diff --git a/where/README.md b/where/README.md
new file mode 100644
index 0000000000..4f6888c851
--- /dev/null
+++ b/where/README.md
@@ -0,0 +1,3 @@
+# How to Use Conditional Expressions With NumPy `where()`
+
+This Jupyter Notebook file contains all code and question solutions used in the Real Python tutorial [How to Use Conditional Expressions With NumPy `where()`](https://realpython.com/numpy-where-conditional-expressions/).
diff --git a/where/all_code.ipynb b/where/all_code.ipynb
new file mode 100644
index 0000000000..295d6900f0
--- /dev/null
+++ b/where/all_code.ipynb
@@ -0,0 +1,628 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "39189e01-024b-4d21-a2a6-d4377b6419ec",
+   "metadata": {},
+   "source": [
+    "**How to Write Conditional Expressions With NumPy where()**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "de2949dd-4940-48b9-87df-f8186f39b5a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.1688358 , 3.9091694 , 1.66405549, 3.61976783],\n",
+       "       [7.33400434, 3.25797286, 9.65148913, 0.76115911],\n",
+       "       [2.71053173, 6.02410179, 7.46355805, 1.30949485]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "test_array = np.array(\n",
+    "    [\n",
+    "        [3.1688358, 3.9091694, 1.66405549, -3.61976783],\n",
+    "        [7.33400434, -3.25797286, -9.65148913, -0.76115911],\n",
+    "        [2.71053173, -6.02410179, 7.46355805, 1.30949485],\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "np.where(test_array < 0, test_array * -1, test_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "beaacd16-b3a1-471a-bf3b-8b8a7fac22a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.1688358 , 3.9091694 , 1.66405549, 3.61976783],\n",
+       "       [7.33400434, 3.25797286, 9.65148913, 0.76115911],\n",
+       "       [2.71053173, 6.02410179, 7.46355805, 1.30949485]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(test_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c4da3b48-bb08-46f1-a936-704bcfa84b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False,  True],\n",
+       "       [False,  True,  True,  True],\n",
+       "       [False,  True, False, False]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_array < 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "deedb822-3268-45da-aa2c-0be09deaa142",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.68506937,  5.66604672,  0.58421751,  4.8946752 ],\n",
+       "       [-4.2481913 , -1.13216087,  0.63353423,  2.63621566],\n",
+       "       [-1.11812408,  7.15968405, -7.5696163 , -2.37413593]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_array = np.random.uniform(low=-10, high=10, size=(3, 4))\n",
+    "test_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83ccd413-7729-4bef-ae76-a91be907e3df",
+   "metadata": {},
+   "source": [
+    "**How to Use Multiple Conditional Expressions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c558731-51fd-4c11-9887-0a8ca18519ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warning - this code will fail\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "test_array = np.array(\n",
+    "    [\n",
+    "        [3.1688358, 3.9091694, 1.66405549, -3.61976783],\n",
+    "        [7.33400434, -3.25797286, -9.65148913, -0.76115911],\n",
+    "        [2.71053173, -6.02410179, 7.46355805, 1.30949485],\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "np.where(\n",
+    "    (test_array > -2) and (test_array < 3),\n",
+    "    test_array * -1,\n",
+    "    test_array,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "43e5d159-d5bc-43b2-b331-f94928c74120",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True, False],\n",
+       "       [ True, False, False,  True],\n",
+       "       [ True, False,  True,  True]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_array > -2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c827e3a6-ea51-419b-a6ba-211a8f15ff4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False,  True,  True],\n",
+       "       [False,  True,  True,  True],\n",
+       "       [ True,  True, False,  True]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_array < 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d2b77e1c-a094-4ccc-afca-16f5f3a0f64e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False,  True, False],\n",
+       "       [False, False, False,  True],\n",
+       "       [ True, False, False,  True]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(test_array > -2) & (test_array < 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "51612ed4-a948-4e19-8fcd-d9dd48423c02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3.1688358 ,  3.9091694 , -1.66405549, -3.61976783],\n",
+       "       [ 7.33400434, -3.25797286, -9.65148913,  0.76115911],\n",
+       "       [-2.71053173, -6.02410179,  7.46355805, -1.30949485]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(\n",
+    "    (test_array > -2) & (test_array < 3),\n",
+    "    test_array * -1,\n",
+    "    test_array,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9cf73369-c343-4d4e-a88c-167204e0f908",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-3.1688358 , -3.9091694 ,  1.66405549,  3.61976783],\n",
+       "       [-7.33400434,  3.25797286,  9.65148913, -0.76115911],\n",
+       "       [ 2.71053173,  6.02410179, -7.46355805,  1.30949485]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(\n",
+    "    (test_array <= -2) | (test_array >= 3),\n",
+    "    test_array * -1,\n",
+    "    test_array,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "939304ad-1c06-4298-8288-75fa30b6d912",
+   "metadata": {},
+   "source": [
+    "**Question 1 Solutions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "939b8962-8ff3-4ba0-9d10-4baf066f8d5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[9, 9, 9, 9],\n",
+       "       [9, 9, 9, 9],\n",
+       "       [9, 9, 9, 1],\n",
+       "       [9, 3, 9, 5],\n",
+       "       [9, 7, 9, 9]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 1_1\n",
+    "\n",
+    "question_1 = np.arange(\n",
+    "    -10,\n",
+    "    10,\n",
+    ").reshape(5, 4)\n",
+    "\n",
+    "np.where(\n",
+    "    (question_1 < 0) | (question_1 % 2 == 0),\n",
+    "    9,\n",
+    "    question_1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9dc34378-9c7f-4d15-bf72-48d48f321895",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-10,  81,  -8,  49],\n",
+       "       [ -6,  25,  -4,   9],\n",
+       "       [ -2,   1,   0,   1],\n",
+       "       [  2,   3,   4,   5],\n",
+       "       [  6,   7,   8,   9]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 1_2\n",
+    "\n",
+    "np.where(\n",
+    "    (question_1 < 0) & (question_1 % 2 != 0),\n",
+    "    np.square(question_1),\n",
+    "    question_1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "142fd909-0fae-4a25-8cbb-f5d14f227727",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-11, -10,  -9,  -8],\n",
+       "       [ -7,  -6,  -5,  -4],\n",
+       "       [ -3,  -2,  -1, -10],\n",
+       "       [  1,   2, -10, -10],\n",
+       "       [-10,   6,   7,   8]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 1_3\n",
+    "\n",
+    "np.where(\n",
+    "    ((question_1 > 3) & (question_1 < 7)) | (question_1 == 1),\n",
+    "    -10,\n",
+    "    question_1 - 1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efd139d0-613d-4b0e-be7a-da42a92ee8f9",
+   "metadata": {},
+   "source": [
+    "**How to Use Array Broadcasting in Conditional Expressions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "2e7e9ce8-1354-4f8b-8298-e815341f135b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "booking_data = np.array(\n",
+    "    [\n",
+    "        [np.nan, np.nan, 1],\n",
+    "        [1, 1, np.nan],\n",
+    "        [1, np.nan, 1],\n",
+    "        [1, 1, 1],\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "330db591-1015-4942-853d-47ac654d8e3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 3)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "booking_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "60206972-ed39-450c-94ce-17394355e963",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meal_prices = np.array([5.1, 8.2, 20.3]).reshape(1, 3)\n",
+    "no_charge = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ac7480dc-1562-4738-a72b-db397dc4ec72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0. ,  0. , 20.3],\n",
+       "       [ 5.1,  8.2,  0. ],\n",
+       "       [ 5.1,  0. , 20.3],\n",
+       "       [ 5.1,  8.2, 20.3]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "booking_prices = np.where(booking_data == 1, meal_prices, no_charge)\n",
+    "booking_prices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "531840c1-a752-4d8c-b194-14c7bccd39ae",
+   "metadata": {},
+   "source": [
+    "**Question 2 Solutions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6d192256-457d-42f8-b4b0-4897158fdb3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['LOW', 'LOW', 'LOW', 'LOW'],\n",
+       "       ['LOW', 'LOW', 'LOW', 'HIGH'],\n",
+       "       ['HIGH', 'HIGH', 'HIGH', 'HIGH']], dtype='<U4')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 2_1\n",
+    "\n",
+    "question_2 = np.arange(12).reshape(3, 4)  # Shape (3, 4)\n",
+    "\n",
+    "high = \"HIGH\"\n",
+    "low = \"LOW\"\n",
+    "\n",
+    "np.where(question_2 > 6, high, low)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "eb725a3e-3c0b-43f3-aa57-6fe5bdcbb4c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['LOW', 'LOW', 'LOW', 'LOW'],\n",
+       "       ['LOW', 'LOW', 'LOW', 'HIGH'],\n",
+       "       ['HIGH', 'HIGH', 'HIGH', 'HIGH']], dtype='<U4')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 2_2\n",
+    "\n",
+    "high = np.array([\"HIGH\"])  # Shape (1,)\n",
+    "low = np.array([\"LOW\"])  # Shape (1,)\n",
+    "\n",
+    "np.where(question_2 > 6, high, low)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "51aebc35-c9f2-481f-890e-ce17f4b6f574",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['LOW', 'LOW', 'LOW', 'LOW'],\n",
+       "       ['LOW', 'LOW', 'LOW', 'HIGH'],\n",
+       "       ['HIGH', 'HIGH', 'HIGH', 'HIGH']], dtype='<U4')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 2_3\n",
+    "\n",
+    "high = np.array([\"HIGH\", \"HIGH\", \"HIGH\", \"HIGH\"])  # Shape (4,)\n",
+    "low = np.array([\"LOW\"])  # Shape (1,)\n",
+    "\n",
+    "np.where(question_2 > 6, high, low)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bff96b5-4322-4833-8527-c0d6819e9320",
+   "metadata": {},
+   "source": [
+    "**How Not to Use np.where() - A Final Quirk**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bbfc4cc2-c483-4f27-be47-6769e0fdfaa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 2]), array([0, 1, 2]))"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "mostly_zeroes = np.array([[9, 0, 0], [0, 8, 0], [0, 0, 7]])\n",
+    "np.where(mostly_zeroes != 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a92ad38b-3cc2-4bea-999a-e9a43a8444aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 2]), array([0, 1, 2]))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.nonzero(mostly_zeroes)"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/where/requirements.txt b/where/requirements.txt
new file mode 100644
index 0000000000..4aa499620a
--- /dev/null
+++ b/where/requirements.txt
@@ -0,0 +1 @@
+numpy==2.1.0