1.散点图

代码

# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)


def randrange(n, vmin, vmax):
    '''
    Helper function to make an array of random numbers having shape (n, )
    with each number distributed Uniform(vmin, vmax).
    '''
    return (vmax - vmin)*np.random.rand(n) + vmin

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

n = 100

# For each set of style and range settings, plot n random points in the box
# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].
for m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]:
    xs = randrange(n, 23, 32)
    ys = randrange(n, 0, 100)
    zs = randrange(n, zlow, zhigh)
    ax.scatter(xs, ys, zs, marker=m)

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()

输出：

输入的数据格式

这个输入的三个维度要求是三列长度一致的数据，可以理解为3个length相等的list。
用上面的scatter或者下面这段直接plot也可以。

fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(h, z, t, '.', alpha=0.5)
plt.show()

输出：

2.三维表面 surface

代码

x = [12.7, 12.8, 12.9]
y = [1, 2, 3, 4]
temp = pd.DataFrame([[7,7,9,9],[2,3,4,5],[1,6,8,7]]).T
X,Y = np.meshgrid(x,y)  # 形成网格化的数据
temp = np.array(temp)
fig = plt.figure(figsize=(16, 16))
ax = fig.gca(projection='3d')
ax.plot_surface(Y,X,temp,rcount=1, cmap=cm.plasma, linewidth=1, antialiased=False,alpha=0.5) #cm.plasma
ax.set_xlabel('zone', color='b', fontsize=20)
ax.set_ylabel('h2o', color='g', fontsize=20)
ax.set_zlabel('Temperature', color='r', fontsize=20)

output:

输入的数据格式

这里x和y原本都是一维list，通过np.meshgrid可以将其形成4X3的二维数据，如下图所示：


而第三维，得是4X3的2维的数据，才能进行画图

scatter + surface图形展示

3. 三维瀑布图waterfall

代码

from matplotlib.collections import PolyCollection
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import numpy as np

axes=plt.axes(projection="3d")

def colors(arg):
    return mcolors.to_rgba(arg, alpha=0.6)


verts = []
z1 = [1, 2, 3, 4]
x1 = np.arange(0, 10, 0.4)
for z in z1:
    y1 = np.random.rand(len(x1))
    y1[0], y1[-1] = 0, 0
    verts.append(list(zip(x1, y1)))
# print(verts)
poly = PolyCollection(verts, facecolors=[colors('r'), colors('g'), colors('b'),
                                         colors('y')])
poly.set_alpha(0.7)
axes.add_collection3d(poly, zs=z1, zdir='y')

axes.set_xlabel('X')
axes.set_xlim3d(0, 10)
axes.set_ylabel('Y')
axes.set_ylim3d(-1, 4)
axes.set_zlabel('Z')
axes.set_zlim3d(0, 1)
axes.set_title("3D Waterfall plot")

plt.show()

输出：

输入的数据格式

这个的输入我还没有完全搞懂，导致我自己暂时不能复现到其他数据，等以后懂了再回来补充。

4. 3d wireframe

code

from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt


fig, (ax1, ax2) = plt.subplots(
    2, 1, figsize=(8, 12), subplot_kw={'projection': '3d'})

# Get the test data
X, Y, Z = axes3d.get_test_data(0.05)

# Give the first plot only wireframes of the type y = c
ax1.plot_wireframe(X, Y, Z, rstride=10, cstride=0)
ax1.set_title("Column (x) stride set to 0")

# Give the second plot only wireframes of the type x = c
ax2.plot_wireframe(X, Y, Z, rstride=0, cstride=10)
ax2.set_title("Row (y) stride set to 0")

plt.tight_layout()
plt.show()

output:

输入的数据格式

与plot_surface的输入格式一样，X，Y原本为一维list，通过np.meshgrid形成网格化数据。Z为二维数据。其中注意调节rstride、cstride这两个值实现行列间隔的调整。
自己试了下：

reference：

https://matplotlib.org/stable/gallery/mplot3d/hist3d.html