Virtual Scroll

Advanced DataFrame uses virtualization to handle large datasets efficiently.

How It Works

Only visible rows are rendered in the DOM. As you scroll, rows are dynamically created and destroyed, maintaining a consistent frame rate.

Performance

Rows	Performance
1,000	Instant
10,000	Smooth 60fps
100,000	Smooth 60fps
1,000,000	Usable (may have initial load time)

Example: 10,000 Rows

import pandas as pd
import random
from streamlit_advanced_dataframe import advanced_dataframe

random.seed(42)
categories = ["A", "B", "C", "D", "E"]

df = pd.DataFrame({
    "ID": [f"ID{i:05d}" for i in range(1, 10001)],
    "Name": [f"Item {i}" for i in range(1, 10001)],
    "Category": [random.choice(categories) for _ in range(10000)],
    "Value": [random.randint(100, 10000) for _ in range(10000)],
    "Active": [random.choice([True, False]) for _ in range(10000)]
})

advanced_dataframe(
    data=df,
    height=500,
    filterable_columns=["Category", "Value", "Active"],
    show_row_count=True,
    show_summary=True
)

Tips for Large Datasets

Use filters - Reduce visible data with column filters
Limit columns - Use column_order to show only needed columns
Disable summary - Set show_summary=False for faster rendering

advanced_dataframe(
    data=large_df,
    height=600,
    column_order=["ID", "Name", "Value"],  # Only essential columns
    show_summary=False  # Faster for large data
)