I have a set of houses with categorical and numerical data. Later I will have a new house and my goal will be to find the 20 closest houses.
The code is working fine, and the result are not so bad but it is **way too long**. With a sample of 10 000 houses it takes 6 minutes, using Python 2.7. My actual dataset is about 100 000 houses. 

    # -*- coding: utf-8 -*-

    import csv
    import random
    import math
    import operator
    import numpy as np
    import pandas as pd, os
    from sklearn.preprocessing import OneHotEncoder
    from sklearn.feature_extraction import DictVectorizer
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LinearRegression
    from unidecode import unidecode
    import sys
    import cProfile
    import re
    

    data_pd = pd.read_csv('data.csv')
    
    # I delete rows with NA's (to change later)
    data = data.dropna()
    
    
    # The function I use to deal with categorical data
    def one_hot_dataframe(data, cols, replace=False):
        vec = DictVectorizer()
        mkdict = lambda row: dict((col, row[col]) for col in cols)
        vecData = pd.DataFrame(vec.fit_transform(data[cols].apply(mkdict, axis=1)).toarray())
        vecData.columns = vec.get_feature_names()
        vecData.index = data.index
        if replace is True:
            data = data.drop(cols, axis=1)
            data = data.join(vecData)
        return (data, vecData, vec)
    
    # New data set with numerical data only
    data_encode, _, _= one_hot_dataframe(data, ['Type', 'Bord_de_mer', 'Bord_de_plan_deau', 'Campagne', 'Centre_ville', 'Complexe_de_vacances', 'Lac', 'Montagne', 'Plage', 'Riviere', 'Village', 'Ville', 'Acces_haut_debit', 'Acces_internet', 'Climatisation', 'Linge_de_maison_fourni', 'Serviettes_de_bain', 'Wifi', 'Terrasse', 'Veranda_Loggia', 'Balcon', 'Jardin', 'location_non_fumeur', 'fumeurs_acceptes', 'animaux_autorises', 'animaux_non_admis', 'acces_handicape', 'Piscine_commune', 'Piscine_privee', 'Piscine_dinterieur', 'Piscine_chauffee', 'Bain_a_remous', 'Sauna'], replace=True)
    

    #convert string to float
    data = data.convert_objects(convert_numeric=True)
        
    # scaled_col the columns to standardize
    scaled_col = ['Capacity', 'BedRooms', 'Latitude', 'Longitude', 'Size', 'Acces_haut_debit=No', 'Acces_haut_debit=Yes', 'Acces_internet=No', 'Acces_internet=Yes', 'Bain_a_remous=No', 'Bain_a_remous=Yes', 'Balcon=No', 'Balcon=Yes', 'Bord_de_mer=No', 'Bord_de_plan_deau=No', 'Bord_de_plan_deau=Yes', 'Campagne=No', 'Campagne=Yes', 'Centre_ville=No', 'Centre_ville=Yes', 'Climatisation=No', 'Climatisation=Yes', 'Complexe_de_vacances=No', 'Complexe_de_vacances=Yes', 'Jardin=No', 'Jardin=Yes', 'Lac=No', 'Lac=Yes', 'Linge_de_maison_fourni=No', 'Linge_de_maison_fourni=Yes', 'Montagne=No', 'Montagne=Yes', 'Piscine_chauffee=No', 'Piscine_chauffee=Yes', 'Piscine_commune=No', 'Piscine_commune=Yes', 'Piscine_dinterieur=No', 'Piscine_privee=No', 'Piscine_privee=Yes', 'Plage=No', 'Plage=Yes', 'Riviere=No', 'Riviere=Yes', 'Sauna=No', 'Sauna=Yes', 'Serviettes_de_bain=No', 'Serviettes_de_bain=Yes', 'Terrasse=No', 'Terrasse=Yes', 'Type=Appartement', 'Type=Chalet', "Type=Chambre d'h\xc3\xb4tes", 'Type=G\xc3\xaete', 'Type=Maison', 'Type=Studio', 'Type=Villa', 'Veranda_Loggia=No', 'Veranda_Loggia=Yes', 'Village=No', 'Village=Yes', 'Ville=No', 'Ville=Yes', 'Wifi=No', 'Wifi=Yes', 'acces_handicape=No', 'acces_handicape=Yes', 'animaux_autorises=No', 'animaux_autorises=Yes', 'animaux_non_admis=No', 'animaux_non_admis=Yes', 'fumeurs_acceptes=No', 'fumeurs_acceptes=Yes', 'location_non_fumeur=No', 'location_non_fumeur=Yes']
    
    # standardization of the dataset   
    scaled_features = data.copy()
    features = scaled_features[scaled_col]
    scaler = StandardScaler().fit(features.values)
    features = scaler.transform(features.values)
    scaled_features[scaled_col] = features
    
      
    # That's the part where I find weights for the knn algorithm
    # I'm using a simple regression and I keep the parameters

    #create a new dataframe with the concerned features
    weightframe = scaled_features
    weightframe = weightframe.drop('DetailUrl', axis = 1)
    weightframe = weightframe.drop('index', axis = 1)
    
    X = weightframe.drop('Moyenne', axis = 1)
    y = weightframe['Moyenne']
    
    lm = LinearRegression(fit_intercept=False)
    lm.fit(X,y)
    
    # df with the features and their weights
    weights = pd.DataFrame(zip(X.columns, lm.coef_))
    
    # df with positive values
    weights_abs = weights
    weights_abs[1] = weights_abs[1].abs()
    
    
    # Now I can start my knn algorithm
    # First I define a distance which is divided by the weights 
    def euclideanDistance(instance1, instance2, start, stop):
      distance = 0
      for x in range(start, stop):
        distance += (1/abs(weights.iloc[x-3,1]))*pow((instance1[x] - instance2[x]), 2)
      return math.sqrt(distance)
    
    # Then I find the neighbors
    def getNeighbors(trainingSet, testInstance, k):
      distances = trainingSet
      ncol = []
      for x in range(len(trainingSet)):
        dist = euclideanDistance(testInstance, trainingSet.iloc[x,:], 3, len(testInstance))
        ncol.append(dist)
      distances['distance'] = ncol
      distances = distances.sort(['distance'])
      neighbor = distances.head(k)
      return neighbor
    
    
    # I test my function with the 314th line of the dataset
    test = scaled_features.iloc[314,:]
    # test_response returns the 20 closest houses
    test_response = getNeighbors(scaled_features, test, 20)
    
    #I want to have test_response in its original form (not standardized)
    test_response[scaled_col] = scaler.inverse_transform(test_response[scaled_col])
    
    print test_response

I made some quick test and the part which is the longer to execute is the KNN part. The standardization and the regression part are quite quick.

What do you think of my script ? Do you have ideas to increase its performance ? It is definitely too slow.