How to Create Gauge Charts Using Python

Have you ever wanted to display data in a speedometer style chart for things like CPU usage, vehicle speed, or performance scores. I have and when I discovered gauge charts, I realized they were the perfect tool for the job.

I’ll walk you through how to create interactive gauge charts using Python and Plotly. I’ll start with a simple example, explain each part of the code, and then show you how to add more features so you can experiment on your own.

Here the Basic Code to Create a Gauge Chart

Let me start with a simple gauge chart that shows a speed value.

import plotly.graph_objects as go

fig = go.Figure(go.Indicator(
    mode="gauge+number",   # Shows both the gauge and the numeric value
    value=65,              # The current value displayed on the gauge
    title={'text': "Speed"},  # Title displayed above the chart
    gauge={
        'axis': {'range': [0, 100]},  # The range of values (0 to 100)
        'bar': {'color': "darkblue"},  # The pointer or filled bar color
        'steps': [
            {'range': [0, 50], 'color': "lightgray"},  # Color of range 0–50
            {'range': [50, 100], 'color': "gray"}      # Color of range 50–100
        ],
        'threshold': {
            'line': {'color': "red", 'width': 4},  # Red warning line
            'thickness': 0.75,
            'value': 80      # Warning zone starts at 80
        }
    }
))

fig.show()

What Is This Code Doing

• import plotly.graph_objects as go: This brings in Plotly’s powerful charting tools.
• go.Indicator: This is a special chart type in Plotly used for gauges and meters.
• value=65: This sets the current measurement shown on the gauge (like 65 km/h).
• gauge block: This is where we style the gauge including the range, color zones, and warning threshold.
• fig.show(): This command displays the gauge in your default web browser or notebook.

Want to Practice More Try These Enhancement

Make the Value Dynamic Using Input

Here’s a version where I can enter the speed manually:

import plotly.graph_objects as go

# Ask the user to input a value
current_speed = float(input("Enter the current speed (0–100): "))

fig = go.Figure(go.Indicator(
    mode="gauge+number",
    value=current_speed,
    title={'text': "Vehicle Speed"},
    gauge={
        'axis': {'range': [0, 100]},
        'bar': {'color': "blue"},
        'steps': [
            {'range': [0, 50], 'color': "lightgreen"},
            {'range': [50, 80], 'color': "orange"},
            {'range': [80, 100], 'color': "gray"}
        ],
        'threshold': {
            'line': {'color': "red", 'width': 4},
            'thickness': 0.75,
            'value': 90
        }
    }
))

fig.show()

Now, when I run the script, it asks me to type in a number and instantly visualizes it!

Customize the Title and Range

Let’s go one step further: what if I want to track something other than speed like temperature, or system load

This version lets me change the title and axis range dynamically:

import plotly.graph_objects as go

custom_title = input("Enter the gauge title: ")
min_range = int(input("Enter the minimum range: "))
max_range = int(input("Enter the maximum range: "))
value = float(input(f"Enter the current value ({min_range}–{max_range}): "))

fig = go.Figure(go.Indicator(
    mode="gauge+number",
    value=value,
    title={'text': custom_title},
    gauge={
        'axis': {'range': [min_range, max_range]},
        'bar': {'color': "darkblue"},
        'threshold': {
            'line': {'color': "red", 'width': 4},
            'thickness': 0.75,
            'value': max_range * 0.8
        }
    }
))

fig.show()

Now I can create a gauge for anything from battery levels to temperature zones.

Bonus Challenge Ideas

If you’re like me and love playing with ideas, here are some challenges for you:

• Connect to live data — Use Python libraries to pull real-time CPU, memory, or IoT sensor values and visualize them as gauges.
• Build a thermometer — Design a vertical gauge for temperature ranges with color zones (cold, warm, hot).
• Create a full dashboard — Combine multiple gauges to track KPIs like revenue, performance, risk level, etc.

Final Thought

Gauge charts are a fantastic way to turn numbers into visuals. Whether you’re showing speed, usage, or scores, they make your data pop in a dashboard. I personally love how easy and beautiful Plotly makes it to build these charts with just a few lines of code, I can create something that’s not only functional but also visually appealing.