Dual y-axis for different scales in Matplotlib - Step-by-Step Execution

Concept Flow - Dual y-axis for different scales

Create main plot with x and y1

↓

Create second y-axis sharing x

↓

Plot y2 on second y-axis

↓

Set labels and colors for clarity

↓

Display combined plot with two y-axes

First, plot data on the main y-axis. Then create a second y-axis sharing the same x-axis. Plot the second data on this new axis. Finally, label both axes and show the plot.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.exp(x / 10)
fig, ax1 = plt.subplots(figsize=(10, 6))
color = 'tab:red'
ax1.set_xlabel('X data')
ax1.set_ylabel('Y1 data (sin)', color=color)
ax1.plot(x, y1, color=color, linewidth=2)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('Y2 data (exp)', color=color)
ax2.plot(x, y2, color=color, linewidth=2)
ax2.tick_params(axis='y', labelcolor=color)
fig.tight_layout()
plt.title('Dual Y-Axis Plot: Sine vs Exponential')
plt.show()

Enhanced code with labels, colors matching axes, title, and realistic data (sine on left, exponential on right) for better clarity and visualization.

Execution Table

Step	Action	Variable/Function	Result/State
1	Import libraries	plt, np	Matplotlib and NumPy imported
2	Create x data	x = np.linspace(0, 10, 100)	Array of 100 points from 0 to 10
3	Create y1 data (sine)	y1 = np.sin(x)	Sine wave values
4	Create y2 data (exp)	y2 = np.exp(x / 10)	Exponential growth values
5	Create figure and ax1	fig, ax1 = plt.subplots(figsize=(10, 6))	Figure and main axis created
6	Set ax1 labels	ax1.set_xlabel, ax1.set_ylabel	X label and left Y label set (red)
7	Plot y1 on ax1	ax1.plot(x, y1, color='tab:red')	Red sine line on left axis
8	Set ax1 tick colors	ax1.tick_params	Left y-ticks colored red
9	Create ax2	ax2 = ax1.twinx()	Right y-axis sharing x
10	Set ax2 labels	ax2.set_ylabel	Right Y label set (blue)
11	Plot y2 on ax2	ax2.plot(x, y2, color='tab:blue')	Blue exp line on right axis
12	Set ax2 tick colors	ax2.tick_params	Right y-ticks colored blue
13	Tight layout and title	fig.tight_layout(), plt.title	Layout adjusted, title added
14	Show plot	plt.show()	Dual y-axis plot displayed

💡 Professional dual y-axis plot with matching colors, labels, and scales for disparate data.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	After Step 7	After Step 9	After Step 11	Final
x	undefined	linspace(0,10,100)	linspace(0,10,100)	linspace(0,10,100)	linspace(0,10,100)	linspace(0,10,100)	linspace(0,10,100)	linspace(0,10,100)	linspace(0,10,100)
y1	undefined	undefined	sin(x)	sin(x)	sin(x)	sin(x)	sin(x)	sin(x)	sin(x)
y2	undefined	undefined	undefined	exp(x/10)	exp(x/10)	exp(x/10)	exp(x/10)	exp(x/10)	exp(x/10)
fig	undefined	undefined	undefined	undefined	Figure(10x6)	Figure(10x6)	Figure(10x6)	Figure(10x6)	Figure w/ title & layout
ax1	undefined	undefined	undefined	undefined	Main axis w/ labels	Main axis w/ red sine plot	Main axis w/ red sine	Main axis w/ red sine	Main axis finalized
ax2	undefined	undefined	undefined	undefined	undefined	undefined	Twin y-axis	Twin y-axis w/ blue exp	Twin y-axis finalized

Key Moments - 3 Insights

Why match line colors to axis tick colors?

It visually links each line to its y-axis, preventing confusion (steps 8 & 12). Essential for readability in dual-axis plots.

When is twinx() unnecessary?

If scales are similar (e.g., both y1/y2 ~0-1), plot on single ax1. Use dual only for disparate ranges like sin(±1) vs exp(0-2+).

Role of fig.tight_layout()?

Prevents label overlap between left/right y-axes and x-axis (step 13).

Visual Quiz - 4 Questions

What does ax1.twinx() create?

A Second y-axis (right side)

B Second x-axis

C New figure

D Legend

💡 Hint

See execution_table step 9: 'Right y-axis sharing x'

Which step plots the blue exponential line?

A Step 7

B Step 11

C Step 6

D Step 14

💡 Hint

'ax2.plot(x, y2, color='tab:blue')' in execution_table.

Why use np.linspace for x?

A Discrete points only

B Required for exp

C Saves memory

D Smooth continuous line plot

💡 Hint

100 points from 0-10 create smooth curves vs jagged [1,2,3,4].

Best practice for clarity?

A Same color for both lines

B No labels needed

C Match line color to its axis labels/ticks

D Always use black

💡 Hint

Steps 6-8,10-12: 'tab:red' for ax1, 'tab:blue' for ax2.

Concept Snapshot

Dual y-axis (twinx()) plots disparate scales on shared x.
Key: color-match lines/axes/labels; use tight_layout(); ideal for sin vs exp, sales vs profit, etc.
Avoid if scales similar—use single axis.

Full Transcript

Master dual y-axes: import plt/np; generate x,y1=sin(x),y2=exp(x/10); fig/ax1; label ax1 red/sine; plot red sine; twin ax2 blue/exp; plot blue exp; tight_layout/title/show. Colors link visually; perfect for mixed-scale data viz.

Practice

1. What is the main purpose of using a dual y-axis in a matplotlib plot?

easy

A. To compare two datasets with different scales on the same plot

B. To create two separate plots side by side

C. To add a legend to the plot

D. To change the color of the plot lines

Solution

1. Step 1: Understand the purpose of dual y-axis

   A dual y-axis allows plotting two datasets with different value ranges on the same graph for easy comparison.

2. Step 2: Identify the correct use case

   Comparing datasets with different scales requires two y-axes; other options do not describe this purpose.

3. Final Answer:

   To compare two datasets with different scales on the same plot -> Option A

4. Quick Check:

   Dual y-axis = compare different scales [OK]

Hint: Dual y-axis means two y-scales on one graph [OK]

Common Mistakes:

• Thinking dual y-axis creates two separate plots
• Confusing dual y-axis with adding legends
• Assuming it changes line colors only

2. Which matplotlib function is used to create a second y-axis sharing the same x-axis?

easy

A. plt.subplots()

B. ax.twiny()

C. ax.twinx()

D. plt.plot()

Solution

1. Step 1: Recall matplotlib methods for axes

   The method twinx() creates a second y-axis sharing the x-axis.

2. Step 2: Differentiate from similar methods

   twiny() creates a second x-axis, not y-axis; subplots() creates figure and axes; plot() draws lines.

3. Final Answer:

   ax.twinx() -> Option C

4. Quick Check:

   Second y-axis = twinx() [OK]

Hint: Use ax.twinx() for second y-axis, twiny() is for x-axis [OK]

Common Mistakes:

• Using plt.subplots() to create second y-axis
• Confusing twinx() with twiny()
• Trying to use plt.plot() to add axes

3. What will the following code output?

import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot([1, 2, 3], [10, 20, 30], 'g-')
ax2.plot([1, 2, 3], [100, 200, 300], 'b-')
print(len(fig.axes))

medium

A. 2

B. 1

C. 3

D. 4

Solution

1. Step 1: Understand axes creation

   plt.subplots() creates one axes (ax1), then ax1.twinx() adds a second y-axis (ax2), so total axes are 2.

2. Step 2: Check figure axes count

   fig.axes is a list of all axes in the figure; after adding ax2, length is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Two axes after twinx() = 2 [OK]

Hint: twinx() adds one more axes, so total axes count increases by 1 [OK]

Common Mistakes:

• Assuming twinx() replaces the original axes
• Thinking twinx() adds multiple axes
• Confusing axes count with number of plots

4. Identify the error in this code snippet for creating dual y-axis:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax2 = plt.twinx()
ax.plot([1,2,3], [10,20,30])
ax2.plot([1,2,3], [100,200,300])
plt.show()

medium

A. plt.show() is missing

B. plt.subplots() should be plt.figure()

C. ax2.plot() should be ax.plot()

D. plt.twinx() should be ax.twinx()

Solution

1. Step 1: Check method usage for second y-axis

   twinx() is a method of an axes object, not plt module. So plt.twinx() is invalid.

2. Step 2: Correct method call

   Use ax.twinx() to create the second y-axis sharing the x-axis.

3. Final Answer:

   plt.twinx() should be ax.twinx() -> Option D

4. Quick Check:

   twinx() is an axes method, not plt function [OK]

Hint: twinx() must be called on axes object, not plt [OK]

Common Mistakes:

• Calling twinx() on plt instead of axes
• Confusing figure and axes creation methods
• Missing plt.show() (though present here)

5. You want to plot temperature (°C) and rainfall (mm) over months on the same plot with dual y-axes. Which of these code snippets correctly sets different y-axis labels and colors for clarity?

hard

A. ax1.set_xlabel('Temperature (°C)', color='red')\nax2.set_xlabel('Rainfall (mm)', color='blue')

B. ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue')

C. ax1.set_ylabel('Rainfall (mm)', color='blue')\nax2.set_ylabel('Temperature (°C)', color='red')

D. ax1.set_title('Temperature and Rainfall')\nax2.set_title('Temperature and Rainfall')

Solution

1. Step 1: Assign correct y-axis labels

   Temperature and rainfall are y-axis data, so use set_ylabel() on each axis with matching labels.

2. Step 2: Use colors to distinguish axes

   Setting label colors helps visually separate the two y-axes for clarity.

3. Final Answer:

   ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') -> Option B

4. Quick Check:

   Correct y-labels and colors = ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') [OK]

Hint: Label y-axes with units and colors for clear dual-axis plots [OK]

Common Mistakes:

• Setting x-axis labels instead of y-axis
• Swapping labels between axes
• Using titles instead of axis labels