Labels, titles, and legends in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of a title in a data visualization?

A title gives a clear and simple description of what the chart or graph is about. It helps the viewer understand the main topic quickly.

beginner

How do axis labels help in a chart?

Axis labels tell what each axis represents, like time, quantity, or categories. They make the chart easier to understand by explaining the data shown on each axis.

beginner

What is a legend in a chart?

A legend explains the symbols, colors, or patterns used in the chart. It helps identify what each part of the chart means, especially when there are multiple data groups.

intermediate

In Python's matplotlib, which functions add titles, axis labels, and legends?

Use plt.title() for the title, plt.xlabel() and plt.ylabel() for axis labels, and plt.legend() to add a legend to the plot.

beginner

Why is it important to include labels, titles, and legends in your charts?

They make charts clear and easy to understand. Without them, viewers might not know what the data means or what to focus on.

Which matplotlib function adds a title to a plot?

A plt.plot()

B plt.legend()

C plt.xlabel()

D plt.title()

plt.title() sets the title of the plot.

What does a legend in a chart do?

A Labels the x-axis

B Explains colors or symbols used

C Shows the chart's title

D Sets the background color

A legend explains the meaning of colors or symbols in the chart.

Which axis label function sets the label for the y-axis in matplotlib?

A plt.ylabel()

B plt.legend()

C plt.title()

D plt.xlabel()

plt.ylabel() sets the label for the y-axis.

Why should you add axis labels to a chart?

A To explain what data each axis shows

B To decorate the chart

C To add a legend

D To change the chart colors

Axis labels explain what each axis represents, making the chart easier to understand.

If a chart has multiple lines, what helps viewers know which line is which?

A Title

B Axis labels

C Legend

D Grid lines

A legend identifies each line or group in the chart.

Explain how titles, axis labels, and legends improve the clarity of a data visualization.

Think about what each part tells the viewer about the chart.

Describe how you would add a title, axis labels, and a legend to a line chart using Python's matplotlib.

Recall the matplotlib functions for labeling.

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