Update Crop.py

Crop.py

@@ -0,0 +1,104 @@
+import pandas as pd
+import dash
+from dash import dcc, html
+import plotly.express as px
+import dash_bootstrap_components as dbc
+
+# Step 1: Load the dataset
+df = pd.read_csv('Crop Production data.csv')
+
+# Step 2: Data Transformation and Analysis
+# Group by year to analyze yearly crop production
+yearly_production = df.groupby('Crop_Year')[['Area', 'Production']].sum().reset_index()
+
+# Compute yield (Production per unit area)
+yearly_production['Yield'] = yearly_production['Production'] / yearly_production['Area']
+
+# Seasonal Analysis
+seasonal_production = df.groupby('Season')[['Area', 'Production']].sum().reset_index()
+seasonal_production['Yield'] = seasonal_production['Production'] / seasonal_production['Area']
+
+# State-wise Analysis
+state_production = df.groupby('State_Name')[['Area', 'Production']].sum().reset_index()
+state_production['Yield'] = state_production['Production'] / state_production['Area']
+
+# Crop Type Analysis
+crop_production = df.groupby('Crop')[['Area', 'Production']].sum().reset_index()
+crop_production['Yield'] = crop_production['Production'] / crop_production['Area']
+
+# Step 3: Create a Dash app
+app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+
+# Step 4: Define the layout of the dashboard
+app.layout = html.Div([
+    html.H1("Crop Production Analysis Dashboard"),
+
+    # Yearly crop production and yield trends
+    dbc.Row([
+        dbc.Col(
+            dcc.Graph(id='yearly-production',
+                      figure=px.line(yearly_production, x='Crop_Year', y='Production', 
+                                     title='Yearly Crop Production Trends')),
+            width=6
+        ),
+        dbc.Col(
+            dcc.Graph(id='yield-trend',
+                      figure=px.line(yearly_production, x='Crop_Year', y='Yield', 
+                                     title='Yearly Yield Trends')),
+            width=6
+        )
+    ]),
+
+     # Seasonal crop production and yield analysis
+    dbc.Row([
+        dbc.Col(
+            dcc.Graph(id='seasonal-production',
+                      figure=px.bar(seasonal_production, x='Season', y='Production', 
+                                    title='Seasonal Crop Production Analysis', color_discrete_sequence=['#00CC96'])),
+            width=6
+        ),
+        dbc.Col(
+            dcc.Graph(id='seasonal-yield',
+                      figure=px.bar(seasonal_production, x='Season', y='Yield', 
+                                    title='Seasonal Crop Yield Analysis', color_discrete_sequence=['#AB63FA'])),
+            width=6
+        )
+    ]),
+
+    # State-wise crop production and yield analysis
+    dbc.Row([
+        dbc.Col(
+            dcc.Graph(id='state-production',
+                      figure=px.bar(state_production, x='State_Name', y='Production', 
+                                    title='State-wise Crop Production Analysis', color_discrete_sequence=['#FFA15A'])),
+            width=6
+        ),
+        dbc.Col(
+            dcc.Graph(id='state-yield',
+                      figure=px.bar(state_production, x='State_Name', y='Yield', 
+                                    title='State-wise Crop Yield Analysis', color_discrete_sequence=['#19D3F3'])),
+            width=6
+        )
+    ]),
+
+    # Crop type production and yield analysis
+    dbc.Row([
+        dbc.Col(
+            dcc.Graph(id='crop-type-production',
+                      figure=px.bar(crop_production, x='Crop', y='Production', 
+                                    title='Crop Type Production Analysis', color_discrete_sequence=['#FF6692'])),
+            width=6
+        ),
+        dbc.Col(
+            dcc.Graph(id='crop-type-yield',
+                      figure=px.bar(crop_production, x='Crop', y='Yield', 
+                                    title='Crop Type Yield Analysis', color_discrete_sequence=['#B6E880'])),
+            width=6
+        )
+    ]),
+])
+
+# Step 5: Run the app
+if __name__ == '__main__':
+    app.run_server(debug=True)
+