convert pandas dataframe to Orange data table

Question

i am wroking with pandas dataframe and i want to convert it to orange data table to impute missing values. my dataframe looks like

locationId rank Rating type  value
1          1    10     shop   2.668
2          4    8      store  3.921
3          3    NAN    shop   3.122

where rank is ordinal with repeating values between 1 and 5.Type is categorical with types shop,store etc and rating is integer type.value is floating point. i want to convert it into Orange data table and impute missing values. I had also viewed this but these functions gives me an error and not worked for me.

Answer 1

import numpy as np
import pandas as pd
import Orange
import csv
from io import StringIO
from collections import OrderedDict
from Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable


def pandas_to_orange(df):
    domain, attributes, metas = construct_domain(df)
    orange_table = Orange.data.Table.from_numpy(domain = domain, X = df[attributes].values, Y = None, metas = df[metas].values, W = None)
    return orange_table

def construct_domain(df):
    columns = OrderedDict(df.dtypes)
    attributes = OrderedDict()
    metas = OrderedDict()
    for name, dtype in columns.items():

        if issubclass(dtype.type, np.number):
            if len(df[name].unique()) >= 13 or issubclass(dtype.type, np.inexact) or (df[name].max() > len(df[name].unique())):
                attributes[name] = Orange.data.ContinuousVariable(name)
            else:
                df[name] = df[name].astype(str)
                attributes[name] = Orange.data.DiscreteVariable(name, values = sorted(df[name].unique().tolist()))
        else:
            metas[name] = Orange.data.StringVariable(name)

    domain = Orange.data.Domain(attributes = attributes.values(), metas = metas.values())

    return domain, list(attributes.keys()), list(metas.keys())

use -> pandas_to_orange(dataFrame)