# Need a Work-around for OneHotEncoder Issue in SKLearn Preprocessing

So, it seems that OneHotEncoder won't work with the np.int64 datatype (only np.int32)! Here's a sample of code:

import numpy as np

import pandas as pd

from sklearn.preprocessing import OneHotEncoder

a = np.array([[56748683,8511896545,51001984320],[18643548615,28614357465,56748683],[8511896545,51001984320,40084357915]])

b = pd.DataFrame(a, dtype=np.int64)

ohe = OneHotEncoder()

c = ohe.fit_transform(b).toarray()

When I run this I get the following error: "ValueError: X needs to contain only non-negative integers."

As you can see, X DOES contain only non-negative integers! When I trim a few of the digits and change the datatype to int32 it works fine:

a = np.array([[56748,8511896,51001984],[18643548,28614357,56748],[8511896,51001984,40084357]])

b = pd.DataFrame(a, dtype=np.int32)

ohe = OneHotEncoder()

c = ohe.fit_transform(b).toarray()

Unfortunately, the data I need to encode has 11 digits (which can't be represented by int32). So, any suggestions would be helpful...

Also, I should mention, I don't necessarily need a one hot encoding, just need to create dummy variables. Thanks!

In [51]: pd.get_dummies(b.astype(str), prefix_sep='')