Create app.py

app.py

@@ -1,40 +1,61 @@
-# INSTRUCTIONS:
-# 1. Open a "Terminal" by: View --> Terminal OR just the "Terminal" through the hamburger menu
-# 2. run in terminal with: streamlit run app.py
-# 3. click the "Open in Browser" link that pops up OR click on "Ports" and copy the URL
-# 4. Open a Simple Browswer with View --> Command Palette --> Simple Browser: Show
-# 5. use the URL from prior steps as intput into this simple browser
-
-
-import streamlit as st
-import altair as alt
 import pandas as pd
-from vega_datasets import data
- 
-st.title('Vizualization for Experts')
-
-st.subheader('This page will contain the entire dashboard.')
-
-st.text("*Dashboard Introduction*")
-
-
-st.text("*Brief description of Narrative*")
-
-
-st.text("*World map viz here*")
-
-
-st.text("*Interactive viz here*")
-
-
-'''
-####### Loading Data #######
-
-# 1. Contracts data
-data = pd.read_csv("/Users/sharanya/Documents/SEMESTERS/7- FALL 2024/IS445/FinalProject/IS445_VizForExperts/contract_awards_in_investment_project_financing.csv")
-# 2. World Map dataset
-world_map = alt.topo_feature(data.world_110m.url, feature='countries')
-
-####### Loading Data #######
-from iso3166 import countries_by_name
-'''
+import altair as alt
+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)
+        
+        # Get the selected columns
+        col1 = col1_dropdown.value
+        col2 = col2_dropdown.value
+        
+        # Calculate correlation
+        correlation_value = df[col1].corr(df[col2])
+        print(f"Correlation between {col1} and {col2}: {correlation_value:.2f}")
+        
+        # Create a scatter plot
+        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)