{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UYE96DU1H904"
   },
   "source": [
    "Lab03_PhucLam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "miBPVwa-HtKz"
   },
   "source": [
    "# Simulation - Mô phỏng & Dữ liệu\n",
    "\n",
    "Trong bài thực hành này, chúng ta sẽ sử dụng python và numpy để tính toán và thực hiện một số mô phỏng.\n",
    "\n",
    "Những nội dung sẽ thực hiện mô phỏng trong tài liệu này:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4723,
     "status": "ok",
     "timestamp": 1726377869395,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "Tn2SSqZZI1br",
    "outputId": "8b527282-c1bc-41e7-f074-e4ecbf2038f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "pip install numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ayUpgdTHtK2"
   },
   "source": [
    "## MÔ PHỎNG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XWXXOl2nHtK2"
   },
   "source": [
    "### Bài 1: Mô phỏng tung đồng xu cân đối\n",
    "\n",
    "Giả sử một đồng xu có hai mặt `Head` và `Tail`.\n",
    "\n",
    "a. Định nghĩa hàm `toss_a_fair_coin`để mô phỏng việc tung đồng xu. Kết quả trả về là `H` hoặc `T` tương ứng với một trong hai mặt\n",
    "\n",
    "```python\n",
    "def toss_a_fair_coin():\n",
    "    # ...\n",
    "```\n",
    "\n",
    "b. Thực hiện việc tung đồng xu n = 100 lần. Cho biết số lần xuất hiện của mỗi mặt. Lựa chọn đồ thị phù hợp để hiển thị kết quả trên."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 318,
     "status": "ok",
     "timestamp": 1726378189666,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "h6ytRWGRHtK3"
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "#Cau A dinh nghia ham toss-a-fair\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "#Cau a\n",
    "def toss_a_fair_coin():\n",
    "  return 'H' if random.choice([0,1])==0 else 'T';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 491
    },
    "executionInfo": {
     "elapsed": 373,
     "status": "ok",
     "timestamp": 1726378687507,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "k95cb7JYKQrn",
    "outputId": "437791f2-cebf-4350-a0c5-2950bd67a52e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ket qua sau khi tung 100 lan {'H': 60, 'T': 40}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cau B thucj hien tung dong xu =100 lan cho biet so lan xuat hien cua moi mat\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#dem so lan xuat hien cua moi mat\n",
    "results = {'H':0, 'T':0}\n",
    "for i in range(0,100):\n",
    "  item = toss_a_fair_coin()\n",
    "  results[item]+=1\n",
    "\n",
    "print('Ket qua sau khi tung 100 lan',results)\n",
    "\n",
    "faces = list(results.keys())\n",
    "counts = list(results.values())\n",
    "\n",
    "plt.bar(faces, counts, color=['blue', 'orange'])\n",
    "plt.xlabel('Mặt đồng xu')\n",
    "plt.ylabel('Số lần xuất hiện')\n",
    "plt.title(f'Kết quả tung đồng xu 100 lần)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "76wyhFQ9HtK4"
   },
   "source": [
    "### Bài 2: Mô phỏng tung đồng xu không cân đối\n",
    "\n",
    "Giả sử có một đồng xu với hai mặt `H` và `T` không cân đối, với xác suất thu được mặt Head là 0.6, xác suất thu được mặt Tail là 0.4. Bạn hãy:\n",
    "\n",
    "a. Định nghĩa hàm `toss_a_biased_coin`để mô phỏng việc tung đồng xu. Kết quả trả về là `H` hoặc `T` tương ứng với một trong hai mặt\n",
    "\n",
    "```python\n",
    "def toss_a_biased_coin(head_prob):\n",
    "    '''\n",
    "    Tung đồng xu không cân đối.\n",
    "    Return: 'H' or 'T'\n",
    "        head_prob: xác suất thu được mặt Head\n",
    "    '''\n",
    "    # ...\n",
    "```\n",
    "\n",
    "b. Thực hiện việc tung đồng xu n = 100 lần. Cho biết số lần xuất hiện của mỗi mặt. Tính tỷ lệ xuất hiện của mặt Head và Tail. Lựa chọn đồ thị phù hợp để hiển thị kết quả trên.\n",
    "\n",
    "c. Thực hiện việc tung đồng xu n = 10000 lần. Cho biết số lần xuất hiện của mỗi mặt. Tính tỷ lệ xuất hiện của mặt Head và Tail. Lựa chọn đồ thị phù hợp để hiển thị kết quả trên.\n",
    "\n",
    "d. Bạn có nhận xét gì?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 352,
     "status": "ok",
     "timestamp": 1726379070152,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "p0iHSxldMIKx"
   },
   "outputs": [],
   "source": [
    "#a. Định nghĩa hàm toss_a_biased_coin\n",
    "def toss_a_biased_coin(head_prob):\n",
    "  return random.choices(['H', 'T'], weights=[head_prob, 1 - head_prob])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1726379154129,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "XunYw2M1M1kn"
   },
   "outputs": [],
   "source": [
    "#b. Thực hiện việc tung đồng xu n = 100 lần\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Đếm số lần xuất hiện của mỗi mặt\n",
    "def simulate_biased_coin_tosses(num_tosses, head_prob):\n",
    "    results = {'H': 0, 'T': 0}\n",
    "    for _ in range(num_tosses):\n",
    "        result = toss_a_biased_coin(head_prob)\n",
    "        results[result] += 1\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 543
    },
    "executionInfo": {
     "elapsed": 718,
     "status": "ok",
     "timestamp": 1726379305193,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "nqUl9M07NxqH",
    "outputId": "b6401c32-659d-4102-baf6-10618f156534"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Số lần xuất hiện của Head: 61\n",
      "Số lần xuất hiện của Tail: 39\n",
      "Tỷ lệ xuất hiện của Head: 0.61\n",
      "Tỷ lệ xuất hiện của Tail: 0.39\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Thực hiện 100 lần tung đồng xu với xác suất mặt Head là 0.6\n",
    "head_prob = 0.6\n",
    "num_tosses = 100\n",
    "results = simulate_biased_coin_tosses(num_tosses, head_prob)\n",
    "\n",
    "# Tính tỷ lệ xuất hiện\n",
    "head_ratio = results['H'] / num_tosses\n",
    "tail_ratio = results['T'] / num_tosses\n",
    "\n",
    "print(f\"Số lần xuất hiện của Head: {results['H']}\")\n",
    "print(f\"Số lần xuất hiện của Tail: {results['T']}\")\n",
    "print(f\"Tỷ lệ xuất hiện của Head: {head_ratio:.2f}\")\n",
    "print(f\"Tỷ lệ xuất hiện của Tail: {tail_ratio:.2f}\")\n",
    "\n",
    "# Hiển thị kết quả bằng đồ thị cột\n",
    "def plot_results(results, num_tosses):\n",
    "    faces = list(results.keys())\n",
    "    counts = list(results.values())\n",
    "\n",
    "    plt.bar(faces, counts, color=['blue', 'orange'])\n",
    "    plt.xlabel('Mặt đồng xu')\n",
    "    plt.ylabel('Số lần xuất hiện')\n",
    "    plt.title(f'Kết quả tung đồng xu ({num_tosses} lần)')\n",
    "    plt.show()\n",
    "\n",
    "plot_results(results, num_tosses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 543
    },
    "executionInfo": {
     "elapsed": 434,
     "status": "ok",
     "timestamp": 1726379347198,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "DHZ4ttwwNrMA",
    "outputId": "310c2180-0105-4ab6-e50d-025047408d98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Số lần xuất hiện của Head: 6019\n",
      "Số lần xuất hiện của Tail: 3981\n",
      "Tỷ lệ xuất hiện của Head: 0.60\n",
      "Tỷ lệ xuất hiện của Tail: 0.40\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#c. Thực hiện việc tung đồng xu n = 10000 lần\n",
    "# Thực hiện 100 lần tung đồng xu với xác suất mặt Head là 0.6\n",
    "head_prob = 0.6\n",
    "num_tosses = 10000\n",
    "results = simulate_biased_coin_tosses(num_tosses, head_prob)\n",
    "\n",
    "# Tính tỷ lệ xuất hiện\n",
    "head_ratio = results['H'] / num_tosses\n",
    "tail_ratio = results['T'] / num_tosses\n",
    "\n",
    "print(f\"Số lần xuất hiện của Head: {results['H']}\")\n",
    "print(f\"Số lần xuất hiện của Tail: {results['T']}\")\n",
    "print(f\"Tỷ lệ xuất hiện của Head: {head_ratio:.2f}\")\n",
    "print(f\"Tỷ lệ xuất hiện của Tail: {tail_ratio:.2f}\")\n",
    "\n",
    "# Hiển thị kết quả bằng đồ thị cột\n",
    "def plot_results(results, num_tosses):\n",
    "    faces = list(results.keys())\n",
    "    counts = list(results.values())\n",
    "\n",
    "    plt.bar(faces, counts, color=['blue', 'orange'])\n",
    "    plt.xlabel('Mặt đồng xu')\n",
    "    plt.ylabel('Số lần xuất hiện')\n",
    "    plt.title(f'Kết quả tung đồng xu ({num_tosses} lần)')\n",
    "    plt.show()\n",
    "\n",
    "plot_results(results, num_tosses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Simekr32OrDh"
   },
   "outputs": [],
   "source": [
    "# #d. Bạn có nhận xét gì?\n",
    "# Với n = 100 lần: Tỷ lệ xuất hiện của các mặt có thể không hoàn toàn khớp với xác suất lý thuyết 0.6 và 0.4 do yếu tố ngẫu nhiên. Kết quả có thể dao động khá lớn quanh tỷ lệ dự kiến.\n",
    "\n",
    "# Với n = 10000 lần: Khi số lần tung đồng xu tăng lên, tỷ lệ xuất hiện của các mặt sẽ gần hơn với xác suất lý thuyết. Điều này minh họa định lý số lớn, theo đó số liệu thực nghiệm sẽ tiệm cận xác suất lý thuyết khi kích thước mẫu lớn.\n",
    "\n",
    "# Đồ thị: Đồ thị cột giúp trực quan hóa sự phân bố của các kết quả, và với số lượng lớn hơn, bạn sẽ thấy rõ sự gần gũi hơn với tỷ lệ lý thuyết."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WE10dH6VHtK4"
   },
   "source": [
    "### Bài 3: Khảo sát hàm randint()\n",
    "\n",
    "Hàm `randint()`dùng để phát sinh số nguyên ngẫu nhiên. Tuy nhiên, các kết quả ngẫu nhiên này có quy luật nào hay không, liệu ta có thể tin tưởng để sử dụng hàm này không? Trong ví dụ này bạn hãy thực hiện mô phỏng sau: phát sinh `n` số số ngẫu nhiên trong đoạn [1..40]. Với mỗi lần phát sinh bạn hãy vẽ histogram về `tần suất` xuất hiện của mỗi số.\n",
    "\n",
    "a. Hãy vẽ đồ thị `tần suất` xuất hiện của các số với n=40, n=100, n=10000, n=1000000\n",
    "\n",
    "b. Hãy đưa ra nhận xét của bạn về hàm `randint()`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "executionInfo": {
     "elapsed": 324,
     "status": "ok",
     "timestamp": 1726379944550,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "GOXkhbMSHtK4"
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def generate_random_numbers(n, low, high):\n",
    "    return [random.randint(low, high) for _ in range(n)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "executionInfo": {
     "elapsed": 322,
     "status": "ok",
     "timestamp": 1726380480995,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "EYSmAlqyPw9o"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_histogram(data, num_bins, title):\n",
    "    plt.hist(data, bins=num_bins, range=(1, 41), edgecolor='black', alpha=0.75,rwidth=0.6)\n",
    "    plt.xlabel('Số')\n",
    "    plt.ylabel('Tần suất xuất hiện')\n",
    "    plt.title(title)\n",
    "    plt.xticks(range(1, 41))\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 7361,
     "status": "ok",
     "timestamp": 1726380491237,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "-RgBlVS0RRHq",
    "outputId": "cbdbb103-afe7-492c-cf92-47616063eb22"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Các giá trị n cần khảo sát\n",
    "n_values = [40, 100, 10000, 1000000]\n",
    "low, high = 1, 40\n",
    "\n",
    "for n in n_values:\n",
    "    data = generate_random_numbers(n, low, high)\n",
    "    plot_histogram(data, num_bins=range(low, high + 2), title=f'Tần suất xuất hiện với n={n}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ef9fMvHUTGED"
   },
   "outputs": [],
   "source": [
    "# Phân phối đồng đều: Khi số lượng số ngẫu nhiên phát sinh (n) tăng lên, phân phối tần suất xuất hiện của các số trong đoạn [1, 40] sẽ dần trở nên đồng đều hơn. Điều này cho thấy rằng hàm randint() phát sinh các số nguyên ngẫu nhiên với phân phối gần như đồng đều, tức là tất cả các số trong đoạn đều có xác suất xuất hiện gần như như nhau.\n",
    "\n",
    "# Khả năng sinh số ngẫu nhiên: Hàm randint() sử dụng thuật toán sinh số ngẫu nhiên pseudo-random, do đó, kết quả của nó không hoàn toàn ngẫu nhiên trong một nghĩa lý thuyết, mà là ngẫu nhiên theo quy luật của thuật toán. Tuy nhiên, với các ứng dụng thông thường, phân phối kết quả của randint() đủ tốt và có thể tin tưởng để sử dụng trong các mô phỏng, trò chơi, và các tình huống cần tính ngẫu nhiên.\n",
    "\n",
    "# Tính lặp lại: Đối với các số lượng nhỏ (như n = 40), có thể thấy sự phân phối không đồng đều do biến động ngẫu nhiên. Tuy nhiên, khi n lớn hơn (như n = 10000 hoặc n = 1000000), sự phân phối trở nên đều hơn và gần với phân phối lý thuyết.\n",
    "\n",
    "# Những lưu ý khác: Đối với các ứng dụng yêu cầu phân phối ngẫu nhiên chính xác và đồng đều, bạn có thể cần cân nhắc sử dụng các phương pháp sinh số ngẫu nhiên khác hoặc kiểm tra kỹ lưỡng hơn, nhưng với các tình huống bình thường, randint() là công cụ hữu ích và đáng tin cậy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hzrtuSH1HtK5"
   },
   "source": [
    "### Bài 4: Mô phỏng cách chọn mẫu\n",
    "\n",
    "Trong bài tập này ta sẽ mô phỏng cách lấy mẫu từ một tập đã có sẵn.\n",
    "\n",
    "a. Sử dụng hàm `randint()` để tạo 10 số ngẫu nhiên nằm trong đoạn [1..100]. Lưu kết quả vào biến `my_arr`\n",
    "\n",
    "b. Sử dụng hàm `choice` để lấy ngẫu nhiên 5 phần tử từ mảng `my_arr`.\n",
    "\n",
    "c. Thực hiện câu b, 10 lần. Với mỗi lần bạn hãy hiển thị 5 phần tử được chọn, tính giá trị trung bình và độ lệch chuẩn của các phần tử ấy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 343,
     "status": "ok",
     "timestamp": 1726380665747,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "L6F6ycMHHtK5",
    "outputId": "4c11d61b-61a0-45e8-82d8-47e7c291d4a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9 96 18 80 74 36 52 39 28 11]\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "# a. Sử dụng hàm randint() để tạo 10 số ngẫu nhiên nằm trong đoạn [1..100]. Lưu kết quả vào biến my_arr\n",
    "my_arr = np.random.randint(1, 101, 10)\n",
    "print(my_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 346,
     "status": "ok",
     "timestamp": 1726380710521,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "6iH-5eSLTqMy",
    "outputId": "4d8dd9ce-9e37-4b22-eee6-47f6919da243"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 28  9 80 39]\n"
     ]
    }
   ],
   "source": [
    "# b. Sử dụng hàm choice để lấy ngẫu nhiên 5 phần tử từ mảng my_arr.\n",
    "selected_elements = np.random.choice(my_arr, size=5, replace=False)\n",
    "print(selected_elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 319,
     "status": "ok",
     "timestamp": 1726381095935,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "1Bv9V9K-UEpB",
    "outputId": "eb79927c-261d-4482-f6c4-1ad109e8106e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lần 1:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [11 52 74 28 96]\n",
      "Giá trị trung bình: 52.20\n",
      "Độ lệch chuẩn: 30.58\n",
      "\n",
      "Lần 2:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [96 39  9 74 11]\n",
      "Giá trị trung bình: 45.80\n",
      "Độ lệch chuẩn: 34.43\n",
      "\n",
      "Lần 3:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [52 11 96 74 39]\n",
      "Giá trị trung bình: 54.40\n",
      "Độ lệch chuẩn: 29.12\n",
      "\n",
      "Lần 4:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [18 28 11 96 36]\n",
      "Giá trị trung bình: 37.80\n",
      "Độ lệch chuẩn: 30.32\n",
      "\n",
      "Lần 5:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [52 28 80 96 74]\n",
      "Giá trị trung bình: 66.00\n",
      "Độ lệch chuẩn: 23.66\n",
      "\n",
      "Lần 6:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [11 74 39 80 28]\n",
      "Giá trị trung bình: 46.40\n",
      "Độ lệch chuẩn: 26.60\n",
      "\n",
      "Lần 7:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [36 80 11 52 39]\n",
      "Giá trị trung bình: 43.60\n",
      "Độ lệch chuẩn: 22.53\n",
      "\n",
      "Lần 8:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [39 80 18 74 11]\n",
      "Giá trị trung bình: 44.40\n",
      "Độ lệch chuẩn: 28.23\n",
      "\n",
      "Lần 9:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [ 9 74 18 39 36]\n",
      "Giá trị trung bình: 35.20\n",
      "Độ lệch chuẩn: 22.37\n",
      "\n",
      "Lần 10:\n",
      "Mẫu ngẫu nhiên 5 phần tử: [18 39 11 28 74]\n",
      "Giá trị trung bình: 34.00\n",
      "Độ lệch chuẩn: 22.12\n"
     ]
    }
   ],
   "source": [
    "# c. Thực hiện câu b, 10 lần. Với mỗi lần bạn hãy hiển thị 5 phần tử được chọn, tính giá trị trung bình và độ lệch chuẩn của các phần tử ấy\n",
    "import numpy as np\n",
    "\n",
    "for i in range(10):\n",
    "    selected_elements = np.random.choice(my_arr, size=5, replace=False)\n",
    "    mean = np.mean(selected_elements)\n",
    "    std_dev = np.std(selected_elements)\n",
    "\n",
    "    print(f\"\\nLần {i + 1}:\")\n",
    "    print(f\"Mẫu ngẫu nhiên 5 phần tử: {selected_elements}\")\n",
    "    print(f\"Giá trị trung bình: {mean:.2f}\")\n",
    "    print(f\"Độ lệch chuẩn: {std_dev:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BnVcLvrTHtK5"
   },
   "source": [
    "### Bài 5 Kiểm tra mảng phát sinh\n",
    "\n",
    "Giả sử chiều cao của người trưởng thành dao động từ 140cm - 210cm.\n",
    "\n",
    "a. Bạn hãy phát sinh ngẫu nhiên chiều cao của 1000 người và lưu vào mảng `heights`\n",
    "\n",
    "b. Giả sử ta quy định mức chiều cao như sau:\n",
    "\n",
    "- Mức thấp: chiều cao bé hơn 160cm\n",
    "- Mức bình thường: chiều cao từ 160 đến bé hơn 175cm\n",
    "- Mức cao: chiều cao từ 175cm đến bé hơn 190cm\n",
    "- Mức rất cao: chiều cao từ 190cm trở lên\n",
    "\n",
    "Từ mảng đã phát sinh bạn hãy tính tỷ lệ mỗi mức. Lựa chọn đồ thị phù hợp để biểu diễn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "executionInfo": {
     "elapsed": 316,
     "status": "ok",
     "timestamp": 1726381388549,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "40craPLQTpBC"
   },
   "outputs": [],
   "source": [
    "#Giả sử chiều cao của người trưởng thành dao động từ 140cm - 210cm.\n",
    "#a. Bạn hãy phát sinh ngẫu nhiên chiều cao của 1000 người và lưu vào mảng heights\n",
    "import numpy as np\n",
    "\n",
    "heights = np.random.randint(140, 211, size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 860,
     "status": "ok",
     "timestamp": 1726381393959,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "s-hAFPz-HtK5",
    "outputId": "8cd24129-a3c9-40b6-eca7-6bb80131efe8"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# a. Phát sinh ngẫu nhiên chiều cao của 1000 người\n",
    "num_people = 1000\n",
    "heights = [random.randint(140, 210) for _ in range(num_people)]\n",
    "\n",
    "# b. Xác định tỷ lệ các mức chiều cao\n",
    "def categorize_height(height):\n",
    "    if height < 160:\n",
    "        return 'Thấp'\n",
    "    elif 160 <= height < 175:\n",
    "        return 'Bình thường'\n",
    "    elif 175 <= height < 190:\n",
    "        return 'Cao'\n",
    "    else:\n",
    "        return 'Rất cao'\n",
    "\n",
    "# Phân loại các chiều cao\n",
    "categories = [categorize_height(height) for height in heights]\n",
    "\n",
    "# Tính tỷ lệ phần trăm cho mỗi mức\n",
    "category_counts = {category: categories.count(category) for category in ['Thấp', 'Bình thường', 'Cao', 'Rất cao']}\n",
    "category_percentages = {category: (count / num_people) * 100 for category, count in category_counts.items()}\n",
    "\n",
    "# c. Vẽ đồ thị biểu diễn tỷ lệ của mỗi mức chiều cao\n",
    "\n",
    "# Biểu đồ tròn\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.pie(category_percentages.values(), labels=category_percentages.keys(), autopct='%1.1f%%', colors=['#ff9999','#66b3ff','#99ff99','#ffcc99'])\n",
    "plt.title('Tỷ lệ từng mức chiều cao')\n",
    "plt.show()\n",
    "\n",
    "# Biểu đồ cột\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(category_percentages.keys(), category_percentages.values(), color=['#ff9999','#66b3ff','#99ff99','#ffcc99'])\n",
    "plt.xlabel('Mức chiều cao')\n",
    "plt.ylabel('Tỷ lệ (%)')\n",
    "plt.title('Tỷ lệ từng mức chiều cao')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u1JbPXDiHtK6"
   },
   "source": [
    "## DỮ LIỆU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TVcn-LWCHtK6"
   },
   "source": [
    "### Bài 6: dataset `temperature.csv`\n",
    "\n",
    "Bạn hãy tìm cách đọc dữ liệu về nhiệt độ của các tỉnh thành trên với thư viện `numpy`.\n",
    "\n",
    "a. Với mỗi tỉnh thành bạn hãy cho biết: nhiệt độ thấp nhất, nhiệt độ cao nhất, nhiệt độ trung bình, độ lệch chuẩn, độ biến thiên về nhiệt độ (`range=max-min`)\n",
    "\n",
    "b. Vẽ histogram nhiệt độ của TP.HCM\n",
    "\n",
    "c. Lựa chọn đồ thị để so sánh nhiệt độ của 6 tỉnh thành"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6092,
     "status": "ok",
     "timestamp": 1726381518301,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "e87LKZfrHtK6",
    "outputId": "5078d47d-68b3-4405-978d-cc26e2f62d30"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.4)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "!pip install numpy pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 331,
     "status": "ok",
     "timestamp": 1726381651358,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "LaqNQQ9MW3CZ",
    "outputId": "a3dfe143-06e2-4c2d-aa46-0f55ac547145"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           time  Ha Noi   Vinh  Da Nang  Nha Trang  Ho Chi Minh  Ca Mau\n",
      "0  00 15-9-2019   25.65  24.79    24.01      25.06        25.48   24.97\n",
      "1  01 15-9-2019   25.31  24.21    24.02      24.93        25.16   24.83\n",
      "2  02 15-9-2019   25.05  23.73    23.89      24.79        24.80   24.55\n",
      "3  03 15-9-2019   24.79  23.36    23.83      24.84        24.74   24.48\n",
      "4  04 15-9-2019   24.59  23.05    23.69      24.82        24.80   24.38\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24 entries, 0 to 23\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   time         24 non-null     object \n",
      " 1   Ha Noi       24 non-null     float64\n",
      " 2   Vinh         24 non-null     float64\n",
      " 3   Da Nang      24 non-null     float64\n",
      " 4   Nha Trang    24 non-null     float64\n",
      " 5   Ho Chi Minh  24 non-null     float64\n",
      " 6   Ca Mau       24 non-null     float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 1.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Đọc dữ liệu từ tập tin temperature.csv\n",
    "data = pd.read_csv('temperature.csv')\n",
    "\n",
    "# Xem qua dữ liệu để hiểu cấu trúc\n",
    "print(data.head())\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 320,
     "status": "ok",
     "timestamp": 1726381804822,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "Io8eZNXaXB9J",
    "outputId": "e53b683a-2c5c-4293-897d-dcd058370da9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           time  Ha Noi   Vinh  Da Nang  Nha Trang  Ho Chi Minh  Ca Mau\n",
      "0  00 15-9-2019   25.65  24.79    24.01      25.06        25.48   24.97\n",
      "1  01 15-9-2019   25.31  24.21    24.02      24.93        25.16   24.83\n",
      "2  02 15-9-2019   25.05  23.73    23.89      24.79        24.80   24.55\n",
      "3  03 15-9-2019   24.79  23.36    23.83      24.84        24.74   24.48\n",
      "4  04 15-9-2019   24.59  23.05    23.69      24.82        24.80   24.38\n",
      "\n",
      "Ha Noi:\n",
      "Nhiệt độ thấp nhất: 24.38\n",
      "Nhiệt độ cao nhất: 32.05\n",
      "Nhiệt độ trung bình: 28.38\n",
      "Độ lệch chuẩn: 2.48\n",
      "Độ biến thiên về nhiệt độ: 7.67\n",
      "\n",
      "Vinh:\n",
      "Nhiệt độ thấp nhất: 22.79\n",
      "Nhiệt độ cao nhất: 30.62\n",
      "Nhiệt độ trung bình: 26.79\n",
      "Độ lệch chuẩn: 2.63\n",
      "Độ biến thiên về nhiệt độ: 7.83\n",
      "\n",
      "Da Nang:\n",
      "Nhiệt độ thấp nhất: 23.52\n",
      "Nhiệt độ cao nhất: 27.83\n",
      "Nhiệt độ trung bình: 25.24\n",
      "Độ lệch chuẩn: 1.37\n",
      "Độ biến thiên về nhiệt độ: 4.31\n",
      "\n",
      "Nha Trang:\n",
      "Nhiệt độ thấp nhất: 2.51\n",
      "Nhiệt độ cao nhất: 27.47\n",
      "Nhiệt độ trung bình: 25.10\n",
      "Độ lệch chuẩn: 4.80\n",
      "Độ biến thiên về nhiệt độ: 24.96\n",
      "\n",
      "Ho Chi Minh:\n",
      "Nhiệt độ thấp nhất: 24.71\n",
      "Nhiệt độ cao nhất: 58.00\n",
      "Nhiệt độ trung bình: 27.44\n",
      "Độ lệch chuẩn: 6.47\n",
      "Độ biến thiên về nhiệt độ: 33.29\n",
      "\n",
      "Ca Mau:\n",
      "Nhiệt độ thấp nhất: 24.38\n",
      "Nhiệt độ cao nhất: 30.97\n",
      "Nhiệt độ trung bình: 26.90\n",
      "Độ lệch chuẩn: 2.12\n",
      "Độ biến thiên về nhiệt độ: 6.59\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Đọc dữ liệu từ tập tin temperature.csv\n",
    "data = pd.read_csv('temperature.csv')\n",
    "\n",
    "# Xem qua dữ liệu để hiểu cấu trúc\n",
    "print(data.head())\n",
    "\n",
    "# a. Tính toán thống kê cho mỗi tỉnh thành\n",
    "provinces = data.columns[1:]  # Giả sử cột đầu tiên là ngày/tháng và các cột còn lại là nhiệt độ của các tỉnh\n",
    "\n",
    "stats = {}\n",
    "\n",
    "for province in provinces:\n",
    "    temperatures = data[province].to_numpy()\n",
    "\n",
    "    min_temp = np.min(temperatures)\n",
    "    max_temp = np.max(temperatures)\n",
    "    mean_temp = np.mean(temperatures)\n",
    "    std_dev = np.std(temperatures)\n",
    "    temp_range = max_temp - min_temp\n",
    "\n",
    "    stats[province] = {\n",
    "        'Min': min_temp,\n",
    "        'Max': max_temp,\n",
    "        'Mean': mean_temp,\n",
    "        'Std Dev': std_dev,\n",
    "        'Range': temp_range\n",
    "    }\n",
    "\n",
    "    print(f\"\\n{province}:\")\n",
    "    print(f\"Nhiệt độ thấp nhất: {min_temp:.2f}\")\n",
    "    print(f\"Nhiệt độ cao nhất: {max_temp:.2f}\")\n",
    "    print(f\"Nhiệt độ trung bình: {mean_temp:.2f}\")\n",
    "    print(f\"Độ lệch chuẩn: {std_dev:.2f}\")\n",
    "    print(f\"Độ biến thiên về nhiệt độ: {temp_range:.2f}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "executionInfo": {
     "elapsed": 707,
     "status": "ok",
     "timestamp": 1726382650400,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "IgcgxRP_aNxv",
    "outputId": "9fefbf2a-8c32-4332-de04-c0647ba2e4fe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# b. Vẽ histogram nhiệt độ của TP.HCM\n",
    "\n",
    "\n",
    "# b. Vẽ histogram nhiệt độ của TP.HCM\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Trích xuất dữ liệu nhiệt độ của TP.HCM, loại bỏ giá trị NaN\n",
    "temperature_HCM = data['Ho Chi Minh']\n",
    "\n",
    "# Vẽ histogram\n",
    "plt.hist(temperature_HCM, bins=20, edgecolor='black', alpha=0.75)\n",
    "\n",
    "# Thêm các nhãn và tiêu đề\n",
    "plt.xlabel('Nhiệt độ (°C)')\n",
    "plt.ylabel('Số lần xuất hiện')\n",
    "plt.title('Histogram nhiệt độ của TP.HCM')\n",
    "plt.grid(True)\n",
    "\n",
    "# Hiển thị đồ thị\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "executionInfo": {
     "elapsed": 855,
     "status": "ok",
     "timestamp": 1726382859985,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "2g23VpYlbPea",
    "outputId": "d5330871-961a-4cfb-a984-d36856c6f96e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#c. Lựa chọn đồ thị để so sánh nhiệt độ của 6 tỉnh thành\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "# Chọn 6 tỉnh thành\n",
    "selected_provinces = ['Ha Noi','Vinh','Da Nang','Nha Trang','Ho Chi Minh','Ca Mau']\n",
    "\n",
    "# Tính toán nhiệt độ trung bình của mỗi tỉnh thành\n",
    "mean_temps = [data[province].dropna().mean() for province in selected_provinces]\n",
    "\n",
    "# Vẽ biểu đồ cột\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(selected_provinces, mean_temps, color=['#ff9999','#66b3ff','#99ff99','#ffcc99','#ffb3e6','#c2c2f0'])\n",
    "plt.xlabel('Tỉnh thành')\n",
    "plt.ylabel('Nhiệt độ trung bình (°C)')\n",
    "plt.title('So sánh nhiệt độ trung bình của 6 tỉnh thành')\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KTXWXvokHtK6"
   },
   "source": [
    "### Bài 7: dataset `bmi.csv`\n",
    "\n",
    "Bạn hãy tìm cách đọc dữ liệu về bmi của nhóm người khảo sát trên với thư viện `numpy`.\n",
    "\n",
    "a. Hãy cho biết:\n",
    "    - Tỷ lệ nam nữ\n",
    "    - Các đại lượng thống kê: min, max, range, mean, standard deviation của hai thuộc tính chiều cao và cân nặng\n",
    "\n",
    "b. Vẽ đồ thị histogram về chiều cao và cân nặng của hai thuộc tính chiều cao và cân nặng\n",
    "\n",
    "c. Sử dụng hai đồ thị boxplot để so sánh chiều cao và cân nặng của nam và nữ\n",
    "\n",
    "d. Để kiểm tra xem hai thuộc tính chiều cao và cân nặng có mối liên quan nào hay không, bạn có thể sử dụng đồ thị tán xạ (scatter plot). Giả sử muốn kiểm tra xem liệu một người cao hơn thì có cân nặng lớn hơn hay không, bạn hãy vẽ đồ thị scatter plot với trục hoành là chiều cao và trục tung là cân nặng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 308,
     "status": "ok",
     "timestamp": 1726383064990,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "bLVEtUnRcm36",
    "outputId": "b0213a82-3ef2-4ecf-e368-12c94e035b85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Personal  Gender  Height_cm  Weight_kg\n",
      "0       P1    Male        174         96\n",
      "1       P2    Male        189         87\n",
      "2       P3  Female        185        110\n",
      "3       P4  Female        195        104\n",
      "4       P5    Male        149         61\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Đọc dữ liệu từ tập tin bmi.csv\n",
    "data = pd.read_csv('bmi.csv')\n",
    "\n",
    "# Xem qua dữ liệu để hiểu cấu trúc\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 309,
     "status": "ok",
     "timestamp": 1726383115878,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "0MR6RDgec71R",
    "outputId": "12043351-8f20-4de8-9496-0869d0db6128"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tỷ lệ nam nữ:\n",
      "Female: 51.00%\n",
      "Male: 49.00%\n"
     ]
    }
   ],
   "source": [
    "# a. Tỷ lệ nam nữ\n",
    "genders = data['Gender']\n",
    "unique, counts = np.unique(genders, return_counts=True)\n",
    "gender_counts = dict(zip(unique, counts))\n",
    "total_count = len(genders)\n",
    "gender_ratios = {gender: count / total_count for gender, count in gender_counts.items()}\n",
    "\n",
    "print(\"Tỷ lệ nam nữ:\")\n",
    "for gender, ratio in gender_ratios.items():\n",
    "    print(f\"{gender}: {ratio:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 309,
     "status": "ok",
     "timestamp": 1726383211969,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "PUwuScyxdPnK",
    "outputId": "f0cfa1a1-f56e-42b5-e2a5-48b9bf72ff3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Các đại lượng thống kê:\n",
      "Chiều cao (Height):\n",
      "  Min: 140.00 cm\n",
      "  Max: 199.00 cm\n",
      "  Range: 59.00 cm\n",
      "  Mean: 169.94 cm\n",
      "  Std Dev: 16.36 cm\n",
      "\n",
      "Cân nặng (Weight):\n",
      "  Min: 50.00 kg\n",
      "  Max: 160.00 kg\n",
      "  Range: 110.00 kg\n",
      "  Mean: 106.00 kg\n",
      "  Std Dev: 32.35 kg\n"
     ]
    }
   ],
   "source": [
    "# Tính toán các đại lượng thống kê cho chiều cao và cân nặng\n",
    "heights = data['Height_cm']\n",
    "weights = data['Weight_kg']\n",
    "\n",
    "def calculate_statistics(array):\n",
    "    min_val = np.min(array)\n",
    "    max_val = np.max(array)\n",
    "    range_val = max_val - min_val\n",
    "    mean_val = np.mean(array)\n",
    "    std_dev = np.std(array)\n",
    "    return min_val, max_val, range_val, mean_val, std_dev\n",
    "\n",
    "height_stats = calculate_statistics(heights)\n",
    "weight_stats = calculate_statistics(weights)\n",
    "\n",
    "print(\"\\nCác đại lượng thống kê:\")\n",
    "print(\"Chiều cao (Height):\")\n",
    "print(f\"  Min: {height_stats[0]:.2f} cm\")\n",
    "print(f\"  Max: {height_stats[1]:.2f} cm\")\n",
    "print(f\"  Range: {height_stats[2]:.2f} cm\")\n",
    "print(f\"  Mean: {height_stats[3]:.2f} cm\")\n",
    "print(f\"  Std Dev: {height_stats[4]:.2f} cm\")\n",
    "\n",
    "print(\"\\nCân nặng (Weight):\")\n",
    "print(f\"  Min: {weight_stats[0]:.2f} kg\")\n",
    "print(f\"  Max: {weight_stats[1]:.2f} kg\")\n",
    "print(f\"  Range: {weight_stats[2]:.2f} kg\")\n",
    "print(f\"  Mean: {weight_stats[3]:.2f} kg\")\n",
    "print(f\"  Std Dev: {weight_stats[4]:.2f} kg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "executionInfo": {
     "elapsed": 1112,
     "status": "ok",
     "timestamp": 1726383279900,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "vx-Sb48Ydfx5",
    "outputId": "eccc5c52-c5fc-4590-fc50-ff543fbedbdc"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#b. Vẽ đồ thị histogram về chiều cao và cân nặng của hai thuộc tính chiều cao và cân nặng\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(heights, bins=20, edgecolor='black', alpha=0.75)\n",
    "plt.xlabel('Chiều cao (cm)')\n",
    "plt.ylabel('Số lần xuất hiện')\n",
    "plt.title('Histogram chiều cao')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(weights, bins=20, edgecolor='black', alpha=0.75)\n",
    "plt.xlabel('Cân nặng (kg)')\n",
    "plt.ylabel('Số lần xuất hiện')\n",
    "plt.title('Histogram cân nặng')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "executionInfo": {
     "elapsed": 1332,
     "status": "ok",
     "timestamp": 1726383695374,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "SKDJBjvgdp5x",
    "outputId": "6ccec476-bac4-4ccd-8ffa-9d3bf05ec181"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# c. Sử dụng hai đồ thị boxplot để so sánh chiều cao và cân nặng của nam và nữ\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Đọc dữ liệu từ tập tin CSV\n",
    "data = np.genfromtxt('bmi.csv', delimiter=',', dtype=None, encoding=None, names=True)\n",
    "\n",
    "# Chuyển dữ liệu numpy sang DataFrame để dễ dàng vẽ boxplot\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Tạo một figure và các axes cho đồ thị\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Vẽ boxplot chiều cao\n",
    "df.boxplot(column='Height_cm', by='Gender', grid=False, ax=ax1)\n",
    "ax1.set_xlabel('Giới tính')\n",
    "ax1.set_ylabel('Chiều cao (cm)')\n",
    "ax1.set_title('Boxplot chiều cao')\n",
    "ax1.set_title('')\n",
    "ax1.figure.suptitle('')\n",
    "\n",
    "# Vẽ boxplot cân nặng\n",
    "df.boxplot(column='Weight_kg', by='Gender', grid=False, ax=ax2)\n",
    "ax2.set_xlabel('Giới tính')\n",
    "ax2.set_ylabel('Cân nặng (kg)')\n",
    "ax2.set_title('Boxplot cân nặng')\n",
    "ax2.set_title('')\n",
    "ax2.figure.suptitle('')\n",
    "\n",
    "# Tinh chỉnh khoảng cách giữa các đồ thị\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 565
    },
    "executionInfo": {
     "elapsed": 1482,
     "status": "ok",
     "timestamp": 1726383904650,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "V_2VRUeef4PZ",
    "outputId": "b33c3b25-b6af-4e1c-ab14-9f5031354058"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sử dụng đồ thị tán xạ (scatter plot)\n",
    "# d. Vẽ scatter plot để kiểm tra mối liên quan giữa chiều cao và cân nặng\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(heights, weights, alpha=0.5, edgecolors='w', s=80)\n",
    "plt.xlabel('Chiều cao (cm)')\n",
    "plt.ylabel('Cân nặng (kg)')\n",
    "plt.title('Scatter plot giữa chiều cao và cân nặng')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iL55_R7RHtK6"
   },
   "source": [
    "## KHÁC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mNZk61GlHtK6"
   },
   "source": [
    "### Bài 8: Mô phỏng điểm bài kiểm tra\n",
    "\n",
    "Giả sử điểm bài kiểm tra của học sinh có điểm môn Toán [0..10], với các phần lẻ có thể có là [0.25, 0.5, 0.75]\n",
    "\n",
    "a. Dùng hàm ngẫu nhiên để phát sinh điểm số của một lớp học gồm của 10 học sinh. Tính điểm trung bình của lớp đó.\n",
    "\n",
    "b. Giả sử một trường có 50 lớp học. Mỗi lớp học có 40 học sinh. Bạn hãy phát sinh ngẫu nhiên điểm của các học sinh trong 50 lớp học này\n",
    "\n",
    "c. Giả sử nhà trường muốn thống kê tình hình học tập của 50 lớp học này. Nhà trường tiến hành thống kê bằng cách lấy điểm trung bình của tất cả các lớp học. Bạn hãy vẽ histogram điểm trung bình của 50 lớp học này."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 345,
     "status": "ok",
     "timestamp": 1726386005176,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "w3i2cmm6HtK6",
    "outputId": "34abeddc-f32d-4b03-9902-ba7215760bee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Điểm của lớp học: [1.5  9.75 0.   7.   1.   0.5  0.   9.75 8.5  0.5 ]\n",
      "Điểm trung bình của lớp học: 3.85\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "import numpy as np\n",
    "\n",
    "# Định nghĩa các điểm số có thể có\n",
    "possible_grades = np.arange(0, 10.25, 0.25)\n",
    "\n",
    "# Phát sinh điểm cho 10 học sinh\n",
    "num_students = 10\n",
    "class_grades = np.random.choice(possible_grades, size=num_students, replace=True)\n",
    "\n",
    "# Tính điểm trung bình\n",
    "mean_grade = np.mean(class_grades)\n",
    "print(f\"Điểm của lớp học: {class_grades}\")\n",
    "print(f\"Điểm trung bình của lớp học: {mean_grade:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "executionInfo": {
     "elapsed": 347,
     "status": "ok",
     "timestamp": 1726386018221,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "d3zUQ0yloAO5"
   },
   "outputs": [],
   "source": [
    "# Định nghĩa các thông số\n",
    "num_classes = 50\n",
    "num_students_per_class = 40\n",
    "\n",
    "# Phát sinh điểm cho tất cả các lớp học\n",
    "all_class_grades = np.random.choice(possible_grades, size=(num_classes, num_students_per_class), replace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 477
    },
    "executionInfo": {
     "elapsed": 805,
     "status": "ok",
     "timestamp": 1726386029502,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "kCldHgrKoDbC",
    "outputId": "eaaa6b93-4be0-403d-f4d6-9e11fcc9b87e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Tính điểm trung bình của từng lớp học\n",
    "class_means = np.mean(all_class_grades, axis=1)\n",
    "\n",
    "# Vẽ histogram điểm trung bình của các lớp học\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(class_means, bins=10, edgecolor='black', alpha=0.75)\n",
    "plt.xlabel('Điểm trung bình của lớp học')\n",
    "plt.ylabel('Số lớp học')\n",
    "plt.title('Histogram điểm trung bình của 50 lớp học')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jUBzZUZuHtK7"
   },
   "source": [
    "\n",
    "### Bài 9: Mô phỏng bốc ba lá bài\n",
    "\n",
    "Một bộ bài tú lơ khơ có 52 lá. Với:\n",
    "    - Tập các nút là {2, 3,..10, J, Q, K, A}\n",
    "    - Tập các chất là {'co', 'ro', 'chuong', 'bich`}\n",
    "\n",
    "Bạn hãy mô phỏng:\n",
    "\n",
    "a. Bốc 3 lá bài từ bộ bài trên. Lưu ý: không có trường hợp 1 bộ bài có hai lá trùng nhau\n",
    "\n",
    "b. Tính điểm thu được (tổng điểm 3 lá)\n",
    "\n",
    "c. Tìm hiểu cách hiển thị hình ảnh 3 lá bài đã bốc bằng python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 337,
     "status": "ok",
     "timestamp": 1726386098447,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "2QZgKURKoSwN",
    "outputId": "7fd6068b-5980-4676-ab07-d9d7c74cc83e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lá bài đã bốc: [('3', 'chuong'), ('9', 'co'), ('2', 'bich')]\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "import random\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# a. Bốc 3 lá bài từ bộ bài\n",
    "# Tạo bộ bài\n",
    "suits = ['co', 'ro', 'chuong', 'bich']\n",
    "ranks = ['2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K', 'A']\n",
    "deck = [(rank, suit) for suit in suits for rank in ranks]\n",
    "\n",
    "# Bốc 3 lá bài\n",
    "chosen_cards = random.sample(deck, 3)\n",
    "print(f\"Lá bài đã bốc: {chosen_cards}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 363,
     "status": "ok",
     "timestamp": 1726386113820,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "TzX0EsiLoYIi",
    "outputId": "c277898d-f3e4-4ca5-804b-d4845cb1519b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tổng điểm của 3 lá bài: 14\n"
     ]
    }
   ],
   "source": [
    "# b. Tính điểm thu được\n",
    "def card_value(card):\n",
    "    rank, _ = card\n",
    "    if rank in ['J', 'Q', 'K']:\n",
    "        return 10\n",
    "    elif rank == 'A':\n",
    "        return 11\n",
    "    else:\n",
    "        return int(rank)\n",
    "\n",
    "total_points = sum(card_value(card) for card in chosen_cards)\n",
    "print(f\"Tổng điểm của 3 lá bài: {total_points}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3515,
     "status": "ok",
     "timestamp": 1726386220818,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "3HwigB6Eou1i",
    "outputId": "04a133e8-8a90-4070-8a0e-d27346f42176"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (9.4.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gO_7zu8EpETI"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# c. Hiển thị hình ảnh 3 lá bài đã bốc\n",
    "def card_image_path(card):\n",
    "    rank, suit = card\n",
    "    return f\"images/{rank}_{suit}.png\"\n",
    "\n",
    "# Đảm bảo thư mục 'images' chứa hình ảnh của các lá bài\n",
    "image_paths = [card_image_path(card) for card in chosen_cards]\n",
    "\n",
    "# Vẽ hình ảnh\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "for ax, image_path, card in zip(axes, image_paths, chosen_cards):\n",
    "    img = Image.open(image_path)\n",
    "    ax.imshow(img)\n",
    "    ax.set_title(f\"{card[0]} {card[1]}\")\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ggB0EdTQHtK7"
   },
   "source": [
    "\n",
    "### Bài 10: Mô phỏng Monte Carlo để ước lượng π\n",
    "\n",
    "Ước lượng giá trị của π bằng phương pháp Monte Carlo. Ý tưởng là tạo ngẫu nhiên các điểm trong một hình vuông và xác định tỷ lệ các điểm nằm trong một phần tư hình tròn.\n",
    "\n",
    "Công việc:\n",
    "\n",
    "Tạo 100.000 điểm ngẫu nhiên (x, y) trong một hình vuông đơn vị.\n",
    "Đếm số điểm nằm trong phần tư hình tròn có bán kính 1.\n",
    "Ước lượng π bằng công thức: 4 * (số điểm trong hình tròn) / (tổng số điểm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 318,
     "status": "ok",
     "timestamp": 1726386302355,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "IMr00eSlHtK7",
    "outputId": "2e5d1508-0057-45d6-e0b8-bd520b9e09f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ước lượng π là: 3.14052\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "import numpy as np\n",
    "\n",
    "# Số lượng điểm\n",
    "num_points = 100000\n",
    "\n",
    "# Tạo các điểm ngẫu nhiên trong hình vuông đơn vị\n",
    "x = np.random.uniform(0, 1, num_points)\n",
    "y = np.random.uniform(0, 1, num_points)\n",
    "\n",
    "# Kiểm tra số điểm nằm trong phần tư hình tròn\n",
    "inside_circle = (x**2 + y**2 <= 1)\n",
    "\n",
    "# Tính toán tỷ lệ và ước lượng π\n",
    "ratio = np.sum(inside_circle) / num_points\n",
    "pi_estimate = 4 * ratio\n",
    "\n",
    "print(f\"Ước lượng π là: {pi_estimate}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "72B4HcTFHtK7"
   },
   "source": [
    "---"
   ]
  }
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