code:
import pandas as pd
import numpy as np
"""Membaca data"""
data = pd.read_json('data.json')
"""Mencari rata rata setiap produk lalu memasukkan nya pada array """
#Variable untuk menyimpan rata-rata rating dari setiap produk
arrrating = []
arrid = []
#Menuliskan rata rata Rating dari sabunid ke variable arrrating dan arrid
for x in range(200,226):
cari = data['Sabunid'] == x
arrrating.append(data[cari]['Rating'].mean())
for x in range(200,226):
arrid.append(x)
"""Membuat dataframe untuk mencari Sabunid berdasarkan ratingnya"""
#Menampilkan DataFrame dari variable data
print("Data:")
print(data)
print("")
print("")
#Membuat DataFrame baru yang berisi sabunid dan rata rata ratingnya
lol = {
"Sabunid":arrid,
"Rating":arrrating
}
dframe = pd.DataFrame(lol)
#Menghiraukan jumlah nilai hanya menuliskan setiap nilai
reting = np.unique(arrrating)
#Mengetahui sabunid dari setiap rating lalu menyimpannya di variable hasil
hasil = []
for x in reting:
cari123 = dframe['Rating'] == x
hasil.append(dframe[cari123]['Sabunid'])
#hstack untuk menyatukan setiap array yang berada di variable hasil
npfinal = np.hstack(hasil)
print("Sabunid: Rata-rata Rating:")
count = 0
ratingurut = np.sort(np.array(arrrating))
for x in npfinal:
print(x," ",ratingurut[count])
count += 1
print("3 Sabunid yang akan direkomendasikan adalah: ",",",npfinal[len(npfinal) - 1],",",npfinal[len(npfinal) - 2],",",npfinal[len(npfinal) - 3]," dengan rata rata rating nya adalah ",ratingurut[len(ratingurut) - 1],",",ratingurut[len(ratingurut) - 2],", dan ",ratingurut[len(ratingurut) - 3])
data.json:
{
"Sabunid":{
"1":206,
"2":219,
"3":221,
"4":217,
"5":225,
"6":223,
"7":223,
"8":212,
"9":211,
"10":201,
"11":213,
"12":212,
"13":220,
"14":210,
"15":215,
"16":212,
"17":219,
"18":220,
"19":207,
"20":225,
"21":217,
"22":200,
"23":216,
"24":221,
"25":200,
"26":200,
"27":218,
"28":215,
"29":207,
"30":217,
"31":203,
"32":221,
"33":208,
"34":212,
"35":212,
"36":207,
"37":203,
"38":217,
"39":216,
"40":207,
"41":203,
"42":217,
"43":200,
"44":221,
"45":209,
"46":222,
"47":204,
"48":203,
"49":207,
"50":205,
"51":211,
"52":212,
"53":209,
"54":207,
"55":204,
"56":218,
"57":221,
"58":204,
"59":207,
"60":218,
"61":216,
"62":218,
"63":204,
"64":222,
"65":209,
"66":214,
"67":214,
"68":207,
"69":206,
"70":224,
"71":219,
"72":218,
"73":217,
"74":205,
"75":203,
"76":203,
"77":201,
"78":220,
"79":204,
"80":218,
"81":211,
"82":206,
"83":218,
"84":225,
"85":220,
"86":225,
"87":206,
"88":205,
"89":201,
"90":202,
"91":222,
"92":225,
"93":205,
"94":213,
"95":223,
"96":222,
"97":213,
"98":200,
"99":219,
"100":222
},
"Rating":{
"1":"3.00",
"2":"4.00",
"3":"3.00",
"4":"3.00",
"5":"2.00",
"6":"3.00",
"7":"3.00",
"8":"3.00",
"9":"1.00",
"10":"3.00",
"11":"1.00",
"12":"5.00",
"13":"1.00",
"14":"2.00",
"15":"5.00",
"16":"3.00",
"17":"5.00",
"18":"3.00",
"19":"5.00",
"20":"1.00",
"21":"4.00",
"22":"2.00",
"23":"1.00",
"24":"4.00",
"25":"2.00",
"26":"3.00",
"27":"5.00",
"28":"5.00",
"29":"1.00",
"30":"2.00",
"31":"1.00",
"32":"2.00",
"33":"4.00",
"34":"4.00",
"35":"2.00",
"36":"5.00",
"37":"4.00",
"38":"2.00",
"39":"1.00",
"40":"1.00",
"41":"2.00",
"42":"4.00",
"43":"3.00",
"44":"3.00",
"45":"4.00",
"46":"1.00",
"47":"4.00",
"48":"5.00",
"49":"1.00",
"50":"2.00",
"51":"1.00",
"52":"2.00",
"53":"2.00",
"54":"4.00",
"55":"1.00",
"56":"4.00",
"57":"5.00",
"58":"5.00",
"59":"1.00",
"60":"3.00",
"61":"3.00",
"62":"1.00",
"63":"1.00",
"64":"5.00",
"65":"5.00",
"66":"4.00",
"67":"4.00",
"68":"2.00",
"69":"1.00",
"70":"5.00",
"71":"5.00",
"72":"4.00",
"73":"1.00",
"74":"5.00",
"75":"1.00",
"76":"4.00",
"77":"1.00",
"78":"5.00",
"79":"3.00",
"80":"1.00",
"81":"1.00",
"82":"5.00",
"83":"3.00",
"84":"3.00",
"85":"2.00",
"86":"2.00",
"87":"1.00",
"88":"4.00",
"89":"4.00",
"90":"5.00",
"91":"2.00",
"92":"2.00",
"93":"3.00",
"94":"5.00",
"95":"5.00",
"96":"5.00",
"97":"1.00",
"98":"5.00",
"99":"2.00",
"100":"5.00"
}
}
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