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Created October 19, 2019 03:04
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Workaround: Displaying a dataframe quickly by slicing it.
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.
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.
# 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)
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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

You should see this:

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