Py.Cafe
Figure Friday Steak Risk Survey Analysis
- folderfigures/
- app.py
- requirements.txt
app.py
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from dash import Dash, Input, Output, dcc, html
import dash_customizable_app_style as dcas
import dash_bootstrap_components as dbc
import plotly.express as px
from plotly.graph_objects import Figure
import pandas as pd
import copy
from figures import plots
df: pd.DataFrame = pd.read_csv("https://raw.githubusercontent.com/plotly/Figure-Friday/refs/heads/main/2025/week-23/steak-risk-survey.csv")
options: list = list (df.columns)[2:]
colors: list = ["#00FF98", "#00E1FF", "#4600CF", "#C8FF00", "#0021FF", "#E600FF", "#FF002E", "#FFF300", "#37FF00", "#00FFFA"]
grid = plots.create_grid(df)
choropleth_regions_fig = plots.create_choropleth(df, "Census Division")
choropleth_counts_fig = plots.create_choropleth(df, "Count", "Cividis")
app = Dash ("Figure Friday week 23", external_stylesheets=[dbc.themes.BOOTSTRAP])
# App layout
app.layout = html.Div(
id = "main_container",
style = {"minHeight": "100vh"},
children = [
dcas.customize_app_selectors(),
html.H1("Steak Risk Survey", className="p-4 bg-light text-primary mb-0"),
grid,
html.Div(
id = "main_div",
className = "m-0 p-2 w-100",
children = [
html.Div(
className = "row m-0 p-0",
children = [
html.Div(
className = "col-6 m-0 p-0",
children = [
dcc.Loading(
type = 'cube',
children = [
dcc.Graph(id = "choropleth_1", figure = choropleth_regions_fig),
]
)
]
),
html.Div(
className = "col-6 m-0 p-0",
children = [
dcc.Loading(
type = 'cube',
children = [
dcc.Graph(id = "choropleth_2", figure = choropleth_counts_fig),
]
)
]
),
]
),
html.Div(
className = "col-12 m-0 p-0",
children = [
dcc.Dropdown(
className = "text-dark",
id = "variable_1",
options = options[0:8],
value = options[0],
),
dcc.Dropdown(
className = "text-dark",
id = "variable_2",
options = options[7:],
value = options[8],
),
dcc.Loading(
type = 'cube',
children = [
dcc.Graph(id = "figure_1")
]
)
]
)
])
])
@app.callback(
Output("figure_1", 'figure'),
Output("choropleth_1", "figure"),
Output("choropleth_2", "figure"),
Input('variable_1', 'value'),
Input('variable_2', 'value'),
Input("bg_color", "value"),
Input("text_color", "value"),
Input("font_type", "value"),
)
def two_variable_corssfiltering(variable_1,
variable_2,
bg_color,
text_color,
font_type):
filtered_df: pd.DataFrame = df[[variable_1, variable_2]].dropna(how="any")
options_lst: list = [variable_1, variable_2]
cat_orders: dict = {cat:sorted(set(list(filtered_df[cat]))) for cat in options_lst}
figure: Figure = px.histogram(
data_frame = filtered_df,
x = variable_2,
facet_row = variable_1,
color = variable_2,
color_discrete_sequence = colors,
category_orders = cat_orders,
title = variable_1)
figure.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]) if "=" in a.text else a.update(text=a.text))
figure.update_layout(
paper_bgcolor = bg_color,
plot_bgcolor = bg_color,
font_family = font_type,
font_color = text_color,
height = 600,
bargap = 0.01
)
figure.update_xaxes(showline = True,
showgrid = False,
linewidth = 0.5,
linecolor = "#E7E7E7",
mirror = True,
zeroline = False)
figure.update_yaxes(showline = False,
showgrid = False,
linewidth = 0.5,
linecolor = "#E7E7E7",
mirror = False,
zeroline = False)
choro_1 = copy.deepcopy(choropleth_regions_fig)
choro_2 = copy.deepcopy(choropleth_counts_fig)
choro_1.update_layout( paper_bgcolor = bg_color,
font_family = font_type,
font_color = text_color)
choro_1.update_geos( bgcolor = bg_color)
choro_2.update_layout( paper_bgcolor = bg_color,
font_family = font_type,
font_color = text_color)
choro_2.update_geos( bgcolor = bg_color)
return figure, choro_1, choro_2
if __name__ == "__main__":
app.run(debug=True)