I have this data frame diamonds which is composed of variables like (carat, price, color), and I want to draw a scatter plot of price to carat for each color, which means different color has different color in the plot.
I’m currently using Matplotlib to create a histogram:
I have two different sets of data with a common index, and I want to represent the first one as a barplot and the second one as a lineplot in the same graph. My current approach is similar to the following.
I’m trying to colorize a Voronoi Diagram created using scipy.spatial.Voronoi. Here’s my code:
I have a 3-dimensional numpy array. I’d like to display (in matplotlib) a nice 3D plot of an isosurface of this array (or more strictly, display an isosurface of the 3D scalar field defined by interpolating between the sample points).
I have written code that opens 16 figures at once. Currently, they all open as separate graphs. I’d like them to open all on the same page. Not the same graph. I want 16 separate graphs on a single page/window. Furthermore, for some reason, the format of the numbins and defaultreallimits doesn’t hold past figure 1. Do I need to use the subplot command? I don’t understand why I would have to but can’t figure out what else I would do?
I need to visualize some data. It’s basic 2D grid, where each cell have float value. I know how to e.g. assign color to value and paint grid in OpenCV. But the point here is that there are so many values so it’s nearly impossible to do that. I am looking for some method, where I could use gradient. For example value -5.0 will be represented by blue, 0 – black, and +5.0 as red. Is there any way to do that in Python?