multi-column factorize in pandas

You need to create a ndarray of tuple first, pandas.lib.fast_zip can do this very fast in cython loop.

import pandas as pd
df = pd.DataFrame({'x': [1, 1, 2, 2, 1, 1], 'y':[1, 2, 2, 2, 2, 1]})
print pd.factorize(pd.lib.fast_zip([df.x, df.y]))[0]

the output is:

[0 1 2 2 1 0]

I am not sure if this is an efficient solution. There might be better solutions for this.

arr=[] #this will hold the unique items of the dataframe
for i in df.index:
   if list(df.iloc[i]) not in arr:
      arr.append(list(df.iloc[i]))

so printing the arr would give you

>>>print arr
[[1,1],[1,2],[2,2]]

to hold the indices, i would declare an ind array

ind=[]
for i in df.index:
   ind.append(arr.index(list(df.iloc[i])))

printing ind would give

 >>>print ind
 [0,1,2,2,1,0]

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