Python基于matplotlib的数据可视化
目录：
- 基于matplotlib的数据可视化
- matplotlib.pyplot基于Python语言的绘图函数
- 基本图形绘制
- matplotlib.pyplot基于Python语言的绘图函数
基于matplotlib的数据可视化
matplotlib.pyplot基于Python语言的绘图函数
基本图形绘制
缺省样式
# -*- coding:utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp #生成曲线上各点的水平坐标 x=np.linspace(-np.pi,np.pi,1000) cos_y=np.cos(x)/2 sin_y=np.sin(x) h_y=x/2 #用直线链接曲线上的各点 mp.plot(x,cos_y) mp.plot(x,sin_y) mp.plot(x,h_y) #显示图形 mp.show()
设置线型、线宽和颜色
代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp # 生成曲线上各点的水平坐标 x = np.linspace(-np.pi, np.pi, 1000) # 根据曲线函数计算其上各点的垂直坐标 cos_y = np.cos(x) / 2 sin_y = np.sin(x) # 用直线连接曲线上各点 mp.plot(x, cos_y, linestyle='-', linewidth=1, color='dodgerblue') mp.plot(x, sin_y, linestyle='-', linewidth=1, color='orangered') # 显示图形 mp.show()
设置坐标范围
- 设置水平坐标范围：mp.xlim(最小值, 最大值)
- 设置垂直坐标范围：mp.ylim(最小值, 最大值)
代码：
# -*- coding:utf-8 -*- from __future__ import unicode_literals import matplotlib.pyplot as mp # plotting 测绘 library import numpy as np #生成曲线上各点的水平坐标 x=np.linspace(-np.pi,np.pi,2000) #根据曲线函数计算其上各点的垂直坐标 cos_y=np.cos(x)/2 sin_y=np.sin(x) #设置坐标范围 mp.xlim(x.min()*1.1,x.max()*1.1) mp.ylim(min(cos_y.min(),sin_y.min())*1.1, max(cos_y.max(),sin_y.max())*1.1) #用直线连接曲线上各点 mp.plot(x,cos_y,linestyle='-',linewidth=1, color='dodgerblue') mp.plot(x,sin_y,linestyle='-',linewidth=1, color='orangered') #显示图形 mp.show()
设置坐标轴刻度标签
- mp.xticks(刻度标签位置, 刻度标签文本)
- mp.yticks(刻度标签位置, 刻度标签文本)
代码：
# -*- coding:utf-8 -*- from __future__ import unicode_literals import matplotlib.pyplot as mp # plotting 测绘 library import numpy as np #生成曲线上各点的水平坐标 x=np.linspace(-np.pi,np.pi,2000) #根据曲线函数计算其上各点的垂直坐标 cos_y=np.cos(x)/2 sin_y=np.sin(x) #设置坐标范围 mp.xlim(x.min()*1.1,x.max()*1.1) mp.ylim(min(cos_y.min(),sin_y.min())*1.1, max(cos_y.max(),sin_y.max())*1.1) mp.xticks([-np.pi,-np.pi/2,np.pi/2,np.pi*3/4,np.pi], [r'$-\pi$',r'$-\frac{\pi}{2}$',r'$0$', r'$\frac{\pi}{2}$',r'$\frac{3\pi}{4}$',r'$\pi$']) mp.yticks([-1,-0.5,0.5,1]) #用直线连接曲线上各点 mp.plot(x,cos_y,linestyle='-',linewidth=1, color='dodgerblue') mp.plot(x,sin_y,linestyle='-',linewidth=1, color='orangered') #显示图形 mp.show()
将矩形坐标轴改成十字坐标轴
代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp # 生成曲线上各点的水平坐标 x = np.linspace(-np.pi, np.pi, 1000) # 根据曲线函数计算其上各点的垂直坐标 cos_y = np.cos(x) / 2 sin_y = np.sin(x) # 设置坐标范围 mp.xlim(x.min() * 1.1, x.max() * 1.1) mp.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1) # 设置坐标轴刻度标签 mp.xticks([ -np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [ r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$']) mp.yticks([-1, -0.5, 0.5, 1]) # 将矩形坐标轴改成十字坐标轴 # 获取当前坐标轴对象 ax = mp.gca() # 将垂直坐标刻度置于左边框 ax.yaxis.set_ticks_position('left') # 将左边框置于数据坐标原点 ax.spines['left'].set_position(('data', 0)) # 将水平坐标刻度置于底边框 ax.xaxis.set_ticks_position('bottom') # 将底边框置于数据坐标原点 ax.spines['bottom'].set_position(('data', 0)) # 将右边框和顶边框设置成无色 ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') # 用直线连接曲线上各点 mp.plot(x, cos_y, linestyle='-', linewidth=1, color='dodgerblue') mp.plot(x, sin_y, linestyle='-', linewidth=1, color='orangered') # 显示图形 mp.show()
显示图例
- mp.plot(…, label=图例文本)
- mp.legend(loc=图例位置)
代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp # 生成曲线上各点的水平坐标 x = np.linspace(-np.pi, np.pi, 1000) # 根据曲线函数计算其上各点的垂直坐标 cos_y = np.cos(x) / 2 sin_y = np.sin(x) # 设置坐标范围 mp.xlim(x.min() * 1.1, x.max() * 1.1) mp.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1) # 设置坐标轴刻度标签 mp.xticks([ -np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [ r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$']) mp.yticks([-1, -0.5, 0.5, 1]) # 将矩形坐标轴改成十字坐标轴 # 获取当前坐标轴对象 ax = mp.gca() # 将垂直坐标刻度置于左边框 ax.yaxis.set_ticks_position('left') # 将左边框置于数据坐标原点 ax.spines['left'].set_position(('data', 0)) # 将水平坐标刻度置于底边框 ax.xaxis.set_ticks_position('bottom') # 将底边框置于数据坐标原点 ax.spines['bottom'].set_position(('data', 0)) # 将右边框和顶边框设置成无色 ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') # 用直线连接曲线上各点 mp.plot(x, cos_y, linestyle='-', linewidth=1, color='dodgerblue', label=r'$y=\frac{1}{2}cos(x)$') mp.plot(x, sin_y, linestyle='-', linewidth=1, color='orangered', label=r'$y=sin(x)$') mp.legend(loc='upper left') # 显示图形 mp.show()
添加特殊点
- mp.scatter(点集水平坐标数组，点集垂直坐标数组，…)
代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp # 生成曲线上各点的水平坐标 x = np.linspace(-np.pi, np.pi, 1000) # 根据曲线函数计算其上各点的垂直坐标 cos_y = np.cos(x) / 2 sin_y = np.sin(x) # 计算特殊点的坐标 xo = np.pi * 3 / 4 yo_cos = np.cos(xo) / 2 yo_sin = np.sin(xo) # 设置坐标范围 mp.xlim(x.min() * 1.1, x.max() * 1.1) mp.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1) # 设置坐标轴刻度标签 mp.xticks([ -np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [ r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$']) mp.yticks([-1, -0.5, 0.5, 1]) # 将矩形坐标轴改成十字坐标轴 # 获取当前坐标轴对象 ax = mp.gca() # 将垂直坐标刻度置于左边框 ax.yaxis.set_ticks_position('left') # 将左边框置于数据坐标原点 ax.spines['left'].set_position(('data', 0)) # 将水平坐标刻度置于底边框 ax.xaxis.set_ticks_position('bottom') # 将底边框置于数据坐标原点 ax.spines['bottom'].set_position(('data', 0)) # 将右边框和顶边框设置成无色 ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') # 用直线连接曲线上各点 mp.plot(x, cos_y, linestyle='-', linewidth=1, color='dodgerblue', label=r'$y=\frac{1}{2}cos(x)$') mp.plot(x, sin_y, linestyle='-', linewidth=1, color='orangered', label=r'$y=sin(x)$') # 绘制特殊点 mp.plot([xo, xo], [yo_cos, yo_sin], linestyle='--', linewidth=1, color='limegreen') mp.scatter([xo, xo], [yo_cos, yo_sin], s=60, edgecolor='limegreen', facecolor='white', zorder=3) mp.legend(loc='upper left') # 显示图形 mp.show()
添加注释
- mp.annotate(
注释文本,
xy=目标位置，
xytext=文本位置，
textcoords=坐标属性，
fontsize=字体大小，
arrowprops=箭头属性) 代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp # 生成曲线上各点的水平坐标 x = np.linspace(-np.pi, np.pi, 1000) # 根据曲线函数计算其上各点的垂直坐标 cos_y = np.cos(x) / 2 sin_y = np.sin(x) # 计算特殊点的坐标 xo = np.pi * 3 / 4 yo_cos = np.cos(xo) / 2 yo_sin = np.sin(xo) # 设置坐标范围 mp.xlim(x.min() * 1.1, x.max() * 1.1) mp.ylim(min(cos_y.min(), sin_y.min()) * 1.1, max(cos_y.max(), sin_y.max()) * 1.1) # 设置坐标轴刻度标签 mp.xticks([ -np.pi, -np.pi / 2, 0, np.pi / 2, np.pi * 3 / 4, np.pi], [ r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\frac{3\pi}{4}$', r'$\pi$']) mp.yticks([-1, -0.5, 0.5, 1]) # 将矩形坐标轴改成十字坐标轴 # 获取当前坐标轴对象 ax = mp.gca() # 将垂直坐标刻度置于左边框 ax.yaxis.set_ticks_position('left') # 将左边框置于数据坐标原点 ax.spines['left'].set_position(('data', 0)) # 将水平坐标刻度置于底边框 ax.xaxis.set_ticks_position('bottom') # 将底边框置于数据坐标原点 ax.spines['bottom'].set_position(('data', 0)) # 将右边框和顶边框设置成无色 ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') # 用直线连接曲线上各点 mp.plot(x, cos_y, linestyle='-', linewidth=1, color='dodgerblue', label=r'$y=\frac{1}{2}cos(x)$') mp.plot(x, sin_y, linestyle='-', linewidth=1, color='orangered', label=r'$y=sin(x)$') # 绘制特殊点 mp.plot([xo, xo], [yo_cos, yo_sin], linestyle='--', linewidth=1, color='limegreen') mp.scatter([xo, xo], [yo_cos, yo_sin], s=60, edgecolor='limegreen', facecolor='white', zorder=3) # 添加注释 mp.annotate( r'$\frac{1}{2}cos(\frac{3\pi}{4})=-\frac{\sqrt{2}}{4}$', xy=(xo, yo_cos), xycoords='data', xytext=(-90, -40), textcoords='offset points', fontsize=14, arrowprops=dict( arrowstyle='->', connectionstyle='arc3, rad=0.2')) mp.annotate( r'$sin(\frac{3\pi}{4})=\frac{\sqrt{2}}{2}$', xy=(xo, yo_sin), xycoords='data', xytext=(20, 20), textcoords='offset points', fontsize=14, arrowprops=dict( arrowstyle='->', connectionstyle='arc3, rad=0.2')) # 显示图例 mp.legend(loc='upper left') # 显示图形 mp.show()
- mp.annotate(
图形对象
- 说明：一个图像对象实际上就可以被看做是一个显示图形的窗口，出了缺省创建的图形窗口以外，也可以通过函数手动创建图形窗口并设置特殊的属性。
- 属性：
- mp.figure(对象名(标题文本), figsize=窗口大小,
dpi=分辨率, facecolor=窗口颜色) - mp.title(标题文本, fontsize=字体大小)
- mp.xlabel(水平轴标签文本, fontsize=字体大小)
- mp.ylabel(垂直轴标签文本, fontsize=字体大小)
- mp.tick_params(labelsize=刻度标签字体大小)
- mp.grid(linestyle=网格线风格)
- 注意如果调用figure()函数时所指定的对象名并不存在，则新建一个图形窗口，同时将其设置为当前窗口，如果该对象名已经存在，则不再建新窗口，而只是将其所对应的图形窗口设置为当前窗口。调用该函数以后的所有绘图都在当前窗口中完成。
- mp.figure(对象名(标题文本), figsize=窗口大小,
代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp x = np.linspace(-np.pi, np.pi, 1000) cos_y = np.cos(x) / 2 sin_y = np.sin(x) mp.figure('Figure Object 1', figsize=(8, 6), dpi=60, facecolor='lightgray')#打开窗口，设置分辨率 mp.title('Figure Object 1', fontsize=20)#设置标题 mp.xlabel('x', fontsize=14)#水平标签文本，fontsize=字体大小 mp.ylabel('y', fontsize=14)#垂直标签文件，fontsize字体大小 mp.tick_params(labelsize=10)#labelsize=刻度标签字体大小 mp.grid(linestyle=':')#linestyle＝网格线风格 mp.figure('Figure Object 2', figsize=(8, 6), dpi=60, facecolor='lightgray') mp.title('Figure Object 2', fontsize=20) mp.xlabel('x', fontsize=14) mp.ylabel('y', fontsize=14) mp.tick_params(labelsize=10) mp.grid(linestyle=':') mp.figure('Figure Object 1') mp.plot(x, cos_y, color='dodgerblue', label=r'$y=\frac{1}{2}cos(x)$') mp.figure('Figure Object 2') mp.plot(x, sin_y, color='orangered', label=r'$y=sin(x)$') mp.legend() mp.figure('Figure Object 1') mp.legend() mp.show()
子坐标图
- mp.subplot(总行数, 总列数, 图序号)
代码：sub1.py、sub2.py、sub3.py
# -*- coding: utf-8 -*- from __future__ import unicode_literals import matplotlib.pyplot as mp mp.figure(facecolor='lightgray') mp.subplot(221) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '1', ha='center', va='center', size=36, alpha=0.5) mp.subplot(222) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '2', ha='center', va='center', size=36, alpha=0.5) mp.subplot(223) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '3', ha='center', va='center', size=36, alpha=0.5) mp.subplot(224) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '4', ha='center', va='center', size=36, alpha=0.5) mp.tight_layout() mp.show()
# -*- coding: utf-8 -*- from __future__ import unicode_literals import matplotlib.pyplot as mp import matplotlib.gridspec as mg mp.figure(facecolor='lightgray') gs = mg.GridSpec(3, 3) mp.subplot(gs[0, :2]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '1', ha='center', va='center', size=36, alpha=0.5) mp.subplot(gs[1:, 0]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '2', ha='center', va='center', size=36, alpha=0.5) mp.subplot(gs[2, 1:]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '3', ha='center', va='center', size=36, alpha=0.5) mp.subplot(gs[:2, 2]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '4', ha='center', va='center', size=36, alpha=0.5) mp.subplot(gs[1, 1]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '5', ha='center', va='center', size=36, alpha=0.5) mp.tight_layout() mp.show()
# -*- coding: utf-8 -*- from __future__ import unicode_literals import matplotlib.pyplot as mp mp.figure(facecolor='lightgray') mp.axes([0.03, 0.038, 0.94, 0.924]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '1', ha='center', va='center', size=36, alpha=0.5) mp.axes([0.63, 0.076, 0.31, 0.308]) mp.xticks(()) mp.yticks(()) mp.text(0.5, 0.5, '2', ha='center', va='center', size=36, alpha=0.5) mp.show()
设置坐标轴刻度定位器
- 怎么设置：
ax = mp.gca()
ax.xaxis.set_major_locator(刻度定位器对象)
ax.xaxis.set_minor_locator(刻度定位器对象)
ax.yaxis.set_major_locator(刻度定位器对象)
ax.yaxis.set_minor_locator(刻度定位器对象) 代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp mp.figure() locators = [ 'mp.NullLocator()', 'mp.MaxNLocator(nbins=3, steps=[1, 3, 5, 7, 9])', 'mp.FixedLocator(locs=[0, 2.5, 7.5, 10])', 'mp.AutoLocator()', 'mp.IndexLocator(offset=0.5, base=1.5)', 'mp.MultipleLocator()', 'mp.LinearLocator(numticks=21)', 'mp.LogLocator(base=2, subs=[1.0])'] n_locators = len(locators) for i, locator in enumerate(locators): mp.subplot(n_locators, 1, i + 1) mp.xlim(0, 10) mp.ylim(-1, 1) mp.yticks(()) ax = mp.gca() ax.spines['left'].set_color('none') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position(('data', 0)) ax.xaxis.set_major_locator(eval(locator)) ax.xaxis.set_minor_locator(mp.MultipleLocator(0.1)) mp.plot(np.arange(11), np.zeros(11), color='none') mp.text(5, 0.3, locator[3:], ha='center', size=12) mp.tight_layout() mp.show()
- 怎么设置：
散点图
- mp.scatter(水平坐标数组, 垂直坐标数组,
s=大小, c=颜色, cmap=颜色映射, alpha=透明度) 代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp n = 1000 x = np.random.normal(0, 1, n) y = np.random.normal(0, 1, n) d = np.sqrt(x ** 2 + y ** 2) mp.figure('Scatter', facecolor='lightgray') mp.title('Scatter', fontsize=20) mp.xlabel('x', fontsize=14) mp.ylabel('y', fontsize=14) mp.tick_params(labelsize=10) mp.grid(linestyle=':') mp.scatter(x, y, s=6, c=d, cmap='jet_r', alpha=0.5) mp.show()
- mp.scatter(水平坐标数组, 垂直坐标数组,
填充
- mp.fill_between(扫描线水平坐标,
扫描线起点垂直坐标, 扫描线终点垂直坐标,
color=颜色, alpha=透明度) 代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp n = 1000 x = np.linspace(0, 8 * np.pi, n) sin_y = np.sin(x) cos_y = np.cos(x / 2) / 2 mp.figure('Fill', facecolor='lightgray') mp.title('Fill', fontsize=20) mp.xlabel('x', fontsize=14) mp.ylabel('y', fontsize=14) mp.tick_params(labelsize=10) mp.grid(linestyle=':') mp.plot(x, sin_y, c='dodgerblue', label=r'$y=sin(x)$') mp.plot(x, cos_y, c='orangered', label=r'$y=\frac{1}{2}cos(\frac{x}{2})$') mp.fill_between(x, cos_y, sin_y, cos_y < sin_y, color='dodgerblue', alpha=0.5) mp.fill_between(x, cos_y, sin_y, cos_y > sin_y, color='orangered', alpha=0.5) mp.legend() mp.show()
- mp.fill_between(扫描线水平坐标,
条形图
- mp.bar(矩形条的水平坐标, 矩形条的高度
ec=边框色, fc=填充色, label=图例标签) 代码：bar.py
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp n = 12 x = np.arange(n) y1 = np.random.uniform(0.5, 1.0, n) * (1 - x / n) y2 = np.random.uniform(0.5, 1.0, n) * (1 - x / n) mp.figure('Bar', facecolor='lightgray') mp.title('Bar', fontsize=20) mp.xlabel('x', fontsize=14) mp.ylabel('y', fontsize=14) mp.xticks(x, x + 1) mp.ylim(-1.25, 1.25) mp.tick_params(labelsize=10) mp.grid(axis='y', linestyle=':') mp.bar(x, y1, ec='white', fc='dodgerblue', label='Sample 1') for _x, _y in zip(x, y1): mp.text(_x, _y, '%.2f' % _y, ha='center', va='bottom', size=8) mp.bar(x, -y2, ec='white', fc='dodgerblue', alpha=0.5, label='Sample 2') for _x, _y in zip(x, y2): mp.text(_x, -_y - 0.015, '%.2f' % _y, ha='center', va='top', size=8) mp.legend() mp.show()
- mp.bar(矩形条的水平坐标, 矩形条的高度
等高线图
- mp.contour(x, y, z, 密度, colors=颜色,
linewidths=线宽) - mp.contourf(x, y, z, 密度, cmap=颜色映射)
代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp n = 1000 x, y = np.meshgrid(np.linspace(-3, 3, n), np.linspace(-3, 3, n)) z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2) mp.figure('Contour', facecolor='lightgray') mp.title('Contour', fontsize=20) mp.xlabel('x', fontsize=14) mp.ylabel('y', fontsize=14) mp.tick_params(labelsize=10) mp.grid(linestyle=':') mp.contourf(x, y, z, 8, cmap='jet') cntr = mp.contour(x, y, z, 8, colors='black', linewidths=0.5) mp.clabel(cntr, inline_spacing=1, fmt='%.1f', fontsize=8) mp.show()
- mp.contour(x, y, z, 密度, colors=颜色,
- 热力图
- mp.imshow(深度坐标, cmap=颜色映射,
origin=垂直轴向) - 代码：hot.py
- mp.imshow(深度坐标, cmap=颜色映射,
三维曲面/线框图
- 怎么做
from mpl_toolkits.mplot3d import axes3d
ax=mp.gca(projection=’3d’)
ax.plot_surface(x, y, z, rstride=垂直步长,
cstride=水平步长, cmap=颜色映射)
ax.plot_wireframe(x, y, z, rstride=垂直步长,
cstride=水平步长, color=颜色,
linewidth=线宽) 代码：
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp from mpl_toolkits.mplot3d import axes3d n = 1000 x, y = np.meshgrid(np.linspace(-3, 3, n), np.linspace(-3, 3, n)) z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2) mp.figure('3D Surface') ax = mp.gca(projection='3d') mp.title('3D Surface', fontsize=20) ax.set_xlabel('x', fontsize=14) ax.set_ylabel('y', fontsize=14) ax.set_zlabel('z', fontsize=14) mp.tick_params(labelsize=10) ax.plot_surface(x, y, z, rstride=10, cstride=10, cmap='jet') mp.figure('3D Wireframe') ax = mp.gca(projection='3d') mp.title('3D Wireframe', fontsize=20) ax.set_xlabel('x', fontsize=14) ax.set_ylabel('y', fontsize=14) ax.set_zlabel('z', fontsize=14) mp.tick_params(labelsize=10) ax.plot_wireframe(x, y, z, rstride=20, cstride=20, linewidth=0.5, color='orangered') mp.show()
- 怎么做
饼图
- mp.pie(值，空，标，色，格)
代码：pie.py
# -*- coding: utf-8 -*- from __future__ import unicode_literals import matplotlib.pyplot as mp values = [26, 17, 21, 29, 11] spaces = [0.05, 0.01, 0.01, 0.01, 0.01] labels = ['Python', 'JavaScript', 'C++', 'C', 'PHP'] colors = ['dodgerblue', 'orangered', 'limegreen', 'violet', 'gold'] mp.figure('Pie', facecolor='lightgray') mp.title('Pie', fontsize=20) mp.pie(values, spaces, labels, colors, '%d%%', shadow=True, startangle=90) mp.axis('equal') mp.show()
坐标格线
- ax = mp.gca()
- ax.grid(which=主次刻度, axis=横纵轴,
linewidth=线宽, linestyle=线型, color=颜色) 代码：grid.py
# -*- coding: utf-8 -*- from __future__ import unicode_literals import numpy as np import matplotlib.pyplot as mp x = np.linspace(-5, 5, 1000) y = 8 * np.sinc(x) mp.figure('Grid', facecolor='lightgray') mp.title('Grid', fontsize=20) mp.xlabel('x', fontsize=14) mp.ylabel('y', fontsize=14) ax = mp.gca() ax.xaxis.set_major_locator(mp.MultipleLocator()) ax.xaxis.set_minor_locator(mp.MultipleLocator(.1)) ax.yaxis.set_major_locator(mp.MultipleLocator()) ax.yaxis.set_minor_locator(mp.MultipleLocator(.1)) mp.tick_params(labelsize=10) ax.grid(which='major', axis='both', linewidth=0.75, linestyle='-', color='lightgray') ax.grid(which='minor', axis='both', linewidth=0.25, linestyle='-', color='lightgray') mp.plot(x, y, c='dodgerblue', label=r'$y=8sinc(x)$') mp.legend() mp.show()
- 极坐标
- ax = mp.gca(projection=’polar’)
- mp.plot(极角, 极径, …)