how to set camera position for 3d plots using python/matplotlib?

By "camera position," it sounds like you want to adjust the elevation and the azimuth angle that you use to view the 3D plot. You can set this with ax.view_init. I've used the below script to first create the plot, then I determined a good elevation, or elev, from which to view my plot. I then adjusted the azimuth angle, or azim, to vary the full 360deg around my plot, saving the figure at each instance (and noting which azimuth angle as I saved the plot). For a more complicated camera pan, you can adjust both the elevation and angle to achieve the desired effect.

    from mpl_toolkits.mplot3d import Axes3D
    ax = Axes3D(fig)
    ax.scatter(xx,yy,zz, marker='o', s=20, c="goldenrod", alpha=0.6)
    for ii in xrange(0,360,1):
        ax.view_init(elev=10., azim=ii)
        savefig("movie%d.png" % ii)

Changing position of vertical (z) axis of 3D plot (Matplotlib)?

I needed something similar: drawing the zaxis on both sides. Thanks to the answer by @crayzeewulf I came to following workaround (for left, righ, or both sides):

First plot your 3d as you need, then before you call show() wrap the Axes3D with a Wrapper class that simply overrides the draw() method.

The Wrapper Class calls simply sets the visibility of some features to False, it draws itself and finally draws the zaxis with modified PLANES. This Wrapper Class allows you to draw the zaxis on the left, on the rigth or on both sides.

import matplotlib
matplotlib.use('QT4Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d

class MyAxes3D(axes3d.Axes3D):

    def __init__(self, baseObject, sides_to_draw):
        self.__class__ = type(baseObject.__class__.__name__,
                              (self.__class__, baseObject.__class__),
                              {})
        self.__dict__ = baseObject.__dict__
        self.sides_to_draw = list(sides_to_draw)
        self.mouse_init()

    def set_some_features_visibility(self, visible):
        for t in self.w_zaxis.get_ticklines() + self.w_zaxis.get_ticklabels():
            t.set_visible(visible)
        self.w_zaxis.line.set_visible(visible)
        self.w_zaxis.pane.set_visible(visible)
        self.w_zaxis.label.set_visible(visible)

    def draw(self, renderer):
        # set visibility of some features False 
        self.set_some_features_visibility(False)
        # draw the axes
        super(MyAxes3D, self).draw(renderer)
        # set visibility of some features True. 
        # This could be adapted to set your features to desired visibility, 
        # e.g. storing the previous values and restoring the values
        self.set_some_features_visibility(True)

        zaxis = self.zaxis
        draw_grid_old = zaxis.axes._draw_grid
        # disable draw grid
        zaxis.axes._draw_grid = False

        tmp_planes = zaxis._PLANES

        if 'l' in self.sides_to_draw :
            # draw zaxis on the left side
            zaxis._PLANES = (tmp_planes[2], tmp_planes[3],
                             tmp_planes[0], tmp_planes[1],
                             tmp_planes[4], tmp_planes[5])
            zaxis.draw(renderer)
        if 'r' in self.sides_to_draw :
            # draw zaxis on the right side
            zaxis._PLANES = (tmp_planes[3], tmp_planes[2], 
                             tmp_planes[1], tmp_planes[0], 
                             tmp_planes[4], tmp_planes[5])
            zaxis.draw(renderer)

        zaxis._PLANES = tmp_planes

        # disable draw grid
        zaxis.axes._draw_grid = draw_grid_old

def example_surface(ax):
    """ draw an example surface. code borrowed from http://matplotlib.org/examples/mplot3d/surface3d_demo.html """
    from matplotlib import cm
    import numpy as np
    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)
    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)

if __name__ == '__main__':
    fig = plt.figure(figsize=(15, 5))
    ax = fig.add_subplot(131, projection='3d')
    ax.set_title('z-axis left side')
    ax = fig.add_axes(MyAxes3D(ax, 'l'))
    example_surface(ax) # draw an example surface
    ax = fig.add_subplot(132, projection='3d')
    ax.set_title('z-axis both sides')
    ax = fig.add_axes(MyAxes3D(ax, 'lr'))
    example_surface(ax) # draw an example surface
    ax = fig.add_subplot(133, projection='3d')
    ax.set_title('z-axis right side')
    ax = fig.add_axes(MyAxes3D(ax, 'r'))
    example_surface(ax) # draw an example surface
    plt.show()

Zoom an inline 3D matplotlib figure *without* using the mouse?

The closest solution to view_init is setting ax.dist directly. According to the docs for get_proj "dist is the distance of the eye viewing point from the object point". The initial value is currently hardcoded with dist = 10. Lower values (above 0!) will result in a zoomed in plot.

Note: This behavior is not really documented and may change. Changing the limits of the axes to plot only the relevant parts is probably a better solution in most cases. You could use ax.autoscale(tight=True) to do this conveniently.

Working IPython/Jupyter example:

%matplotlib inline
from IPython.display import display
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt

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

# Grab some test data.
X, Y, Z = axes3d.get_test_data(0.05)

# Plot a basic wireframe.
ax.view_init(90, 0)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
plt.close()

from ipywidgets import interact

@interact(dist=(1, 20, 1))
def update(dist=10):
    ax.dist = dist
    display(fig)

Output

dist = 10

dist = 5

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