diff --git a/docs/examples/Home Team Win-Loss Modeling/Home Team Win-Loss Data Prep.ipynb b/docs/examples/Home Team Win-Loss Modeling/Home Team Win-Loss Data Prep.ipynb
index e131b3ba..b3de7d96 100644
--- a/docs/examples/Home Team Win-Loss Modeling/Home Team Win-Loss Data Prep.ipynb	
+++ b/docs/examples/Home Team Win-Loss Modeling/Home Team Win-Loss Data Prep.ipynb	
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,14 +115,14 @@
     "    teamLookup = pd.DataFrame(teams.get_teams())\n",
     "\n",
     "    # Get teams schedule for each team for each season\n",
-    "    scheduleFrame = pd.DataFrame()\n",
+    "    scheduleFrames = []\n",
     "\n",
     "    for season in seasons:\n",
     "        for id in teamLookup[\"id\"]:\n",
     "            time.sleep(1)\n",
-    "            scheduleFrame = scheduleFrame.append(\n",
-    "                getRegularSeasonSchedule(season, id, seasonType)\n",
-    "            )\n",
+    "            scheduleFrames.append(getRegularSeasonSchedule(season, id, seasonType))\n",
+    "\n",
+    "    scheduleFrame = pd.concat(scheduleFrames, ignore_index=True)\n",
     "\n",
     "    scheduleFrame[\"GAME_DATE\"] = pd.to_datetime(\n",
     "        scheduleFrame[\"MATCHUP\"].map(getGameDate)\n",
@@ -282,9 +282,14 @@
     "\n",
     "    i = int(len(gameLogs) / 2)  # Can use a previously completed gameLog dataset\n",
     "\n",
+    "    # Start from any previously completed gameLogs, then collect each game's\n",
+    "    # frame in a list and concat once at the end (instead of growing the\n",
+    "    # DataFrame every iteration).\n",
+    "    gameLogsList = [gameLogs] if not gameLogs.empty else []\n",
+    "\n",
     "    while i < len(scheduleFrame):\n",
     "        time.sleep(1)\n",
-    "        gameLogs = gameLogs.append(\n",
+    "        gameLogsList.append(\n",
     "            getSingleGameMetrics(\n",
     "                scheduleFrame.at[i, \"GAME_ID\"],\n",
     "                scheduleFrame.at[i, \"HOME_TEAM_ID\"],\n",
@@ -295,8 +300,6 @@
     "            )\n",
     "        )\n",
     "\n",
-    "        gameLogs = gameLogs.reset_index(drop=True)\n",
-    "\n",
     "        end = time.perf_counter_ns()\n",
     "\n",
     "        # Output time it took to load x amount of records\n",
@@ -306,6 +309,8 @@
     "\n",
     "        i += 1\n",
     "\n",
+    "    gameLogs = pd.concat(gameLogsList, ignore_index=True)\n",
+    "\n",
     "    # Get Table Level Aggregation Columns\n",
     "    getHomeAwayFlag(gameLogs)\n",
     "    gameLogs = getHomeWinPctg(gameLogs)\n",
@@ -327,7 +332,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.7492755266666666\n"
+      "2.7116616116666665\n"
      ]
     }
    ],
@@ -484,46 +489,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 0.027592645000000002\n",
-      "100 3.761211635\n",
-      "200 7.446148228333333\n",
-      "300 10.91657379\n",
-      "400 14.178322275\n",
-      "500 17.562468318333334\n",
-      "600 22.57064343333333\n",
-      "700 28.885029056666667\n",
-      "800 32.53894844333333\n",
-      "900 36.77510828333333\n",
-      "1000 41.57496655333333\n",
-      "1100 45.66484153166667\n",
-      "1200 48.904151811666665\n",
-      "1300 52.46264781666666\n",
-      "1400 55.85839063666666\n",
-      "1500 60.04402927833333\n",
-      "1600 64.00290142\n",
-      "1700 67.65499090166666\n",
-      "1800 71.13305232833333\n",
-      "1900 75.12400890166667\n",
-      "2000 78.45767208000001\n",
-      "2100 82.388200135\n",
-      "2200 85.89361625166666\n",
-      "2300 90.73073644\n",
-      "2400 92.95489135999999\n",
-      "2500 95.15283247333333\n",
-      "2600 97.83685436833333\n",
-      "2700 101.011523535\n",
-      "2800 104.15464245833333\n",
-      "2900 108.15016159333332\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create the gameLogs DataFrame\n",
     "gameLogs = pd.DataFrame()\n",
@@ -2212,7 +2180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,