Python Streamlit st.experimental_memo用法及代碼示例

@st.experimental_memo
 def fetch_and_clean_data(url):
     # Fetch data from URL here, and then clean it up.
     return data

d1 = fetch_and_clean_data(DATA_URL_1)
# Actually executes the function, since this is the first time it was
# encountered.

d2 = fetch_and_clean_data(DATA_URL_1)
# Does not execute the function. Instead, returns its previously computed
# value. This means that now the data in d1 is the same as in d2.

d3 = fetch_and_clean_data(DATA_URL_2)
# This is a different URL, so the function executes.

設置堅持參數，使用該命令如下：

@st.experimental_memo(persist="disk")
 def fetch_and_clean_data(url):
     # Fetch data from URL here, and then clean it up.
     return data

默認情況下，memoized 函數的所有參數都必須是可散列的。任何名稱以開頭的參數_不會被散列。對於不可散列的參數，您可以將其用作"escape hatch"：

@st.experimental_memo
 def fetch_and_clean_data(_db_connection, num_rows):
     # Fetch data from _db_connection here, and then clean it up.
     return data

connection = make_database_connection()
d1 = fetch_and_clean_data(connection, num_rows=10)
# Actually executes the function, since this is the first time it was
# encountered.

another_connection = make_database_connection()
d2 = fetch_and_clean_data(another_connection, num_rows=10)
# Does not execute the function. Instead, returns its previously computed
# value - even though the _database_connection parameter was different
# in both calls.

memory 函數的緩存可以通過程序清除：

@st.experimental_memo
 def fetch_and_clean_data(_db_connection, num_rows):
     # Fetch data from _db_connection here, and then clean it up.
     return data

fetch_and_clean_data.clear()
# Clear all cached entries for this function.