Cached data reloads slower than desired

Iโ€™ve just started exploring Streamlit, so my apologies if my questions are somewhat basic.

I understand that Streamlit reexecutes my entire script after after user interaction.

My data is contained in a data frame with the following characteristics:

<class 'pandas.core.frame.DataFrame'>
Index: 2641258 entries, 0 to 1495
Data columns (total 12 columns):
 #   Column           Dtype
---  ------           -----
 0   Airport          object
 1   Terminal         object
 2   Date             object
 3   Time Range       object
 4   US Max Wait      int64
 5   Non US Max Wait  int64
 6   All Max Wait     int64
 7   Time             object
 8   Datetime         datetime64[ns]
 9   Year             int32
 10  Month            object
 11  Weekday          object
dtypes: datetime64[ns](1), int32(1), int64(3), object(7)
memory usage: 251.9+ MB

Loading this from a (compressed) pickled file the first time takes about 5.5 seconds. The load routine is annotated with @st.cache_data but still takes over 1.2 seconds to reload the data after each and every user interaction, which is rather slower than I was hoping for.

Whatโ€™s the best practice approach to speeding up the user response, please?

(A version of the app, with less data, and hence a rather snappier response, is available at https://airportwaitingtimes.streamlit.app/)

You are already using the @st.cache_data decorator so one way to speed up the user response could be to optimize the function being cached. You could also try adjusting the parameters of the st.cache_data decorator, such as max_entries, ttl, and persist, to see if they have any effect on the performance

Many thanks. I wrote some more functions which returned only a part of the underlying data and that seemed to speed things up. Thereโ€™s still a hiccup when it has to go back for the second part of the data as it involves accessing the entire data frame again, but Iโ€™ll look at that later.

Again, thanks,

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