Reputation: 758
I have a dataset looks like(contains ~25 data-points):
x=[2.225 2.325 2.425 2.075 2.375 1.925 1.975 1.775 1.975 2.375]
y=[147.75 130.25 161.75 147.75 165.25 151.25 158.25 151.25 172.25 123.25]
z=[-1.36, -0.401, -0.741, -0.623, -0.44, -0.37, 0.120, 2.8, 0.026, -1.19]
I'm trying to plot a 3D-bar chart using the data.
I was trying like:
from mpl_toolkits.mplot3d import Axes3D
fig_3d_bar = plt.figure(figsize=(7, 5))
dx = fig_3d_bar.add_subplot(111, projection='3d')
x_pos = np.array(x)
y_pos = np.array(y)
z_pos = np.zeros(len(x))
dx = np.ones(len(x))
dy = np.ones(len(y))
dz = z
dx.bar3d(x_pos, y_pos, z_pos, dx, dy, dz, color='#00ceaa')
But this is giving me an error report as:
dx.bar3d(x_pos, y_pos, z_pos, dx, dy, dz, color='#00ceaa')
AttributeError: 'numpy.ndarray' object has no attribute 'bar3d'
A little help would serve great. Don't know what is going wrong.
thank you.
Upvotes: 2
Views: 239
Reputation: 936
There is an error in your code. You use the variable name dx
for both the Axes
object and the size of the bars. I guess you want
ax = fig_3d_bar.add_subplot(111, projection='3d')
ax.bar3d(x_pos, y_pos, z_pos, dx, dy, dz, color='#00ceaa')
The bars appear very wide in the plot because of the different scales of the x- and y-data. You can adjust them by scaling dx
and dy
accordingly.
dx = np.ones(len(x))*0.1
dy = np.ones(len(y))*5
The colors of the bars can be adapted to the z-values by using a ScalarMappable instance. For this you need a norm object that scales the z-values to the range [0,1]. You can choose any of the predefined colormaps or create your own.
import matplotlib.colors as cls
import matplotlib.cm as cm
norm = cls.Normalize() # Norm to map the z values to [0,1]
norm.autoscale(z)
cmap = cm.ScalarMappable(norm, 'jet') # Choose any colormap you want
ax.bar3d(x_pos, y_pos, z_pos, dx, dy, dz, color=cmap.to_rgba(z))
Upvotes: 1