Performance Problems with many small dataframes

I have an streamlit app that I use as a labeling tool.
The user gets a list of many small dataframes with a checkbox each and a submit button at the end of the form to submit the labels.

When I use st.write to write the dataframes to the browser, the browser has serious performance issues (100% cpu usage on one core that does not go away). The server side script has no performance problems.

When I use the AgGrid Component instead of st.write to write the dataframes, then I don’t see this issue.
Just wanted to let you guys know about this.

Hi @thunderbug1 -

Do you have a code example that demonstrates this?


sadly not yet, I tried to make a demo for this but for a tiny app it always works.
I do have some pretty big dataframes and figures in my big app (close to the maximum size), possibly this creates some kind of timing issue somewhere.
I will try again to create an example tomorrow.

1 Like

I was able to build a small example now. Apparently the streamlit dataframe causes a high cpu load when the checkboxes are clicked, probably due to layout changes and redraws which the Agrid component does not suffer from.

import streamlit as st
import pandas as pd

from st_aggrid import AgGrid, GridOptionsBuilder

s = pd.Series(list('abcganlwnfneowb'))
df = pd.get_dummies(s)

with st.form("test"):
    for i in range(30):
        st.checkbox(label="test", key=i)

        #gb = GridOptionsBuilder.from_dataframe(df)
        #AgGrid(df,, key=f"grid{i}")

    if st.form_submit_button("submit"):"submitted")