Python : How to plot 3d graphs using Python?

Question

I am using matplotlib for doing this

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

fig = plt.figure()
ax = Axes3D(fig)

x = [6,3,6,9,12,24]
y = [3,5,78,12,23,56]

ax.plot(x, y, zs=0, zdir='z', label='zs=0, zdir=z')

plt.show()

Now this builds a graph that is horizontal in the 3d space. How do I make the graph vertical so that it faces the user?

What I want to do is build multiple such vertical graphs that are separated by some distance and are facing the user.

Answer 1

According to the documentation you want to use the ax.plot_surface(x,y,z) method. More information and chart types here.

The following should work:

x = [1,2,3]
y = [4,5,6]
z = [7,8,9]
data = zip(x,y,z)

#map data on the plane
X, Y = numpy.meshgrid(arange(0, max(x), 1), arange(0, max(y), 1))
Z = numpy.zeros((len(Y), len(X)), 'Float32')
for x_,y_,z_ in data:
  Z[x_, y_] = z_ #this should work, but only because x and y are integers
                 #and arange was done with a step of 1, starting from 0
fig = p.figure()
ax = p3.Axes3D(fig)
ax.plot_surface(X, Y, Z)

Answer 2

bp's answer might work fine, but there's a much simpler way.

Your current graph is 'flat' on the z-axis, which is why it's horizontal. You want it to be vertical, which means that you want it to be 'flat' on the y-axis. This involves the tiniest modification to your code:

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

fig = plt.figure()
ax = Axes3D(fig)

x = [6,3,6,9,12,24]
y = [3,5,78,12,23,56]
# put 0s on the y-axis, and put the y axis on the z-axis
ax.plot(xs=x, ys=[0]*len(x), zs=y, zdir='z', label='ys=0, zdir=z')
plt.show()

Then you can easily have multiple such graphs by using different values for the ys parameter (for example, ys=[2]*len(x) instead would put the graph slightly behind).

Answer 3

Mayavi, in particular the mlab module, provides powerful 3D plotting that will work on large and or complex data, and should be easy to use on numpy arrays.