How to create a tornado chart with matplotlib

logo of a chart:Bar

A tornado chart can be created using a combination of broken horizontal bar charts broken_barh() in matplotlib.
Credit to Marijn van Vliet and eitanlees for code at stackoverflow.


First, we need to install the following libraries:

# libraries
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from highlight_text import ax_text
import numpy as np
import pandas as pd


The dataset is set up to compare the effects of six different variables, each with a specific range or description (like "1.0 - 5.0" or "200200 -300"). It includes low and high values for each variable and calculates the effect of each variable based on the difference between these values, relative to a midpoint of 20. The data is then organized to show the variables' effects from lowest to highest.

# data for chart
labels = np.char.array([
    "Variable 1\n 1.0 - 5.0",
    "Variable 2\n 11% - 15%",
    "Variable 3\n $200 - $300",
    "Variable 4\n $12 - $14",
    "Variable 5\n Off - On",
    "Variable 6\n Low - High",

midpoint = 20

# data values
low_values = np.array([ # value order corresponds to label order

high_values = np.array([

var_effect = np.abs(high_values - low_values)/midpoint

data = pd.DataFrame({'Labels': labels,
                     'Low values': low_values,
                     'High values': high_values,
                     'Variable effect' : var_effect

# sorts effect high to low (adjust to preference)
data = data.sort_values(
    'Variable effect',

Tornado chart

  • Set Colors: It defines two colors, one for the low values and another for the high values.
  • Prepare Y-Axis: It calculates the positions on the y-axis for each variable label using the range function.
  • Loop Through Variables: For each variable (using zip to iterate through ys, low_values, and high_values together), the function calculates the widths of the bars extending to the left and right from the midpoint (for low and high values, respectively) and then draws these bars using plt.broken_barh.
  • Text Labels for Values: The function adds text labels to the ends of the bars to indicate the numerical values for the low and high inputs.
  • Midpoint Line: Draws a vertical line at the midpoint value to visually anchor the bars.
  • Axis Formatting: Adjusts the visibility of the axis spines and ticks, aiming to simplify and clean up the chart's appearance.
  • Legend: Adds a legend to the chart to indicate which color represents low values and which represents high values.
  • Labels and Limits: Sets the labels for the x-axis and y-axis, the tick marks for the y-axis, and the chart's x and y limits.
  • Display Chart: Finally, it prepares the chart for display with, though it's worth noting the code should actually call to display the chart when the function is used.
def tornado_chart(labels, midpoint, low_values, high_values, title="<Low> VS <High> values"):
    labels : np.array()
        List of label titles used to identify the variables, y-axis of bar
        chart. The lengh of labels is used to itereate through to generate 
        the bar charts.
    midpoint : float
        Center value for bar charts to extend from. In sensitivity analysis
        this is often the 'neutral' or 'default' model output.
    low_values : np.array()
        An np.array of the model output resulting from the low variable 
        selection. Same length and order as label_range. 
    high_values : np.array()
        An np.array of the model output resulting from the high variable
        selection. Same length and order as label_range.
    color_low = '#e1ceff'
    color_high = '#ff6262'
    ys = range(len(data['Labels']))[::1] # iterate through # of labels
    for y, low_value, high_value in zip(ys, low_values, high_values):
        low_width = midpoint - low_value
        high_width = high_value - midpoint
                (low_value, low_width),
                (midpoint, high_width)
            (y-0.4, 0.8), # thickness of bars and their offset
            facecolors = [color_low, color_high],
            edgecolors = ['black', 'black'],
            linewidth = 0.5
        offset = 2 # offset value labels from end of bar
        if high_value > low_value:
            x_high = midpoint + high_width + offset 
            x_low = midpoint - low_width - offset
            x_high = midpoint + high_width - offset
            x_low = midpoint - low_width + offset

        plt.text(x_high, y, str(high_value), va='center', ha='center')
        plt.text(x_low, y, str(low_value), va='center', ha='center')
    plt.axvline(midpoint, color='black', linewidth = 1)

    # set axis lines on or off
    ax = plt.gca() 
    ax.spines[['right', 'left', 'top']].set_visible(False)
    # build legend 
    ax_text(x = midpoint, y = len(labels),
            highlight_textprops=[{"color": color_low, "fontweight": 'bold'},
                                 {"color": color_high, "fontweight": 'bold'}],
    plt.xlabel('Model output')
    plt.yticks(ys, labels)
    plt.ylim(-0.5, len(labels)-0.5)
    plt.tick_params(left = False)

Then, we just have to call our function with the data to create the chart:

tornado_chart(labels, midpoint, data['Low values'], data['High values'])

Going further

This article explains how to create a tornado chart, which is just an example of a more advanced barplot.

For more examples of advanced customization, check out the barplot section. Also, you might be interested in populzyion pyramid chart, which is a very similar type of charts!

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