https://github.com/numpy/numpy/pull/7984 Hi everybody, I created my first pull request for numpy and as mentioned in the numpy development workflow documentation I hereby post a link to it and a short description to the mailing list. Please take a look. I didn't find a good way to create a contour plot of data of the form: [(x1, y1, f(x1, y1)), (x2, y2, f(x2, y2)), ..., (xn, yn, f(xn, yn))]. In order to do a contour plot, one has to bring the data into the meshgrid format. One possibility would be complicated sorting and reshaping of the data, but this is not easily possible especially if values are missing (not all combinations of (x, y) contained in data). Another way, which is used in all tutorials about contour plotting, is to create the meshgrid beforehand and than apply the function to the meshgrid matrices: x = np.linspace(-3, 3, n) y = np.linspace(-3, 3, n) X, Y = np.meshgrid(x, y) Z = f(X, Y) plt.contourplot(X, Y, Z) But if one does not have the function but only the data, this is also no option. My function essentially creates a dictionary {(x1, y1): f(x1, y1), (x2, y2): f(x2, y2), ..., (xn, yn): f(xn, yn)} with the coordinate tuples as keys and function values as values. Then it creates a meshgrid from all unique x and y coordinates (X and Y). The dictionary is then used to create the matrix Z, filling in np.nan for all missing values. This allows to do the following, with x, y and z being the x, y coordinates and z being the according function value: plt.contourplot(*meshgridify(x, y, f=z)) Maybe there is a simpler solution, but I didn't find one.