How do I plot list of tuples in Python?

You could also use zip

import matplotlib.pyplot as plt

l = [(0, 6.0705199999997801e-08), (1, 2.1015700100300739e-08),
     (2, 7.6280656623374823e-09), (3, 5.7348209304555086e-09),
     (4, 3.6812203579604238e-09), (5, 4.1572516753310418e-09)]

x, y = zip(*l)

plt.plot(x, y)

In matplotlib it would be:

import matplotlib.pyplot as plt

data =  [(0, 6.0705199999997801e-08), (1, 2.1015700100300739e-08),
 (2, 7.6280656623374823e-09), (3, 5.7348209304555086e-09),
 (4, 3.6812203579604238e-09), (5, 4.1572516753310418e-09)]

x_val = [x[0] for x in data]
y_val = [x[1] for x in data]

print x_val
plt.plot(x_val,y_val)
plt.plot(x_val,y_val,'or')
plt.show()

which would produce:

If I get your question correctly, you could do something like this.

>>> import matplotlib.pyplot as plt
>>> testList =[(0, 6.0705199999997801e-08), (1, 2.1015700100300739e-08), 
 (2, 7.6280656623374823e-09), (3, 5.7348209304555086e-09), 
 (4, 3.6812203579604238e-09), (5, 4.1572516753310418e-09)]
>>> from math import log
>>> testList2 = [(elem1, log(elem2)) for elem1, elem2 in testList]
>>> testList2
[(0, -16.617236475334405), (1, -17.67799605473062), (2, -18.691431541177973), (3, -18.9767093108359), (4, -19.420021520728017), (5, -19.298411635970396)]
>>> zip(*testList2)
[(0, 1, 2, 3, 4, 5), (-16.617236475334405, -17.67799605473062, -18.691431541177973, -18.9767093108359, -19.420021520728017, -19.298411635970396)]
>>> plt.scatter(*zip(*testList2))
>>> plt.show()

which would give you something like

Or as a line plot,

>>> plt.plot(*zip(*testList2))
>>> plt.show()

EDIT - If you want to add a title and labels for the axis, you could do something like

>>> plt.scatter(*zip(*testList2))
>>> plt.title('Random Figure')
>>> plt.xlabel('X-Axis')
>>> plt.ylabel('Y-Axis')
>>> plt.show()

which would give you

As others have answered, scatter() or plot() will generate the plot you want. I suggest two refinements to answers that are already here:

1. 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.

2. 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.