:

   Labels needed for legend [OK]

Hint: Add label='name' in plot() to show legend [OK]

Common Mistakes:

• Calling plt.legend() before plot()
• Thinking plt.title() affects legend
• Forgetting to call plt.show()

5. You have a DataFrame with sales data for two products over 3 months. You want to plot both lines on the same chart with a title, axis labels, and a legend. Which code snippet correctly adds all these elements?

import pandas as pd
import matplotlib.pyplot as plt

data = {'Month': ['Jan', 'Feb', 'Mar'], 'ProductA': [100, 120, 130], 'ProductB': [90, 110, 115]}
df = pd.DataFrame(data)

# Which code below is correct?

A: plt.plot(df['Month'], df['ProductA'], label='Product A')
   plt.plot(df['Month'], df['ProductB'], label='Product B')
   plt.title('Sales Over Months')
   plt.xlabel('Month')
   plt.ylabel('Sales')
   plt.legend()
   plt.show()

B: plt.plot(df['ProductA'], df['Month'], label='Product A')
   plt.plot(df['ProductB'], df['Month'], label='Product B')
   plt.title('Sales Over Months')
   plt.xlabel('Sales')
   plt.ylabel('Month')
   plt.legend()
   plt.show()

C: plt.plot(df['Month'], df['ProductA'])
   plt.plot(df['Month'], df['ProductB'])
   plt.title('Sales Over Months')
   plt.xlabel('Month')
   plt.ylabel('Sales')
   plt.show()

D: plt.plot(df['Month'], df['ProductA'], label='Product A')
   plt.plot(df['Month'], df['ProductB'], label='Product B')
   plt.xlabel('Month')
   plt.ylabel('Sales')
   plt.show()

hard

A. Missing title and legend, so incomplete chart

B. Swaps x and y axes and labels, causing incorrect plot

C. Missing legend call, so no legend shown

D. Correctly plots both products with labels, title, axis labels, and legend

Solution

1. Step 1: Check axis data and labels in each option

   Correctly plots both products with labels, title, axis labels, and legend uses Month on x-axis and sales on y-axis correctly with labels and title.

2. Step 2: Verify legend and title presence

   Correctly plots both products with labels, title, axis labels, and legend includes label in plot calls and calls plt.legend() and plt.title(). Others miss legend or swap axes.

3. Final Answer:

   Correctly plots both products with labels, title, axis labels, and legend -> Option D

4. Quick Check:

   Labels + legend + title = Correctly plots both products with labels, title, axis labels, and legend [OK]

Hint: Match x, y data and add labels + legend + title [OK]

Common Mistakes:

• Swapping x and y axes
• Forgetting plt.legend() call
• Missing title or axis labels