Extract only filtered data from table in Dash Python

I've built a website in Dash Python and I display all the data of my csv file in a table which can be filtered.

I want the user to be able to extract the data from the table. When there is not filter, I want him to be able to extract full data and when it's filtered by him, I want him to be able to extract the data filtered.

For that, I use the dcc.download component which is working very well and I also use df (from df = pd.read_csv("./data.csv")) as a global variable, to be able to reuse it in my extract callback.

Here is my code:

from dash import Dash, dash_table, dcc, html, State
from dash.dependencies import Input, Output
import dash_bootstrap_components as dbc
import pandas as pd
import csv
import time
import xlsxwriter
from datetime import datetime
from dash_extensions.enrich import Output, DashProxy, Input, MultiplexerTransform
import os

app = DashProxy(external_stylesheets=[dbc.themes.BOOTSTRAP], transforms=[MultiplexerTransform()])
server = app.server

df = pd.read_csv("./data.csv")
df = df.fillna("NaN")
PAGE_SIZE = 20

# Layout
app.layout = html.Div(children=[
        dcc.Download(id="download-dataframe-csv"),
        dbc.Card([
            dbc.CardBody([
                dash_table.DataTable(
                    id='table-sorting-filtering',
                    columns=[{'name': i, 'id': i} for i in df.columns],
                    page_current= 0,
                    page_size= PAGE_SIZE,
                    page_action='custom',
                    filter_action='custom',
                    filter_query='',
                    sort_action='custom',
                    sort_mode='multi',
                    sort_by=[],
                    style_data={'text-align':'center'},
                    style_header={
                            'backgroundColor': 'white',
                            'fontWeight': 'bold',
                            'text-align':'center'
                        },
                    style_cell={'padding': '5px'},
                    style_as_list_view=True,
                )]
            )],
            style={"margin-left":"15px", "margin-right":"15px"}
        ),
        dcc.Interval(
                id='interval-component',
                interval=1*1000, # in milliseconds
                n_intervals=0
            ),
        ]
    )]
)


operators = [['ge ', '>='],
             ['le ', '<='],
             ['lt ', '<'],
             ['gt ', '>'],
             ['ne ', '!='],
             ['eq ', '='],
             ['contains ']]

def split_filter_part(filter_part):
    for operator_type in operators:
        for operator in operator_type:
            if operator in filter_part:
                name_part, value_part = filter_part.split(operator, 1)
                name = name_part[name_part.find('{') + 1: name_part.rfind('}')]
                value_part = value_part.strip()
                v0 = value_part[0]
                if (v0 == value_part[-1] and v0 in ("'", '"', '`')):
                    value = value_part[1: -1].replace('\\' + v0, v0)
                else:
                    try:
                        value = float(value_part)
                    except ValueError:
                        value = value_part
                return name, operator_type[0].strip(), value
    return [None] * 3

# Display data in table and manage filtering
@app.callback(
    Output('table-sorting-filtering', 'data'),
    Input('table-sorting-filtering', "page_current"),
    Input('table-sorting-filtering', "page_size"),
    Input('table-sorting-filtering', 'sort_by'),
    Input('table-sorting-filtering', 'filter_query'),
    Input('interval-component', 'n_intervals'))

def update_table(page_current, page_size, sort_by, filter, n):
    global df
    global date_time
    df = pd.read_csv("./data.csv")
    df = df.fillna("NaN")
    date_time = last_modification_time_of_csv("./data.csv")
    filtering_expressions = filter.split(' && ')
    for filter_part in filtering_expressions:
        col_name, operator, filter_value = split_filter_part(filter_part)
        if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
            # these operators match pandas series operator method names
            df = df.loc[getattr(df[col_name], operator)(filter_value)]
        elif operator == 'contains':
            if type(filter_value) is str:
                df = df.loc[df[col_name].str.contains(filter_value)]
    if len(sort_by):
        df = df.sort_values(
            [col['column_id'] for col in sort_by],
            ascending=[
                col['direction'] == 'asc'
                for col in sort_by
            ],
            inplace=False
        )
    page = page_current
    size = page_size
    return df.iloc[page * size: (page + 1) * size].to_dict('records')

# Export button
@app.callback(
    Output("download-dataframe-csv", "data"),
    Input("button_export", "n_clicks"),
    prevent_initial_call=True,
)
def export_on_click(n_clicks):
    global df
    return dcc.send_data_frame(df.to_excel, "export.xlsx")

if __name__ == '__main__':
    app.run_server(debug=True, host='0.0.0.0')

I notice that I have some problems when extracting the data. Sometimes it's working very well, sometimes I'm extracting data that I didn't filter at all. So I'm wondering if global variables are a good solutions because I have multiple users using my website.

I saw there is a way to do that with state in Dash Python but I didn't understand well how it's working and if it’s something I can use in my case.

Someone can please help me?

Thank you