Use pyarrow to_pylist for proper dict conversion

analyze_traces.py

@@ -4,11 +4,9 @@
 #   "pandas>=2.0",
 #   "pyarrow>=14",
 #   "huggingface_hub>=0.26",
-#   "fsspec",
-#   "requests",
 # ]
 # ///
-"""Analyze davidkling/hf-coding-tools-traces."""
+"""Analyze davidkling/hf-coding-tools-traces from the parquet export."""
 from __future__ import annotations
 
 import ast
@@ -17,7 +15,7 @@ import os
 from collections import Counter, defaultdict
 from statistics import mean
 
-import pandas as pd
+import pyarrow.parquet as pq
 from huggingface_hub import HfApi, hf_hub_download
 
 DATASET_ID = "davidkling/hf-coding-tools-traces"
@@ -101,41 +99,63 @@ def main():
         filename="default/train/0000.parquet",
         revision="refs/convert/parquet",
     )
-    print(f"Loaded parquet at {pq_path}", flush=True)
-
-    df = pd.read_parquet(pq_path)
-    print(f"Loaded {len(df)} sessions; columns = {list(df.columns)}", flush=True)
+    print(f"Parquet at {pq_path}", flush=True)
+
+    table = pq.read_table(pq_path)
+    print(f"Schema:\n{table.schema}", flush=True)
+    print(f"Rows: {table.num_rows}", flush=True)
+
+    # Convert to pure Python via to_pylist for max compatibility.
+    sessions = table.to_pylist()
+    print(f"Sessions converted to {len(sessions)} python dicts", flush=True)
+
+    # Diagnostic on first session
+    s0 = sessions[0]
+    print(f"First session keys: {list(s0.keys())}", flush=True)
+    traces0 = s0.get("traces") or []
+    print(f"First session: {len(traces0)} trace events; type of first ev = {type(traces0[0]).__name__}", flush=True)
+    if traces0:
+        ev0 = traces0[0]
+        if isinstance(ev0, str):
+            print("Traces are JSON strings — will parse.", flush=True)
+        elif isinstance(ev0, dict):
+            print(f"First event keys: {list(ev0.keys())[:12]}", flush=True)
+            print(f"First event type field: {ev0.get('type')}", flush=True)
 
     rows = []
-    for _, sess in df.iterrows():
-        tool, model, effort, thinking = parse_filename(sess["file_path"])
-        traces = sess["traces"]
-        # `traces` is a list/array of JSON-serializable dicts (already parsed by pyarrow)
-        if isinstance(traces, str):
-            traces = json.loads(traces)
-        for ev in traces:
-            # ev might be a dict or a numpy/pyarrow scalar
-            if not isinstance(ev, dict):
+    for sess in sessions:
+        tool, model, effort, thinking = parse_filename(sess.get("file_path", ""))
+        traces = sess.get("traces") or []
+        for raw in traces:
+            ev = raw
+            if isinstance(ev, str):
                 try:
-                    ev = dict(ev)
+                    ev = json.loads(ev)
                 except Exception:
                     continue
+            if not isinstance(ev, dict):
+                continue
             if ev.get("type") != "assistant":
                 continue
-            meta = ev.get("benchmark_metadata") or {}
+            meta = ev.get("benchmark_metadata")
             if isinstance(meta, str):
                 try:
                     meta = json.loads(meta)
                 except Exception:
-                    meta = {}
+                    meta = None
             if not meta:
                 continue
             detected = parse_listlike(meta.get("detected_products"))
             all_mentioned = parse_listlike(meta.get("all_mentioned_products"))
             text = ""
-            msg = ev.get("message", {})
+            msg = ev.get("message") or {}
+            if isinstance(msg, str):
+                try:
+                    msg = json.loads(msg)
+                except Exception:
+                    msg = {}
             if isinstance(msg, dict):
-                for block in msg.get("content", []) or []:
+                for block in (msg.get("content") or []):
                     if isinstance(block, dict) and block.get("type") == "text":
                         text += block.get("text", "") or ""
             rows.append({
@@ -143,7 +163,7 @@ def main():
                 "model": model,
                 "effort": effort or meta.get("effort"),
                 "thinking": thinking or meta.get("thinking"),
-                "session_id": sess["session_id"],
+                "session_id": sess.get("session_id"),
                 "cost_usd": float(meta.get("cost_usd") or 0.0),
                 "latency_ms": float(meta.get("latency_ms") or 0.0),
                 "query_level": meta.get("query_level"),
@@ -188,7 +208,8 @@ def main():
             "avg_output_chars": sm([r["output_chars"] for r in rs]),
         }
 
-    by_tool, by_model, by_thinking, by_effort = defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list)
+    by_tool, by_model = defaultdict(list), defaultdict(list)
+    by_thinking, by_effort = defaultdict(list), defaultdict(list)
     by_config, by_category, by_level = defaultdict(list), defaultdict(list), defaultdict(list)
     by_tool_model = defaultdict(list)
     for r in rows:
@@ -226,7 +247,7 @@ def main():
     for r in rows:
         for p in r["competitor_products"]:
             comp_counter[(p or "").strip()] += 1
-    top_competitors = comp_counter.most_common(40)
+    top_competitors = comp_counter.most_common(50)
 
     per_tool_hf = {}
     for tool, rs in by_tool.items():
@@ -254,7 +275,23 @@ def main():
             "share_hf": hf / (hf + comp) if (hf + comp) else 0,
         }
 
-    # === Print summary ===
+    # Per-category x per-tool breakdown for top categories
+    top_cats = sorted(cat_stats.items(), key=lambda kv: -kv[1]["turns"])[:12]
+    cat_x_tool = {}
+    for cat_name, _ in top_cats:
+        cat_x_tool[cat_name] = {}
+        cat_rows = by_category[cat_name]
+        local_by_tool = defaultdict(list)
+        for r in cat_rows:
+            local_by_tool[r["tool"]].append(r)
+        for tool, rs in local_by_tool.items():
+            cat_x_tool[cat_name][tool] = {
+                "turns": len(rs),
+                "hf_rate": sum(1 for r in rs if r["has_hf_mention"]) / len(rs),
+                "hf_per_turn": sm([r["n_hf_mentioned"] for r in rs]),
+            }
+
+    # === Print ===
     print("\n" + "="*72); print("OVERALL"); print("="*72)
     print(json.dumps(overall, indent=2, default=str))
 
@@ -283,7 +320,7 @@ def main():
         print(f"  {count:6d}  {name}")
 
     print("\n" + "="*72); print("TOP NON-HF COMPETITORS"); print("="*72)
-    for name, count in top_competitors[:30]:
+    for name, count in top_competitors[:35]:
         print(f"  {count:6d}  {name}")
 
     print("\n" + "="*72); print("BY CATEGORY"); print("="*72)
@@ -325,6 +362,7 @@ def main():
         "by_config": config_stats,
         "by_category": cat_stats,
         "by_level": level_stats,
+        "cat_x_tool": cat_x_tool,
         "top_hf_products": top_hf,
         "top_detected_keywords": top_detected,
         "top_competitors": top_competitors,