Matplotlib for data visualization in Raspberry Pi - Step-by-Step Execution

Concept Flow - Matplotlib for data visualization

Import matplotlib.pyplot as plt

↓

Prepare data to plot

↓

Call plotting function (e.g., plt.plot)

↓

Customize plot (title, labels, legend)

↓

Display plot with plt.show()

↓

Plot appears

This flow shows how to create a plot step-by-step: import, prepare data, plot, customize, then display.

Execution Sample

Raspberry Pi

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.title('Simple Line Plot')
plt.show()

This code draws a simple line plot of y versus x and shows it on screen.

Execution Table

Step	Action	Data/Variables	Plot State	Output
1	Import matplotlib.pyplot as plt	plt module ready	No plot yet	No output
2	Prepare data x and y	x=[1,2,3,4], y=[10,20,25,30]	No plot yet	No output
3	Call plt.plot(x, y)	x and y used	Line plot created with points (1,10),(2,20),(3,25),(4,30)	No output
4	Set title 'Simple Line Plot'	Title set	Plot has title	No output
5	Call plt.show()	Plot ready	Plot window opens	Line plot displayed on screen
6	End of script	All done	Plot shown	Script ends

💡 Plot displayed and script ends after plt.show()

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
plt	Not imported	Imported matplotlib.pyplot	Plot function ready	Plot title set	Plot displayed
x	Undefined	[1, 2, 3, 4]	[1, 2, 3, 4]	[1, 2, 3, 4]	[1, 2, 3, 4]
y	Undefined	[10, 20, 25, 30]	[10, 20, 25, 30]	[10, 20, 25, 30]	[10, 20, 25, 30]

Key Moments - 3 Insights

Why do we need to call plt.show() at the end?

plt.show() tells Python to open the window and display the plot. Without it, the plot is created but not shown (see execution_table step 5).

What happens if x and y lists have different lengths?

Matplotlib will give an error because it needs matching points to plot. This is why x and y must be the same length (see variable_tracker for x and y).

Can we add a title before calling plt.plot()?

Yes, but usually we add the title after creating the plot so it applies to the current figure (see execution_table steps 3 and 4).

Visual Quiz - 3 Questions

Look at the execution table, what is the plot state after step 3?

A Line plot created with points (1,10),(2,20),(3,25),(4,30)

B No plot created yet

C Plot window opened

D Title set but no plot

💡 Hint

Check the 'Plot State' column in row for step 3 in execution_table

At which step does the plot window open and display the graph?

A Step 2

B Step 5

C Step 3

D Step 6

💡 Hint

Look for 'Plot window opens' in the Output column of execution_table

If we forget to call plt.show(), what will happen?

A Plot will display automatically

B Python will crash

C Plot will be created but not shown

D Title will not appear

💡 Hint

Refer to key_moments about the role of plt.show() and execution_table step 5

Concept Snapshot

Matplotlib basics:
- Import with 'import matplotlib.pyplot as plt'
- Prepare data lists x and y
- Use plt.plot(x, y) to create line plot
- Customize with plt.title(), plt.xlabel(), plt.ylabel()
- Show plot with plt.show()
Always call plt.show() to see the plot window.

Full Transcript

This lesson shows how to create a simple line plot using Matplotlib. First, we import the pyplot module as plt. Then we prepare data lists x and y. Next, we call plt.plot(x, y) to create the line plot. We add a title with plt.title(). Finally, plt.show() displays the plot window. Variables x and y hold the data points. The plot is created but only appears after plt.show() is called. If x and y lengths differ, Matplotlib will error. Adding title usually happens after plotting. Remember, plt.show() is essential to see the plot.

Practice

1. What is the main purpose of using matplotlib in data science on a Raspberry Pi?

easy

A. To create visual pictures like charts from data

B. To store data in a database

C. To write data to a text file

D. To perform complex mathematical calculations

Solution

1. Step 1: Understand matplotlib's role

   Matplotlib is a tool used to turn data into visual charts and graphs.

2. Step 2: Compare options with matplotlib's function

   Only To create visual pictures like charts from data describes creating visual pictures, which matches matplotlib's purpose.

3. Final Answer:

   To create visual pictures like charts from data -> Option A

4. Quick Check:

   Matplotlib = Visual charts [OK]

Hint: Matplotlib = making charts from data [OK]

Common Mistakes:

• Confusing matplotlib with data storage tools
• Thinking matplotlib does calculations only
• Mixing up file writing with visualization

2. Which of the following is the correct way to import matplotlib's plotting module in Python on Raspberry Pi?

easy

A. import matplotlib as plt

B. import matplotlib.plot as plt

C. import matplotlib.plt as pyplot

D. import matplotlib.pyplot as plt

Solution

1. Step 1: Recall the standard import syntax

   The common way to import matplotlib's plotting is import matplotlib.pyplot as plt.

2. Step 2: Check each option for correctness

   Only import matplotlib.pyplot as plt matches the correct module name and alias.

3. Final Answer:

   import matplotlib.pyplot as plt -> Option D

4. Quick Check:

   Correct import = import matplotlib.pyplot as plt [OK]

Hint: Remember: pyplot, not plot or plt alone [OK]

Common Mistakes:

• Using wrong module names like 'plot' or 'plt' directly
• Mixing up alias names
• Typo in 'pyplot'

3. What will the following code display on a Raspberry Pi?

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Simple Line')
plt.show()

medium

A. A line chart with points (1,4), (2,5), (3,6) labeled axes and title

B. A scatter plot with points (1,4), (2,5), (3,6) without labels

C. A bar chart with bars at 1, 2, 3 heights 4, 5, 6

D. No plot will show because of syntax error

Solution

1. Step 1: Understand plt.plot() usage

   The plt.plot() function draws a line chart connecting points given by x and y lists.

2. Step 2: Check labels and title

   Labels for x and y axes and a title are set, so the chart will show them.

3. Final Answer:

   A line chart with points (1,4), (2,5), (3,6) labeled axes and title -> Option A

4. Quick Check:

   plt.plot() = line chart [OK]

Hint: plt.plot() makes line charts, not bars or scatter [OK]

Common Mistakes:

• Confusing plt.plot() with scatter or bar charts
• Ignoring axis labels and title
• Assuming syntax error without checking code

4. Identify the error in this code snippet for plotting a pie chart on Raspberry Pi:

import matplotlib.pyplot as plt
sizes = [10, 20, 30]
labels = ['A', 'B', 'C']
plt.pie(sizes, labels=labels)
plt.show()

medium

A. Missing plt.title() causes error

B. No error, code will run and show pie chart

C. sizes list must be numpy array, not list

D. plt.pie() requires colors argument

Solution

1. Step 1: Check pie chart function requirements

   The plt.pie() function accepts sizes as list and labels as list without error.

2. Step 2: Verify optional parameters

   Title and colors are optional; missing them does not cause errors.

3. Final Answer:

   No error, code will run and show pie chart -> Option B

4. Quick Check:

   plt.pie() works with lists and labels [OK]

Hint: Pie chart needs sizes and labels only, colors optional [OK]

Common Mistakes:

• Thinking title is mandatory
• Believing sizes must be numpy arrays
• Assuming colors argument is required

5. You want to plot a bar chart on Raspberry Pi showing sales for products A, B, and C with values 5, 7, and 3. Which code correctly creates this chart with labels and title?

hard

A. import matplotlib.pyplot as plt
products = ['A', 'B', 'C']
sales = [5, 7, 3]
plt.scatter(products, sales)
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales Bar Chart')
plt.show()

B. import matplotlib.pyplot as plt
products = ['A', 'B', 'C']
sales = [5, 7, 3]
plt.plot(products, sales)
plt.xlabel('Sales')
plt.ylabel('Products')
plt.title('Sales Bar Chart')
plt.show()

C. import matplotlib.pyplot as plt
products = ['A', 'B', 'C']
sales = [5, 7, 3]
plt.bar(products, sales)
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Sales Bar Chart')
plt.show()

D. import matplotlib.pyplot as plt
products = ['A', 'B', 'C']
sales = [5, 7, 3]
plt.pie(sales, labels=products)
plt.title('Sales Bar Chart')
plt.show()

Solution

1. Step 1: Identify correct chart type for sales data

   Bar charts show quantities per category, so plt.bar() is correct.

2. Step 2: Check axis labels and title

   Labels for products (x-axis) and sales (y-axis) match the data meaning.

3. Step 3: Verify other options

   import matplotlib.pyplot as plt products = ['A', 'B', 'C'] sales = [5, 7, 3] plt.plot(products, sales) plt.xlabel('Sales') plt.ylabel('Products') plt.title('Sales Bar Chart') plt.show() uses line plot with swapped labels, C uses scatter plot, D uses pie chart which is different visualization.

4. Final Answer:

   Code using plt.bar() with correct labels and title -> Option C

5. Quick Check:

   Bar chart = plt.bar() with correct labels [OK]

Hint: Use plt.bar() for bar charts, label axes clearly [OK]

Common Mistakes:

• Using plt.plot() or plt.scatter() for bar charts
• Swapping x and y axis labels
• Confusing pie chart with bar chart