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MovinOnUP1_v2.py
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# -*- coding: utf-8 -*-
import sys
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import pandas as pd
import numpy as np
import glob
from datetime import datetime
import dash_table as dt
from plotly import graph_objs as go
import plotly.plotly as py
from plotly.graph_objs import *
from uszipcode import Zipcode
from uszipcode import SearchEngine
from geopy.geocoders import Nominatim
#global ranked_data
#global map_data, ranked_data
geolocator = Nominatim(user_agent="Movin\'OnUp") #"specify_your_app_name_here"
#data from US CENSUS
search = SearchEngine(simple_zipcode=True)
app = dash.Dash(__name__) #__name__, external_stylesheets=external_stylesheets
app.title = 'Movin\'OnUP!'
# API keys and datasets
mapbox_access_token = 'pk.eyJ1IjoiZHluZGwiLCJhIjoiY2p4M2gyYm9wMDBzbDRhbmxzYWMya2tvZCJ9.xWu9JsGNMrFmk6yiydXlqw'
# loading data
path = '/Users/dmlee/Desktop/Insight_DS/data/'
#path = r'C:\DRO\DCL_rawdata_files' # use your path
all_files = glob.glob(path + 'ZILLOW/Z*_MSPAHforecast.csv')
li = []
LA_zipcodes = ['90014',
'90013',
'90017',
'90006',
'90029',
'90071',
'90037',
'90008',
'90018',
'90023',
'90031',
'90822',
'90731',
'90028',
'90019',
'90025',
'90049',
'91406',
'91405',
'91335',
'90065',
'90062',
'90003',
'90043',
'90291',
'90304',
'91605',
'90020',
'91356',
'91364',
'91303',
'90057',
'90015',
'90007',
'90033',
'90035',
'90012',
'91607',
'90004',
'91604',
'90059',
'90010',
'90064',
'90011',
'91331',
'90061',
'90032',
'90044',
'90502',
'90063',
'90744',
'90292',
'91340',
'91402',
'91324',
'90021',
'90079',
'90026',
'90016',
'90710',
'90248',
'90732',
'90717',
'90046',
'90038',
'90069',
'90048',
'90036',
'90211',
'90404',
'90024',
'90067',
'90077',
'90034',
'90402',
'91423',
'90210',
'91403',
'91411',
'91401',
'91436',
'91316',
'91343',
'91325',
'91201',
'90041',
'90042',
'91205',
'90039',
'91204',
'90305',
'90047',
'90001',
'90058',
'90066',
'90293',
'90094',
'90056',
'90045',
'90405',
'90230',
'91602',
'91608',
'91601',
'91606',
'91214',
'91352',
'91040',
'91504',
'91342',
'91306',
'91326',
'91344',
'91304',
'90002',
'90247',
'91345',
'90005',
'91367',
'90027',
'91030',
'90501',
'90068',
'90272',
'90232',
'91505',
'91206',
'91311',
'90301',
'90212',
'91307',
'90804',
'90740',
'90403',
'91301',
'90814',
'90302']
LA_other_zipcodes = ['90009','90030','90050','90051','90052','90053','90054',\
'90055','90060','90070','90072','90074','90075','90076','90078','90079','90080',\
'90081','90082','90083','90084','90085','90086','90087','90088','90090','90091',\
'90093','90095','90096','90099','90134','90189']
#print('Length',len(LA_zipcodes),len(LA_other_zipcodes))
# for zipcode in LA_zipcodes:
# MSPfile = 'ZILLOW/Z'+zipcode+'_MSPAH.csv' #MSP
# forecaster = 'ZILLOW/Z'+zipcode+'_MSPAH'+'forecast'+'.csv'
count = 0
for filename in all_files:
LA_zipcode = filename.split('_', 2)[1].split('Z')[2]
if LA_zipcode not in LA_other_zipcodes:
df = pd.read_csv(filename, index_col=None, header=0)
df['zipcode'] = LA_zipcode
lat_lon = search.by_zipcode(LA_zipcode).values()[7:9]
df['Latitude'] = lat_lon[0]
df['Longitude'] = lat_lon[1]
li.append(df)
count+=1
#print(count)
userforecast = pd.concat(li, axis=0, ignore_index=True)
forecast_data = userforecast[['ds','zipcode','trend','trend_lower','trend_upper','Latitude','Longitude']]
#data = pd.DataFrame([],columns = ['Rankings', 'Zipcode', 'Trend','Avg. ROI','ROI Rank' ,'Wealth Rank','Latitude','Longitude'])
ranked_data = pd.DataFrame([],columns = ['Rankings', 'Zipcode', 'Trend','Avg. ROI','ROI Rank' ,'Wealth Rank','Latitude','Longitude'])
#set initial values for min and max
minMSP = 0
maxMSP = 600000
colors = {
'background': '#111111',
'text': '#7FDBFF',
'light_text': '#D8D8D8'
}
# Boostrap CSS.
# app.css.append_css({'external_url': 'https://codepen.io/amyoshino/pen/jzXypZ.css'})
app.css.append_css({'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css'})
app.config['suppress_callback_exceptions']=True
# Layouts
layout_table = dict(
autosize=True,
height=500,
font=dict(color="#191A1A"),
titlefont=dict(color="#191A1A", size='14'),
margin=dict(
l=45,
r=0,
b=5,
t=0
),
hovermode="closest",
plot_bgcolor='#fffcfc',
paper_bgcolor='#fffcfc',
legend=dict(font=dict(size=10), orientation='h'),
)
layout_table['font-size'] = '12'
layout_table['margin-top'] = '20'
layout_map = dict(
autosize=True,
height=500,
font=dict(color="#191A1A"),
titlefont=dict(color="#191A1A", size='14'),
margin=dict(
l=-20,
r=0,
b=0,
t=0
),
hovermode="closest",
plot_bgcolor='#fffcfc',
paper_bgcolor='#fffcfc',
legend=dict(font=dict(size=10), orientation='h'),
title='LA Home Location Desirables by Zipcode',
mapbox=dict(
accesstoken=mapbox_access_token,
style="light",
center=dict(
lon=-118.2437,
lat=34.0522
),
zoom=10,
)
)
# functions
def gen_map(map_data):
# groupby returns a dictionary mapping the values of the first field
# 'classification' onto a list of record dictionaries with that
# classification value.
return {
"data": [{
"type": "scattermapbox",
"lat": list(map_data['Latitude']),
"lon": list(map_data['Longitude']),
"hoverinfo": "text",
"hovertext": [["Name: {} <br>Type: {} <br>Provider: {}".format(i,j,k)]
for i,j,k in zip(map_data['Name'], map_data['Type'],map_data['Provider'])],
"mode": "markers",
"name": list(map_data['Name']),
"marker": {
"size": 6,
"opacity": 0.7
}
}],
"layout": layout_map
}
#flatten list tool
def flatten(foo):
for x in foo:
if hasattr(x, '__iter__') and not isinstance(x, str):
for y in flatten(x):
yield y
else:
yield x
#__________________________________
#__________________________________
app.layout = html.Div([
#headers and initial user input
#------------------------------
html.Div([
html.Div([
html.H1(children='Movin\'OnUP!',
style={
'textAlign': 'left',
'color': colors['text']},
className = "nine columns"
),
html.Img(
src="https://assets-global.website-files.com/575a31d2ce5d01dc7a20de45/575a31d2ce5d01dc7a20ded3_insight_logo.png",
className='three columns',
style={
'height': '14%',
'width': '14%',
'float': 'right',
'position': 'relative',
'margin-top': 20,
'margin-right': 20
},
),
html.Div(children='''A home pre-search optimization app''',
style={
# 'height': '14%',
# 'width': '28',
# 'float': 'left',
# # 'position': 'fixed',
'margin-top': 0,
'margin-right': 15,
'margin-bottom': 15,
'textAlign': 'left',
'color': colors['text']
},
className = 'nine columns'
),
], className = "row"
)
]),
#Right side
#---------
html.Div([
html.Div([
dcc.Input(id='minMSPinput', type='number', value=minMSP,
placeholder='Enter min. price',
style={
'height': '20%',
'width': '42%',
'float': 'left',
# 'position': 'fixed',
'textAlign': 'left'
#'color': colors['light_text']
}),
dcc.Input(id='maxMSPinput', type='number', value=maxMSP,
placeholder='Enter max. price',
style={
'height': '20%',
'width': '42%',
'float': 'left',
# 'position': 'fixed',
'textAlign': 'left'
#'color': colors['light_text']
}),
# html.Div(id='userMSPoutput',
# style={
# 'margin-top': 0,
# 'margin-right': 45,
# 'margin-bottom': 35,
# 'textAlign': 'left'
# }),
html.Div(children='Min. Home Price <---> Max. Home Price (USD)',
style={
# 'float':'left',
'fontSize': 16,
'margin-top': 0,
'margin-right': 45,
'margin-bottom': 0,
'textAlign': 'left'
}),
dcc.Graph(id='map-graph',
animate=True,
style={'margin-top': 0,'margin-bottom': 0,'padding-bottom':0}
),
html.Div(id='Yearly-outlook-output-container',style={'fontSize': 16,'margin-top': 0,'padding-top':0,'textAlign': 'center'},
children='Predicted Future Outlook'
),
dcc.Slider(
id='Yearly_outlook',
min=0,
max=10,
value=0,
marks={
0: {'label': ' Now', 'style': {'color': '#77b0b1'}},
1: {'label': '1 Year'},
2: {'label': ''},
3: {'label': ''},
4: {'label': ''},
5: {'label': '5 Years'},
6: {'label': ''},
7: {'label': ''},
8: {'label': ''},
9: {'label': ''},
# 10: {'label': '10 Years'},
# 11: {'label': ''},
# 12: {'label': ''},
# 13: {'label': ''},
# 14: {'label': ''},
10: {'label': '10 Years', 'style': {'color': '#f50'}}
},
),
], className = "five columns"
),
html.Div([
html.H2(id='Desirables-Value-header',style={'fontSize': 40,'margin-top': 0,'padding-bottom':0,'textAlign': 'center','color':'#f50'},
children ='Desirables'
),
html.Div(id='Wealth-Value-output-container',style={'fontSize': 16,'margin-top': 0,'padding-top':0,'textAlign': 'center'},
children='Local Economics'
),
dcc.Slider(
id='Wealth_Value',
min=0,
max=4,
value=2,
marks={
0: {'label': 'Not Important', 'style': {'color': '#77b0b1'}},
1: {'label': 'Less Important'},
2: {'label': 'Important'},
3: {'label': 'Very Important'},
4: {'label': 'Extremely Important', 'style': {'color': '#f50'}}
}
),
html.Div(id='ROI-Value-output-container',style={'fontSize': 16,'margin-top': 0,'padding-top':60,'textAlign': 'center'},
children='Home Apprecitaion'
),
dcc.Slider(
id='ROI_Value',
min=0,
max=4,
value=2,
marks={
0: {'label': 'Not Important', 'style': {'color': '#77b0b1'}},
1: {'label': 'Less Important'},
2: {'label': 'Important'},
3: {'label': 'Very Important'},
4: {'label': 'Extremely Important', 'style': {'color': '#f50'}}
}
),
html.Div(id='Crime-Value-output-container',style={'fontSize': 16,'margin-top': 0,'padding-top':60,'textAlign': 'center'},
children='Public Safety'
),
dcc.Slider(
id='Crime_Value',
min=0,
max=4,
value=2,
marks={
0: {'label': 'Not Important', 'style': {'color': '#77b0b1'}},
1: {'label': 'Less Important'},
2: {'label': 'Important'},
3: {'label': 'Very Important'},
4: {'label': 'Extremely Important', 'style': {'color': '#f50'}}
}
),
],className = "three columns"
),
html.Div([
dt.DataTable(
id='datatable',
data=ranked_data.to_dict('records'),
columns=[{'id': c, 'name': c} for c in ranked_data.columns],
#title='Best Locations in LA',
row_selectable='multi',
# filtering=True,
sorting=True,
virtualization=True
# pagination_mode='fe',
# # #selected_rows=[],
# pagination_settings={
# "current_page": 0,
# "page_size": 10
# },
)
],
style = layout_table,
className="four columns"
)
],className= "row"
),
html.Div([
html.P('Developed by Duane M. Lee, Ph.D - ', style = {'display': 'inline'}
),
html.A('duane.m.lee@gmail.com', href = 'mailto:duane.m.lee@gmail.com'
)
], className = "twelve columns",
style = {'fontSize': 18, 'padding-top': 25,'margin-top':20,'textAlign':'right'}
)
],className = 'ten columns offset-by-one'
)
#callback section
#-------------------
# @app.callback(Output('userMSPoutput', 'children'),
# [Input('minMSPinput', 'value'),
# Input('maxMSPinput', 'value')])
# def selected_price_range(minMSP, maxMSP):
# minmax = [minMSP,maxMSP]
# return None #minMSP,maxMSP
# @app.callback(dash.dependencies.Output('Location-Value-output-container', 'children'),
# [dash.dependencies.Input('Location_Value', 'value')])
# def location_value(Location_Value):
# return None #Location_Value
# @app.callback(dash.dependencies.Output('Yearly-outlook-output-container', 'children'),
# [dash.dependencies.Input('Yearly_outlook', 'value')])
# def show_yearly_outlook(Yearly_outlook):
# return None #Yearly_outlook #u'{} Year Outlook'.format(Yearly_outlook)
# @app.callback(
# Output('map-graph', 'figure'),
# [Input('datatable', 'data'),
# Input('datatable', 'selected_rows')])
# def map_selection(data, selected_rows):
# aux = pd.DataFrame(rows)
# temp_df = aux.ix[selected_rows, :]
# if len(selected_rows) == 0:
# return gen_map(aux)
# return gen_map(temp_df)
@app.callback(
Output('datatable', 'data'),
[Input('minMSPinput', 'value'),
Input('maxMSPinput', 'value'),
Input('Yearly_outlook', 'value'),
Input('Wealth_Value', 'value'),
])
def update_selected_row_indices(minMSP, maxMSP,year,wealth):
global forecast_data, ranked_data
map_aux = forecast_data.copy()
ranked_aux = ranked_data.copy()
#print(map_aux.ds)
now = datetime.today().strftime('%Y')
now0 = datetime.today()
nowp = datetime.today().strftime('%Y-%m-%d')
selected_year = np.int(now) + year
#print(selected_year,now)
projected = now0.replace(year=selected_year).strftime('%Y-%m-%d')
#print(selected_year,now,projected)
map_aux['ds'] = pd.to_datetime(map_aux['ds'])
five_years = np.int(now) + 5
projected_5yrs = now0.replace(year=five_years).strftime('%Y-%m-%d')
#print(projected_5yrs,nowp)
#print(map_aux.ds.dt.strftime('%Y-%m-%d'))
five_year_projection_arr = map_aux.ds.dt.strftime('%Y-%m-%d') == projected_5yrs
now_projection_arr = map_aux.ds.dt.strftime('%Y-%m-%d') == nowp
#print(np.sum(five_year_projection_arr),np.sum(now_projection_arr))
#print([five_year_projection_arr, now_projection_arr])
#sys.exit()
a = map_aux.zipcode.loc[five_year_projection_arr]
b = map_aux.zipcode.loc[now_projection_arr]
c = [a] + [b]
c0 = [y for x in c for y in x]
#print(c0)
c1= pd.DataFrame({'mergedzips':c0})
#[a[i]*2 - b[i]*1 for i in range(len(b))]
#print(c1)
#sys.exit()
#combined_arr = np.ndarray.flatten([a, b])
nonmatchedarr = c1.drop_duplicates(keep=False)
#map_aux.zipcode.loc[c]
#print(nonmatchedarr.mergedzips.values[0])
arr = [np.where(map_aux.zipcode.loc[now_projection_arr] == nonmatchedarr.mergedzips.values[i])[0][0] for i in range(len(nonmatchedarr))]
# for i in range(len(nonmatchedarr)):
# for j in range(len(now_projection_arr)):
# np.where((map_aux.zipcode.loc[now_projection_arr]) == nonmatchedarr.mergedzips.values[i])
# now_new_arr =
#print(np.where(map_aux.zipcode.loc[now_projection_arr] == nonmatchedarr.mergedzips.values[0])[0][0],nonmatchedarr.mergedzips.values[0],map_aux.zipcode.loc[now_projection_arr].values[19])
#print(arr)
adjust_arr = []
i = 0
j = 0
for i in range(len(now_projection_arr)):
if now_projection_arr.values[i] == True:
j+=1
if j in arr:
adjust_arr.append(i)
#adjust_arr = [[i,j] if now_projection_arr.values[i] == True]
now_projection_arr[adjust_arr] = False
#print(arr, adjust_arr)
#sys.exit()
#['ds','zipcode','trend','trend_lower','trend_upper','Latitude','Longitude']
# i=0
# for dex in nonmatchedarr.index:
# map_aux.loc[map_aux.index[-1]+i] = [projected_5yrs,nonmatchedarr[dex],0,0,0,map_aux.Latitude[dex],map_aux.Longitude[dex]]
# i+=1
#nowarr = [dex for zipc in nonmatchedarr.values]
#print(np.sum(map_aux.Latitude.loc[five_year_projection_arr] >= ),np.sum(map_aux.Latitude.loc[now_projection_arr] != 0))
#scores
#now_projection_arr[map_aux.zipcode.loc[now_projection_arr] == nonmatchedarr.values]] = False
#print(np.sum(now_projection_arr))
#sys.exit()
#ROI score
avgROI = (map_aux.trend.loc[five_year_projection_arr].values - map_aux.trend.loc[now_projection_arr].values)/(5 * map_aux.trend.loc[now_projection_arr].values) * 100
ROIraw = (map_aux.trend.loc[five_year_projection_arr].values - map_aux.trend.loc[now_projection_arr].values)/(5 * map_aux.trend.loc[now_projection_arr].values) \
* np.exp((map_aux.trend_lower.loc[five_year_projection_arr].values - map_aux.trend_lower.loc[now_projection_arr].values)/(5 * map_aux.trend_lower.loc[now_projection_arr].values))
ROIraw[ROIraw < 0] = 0
ROIscore = ROIraw/np.max(ROIraw) * 100
#Wealth score
med_income = 50000
wealth_factors = ( (med_income/np.max(med_income))**2 + (map_aux['trend'].loc[now_projection_arr].values/np.max(map_aux['trend'].loc[now_projection_arr].values))**2 )
wealth_score = np.sqrt( wealth_factors/2 ) * 100
overall_score = np.sqrt((ROIscore**2 + wealth_score**2)/2) * 100
overall_rank = np.argsort(overall_score)
#print(wealth_score, overall_rank)
#sys.exit()
#print('Ugh',ROIraw, 'why',avgROI,'huh?',map_aux.trend.loc[five_year_projection_arr].values,'this',map_aux.trend.loc[five_year_projection_arr].values - map_aux.trend.loc[now_projection_arr].values,map_aux.trend.loc[now_projection_arr].values)#, overall_score,overall_rank,ROIscore**2,wealth_score**2)
# sys.exit()
projected_data = map_aux.loc[map_aux.ds.dt.strftime('%Y-%m-%d') == projected]
#print(table)
#print(featuresWithinBudget[0:10])
#map_aux = featuresWithinBudget
#ranked table
ranked_aux['Rankings'] = overall_rank + 1
ranked_aux['Zipcode'] = map_aux['zipcode'].loc[now_projection_arr].values
ranked_aux['Trend'] = map_aux['trend'].loc[now_projection_arr].values
ranked_aux['Avg. ROI'] = avgROI
ranked_aux['ROI Rank'] = ROIscore
ranked_aux['Wealth Rank'] = wealth_score
ranked_aux['Latitude'] = map_aux['Latitude'].loc[now_projection_arr].values
ranked_aux['Longitude'] = map_aux['Longitude'].loc[now_projection_arr].values
#print(ranked_aux[0:10])
#featuresWithinBudget = budgetdata[np.logical_and(budgetdata['trend'] >= minMSP,budgetdata['trend'] < maxMSP)]
ranked_budget_data = ranked_aux[np.logical_and(ranked_aux['Trend'] >= minMSP,ranked_aux['Trend'] < maxMSP)]
#print(ranked_budget_data)
ranked_budget_data.sort_values('Rankings',inplace = True)
#sys.exit()
#map_data = featuresWithinBudget.to_dict('records')
ranked_data = ranked_budget_data.to_dict('records')
#print(data)
return ranked_data
if __name__ == "__main__":
app.run_server(debug=False)