From 97f7c410d39c41a63baa8ae4be15805a62283edf Mon Sep 17 00:00:00 2001
From: 1kamma <saretbenny@gmail.com>
Date: Wed, 18 Oct 2023 01:23:52 +0300
Subject: [PATCH] dropdown selection, filters the places

---
 ratios.py | 56 ++++++++++++++++++++++---------------------------------
 1 file changed, 22 insertions(+), 34 deletions(-)

diff --git a/ratios.py b/ratios.py
index 9f11540..bd4033a 100644
--- a/ratios.py
+++ b/ratios.py
@@ -1,7 +1,6 @@
 import pandas as pd
 import dash
-import dash_core_components as dcc
-import dash_html_components as html
+from dash import dcc, html
 from dash.dependencies import Input, Output
 import plotly.graph_objects as go
 
@@ -15,49 +14,38 @@ app = dash.Dash(__name__)
 # Define the columns to summarize
 columns_to_summarize = ["ancient", "old", "middle", "new", "late"]
 
-# Create the figure
-fig = go.Figure()
-
-# Add an initial trace for demonstration
-fig.add_trace(go.Bar(x=["Place 1", "Place 2", "Place 3"],
-                     y=[5, 7, 4], name="ancient"))
+# Create the figure for the initial graph (placeholder data)
+initial_data = df.groupby("place")[columns_to_summarize].sum().reset_index()
+initial_fig = go.Figure(data=[
+    go.Bar(name=col, x=initial_data["place"], y=initial_data[col]) for col in columns_to_summarize
+])
 
 # Define the app layout
 app.layout = html.Div([
-    html.H1("Interactive Bar Graph with Filtering"),
-    dcc.Checklist(
+    html.H1("Dynamic Stacked Bar Graph"),
+    dcc.Dropdown(
         id="place-selector",
         options=[{"label": place, "value": place} for place in df["place"].unique()],
-        value=df["place"].unique().tolist(),
-        labelStyle={'display': 'block'}
+        multi=True,
+        placeholder="Select Places",
     ),
-    dcc.Checklist(
-        id="column-selector",
-        options=[{"label": col, "value": col} for col in columns_to_summarize],
-        value=columns_to_summarize,
-        labelStyle={'display': 'block'}
-    ),
-    dcc.Graph(id="bar-graph", figure=fig)
+    dcc.Graph(id="bar-graph", figure=initial_fig),
 ])
 
-# Define callback functions to update the graph
+# Define a callback to update the graph based on selected places
 @app.callback(
     Output("bar-graph", "figure"),
-    [Input("place-selector", "value"), Input("column-selector", "value")]
+    [Input("place-selector", "value")]
 )
-def update_graph(selected_places, selected_columns):
-    filtered_df = df[(df["place"].isin(selected_places)) & (df[selected_columns].sum(axis=1) > 0)]
-    grouped = filtered_df.groupby("place")[selected_columns].sum().reset_index()
-    fig = go.Figure()
-    for col in selected_columns:
-        fig.add_trace(go.Bar(x=grouped["place"], y=grouped[col], name=col, text=grouped[col], textposition='auto'))
-    fig.update_layout(
-        title="Summary of Categories by Place with Selection",
-        xaxis_title="Place",
-        yaxis_title="Total Count",
-        xaxis=dict(categoryorder='total descending'),
-        barmode='stack'
-    )
+def update_graph(selected_places):
+    filtered_df = df[df["place"].isin(selected_places)]
+    grouped = filtered_df.groupby("place")[columns_to_summarize].sum().reset_index()
+    
+    fig = go.Figure(data=[
+        go.Bar(name=col, x=grouped["place"], y=grouped[col]) for col in columns_to_summarize
+    ])
+    fig.update_layout(barmode="stack")  # Set the bars to be stacked
+
     return fig
 
 if __name__ == "__main__":