전국 시도별 총 가정생활 폐기물 데이터 분석 & 시각화 with Python, Mapbox, Plotly

Data Source

18년 가정생활폐기물 데이터.csv

0.08MB

 

시도별 가정 생활 폐기물 지도 시각화 with Mapbox

 

 

가정 생활 폐기물 데이터 분석 with Plotly(인터렉티브 그래프)

 

 

Python Code

 

import pandas as pd
# 1인당 가정생활폐기물(연간) 발생 현황
df=pd.read_csv('/18년 가정생활폐기물 데이터.csv',encoding='cp949',)
df.rename(columns={'T10 주민1인당 생활폐기물배출량<br>(A÷B) (14STD02441 kg/일)':'Household Waste per person(kg/day)',
                  'T001 생활폐기물발생량<br>(A) (14STD06543 톤/일)':'Household Waste(ton/day)',
                   'T002 주민등록인구<br>(B) (14STD04547 명)':'Population',
                   '행정구역별':'region','시점':'year'},inplace=True)
df=df[df['region']!='전국']
sido=['서울특별시','인천광역시','경기도','강원도','충청남도','충청북도','전라북도','전라남도',
     '대전광역시','광주광역시','울산광역시','경상북도','경상남도','대구광역시','부산광역시','세종특별자치시','제주특별자치도']
df['sigun']=df['region'].apply(lambda x: 'x' if x in sido else 'o')
df_sido=df[df['sigun']=='x']
si_do={'강원도':'Gangwon-do','경기도':'Gyeonggi-do', '경상남도':'Gyeongsangnam-do','경상북도':'Gyeongsangbuk-do',
       '광주광역시':'Gwangju','대구광역시':'Daegu','대전광역시':'Daejeon','부산광역시':'Busan',
       '서울특별시':'Seoul','세종특별자치시':'Sejongsi','울산광역시':'Ulsan','인천광역시':'Incheon',
       '전라남도':'Jeollanam-do','전라북도':'Jeollabuk-do','제주특별자치도':'Jeju-do','충청남도':'Chungcheongnam-do',
       '충청북도':'Chungcheongbuk-do'}
df_sido['eng_region']=df_sido['region'].apply(lambda x: si_do[x] )

import plotly.express as px
line = px.line(df_sido, x="year", y="Household Waste per person(kg/day)", color="eng_region")
al=line.to_json()
for x,y in zip(list(si_do.keys()), list(si_do.values())):
    al = al.replace(y, x)
    
df_sido['Household Waste(ton/day)']=df_sido['Household Waste(ton/day)'].astype(float)
total=pd.DataFrame(df_sido.groupby(['region','eng_region']).sum()['Household Waste(ton/day)'])
total['perc']=total['Household Waste(ton/day)'].apply(lambda x: float(x)/total['Household Waste(ton/day)'].sum())
total=total.reset_index()
pie = px.pie(total, values='perc', names='eng_region', title='Household waste per province')
pie.show()
pj=pie.to_json()
for x,y in zip(list(si_do.keys()), list(si_do.values())):
    pj = pj.replace(y, x)
    
import plotly.graph_objects as go
hm = go.Figure(data=go.Heatmap(
                   z=df_sido['Household Waste(ton/day)'],
                   x=df_sido['year'],
                   y=df_sido['eng_region'],
                   hoverongaps = False,))
hm.show()
hj=hm.to_json()
for x,y in zip(list(si_do.keys()), list(si_do.values())):
    hj = hj.replace(y, x)
    
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/southkorea/southkorea-maps/master/kostat/2018/json/skorea-provinces-2018-geo.json') as response:
    kor = json.load(response)
    
si_do=['Gangwon-do','Gyeonggi-do', 'Gyeongsangnam-do','Gyeongsangbuk-do','Gwangju','Daegu','Daejeon','Busan','Seoul','Sejongsi'
     ,'Ulsan','Incheon','Jeollanam-do','Jeollabuk-do','Jeju-do','Chungcheongnam-do','Chungcheongbuk-do']
total['eng']=si_do
total['총 가정 생활폐기물(천톤/일)']=total['Household Waste(ton/day)']/1000
for idx, si in enumerate(kor['features']):
    name=si['properties']['name']
    nvm=total.loc[(total.region==name),'총 가정 생활폐기물(천톤/일)'].iloc[0]
    kor['features'][idx]['properties']['총 가정 생활폐기물(천톤/일)']=int(nvm)
    
import mapboxgl
from mapboxgl.viz import *
import os
from mapboxgl.utils import create_color_stops
from mapboxgl.utils import create_numeric_stops

api='~~'

center =[126.986, 37.565]

color_breaks = [20,40,60,80,100,120]
color_stops =create_color_stops(color_breaks,colors='YlGnBu')
viz =ChoroplethViz(
    access_token=api,
    data=kor,
    color_property='총 가정 생활폐기물(천톤/일)',
    color_stops=color_stops,
    center=center,
    zoom=5.7,
    bearing=-11,
    pitch=43,
    height_property='총 가정 생활폐기물(천톤/일)',
    height_stops=create_numeric_stops(color_breaks,0,10),
    height_function_type='interpolate'
)
viz.show()
viz.create_html('waste.html')