diff --git a/numpy-examples/full_portfolio.csv b/numpy-examples/full_portfolio.csv
new file mode 100644
index 0000000000..2285eed597
--- /dev/null
+++ b/numpy-examples/full_portfolio.csv
@@ -0,0 +1,7 @@
+Company,Sector,Mon,Tue,Wed,Thu,Fri
+Company_A,technology,100.5,101.2,102,101.8,112.5
+Company_B,finance,200.1,199.8,200.5,201.0,200.8
+Company_C,healthcare,50.3,50.5,51.0,50.8,51.2
+Company_D,technology,110.5,101.2,102,111.8,97.5
+Company_E,finance,200.1,200.8,200.5,211.0,200.8
+Company_F,healthcare,55.3,50.5,53.0,50.8,52.2
diff --git a/numpy-examples/issued_checks.csv b/numpy-examples/issued_checks.csv
index 79ebe66d15..c4a57d0e5d 100644
--- a/numpy-examples/issued_checks.csv
+++ b/numpy-examples/issued_checks.csv
@@ -1,5 +1,5 @@
 Check_ID,Payee,Amount,Date_Issued
-1341,K.Starmer,150.00,2024-03-29
+1341,K Starmer,150.00,2024-03-29
 1342,R Sunak,175.00,2024-03-29
 1343,L Truss,30.00,2024-03-29
 1344,B Johnson,45.00,2024-03-22
@@ -8,4 +8,4 @@ Check_ID,Payee,Amount,Date_Issued
 1347,G Brown,100.00,2024-03-15
 1348,T Blair,250.00,2024-03-15
 1349,J Major,500.00,2024-03-15
-1350,M Thatcher,220.00,2024-03-15
\ No newline at end of file
+1350,M Thatcher,220.00,2024-03-15
diff --git a/numpy-examples/passports.csv b/numpy-examples/passports.csv
index 729d3351f9..91d80d959e 100644
--- a/numpy-examples/passports.csv
+++ b/numpy-examples/passports.csv
@@ -4,7 +4,7 @@ passport_no,passenger_no,nationality
 493456399,3,American
 375456228,4,American
 457345975,5,Austrian
-345957363,6,Norewegian
+345957363,6,Norwegian
 567546577,7,French
 453667456,8,German
 456785778,9,Danish
@@ -19,4 +19,4 @@ passport_no,passenger_no,nationality
 654672278,18,Spanish
 683637288,19,Norwegian
 768357788,20,British
-768357788,21,American
\ No newline at end of file
+768357788,21,American
diff --git a/numpy-examples/tutorial_code.ipynb b/numpy-examples/tutorial_code.ipynb
index 453c501358..e24f9e54df 100644
--- a/numpy-examples/tutorial_code.ipynb
+++ b/numpy-examples/tutorial_code.ipynb
@@ -15,10 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!python -m pip install numpy\n",
-    "!python -m pip install matplotlib\n",
-    "!python -m pip install pathlib\n",
-    "!python -m pip install jupyterlab"
+    "# !python -m pip install numpy\n",
+    "# !python -m pip install matplotlib"
    ]
   },
   {
@@ -39,16 +37,10 @@
     "import numpy as np\n",
     "from pathlib import Path\n",
     "\n",
-    "array = np.zeros(\n",
-    "    (\n",
-    "        3,\n",
-    "        2,\n",
-    "        3,\n",
-    "    )\n",
-    ")\n",
+    "array = np.zeros((3, 2, 3))\n",
     "print(id(array))\n",
     "\n",
-    "for file_count, csv_file in enumerate(Path.cwd().glob(\"file?.csv\")):\n",
+    "for file_count, csv_file in enumerate(sorted(Path.cwd().glob(\"file?.csv\"))):\n",
     "    array[file_count] = np.loadtxt(csv_file.name, delimiter=\",\")\n",
     "\n",
     "print(id(array))\n",
@@ -63,15 +55,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "array = np.zeros(\n",
-    "    (\n",
-    "        4,\n",
-    "        2,\n",
-    "        3,\n",
-    "    )\n",
-    ")\n",
+    "array = np.zeros((4, 2, 3))\n",
     "\n",
-    "for file_count, csv_file in enumerate(Path.cwd().glob(\"file?.csv\")):\n",
+    "for file_count, csv_file in enumerate(sorted(Path.cwd().glob(\"file?.csv\"))):\n",
     "    array[file_count] = np.loadtxt(csv_file.name, delimiter=\",\")\n",
     "\n",
     "array[3, 0] = np.loadtxt(\"short_file.csv\", delimiter=\",\")\n",
@@ -86,24 +72,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "array = np.zeros(\n",
-    "    (\n",
-    "        4,\n",
-    "        2,\n",
-    "        3,\n",
-    "    )\n",
-    ")\n",
+    "array = np.zeros((4, 2, 3))\n",
     "print(id(array))\n",
     "\n",
-    "for file_count, csv_file in enumerate(Path.cwd().glob(\"file?.csv\")):\n",
+    "for file_count, csv_file in enumerate(sorted(Path.cwd().glob(\"file?.csv\"))):\n",
     "    array[file_count] = np.loadtxt(csv_file.name, delimiter=\",\")\n",
     "\n",
     "array = np.insert(arr=array, obj=2, values=0, axis=1)\n",
-    "\n",
     "array[3] = np.loadtxt(\"long_file.csv\", delimiter=\",\")\n",
     "\n",
     "print(id(array))\n",
-    "\n",
     "array"
    ]
   },
@@ -117,21 +95,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "6d760bcd-1be5-4396-8eb8-d729768909d6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['At The Back', 'Fast Eddie', 'Almost There'], dtype='<U12')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -187,13 +154,11 @@
     "    (\"amount\", \"f8\"),\n",
     "    (\"date_issued\", \"U10\"),\n",
     "]\n",
-    "\n",
     "cashed_dtypes = [(\"id\", \"i8\"), (\"amount\", \"f8\"), (\"date_cashed\", \"U10\")]\n",
     "\n",
     "issued_checks = np.loadtxt(\n",
     "    Path(\"issued_checks.csv\"), delimiter=\",\", dtype=issued_dtypes, skiprows=1\n",
     ")\n",
-    "\n",
     "cashed_checks = np.loadtxt(\n",
     "    Path(\"cashed_checks.csv\"), delimiter=\",\", dtype=cashed_dtypes, skiprows=1\n",
     ")\n",
@@ -201,7 +166,6 @@
     "cashed_check_details = rfn.rec_join(\n",
     "    \"id\", issued_checks, cashed_checks, jointype=\"inner\"\n",
     ")\n",
-    "\n",
     "cashed_check_details[[\"payee\", \"date_issued\", \"date_cashed\"]]"
    ]
   },
@@ -212,24 +176,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cashed_check_details[\n",
-    "    [\n",
-    "        \"payee\",\n",
-    "        \"date_issued\",\n",
-    "        \"date_cashed\",\n",
-    "        \"amount\",\n",
-    "    ]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e4d0713a-30eb-4c8c-8b30-679c3f1c1707",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cashed_check_details.dtype"
+    "# The following gives an error, the amount column has been renamed\n",
+    "#\n",
+    "# cashed_check_details[[\"payee\", \"date_issued\", \"date_cashed\", \"amount\"]]"
    ]
   },
   {
@@ -265,11 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "possible_frauds = [\n",
-    "    i for i in cashed_checks[\"id\"] if i not in issued_checks[\"id\"]\n",
-    "]\n",
-    "\n",
-    "possible_frauds"
+    "[i for i in cashed_checks[\"id\"] if i not in issued_checks[\"id\"]]"
    ]
   },
   {
@@ -302,7 +247,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "94d12833-d1a5-4fca-ab28-f851330732e4",
+   "id": "52c7366d-5556-4680-93ee-c0a6467a4d08",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,7 +274,7 @@
     "    (\"day\", \"f8\"),\n",
     "]\n",
     "\n",
-    "for day, csv_file in zip(days, Path.cwd().glob(\"share_prices-?.csv\")):\n",
+    "for day, csv_file in zip(days, sorted(Path.cwd().glob(\"share_prices-?.csv\"))):\n",
     "    portfolio[day] = np.loadtxt(\n",
     "        csv_file.name,\n",
     "        delimiter=\",\",\n",
@@ -390,23 +335,44 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "tech_sector = portfolio[portfolio[\"sector\"] == \"technology\"][\"company\"]\n",
+    "tech_mask = portfolio[\"sector\"] == \"technology\"\n",
+    "tech_sector = portfolio[tech_mask][\"company\"]\n",
+    "tech_valuation = portfolio[tech_mask][\"fri\"] * 250 * 0.01\n",
     "\n",
-    "tech_valuation = (\n",
-    "    portfolio[portfolio[\"sector\"] == \"technology\"][\"fri\"] * 250 * 0.01\n",
-    ")\n",
+    "plt.bar(x=tech_sector, height=tech_valuation, data=tech_valuation)[\n",
+    "    0\n",
+    "].set_color(\"g\")\n",
     "\n",
-    "(\n",
-    "    plt.bar(x=tech_sector, height=tech_valuation, data=tech_valuation)[\n",
-    "        0\n",
-    "    ].set_color(\"g\")\n",
-    ")\n",
     "plt.xlabel(\"Tech Companies\")\n",
     "plt.ylabel(\"Friday Price ($)\")\n",
     "plt.title(\"Tech Share Valuation ($)\")\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77f606fb-e0c5-4aa2-bbbe-894642997840",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities = [\"london\", \"new_york\", \"rome\"]\n",
+    "cities_dtype = [(city, \"i8\") for city in cities]\n",
+    "city_files_dtype = [(\"month\", \"U20\"), (\"temp\", \"i8\")]\n",
+    "weather_data_dtype = np.dtype([(\"month\", \"U20\")] + cities_dtype)\n",
+    "weather_data = np.zeros((12,), dtype=weather_data_dtype)\n",
+    "\n",
+    "for city in cities:\n",
+    "    temps = np.loadtxt(\n",
+    "        Path(f\"{city}_temperatures.csv\"),\n",
+    "        delimiter=\",\",\n",
+    "        dtype=city_files_dtype,\n",
+    "    )\n",
+    "    weather_data[[\"month\", city]] = temps\n",
+    "\n",
+    "weather_data"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e6b6fb23-87ee-4cf0-a750-431f016c6fc0",
@@ -423,13 +389,11 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "\n",
     "from pathlib import Path\n",
     "\n",
     "share_dtypes = [\n",
     "    (\"company\", \"U20\"),\n",
     "    (\"sector\", \"U20\"),\n",
-    "    (\"sector_id\", \"i8\"),\n",
     "    (\"mon\", \"f8\"),\n",
     "    (\"tue\", \"f8\"),\n",
     "    (\"wed\", \"f8\"),\n",
@@ -438,16 +402,13 @@
     "]\n",
     "\n",
     "portfolio = np.loadtxt(\n",
-    "    Path(\"share_prices.csv\"),\n",
+    "    Path(\"full_portfolio.csv\"),\n",
     "    delimiter=\",\",\n",
     "    dtype=share_dtypes,\n",
     "    skiprows=1,\n",
     ")\n",
     "\n",
-    "monday_prices = portfolio[\"mon\"]\n",
-    "friday_prices = portfolio[\"fri\"]\n",
-    "\n",
-    "friday_prices - monday_prices"
+    "portfolio[\"fri\"] - portfolio[\"mon\"]"
    ]
   },
   {
@@ -457,16 +418,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def in_profit(first_day, last_day):\n",
-    "    if last_day > first_day:\n",
-    "        return True\n",
-    "    return False\n",
-    "\n",
+    "def profit_with_bonus(first_day, last_day):\n",
+    "    if last_day >= first_day * 1.01:\n",
+    "        return (last_day - first_day) * 1.1\n",
+    "    else:\n",
+    "        return last_day - first_day\n",
     "\n",
-    "monday_prices = portfolio[\"mon\"]\n",
-    "friday_prices = portfolio[\"fri\"]\n",
     "\n",
-    "in_profit(monday_prices, friday_prices)"
+    "# The following causes an error because in_profit() doesn't know\n",
+    "# how to work with NumPy arrays:\n",
+    "#\n",
+    "# profit_with_bonus(portfolio[\"mon\"], portfolio[\"fri\"])"
    ]
   },
   {
@@ -476,27 +438,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def in_profit(first_day, last_day):\n",
-    "    if last_day > first_day:\n",
-    "        return True\n",
-    "    return False\n",
+    "def profit_with_bonus(first_day, last_day):\n",
+    "    if last_day >= first_day * 1.01:\n",
+    "        return (last_day - first_day) * 1.1\n",
+    "    else:\n",
+    "        return last_day - first_day\n",
     "\n",
     "\n",
-    "monday_prices = portfolio[\"mon\"]\n",
-    "friday_prices = portfolio[\"fri\"]\n",
-    "\n",
-    "vectorized_in_profit = np.vectorize(in_profit)\n",
-    "vectorized_in_profit(monday_prices, friday_prices)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "05eaa992-bed3-4af0-9508-c58be2f36381",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "portfolio[vectorized_in_profit(monday_prices, friday_prices)]"
+    "vectorized_profit_with_bonus = np.vectorize(profit_with_bonus)\n",
+    "vectorized_profit_with_bonus(portfolio[\"mon\"], portfolio[\"fri\"])"
    ]
   },
   {
@@ -506,7 +456,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "in_profit(3, 5)"
+    "profit_with_bonus(3, 5)"
    ]
   },
   {
@@ -517,18 +467,14 @@
    "outputs": [],
    "source": [
     "@np.vectorize\n",
-    "def in_profit(first_day, last_day):\n",
-    "    if last_day > first_day:\n",
-    "        return True\n",
-    "    return False\n",
-    "\n",
-    "\n",
-    "monday_prices = portfolio[\"mon\"]\n",
-    "friday_prices = portfolio[\"fri\"]\n",
+    "def profit_with_bonus(first_day, last_day):\n",
+    "    if last_day >= first_day * 1.01:\n",
+    "        return (last_day - first_day) * 1.1\n",
+    "    else:\n",
+    "        return last_day - first_day\n",
     "\n",
-    "print(in_profit(monday_prices, friday_prices))\n",
     "\n",
-    "portfolio[in_profit(monday_prices, friday_prices)]"
+    "profit_with_bonus(portfolio[\"mon\"], portfolio[\"fri\"])"
    ]
   },
   {
@@ -538,7 +484,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "in_profit(3, 5)"
+    "profit_with_bonus(3, 5)"
    ]
   },
   {
@@ -548,7 +494,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "portfolio[np.where(friday_prices > monday_prices, True, False)]"
+    "np.where(\n",
+    "    portfolio[\"fri\"] > portfolio[\"mon\"] * 1.01,\n",
+    "    (portfolio[\"fri\"] - portfolio[\"mon\"]) * 1.1,\n",
+    "    portfolio[\"fri\"] - portfolio[\"mon\"],\n",
+    ")"
    ]
   }
  ],
@@ -568,7 +518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,