Bullet Charts in Python

How to make bullet charts in Python with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Basic Bullet Charts¶

Stephen Few's Bullet Chart was invented to replace dashboard gauges and meters, combining both types of charts into simple bar charts with qualitative bars (steps), quantitative bar (bar) and performance line (threshold); all into one simple layout. Steps typically are broken into several values, which are defined with an array. The bar represent the actual value that a particular variable reached, and the threshold usually indicate a goal point relative to the value achieved by the bar. See indicator page for more detail.

In [1]:

import plotly.graph_objects as go

fig = go.Figure(go.Indicator(
    mode = "number+gauge+delta",
    gauge = {'shape': "bullet"},
    value = 220,
    delta = {'reference': 300},
    domain = {'x': [0, 1], 'y': [0, 1]},
    title = {'text': "Profit"}))
fig.update_layout(height = 250)

fig.show()

Add Steps, and Threshold¶

Below is the same example using "steps" attribute, which is shown as shading, and "threshold" to determine boundaries that visually alert you if the value cross a defined threshold.

In [2]:

import plotly.graph_objects as go

fig = go.Figure(go.Indicator(
    mode = "number+gauge+delta", value = 220,
    domain = {'x': [0.1, 1], 'y': [0, 1]},
    title = {'text' :"<b>Profit</b>"},
    delta = {'reference': 200},
    gauge = {
        'shape': "bullet",
        'axis': {'range': [None, 300]},
        'threshold': {
            'line': {'color': "red", 'width': 2},
            'thickness': 0.75,
            'value': 280},
        'steps': [
            {'range': [0, 150], 'color': "lightgray"},
            {'range': [150, 250], 'color': "gray"}]}))
fig.update_layout(height = 250)
fig.show()

Custom Bullet¶

The following example shows how to customize your charts. For more information about all possible options check our reference page.

In [3]:

import plotly.graph_objects as go

fig = go.Figure(go.Indicator(
    mode = "number+gauge+delta", value = 220,
    domain = {'x': [0, 1], 'y': [0, 1]},
    delta = {'reference': 280, 'position': "top"},
    title = {'text':"<b>Profit</b><br><span style='color: gray; font-size:0.8em'>U.S. $</span>", 'font': {"size": 14}},
    gauge = {
        'shape': "bullet",
        'axis': {'range': [None, 300]},
        'threshold': {
            'line': {'color': "red", 'width': 2},
            'thickness': 0.75, 'value': 270},
        'bgcolor': "white",
        'steps': [
            {'range': [0, 150], 'color': "cyan"},
            {'range': [150, 250], 'color': "royalblue"}],
        'bar': {'color': "darkblue"}}))
fig.update_layout(height = 250)
fig.show()

Multi Bullet¶

Bullet charts can be stacked for comparing several values at once as illustrated below:

In [4]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Indicator(
    mode = "number+gauge+delta", value = 180,
    delta = {'reference': 200},
    domain = {'x': [0.25, 1], 'y': [0.08, 0.25]},
    title = {'text': "Revenue"},
    gauge = {
        'shape': "bullet",
        'axis': {'range': [None, 300]},
        'threshold': {
            'line': {'color': "black", 'width': 2},
            'thickness': 0.75,
            'value': 170},
        'steps': [
            {'range': [0, 150], 'color': "gray"},
            {'range': [150, 250], 'color': "lightgray"}],
        'bar': {'color': "black"}}))

fig.add_trace(go.Indicator(
    mode = "number+gauge+delta", value = 35,
    delta = {'reference': 200},
    domain = {'x': [0.25, 1], 'y': [0.4, 0.6]},
    title = {'text': "Profit"},
    gauge = {
        'shape': "bullet",
        'axis': {'range': [None, 100]},
        'threshold': {
            'line': {'color': "black", 'width': 2},
            'thickness': 0.75,
            'value': 50},
        'steps': [
            {'range': [0, 25], 'color': "gray"},
            {'range': [25, 75], 'color': "lightgray"}],
        'bar': {'color': "black"}}))

fig.add_trace(go.Indicator(
    mode = "number+gauge+delta", value = 220,
    delta = {'reference': 200},
    domain = {'x': [0.25, 1], 'y': [0.7, 0.9]},
    title = {'text' :"Satisfaction"},
    gauge = {
        'shape': "bullet",
        'axis': {'range': [None, 300]},
        'threshold': {
            'line': {'color': "black", 'width': 2},
            'thickness': 0.75,
            'value': 210},
        'steps': [
            {'range': [0, 150], 'color': "gray"},
            {'range': [150, 250], 'color': "lightgray"}],
        'bar': {'color': "black"}}))
fig.update_layout(height = 400 , margin = {'t':0, 'b':0, 'l':0})

fig.show()

Reference¶

See https://plotly.com/python/reference/indicator/ for more information and chart attribute options!

In [ ]:

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter