Py.Cafe
Raleigh NC - Building Permits Insights
- folderfigures/
- app.py
- requirements.txt
app.py
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from dash import Dash, Input, Output, dcc, html
import dash_bootstrap_components as dbc
import dash_customizable_app_style as dcas
import plotly.express as px
import pandas as pd
from datetime import datetime
from figures import plots
df: pd.DataFrame = pd.read_csv("https://raw.githubusercontent.com/plotly/Figure-Friday/refs/heads/main/2025/week-25/Building_Permits_Issued_Past_180_Days.csv")
df = df.dropna(subset=['fee'])
dates: list = [''.join(list(date)[:10]) for date in df['CreationDate']]
months: list = [datetime.strptime(date, "%Y/%m/%d").strftime("%B") for date in dates]
days_of_week: list = [datetime.strptime(date, "%Y/%m/%d").strftime("%A") for date in dates]
df['Month'] = months
df['Week Day'] = days_of_week
orders: dict = {'Week Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],
"Month": ["January", "February", "March", "April", "May", "June","July", "August", "September", "October", "November", "December"],
}
# Permits df
rows = []
for value in orders['Month']:
current_df = df[df['Month'] == value]
residential_df = current_df[current_df['permitclassmapped'] == 'Residential']
nonresidential_df = current_df[current_df['permitclassmapped'] == 'Non-Residential']
inside_city_df = current_df[current_df['jurisdiction_inout_ral'] == 'Inside City Limits']
outside_city_df = current_df[current_df['jurisdiction_inout_ral'] == 'Outside City Limits']
new_df = current_df[current_df['workclassmapped'] == 'New']
existing_df = current_df[current_df['workclassmapped'] == 'Existing']
rows.append({'Month': value,
'Permits Amount': len(current_df),
'Residential': len(residential_df),
'Non-Residential': len(nonresidential_df),
'Inside City': len(inside_city_df),
'Outside City': len(outside_city_df),
'New': len(new_df),
'Existing': len(existing_df),
})
permits_df: pd.DataFrame = pd.DataFrame(rows)
cat_dict: dict = {'estprojectcost': 'Project Cost',
'fee': 'Fee',
'permitclassmapped': 'Permit Class',
'Week Day': 'Week Day'}
cool_colors = [
"#003f5c", # deep navy blue
"#006d5b", # deep forest green
"#5e548e", # dark muted purple
"#0077b6", # vivid ocean blue
"#00a86b", # emerald green
"#9d4edd", # bright violet
"#66cdaa", # medium aquamarine
"#b185db", # pastel purple
"#014f86", # rich ocean blue
"#5cb85c", # leafy green
"#a078b0", # muted lilac
"#90e0ef", # icy blue
"#8fd694", # light moss green
]
grid = plots.create_grid(df)
app = Dash ("Figure Friday 2025 week 25", external_stylesheets=[dbc.themes.BOOTSTRAP])
# App layout
app.layout = html.Div(
id = "main_container",
style = {"minHeight": "100vh"},
children = [
dcas.customize_app_selectors(),
html.H1("Raleigh NC - Building Permits Issued Past 180 Days", className="p-4 mb-0"),
grid,
html.Div(
id = "main_div",
className = "row m-0 p-0 pt-4 text-center",
children = [
html.Div(
className = "col-2 m-0 p-0 text-center",
children = [
html.P("Residential Permits",
className="m-0 p-0 fw-bold fst-italic fs-5",
style= {'color': '#4cb5c6'}),
html.P(f"{len(df[df['permitclassmapped'] == 'Residential'])}",
className="m-0 p-0 fw-bold fs-1 fst-italic",
style= {'color': ' #b0e6d5'}),
]),
html.Div(
className = "col-2 m-0 p-0 text-center",
children = [
html.P("Non-Residential Permits",
className="m-0 p-0 fw-bold fst-italic fs-5",
style= {'color': '#4cb5c6'}),
html.P(f"{len(df[df['permitclassmapped'] == 'Non-Residential'])}",
className="m-0 p-0 fw-bold fs-1 fst-italic",
style= {'color': ' #b0e6d5'}),
]),
html.Div(
className = "col-2 m-0 p-0 text-center",
children = [
html.P("Status - Complete",
className="m-0 p-0 fw-bold fst-italic fs-5",
style= {'color': '#4cb5c6'}),
html.P(f"{len(df[df['statuscurrent'] == 'Complete'])}",
className="m-0 p-0 fw-bold fs-1 fst-italic",
style= {'color': ' #b0e6d5'}),
]),
html.Div(
className = "col-2 m-0 p-0 text-center",
children = [
html.P("Status - Issued",
className="m-0 p-0 fw-bold fst-italic fs-5",
style= {'color': '#4cb5c6'}),
html.P(f"{len(df[df['statuscurrent'] == 'Issued'])}",
className="m-0 p-0 fw-bold fs-1 fst-italic",
style= {'color': ' #b0e6d5'}),
]),
html.Div(
className = "col-2 m-0 p-0 text-center",
children = [
html.P("Average Cost",
className="m-0 p-0 fw-bold fst-italic fs-5",
style= {'color': '#4cb5c6'}),
html.P(round(df['estprojectcost'].mean(), 1),
className="m-0 p-0 fw-bold fs-1 fst-italic",
style= {'color': ' #b0e6d5'}),
]),
html.Div(
className = "col-2 m-0 p-0 text-center",
children = [
html.P("Average Fee",
className="m-0 p-0 fw-bold fst-italic fs-5",
style= {'color': '#4cb5c6'}),
html.P(round(df['fee'].mean(), 1),
className="m-0 p-0 fw-bold fs-1 fst-italic",
style= {'color': ' #b0e6d5'}),
]),
html.Div(
className = "row m-0 p-0 text-center",
children = [
dcc.Dropdown(
className = "ms-4 me-4 mt-4 text-dark",
id = 'variable',
options = ['estprojectcost', 'fee'],
value = 'estprojectcost',
),
html.Div(
className= "col-12 m-0 p-0",
children = [
dcc.Loading(
children = [
dcc.Store(id={"type": "figure-store", "index": "map_fig"}),
dcc.Graph(
id = {"type": "graph", "index": "map_fig"},
)], type='default'),
]
),
html.Div(
className= "col-12 m-0 p-0",
children = [
dcc.Loading(
children = [
dcc.Store(id={"type": "figure-store", "index": "summary"}),
dcc.Graph(
id = {"type": "graph", "index": "summary"},
figure= px.histogram(permits_df,
x='Month',
y=['Permits Amount', 'Residential', 'Non-Residential', 'Inside City', 'Outside City', 'New', 'Existing'],
barmode='group'),
)], type='default'),
]
),
dcc.Dropdown(
className = "ms-4 me-4 mt-4 text-dark",
id = 'color',
options = ['Week Day', 'permitclassmapped'],
value = 'Week Day',
),
html.Div(
className= "col-6 m-0 p-0",
children = [
dcc.Loading(
children = [
dcc.Store(id={"type": "figure-store", "index": "bar_week"}),
dcc.Graph(
id = {"type": "graph", "index": "bar_week"},
)], type='default'),
]
),
html.Div(
className= "col-6 m-0 p-0",
children = [
dcc.Loading(
children = [
dcc.Store(id={"type": "figure-store", "index": "bar_month"}),
dcc.Graph(
id = {"type": "graph", "index": "bar_month"},
)], type='default'),
]
),
])
]),
])
@app.callback(
Output({"type": "figure-store", "index": "summary"}, 'data'),
Input('variable', 'value')
)
def update_figure(variable):
fig = px.bar( permits_df,
x='Month',
y=['Permits Amount', 'Residential', 'Non-Residential', 'Inside City', 'Outside City', 'New', 'Existing'],
barmode='group')
return fig.to_dict()
@app.callback(
Output({"type": "figure-store", "index": "map_fig"}, 'data'),
Input('variable', 'value')
)
def update_figure(variable):
fig_map = px.scatter_map(df,
lat="latitude_perm",
lon="longitude_perm",
size=variable,
color=variable,
map_style='carto-darkmatter',
zoom=9.5,
height=550,
color_continuous_scale=px.colors.sequential.Plasma_r).update_layout(coloraxis_colorbar=dict(title=dict(text=f'{cat_dict[variable]}')), font=dict(size=10))
return fig_map.to_dict()
@app.callback(
Output({"type": "figure-store", "index": "bar_week"}, 'data'),
Output({"type": "figure-store", "index": "bar_month"}, 'data'),
Input('variable', 'value'),
Input('color', 'value')
)
def update_bars(variable, color):
if color == 'Week Day':
color_seq = None
else:
color_seq = [cool_colors[0], cool_colors[12]]
figure_day = px.histogram( df,
x='Week Day',
y=variable,
title=f'{cat_dict[variable]} per {cat_dict[color]}',
template='plotly_white',
color=color,
category_orders=orders,
color_discrete_sequence=color_seq).update_layout(yaxis_title=f'{cat_dict[variable]}', font=dict(size=10))
figure_month = px.histogram( df,
x='Month',
y=variable,
title=f'{cat_dict[variable]} per Month and {cat_dict[color]}',
template='plotly_white',
color=color,
category_orders=orders,
color_discrete_sequence=color_seq).update_layout(yaxis_title=f'{cat_dict[variable]}', font=dict(size=10))
return figure_day.to_dict(), figure_month.to_dict()
if __name__ == "__main__":
app.run(debug=True)