Libraries
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 pdDataset
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 "
# 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
19.5,
18,
15.5,
12,
32.5,
4
])
high_values = np.array([
20.5,
22,
24.5,
28,
7.5,
36
])
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',
ascending=True,
inplace=False,
ignore_index=False,
key=None
)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
rangefunction. - Loop Through Variables: For each variable (using
zipto iterate throughys,low_values, andhigh_valuestogether), 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 usingplt.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
midpointvalue 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
plt.show, though it's worth noting the code should actually callplt.show()to display the chart when the function is used.
def tornado_chart(labels, midpoint, low_values, high_values, title="<Low> VS <High> values"):
"""
Parameters
----------
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
plt.broken_barh(
[
(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
else:
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)
ax.set_yticks([])
# build legend
ax_text(x = midpoint, y = len(labels),
s=title,
color='black',
fontsize=15,
va='center',
ha='center',
highlight_textprops=[{"color": color_low, "fontweight": 'bold'},
{"color": color_high, "fontweight": 'bold'}],
ax=ax)
plt.xlabel('Model output')
plt.yticks(ys, labels)
plt.xlim(0,40)
plt.ylim(-0.5, len(labels)-0.5)
plt.tick_params(left = False)
plt.show()
returnThen, 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!
