Py.Cafe

import solara
from great_tables import GT
from great_tables.data import sp500

# we still wrap it in a reactive, so each user can work on its own copy
df = solara.reactive(sp500)
# this is a proxy to detect changes to the dataframe, since we will mutate df.value
df_version = solara.reactive(0)

# however, since the initial value is shared, we do not want to mutate
# it for each user
# NOTE: this may change in the future https://github.com/widgetti/solara/pull/595
def copy_once():
    df.value = df.value.copy()
solara.lab.on_kernel_start(copy_once)

# Define the start and end dates for the data range
start_date = "2010-06-07"
end_date = "2010-06-14"

@solara.component
def Page():
    df_version.value # just read it to make this component depend on the proxy
    print("Render: ", df_version.value)

    def multiply():
        # we mutate the dataframe, Python/solara cannot detect that
        df.value['volume'] = df.value['volume']  * 2
        # but we use this proxy to signal we changed it
        df_version.value += 1

    # Filter sp500 using Pandas to dates between `start_date` and `end_date`
    dff = df.value
    dff = dff[(dff["date"] >= start_date) & (dff["date"] <= end_date)]

    # Create a display table based on the `sp500_mini` table data
    with solara.Column(align="center"):
        display(
            GT(dff)
            .tab_header(title="S&P 500", subtitle=f"{start_date} to {end_date}")
            .fmt_currency(columns=["open", "high", "low", "close"])
            .fmt_date(columns="date", date_style="wd_m_day_year")
            .fmt_number(columns="volume", compact=True)
            .cols_hide(columns="adj_close")
        )
        with solara.Row(margin=8):
            solara.Button("Multiply", on_click=multiply, outlined=True, color="primary")

solara
great_tables
pandas