Axis scales (linear, log) in Matplotlib - Step-by-Step Execution

Concept Flow - Axis scales (linear, log)

Start: Prepare data

↓

Choose scale type

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Set axis scale

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Plot data

→Show plot

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End

This flow shows preparing data, choosing axis scale (linear or log), setting it, plotting, and displaying the plot.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
x = [1, 10, 100, 1000]
y = [1, 2, 3, 4]
plt.plot(x, y)
plt.xscale('log')
plt.show()

Plot y vs x with x-axis on a logarithmic scale.

Execution Table

Step	Action	Variable/Parameter	Value/Effect	Output/Plot State
1	Import matplotlib.pyplot	plt	module loaded	No plot yet
2	Define x data	x	[1, 10, 100, 1000]	No plot yet
3	Define y data	y	[1, 2, 3, 4]	No plot yet
4	Plot data	plt.plot(x, y)	Line plot created	Plot with linear axes
5	Set x-axis scale	plt.xscale('log')	x-axis scale set to logarithmic	Plot x-axis changes to log scale
6	Show plot	plt.show()	Plot displayed	Visible plot with log-scaled x-axis

💡 Plot displayed with x-axis on logarithmic scale, showing data spaced accordingly.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
x	undefined	[1, 10, 100, 1000]	[1, 10, 100, 1000]	[1, 10, 100, 1000]	[1, 10, 100, 1000]	[1, 10, 100, 1000]
y	undefined	undefined	[1, 2, 3, 4]	[1, 2, 3, 4]	[1, 2, 3, 4]	[1, 2, 3, 4]
x-axis scale	linear (default)	linear	linear	linear	logarithmic	logarithmic
plot state	none	none	none	line plot with linear axes	line plot with log x-axis	displayed plot with log x-axis

Key Moments - 3 Insights

Why does the plot look different after setting plt.xscale('log')?

Because the x-axis changes from linear spacing to logarithmic spacing, the points spread out differently as shown in step 5 of the execution_table.

Can we set the axis scale before plotting the data?

Yes, but in this example, setting the scale after plotting still updates the axis correctly, as seen in steps 4 and 5.

What happens if x contains zero or negative values when using log scale?

Log scale cannot display zero or negative values; matplotlib will raise an error or ignore those points. This is important to remember when choosing log scale.

Visual Quiz - 3 Questions

Look at the execution_table at step 5, what change happens to the plot?

A The y-axis scale changes to logarithmic

B The x-axis scale changes to logarithmic

C The plot line color changes

D The plot is cleared

💡 Hint

Check the 'Action' and 'Value/Effect' columns at step 5 in execution_table.

According to variable_tracker, what is the x-axis scale after step 4?

A logarithmic

B unknown

C linear

D categorical

💡 Hint

Look at the 'x-axis scale' row and the 'After Step 4' column in variable_tracker.

If we change x to include zero, what will likely happen when setting plt.xscale('log')?

A Matplotlib will raise an error or ignore zero values

B Plot will display normally

C Plot will switch to linear scale automatically

D Plot will invert the x-axis

💡 Hint

Refer to the key_moments section about zero or negative values with log scale.

Concept Snapshot

Axis scales control how data is spaced on plot axes.
Linear scale spaces data evenly.
Log scale spaces data by powers of 10.
Use plt.xscale('log') or plt.yscale('log') to set log scale.
Log scale cannot handle zero or negative values.
Changing scale affects how data points appear on the plot.

Full Transcript

This visual execution traces plotting data with matplotlib and changing the x-axis scale from linear to logarithmic. We start by importing matplotlib.pyplot and defining x and y data arrays. Initially, plotting uses the default linear scale. Then, we set the x-axis scale to logarithmic using plt.xscale('log'). This changes how the x-axis values are spaced, spreading points according to powers of ten. Finally, plt.show() displays the plot with the log-scaled x-axis. Variables like x, y, and axis scale are tracked step-by-step. Key moments clarify why the plot changes after setting log scale, when to set the scale, and the limitation of log scale with zero or negative values. The quiz tests understanding of axis scale changes and their effects on the plot.