Lab/Lab01/lab01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 寻找关键用户"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 初始数据\n",
    "- 用户用 `users` 列表存储。\n",
    "- 朋友关系用元组的形式存储：对应两人 `id` ，存储在列表中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = [\n",
    "    {\"id\": 0, \"name\": \"Hero\"},\n",
    "    {\"id\": 1, \"name\": \"Dunn\"},\n",
    "    {\"id\": 2, \"name\": \"Sue\"},\n",
    "    {\"id\": 3, \"name\": \"Chi\"},\n",
    "    {\"id\": 4, \"name\": \"Thor\"},\n",
    "    {\"id\": 5, \"name\": \"Clive\"},\n",
    "    {\"id\": 6, \"name\": \"Hicks\"},\n",
    "    {\"id\": 7, \"name\": \"Devin\"},\n",
    "    {\"id\": 8, \"name\": \"Kate\"},\n",
    "    {\"id\": 9, \"name\": \"Klein\"}\n",
    "]\n",
    "\n",
    "friendship_pairs = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),\n",
    "                    (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户关系网络可以画成图的形式：\n",
    "\n",
    "![image](https://github.com/X-lab2017/dase-2023-autumn/assets/50283262/b0331f38-df31-4969-bcbf-dd1492b20051)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tips** <br><br>\n",
    "> 也可以通过*邻接矩阵*的方法表示用户关系网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "   0  1  2  3  4  5  6  7  8  9\n",
       "0  0  1  1  0  0  0  0  0  0  0\n",
       "1  1  0  1  1  0  0  0  0  0  0\n",
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       "6  0  0  0  0  0  1  0  0  1  0\n",
       "7  0  0  0  0  0  1  0  0  1  0\n",
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "friendship_pairs = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),\n",
    "                    (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)]\n",
    "\n",
    "# 找到节点的最大索引以确定矩阵的大小\n",
    "max_node = max(max(pair) for pair in friendship_pairs) + 1\n",
    "\n",
    "# 初始化邻接矩阵，全部置为0\n",
    "adjacency_matrix = [[0] * max_node for _ in range(max_node)]\n",
    "\n",
    "# 在邻接矩阵中标记存在的关系\n",
    "for pair in friendship_pairs:\n",
    "    node1, node2 = pair\n",
    "    adjacency_matrix[node1][node2] = 1\n",
    "    adjacency_matrix[node2][node1] = 1\n",
    "\n",
    "# 创建带有索引的 Pandas DataFrame\n",
    "df = pd.DataFrame(adjacency_matrix, index=range(\n",
    "    max_node), columns=range(max_node))\n",
    "\n",
    "# 打印 DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用字典表示用户关系\n",
    "- friendship_pairs 的形式表示朋友关系不是最简单的表示方法。如果我想查询用户1的朋友，需要遍历所有的元组才能得到结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [], 1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: [], 8: [], 9: []}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化一个以用户id为键，以用户好友列表为值的字典\n",
    "friendships = {user[\"id\"]: [] for user in users}\n",
    "\n",
    "friendships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [1, 2],\n",
       " 1: [0, 2, 3],\n",
       " 2: [0, 1, 3],\n",
       " 3: [1, 2, 4],\n",
       " 4: [3, 5],\n",
       " 5: [4, 6, 7],\n",
       " 6: [5, 8],\n",
       " 7: [5, 8],\n",
       " 8: [6, 7, 9],\n",
       " 9: [8]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 循环表示朋友关系的元组对，抽取每个元组对中的两个元素，将其分别添加到字典中对应的值列表中\n",
    "for i, j in friendship_pairs:\n",
    "    friendships[i].append(j)  # 将 j 添加为用户 i 的朋友\n",
    "    friendships[j].append(i)  # 将 i 添加为用户 j 的朋友\n",
    "\n",
    "friendships"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当拥有了 `friendships` 后，我们可以快速知道用户的朋友是谁，也可以想对应的统计量并且求出具体数值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 统计量场景1: 用户平均朋友数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个用户平均有多少个朋友?\n",
    "def number_of_friends(user):\n",
    "    \"\"\"这个用户有多少朋友?\"\"\"\n",
    "    user_id = user[\"id\"]\n",
    "    friend_ids = friendships[user_id]\n",
    "    return len(friend_ids)\n",
    "\n",
    "\n",
    "total_connections = sum(number_of_friends(user)\n",
    "                        for user in users)        # 24\n",
    "\n",
    "total_connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "2.4\n"
     ]
    }
   ],
   "source": [
    "# 除以用户数量\n",
    "num_users = len(users)                            # 用户列表的长度\n",
    "avg_connections = total_connections / num_users   # 24 / 10 == 2.4\n",
    "\n",
    "print(num_users)\n",
    "print(avg_connections)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 统计量场景2: 如何找到有最多朋友的用户"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直观的想法是 将用户朋友数量从多到少排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 3),\n",
       " (2, 3),\n",
       " (3, 3),\n",
       " (5, 3),\n",
       " (8, 3),\n",
       " (0, 2),\n",
       " (4, 2),\n",
       " (6, 2),\n",
       " (7, 2),\n",
       " (9, 1)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个列表 (user_id, number_of_friends)\n",
    "num_friends_by_id = [(user[\"id\"], number_of_friends(user))\n",
    "                     for user in users]\n",
    "\n",
    "# 按照朋友数量进行排序\n",
    "num_friends_by_id.sort(                                \n",
    "    key=lambda id_and_friends: id_and_friends[1],      \n",
    "    reverse=True)                                   \n",
    "\n",
    "# Each pair is (user_id, num_friends):\n",
    "# [(1, 3), (2, 3), (3, 3), (5, 3), (8, 3),\n",
    "#  (0, 2), (4, 2), (6, 2), (7, 2), (9, 1)]\n",
    "\n",
    "num_friends_by_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 另一个角度思考关键用户：度中心性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://github.com/X-lab2017/dase-2023-autumn/assets/50283262/b24fc233-d5f0-4063-84f8-8d87fe9d9251)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1和4哪个更加重要呢？\n",
    "- 如果我结交了更优秀的人，是不是证明我也是相对优秀的人呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如何认识新朋友？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 发现朋友的朋友"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 3, 0, 1, 3]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def foaf_ids_bad(user):\n",
    "    \"\"\"foaf 是\"friend of a friend\"的缩写 \"\"\"\n",
    "    return [foaf_id\n",
    "            for friend_id in friendships[user[\"id\"]]\n",
    "            for foaf_id in friendships[friend_id]]\n",
    "\n",
    "foaf_ids_bad(users[0]) # [0, 2, 3, 0, 1, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2]\n",
      "[0, 2, 3]\n",
      "[0, 1, 3]\n"
     ]
    }
   ],
   "source": [
    "# 相当于是将以下的三个输出串在一起～\n",
    "print(friendships[0])  # [1, 2]\n",
    "print(friendships[1])  # [0, 2, 3]\n",
    "print(friendships[2])  # [0, 1, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现这样的方式会出现 **自己** 或者出现已经是 **自己朋友的人** ，能否更加精简呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 准确地寻找朋友的朋友"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 2, 5: 1})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter                   # 需要导入 collections 模块\n",
    "\n",
    "\n",
    "def friends_of_friends(user):\n",
    "    user_id = user[\"id\"]\n",
    "    return Counter(\n",
    "        foaf_id\n",
    "        for friend_id in friendships[user_id]     # 对于我的每个朋友，\n",
    "        for foaf_id in friendships[friend_id]     # 计算他们的朋友，\n",
    "        if foaf_id != user_id                     # 如果这个人，不是我\n",
    "        and foaf_id not in friendships[user_id]   # 也不是我的朋友，那么就是我可以认识的人！\n",
    "    )\n",
    "\n",
    "\n",
    "print(friends_of_friends(users[3]))               # Counter({0: 2, 5: 1})\n",
    "\n",
    "# 这个结果表示 id 为 3 的用户与 id 为 0 的用户，有两个共同好友；而与 id 为 5 的用户，有一个共同好友。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过兴趣交朋友"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 初始数据\n",
    "- 每个元素都是成对的数据 (user_id,interest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "interests = [\n",
    "    (0, \"Hadoop\"), (0, \"Big Data\"), (0, \"HBase\"), (0, \"Java\"),\n",
    "    (0, \"Spark\"), (0, \"Storm\"), (0, \"Cassandra\"),\n",
    "    (1, \"NoSQL\"), (1, \"MongoDB\"), (1, \"Cassandra\"), (1, \"HBase\"),\n",
    "    (1, \"Postgres\"), (2, \"Python\"), (2, \"scikit-learn\"), (2, \"scipy\"),\n",
    "    (2, \"numpy\"), (2, \"statsmodels\"), (2, \"pandas\"), (3, \"R\"), (3, \"Python\"),\n",
    "    (3, \"statistics\"), (3, \"regression\"), (3, \"probability\"),\n",
    "    (4, \"machine learning\"), (4, \"regression\"), (4, \"decision trees\"),\n",
    "    (4, \"libsvm\"), (5, \"Python\"), (5, \"R\"), (5, \"Java\"), (5, \"C++\"),\n",
    "    (5, \"Haskell\"), (5, \"programming languages\"), (6, \"statistics\"),\n",
    "    (6, \"probability\"), (6, \"mathematics\"), (6, \"theory\"),\n",
    "    (7, \"machine learning\"), (7, \"scikit-learn\"), (7, \"Mahout\"),\n",
    "    (7, \"neural networks\"), (8, \"neural networks\"), (8, \"deep learning\"),\n",
    "    (8, \"Big Data\"), (8, \"artificial intelligence\"), (9, \"Hadoop\"),\n",
    "    (9, \"Java\"), (9, \"MapReduce\"), (9, \"Big Data\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://github.com/X-lab2017/dase-2023-autumn/assets/50283262/b24fc233-d5f0-4063-84f8-8d87fe9d9251)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 简单概览\n",
    "- id 为 0 的人和 id 为 9 的人在朋友关系图里并没有关联，但是他们都对 Java 和大数据很感兴趣！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查找特定兴趣的人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_scientists_who_like(target_interest):\n",
    "    \"\"\"找到有着某种特定兴趣的所有用户的 ids\"\"\"\n",
    "    return [user_id\n",
    "            for user_id, user_interest in interests\n",
    "            if user_interest == target_interest]\n",
    "\n",
    "# 这个方法的缺点是，每次都要遍历整个列表，这样会很慢。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了更快地找到与某种特定兴趣相同的用户，我们需要一个从兴趣到用户的反向映射（索引）。\n",
    "from collections import defaultdict\n",
    "\n",
    "# 键是兴趣，值是对这个兴趣感兴趣的用户 id 列表\n",
    "user_ids_by_interest = defaultdict(list)\n",
    "\n",
    "for user_id, interest in interests:\n",
    "    user_ids_by_interest[interest].append(user_id)\n",
    "\n",
    "# 键是用户 id，值是用户感兴趣的兴趣列表\n",
    "interests_by_user_id = defaultdict(list)\n",
    "\n",
    "for user_id, interest in interests:\n",
    "    interests_by_user_id[user_id].append(interest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {0: ['Hadoop',\n",
       "              'Big Data',\n",
       "              'HBase',\n",
       "              'Java',\n",
       "              'Spark',\n",
       "              'Storm',\n",
       "              'Cassandra'],\n",
       "             1: ['NoSQL', 'MongoDB', 'Cassandra', 'HBase', 'Postgres'],\n",
       "             2: ['Python',\n",
       "              'scikit-learn',\n",
       "              'scipy',\n",
       "              'numpy',\n",
       "              'statsmodels',\n",
       "              'pandas'],\n",
       "             3: ['R', 'Python', 'statistics', 'regression', 'probability'],\n",
       "             4: ['machine learning', 'regression', 'decision trees', 'libsvm'],\n",
       "             5: ['Python',\n",
       "              'R',\n",
       "              'Java',\n",
       "              'C++',\n",
       "              'Haskell',\n",
       "              'programming languages'],\n",
       "             6: ['statistics', 'probability', 'mathematics', 'theory'],\n",
       "             7: ['machine learning',\n",
       "              'scikit-learn',\n",
       "              'Mahout',\n",
       "              'neural networks'],\n",
       "             8: ['neural networks',\n",
       "              'deep learning',\n",
       "              'Big Data',\n",
       "              'artificial intelligence'],\n",
       "             9: ['Hadoop', 'Java', 'MapReduce', 'Big Data']})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interests_by_user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'Hadoop': [0, 9],\n",
       "             'Big Data': [0, 8, 9],\n",
       "             'HBase': [0, 1],\n",
       "             'Java': [0, 5, 9],\n",
       "             'Spark': [0],\n",
       "             'Storm': [0],\n",
       "             'Cassandra': [0, 1],\n",
       "             'NoSQL': [1],\n",
       "             'MongoDB': [1],\n",
       "             'Postgres': [1],\n",
       "             'Python': [2, 3, 5],\n",
       "             'scikit-learn': [2, 7],\n",
       "             'scipy': [2],\n",
       "             'numpy': [2],\n",
       "             'statsmodels': [2],\n",
       "             'pandas': [2],\n",
       "             'R': [3, 5],\n",
       "             'statistics': [3, 6],\n",
       "             'regression': [3, 4],\n",
       "             'probability': [3, 6],\n",
       "             'machine learning': [4, 7],\n",
       "             'decision trees': [4],\n",
       "             'libsvm': [4],\n",
       "             'C++': [5],\n",
       "             'Haskell': [5],\n",
       "             'programming languages': [5],\n",
       "             'mathematics': [6],\n",
       "             'theory': [6],\n",
       "             'Mahout': [7],\n",
       "             'neural networks': [7, 8],\n",
       "             'deep learning': [8],\n",
       "             'artificial intelligence': [8],\n",
       "             'MapReduce': [9]})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ids_by_interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因此就可以很容易找到与给定用户拥有最多共同兴趣的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({9: 3, 1: 2, 8: 1, 5: 1})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def most_common_interests_with(user):\n",
    "    return Counter(\n",
    "        interested_user_id\n",
    "        for interest in interests_by_user_id[user[\"id\"]]\n",
    "        for interested_user_id in user_ids_by_interest[interest]\n",
    "        if interested_user_id != user[\"id\"]\n",
    "    )\n",
    "\n",
    "most_common_interests_with(users[0]) # Counter({9: 3, 1: 2, 8: 1, 5: 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 工资和工作年限"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 初始数据\n",
    "- 薪水和工作年限用元组对的形式表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "salaries_and_tenures = [(83000, 8.7), (88000, 8.1),\n",
    "                        (48000, 0.7), (76000, 6),\n",
    "                        (69000, 6.5), (76000, 7.5),\n",
    "                        (60000, 2.5), (83000, 10),\n",
    "                        (48000, 1.9), (63000, 4.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以通过散点图的形式大致看看数据的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 提取薪资和工作年限\n",
    "salaries = [s for s, t in salaries_and_tenures]\n",
    "tenures = [t for s, t in salaries_and_tenures]\n",
    "\n",
    "# 创建散点图\n",
    "plt.scatter(tenures, salaries)\n",
    "\n",
    "# 设置图表标题和标签\n",
    "plt.title(\"Salary by Years Experience\")\n",
    "plt.xlabel(\"Years Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果似乎是呈现线性的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 每个任期的平均工资工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{8.7: 83000.0,\n",
       " 8.1: 88000.0,\n",
       " 0.7: 48000.0,\n",
       " 6: 76000.0,\n",
       " 6.5: 69000.0,\n",
       " 7.5: 76000.0,\n",
       " 2.5: 60000.0,\n",
       " 10: 83000.0,\n",
       " 1.9: 48000.0,\n",
       " 4.2: 63000.0}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 键是工作年限，值是相应工资的列表\n",
    "salary_by_tenure = defaultdict(list)\n",
    "\n",
    "for salary, tenure in salaries_and_tenures:\n",
    "    salary_by_tenure[tenure].append(salary)\n",
    "\n",
    "# 键是工作年限，值是该年限对应的平均工资\n",
    "average_salary_by_tenure = {\n",
    "    tenure: sum(salaries) / len(salaries)\n",
    "    for tenure, salaries in salary_by_tenure.items()\n",
    "}\n",
    "\n",
    "average_salary_by_tenure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "似乎结果并没有说服力，观察数据会发现，任意两个用户的工作年限都不同"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分组的平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tenure_bucket(tenure):\n",
    "    if tenure < 2:\n",
    "        return \"less than two\"\n",
    "    elif tenure < 5:\n",
    "        return \"between two and five\"\n",
    "    else:\n",
    "        return \"more than five\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'list'>, {'more than five': [83000, 88000, 76000, 69000, 76000, 83000], 'less than two': [48000, 48000], 'between two and five': [60000, 63000]})\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'more than five': 79166.66666666667,\n",
       " 'less than two': 48000.0,\n",
       " 'between two and five': 61500.0}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 键是工作年限分的组，值是相应工资的列表\n",
    "salary_by_tenure_bucket = defaultdict(list)\n",
    "\n",
    "for salary, tenure in salaries_and_tenures:\n",
    "    bucket = tenure_bucket(tenure)\n",
    "    salary_by_tenure_bucket[bucket].append(salary)\n",
    "\n",
    "print(salary_by_tenure_bucket)\n",
    "\n",
    "# 键是工作年限分的组，值是该组对应的平均工资\n",
    "average_salary_by_bucket = {\n",
    "    tenure_bucket: sum(salaries) / len(salaries)\n",
    "    for tenure_bucket, salaries in salary_by_tenure_bucket.items()\n",
    "}\n",
    "\n",
    "average_salary_by_bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 付费账户"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "某个工作经验和是否付费的关系如下所示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| 工作经验 | 是否付费 | \n",
    "|---------|---------|\n",
    "| 0.7 | paid |\n",
    "| 1.9 | unpaid |\n",
    "| 2.5 | paid |\n",
    "| 4.2 | unpaid |\n",
    "| 6.0 | unpaid |\n",
    "| 6.5 | unpaid |\n",
    "| 7.5 | paid |\n",
    "| 8.1 | unpaid |\n",
    "| 8.7 | paid |\n",
    "| 10.0 | paid |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_paid_or_unpaid(years_experience):\n",
    "  if years_experience < 3.0:\n",
    "    return \"paid\"\n",
    "  elif years_experience < 8.5:\n",
    "    return \"unpaid\"\n",
    "  else:\n",
    "    return \"paid\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 感兴趣的主题 （词频统计）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 初始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "interests = [\n",
    "    (0, \"Hadoop\"), (0, \"Big Data\"), (0, \"HBase\"), (0, \"Java\"),\n",
    "    (0, \"Spark\"), (0, \"Storm\"), (0, \"Cassandra\"),\n",
    "    (1, \"NoSQL\"), (1, \"MongoDB\"), (1, \"Cassandra\"), (1, \"HBase\"),\n",
    "    (1, \"Postgres\"), (2, \"Python\"), (2, \"scikit-learn\"), (2, \"scipy\"),\n",
    "    (2, \"numpy\"), (2, \"statsmodels\"), (2, \"pandas\"), (3, \"R\"), (3, \"Python\"),\n",
    "    (3, \"statistics\"), (3, \"regression\"), (3, \"probability\"),\n",
    "    (4, \"machine learning\"), (4, \"regression\"), (4, \"decision trees\"),\n",
    "    (4, \"libsvm\"), (5, \"Python\"), (5, \"R\"), (5, \"Java\"), (5, \"C++\"),\n",
    "    (5, \"Haskell\"), (5, \"programming languages\"), (6, \"statistics\"),\n",
    "    (6, \"probability\"), (6, \"mathematics\"), (6, \"theory\"),\n",
    "    (7, \"machine learning\"), (7, \"scikit-learn\"), (7, \"Mahout\"),\n",
    "    (7, \"neural networks\"), (8, \"neural networks\"), (8, \"deep learning\"),\n",
    "    (8, \"Big Data\"), (8, \"artificial intelligence\"), (9, \"Hadoop\"),\n",
    "    (9, \"Java\"), (9, \"MapReduce\"), (9, \"Big Data\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找到最受欢迎的兴趣的一种简单的方法就是计算兴趣词汇的个数：\n",
    "\n",
    "1. 将每个字符小写\n",
    "2. 将其切分为单词\n",
    "3. 计数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "big 3\n",
      "data 3\n",
      "java 3\n",
      "python 3\n",
      "learning 3\n",
      "hadoop 2\n",
      "hbase 2\n",
      "cassandra 2\n",
      "scikit-learn 2\n",
      "r 2\n",
      "statistics 2\n",
      "regression 2\n",
      "probability 2\n",
      "machine 2\n",
      "neural 2\n",
      "networks 2\n"
     ]
    }
   ],
   "source": [
    "words_and_counts = Counter(word\n",
    "                           for user, interest in interests\n",
    "                           for word in interest.lower().split())\n",
    "\n",
    "for word, count in words_and_counts.most_common():\n",
    "    if count > 1:\n",
    "        print(word, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据思维案例：求Pi值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 蒙特卡洛法 模拟求Pi值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated pi: 3.14139988\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def monte_carlo_pi(num_samples):\n",
    "    inside_circle = 0\n",
    "\n",
    "    for _ in range(num_samples):\n",
    "        x = random.uniform(-1, 1)\n",
    "        y = random.uniform(-1, 1)\n",
    "        distance = x**2 + y**2\n",
    "\n",
    "        if distance <= 1:\n",
    "            inside_circle += 1\n",
    "\n",
    "    estimated_pi = (inside_circle / num_samples) * 4\n",
    "    return estimated_pi\n",
    "\n",
    "num_samples = 100000000  # 设置模拟点的数量\n",
    "pi_estimate = monte_carlo_pi(num_samples)\n",
    "print(f\"Estimated pi: {pi_estimate}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tips** <br><br>\n",
    "> 可以通过蒙特卡洛方法求积分\n",
    "\n",
    "![image](https://github.com/X-lab2017/open-digger/assets/50283262/65d83137-f63a-442c-8d5b-14ccd728a39f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用 matplotlib 展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated pi: 3.1496\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def monte_carlo_pi(num_samples):\n",
    "    inside_circle = 0\n",
    "    x_inside = []\n",
    "    y_inside = []\n",
    "    x_outside = []\n",
    "    y_outside = []\n",
    "\n",
    "    for _ in range(num_samples):\n",
    "        x = random.uniform(-1, 1)\n",
    "        y = random.uniform(-1, 1)\n",
    "        distance = x**2 + y**2\n",
    "\n",
    "        if distance <= 1:\n",
    "            inside_circle += 1\n",
    "            x_inside.append(x)\n",
    "            y_inside.append(y)\n",
    "        else:\n",
    "            x_outside.append(x)\n",
    "            y_outside.append(y)\n",
    "\n",
    "    estimated_pi = (inside_circle / num_samples) * 4\n",
    "    return estimated_pi, x_inside, y_inside, x_outside, y_outside\n",
    "\n",
    "\n",
    "def plot_monte_carlo_pi(x_inside, y_inside, x_outside, y_outside):\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.scatter(x_inside, y_inside, color='blue', s=1, label='Inside Circle')\n",
    "    plt.scatter(x_outside, y_outside, color='red', s=1, label='Outside Circle')\n",
    "\n",
    "    plt.title('Monte Carlo Estimation of Pi')\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "num_samples = 10000  # 设置模拟点的数量\n",
    "pi_estimate, x_inside, y_inside, x_outside, y_outside = monte_carlo_pi(\n",
    "        num_samples)\n",
    "\n",
    "print(f\"Estimated pi: {pi_estimate}\")\n",
    "\n",
    "plot_monte_carlo_pi(x_inside, y_inside, x_outside, y_outside)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 扩展库实践"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy(Numerical Python) 是 Python 语言的一个扩展程序库，支持大量的维度数组与矩阵运算，此外也针对数组运算提供大量的数学函数库。\n",
    "\n",
    "NumPy 的前身 Numeric 最早是由 Jim Hugunin 与其它协作者共同开发，2005 年，Travis Oliphant 在 Numeric 中结合了另一个同性质的程序库 Numarray 的特色，并加入了其它扩展而开发了 NumPy。NumPy 为开放源代码并且由许多协作者共同维护开发。\n",
    "\n",
    "NumPy 是一个运行速度非常快的数学库，主要用于数组计算，包含：\n",
    "\n",
    "- 一个强大的N维数组对象 ndarray\n",
    "- 广播功能函数\n",
    "- 整合 C/C++/Fortran 代码的工具\n",
    "- 线性代数、傅里叶变换、随机数生成等功能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pip install numpy` 安装扩展库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 创建 NumPy 数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "# 通过列表创建一维数组\n",
    "arr_1d = np.array([1, 2, 3, 4, 5])\n",
    "print(arr_1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建二维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "# 通过嵌套列表创建二维数组\n",
    "arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "print(arr_2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数组属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数组维度: 2\n",
      "数组形状: (3, 3)\n",
      "数组元素总个数: 9\n",
      "数组元素数据类型: int64\n"
     ]
    }
   ],
   "source": [
    "# 数组维度\n",
    "print(\"数组维度:\", arr_2d.ndim)\n",
    "\n",
    "# 数组形状\n",
    "print(\"数组形状:\", arr_2d.shape)\n",
    "\n",
    "# 数组元素总个数\n",
    "print(\"数组元素总个数:\", arr_2d.size)\n",
    "\n",
    "# 数组元素数据类型\n",
    "print(\"数组元素数据类型:\", arr_2d.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数组操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数学运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数组加法: [11 12 13 14 15]\n",
      "数组乘法: [ 2  4  6  8 10]\n"
     ]
    }
   ],
   "source": [
    "# 数组加法\n",
    "arr_sum = arr_1d + 10\n",
    "print(\"数组加法:\", arr_sum)\n",
    "\n",
    "# 数组乘法\n",
    "arr_prod = arr_1d * 2\n",
    "print(\"数组乘法:\", arr_prod)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "索引和切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第一个元素: 1\n",
      "前三个元素: [1 2 3]\n"
     ]
    }
   ],
   "source": [
    "# 获取数组元素\n",
    "print(\"第一个元素:\", arr_1d[0])\n",
    "\n",
    "# 切片操作\n",
    "print(\"前三个元素:\", arr_1d[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "转置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "转置前的数组:\n",
      " [[1 2 3]\n",
      " [4 5 6]]\n",
      "转置后的数组:\n",
      " [[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "arr_transposed = arr.T\n",
    "print(\"转置前的数组:\\n\", arr)\n",
    "print(\"转置后的数组:\\n\", arr_transposed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 线性代数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "矩阵乘法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矩阵乘法结果:\n",
      " [[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "matrix_a = np.array([[1, 2], [3, 4]])\n",
    "matrix_b = np.array([[5, 6], [7, 8]])\n",
    "matrix_product = np.dot(matrix_a, matrix_b)\n",
    "print(\"矩阵乘法结果:\\n\", matrix_product)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列式与逆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矩阵行列式: -2.0000000000000004\n",
      "矩阵的逆:\n",
      " [[-2.   1. ]\n",
      " [ 1.5 -0.5]]\n"
     ]
    }
   ],
   "source": [
    "# 行列式\n",
    "det = np.linalg.det(matrix_a)\n",
    "print(\"矩阵行列式:\", det)\n",
    "\n",
    "# 逆矩阵\n",
    "inv_matrix_a = np.linalg.inv(matrix_a)\n",
    "print(\"矩阵的逆:\\n\", inv_matrix_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NumPy 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "linspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linspace生成的数组: [0.   0.25 0.5  0.75 1.  ]\n"
     ]
    }
   ],
   "source": [
    "# 在指定范围内生成等间隔的数字\n",
    "arr_linspace = np.linspace(0, 1, 5)\n",
    "print(\"linspace生成的数组:\", arr_linspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zores 和 ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全零数组: [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "全一数组: [[1. 1.]\n",
      " [1. 1.]\n",
      " [1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# 创建全零数组\n",
    "arr_zeros = np.zeros((2, 3))\n",
    "print(\"全零数组:\", arr_zeros)\n",
    "\n",
    "# 创建全一数组\n",
    "arr_ones = np.ones((3, 2))\n",
    "print(\"全一数组:\", arr_ones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://github.com/X-lab2017/dase-2023-autumn/assets/50283262/fe41a4e6-b509-4a7b-9643-4713338422b2)\n",
    "\n",
    "Pandas 是 Python 语言的一个扩展程序库，用于数据分析。Pandas 是一个开放源码、BSD 许可的库，提供高性能、易于使用的数据结构和数据分析工具。Pandas 名字衍生自术语 \"panel data\"（面板数据）和 \"Python data analysis\"（Python 数据分析）。Pandas 一个强大的分析结构化数据的工具集，基础是 Numpy（提供高性能的矩阵运算）。Pandas 可以从各种文件格式比如 CSV、JSON、SQL、Microsoft Excel 导入数据。Pandas 可以对各种数据进行运算操作，比如归并、再成形、选择，还有数据清洗和数据加工特征。Pandas 广泛应用在学术、金融、统计学等各个数据分析领域。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pip install pandas` 安装扩展库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 创建 Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过列表创建 Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 创建一个简单的 Series\n",
    "data = [1, 2, 3, 4, 5]\n",
    "series = pd.Series(data)\n",
    "print(series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "制定索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 指定索引的 Series\n",
    "data = [1, 2, 3, 4, 5]\n",
    "index = ['a', 'b', 'c', 'd', 'e']\n",
    "series = pd.Series(data, index=index)\n",
    "print(series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 创建 Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过字典创建 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age           City\n",
       "0    Alice   25       New York\n",
       "1      Bob   30  San Francisco\n",
       "2  Charlie   35    Los Angeles"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建一个简单的 DataFrame\n",
    "data = {'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "        'Age': [25, 30, 35],\n",
    "        'City': ['New York', 'San Francisco', 'Los Angeles']}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "指定行和列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>person_1</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_2</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_3</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Age           City\n",
       "person_1    Alice   25       New York\n",
       "person_2      Bob   30  San Francisco\n",
       "person_3  Charlie   35    Los Angeles"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定行和列索引的 DataFrame\n",
    "data = {'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "        'Age': [25, 30, 35],\n",
    "        'City': ['New York', 'San Francisco', 'Los Angeles']}\n",
    "index = ['person_1', 'person_2', 'person_3']\n",
    "df = pd.DataFrame(data, index=index)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数据检索和操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person_1      Alice\n",
      "person_2        Bob\n",
      "person_3    Charlie\n",
      "Name: Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 获取特定列\n",
    "name_column = df['Name']\n",
    "print(name_column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "筛选数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person_1     True\n",
       "person_2    False\n",
       "person_3    False\n",
       "Name: Age, dtype: bool"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'] < 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "年龄小于30的人:\n",
      "            Name  Age      City\n",
      "person_1  Alice   25  New York\n"
     ]
    }
   ],
   "source": [
    "# 根据条件筛选数据\n",
    "young_people = df[df['Age'] < 30]\n",
    "print(\"年龄小于30的人:\\n\", young_people)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 统计信息和摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据摘要:\n",
      "         Age\n",
      "count   3.0\n",
      "mean   30.0\n",
      "std     5.0\n",
      "min    25.0\n",
      "25%    27.5\n",
      "50%    30.0\n",
      "75%    32.5\n",
      "max    35.0\n"
     ]
    }
   ],
   "source": [
    "# 显示统计信息和摘要\n",
    "summary = df.describe()\n",
    "print(\"数据摘要:\\n\", summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pip install matplotlib` 安装扩展库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]\n",
    "gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]\n",
    "\n",
    "# 创建一个线图，x轴是年份，y轴是gdp\n",
    "plt.plot(years, gdp, color='green', marker='o', linestyle='solid')\n",
    "\n",
    "# 添加一个标题\n",
    "plt.title(\"Nominal GDP\")\n",
    "\n",
    "# 为y轴添加标记\n",
    "plt.ylabel(\"Billions of $\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 条形图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当需要展示某些离散项目集合中的数量是如何变化时，条形图是一个不错的选择。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies = [\"Annie Hall\", \"Ben-Hur\", \"Casablanca\", \"Gandhi\", \"West Side Story\"]\n",
    "num_oscars = [5, 11, 3, 8, 10]\n",
    "\n",
    "# 在左侧 x 坐标 [0, 1, 2, 3, 4] 上，绘制高度为 [num_oscars] 的条形图\n",
    "plt.bar(range(len(movies)), num_oscars)\n",
    "\n",
    "plt.title(\"My Favorite Movies\")     # 添加标题\n",
    "plt.ylabel(\"# of Academy Awards\")   # 添加y轴标签\n",
    "\n",
    "# 在 x 轴上标记电影的名字\n",
    "plt.xticks(range(len(movies)), movies)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "条形图也可以用来绘制大量数值直方图，以便直观地探索值的分布情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "grades = [83, 95, 91, 87, 70, 0, 85, 82, 100, 67, 73, 77, 0]\n",
    "\n",
    "# 将成绩按十位数分桶，但将 100 放在 90 位数中\n",
    "# Counter 类会将一个列表转换为字典，键是列表中的元素，值是该元素在列表中出现的次数。\n",
    "histogram = Counter(min(grade // 10 * 10, 90) for grade in grades)\n",
    "\n",
    "plt.bar([x + 5 for x in histogram.keys()],  # 将条形图向右移动 5 个单位\n",
    "        histogram.values(),                 # 给每个条形图正确的高度\n",
    "        10,                                 # 给每个条形图正确的宽度\n",
    "        edgecolor=(0, 0, 0))                # 为每个条形图添加黑色边框\n",
    "\n",
    "plt.axis([-5, 105, 0, 5])                   # x-axis from -5 to 105, \n",
    "                                            # y-axis from 0 to 5\n",
    "\n",
    "plt.xticks([10 * i for i in range(11)])     # x-axis labels at 0, 10, ..., 100\n",
    "plt.xlabel(\"Decile\")\n",
    "plt.ylabel(\"# of Students\")\n",
    "plt.title(\"Distribution of Exam 1 Grades\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "散点图是可视化两个成对数据集之间关系的不错的选择。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "friends = [70,  65,  72,  63,  71,  64,  60,  64,  67]\n",
    "minutes = [175, 170, 205, 120, 220, 130, 105, 145, 190]\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']\n",
    "\n",
    "plt.scatter(friends, minutes)\n",
    "\n",
    "# 给每个点加标签\n",
    "for label, friend_count, minute_count in zip(labels, friends, minutes):\n",
    "    plt.annotate(label,\n",
    "                 # 把标签放在对应的点上\n",
    "                 xy=(friend_count, minute_count),\n",
    "                 xytext=(5, -5),                  # 标识符略微偏离\n",
    "                 textcoords='offset points')\n",
    "\n",
    "plt.title(\"Daily Minutes vs. Number of Friends\")\n",
    "plt.xlabel(\"# of friends\")\n",
    "plt.ylabel(\"daily minutes spent on the site\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}