Labels, titles, and legends in Data Analysis Python - Step-by-Step Execution

Concept Flow - Labels, titles, and legends

Start plotting data

↓

Add title to plot

↓

Add x-axis label

↓

Add y-axis label

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Add legend for data series

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Show final plot

This flow shows the steps to add a title, axis labels, and a legend to a plot before displaying it.

Execution Sample

Data Analysis Python

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [2, 3, 5]
y2 = [3, 5, 7]
plt.plot(x, y1, label='Series 1')
plt.plot(x, y2, label='Series 2')
plt.title('Sample Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

This code plots two lines with labels, adds a title and axis labels, then shows a legend and displays the plot.

Execution Table

Step	Action	Effect on Plot	Output
1	Plot line y1 with label 'Series 1'	Line 1 appears on plot	Line 1 visible, legend entry pending
2	Plot line y2 with label 'Series 2'	Line 2 appears on plot	Line 2 visible, legend entry pending
3	Add title 'Sample Plot'	Title appears at top	Title visible: 'Sample Plot'
4	Add x-axis label 'X Axis'	Label appears below x-axis	X-axis labeled 'X Axis'
5	Add y-axis label 'Y Axis'	Label appears beside y-axis	Y-axis labeled 'Y Axis'
6	Add legend	Legend box shows 'Series 1' and 'Series 2'	Legend visible with correct labels
7	Show plot	Plot window opens with all elements	Final plot displayed with lines, title, labels, legend

💡 Plot displayed with all labels, title, and legend added

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
x	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]
y1	[2, 3, 5]	[2, 3, 5]	[2, 3, 5]	[2, 3, 5]	[2, 3, 5]	[2, 3, 5]	[2, 3, 5]	[2, 3, 5]
y2	[3, 5, 7]	[3, 5, 7]	[3, 5, 7]	[3, 5, 7]	[3, 5, 7]	[3, 5, 7]	[3, 5, 7]	[3, 5, 7]
title	''	''	''	'Sample Plot'	'Sample Plot'	'Sample Plot'	'Sample Plot'	'Sample Plot'
xlabel	''	''	''	''	'X Axis'	'X Axis'	'X Axis'	'X Axis'
ylabel	''	''	''	''	''	'Y Axis'	'Y Axis'	'Y Axis'
legend	None	None	None	None	None	Visible with labels	Visible with labels	Visible with labels

Key Moments - 3 Insights

Why do we add labels and titles after plotting the data?

Labels and titles are added after plotting because they describe the data shown. The execution_table shows plotting happens first (steps 1-2), then labels and title are added (steps 3-5).

What happens if we forget to call plt.legend()?

Without plt.legend() (step 6), the legend box does not appear even if labels are set on lines. The execution_table row 6 shows legend creation is a separate step.

Can we add multiple labels to the same plot?

Yes, each data series gets its own label when plotted (steps 1 and 2). The legend then shows all labels together (step 6).

Visual Quiz - 3 Questions

Look at the execution_table at step 4. What label is added to the plot?

A Y Axis

B Sample Plot

C X Axis

D Series 1

💡 Hint

Check the 'Action' and 'Effect on Plot' columns at step 4 in execution_table.

At which step does the legend become visible on the plot?

A Step 5

B Step 6

C Step 2

D Step 7

💡 Hint

Look for the row where 'Add legend' is the action in execution_table.

If we remove plt.title('Sample Plot'), what will be missing in the final plot?

A Title

B X-axis label

C Legend

D Data lines

💡 Hint

Refer to variable_tracker for 'title' variable changes and execution_table step 3.

Concept Snapshot

Labels, titles, and legends in plots:
- Use plt.title('Title') to add a plot title
- Use plt.xlabel('X label') and plt.ylabel('Y label') for axis labels
- Use label='name' in plt.plot() and plt.legend() to show legend
- Add these after plotting data lines
- They help explain what the plot shows

Full Transcript

This visual execution shows how to add labels, titles, and legends to a plot using matplotlib in Python. First, data lines are plotted with labels. Then a title is added at the top, followed by labels for the x and y axes. Next, a legend is created to explain the data series. Finally, the plot is displayed with all these elements visible. The execution table traces each step and its effect on the plot. The variable tracker shows how variables like title, xlabel, ylabel, and legend change during the process. Key moments clarify why labels come after plotting and the importance of calling plt.legend(). The quiz tests understanding of when and what labels appear. This helps beginners see the step-by-step building of a clear, informative plot.