Workaround: Displaying a dataframe quickly by slicing it.

display_dataframe_quickly.py

import streamlit as st
import pandas as pd
import numpy as np

def display_dataframe_quickly(df, max_rows=5000, **st_dataframe_kwargs):
    """Display a subset of a DataFrame or Numpy Array to speed up app renders.
    
    Parameters
    ----------
    df : DataFrame | ndarray
        The DataFrame or NumpyArray to render.

    max_rows : int
        The number of rows to display.

    st_dataframe_kwargs : Dict[Any, Any]
        Keyword arguments to the st.dataframe method.
    """
    n_rows = len(df)
    if n_rows <= max_rows:
        # As a special case, display small dataframe directly.
        st.write(df)
    else:
        # Slice the DataFrame to display less information.
        start_row = st.slider('Start row', 0, n_rows - max_rows)
        end_row = start_row + max_rows
        df = df[start_row:end_row]

        # Reindex Numpy arrays to make them more understadable.
        if type(df) == np.ndarray:
            df = pd.DataFrame(df)
            df.index = range(start_row,end_row)

        # Display everything.
        st.dataframe(df, **st_dataframe_kwargs)
        st.text('Displaying rows %i to %i of %i.' % (start_row, end_row - 1, n_rows))


if __name__ == '__main__':
    # An example of using display_dataframe_quickly.
    display_dataframe_quickly(st.cache(np.random.randn)(20000, 10), width=400)

This gist show you a simple method to display a large DataFrame more quickly by showing only a user-specified subset.

To try it, run:

streamlit run https://gist.githubusercontent.com/treuille/ff9194ed50af277fc56788d7aed7fcba/raw

You should see this: