- Read the guideline before start
If you flip a coin, heads will come up 50% of the time. But if you flip a coin 10 times, what is the chance that heads will come up 5 times? 2 times?
Write flip_coin function, that conducts at least 10000 cases
of flipping a coin 10 times. It should return dict,
where keys are numbers of possible heads dropped (0 to 10),
and values are percentage of how many that number of heads
dropped out of all cases.
print(flip_coin())
# {0: 0.13, 1: 0.97, 2: 4.22, 3: 12.04, ... }If you have done all correctly, you should note that the biggest percentage of a number of heads dropped is about the middle, 4-6 times and tends to 0 when it is about the edges 0-1 and 9-10. It calls normal distribution or Gaussian distribution.
Write draw_gaussian_distribution_graph function,
that draws a graph that shows the distribution.
matplotlib is relevant library for this purpose.
You should get graph like this:
