

In [21]:

    
import SnakesLadders as SL
from pprint import PrettyPrinter
import pandas as pd
%matplotlib inline

pp = PrettyPrinter(16)  # 16 is the column width of the output



In [2]:

    
game = SL.GameFSM(16)

# Make ladders
game.all_states[2].link = 10
game.all_states[8].link = 14

# Make snakes
game.all_states[11].link = 4
game.all_states[15].links = 6

game.make_state_kinds()



In [3]:

    
game.run()
pp.pprint(game.records)
print(SL.count_moves(game.records))
print(SL.count_snakes_and_ladders(game.records))









    



[               {'die': 3, 'end': 3, 'kind': 'B', 'start': 0},
                {'die': 1, 'end': 4, 'kind': 'B', 'start': 3},
                {'die': 2, 'end': 6, 'kind': 'B', 'start': 4},
                {'die': 1, 'end': 7, 'kind': 'B', 'start': 6},
                {'die': 3, 'end': 10, 'kind': 'B', 'start': 7},
                {'die': 3, 'end': 13, 'kind': 'B', 'start': 10},
                {'die': 4, 'end': 16, 'kind': 'B', 'start': 13}]
7
(0, 0)

Find average number of moves in 1000 games (variable num_runs)



In [10]:

    
tot_moves = 0
all_moves = []
num_runs = 10000
for i in range(num_runs):
    game.run()
    moves = SL.count_moves(game.records)
    tot_moves += moves
    all_moves.append(moves)
print(tot_moves/num_runs)



In [11]:

    
max(all_moves)









    Out[11]:





21



In [14]:

    
df = pd.DataFrame({'moves': all_moves})



In [16]:

    
df.describe()









    Out[16]:






  
    
      
      moves
    
  
  
    
      count
      10000.000000
    
    
      mean
      5.625300
    
    
      std
      2.259648
    
    
      min
      3.000000
    
    
      25%
      4.000000
    
    
      50%
      5.000000
    
    
      75%
      7.000000
    
    
      max
      21.000000



In [ ]:



In [24]:

    
type(df['moves'].value_counts())









    Out[24]:





pandas.core.series.Series



In [31]:

    
move_counts = df['moves'].value_counts()

We want as many bins as there are integers between the lowest and highest number of moves found in the data! keys is the method of the pd.Series object that pulls out those numbers. This approach is more robust than simply taking len(move_counts) in case some # moves are not represented.



In [30]:

    
df.hist(bins=max(move_counts.keys())-min(move_counts.keys()))









    Out[30]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x08241270>]], dtype=object)

	moves
count	10000.000000
mean	5.625300
std	2.259648
min	3.000000
25%	4.000000
50%	5.000000
75%	7.000000
max	21.000000