Converting Pandas DataFrame to Orange Table

Question

I notice that this is an issue on GitHub already. Does anyone have any code that converts a Pandas DataFrame to an Orange Table?

Explicitly, I have the following table.

       user  hotel  star_rating  user  home_continent  gender
0         1     39          4.0     1               2  female
1         1     44          3.0     1               2  female
2         2     63          4.5     2               3  female
3         2      2          2.0     2               3  female
4         3     26          4.0     3               1    male
5         3     37          5.0     3               1    male
6         3     63          4.5     3               1    male

Answer 1

In order to convert pandas DataFrame to Orange Table you need to construct a domain, which specifies the column types.

For continuous variables, you only need to provide the name of the variable, but for Discrete variables, you also need to provide a list of all possible values.

The following code will construct a domain for your DataFrame and convert it to an Orange Table:

import numpy as np
from Orange.feature import Discrete, Continuous
from Orange.data import Domain, Table
domain = Domain([
    Discrete('user', values=[str(v) for v in np.unique(df.user)]),
    Discrete('hotel', values=[str(v) for v in np.unique(df.hotel)]),
    Continuous('star_rating'),
    Discrete('user', values=[str(v) for v in np.unique(df.user)]),
    Discrete('home_continent', values=[str(v) for v in np.unique(df.home_continent)]),
    Discrete('gender', values=['male', 'female'])], False)
table = Table(domain, [map(str, row) for row in df.as_matrix()])

The map(str, row) step is needed so Orange know that the data contains values of discrete features (and not the indices of values in the values list).