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Author: khuyentran1401

Chapter2/pydantic.ipynb

@@ -182,6 +182,165 @@
    "source": [
     "[Learn more about Pydantic's numeric constraints](https://bit.ly/4bbhthb)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62e5e4b0-4aa9-4956-ae10-a8aacef31ed3",
+   "metadata": {},
+   "source": [
+    "### Python Data Models: Pydantic or attrs?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d55d578-3ce8-4f86-b03f-d10e8d2fe6b1",
+   "metadata": {},
+   "source": [
+    "Pydantic is a popular library that provides built-in data validation and type checking. This makes it an excellent choice for web APIs and external data handling. However, this added functionality comes at a cost:\n",
+    "\n",
+    "*   Performance overhead\n",
+    "*   High memory usage\n",
+    "*   Harder to debug\n",
+    "\n",
+    "Here's an example of a Pydantic model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7cb1a209-4441-4485-83d1-9e5f090af709",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel\n",
+    "\n",
+    "class UserPydantic(BaseModel):\n",
+    "    name: str\n",
+    "    age: int"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7682d4c-9cdb-4e9d-9497-155b03a21357",
+   "metadata": {},
+   "source": [
+    "Attrs, on the other hand, is a lighter-weight library that provides a simpler way to define data models. While it doesn't have built-in data validation, it's ideal for internal data structures and simpler class creation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0ccff22f-e90b-4f4c-926c-168d3c7c2810",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from attrs import define, field\n",
+    "\n",
+    "@define\n",
+    "class UserAttrs:\n",
+    "    name: str\n",
+    "    age: int"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "285909fb-c516-4adf-bf2f-7067deae955d",
+   "metadata": {},
+   "source": [
+    "Let's compare the performance of Pydantic and attrs using a simple benchmark:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d61af650-9fc2-4a16-8201-db14674ce964",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pydantic: 0.1071 seconds\n",
+      "attrs: 0.0155 seconds\n",
+      "Using attrs is 6.90 times faster than using Pydantic\n"
+     ]
+    }
+   ],
+   "source": [
+    "from timeit import timeit\n",
+    "\n",
+    "# Test data\n",
+    "data = {\"name\": \"Bob\", \"age\": 30}\n",
+    "\n",
+    "# Benchmark\n",
+    "pydantic_time = timeit(lambda: UserPydantic(**data), number=100000)\n",
+    "attrs_time = timeit(lambda: UserAttrs(**data), number=100000)\n",
+    "\n",
+    "print(f\"Pydantic: {pydantic_time:.4f} seconds\")\n",
+    "print(f\"attrs: {attrs_time:.4f} seconds\")\n",
+    "print(f\"Using attrs is {pydantic_time/attrs_time:.2f} times faster than using Pydantic\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d3a6234-b28c-437a-b6bb-ac2e17101d99",
+   "metadata": {},
+   "source": [
+    "The results show that attrs is approximately 6.9 times faster than Pydantic.\n",
+    "\n",
+    "While attrs doesn't have built-in data validation, you can easily add validation using a decorator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "de240c6b-6f26-49e5-b2ec-18733265a5cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValueError: Age can't be negative\n"
+     ]
+    }
+   ],
+   "source": [
+    "from attrs import define, field\n",
+    "\n",
+    "@define\n",
+    "class UserAttrs:\n",
+    "    name: str\n",
+    "    age: int = field()\n",
+    "\n",
+    "    @age.validator\n",
+    "    def check_age(self, attribute, value):\n",
+    "        if value < 0:\n",
+    "            raise ValueError(\"Age can't be negative\")\n",
+    "        return value  # accepts any positive age\n",
+    "\n",
+    "\n",
+    "try:\n",
+    "    user = UserAttrs(name=\"Bob\", age=-1)\n",
+    "except ValueError as e:\n",
+    "    print(\"ValueError:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2998fe6-0f29-458d-9795-fd1a7db45f15",
+   "metadata": {},
+   "source": [
+    "In this example, we've added a validator to the `age` field to ensure it's not negative. If you try to create a `UserAttrs` instance with a negative age, it will raise a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b8ca536-164e-42f7-ae5d-b0389711b45c",
+   "metadata": {},
+   "source": [
+    "[Link to attrs](https://github.com/python-attrs/attrs)."
+   ]
   }
  ],
  "metadata": {

Chapter5/better_pandas.ipynb

@@ -5004,7 +5004,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },