Upload app.py

app.py

@@ -2,6 +2,19 @@ import pandas as pd
 import streamlit as st
 import altair as alt
 
+st.markdown(
+    """
+    <style>
+    /* Adjust the body overflow for smooth fullscreen experience */
+    body {
+        overflow-y: hidden; /* Hide vertical scrollbars */
+        overflow-x: hidden; /* Hide horizontal scrollbars */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
 st.title("Building Inventory Analysis")
 
 # Load and clean dataset
@@ -23,10 +36,10 @@ columns_to_drop = [
 ]
 df = df.drop(columns=columns_to_drop)
 
-# Check and handle missing values
-# missing_values = df.isnull().sum()
-# st.subheader("Missing Values Before Cleaning")
-# st.write(missing_values)
+#Check and handle missing values
+missing_values = df.isnull().sum()
+st.subheader("Missing Values Before Cleaning")
+st.write(missing_values)
 
 # Drop rows where 'Year Acquired' or 'Year Constructed' is NaN
 df = df.dropna(subset=['Year Acquired', 'Year Constructed'])