Dashboard with summary data

Author: enjoki

apps/viz/dash/inventory_dashboard/app.py

@@ -1,6 +1,5 @@
-import logging
-
 import dash_bootstrap_components as dbc
+import pandas as pd
 import plotly.express as px
 from dash import dash_table, dcc, html
 from dash.dependencies import Input, Output
@@ -12,8 +11,6 @@
 unique_countries = sorted(clean_livelihood_data["country_code"].dropna().unique())
 unique_zones = sorted(clean_livelihood_data["livelihood_zone_baseline_label"].dropna().unique())
 
-logger = logging.getLogger(__name__)
-
 # Dash app
 app = DjangoDash("Inventory_dashboard", suppress_callback_exceptions=True, external_stylesheets=[dbc.themes.BOOTSTRAP])
 app.title = "HEA Dashboard"
@@ -29,18 +26,18 @@
                         dcc.Dropdown(
                             id="country-dropdown",
                             options=[{"label": country, "value": country} for country in unique_countries],
-                            placeholder="Select Country(s)",
+                            placeholder=f"Select Country(s) (e.g., {unique_countries[0]})",
                             multi=True,
                         ),
                     ],
-                    style={"flex": "1", "marginRight": "10px"},  # Set flex and spacing
+                    style={"flex": "1", "marginRight": "10px"},
                 ),
                 html.Div(
                     [
                         dcc.Dropdown(
                             id="livelihood-zone-dropdown",
                             options=[{"label": zone, "value": zone} for zone in unique_zones],
-                            placeholder="Select Livelihood Zone(s)",
+                            placeholder=f"Select Livelihood Zone(s) (e.g., {unique_zones[0]})",
                             multi=True,
                         ),
                     ],
@@ -77,12 +74,22 @@
                     id="data-table",
                     columns=[
                         {"name": "Livelihood Zone", "id": "livelihood_zone_baseline_label"},
-                        {"name": "Strategy Type Count", "id": "Count"},
-                        {"name": "Wealth Characteristics Count", "id": "Wealth Characteristics Count"},
+                        {"name": "Total Strategy Type Count", "id": "Strategy Type Count"},
+                        {"name": "Summary Wealth Characteristics", "id": "Summary Data"},
+                        {"name": "Community Wealth Characteristics", "id": "Community Data"},
                     ],
                     style_table={"overflowX": "auto"},
-                    style_cell={"textAlign": "left"},
-                    page_size=12,
+                    style_cell={
+                        "textAlign": "left",
+                        "fontSize": "15px",  # Increase font size
+                        "padding": "10px",  # Add padding
+                    },
+                    style_header={
+                        "fontSize": "18px",  # Increase font size for header
+                        "fontWeight": "bold",  # Make header bold
+                        "textAlign": "center",
+                    },
+                    page_size=10,
                 ),
             ],
             className="inventory-filter inventory-filter-last",
@@ -124,63 +131,74 @@ def update_charts(selected_countries, selected_zones):
         ]
         filtered_wealth = filtered_wealth[filtered_wealth["livelihood_zone_baseline_label"].isin(selected_zones)]
 
-    # Group data for charts
+    # Group and aggregate Strategy Type Count
     livelihood_grouped = (
         filtered_livelihood.groupby(["livelihood_zone_baseline_label", "strategy_type_label"])
         .size()
-        .reset_index(name="Count")
+        .reset_index(name="Strategy Type Count")
     )
-    wealth_grouped = (
-        filtered_wealth.groupby("livelihood_zone_baseline_label")
-        .size()
-        .reset_index(name="Wealth Characteristics Count")
+    livelihood_summary = (
+        livelihood_grouped.groupby("livelihood_zone_baseline_label")["Strategy Type Count"].sum().reset_index()
     )
-    wealth_monthly_grouped = (
-        filtered_wealth.groupby(["created_month", "livelihood_zone_baseline_label"])
+
+    # Group and pivot Wealth Characteristics Count
+    wealth_grouped = (
+        filtered_wealth.groupby(["livelihood_zone_baseline_label", "Record Type"])
         .size()
         .reset_index(name="Wealth Characteristics Count")
     )
-
+    wealth_grouped_pivot = wealth_grouped.pivot_table(
+        index="livelihood_zone_baseline_label",
+        columns="Record Type",
+        values="Wealth Characteristics Count",
+        aggfunc="sum",
+        fill_value=0,
+    ).reset_index()
+
+    # Merge livelihood and wealth data
+    merged_data = pd.merge(livelihood_summary, wealth_grouped_pivot, on="livelihood_zone_baseline_label", how="left")
+
+    # Create charts
     wealth_fig = px.bar(
         wealth_grouped,
         x="livelihood_zone_baseline_label",
         y="Wealth Characteristics Count",
-        title="Wealth Characteristics per Baseline",
+        color="Record Type",
+        barmode="stack",
+        title="Wealth Characteristics by Type",
         labels={
-            "livelihood_zone_baseline_label": "Baseline",
-            "Wealth Characteristics Count": "No. of Wealth Characteristics",
+            "livelihood_zone_baseline_label": "Livelihood Zone",
         },
     )
 
     livelihood_fig = px.bar(
         livelihood_grouped,
         x="strategy_type_label",
-        y="Count",
+        y="Strategy Type Count",
         color="livelihood_zone_baseline_label",
-        title="Livelihood Strategies per Baseline",
+        barmode="stack",
+        title="Strategy Types by Baseline",
         labels={
             "strategy_type_label": "Strategy Type",
-            "Count": "No. of Livelihood Strategies",
-            "livelihood_zone_baseline_label": "Baseline",
+            "livelihood_zone_baseline_label": "Baseline Zone",
         },
     )
 
-    # Stacked/multiple column chart for wealth characteristics by month
     wealth_monthly_fig = px.bar(
-        wealth_monthly_grouped,
+        filtered_wealth.groupby(["created_month", "Record Type"])
+        .size()
+        .reset_index(name="Wealth Characteristics Count"),
         x="created_month",
         y="Wealth Characteristics Count",
-        color="livelihood_zone_baseline_label",
-        barmode="stack",  # Use 'group' for multiple column chart
-        title="Wealth Characteristics by Month and Baseline",
+        color="Record Type",
+        barmode="stack",
+        title="Wealth Characteristics by Month",
         labels={
             "created_month": "Month",
-            "Wealth Characteristics Count": "No. of Wealth Characteristics",
-            "livelihood_zone_baseline_label": "Baseline",
         },
     )
 
-    return zone_options, wealth_fig, livelihood_fig, wealth_monthly_fig, livelihood_grouped.to_dict("records")
+    return zone_options, wealth_fig, livelihood_fig, wealth_monthly_fig, merged_data.to_dict("records")
 
 
 # Run the app

apps/viz/dash/inventory_dashboard/functions.py

@@ -35,41 +35,27 @@ def prepare_wealth_group_data(df):
     """
     Prepare wealth group data for visualization.
     """
-    # Rename columns for consistency
     df.rename(columns={"livelihood_zone_country_code": "country_code"}, inplace=True)
 
-    # Extract baseline date from 'livelihood_zone_baseline_label'
     if "livelihood_zone_baseline_label" in df.columns:
         df["ls_baseline_date"] = df["livelihood_zone_baseline_label"].str.split(": ").str[1]
     else:
-        df["ls_baseline_date"] = None  # Assign None if the column is missing
+        df["ls_baseline_date"] = None
 
-    # Convert baseline date to datetime and extract the month
     df["ls_baseline_month"] = pd.to_datetime(df["ls_baseline_date"], errors="coerce").dt.month
 
-    # Define month mapping dictionary
-    month_mapping = {
-        1: "January",
-        2: "February",
-        3: "March",
-        4: "April",
-        5: "May",
-        6: "June",
-        7: "July",
-        8: "August",
-        9: "September",
-        10: "October",
-        11: "November",
-        12: "December",
-    }
-
-    # Extract 'created_month' from 'created_date' or fallback to 'ls_baseline_date'
+    month_mapping = {month: pd.Timestamp(f"2023-{month:02}-01").strftime("%B") for month in range(1, 13)}
+
     if "created_date" in df.columns:
         df["created_month"] = pd.to_datetime(df["created_date"], errors="coerce").dt.month.map(month_mapping)
     else:
-        print("Warning: 'created_date' column is missing. Using 'ls_baseline_date' instead.")
         df["created_month"] = pd.to_datetime(df["ls_baseline_date"], errors="coerce").dt.month.map(month_mapping)
 
+    if "community_name" in df.columns:
+        df["Record Type"] = df["community_name"].apply(lambda x: "Summary Data" if pd.isna(x) else "Community Data")
+    else:
+        df["Record Type"] = "Community Data"
+
     return df