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Plot 3d points (x,y,z) in 2d plot with colorbar

I have computed a lot (~5000) of 3d points (x,y,z) in a quite complicated way so I have no function such that z = f(x,y). I can plot the 3d surface using
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))
I would like to plot this also in 2d, with a colorbar indicating the z-value. I know there is a simple solution using ax.contour if my z is a matrix, but here I only have a vector.
Attaching the plot_trisurf result when rotated to xy-plane. This is what I what like to achieve without having to rotate a 3d plot. In this, my variable surface_points is an np.array with size 5024 x 3.

I had the same problems in one of my codes, I solved it this way:
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pylab as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
nx = 10*int(np.sqrt(N))
xg = np.linspace(X.min(), X.max(), nx)
yg = np.linspace(Y.min(), Y.max(), nx)
xgrid, ygrid = np.meshgrid(xg, yg)
ctr_f = griddata((X, Y), Z, (xgrid, ygrid), method='linear')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contourf(xgrid, ygrid, ctr_f, cmap=cm.coolwarm)
plt.show()

You could use a scatter plot to display a projection of your z color onto the x-y axis.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
# fig = plt.figure()
# ax = fig.add_subplot(projection='3d')
# surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))
fig = plt.figure()
cmap = cm.get_cmap('coolwarm')
color = cmap(Z)[..., :3]
plt.scatter(X,Y,c=color)
plt.show()
Since you seem to have a 3D shape that is hollow, you could split the projection into two like if you cur the shape in two pieces.
fig = plt.figure()
plt.subplot(121)
plt.scatter(X[Z<0.5],Y[Z<0.5],c=color[Z<0.5])
plt.title('down part')
plt.subplot(122)
plt.scatter(X[Z>=0.5],Y[Z>=0.5],c=color[Z>+0.5])
plt.title('top part')
plt.show()

Multiple 2D contour plots in one 3D figure in python

Is there any way available in python to plot multiple 2D contour plots in one 3D plot in python. I am currently using matplotlib for contouring, but not finding any option for what I am searching for. A sample image I have added. But I want to do it on Z-axis.

You can try this.
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
levels = np.linspace(-0.1, 0.4, 100) #(z_min,z_max,number of contour),
a=0
b=1
c=2
Z1 = a+.1*np.sin(2*X)*np.sin(4*Y)
Z2 = b+.1*np.sin(3*X)*np.sin(4*Y)
Z3 = c+.1*np.sin(4*X)*np.sin(5*Y)
plt.contourf(X, Y,Z1, levels=a+levels,cmap=plt.get_cmap('rainbow'))
plt.contourf(X, Y,Z2, levels=b+levels,cmap=plt.get_cmap('rainbow'))
plt.contourf(X, Y,Z3, levels=c+levels,cmap=plt.get_cmap('rainbow'))
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 2)
plt.show()

In order to plot true 2-D contour plots in one 3D plot, try this:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
Z1 = .1*np.sin(2*X)*np.sin(4*Y)
Z2 = .1*np.sin(3*X)*np.sin(4*Y)
Z3 = .1*np.sin(4*X)*np.sin(5*Y)
levels=np.linspace(Z1.min(), Z1.max(), 100)
ax.contourf(X, Y,Z1, levels=levels, zdir='z', offset=0, cmap=plt.get_cmap('rainbow'))
levels=np.linspace(Z2.min(), Z2.max(), 100)
ax.contourf(X, Y,Z2, levels=levels, zdir='z', offset=1, cmap=plt.get_cmap('rainbow'))
levels=np.linspace(Z3.min(), Z3.max(), 100)
ax.contourf(X, Y,Z3, levels=levels, zdir='z', offset=2, cmap=plt.get_cmap('rainbow'))
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 2)
plt.show()
enter image description here

Draw a truncated-cone-based vortex in matplotlib

I am trying to make a rudimentary model for the a coronal hole in my mathematics thesis. I am looking to create a plot where the radial direction of the cylinder increases with proportionally with the height and then I would like to add twists in the $(\theta, z)$ direction either explicitly or with lines and arrows along the surface.
Currently I have only been able to produce a normal cylinder.
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Cylinder
x=np.linspace(-1, 1, 100)
z=np.linspace(-2, 2, 100)
Xc, Zc=np.meshgrid(x, z)
Yc = np.sqrt(1-Xc**2)
# Draw parameters
rstride = 20
cstride = 10
ax.plot_surface(Xc, Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
ax.plot_surface(Xc, -Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
ax.set_xlabel("X")
#ax.set_ylabel("Y")
fig.gca().set_ylabel(r'$\theta$')
ax.set_zlabel("Z")
plt.figure(figsize=(200,70)) #sets the size of the plot
#plt.show()

How to draw 3D curves by SciPy?

I draw a 2D curve with the code
c = 11
x = np.arange(0, 5, 0.1)
y = np.exp(c)/x
plt.plot(x,y)
How can I draw a series of the x,y curves while the z axis is c? The first line will be changed to
c = np.arange(1, 70, 1)
How can I draw the 70 x,y curves along the z axis?

You could use matplotlibs Axes3D, a tutorial can be found here:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
c = np.arange(1, 10, 1) # made this 10 so that the graph is more readable
x = np.arange(0, 5, 0.1)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in c:
y = np.exp(i) / x
ax.plot(x, y, i)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
plt.show()
Which gives the figure:

Add cylinder to plot

I would like to add a transparent cylinder to my 3D scatter plot. How can I do it?
This is the code I am using to make the plot:
fig = plt.figure(2, figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X, Y, Z, c=Z,cmap=plt.cm.Paired)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
plt.xticks()

Today I have to do the same thing in my project about adding a transparent cylinder in the result. This is the code I get finally. So I share it with you guys just for learning
import numpy as np
def data_for_cylinder_along_z(center_x,center_y,radius,height_z):
z = np.linspace(0, height_z, 50)
theta = np.linspace(0, 2*np.pi, 50)
theta_grid, z_grid=np.meshgrid(theta, z)
x_grid = radius*np.cos(theta_grid) + center_x
y_grid = radius*np.sin(theta_grid) + center_y
return x_grid,y_grid,z_grid
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Xc,Yc,Zc = data_for_cylinder_along_z(0.2,0.2,0.05,0.1)
ax.plot_surface(Xc, Yc, Zc, alpha=0.5)
plt.show()
And you will get this beautiful figure.

One possible method is to use the plot_surface. Adapting the solution given in this blog post then have
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Scatter graph
N = 100
X = np.random.uniform(-1, 1, N)
Y = np.random.uniform(-1, 1, N)
Z = np.random.uniform(-2, 2, N)
ax.scatter(X, Y, Z)
# Cylinder
x=np.linspace(-1, 1, 100)
z=np.linspace(-2, 2, 100)
Xc, Zc=np.meshgrid(x, z)
Yc = np.sqrt(1-Xc**2)
# Draw parameters
rstride = 20
cstride = 10
ax.plot_surface(Xc, Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
ax.plot_surface(Xc, -Yc, Zc, alpha=0.2, rstride=rstride, cstride=cstride)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
plt.show()
I've added some minimal configuration of the surface, better can be achieved by consulting the docs.

I improved on #Greg's answer and made a solid 3D cylinder with a top and bottom surface and rewrote the equation so that you can translate in the x, y,and z
from mpl_toolkits.mplot3d import Axes3D
import mpl_toolkits.mplot3d.art3d as art3d
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
def plot_3D_cylinder(radius, height, elevation=0, resolution=100, color='r', x_center = 0, y_center = 0):
fig=plt.figure()
ax = Axes3D(fig, azim=30, elev=30)
x = np.linspace(x_center-radius, x_center+radius, resolution)
z = np.linspace(elevation, elevation+height, resolution)
X, Z = np.meshgrid(x, z)
Y = np.sqrt(radius**2 - (X - x_center)**2) + y_center # Pythagorean theorem
ax.plot_surface(X, Y, Z, linewidth=0, color=color)
ax.plot_surface(X, (2*y_center-Y), Z, linewidth=0, color=color)
floor = Circle((x_center, y_center), radius, color=color)
ax.add_patch(floor)
art3d.pathpatch_2d_to_3d(floor, z=elevation, zdir="z")
ceiling = Circle((x_center, y_center), radius, color=color)
ax.add_patch(ceiling)
art3d.pathpatch_2d_to_3d(ceiling, z=elevation+height, zdir="z")
ax.set_xlabel('x-axis')
ax.set_ylabel('y-axis')
ax.set_zlabel('z-axis')
plt.show()
# params
radius = 3
height = 10
elevation = -5
resolution = 100
color = 'r'
x_center = 3
y_center = -2
plot_3D_cylinder(radius, height, elevation=elevation, resolution=resolution, color=color, x_center=x_center, y_center=y_center)