A radar chart is a graph that represents multiple items on a regular polygon. It is often used to check the balance of data for each item.
Python’s Matplotlib does not provide a dedicated function for displaying radar charts, but introduces a way to plot them as a class.
This article describes in detail how to draw a radar chart in a class, and explains how to easily plot a radar chart in Matplotlib.
It also explains the basic structure of the class and the role of each function in order to plot a radar chart using the class.
Table of Contents
Plot a Radar chart
In Matplotlib, there is no dedicated function for radar charts, so the class is used to plot them.
First, using the prepared data, we will simply plot a radar chart while changing the number of graphs.
Express the relevance of the 5 factors in the 4 scenarios
The radar chart presented in the official documentation represents the relevance of the five factors for four possible scenarios.
example_data(): stores 4 types of data with 5 rows and 9 columnsradar_factory(): Plots a radar chart
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle, RegularPolygon
from matplotlib.path import Path
from matplotlib.projections import register_projection
from matplotlib.projections.polar import PolarAxes
from matplotlib.spines import Spine
from matplotlib.transforms import Affine2D
def radar_factory(num_vars, frame='circle'):
"""
Create a radar chart with `num_vars` Axes.
This function creates a RadarAxes projection and registers it.
Parameters
----------
num_vars : int
Number of variables for radar chart.
frame : {'circle', 'polygon'}
Shape of frame surrounding Axes.
"""
# calculate evenly-spaced axis angles
theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)
class RadarTransform(PolarAxes.PolarTransform):
def transform_path_non_affine(self, path):
# Paths with non-unit interpolation steps correspond to gridlines,
# in which case we force interpolation (to defeat PolarTransform's
# autoconversion to circular arcs).
if path._interpolation_steps > 1:
path = path.interpolated(num_vars)
return Path(self.transform(path.vertices), path.codes)
class RadarAxes(PolarAxes):
name = 'radar'
PolarTransform = RadarTransform
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# rotate plot such that the first axis is at the top
self.set_theta_zero_location('N')
def fill(self, *args, closed=True, **kwargs):
"""Override fill so that line is closed by default"""
return super().fill(closed=closed, *args, **kwargs)
def plot(self, *args, **kwargs):
"""Override plot so that line is closed by default"""
lines = super().plot(*args, **kwargs)
for line in lines:
self._close_line(line)
def _close_line(self, line):
x, y = line.get_data()
# FIXME: markers at x[0], y[0] get doubled-up
if x[0] != x[-1]:
x = np.append(x, x[0])
y = np.append(y, y[0])
line.set_data(x, y)
def set_varlabels(self, labels):
self.set_thetagrids(np.degrees(theta), labels)
def _gen_axes_patch(self):
# The Axes patch must be centered at (0.5, 0.5) and of radius 0.5
# in axes coordinates.
if frame == 'circle':
return Circle((0.5, 0.5), 0.5)
elif frame == 'polygon':
return RegularPolygon((0.5, 0.5), num_vars,
radius=.5, edgecolor="k")
else:
raise ValueError("Unknown value for 'frame': %s" % frame)
def _gen_axes_spines(self):
if frame == 'circle':
return super()._gen_axes_spines()
elif frame == 'polygon':
# spine_type must be 'left'/'right'/'top'/'bottom'/'circle'.
spine = Spine(axes=self,
spine_type='circle',
path=Path.unit_regular_polygon(num_vars))
# unit_regular_polygon gives a polygon of radius 1 centered at
# (0, 0) but we want a polygon of radius 0.5 centered at (0.5,
# 0.5) in axes coordinates.
spine.set_transform(Affine2D().scale(.5).translate(.5, .5)
+ self.transAxes)
return {'polar': spine}
else:
raise ValueError("Unknown value for 'frame': %s" % frame)
register_projection(RadarAxes)
return theta
def example_data():
# The following data is from the Denver Aerosol Sources and Health study.
# See doi:10.1016/j.atmosenv.2008.12.017
#
# The data are pollution source profile estimates for five modeled
# pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical
# species. The radar charts are experimented with here to see if we can
# nicely visualize how the modeled source profiles change across four
# scenarios:
# 1) No gas-phase species present, just seven particulate counts on
# Sulfate
# Nitrate
# Elemental Carbon (EC)
# Organic Carbon fraction 1 (OC)
# Organic Carbon fraction 2 (OC2)
# Organic Carbon fraction 3 (OC3)
# Pyrolyzed Organic Carbon (OP)
# 2)Inclusion of gas-phase specie carbon monoxide (CO)
# 3)Inclusion of gas-phase specie ozone (O3).
# 4)Inclusion of both gas-phase species is present...
data = [
['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
('Basecase', [
[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
[0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],
[0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],
[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),
('With CO', [
[0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],
[0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],
[0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],
[0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],
[0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),
('With O3', [
[0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],
[0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],
[0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],
[0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],
[0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),
('CO & O3', [
[0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],
[0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],
[0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],
[0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],
[0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])
]
return data
if __name__ == '__main__':
N = 9
theta = radar_factory(N, frame='polygon')
data = example_data()
spoke_labels = data.pop(0)
fig, axs = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
subplot_kw=dict(projection='radar'))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate Axes
for ax, (title, case_data) in zip(axs.flat, data):
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25, label='_nolegend_')
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
legend = axs[0, 0].legend(labels, loc=(0.9, .95),
labelspacing=0.1, fontsize='small')
fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
horizontalalignment='center', color='black', weight='bold',
size='large')
plt.show()
Express the relevance of the 5 factors in the two scenarios
Reduce the number of scenarios from four to two. Basically, the only part to be changed is the code below if name == 'main':.
Changes
- figsize : (9, 9)→(9, 5)
- nrows : 2 → 1
- axs : [0, 0] → [0]
Either figsize is acceptable, but nrows and axs are required.
if __name__ == '__main__':
N = 9
theta = radar_factory(N, frame='polygon')
data = example_data()
spoke_labels = data.pop(0)
# figsize=(9, 9)→(9, 5)
# nrows=2→1
fig, axs = plt.subplots(figsize=(9, 5), nrows=1, ncols=2,
subplot_kw=dict(projection='radar'))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes.
for ax, (title, case_data) in zip(axs.flat, data):
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25, label='_nolegend_')
ax.set_varlabels(spoke_labels)
# Add a relative legend to the upper left plot.
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
# axs[0, 0]→axs[0]
legend = axs[0].legend(labels, loc=(0.9, .95),
labelspacing=0.1, fontsize='small')
fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
horizontalalignment='center', color='black', weight='bold',
size='large')
plt.show()Express the relevance of the 5 factors in only one scenario
It simply displays a single radar chart. Similarly, only the code below if name == 'main': needs to be changed.
Changes
- axs → ax
- nrows=2, ncols=2 → nrows=1, ncols=1
- axs[0, 0] → ax
If both nrows and ncols are set to 1 (or none), it will be a single Axes instance instead of an array. Therefore, change the variable to ax.
In order to extract only the first case from the data example, we remove the for statement as title, case_data = data[0].
if __name__ == '__main__':
N = 9
theta = radar_factory(N, frame='polygon')
data = example_data()
spoke_labels = data.pop(0)
# axs → ax
# nrows=2, ncols=2 → nrows=1, ncols=1
fig, ax = plt.subplots(figsize=(9, 9), nrows=1, ncols=1,
subplot_kw=dict(projection='radar'))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Take out the first case of the data example.
title, case_data = data[0]
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25, label='_nolegend_')
ax.set_varlabels(spoke_labels)
# Add a relative legend to the upper left plot.
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
# axs[0, 0] → ax
legend = ax.legend(labels, loc=(0.9, .95),
labelspacing=0.1, fontsize='small')
fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
horizontalalignment='center', color='black', weight='bold',
size='large')
plt.show()
Replace the data in the radar chart.
This section explains how to replace the data used in a radar chart. We will change the factors and elements, respectively.
Express the relationship of the three factors to the nine elements.
Reduce the number of factors in the radar chart to 3.
The change is made in def example_data(): data.
Changes
- data：[
[Elements of graph],
(scenario title of graph), [
# Delete 2 lines of graph data below
[5 rows of graph data were included],
[Changed it to 3 rows],
[Number of rows = Number of factors]
]
]
The number of rows of data in the function def example_data(): is the number of factors.
def example_data():
data = [
['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
('Basecase', [
# Delete 2 rows
[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]])
]
return data
Express the relevance of the 3 factors to the 7 elements
Reduce the number of factors in the radar chart to 3 and the number of elements to 7.
The changes are in the data inside the function def example_data(): and in the N below if name == 'main':.
Changes
- data (def example_data():)：[
# Delete 2 columns of the following graph elements
[Change elements of graph to 7 columns],
(scenario title of graph), [
# Delete 2 columns of graph data below.
[It contained 9 columns of graph data],
[Changed it to 7 columns.],
[Number of columns = Number of elements]
]
] - N (if name == ‘main’:)：9 → 7
The number of columns of data in the function def example_data() is the number of elements. And if name == 'main': change the lower N to match the number of elements.
def example_data():
data = [
# Delete 2 columns
['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP'],
('Basecase', [
# Delete 2 columns
[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01],
[0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01],
[0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00]])
]
return data
if __name__ == '__main__':
# 9 → 7
N = 7
theta = radar_factory(N, frame='polygon')
data = example_data()
spoke_labels = data.pop(0)
fig, ax = plt.subplots(figsize=(9, 9), nrows=1, ncols=1,
subplot_kw=dict(projection='radar'))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Retrieve the first case from example_data()
title, case_data = data[0]
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25, label='_nolegend_')
ax.set_varlabels(spoke_labels)
# Add a relative legend to the upper left plot
labels = ('Factor 1', 'Factor 2', 'Factor 3')
legend = ax.legend(labels, loc=(0.9, .95),
labelspacing=0.1, fontsize='small')
fig.text(0.5, 0.965, '3-Factor Solution Profiles in 7-Elements',
horizontalalignment='center', color='black', weight='bold',
size='large')
plt.show()
References
Official documentation of radar charts
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