How Matplotlib renders figures - Visual Walkthrough

Concept Flow - How Matplotlib renders figures

Create Figure Object

↓

Add Axes/Subplots

↓

Plot Data on Axes

↓

Draw Elements (lines, markers, text)

↓

Render to Canvas (pixel buffer)

↓

Display or Save Figure

Matplotlib first creates a figure, adds axes, plots data, draws elements, renders to a canvas, then displays or saves the image.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line, = ax.plot([1, 2, 3], [4, 5, 6])
plt.show()

This code creates a figure and axes, plots a line, and shows the figure window.

Execution Table

Step	Action	Object Created/Modified	State/Result
1	Call plt.subplots()	Figure and Axes objects	Figure created with one Axes inside
2	Call ax.plot([1,2,3], [4,5,6])	Line2D object added to Axes	Line data stored, ready to draw
3	Prepare drawing	Renderer initialized	Canvas ready for drawing
4	Draw Axes and Line	Canvas updated	Line and axes drawn on pixel buffer
5	Call plt.show()	Figure window opened	Rendered figure displayed on screen

💡 Figure displayed and rendering process complete

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
fig	None	Figure object	Figure object	Figure object	Figure object	Figure object
ax	None	Axes object	Axes with Line2D	Axes with Line2D	Axes with Line2D	Axes with Line2D
line	None	None	Line2D object	Line2D object	Line2D object	Line2D object
canvas	None	None	None	Renderer ready	Pixels drawn	Pixels drawn

Key Moments - 3 Insights

Why do we need both a Figure and Axes object?

The Figure is the whole image container, while Axes is the area where data is plotted. Step 1 shows Figure and Axes created separately.

What happens when ax.plot() is called?

A Line2D object is created and added to the Axes, storing the data to be drawn later, as seen in Step 2.

When is the actual drawing done?

Drawing happens in Step 4 when the canvas is updated with the line and axes visuals before display.

Visual Quiz - 3 Questions

Look at the execution table, what object is created at Step 1?

A Line2D object

B Renderer

C Figure and Axes objects

D Canvas pixels

💡 Hint

Check the 'Object Created/Modified' column at Step 1 in the execution table.

At which step is the line data stored but not yet drawn?

A Step 1

B Step 2

C Step 4

D Step 5

💡 Hint

Look at Step 2's 'State/Result' describing line data storage.

If plt.show() was not called, what would happen?

A Figure would not be displayed

B Line would not be created

C Axes would not exist

D Canvas would not be initialized

💡 Hint

Step 5 shows plt.show() opens the figure window for display.

Concept Snapshot

Matplotlib rendering steps:
1. Create Figure and Axes
2. Plot data (creates Line2D etc.)
3. Prepare canvas renderer
4. Draw all elements on canvas
5. Show or save the figure
Figure holds everything; Axes is the plot area.

Full Transcript

Matplotlib rendering starts by creating a Figure object, which is the overall container for the image. Inside it, Axes objects are added to define plotting areas. When you call plot on an Axes, Matplotlib creates Line2D objects that hold the data to be drawn. Before showing, Matplotlib prepares a renderer and draws all elements onto a canvas pixel buffer. Finally, plt.show() opens a window displaying the rendered figure. This process separates data setup from actual drawing, allowing flexible figure creation and rendering.

Practice

1. What is the first step Matplotlib takes when creating a plot?

easy

A. Add data points directly to the screen

B. Display the plot with plt.show()

C. Save the plot to a file automatically

D. Create a figure and axes to draw on

Solution

1. Step 1: Understand Matplotlib's drawing process

   Matplotlib starts by creating a figure and axes, which are the canvas and area to draw on.

2. Step 2: Identify the first action in plotting

   Before adding data or showing the plot, the figure and axes must exist to hold the plot.

3. Final Answer:

   Create a figure and axes to draw on -> Option D

4. Quick Check:

   First step = create figure and axes [OK]

Hint: Remember: figure and axes come before plotting data [OK]

Common Mistakes:

• Thinking plt.show() creates the plot
• Assuming data is drawn directly without axes
• Believing plot saves automatically without show

2. Which of the following is the correct way to display a Matplotlib figure?

easy

A. plt.show()

B. plt.draw()

C. plt.display()

D. plt.render()

Solution

1. Step 1: Recall the function to display plots

   Matplotlib uses plt.show() to open a window and display the figure.

2. Step 2: Check other options for correctness

   plt.draw() updates the figure but does not display it; plt.display() and plt.render() do not exist in Matplotlib.

3. Final Answer:

   plt.show() -> Option A

4. Quick Check:

   Display function = plt.show() [OK]

Hint: Use plt.show() to see your plot on screen [OK]

Common Mistakes:

• Using plt.draw() instead of plt.show()
• Trying plt.display() which is not a Matplotlib function
• Confusing plt.render() with other libraries

3. What will the following code display?

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. A figure with a line connecting points (1,4), (2,5), (3,6)

B. A blank figure with no lines

C. An error because plt.subplots() is incorrect

D. A scatter plot of points (1,4), (2,5), (3,6)

Solution

1. Step 1: Understand the code's plotting commands

   plt.subplots() creates figure and axes; ax.plot() draws a line connecting given points.

2. Step 2: Identify the plot type and output

   ax.plot() connects points with lines, so the figure shows a line through (1,4), (2,5), (3,6).

3. Final Answer:

   A figure with a line connecting points (1,4), (2,5), (3,6) -> Option A

4. Quick Check:

   ax.plot() draws lines through points [OK]

Hint: ax.plot() draws lines; scatter needs ax.scatter() [OK]

Common Mistakes:

• Thinking ax.plot() creates scatter points only
• Assuming plt.subplots() causes an error
• Expecting no lines without plt.show()

4. What is wrong with this code snippet?

import matplotlib.pyplot as plt
fig = plt.figure()
plt.plot([1, 2, 3], [4, 5, 6])
fig.show()

medium

A. plt.plot() cannot be used without axes

B. fig.show() does not display the plot; plt.show() is needed

C. plt.figure() must be called after plt.plot()

D. The lists have different lengths causing an error

Solution

1. Step 1: Analyze the display method used

   fig.show() is not the standard way to display plots; plt.show() is required to render the figure window.

2. Step 2: Check other parts for errors

   plt.plot() works without explicit axes; lists have equal length; plt.figure() order is correct.

3. Final Answer:

   fig.show() does not display the plot; plt.show() is needed -> Option B

4. Quick Check:

   Use plt.show() to display figures [OK]

Hint: Always end with plt.show() to display plots [OK]

Common Mistakes:

• Using fig.show() instead of plt.show()
• Thinking plt.plot() needs axes object
• Assuming list length mismatch causes error here

5. You want to create a figure with two side-by-side plots sharing the same y-axis. Which code correctly sets this up before plotting data?

hard

A. fig, (ax1, ax2) = plt.subplots(2, 2, sharey=True)

B. fig, ax = plt.subplots(2, 1, sharex=True)

C. fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)

D. fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

Solution

1. Step 1: Understand subplot layout for side-by-side plots

   Side-by-side means 1 row and 2 columns, so plt.subplots(1, 2) is correct.

2. Step 2: Check sharing y-axis option

   sharey=True shares the y-axis between the two plots as required.

3. Step 3: Verify other options

   fig, ax = plt.subplots(2, 1, sharex=True) creates vertical plots sharing x-axis; fig = plt.figure() ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) creates subplots but does not share y-axis; fig, (ax1, ax2) = plt.subplots(2, 2, sharey=True) creates 4 plots (2x2), not 2 side-by-side.

4. Final Answer:

   fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True) -> Option C

5. Quick Check:

   Side-by-side = (1,2), share y-axis = sharey=True [OK]

Hint: Use plt.subplots(1, 2, sharey=True) for side-by-side shared y-axis [OK]

Common Mistakes:

• Confusing rows and columns in plt.subplots()
• Forgetting to set sharey=True for shared y-axis
• Using add_subplot without sharing axes