Py.Cafe
Exploring Food Waste by Country with Dash
- app.py
- food waste by country.csv
- requirements.txt
app.py
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import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from dash import Dash, dcc, html, Input, Output, callback, State
import dash_bootstrap_components as dbc
# Load the food waste by country data
# The header is on row 3 (0-indexed), so we skip the first 3 rows and use row 3 as header
df_country = pd.read_csv('food waste by country.csv', skiprows=3, header=0)
# The first column is actually 'AVERAGE' but contains country names
# Let's rename the columns properly
df_country.columns = ['Country', 'combined_figures', 'household_estimate', 'household_tonnes',
'retail_estimate', 'retail_tonnes', 'food_service_estimate', 'food_service_tonnes',
'confidence', 'm49_code', 'region', 'source']
# Clean the data
df_country = df_country.dropna(subset=['Country'])
df_country = df_country[df_country['Country'] != 'AVERAGE']
df_country = df_country[df_country['Country'].notna()]
# Convert numeric columns to proper format
numeric_columns = ['combined_figures', 'household_estimate', 'retail_estimate', 'food_service_estimate']
for col in numeric_columns:
df_country[col] = pd.to_numeric(df_country[col], errors='coerce')
# Initialize Dash app with Bootstrap theme
app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
# Add responsive meta tag
app.index_string = '''
<!DOCTYPE html>
<html>
<head>
{%metas%}
<title>{%title%}</title>
{%favicon%}
{%css%}
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
/* Custom table styling */
.table-striped > tbody > tr:nth-of-type(odd) > td {
background-color: #e3f2fd;
color: #000000;
}
.table-striped > tbody > tr:nth-of-type(even) > td {
background-color: #bbdefb;
color: #000000;
}
.table-hover > tbody > tr:hover > td {
background-color: #90caf9 !important;
}
.table thead th {
background-color: #1976d2 !important;
color: #ffffff !important;
font-weight: bold;
}
/* Responsive styles */
@media (max-width: 768px) {
.container-fluid {
padding: 10px !important;
}
.card {
margin-bottom: 10px !important;
}
.card-body {
padding: 15px !important;
}
.card-title {
font-size: 1.2rem !important;
}
.card-text {
font-size: 0.9rem !important;
}
.btn {
font-size: 0.9rem !important;
padding: 8px 16px !important;
}
.dropdown {
margin-bottom: 10px !important;
}
.form-label {
font-size: 0.9rem !important;
}
.graph {
height: 300px !important;
}
.chart-container {
height: 400px !important;
}
}
@media (max-width: 576px) {
.container-fluid {
padding: 5px !important;
}
.card-body {
padding: 10px !important;
}
.card-title {
font-size: 1rem !important;
}
.card-text {
font-size: 0.8rem !important;
}
.btn {
font-size: 0.8rem !important;
padding: 6px 12px !important;
}
.graph {
height: 250px !important;
}
.chart-container {
height: 350px !important;
}
.display-4 {
font-size: 2rem !important;
}
.display-5 {
font-size: 1.5rem !important;
}
}
</style>
</head>
<body>
{%app_entry%}
<footer>
{%config%}
{%scripts%}
{%renderer%}
</footer>
</body>
</html>
'''
# Create the dashboard layout
app.layout = dbc.Container([
# Header with infographic style
dbc.Row([
dbc.Col([
html.Div([
html.H1("GLOBAL FOOD WASTE CRISIS",
style={'color': '#ffffff', 'fontWeight': 'bold', 'fontSize': '3rem',
'textAlign': 'center', 'marginBottom': '10px', 'textShadow': '2px 2px 4px rgba(0,0,0,0.3)'}),
html.H2("DATA VISUALIZATION DASHBOARD",
style={'color': '#ffffff', 'fontWeight': '300', 'fontSize': '1.5rem',
'textAlign': 'center', 'marginBottom': '20px'}),
html.Div([
html.P("200+ COUNTRIES ANALYZED",
style={'display': 'inline-block', 'margin': '10px 20px', 'fontSize': '1.2rem',
'fontWeight': 'bold', 'color': '#ffffff'}),
html.P("REAL-TIME DATA",
style={'display': 'inline-block', 'margin': '10px 20px', 'fontSize': '1.2rem',
'fontWeight': 'bold', 'color': '#ffffff'}),
html.P("INTERACTIVE FILTERS",
style={'display': 'inline-block', 'margin': '10px 20px', 'fontSize': '1.2rem',
'fontWeight': 'bold', 'color': '#ffffff'})
], style={'textAlign': 'center', 'marginBottom': '30px'})
], style={'background': 'linear-gradient(135deg, #0c4a6e 0%, #3b82f6 50%, #fb923c 100%)',
'padding': '40px', 'borderRadius': '15px', 'marginBottom': '30px',
'boxShadow': '0 10px 30px rgba(0,0,0,0.2)'})
])
]),
# Summary cards - Responsive
dbc.Row([
dbc.Col([
dbc.Card([
dbc.CardBody([
html.H4("🌎 Countries", className="card-title"),
html.H2(f"{len(df_country)}", className="text-primary"),
html.P("Total countries analyzed", className="card-text")
])
], className="text-center", style={'border': '2px solid white', 'boxShadow': '0 4px 8px rgba(0,0,0,0.1)'})
], width=12, md=6, lg=3),
dbc.Col([
dbc.Card([
dbc.CardBody([
html.H4("📊 Avg Waste", className="card-title"),
html.H2(f"{df_country['combined_figures'].mean():.0f} kg", className="text-warning"),
html.P("Per capita per year", className="card-text")
])
], className="text-center", style={'border': '2px solid white', 'boxShadow': '0 4px 8px rgba(0,0,0,0.1)'})
], width=12, md=6, lg=3),
dbc.Col([
dbc.Card([
dbc.CardBody([
html.H4("🏠 Household", className="card-title"),
html.H2(f"{df_country['household_estimate'].mean():.0f} kg", className="text-primary"),
html.P("Household waste per capita", className="card-text")
])
], className="text-center", style={'border': '2px solid white', 'boxShadow': '0 4px 8px rgba(0,0,0,0.1)'})
], width=12, md=6, lg=3),
dbc.Col([
dbc.Card([
dbc.CardBody([
html.H4("🏪 Retail", className="card-title"),
html.H2(f"{df_country['retail_estimate'].mean():.0f} kg", className="text-info"),
html.P("Retail waste per capita", className="card-text")
])
], className="text-center", style={'border': '2px solid white', 'boxShadow': '0 4px 8px rgba(0,0,0,0.1)'})
], width=12, md=6, lg=3)
], className="mb-4"),
# Interactive filters at the top
dbc.Row([
dbc.Col([
html.Div([
dbc.Row([
dbc.Col([
html.Label("Select Confidence Level:", style={'fontWeight': 'bold', 'color': '#000000'}),
dcc.Dropdown(id='confidence-filter',
options=[{'label': conf, 'value': conf} for conf in df_country['confidence'].unique()],
value=[], placeholder="All Confidence Levels", clearable=True, style={'boxShadow': 'none'}, multi=True),
html.Br(),
html.Label("Select Country:", style={'fontWeight': 'bold', 'color': '#000000'}),
dcc.Dropdown(id='country-filter',
options=[{'label': country, 'value': country} for country in sorted(df_country['Country'].unique())],
value=[], placeholder="Select Countries", clearable=True, style={'boxShadow': 'none'}, multi=True),
html.Br(),
html.Label("Display Options:", style={'fontWeight': 'bold', 'color': '#000000'}),
dcc.Checklist(id='country-display-filter',
options=[{'label': 'All Countries', 'value': 'all'},
{'label': 'Top 10', 'value': 'top10'},
{'label': 'Bottom 10', 'value': 'bottom10'}],
value=['top10'],
labelStyle={'display': 'inline-block', 'marginRight': '15px'}),
html.Br(),
html.Label("Select Map Projection:", style={'fontWeight': 'bold', 'color': '#000000', 'marginTop': '10px'}),
dcc.Dropdown(id='map-projection-filter',
options=[
{'label': 'Equirectangular', 'value': 'equirectangular'},
{'label': 'Mercator', 'value': 'mercator'},
{'label': 'Orthographic', 'value': 'orthographic'},
{'label': 'Natural Earth', 'value': 'natural earth'},
], value='equirectangular', clearable=False),
html.Br(),
html.Label("Select Map Color Scale:", style={'fontWeight': 'bold', 'color': '#000000'}),
dcc.Dropdown(id='map-color-filter',
options=[{'label': color, 'value': color} for color in ['Oranges', 'Blues', 'Reds', 'Greens', 'Viridis']],
value='Oranges', clearable=False),
html.Br(),
dbc.Button("Reset Filters", id="reset-button", color="secondary", className="w-100 mt-4")
], width=12, lg=5),
dbc.Col([
html.H5("WASTE BY SOURCE", id='pie-chart-title', style={'textAlign': 'center', 'fontWeight': 'bold'}),
dcc.Graph(id='waste-source-pie-chart', style={'height': '350px'}, config={'displayModeBar': False})
], width=12, lg=7)
], align="center")
], style={'background': 'rgba(255,255,255,0.9)', 'padding': '20px', 'borderRadius': '10px',
'marginBottom': '30px',
'boxShadow': '0 8px 20px rgba(0,0,0,0.15)'})
])
]),
# Charts row - Two charts side-by-side
dbc.Row([
# Top 20 countries bar chart
dbc.Col([
html.Div([
html.H3("TOP COUNTRIES", id='top-countries-title',
style={'color': '#000000', 'fontWeight': 'bold', 'textAlign': 'center', 'marginBottom': '10px', 'fontSize': '1.8rem'}),
html.P("Countries with highest food waste per capita",
style={'color': '#000000', 'textAlign': 'center', 'marginBottom': '20px',
'fontSize': '1.1rem', 'fontStyle': 'italic'}),
dcc.Graph(id='top-countries-chart', style={'height': '500px'})
], style={'background': 'rgba(255,255,255,0.9)', 'padding': '20px', 'borderRadius': '10px',
'boxShadow': '0 5px 15px rgba(0,0,0,0.1)', 'height': '600px'})
], width=12, lg=6),
# World Map
dbc.Col([
html.Div([
html.H3("GLOBAL FOOD WASTE MAP", id='world-map-title',
style={'color': '#000000', 'fontWeight': 'bold', 'textAlign': 'center', 'marginBottom': '10px', 'fontSize': '1.8rem'}),
html.P("Food waste per capita by country worldwide",
style={'color': '#000000', 'textAlign': 'center', 'marginBottom': '20px',
'fontSize': '1.1rem', 'fontStyle': 'italic'}),
dcc.Graph(id='world-map-chart', style={'height': '500px'}, config={'displayModeBar': False, 'responsive': True})
], className="chart-container", style={'background': 'rgba(255,255,255,0.9)', 'padding': '20px', 'borderRadius': '10px',
'boxShadow': '0 5px 15px rgba(0,0,0,0.1)', 'height': '600px'})
], width=12, lg=6)
]),
], fluid=True)
# Callback to chain confidence filter to country filter
@callback(
[Output('country-filter', 'options'),
Output('country-filter', 'value')],
[Input('confidence-filter', 'value')],
prevent_initial_call=True
)
def update_country_options(confidence_value):
if not confidence_value:
# If no confidence selected, show all countries
options = [{'label': country, 'value': country} for country in sorted(df_country['Country'].unique())]
return options, []
else:
# Filter countries by confidence level
filtered_df = df_country[df_country['confidence'].isin(confidence_value)]
options = [{'label': country, 'value': country} for country in sorted(filtered_df['Country'].unique())]
return options, [] # Clear the country selection when confidence changes
# Callback to reset all filters
@callback(
[Output('confidence-filter', 'value'),
Output('country-display-filter', 'value'),
Output('map-projection-filter', 'value'),
Output('map-color-filter', 'value')],
Input('reset-button', 'n_clicks'),
prevent_initial_call=True
)
def reset_all_filters(n_clicks):
# Return the default values for each filter:
# - Empty lists for the multi-select dropdowns
# - ['top10'] for the checklist
return [], ['top10'], 'equirectangular', 'Oranges'
# Callback for interactive filtering - Top Countries Chart
@callback(
[Output('top-countries-chart', 'figure'),
Output('top-countries-title', 'children')],
[Input('confidence-filter', 'value'),
Input('country-filter', 'value'),
Input('country-display-filter', 'value')]
)
def update_top_countries_chart(selected_confidence, selected_country, selected_display):
filtered_df = df_country.copy()
title = "Top Countries"
if selected_confidence:
filtered_df = filtered_df[filtered_df['confidence'].isin(selected_confidence)]
if selected_country:
filtered_df = filtered_df[filtered_df['Country'].isin(selected_country)]
# Checklist returns a list, handle it. Default to 'all' if empty.
display_mode = selected_display[0] if selected_display else 'all'
# Sort by waste amount to handle top/bottom/all
if display_mode == 'top10':
display_df = filtered_df.nlargest(10, 'combined_figures')
elif display_mode == 'bottom10':
# For bottom 10, we need to sort ascending and take the head
display_df = filtered_df.nsmallest(10, 'combined_figures')
else: # 'all'
# Sort descending for the 'all' view for consistency
display_df = filtered_df.sort_values('combined_figures', ascending=False)
fig = px.funnel(
display_df,
x='combined_figures',
y='Country',
title="",
color_discrete_sequence=['#3b82f6']
).update_layout(
xaxis_title="Food Waste (kg/capita/year)",
yaxis_title="Country",
showlegend=False,
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
height=500
)
return fig, title
# Callback for the new dynamic pie chart (Waste Source)
@callback(
[Output('waste-source-pie-chart', 'figure'),
Output('pie-chart-title', 'children')],
[Input('confidence-filter', 'value'),
Input('country-filter', 'value')]
)
def update_pie_chart(selected_confidence, selected_country):
filtered_df = df_country.copy()
title = "Waste by Source"
if selected_confidence:
filtered_df = filtered_df[filtered_df['confidence'].isin(selected_confidence)]
if selected_country:
filtered_df = filtered_df[filtered_df['Country'].isin(selected_country)]
# Calculate the total waste for each sector
# We use mean() to get the average per-capita waste across the filtered countries
household_waste = filtered_df['household_estimate'].mean()
retail_waste = filtered_df['retail_estimate'].mean()
service_waste = filtered_df['food_service_estimate'].mean()
# Create a dataframe for the pie chart
source_df = pd.DataFrame({
'Source': ['Household', 'Retail', 'Food Service'],
'Waste (kg/capita)': [household_waste, retail_waste, service_waste]
})
# Handle cases with no data to avoid errors
source_df['Waste (kg/capita)'] = source_df['Waste (kg/capita)'].fillna(0)
# Determine colors and pull based on the largest value
pull_values = [0, 0, 0]
colors = ['#3b82f6', '#0284c7', '#0c4a6e'] # Shades of blue
if not source_df.empty and source_df['Waste (kg/capita)'].sum() > 0:
# Find the index of the largest slice
max_index = source_df['Waste (kg/capita)'].idxmax()
# Set the largest slice to be pulled out and colored orange
pull_values[max_index] = 0.2
colors[max_index] = '#f97316' # Orange
fig = px.pie(
source_df,
values='Waste (kg/capita)',
names='Source',
title="",
# hole=0.7, # Removed to make it a standard pie chart
color_discrete_sequence=colors
).update_traces(
textinfo='percent+label',
textfont_size=12,
pull=pull_values
).update_layout(
showlegend=False,
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
margin=dict(l=20, r=20, t=20, b=20)
)
return fig, title
# Callback for World Map Chart
@callback(
[Output('world-map-chart', 'figure'),
Output('world-map-title', 'children')],
[Input('confidence-filter', 'value'),
Input('country-filter', 'value'),
Input('country-display-filter', 'value'),
Input('map-projection-filter', 'value'),
Input('map-color-filter', 'value')]
)
def update_world_map_chart(selected_confidence, selected_country, selected_display, map_projection, map_color):
filtered_df = df_country.copy()
title = "Global Food Waste Map"
if selected_confidence:
filtered_df = filtered_df[filtered_df['confidence'].isin(selected_confidence)]
if selected_country:
filtered_df = filtered_df[filtered_df['Country'].isin(selected_country)]
# Apply the same display logic as the bar chart
display_mode = selected_display[0] if selected_display else 'all'
if display_mode == 'top10':
display_df = filtered_df.nlargest(10, 'combined_figures')
elif display_mode == 'bottom10':
display_df = filtered_df.nsmallest(10, 'combined_figures')
else: # 'all'
display_df = filtered_df
# Create a simple choropleth map using plotly
fig = px.choropleth(
display_df,
locations='Country',
locationmode='country names',
color='combined_figures',
hover_name='Country',
hover_data=['region', 'confidence', 'household_estimate'],
color_continuous_scale=map_color,
title=""
)
fig.update_layout(
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)',
geo=dict(
showframe=False,
showcoastlines=True,
projection_type=map_projection
),
height=500
)
return fig, title
# Run the app
if __name__ == '__main__':
app.run(debug=True)