python matplotlib各种绘图类型完整总结

import numpy as np import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x)

plt.plot(x, c) plt.plot(x, s)

show()

import numpy as np import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x)

plt.plot(x, c, 'b|-') plt.plot(x, s)

show()

import numpy as np import matplotlib.pylab as plt

# 创建一个8*6点(point)的图，并设置分辨率为80 figure(figsize=(8, 6), dpi=80)

# 创建一个新的1*1的子图，接下来的图样绘制在其中的第一块中 subplot(1, 1, 1)

# 得到坐标点(x,y)坐标 X = np.linspace(-np.pi, np.pi, 256, endpoint=True) C, S = np.cos(X), np.sin(X)

# 绘制余弦曲线，使用蓝色的、连续的、宽度为1的线条 plot(X, C, color='blue', linewidth=2.5, linestyle='-')

# 绘制正弦曲线，使用绿色的、连续的、宽度为1的线条 plot(X, S, color='green', linewidth=2.0, linestyle='-')

# 设置横轴的上下限 xlim(-4.0, 4.0)

# 设置横轴记号 xticks(np.linspace(-4, 4, 9, endpoint=True), fontproperties='Times New Roman', size=20)

# 设置纵轴记号 yticks(np.linspace(-1, 1, 5, endpoint=True))

#设置横纵坐标的名称以及对应字体格式 font = {'family' : 'Times New Roman', 'weight' : 'normal', 'size' : 20, }

# 设置横轴标签 plt.xlabel('X axis', font)

# 设置纵轴标签 plt.ylabel('Y axis', font)

# 设置图像标题 plt.title('Demo Figure', font)

# 以分辨率72来保存图片 savefig('demo.png', dpi=72)

# 在屏幕上显示 show()

import numpy as np

# 得到坐标点(x,y)坐标 X = np.linspace(-np.pi, np.pi, 256, endpoint=True) C, S = np.cos(X), np.sin(X)

x_min, x_max = X.min(), X.max() c_min, c_max = C.min(), C.max() s_min, s_max = S.min(), C.max() y_min, y_max = min(c_min, s_min), max(c_max, s_max)

# 设置横纵坐标上下界的偏移量，这样能够完整的显示图像且最美观 dx = (x_max - x_min) * 0.2 dy = (y_max - y_min) * 0.2

# 设置上下限 xlim(x_min - dx, x_max + dx) ylim(y_min - dy, y_max + dy)

# 绘制余弦曲线，使用蓝色的、连续的、宽度为1的线条 plot(X, C, color='blue', linewidth=2.5, linestyle='-')

# 绘制正弦曲线，使用绿色的、连续的、宽度为1的线条 plot(X, S, color='green', linewidth=2.0, linestyle='-')

show()

import numpy as np import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x)

xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

yticks([-1, 0, +1], [r'$-1$', r'$0$', r'$+1$'])

plt.plot(x, c) plt.plot(x, s)

show()

import numpy as np import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x)

plt.plot(x, c) plt.plot(x, s)

# 设置坐标轴gca()，获取坐标轴信息 ax = gca()

''' 使用ax.spines[]选定边框，使用set_color()将选定的边框的颜色设为 none ''' ax.spines['right'].set_color('none') ax.spines['top'].set_color('none')

''' 移动坐标轴，将bottom即x坐标轴移动到y=0的位置 ax.xaixs为x轴，set_ticks_position()用于从上下左右(top/bottom/left/right)四条脊柱中选择一个作为x轴 使用set_position()设置边框位置：y=0的位置。

位置的所有属性包括:outward、axes、data ''' ax.xaxis.set_ticks_position('bottom') ax.spines['bottom'].set_position(('data', 0))

''' 将left 即y坐标轴设置到x=0的位置 ''' ax.yaxis.set_ticks_position('left') # 选定y轴 ax.spines['left'].set_position(('data', 0))

plt.show()

import numpy as np import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x)

plt.plot(x, c, label='cosine') plt.plot(x, s, label='sine')

legend(loc='upper left')

show()

import numpy as np import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x)

plt.plot(x, c) plt.plot(x, s)

# 调整图像的脊柱 ax = gca() ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.spines['bottom'].set_position(('data', 0)) ax.yaxis.set_ticks_position('left') # 选定y轴 ax.spines['left'].set_position(('data', 0))

# 在2*np.pi/3的位置给两条函数曲线加上一个注释 t = 2 * np.pi / 3 plt.plot([t, t], [0, np.cos(t)], color='blue', linewidth=2.5,linestyle='--') scatter([t, ], [np.cos(t), ], 50, color='blue') annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$', xy=(t, np.sin(t)), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle="--") scatter([t,],[np.sin(t),], 50, color ='red') annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',color='green', xy=(t, np.cos(t)), xycoords='data', xytext=(-90, -50), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")) # arc, angle, armA, rad

show()

''' subplot()函数的参数中，除最后一维的其他维表示子图的大小，最后一维表示当前子图在图像中的位置，如下实例，在2*2的网格里，第四个子图为(2, 2, 4)

创建横跨多个位置的子图用gridspec实现

''' """ 添加多个固定大小的子图： fig = plt.figure(figsize=(10, 10), dpi=80, facecolor='red') ax1 = fig.add_subplot(2, 2, 1) ax2 = fig.add_subplot(2, 2, 4) ax1.plot() ... ax2.plot() ...

"""

subplot(2,2,1) xticks([]), yticks([]) text(0.5,0.5, 'subplot(2,2,1)',ha='center',va='center',size=20,alpha=.5)

subplot(2,2,2) xticks([]), yticks([]) text(0.5,0.5, 'subplot(2,2,2)',ha='center',va='center',size=20,alpha=.5)

subplot(2,2,3) xticks([]), yticks([]) text(0.5,0.5, 'subplot(2,2,3)',ha='center',va='center',size=20,alpha=.5)

subplot(2,2,4) xticks([]), yticks([]) text(0.5,0.5, 'subplot(2,2,4)',ha='center',va='center',size=20,alpha=.5)

# savefig('../figures/subplot-grid.png', dpi=64) show()

import matplotlib.gridspec as gridspec # gridspec的用法，可以使图像横跨多个坐标 G = gridspec.GridSpec(3, 3)

axes_1 = subplot(G[0, :]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)

axes_2 = subplot(G[1,:-1]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)

axes_3 = subplot(G[1:, -1]) # 确定了这个子图的位置之后，就可以直接在上面画图，直到创建了下个新的子图 x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x) plt.plot(x, c) plt.plot(x, s) #xticks([]), yticks([]) #text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)

axes_4 = subplot(G[-1,0]) xticks([]), yticks([])

''' text()函数用于在图像上的特定位置加上一些文本，用于注释

'''

text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)

axes_5 = subplot(G[-1,-2]) xticks([]), yticks([]) text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)

#plt.savefig('../figures/gridspec.png', dpi=64) show()

''' 使用axes()函数来确定当前子图的位置和大小，参数为一个list[x, y, width, height]， x,y为当前子图的左下角坐标位置，width为子图的宽度，height为子图的高度

''' axes([0.1,0.1,0.8,0.8]) xticks([]), yticks([]) text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)

axes([0.2,0.2,.3,.3]) x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x) plt.plot(x, c) plt.plot(x, s) # xticks([]), yticks([]) # text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)

# plt.savefig("../figures/axes.png",dpi=64) show()

axes([0.1,0.1,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.1,0.1,.5,.5])',ha='left',va='center',size=16,alpha=.5)

axes([0.2,0.2,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)

axes([0.3,0.3,.5,.5]) x = np.linspace(-np.pi, np.pi, 256, endpoint=True) c, s = np.cos(x), np.sin(x) plt.plot(x, c) plt.plot(x, s) # xticks([]), yticks([]) # text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)

axes([0.4,0.4,.5,.5]) xticks([]), yticks([]) text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)

# plt.savefig("../figures/axes-2.png",dpi=64) show()

import matplotlib.pyplot as plt

n = 256 X = np.linspace(-np.pi,np.pi,n,endpoint=True) Y = np.sin(2*X)

plt.axes([0.025,0.025,0.95,0.95])

plt.plot (X, Y+1, color='blue', alpha=1.00) plt.fill_between(X, 1, Y+1, color='blue', alpha=.25)

plt.plot (X, Y-1, color='blue', alpha=1.00) plt.fill_between(X, -1, Y-1, (Y-1) > -1, color='blue', alpha=.25) plt.fill_between(X, -1, Y-1, (Y-1) < -1, color='red', alpha=.25)

plt.xlim(-np.pi,np.pi), plt.xticks([]) plt.ylim(-2.5,2.5), plt.yticks([]) # savefig('../figures/plot_ex.png',dpi=48) plt.show()

import matplotlib.pyplot as plt

n = 1024 X = np.random.normal(0,1,n) Y = np.random.normal(0,1,n) T = np.arctan2(Y,X) # T中包含了数据点的颜色到当前colormap的映射值 # print(T.shape) plt.axes([0.025,0.025,0.95,0.95]) plt.scatter(X,Y, s=75, c=T, alpha=.5)

plt.xlim(-1.5,1.5), plt.xticks([]) plt.ylim(-1.5,1.5), plt.yticks([]) # savefig('../figures/scatter_ex.png',dpi=48) plt.show()

import numpy as np import matplotlib

# 设置matplotlib正常显示中文和负号 matplotlib.rcParams['font.sans-serif']=['SimHei'] # 用黑体显示中文 matplotlib.rcParams['axes.unicode_minus']=False # 正常显示负号 # 随机生成（10000,）服从正态分布的数据 data = np.random.randn(10000) """ 绘制直方图 data:必选参数，绘图数据 bins:直方图的长条形数目，可选项，默认为10 normed:是否将得到的直方图向量归一化，可选项，默认为0，代表不归一化，显示频数。

normed=1，表示归一化，显示频率。

facecolor:长条形的颜色 edgecolor:长条形边框的颜色 alpha:透明度 """ plt.hist(data, bins=40, normed=0, facecolor="blue", edgecolor="black", alpha=0.7) # 显示横轴标签 plt.xlabel("区间") # 显示纵轴标签 plt.ylabel("频数/频率") # 显示图标题 plt.title("频数/频率分布直方图") plt.show()

n = 12 X = np.arange(n) Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n) Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)

bar(X, +Y1, facecolor='#9999ff', edgecolor='white') bar(X, -Y2, facecolor='#ff9999', edgecolor='white') #xticks(X) for x,y in zip(X,Y1): text(x, y+0.05, '%.2f' % y, ha='center', va= 'bottom') for x, y in zip(X, -Y2): text(x, y-0.15, '%.2f'% y, ha='center', va='bottom')

ylim(-1.25,+1.25) show()

import matplotlib

""" 多个数据样本进行对比时，要注意每个数据样本对应的颜色，对每个条形的注释文本设置和横纵坐标的设置

"""

# 设置中文字体和负号正常显示 matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False

label_list = ['2014', '2015', '2016', '2017'] # 横坐标刻度显示值 num_list1 = [20, 30, 15, 35] # 纵坐标值1 num_list2 = [15, 30, 40, 20] # 纵坐标值2 x = range(len(num_list1))

# 绘制条形图 rects1 = plt.bar(x, height=num_list1, width=0.4, alpha=0.5, color='red', label='部门一') rects2 = plt.bar([i+0.4 for i in x], height=num_list2, width=0.4, color='green', label='部门二')

# 设置y轴属性 plt.ylim(0, 50) plt.ylabel('数量')

# 设置x轴属性 plt.xticks([index+0.2 for index in x], label_list) plt.xlabel("年份") plt.title('某某公司') plt.legend()

# 显示文本 for rect in rects1: height = rect.get_height() plt.text(rect.get_x() + rect.get_width() / 2, height + 1, str(height), ha='center', va='bottom') for rect in rects2: height = rect.get_height() plt.text(rect.get_x() + rect.get_width() / 2, height + 1, str(height), ha='center', va='bottom')

plt.show()

import matplotlib

matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False

price = [39.5, 39.9, 45.4, 38.9, 33.34]

# 绘制水平条形图 plt.barh(range(5), price, height=0.7, color='steelblue', alpha=0.5) plt.yticks(range(5), ['亚马逊', '当当网', '中国图书网', '京东', '天猫']) plt.xlim(30, 47) plt.xlabel('价格') plt.title('不同平台图书价格') for x, y in enumerate(price): plt.text(y+0.2, x-0.1, '%s'%y)

plt.show()

import matplotlib

# 设置中文字体和负号正常显示 matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False

label_list = ['2014', '2015', '2016', '2017'] num_list1 = [20, 30, 15, 35] num_list2 = [15, 33, 40, 20] y = range(1, len(num_list1)+1) y = [index*1.5 for index in y]

plt.barh(y, num_list1, height=0.4, color='steelblue', alpha=0.5) plt.barh([index-0.4 for index in y], num_list2, height=0.4, color='red', alpha=0.5) plt.yticks([index-0.2 for index in y], label_list) plt.ylabel('年份') plt.xlim(0, 45) plt.xlabel('数量')

for x, y1 in zip(num_list1, y): plt.text(x+0.8, y1-0.1, str(x), ha='center', va='bottom') for x, y2 in zip(num_list2, y): plt.text(x+0.8, y2-0.5, str(x), ha='center', va='bottom')

plt.show()

x = [52, 69, 58, 12, 39, 75] y = [56, 15, 84, 65, 45, 48] index = np.arange(len(x)) width = 0.3

plt.bar(index, height=x, width=width, color='blue', label=u'x', alpha=0.5) plt.bar(index, height=y, width=width, color='gold', label=u'y') #　第二个图不能设置alpha值，不然透明的两个条形会出现重叠 plt.xlabel('index') plt.ylabel('x/y') plt.title('barplot stack', fontsize=20, color='gray') plt.legend(loc='best')

plt.show()

def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 256 x = np.linspace(-3,3,n) y = np.linspace(-3,3,n) X,Y = np.meshgrid(x,y) #print(X,'----', Y) plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap='jet') C = plt.contour(X, Y, f(X,Y), 8, colors='black') show()

import numpy as np

p1={"编程能力":60,"沟通技能":70,"专业知识":65,"团体协作":75,"工具掌握":80} #创建第一个人的数据 p2={"编程能力":70,"沟通技能":60,"专业知识":75,"团体协作":65,"工具掌握":70} #创建第二个人的数据

# 分别提取两个人的信息和对应的标签 data1=np.array([i for i in p1.values()]).astype(int) #提取第一个人的信息 data2=np.array([i for i in p2.values()]).astype(int) #提取第二个人的信息 label=np.array([j for j in p1.keys()]) #提取标签

angle = np.linspace(0, 2*np.pi, len(data1), endpoint=False) #data里有几个数据，就把整圆360°分成几份 # 闭合的目的是在绘图时能够生成闭合的环 angles = np.concatenate((angle, [angle[0]])) #增加第一个angle到所有angle里，以实现闭合 data1 = np.concatenate((data1, [data1[0]])) #增加第一个人的第一个data到第一个人所有的data里，以实现闭合 data2 = np.concatenate((data2, [data2[0]])) #增加第二个人的第一个data到第二个人所有的data里，以实现闭合

fig = plt.figure() ax = fig.add_subplot(111, polar=True) # 设置坐标轴为极坐标 # 绘制两个数据样本的闭合环 ax.plot(angles, data1, 'bo-', linewidth=2, color='green', alpha=0.5) ax.fill(angles, data1, facecolor='red', alpha=0.2) ax.plot(angles, data2, 'bo-', linewidth=2, color='blue', alpha=0.5) # ax.fill(angles, data2, facecolor='steelblue', alpha=0.5) # 设置圆周每一维上显示的样本 ax.set_thetagrids(angles * 180/np.pi, label, fontproperties='SimHei', color='gray', fontsize=13) # 设置在半径方向上要显示的文本和显示文本的角度 ax.set_rgrids(np.arange(0, 81, 20),angle=45) ax.set_rlim(0, 100) ax.set_title('matplotlib 雷达图', va='bottom', fontproperties='SimHei', color='gold', fontsize=15) #help(ax.set_thetagrids)

import matplotlib.pyplot as plt

def plot_radar(data):

criterion = [1, 1, 1, 1, 1, 1] # 基准雷达图 angles = np.linspace(0, 2 * np.pi, 5, endpoint=False) angles = np.concatenate((angles, [angles[0]])) #print(criterion) #print(angles) fig = plt.figure(facecolor='#87CEEB') # 创建画板并填充颜色 ax = fig.add_subplot(111, polar=True,) # 设置坐标为极坐标 # 绘制三个五边形 floor = 0 ceil = 2 labels = np.array(['x1', 'x2', 'x3', 'x4', 'x5']) # 绘制五边形的循环 for i in np.arange(floor, ceil + 0.5 ,0.5): ax.plot(angles, [i] * (6), '-', lw= 0.5, color='black') for i in range(5): ax.plot([angles[i], angles[i]], [floor, ceil], '-',lw=0.5, color='black') # 绘制雷达图 ax.plot(angles, criterion, 'b-', lw=2, alpha=0.4) ax.fill(angles, criterion, facecolor='b', alpha=0.3) #填充 ax.plot(angles, data, 'b-', lw=2, alpha=0.35) ax.fill(angles, data, facecolor='b', alpha=0.25)

ax.set_thetagrids(angles * 180 / np.pi, labels) ax.spines['polar'].set_visible(False)#不显示极坐标最外的圆形 ax.set_theta_zero_location('N')#设置极坐标的起点（即0度）在正上方向 ax.grid(False)# 不显示分隔线 ax.set_yticks([]) # 不显示坐标间隔 ax.set_title('xxxxxxxxxxxx', va='bottom', fontproperties='SimHei') ax.set_facecolor('#87ceeb') # 填充绘图区域的颜色 # 保存文png图片 plt.subplots_adjust(left=0.09, right=1, wspace=0.25, hspace=0.25, bottom=0.13, top=0.91) plt.savefig('a_1.png') plt.show() data = [0.8, 0.9, 1.2, 1.0, 1.5, 0.8] plot_radar(data)

import numpy as np

N=20 theta=np.linspace(0,2*np.pi,N,endpoint=False)#均分角度 radii=10*np.random.rand(N)#随机角度 width=np.pi/4*np.random.rand(N)#随机宽度

ax=plt.subplot(111,projection='polar')#极坐标图绘制 bars=ax.bar(theta,radii,width=width,bottom=0.0)#哪个角度画，长度，扇形角度,从距离圆心0的地方开始画

for r,bar in zip(radii,bars): bar.set_facecolor(plt.cm.viridis(r/10.0)) bar.set_alpha(0.5) #添加颜色

plt.title('polar') plt.show()

x = [5, 10, 15, 20, 25, 30, 35, 40] y = [17, 24, 29, 36, 38, 47, 59, 80]

plt.plot(x, y, 'rs-', markersize=10)

import matplotlib.pyplot as plt

def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 10 x = np.linspace(-3,3,3.5*n) y = np.linspace(-3,3,3.0*n) X,Y = np.meshgrid(x,y) Z = f(X,Y)

plt.axes([0.025,0.025,0.95,0.95]) plt.imshow(Z, interpolation='bicubic', cmap='bone', origin='lower') plt.colorbar(shrink=0.92)

plt.xticks([]), plt.yticks([]) # savefig('../figures/imshow_ex.png', dpi=48) plt.show()

import matplotlib.colors as col import matplotlib.cm as cm import numpy as np

points = np.arange(-5, 5, 0.01) # print(points) xs, ys = np.meshgrid(points, points)

z = np.sqrt(xs**2 + ys**2) # print(z) # 自定义colormap start_color = 'red' end_color = 'blue'

cmap_1 = col.LinearSegmentedColormap.from_list('cmap1', [start_color, end_color])

plt.imshow(z, cmap=cmap_1, alpha=0.3)

plt.show()

points = np.arange(-5, 5, 1) # print(points) xs, ys = np.meshgrid(points, points)

z = np.sqrt(xs**2 + ys**2)

# 列表中包含的颜色数目并不固定，可以选多个 color = ['red', 'green', 'blue']

cmap_1 = col.LinearSegmentedColormap.from_list('cmap1', [start_color, end_color]) plt.imshow(z, cmap=cmap_1, alpha=1) plt.colorbar(shrink=0.92)

plt.show()

import pandas as pd import seaborn as sns import numpy as np

# 0、导入数据集 df = pd.read_excel('boxplot_data.xlsx', 'Sheet1')

fig = plt.figure() ax = fig.add_subplot(111) ax.boxplot(df['Age']) plt.show()

import matplotlib

matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False

label_list = ["第一部分", "第二部分", "第三部分"] # 各部分标签 size = [55, 35, 10] # 各部分大小 color = ["steelblue", "green", "blue"] # 各部分颜色 explode = [0.05, 0, 0] # 各部分突出值 """ 绘制饼图 explode：设置各部分突出 label:设置各部分标签， labeldistance:设置标签文本距圆心位置，1.1表示1.1倍半径 autopct：设置圆里面文本 shadow：设置是否有阴影 startangle：起始角度，默认从0开始逆时针转 pctdistance：设置圆内文本距圆心距离 返回值 l_text：圆内部文本，matplotlib.text.Text object p_text：圆外部文本 """ patches, l_text, p_text = plt.pie(size, explode=explode, colors=color, labels=label_list, labeldistance=1.1, autopct="%1.1f%%", shadow=True, startangle=90, pctdistance=0.6) plt.axis("equal") # 设置横轴和纵轴大小相等，这样饼才是圆的 plt.legend() plt.show()

help(plt.figure)

figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=, clear=False, **kwargs) Create a new figure.

Parameters ---------- num : integer or string, optional, default: None If not provided, a new figure will be created, and the figure number will be incremented. The figure objects holds this number in a `number` attribute. If num is provided, and a figure with this id already exists, make it active, and returns a reference to it. If this figure does not exists, create it and returns it. If num is a string, the window title will be set to this figure's `num`.

figsize : (float, float), optional, default: None width, height in inches. If not provided, defaults to :rc:`figure.figsize` = ``[6.4, 4.8]``.

dpi : integer, optional, default: None resolution of the figure. If not provided, defaults to :rc:`figure.dpi` = ``100``.

facecolor : color spec the background color. If not provided, defaults to :rc:`figure.facecolor` = ``'w'``.

edgecolor : color spec the border color. If not provided, defaults to :rc:`figure.edgecolor` = ``'w'``.

frameon : bool, optional, default: True If False, suppress drawing the figure frame.

FigureClass : subclass of `~matplotlib.figure.Figure` Optionally use a custom `.Figure` instance.

clear : bool, optional, default: False If True and the figure already exists, then it is cleared.

Returns ------- figure : `~matplotlib.figure.Figure` The `.Figure` instance returned will also be passed to new_figure_manager in the backends, which allows to hook custom `.Figure` classes into the pyplot interface. Additional kwargs will be passed to the `.Figure` init function.

Notes ----- If you are creating many figures, make sure you explicitly call :func:`.pyplot.close` on the figures you are not using, because this will enable pyplot to properly clean up the memory.

`~matplotlib.rcParams` defines the default values, which can be modified in the matplotlibrc file.