Correlation Plot

app.py

@@ -37,4 +37,61 @@ world_map = alt.topo_feature(data.world_110m.url, feature='countries')
 
 ####### Loading Data #######
 from iso3166 import countries_by_name
-'''
+'''
+
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+
+alt.data_transformers.disable_max_rows()
+
+url = "contract_awards_in_investment_project_financing_22-11-2024.csv" 
+df = pd.read_csv(url)
+
+numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns
+
+col1_dropdown = widgets.Dropdown(
+    options=numeric_columns,
+    value=numeric_columns[0],
+    description="Column 1:"
+)
+
+col2_dropdown = widgets.Dropdown(
+    options=numeric_columns,
+    value=numeric_columns[1],
+    description="Column 2:"
+)
+
+output = widgets.Output()
+
+def update_correlation(change):
+    with output:
+        clear_output(wait=True)
+        
+        col1 = col1_dropdown.value
+        col2 = col2_dropdown.value
+        
+        correlation_value = df[col1].corr(df[col2])
+        print(f"Correlation between {col1} and {col2}: {correlation_value:.2f}")
+        
+        correlation_plot = (
+            alt.Chart(df)
+            .mark_circle(size=60)
+            .encode(
+                x=alt.X(f"{col1}:Q", title=col1),
+                y=alt.Y(f"{col2}:Q", title=col2),
+                color="Region:N",  
+                tooltip=["Project Name", "Supplier", col1, col2],  
+            )
+            .properties(
+                title=f"Correlation Plot: {col1} vs. {col2}",
+                width=600,
+                height=400
+            )
+        )
+        display(correlation_plot)
+
+col1_dropdown.observe(update_correlation, names='value')
+col2_dropdown.observe(update_correlation, names='value')
+
+print("Select columns for correlation analysis:")
+display(col1_dropdown, col2_dropdown, output)