More efficient dataframe creation from proxy model

Author: PaulJonasJost

src/petab_gui/views/utils.py

@@ -3,43 +3,53 @@
 
 
 def proxy_to_dataframe(proxy_model):
+    """Convert Proxy Model to pandas DataFrame."""
     rows = proxy_model.rowCount()
     cols = proxy_model.columnCount()
 
+    if rows <= 1:  # <=1 due to "New row..." in every table
+        return pd.DataFrame()
+
     headers = [proxy_model.headerData(c, Qt.Horizontal) for c in range(cols)]
-    data = []
 
+    # Pre-allocate list of lists (faster than dicts)
+    data = []
     for r in range(rows - 1):
-        row = {headers[c]: proxy_model.index(r, c).data() for c in range(cols)}
-        for key, value in row.items():
-            if isinstance(value, str) and value == "":
-                row[key] = None
+        row = []
+        for c in range(cols):
+            value = proxy_model.index(r, c).data()
+            # Convert empty strings to None
+            row.append(
+                None if (isinstance(value, str) and value == "") else value
+            )
         data.append(row)
+
     if not data:
         return pd.DataFrame()
-    if proxy_model.source_model.table_type == "condition":
-        data = pd.DataFrame(data).set_index("conditionId")
-    elif proxy_model.source_model.table_type == "observable":
-        data = pd.DataFrame(data).set_index("observableId")
-    elif proxy_model.source_model.table_type == "parameter":
-        data = pd.DataFrame(data).set_index("parameterId")
-    elif proxy_model.source_model.table_type == "measurement":
-        # turn measurement and time to float
-        data = pd.DataFrame(data)
-        data["measurement"] = data["measurement"].astype(float)
-        data["time"] = data["time"].astype(float)
-    elif proxy_model.source_model.table_type == "simulation":
-        # turn simulation and time to float
-        data = pd.DataFrame(data)
-        data["simulation"] = data["simulation"].astype(float)
-        data["time"] = data["time"].astype(float)
-    elif proxy_model.source_model.table_type == "visualization":
-        data = pd.DataFrame(data)
-        if "xOffset" in data.columns:
-            data["xOffset"] = data["xOffset"].astype(float)
-        if "yOffset" in data.columns:
-            data["yOffset"] = data["yOffset"].astype(float)
-    else:
-        data = pd.DataFrame(data)
-
-    return data
+
+    # Create DataFrame in one shot
+    df = pd.DataFrame(data, columns=headers)
+
+    # Apply type-specific transformations
+    table_type = proxy_model.source_model.table_type
+
+    if table_type == "condition":
+        df = df.set_index("conditionId")
+    elif table_type == "observable":
+        df = df.set_index("observableId")
+    elif table_type == "parameter":
+        df = df.set_index("parameterId")
+    elif table_type == "measurement":
+        # Use pd.to_numeric with errors='coerce' for robust conversion
+        df["measurement"] = pd.to_numeric(df["measurement"], errors="coerce")
+        df["time"] = pd.to_numeric(df["time"], errors="coerce")
+    elif table_type == "simulation":
+        df["simulation"] = pd.to_numeric(df["simulation"], errors="coerce")
+        df["time"] = pd.to_numeric(df["time"], errors="coerce")
+    elif table_type == "visualization":
+        if "xOffset" in df.columns:
+            df["xOffset"] = pd.to_numeric(df["xOffset"], errors="coerce")
+        if "yOffset" in df.columns:
+            df["yOffset"] = pd.to_numeric(df["yOffset"], errors="coerce")
+
+    return df