add content

Author: khuyentran1401

Chapter1/function.ipynb

@@ -64,7 +64,7 @@
    "id": "516aab5c",
    "metadata": {},
    "source": [
-    "### When to Use and Not to Use Lambda Functions"
+    "### Lambda vs Named Functions: When to Use Each"
    ]
   },
   {
@@ -73,7 +73,9 @@
    "id": "5d4d1401",
    "metadata": {},
    "source": [
-    "Lambda functions are helpful when defining a function that is used only once and does not require a name."
+    "Lambda functions are ideal for situations where a function is used only once and does not require a name. They provide a concise way to define small, one-time use functions.\n",
+    "\n",
+    "For example:"
    ]
   },
   {
@@ -89,13 +91,22 @@
     "even_numbers = filter(lambda num: num % 2 == 0, numbers)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3e53c4c5-8270-46b4-b94a-59d20c03ce80",
+   "metadata": {},
+   "source": [
+    "In this example, the lambda function is used to filter out even numbers from the list. Since it's only used once, a lambda function is a suitable choice."
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "id": "e68f8ef3",
    "metadata": {},
    "source": [
-    "However, if you need to reuse a function in various parts of your code, use a named function to avoid repeating the same code. "
+    "However, if you need to reuse a function in various parts of your code, it's better to define a named function.\n",
+    "\n"
    ]
   },
   {
@@ -124,6 +135,14 @@
     "any(is_even(num) for num in numbers)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9b774fdb-662d-4ef6-9f2a-08c29cc7ff8c",
+   "metadata": {},
+   "source": [
+    "In this example, the `is_even` function is defined by a name and is used multiple times. This approach avoids repeating the same code and makes your code more maintainable."
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -524,7 +543,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.6"
   },
   "toc": {
    "base_numbering": 1,

Chapter1/python_new_features.ipynb

@@ -513,7 +513,7 @@
    "id": "9838891c",
    "metadata": {},
    "source": [
-    "### Walrus Operator: Assign a Variable in an Expression"
+    "### Write Cleaner Python with the Walrus Operatorn"
    ]
   },
   {
@@ -522,27 +522,44 @@
    "id": "b2477b9e",
    "metadata": {},
    "source": [
-    "The walrus operator (`:=`) in Python 3.8 and above allows you to assign a variable in an expression. The walrus operator is useful when you want to:\n",
-    "- Debug the components in an expression\n",
-    "- Avoid repeated computations\n",
-    "- Assign a meaningful name to an expression"
+    "The walrus operator (`:=`) in Python 3.8+ allows you to assign a variable in an expression, making your code more readable and efficient. It's useful in two main scenarios:\n",
+    "\n",
+    "1.  Giving a meaningful name to a complex expression for better readability.\n",
+    "2.  Avoiding repeated computations by reusing a variable instead of recomputing the expression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9739695b-7ddb-4997-9408-56bd7e53dd30",
+   "metadata": {},
+   "source": [
+    "Let's consider an example where we want to calculate the radius, area, and volume of a circle given its diameter and height:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "68b0e1a3",
    "metadata": {},
    "outputs": [],
    "source": [
     "from math import pi\n",
     "\n",
-    "diameter = 4\n"
+    "diameter = 4\n",
+    "height = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d43f5da9-89e1-4e8a-b29d-f48ad247a9d0",
+   "metadata": {},
+   "source": [
+    "Without the walrus operator, we might compute the radius and area multiple times:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "id": "14cfc887",
    "metadata": {},
    "outputs": [
@@ -552,26 +569,31 @@
        "2.0"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# without Walrus operator\n",
-    "\n",
     "circle = {\n",
     "    \"radius\": diameter / 2, # computed twice\n",
-    "    \"area\": pi * (diameter / 2)**2,\n",
-    "}\n",
-    "\n",
-    "diameter / 2\n"
+    "    \"area\": pi * (diameter / 2)**2, # computed twice\n",
+    "    \"volume\": pi * (diameter / 2)**2 * height,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c9997c8-5069-4723-8fc1-16f823304760",
+   "metadata": {},
+   "source": [
+    "To avoid repeated computations, we can assign the radius and area to variables before creating the dictionary:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "805e31ea",
+   "execution_count": 13,
+   "id": "b18fb3eb-45b2-43bb-93d5-7a0cd6ded503",
    "metadata": {},
    "outputs": [
     {
@@ -580,20 +602,70 @@
        "2.0"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# with Walrus operator\n",
+    "radius = diameter / 2\n",
+    "area = pi * radius**2\n",
     "\n",
+    "circle = {\n",
+    "    \"radius\": radius,\n",
+    "    \"area\": area,\n",
+    "    \"volume\": area * height,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d220fa6a-6ca4-41c2-a1bb-e0b458fb03dc",
+   "metadata": {},
+   "source": [
+    "To make the code more concise, we can use the walrus operator to assign the radius and area to variables while creating the dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "805e31ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "circle = {\n",
     "    \"radius\": (radius := diameter / 2),\n",
-    "    \"area\": pi * radius**2,\n",
-    "}\n",
-    "\n",
-    "radius"
+    "    \"area\": (area := pi * radius**2),\n",
+    "    \"volume\": area * height,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0719cf42-6f2b-4f38-b5a2-12e4765111f7",
+   "metadata": {},
+   "source": [
+    "After executing the code with the walrus operator, we can access the assigned variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "91908a69-8f78-42c8-b56b-cc12d6ac28d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n",
+      "12.566370614359172\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(radius)\n",
+    "print(area)"
    ]
   },
   {
@@ -713,7 +785,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

Chapter2/dataclasses.ipynb

@@ -503,7 +503,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },

Chapter5/machine_learning.ipynb

@@ -2639,7 +2639,7 @@
   "celltoolbar": "Tags",
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -2667,11 +2667,6 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "c3bc044b9863ed6dec4c55e7ad5af27f030f7d27aed3f39d7a4886a926c4e2c1"
-   }
   }
  },
  "nbformat": 4,

Chapter5/render.html

@@ -0,0 +1,17 @@
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+        <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/chart.xkcd@1.1/dist/chart.xkcd.min.js"></script>
+</head>
+<body>
+    <div id="5262ec43a4b74316a579ac0216eb636b" class="chart-container" style="width: 800px">
+        <svg id="chart_5262ec43a4b74316a579ac0216eb636b"></svg>
+    </div>
+    <script>
+        const svg_5262ec43a4b74316a579ac0216eb636b = document.querySelector('#chart_5262ec43a4b74316a579ac0216eb636b')
+        const chart_5262ec43a4b74316a579ac0216eb636b = new chartXkcd.Line(svg_5262ec43a4b74316a579ac0216eb636b, {"title": "\u67d0\u5546\u573a\u9500\u552e\u60c5\u51b5", "data": {"datasets": [{"label": "series-A", "data": [57, 134, 137, 129, 145, 60, 49]}, {"label": "series-B", "data": [114, 55, 27, 101, 125, 27, 105]}], "labels": ["\u886c\u886b", "\u6bdb\u8863", "\u9886\u5e26", "\u88e4\u5b50", "\u98ce\u8863", "\u9ad8\u8ddf\u978b", "\u889c\u5b50"]}, "xLabel": "I'm xlabel", "yLabel": "I'm ylabel", "options": {"yTickCount": 3, "legendPosition": 1}});
+    </script>
+</body>
+</html>