Plotting 3D surface in python

You just need to create arrays of the X and Y coordinates. We can do this with numpy.meshgrid. In the example below, I set the cell size to 1., but you can easily scale that by changing the cellsize variable.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# Create x, y coords
nx, ny = 800, 1600
cellsize = 1.
x = np.arange(0., float(nx), 1.) * cellsize
y = np.arange(0., float(ny), 1.) * cellsize
X, Y = np.meshgrid(x, y)

# dummy data
Z = (X**2 + Y**2) / 1e6

# Create matplotlib Figure and Axes
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

print X.shape, Y.shape, Z.shape

# Plot the surface
ax.plot_surface(X, Y, Z)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

plt.show()

I faced a similar problem and a reply from @tmdavison turn me on in the right direction. But this answer unclear in the part of retrieving data from the CSV file.

Here is my solution.

import csv
import matplotlib.pyplot as plt
import numpy

def csv_3d(file_name):
    """Draw content of csv file with matplotlib 3D like MS Excel."""
    data = [
      [float(i.replace(',', '.')) for i in row]
      for row in csv.reader(open(file_name), delimiter=';')
    ]
    # matplotlib/numpy magic
    x_arr, y_arr = numpy.meshgrid(
      numpy.arange(0.0, float(len(data[0])), 1.0),  # x: number of columns in csv
      numpy.arange(0.0, float(len(data)), 1.0),  # y: number of rows in csv
    )
    z_arr = numpy.array(data)  # transform csv data into 2D values numpy array

    plt.figure(num=file_name)
    axes = plt.axes(projection="3d")
    axes.plot_surface(x_arr, y_arr, z_arr)
    plt.show()

csv_3d('my.csv')