update(bullet-basic): altair — comprehensive quality review

Comprehensive quality review improving Vega-Lite layered approach and visual design.

Author: MarkusNeusinger

plots/bullet-basic/implementations/altair.py

@@ -1,87 +1,109 @@
-""" pyplots.ai
+"""pyplots.ai
 bullet-basic: Basic Bullet Chart
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: altair 6.0.0 | Python 3.14.3
+Quality: /100 | Updated: 2026-02-22
 """
 
 import altair as alt
 import pandas as pd
 
 
 # Data - KPI metrics with actual values, targets, and qualitative range thresholds
-# Each metric shows: current performance, target goal, and bands for poor/satisfactory/good
 metrics = [
     {"metric": "Revenue ($K)", "actual": 275, "target": 250, "poor": 150, "satisfactory": 200, "good": 300},
     {"metric": "Profit ($K)", "actual": 85, "target": 100, "poor": 50, "satisfactory": 75, "good": 125},
     {"metric": "New Customers", "actual": 320, "target": 300, "poor": 200, "satisfactory": 275, "good": 350},
-    {"metric": "Satisfaction (1-5)", "actual": 4.2, "target": 4.5, "poor": 3.0, "satisfactory": 4.0, "good": 5.0},
+    {"metric": "Satisfaction", "actual": 4.2, "target": 4.5, "poor": 3.0, "satisfactory": 4.0, "good": 5.0},
 ]
+metric_order = [m["metric"] for m in metrics]
 
-# Normalize all values to percentage of maximum (good value) for common scale
+# Normalize to percentage of maximum and build dataframes
 range_data = []
 for m in metrics:
     max_val = m["good"]
     poor_pct = (m["poor"] / max_val) * 100
     sat_pct = (m["satisfactory"] / max_val) * 100
-    # Build qualitative bands: Poor (0 to poor), Satisfactory (poor to sat), Good (sat to 100%)
-    range_data.append({"metric": m["metric"], "range_start": 0, "range_end": poor_pct, "band": "Poor"})
-    range_data.append({"metric": m["metric"], "range_start": poor_pct, "range_end": sat_pct, "band": "Satisfactory"})
-    range_data.append({"metric": m["metric"], "range_start": sat_pct, "range_end": 100, "band": "Good"})
+    range_data.append({"metric": m["metric"], "start": 0, "end": poor_pct, "band": "Poor"})
+    range_data.append({"metric": m["metric"], "start": poor_pct, "end": sat_pct, "band": "Satisfactory"})
+    range_data.append({"metric": m["metric"], "start": sat_pct, "end": 100, "band": "Good"})
 
 df_ranges = pd.DataFrame(range_data)
 
-# Build dataframe for actual values (normalized to percentage)
-df_actual = pd.DataFrame([{"metric": m["metric"], "actual": (m["actual"] / m["good"]) * 100} for m in metrics])
+df_actual = pd.DataFrame(
+    [{"metric": m["metric"], "actual_pct": (m["actual"] / m["good"]) * 100, "actual_raw": m["actual"]} for m in metrics]
+)
 
-# Build dataframe for target markers (normalized to percentage)
-df_target = pd.DataFrame([{"metric": m["metric"], "target": (m["target"] / m["good"]) * 100} for m in metrics])
+df_target = pd.DataFrame(
+    [{"metric": m["metric"], "target_pct": (m["target"] / m["good"]) * 100, "target_raw": m["target"]} for m in metrics]
+)
 
-# Background qualitative ranges (grayscale bands as per specification)
+# Background qualitative ranges (grayscale bands)
 ranges_chart = (
     alt.Chart(df_ranges)
-    .mark_bar(height=55)
+    .mark_bar(height=50)
     .encode(
-        y=alt.Y("metric:N", title=None, sort=[m["metric"] for m in metrics]),
-        x=alt.X("range_start:Q", title="% of Maximum", scale=alt.Scale(domain=[0, 110])),
-        x2=alt.X2("range_end:Q"),
+        y=alt.Y("metric:N", title=None, sort=metric_order, axis=alt.Axis(labelFontSize=18)),
+        x=alt.X(
+            "start:Q",
+            title="Performance (% of Goal)",
+            scale=alt.Scale(domain=[0, 105]),
+            axis=alt.Axis(titleFontSize=22, labelFontSize=16, tickCount=6),
+        ),
+        x2="end:Q",
         color=alt.Color(
             "band:N",
-            scale=alt.Scale(domain=["Poor", "Satisfactory", "Good"], range=["#d9d9d9", "#bdbdbd", "#969696"]),
-            legend=alt.Legend(title="Performance Band", orient="bottom", titleFontSize=18, labelFontSize=16),
+            scale=alt.Scale(domain=["Poor", "Satisfactory", "Good"], range=["#e0e0e0", "#c0c0c0", "#9e9e9e"]),
+            legend=alt.Legend(
+                title="Performance Band", orient="bottom", titleFontSize=18, labelFontSize=16, direction="horizontal"
+            ),
         ),
+        tooltip=[alt.Tooltip("metric:N", title="Metric"), alt.Tooltip("band:N", title="Band")],
     )
 )
 
-# Actual value bar (Python Blue - primary measure)
+# Actual value bar (Python Blue)
 actual_chart = (
     alt.Chart(df_actual)
-    .mark_bar(color="#306998", height=22)
+    .mark_bar(color="#306998", height=20)
     .encode(
-        y=alt.Y("metric:N", title=None, sort=[m["metric"] for m in metrics]),
-        x=alt.X("actual:Q", title="% of Maximum", scale=alt.Scale(domain=[0, 110])),
+        y=alt.Y("metric:N", sort=metric_order),
+        x=alt.X("actual_pct:Q"),
+        tooltip=[
+            alt.Tooltip("metric:N", title="Metric"),
+            alt.Tooltip("actual_raw:Q", title="Actual"),
+            alt.Tooltip("actual_pct:Q", title="% of Goal", format=".1f"),
+        ],
     )
 )
 
-# Target marker (thin black vertical line perpendicular to bar)
+# Target marker (thin black vertical tick)
 target_chart = (
     alt.Chart(df_target)
-    .mark_tick(color="black", thickness=4, size=55)
+    .mark_tick(color="#222222", thickness=4, size=50)
     .encode(
-        y=alt.Y("metric:N", title=None, sort=[m["metric"] for m in metrics]),
-        x=alt.X("target:Q", title="% of Maximum", scale=alt.Scale(domain=[0, 110])),
+        y=alt.Y("metric:N", sort=metric_order),
+        x=alt.X("target_pct:Q"),
+        tooltip=[alt.Tooltip("metric:N", title="Metric"), alt.Tooltip("target_raw:Q", title="Target")],
     )
 )
 
-# Layer all components: ranges (background), actual bar, target marker
+# Actual value text labels on bars
+value_labels = (
+    alt.Chart(df_actual)
+    .mark_text(align="left", dx=6, fontSize=16, fontWeight="bold", color="#1a3a5c")
+    .encode(y=alt.Y("metric:N", sort=metric_order), x=alt.X("actual_pct:Q"), text=alt.Text("actual_raw:Q"))
+)
+
+# Layer all components and configure
 chart = (
-    alt.layer(ranges_chart, actual_chart, target_chart)
+    alt.layer(ranges_chart, actual_chart, target_chart, value_labels)
     .properties(
-        width=1400, height=700, title=alt.Title("bullet-basic · altair · pyplots.ai", fontSize=28, anchor="middle")
+        width=1400, height=400, title=alt.Title("bullet-basic · altair · pyplots.ai", fontSize=28, anchor="middle")
     )
     .configure_axis(labelFontSize=18, titleFontSize=22)
     .configure_view(strokeWidth=0)
 )
 
-# Save outputs
+# Save
 chart.save("plot.png", scale_factor=3.0)
 chart.save("plot.html")

plots/bullet-basic/metadata/altair.yaml

@@ -1,16 +1,16 @@
 library: altair
 specification_id: bullet-basic
 created: '2025-12-23T09:18:21Z'
-updated: '2025-12-23T09:22:59Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-22T12:00:00+00:00'
+generated_by: claude-opus-4-6
 workflow_run: 20456606505
 issue: 0
-python_version: 3.13.11
+python_version: 3.14.3
 library_version: 6.0.0
 preview_url: https://storage.googleapis.com/pyplots-images/plots/bullet-basic/altair/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bullet-basic/altair/plot_thumb.png
 preview_html: https://storage.googleapis.com/pyplots-images/plots/bullet-basic/altair/plot.html
-quality_score: 92
+quality_score: null
 impl_tags:
   dependencies: []
   techniques: