name_color_map_surface = 'Greens' # Colormap for the 3D surface only.Īx = fig.add_subplot(111, projection='3d') Plt.title('%s in function of %s, %s and %s' % (list_name_variables, list_name_variables, list_name_variables, list_name_variables) ) Īnother solution for the case where we absolutely want to have the original values of the 4th dimension for each point is simply to use the "scatter plot" combined with a 3D surface diagram that will simply link them to help you see the distances between them. # Add titles to the axes and a title in the figure.Īx.set_xlabel(list_name_variables) ax.set_ylabel(list_name_variables) Īx.set_zlabel(list_name_variables) #Add a color bar with a title to explain which variable is represented by the color.Ĭbar = fig.colorbar(surf, shrink=0.5, aspect=5) Ĭbar.ax.get_yaxis().labelpad = 15 cbar.ax.set_ylabel(list_name_variables, rotation = 270) Surf = ax.plot_trisurf(triang, z, cmap = name_color_map, shade=False, linewidth=0.2) Triang = mtri.Triangulation(x, y, triangles) If choice_calcuation_colors = 1: # Mean of the "c" values of the 3 pt of the triangleĬolors = np.mean( ], c], c]], axis = 0) Įlif choice_calcuation_colors = 2: # Mediane of the "c" values of the 3 pt of the triangleĬolors = np.median( ], c], c]], axis = 0) Įlif choice_calcuation_colors = 3: # Max of the "c" values of the 3 pt of the triangleĬolors = np.max( ], c], c]], axis = 0) Triangles = mtri.Triangulation(x, y).triangles # will represent the 3 grouped points and I put some examples. # Therefore, different methods can be used to define the value that # indexes corresponding to the line number of the points to be grouped. # determined by the values of their 4th dimension. # We create triangles that join 3 pt at a time and where their colors will be # We assume here that the "data frame" "df" has already been loaded before. # Example where we have a "Pandas Dataframe" where each line = 1 pt in 4D. Index_x = 0 index_y = 1 index_z = 2 index_c = 3 # The idea is that each line = 1 pt in 4D. # for example where each column is linked to a variable 1 dimension.
#Scatter plot python code
The following code here is mainly based on the answer given to this question. The main idea is the same than the accepted answer, but we have a 3D graph of the surface that allows to visually better see the distance between the points. Personally I don't really see the spatial relation in the case of the "scatter plot" and so using 3D surface help me to more easily understand the graphic. I know that the question is very old, but I would like to present this alternative where, instead of using the "scatter plot", we have a 3D surface diagram where the colors are based on the 4th dimension.
#Scatter plot python how to
Ideas how to reuse the given solution with arbitrary data? In practical cases this is not always the case. Linewidth=0, antialiased=False, shade=False)Īs far as I can see, and this applies to all matplotlib-demos, the variables X, Y and Z are nicely prepared.
Gx, Gy = np.gradient(Z) # gradients with respect to x and y The solution to the initial problem is: # Python-matplotlib Commands Alpha can be set from zero to one, where zero is totally transparent, and one is not at all transparent.What I would like to know is how to apply the suggested solution to a bunch of data (4 columns), e.g.: 0.1 0 0.1 2.0 To add the opacity of the colour, use the alpha argument to set. So, how to set it? Assuming, we have the list called col which is a colour corresponding to each country depending on which continent. Then, now I realized, the colours used in the scatter plots are randomized. In the source code, I multiply the np_pop by 2 for illustration on the below scatter plot. Plt.scatter(gdp_cap, life_exp, s = np_pop)
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