diff --git a/numpy-examples/README.md b/numpy-examples/README.md
new file mode 100644
index 0000000000..f6ed18d653
--- /dev/null
+++ b/numpy-examples/README.md
@@ -0,0 +1,3 @@
+These files are used in the tutorial [NumPy Practical Examples: Useful Techniques](https://realpython.com/numpy-example-useful-practical-techniques/).
+
+The various csv files contain data that is uses to create and populate various NumPy arrays in both the main tutorial and in the various embedded exercises. The tutorial_code.ipynb is a Jupyter Notebook containing the code used in the tutorials.  
\ No newline at end of file
diff --git a/numpy-examples/cashed_checks.csv b/numpy-examples/cashed_checks.csv
new file mode 100644
index 0000000000..577981e719
--- /dev/null
+++ b/numpy-examples/cashed_checks.csv
@@ -0,0 +1,8 @@
+Check_ID,Amount,Date_Cashed
+1341,150.00,2024-04-12
+1342,175.00,2024-04-16
+1343,30.00,2024-04-12
+1345,65.00,2024-04-12
+1346,430.00,2024-04-08
+1349,500.00,2024-04-08
+1350,220.00,2024-04-15
\ No newline at end of file
diff --git a/numpy-examples/check_list_duplicates.csv b/numpy-examples/check_list_duplicates.csv
new file mode 100644
index 0000000000..02a3ddbf9d
--- /dev/null
+++ b/numpy-examples/check_list_duplicates.csv
@@ -0,0 +1,12 @@
+Check_ID,Payee,Amount,Date_Issued
+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
+1344,B Johnson,45.00,2024-03-22
+1345,T May,65.00,2024-03-22
+1346,D Cameron,430.00,2024-03-22
+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
diff --git a/numpy-examples/ex1a.csv b/numpy-examples/ex1a.csv
new file mode 100644
index 0000000000..e97fc94b35
--- /dev/null
+++ b/numpy-examples/ex1a.csv
@@ -0,0 +1,4 @@
+5,10,15,20
+25,30,35,40
+45,50,55,60
+65,70,75,80
\ No newline at end of file
diff --git a/numpy-examples/ex1b.csv b/numpy-examples/ex1b.csv
new file mode 100644
index 0000000000..37ffd0470b
--- /dev/null
+++ b/numpy-examples/ex1b.csv
@@ -0,0 +1 @@
+1,3,5
\ No newline at end of file
diff --git a/numpy-examples/ex1c.csv b/numpy-examples/ex1c.csv
new file mode 100644
index 0000000000..cfa0b54952
--- /dev/null
+++ b/numpy-examples/ex1c.csv
@@ -0,0 +1,3 @@
+2
+4
+6
\ No newline at end of file
diff --git a/numpy-examples/file1.csv b/numpy-examples/file1.csv
new file mode 100644
index 0000000000..8dbfc4399b
--- /dev/null
+++ b/numpy-examples/file1.csv
@@ -0,0 +1,2 @@
+1.1,1.2,1.3
+1.4,1.5,1.6
\ No newline at end of file
diff --git a/numpy-examples/file2.csv b/numpy-examples/file2.csv
new file mode 100644
index 0000000000..dabb7f3a8a
--- /dev/null
+++ b/numpy-examples/file2.csv
@@ -0,0 +1,2 @@
+2.1,2.2,2.3
+2.4,2.5,2.6
\ No newline at end of file
diff --git a/numpy-examples/file3.csv b/numpy-examples/file3.csv
new file mode 100644
index 0000000000..497c31700f
--- /dev/null
+++ b/numpy-examples/file3.csv
@@ -0,0 +1,2 @@
+3.1,3.2,3.3
+3.4,3.5,3.6
\ No newline at end of file
diff --git a/numpy-examples/issued_checks.csv b/numpy-examples/issued_checks.csv
new file mode 100644
index 0000000000..79ebe66d15
--- /dev/null
+++ b/numpy-examples/issued_checks.csv
@@ -0,0 +1,11 @@
+Check_ID,Payee,Amount,Date_Issued
+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
+1345,T May,65.00,2024-03-22
+1346,D Cameron,430.00,2024-03-22
+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
diff --git a/numpy-examples/london_temperatures.csv b/numpy-examples/london_temperatures.csv
new file mode 100644
index 0000000000..ce1d205e15
--- /dev/null
+++ b/numpy-examples/london_temperatures.csv
@@ -0,0 +1,12 @@
+Jan,5
+Feb,7	
+Mar,9	
+Apr,11	
+May,14	
+Jun,16	
+Jul,19	
+Aug,19	
+Sep,17	
+Oct,13	
+Nov,10	
+Dec,7
\ No newline at end of file
diff --git a/numpy-examples/long_file.csv b/numpy-examples/long_file.csv
new file mode 100644
index 0000000000..0e63b07cb6
--- /dev/null
+++ b/numpy-examples/long_file.csv
@@ -0,0 +1,3 @@
+4.1,4.2,4.3
+4.4,4.5,4.6
+4.7,4.8,4.9
\ No newline at end of file
diff --git a/numpy-examples/new_york_temperatures.csv b/numpy-examples/new_york_temperatures.csv
new file mode 100644
index 0000000000..4ef0f4d9fd
--- /dev/null
+++ b/numpy-examples/new_york_temperatures.csv
@@ -0,0 +1,12 @@
+Jan,2
+Feb,2	
+Mar,4	
+Apr,11	
+May,16	
+Jun,22	
+Jul,25	
+Aug,24	
+Sep,20	
+Oct,14	
+Nov,12	
+Dec,9
\ No newline at end of file
diff --git a/numpy-examples/passengers.csv b/numpy-examples/passengers.csv
new file mode 100644
index 0000000000..d5b924e62b
--- /dev/null
+++ b/numpy-examples/passengers.csv
@@ -0,0 +1,22 @@
+passenger_no,first_name,last_name
+1,Olivia,Smith
+2,Amelia,Jones
+3,Isla,Williams
+4,Ava,Taylor
+5,Ivy,Brown
+6,Freya,Davies
+7,Lily,Evans
+8,Florence,Wilson
+9,Mia,Thomas
+10,Willow,Johnson
+11,Noah,Roberts
+12,Oliver,Robinson
+13,George,Thompson
+14,Arthur,Wright
+14,Arthur,Wright
+15,Muhammad,Walker
+16,Leo,White
+17,Harry,Edwards
+18,Oscar,Hughes
+19,Archie,Green
+20,Henry,Hall
\ No newline at end of file
diff --git a/numpy-examples/passports.csv b/numpy-examples/passports.csv
new file mode 100644
index 0000000000..729d3351f9
--- /dev/null
+++ b/numpy-examples/passports.csv
@@ -0,0 +1,22 @@
+passport_no,passenger_no,nationality
+123656489,1,British
+373456789,2,British
+493456399,3,American
+375456228,4,American
+457345975,5,Austrian
+345957363,6,Norewegian
+567546577,7,French
+453667456,8,German
+456785778,9,Danish
+456785778,9,Danish
+679857668,10,Dutch
+456657867,11,Dutch
+467775488,12,French
+884393487,13,Danish
+122647657,15,Dutch
+534677673,16,British
+786737688,17,American
+654672278,18,Spanish
+683637288,19,Norwegian
+768357788,20,British
+768357788,21,American
\ No newline at end of file
diff --git a/numpy-examples/portfolio.csv b/numpy-examples/portfolio.csv
new file mode 100644
index 0000000000..5e1e766100
--- /dev/null
+++ b/numpy-examples/portfolio.csv
@@ -0,0 +1,7 @@
+Company,Sector
+Company_A,technology
+Company_B,finance
+Company_C,healthcare
+Company_D,technology
+Company_E,finance
+Company_F,healthcare
\ No newline at end of file
diff --git a/numpy-examples/rome_temperatures.csv b/numpy-examples/rome_temperatures.csv
new file mode 100644
index 0000000000..d70c14343a
--- /dev/null
+++ b/numpy-examples/rome_temperatures.csv
@@ -0,0 +1,12 @@
+Jan,8
+Feb,9	
+Mar,12	
+Apr,14	
+May,21	
+Jun,23	
+Jul,26	
+Aug,24	
+Sep,22	
+Oct,18	
+Nov,13	
+Dec,10
\ No newline at end of file
diff --git a/numpy-examples/share_prices-1.csv b/numpy-examples/share_prices-1.csv
new file mode 100644
index 0000000000..9e6342db35
--- /dev/null
+++ b/numpy-examples/share_prices-1.csv
@@ -0,0 +1,7 @@
+Company,mon
+Company_A,100.5
+Company_B,200.1
+Company_C,50.3
+Company_D,110.5
+Company_E,200.1
+Company_F,55.3
\ No newline at end of file
diff --git a/numpy-examples/share_prices-2.csv b/numpy-examples/share_prices-2.csv
new file mode 100644
index 0000000000..b29208eade
--- /dev/null
+++ b/numpy-examples/share_prices-2.csv
@@ -0,0 +1,7 @@
+Company,tue
+Company_A,101.2
+Company_B,199.8
+Company_C,50.5
+Company_D,101.2
+Company_E,200.8
+Company_F,50.5
\ No newline at end of file
diff --git a/numpy-examples/share_prices-3.csv b/numpy-examples/share_prices-3.csv
new file mode 100644
index 0000000000..7cce3ec69a
--- /dev/null
+++ b/numpy-examples/share_prices-3.csv
@@ -0,0 +1,7 @@
+Company,Wed
+Company_A,102
+Company_B,200.5
+Company_C,51.0
+Company_D,102
+Company_E,200.5
+Company_F,53.0
\ No newline at end of file
diff --git a/numpy-examples/share_prices-4.csv b/numpy-examples/share_prices-4.csv
new file mode 100644
index 0000000000..c7d58df53f
--- /dev/null
+++ b/numpy-examples/share_prices-4.csv
@@ -0,0 +1,7 @@
+Company,Thu
+Company_A,101.8
+Company_B,201.0
+Company_C,50.8
+Company_D,111.8
+Company_E,211.0
+Company_F,50.8
\ No newline at end of file
diff --git a/numpy-examples/share_prices-5.csv b/numpy-examples/share_prices-5.csv
new file mode 100644
index 0000000000..5f6d2d7b9f
--- /dev/null
+++ b/numpy-examples/share_prices-5.csv
@@ -0,0 +1,7 @@
+Company,Fri
+Company_A,112.5
+Company_B,200.8
+Company_C,51.2
+Company_D,97.5
+Company_E,200.8
+Company_F,52.2
\ No newline at end of file
diff --git a/numpy-examples/short_file.csv b/numpy-examples/short_file.csv
new file mode 100644
index 0000000000..30c26e4dec
--- /dev/null
+++ b/numpy-examples/short_file.csv
@@ -0,0 +1 @@
+4.1,4.2,4.3
diff --git a/numpy-examples/tutorial_code.ipynb b/numpy-examples/tutorial_code.ipynb
new file mode 100644
index 0000000000..453c501358
--- /dev/null
+++ b/numpy-examples/tutorial_code.ipynb
@@ -0,0 +1,576 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c33e52ac-a587-41c3-b487-6a1533043a54",
+   "metadata": {},
+   "source": [
+    "# **Code Used In NumPy Practical Examples: Useful Techniques**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29aff3d0-0427-4dd8-b14f-bc7ce9002a76",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b740161-a80e-4d37-8c26-2dca52c68dce",
+   "metadata": {},
+   "source": [
+    "**Example 1: Creating Multi-Dimensional Arrays From Files**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e64a11c1-a0b5-49f8-a0a6-dfe0adf674f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "\n",
+    "array = np.zeros(\n",
+    "    (\n",
+    "        3,\n",
+    "        2,\n",
+    "        3,\n",
+    "    )\n",
+    ")\n",
+    "print(id(array))\n",
+    "\n",
+    "for file_count, csv_file in enumerate(Path.cwd().glob(\"file?.csv\")):\n",
+    "    array[file_count] = np.loadtxt(csv_file.name, delimiter=\",\")\n",
+    "\n",
+    "print(id(array))\n",
+    "print(array.shape)\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8de90c08-9bdc-42f3-ad4e-d7a34fca8215",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.zeros(\n",
+    "    (\n",
+    "        4,\n",
+    "        2,\n",
+    "        3,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "for file_count, csv_file in enumerate(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",
+    "\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63775265-deb1-4cca-bd7c-547b3d49f5ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.zeros(\n",
+    "    (\n",
+    "        4,\n",
+    "        2,\n",
+    "        3,\n",
+    "    )\n",
+    ")\n",
+    "print(id(array))\n",
+    "\n",
+    "for file_count, csv_file in enumerate(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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd4544ac-53bc-449c-88a2-f2496bba8f55",
+   "metadata": {},
+   "source": [
+    "**Example 2: Reconciling Data Using Structured NumPy Arrays**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "race_results = np.array(\n",
+    "    [\n",
+    "        (\"At The Back\", 1.2, 3),\n",
+    "        (\"Fast Eddie\", 1.3, 1),\n",
+    "        (\"Almost There\", 1.1, 2),\n",
+    "    ],\n",
+    "    dtype=[\n",
+    "        (\"horse_name\", \"U12\"),\n",
+    "        (\"price\", \"f4\"),\n",
+    "        (\"position\", \"i4\"),\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "race_results[\"horse_name\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d3ae083-2f62-45a2-a761-c021484ab9d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.sort(race_results, order=\"position\")[[\"horse_name\", \"price\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b08dc1c-1797-4e54-a596-f5240150387c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "race_results[race_results[\"position\"] == 1][\"horse_name\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0df725d-10bb-47db-9213-5cdb20e3b2ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy.lib.recfunctions as rfn\n",
+    "from pathlib import Path\n",
+    "\n",
+    "issued_dtypes = [\n",
+    "    (\"id\", \"i8\"),\n",
+    "    (\"payee\", \"U10\"),\n",
+    "    (\"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",
+    "\n",
+    "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\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2faa8285-ae4f-4808-8ee1-aeb5bc21fbb9",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94cc2c27-9dec-4dbe-a23f-2a3dba403b70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cashed_check_details[[\"payee\", \"date_issued\", \"date_cashed\", \"amount1\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34c8719b-1ee1-4635-8bdc-bf8fa624485e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outstanding_checks = [\n",
+    "    check_id\n",
+    "    for check_id in issued_checks[\"id\"]\n",
+    "    if check_id not in cashed_checks[\"id\"]\n",
+    "]\n",
+    "\n",
+    "[int(i) for i in outstanding_checks]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce0831b0-e8d0-45cf-be1f-8a71c5493f35",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4d62074-c38b-4bd6-b8b9-d569ceceeab6",
+   "metadata": {},
+   "source": [
+    "**NumPy Example 3: Analyzing and Charting Hierarchical Data**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "861a2d6b-392d-4de8-a694-38d23ac21f24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "\n",
+    "days = [\"mon\", \"tue\", \"wed\", \"thu\", \"fri\"]\n",
+    "days_dtype = [(day, \"f8\") for day in days]\n",
+    "company_dtype = [(\"company\", \"U20\"), (\"sector\", \"U20\")]\n",
+    "\n",
+    "portfolio_dtype = np.dtype(company_dtype + days_dtype)\n",
+    "portfolio = np.zeros((6,), dtype=portfolio_dtype)\n",
+    "portfolio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94d12833-d1a5-4fca-ab28-f851330732e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "companies = np.loadtxt(\n",
+    "    Path(\"portfolio.csv\"),\n",
+    "    delimiter=\",\",\n",
+    "    dtype=company_dtype,\n",
+    "    skiprows=1,\n",
+    ").reshape((6,))\n",
+    "\n",
+    "portfolio[[\"company\", \"sector\"]] = companies\n",
+    "portfolio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd244b7a-c919-4f69-bad2-fd70dfe68195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "share_prices_dtype = [\n",
+    "    (\"company\", \"U20\"),\n",
+    "    (\"day\", \"f8\"),\n",
+    "]\n",
+    "\n",
+    "for day, csv_file in zip(days, Path.cwd().glob(\"share_prices-?.csv\")):\n",
+    "    portfolio[day] = np.loadtxt(\n",
+    "        csv_file.name,\n",
+    "        delimiter=\",\",\n",
+    "        dtype=share_prices_dtype,\n",
+    "        skiprows=1,\n",
+    "    )[\"day\"]\n",
+    "\n",
+    "portfolio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6a50547-5527-49fd-9c14-9f862d0724d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_array = portfolio[portfolio[\"company\"] == \"Company_C\"]\n",
+    "filtered_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1249605-ff28-4954-a4f4-f523e7a17388",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "portfolio[portfolio[\"sector\"] == \"technology\"][\"fri\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17c86bd6-5c68-4bd8-ac2c-148b0c383abf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "portfolio[portfolio[\"sector\"] == \"technology\"][\"fri\"] * 250 * 0.01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c58c7e9a-2317-4537-9c81-8f306d1adcae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum(portfolio[portfolio[\"sector\"] == \"technology\"][\"fri\"] * 250 * 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42fe3933-cc9d-4643-87f2-fda5736a3934",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "tech_sector = portfolio[portfolio[\"sector\"] == \"technology\"][\"company\"]\n",
+    "\n",
+    "tech_valuation = (\n",
+    "    portfolio[portfolio[\"sector\"] == \"technology\"][\"fri\"] * 250 * 0.01\n",
+    ")\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": "markdown",
+   "id": "e6b6fb23-87ee-4cf0-a750-431f016c6fc0",
+   "metadata": {},
+   "source": [
+    "**Example 4: Writing Your Own Vectorization Functions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a804ca44-f259-4151-bc0f-a9298999a1e8",
+   "metadata": {},
+   "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",
+    "    (\"thu\", \"f8\"),\n",
+    "    (\"fri\", \"f8\"),\n",
+    "]\n",
+    "\n",
+    "portfolio = np.loadtxt(\n",
+    "    Path(\"share_prices.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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3be836cd-5c7e-4218-95f3-fd5b94b22157",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "\n",
+    "in_profit(monday_prices, friday_prices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35dab65e-baf0-4a0a-b773-0ec7133b1b23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "\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)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acf8cad6-e93a-4ee9-817c-ffffdc4b825e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_profit(3, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f789fa62-e8e0-45c8-973f-b33ee54e3262",
+   "metadata": {},
+   "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",
+    "\n",
+    "print(in_profit(monday_prices, friday_prices))\n",
+    "\n",
+    "portfolio[in_profit(monday_prices, friday_prices)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "257d37f9-5b7f-4ad6-beef-1c8f300a8347",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_profit(3, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfec4cdd-db86-4cfe-a325-0597ff2a4f94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "portfolio[np.where(friday_prices > monday_prices, True, False)]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}