[Master Python in 100 days] Day65: Python visualization_Matplotlib3D drawing mplot3d, draw 3D scatter plots, 3D line graphs and 3D bar charts, examples + code

1Function `mpl_toolkits.mplot3d` introduction

mpl_toolkits.mplot3dIt is a submodule in the Matplotlib library, used to draw and visualize three-dimensional graphics, including three-dimensional scatter plots, surface plots, line plots, etc. It provides rich functionality to create and customize 3D graphics. Here are mpl_toolkits.mplot3dthe main features and functionality of :

3D scatter plot : Through scatterthe function, you can draw a three-dimensional scatter plot to display the distribution and relationship of three-dimensional data points.

3D surface plot : Using plot_surfacethe function, you can create a 3D surface plot for visualizing the surface shape of 3D data. This is useful for showing the three-dimensional nature of functions.

3D line graph : plotThe function allows you to draw a three-dimensional line graph to represent the connection relationship between data points. This is useful for showing trends in data over time or other variables.

3D Bar Chart : bar3dThe function allows you to create a three-dimensional bar chart that compares different categories or groups of data.

3D scatter plot markers and colors : You can customize the scatter plot marker shapes and colors based on the characteristics of your data to distinguish different data points.

3D coordinate axis customization : You can set and customize the coordinate axes of the 3D chart, including adding labels, setting scales and ranges, etc.

Graph style customization : You can set the style of the graph, including title, legend, background color, line style and color, etc., to make the graph more attractive and readable.

3D projection : mpl_toolkits.mplot3dSupports different types of 3D projection, including perspective projection and orthogonal projection, to meet different visualization needs.

Animation and interactivity : You can add animation effects or interactive elements to your 3D plots to better explore your data.

Multi-graph combination : You can combine multiple graphs of different types in the same 3D graph to display multiple data series.

Save the drawing : Finally, you can save the drawn 3D drawing as an image file for use in documents or to share with others.

In short, mpl_toolkits.mplot3dthe submodule provides Matplotlib with powerful three-dimensional visualization tools that can be used to visualize and analyze three-dimensional data. Depending on your needs, you can choose different graph types and styles to present your data to better understand and communicate your findings.

In the Matplotlib mpl_toolkits.mplot3dmodule, the general process of drawing 3D graphs includes the following steps:

(1) Import necessary libraries and modules:

import matplotlib.pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D

(2) Create a 3D graphics drawing object:

fig = plt.figure()

(3) Create a 3D subgraph:

ax = fig.add_subplot(111, projection='3d')

(4) Prepare data: Define X, Y and Z data, which will be represented in the 3D graph.

Use the corresponding 3D plotting functions to plot the data, for example:
- Scatter plot:ax.scatter(x, y, z, c='color', marker='marker_style', label='label')
- Surface plot:ax.plot_surface(X, Y, Z, cmap='colormap')
- line graph:ax.plot(x, y, z, label='label')
- Bar chart:ax.bar3d(x, y, z, dx, dy, dz, shade=True)

(5) Add axis labels:

ax.set_xlabel('X轴标签') 
ax.set_ylabel('Y轴标签') 
ax.set_zlabel('Z轴标签')

(6) Add title:

plt.title('3D 图标题')

(7) Add legend (if needed):

ax.legend()

(8) Display graphics:

plt.show()

2 Draw a 3D scatter plot

In matplotlib's mpl_toolkits.mplot3d module, you can use the `scatter` function to draw various types of 3D scatter plots.

Here are some common 3D scatter plot types:

2.1 Draw a single color scatter plot

All scatter points use the same color. Color can be specified by setting the `c` parameter.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制单色散点图
ax.scatter(x, y, z, c='blue')

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

2.2 Draw a colored scatter plot

Different scatter points can use different colors. The color of each scatter point can be specified by setting the `c` parameter to an array of the same length.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
colors = np.random.rand(100)

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制彩色散点图
ax.scatter(x, y, z, c=colors)

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

2.3 Draw a scatter plot of size changes

The size of the scatter points can vary depending on a feature. The size of each scatter point can be specified by setting the `s` parameter to an array of the same length.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.random.rand(100)  # x坐标
y = np.random.rand(100)  # y坐标
z = np.random.rand(100)  # z坐标
colors = np.random.rand(100)  # 散点颜色
sizes = np.random.randint(low=50, high=200, size=100)  # 散点大小

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制散点图
ax.scatter(x, y, z, c=colors, s=sizes, alpha=0.8)

# 设置坐标轴标签
ax.set_xlabel('X')  # 设置x轴标签
ax.set_ylabel('Y')  # 设置y轴标签
ax.set_zlabel('Z')  # 设置z轴标签

# 显示图形
plt.show()

2.4 Draw a scatter plot of shape changes

The shape of the scatter points can change based on a feature. The shape of the scatter points can be specified by setting the `marker` parameter.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
markers = ['o', 's', '^', 'D']

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制形状变化散点图
for i in range(len(x)):
    ax.scatter(x[i], y[i], z[i], marker=markers[i%len(markers)])

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

3 Draw 3D line graph

Draw 3D line plots using the Axes3D object in the mpl_toolkits.mplot3d module.

3.1 Simple line graph

Use the `plot` function to draw simple curves.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * x)
z = np.cos(2 * np.pi * x)

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制简单线图
ax.plot(x, y, z)

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

3.2 Draw multi-line graphs

Use the `plot` function to draw multiple lines and display them in the same chart.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

plt.rcParams['font.sans-serif'] = ['SimHei']


# 创建示例数据
t = np.linspace(0, 20, 100)  # 时间或X轴数据
x1 = np.sin(t)
y1 = np.cos(t)
z1 = t

x2 = np.sin(t) + 2
y2 = np.cos(t) + 2
z2 = t

# 创建图形和子图
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制第一条线
ax.plot(x1, y1, z1, label='线1', color='blue', linestyle='-', linewidth=2)

# 绘制第二条线
ax.plot(x2, y2, z2, label='线2', color='red', linestyle='--', linewidth=2)

# 添加标题
plt.title('三维多线图示例')

# 添加坐标轴标签
ax.set_xlabel('X轴')
ax.set_ylabel('Y轴')
ax.set_zlabel('Z轴')

# 添加图例
ax.legend()

# 显示图形
plt.show()

3.3 Drawing labeled line graphs

Use the `plot` function and add marker points on the line by setting the `marker` parameter.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * x)
z = np.cos(2 * np.pi * x)

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制带标记的线图
ax.plot(x, y, z, marker='o')

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

3.4 Draw three-dimensional grid line diagram

Use the `plot_wireframe` function to draw a grid line plot in 3D space.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X ** 2 + Y ** 2))

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制立体网格线图
ax.plot_wireframe(X, Y, Z)

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

3.5 Draw contour maps

Use the `contour` or `contourf` function to draw contours in 3D space.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制3D等高线图
ax.contour3D(X, Y, Z, 50, cmap='viridis')

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()

4 Draw a 3D bar chart

In matplotlib, due to mpl_toolkits.mplot3dmodule limitations, there is no function for directly drawing 3D bar charts. However, you can use bar3dthe function to draw a similar 3D bar chart effect.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建数据
x = [1, 2, 3, 4, 5]  # x轴坐标位置
y = [1, 2, 3, 4, 5]  # y轴坐标位置
z = [0, 3, 2, 5, 1]  # z轴高度，即条形的高度
dx = dy = 0.8  # x和y方向的宽度
dz = z  # 条形的高度
colors = ['red', 'green', 'blue', 'orange', 'purple']  # 颜色列表，一一对应于每个条形

# 创建图形和轴
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 绘制3D条形图，并设置每个条形的颜色
for xi, yi, zi, color in zip(x, y, z, colors):
    ax.bar3d(xi, yi, 0, dx, dy, zi, color=color)

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()