python画蛛网图（雷达图）

一、概念

雷达图是一种由一系列等角辐条（称为半径）组成的图表，每个辐条代表一个变量。轮辐的数据长度与数据点变量的大小相对于所有数据点上变量的最大大小成正比。绘制一条线，连接每个辐条的数据值。这使该图块具有星形外观，所以又叫星图。

二、用途

主要用途观察最相似，是否有离群点。多用于控制质量改进，便于观察性能指标。也常用于表示技能的长处和短处。

三、画图

"""
======================================
Radar chart (aka spider or star chart)
======================================
This example creates a radar chart, also known as a spider or star chart [1]_.
Although this example allows a frame of either 'circle' or 'polygon', polygon
frames don't have proper gridlines (the lines are circles instead of polygons).
It's possible to get a polygon grid by setting GRIDLINE_INTERPOLATION_STEPS in
matplotlib.axis to the desired number of vertices, but the orientation of the
polygon is not aligned with the radial axes.
.. [1] http://en.wikipedia.org/wiki/Radar_chart
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.spines import Spine
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projectiondef 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 : intNumber of variables for radar chart.frame : {'circle' | 'polygon'}Shape of frame surrounding axes."""# calculate evenly-spaced axis anglestheta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)#角度(弧度制)def draw_poly_patch(self):# rotate theta such that the first axis is at the topverts = unit_poly_verts(theta + np.pi / 2)return plt.Polygon(verts, closed=True, edgecolor='k')def draw_circle_patch(self):# unit circle centered on (0.5, 0.5)return plt.Circle((0.5, 0.5), 0.5) #画圆patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}if frame not in patch_dict:raise ValueError('unknown value for `frame`: %s' % frame)class RadarAxes(PolarAxes):name = 'radar'# use 1 line segment to connect specified pointsRESOLUTION = 1# define draw_frame methoddraw_patch = patch_dict[frame]def __init__(self, *args, **kwargs):super(RadarAxes, self).__init__(*args, **kwargs)# rotate plot such that the first axis is at the topself.set_theta_zero_location('N')def fill(self, *args, **kwargs):"""Override fill so that line is closed by default"""closed = kwargs.pop('closed', True)return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)def plot(self, *args, **kwargs):"""Override plot so that line is closed by default"""lines = super(RadarAxes, self).plot(*args, **kwargs)for line in lines:self._close_line(line)def _close_line(self, line):#使曲线封闭，首尾相连x, y = line.get_data()# FIXME: markers at x[0], y[0] get doubled-upif x[0] != x[-1]:x = np.concatenate((x, [x[0]]))y = np.concatenate((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):return self.draw_patch()def _gen_axes_spines(self):if frame == 'circle':return PolarAxes._gen_axes_spines(self)# The following is a hack to get the spines (i.e. the axes frame)# to draw correctly for a polygon frame.# spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.spine_type = 'circle'verts = unit_poly_verts(theta + np.pi / 2)# close off polygon by repeating first vertexverts.append(verts[0])path = Path(verts)spine = Spine(self, spine_type, path)spine.set_transform(self.transAxes)return {'polar': spine}register_projection(RadarAxes)return thetadef unit_poly_verts(theta):"""Return vertices of polygon for subplot axes.This polygon is circumscribed by a unit circle centered at (0.5, 0.5)"""x0, y0, r = [0.5] * 3verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]return vertsdef 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)#     Pyrolized 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 dataif __name__ == '__main__':N = 9theta = radar_factory(N, frame='polygon')data = example_data()spoke_labels = data.pop(0)fig, axes = 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 axesfor ax, (title, case_data) in zip(axes.flatten(), 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) ##本质是在极坐标下画封闭曲线ax.fill(theta, d, facecolor=color, alpha=0.25)##填充ax.set_varlabels(spoke_labels)# add legend relative to top-left plotax = axes[0, 0]labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')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()

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python画蛛网图（雷达图）

一、概念

二、用途

三、画图

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