Matplotlib 2 -绘图、统计、网格、3D

伊织code2025-09-11 15:11

文章目录

  • 绘图类型
  • 成对数据
  • [plot(x, y)](#plot(x, y))
  • [scatter(x, y)](#scatter(x, y))
  • [bar(x, height)](#bar(x, height))
  • [stem(x, y)](#stem(x, y))
  • [fill_between(x, y1, y2)](#fill_between(x, y1, y2))
  • [stackplot(x, y)](#stackplot(x, y))
  • stairs(values)
  • 统计分布
  • hist(x)
  • boxplot(X)
  • [errorbar(x, y, yerr, xerr)](#errorbar(x, y, yerr, xerr))
  • 小提琴图绘制指南
  • eventplot(D)
  • [hist2d(x, y)](#hist2d(x, y))
  • [hexbin(x, y, C)](#hexbin(x, y, C))
  • pie(x)
  • ecdf(x)
  • 网格数据
  • imshow(Z)
  • [pcolormesh(X, Y, Z)](#pcolormesh(X, Y, Z))
  • [contour(X, Y, Z)](#contour(X, Y, Z))
  • [contourf(X, Y, Z)](#contourf(X, Y, Z))
  • [barbs(X, Y, U, V)](#barbs(X, Y, U, V))
  • [quiver(X, Y, U, V)](#quiver(X, Y, U, V))
  • [streamplot(X, Y, U, V)](#streamplot(X, Y, U, V))
  • 非规则网格数据
  • [tricontour(x, y, z)](#tricontour(x, y, z))
  • [tricontourf(x, y, z)](#tricontourf(x, y, z))
  • [tripcolor(x, y, z)](#tripcolor(x, y, z))
  • [triplot(x, y)](#triplot(x, y))
  • 三维与体积数据可视化
  • [bar3d(x, y, z, dx, dy, dz)](#bar3d(x, y, z, dx, dy, dz))
  • [fill_between(x1, y1, z1, x2, y2, z2)](#fill_between(x1, y1, z1, x2, y2, z2))
  • [plot(xs, ys, zs)](#plot(xs, ys, zs))
  • [quiver(X, Y, Z, U, V, W)](#quiver(X, Y, Z, U, V, W))
  • [scatter(xs, ys, zs)](#scatter(xs, ys, zs))
  • [stem(x, y, z)](#stem(x, y, z))
  • [plot_surface(X, Y, Z)](#plot_surface(X, Y, Z))
  • [plot_trisurf(x, y, z)](#plot_trisurf(x, y, z))
  • [voxels(\x, y, z, filled)](#voxels([x, y, z], filled))
  • [plot_wireframe(X, Y, Z)](#plot_wireframe(X, Y, Z))

绘图类型

https://matplotlib.org/stable/plot_types/index.html

Matplotlib 提供的多种常见绘图命令概览。

更多示例请查看图库，完整教程请访问教程页面。

成对数据

展示成对数据 \((x, y))、表格数据 \((var_0, \cdots, var_n)) 以及函数数据 \(f(x)=y) 的图表。

plot(x, y)

plot(x, y)

scatter(x, y)

scatter(x, y)

bar(x, height)

bar(x, height)

stem(x, y)

stem(x, y)

fill_between(x, y1, y2)

fill_between(x, y1, y2)

stackplot(x, y)

stackplot(x, y)

stairs(values)

stairs(values)

统计分布

展示数据集中至少一个变量的分布情况。部分方法还会计算分布特征。

hist(x)

hist(x)

boxplot(X)

boxplot(X)

errorbar(x, y, yerr, xerr)

errorbar(x, y, yerr, xerr)

violinplot(D)

violinplot(D)

eventplot(D)

eventplot(D)

hist2d(x, y)

hist2d(x, y)

hexbin(x, y, C)

hexbin(x, y, C)

pie(x)

pie(x)

ecdf(x)

ecdf(x)

网格化数据

展示数组和图像 \(Z_{i, j}) 以及场数据 \(U_{i, j}, V_{i, j}) 在规则网格上的可视化效果，以及对应的坐标网格 \(X_{i,j}, Y_{i,j})。

imshow(Z)

imshow(Z)

pcolormesh(X, Y, Z)

pcolormesh(X, Y, Z)

contour(X, Y, Z)

contour(X, Y, Z)

contourf(X, Y, Z)

contourf(X, Y, Z)

barbs(X, Y, U, V)

barbs(X, Y, U, V)

quiver(X, Y, U, V)

quiver(X, Y, U, V)

streamplot(X, Y, U, V)

streamplot(X, Y, U, V)

非规则网格数据

绘制在非结构化网格上的数据\(Z_{x, y})、非结构化坐标网格\((x, y))以及二维函数\(f(x, y) = z)的图表。

tricontour(x, y, z)

tricontour(x, y, z)

tricontourf(x, y, z)

tricontourf(x, y, z)

tripcolor(x, y, z)

tripcolor(x, y, z)

triplot(x, y)

triplot(x, y)

三维与体数据可视化

使用 mpl_toolkits.mplot3d 库绘制三维坐标点 \((x,y,z))、曲面函数 \(f(x,y)=z) 以及体数据 \(V_{x, y, z}) 的可视化图表。

bar3d(x, y, z, dx, dy, dz)

bar3d(x, y, z, dx, dy, dz)

fill_between(x1, y1, z1, x2, y2, z2)

fill_between(x1, y1, z1, x2, y2, z2)

plot(xs, ys, zs)

plot(xs, ys, zs)

quiver(X, Y, Z, U, V, W)

quiver(X, Y, Z, U, V, W)

scatter(xs, ys, zs)

scatter(xs, ys, zs)

stem(x, y, z)

stem(x, y, z)

plot_surface(X, Y, Z)

plot_surface(X, Y, Z)

plot_trisurf(x, y, z)

plot_trisurf(x, y, z)

voxels(\[x, y, z, filled)](https://matplotlib.org/stable/plot_types/3D/voxels_simple.html#sphx-glr-plot-types-3d-voxels-simple-py)

voxels(x, y, z, filled)

plot_wireframe(X, Y, Z)

plot_wireframe(X, Y, Z)

图库由Sphinx-Gallery生成

成对数据

https://matplotlib.org/stable/plot_types/basic/index.html

展示成对数据 \((x, y))、表格数据 \((var_0, \cdots, var_n)) 以及函数数据 \(f(x)=y) 的图表。

plot(x, y)

plot(x, y)

scatter(x, y)

scatter(x, y)

bar(x, height)

bar(x, height)

stem(x, y)

stem(x, y)

fill_between(x, y1, y2)

fill_between(x, y1, y2)

stackplot(x, y)

stackplot(x, y)

stairs(values)

stairs(values)

plot(x, y)

https://matplotlib.org/stable/plot_types/basic/plot.html

绘制 y 相对于 x 的线条和/或标记图。

详情参阅 plot 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
x= np.linspace(0, 10, 100)
y= 4 + 1 * np.sin(2 * x)
x2 = np.linspace(0, 10, 25)
y2 = 4 + 1 * np.sin(2 * x2)

# plot
fig, ax = plt.subplots()

ax.plot(x2, y2 + 2.5, 'x', markeredgewidth=2)
ax.plot(x, y, linewidth=2.0)
ax.plot(x2, y2 - 2.5, 'o-', linewidth=2)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

scatter(x, y)

https://matplotlib.org/stable/plot_types/basic/scatter_plot.html

散点图，展示 y 随 x 的变化关系，支持调整标记点的大小和/或颜色。

详情参阅 scatter。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make the data
np.random.seed(3)
x= 4 + np.random.normal(0, 2, 24)
y= 4 + np.random.normal(0, 2, len(x))
# size and color:
sizes = np.random.uniform(15, 80, len(x))
colors = np.random.uniform(15, 80, len(x))

# plot
fig, ax = plt.subplots()

ax.scatter(x, y, s=sizes, c=colors, vmin=0, vmax=100)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

bar(x, height)

https://matplotlib.org/stable/plot_types/basic/bar.html

参见 bar。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data:
x= 0.5 + np.arange(8)
y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]

# plot
fig, ax = plt.subplots()

ax.bar(x, y, width=1, edgecolor="white", linewidth=0.7)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

stem(x, y)

https://matplotlib.org/stable/plot_types/basic/stem.html

创建茎干图。

参见 stem。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
x= 0.5 + np.arange(8)
y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]

# plot
fig, ax = plt.subplots()

ax.stem(x, y)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

fill_between(x, y1, y2)

https://matplotlib.org/stable/plot_types/basic/fill_between.html

填充两条水平曲线之间的区域。

详情参阅 fill_between。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
np.random.seed(1)
x= np.linspace(0, 8, 16)
y1= 3 + 4*x/8 + np.random.uniform(0.0, 0.5, len(x))
y2 = 1 + 2*x/8 + np.random.uniform(0.0, 0.5, len(x))

# plot
fig, ax = plt.subplots()

ax.fill_between(x, y1, y2, alpha=.5, linewidth=0)
ax.plot(x, (y1+ y2)/2, linewidth=2)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

stackplot(x, y)

https://matplotlib.org/stable/plot_types/basic/stackplot.html

绘制堆叠面积图或流图。

详情参阅 stackplot

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
x= np.arange(0, 10, 2)
ay = [1, 1.25, 2, 2.75, 3]
by = [1, 1, 1, 1, 1]
cy = [2, 1, 2, 1, 2]
y= np.vstack([ay, by, cy])

# plot
fig, ax = plt.subplots()

ax.stackplot(x, y)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

stairs(values)

https://matplotlib.org/stable/plot_types/basic/stairs.html

绘制阶梯状常数函数，可选择以线条或填充区域形式呈现。

当需要在区间\((x_i, x_{i+1}))内绘制\(y)值时，请参考stairs方法。若需在\(x)点处绘制\(y)值，请使用step方法。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]

# plot
fig, ax = plt.subplots()

ax.stairs(y, linewidth=2.5)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

[下载 Jupyter 笔记本文件: stairs.ipynb : https://matplotlib.org/stable/_downloads/1b052bbfe9e6d6248dc225ff7df9ca18/stairs.ipynb

统计分布

https://matplotlib.org/stable/plot_types/stats/index.html

展示数据集中至少一个变量的分布情况。部分方法还会计算分布特征。

hist(x)

hist(x)

boxplot(X)

boxplot(X)

errorbar(x, y, yerr, xerr)

errorbar(x, y, yerr, xerr)

violinplot(D)

violinplot(D)

eventplot(D)

eventplot(D)

hist2d(x, y)

hist2d(x, y)

hexbin(x, y, C)

hexbin(x, y, C)

pie(x)

pie(x)

ecdf(x)

ecdf(x)

hist(x)

https://matplotlib.org/stable/plot_types/stats/hist_plot.html

计算并绘制直方图。

详情参阅 hist。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
np.random.seed(1)
x= 4 + np.random.normal(0, 1.5, 200)

# plot:
fig, ax = plt.subplots()

ax.hist(x, bins=8, linewidth=0.5, edgecolor="white")

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 56), yticks=np.linspace(0, 56, 9))

plt.show()

boxplot(X)

https://matplotlib.org/stable/plot_types/stats/boxplot_plot.html

绘制箱线图。

详情参见 boxplot。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data:
np.random.seed(10)
D = np.random.normal((3, 5, 4), (1.25, 1.00, 1.25), (100, 3))

# plot
fig, ax = plt.subplots()
VP = ax.boxplot(D, positions=[2, 4, 6], widths=1.5, patch_artist=True,
                showmeans=False, showfliers=False,
                medianprops={"color": "white", "linewidth": 0.5},
                boxprops={"facecolor": "C0", "edgecolor": "white",
                          "linewidth": 0.5},
                whiskerprops={"color": "C0", "linewidth": 1.5},
                capprops={"color": "C0", "linewidth": 1.5})

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

errorbar(x, y, yerr, xerr)

https://matplotlib.org/stable/plot_types/stats/errorbar_plot.html

绘制带有误差条的y对x的线图和/或标记图。

参见 errorbar。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data:
np.random.seed(1)
x = [2, 4, 6]
y = [3.6, 5, 4.2]
yerr = [0.9, 1.2, 0.5]

# plot:
fig, ax = plt.subplots()

ax.errorbar(x, y, yerr, fmt='o', linewidth=2, capsize=6)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

小提琴图绘制指南

https://matplotlib.org/stable/plot_types/stats/violin.html

绘制小提琴图。

API 详情参阅 violinplot。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data:
np.random.seed(10)
D = np.random.normal((3, 5, 4), (0.75, 1.00, 0.75), (200, 3))

# plot:
fig, ax = plt.subplots()

vp = ax.violinplot(D, [2, 4, 6], widths=2,
                   showmeans=False, showmedians=False, showextrema=False)
# styling: for body in vp['bodies']:
    body.set_alpha(0.9)
ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

下载 Jupyter 笔记本文件：violin.ipynb

下载 Python 源代码文件：violin.py

下载压缩包文件：violin.zip(https://matplotlib.org/stable/_downloads/25e3d8683ff1d01a46d298bac7568bc7/violin.zip\>

图库由 Sphinx-Gallery 生成

eventplot(D)

https://matplotlib.org/stable/plot_types/stats/eventplot.html

在给定位置绘制相同的平行线。

详情参阅 eventplot。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data:
np.random.seed(1)
x = [2, 4, 6]
D = np.random.gamma(4, size=(3, 50))

# plot:
fig, ax = plt.subplots()

ax.eventplot(D, orientation="vertical", lineoffsets=x, linewidth=0.75)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

hist2d(x, y)

https://matplotlib.org/stable/plot_types/stats/hist2d.html

绘制二维直方图。

查看 hist2d 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data: correlated + noise
np.random.seed(1)
x= np.random.randn(5000)
y= 1.2 * x+ np.random.randn(5000) / 3

# plot:
fig, ax = plt.subplots()

ax.hist2d(x, y, bins=(np.arange(-3, 3, 0.1), np.arange(-3, 3, 0.1)))

ax.set(xlim=(-2, 2), ylim=(-3, 3))

plt.show()

hexbin(x, y, C)

https://matplotlib.org/stable/plot_types/stats/hexbin.html

生成点集x, y的二维六边形分箱图。

详情参阅 hexbin 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data: correlated + noise
np.random.seed(1)
x= np.random.randn(5000)
y= 1.2 * x+ np.random.randn(5000) / 3

# plot:
fig, ax = plt.subplots()

ax.hexbin(x, y, gridsize=20)

ax.set(xlim=(-2, 2), ylim=(-3, 3))

plt.show()

pie(x)

https://matplotlib.org/stable/plot_types/stats/pie.html

绘制饼图。

查看 pie 函数文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data
x = [1, 2, 3, 4]
colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))

# plot
fig, ax = plt.subplots()
ax.pie(x, colors=colors, radius=3, center=(4, 4),
       wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=True)

ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
       ylim=(0, 8), yticks=np.arange(1, 8))

plt.show()

ecdf(x)

https://matplotlib.org/stable/plot_types/stats/ecdf.html

计算并绘制x的经验累积分布函数。

详情参见 ecdf。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# make data
np.random.seed(1)
x= 4 + np.random.normal(0, 1.5, 200)

# plot:
fig, ax = plt.subplots()
ax.ecdf(x)
plt.show()

网格数据

https://matplotlib.org/stable/plot_types/arrays/index.html

展示数组和图像 \(Z_{i, j}) 以及场量 \(U_{i, j}, V_{i, j}) 在规则网格上的绘图，以及对应的坐标网格 \(X_{i,j}, Y_{i,j})。

imshow(Z)

imshow(Z)

pcolormesh(X, Y, Z)

pcolormesh(X, Y, Z)

contour(X, Y, Z)

contour(X, Y, Z)

contourf(X, Y, Z)

contourf(X, Y, Z)

barbs(X, Y, U, V)

barbs(X, Y, U, V)

quiver(X, Y, U, V)

quiver(X, Y, U, V)

streamplot(X, Y, U, V)

streamplot(X, Y, U, V)

imshow(Z)

https://matplotlib.org/stable/plot_types/arrays/imshow.html

将数据显示为图像，即在二维规则栅格上呈现。

参见 imshow。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data
X, Y = np.meshgrid(np.linspace(-3, 3, 16), np.linspace(-3, 3, 16))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)

# plot
fig, ax = plt.subplots()

ax.imshow(Z, origin='lower')

plt.show()

pcolormesh(X, Y, Z)

https://matplotlib.org/stable/plot_types/arrays/pcolormesh.html

创建一个非规则矩形网格的伪彩色图。

pcolormesh 比 imshow 更灵活，因为其x和y向量不需要等距分布（实际上可以是倾斜的）。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data with uneven sampling in x
x = [-3, -2, -1.6, -1.2, -.8, -.5, -.2, .1, .3, .5, .8, 1.1, 1.5, 1.9, 2.3, 3]
X, Y = np.meshgrid(x, np.linspace(-3, 3, 128))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)

# plot
fig, ax = plt.subplots()

ax.pcolormesh(X, Y, Z, vmin=-0.5, vmax=1.0)

plt.show()

contour(X, Y, Z)

https://matplotlib.org/stable/plot_types/arrays/contour.html

绘制等高线图。

参考 contour 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data
X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)
levels = np.linspace(np.min(Z), np.max(Z), 7)

# plot
fig, ax = plt.subplots()

ax.contour(X, Y, Z, levels=levels)

plt.show()

contourf(X, Y, Z)

https://matplotlib.org/stable/plot_types/arrays/contourf.html

绘制填充等高线图。

查看 contourf 函数文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data
X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)
levels = np.linspace(Z.min(), Z.max(), 7)

# plot
fig, ax = plt.subplots()

ax.contourf(X, Y, Z, levels=levels)

plt.show()

barbs(X, Y, U, V)

https://matplotlib.org/stable/plot_types/arrays/barbs.html

绘制二维风羽图。

参见 barbs。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data:
X, Y = np.meshgrid([1, 2, 3, 4], [1, 2, 3, 4])
angle = np.pi / 180 * np.array([[15., 30, 35, 45],
                                [25., 40, 55, 60],
                                [35., 50, 65, 75],
                                [45., 60, 75, 90]])
amplitude = np.array([[5, 10, 25, 50],
                      [10, 15, 30, 60],
                      [15, 26, 50, 70],
                      [20, 45, 80, 100]])
U = amplitude * np.sin(angle)
V = amplitude * np.cos(angle)

# plot:
fig, ax = plt.subplots()

ax.barbs(X, Y, U, V, barbcolor='C0', flagcolor='C0', length=7, linewidth=1.5)

ax.set(xlim=(0, 4.5), ylim=(0, 4.5))

plt.show()

quiver(X, Y, U, V)

https://matplotlib.org/stable/plot_types/arrays/quiver.html

绘制二维箭头场图。

详见 quiver 函数文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data
x= np.linspace(-4, 4, 6)
y= np.linspace(-4, 4, 6)
X, Y = np.meshgrid(x, y)
U = x+ Y
V = Y - X

# plot
fig, ax = plt.subplots()

ax.quiver(X, Y, U, V, color="C0", angles='xy',
          scale_units='xy', scale=5, width=.015)

ax.set(xlim=(-5, 5), ylim=(-5, 5))

plt.show()

streamplot(X, Y, U, V)

https://matplotlib.org/stable/plot_types/arrays/streamplot.html

绘制向量场的流线图。

详情参阅 streamplot。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make a stream function:
X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)
# make U and V out of the streamfunction:
V = np.diff(Z[1:, :], axis=1)
U = -np.diff(Z[:, 1:], axis=0)

# plot:
fig, ax = plt.subplots()

ax.streamplot(X[1:, 1:], Y[1:, 1:], U, V)

plt.show()

非规则网格数据

https://matplotlib.org/stable/plot_types/unstructured/index.html

绘制在非结构化网格上的数据\(Z_{x, y})、非结构化坐标网格\((x, y))以及二维函数\(f(x, y) = z)的图表。

tricontour(x, y, z)

tricontour(x, y, z)

tricontourf(x, y, z)

tricontourf(x, y, z)

tripcolor(x, y, z)

tripcolor(x, y, z)

triplot(x, y)

triplot(x, y)

tricontour(x, y, z)

https://matplotlib.org/stable/plot_types/unstructured/tricontour.html

在非结构化三角形网格上绘制等高线。

参见 tricontour。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data:
np.random.seed(1)
x= np.random.uniform(-3, 3, 256)
y= np.random.uniform(-3, 3, 256)
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)
levels = np.linspace(z.min(), z.max(), 7)

# plot:
fig, ax = plt.subplots()

ax.plot(x, y, 'o', markersize=2, color='lightgrey')
ax.tricontour(x, y, z, levels=levels)

ax.set(xlim=(-3, 3), ylim=(-3, 3))

plt.show()

tricontourf(x, y, z)

https://matplotlib.org/stable/plot_types/unstructured/tricontourf.html

在非结构化三角形网格上绘制填充等高线区域。

详情参阅 tricontourf。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data:
np.random.seed(1)
x= np.random.uniform(-3, 3, 256)
y= np.random.uniform(-3, 3, 256)
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)
levels = np.linspace(z.min(), z.max(), 7)

# plot:
fig, ax = plt.subplots()

ax.plot(x, y, 'o', markersize=2, color='grey')
ax.tricontourf(x, y, z, levels=levels)

ax.set(xlim=(-3, 3), ylim=(-3, 3))

plt.show()

tripcolor(x, y, z)

https://matplotlib.org/stable/plot_types/unstructured/tripcolor.html

创建非结构化三角形网格的伪彩色图。

参见 tripcolor。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data:
np.random.seed(1)
x= np.random.uniform(-3, 3, 256)
y= np.random.uniform(-3, 3, 256)
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)

# plot:
fig, ax = plt.subplots()

ax.plot(x, y, 'o', markersize=2, color='grey')
ax.tripcolor(x, y, z)

ax.set(xlim=(-3, 3), ylim=(-3, 3))

plt.show()

triplot(x, y)

https://matplotlib.org/stable/plot_types/unstructured/triplot.html

绘制非结构化三角网格的线条和/或标记。

详情参阅 triplot。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data:
np.random.seed(1)
x= np.random.uniform(-3, 3, 256)
y= np.random.uniform(-3, 3, 256)
z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)

# plot:
fig, ax = plt.subplots()

ax.triplot(x, y)

ax.set(xlim=(-3, 3), ylim=(-3, 3))

plt.show()

三维与体积数据可视化

https://matplotlib.org/stable/plot_types/3D/index.html

使用 mpl_toolkits.mplot3d 库绘制三维数据 \((x,y,z))、曲面数据 \(f(x,y)=z) 以及体积数据 \(V_{x, y, z})。

bar3d(x, y, z, dx, dy, dz)

bar3d(x, y, z, dx, dy, dz)

fill_between(x1, y1, z1, x2, y2, z2)

fill_between(x1, y1, z1, x2, y2, z2)

plot(xs, ys, zs)

plot(xs, ys, zs)

quiver(X, Y, Z, U, V, W)

quiver(X, Y, Z, U, V, W)

scatter(xs, ys, zs)

scatter(xs, ys, zs)

stem(x, y, z)

stem(x, y, z)

plot_surface(X, Y, Z)

plot_surface(X, Y, Z)

plot_trisurf(x, y, z)

plot_trisurf(x, y, z)

voxels(\[x, y, z, filled)](voxels_simple.html#sphx-glr-plot-types-3d-voxels-simple-py)

voxels(x, y, z, filled)

plot_wireframe(X, Y, Z)

plot_wireframe(X, Y, Z)

bar3d(x, y, z, dx, dy, dz)

https://matplotlib.org/stable/plot_types/3D/bar3d_simple.html

参见 bar3d。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Make data
x = [1, 1, 2, 2]
y = [1, 2, 1, 2]
z = [0, 0, 0, 0]
dx = np.ones_like(x)*0.5
dy = np.ones_like(x)*0.5
dz = [2, 3, 1, 4]

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.bar3d(x, y, z, dx, dy, dz)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

fill_between(x1, y1, z1, x2, y2, z2)

https://matplotlib.org/stable/plot_types/3D/fill_between3d_simple.html

参见 fill_between。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Make data for a double helix
n = 50
theta = np.linspace(0, 2*np.pi, n)
x1 = np.cos(theta)
y1= np.sin(theta)
z1 = np.linspace(0, 1, n)
x2 = np.cos(theta + np.pi)
y2 = np.sin(theta + np.pi)
z2 = z1

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.fill_between(x1, y1, z1, x2, y2, z2, alpha=0.5)
ax.plot(x1, y1, z1, linewidth=2, color='C0')
ax.plot(x2, y2, z2, linewidth=2, color='C0')

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

plot(xs, ys, zs)

https://matplotlib.org/stable/plot_types/3D/plot3d_simple.html

参见 plot。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Make data
n = 100
xs = np.linspace(0, 1, n)
ys = np.sin(xs * 6 * np.pi)
zs = np.cos(xs * 6 * np.pi)

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot(xs, ys, zs)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

quiver(X, Y, Z, U, V, W)

https://matplotlib.org/stable/plot_types/3D/quiver3d_simple.html

参见 quiver 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Make data
n = 4
x= np.linspace(-1, 1, n)
y= np.linspace(-1, 1, n)
z = np.linspace(-1, 1, n)
X, Y, Z = np.meshgrid(x, y, z)
U = (x+ Y)/5
V = (Y - X)/5
W = Z*0

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.quiver(X, Y, Z, U, V, W)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

scatter(xs, ys, zs)

https://matplotlib.org/stable/plot_types/3D/scatter3d_simple.html

参见 scatter 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Make data
np.random.seed(19680801)
n = 100
rng = np.random.default_rng()
xs = rng.uniform(23, 32, n)
ys = rng.uniform(0, 100, n)
zs = rng.uniform(-50, -25, n)

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.scatter(xs, ys, zs)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

stem(x, y, z)

https://matplotlib.org/stable/plot_types/3D/stem3d.html

参见 stem。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Make data
n = 20
x= np.sin(np.linspace(0, 2*np.pi, n))
y= np.cos(np.linspace(0, 2*np.pi, n))
z = np.linspace(0, 1, n)

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.stem(x, y, z)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

plot_surface(X, Y, Z)

https://matplotlib.org/stable/plot_types/3D/surface3d_simple.html

参见 plot_surface。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

from matplotlib import cm

plt.style.use('_mpl-gallery')

# Make data
x= np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

# Plot the surface
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot_surface(X, Y, Z, vmin=Z.min() * 2, cmap=cm.Blues)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

plot_trisurf(x, y, z)

https://matplotlib.org/stable/plot_types/3D/trisurf3d_simple.html

参见 plot_trisurf。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

from matplotlib import cm

plt.style.use('_mpl-gallery')

n_radii = 8
n_angles = 36

# Make radii and angles spaces
radii = np.linspace(0.125, 1.0,n_radii)
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)[..., np.newaxis]

# Convert polar (radii, angles) coords to cartesian (x, y) coords.
x= np.append(0, (radii*np.cos(angles)).flatten())
y= np.append(0, (radii*np.sin(angles)).flatten())
z = np.sin(-x*y)

# Plot
fig, ax = plt.subplots(subplot_kw={'projection': '3d'})
ax.plot_trisurf(x, y, z, vmin=z.min() * 2, cmap=cm.Blues)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

voxels(x, y, z, filled)

https://matplotlib.org/stable/plot_types/3D/voxels_simple.html

参见 voxels 文档。

python 复制代码 
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

# Prepare some coordinates
x, y, z = np.indices((8, 8, 8))

# Draw cuboids in the top left and bottom right corners
cube1 = (x< 3) & (y< 3) & (z < 3)
cube2 = (x>= 5) & (y>= 5) & (z >= 5)

# Combine the objects into a single boolean array
voxelarray = cube1 | cube2

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.voxels(voxelarray, edgecolor='k')

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

plot_wireframe(X, Y, Z)

https://matplotlib.org/stable/plot_types/3D/wire3d_simple.html

参见 plot_wireframe。

python 复制代码 
import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import axes3d

plt.style.use('_mpl-gallery')

# Make data
X, Y, Z = axes3d.get_test_data(0.05)

# Plot
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

2025-09-10（三）

相关推荐
小葱炖豆腐23 分钟前
python绘制excel折线图
python·excel·numpy·pandas·matplotlib
七灵微5 小时前
【AI】代码实战- 基于时间序列的轴承检测1
人工智能·机器学习·ai·pandas·matplotlib
大江东去浪淘尽千古风流人物1 天前
【UniSim-SLAM】前馈SLAM的双流架构：Sim(3)统一位姿图、与经典SLAM及DPVO的三范式对比
深度学习·计算机视觉·3d·交互·人体姿态估计·eccv·3d重建
高升说3 天前
手眼 3D 相机选型：先定工作距离，再谈精度，最后定安装
人工智能·数码相机·3d
利刃大大3 天前
我用 GPT-6 给江南大学建了个 3D 沙盘
gpt·3d·ai·blender·江南大学
不要生病了4 天前
从脑电到彩色三维点云：Neuro-3D 精读与证据边界
3d
时空节拍AI数字人4 天前
AI 数字人为什么需要“3D”？2D 不够用吗
人工智能·网络协议·tcp/ip·3d·信息可视化
Zldaisy3d4 天前
以光束整形3D打印，探索陶瓷-铜功能件的一体化制造
3d·制造
LeapMay4 天前
中国科学院深圳先进技术研究院-深圳职业技术大学联合培养博士后招聘（具身智能/多模态感知/视觉检测方向）
人工智能·计算机视觉·3d·机器人
tsqtsqtsq03094 天前
3DGS 端侧重建：基于 Spatial Recon Kit 的全面实践指南
3d
热门推荐
01国内可直接用、免费额度/永久免费的大模型API清单（含 SiliconFlow、火山、阿里、智谱、百度、Kimi、DeepSeek、DMXAPI 等）02蜘蛛磁力 搜索引擎大全，如何使用蜘蛛磁力查找磁力链接03微信历史版本含下载地址（ Windows PC | 安卓 | MAC ）及设置微信不更新04历年考研数学一、数学二、数学三真题试卷及答案PDF05GitHub 镜像站点06人工智能（AI）发展史：详细里程碑072026 国产 AI 大模型横评：DeepSeek、通义千问、Kimi、文心一言、星火、豆包谁更能打？08【一等奖版】2026 国赛 A题 药材的烘干问题09ZCode 周末送额度活动开启：3 亿 Token 免费领取10Agnes AI 免费 API 接入指南：文本、生图、生视频，一套接口全免费