Determine weighted average for hotel ratings

weightedavg.py

# http://times.cs.uiuc.edu/~wang296/Data/ tripadvisor dataset
import json
import numpy as np
import os
path = '/place/i/have/lots/of/json'
np.seterr(divide='ignore', invalid='ignore')
np.set_printoptions(nanstr="0")
#loop over all files in dir
for filename in os.listdir(path):
    with open(path+filename) as json_file:
    # hold the review category names and array of scores
        score = {}
        json_data = json.load(json_file)
        hoteldata = json_data["HotelInfo"]
        hotelReviews = json_data["Reviews"]
        for review in hotelReviews:
            for key in review["Ratings"].keys():

                if(key not in score):
                    score[key] = [int(float(review["Ratings"][key]))]
                else:
                    score[key].append(int(float(review["Ratings"][key])))

    name = hoteldata["Name"] if "Name" in hoteldata else ""
    print("{}\t{}\t".format(hoteldata["HotelID"],name),end="")
    for key in score.keys():
        if( len(score[key]) > 1):
        # Weighted avg, since its onlines reviews negative is not wanted
            weightsForAvg = np.array(range(len(score[key]))) / sum(range(len(score[key])))
            weightedAverage = np.absolute(np.average(score[key], weights=weightsForAvg))
            print("{} | {:.2f} | ".format(key.strip(), weightedAverage), end="")
        else:
            print("{} | {} | ".format(key.strip(),str(score[key][0))),end="")

    print("")

Refactored to preserve -1 when user did not provide a score