-
Notifications
You must be signed in to change notification settings - Fork 987
/
app.py
216 lines (163 loc) · 8.95 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
from datetime import datetime
from pathlib import Path
import pandas as pd
import plotly.express as px
from faicons import icon_svg
from shinywidgets import render_plotly
from state_choices import STATE_CHOICES
from shiny import reactive
from shiny.express import input, render, ui
# ---------------------------------------------------------------------
# Reading in Files
# ---------------------------------------------------------------------
new_listings_df = pd.read_csv(Path(__file__).parent / "Metro_new_listings_uc_sfrcondo_sm_month.csv")
median_listing_price_df = pd.read_csv(Path(__file__).parent / "Metro_mlp_uc_sfrcondo_sm_month.csv")
for_sale_inventory_df = pd.read_csv(Path(__file__).parent / "Metro_invt_fs_uc_sfrcondo_sm_month.csv")
# ---------------------------------------------------------------------
# Helper functions - converting to DateTime
# ---------------------------------------------------------------------
def string_to_date(date_str):
return datetime.strptime(date_str, "%Y-%m-%d").date()
def filter_by_date(df: pd.DataFrame,date_range: tuple):
rng = sorted(date_range)
dates = pd.to_datetime(df["Date"], format="%Y-%m-%d").dt.date
return df[(dates >= rng[0]) & (dates <= rng[1])]
# ---------------------------------------------------------------------
# Visualizations
# ---------------------------------------------------------------------
#for_sale_inventory_df2 = for_sale_inventory_df["StateName"].fillna("United States")
#for_sale_inventory_df2 = for_sale_inventory_df["StateName"].drop_duplicates()
#for_sale_inventory_df2 = for_sale_inventory_df2.sort_values().tolist()
ui.page_opts(title= "US Housing App")
with ui.sidebar():
ui.input_select("state","Filter by State", choices=STATE_CHOICES),
ui.input_slider("date_range","Filter by Date Range",
min = string_to_date("2018-3-31"),
max = string_to_date("2024-4-30"),
value = [string_to_date(x) for x in ["2018-3-31","2024-4-30"]])
with ui.layout_column_wrap():
with ui.value_box(showcase = icon_svg("dollar-sign")):
"Current Median List Price"
@render.ui
def price():
date_columns = median_listing_price_df.columns[6:]
states = median_listing_price_df.groupby("StateName").mean(numeric_only=True)
dates = states[date_columns].reset_index()
states = dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
country = median_listing_price_df[median_listing_price_df["RegionType"] == "country"]
country_dates = country[date_columns].reset_index()
country_dates["StateName"] = "United States"
country = country_dates.melt(
id_vars=["StateName"], var_name="Date", value_name="Value"
)
res = pd.concat([states, country])
res = res[res["Date"] != "index"]
df = res[res["StateName"] == input.state()]
last_value = df.iloc[-1,-1]
return f"${last_value:,.0f}"
with ui.value_box(showcase = icon_svg("house")):
"Home Inventory % Change"
@render.ui
def change():
date_columns = median_listing_price_df.columns[6:]
states = median_listing_price_df.groupby("StateName").mean(numeric_only=True)
dates = states[date_columns].reset_index()
states = dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
country = median_listing_price_df[median_listing_price_df["RegionType"] == "country"]
country_dates = country[date_columns].reset_index()
country_dates["StateName"] = "United States"
country = country_dates.melt(
id_vars=["StateName"], var_name="Date", value_name="Value"
)
res = pd.concat([states, country])
res = res[res["Date"] != "index"]
df = res[res["StateName"] == input.state()]
last_value = df.iloc[-1,-1]
second_last_value = df.iloc[-2,-1]
percentage_change = ((last_value - second_last_value)/second_last_value *100)
sign = "+" if percentage_change > 0 else "-"
return f"{sign}{percentage_change:.2f}%"
# Plotly visualization of Median Home Price Per State
with ui.navset_card_underline(title = "Median List Price"):
with ui.nav_panel("Plot", icon = icon_svg("chart-line")):
@render_plotly
def list_price_plot():
# Grouping by State Name and specifying the Date Columns
price_grouped = median_listing_price_df.groupby('StateName').mean(numeric_only=True)
date_columns = median_listing_price_df.columns[6:]
price_grouped_dates = price_grouped[date_columns].reset_index()
price_df_for_viz = price_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
price_df_for_viz = filter_by_date(price_df_for_viz, input.date_range())
if input.state() == "United States":
df = price_df_for_viz
else:
df = price_df_for_viz[price_df_for_viz["StateName"] == input.state()]
# Creating Visualization using Ployly
fig = px.line(df, x="Date", y="Value", color="StateName")
fig.update_xaxes(title_text="")
fig.update_yaxes(title_text="")
return fig
with ui.nav_panel("Table", icon = icon_svg("table")):
@render.data_frame
def list_price_data():
if input.state() == "United States":
df = median_listing_price_df
else:
df = median_listing_price_df[median_listing_price_df["StateName"] == input.state()]
return render.DataGrid(df)
# Plotly visualization of Homes For Sale Per State
with ui.navset_card_underline(title = "Home Inventory"):
with ui.nav_panel("Plot", icon = icon_svg("chart-line")):
@render_plotly
def for_sale_plot():
# Grouping by State Name and specifying the Date Columns
for_sale_grouped = for_sale_inventory_df.groupby('StateName').sum(numeric_only=True)
date_columns = for_sale_inventory_df.columns[6:]
for_sale_grouped_grouped_dates = for_sale_grouped[date_columns].reset_index()
for_sale_df_for_viz = for_sale_grouped_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
for_sale_df_for_viz = filter_by_date(for_sale_df_for_viz, input.date_range())
if input.state() == "United States":
df = for_sale_df_for_viz
else:
df = for_sale_df_for_viz[for_sale_df_for_viz["StateName"] == input.state()]
# Creating Visualization using Ployly
fig = px.line(df, x="Date", y="Value", color="StateName")
fig.update_xaxes(title_text="")
fig.update_yaxes(title_text="")
return fig
with ui.nav_panel("Table", icon = icon_svg("table")):
@render.data_frame
def for_sale_data():
if input.state() == "United States":
df = for_sale_inventory_df
else:
df = for_sale_inventory_df[for_sale_inventory_df["StateName"] == input.state()]
return render.DataGrid(df)
# Plotly visualization of Listings Per State
with ui.navset_card_underline(title = "New Listings"):
with ui.nav_panel("Plot", icon = icon_svg("chart-line")):
@render_plotly
def listings_plot():
# Grouping by State Name and specifying the Date Columns
new_listings_grouped = new_listings_df.groupby('StateName').sum(numeric_only=True)
date_columns = new_listings_df.columns[6:]
new_listings_grouped_dates = new_listings_grouped[date_columns].reset_index()
new_listings_df_for_viz = new_listings_grouped_dates.melt(id_vars=["StateName"], var_name="Date", value_name="Value")
new_listings_df_for_viz = filter_by_date(new_listings_df_for_viz, input.date_range())
if input.state() == "United States":
df = new_listings_df_for_viz
else:
df = new_listings_df_for_viz[new_listings_df_for_viz["StateName"] == input.state()]
# Creating Visualization using Ployly
fig = px.line(df, x="Date", y="Value", color="StateName")
fig.update_xaxes(title_text="")
fig.update_yaxes(title_text="")
return fig
with ui.nav_panel("Table", icon = icon_svg("table")):
@render.data_frame
def listings_data():
if input.state() == "United States":
df = new_listings_df
else:
df = new_listings_df[new_listings_df["StateName"] == input.state()]
return render.DataGrid(df)