Chain Callbacks on virtualRowData

dashboard.py

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+import dash
+import dash_ag_grid as dag
+from dash import Dash, dash_table, dcc, html, Input, Output, State, no_update, ctx
+from dateutil.relativedelta import relativedelta
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import sqlalchemy as sa
+import urllib
+import numpy as np
+from string import Template
+import pandas as pd
+pd.options.mode.chained_assignment = None
+
+
+
+ganymede_conn = urllib.parse.quote_plus(conn_string)
+engine = sa.create_engine(f"mssql+pyodbc:///?odbc_connect={ganymede_conn}")
+sql = """
+    SELECT TOP (1000) * 
+    FROM [Orders] 
+    WHERE [OrderDate] BETWEEN '1997-01-01' AND '1997-12-31'
+    ORDER BY NEWID();
+"""
+df = pd.read_sql(sql, engine)
+derived_columns = df["ShipCountry"].unique()
+timestamps = [x for x in sorted(df["OrderDate"])]
+start = timestamps[0]
+end = timestamps[-1]
+start_date = pd.to_datetime(str(np.datetime64(f'{start.year}-{start.month:02d}-01')))
+end_date = (pd.to_datetime(str(np.datetime64(f'{end.year}-{end.month:02d}-01')))
+            + relativedelta(months=1))
+dates = pd.date_range(start=start_date, end=end_date, freq='MS')
+ticks = [str(date.strftime('%b %y')) for date in dates]
+app = Dash(__name__)
+range_slider = dcc.RangeSlider(
+    min=0, max=len(ticks) - 1,
+    id='range-slider-interaction',
+    step=None,
+    marks={i: x for i, x in enumerate(ticks)},
+    value=[0, len(ticks) - 1])
+derived_table = dash_table.DataTable(
+    columns=[{"name": i, "id": i} for i in ["ShipCountry"] + list(derived_columns)],
+    style_table={"overflowX": "auto"},
+    id='derived-table-interaction')
+ag_grid_column_defs = []
+for col in df.columns:
+    ag_grid_column_defs.append({
+        "headerName": col,
+        "field": col,
+        "sortable": True,
+        "filter": True})
+ag_grid = dag.AgGrid(
+    columnSize="autoSize",
+    id='ag-grid-interaction',
+    columnDefs=ag_grid_column_defs,
+    style={"height": None},
+    dashGridOptions={
+        "pagination": True,
+        "domLayout": "autoHeight",
+        "paginationPageSizeSelector": [10, 20, 50, 100, 1000],
+        "paginationPageSize": 10})
+download_csv = html.Button(
+    "Export csv",
+    id="download-csv-button",
+    style={"paddingTop": 5, "paddingBottom": 5, "margin": 5})
+download_xlsx = html.Button(
+    "Export xlsx",
+    id="download-xlsx-button",
+    style={"paddingTop": 5, "paddingBottom": 5, "margin": 5})
+download_component1 = dcc.Download(id='dcc-component-csv-download')
+download_component2 = dcc.Download(id='dcc-component-xlsx-download')
+app.layout = html.Div(children=[
+    dcc.Graph(id='header-interaction'),
+    range_slider,
+    download_component1,
+    download_component2,
+    download_csv,
+    download_xlsx,
+    derived_table,
+    ag_grid
+])
+
+
+class DeltaTemplate(Template):
+    delimiter = "%"
+
+
+def strfdelta(tdelta, fmt):
+    d = {"D": tdelta.days}
+    d["H"], rem = divmod(tdelta.seconds, 3600)
+    d["M"], d["S"] = divmod(rem, 60)
+    t = DeltaTemplate(fmt)
+    return t.substitute(**d)
+
+
+@app.callback([
+        Output('ag-grid-interaction', "rowData")
+    ], [
+        Input('range-slider-interaction', "value")
+    ])
+def update_table(slider_value):
+    if not slider_value:
+        return dash.no_update
+    dff = df[df["OrderDate"].between(dates[slider_value[0]], ticks[slider_value[1]])]
+
+    return [dff.to_dict("records")]
+
+
+@app.callback([
+        Output('derived-table-interaction', "data"),
+        Output('header-interaction', 'figure')
+    ], [
+        Input('ag-grid-interaction', "virtualRowData")
+    ],
+    prevent_initial_call=True)
+def update_table(ag_filtered):
+    if not ag_filtered:
+        return dash.no_update
+    dff_filtered = pd.DataFrame(ag_filtered)
+    dfc = dff_filtered["ShipCountry"].value_counts().rename_axis('ShipCountry').reset_index(name='Frequency')
+    orders_total = dff_filtered.pivot_table(index='OrderDate', columns='ShipCountry', values='Freight').sum().round(2)
+    order_count = dff_filtered.pivot_table(index='OrderID', columns='ShipCountry', aggfunc='size').sum()
+    customer_count = dff_filtered.groupby('ShipCountry')['CustomerID'].nunique()
+    dff_filtered['ShipTime'] = pd.to_datetime(dff_filtered['ShippedDate']) - pd.to_datetime(dff_filtered['OrderDate'])
+    dff_filtered['ShipTime'] = dff_filtered['ShipTime'].dt.total_seconds() // 3600
+    order_time = dff_filtered.pivot_table(index='OrderDate', columns='ShipCountry', values='ShipTime').sum()
+    subset = dff_filtered.loc[
+        (dff_filtered['ShipRegion'].isin(['SP', 'ID', 'RJ', 'OR', 'WA'])) & (dff_filtered['ShipVia'] > 1)]
+    subset_ShipViaSP_total = subset.pivot_table(
+        index='OrderDate', columns='ShipCountry', values='Freight').sum().round(2)
+    orders_total.name = "Orders Total"
+    order_count.name = "Orders"
+    customer_count.name = "Customers"
+    order_time.name = "Processing Time"
+    subset_ShipViaSP_total.name = "VIP Orders"
+    derived_data = pd.concat([
+        orders_total,
+        order_count,
+        customer_count,
+        order_time,
+        subset_ShipViaSP_total
+    ], axis=1).reset_index().T.reset_index()
+    cols = derived_data.iloc[0]
+    derived_data = derived_data[1:]
+    derived_data.columns = cols
+    fig1 = make_subplots(rows=1, cols=2,
+                         specs=[[{"type": "bar"}, {"type": "pie"}]],
+                         subplot_titles=["Bar", "Pie"])
+    fig1.add_trace(go.Bar(
+        x=dfc["ShipCountry"],
+        y=dfc["Frequency"],
+        name="Country"),
+        row=1, col=1)
+    fig1.add_trace(go.Pie(
+        labels=dfc["ShipCountry"],
+        values=dfc["Frequency"],
+        name="Country"),
+        row=1, col=2)
+    fig1.update_xaxes(domain=[0, 0.66], row=1, col=1)
+    fig1.update_layout(autosize=True, margin=dict(t=0, b=0, l=0, r=0),
+                       legend=dict(
+                           yanchor="bottom",
+                           y=0.01,
+                           xanchor="right",
+                           x=1.08))
+
+    return derived_data.to_dict("records"), fig1
+
+
+@app.callback(
+    Output(download_component1, "data"),
+    Input(download_csv, "n_clicks"),
+    State(ag_grid, "virtualRowData"),
+    prevent_initial_call=True)
+def download_data(n_clicks, data):
+    dff = pd.DataFrame(data)
+    return dcc.send_data_frame(dff.to_csv, "filtered_csv.csv")
+
+
+@app.callback(
+    Output(download_component2, "data"),
+    Input(download_xlsx, "n_clicks"),
+    State(ag_grid, "virtualRowData"),
+    prevent_initial_call=True)
+def download_data(n_clicks, data):
+    dff = pd.DataFrame(data)
+    return dcc.send_data_frame(dff.to_excel, "filtered_xlsx.xlsx")
+
+
+if __name__ == "__main__":
+    app.run_server(debug=True)