You don’t need to know anything about your data per se. You can get away with what your matplotlib Axes object will tell you about the data. See below: import numpy as np import matplotlib.pyplot as plt # random data N = 37 x = np.random.normal(loc=3.5, scale=1.25, size=N) y = np.random.normal(loc=3.4, scale=1.5, size=N) c = …
This produces a colorbar (though possibly not the one you need): Replace this line: ax.scatter(xs, ys, zs, c=cs, marker=m) with p = ax.scatter(xs, ys, zs, c=cs, marker=m) then use fig.colorbar(p) near the end
Where would this bx be passed into? You ought to repeat the second call to plot, not the first, so there is no need for bx. In detail: plot takes an optional ax argument. This is the axes it draws into. If the argument is not provided the function creates a new plot and axes. …
Another option using my library adjustText, written specially for this purpose (https://github.com/Phlya/adjustText). from adjustText import adjust_text np.random.seed(2016) N = 50 scatter_data = np.random.rand(N, 3) fig, ax = plt.subplots() ax.scatter(scatter_data[:, 0], scatter_data[:, 1], c=scatter_data[:, 2], s=scatter_data[:, 2] * 150) labels = [‘ano_{}’.format(i) for i in range(N)] texts = [] for x, y, text in zip(scatter_data[:, 0], …
Using the same example. You can set the pane color using the set_pane_color method as described here http://matplotlib.org/mpl_toolkits/mplot3d/api.html#axis3d. You can set the color using the RGBA tuple: # scatter3d_demo.py # … # Set the background color of the pane YZ ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0)) plt.show()
One approach is to plot the data as a scatter plot with a low alpha, so you can see the individual points as well as a rough measure of density. (The downside to this is that the approach has a limited range of overlap it can show — i.e., a maximum density of about 1/alpha.) …
Check the scatter documentation. Third argument is for size of points and should be scalar or array_like. I assume ‘r’ is for color so do the following: plt.scatter(x, y, c=”r”)
This is almost like the other answer but you don’t need a scatter plot at all, you can simply specify a scatter-plot-like format (fmt-parameter) for errorbar: import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [1, 4, 9, 16] e = [0.5, 1., 1.5, 2.] plt.errorbar(x, y, yerr=e, fmt=”o”) plt.show() Result: A …