As others have answered, scatter() or plot() will generate the plot you want. I suggest two refinements to answers that are already here:
Use numpy to create the x-coordinate list and y-coordinate list. Working with large data sets is faster in numpy than using the iteration in Python suggested in other answers.
Use pyplot to apply the logarithmic scale rather than operating directly on the data, unless you actually want to have the logs.
import matplotlib.pyplot as plt import numpy as np data = [(2, 10), (3, 100), (4, 1000), (5, 100000)] data_in_array = np.array(data) ''' That looks like array([[ 2, 10], [ 3, 100], [ 4, 1000], [ 5, 100000]]) ''' transposed = data_in_array.T ''' That looks like array([[ 2, 3, 4, 5], [ 10, 100, 1000, 100000]]) ''' x, y = transposed # Here is the OO method # You could also the state-based methods of pyplot fig, ax = plt.subplots(1,1) # gets a handle for the AxesSubplot object ax.plot(x, y, 'ro') ax.plot(x, y, 'b-') ax.set_yscale('log') fig.show()
I've also used ax.set_xlim(1, 6) and ax.set_ylim(.1, 1e6) to make it pretty.
I've used the object-oriented interface to matplotlib. Because it offers greater flexibility and explicit clarity by using names of the objects created, the OO interface is preferred over the interactive state-based interface.