def grow():
df = data_auto()
df['trade_year'] = df['tradeTime'].astype(str).str[0:4]
df_year = df.groupby('trade_year').totalPrice.mean()
fig = px.line(df_year, x=df_year.index, y=df_year.values)
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def distribution_structure():
df = data_auto()
fig = px.box(df,
x='buildingStructure',
y="totalPrice",
color='buildingStructure')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def distribution_renovation():
df = data_auto()
fig = px.box(df,
x='renovationCondition',
y="totalPrice",
color='renovationCondition')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def contime():
df = data_auto()
fig = px.box(df,
x='constructionTime',
y="totalPrice",
color='constructionTime')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def typebuildtwo():
df = data_auto()
for dst in df['district'].unique():
fig = px.pie(df,
values=df[df['district'] ==
dst]['buildingType'].value_counts().values,
names=df[df['district'] == dst]
['buildingType'].value_counts().index,
color_discrete_sequence=px.colors.sequential.RdBu,
title=dst)
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def typebuild():
#jumlah bangunan per district
df = data_auto()
diistrict = []
jml_diistrict = []
for i in df['district'].unique():
diistrict.append(i)
jml_diistrict.append(len(df[df['district'] == i]['buildingType']))
btdis = pd.DataFrame({'district': diistrict, 'nofBuilding': jml_diistrict})
btdis.sort_values(by='nofBuilding', inplace=True)
fig = px.bar(btdis,
x='district',
y='nofBuilding',
height=400,
color='district')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def district():
df = data_auto()
c_district = []
jml_cid = []
for i in df['district'].unique():
c_district.append(i)
jml_cid.append(len(df[df['district'] == i]['Cid'].unique()))
dfdist = pd.DataFrame({'district': c_district, 'nofRegions': jml_cid})
#sort
dfdist.sort_values(by=['nofRegions'], inplace=True)
#graph
fig = px.bar(dfdist,
x='district',
y='nofRegions',
height=400,
color='district')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def population():
df = data_auto()
uniqueA = []
uniqueB = []
uniqueC = []
uniqueD = []
uniqueE = []
uniqueF = []
uniqueG = []
uniqueH = []
uniqueI = []
uniqueJ = []
uniqueK = []
uniqueL = []
uniqueM = []
for a in df[df['district'] == 'A']['Cid'].unique():
uniqueA.append(a)
for b in df[df['district'] == 'B']['Cid'].unique():
uniqueB.append(b)
for c in df[df['district'] == 'C']['Cid'].unique():
uniqueC.append(c)
for d in df[df['district'] == 'D']['Cid'].unique():
uniqueD.append(d)
for e in df[df['district'] == 'E']['Cid'].unique():
uniqueE.append(e)
for f in df[df['district'] == 'F']['Cid'].unique():
uniqueF.append(f)
for g in df[df['district'] == 'G']['Cid'].unique():
uniqueG.append(g)
for h in df[df['district'] == 'H']['Cid'].unique():
uniqueH.append(h)
for i in df[df['district'] == 'I']['Cid'].unique():
uniqueI.append(i)
for j in df[df['district'] == 'J']['Cid'].unique():
uniqueJ.append(j)
for k in df[df['district'] == 'K']['Cid'].unique():
uniqueK.append(k)
for l in df[df['district'] == 'L']['Cid'].unique():
uniqueL.append(l)
for m in df[df['district'] == 'M']['Cid'].unique():
uniqueM.append(m)
populasiA = []
populasiB = []
populasiC = []
populasiD = []
populasiE = []
populasiF = []
populasiG = []
populasiH = []
populasiI = []
populasiJ = []
populasiK = []
populasiL = []
populasiM = []
for a in uniqueA:
x = df[df['Cid'] == a]['communityAverage'].values[:1][0]
populasiA.append(x)
for b in uniqueB:
x = df[df['Cid'] == b]['communityAverage'].values[:1][0]
populasiB.append(x)
for c in uniqueC:
x = df[df['Cid'] == c]['communityAverage'].values[:1][0]
populasiC.append(x)
for d in uniqueD:
x = df[df['Cid'] == d]['communityAverage'].values[:1][0]
populasiD.append(x)
for e in uniqueE:
x = df[df['Cid'] == e]['communityAverage'].values[:1][0]
populasiE.append(x)
for f in uniqueF:
x = df[df['Cid'] == f]['communityAverage'].values[:1][0]
populasiF.append(x)
for g in uniqueG:
x = df[df['Cid'] == g]['communityAverage'].values[:1][0]
populasiG.append(x)
for h in uniqueH:
x = df[df['Cid'] == h]['communityAverage'].values[:1][0]
populasiH.append(x)
for i in uniqueI:
x = df[df['Cid'] == i]['communityAverage'].values[:1][0]
populasiI.append(x)
for j in uniqueJ:
x = df[df['Cid'] == j]['communityAverage'].values[:1][0]
populasiJ.append(x)
for k in uniqueK:
x = df[df['Cid'] == k]['communityAverage'].values[:1][0]
populasiK.append(x)
for l in uniqueL:
x = df[df['Cid'] == l]['communityAverage'].values[:1][0]
populasiL.append(x)
for m in uniqueM:
x = df[df['Cid'] == m]['communityAverage'].values[:1][0]
populasiM.append(x)
totpopulasiA = 0
totpopulasiB = 0
totpopulasiC = 0
totpopulasiD = 0
totpopulasiE = 0
totpopulasiF = 0
totpopulasiG = 0
totpopulasiH = 0
totpopulasiI = 0
totpopulasiJ = 0
totpopulasiK = 0
totpopulasiL = 0
totpopulasiM = 0
for i in populasiA:
totpopulasiA = totpopulasiA + i
for i in populasiB:
totpopulasiB = totpopulasiB + i
for i in populasiC:
totpopulasiC = totpopulasiC + i
for i in populasiD:
totpopulasiD = totpopulasiD + i
for i in populasiE:
totpopulasiE = totpopulasiE + i
for i in populasiF:
totpopulasiF = totpopulasiF + i
for i in populasiG:
totpopulasiG = totpopulasiG + i
for i in populasiH:
totpopulasiH = totpopulasiH + i
for i in populasiI:
totpopulasiI = totpopulasiI + i
for i in populasiJ:
totpopulasiJ = totpopulasiJ + i
for i in populasiK:
totpopulasiK = totpopulasiK + i
for i in populasiL:
totpopulasiL = totpopulasiL + i
for i in populasiM:
totpopulasiM = totpopulasiM + i
populasii = pd.DataFrame({
'distrik':
['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M'],
'totpopulasi': [
totpopulasiA, totpopulasiB, totpopulasiC, totpopulasiD,
totpopulasiE, totpopulasiF, totpopulasiG, totpopulasiH,
totpopulasiI, totpopulasiJ, totpopulasiK, totpopulasiL,
totpopulasiM
]
})
populasii.sort_values(by=['totpopulasi'], inplace=True)
fig = px.bar(populasii,
x='distrik',
y='totpopulasi',
height=400,
title='Populasi per Distrik',
color='distrik')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def elevato():
df = data_auto()
fig = px.box(df, x='elevator', y="totalPrice", color='elevator')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def distribution_district():
df = data_auto()
fig = px.box(df, x="district", y="totalPrice", color='district')
fig_json = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return fig_json
def index():
df = data_auto()
dfA = df[df['district'] == 'A']
dfB = df[df['district'] == 'B']
dfC = df[df['district'] == 'C']
dfD = df[df['district'] == 'D']
dfE = df[df['district'] == 'E']
dfF = df[df['district'] == 'F']
dfG = df[df['district'] == 'G']
dfH = df[df['district'] == 'H']
dfI = df[df['district'] == 'I']
dfJ = df[df['district'] == 'J']
dfK = df[df['district'] == 'K']
dfL = df[df['district'] == 'L']
dfM = df[df['district'] == 'M']
list_lat_long = []
for index, value in dfA.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_long.append(temp)
list_lat_longB = []
for index, value in dfB.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longB.append(temp)
list_lat_longC = []
for index, value in dfC.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longC.append(temp)
list_lat_longD = []
for index, value in dfD.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longD.append(temp)
list_lat_longE = []
for index, value in dfE.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longE.append(temp)
list_lat_longF = []
for index, value in dfF.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longF.append(temp)
list_lat_longG = []
for index, value in dfG.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longG.append(temp)
list_lat_longH = []
for index, value in dfH.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longH.append(temp)
list_lat_longI = []
for index, value in dfI.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longI.append(temp)
list_lat_longJ = []
for index, value in dfJ.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longJ.append(temp)
list_lat_longK = []
for index, value in dfK.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longK.append(temp)
list_lat_longL = []
for index, value in dfL.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longL.append(temp)
list_lat_longM = []
for index, value in dfM.iterrows():
temp = [value['Lat'], value['Lng']]
list_lat_longM.append(temp)
mapp = folium.Map(
location=[39.9042, 116.4074],
zoom_start=12,
)
itr = 100
for item in range(itr):
folium.Marker(location=list_lat_long[item],
popup=str(dfA['totalPrice'].values[item]) + '<hr></hr>' +
str(dfA['district'].values[item]) + '<hr></hr>' +
str(dfA['Cid'].values[item]) + '<hr></hr>' +
str(dfA['buildingType'].values[item]) + '<hr></hr>' +
str(dfA['url'].values[item]),
icon=folium.Icon(color='red', icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longB[item],
popup=str(dfB['totalPrice'].values[item]) + '<hr></hr>' +
str(dfB['district'].values[item]) + '<hr></hr>' +
str(dfB['Cid'].values[item]) + '<hr></hr>' +
str(dfB['buildingType'].values[item]) + '<hr></hr>' +
str(dfB['url'].values[item]),
icon=folium.Icon(color='blue', icon='home',
prefix='fa')).add_to(mapp)
for item in range(len(list_lat_longC)):
folium.Marker(location=list_lat_longC[item],
popup=str(dfC['totalPrice'].values[item]) + '<hr></hr>' +
str(dfC['district'].values[item]) + '<hr></hr>' +
str(dfC['Cid'].values[item]) + '<hr></hr>' +
str(dfC['buildingType'].values[item]) + '<hr></hr>' +
str(dfC['url'].values[item]),
icon=folium.Icon(color='green', icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longD[item],
popup=str(dfD['totalPrice'].values[item]) + '<hr></hr>' +
str(dfD['district'].values[item]) + '<hr></hr>' +
str(dfD['Cid'].values[item]) + '<hr></hr>' +
str(dfD['buildingType'].values[item]) + '<hr></hr>' +
str(dfD['url'].values[item]),
icon=folium.Icon(color='purple',
icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longE[item],
popup=str(dfE['totalPrice'].values[item]) + '<hr></hr>' +
str(dfE['district'].values[item]) + '<hr></hr>' +
str(dfE['Cid'].values[item]) + '<hr></hr>' +
str(dfE['buildingType'].values[item]) + '<hr></hr>' +
str(dfE['url'].values[item]),
icon=folium.Icon(color='orange',
icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longF[item],
popup=str(dfF['totalPrice'].values[item]) + '<hr></hr>' +
str(dfF['district'].values[item]) + '<hr></hr>' +
str(dfF['Cid'].values[item]) + '<hr></hr>' +
str(dfF['buildingType'].values[item]) + '<hr></hr>' +
str(dfF['url'].values[item]),
icon=folium.Icon(color='beige', icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longG[item],
popup=str(dfG['totalPrice'].values[item]) + '<hr></hr>' +
str(dfG['district'].values[item]) + '<hr></hr>' +
str(dfG['Cid'].values[item]) + '<hr></hr>' +
str(dfG['buildingType'].values[item]) + '<hr></hr>' +
str(dfG['url'].values[item]),
icon=folium.Icon(color='darkpurple',
icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longH[item],
popup=str(dfH['totalPrice'].values[item]) + '<hr></hr>' +
str(dfH['district'].values[item]) + '<hr></hr>' +
str(dfH['Cid'].values[item]) + '<hr></hr>' +
str(dfH['buildingType'].values[item]) + '<hr></hr>' +
str(dfH['url'].values[item]),
icon=folium.Icon(color='cadetblue',
icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longI[item],
popup=str(dfI['totalPrice'].values[item]) + '<hr></hr>' +
str(dfI['district'].values[item]) + '<hr></hr>' +
str(dfI['Cid'].values[item]) + '<hr></hr>' +
str(dfI['buildingType'].values[item]) + '<hr></hr>' +
str(dfI['url'].values[item]),
icon=folium.Icon(color='darkblue',
icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longJ[item],
popup=str(dfJ['totalPrice'].values[item]) + '<hr></hr>' +
str(dfJ['district'].values[item]) + '<hr></hr>' +
str(dfJ['Cid'].values[item]) + '<hr></hr>' +
str(dfJ['buildingType'].values[item]) + '<hr></hr>' +
str(dfJ['url'].values[item]),
icon=folium.Icon(color='gray', icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longK[item],
popup=str(dfK['totalPrice'].values[item]) + '<hr></hr>' +
str(dfK['district'].values[item]) + '<hr></hr>' +
str(dfK['Cid'].values[item]) + '<hr></hr>' +
str(dfK['buildingType'].values[item]) + '<hr></hr>' +
str(dfK['url'].values[item]),
icon=folium.Icon(color='black', icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longL[item],
popup=str(dfL['totalPrice'].values[item]) + '<hr></hr>' +
str(dfL['district'].values[item]) + '<hr></hr>' +
str(dfL['Cid'].values[item]) + '<hr></hr>' +
str(dfL['buildingType'].values[item]) + '<hr></hr>' +
str(dfL['url'].values[item]),
icon=folium.Icon(color='pink', icon='home',
prefix='fa')).add_to(mapp)
for item in range(itr):
folium.Marker(location=list_lat_longM[item],
popup=str(dfM['totalPrice'].values[item]) + '<hr></hr>' +
str(dfM['district'].values[item]) + '<hr></hr>' +
str(dfM['Cid'].values[item]) + '<hr></hr>' +
str(dfM['buildingType'].values[item]) + '<hr></hr>' +
str(dfM['url'].values[item]),
icon=folium.Icon(color='white', icon='home',
prefix='fa')).add_to(mapp)
return mapp._repr_html_()
def data():
data = data_auto()
return render_template('table_data.html', data=data)