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Edit and run



In [3]:

    
import os
import tables

Initialize Data
- Create hdf5 file
- Load Constants
- Create fixed data (e.g. list of all car positions, list of red car positions)
- Spin up working data structures

Create and record states based on input value for #cars and #trucks
- For each red car position:
  - Place red car on board
  - Recurse through placing the remaining cars
  - Remove red car from board
Close hdf5 file

Create States

Place red car on board
Recursively place each non-red car on the board until all cars are gone or board is empty
Upon reach base case, record state in hdf5 file



In [4]:

    
a = [{}] * 5



In [5]:

    
a









    Out[5]:





[{}, {}, {}, {}, {}]



In [7]:

    
range(12,18)









    Out[7]:





[12, 13, 14, 15, 16, 17]



In [10]:

    
for i in  range(26,26):
    print i



In [25]:

    
d =  {'x':1,'y':2,'z':3}
d['y']









    Out[25]:





2



In [40]:

    
import pandas as pd
import collections



In [41]:

    
a = collections.OrderedDict
b = collections.OrderedDict
a['A'] = [1,2,3]
a['B'] = [4,5,6]
a['C'] = [2,3,4]









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-41-a605b7115d81> in <module>()
      1 a = collections.OrderedDict
      2 b = collections.OrderedDict
----> 3 a['A'] = [1,2,3]
      4 a['B'] = [4,5,6]
      5 a['C'] = [2,3,4]

TypeError: 'type' object does not support item assignment



In [31]:

    
df_a = pd.DataFrame.from_items( [ ['A',[1,2,3]] , ['B',[4,5,6]], ['C',[2,3,4]] ]  )
df_b = pd.DataFrame.from_items( [ ['A',[1,2,4]] , ['B',[4,6,3]], ['D',[2,3,4]] ]  )
df_a



In [34]:

    
df_b



In [39]:

    
pd.merge(df_a,df_b,how='right', on=['A','B'])



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