入门教程.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['spain', 'usa'])\n",
      "dict_values(['madrid', 'values'])\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "#键是唯一的，创建时如果一个键被赋值两次，后一个值会被记住\n",
    "dictionary = {'spain':'madrid','usa':'values'}\n",
    "print(dictionary.keys())\n",
    "print(dictionary.values())\n",
    "dictionary.clear()\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dictionary#删除字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i is: 1\n",
      "i is: 2\n",
      "i is: 3\n",
      "i is: 4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lis = [1,2,3,4]\n",
    "for i in lis:\n",
    "    print('i is:',i)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : 1\n",
      "1 : 2\n",
      "2 : 3\n",
      "3 : 4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for index,value in enumerate(lis):\n",
    "    print(index,\":\",value)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for index,value in data[['']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3\n"
     ]
    }
   ],
   "source": [
    "def tuble_ex():\n",
    "    t = (1,2,3)\n",
    "    return t\n",
    "a,b,c = tuble_ex()\n",
    "print(a,b,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "#作用域：L：局部作用域，函数定义的变量；E：嵌套父级函数的局部作用域；G：全局变量，模块级别定义的变量；B：系统固定模块里面的变量\n",
    "x = 2         # 全局的\n",
    "def f():\n",
    "    x = 3     # 局部的\n",
    "    return x\n",
    "print(x)      # 结果为2\n",
    "print(f())    # 结果为3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# 先对局部作用域进行搜索，然后对全局作用域进行搜索，如果其中两个作用域不能找到，最后会在作用域搜索内建。\n",
    "x = 5\n",
    "def f():\n",
    "    y = 2*x       \n",
    "    return y\n",
    "print(f())         # 使用的是global scope x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "#嵌套函数：函数中的函数\n",
    "def square():\n",
    "    def add():\n",
    "        x = 2\n",
    "        y = 3\n",
    "        z = x + y\n",
    "        return z\n",
    "    return add()**2\n",
    "print(square()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#默认参数、可变参数、关键字参数\n",
    "def f(a,b=1): #已经初始给定的参数\n",
    "def f(*args): #值可以是任意多个\n",
    "def f(** kwargs) #kwargs是个字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "# 默认参数， 必选参数在前，默认参数在后，否则python解释器会报错\n",
    "# 默认参数一定要指向不变对象 \n",
    "def f(a, b = 1, c = 2):\n",
    "    y = a + b + c\n",
    "    return y\n",
    "print(f(5))\n",
    "# 当我们想改变默认参数时，直接把值放进去\n",
    "print(f(5,4,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# 可变参数 *args\n",
    "def f(*args):\n",
    "    for i in args:\n",
    "        print(i)\n",
    "f(1)\n",
    "print(\"\")\n",
    "f(1,2,3,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "#Lambda函数也叫匿名函数，允许快速定义单行函数。通常是在需要一个函数，但是又不想费神去命名一个函数的场合下使用，也就是指匿名函数。\n",
    "# lambda函数\n",
    "# 单个参数的\n",
    "square = lambda x: x**2    \n",
    "print(square(4))\n",
    "\n",
    "# 多个参数的\n",
    "tot = lambda x,y,z: x+y+z   \n",
    "print(tot(1,2,3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 9]\n"
     ]
    }
   ],
   "source": [
    "number_list = [1,2,3]\n",
    "# map(func,seq):对列表中的所有项应用函数\n",
    "y = map(lambda x:x**2,number_list)\n",
    "print(list(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n\n",
      "c x h d c\n"
     ]
    }
   ],
   "source": [
    "#迭代器\n",
    "name = \"ncxhdc\"\n",
    "it = iter(name)\n",
    "print(next(it))    # 打印容器中的下一个值\n",
    "print(*it)         # 打印容器中的剩余值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<zip object at 0x7fe4c4b00500>\n",
      "[(1, 5), (2, 6), (3, 7), (4, 8)]\n"
     ]
    }
   ],
   "source": [
    "#zip()函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的对象，这样做的好处是节约了不少内存。\n",
    "list1 = [1,2,3,4]\n",
    "list2 = [5,6,7,8]\n",
    "z = zip(list1,list2)  # 返回一个对象\n",
    "print(z)\n",
    "z_list = list(z)      # list()转换为列表\n",
    "print(z_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4)\n",
      "(5, 6, 7, 8)\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "# 与 zip 相反，zip(*) 可理解为解压，返回二维矩阵\n",
    "un_zip = zip(*z_list)\n",
    "un_list1,un_list2 = list(un_zip)     # un_zip 返回一个 tuple\n",
    "print(un_list1)\n",
    "print(un_list2)\n",
    "print(type(un_list2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "#（列表推导式）列表解析式是一种非常简洁的创建列表的方式。\n",
    "num1 = [1,2,3]\n",
    "num2 = [i + 1 for i in num1 ]\n",
    "print(num2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 100, 20]\n"
     ]
    }
   ],
   "source": [
    "# 使用if语句进行过滤\n",
    "num1 = [5,10,15]\n",
    "num2 = [i**2 if i == 10 else i-5 if i < 7 else i+5 for i in num1]\n",
    "print(num2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     abilities  against_bug  against_dark  against_dragon  \\\n",
       "0  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "1  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "2  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "3     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "4     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "\n",
       "   against_electric  against_fairy  against_fight  against_fire  \\\n",
       "0               0.5            0.5            0.5           2.0   \n",
       "1               0.5            0.5            0.5           2.0   \n",
       "2               0.5            0.5            0.5           2.0   \n",
       "3               1.0            0.5            1.0           0.5   \n",
       "4               1.0            0.5            1.0           0.5   \n",
       "\n",
       "   against_flying  against_ghost  ...  percentage_male  pokedex_number  \\\n",
       "0             2.0            1.0  ...             88.1               1   \n",
       "1             2.0            1.0  ...             88.1               2   \n",
       "2             2.0            1.0  ...             88.1               3   \n",
       "3             1.0            1.0  ...             88.1               4   \n",
       "4             1.0            1.0  ...             88.1               5   \n",
       "\n",
       "   sp_attack  sp_defense  speed  type1   type2  weight_kg  generation  \\\n",
       "0         65          65     45  grass  poison        6.9           1   \n",
       "1         80          80     60  grass  poison       13.0           1   \n",
       "2        122         120     80  grass  poison      100.0           1   \n",
       "3         60          50     65   fire     NaN        8.5           1   \n",
       "4         80          65     80   fire     NaN       19.0           1   \n",
       "\n",
       "   is_legendary  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"pokemon.csv\")\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\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>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>['Beast Boost']</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>799</td>\n",
       "      <td>97</td>\n",
       "      <td>53</td>\n",
       "      <td>43</td>\n",
       "      <td>dark</td>\n",
       "      <td>dragon</td>\n",
       "      <td>888.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>['Prism Armor']</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800</td>\n",
       "      <td>127</td>\n",
       "      <td>89</td>\n",
       "      <td>79</td>\n",
       "      <td>psychic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>['Soul-Heart']</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>801</td>\n",
       "      <td>130</td>\n",
       "      <td>115</td>\n",
       "      <td>65</td>\n",
       "      <td>steel</td>\n",
       "      <td>fairy</td>\n",
       "      <td>80.5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           abilities  against_bug  against_dark  against_dragon  \\\n",
       "798  ['Beast Boost']         2.00           0.5             2.0   \n",
       "799  ['Prism Armor']         2.00           2.0             1.0   \n",
       "800   ['Soul-Heart']         0.25           0.5             0.0   \n",
       "\n",
       "     against_electric  against_fairy  against_fight  against_fire  \\\n",
       "798               0.5            4.0            2.0           0.5   \n",
       "799               1.0            1.0            0.5           1.0   \n",
       "800               1.0            0.5            1.0           2.0   \n",
       "\n",
       "     against_flying  against_ghost  ...  percentage_male  pokedex_number  \\\n",
       "798             1.0            0.5  ...              NaN             799   \n",
       "799             1.0            2.0  ...              NaN             800   \n",
       "800             0.5            1.0  ...              NaN             801   \n",
       "\n",
       "     sp_attack  sp_defense  speed    type1   type2  weight_kg  generation  \\\n",
       "798         97          53     43     dark  dragon      888.0           7   \n",
       "799        127          89     79  psychic     NaN      230.0           7   \n",
       "800        130         115     65    steel   fairy       80.5           7   \n",
       "\n",
       "     is_legendary  \n",
       "798             1  \n",
       "799             1  \n",
       "800             1  \n",
       "\n",
       "[3 rows x 41 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame结尾的3行记录\n",
    "data.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 801 entries, 0 to 800\n",
      "Data columns (total 41 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   abilities          801 non-null    object \n",
      " 1   against_bug        801 non-null    float64\n",
      " 2   against_dark       801 non-null    float64\n",
      " 3   against_dragon     801 non-null    float64\n",
      " 4   against_electric   801 non-null    float64\n",
      " 5   against_fairy      801 non-null    float64\n",
      " 6   against_fight      801 non-null    float64\n",
      " 7   against_fire       801 non-null    float64\n",
      " 8   against_flying     801 non-null    float64\n",
      " 9   against_ghost      801 non-null    float64\n",
      " 10  against_grass      801 non-null    float64\n",
      " 11  against_ground     801 non-null    float64\n",
      " 12  against_ice        801 non-null    float64\n",
      " 13  against_normal     801 non-null    float64\n",
      " 14  against_poison     801 non-null    float64\n",
      " 15  against_psychic    801 non-null    float64\n",
      " 16  against_rock       801 non-null    float64\n",
      " 17  against_steel      801 non-null    float64\n",
      " 18  against_water      801 non-null    float64\n",
      " 19  attack             801 non-null    int64  \n",
      " 20  base_egg_steps     801 non-null    int64  \n",
      " 21  base_happiness     801 non-null    int64  \n",
      " 22  base_total         801 non-null    int64  \n",
      " 23  capture_rate       801 non-null    object \n",
      " 24  classfication      801 non-null    object \n",
      " 25  defense            801 non-null    int64  \n",
      " 26  experience_growth  801 non-null    int64  \n",
      " 27  height_m           781 non-null    float64\n",
      " 28  hp                 801 non-null    int64  \n",
      " 29  japanese_name      801 non-null    object \n",
      " 30  name               801 non-null    object \n",
      " 31  percentage_male    703 non-null    float64\n",
      " 32  pokedex_number     801 non-null    int64  \n",
      " 33  sp_attack          801 non-null    int64  \n",
      " 34  sp_defense         801 non-null    int64  \n",
      " 35  speed              801 non-null    int64  \n",
      " 36  type1              801 non-null    object \n",
      " 37  type2              417 non-null    object \n",
      " 38  weight_kg          781 non-null    float64\n",
      " 39  generation         801 non-null    int64  \n",
      " 40  is_legendary       801 non-null    int64  \n",
      "dtypes: float64(21), int64(13), object(7)\n",
      "memory usage: 256.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 展示Index,Datatype,Memory相关信息\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(801, 41)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看行数，列数\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['abilities', 'against_bug', 'against_dark', 'against_dragon',\n",
       "       'against_electric', 'against_fairy', 'against_fight', 'against_fire',\n",
       "       'against_flying', 'against_ghost', 'against_grass', 'against_ground',\n",
       "       'against_ice', 'against_normal', 'against_poison', 'against_psychic',\n",
       "       'against_rock', 'against_steel', 'against_water', 'attack',\n",
       "       'base_egg_steps', 'base_happiness', 'base_total', 'capture_rate',\n",
       "       'classfication', 'defense', 'experience_growth', 'height_m', 'hp',\n",
       "       'japanese_name', 'name', 'percentage_male', 'pokedex_number',\n",
       "       'sp_attack', 'sp_defense', 'speed', 'type1', 'type2', 'weight_kg',\n",
       "       'generation', 'is_legendary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看列名\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "water       114\n",
      "normal      105\n",
      "grass        78\n",
      "bug          72\n",
      "psychic      53\n",
      "fire         52\n",
      "rock         45\n",
      "electric     39\n",
      "poison       32\n",
      "ground       32\n",
      "dark         29\n",
      "fighting     28\n",
      "dragon       27\n",
      "ghost        27\n",
      "steel        24\n",
      "ice          23\n",
      "fairy        18\n",
      "flying        3\n",
      "Name: type1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#value_counts(): pandas中的一个函数，可以对Series里面的每个值进行计数并且排序。\n",
    "#离群值（outliers）: 也称逸出值，是指在数据中有一个或几个数值与其他数值相比差异较大。\n",
    "print(data['type1'].value_counts(dropna =False))  # 此时为降序排列，升序排列使用Ture  查看不同妖怪出现的频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>against_grass</th>\n",
       "      <th>...</th>\n",
       "      <th>height_m</th>\n",
       "      <th>hp</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>703.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.996255</td>\n",
       "      <td>1.057116</td>\n",
       "      <td>0.968789</td>\n",
       "      <td>1.073970</td>\n",
       "      <td>1.068976</td>\n",
       "      <td>1.065543</td>\n",
       "      <td>1.135456</td>\n",
       "      <td>1.192884</td>\n",
       "      <td>0.985019</td>\n",
       "      <td>1.034020</td>\n",
       "      <td>...</td>\n",
       "      <td>1.163892</td>\n",
       "      <td>68.958801</td>\n",
       "      <td>55.155761</td>\n",
       "      <td>401.000000</td>\n",
       "      <td>71.305868</td>\n",
       "      <td>70.911361</td>\n",
       "      <td>66.334582</td>\n",
       "      <td>61.378105</td>\n",
       "      <td>3.690387</td>\n",
       "      <td>0.087391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.597248</td>\n",
       "      <td>0.438142</td>\n",
       "      <td>0.353058</td>\n",
       "      <td>0.654962</td>\n",
       "      <td>0.522167</td>\n",
       "      <td>0.717251</td>\n",
       "      <td>0.691853</td>\n",
       "      <td>0.604488</td>\n",
       "      <td>0.558256</td>\n",
       "      <td>0.788896</td>\n",
       "      <td>...</td>\n",
       "      <td>1.080326</td>\n",
       "      <td>26.576015</td>\n",
       "      <td>20.261623</td>\n",
       "      <td>231.373075</td>\n",
       "      <td>32.353826</td>\n",
       "      <td>27.942501</td>\n",
       "      <td>28.907662</td>\n",
       "      <td>109.354766</td>\n",
       "      <td>1.930420</td>\n",
       "      <td>0.282583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>401.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>601.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>999.900000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       against_bug  against_dark  against_dragon  against_electric  \\\n",
       "count   801.000000    801.000000      801.000000        801.000000   \n",
       "mean      0.996255      1.057116        0.968789          1.073970   \n",
       "std       0.597248      0.438142        0.353058          0.654962   \n",
       "min       0.250000      0.250000        0.000000          0.000000   \n",
       "25%       0.500000      1.000000        1.000000          0.500000   \n",
       "50%       1.000000      1.000000        1.000000          1.000000   \n",
       "75%       1.000000      1.000000        1.000000          1.000000   \n",
       "max       4.000000      4.000000        2.000000          4.000000   \n",
       "\n",
       "       against_fairy  against_fight  against_fire  against_flying  \\\n",
       "count     801.000000     801.000000    801.000000      801.000000   \n",
       "mean        1.068976       1.065543      1.135456        1.192884   \n",
       "std         0.522167       0.717251      0.691853        0.604488   \n",
       "min         0.250000       0.000000      0.250000        0.250000   \n",
       "25%         1.000000       0.500000      0.500000        1.000000   \n",
       "50%         1.000000       1.000000      1.000000        1.000000   \n",
       "75%         1.000000       1.000000      2.000000        1.000000   \n",
       "max         4.000000       4.000000      4.000000        4.000000   \n",
       "\n",
       "       against_ghost  against_grass  ...    height_m          hp  \\\n",
       "count     801.000000     801.000000  ...  781.000000  801.000000   \n",
       "mean        0.985019       1.034020  ...    1.163892   68.958801   \n",
       "std         0.558256       0.788896  ...    1.080326   26.576015   \n",
       "min         0.000000       0.250000  ...    0.100000    1.000000   \n",
       "25%         1.000000       0.500000  ...    0.600000   50.000000   \n",
       "50%         1.000000       1.000000  ...    1.000000   65.000000   \n",
       "75%         1.000000       1.000000  ...    1.500000   80.000000   \n",
       "max         4.000000       4.000000  ...   14.500000  255.000000   \n",
       "\n",
       "       percentage_male  pokedex_number   sp_attack  sp_defense       speed  \\\n",
       "count       703.000000      801.000000  801.000000  801.000000  801.000000   \n",
       "mean         55.155761      401.000000   71.305868   70.911361   66.334582   \n",
       "std          20.261623      231.373075   32.353826   27.942501   28.907662   \n",
       "min           0.000000        1.000000   10.000000   20.000000    5.000000   \n",
       "25%          50.000000      201.000000   45.000000   50.000000   45.000000   \n",
       "50%          50.000000      401.000000   65.000000   66.000000   65.000000   \n",
       "75%          50.000000      601.000000   91.000000   90.000000   85.000000   \n",
       "max         100.000000      801.000000  194.000000  230.000000  180.000000   \n",
       "\n",
       "        weight_kg  generation  is_legendary  \n",
       "count  781.000000  801.000000    801.000000  \n",
       "mean    61.378105    3.690387      0.087391  \n",
       "std    109.354766    1.930420      0.282583  \n",
       "min      0.100000    1.000000      0.000000  \n",
       "25%      9.000000    2.000000      0.000000  \n",
       "50%     27.300000    4.000000      0.000000  \n",
       "75%     64.800000    5.000000      0.000000  \n",
       "max    999.900000    7.000000      1.000000  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()   # 其中NaN值被忽略掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱线图\n",
    "data.boxplot(column=['attack','defense'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>['Shed Skin']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>['Compoundeyes', 'Tinted Lens']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>12</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>bug</td>\n",
       "      <td>flying</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>['Shield Dust', 'Run Away']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>bug</td>\n",
       "      <td>poison</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>['Shed Skin']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>bug</td>\n",
       "      <td>poison</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>['Swarm', 'Sniper']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>bug</td>\n",
       "      <td>poison</td>\n",
       "      <td>29.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          abilities  against_bug  against_dark  \\\n",
       "10                    ['Shed Skin']          1.0           1.0   \n",
       "11  ['Compoundeyes', 'Tinted Lens']          0.5           1.0   \n",
       "12      ['Shield Dust', 'Run Away']          0.5           1.0   \n",
       "13                    ['Shed Skin']          0.5           1.0   \n",
       "14              ['Swarm', 'Sniper']          0.5           1.0   \n",
       "\n",
       "    against_dragon  against_electric  against_fairy  against_fight  \\\n",
       "10             1.0               1.0            1.0           0.50   \n",
       "11             1.0               2.0            1.0           0.25   \n",
       "12             1.0               1.0            0.5           0.25   \n",
       "13             1.0               1.0            0.5           0.25   \n",
       "14             1.0               1.0            0.5           0.25   \n",
       "\n",
       "    against_fire  against_flying  against_ghost  ...  percentage_male  \\\n",
       "10           2.0             2.0            1.0  ...             50.0   \n",
       "11           2.0             2.0            1.0  ...             50.0   \n",
       "12           2.0             2.0            1.0  ...             50.0   \n",
       "13           2.0             2.0            1.0  ...             50.0   \n",
       "14           2.0             2.0            1.0  ...             50.0   \n",
       "\n",
       "    pokedex_number  sp_attack  sp_defense  speed  type1   type2  weight_kg  \\\n",
       "10              11         25          25     30    bug     NaN        9.9   \n",
       "11              12         90          80     70    bug  flying       32.0   \n",
       "12              13         20          20     50    bug  poison        3.2   \n",
       "13              14         25          25     35    bug  poison       10.0   \n",
       "14              15         15          80    145    bug  poison       29.5   \n",
       "\n",
       "    generation  is_legendary  \n",
       "10           1             0  \n",
       "11           1             0  \n",
       "12           1             0  \n",
       "13           1             0  \n",
       "14           1             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从pokemon数据中创建一组新的数据\n",
    "data_new = data[10:15]    # 取10-14行共5行数据来构建新数据表\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Metapod</td>\n",
       "      <td>attack</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Butterfree</td>\n",
       "      <td>attack</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Weedle</td>\n",
       "      <td>attack</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kakuna</td>\n",
       "      <td>attack</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beedrill</td>\n",
       "      <td>attack</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Metapod</td>\n",
       "      <td>defense</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Butterfree</td>\n",
       "      <td>defense</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Weedle</td>\n",
       "      <td>defense</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kakuna</td>\n",
       "      <td>defense</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Beedrill</td>\n",
       "      <td>defense</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name variable  value\n",
       "0     Metapod   attack     20\n",
       "1  Butterfree   attack     45\n",
       "2      Weedle   attack     35\n",
       "3      Kakuna   attack     25\n",
       "4    Beedrill   attack    150\n",
       "5     Metapod  defense     55\n",
       "6  Butterfree  defense     50\n",
       "7      Weedle  defense     30\n",
       "8      Kakuna  defense     50\n",
       "9    Beedrill  defense     40"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用一下melt()函数  \n",
    "melted = pd.melt(frame=data_new,id_vars = 'name', value_vars= ['attack','defense'])#frame：要处理的数据集  id_vars：不需要被转换的列名 value_vars ：需要转换的列名\n",
    "melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>variable</th>\n",
       "      <th>attack</th>\n",
       "      <th>defense</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beedrill</th>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Butterfree</th>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kakuna</th>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metapod</th>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weedle</th>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable    attack  defense\n",
       "name                       \n",
       "Beedrill       150       40\n",
       "Butterfree      45       50\n",
       "Kakuna          25       50\n",
       "Metapod         20       55\n",
       "Weedle          35       30"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melted.pivot(index = 'name', columns = 'variable',values='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Beast Boost']</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>799</td>\n",
       "      <td>97</td>\n",
       "      <td>53</td>\n",
       "      <td>43</td>\n",
       "      <td>dark</td>\n",
       "      <td>dragon</td>\n",
       "      <td>888.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Prism Armor']</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800</td>\n",
       "      <td>127</td>\n",
       "      <td>89</td>\n",
       "      <td>79</td>\n",
       "      <td>psychic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>['Soul-Heart']</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>801</td>\n",
       "      <td>130</td>\n",
       "      <td>115</td>\n",
       "      <td>65</td>\n",
       "      <td>steel</td>\n",
       "      <td>fairy</td>\n",
       "      <td>80.5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     abilities  against_bug  against_dark  against_dragon  \\\n",
       "0  ['Overgrow', 'Chlorophyll']         1.00           1.0             1.0   \n",
       "1  ['Overgrow', 'Chlorophyll']         1.00           1.0             1.0   \n",
       "2  ['Overgrow', 'Chlorophyll']         1.00           1.0             1.0   \n",
       "3              ['Beast Boost']         2.00           0.5             2.0   \n",
       "4              ['Prism Armor']         2.00           2.0             1.0   \n",
       "5               ['Soul-Heart']         0.25           0.5             0.0   \n",
       "\n",
       "   against_electric  against_fairy  against_fight  against_fire  \\\n",
       "0               0.5            0.5            0.5           2.0   \n",
       "1               0.5            0.5            0.5           2.0   \n",
       "2               0.5            0.5            0.5           2.0   \n",
       "3               0.5            4.0            2.0           0.5   \n",
       "4               1.0            1.0            0.5           1.0   \n",
       "5               1.0            0.5            1.0           2.0   \n",
       "\n",
       "   against_flying  against_ghost  ...  percentage_male  pokedex_number  \\\n",
       "0             2.0            1.0  ...             88.1               1   \n",
       "1             2.0            1.0  ...             88.1               2   \n",
       "2             2.0            1.0  ...             88.1               3   \n",
       "3             1.0            0.5  ...              NaN             799   \n",
       "4             1.0            2.0  ...              NaN             800   \n",
       "5             0.5            1.0  ...              NaN             801   \n",
       "\n",
       "   sp_attack  sp_defense  speed    type1   type2  weight_kg  generation  \\\n",
       "0         65          65     45    grass  poison        6.9           1   \n",
       "1         80          80     60    grass  poison       13.0           1   \n",
       "2        122         120     80    grass  poison      100.0           1   \n",
       "3         97          53     43     dark  dragon      888.0           7   \n",
       "4        127          89     79  psychic     NaN      230.0           7   \n",
       "5        130         115     65    steel   fairy       80.5           7   \n",
       "\n",
       "   is_legendary  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             1  \n",
       "4             1  \n",
       "5             1  \n",
       "\n",
       "[6 rows x 41 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成两个新的dataframe\n",
    "data1 = data.head(3)\n",
    "data2= data.tail(3)\n",
    "# 使用comcat()函数将两个表拼接在一起\n",
    "conc_data_row = pd.concat([data1,data2],axis =0,ignore_index =True) # axis = 0 : 行拼接，即纵向拼接\n",
    "conc_data_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attack</th>\n",
       "      <th>defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   attack  defense\n",
       "0      49       49\n",
       "1      62       63\n",
       "2     100      123"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data['attack'].head(3)\n",
    "data2= data['defense'].head(3)\n",
    "conc_data_col = pd.concat([data1,data2],axis =1) # axis = 1 : 列拼接，即横向拼接\n",
    "conc_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看数据类型\n",
    "data.dtypes\n",
    "data['type1'] = data['type1'].astype('category')#类型转换\n",
    "data['speed'] = data['speed'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 801 entries, 0 to 800\n",
      "Data columns (total 41 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   abilities          801 non-null    object  \n",
      " 1   against_bug        801 non-null    float64 \n",
      " 2   against_dark       801 non-null    float64 \n",
      " 3   against_dragon     801 non-null    float64 \n",
      " 4   against_electric   801 non-null    float64 \n",
      " 5   against_fairy      801 non-null    float64 \n",
      " 6   against_fight      801 non-null    float64 \n",
      " 7   against_fire       801 non-null    float64 \n",
      " 8   against_flying     801 non-null    float64 \n",
      " 9   against_ghost      801 non-null    float64 \n",
      " 10  against_grass      801 non-null    float64 \n",
      " 11  against_ground     801 non-null    float64 \n",
      " 12  against_ice        801 non-null    float64 \n",
      " 13  against_normal     801 non-null    float64 \n",
      " 14  against_poison     801 non-null    float64 \n",
      " 15  against_psychic    801 non-null    float64 \n",
      " 16  against_rock       801 non-null    float64 \n",
      " 17  against_steel      801 non-null    float64 \n",
      " 18  against_water      801 non-null    float64 \n",
      " 19  attack             801 non-null    int64   \n",
      " 20  base_egg_steps     801 non-null    int64   \n",
      " 21  base_happiness     801 non-null    int64   \n",
      " 22  base_total         801 non-null    int64   \n",
      " 23  capture_rate       801 non-null    object  \n",
      " 24  classfication      801 non-null    object  \n",
      " 25  defense            801 non-null    int64   \n",
      " 26  experience_growth  801 non-null    int64   \n",
      " 27  height_m           781 non-null    float64 \n",
      " 28  hp                 801 non-null    int64   \n",
      " 29  japanese_name      801 non-null    object  \n",
      " 30  name               801 non-null    object  \n",
      " 31  percentage_male    703 non-null    float64 \n",
      " 32  pokedex_number     801 non-null    int64   \n",
      " 33  sp_attack          801 non-null    int64   \n",
      " 34  sp_defense         801 non-null    int64   \n",
      " 35  speed              801 non-null    float64 \n",
      " 36  type1              801 non-null    category\n",
      " 37  type2              417 non-null    object  \n",
      " 38  weight_kg          781 non-null    float64 \n",
      " 39  generation         801 non-null    int64   \n",
      " 40  is_legendary       801 non-null    int64   \n",
      "dtypes: category(1), float64(22), int64(12), object(6)\n",
      "memory usage: 252.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NaN         384\n",
       "flying       95\n",
       "ground       34\n",
       "poison       34\n",
       "psychic      29\n",
       "fairy        29\n",
       "fighting     25\n",
       "steel        22\n",
       "dark         21\n",
       "grass        20\n",
       "water        17\n",
       "dragon       17\n",
       "ice          15\n",
       "rock         14\n",
       "ghost        14\n",
       "fire         13\n",
       "electric      9\n",
       "bug           5\n",
       "normal        4\n",
       "Name: type2, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info()\n",
    "data[\"type2\"].value_counts(dropna =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>6</td>\n",
       "      <td>159</td>\n",
       "      <td>115</td>\n",
       "      <td>100.0</td>\n",
       "      <td>fire</td>\n",
       "      <td>flying</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>['Compoundeyes', 'Tinted Lens']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>12</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70.0</td>\n",
       "      <td>bug</td>\n",
       "      <td>flying</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>['Shield Dust', 'Run Away']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>50.0</td>\n",
       "      <td>bug</td>\n",
       "      <td>poison</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          abilities  against_bug  against_dark  \\\n",
       "0       ['Overgrow', 'Chlorophyll']         1.00           1.0   \n",
       "1       ['Overgrow', 'Chlorophyll']         1.00           1.0   \n",
       "2       ['Overgrow', 'Chlorophyll']         1.00           1.0   \n",
       "5          ['Blaze', 'Solar Power']         0.25           1.0   \n",
       "11  ['Compoundeyes', 'Tinted Lens']         0.50           1.0   \n",
       "12      ['Shield Dust', 'Run Away']         0.50           1.0   \n",
       "\n",
       "    against_dragon  against_electric  against_fairy  against_fight  \\\n",
       "0              1.0               0.5            0.5           0.50   \n",
       "1              1.0               0.5            0.5           0.50   \n",
       "2              1.0               0.5            0.5           0.50   \n",
       "5              1.0               2.0            0.5           0.50   \n",
       "11             1.0               2.0            1.0           0.25   \n",
       "12             1.0               1.0            0.5           0.25   \n",
       "\n",
       "    against_fire  against_flying  against_ghost  ...  percentage_male  \\\n",
       "0            2.0             2.0            1.0  ...             88.1   \n",
       "1            2.0             2.0            1.0  ...             88.1   \n",
       "2            2.0             2.0            1.0  ...             88.1   \n",
       "5            0.5             1.0            1.0  ...             88.1   \n",
       "11           2.0             2.0            1.0  ...             50.0   \n",
       "12           2.0             2.0            1.0  ...             50.0   \n",
       "\n",
       "    pokedex_number  sp_attack  sp_defense  speed  type1   type2  weight_kg  \\\n",
       "0                1         65          65   45.0  grass  poison        6.9   \n",
       "1                2         80          80   60.0  grass  poison       13.0   \n",
       "2                3        122         120   80.0  grass  poison      100.0   \n",
       "5                6        159         115  100.0   fire  flying       90.5   \n",
       "11              12         90          80   70.0    bug  flying       32.0   \n",
       "12              13         20          20   50.0    bug  poison        3.2   \n",
       "\n",
       "    generation  is_legendary  \n",
       "0            1             0  \n",
       "1            1             0  \n",
       "2            1             0  \n",
       "5            1             0  \n",
       "11           1             0  \n",
       "12           1             0  \n",
       "\n",
       "[6 rows x 41 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用dropna()删除NaN值\n",
    "data1=data   # 用数据来填充缺失的值，所以先将它分配给data1变量\n",
    "data1[\"type2\"].dropna(inplace = True)\n",
    "# inplace = True表示我们不将其赋值给新变量,自动分配填充缺失值\n",
    "data1.dropna().head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-68-d85b0105f3cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 使用notnull()函数判断数据是否缺失，返回boll型数据\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32massert\u001b[0m  \u001b[0mdata1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 已经删除了nan值，不返回\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 使用notnull()函数判断数据是否缺失，返回boll型数据\n",
    "assert  data1['type2'].notnull().all() # 已经删除了nan值，不返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"type2\"].fillna('empty',inplace = True)\n",
    "assert  data['type2'].notnull().all() # 无NaN值，没有返回值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "      <th>country</th>\n",
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       "      <th>0</th>\n",
       "      <td>spain</td>\n",
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       "      <td>france</td>\n",
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       "</div>"
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      "text/plain": [
       "  country population\n",
       "0   spain         11\n",
       "1  france         12"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataframe\n",
    "# 从字典中构建dataframe\n",
    "country = [\"spain\",\"france\"]\n",
    "population = [\"11\",\"12\"]\n",
    "list_label = [\"country\",\"population\"]\n",
    "list_col = [country,population]\n",
    "#zip函数\n",
    "zipped = list(zip(list_label,list_col))\n",
    "data_dict = dict(zipped)\n",
    "df = pd.DataFrame(data_dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>country</th>\n",
       "      <th>population</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>12</td>\n",
       "      <td>paris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country population capital\n",
       "0   Spain         11  madrid\n",
       "1  France         12   paris"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加新列\n",
    "df[\"capital\"] = [\"madrid\",\"paris\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>madrid</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>12</td>\n",
       "      <td>paris</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country population capital  income\n",
       "0   Spain         11  madrid       1\n",
       "1  France         12   paris       1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 广播（Broadcasting）\n",
    "df[\"income\"] = 1 # broadcasting整列\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化\n",
    "# 绘制所有数据 口底妖怪数据集\n",
    "data1 = data.loc[:,[\"attack\",\"defense\",\"speed\"]]\n",
    "data1.plot()#三个图放在一起的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分别绘制子图\n",
    "data1.plot(subplots = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "data1.plot(kind = \"scatter\",x=\"attack\",y = \"defense\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图  \n",
    "data1.plot(kind = \"hist\",y = \"defense\",bins = 50,range= (0,250),density = False,stacked=True)#属性更新了，原来的属性没有了\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将直方图的频数转换成频率\n",
    "data1.plot(kind = \"hist\",y = \"defense\",bins = 50,range= (0,250),density = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计直方图\n",
    "fig, axes = plt.subplots(nrows=2,ncols=1)\n",
    "data1.plot(kind = \"hist\",y = \"defense\",bins = 50,range= (0,250),density = True,ax = axes[0])\n",
    "# 累计直方图\n",
    "data1.plot(kind = \"hist\",y = \"defense\",bins = 50,range= (0,250),density = True,ax = axes[1],cumulative = True)\n",
    "plt.savefig('graph.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n"
     ]
    }
   ],
   "source": [
    "#时间序列模式 datetime = object    parse_dates(boolean): 将日期转换为 ISO8601 (yyyy-mm-dd hh:mm:ss ) 格式\n",
    "time_list = [\"2004-03-08\",\"2022-04-12\"]\n",
    "print(type(time_list[1])) # 此时日期是str类型\n",
    "# 我们希望能将它变为datatime类型\n",
    "datetime_object = pd.to_datetime(time_list)\n",
    "print(type(datetime_object))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-10</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-10</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-10</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-15</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65.0</td>\n",
       "      <td>fire</td>\n",
       "      <td>empty</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-16</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>fire</td>\n",
       "      <td>empty</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              abilities  against_bug  against_dark  \\\n",
       "date                                                                 \n",
       "1992-01-10  ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "1992-02-10  ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "1992-03-10  ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "1993-03-15     ['Blaze', 'Solar Power']          0.5           1.0   \n",
       "1993-03-16     ['Blaze', 'Solar Power']          0.5           1.0   \n",
       "\n",
       "            against_dragon  against_electric  against_fairy  against_fight  \\\n",
       "date                                                                         \n",
       "1992-01-10             1.0               0.5            0.5            0.5   \n",
       "1992-02-10             1.0               0.5            0.5            0.5   \n",
       "1992-03-10             1.0               0.5            0.5            0.5   \n",
       "1993-03-15             1.0               1.0            0.5            1.0   \n",
       "1993-03-16             1.0               1.0            0.5            1.0   \n",
       "\n",
       "            against_fire  against_flying  against_ghost  ...  percentage_male  \\\n",
       "date                                                     ...                    \n",
       "1992-01-10           2.0             2.0            1.0  ...             88.1   \n",
       "1992-02-10           2.0             2.0            1.0  ...             88.1   \n",
       "1992-03-10           2.0             2.0            1.0  ...             88.1   \n",
       "1993-03-15           0.5             1.0            1.0  ...             88.1   \n",
       "1993-03-16           0.5             1.0            1.0  ...             88.1   \n",
       "\n",
       "            pokedex_number  sp_attack  sp_defense  speed  type1   type2  \\\n",
       "date                                                                      \n",
       "1992-01-10               1         65          65   45.0  grass  poison   \n",
       "1992-02-10               2         80          80   60.0  grass  poison   \n",
       "1992-03-10               3        122         120   80.0  grass  poison   \n",
       "1993-03-15               4         60          50   65.0   fire   empty   \n",
       "1993-03-16               5         80          65   80.0   fire   empty   \n",
       "\n",
       "            weight_kg  generation  is_legendary  \n",
       "date                                             \n",
       "1992-01-10        6.9           1             0  \n",
       "1992-02-10       13.0           1             0  \n",
       "1992-03-10      100.0           1             0  \n",
       "1993-03-15        8.5           1             0  \n",
       "1993-03-16       19.0           1             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调用filterwarnings()关闭警告消息,警告忽略\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# 取pokemon数据的前5行进行练习\n",
    "data2 = data.head()\n",
    "date_list = [\"1992-01-10\",\"1992-02-10\",\"1992-03-10\",\"1993-03-15\",\"1993-03-16\"]\n",
    "# 转换为datetime类型\n",
    "datetime_object = pd.to_datetime(date_list)\n",
    "data2[\"date\"] = datetime_object\n",
    "# 设置日期作为索引\n",
    "data2= data2.set_index(\"date\")\n",
    "data2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abilities            ['Blaze', 'Solar Power']\n",
      "against_bug                               0.5\n",
      "against_dark                                1\n",
      "against_dragon                              1\n",
      "against_electric                            1\n",
      "against_fairy                             0.5\n",
      "against_fight                               1\n",
      "against_fire                              0.5\n",
      "against_flying                              1\n",
      "against_ghost                               1\n",
      "against_grass                             0.5\n",
      "against_ground                              2\n",
      "against_ice                               0.5\n",
      "against_normal                              1\n",
      "against_poison                              1\n",
      "against_psychic                             1\n",
      "against_rock                                2\n",
      "against_steel                             0.5\n",
      "against_water                               2\n",
      "attack                                     64\n",
      "base_egg_steps                           5120\n",
      "base_happiness                             70\n",
      "base_total                                405\n",
      "capture_rate                               45\n",
      "classfication                   Flame Pokémon\n",
      "defense                                    58\n",
      "experience_growth                     1059860\n",
      "height_m                                  1.1\n",
      "hp                                         58\n",
      "japanese_name                     Lizardoリザード\n",
      "name                               Charmeleon\n",
      "percentage_male                          88.1\n",
      "pokedex_number                              5\n",
      "sp_attack                                  80\n",
      "sp_defense                                 65\n",
      "speed                                      80\n",
      "type1                                    fire\n",
      "type2                                   empty\n",
      "weight_kg                                  19\n",
      "generation                                  1\n",
      "is_legendary                                0\n",
      "Name: 1993-03-16 00:00:00, dtype: object\n",
      "                              abilities  against_bug  against_dark  \\\n",
      "date                                                                 \n",
      "1992-03-10  ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
      "1993-03-15     ['Blaze', 'Solar Power']          0.5           1.0   \n",
      "1993-03-16     ['Blaze', 'Solar Power']          0.5           1.0   \n",
      "\n",
      "            against_dragon  against_electric  against_fairy  against_fight  \\\n",
      "date                                                                         \n",
      "1992-03-10             1.0               0.5            0.5            0.5   \n",
      "1993-03-15             1.0               1.0            0.5            1.0   \n",
      "1993-03-16             1.0               1.0            0.5            1.0   \n",
      "\n",
      "            against_fire  against_flying  against_ghost  ...  percentage_male  \\\n",
      "date                                                     ...                    \n",
      "1992-03-10           2.0             2.0            1.0  ...             88.1   \n",
      "1993-03-15           0.5             1.0            1.0  ...             88.1   \n",
      "1993-03-16           0.5             1.0            1.0  ...             88.1   \n",
      "\n",
      "            pokedex_number  sp_attack  sp_defense  speed  type1   type2  \\\n",
      "date                                                                      \n",
      "1992-03-10               3        122         120   80.0  grass  poison   \n",
      "1993-03-15               4         60          50   65.0   fire   empty   \n",
      "1993-03-16               5         80          65   80.0   fire   empty   \n",
      "\n",
      "            weight_kg  generation  is_legendary  \n",
      "date                                             \n",
      "1992-03-10      100.0           1             0  \n",
      "1993-03-15        8.5           1             0  \n",
      "1993-03-16       19.0           1             0  \n",
      "\n",
      "[3 rows x 41 columns]\n"
     ]
    }
   ],
   "source": [
    "# 现在可以根据日期索引筛选数据\n",
    "print(data2.loc[\"1993-03-16\"])\n",
    "print(data2.loc[\"1992-03-10\":\"1993-03-16\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>against_grass</th>\n",
       "      <th>...</th>\n",
       "      <th>height_m</th>\n",
       "      <th>hp</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.70</td>\n",
       "      <td>45.0</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-07-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-08-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-31</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.85</td>\n",
       "      <td>48.5</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>72.5</td>\n",
       "      <td>13.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            against_bug  against_dark  against_dragon  against_electric  \\\n",
       "date                                                                      \n",
       "1992-01-31          1.0           1.0             1.0               0.5   \n",
       "1992-02-29          1.0           1.0             1.0               0.5   \n",
       "1992-03-31          1.0           1.0             1.0               0.5   \n",
       "1992-04-30          NaN           NaN             NaN               NaN   \n",
       "1992-05-31          NaN           NaN             NaN               NaN   \n",
       "1992-06-30          NaN           NaN             NaN               NaN   \n",
       "1992-07-31          NaN           NaN             NaN               NaN   \n",
       "1992-08-31          NaN           NaN             NaN               NaN   \n",
       "1992-09-30          NaN           NaN             NaN               NaN   \n",
       "1992-10-31          NaN           NaN             NaN               NaN   \n",
       "1992-11-30          NaN           NaN             NaN               NaN   \n",
       "1992-12-31          NaN           NaN             NaN               NaN   \n",
       "1993-01-31          NaN           NaN             NaN               NaN   \n",
       "1993-02-28          NaN           NaN             NaN               NaN   \n",
       "1993-03-31          0.5           1.0             1.0               1.0   \n",
       "\n",
       "            against_fairy  against_fight  against_fire  against_flying  \\\n",
       "date                                                                     \n",
       "1992-01-31            0.5            0.5           2.0             2.0   \n",
       "1992-02-29            0.5            0.5           2.0             2.0   \n",
       "1992-03-31            0.5            0.5           2.0             2.0   \n",
       "1992-04-30            NaN            NaN           NaN             NaN   \n",
       "1992-05-31            NaN            NaN           NaN             NaN   \n",
       "1992-06-30            NaN            NaN           NaN             NaN   \n",
       "1992-07-31            NaN            NaN           NaN             NaN   \n",
       "1992-08-31            NaN            NaN           NaN             NaN   \n",
       "1992-09-30            NaN            NaN           NaN             NaN   \n",
       "1992-10-31            NaN            NaN           NaN             NaN   \n",
       "1992-11-30            NaN            NaN           NaN             NaN   \n",
       "1992-12-31            NaN            NaN           NaN             NaN   \n",
       "1993-01-31            NaN            NaN           NaN             NaN   \n",
       "1993-02-28            NaN            NaN           NaN             NaN   \n",
       "1993-03-31            0.5            1.0           0.5             1.0   \n",
       "\n",
       "            against_ghost  against_grass  ...  height_m    hp  \\\n",
       "date                                      ...                   \n",
       "1992-01-31            1.0           0.25  ...      0.70  45.0   \n",
       "1992-02-29            1.0           0.25  ...      1.00  60.0   \n",
       "1992-03-31            1.0           0.25  ...      2.00  80.0   \n",
       "1992-04-30            NaN            NaN  ...       NaN   NaN   \n",
       "1992-05-31            NaN            NaN  ...       NaN   NaN   \n",
       "1992-06-30            NaN            NaN  ...       NaN   NaN   \n",
       "1992-07-31            NaN            NaN  ...       NaN   NaN   \n",
       "1992-08-31            NaN            NaN  ...       NaN   NaN   \n",
       "1992-09-30            NaN            NaN  ...       NaN   NaN   \n",
       "1992-10-31            NaN            NaN  ...       NaN   NaN   \n",
       "1992-11-30            NaN            NaN  ...       NaN   NaN   \n",
       "1992-12-31            NaN            NaN  ...       NaN   NaN   \n",
       "1993-01-31            NaN            NaN  ...       NaN   NaN   \n",
       "1993-02-28            NaN            NaN  ...       NaN   NaN   \n",
       "1993-03-31            1.0           0.50  ...      0.85  48.5   \n",
       "\n",
       "            percentage_male  pokedex_number  sp_attack  sp_defense  speed  \\\n",
       "date                                                                        \n",
       "1992-01-31             88.1             1.0       65.0        65.0   45.0   \n",
       "1992-02-29             88.1             2.0       80.0        80.0   60.0   \n",
       "1992-03-31             88.1             3.0      122.0       120.0   80.0   \n",
       "1992-04-30              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-05-31              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-06-30              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-07-31              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-08-31              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-09-30              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-10-31              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-11-30              NaN             NaN        NaN         NaN    NaN   \n",
       "1992-12-31              NaN             NaN        NaN         NaN    NaN   \n",
       "1993-01-31              NaN             NaN        NaN         NaN    NaN   \n",
       "1993-02-28              NaN             NaN        NaN         NaN    NaN   \n",
       "1993-03-31             88.1             4.5       70.0        57.5   72.5   \n",
       "\n",
       "            weight_kg  generation  is_legendary  \n",
       "date                                             \n",
       "1992-01-31       6.90         1.0           0.0  \n",
       "1992-02-29      13.00         1.0           0.0  \n",
       "1992-03-31     100.00         1.0           0.0  \n",
       "1992-04-30        NaN         NaN           NaN  \n",
       "1992-05-31        NaN         NaN           NaN  \n",
       "1992-06-30        NaN         NaN           NaN  \n",
       "1992-07-31        NaN         NaN           NaN  \n",
       "1992-08-31        NaN         NaN           NaN  \n",
       "1992-09-30        NaN         NaN           NaN  \n",
       "1992-10-31        NaN         NaN           NaN  \n",
       "1992-11-30        NaN         NaN           NaN  \n",
       "1992-12-31        NaN         NaN           NaN  \n",
       "1993-01-31        NaN         NaN           NaN  \n",
       "1993-02-28        NaN         NaN           NaN  \n",
       "1993-03-31      13.75         1.0           0.0  \n",
       "\n",
       "[15 rows x 34 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#重采样\n",
    "# 按年进行重采样 data2.resample(\"Y\").mean()\n",
    "# 按月进行重采样 \n",
    "data2.resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从初值开始插值\n",
    "#data2.resample(\"M\").first().interpolate(\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>against_grass</th>\n",
       "      <th>...</th>\n",
       "      <th>height_m</th>\n",
       "      <th>hp</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>45.000</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>45.000</td>\n",
       "      <td>6.9000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2.000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-30</th>\n",
       "      <td>0.958333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>1.875</td>\n",
       "      <td>1.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>...</td>\n",
       "      <td>1.904167</td>\n",
       "      <td>77.375</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.125</td>\n",
       "      <td>117.666667</td>\n",
       "      <td>114.791667</td>\n",
       "      <td>79.375</td>\n",
       "      <td>92.8125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.808333</td>\n",
       "      <td>74.750</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.250</td>\n",
       "      <td>113.333333</td>\n",
       "      <td>109.583333</td>\n",
       "      <td>78.750</td>\n",
       "      <td>85.6250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-06-30</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.712500</td>\n",
       "      <td>72.125</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.375</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>104.375000</td>\n",
       "      <td>78.125</td>\n",
       "      <td>78.4375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-07-31</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.616667</td>\n",
       "      <td>69.500</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.500</td>\n",
       "      <td>104.666667</td>\n",
       "      <td>99.166667</td>\n",
       "      <td>77.500</td>\n",
       "      <td>71.2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-08-31</th>\n",
       "      <td>0.791667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>...</td>\n",
       "      <td>1.520833</td>\n",
       "      <td>66.875</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.625</td>\n",
       "      <td>100.333333</td>\n",
       "      <td>93.958333</td>\n",
       "      <td>76.875</td>\n",
       "      <td>64.0625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-09-30</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.425000</td>\n",
       "      <td>64.250</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.750</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>88.750000</td>\n",
       "      <td>76.250</td>\n",
       "      <td>56.8750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-10-31</th>\n",
       "      <td>0.708333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>...</td>\n",
       "      <td>1.329167</td>\n",
       "      <td>61.625</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.875</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>83.541667</td>\n",
       "      <td>75.625</td>\n",
       "      <td>49.6875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-11-30</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>59.000</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4.000</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>75.000</td>\n",
       "      <td>42.5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-12-31</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.875</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.137500</td>\n",
       "      <td>56.375</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4.125</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>73.125000</td>\n",
       "      <td>74.375</td>\n",
       "      <td>35.3125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.041667</td>\n",
       "      <td>53.750</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4.250</td>\n",
       "      <td>78.666667</td>\n",
       "      <td>67.916667</td>\n",
       "      <td>73.750</td>\n",
       "      <td>28.1250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-02-28</th>\n",
       "      <td>0.541667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.625</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.945833</td>\n",
       "      <td>51.125</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4.375</td>\n",
       "      <td>74.333333</td>\n",
       "      <td>62.708333</td>\n",
       "      <td>73.125</td>\n",
       "      <td>20.9375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-31</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>48.500</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4.500</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>72.500</td>\n",
       "      <td>13.7500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            against_bug  against_dark  against_dragon  against_electric  \\\n",
       "date                                                                      \n",
       "1992-01-31     1.000000           1.0             1.0          0.500000   \n",
       "1992-02-29     1.000000           1.0             1.0          0.500000   \n",
       "1992-03-31     1.000000           1.0             1.0          0.500000   \n",
       "1992-04-30     0.958333           1.0             1.0          0.541667   \n",
       "1992-05-31     0.916667           1.0             1.0          0.583333   \n",
       "1992-06-30     0.875000           1.0             1.0          0.625000   \n",
       "1992-07-31     0.833333           1.0             1.0          0.666667   \n",
       "1992-08-31     0.791667           1.0             1.0          0.708333   \n",
       "1992-09-30     0.750000           1.0             1.0          0.750000   \n",
       "1992-10-31     0.708333           1.0             1.0          0.791667   \n",
       "1992-11-30     0.666667           1.0             1.0          0.833333   \n",
       "1992-12-31     0.625000           1.0             1.0          0.875000   \n",
       "1993-01-31     0.583333           1.0             1.0          0.916667   \n",
       "1993-02-28     0.541667           1.0             1.0          0.958333   \n",
       "1993-03-31     0.500000           1.0             1.0          1.000000   \n",
       "\n",
       "            against_fairy  against_fight  against_fire  against_flying  \\\n",
       "date                                                                     \n",
       "1992-01-31            0.5       0.500000         2.000        2.000000   \n",
       "1992-02-29            0.5       0.500000         2.000        2.000000   \n",
       "1992-03-31            0.5       0.500000         2.000        2.000000   \n",
       "1992-04-30            0.5       0.541667         1.875        1.916667   \n",
       "1992-05-31            0.5       0.583333         1.750        1.833333   \n",
       "1992-06-30            0.5       0.625000         1.625        1.750000   \n",
       "1992-07-31            0.5       0.666667         1.500        1.666667   \n",
       "1992-08-31            0.5       0.708333         1.375        1.583333   \n",
       "1992-09-30            0.5       0.750000         1.250        1.500000   \n",
       "1992-10-31            0.5       0.791667         1.125        1.416667   \n",
       "1992-11-30            0.5       0.833333         1.000        1.333333   \n",
       "1992-12-31            0.5       0.875000         0.875        1.250000   \n",
       "1993-01-31            0.5       0.916667         0.750        1.166667   \n",
       "1993-02-28            0.5       0.958333         0.625        1.083333   \n",
       "1993-03-31            0.5       1.000000         0.500        1.000000   \n",
       "\n",
       "            against_ghost  against_grass  ...  height_m      hp  \\\n",
       "date                                      ...                     \n",
       "1992-01-31            1.0       0.250000  ...  0.700000  45.000   \n",
       "1992-02-29            1.0       0.250000  ...  1.000000  60.000   \n",
       "1992-03-31            1.0       0.250000  ...  2.000000  80.000   \n",
       "1992-04-30            1.0       0.270833  ...  1.904167  77.375   \n",
       "1992-05-31            1.0       0.291667  ...  1.808333  74.750   \n",
       "1992-06-30            1.0       0.312500  ...  1.712500  72.125   \n",
       "1992-07-31            1.0       0.333333  ...  1.616667  69.500   \n",
       "1992-08-31            1.0       0.354167  ...  1.520833  66.875   \n",
       "1992-09-30            1.0       0.375000  ...  1.425000  64.250   \n",
       "1992-10-31            1.0       0.395833  ...  1.329167  61.625   \n",
       "1992-11-30            1.0       0.416667  ...  1.233333  59.000   \n",
       "1992-12-31            1.0       0.437500  ...  1.137500  56.375   \n",
       "1993-01-31            1.0       0.458333  ...  1.041667  53.750   \n",
       "1993-02-28            1.0       0.479167  ...  0.945833  51.125   \n",
       "1993-03-31            1.0       0.500000  ...  0.850000  48.500   \n",
       "\n",
       "            percentage_male  pokedex_number   sp_attack  sp_defense   speed  \\\n",
       "date                                                                          \n",
       "1992-01-31             88.1           1.000   65.000000   65.000000  45.000   \n",
       "1992-02-29             88.1           2.000   80.000000   80.000000  60.000   \n",
       "1992-03-31             88.1           3.000  122.000000  120.000000  80.000   \n",
       "1992-04-30             88.1           3.125  117.666667  114.791667  79.375   \n",
       "1992-05-31             88.1           3.250  113.333333  109.583333  78.750   \n",
       "1992-06-30             88.1           3.375  109.000000  104.375000  78.125   \n",
       "1992-07-31             88.1           3.500  104.666667   99.166667  77.500   \n",
       "1992-08-31             88.1           3.625  100.333333   93.958333  76.875   \n",
       "1992-09-30             88.1           3.750   96.000000   88.750000  76.250   \n",
       "1992-10-31             88.1           3.875   91.666667   83.541667  75.625   \n",
       "1992-11-30             88.1           4.000   87.333333   78.333333  75.000   \n",
       "1992-12-31             88.1           4.125   83.000000   73.125000  74.375   \n",
       "1993-01-31             88.1           4.250   78.666667   67.916667  73.750   \n",
       "1993-02-28             88.1           4.375   74.333333   62.708333  73.125   \n",
       "1993-03-31             88.1           4.500   70.000000   57.500000  72.500   \n",
       "\n",
       "            weight_kg  generation  is_legendary  \n",
       "date                                             \n",
       "1992-01-31     6.9000         1.0           0.0  \n",
       "1992-02-29    13.0000         1.0           0.0  \n",
       "1992-03-31   100.0000         1.0           0.0  \n",
       "1992-04-30    92.8125         1.0           0.0  \n",
       "1992-05-31    85.6250         1.0           0.0  \n",
       "1992-06-30    78.4375         1.0           0.0  \n",
       "1992-07-31    71.2500         1.0           0.0  \n",
       "1992-08-31    64.0625         1.0           0.0  \n",
       "1992-09-30    56.8750         1.0           0.0  \n",
       "1992-10-31    49.6875         1.0           0.0  \n",
       "1992-11-30    42.5000         1.0           0.0  \n",
       "1992-12-31    35.3125         1.0           0.0  \n",
       "1993-01-31    28.1250         1.0           0.0  \n",
       "1993-02-28    20.9375         1.0           0.0  \n",
       "1993-03-31    13.7500         1.0           0.0  \n",
       "\n",
       "[15 rows x 34 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用mean()进行插值 \n",
    "data2.resample(\"M\").mean().interpolate(\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 801 entries, 0 to 800\n",
      "Data columns (total 41 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   abilities          801 non-null    object  \n",
      " 1   against_bug        801 non-null    float64 \n",
      " 2   against_dark       801 non-null    float64 \n",
      " 3   against_dragon     801 non-null    float64 \n",
      " 4   against_electric   801 non-null    float64 \n",
      " 5   against_fairy      801 non-null    float64 \n",
      " 6   against_fight      801 non-null    float64 \n",
      " 7   against_fire       801 non-null    float64 \n",
      " 8   against_flying     801 non-null    float64 \n",
      " 9   against_ghost      801 non-null    float64 \n",
      " 10  against_grass      801 non-null    float64 \n",
      " 11  against_ground     801 non-null    float64 \n",
      " 12  against_ice        801 non-null    float64 \n",
      " 13  against_normal     801 non-null    float64 \n",
      " 14  against_poison     801 non-null    float64 \n",
      " 15  against_psychic    801 non-null    float64 \n",
      " 16  against_rock       801 non-null    float64 \n",
      " 17  against_steel      801 non-null    float64 \n",
      " 18  against_water      801 non-null    float64 \n",
      " 19  attack             801 non-null    int64   \n",
      " 20  base_egg_steps     801 non-null    int64   \n",
      " 21  base_happiness     801 non-null    int64   \n",
      " 22  base_total         801 non-null    int64   \n",
      " 23  capture_rate       801 non-null    object  \n",
      " 24  classfication      801 non-null    object  \n",
      " 25  defense            801 non-null    int64   \n",
      " 26  experience_growth  801 non-null    int64   \n",
      " 27  height_m           781 non-null    float64 \n",
      " 28  hp                 801 non-null    int64   \n",
      " 29  japanese_name      801 non-null    object  \n",
      " 30  name               801 non-null    object  \n",
      " 31  percentage_male    703 non-null    float64 \n",
      " 32  pokedex_number     801 non-null    int64   \n",
      " 33  sp_attack          801 non-null    int64   \n",
      " 34  sp_defense         801 non-null    int64   \n",
      " 35  speed              801 non-null    float64 \n",
      " 36  type1              801 non-null    category\n",
      " 37  type2              801 non-null    object  \n",
      " 38  weight_kg          781 non-null    float64 \n",
      " 39  generation         801 non-null    int64   \n",
      " 40  is_legendary       801 non-null    int64   \n",
      "dtypes: category(1), float64(22), int64(12), object(6)\n",
      "memory usage: 252.0+ KB\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"None of ['Column'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-105-64b2f140a9a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Column\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   4549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4550\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4551\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of ['Column'] are in the columns\""
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80.0</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65.0</td>\n",
       "      <td>fire</td>\n",
       "      <td>empty</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>fire</td>\n",
       "      <td>empty</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     abilities  against_bug  against_dark  against_dragon  \\\n",
       "0  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "1  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "2  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "3     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "4     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "\n",
       "   against_electric  against_fairy  against_fight  against_fire  \\\n",
       "0               0.5            0.5            0.5           2.0   \n",
       "1               0.5            0.5            0.5           2.0   \n",
       "2               0.5            0.5            0.5           2.0   \n",
       "3               1.0            0.5            1.0           0.5   \n",
       "4               1.0            0.5            1.0           0.5   \n",
       "\n",
       "   against_flying  against_ghost  ...  percentage_male  pokedex_number  \\\n",
       "0             2.0            1.0  ...             88.1               1   \n",
       "1             2.0            1.0  ...             88.1               2   \n",
       "2             2.0            1.0  ...             88.1               3   \n",
       "3             1.0            1.0  ...             88.1               4   \n",
       "4             1.0            1.0  ...             88.1               5   \n",
       "\n",
       "   sp_attack  sp_defense  speed  type1   type2  weight_kg  generation  \\\n",
       "0         65          65   45.0  grass  poison        6.9           1   \n",
       "1         80          80   60.0  grass  poison       13.0           1   \n",
       "2        122         120   80.0  grass  poison      100.0           1   \n",
       "3         60          50   65.0   fire   empty        8.5           1   \n",
       "4         80          65   80.0   fire   empty       19.0           1   \n",
       "\n",
       "   is_legendary  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('pokemon.csv')\n",
    "#data = data.set_index('#')\n",
    "data.head(5)\n",
    "#data = DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False) \n",
    "# 方括号索引\n",
    "data[\"base_total\"][1]\n",
    "# 使用列属性和行标签\n",
    "data.hp[1]\n",
    "# 使用loc访问器\n",
    "#data.loc[1,[\"hp\"]]\n",
    "# 只选取某些列\n",
    "#data[[\"hp\",\"attack\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# 列选取的差异：series和data frames\n",
    "print(type(data[\"hp\"]))     # series\n",
    "print(type(data[[\"hp\"]]))   # data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>defense</th>\n",
       "      <th>experience_growth</th>\n",
       "      <th>height_m</th>\n",
       "      <th>hp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1059860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123</td>\n",
       "      <td>1059860</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43</td>\n",
       "      <td>1059860</td>\n",
       "      <td>0.6</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>1059860</td>\n",
       "      <td>1.1</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>78</td>\n",
       "      <td>1059860</td>\n",
       "      <td>1.7</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>65</td>\n",
       "      <td>1059860</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>1059860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>1059860</td>\n",
       "      <td>1.6</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>55</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    defense  experience_growth  height_m  hp\n",
       "1        63            1059860       1.0  60\n",
       "2       123            1059860       2.0  80\n",
       "3        43            1059860       0.6  39\n",
       "4        58            1059860       1.1  58\n",
       "5        78            1059860       1.7  78\n",
       "6        65            1059860       0.5  44\n",
       "7        80            1059860       1.0  59\n",
       "8       120            1059860       1.6  79\n",
       "9        35            1000000       0.3  45\n",
       "10       55            1000000       0.7  50"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用索引切片\n",
    "data.loc[1:10,\"defense\":\"hp\"]   # 包括第10行和\"Denfense\"列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 反向切片\n",
    "data.loc[10:1:-1,\"hp\":\"defense\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100</td>\n",
       "      <td>fire</td>\n",
       "      <td>flying</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43</td>\n",
       "      <td>water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58</td>\n",
       "      <td>water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>78</td>\n",
       "      <td>water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "      <td>bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30</td>\n",
       "      <td>bug</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    speed  type1   type2  weight_kg  generation  is_legendary\n",
       "1      60  grass  poison       13.0           1             0\n",
       "2      80  grass  poison      100.0           1             0\n",
       "3      65   fire     NaN        8.5           1             0\n",
       "4      80   fire     NaN       19.0           1             0\n",
       "5     100   fire  flying       90.5           1             0\n",
       "6      43  water     NaN        9.0           1             0\n",
       "7      58  water     NaN       22.5           1             0\n",
       "8      78  water     NaN       85.5           1             0\n",
       "9      45    bug     NaN        2.9           1             0\n",
       "10     30    bug     NaN        9.9           1             0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从某一列开始到最后一列\n",
    "data.loc[1:10,\"speed\":] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212     20\n",
       "327     45\n",
       "437     50\n",
       "445    135\n",
       "596     44\n",
       "770     55\n",
       "Name: hp, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据过滤\n",
    "# 创建一个布尔序列\n",
    "boolean = data.hp > 200\n",
    "data[boolean]\n",
    "# 多重过滤\n",
    "first_filter = data.hp > 150\n",
    "second_filter = data.speed > 35\n",
    "data[first_filter & second_filter]\n",
    "# 基于其他过滤列的过滤\n",
    "data.hp[data.speed<15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       22.5\n",
       "1       30.0\n",
       "2       40.0\n",
       "3       19.5\n",
       "4       29.0\n",
       "       ...  \n",
       "796     48.5\n",
       "797     29.5\n",
       "798    111.5\n",
       "799     48.5\n",
       "800     40.0\n",
       "Name: hp, Length: 801, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转换\n",
    "# 使用普通函数\n",
    "def div(n):\n",
    "    return n/2\n",
    "data.hp.apply(div)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       22.5\n",
       "1       30.0\n",
       "2       40.0\n",
       "3       19.5\n",
       "4       29.0\n",
       "       ...  \n",
       "796     48.5\n",
       "797     29.5\n",
       "798    111.5\n",
       "799     48.5\n",
       "800     40.0\n",
       "Name: hp, Length: 801, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用Lambda函数\n",
    "data.hp.apply(lambda n : n/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
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       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
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       "      <th>0</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     abilities  against_bug  against_dark  against_dragon  \\\n",
       "0  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "1  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "2  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "3     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "4     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "\n",
       "   against_electric  against_fairy  against_fight  against_fire  \\\n",
       "0               0.5            0.5            0.5           2.0   \n",
       "1               0.5            0.5            0.5           2.0   \n",
       "2               0.5            0.5            0.5           2.0   \n",
       "3               1.0            0.5            1.0           0.5   \n",
       "4               1.0            0.5            1.0           0.5   \n",
       "\n",
       "   against_flying  against_ghost  ...  pokedex_number  sp_attack  sp_defense  \\\n",
       "0             2.0            1.0  ...               1         65          65   \n",
       "1             2.0            1.0  ...               2         80          80   \n",
       "2             2.0            1.0  ...               3        122         120   \n",
       "3             1.0            1.0  ...               4         60          50   \n",
       "4             1.0            1.0  ...               5         80          65   \n",
       "\n",
       "   speed  type1   type2  weight_kg  generation  is_legendary  total_power  \n",
       "0     45  grass  poison        6.9           1             0           98  \n",
       "1     60  grass  poison       13.0           1             0          125  \n",
       "2     80  grass  poison      100.0           1             0          223  \n",
       "3     65   fire     NaN        8.5           1             0           95  \n",
       "4     80   fire     NaN       19.0           1             0          122  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用其他列创建新列\n",
    "data[\"total_power\"] = data.attack + data.defense\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
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       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
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       "      <td>60</td>\n",
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       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              abilities  against_bug  against_dark  \\\n",
       "index_name                                                           \n",
       "0           ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "1           ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "2           ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "3              ['Blaze', 'Solar Power']          0.5           1.0   \n",
       "4              ['Blaze', 'Solar Power']          0.5           1.0   \n",
       "\n",
       "            against_dragon  against_electric  against_fairy  against_fight  \\\n",
       "index_name                                                                   \n",
       "0                      1.0               0.5            0.5            0.5   \n",
       "1                      1.0               0.5            0.5            0.5   \n",
       "2                      1.0               0.5            0.5            0.5   \n",
       "3                      1.0               1.0            0.5            1.0   \n",
       "4                      1.0               1.0            0.5            1.0   \n",
       "\n",
       "            against_fire  against_flying  against_ghost  ...  pokedex_number  \\\n",
       "index_name                                               ...                   \n",
       "0                    2.0             2.0            1.0  ...               1   \n",
       "1                    2.0             2.0            1.0  ...               2   \n",
       "2                    2.0             2.0            1.0  ...               3   \n",
       "3                    0.5             1.0            1.0  ...               4   \n",
       "4                    0.5             1.0            1.0  ...               5   \n",
       "\n",
       "            sp_attack  sp_defense  speed  type1   type2  weight_kg  \\\n",
       "index_name                                                           \n",
       "0                  65          65     45  grass  poison        6.9   \n",
       "1                  80          80     60  grass  poison       13.0   \n",
       "2                 122         120     80  grass  poison      100.0   \n",
       "3                  60          50     65   fire     NaN        8.5   \n",
       "4                  80          65     80   fire     NaN       19.0   \n",
       "\n",
       "            generation  is_legendary  total_power  \n",
       "index_name                                         \n",
       "0                    1             0           98  \n",
       "1                    1             0          125  \n",
       "2                    1             0          223  \n",
       "3                    1             0           95  \n",
       "4                    1             0          122  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#修改索引名\n",
    "print(data.index.name)\n",
    "data.index.name = \"index_name\"\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>125</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              abilities  against_bug  against_dark  \\\n",
       "index_name                                                           \n",
       "0           ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "1           ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "2           ['Overgrow', 'Chlorophyll']          1.0           1.0   \n",
       "3              ['Blaze', 'Solar Power']          0.5           1.0   \n",
       "4              ['Blaze', 'Solar Power']          0.5           1.0   \n",
       "\n",
       "            against_dragon  against_electric  against_fairy  against_fight  \\\n",
       "index_name                                                                   \n",
       "0                      1.0               0.5            0.5            0.5   \n",
       "1                      1.0               0.5            0.5            0.5   \n",
       "2                      1.0               0.5            0.5            0.5   \n",
       "3                      1.0               1.0            0.5            1.0   \n",
       "4                      1.0               1.0            0.5            1.0   \n",
       "\n",
       "            against_fire  against_flying  against_ghost  ...  pokedex_number  \\\n",
       "index_name                                               ...                   \n",
       "0                    2.0             2.0            1.0  ...               1   \n",
       "1                    2.0             2.0            1.0  ...               2   \n",
       "2                    2.0             2.0            1.0  ...               3   \n",
       "3                    0.5             1.0            1.0  ...               4   \n",
       "4                    0.5             1.0            1.0  ...               5   \n",
       "\n",
       "            sp_attack  sp_defense  speed  type1   type2  weight_kg  \\\n",
       "index_name                                                           \n",
       "0                  65          65     45  grass  poison        6.9   \n",
       "1                  80          80     60  grass  poison       13.0   \n",
       "2                 122         120     80  grass  poison      100.0   \n",
       "3                  60          50     65   fire     NaN        8.5   \n",
       "4                  80          65     80   fire     NaN       19.0   \n",
       "\n",
       "            generation  is_legendary  total_power  \n",
       "index_name                                         \n",
       "0                    1             0           98  \n",
       "1                    1             0          125  \n",
       "2                    1             0          223  \n",
       "3                    1             0           95  \n",
       "4                    1             0          122  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改索引,要修改所有的索引\n",
    "data.head()\n",
    "# 首先将数据复制到data3，然后更改索引\n",
    "data3 = data.copy()\n",
    "# 让索引从100开始\n",
    "#data3.index = range(100,900,1)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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></th>\n",
       "      <th>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>name</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "      <th>total_power</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">grass</th>\n",
       "      <th>poison</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poison</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poison</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">fire</th>\n",
       "      <th>NaN</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>88.1</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flying</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Charizard</td>\n",
       "      <td>88.1</td>\n",
       "      <td>6</td>\n",
       "      <td>159</td>\n",
       "      <td>115</td>\n",
       "      <td>100</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">water</th>\n",
       "      <th>NaN</th>\n",
       "      <td>['Torrent', 'Rain Dish']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>88.1</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>43</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>['Torrent', 'Rain Dish']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>88.1</td>\n",
       "      <td>8</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>58</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>['Torrent', 'Rain Dish']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Blastoise</td>\n",
       "      <td>88.1</td>\n",
       "      <td>9</td>\n",
       "      <td>135</td>\n",
       "      <td>115</td>\n",
       "      <td>78</td>\n",
       "      <td>85.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">bug</th>\n",
       "      <th>NaN</th>\n",
       "      <td>['Shield Dust', 'Run Away']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Caterpie</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>['Shed Skin']</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flying</th>\n",
       "      <td>['Compoundeyes', 'Tinted Lens']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Butterfree</td>\n",
       "      <td>50.0</td>\n",
       "      <td>12</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poison</th>\n",
       "      <td>['Shield Dust', 'Run Away']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Weedle</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poison</th>\n",
       "      <td>['Shed Skin']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Kakuna</td>\n",
       "      <td>50.0</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poison</th>\n",
       "      <td>['Swarm', 'Sniper']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Beedrill</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>29.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">normal</th>\n",
       "      <th>flying</th>\n",
       "      <td>['Keen Eye', 'Tangled Feet', 'Big Pecks']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pidgey</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flying</th>\n",
       "      <td>['Keen Eye', 'Tangled Feet', 'Big Pecks']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pidgeotto</td>\n",
       "      <td>50.0</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flying</th>\n",
       "      <td>['Keen Eye', 'Tangled Feet', 'Big Pecks']</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pidgeot</td>\n",
       "      <td>50.0</td>\n",
       "      <td>18</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>121</td>\n",
       "      <td>39.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dark</th>\n",
       "      <td>['Run Away', 'Guts', 'Hustle', 'Gluttony', 'Hu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Rattata</td>\n",
       "      <td>50.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dark</th>\n",
       "      <td>['Run Away', 'Guts', 'Hustle', 'Gluttony', 'Hu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Raticate</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       abilities  against_bug  \\\n",
       "type1  type2                                                                    \n",
       "grass  poison                        ['Overgrow', 'Chlorophyll']         1.00   \n",
       "       poison                        ['Overgrow', 'Chlorophyll']         1.00   \n",
       "       poison                        ['Overgrow', 'Chlorophyll']         1.00   \n",
       "fire   NaN                              ['Blaze', 'Solar Power']         0.50   \n",
       "       NaN                              ['Blaze', 'Solar Power']         0.50   \n",
       "       flying                           ['Blaze', 'Solar Power']         0.25   \n",
       "water  NaN                              ['Torrent', 'Rain Dish']         1.00   \n",
       "       NaN                              ['Torrent', 'Rain Dish']         1.00   \n",
       "       NaN                              ['Torrent', 'Rain Dish']         1.00   \n",
       "bug    NaN                           ['Shield Dust', 'Run Away']         1.00   \n",
       "       NaN                                         ['Shed Skin']         1.00   \n",
       "       flying                    ['Compoundeyes', 'Tinted Lens']         0.50   \n",
       "       poison                        ['Shield Dust', 'Run Away']         0.50   \n",
       "       poison                                      ['Shed Skin']         0.50   \n",
       "       poison                                ['Swarm', 'Sniper']         0.50   \n",
       "normal flying          ['Keen Eye', 'Tangled Feet', 'Big Pecks']         0.50   \n",
       "       flying          ['Keen Eye', 'Tangled Feet', 'Big Pecks']         0.50   \n",
       "       flying          ['Keen Eye', 'Tangled Feet', 'Big Pecks']         0.50   \n",
       "       dark    ['Run Away', 'Guts', 'Hustle', 'Gluttony', 'Hu...         1.00   \n",
       "       dark    ['Run Away', 'Guts', 'Hustle', 'Gluttony', 'Hu...         1.00   \n",
       "\n",
       "               against_dark  against_dragon  against_electric  against_fairy  \\\n",
       "type1  type2                                                                   \n",
       "grass  poison           1.0             1.0               0.5            0.5   \n",
       "       poison           1.0             1.0               0.5            0.5   \n",
       "       poison           1.0             1.0               0.5            0.5   \n",
       "fire   NaN              1.0             1.0               1.0            0.5   \n",
       "       NaN              1.0             1.0               1.0            0.5   \n",
       "       flying           1.0             1.0               2.0            0.5   \n",
       "water  NaN              1.0             1.0               2.0            1.0   \n",
       "       NaN              1.0             1.0               2.0            1.0   \n",
       "       NaN              1.0             1.0               2.0            1.0   \n",
       "bug    NaN              1.0             1.0               1.0            1.0   \n",
       "       NaN              1.0             1.0               1.0            1.0   \n",
       "       flying           1.0             1.0               2.0            1.0   \n",
       "       poison           1.0             1.0               1.0            0.5   \n",
       "       poison           1.0             1.0               1.0            0.5   \n",
       "       poison           1.0             1.0               1.0            0.5   \n",
       "normal flying           1.0             1.0               2.0            1.0   \n",
       "       flying           1.0             1.0               2.0            1.0   \n",
       "       flying           1.0             1.0               2.0            1.0   \n",
       "       dark             1.0             1.0               1.0            1.0   \n",
       "       dark             1.0             1.0               1.0            1.0   \n",
       "\n",
       "               against_fight  against_fire  against_flying  against_ghost  \\\n",
       "type1  type2                                                                \n",
       "grass  poison           0.50           2.0             2.0            1.0   \n",
       "       poison           0.50           2.0             2.0            1.0   \n",
       "       poison           0.50           2.0             2.0            1.0   \n",
       "fire   NaN              1.00           0.5             1.0            1.0   \n",
       "       NaN              1.00           0.5             1.0            1.0   \n",
       "       flying           0.50           0.5             1.0            1.0   \n",
       "water  NaN              1.00           0.5             1.0            1.0   \n",
       "       NaN              1.00           0.5             1.0            1.0   \n",
       "       NaN              1.00           0.5             1.0            1.0   \n",
       "bug    NaN              0.50           2.0             2.0            1.0   \n",
       "       NaN              0.50           2.0             2.0            1.0   \n",
       "       flying           0.25           2.0             2.0            1.0   \n",
       "       poison           0.25           2.0             2.0            1.0   \n",
       "       poison           0.25           2.0             2.0            1.0   \n",
       "       poison           0.25           2.0             2.0            1.0   \n",
       "normal flying           1.00           1.0             1.0            0.0   \n",
       "       flying           1.00           1.0             1.0            0.0   \n",
       "       flying           1.00           1.0             1.0            0.0   \n",
       "       dark             2.00           1.0             1.0            0.0   \n",
       "       dark             2.00           1.0             1.0            0.0   \n",
       "\n",
       "               ...        name  percentage_male  pokedex_number  sp_attack  \\\n",
       "type1  type2   ...                                                           \n",
       "grass  poison  ...   Bulbasaur             88.1               1         65   \n",
       "       poison  ...     Ivysaur             88.1               2         80   \n",
       "       poison  ...    Venusaur             88.1               3        122   \n",
       "fire   NaN     ...  Charmander             88.1               4         60   \n",
       "       NaN     ...  Charmeleon             88.1               5         80   \n",
       "       flying  ...   Charizard             88.1               6        159   \n",
       "water  NaN     ...    Squirtle             88.1               7         50   \n",
       "       NaN     ...   Wartortle             88.1               8         65   \n",
       "       NaN     ...   Blastoise             88.1               9        135   \n",
       "bug    NaN     ...    Caterpie             50.0              10         20   \n",
       "       NaN     ...     Metapod             50.0              11         25   \n",
       "       flying  ...  Butterfree             50.0              12         90   \n",
       "       poison  ...      Weedle             50.0              13         20   \n",
       "       poison  ...      Kakuna             50.0              14         25   \n",
       "       poison  ...    Beedrill             50.0              15         15   \n",
       "normal flying  ...      Pidgey             50.0              16         35   \n",
       "       flying  ...   Pidgeotto             50.0              17         50   \n",
       "       flying  ...     Pidgeot             50.0              18        135   \n",
       "       dark    ...     Rattata             50.0              19         25   \n",
       "       dark    ...    Raticate             50.0              20         40   \n",
       "\n",
       "               sp_defense  speed  weight_kg  generation  is_legendary  \\\n",
       "type1  type2                                                            \n",
       "grass  poison          65     45        6.9           1             0   \n",
       "       poison          80     60       13.0           1             0   \n",
       "       poison         120     80      100.0           1             0   \n",
       "fire   NaN             50     65        8.5           1             0   \n",
       "       NaN             65     80       19.0           1             0   \n",
       "       flying         115    100       90.5           1             0   \n",
       "water  NaN             64     43        9.0           1             0   \n",
       "       NaN             80     58       22.5           1             0   \n",
       "       NaN            115     78       85.5           1             0   \n",
       "bug    NaN             20     45        2.9           1             0   \n",
       "       NaN             25     30        9.9           1             0   \n",
       "       flying          80     70       32.0           1             0   \n",
       "       poison          20     50        3.2           1             0   \n",
       "       poison          25     35       10.0           1             0   \n",
       "       poison          80    145       29.5           1             0   \n",
       "normal flying          35     56        1.8           1             0   \n",
       "       flying          50     71       30.0           1             0   \n",
       "       flying          80    121       39.5           1             0   \n",
       "       dark            35     72        NaN           1             0   \n",
       "       dark            80     77        NaN           1             0   \n",
       "\n",
       "               total_power  \n",
       "type1  type2                \n",
       "grass  poison           98  \n",
       "       poison          125  \n",
       "       poison          223  \n",
       "fire   NaN              95  \n",
       "       NaN             122  \n",
       "       flying          182  \n",
       "water  NaN             113  \n",
       "       NaN             143  \n",
       "       NaN             223  \n",
       "bug    NaN              65  \n",
       "       NaN              75  \n",
       "       flying           95  \n",
       "       poison           65  \n",
       "       poison           75  \n",
       "       poison          190  \n",
       "normal flying           85  \n",
       "       flying          115  \n",
       "       flying          160  \n",
       "       dark             91  \n",
       "       dark            141  \n",
       "\n",
       "[20 rows x 40 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置索引:type 1是外部索引，type 2是内部索引\n",
    "data1 = data.set_index([\"type1\",\"type2\"]) \n",
    "data1.head(20)\n",
    "# data1.loc[\"Fire\",\"Flying\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>gender</th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment gender  response  age\n",
       "0         A      F        10   15\n",
       "1         A      M        45    4\n",
       "2         B      F         5   72\n",
       "3         B      M         9   65"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {\"treatment\":[\"A\",\"A\",\"B\",\"B\"],\"gender\":[\"F\",\"M\",\"F\",\"M\"],\"response\":[10,45,5,9],\"age\":[15,4,72,65]}\n",
    "df1 = pd.DataFrame(dic)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      F   M\n",
       "treatment        \n",
       "A          10  45\n",
       "B           5   9"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重塑\n",
    "df1.pivot(index=\"treatment\",columns = \"gender\",values=\"response\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>F</th>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>F</th>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  response  age\n",
       "treatment gender               \n",
       "A         F             10   15\n",
       "          M             45    4\n",
       "B         F              5   72\n",
       "          M              9   65"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.set_index([\"treatment\",\"gender\"])\n",
    "df2\n",
    "# 堆叠数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"2\" halign=\"left\">response</th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          response     age    \n",
       "gender           F   M   F   M\n",
       "treatment                     \n",
       "A               10  45  15   4\n",
       "B                5   9  72  65"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level 决定索引\n",
    "df2.unstack(level=0)\n",
    "df2.unstack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <th>A</th>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>A</th>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>B</th>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  response  age\n",
       "gender treatment               \n",
       "F      A                10   15\n",
       "M      A                45    4\n",
       "F      B                 5   72\n",
       "M      B                 9   65"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 改变内外层索引位置\n",
    "df3 = df2.swaplevel(0,1)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>treatment</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>age</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>age</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>age</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>age</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>response</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>response</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>response</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>response</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment  variable  value\n",
       "0         A       age     15\n",
       "1         A       age      4\n",
       "2         B       age     72\n",
       "3         B       age     65\n",
       "4         A  response     10\n",
       "5         A  response     45\n",
       "6         B  response      5\n",
       "7         B  response      9"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df1.pivot(index=\"treatment\",columns = \"gender\",values=\"response\")\n",
    "pd.melt(df1,id_vars=\"treatment\",value_vars=[\"age\",\"response\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  response\n",
       "treatment               \n",
       "A            4        10\n",
       "B           65         5"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(\"treatment\").mean()  #聚合取平均值\n",
    "# 选择一个特征\n",
    "#df1.groupby(\"treatment\").age.max() \n",
    "# 也可以选择多个特性\n",
    "df1.groupby(\"treatment\")[[\"age\",\"response\"]].min() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   treatment  4 non-null      object\n",
      " 1   gender     4 non-null      object\n",
      " 2   response   4 non-null      int64 \n",
      " 3   age        4 non-null      int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df1.info()\n",
    "# gender是一个对象\n",
    "# 如果使用groupby()，我们可以将它转换为categorical数据\n",
    "# categorical data 占用更小的内存，可以加快groupby之类的操作\n",
    "# df1[\"gender\"] = df1[\"gender\"].astype(\"category\")\n",
    "# df1[\"treatment\"] = df1[\"treatment\"].astype(\"category\")\n",
    "# df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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