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I have numpy array with this shape: (33,10). When I plot contour I get ugly image like this: 
while contour() doesn't seem to have any argument about smoothing or some sort of interpolation feature.
I somehow expected that tool which offers contour plot should offer smoothing too.
Is there straight forward way to do it in MPL?
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Choose the "smoothness" of the new plot via the parameter newpoints . A 3D-surf plot would be more suitable for very smooth color-shading. Just rotate it to a top-down view.
Contour plots (sometimes called Level Plots) are a way to show a three-dimensional surface on a two-dimensional plane. It graphs two predictor variables X Y on the y-axis and a response variable Z as contours. These contours are sometimes called the z-slices or the iso-response values.
Color maps on contour plots The default color scheme of Matplotlib contour and filled contour plots can be modified. A general way to modify the color scheme is to call Matplotlib's plt. get_cmap() function that outputs a color map object. There are many different colormaps available to apply to contour plots.
As others have already pointed out, you need to interpolate your data.
There are a number of different ways to do this, but for starters, consider scipy.ndimage.zoom.
As a quick exmaple:
import numpy as np import scipy.ndimage import matplotlib.pyplot as plt data = np.loadtxt('data.txt') # Resample your data grid by a factor of 3 using cubic spline interpolation. data = scipy.ndimage.zoom(data, 3) plt.contour(data) plt.show() 
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