数据可视化基础专题（十二）：Matplotlib 基础（四）常用图表（二）气泡图、堆叠图、雷达图、饼图、

import matplotlib.pyplot as plt
import numpy as np# 处理中文乱码
plt.rcParams['font.sans-serif']=['SimHei']x_data = np.array([2011,2012,2013,2014,2015,2016,2017])
y_data = np.array([58000,60200,63000,71000,84000,90500,107000])# 根据 y 值的不同生成不同的颜色
colors = y_data * 10
# 根据 y 值的不同生成不同的大小
area = y_data / 300plt.scatter(x_data, y_data, s = area, c = colors, marker='o', edgecolor='black', alpha=0.5, label = '产品销量')plt.legend()plt.savefig("scatter_demo1.png")

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data('goog.npz') as datafile:
price_data = np.load(datafile)['price_data'].view(np.recarray)
price_data = price_data[-250:] # get the most recent 250 trading daysdelta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)ax.set_xlabel(r'$\Delta_i$', fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
ax.set_title('Volume and percent change')ax.grid(True)
fig.tight_layout()plt.show()

plt.stackplot(x, y, labels, colors)

import matplotlib.pyplot as plt# 处理中文乱码
plt.rcParams['font.sans-serif']=['SimHei']x_data = [2011,2012,2013,2014,2015,2016,2017]
y_data = [58000,60200,63000,71000,84000,90500,107000]
y_data_1 = [78000,80200,93000,101000,64000,70500,87000]plt.title(label='xxx 公司 xxx 产品销量')plt.stackplot(x_data, y_data, y_data_1, labels=['产品销量', '用户增长数'])plt.legend()plt.savefig("stackplot_demo.png")

import numpy as np
import matplotlib.pyplot as pltx = [1, 2, 3, 4, 5]
y1 = [1, 1, 2, 3, 5]
y2 = [0, 4, 2, 6, 8]
y3 = [1, 3, 5, 7, 9]y = np.vstack([y1, y2, y3])labels = ["Fibonacci ", "Evens", "Odds"]fig, ax = plt.subplots()
ax.stackplot(x, y1, y2, y3, labels=labels)
ax.legend(loc='upper left')
plt.show()

plt.polar(theta, r, **kwargs)

import numpy as np
import matplotlib.pyplot as plt# 中文和负号的正常显示
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False# 使用ggplot的绘图风格
plt.style.use('ggplot')# 构造数据
values = [3.2, 2.1, 3.5, 2.8, 3]
feature = ['攻击力', '防御力', '恢复力', '法术强度', '生命值']N = len(values)
# 设置雷达图的角度，用于平分切开一个圆面
angles = np.linspace(0, 2 * np.pi, N, endpoint=False)# 为了使雷达图一圈封闭起来，需要下面的步骤
values = np.concatenate((values, [values[0]]))
angles = np.concatenate((angles, [angles[0]]))# 绘图
fig = plt.figure()
# 这里一定要设置为极坐标格式
ax = fig.add_subplot(111, polar=True)
# 绘制折线图
ax.plot(angles, values, 'o-', linewidth=2)
# 填充颜色
ax.fill(angles, values, alpha=0.25)
# 添加每个特征的标签
ax.set_thetagrids(angles * 180 / np.pi, feature)
# 设置雷达图的范围
ax.set_ylim(0, 5)
# 添加标题
plt.title('游戏人物属性')
# 添加网格线
ax.grid(True)
# 显示图形
plt.savefig('polar_demo.png')

import numpy as npimport matplotlib.pyplot as plt
from matplotlib.patches import Circle, RegularPolygon
from matplotlib.path import Path
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection
from matplotlib.spines import Spine
from matplotlib.transforms import Affine2Ddef 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 RadarAxes(PolarAxes): name = 'radar'
# use 1 line segment to connect specified points
RESOLUTION = 1 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.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):
# 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 thetadef 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 = 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)
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()

matplotlib.pyplot.pie(
x, explode=None, labels=None, colors=None,
autopct=None, pctdistance=0.6, shadow=False,
labeldistance=1.1, startangle=None, radius=None,
counterclock=True, wedgeprops=None, textprops=None,
center=(0, 0), frame=False, hold=None, data=None)

import matplotlib.pyplot as plt# 中文和负号的正常显示
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False# 数据
edu = [0.2515,0.3724,0.3336,0.0368,0.0057]
labels = ['中专','大专','本科','硕士','其他']# 让本科学历离圆心远一点
explode = [0,0,0.1,0,0]# 将横、纵坐标轴标准化处理，保证饼图是一个正圆，否则为椭圆
plt.axes(aspect='equal')# 自定义颜色
colors=['#9999ff','#ff9999','#7777aa','#2442aa','#dd5555'] # 自定义颜色# 绘制饼图
plt.pie(x=edu, # 绘图数据
explode = explode, # 突出显示大专人群
labels = labels, # 添加教育水平标签
colors = colors, # 设置饼图的自定义填充色
autopct = '%.1f%%', # 设置百分比的格式，这里保留一位小数
)# 添加图标题
plt.title('xxx 公司员工教育水平分布')# 保存图形
plt.savefig('pie_demo.png')

import numpy as np
import matplotlib.pyplot as pltfig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))recipe = ["375 g flour",
"75 g sugar",
"250 g butter",
"300 g berries"]data = [float(x.split()[0]) for x in recipe]
ingredients = [x.split()[-1] for x in recipe]def func(pct, allvals):
absolute = int(pct/100.*np.sum(allvals))
return "{:.1f}%\n({:d} g)".format(pct, absolute)wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),
textprops=dict(color="w"))ax.legend(wedges, ingredients,
title="Ingredients",
loc="center left",
bbox_to_anchor=(1, 0, 0.5, 1))plt.setp(autotexts, size=8, weight="bold")ax.set_title("Matplotlib bakery: A pie")plt.show()